Download Quantification of ribosomal proteins by mass Spectrometry: an

Quantification of ribosomal proteins by mass
Spectrometry: an approach to drug target
identification.
A thesis submitted to The University of Manchester for the degree of
Doctor of Philosophy
in the Faculty of Medical and Human Sciences
2013
Zubida Millad Al-majdoub
School of Pharmacy and Pharmaceutical Sciences
Table of contents
Table of Contents
2
List of Abbreviations
6
Abstract
8
Declaration
9
Copyright statement
10
Acknowledgements
11
Introduction
12
Chapter 1
12
1.1-Ribosome
12
1.2-Composition and structure features of ribosome
12
1.3-Crystal structure of ribosomes
14
1.4-Ribosome function
17
1.5-Ribosomal proteins (RPs)
19
1.6-Properties of ribosomal proteins
21
1.7-Conservation of ribosomal proteins
21
1.8-Copy number and organisation of ribosomal proteins genes
23
1.9-The Effects of antibiotics on ribosomes and ribosomal proteins
25
1.10-Antibiotic-target identification
26
1.10.1 Protein synthesis
26
1.11-The modes of action of anti-ribosomal antibiotics
27
1.11.1-Gentamicin
30
1.11.2-Azithromycin
31
1.12-Proteomics
32
1.12.1-Proteomics in drug discovery
32
1.12.2- Techniques in proteomics
33
1.12.3- Sample preparation
34
1.12.4-Prefractionation techniques in proteomics
35
1.12.4.1-Electrophoretic approaches
35
1.12.4.2-Sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE)
36
1.12.4.3-Iso-electric focusing (IEF) fractionation technique
37
1.12.5-Chromatographic approaches
38
1.12.5.1-Reversed-phase (RP) chromatography
38
1.12.5.2-Ion-exchange chromatography (IEC)
39
2
1.13-Bottom-Up and Top-down approaches
40
1.14-Protein identification and bioinformatics
41
1.15-Mass Spectrometry
42
1.15.1-Ionization Sources
43
1.15.1.1-Matrix-assisted laser desorption/ionization (MALDI)
44
1.15.1.2-Electrospray ionization (ESI)
45
1.15.2-Mass analyzers
47
1.15.2.1-Time of flight (ToF)
48
1.15.2.2-Quadrupole mass filter
50
1.15.2.3-Quadruple ion trap
51
1.15.2.4-Orbitrap mass analyzer
52
1.16-Tandem mass spectrometry (MS/MS)
54
1.17-Activation methods in tandem mass spectrometry
55
1.17.1-Collision induced dissociation (CID)
55
1.17.2-Electron transfer dissociation (ETD)
59
1.18-Types of tandem mass spectrometric experiments available on tandem 62
quadrupole instruments
1.19-Tandem mass spectrometry in a MALDI-ToF/ToF instrument
63
1.20-Quantitative techniques in proteomics
65
1.21-Aims of the work
83
1.22-References
85
Chapter 2- General experimental procedures used for the experiments described in this 100
thesis
100
2.1-Determination of protein concentration by Bradford assay
2.2-One-dimensional sodium dodecylsulphate-polyacrylamide gel electrophoresis 100
(1D-SDS-PAGE)
101
2.3-In-gel digestion
2.4-OFFGEL IEF of peptides (OG-IEF)
102
2.5-Isolation and purification of ribosomes
103
2.6-Extraction of ribosomal proteins with acetic acid
104
2.7-LC-MS and LC-MS/MS analysis using the Q-ToF Global
104
2.8-MS and MS/MS analysis on the Amazon ion trap
105
2.9-MALDI-ToF and MALDI-ToF/ToF mass spectrometry
106
2.10-Peptide desalting using C18 ZipTip or C18 StageTips
106
2.11-Statistical analysis (for chapter 6, 7 and 8)
107
Chapter 3-Results
108
3
3.1-Evaluation of different MS approaches and protein and peptide fractionation 108
methods to select suitable signature peptides to design ribosomal QconCAT
3.1.1-Isolation and identification of 70S ribosomal proteins and peptides with 108
electrophoresis and MALDI-ToF
3.1.2-RNA extraction approach
111
3.1.3-Direct analysis of 70S ribosomal proteins treated and untreated sample by LC 111
MS/MS
3.1.4- Comparison of ion Trap and Q-TOF mass spectrometers for 70S ribosomal 112
protein identification
113
3.1.5-The Lys-C strategy
3.1.6-OFFgel Peptide isoelectric focussing (OG-IEF)
115
3.1.7-Conclusions
119
Chapter 4-A tool for the quantification of 30S ribosomal proteins-the E.coli 30S 121
ribosomal QconCAT
Introduction
124
Experimental
125
results
136
Discussion
160
References
163
Chapter 5-A tool for the quantification of 50S ribosomal proteins-the E.coli 50S 165
ribosomal QconCAT
Introduction
165
Experimental
166
Results and discussion
168
References
184
Chapter 6-The effects of gentamicin on the proteomes of aerobic and oxygen-limited 186
Escherichia coli
Introduction
187
Results
188
Discussion
195
Experimental
200
References
204
Supporting information
206
Chapter 7-A proteomic analysis for discovery of drug targets in Escherichia coli
213
Introduction
213
Experimental
214
Results
215
Discussion
218
4
References
218
Supporting information
221
Chapter 8-Aproteomic approach to the identification of drug targets in Staphylococcus 223
epidermidis
Introduction
223
Results and discussion
224
References
227
Supporting information
229
Chapter 9- Conclusions
236
Note: a CD attached with this thesis includes data in an excel files for chapters 6, 7 and 8
5
List of abbreviations
m(RNA)
RNA
RPs
rRNA
GTP
MDa
PTC
tRNA
RFs
EFs
CDK
PAE
MIC
EFG
MS/MS
LC
SDS-PAGE
1D
2D
DDT
IAA
MALDI
MS
IEF
IPG
ESI
HPLC
SCX
RP
IEC
PMF
ToF
m/z
Xe
Ar
FAB
DC
AC
RF
Q-ToF
AGC
3D
LTQ
FT-ICR
Messenger RNA
Ribonucleic Acid
Ribosomal proteins
Ribosomal RNA
Guanosine triphosphate
MegaDalton
Peptidyl transferase center
Transfer RNA
Release factors
Elongation factors
Concentration dependent killing
Post-antibiotic effect
Minimum inhibitory concentration
Elongation factor G
Tandem mass spectrometry
Liquid chromatography
Sodium dodecyl sulfate polyacrylamide gel electrophoresis
One dimensional
Two dimensional
Dithiotreitol
Iodoacetamide
Matrix-assisted laser desorption /ionization
Mass spectrometry
Iso-electric focusing
Immobilized pH gradients
Electrospray ionization
High performance liquid chromatography
Strong cation-exchange
Reverse phase
Ion exchange chromatography
Peptide mass fingerprint
Time-of-flight
Mass to charge ratio
Xenon
Argon
Fast atom bombardment
Direct current
Alternating current
Radio frequency
Quadrupole time-of-flight
Automatic Gain Control
Three dimensional
Linear trap quadrupole
Fourier transform ion cyclotron resonance
6
CID
ETD
ECD
CAD
SRM
MRM
PSD
ICAT
iTRAQ
PAI
emPAI
SILAC
QconCAT
FA
TFA
ACN
rpm
PTM
BSA
UV
ppm
Collision induced dissociation
Electron-transfer dissociation
Electron-capture dissociation
Collisional activated dissociation
Selected reaction monitoring
Multiple reaction monitor
Post-source decay
Isotope coded affinity tag
Isobaric tags for relative and absolute quantification
Protein abundance index
exponentially modified protein abundance index
Stable isotope labelling with amino acids in cell culture
Quantification conCATamer
Formic acid
Trifluoroacetic acid
Acetonitrile
Revolutions per minute
Post-translational modification
Bovine serum albumin
Ultraviolet
Parts per million
7
Abstract
Absolute quantitative proteomics typically relies on the use of stable isotope labelled internal
standards introduced in a known amount. Comparative signal intensities of the labelled and
unlabelled peptides allow the inference of protein concentration. However, the availability of
standard labelled signature peptides in accurately known amounts is a limitation to the
widespread of this approach.
Here, new approach termed ‘‘QconCAT’’ has been developed for absolute quantification and
for the determination of 30S and 50S ribosomal proteins stoichiometry. These QconCAT
proteins are concatamers of Lys C/tryptic peptides for 54 ribosomal proteins. Trypsin, the
most widely used enzyme in proteomics, has a few caveats especially with ribosomal proteins
as it does not perform well due to the presence of high numbers of small and basic residue
and creates relatively short peptides. There is, thus, room for improvement using alternative
proteases. Here, we evaluate the performance of such a sequential strategy (Lys-C/Trypsin).
We show this strategy, is capable of increasing number of suitable signature peptides for
designing ribosomal proteins QconCATs.
Secondly, the method for multiplexed absolute quantification, 30S QconCAT, has been
extended to enable the determination of stoichiometry of ribosomal proteins in control and
gentamicin-treated cultures in a single experiment using LTQ-Orbitrap. Ribosomal samples
were isolated from the whole cell lysate by sucrose density centrifugation, and the relevant
fractions were pooled and analyzed.
Specifically we targeted ribosomes with sub-lethal concentrations of gentamicin and
compared that with ribosome untreated samples. Our results showed that the stoichiometry of
30S ribosomal proteins and 3 others from large subunit was measured to be close to 1:1 copy
number per ribosome along with some unique variations, such as ribosomal proteins S1 with
low copy number and S2 with high copy number as established in the literature. Among
these, ribosomal protein S4, S7 and S8 were selected for investigations as these proteins are
early-assembly proteins and our result confirmed the stoichiometry of these proteins in
untreated samples are very similar. We also found there is a variation in up-regulation of
ribosomal proteins in treated cultures, result from the gentamicin cell injury.
The last three chapters of this thesis dealing with the appearance of E.coli and S.epidermidis
bacterial resistant to gentamicin and azithromycin, and this problem has inspired extensive
searches towards the goal of obtaining novel drug targets that have a synergistic effect with
these two anti-ribosomal drugs with improved antibacterial activity and reduced toxicity. We
use label-free quantitative proteomic analysis coupled with mass spectrometry to investigate
how E.coli and S.epidermidis adapts to the presence of sub-lethal concentrations of
gentamicin and azithromycin in aerobic and oxygen-limited cultures as gentamicin effect on
both conditions. We found that gentamicin caused the up-regulation of proteins involved in
translation such as L1, L10, S2, S1, S8 , S10 and L9 in E.coli cultures.
Three ribosomal proteins were up-regulated by gentamicin in both aerobic and oxygenlimited cultures: L1, L10 and S2. Similar expression for azithromycin on E.coli was
observed, up-regulation of proteins involved in translation such as ribosomal proteins L6 and
L11, whereas expression patterns of gentamicin-treated cells on S.epidermidis were unexpected as proteins necessary for pathogenesis were up-regulated by gentamicin. Our data
provide several ribosomal proteins as drug targets that could produce a synergistic effect with
gentamicin and azithromycin.
8
Declaration
No portion of the work referred to in this thesis has been submitted in support of an
application for another degree or qualification of this or any other university or other
institute of learning.
9
Copyright Statement
i. The author of this thesis (including any appendices and/or schedules to this thesis) owns
certain copyright or related rights in it (the “Copyright”) and she has given The University of
Manchester certain rights to use such Copyright, including for administrative purposes.
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10
Acknowledgements
I would like to sincerely thank my supervisors Dr Jill Barber and Professor Simon Gaskell for
giving me the opportunity to study for a Ph.D. at the Michael Barber Centre for Mass
Spectrometry.
I also warmly thank Dr Narciso Couto, Dr Onrapak R, Dr Waleed Algamdi, they are a kind
people, and I enjoyed the interesting conversations, whether scientific or not. Their help made
my life easier during the Ph.D time and I wish them all the best for their future life. Next, I
would like to thank Dr Kathleen Carroll for her help with Orbitrap MS study. Dr Neil
Swainston is also thanked for allowing me to use his software (QconCAT Pride Wizard).
This thesis would have been difficult without the help and support of my past and present
friends and colleagues of Michael barber centre of mass spectrometry. A special thanks to Dr
Francesco, Andrew, Brahim and Dr Louise, my truly helpful colleagues, who share reading
my thesis and gave me their best advice and support at the end, and with whom I shared so
many interesting talks. To Dr Belen, the spain spirit of the lab, my out of category friend,
with whom I had many good laughing sessions and nice times. I express all my gratefulness
to them, from the bottom of my heart.
And finally, but most importantly, I would not be here today, with what I am and what I have
done, without the help, and support of my husband (Khaled), my lovely children (Mohamed
and Suhybe). I owe them everything. This thesis is dedicated to them, with all my love.
11
Chapter 1
Introduction:
Chapter 1:
1.1-Ribosomes
The ribosome is amongst the most marvellous molecular targets in all biology. It is
constructed from both protein and ribonucleic acid (RNA) components, and is of more than
2.3 MDa in size. Initially, ribosomal proteins (RPs) were believed to be the functionally
active part of the ribosome, catalyzing the polymerization of amino acids to form
polypeptides, but it was later discovered that ribosomal RNA (rRNA) is the “ribozyme” that
catalyzes protein synthesis.1 It is a fascinating fact that about one third of the mass of a
bacterial ribosome consists of protein and the other two thirds of rRNA.2,3 The complex
folding and assembly of individual components, as well as the detail of ribosomal structure
and function, are of great interest to researchers.4
In order for this complex catalytic machinery to translate genetic information into proteins, it
requires a messenger RNA (mRNA) as a carrier, transfer RNAs (tRNAs) as adaptor
molecules and guanosine triphosphate (GTP) as a the source of energy. This translation
occurs through base-pairing interactions between the codon on mRNA and the anticodon on
transfer RNA (tRNA).5,6
Ribosomes are abundant in actively growing cells. In E.coli the rate of ribosome production
is directly proportional to the nutritional content of the growth medium.7,8 Ribosomes can
amount to more than 30% of the dry cell mass.9 The number of ribosomes per cell will vary
depending on the type of cell to which the ribosome belongs. Due to their sheer mass within
the cell, and the considerable amount of energy expenditure required for their manufacture
and function, it is not surprising that the synthesis and function of ribosomes are tightly
regulated.10
1.2-Composition and structure features of ribosome
The basic structure of the ribosome has been conserved in all kingdoms. Ribosomes are large
ribonucleoprotein complexes. In prokaryotes, 30% of the cells mass is accounted for by
12
Chapter 1
ribosomes; they contain roughly 10% of the total bacterial protein and about 80% of the total
mass of cellular RNA.
In eukaryotes, ribosomes are composed of ~ 65% rRNA and ~ 35% ribosomal proteins. Each
ribosome is composed of two subunits of unequal size.
Figure 1.1: The complete structure of the 70S ribosome. The rRNA and ribosomal
proteins of 30S are coloured in light and dark blue, while 23rRNA and 50S ribosomal
proteins are coloured in light and dark grey.11
Prokaryotic ribosomes consist of a large (50S) and a small (30S) subunit, which together
form the 70S ribosome, with an approximate mass of 2.6–2.8 MDa;
12
their eukaryotic
counterparts are the 60S and 40S subunits, forming the 80S ribosome with a mass of >3.5
MDa. The bacterial ribosome has a diameter of 200-250 Ǻ and a sedimentation coefficient of
70S.
The prokaryotic 50S ribosomal subunit consists of 23S RNA (about 2904 nucleotides), 5S
RNA (120 nucleotides) and about 34 proteins (numbered L1-L36, where L8 is an artefact, L7
and L12 are essentially identical, the only difference being that L7 is acetylated at its Nterminus whereas L12 is not). No difference was found between S20 from the small and L26
from the large subunit. The 30S subunit consists of 16S RNA (approximately 1,500
nucleotides) and about 21 proteins (S1-S21).13 In contrast, eukaryotes have 4 rRNA subunits
and about 80 proteins.15 In the 50S subunit, the proteins are distributed over the cytosolic
13
Chapter 1
surface, whereas in the 30S they are concentrated mainly in the head, and shoulder and
platform regions of the body (Figure 1.2 and 1.3).16 The peptidyl transferase centre is located
on the 50S subunit and drives peptide bond formation. The peptidyl transferase centre
consists solely of rRNA suggesting that the ribosome is a ribozyme.17 The 30S subunit also
contains the decoding domain, which pairs the codon on the mRNA with the anticodon of the
corresponding tRNA.18
Figure 1.2: Key of 50S ribosomal proteins, the RNA of the subunit is shown in
grey; left side, the top view facing the small subunit; right side: back side of the subunit (solvent
side) in the 180º rotated top view orientation; bottom structure, view from the bottom
of the 50S subunit.19
1.3-Crystal structure of ribosomes
After lengthy studies, the most recent high-resolution crystal structure of the ribosome was
published in Science in 2005, and was for the intact 70S ribosome of the E.coli at 3.5Å
resolution.19 In addition to structures were reported for the 70S ribosome for T. thermophilus
at 5.5Å11. Two independent structures of the 30S subunit from bacterium T.thermophilus 30S
14
Chapter 1
at 3.3 Ǻ and 3.0 Ǻ have been reported.20,21,22 In 2000, the structure of the 50S subunit of the
H. marismortuii at a resolution of 2.4 A was reported23.
A
B
Figure 1.3: Distribution of ribosomal proteins in 30S small subunit. A; seen form
the 50S. B; the cytosolic side of the 30S. Blue, proteins; grey, RNA.19
A 3.1Å resolution structure for the large subunit from D. radiodurans was described in
200123. There are a number of significant differences in RNA and protein composition
between the two structures, and these have been discussed elsewhere.24 The availability of
atomic structures of both 30S and 50S subunits facilitated the construction of a complete
structure of 70S ribosomal of T. thermophilus with mRNA and tRNA in the A, P and E sites
and also shows how the two subunits come together at a primarily RNA-RNA interface
(Figure 1.1)11.
The prominent features of the 50S subunit such as the L1 or L7/L12 stalks that are partly or
completely disordered in most high-resolution structures of the ribosome of the 50S subunit
have been solved in isolation.25,26 Based on neutron scattering26, there are number of features
shared between the two subunits: for example the interface between the two subunits is
largely free of protein17 and consists mainly of RNA; this was later confirmed by cryoelectron microscopy, not only for bacterial ribosomes but also for yeast ribosomes28. This
interface between the subunits is composed of parts of the 16S and 23S RNA subunits and of
15
Chapter 1
some ribosomal proteins, e.g. S7, S11 and L2, L3, L4, L23, L27. These latter proteins (L2,
L3, L4, L23 and L27) together with the 5S RNA and parts of the 23S RNA form the central
part of the large subunit and are flanked by L1 and L7/L12 domains - the two outer
protuberances of the large subunit. Most of the proteins tend to cover the outer surfaces of the
ribosome (Figure 1.2). A typical feature of many ribosomal proteins is the presence of a
globular domain, which is found generally on the surface of the subunit and has long
extensions that pass through and penetrate deep into the core of the ribosome and interact
with rRNA. These extensions contain multiple glycine residues to allow flexibility and tight
packing, and are rich in basic amino acids to interact with rRNA. This extension is one of the
reasons why previous attempts to crystallise ribosomal proteins such as L2 and L4 were
unsuccessful.29
Ribosomal proteins S2, S6, S9, S11, S12, S14, S16 and S17 as well as many 50S proteins
such as L3, L15, L18, L19, L22, L31 and L35 exhibit globular domain with long extensions.
The function of the long extension in the 30S subunit is to fix the folding of rRNA at late
assembly stage-whereas in the 50S subunit, proteins L3, L4, L22 and L25 fix the 23rRNA
folding in early ribosome assembly.29 Hence, RNA domains and ribosomal proteins form
inter-subunit bridges which are important for the movement of the subunits during
translation, and also stabilise the ribosomal tertiary structure. These extension lack tertiary,
and even secondary structure – this is illustrated by the proteins that penetrate to the peptidyl
transferase centre of the large subunit1 in Figure 1.4.
Figure 1.4: The ribosomal proteins with closest extensions to the entrance of the
active PTC site (purple) through the 50S subunit1.
16
Chapter 1
A culmination of the work leading to the atomic architecture of the ribosome, led largely by
Yonath, Steitz and Ramakrishnan groups attracted the 2009 Nobel Prize in Chemistry.
1.4-Ribosome function
The job of every ribosome is to ensure that mRNA is in the correct frame, and to ensure that
each successive codon in mRNA interacts precisely with the anticodon tRNA molecule so
that the correct amino acid is added to the polypeptide chain, and subsequently exits the
ribosome, folding into the correct, biologically active protein. A number of ribosomes may be
attached to the same messenger RNA, each manufacturing its own chain of polypeptides, and
this total structure is called a polysome. Ribosomes are assisted in translation of the genetic
code by a number of translation factors.
Aminoacyl-tRNA synthetases covalently link an amino acid to a nucleic acid adapter
molecule, a tRNA. They are in fact the only components of the translation machinery which
really 'know the genetic code’ and are thus crucial guarantors of the fidelity of protein
synthesis, such that for each of the 20 amino acids there are corresponding synthetases to
secure this process.15 The protein synthesis process is performed with amazing accuracy and
at high speed. Protein synthesis can be divided into three functional phases: initiation,
elongation, and termination,31 illustrated in Figure 1.1. Each phase is directed and controlled
by additional factors, namely initiation factors (IFs), elongation factors (EFs) and
termination/release factors (RFs).
Initiation
Bacterial initiation is mainly a function of the 30S subunit together with three Ifs32. The 30S
and mRNA bind together in the presence of IF-1 and IF-3. IF-2 together with GTP ensures
the binding of fMet-tRNAfMet to the start codon, usually AUG, and this is a substrate for the
ribosomal A site. After primary association of the mRNA to the 30S subunit via codonanticodon interaction, the initiator tRNA complex (fMet-tRNAfMet) associates with a free 50S
subunit to form a 70S ribosome, with mRNA and fMet-tRNAfMet in the binding site, the P
site.33 GTP is hydrolyzed on IF-2, and the factor is released from the initiation complex.
17
Chapter 1
Elongation
After association of the 30S and 50S subunits at the end of the initiation step, the ribosomal P
site holds the aminoacylated initiator tRNA, while the A site is empty and ready to receive an
aminoacylated tRNA. It is brought to the ribosome as a ternary complex with EF-Tu and
GTP. EF-Tu accounts for almost two thirds of the mass of the ternary complex and is mainly
responsible for the high affinity of the ternary complex for the A site.
Figure 1.5: The diagram above shows overview of bacterial translation.104
18
Chapter 1
After GTP hydrolysis, EF-Tu releases the aminoacyl end of the A site tRNA, allowing it to
move into the P site. Thus, the ends of the A and P site tRNAs are positioned at the peptidyl
transferase centre on the 50S subunit, and peptide bond formation can occur. Peptide bond
formation is catalyzed by the large ribosomal subunit and it involves nucleophilic attack of
the amino group of the amino acid that is linked to 3` hydroxyl of the terminal adenosine of
the tRNA in the A-site on the carbonyl group of the amino acid (with attached nascent
polypeptide) in ester linkage to the tRNA in the P-site. As part of the reaction a proton is
extracted from the attacking amino group. This proton is then donated to the hydroxyl of the
tRNA in the P site as the ester linkage is cleaved. The deacylated tRNA is moved from the P
site, eventually to be ejected from the ribosome, while the peptidyl tRNA currently in the A
site is elongated by one aminoacyl residue and is then translocated to the P site. The
translocation reaction is carried out by GTP hydrolysis and EF-G.
The result of translocation is a ribosome ready for the next round of elongation. In this
translocation reaction, the mRNA-tRNAs complex is shifted by one codon length, whereby
the tRNAs move from the A and P sites to the P and E sites, respectively.
Termination
At the end of the elongation phase, the stop codon in the mRNA is positioned in the A site.
The termination codon is recognized by either release factor 1 (RF1) or release factor 2
(RF2); RF1 terminates at stop codons UAA and UAG, while RF2 terminates at UAA and
UGA.34 One of two protein release factors (RFs), RF1 or RF2, binds to the A site and
promotes the deacylation of the peptidyl-tRNA. A recycling factor, with the help of EF-G,
then leads to the dissociation of the release factor and the ribosome enters into the next
elongation cycle.
1.5-Ribosomal proteins
It has been established that most of the ribosomal proteins were evolutionarily conserved
from bacteria to humans: their nucleotide and amino acid sequence is a necessary prerequisite
for studying the phylogenetic relationships between organisms.35 Wool et al 197936,
19
Chapter 1
characterised the first ribosomal proteins from eukaryotes. The amino acid sequence and
biochemical properties of human ribosomal proteins have been also described.37,38 The
number of and molecular weights of ribosomal proteins from many species have been
observed to be conserved.39 The conservation of about 20 ribosomal proteins in the L3 and
L4 operons across almost the entire eubacterial and archaebacterial domains is remarkable.
This strongly supports that eubacteria and archaebacteria originated from one common
ancestry.
The number of ribosomal proteins in E. coli was first determined by Kaltschmidt and
Wittmann (1970)4 using a two dimensional polyacrylamide gel electrophoresis procedure that
is able to separate all E. coli ribosomal proteins. There are 21 proteins, designated S1-S21, in
the small (30S) subunit and 34 proteins (L1-L36) in the large (50S) subunit. The smallest E.
coli ribosomal protein has a molecular weight of 5400 Da and the largest, 61,000 Da. The
molecular weights of most of the proteins range between 10,000 Da and 25,000 Da and the
isoelectric point of the vast majority of these is below 7.0. The large proportion of basic
proteins in ribosomal particles is in direct contrast with the mostly acidic nature of nonribosomal proteins.
Geisser et al. 197339 showed that most ribosomal proteins from members of the prokaryotic
family Bacillaceae did not co-migrate with those from E. coli, although co-migration was
observed with a larger number of ribosomal proteins from other species of the prokaryotic
family Enterobactericeae. They suggested that the latter family could be separated into 12
groups of organisms on the basis of electropherogram similarity. Each group included species
whose ribosomal protein composition revealed more than 90 % similarity. There were no
detectable similarities between E. coli ribosomes and those of eukaryotic organisms, such as
mammals, plants, and yeasts.
Immunological studies using antibodies against purified, isolated E. coli ribosomal proteins
showed the same general results. However, two highly acidic proteins (L7/L12) seem to be at
least partially conserved through evolution. These proteins appear to be present in both E.
coli and spinach chloroplast ribosomes.40 There is, however, a high degree of homology
20
Chapter 1
between ribosomal proteins from various eukaryotic organisms such as rat, shrimp, wheat,
and yeast.41 This suggests that 70S and 80S ribosomes evolved differently from one another.
1.6-Properties of ribosomal proteins
Ribosomal proteins are vital components of the ribosome. Most ribosomal proteins are
extremely basic. These proteins are typically rich in Lys and Arg and have few aromatic
amino acid residues, properties appropriate to proteins intended to interact strongly with
phosphate residues in the RNA backbone. These RPs are characterised by high pI average
values of about 10 compared to pI=4 to 5 for most translation factors.42
There is one single gene for each ribosomal protein per 70S ribosome, apart from L7/L12 (L7
is the acetylated form of L12), which have four. The ribosomal protein S20 in small subunit
is identical to ribosomal protein L26 and there is only one copy of this protein in the
ribosome, situated at the interface of the two subunits. For the E. coli ribosome complete
sequences of proteins have been determined contained in SwissProt database.9 The largest
prokaryotic ribosomal protein is S1 (61.2 kDa); the smallest is L34 (5.4 kDa).42,43
The E. coli ribosome contains 21 proteins in the small subunit and 34 proteins in the large
subunit with 70S sedimentation coefficient. The eukaryotic ribosome has a low
rRNA content (made up with a higher protein content – about 80 ribosomal proteins in total),
giving it a lower density and sedimentation rate.44 Despite the high rRNA content and high
sedimentation coefficient, prokaryotic ribosomes (E. coli) are smaller than eukaryotic ones
based on particle mass and physical dimension.
1.7-Conservation of ribosomal proteins
The ribosomes and their components provide an appropriate means for the study of
evolutionary aspects, since this organelle performs the translation of the genetic information
into proteins and occurs in all organisms. While determinations of gross structure, such as
those outlined above, show clear differences between prokaryotic and eukaryotic ribosomes,
there are similarities that can be explored at the level of protein and nucleic acid sequence.
21
Chapter 1
Generally amino acid sequences of ribosomal proteins are short and conserved less than
nucleotide sequences45. Although particular ribosomal proteins are unique to only one or two
domains, many proteins are conserved throughout all phylogenetic trees.46 It has been
reported by Lecompte,42 that around 30% of E.coli ribosomal proteins, especially those vital
for ribosomal function and assembly, have conserved sequence in eukaryotic and archaeal
ribosomes.
Prokaryotic proteins share sequence similarity and properties with the eukaryotic kingdom,
but the latter have additional proteins that show no sequence similarity with prokaryotic
proteins. This is demonstrated by the similarities maintained within L7/L12 of E.coli and
L40/L41 of eukaryotes. Conversely, a few ribosomal proteins are found only in bacteria, and
not in eukaryotes, such as ribosomal protein L1.47 Previous phylogenetic studies have been
applied to ribosomal proteins L2, L10, L11, L12, L15, L30, S8, S11, S12 and S17.48,49
Prokaryotic S11 and S12 show a 30% sequence homology with respect to the corresponding
eukaryotic proteins.
In a study by Cooperman et al.,50 it was reported that L2 proteins are well conserved and
exhibit around 30% homology from eukarya, archaea and bacteria. In some case, the high
degree of conservation, whether in rRNA or ribosomal proteins, may reflect the functional
importance of the corresponding rRNA or ribosomal protein region. To this end, the
conservation of L2 is maintained due to its involvement in peptidyl transferase activity.51
Other examples: prokaryotic ribosomal proteins S7, S11, S12, S19 and L2 with functional
importance were found with high sequence similarity. E. coli ribosomal protein S12 is
essential for maintaining the accurate message translation by the ribosome and is related to
the S12 counterparts, in human and rat.52,53
The most conserved proteins from eubacteria, chloroplasts, mitochondria, archaea and
eukarya with respect to E.coli are S11, S12, S19, L14, L2, L5, S3-S5, S7, S8, S17, and L6.
Ribosomal protein S4 is an essential protein, protecting several 16S rRNA regions from
chemical modification during assembly. Its primary function is to initiate the folding of 16S
RNA in the assembly of the small ribosomal subunit.54 S4 is a highly conserved protein when
22
Chapter 1
E. coli compared with other prokaryotes, however, the similarity between prokaryotic S4 and
eukaryotic S9 is limited to amino acid residues 100 to 159. Ribosomal protein S3 is very
highly conserved phylogenetically. E. coli S3 interacts with 16S rRNA, forming the entry
pore for mRNA, and exerting helicase activity. It resides in the head of the small subunit near
the site to which the polypeptide release factor RF-2 binds.55, 56
Three ribosomal proteins S4, S7 and S8 are collectively called the assembly initiator proteinsthese bind directly to 16S rRNA and play an important role in modulating the folding of 16S
rRNA in the assembly pathway of the small subunit.57 Within the core of the S8 binding site,
there is a remarkable conservation of nucleotide sequence. This forms part of the central
domain of the 16S rRNA (the platform ring), and shows a sequence homology of more than
90% across all kingdoms. The conservation of nucleotides within the conserved core of the
S8 binding site with the central domain of the 16S rRNA (the platform ring) is remarkable.
The conservation level is more than 90% of all known prokaryotes and chloroplasts.58
1.8-Copy number and organisation of ribosomal proteins genes
Almost all of the proteins present in the E. coli ribosome are single copy with the exception
of protein L7/L12 which has four copies.59 rRNA genes generally exist in multiple copies
within the ribosome. About half of the genes for ribosomal proteins in E. coli are organized
into a number of operons (Figure 1.6). These operons are named spc, S10, str, α and β. The
remaining proteins are distributed across the E. coli chromosomes in operons.60
Ribosomal proteins are produced at a level matching the level of rRNA production – proteins
not incorporated into ribosomes are excess, and as such provide negative feedback, downregulating their own synthesis and that of other ribosomal proteins within the same operon at
the level of translation.61,62 This is called “translational autoregulation”.
Many operons encode ribosomal proteins for both subunits, thus an auto-regulatory protein
associated with one subunit can regulate the production of ribosomal protein for another
subunit.
23
Chapter 1
Figure 1.6: The operon structure of the ribosomal proteins. Ribosomal protein
operons regulated by translational feedback. The name of each operon is given. The
regulatory product is indicated by a red circle. The longer black arrows denote the
transcription start site. The non-ribosomal proteins are indicated by yellow and blue
squares. RNA polymerase subunit alpha (α), beta-subunit of RNA polymerase (β)
and beta`-subunit of RNA polymerase (β`) are indicated by yellow squares.
This is demonstrated by L4 which regulates the synthesis of S10, L2, L3, L4 and L23 (the
genes of which are located on the S10 operon)63- all of which are suppressed following
overexpression of L4.64 Similarly, S8 protein regulates the synthesis of two small subunit
proteins (S14 and S5) and five proteins of large subunit (L5, L6, L18, L30 and L15) in the
spc operon.65 Dean and Nomera 1980 also observed a similar result on the regulation of the α
operon, namely inhibition of the first three genes for S13, S4 and L17 by overexpression of
S4 with S11. Furthermore, L1 specifically inhibits the synthesis of the two proteins of the
L11 operon – namely L1 and L11. Thus, S4, S8, L1, L4 and L10 have been identified as
translational repressors.62 Mutations in r-proteins S4, S7 and S17 have been shown to result in
ribosome assembly defects.66
24
Chapter 1
1.9-The effects of antibiotics on ribosomes and ribosomal proteins
The ribosome is a highly complex machine that provides many possible sites for functional
disturbance, thus acts as major target for antibiotics. To date, about half of the total number
of characterised antibiotics act on the ribosome, making it a very important target.67, 68
The enormous progression in the elucidation of ribosomal structure has provided knowledge
and accelerated understanding of the mechanism of antibiotic inhibition and function. It is
also proving useful in the design of new compounds that might help in the attempt to find
antibiotics that are effective against multidrug-resistant bacteria.69
During the past few decades, considerable evidence has accumulated demonstrating that the
functional centres of the ribosomes are composed entirely of rRNA.70-74 interestingly, the
antibiotic structures solved shows that they primarily bind to the RNA component of the
ribosomal subunits, i.e., there is little or no interaction with ribosomal proteins. The large
number of molecular steps involved in initiation, elongation and termination of protein
synthesis by the ribosome provides many targets for both currently used and new antibiotics.
Anti-ribosomal drugs targeting different steps in protein synthesis do so with differing
degrees of specificity.
Clinically important anti-ribosomal drugs used for the treatment of infectious diseases also
display differing degrees of toxicity (e.g. aminoglycosides, macrolides and oxazolidinones),
Aminoglycosides represent gold standard drugs for the treatment of serious Gram-negative
pathogens, but their narrow therapeutic margins can cause significant ototoxicity and
nephrotoxicity. Macrolides and lincosamides, amongst the safest of antibiotics, display little
to no toxicity. Antibiotics targeting the ribosome can be divided into three different classes;
those acting upon the 30S subunit, the 50S subunit, and antibiotics targeting the 70S
ribosome as a whole.
Crystal structural data of ribosomal subunits allows researchers to determine the binding of
antibiotics to the ribosomal subunits and explain many earlier biochemical and genetic
25
Chapter 1
observations regarding anti-ribosomal antibiotics. Moreover, the crystal structures also
provide insight as to how existing drugs might be optimised or new drugs created de novo to
improve binding and prevent resistance. Thus several structures of aminoglycoside antibiotics
such as streptomycin, paromomycin and spectinomycin bound to the 30S subunit were
reported75 and the complexes of tetracycline, hygromycin B and pactamycin soon
afterwards.76
The structures of the 50S subunit alone and in complex with various antibiotics have also
been determined77-79 (for example, several clinically important antibiotics such as macrolides
(e.g., erythromycin and azithromycin), lincosamides, streptogramins, phenylpropanoids (e.g.,
chloramphenicol) and oxazolidinones (e.g., linezolid) bound to one of several binding sites
on the 50S subunit (thereby disrupting polypeptide synthesis).
Aminoglycosides and tetracycline bind to the 30S subunit which is responsible for genetic
decoding and thus incorporation of the proper amino acid in the growing polypeptide chain
and disrupt this function. As a consequence, erroneous proteins that are incorrectly folded
accumulate, which then leads to bacterial cell death.
1.10-Antibiotic-target identification
Antibiotic targets allow researchers to understand how antibiotics work and consequently,
why they cease to be effective. There are two major targets for the main antibiotics:
biosynthesis of cell wall and protein synthesis. The cell wall is a unique target for
prokaryotes., whereas the inhibition of protein synthesis is directed towards highly conserved
structures that can also be found in eukaryotic cells,80 such as mitochondria and the isoleucyltRNA synthetases.81 Both gram positive and gram negative bacteria are surrounded by cell
wall, but in gram positive bacteria the cell wall is simpler and better understood. Major class
of antibiotics inhibiting protein synthesis are briefly described below.
1.10.1-Protein synthesis
Given the fundamental importance of the rRNA, it is not surprising that most anti-ribosome
drugs target the rRNA. Prokaryotic and eukaryotic ribosomes are enough distinguishable that
26
Chapter 1
there are many anti-ribosomal drugs targeting different steps of protein translation with
specific antibacterial action. These anti-ribosomal drugs are among the broadest classes of
antibiotics and can be divided into two subclasses; the 50S ribosome inhibitors and 30S
ribosome inhibitor.
Macrolides of the erythromycin class,82 Clindamycin of the lincosamides class, Dalopristinquinupristin of the class streptogramins, chloramphenicols and oxazolidinones83,84 are
classified as 50S ribosome inhibitors. These anti-ribosomal drugs works by blocking either
initiation of protein synthesis as in case of oxazolidinones85 (for example, linezolid) or
translocation of peptidyl tRNA.
The 30S inhibitors include tetracyclines and the aminocyclitols. The aminocyclitol class
constituent spectinomycin and aminoglycosides (eg, gentamicin, streptomycin was the
establishing member, replaced now by kanamycin). Ribosome inhibitors work either as
bactericidal such as aminoglycosides or bacteriostatic in case of macrolides, streptogramins,
spectinomycin, tetracyclines and chloramphenicol; however, they can be bactericidal in
specific species.86
Finally, cyclohexamide blocks peptidyl transferase activity in eukaryotic ribosomes. The
multiplicity in protein synthesis steps provide a multifaceted target for new antibiotics and
this is the mechanism for the action of oxazolidinones,87 which were approved in the United
States in 2000.
1.11-The modes of action of anti-ribosomal antibiotics
Defining the binding site of the previous anti-ribosomal antibiotics on the ribosome is certain
to provide insights into their mechanism of inhibition. Tetracycline is a classical inhibitor
and the first so-called “broad-spectrum” antibiotic, mainly working on the elongation phase
of protein synthesis.88 It binds to the ribosome and inhibits accommodation of aminoacyltRNA into the A site. New amino acids are prevented from the adding to the growing
polypeptide.89,90 Fusidic acid binds to elongation factor G (EF-G) and inhibits release of EFG from the EF-G/GDP (guanosine diphosphate) complex.
27
Chapter 1
Figure 1.7: The bacterial 70S ribosome from Thermus thermophilus shows the binding
sites of antibiotics. The 30S ribosomal subunit is shown on the left and the 50S ribosomal
subunit is shown on the right. Ribosomal RNAs are shown in yellow and grey and ribosomal
proteins in bronze and blue.11 from Macmillan Publishers Ltd.
Figure 1.8: Aminoglycosides interfering with the translocation of tRNA from the A-site
to the P-site
28
Chapter 1
The binding of aminoglycosides, streptomycine, and the families of the gentamicine,
kanamycine, and neomycine with the ribosome is much more complex. Aminoglycosides
bind in the decoding site of the 30S subunit and induce codon misreading and/or hindrance of
translocation of the amino-acyl tRNA from A-site to P-site, leading to incorrect proteins91
(Figure 1.8).
In 1944, Waksman et al92 discovered Streptomycin, the first aminoglycoside antibiotic,
which proved very effective against Mycobacterium tuberculosis. Streptomycin is a highly
basic trisaccharide, which interacts directly with the ribosome and interferes with the binding
of fMet-tRNAfMet to ribosomes and thereby prevents the correct initiation of protein
synthesis.
Among 50S ribosome inhibitors are macrolides,which bind in the region of domain V
(nucleotides 2000 to 2624 of 23S RNA, these nucleotides causes characteristic world called
domain) of the ribosome in the peptidyltransferase centre. Carbomycin and other 16membered macrolides were found to prevent peptide bond formation through hindering
peptidtransferase activity by binding the A site and blocking the binding of aminoacyl tRNA.
Erythromycin and other 14-membered macrolides were found to have no effect on
peptidyltransferase activity. Macrolides were found to cause accumulation of peptidyl-tRNA,
prompting researchers to suggest that the primary mechanism of action common to all
macrolides was premature expulsion of peptidyl tRNA from the ribosomes.93
Other antibiotic acts at different stages of protein synthesis such as chloramphenicol which
inhibits peptide bond formation by preventing the aminoacyl-tRNA 3 ‫׳‬end from binding in the
peptidyl transferase centre.77 Puromycin is an aminonucleoside antibiotic and first noticed by
Yarmolinsky.94 It inhibits protein synthesis on both prokaryotic and eukaryotic ribosomes by
causing nascent polypeptide chains to be released before their synthesis is completed causes
premature chain termination. Finally, inhibitors of protein synthesis also act on eukaryotic
cells by blocks peptidyl transferase activity such as cyclohexamide.95 It binds the ribosome
and inhibits eEF2-mediated translocation.
29
Chapter 1
1.11.1-Gentamicin
Gentamicin is one of aminoglycoside family and produced by Micromonospora purpurea as
a mixture of three main components called gentamicin C1, C1a, and C2 (Figure 1.9). They
differ slightly structurally, and display approximately the same antibiotic activity. Gentamicin
is bactericidal in action and widely used for the treatment and prevention of life threatening
gram-positive and gram-negative bacterial infections, but it has several side effects that limit
its use.96
The most relevant side effects are nephrotoxicity and ototoxcicity.97 In addition, psychiatric
symptoms related to gentamicin (anorexia, confusion, depression, disorientation and visual
hallucinations) can occur. Gentamicin binds to the decoding region of the 30S subunit to
prevent the formation of an initiation complex with messenger RNA and stimulate
misreading of mRNA, resulting in the incorporation of the wrong amino acid.
Gentamicin interferes with A site binding but only when both P and E sites are occupied.98
Membrane leakage is another important function of gentamicin. Recent studies show that
cationic antibiotic molecules create fissures in the outer cell membrane, resulting in the
leakage of intracellular contents and enhanced antibiotic uptake. The bactericidal activity of
gentamicin is attributed to this rapid action at the outer membrane.99 Anaerobic bacteria are
not susceptible to gentamicin due, at least in part, to a lack of an active transport mechanism
for aminoglycoside uptake.
In case of aerobic gram negative bacteria, gentamicin develops concentration-dependent
killing and post-antibiotic effect. The bactericidal increase as the concentration of gentamicin
increases, thus called concentration-dependent killing.100 post-antibiotic effect is where
inhibition of bacterial growth continues after the antibiotic concentration decreases below the
bacterial MIC. The specificity of post-antibiotic effect can be for bacteria as well as for drug.
The post-antibiotic effect of aminoglycosides is short for most gram-positive organisms (< 2
hours) and longer for gram-negative organisms (2—8 hours), such as E. coli, K.pneumoniae,
and P. aeruginosa.
30
Chapter 1
Figure 1.9: Chemical structure of gentamicin
1.11.2-Azithromycin
Further changes in the basic macrolide structure resulted in a macrolides subclass, the
azalides. Azalides includes azithromycin is a semisynthetic derivative of erythromycin, with
a 15-membered lactone ring possessing additional methyl-substituted nitrogen in the
macrolides ring. This difference produces improvements in spectrum and potency compared
with erythromycin and superior stability to an acid environment (pH 2.0) compared with
erythromycin.101
The superior stability of azithromycin to gastric acid compared with erythromycin base is
evidenced by a 10% loss of compound after 82 min at pH 2.0 compared with 7 sec for
erythromycin. Azithromycin expressed higher plasma AUC values and a longer t1/2 than
erythromycin in all animal species examined; these plasma profiles are related to its good
penetration into and slow elimination from tissues.
Azithromycin possessed potency against gram-positive organisms similar to that of
erythromycin.102 It has been found azithromycin has high potency against gram-negative
organisms. The MIC of azithromycin is lower than erythromycin for inhibition of E.coli.103
Figure 1.10: Chemical structure of azithromycin
31
Chapter 1
Azithromycin, like all macrolide antibiotics, prevents bacteria from growing by interfering
with their ability to make proteins. Azithromycin prevents bacterial protein biosynthesis by
binding to the 50S ribosomal subunit and interfering with the elongation of nascent
polypeptide chains.104
1.12-Proteomics
The term “proteome” was used for the first time in 1995, and is refers to ‘‘protein
complement expressed by a specific genome’’.105 While the genome of an organism is static
over a short timescale, the proteome is highly dynamic and the proteins expressed in a cell
change, depending on the stage of growth and function of the cell.
Proteomics is the study of proteins on a large scale in order to obtain a global, integrated view
of disease processes, cellular processes and networks at the protein level. Proteomics thus
deals with the identification of proteins. It is impossible to determine all of the proteins
present in a cell at one time, and information needs to be pieced together from many different
experiments. Sample complexity, dynamic range of the concentrations of the proteins and a
lack of technology are among the challenges. Proteomics might also be described as
multidisciplinary research in which separation techniques, mass spectrometry (MS) and
bioinformatics play essential roles.
Fractionating the proteins of a cell is one way of reducing the sample complexity. The
proteins are separated using various methods such as liquid chromatography and sub-cellular
fractionation.
1.12.1-Proteomics in drug discovery
The physiological and pathophysiological changes are associated with particular diseases or
conditions, are to some extent reflected by changes in production and metabolism of proteins.
Consequently, drug discovery can benefit tremendously from proteomics, with its ability to
track changes in protein expression, both during the healthy state and during the presence of
disease. Comparative proteomics is a useful method in which a control state can be compared
32
Chapter 1
to treated state of a system in order to find differences that result. For example, in this work
on drug treatment of cells, the protein differences that are discovered between the controls
and treated cells may provide the possibility of identifying novel drug targets. Unique protein
targets (biomarkers) could also be used for clinical diagnosis of diseases and monitoring their
progression. Candidate proteins for detailed functional studies, which might not have been
considered as interesting in a traditional approach can also be identified. Improvements in our
knowledge of drug targets identification obtained by proteomic studies are important for the
understanding of how to control the endogenous cells and in the future may provide novel
therapeutic strategies and targets.
Recently, a few studies applied proteomics approaches to evaluate the cellular response upon
drug treatment. In this approach cells are treated with a drug before proteome analysis to
evaluate globally induced changes in protein abundance and could identify a drug targets. A
particular advantage of this approach is that it is unbiased as the unmodified drug interacts
with its endogenous targets. However, since typically no target enrichment is used, the
observed changes are limited by the analytical depth of the analysis, often restricted to the
most abundant proteins. Also, proteins found to be changed in abundance are not necessarily
part of inhibited signalling pathways but often represent high abundant proteins involved in
stress response and/or housekeeping.106 Hence, identification of direct target proteins is rarely
accomplished by proteome approaches. Illustrative examples of such approaches by Chen et al.
evaluated the differential effect of atenolol, a β1-selective adrenoreceptor blocker, and the
nonsteroidal anti-inflammatory drug ibuprofen on two different cell types by a combination
of two-dimensional peptide separation of trypsin digested cell extracts and quantitative mass
spectrometry.107
1.12.2-Techniques in proteomics
A typical proteomics experiment is equivalent to solving a 10,000 piece jigsaw puzzle in
which the pieces are all of different sizes, some many orders of magnitude larger than others.
The first step in solving the puzzle is to put each piece through a shredder (the equivalent of
digesting with a proteolytic enzyme), giving perhaps 30 times as many pieces as were present
at the start. As a result of the complexity of the proteome (even more of its digest), it is
33
Chapter 1
desirable to apply fractionation techniques that provide a wide dynamic range to be able to
detect as many proteins as possible in the sample. In proteomic analyses, a common issue is
that highly abundant proteins suppress the detection and identification of relatively low
abundance proteins. Reducing the complexity of the protein sample or its enzymatic digestion
products tends to improve the proteome coverage. A number of separation techniques are
available in proteome analyses and the principles of the ones used in this thesis are given
below.
1.12.3-Sample preparation
Perhaps the single most critical point in obtaining useful proteome analysis is reproducible
sample preparation. In order to eliminate the undesirable artefacts during preparation,
requirements such as solubilisation, denaturation, reduction and alkylation should be
addressed. It has been demonstrated that solubilisation media commonly contain reagents,
such as urea, thiourea, dithiotreitol (DTT), and iodoacetamide (IAA), which can have
considerable consequences on the final outcome of protein separation and subsequent
analysis.108
A variety of sample preparation approaches have been developed to deal with the tremendous
complexity of protein samples, both at the intact protein and at the peptide level. For complex
samples containing many thousands of proteins, orthogonal (mutually independent),
multidimensional separation approaches must be performed to provide sufficient
chromatographic separation and resolution for the detection of a high proportion of the
proteins that make up the proteome. Methods such as sonication, mechanically driven rapid
pressure changes and homogenization are often used to disrupt the cells prior to protein
extraction and to obtain a representative protein population sample. The degree of protein
recovery is very variable, with some proteins being completely solubilized. After extraction,
around 10% of the cell protein remains in the pellet. Depletion of highly abundant proteins is
one of the effective and most commonly used approaches to improve the detection of targeted
proteins in a complex sample, such as serum or plasma. The enzymatic digestion process is
also critically important in the sample processing steps. For example, enzymatic digestion of
proteins at concentrations lower than 500 fmol/µl is very inefficient. This is simply due to the
34
Chapter 1
fact that proteases show 50% maximal activity in the range 5-50 pmol/µL. Finally, protein
detection depends on the natural abundance of the protein, the proteotypic features of the
peptides and moreover on the ability of the peptide to ionize under particular instrumental
conditions.
1.12.4-Prefractionation techniques in proteomics
The dynamic range analyzed by liquid chromatography tandem mass spectrometry (LC
MS/MS) or matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) has
been the subject of much technical effort. These technologies remain challenged by biofluid
samples such as plasma in which protein concentrations of interest span a dynamic range
greater than 10 orders of magnitude, while, the dynamic range using LC MS/MS is 105
compared to 102 of MALDI MS.109
Prefractionation techniques, such as ultra-centrifugation, can allow small sub-populations of
cells to be specifically isolated, thus greatly increasing the sensitivity of the analysis.
Prefractionation techniques include chromatographic and electrophoretic approaches, such as
as iso-electric focusing (IEF) and sodium dodecyl sulfate polyacrylamide gel electrophoresis
(SDS-PAGE) techniques, as reviewed below.
1.12.4.1-Electrophoretic approaches
Among the most common fractionation methods in proteomic research are one-dimensional
(1D) and two-dimensional (2D) electrophoresis to separate protein mixtures110. In these
approaches, the proteins are separated according to their ability to move under the influence
of an electric field through the pores of a polymeric gel.
Electrophoretic mobility is proportional to the charge on the protein mixture and inversely
proportional to the retarding forces that are determined by the size and the shape of protein
mixture. This charge can be produced either by coating the protein with anionic detergent or
by acid-base reactions of amine and carboxylic acid moieties in the protein, depending on the
35
Chapter 1
type of electrophoresis experiment being carried out. Complex protein mixtures are often
separated by 2D gel electrophoresis.
This approach is based on two independent protein properties in two different steps; the first
dimension, where the proteins are separated based on their iso-electric point (pI), occurs in
liquid phase.111,112 The pI of a protein is the pH at which the number of negative charges is
equal to the number of positive charges, thus making the protein electrically neutral. When
the proteins reach their isoelectric point (i.e., the protein is neutral) the protein stops
migrating. In the second dimension, the iso-electric focused proteins are further resolved on
SDS-PAGE according to their molecular weight. Each spot will become specifically occupied
by a unique protein or a small number of proteins. For each protein resolved on the gel, its
iso-electric point, molecular weight, and the relative quantity can be calculated.113 This type
of prefractionation allows for higher protein load than direct analysis, and facilitates the
detection of lower abundance proteins.
1.12.4.2-Sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE)
Laemmli shown for the first time that proteins could be fractionated by SDS-PAGE.114 This
approach is a combination of SDS treatment of the proteins and polyacrylamide gel
electrophoresis. Proteins separation is based on their electrophoretic mobility. The protein is
solubilised with a large excess of SDS, an anionic detergent, which binds to proteins leaving
all proteins with negatively charged groups, therefore protein separation is based on
molecular weight only.
The negatively charged proteins are driven through the gel by an electric field and the pore
size of the gel retards the movement according to the molecular weight of the protein. It is
assumed that all proteins are unfolded by the SDS and therefore their travel through the gel is
not affected by their native folding. By changing either the total acrylamide content of the gel
or the cross-linker content of the total acrylamide, the pore size can be controlled and the
degree of the restriction changed.
Proteins lacking hydrophobic groups may run in a way that does not reflect their molecular
weight accurately. Similarly, some types of modification, including phosphorylation and
36
Chapter 1
glycosylation, cause proteins to run more slowly than expected. The fact that all proteins are
negatively charged means that all movement is in the same direction, towards the positive
electrode. Reducing agents such as β-mercaptoethanol are added to the sample loading buffer
to cleave any disulfide bonds. Following electrophoresis, the gel-separated proteins may be
visualised by staining with Coomassie brilliant blue or silver-staining.
1.12.4.3-Iso-electric focusing (IEF) fractionation technique
In 2006, Agilent introduced the OFFGEL fractionator that combines traditional IEF using
immobilized pH gradients (IPG) strips with a liquid phase. IEF is a powerful approach in
separation and concentration of amphoteric biomolecules such as proteins and peptides based
on their pI in a pH gradient under the application of an electric field. During movement
through the pH gradient, the protein will either become deprotonated or protonated. When the
protein eventually arrives at the point along the pH gradient equivalent to its pI, it will stop
migrating because there is no net charge left on its structure.
Because the pI is a unique characteristic of amphoteric substances, IEF is a powerful tool in
zwitterionic separation with high resolution. The amphoteric nature of peptides and proteins
stems from the presence of both carboxylic and amino groups. IEF can resolve proteins with
high fidelity.115 Proteins that have been separated with IEF can then be separated in the
second dimension based on their molecular weights.
The advantage of the OFFGEL for IEF over other techniques is justified by the easy recovery
of liquid fractions of peptides or proteins of small volumes, which are further amenable to
liquid chromatography (as a second dimension separation) or mass spectrometric analyses.
IEF gives higher resolution separation and experimentally derived pI information, which can
be used as a filter criterion for tandem mass spectral data validation.116 However, a major
limitation is the tedious post IEF sample processing that requires cleaning up proteins or
peptides. Also the presence of ampholytes can cause a decrease in detection sensitivity and
suppression of ionization of analytes in mass spectrometry (MS) detection. The technique of
IEF has been applied in this project.
37
Chapter 1
1.12.5-Chromatographic approaches
Chromatographic methods allow the separation of closely related compounds in complex
mixtures based on their differential interaction with a stationary phase and elution by a
mobile phase. These approaches have the advantages of speed, reproducibility and easy
automation. A commonly used separation approach to simplify peptide or protein mixtures,
prior to electrospray ionization mass spectrometry (ESI-MS) analysis, is liquid
chromatography (LC). This technique was originally developed by Russian Botanist M.S
Tswett in 1903117 and since then there has been an enormous development of this technique.
The separation of peptides is based on differences in the rate of migration through the
column. Depending on the binding behaviour, the peptides will elute from the column at
different times.
The defining breakthrough in the field of LC is the introduction of small diameter particles,
such as octadecyl groups bound to silica, in the late 1960s. This technique became the most
powerful separation approach and is called high performance liquid chromatography (HPLC).
For proteins and peptides where the analytes are often non-volatile and/or occur in an
aqueous matrix, the reversed-phase mode, using hydrophilic mobile phases and a
hydrophobic stationary phase is used.
To improve peptide separation prior to MS analysis two dimensional liquid chromatography
(2D-LC) using strong cation-exchange (SCX) chromatography in the first and reverse-phase
liquid chromatography in the second dimension has been shown to provide optimal results.
Washburn et al. successfully introduced the multidimensional protein identification
technology (MuDPIT), where generated peptides are separated on a biphasic microcapillary
column packed with SCX and reverse phase material and are subsequently eluted into the
mass spectrometer.118 A different 2D-LC method using reverse phase in both dimensions but
at different pH values has also been described.119
1.12.5.1-Reversed-phase (RP) chromatography
Reverse-phase liquid chromatography (RP-LC) in conjunction with mass spectrometry is a
powerful tool to reduce the sample complexity. It separates proteins/peptides according to
38
Chapter 1
their hydrophobicity. Proteins or peptides are adsorbed on a stationary phase carrying
hydrophobic groups, and are eluted with a gradient of increasing concentration of organic
solvent normally in binary mixtures with water. The most commonly used RP-LC columns
are packed with silica particles with attached hydrocarbon chains, usually C4, which is
generally used for protein, and C8 or C18, which are used for peptides and small molecules.
By using RP-LC both fractionation of complex mixtures and purification of the samples
occur since buffer salts are eluted in the void volume. Moreover, the LC separation also
increases the concentration of every analyte in the eluent. Decreasing the small inner
diameter of the column leads to increasing the concentration effect of the analyte owing to a
reduced peak volume as a result of a lower flow rate. The direct elution of peptides from an
RP column into the ESI source is one of the most widely-used LC-MS based techniques.
There are two factors that should be considered when combining a chromatographic
instrument directly with an ESI source: a low flow rate between the LC-device and the ESI
source and the selection of the appropriate mobile-phase composition, as they both are crucial
to obtain high sensitivity in LC-ESI-MS.120 High-performance liquid chromatography
(HPLC) is the most used technique in-line with ESI MS.
1.12.5.2-Ion-exchange chromatography (IEC)
Ion exchange chromatography (IEC) uses stationary phases that bind proteins and peptides
based on their charge. Ion exchange chromatography is a popular approach due to its
widespread
applicability,
high
resolving
power,
high
capacity,
and
simplicity.
Chromatography-packed materials are generally water insoluble polymers containing cationic
or anionic groups. The stationary phase of an ion exchange packing consists of functional
groups that have either a positive charge (anion exchange) with general formulae -NHR2+ and
-NR3+.,
(cationic tertiary and quaternary ammonium groups),
or negatively charged
molecules (cation exchange), such as -SO3-, -PO32- and -COO-.
Because the column works by ionic interactions, binding must take place under low ionic
strength conditions. Elution is accomplished by increasing the ionic strength (salt
concentration buffers) to destroy the ionic interactions or by changing the pH of the mobile
phase and therefore the charge density on the proteins/peptides being resolved. Strong cation
39
Chapter 1
exchange belongs to this class of chromatography and has been widely used as a first
dimension separation for proteomics
121,122
or in MuDPIT. Strong cation exchange (SCX) is
carried out near pH 3.0 where the peptide carboxylates will be predominantly in a protonated
form and the separation is then based on difference in positive net charge in solution. The
SCX column is based on sulfonic acid groups linked to the surface of the stationary phase.123
The positively charged peptides are mainly retained on the stationary phase due to ionic
interactions with the negatively charged sulfonic groups. The elution depends on the strength
of the interaction between the peptides and the SCX resin. A higher salt concentration is
required to interfere with the interaction of peptides that bind strongly to the SCX resin.
1.13-Bottom-Up and Top-down approaches
Protein pre-fractionation can be performed prior to top-down and bottom-up proteomics
analyses. In the “bottom up” approach, complex protein mixtures or purified proteins are
subjected to proteolytic digestion prior to mass spectrometry analysis. This method is referred
to as “shotgun proteomics” for the complex protein mixture analysis, which includes the four
following steps: proteolytic digestion, peptide separation, peptide fragmentation in the mass
spectrometer and lastly data analysis (database searching).
This approach is more widely used, both for post-translational modifications studies and for
the identification and characterization of proteins in general and has therefore become a more
established strategy. There are some aspects that can complicate the use of “bottom up”
approach; the complexity of the protein mixture to be analyzed is increased, as each protein
will yield several peptides after digestion. Moreover, the compression of peptide masses into
a narrow mass range, limits the analytical capacity of the mass spectrometer used for the
experiment. By contrast, the “Top-Down” approach relies on analysis of intact proteins by
mass spectrometry.124
Sample complexity is held at the minimum possible, the intact precursor can provide many
clues as to the identity of the protein, and post translational modifications are easier to
identify due to the shift in the precursor mass. By performing MS/MS analysis, improvement
in sequence coverage can be achieved. Another gain of the top-down approach is that a
40
Chapter 1
sequence of product ions is generated without requiring recompilation of the intact protein
from multiple proteolytic fragments. Structural studies, protein-protein interactions,
discovery of disease biomarkers, drug targets, identification of membrane proteins and posttranslational modifications have been studies using these strategies.
1.14-Protein identification and bioinformatics
In the past, scientists could readily interpret the flow of data from low throughput techniques.
Now, no one can handle the quantity and complexity of data being produced without
sophisticated bioinformatics tools. Two approaches can be used to identify proteins that are
generated from bottom up mass spectrometric experiments: peptide mass fingerprinting
(PMF) and MS/MS peptide sequencing. The principle behind PMF is that a group of peptide
masses obtained by mass spectrometric analysis of a proteolytic digestion can act as a
fingerprint, and this property is unique to that particular protein. Therefore, the set of masses
can be compared with the masses calculated from in silico digestion of various entries in
protein database search programs such as Swissprot with the same protease to identify a
protein. This peptide map is often obtained using a MALDI-ToF instrument, usually in
combination with a time of flight analyzer (ToF).
PMF allows a rapid identification of the protein if it is represented in the database. High
sequence coverage, meaning a large representation of the sequence of the protein, must be
obtained in order to identify the protein unambiguously. Peptide mass fingerprinting is
usually the method of choice for non-complex samples, such as 2D gel separated proteins. A
score is generated which reflects the probability that the observed match between the
observed peak and the theoretical value is a chance event.125,126 For this approach to be
successful, the protein must be relatively pure. It is possible to have peptides from two
different proteins present with the same molecular weight, which confuses identification of
the protein.
A second approach of protein identification is using the MS/MS peptide sequencing. In this
approach, fragments arise from the precursor peptide ions as a result of fragmentation
techniques, which will be discussed in detail later in this chapter. The set of fragment masses
acts as a fingerprint for the peptide and can be used to search sequence databases for similar
41
Chapter 1
peptides. The fragment ions contained in the spectrum provide an added degree of specificity
to the peptide mass and can allow identification of proteins based on a single tandem mass
spectrum. The approach to identifying proteins using databases is to submit the MS/MS
spectra to search computer algorithms. A search engine scans against protein sequence
databases using various algorithms which calculate theoretical tandem mass spectra to which
experimental fragment ions are compared.127
1.15-Mass Spectrometry
Mass spectrometry (MS) has become the most widely applicable of all of the analytical tools
in many proteomics studies. MS is not only a sensitive detection technique; it can also be
employed for the detection of a wide range of analytes without derivatization ranging from
small molecules, such as drugs, to large molecules, such as polymers, peptides, proteins and
native protein complexes. MS provides accurate molecular mass measurements and structural
information helping with the identification of unknowns, together with bioinformatics
methods to investigate for example: protein modifications, abundance and conformation,
ultimately leading to new insights in biochemical pathways.
Any MS instrument is essentially built up of three basic components: an ion source in which
the molecules can be converted into ions or ions in solution can be transferred into gas phase,
a mass analyzer which separates ions according to their mass-to-charge (m/z) ratio; a detector,
which registers the number of ions at any given m/z value. The mass spectrum is originated
by signal conversion with m/z on the x-axis and ion count/intensity on the y-axis.
In 1897, an English scientist, Sir J. J. Thomson,128 performed the ground-breaking work
which led to the discovery of electrons; this work also led to the first mass spectrometer.
Thomson was exploring electrical currents inside cathode ray tubes. He noticed that cathode
rays were deflected by both magnetic and electric fields and he was able to measure a cathode
ray's charge/mass (e/m) ratio.
The major achievements in the development of mass spectrometers especially mass analyzers
were in the period from the late 1930 to the early 1970. William E. Stephens proposed the
42
Chapter 1
concept of time-of-flight (ToF) MS in 1946.129 The ideas of quadrupole mass analyzer and
quadrupolar ion trap were introduced in 1953 by Wolfgang Paul.130 In 1989, Paul shared a
Nobel Prize in physics for his invention of the three-dimensional (3D) quadrupole ion trap.131
1.15.1-Ionization Sources
In the early days of mass spectrometry, ion sources were limited to analyzing volatile and
thermally stable compounds with molecular weights less than roughly 103 Dalton. These ion
sources include electron ionization (EI), chemical ionization (CI) and field ionization (FI) in
which the analytes are first volatilized, following which the gaseous components are ionized
in various ways. The volatilization step may be carried out either by external batch inlet
system or internal heated probe. To extend the MS applicability to non-volatile and thermally
unstable compounds with increased molecular weights, ionization sources such as: field
desorption (FD), fast atom bombardment (FAB), secondary ion mass spectrometry (SIMS),
laser
desorption
(LD),
plasma
desorption
(PD),
thermal
desorption
(TD),
electrohydrodynamic ionization (EHMS) and thermospray ionization (TI) were developed.
Ion sources are divided into hard and soft based on the effect of the ionization step on the
stability of the analyte molecule. For example, EI is a hard ion source in which ions are in
highly excited state; this produces very high fragmentation associated with relaxation of these
ions resulting in mass spectra dominated by fragment ions. Conversely, the soft ion sources,
which include CI, electrospray ionization (ESI) and MALDI produce little ion excitation.
Consequently, little fragmentation occurs, producing simple mass spectra. In the early 1980s,
FAB was introduced and had the greatest impact on expectations of future success because
the FAB source has been so easily adapted to all kinds of mass analyzers. With this type of
source, analytes often in a glycerol solution matrix are ionized by bombardment with a beam
of neutral Ar or Xe atoms having a kinetic energy of several thousands eV.
This technique did not solve the problems of extending MS to the analysis of higher masses,
low ionization efficiency and high background peaks arising from the matrix.132,133 FAB has
largely been replaced by electrospray ionization (ESI)134 and matrix assisted laser desorption/
43
Chapter 1
ionization (MALDI).135,136 These ionization methods provide better sensitivity and are much
easier to use.
1.15.1.1-Matrix-assisted laser desorption/ionization (MALDI)
The MALDI technique was first described in 1988137, for which Tanaka was awarded a
portion of the Nobel Prize in chemistry in 2002. The everyday techniques for MALDI as used
today were developed by Karas and Hillenkamp and co-workers.138,139
Matrix-assisted laser desorption/ionization (MALDI) is an ionization technique used for
analysis and investigation of high molecular weight molecules. It is compatible with buffers
and additives commonly used for isolation of proteins or peptides140-142 except sodium
dodecyl sulfate (SDS).143 Detection of sub-picomoles and low femtomoles in special cases
can be achieved. These characteristics make the MALDI technique useful for PMF of the
proteolytic digests of proteins.
In the most common configuration, MALDI sources are coupled to ToF mass analyzers.
Analytes are ionized and their m/z ratio measured in ToF mass analyzer. Briefly, the analyte
is first mixed with molar excess of a matrix compound which is an ultra-violet (UV)
absorbing weak organic acid. The most commonly used matrices are 3,5-dimethoxy-4hydroxycinnamic acid and 2,5-dihydroxybenzoic acid for peptide analysis and sinapinic acid
for protein analysis. The mixture of matrix and analyte are co-crystallized and then irradiated
by a laser pulse and the absorbed energy causes sublimation of matrix crystals and analyte
molecules (Figure 1.11). Consequently, the molecules are ejected into the gas phase.
The laser used in MALDI instruments is generally nitrogen emitted at 337 nm. Depending on
the application, the choice of matrix compound can be important because some matrices do
prompt the fragmentation of the protonated peptides as summarized by Brown.144 Sometimes,
the matrix interference occurs from abundant background ions and can be controlled by
reducing the molar ratio of matrix to analyte.145
44
Chapter 1
Figure 1.11: Schematic representation of the mechanism of MALDI.
http://www.chm.bris.ac.uk/ms/theory/maldi-ionisation.html
1.15.1.2-Electrospray ionization (ESI)
In 1968, Dole el al described the principle of ESI146, which was widely demonstrated by Fenn
in the 1980s134,147 for which he later received a Nobel Prize. ESI is generated directly from
solution containing the analyte ions, which is passed through an electrospray capillary tip
held at high potential (typically 3.5 kV).
Figure 1.12: The electrospray process. A very high voltage is applied to capillary, resulting in
a Taylor cone formation. When coulombic repulsion overcomes the surface tension, highly
charged aerosol droplets will be emitted. This process is repeated until gas phase ions are
finally formed. http://www.magnet.fsu.edu/education/tutorials/tools/ionization_esi.html
45
Chapter 1
When the repulsion between charges on the surface of the liquid exceeds the surface tension,
a Taylor cone is formed and droplets are extracted,148 which results in the formation of a very
fine spray of highly charged droplets (Figure. 1.12). As the droplets traverse from the tip of
the capillary to the orifice, they are desolvated by solvent evaporation, assisted by a flow of
heated nitrogen gas (known as drying gas) then the droplets shrink and the charge density of
ions increases.
A droplet will, however, remain intact as long as its surface tension is stronger than its
internal charge (coulomb) repulsion. The most significant advantage of ESI is the ability to
produce multiple-charged ions; thereby the detection of molecules with larger masses than
the upper m/z limit of the mass spectrometer, such as proteins and other biopolymers,
becomes possible. The precise mechanism of the ion formation is a matter of debate and two
theories have been proposed explaining the final production of gas-phase ions. Dole and coworkers proposed the ‘charge residue model’ (CRM),146 where a droplet fission process
which leads to formation of nanodroplets containing on average one analyte ion or less.
Charged Residue Model (CRM)
Ion Evaporation
Model (IEM)
Figure 1.13: Schematic of the proposed mechanisms for the formation of gas-phase
ions from charged droplets. The upper and the lower parts of the diagram illustrate the
ion formation mechanisms considered in the CRM of Dole et al.123 and the IEM of
Iribarne and Thomson149 respectively.
46
Chapter 1
Iribarne and Thomson149 proposed the second mechanism for the production of gas-phase
ions (Figure. 1.13), the ion evaporation model (IEM), which assumes that before a droplet
reaches the ultimate stage its surface electric field becomes sufficiently large to lift an analyte
ion at the surface of the droplet over the energy barrier that prevents its escape. The general
idea is that the formation of gas phase ions of large biomolecules is mainly dictated by the
CRM mechanism whereas smaller ions are produced by the IEM.150-152 ESI-MS is less
tolerant to high salt concentrations and detergents than MALDI , with the former leading to
signal suppression153 and clustering effects and the latter having a detrimental effect on the
signal to noise ratio, spray formation and moreover saturation of C18 stationary phases in
hyphenated LC-ESI MS instrumental platforms. Improving the sensitivity of ESI can be
achieved by reducing the diameter of the spray tip and lowering sample flow rates.
1.15.2-Mass analyzers
The ionization technique of choice may be combined with a range of different mass
analyzers, for example time of flight (ToF), quadrupole mass filter, ion-trap, magnetic or
electric sectors and Fourier-transform ion cyclotron resonance mass analyzer.154
Gas-phase ions are guided through ion-optics devices into the mass analyzer. The mass
analyzer separates these ions according to their mass to charge ratios (m/z). The selection of
the mass analyzer is a crucial step and depends upon the spectral acquisition speed, mass
accuracy, mass resolution and the detection limit needed for a given application. There are
two subtypes of mass analyzers, scanning and non-scanning mass analyzers. For example, the
quadrupole mass filter is classified as a scanning mass analyzer which only transmits ions of
a selected mass-to-charge ratio to the detector at a given time from a mixture of ions with
different mass to charge ratios and different relative abundance.
Non-scanning mass analyzers, such as ToF, ion cyclotron resonance and orbitrap, permit the
accumulation of an entire ion population from a single pulse of ions. Each of these mass
analyzers can be coupled to either MALDI or ESI ion sources. One common feature to all
mass spectrometers is that both the analyzer and detector are kept under high vacuum to
allow ions travelling from one end of the instrument to the other without any hindrance
47
Chapter 1
originating from air molecules.155 In this thesis four types of mass analyzers have been used
and described below; ToF, ion trap, quadrupole, and the Orbitrap.
1.15.2.1-Time of flight (ToF)
The initial ToF concept was introduced by William Stephens in 1946129. ToF is one of the
simplest of the MS instruments. It measures the flight time of the ions to the detector. Ions of
interest are accelerated (dependent on the number of charges) with the same amount of
kinetic energy in a straight path to the detector. Because all the ions in the acceleration zone
have the same kinetic energy when entering the flight tube, but different masses, the lighter
ions reach the detector first because of their greater velocities. The heavier ions take longer
due to their heavier masses and lower velocities. The equation governing ToF separation is
explained as: an ion with mass m and total charge q=ze has a kinetic energy, Ek:
=
=
=
The time it takes the ion to fly the distance (d) to the detector is given by: = /
=
where ν is the velocity of the ion with mass m and charge z, Vs is the acceleration potential
and d the length of the flight path to the detector.
A ToF mass analyzer can accept a large number of ions with no upper m/z limit and all the
ions flying in the flight tube can be detected. ToF can operate in two different modes named
linear and reflectron. Linear ToF has limited resolving power and low mass accuracy. To
solve these problems that results from minor kinetic energy differences among ions of the
same m/z and to minimise flight time variations, an ion mirror or reflectron can be
incorporated (Figure 1.14bottom) at the end of the flight tube, which was first suggested by
Mamyrin et al.156 It typically consists of multiple rings and insulating spacers held together
with threaded rods, which create a “reflecting” field.
48
Chapter 1
Figure 1.14: Schematic representation of a linear (top) and reflectron (bottom)
time-of-flight mass spectrometer.
http://www.anagnostec.eu/maldi-tof-ms/technology.html
The rings carry the voltages and form the electric field that reflects ions. Ions reach the
reflector and are then turned in an opposite retarding field. Ions will penetrate the field to a
different extent depending on their kinetic energy. As a result, ions with high kinetic energy
move deeper before reflection than ions with lower kinetic energy which are reflected less
deeply.
Ions of the same m/z values which have different initial energies then reach the detector at the
same time, therefore a remarkable improvement in resolution is achieved. The ToF mass
analyzer is ideally fitted for ionization techniques of pulsed nature. It is combined with the
MALDI source in-line with the flight tube.
A ToF mass analyzer can alternatively be coupled to ESI, where the ion-source is placed
perpendicular to the flight tube157 so ions are pulsed into the ToF analyzer. The advantages of
using a ToF analyzers include relatively high resolution, sensitivity, unlimited mass range,
fast analysis, and a relatively low cost.
49
Chapter 1
1.15.2.2-Quadrupole mass filter
Quadrupole mass filter was invented in 1950 by Wolfgang Paul and co-workers.130,131 It
consists of four parallel metal rods, ideally with a hyperbolic cross section to which direct
current (DC) and an oscillating voltage (AC voltage at radio frequency RF) are applied
(Figure 1.15). Ions from an ion source are accelerated into the quadrupole analyzer and by
increasing the magnitude of the DC and RF voltages while maintaining the magnitude of the
DC to RF ratio.
Figure 1.15: Schematic illustration of a quadrupole mass filter (Figure adapted from
http://chemwiki.ucdavis.edu/Analytical_Chemistry/Instrumental_Analysis/Mass_Spectrometry/Mass_
Analyzers_%28Mass_Spectrometry).
The quadrupole can operate in two different modes: in the first mode, the analyzer can scan
over a range of m/z values while in the second mode it is set to transmit only one m/z
value.158,159 When the quadrupole is used as a filter device, stable trajectories are created for
ions of the same m/z, and selected ions will then reach the detector and all other ions deviate
from their stable trajectories, collide with the quadrupole surfaces and are consequently lost.
A quadrupole mass filter can be coupled to a ToF mass analyzer resulting in a hybrid
instrument, namely the quadrupole time of flight (Q-ToF).
50
Chapter 1
1.15.2.3-Quadrupole ion trap
A quadrupole ion trap is a mass analyzer roughly the size of a tennis ball whose size is
inversely proportional to its versatility. It is now called 3D quadrupole ion trap to
differentiate it from the linear quadrupole ion trap and the orbitrap. In a quadrupole ion trap
analyzer, the ions are introduced into the mass analyzer in a pulsing mode as opposed to
normal quadrupoles in which ions continually enter the mass analyzer.130 A quadrupole ion
trap comprises three hyperbolic electrodes, consisting of a ring electrode and two endcap
electrodes, which form the core of this instrument (Figure 1.16).
Figure 1.16: Schematic overview of the quadrupole ion trap. Ions in a quadrupole ion trap
maintain stable trajectories inside the device due to application of a radio frequency
voltage to the ring electrode. Figure adapted from
http://www.nature.com/nrd/journal/v2/n2/fig_tab/nrd1011_F4.html
A quadrupole ion trap uses an oscillating electrical voltage called “fundamental RF” applied
to the ring electrode to stabilize ions in the centre of the trap. An electrostatic ion gate
changes from positive to negative voltages to repel or attract ions towards the entrance
endcap aperture. Helium is used to fill the ion trap. Collisions with helium (1 mTorr) reduce
the kinetic energy of the ions, thereby changing their trajectories toward the centre of the ion
trap. Manipulation of ions in stable trajectories can be achieved using resonance excitation. It
is extremely important in causing particular ion populations to be controlled in the trap.
Resonance excitation may be achieved by matching an AC frequency to the secular
51
Chapter 1
frequency of an ion in the trap. This will cause the ion to gain energy and the amplitude of the
trajectory to linearly approach the end-cap electrodes until the ion is ejected from the trap.
Ion traps are population limited; the “space-charge” effect results from too many ions in the
trap, which leads to degradation in resolution, a reduction in peak height and a shift in mass
assignments. To minimize the “space-charge” effect and maximize the signal, the time during
which ions are allowed into the trap is adjusted. The mass resolution of the ion trap is a
function of scan range and scan speed. Resolution is increased by reducing both scan range
and scan speed.
1.15.2.4-Orbitrap mass analyzer
The orbitrap mass analyzer is a newly developed mass analyser consisting of two electrodes
in a form of outer barrel-like electrode and a central spindle-like electrode as represented in
Figure 2.7. OMA is the latest development in ion trapping by means of electrostatic fields. In
1922, Kingdon160 first implemented the concept of the Orbitrap. The first model was
proposed by Makarov161 for the generation of mass spectra.
Figure 1.17: Cutaway view of the
orbitrap mass analyzer. Ions trajectories
are demonstrated by arrows. The ions
orbit around the central electrode while
oscillating back and forth along the
axis161
The commercially available Orbitrap mass spectrometers are Linear Trap QuadrupoleOrbitraps (LTQ-Orbitraps) (described in more details in the next paragraph) that add together
the benefits of speed, large trapping capacity, MSn capability and versatility with the benefits
of FT-ICR-like high resolution, high sensitivity, high dynamic range and mass accuracy.
After ions are injected into the Orbitrap, a DC voltage is applied to the inner electrodes, while
the outer electrode is set at ground potential during the trapping period (Figure 1.17).
52
Chapter 1
Application of an additional potential to the endcap electrodes can be used to achieve
simultaneous ion trapping in the axial direction, with the ions moving in stable orbits around
the central electrode. The frequency of an ion in axial oscillation (
)is dependent on the
ion mass/charge ratio and the potential between the electrodes which is constant and can be
described as:
=
/
Where k is the axial restoring force (the value is dependent on the shape of the electrode and
the potential applied), m and q are the mass and charge of the ion. The LTQ-Orbitrap (Figure
1.18) is a hybrid mass spectrometer, consisting of a linear trap quadrupole made of four
hyperbolic cross-sectional rods. The ions are generated in the ion source and then
accumulated via RF-only multipoles into the linear trap (LTQ).162 A short prescan is used to
determine the ion current within the mass range of interest therefore allowing storage of a
user-defined number of ions. This acquisition mode is referred to as Automatic Gain Control,
(AGC). Ions are then transferred to a curved linear trap termed C trap in which the injected
ions are tightly focused in time and space.
Ions are finally injected from C-trap into Orbitrap as packets of fast and homogenous ions
during brief interval in which the RF is ramped down and DC is ramped up. The addition of
an ion trap mass analyzer to an Orbitrap detector enables multiple levels of fragmentation
(MSn) as well as multiple fragmentation modes (CID, HCD, and ETD) for the elucidation of
analyte structures. This hybrid instrument has a resolving power of 150,000 (full width at half
maximum, FWHM) and a mass accuracy of 2-5 ppm (LTQ).163, 164
53
Chapter 1
Figure 1.18: Schematic representation of the Thermo LTQ-Orbitrap with an external
Source.163
1.16-Tandem mass spectrometry (MS/MS)
Structural information can be obtained from the product ion spectrum of any ion by inducing
its dissociation. The resulting fragmentation pattern is related to the isolated ion which allows
us to obtain structural information.
Tandem mass spectrometry experiments can be performed either in space or in time. In
tandem in space instruments, such as the Tandem quadrupole, the first mass analyzer is used
to perform isolation of a particular m/z value from the ion population generated by the ion
source. A specific m/z is therefore selected and transferred into the collision cell for ion
dissociation by adding energy, then the ion products of dissociation are transferred to a
second mass analyzer for the determination of their m/z values.
In addition, tandem mass spectrometers in space include hybrid instruments such as Q-ToF in
which two or more mass analyzers are combined together. MS/MS accomplished through
tandem-in-time means that all the individual processes (ion accumulation, selection and
dissociation) are carried out in the same physical space within the instrument, but in a
sequential fashion, as typically done with FT-ICR, orbitrap and quadrupole ion trap
instruments. Ion dissociation can be achieved by collisions with an inert gas within the
54
Chapter 1
collision cell or the ion trap. The inert gas can be nitrogen, helium or argon. The process is
referred to as collision induced dissociation (CID). CID can be divided in terms of energy
into low energy (10-100 eV) or high energy (e.g. 10 keV)165 Triple quadrupole instruments
use low-energy CID in the central quadrupole for precursor ions fragmentation whereas
instruments such as the TOF/TOF use energies that can reach several keV, (high-energy
CID). Fragmentation of peptides by CID depends on several factors; these include the mass
and charge state of the precursor ion, amino acid composition and the residues adjacent to the
backbone cleavage. In addition, another factor is the excitation method used as there are
several alternative fragmentation methods. These include electron-capture dissociation
(ECD)166 and electron transfer dissociation (ETD).167
1.17-Activation methods in tandem mass spectrometry
The most commonly used activation methods in tandem mass spectrometry (MS/MS) of
peptides and proteins are CID and ETD. In CID, labile post-translational modifications (PTM)
such as phosphorylation and glycosylation undergo neutral loss in the gas-phase, resulting in
reduced peptide backbone dissociation and consequentially limited structural information;
Neutral loss of labile groups can be limited by using non ergodic processes such as ETD and
ECD as activation techniques. Both CID and ETD have their obvious advantages and
drawbacks. In general, CID performs very well in sequencing of non-modified peptides with
low charge states (+2 and +3), while ETD has somewhat a less good sequencing capability
but is successful in locating modification sites.
CID generated fragment ions are strongly dependent upon the sequence of the peptide being
analyzed and the instrument used, producing great variability in the mass and intensity of
peaks. Fragmentation of precursor ions by CID and ETD results in the peptide backbone
being cleaved at distinct sites, yielding unique ion fragments (b and y ions versus c and z ions
respectively). This nomenclature was originally proposed by Roepstorff and Fohlman168 and
was later modified by Biemann.169 The two most common fragmentation techniques will be
discussed below.
1.17.1-Collision induced dissociation (CID)
CID is among the earliest processes developed for peptide fragmentation in a tandem mass
spectrometer and is also the most widely studied in bioinformatics. Many researchers have
55
Chapter 1
investigated the fragmentation of peptide precursor ions. Biemann and co-workers have
observed that ions obtained by charge-remote fragmentation (cleavage occuring remotely
from the site of charge (e.g., leading to a series of a, d, and w type ions) can predominate and
that the formation of these ions is related to the location of basic residues along the peptide
backbone.170,171 According to Biemann nomenclature, peptide backbone cleavage with charge
retention on the C-terminus are designated as x-, y-, and z- ions, whilst fragment ions that
retain the charge at the N-terminus are designated as a-, b-, and c- ions (Figure 1.19). The
most comprehensive model currently available to describe the fragmentation observed by
low-energy (eV) dissociation is termed the “mobile proton model”.169,172-177 This model
describes peptide dissociation as resulting from charge-directed fragmentation that is initiated
by intramolecular proton transfer to the incipient fragmentation site, which is the amide bond.
The peptides belonging to this group are multiply charged, where the protons are localized at
the most basic sites in the molecule, these sites a being the N-terminus and the side chains of
basic amino acid residues, particularly the guanidine-side-chain of Arg, the ε-amino group of
Lys, and the imidazole-ring of His. The mobile proton is transferred from the most basic sites
to various backbone amide sites, thus triggering rapid intra-molecular proton transfers which
result in a heterogeneous population of fragment ions.
Fragmentation of a protonated peptide with low-energy CID primarily results in the cleavage
of the peptide-backbone at the amide bonds (CO-NH) generating b and y fragment ions.
Normally, fragment b2 is the first stable member of this series. However, acetylation of the
N-terminal residue can lead to the formation of stable b1 ions.178 b and y ion series are
numbered from the N- and C-terminus, respectively. Additional ions can also be formed due
to neutral losses of ammonia from Asn, Lys and Arg. Elimination of water from serine,
threonine, aspartic and glutamic acid is also common. In addition, if a specific peptide ion
undergoes multiple fragmentation events, internal fragments are generated, including either
acyl ions or immonium ions.179
The fragmentation, however, does not always take place equally along the peptide backbone,
the most frequent fragmentation occurring near glutamic acid, glutamine and proline. Not all
peptide bonds have the same propensity to fragment under CID conditions and the intensity
of the generated fragments varies significantly within the same tandem mass spectrum. For
instance, the presence of Pro in a sequence facilitates cleavage of the peptide bond Nterminal to this residue–because of the slightly higher basicity of the imide nitrogen, yielding
56
Chapter 1
very abundant y-fragments. Similarly, cleavage at the C-terminus of Asp residues is favoured
due to protonation of the peptide bond by the amino acid side chain.180 In addition; the
position of the basic residues in the peptide backbone influences the peak intensity of b and yions. For example, when lysine terminating peptides contain a His residue close to the N
x4
R1
y4
z4
x3
H
z2
O
R3
x1
H
N
y1
O
R5
N
H2N
OH
N
O
a1
R2
b1
c1
H
O
R4
a2
c3
a4
H
O
OH
R4
H3N
b2
H2N
R1
C
H
O
x2
R5
N
C
H
O
y2
R2
H
O
O
NH
H
c2
+
H2N
+
NH3
O
N
OH
R4
O
O
R5
+
C
H
c4
O
+
N
C
b4
O
O
H
N
N
+
a2
+
R1
z1
R4
R2
R5
OH
N
C
H
O
z2
R1
H2N
+
Immonium ion
C
H
Figure 1.19: Common nomenclature of products ions according to Biemann.169
57
Chapter 1
Figure 1.20: Proposed mechanism for the formation of b and y ions.167
58
Chapter 1
terminus, the ion series observed may be controlled by this site and in such a case the
spectrum will be dominated by N-terminal sequence ions. In general, Arg limits the influence
of other basic amino acids,180 while His and Lys display similar basicities in the gas phase.181
CID tandem mass spectra of tryptic peptides, which terminate with a basic residue (Arg or
Lys) are characterised by intense y series.182,183
The presence of modified amino acids in peptides sequences has very noticeable effects on
peptide ion dissociation patterns and reduces the information content of MS/MS spectra
because neutral losses of the modification represent the most thermodynamically favoured
fragmentation pathway.184,185 An additional issue is the presence of multiple Arg residues in
peptide sequences which inhibits random protonation along the peptide backbone resulting in
relatively uninformative MS/MS spectra with a few dominating fragment ions.
Fragmentation mechanisms have been studied extensively by several groups.174,177,198
Substantial evidence has been provided that b2 ions and higher homologues share a
protonated oxazolone structure formed by cyclization involving the next-nearest carbonyl
function (Figure 1.20). This oxazolone structure was confirmed by CID studies.178 The
absence of b1 ion in oxazolone mechanism can be explained by the lack of such carbonyl
group in the b1 ion of an unmodified peptide.
1.17.2-Electron transfer dissociation (ETD)
ETD was introduced by McLafferty and co-workers in 1998166, and is regarded as a
promising method complementary to CID, as it leads to other types of cleavages (Cα-NH),
(Figure 1.21). ETD is better suited for sequencing larger and more basic peptides.
Mechanistically, ETD is similar to ECD. ECD is not practical in quadrupole ion traps
because the thermal electrons are quickly excited and ejected because they can’t be
efficiently trapped by the RF electrostatic fields. As a result, ECD is performed in FT-ICR
mass spectrometers.166 For ETD, radical anions of polyaromatic hydrocarbons, such as
fluoranthene, are formed under chemical ionization conditions, stored in a quadrupole linear
ion trap mass spectrometer, and then allowed to react with multiply charged ions in the gas
phase.
59
Chapter 1
In ETD, electrons are transferred via an ion/ion reaction from sufficiently low electron radical
anions to multicharged peptide cations, thereby converting them into radical cations, and
initiating fragmentation through a variety of pathways.
Dissociation of peptides by ETD maintains modifications such as phosphorylation,
methylation, acetylation, sulfation, nitrosylation and glycosylation,167,186,187 on the peptide
backbone, similar to ECD.188,189 Analyzing these modified peptides by ETD has been shown
to generally improve the sequence information as modifications are left intact for site
determination.
60
Chapter 1
Figure 1.21: Schematic diagram of ETD fragmentation mechanism.
61
Chapter 1
1.18-Types of tandem mass spectrometric experiments available on tandem quadrupole
instruments
The four main scanning modes of a tandem quadrupole instrument are precursor ion
scanning, product ion scanning, neutral loss scanning and selected reaction monitoring.
In product ion scan, the precursor ion is isolated in the first mass analyzer and transferred
into the collision cell - where it is fragmented. The fragment ions are then measured by
scanning the second mass analyzer. This results in an MS/MS spectrum and this modality is
the most common scanning method used for routine peptide identification.
Precursor ion scan. In this case a second mass analyzer is set to measure the occurrence of a
particular fragment ion while the first mass analyzer is used for scanning the ion population
generated by the ion source, allowing the detection of all the precursor ions which undergo a
particular fragmentation pathway leading to the fragment ion on which the third quadrupole
is set. The result is a spectrum of precursor ions that result in that particular product ion.
Neutral loss scan. In this case the first and last MS analyzers are both scanned to look for a
particular transition, such as loss of water, which produces a fragment ion with a mass
difference of 18 Da compared to the precursor ion. In a triple-quadrupole instrument, both
analyzers are scanned simultaneously at the same rate, but with a constant number of m/z
units. Neutral loss scanning is commonly used for PTMs analysis. For example, the neutral
loss of 98 Da is diagnostic of peptide phosphorylation.
Selected reaction monitoring (SRM) and/or Multiple reaction monitor (MRM)
In SRM and MRM, the first and the third mass analyzers act as filters to specifically select
predefined m/z values corresponding to a specific combination of precursor and fragment
ions, whereas the second mass analyzer serves as a collision cell. Several transitions or
“selected reactions” (precursor/product ion pairs) are monitored over time. The two levels of
mass selection with narrow mass windows result in high selectivity, as co-eluting background
ions are filtered out very effectively. SRM is at the base of the unique capabilities of triple
quadrupole MS for quantitative analysis.190
62
Chapter 1
1.19-Tandem mass spectrometry in a MALDI-ToF/ToF instrument
To obtain primary structural information in a MALDI ToF/ToF instrument individual ion
packets consisting of intact and fragment ions have to be selected. The process which occurs
‘post’ ionization in the source is termed post-source decay (PSD).191 PSD utilizes the high
efficacy of “hot” MALDI matrices, in particular of α-cyano-4-hydroxycinnamic acid, to
enhance fragmentation of MALDI generated ions. Post-source decay (metastable decay)
describes the dissociation of intact molecular ions which have acquired sufficient internal
energy during the desorption process (e.g. low energy collisions) and can therefore release
this excess of energy by undergoing dissociation while travelling through the field-free drift
path. The ions that undergo PSD in the flight tube and the resulting product ions continue
towards the detector with the same velocity, and thus are detected as a single peak in a
conventional linear ToF MS mode.192-194
Figure 1.22: Schematic diagram of the Bruker Ultraflex II MALDI-ToF/ToF mass
spectrometer.196
The reflector will separate precursor and metastable decay ions by their difference in kinetic
energy. PSD is routinely employed to obtain sequence information from peptides. The
acquision of a PSD spectrum may require a considerable amount of time (approximately 20
min) when stepping down the reflector voltage, short field free region which means limited
63
Chapter 1
time is available for fragmentation hence the fragmentation is less efficient,195 and PSD often
suffers from low mass accuracy and resolution.
The approach to overcome the disadvantages of PSD mainly relies on using the LIFT
technology, which was developed by Bruker Daltonics to provide a very high performance
MS/MS mode with higher resolution, mass accuracy, and sensitivity in MALDI ToF-ToF. In
LIFT technology, two MS/MS modes are available: Laser-induced dissociation (LID) and
CID. LID is typically used for protein identification and high energy CID for de novo
sequencing. In addition, in source decay can be performed due to the gridless ion optics of the
instrument. LID tandem mass spectra can be recorded in a more sophisticated MALDI
TOF/TOF instrument equipped with the LIFT technology.197
In LIFT method, a relatively low voltage of 8 kV is applied initially for ion acceleration,
which provides long flight time (10-20 µs) within the drift tube. Fragments generated from
LID are subsequently raised to a higher potential (19 kV) in the LIFT cell. This enables the
rapid (seconds) detection of all fragments without changing the reflector voltage, which
compared to conventional PSD is particularly advantageous for the detection of low mass
ions of low abundance.
The selection of the precursor ion together with their respective fragment ions is achieved by
timed ion selector (TIS) which deflects all ions except the ions under investigation by
switching the gate voltage; this TIS is housed before the LIFT cell. The selected “ion family”
leaves the TIS and enters the “LIFT” device. Since ions that have an identical mass but a
small velocity distribution start to drift away from each other, velocity-focusing is required,
so the LIFT cell is useful to correct this effect. The lift cell is the centre of the LIFT
technology. It comprises three regions between four grids: the first one is the actual potential
lift; at this stage as soon as the ions have completely entered the lift cell, the voltage on the
two adjacent grids forming the cell is rapidly increased from ground to 19 kV.
The second region is a focusing cell where the ions continue to fly at the same speed and
enter the focusing cell that is also held at 19 kV. The voltage on the third grid is now reduced
by 2–3 kV and the ions are accelerated towards the third compartment of the LIFT cell. The
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Chapter 1
third region is a post-acceleration cell, in which the ions are accelerated to full speed and
time-focused onto the detector, similar to the delayed extraction in the MALDI source.197
An additional device is located between the LIFT device and reflector, called “post lift
metastable suppressor” (PLMS), which works as a precursor ion suppressor similar to a timed
ion selector (TIS). In order to allow fragment ions to pass through the PLMS, its potential is
set to ground and then increased to a high potential prior to the passage of the precursor ions.
In this case, the precursor ion has been eliminated and further fragmentation in the ToF and
the reflector is impossible, resulting in limited background noise.
There is a gridless two-stage reflector incorporated into the instrument. It space-focuses the
divergent ion beam onto a small area of the detector. In addition, ions that undergo PSD
within the reflector field do not reach the detector and the gridless reflector allows a
reduction in the number of times the ions pass a grid. These properties led to the high
sensitivity and high signal-to-noise ratio.
1.20-Quantitative techniques in proteomics
Mass spectrometry (MS) is not a quantitative technique as ion yields are highly dependent on
the chemical and physical nature of the sample. To be able to measure a quantitative
difference in protein abundance by mass spectrometry, several quantification methods have
been developed. However, in recent years mass spectrometric methods based on stable
isotope labelling have shown great promise for peptide based quantification of proteins in
complex mixtures.199,200
These method involves the addition of a chemically identical form of the peptide(s)
containing stable isotopes (2D,
13
C,
15
N, etc) as internal standards to the sample, prior to
analysis by mass spectrometry. This method creates two versions of each peptide, which are
only distinguished by the isotope label and therefore are not subjected to differences in their
physico-chemical features. In particular, the ionizaion efficiency will be exactly the same for
the light and heavy version of the same peptide. Thus, the ratios between the intensities of the
heavy and light components of these isotopic pairs of provide an accurate measure of the
relative abundance of the peptides. This principle, known as isotopic dilution, allows the
65
Chapter 1
direct measurement of absolute quantities of peptides and therefore proteins within a sample
by mass spectrometry. Stable isotopic labelling can also be used to differentially label
different samples for relative quantification.
Incorporation of stable isotopes is achieved by chemical, metabolic, and enzymatic labelling.
Chemical labelling in the case of ICAT, iTRAQ and ICPL is performed on integrate the label
before, during, or after protein digestion, whereas metabolic labelling such as
15
N-labelling
and SILAC takes place during cell growth and enzymatic labelling during protein hydrolysis.
In cases where use of a labelled amino acid (SILAC) is impractical, organisms can be
labelled through growth on medium containing 15N (as a source of nitrogen) in place of their
natural abundance counterparts via full metabolic labelling.
Performing metabolic labelling guarantees a high labelling efficiency in cell culture, whereas
labelling efficiency using chemical labels is typically lower and should be carefully checked
during data analysis.
Chemical labelling
Isotope Coded Affinity Tag (ICAT)
In 1999, Aebersold and co-workers introduced the ICAT technology in which a biotin affinity
tag and isotope coding are combined together in a single alkylation agent.201 Briefly, ICAT is
a cysteine specific reagent consisting of a iodoacetyl group that specifically alkylates thiol
groups in cysteine residues, a linker containing either the heavy or the light isotopes and a
biotin group for affinity purification of cysteine-derivatized peptides (Figure 1.23).
The presence of cleavable linker is required, to remove the biotin portion of the ICAT reagent
tag prior to MS and MS/MS analysis. It reduces the overall size of the tag, enabling the
analysis of larger peptides. In addition, reduces tag fragmentation, which improves quality of
MS/MS data and significantly increases the number of identified proteins, with high
confidence per experiment.
66
Chapter 1
Figure 1.23: Composition of Isotope Coded Affinity Tag (ICAT) reagents.
Light and heavy ICAT-labelled protein samples are then combined and subsequently
digested. Then the ICAT labelled cysteine-peptides are enriched by streptavidin
chromatography to reduce sample complexity and allow protein quantification in complex
samples. The samples are then analyzed using reverse-phase chromatography and mass
spectrometry. The ratio of ion intensities from co-eluting ICAT-labelled pairs permits the
quantification, while a subsequent MS/MS scan provides peptide identification.
The advantages of ICAT technology come from its compatibility with any amount of protein
from different species and the chemoselectivity of the alkylation reaction which occurs even
in the presence of high concentration of salts and detergents. Moreover, ICAT is compatible
with almost any type of biochemical, immunological or physical fractionation, which makes
this technique appealing for quantification of very low abundance proteins. A major
limitation of the ICAT approach is that only cysteine containing proteins can be quantified;
the ICAT reagent is considerably large and therefore incomplete labelling might occur due to
steric hindrance. The initial commercial ICAT reagents were a pair of deuterated and the
correspondent non-labelled peptide isoforms. The drawback of using deuterium as the
labelled element is its influence on the retention time of the labelled peptide during
chromatographic separation, leading to variation in peptide ratios across the elution profiles
and consequently making quantification less accurate.202 This problem has been solved by
67
Chapter 1
introducing 13C-labeled ICAT reagent with a cleavable linker. This linker allows cleavage of
the purified linker directly from the purification column.203,204
Isotope-Coded Protein Label (ICPL)
One notable disadvantage of ICAT is that it fails to quantify proteins with no cysteine
residues. An alternative chemical modification strategy has been suggested to overcome this
problem: e.g. the amino-reactive labelling strategy ICPL and the successor to ICAT. ICPL,205
which is based on the N-hyroxysuccinimide (NHS) chemistry targeting the epsilon-amino
group of lysine residues in proteins. N-nicotinoyloxy-succinimide is used in a light (d0) and a
heavy (d4) form allowing for relative quantification of two different samples. This method
provides highly accurate and reproducible quantitation, high protein sequence information,
including PTMs and isoforms, and compatibility to all commonly used protein and peptide
separation techniques. However, the slight chromatographic shift during reverse-phase LC
that occur between peptides labelled with d0- and d4-Nic-NHS (N-nicotinoyloxy-succinimide)
is critical for reliable protein quantification. Therefore, the four deuterium isotopes of the
heavy nicotinoyl group were replaced by six 13C-isotopes to receive co-elution of the isotopic
peptide pairs and ensure accurate quantification.
Isobaric Tags for Relative and Absolute Quantification (iTRAQ)
A different approach, based also on N-hyroxysuccinimide (NHS) chemistry, is iTRAQ,206
they are multiplexing reagents, i.e. due to the isotope composition of the reagents several
samples can be compared relative to another in one experiment. This approach allows four (4
differentially isotopically labelled reagents) or eight (8 differentially isotopically labelled
reagents) samples to be profiled in one experiment. Its use of amino-specific isobaric
reagents, therefore resulting in labelling of lysine side chains and amino termini of peptides
and allowing for the identification and quantification of a more general population of
peptides. Unlike ICAT, the amine specificity in iTRAQ makes most peptides in a sample
amenable to this labelling strategy with no loss of information from samples involving posttranslational modifications. The iTRAQ 4-plex consists of a reporter group that is unique to
each of the four reagents, an amine reactive group, which consists of an NHS ester and is
68
Chapter 1
designed to react with the N-termini and lysine side chains of peptides after protease
digestions, and a neutral balance group to maintain an overall mass of 145.
In contrast to ICAT and similar mass-difference labelling strategies, iTRAQ-labelled peptides
(Figure 1.24) are isobaric and do not show a mass difference in the MS. There is no
fragmentation of the precursor signals in the first stage of mass analysis that could lead to
increased spectral complexity by the combination of multiple samples, resulting in enhanced
MS signal. Therefore, quantification is thus performed at the MS/MS stage rather than in MS.
Upon MS/MS fragmentation, the iTRAQ 4-plex gives rise to four unique reporter groups
(m/z=114–117). The attached balance groups are designed to make the total mass of the
balance and reporter group 145 Da for each tag, resulting in neutral fragments of 31 Da, 30
Da, 29 Da, and 28 Da, respectively.
Figure 1.24: protein quantification scheme using iTRAQ shows four different (S1 to S4)
samples, designated by four different colours.206
All other sequence-informative fragment ions (b, y, etc.) remain isobaric, and their individual
ion current signals (signal intensities) are additive. This remains the case even for those
tryptic peptides that are labelled at both the N terminus and lysine side chains, and those
peptides containing internal lysine residues due to incomplete cleavage with trypsin. The
relative concentration of the peptides is thus deduced from the relative intensities of the
corresponding reporter ions. However, the iTRAQ method is still limited by the number of
samples that can be compared in a single experiment. An inherent drawback of the iTRAQ
approach is based on enzymatic digestion of proteins prior to labelling, which artificially
69
Chapter 1
increases sample complexity by a factor of one to two orders of magnitude. So, the
combination of many samples into one for mass spectrometric analysis will logically reduce
the measurable dynamic range because each mass spectrometry experiment used to read out
the proteins and their abundance levels in the sample is limited to a given amount of sample
that may be analyzed. The iTRAQ approach is proposed to be best applicable to samples of
moderate to low complexity that can still be effectively separated by cation exchange
chromatography followed by nano reverse phase-HPLC.
Further, because the marker ions utilized in iTRAQ method reside at low m/z values, the
method is not well suited for analysis on ion trap instrumentation, a common platform for
proteomic analysis. The other potential drawback of this approach is that MS/MS spectra
must be acquired, which requires more analysis time than performing result-dependent
analysis only on differentially expressed peptide pairs in MS (e.g. with ICAT). However, the
ability to identify more proteins with increased confidence and greater peptide coverage
outweighs this disadvantage.
Tandem Mass Tag (TMT)
A very similar approach is the labelling with TMTs.207 The chemical structure resembles the
one of iTRAQ reagents and the quantification procedure is the same. The TMTs permit
simultaneous identification and relative quantification during the MS/MS mode. This
approach is similar in principle to other peptide isotope labelling techniques and enjoys the
same features as these other approaches, while offering other advantages. The TMTs are
reactive toward free amino-terminus peptides and ε-amino functions of lysine residues and
are therefore not restricted to a particular class of peptides. Briefly, the TMT molecules
consist of a protein reactive group that allows labelling on primary amine groups at the Nterminus and internal lysine (K) side chains, a reporter group that reports in MS/MS mode
the abundance of a given peptide after sample mixing, a cleavable linker enabling the release
of the TMT reporter fragment in MS/MS, a mass normalisation group that balance mass
differences from individual reporter fragments to keep the overall mass of the tags from a set
constant (isobaric). Several sets of tags have been developed, depending on the experiment to
be performed. TMT-zero (TMT0) is a cheap tag used for method development that does not
70
Chapter 1
carry any isotopic substitutions. The 2-plex, 4-plex and 6-plex are sets of respectively two,
four, and six isobaric mass labels, with one to five isotopic substitutions per tag, that are used
from simple pairwise comparisons to more complex analysis of protein abundance.
The identical peptides in different samples remain isobaric after tagging and also co-migrate
in chromatographic separations appear as single peaks in MS scans, thus improving the
sensitivity and reducing the probability of peak overlaps. In addition, the TMTs also will act
as more precise reciprocal internal standards, which lead to more accurate quantification.
Upon CID fragmentation in the mass spectrometer, tags give rise to low mass MS/MS
signature ions between 126 and 131 m/z (TMT) corresponding to the reporter loss. The
reporter relative peaks or areas indicate the proportion of labelled peptides of the sample from
which they are derived. Because of the novel TMT strategy relies on a CID-based technique
for quantification of tagged analytes, this feature improves signal to noise ratios by operation
of the MS instrumentation entirely in the MS/MS mode. This allows untagged material to be
ignored, greatly improving data quality.
Enzymatic labelling
18
O Labelling
18
O-labelling is a quantitative method for comparative proteomics in which isotopic labels
can be incorporated during enzymatic proteolysis in one step. It involves digestion of one of
two protein samples in H218O to isotopically label each new C-terminus and a second protein
sample in H216O. The samples can be mixed and subjected to various separation procedures
prior to mass spectrometric analysis.
Very common is also enzymatic labelling after proteolysis in a second incubation step with
the protease. 18O labelling is simple, free of extensive sample manipulations and free of side
reactions. It is two orders of magnitude less costly than ICAT and SILAC, comparing the
price of reagents needed to label 1 mg of protein.
18
O labelling approach amenable to all
protein species, so that eventually any kind of protein sample may be labelled, the same mass
shift is introduced in all the peptides. Incorporation of 18O into C-termini of peptides results
in a mass shift of 2 Da per 18O atom. While trypsin and Glu-C introduce two oxygen atoms
71
Chapter 1
resulting in a 4 Da mass shift which is generally sufficient for differentiation of isotopomers,
Lys-N and other enzymes incorporate only one
18
O molecule and should therefore be
avoided.208-210
Acid and base-catalyzed back exchange with concomitant loss of the isotope label can occur
at extreme pH values, but under the mild acidic conditions typically employed for ESI and
MALDI-MS
18
O-containing carboxyl groups of peptides are sufficiently stable. Because
peptides are enzymatically labelled, artifacts (i.e., side reactions) common to chemical
labeling can be avoided. A practical disadvantage is that full labelling is rarely achieved and
that different peptides incorporate the label at different rates which complicates data analysis.
All of the previous methodologies require complex, time-consuming sample preparation and
can be relatively expensive.211,212
Label-free quantification
Label-free is a method that determines relative or absolute protein amounts without using
stable isotope containing compounds. Label-free analyses have to be carried out when we
cannot employ isotope labelling techniques, which are rather laborious and expensive. The
advantage of label-free quantification is that time-consuming steps of introducing labels to
proteins or peptides are not required and that there are no costs for expensive labelling
reagents. In addition, it is rapid and more sensitive than many other proteomic methods, and
it increases the protein dynamic range 3- to 4-fold as compared to 2DE.213 Label-free methods
can be multiplexed to a higher degree and they can even be applied to already acquired data.
The complexity of mass spectral data (in terms of detected peptide species within a particular
chromatographic time window) is not increased as in the case of differently labelled samples,
which provides a higher analytical depth (i.e., number of detected peptides/proteins in an
experiment) because the MS is not occupied by fragmenting all forms of the labelled peptide.
Unfortunately, label-free approaches are relatively the least accurate among the mass
spectrometric quantification techniques. One critical factor limiting the quantitative
reproducibility of these methods includes the ability to efficiently cluster the detected
peptides. This in turn relies on the accuracy of the mass measurement and the
chromatographic reproducibility.
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Chapter 1
Relative label-free quantification approaches
The ion intensity-based label-free quantitative proteomics methods aim to compare two or
more experiments and based on two categories of measurements. In the first are the
measurements of signal intensity changes such as peptide peak areas or peak heights in
chromatography for any given peptide which correlates well with protein abundances in
complex samples. Bondarenko and co-workers demonstrated linear responses of peptide ion
peak areas between 10 and 1,000 fmol of myoglobin spiked into human plasma.214 i.e the
peak areas were found to increase with increased concentration of injected peptides.
This approach suffers from several limitations; the differences in sample preparation and
injection can result in experimental variations which lead to difference in the peak intensities
of the peptides from run to run. This can be solved by normalization of this type of variation.
In addition, shifts in retention time and m/z may occur as a result of multiple sample
injections using the same reverse-phase LC column, this lead to variability and inaccuracy in
quantification. Moreover, the huge volume of data collected during LC-MS/MS analysis of
complex protein mixtures needs the data analysis of these spectra to be automated. Therefore,
computer algorithms were developed to solve these limitations.
The second label-free quantification approach is based on the spectral counting of identified
proteins after MS/MS analysis and based on counting and comparing the number of MS/MS
spectra observed for peptides derived from a single protein.215,216 In contrast to quantification
by peptide ion intensities, spectral counting benefits from extensive MS/MS data acquisition
across the chromatographic time scale both for protein identification as well as protein
quantification. Also, peptide ion intensities approach requires delicate computer algorithms
for automatic LC-MS peak alignment and comparison, no specific tools or algorithms have
been developed specially for spectral counting due to its ease of implementation.
However, spectral counting can be confused by the fact that the probability of peptide
detection by MS techniques can vary greatly from one peptide to another based on the
particular physicochemical properties of the peptide sequences. This variability in peptide
detection can lead to errors in measuring the abundance of the target protein producing the
73
Chapter 1
tryptic peptides. Therefore, normalization and statistical analysis of spectral counting datasets
are necessary for accurate and reliable detection of proteomic analysis of very complex
peptide mixtures.
These two approaches are simple and cost-effective and have demonstrated high
reproducibility and linearity at both peptide and protein levels.213 Using statistical analysis
with these methods allows detection of small significant changes that are biologically
meaningful. For proteomic analysis of very complex peptide mixtures, spectral counting and
peptide ion intensity measurements requires more robust computing power and algorithm
capable of handling chromatographic peak alignment.
Absolute label-free quantification
Protein abundance index (PAI)
In 2002, Rappsilber developed a label-free method to determine absolute protein amounts of
proteins.217 The PAI, in which the number of observed peptides is divided by the number of
theoretically observable tryptic peptides from a particular protein. In a later paper by the
same group, the PAI was converted to an exponentially modified form (emPAI), which
showed a better correlation to known protein amounts in a protein mixture.218 However,
emPAI values are easily calculated but provide only a rough estimate of the absolute protein
amounts. This approach is useful within a limit protein concentration range whereby the
observed peptides continue to increase linearly as a function of the amount of protein.
However, once a higher protein concentration is reached and all the observable peptides have
been identified, the relationship deteriorates to an asymptotic limit. Moreover, this approach
applies on characterizing peptides using data-directed MS/MS and may therefore be sensitive
enough only to quantify the more abundant proteins present in a mixture.
Absolute protein expression (APEX)
APEX is a modified spectral counting approach termed absolute protein expression (APEX)
profiling, which was described by Lu el al,219 where machine learning techniques are used to
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Chapter 1
improve quantitation results over basic spectral counting. APEX estimates absolute protein
concentration per cell from the proportionality between the abundance of a protein and the
number of its peptides observed versus that of the total protein concentration and all
proteotyic peptides. In other words, for each protein in the sample, the expected number of
peptide observations (spectral counts) is computed based on predicted MS detectability of the
corresponding tryptic peptides. The protein’s absolute abundance is indicated by an APEX
score.
For proteotypic peptide prediction, machine learning classification algorithms are applied to a
training dataset comprised of peptides from a limited set of abundant proteins to build a
classification model for the prediction of proteotypic peptides generated insilico from the
entire proteome. The fundamental to APEX is the introduction of appropriate correction
factors that make the fraction of expected number of peptides and the fraction of observed
number of peptides proportional to one another.
LCMSE
LCMSE is a label-free approach presented by Silva et al220 and ideally suited for determining
the absolute concentration of proteins present in both simple and complex mixtures. The
methods described previously imply data-dependent acquisition in which an MS scan
followed by a number of MS/MS scans of selected peptide precursors. While LCMSE is dataindependent approach, this method is configured to alternate between two collision energy
conditions. A low energy MS survey of eluting precursor peptides and an elevated energy MS
survey of associated product ions with no precursor ion selection applied prior to CID.
During the elevated energy MS survey, the potential energy difference across the collision
cell is ramped in a linear fashion from an initial elevated energy setting to a final value, over
the period of time associated to a single scan.
The LC-MSE data are then collected throughout the entire LC-MS experiment preserving the
chromatographic profile of all the detected peptides and their associated product ions.
Product ion information is obtained from all the isotopes and charge states of any given
precursor peptide as they are simultaneously fragmented. This approach relies heavily on
mass accuracy and reproducible chromatography.
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Chapter 1
Top 3 Protein Quantification (T3PQ)
The T3PQ221 approach is implemented to FT-ICR instruments acquiring in data-dependent
acquisition of the method suggested by Silva et al.220 The method is based on the assumption
that for each protein identified by a set of peptides, the average of the three most efficiently
ionized and therefore highest MS signals directly correlate with the input amount of the
corresponding protein. As most of the label-free quantitation methods, T3PQ is based on the
calculation of the peak intensity of the first peptide isotope at its most abundant charge state.
Moreover, the T3PQ quantitation method is based on the presence of three peptides for each
protein in one single LC-MSMS run.
T3PQ software takes Mascot search result (dat.file) containing the peptide information for all
identified proteins. Also, Mascot ion score threshold considered to only look at reliable
peptides. In addition, it takes the LC–MS/MS run in mzXML format to map the identified
peptides from the dat.file to the ones in the mzXML file and to find the most abundant
precursor peaks for the peptide within a specified retention time window. The three most
intensive precursor ions are selected and averaged to calculate the protein abundance. Unlike
emPAI and APEX, precursor signal intensity based approach (T3PQ) turn out to be more
robust in terms of the linear response and variance of the protein abundance values if at least
three identified peptides per protein are detected.
Metabolic labelling
The earliest possible time point for introducing stable isotopes into proteins is by metabolic
labelling during cell growth and division. Oda et al were the first to describe metabolic
labelling of Saccharomyces cerevisiae by
15
N-enriched cell culture medium.222 Later on,
mammalian cells223 and even small organisms such as C.elegans and D.melanogaster224 have
been fully labelled with
15
N. This method permits complete incorporation of
15
N into all of
the amino acids within the cells/organisms. However, the method has disadvantages. Stableisotope enriched media are expensive and the mass difference between the labelled and
76
Chapter 1
unlabelled peptide is based on the number of nitrogen atoms within the amino acid sequence
of the specific peptide and cannot therefore predicted, thus complicating data analysis.
The most popular method in stable isotope labelling is the SILAC (Stable Isotope Labelling
with Amino acids in Cell culture) approach introduced by Mann and co-workers in 2002.225
In this method, one cell state is metabolically labelled by replacing one or more essential
amino acids in the medium with their labelled versions, such as
13
C6-arginine or 13C6-lysine
when trypsin is used for proteolysis. Potentially all tryptic peptides carry at least one labelled
amino acids resulting in predictable mass shifts between labelled and unlabelled peptides.
Resulting peptides have the same chemical properties,
and therefore identical
chromatographic retentions, ionization efficiencies, and fragmentation characteristics, but
they are distinguishable based on the mass difference. Tandem mass spectrometry of both
‘heavy’ and ‘light’ peptides is used for peptide and therefore protein identification. The
relative intensities of the two peaks in the isotopic pair, corresponding to the heavy and light
forms of the same peptide, are measured to obtain relative quantification data.
One of the advantages of SILAC is that it is easy to apply and compatible with multistage
purification procedures. In contrast to
15
N labelling, the number of incorporated labels in
SILAC is defined, thus simplifying data analysis. The introduction of the SILAC method led
to a high number of quantitative studies that assessed relatively small changes in protein
levels, including relative quantification of post-translational modified proteins.226,227
The main disadvantage of protein labelling using SILAC is many cell lines can be converted
quite readily, some do require special attention. For example, some cell lines require careful
titration of the amount of arginine in the medium in order to prevent metabolic conversion of
excess arginine into proline which in turn complicates data analysis.228 Cell lines that are
sensitive to changes in media composition or are otherwise difficult to grow or maintain in
culture may not be amenable to metabolic labelling at all. Another limitation of SILAC is the
restricted number of available labels. For example, a maximum of three conditions can be
compared in one experiment (unlabelled,
13
C6, and
13
C6 15N4-labelled amino acids) which,
even though possible, complicates the analysis of, e.g., time-course experiments. Because of
77
Chapter 1
the early combination of samples, metabolic labelling and specifically SILAC is probably the
most accurate quantitative MS method in terms of overall experimental process.
Isotope-labelled quantification standards
AQUA (absolute quantification of proteins) Unlike SILAC and iTRAQ, which provide
relative quantitative information, this approach provides an absolute quantification of the
proteins of interest.229 The AQUA method is a variation of isotope dilution mass spectrometry
techniques commonly used for the measurement of small molecules. This method was applied
by Gerber and co-workers in 2003 to quantitatively determine the cell cycle-dependent
phosphorylations of human separase.230 Researchers also used this strategy for the
quantification of candidate biomarkers in clinical studies.231 The absolute quantification can
be achieved spiking a known quantity of a stable isotope-labelled standard peptide (AQUA
peptide) that is chemically synthesized into a protein digest as internal standard. The AQUA
internal standard can be readily introduced to a sample during or after protease digestion.
Since the native peptide and its synthetic counterpart have the same chemical properties, the
mass spectrometry signal from the AQUA synthetic peptide can be compared to the signal of
the native peptide. On the basis of the known amount of AQUA peptides added, the absolute
amount of the endogenous peptide and finally the protein can thus be determined.203
The selection of standard peptides is often empirical232 i.e. peptide selection is based upon
results obtained during sequencing experiments performed by LC MS/MS on the proteins
under investigation. There are several aspects for selecting an AQUA peptide, e.g. the type of
isotope labelled amino acids (13C- and/or
15
N-labelled amino acids as they do not induce
chromatographic retention shifts), peptides shorter than 15 amino acids should be preferred
and chemically reactive residues (Tryptophane, Methionine and Cysteine) or specific
sequence patterns (Aspartate–Glycine, N-terminal Glutamine) should be avoided, Mallick et
al in 2007233 performed a study about predicting frequently detected tryptic peptides in a
given proteomic platform, which might help with selecting standard peptides for absolute
quantification. The AQUA strategy can become time consuming, expensive and it is more
complicated when a complex mixture is to be analyzed as several peptides need to be
synthesized in stable-isotope labelled form, purified and then quantified one by one. Because
78
Chapter 1
the standard is added at late stages of the analytical process, the AQUA strategy is poorly
compatible with sample prefractionation. Due to these technical and financial problems,
AQUA analyses are limited in the number of proteins that can be quantified with only a
single AQUA peptide. For others, dozens of proteins can be targeted. Such limited choice has
to be cautious as a single peptide, even proteotypic, can correspond to different forms of the
target protein (degradation products for example), especially in complex sample such as
plasma. In addition, incomplete digestion is one critical issue in the event of absolute
quantification as it dramatically affects the final result. Practically, it is very difficult to
prepare accurately known amounts of standard peptides for a mixture of proteins, since the
purity of synthetic peptides is variable.
QconCAT technology is a significant advance on AQUA. The QconCAT approach is the
general solution to the generation of peptides as surrogates for the proteins of interest. A
QconCAT is an artificial protein, inducibly expressed in E.coli, consisting of signature
peptides derived from each of the proteins under study concatenated together.234
Figure 1.25: Basic idea of QconCAT
technology
79
Chapter 1
In contrast with AQUA peptides, QconCAT constructs are synthesized biologically, which
expands the range of accessible proteotypic peptides (hydrophobic peptides, peptides with
chemically reactive residues…). Peptide order as well as codon usage are crucial parameters
to be optimized to minimize the occurrence of mRNA secondary structures and to maximize
expression yield.
The artificial protein (named "QconCAT" for "Quantification conCATamer") is expressed
from an artificial gene in an isotopically-labelled growth medium such as
15
NH4Cl as a
nitrogen source and contains a his-tag for purification purposes. Usually, the signature
peptides are arginine or lysine-terminated at the C-terminus. These peptides will be internal
standards for tryptic peptides derived from digestion of the analyte proteins. Choosing the
signature peptides depends on several standards: arginine-terminated tryptic peptides are
preferred to give strong signals in MALDI-ToF MS,235 selected peptides should not contain
histidine, cysteine or methionine so as to minimise down-stream effects. For instance,
cysteine, which can undergo incomplete alkylation, methionine and histidine, which are
easily oxidized in vitro or which could complicate protein expression such as in case of
cysteine by the formation of disulfide bridges. Most importantly, the signature peptides must
be unique. Peptides next to “ragged ends”, with adjacent K and R residues eg
VFQTHSPVVDSISVKR should be avoided because the site of tryptic digestion is unclear.
Finally, the preferred masses for signature peptides are between 1000 and 2000 Da, and this
is because the sensitivity of detection in this range is typically high and interfering signals are
low. A QconCAT normally has additional features such as an initiator codon that would yield
maximal expression in E.coli, a His-tag by adding histidine residues at the C-terminus or Nterminus to facilitate rapid purification of the expressed protein, an optional single cysteine
containing motif for additional quantification using a colorimetric assay and additional amino
acids added to the N-terminus to provide an initiator methionine residue.
The QconCAT method eliminates the intimidating task of preparation and handling of many
synthetic peptides and guarantees addition of multiple standard peptides in equimolar ratio to
the sample. After purification and quantification, the QconCAT proteins are added to the
mixtures of proteins in known amounts and concomitant proteolysis of QconCAT-analyte
80
Chapter 1
mixture releases both the QconCAT peptides which are the stable isotope labelled standards
and the cognate peptides from the analyte. Mass spectrometry analysis is used to absolutely
quantify each representative peptide of the proteins being analysed basing the comparison on
the known quantity of the standard added to the sample.
In principle, there is no reason to expect that QconCATs would adapt any tightly folded
structures, because the QconCAT peptides are concatenated in artificial protein out of their
primary sequence context. Because the tryptic fragments in the QconCAT are adjacent to
other standard peptides, the primary sequence context of the standard and analyte may differ,
and proteolytic excision of the peptide may occur at a different rate in the QconCAT than in
the analyte. Thus, in a QconCAT workflow, it is essential that the digestion of both analyte
and standard are complete. This is an implicit requirement in the QconCAT protocol, since to
be effective for quantification, both hydrolytic reactions must go to completion. The
requirement is therefore to identify reaction conditions that guarantee complete digestion. By
using the QconCAT technology, absolute quantification of mixtures of protein samples can
be achieved. The QconCAT technique can be used for absolute quantification of any peptide
that can be generated by chemical or endoproteolytic cleavage from any protein source.
Protein Standard Absolute Quantification (PSAQ)
Although AQUA and QconCAT approaches have significantly advanced the quantitative
measurement of proteins in biological samples, calibration with AQUA peptides and
QconCAT constructs suffer from some limitations; 1) complete digestion before MS analysis
is required for generation of an equimolar peptide mixture and to prevent quantification of the
digested fraction only; 2) Another source of variation for tryptic digests are post-translational
modifications that may occur during digestion. (i.e some peptides contain sequence or
structural characteristics, such as reactive residues or inappropriate proteolytic cleavage sites,
which make their analysis problematic). All these sources of variations (miscleavages and
PTMs) can affect the equimolarity of the final peptide mixture and result in serious
discrepancies between the real peptide concentration and the calculated value. In addition, an
incompatibility with sample prefractionation, which is often necessary when dealing with
biological samples; and 3) poor protein sequence coverage, limiting the statistical reliability
of the quantification; 4) finally, solubilisation and conservation of AQUA standards and
81
Chapter 1
QconCAT are peptide sequence dependent and this can significantly impact measurements
consistency. Considering the large protein dynamic range in biological samples and body
fluids, sample pre-fractionation is often required for sensitive detection of low abundant
proteins. Therefore, the ideal internal standard for absolute quantification of a specific protein
should behave exactly like the target analyte, not only during the LC/MS analytical step, but
also through all pre-analytical sample treatments. The new quantification method, termed
PSAQ236 is based on the use of in vitro-synthesized isotope-labelled full-length proteins as
standards for MS-based quantification of target proteins in complex matrices. As the
quantification standards display the same biochemical features as the target proteins, they can
be spiked into the samples at early stages of the analytical process. Therefore, protein losses
that may occur during sample prefractionation and/or incomplete proteolysis do not alter
quantification accuracy237-239
In addition, PSAQ also avoids differences in digestion yields between the internal standard
and the target protein. Finally, PSAQ offers the largest sequence coverage for quantification
(all detectable proteotypic peptides are considered), which increases detection specificity and
measurement robustness. In addition, if interferences, such as ionization problem, prevent the
detection of a proteotypic peptide in a complex mixture, PSAQ enables to switch to different
reporter peptides. Along the same line, a given PSAQ standard can be used indifferently with
trypsin or any other conventional endoprotease.
A current limitation of the PSAQ method is the cost and difficulty to produce protein
standards. However, thousands of recombinant proteins have already been synthesized and
purified in the framework of structural genomics. In a recent survey of structural biology
initiatives240, the overall reported production and purification success rates for bacterial and
eukaryote proteins were respectively 30% and 19%. However, this field is moving fast and
the so-called “Human Protein Factory” resource is reportedly able to produce individual
recombinant proteins at a whole proteome scale.241
In summary, PSAQ appears as a reference method for targeted absolute and accurate protein
quantification in complex biological samples and body fluids. PSAQ already appears as the
strategy of choice for MS-based evaluation of candidate protein biomarkers as well as for the
pharmacokinetic study of therapeutic proteins.242
82
Chapter 1
1.21-Aims of this thesis
The prokaryotic ribosome is assembled from more than 50 proteins and 3 stands of RNA and
is responsible for the synthesis of all the proteins in a bacterial cell. During protein synthesis,
it interacts with an estimated 200 additional proteins, including protein synthesis factors and
chaparones. The study of ribosome structure, function and assembly are all active areas of
research being pursued worldwide.
For this reason it is important to understand ribosomal stoichiometry. It is generally believed
that the ribosome contains one copy of each protein, except L7/L12, but this is clearly not the
case for partially-assembled ribosomes. The stoichiometries of protein-ribosome interactions
are generally unknown. The aim of this work was therefore to develop proteomic strategies
for the quantification of ribosomal proteins and of proteins affected by the action of protein
synthesis inhibitors within the cell.
The first objective of this work was therefore to design a ribosomal QconCAT for
quantification of ribosomal proteins. The QconCAT should be flexible enough to permit related
proteins also to be quantified. A QconCAT is an artificial protein, inducibly expressed in E.coli,
consisting of signature peptides derived from each of the proteins under study concatenated
together. The artificial protein (named "QconCAT" for "Quantification conCATamer") is
expressed from an artificial gene in an isotopically-labelled growth medium and contains a His6tag for purification. These processes are presented in Chapter 4 and 5.
Due to increasing resistance to existing antibiotics which often leads to failures in the
treatment of bacterial infections, there is urgent need for strategies to overcome resistance.
Therefore, antimicrobial synergy resulting from combination antibiotic therapy has been a
preferred therapeutic approach to treat serious bacterial infections by broadening antibacterial
spectrum and preventing the development of resistance.
The first antibiotic combinations therapy was the use of aminoglycosides with β-lactam
antibiotics such as the penicillins to create a synergistic therapy. The β-lactams inhibit cell
wall synthesis and thereby increase the permeability of the bacterium to the aminoglycosides.
83
Chapter 1
Other such group of combinations is fluorinated quinolones with β-lactams, where synergy
has been demonstrated against clinical isolates of Pseudomonas aeruginosa.
The second objective of this work was to use label-free proteomic strategies to identify
proteins that might act as drug targets for synergisers for inhibitors of ribosomal function.
Gentamicin is a drug in especial need of a partner, as it is an excellent antibacterial agent with
a poor therapeutic index. Thus, chapter 6, 7 and 8 are focused on identification a potential
drug targets which target different validated proteins for synergistic effect with gentamicin
and azithromycin. This was done by applying proteomics methods in particular label-free
quantification methods.
84
Chapter 1
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Chapter 2
Chapter 2-General experimental procedures used for the experiments described in this
thesis
2.1-Determination of protein concentration by Bradford assay
Concentrations of protein samples were measured by use of the Bradford Protein Assay
(Thermo Scientific Pierce, Rockford, IL, USA) and a UV-Vis spectrophotometer (JENWAY
6300). The Bradford Protein Assay was used according to the manufacturer’s instructions.
Briefly, a calibration curve was obtained using a series of different dilutions of bovine serum
albumin (BSA) aqueous solutions containing 0 (blank), 62, 125, 250, 500 and 1000 µg/ml of
BSA prepared in triplicate. 20 µl of calibrant or sample were mixed with 980 µl of Bradford
assay reagent, and after 5 min the absorbance was measured at 595 nm. If the sample solution
had an absorbance reading outside the range established by the standard curve, the sample
solution was diluted and new absorbance measurements were obtained. A typical standard
curve of absorbance versus BSA protein concentration was plotted. Based on this standard
curve, concentrations of protein samples were determined. BSA and protein samples were
dissolved in the same buffer (eg 10 mM Na2HCO3) for preparation of a standard curve.
2.2-One-dimensional sodium dodecylsulphate-polyacrylamide gel electrophoresis (1DSDS-PAGE)
All 1D SDS-PAGE analyses were performed using 12-15 % polyacrylamide gel (10 cm × 7
cm × 1mm, 10 wells) acrylamide analytical gels (BioRad), a twelve
step wide range
molecular weight marker (6.5-200 kDa, Sigma) using the BioRad gel chamber for criterion
gels. Buffer solutions for SDS-PAGE were prepared according to Table 2.1.
3% Ammonium persulphate solution was added at last to initiate the polymerisation. The
resolving gel was poured into the chamber. During polymerisation (approximately 30 min)
the surface of the gel was covered with a layer of isopropanol to ensure a flat surface of the
resolving gel. After polymerisation, the surface of the gel was washed with water to remove
the isopropanol. A stacking gel was cast on top of the resolving gel (see Table 2.1). Sample
wells in the stacking gel were formed by inserting template combs. Polymerisation of the
stacking gel required approximately 1 h. The gels were placed into the electrophoresis tank
and immersed into running buffer (3.03 g/l Tris, 14.4 g/l glycine, 1 g/l SDS). Protein samples
and standard markers were mixed with 2× SDS sample buffer (62.5 mM Tris-HCl pH 6.8,
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Chapter 2
10% v/v glycerol, 2% w/v SDS, 5% v/v β-mercaptoethanol, 0.05% w/v bromphenol blue) and
incubated at 94° C for 5 minutes immediately prior to 1D SDS-PAGE. Gels were run at a
constant voltage 100 V until the dye front penetrated the resolving gel. The voltage was then
elevated to 160 V and the run was terminated when the dye front reached the bottom of the
gel.
The gels were stained overnight with 0.1 % Coomassie Blue R in 40% methanol, 10% acetic
acid. Stained gels were then developed in 45% methanol and 10% acetic acid (destaining
solution) until clear protein bands appeared and the background staining was removed.
Table 2.1. Gel composition for SDS-PAGE gradient gels
Component
Resolving gel
Solution 1
Solution 2
Stacking
12 % acrylamide 15 % acrylamide
gel
30% w/v acryl-bisacrylamide mix
2.0ml
2.5ml
0.67ml
Resolving gel buffer
(Tris/HCl, 1.5 M, pH 8.8)
1.3ml
1.3ml
-
Stacking gel buffer
(Tris-HCl, 1.5 M, pH 6.8)
-
-
0.5ml
SDS (10% w/v)
50µl
50µl
40µl
Deionised water
1.6ml
1.1ml
2.7ml
TEMED
(N,N,N’,N’- tetramethyl
ethylendiamine)
2µl
2µl
4µl
50µl
50µl
40µl
Ammonium persulphate (30
mg/ml)
2.3-In gel digestion
After electrophoresis, each band to be identified was first digested by cutting it from the gel;
afterwards it was washed three times with 100 µl 25 mM ammonium bicarbonate and 50%
acetonitrile for 40 min with shaking. This process was repeated until gel pieces were white.
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Chapter 2
The gel pieces were then dehydrated with 100% acetonitrile, and incubated with 100 µl 10
mM DTT (in 25 mM ammonium bicarbonate pH 8.0) for 1 h at 56 °C for protein reduction.
The resulting free thiol (-SH) groups were subsequently alkylated by incubating the samples
with 100 µl 55mM iodoacetamide in 25 mM ammonium bicarbonate for 45 min at room
temperature in the dark. The gel pieces were washed twice with 25 mM ammonium
bicarbonate for 10 min while vortexing. After removing the liquid, the gels were dehydrated
with 100% acetonitrile, and then dried. The gel pieces were rehydrated and trypsin or Lys-C
where appropriate was added and the gel pieces incubated for 16 h at 37 °C or 30 °C for
protein digestion. TFA or FA was added to halt the digestion.
Supernatants were transferred to fresh tubes, and the remaining peptides were extracted by
incubating the gel pieces in 50% acetonitrile with 3% TFA or 5 % FA for 30 minutes,
followed by dehydration with 100% acetonitrile. The latter two steps were repeated. The
extracts were combined, and organic solvent was removed in a vacuum concentrator.
Desalting and concentration were carried out on C18-ZipTip and the eluted peptides were
subjected to MALDI ToF or LC MS/MS.
2.4-OFFGEL IEF of peptides (OG-IEF)
70S ribosomal protein samples were digested with Lys C and then with trypsin. The peptides
were separated using an Agilent 3100 OFFGEL Fractionator (Agilent, G3100AA) according
to four protocols. In the first case, the company protocol was used with commercially
available immobilized pH gradient (IPG) DryStrips, 13cm, pH 3–11 (GE Healthcare).
In the second case, strips were rehydrated for 20 min with 20 µl/well of a solution containing
5% glycerol and IPG buffer, pH 3–11 (GE Healthcare,) diluted 1:50. The glycerol content of
the focusing buffer was reduced by 50% compared to the amount given in the manual. The
original glycerol concentration resulted in clogging of the trap column in LC system during
sample loading. Peptides (50 µg for 12-well fractionations) were diluted 1 in 50 in 5%
glycerol and IPG buffer, pH range 3– 11. Peptide solutions (150 µl) were pipetted into each
well, the cover seal was set into place and Immobiline DryStrip Cover Fluid was added to
both ends of the strip. Peptides were focused at 20 kV/h and 12-well fractionations with a
maximum current of 50 µA and power of 200 mW.
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Chapter 2
After OFFgel-IEF, liquid fractions were withdrawn, and a supplementary step to enhance the
protein yield was performed. For this purpose, each well was rinsed with 100 µl of methanol,
allowing the solution to sit for 15 min, and then removed and combined with the initial
extract from the respective well. Fractions (20 µl) were washed in the trap column of the LC
system for 30 min with 0.1% Formic acid, before being injected into the analytical column.
In the third case, a further supplementary step was performed to enhance the peptide yield by
using C18 StageTips before LC-MS/MS. In the fourth case, the isoelectric point focusing was
performed as in third protocol with the exception that samples were dissolved in glycerolfree buffers, resulting in loading of 1.8 ml of sample per well, containing 20% methanol and
1% IPG buffer as final concentrations; the digest solutions were diluted accordingly. To
prevent the outer wells from running dry through excessive water transport during the
glycerol-free focusing process, in addition to the 150 µl sample in each well, 100 µl of
supplemental 1% IPG solution was added to fractions 1 and 12 and 50 µl to fractions 2 and
11.
Each well was rinsed with 100 µl of methanol, allowing the solution to sit for 15 min, and
then removed and combined with the initial extract from the respective well. The recovered
fractions were vacuum-centrifuged to
10−12 µL and resuspended in 20 µL 0.1 % Formic
acid and 20 µl were injected into the Dionex nLC system.
2.5-Isolation and purification of ribosomes
All experiments were carried out in triplicate. The isolation and purification of ribosomes
was achieved by growing E. coli cells at 37 °C in 400 ml of LB media with aeration until the
A600 reached 0.2. At this point, gentamicin (final concentration, 6 µg ml-1) was added; no
antibiotic was added to the control cultures. Cells were harvested at mid-log growth phase
(O.D600 of ~0.8 for control and ~0.6 for treated cultures), by centrifugation at 4200 g for 15
min, and the pellets washed twice in ice-cold phosphate buffer saline (PBS) pH 7.4. Cells
were resuspended in sterile ribosome lysis buffer (RLB) containing tris(hydroxymethyl)aminomethane (Tris) buffer, 20 mM, pH 7.5, 50 mM magnesium acetate, 100 mM
ammonium chloride, 1.0 mM DDT and ethylenediamine tetra-acetic acid (EDTA). The cells
were sonically treated: ten repetitions of 15s burst at 35% power, using a sonicator followed
103
Chapter 2
by 45 s pause to keep the extract cool. Deoxyribonuclease was added directly to the lysate at
a level of 2 µg ml-1 to degrade contaminant DNA and the mixture was incubated for 20 min
at 4 °C. The mixture from the lysed cells was centrifuged at 30000 g in a SS-34 Sorval rotor
for 30 min at 4 °C to remove debris and unbroken cells. This step was repeated twice.
To separate 70S ribosomes from the remaining cellular components, the clarified supernatant
was layered over a 1.1 M sucrose solution in the ribosome lysis buffer and the 70S ribosomes
were pelleted by spinning at 100,000 g at 4 °C in a Ti 50.2 rotor (Beckman Coulter,
Fullerton, CA) for 16 h. The ribosomal pellet was resuspended in a small volume (3 mL) of
RLB. Salts and low-molecular mass components were removed from the ribosome samples
using a Slide-A-Lyzer dialysis cassette (Thermo) comprising a 3500 kDa membrane against
10 mM ammonium carbonate.
2.6-Extraction of ribosomal proteins with acetic acid
Proteins were extracted from ribosomes and from ribosomal subunits by the following
procedure. A volume of 70S ribosomal sample equivalent to 500 µg was resuspended in
1/10th volume of 1M MgCl2 and two volumes of ice cold glacial acetic acid were added in
rapid succession. Following a 45 minute incubation period at 4 ºC, the precipitated RNA was
removed by centrifugation at 20,000 g for 10 minutes.
The pellet was re-extracted with 67% acetic acid and 0.1 M MgCl2 for 30 minutes. The
precipitated RNA was removed as before. The supernatant was removed and placed in
another tube with 5-10 volumes of ice cold acetone. The proteins immediately precipitated,
but the suspension was placed in the freezer at –20°C for 2 hours to facilitate complete
precipitation of the proteins. The mixture was centrifuged at 10,000 g for 30 minutes at 4 ºC
to pellet the proteins. The proteins were washed by the addition of 1 ml of acetone and
centrifuged again. This wash step was repeated once. Protein concentrations were measured
using the Bradford assay method.
2.7-LC MS and LC MS/MS analysis using the Q-ToF Global
70S ribosomal protein LC-MS/MS analyses were performed using an Ultimate 3000 capillary
LC system (Dionex, Surrey, UK) coupled on-line to a Q-ToF global mass spectrometer
104
Chapter 2
(Waters, Manchester, UK). Lys C or tryptic peptides were first concentrated on a 300 µm × 5
mm PepMap C18 precolumn (LC Packings-Dionex, Sunnyvale, CA). Peptide digests were
then passed onto a C18 analytical column (75 µm × 150 mm) (LC Packings, CA, USA) and
eluted with a gradient of aqueous buffer from 2 % (v/v) ACN with 0.1% (v/v) FA to 90%
ACN, 0.1% FA. Two typical gradients of 65 min and 90 min were used to separate peptides
from ribosomal proteins mixtures with a flow rate of 200 nl/min. The ToF analyser was
calibrated using the CID product ion spectrum of the doubly charged peptide ion of [Glu1]fibrinopeptide where the y series ions were used as reference masses for calibration. 1
pmol/µl Glu-Fib solution was directly infused via a gold coated borosilicate nanospray
needles (Proxeon, Odense, Denmark) at flow rates of 10-40 nl/min.
Nano-electrosprayed ions were generated by a distal-coated fused silica PicoTip emitter
(New Objective, Inc., Woburn, MA, USA) using a capillary voltage of 2.0 – 2.7 kV within a
Z-sprayTM ion source. The mass spectrometer was operated in the positive ion electrospray
ionization. Data-dependent analysis was used for MS/MS (three most abundant ions in each
cycle): 1s MS (m/z 300–1600) and maximum 3s or 6s MS/MS (m/z 50–1600, continuum
mode), where the collision offset was determined within the collision energy profile for ions
based on their m/z and charge state. The threshold for selection to CID was set at 50 counts
in the MS survey scan. The CID was performed in the presence of argon with a collision cell
pressure of 1-3 mbar. MS/MS raw data were processed using MassLynxTM version 4.0
(Water, Manchester, UK). Peptide identifications from the resulting MS/MS dataset were
achieved using an in-house MASCOT server.
2.8-MS and MS/MS analysis on the Amazon ion trap
Analysis of Lys-C and trypsin digests of 70S ribosomal proteins was performed with an
Ultimate 3000 capillary LC system (Dionex, Surrey, UK) connected on line with the Amazon
ion trap mass spectrometer (Bruker, Bremen, Germany). All the LC instrumental parameters
were set similarly to the LC system connected to the Q-ToF instrument described above. The
flow rate used in these experiments was set at 300 nl/min. Resolved peptides were introduced
into the MS using electrospray ionisation through a distal-coated fused silica PicoTip emitter
using a capillary voltage at 2 kV with drying gas of 6 L per minute. Source temperature was
105
Chapter 2
set to 150 ºC. The mass range was scanned with a scan rate of 8100 amu/s. The mass range
for detection was 200-1800 m/z. Ions were accumulated in the trap until the ion charge count
(ICC) reached 20000 with a maximum accumulation time of 200 ms.
Sample tables for automatic acquisition were inserted into HyStarTM (Bruker, Bremen,
Germany) which incorporates Esquire control version 6.2 (for the control of the mass
spectrometer) and Chromeleon version 6.8 (for the control of the LC system). Ionized
peptides eluting from the capillary column were selected for CID. Auto MS(n) was
performed for the three most abundant precursor ions in the MS survey scan with a total ion
count (TIC) absolute threshold of 25000 and a relative threshold of 5% of the base peak.
2.9-MALDI-ToF and MALDI-ToF/ToF experiments
MALDI-ToF and MALDI-ToF/ToF analysis were performed on an Ultraflex IITM (Bruker
Daltonics, Germany) equipped with a nitrogen UV laser (337 nm) under the control of the
software FlexControlTM MS spectra were analyzed with software FlexAnalysisTM 2.2. The
ToF spectra were recorded in the positive ion reflector mode over a mass range 700-3000 m/z
for protein digests. The ion acceleration voltage used was 25 kV. A solution of α-cyano-4hydroxycinnamic acid in 70% (vol/vol) ACN, 0.1% (vol/vol) TFA at concentration of 10
mg/ml was used as the matrix. Detection was performed in reflector mode. The data
processed by software FlexAnalysisTM
2.10-Peptide desalting using C18 ZipTip or C18 StageTips
The peptide mixtures were desalted using a C18 ZipTip (Millipore) or StageTips (Proxeon
Biosystems). Briefly, the tip was equilibrated by four times aspirating and then dispensing
50% acetonitrile, followed by four times aspirating and then dispensing 0.1% Trifluoroacetic
acid (TFA). The peptides were bound to the column by aspirating then dispensing the peptide
mixture for 10-15 cycles. The salts were removed by washing with 0.1% TFA or FA four
times. The peptides were eluted by aspirating and then dispensing 0.1% TFA or FA/70%
acetonitrile for 10 cycles. The peptides were dried down and resuspended in 0.1% FA/ 2%
acetonitrile for LC MS/MS. For MALDI-ToF MS, 1 µl of sample solution was directly
106
Chapter 2
spotted on a polished steel MALDI target (MTP 384, Bruker, Bremen, Germany) followed by
1 µl of matrix solution and left to dry and subsequently analyzed.
2.11-Statistical analysis (for chapter 6, 7 and 8)
Statistical analyses were performed to quantify changes between control and treated samples.
To analyze these differences, six parameters were generated: the emPAI score, the number of
peptides detected for a protein and the closely-related percentage of total peptides detected,
the number of residues detected and the closely-related percentage sequence coverage and the
average peptide score of the most intense three peptide signals. Differences of means were
calculated. Because these data are subject to the “look elsewhere” effect (that is a large
number of parameters are compared simultaneously) significance of results was judged in
terms of numbers of standard deviations. A 3σ difference is deemed very likely to be real, a
5 σ difference represents the highest standard of proof.1
2.12-Reference
1. Lyons, L.; Comments on `Look Elsewhere Effect'. Particle Physics 2010, 2, 1-6.
107
Chapter 3
Chapter 3: Results
3.1-Evaluation of different MS approaches and protein and peptide fractionation
methods to select suitable signature peptides to design ribosomal QconCAT
Here, we describe an experimental strategy to maximize the number of ribosomal
proteins/peptides identified by mass spectrometry of a 70S ribosomal protein sample.
Using a combination of MALDI ToF, LC-MS/MS approaches with protein and
peptide fractionation steps we were able to identify a suitable signature peptides, to
design a ribosomal QconCAT, for both absolute quantification and stoichiometric
analysis of ribosomal proteins. A QconCAT is explained with details in chapter 1
(section 1.20). To design a single, huge QconCAT is impractical in several ways: the
artificial gene will be difficult to prepare, the resulting protein will be long and
susceptible to mistranslation and degradation. Most importantly, the mass spectra will
contain many signals of no interest in a particular experiment. Our approach is to
design a core QconCAT plasmid encoding signature peptides from the central
ribosomal proteins L2, L4 and L13 and S4, S7 and S8 and a His-tag will be produced.
Unique so far to this proposal is the incorporation of restriction sites. These restriction
sites will allow insertion of DNA cassettes encoding the remaining ribosomal proteins
on two separate cassettes (one for each subunit). When selecting QconCAT peptides,
several factors must be considered, which mentioned in chapter 1 (section 1.20).
Global Proteome Machine (GPM) and Blast searches were carried out to determine
respectively which tryptic peptides of proteins are frequently observed by MS/MS
analysis and the uniqueness of peptides to the target protein.
3.1.1-Isolation and identification of 70S ribosomal proteins and peptides with
electrophoresis and MALDI-ToF
We first performed 1D-PAGE by loading 20 µg of 70S ribosomal proteins sample
followed by trypsin digestion, and then MALDI-ToF and PMF analysis were
performed. This resulted in the detection of 97 peptides; representing 11 ribosomal
proteins, in single experiments. Similar experiment was performed by loading 30 µg
ribosomal proteins instead of 20 µg on 1D-PAGE (Figure 3.1), resulting in
identification of 27 ribosomal proteins. Seven were found to overlap and a total of 31
different ribosomal proteins were identified. We concluded that even with loading
more sample on SDS-PAGE to improve protein identification (Table 3.1), only 30 of
108
Chapter 3
the expected ribosomal proteins could be observed. However, these experiments
frequently fail to unambiguously identify several ribosomal proteins such as S5, S6,
S8, S12, S13, S14, S15, S17, S18, S20, L7/L12, L11, L20, L21, L22, L23, L24, L27,
L31, L32, L33, L34, L35 and L36 were not seen. Figure 3.2 shows one example of a
spectrum obtained by a MALDI-TOF instrument; the protein band 9 was digested in
the gel using trypsin. The resultant peptides were extracted from the gel, desalted and
analyzed by mass spectrometry. The list of peptides was submitted to the MASCOT
search engine using the SwissProt database. MASCOT returned a list of candidate
proteins.
A score greater than 60 is considered to be significant, this score is above the
statistically relevant threshold (p<0.05), which is represent 95.0% probability and
contain at least one identified peptide with probabilities assigned by the Mascot. Our
false discovery rate was determined by searching the same dataset against the target
database and a decoy database; the latter featured the reversed amino-acid sequences
of all the entries in the SwissProt database. Matches to the decoy database are
considered false discoveries, and the number of matches above the cutoff score
threshold is reported. As shown in the spectrum in figure 3.2, L9 (15,759 Da) with a
contribution from ribosomal protein L16 (15,281 Da) appear in the spectrum.
M
1
2
3
66,000
29,000
Figure 3.1: One-dimensional electrophoresis of 70S ribosomal proteins prepared from
E.coli. Sample was run at 20, 10 and 5 µg protein per well and gels were stained with
Coomassie Blue. Wide range molecular weight marker (206–6.5 kDa) was used as gel
standards. First lane: marker; lanes 1-3: 70S ribosomal proteins 20, 10 and 5 µg,
respectively. The 20 µg lane was chosen in this experiment to be digested and analyzed by
MS.
109
Chapter 3
M
Intens. [a.u.]
Da
29,000
2
1.50
1687.89
1447.81
x104
66,000
1.25
9
0.75
2031.99
2053.97
1953.12
1901.02
1853.98
1726.01
1636.82
1583.84
1407.76
1240.76
1469.79
1047.55
953.58
850.56
14,200
902.59
0.25
1210.66
14, 15
1153.65
1098.68
0.50
1387.83
1045.62
931.59
20.100
1025.57
1.00
0.00
800
1000
1200
1400
1600
1800
Figure 3.2: Protein identification using peptide mass fingerprinting.
2000
m/z
110
Chapter 3
3.1.2-RNA extraction approach
Chapter 4
The previous experiments failed to identify 24 ribosomal proteins from both small and
Development
of a QconCAT for the 30S Subunit of the Escherichia
large
subunits.
coli Ribosome We therefore inserted an additional step before the electrophoretic gel
separation. The addition of acetic acid to the 70S ribosomal proteins sample followed
by acetone protein precipitation was evaluated in order to remove RNA and extend
a,b
a,c
a,b
Zubida
M. Al-majdoub
, Simon.
J. Gaskell
Jill Barber
the protein
identification.
The 30
µg treated, sample
(Figure 3.3) resulted in a total of
17 identified proteins. However, 16 ribosomal proteins from this experiment
overlapped with those identified in previous two experiments– the other ribosomal
Michael Barber Centre for Mass Spectrometry, Manchester Interdisciplinary Biocentre, 131
protein was S5. Furthermore, with RNA removal we did not succeed in identifying
Princess
Street,missing
University
of Manchester,
M1 7DN, United
Kingdom.reason for
(by PMF)
proteins
from twoManchester,
previous experiments.
One possible
a
b
observing
fewer number
proteins is the RNA
extraction
method.
There isUniversity
a risk of of
School
of aPharmacy
and of
Pharmaceutical
Sciences,
Stopford
Building,
reducing the amount of protein in an acetone precipitation step.
Manchester, Manchester, M13 9PT, UK.
c
Present address: Principal’s Office, Queen Mary, University of London, Mile End Road,
3.1.3-Direct analysis of 70S ribosomal proteins treated and untreated sample by
LC MS/MS
London.E1
4NS, UK.
We then performed in-solution tryptic digestion of the untreated and treated 70S
ribosomal
proteinsSchool
E. coli,
the resulting
peptide mixtures
were Stopford
subjectedBuilding,
to LC
Contact
Dr Jill Barber,
of Pharmacy
and Pharmaceutical
Sciences,
MS/MS (60 min gradient). Following this procedure with a Q-ToF mass spectrometer,
University of Manchester, Manchester, M13 9PT, UK., Tel: 44-161-275-2369, fax: 44-161-275we identified a total number of 49 and 45 untreated and treated 70S ribosomal
2369,
email [email protected]
proteins,
representing 170 and 132 peptides respectively in a single E. coli 70S
ribosomal proteins sample. However, these peptide numbers were based on a criterion
where peptides with probability scores p < 0.05 and rank=1 were accepted, even if
Running title: E. coli ribosomal QconCAT
only a single peptide was observed per protein.
Including results of all previous runs, lead to the detection of a total of 50 of the E.
coli 70S ribosomal proteins. While ribosomal proteins L20, L23, L32 and L36 were
completely missing from all of the previous runs.
121
111
Chapter 4
Abbreviations
RNA ribonucleic acid
QconCAT A concatenation of Q-peptides making up an artificial protein, that can be cleaved
proteolytically .
SILAC Stable isotope labelling with amino acids in cell culture
iTRAQ isobaric tag for relative and absolute quantitation
AQUA™ peptides Peptide mixtures for absolute quantification
E. coli Escherichia coli
Lys C Endo proteinase Lys C
DNA deoxyribonucleic acid
LB medium Luria broth
MudPIT Multidimensional Protein Identification Technology
TFA Trifluoroacetic acid
122
Chapter 4
Summary
The bacterial ribosome is a complex of three strands of RNA and some 55 proteins and is the
central machinery responsible for protein synthesis in the cell. During protein synthesis the
ribosome interacts with other proteins, numbered in hundreds, forming some stable, some
transient complexes. The stoichiometries of these complexes and of partially-assembled
ribosomes are often unknown, but are amenable to study by mass spectrometry utilizing
appropriate standards.
We describe the development of a flexible standard for the determination of stoichiometries of
ribosomal particles and complexes. A core QconCAT, an artificial protein consisting of
concatenated signature peptides derived from the ribosomal proteins L2, L4, L13, S4, S7 and S8,
has been developed. The core QconCAT DNA construct incorporates restriction sites for the
insertion of cassettes encoding signature peptides from additional proteins under study. The first
of these cassettes, encoding signature peptides from the remaining 30S ribosomal proteins, has
been prepared and the resulting QconCAT has been expressed, digested and analyzed by mass
spectrometry.
The majority of E. coli ribosomal proteins are small and basic; therefore tryptic digestion alone
yields insufficient signature peptides for quantification of all the proteins. The ribosomal
QconCATs therefore rely on a dual enzyme strategy: endoproteinase Lys C digestion and
analysis is followed by trypsin digestion and further analysis. Endoproteinase Lys C serves to
increase the number of ribosomal peptides amenable to mass spectrometric analysis. Further, in
directing initial cleavage to lysine, rather than arginine, prior endoproteinase Lys C digestion
reduces the extent of missed-cleavage observed in subsequent tryptic digests.
123
Chapter 4
Introduction
The bacterial ribosome is a complex structure consisting of three RNA molecules and about 55
proteins arranged in two distinct subunits. It is the central machinery in bacterial protein
synthesis and is known to interact with more than 100 other proteins during protein synthesis.1
Because of its central importance in cellular function, the ribosome is the target for seven classes
of antibacterial agent, including the aminoglycosides, arguably the most effective antibacterial
agents in the clinic; and the macrolides, probably the safest antibacterial drugs.2 Elegant crystal
structures of prokaryotic ribosomes and subunits have been rewarded with the 2009 Nobel Prize
for Chemistry.3,4,5
Key to understanding the interactions of the ribosome with other proteins is the question of
stoichiometry. How many molecules of these factors bind to one ribosome? Stoichiometry is
also key to understanding ribosomal assembly, and especially the effects of drugs, including the
macrolides and aminoglycosides on this process.6,7 There are many powerful, sensitive methods
for relative (between samples) quantification of proteins based on mass spectrometry, the SILAC
and iTRAQ methods being especially widely-applicable.8,9 For stoichiometric (within sample)
measurements, however, an absolute quantification method is required. The choices here are
quite limited: quantification based on labelled AQUA™ peptides10 is attractive when single
measurements are to be made but, when the stoichiometries of the same proteins are to be
measured repeatedly, QconCAT methodology has many advantages11
A QconCAT is an artificial protein expressed using a gene construct, expressed in E coli. The
protein consists of signature peptides from all the proteins to be quantified, concatenated. When
a known amount of stable isotope-labelled QconCAT is digested with an unlabelled sample, the
signature peptides appear as doublets in the mass spectrum, thus unlabelled signature peptides
from the sample may be quantified by comparison with their isotope-labelled counterparts
derived from the QconCAT. Our aim was to prepare a flexible QconCAT to use for the
determination of ribosomal stoichiometries and the stoichiometries of factors bound to bacterial
ribosomes.
124
Chapter 4
Experimental Procedures
Materials and reagents
Trypsin (sequencing grade) was purchased from Roche Diagnostics, Lysyl endopeptidaseTM (Lys
C) was obtained from Wako (Osaka, Japan) and RapiGestTM from Waters (Bedford, MA, USA).
All other chemicals and solvents (HPLC grade) were purchased from Sigma-Aldrich (UK).
[13C6] K and [13C6] R and an unlabelled AQUA peptide used as an internal standard were
purchased from Cambridge Isotope Laboratories (UK).
In silico digestion of 70S ribosomal proteins
In silico digestion of E coli ribosomal proteins was carried out using ProteinProspector, available
at http://prospector.ucsf.edu/prospector/mshome.htm12. The ribosomal proteins were digested
with trypsin, endoproteinase Lys C, endoproteinase Asp-N and chymotrypsin alone and in
combination. Sequential digestion with endoproteinase Lys C and trypsin was found to yield the
greatest number of proteotypic signature peptides for the bacterial ribosomal proteins. This Lys
C/trypsin sequential strategy was adopted in this study.
QconCAT gene design and construction
Core and 30S QconCAT peptides selection was based on experimental criteria as described in
the result section. A two tryptic or Lys C peptides were chosen to represent each ribosomal
protein. The peptide sequences were then randomly concatenated in silico and used to direct the
design of a gene, codon-optimised for expression in E. coli. The predicted transcript was
analysed for RNA secondary structure that might diminish expression. Additional peptide
sequences were added to provide an initiator methionine (MGTK) and sacrificial peptides
(LEPGR and KLPWR) for the core (see figure 4.3) and LEK, PGR, KLK and PWR for 30S
QconCAT (figure 4.4), which when cleaved would expose a true QconCAT peptide. A His6
sequence (LAAALEHHHHHH) was added for affinity purification. The artificial gene was
synthesised de novo, verified by DNA sequencing and ligated into the NcoI, XhoI, SmaI, Hind
125
Chapter 4
III and BamHI sites of the pET21a expression vector by Entelechon (http://www.qconcat.com/)
to yield the core QconCAT plasmid.
Preparation of recombinant proteins
The core and 30S QconCATs were expressed as C-terminally His6 tagged proteins in E.coli. The
DNA
constructs
for
the
QconCATs
were
produced
by
PolyQuant
GmbH
(http://www.polyquant.com/) (Germany) using the expression vector pET21a encoding
ampicillin and kanamycin resistance. The received 5-10 µg of plasmid was dissolved in 50 µl
distilled water producing a final DNA concentration of approximately 100-200 ng/µl.
Transformation of QconCATs
E.coli strain of BL21 (λDE3) competent cells (Novagen, UK) were used for QconCATs plasmid
transformation. In order to transform this strain, 10 µl of BL21 cells were added into a
microcentrifuge tube to which 0.5 ng of DNA was added. Briefly, competent bacteria were
thawed on ice and mixed with plasmid DNA. After incubation on ice for 10 min, cells were heat
shocked at 42 °C for 30 s and subsequently cooled on ice for 2 min. Then, 150 µl of LB medium
with 50 µg/ml of ampicillin was added followed by incubation at 37 °C for 30 min with shaking.
To select transformed bacteria, cells (100 µl) were plated onto an LB agar plates containing
ampicillin at 50 µg/ml and incubated at 37 °C overnight.
Protein expression
A single colony from the plate was used to inoculate 5 ml of LB medium containing 50 µg/ml
ampicillin and this was incubated for overnight at 37 ºC with shaking at 220 rpm. Glycerol
stocks were prepared from each overnight cell culture either from LB or minimal media each
time a protein was expressed. 700 µl of the overnight cell culture containing transformed cells,
from a single colony, was added to 300 µl of sterile glycerol (to make a 15 % glycerol stock).
This was stored at -80 ºC for future protein expression. Typically 1 µl of glycerol stock is added
126
Chapter 4
to 5 ml of LB containing 50 µg/ml ampicillin and the cells are incubated for 18 h overnight at 37
ºC. The overnight culture was then added to 250 ml of LB broth in a 1l flask. This was incubated
with shaking (220 rpm) until the A600 0.6-0.8. Protein expression from the plasmid was then
induced by addition 500 µM of the isopropyl-D-thiogalactoside (IPTG) to the culture. Incubation
continued for a further 4 hr or otherwise state. Samples were collected at 0, 1, 2, 3 and 4 h
induction for SDS-PAGE analyses. Cells then collected by centrifugation at 4200 g for 10 min at
4 °C.
Protein expression of stable isotope labelled proteins
The proteins were grown in M9 minimal medium (supplemented with 20 % glucose, 1 M
MgSO4, 0.1 M CaCl2 and 0.5 % thiamine) in the presence of ampicillin (50 µg/ml) with a full
complement of labelled amino acids, here [13C6]-R/K. All amino acids excluding labelled
residues were suspended in sterile water at 10 mg/ml, labelling amino acids were weighted out
separately and added as solid to the M9 minimal medium at 0.1 mg/ml. An overnight starter
culture was prepared by inoculating 1 µl glycerol stock into 5 ml of M9 medium without amino
acids and growing for 18 h at 37 °C. The overnight culture was added to 250 ml of M9 with all
the amino acids including labelled arginine and lysine to give a starting A600 of approximately
0.06-0.1. The cells were grown to A600 of 0.6-0.8 at 37 °C, after which isopropyl-Dthiogalactopyranoside (IPTG) was added to a final concentration of 500 µM, and cells were
incubated for another 5 h. Samples were collected at 0, 1, 2, 3, 4 and 5 h induction for SDSPAGE analyses. Cells were harvested by centrifugation at 4200 g for 10 min at 4°C.
Cell Lysis
This procedure was adapted from Beynon et al. with minor modification.11 Pelleted cells were
resuspended in BugbusterTM protein extraction reagent at ratio 5 ml of reagent per 1 gm of wet
cells and suspension was incubated at room temperature (RT) for 20 min on a rocker platform.
20 µl of the suspension (total fraction) was taken and subjected for analysis by SDS-PAGE. The
cells were repelleted, at 16000 g for 20 min at 4 °C. The supernatant (soluble fraction) was
127
Chapter 4
collected in a clean falcon tube and 20 µl was taken and subjected for analysis by SDS-PAGE.
The pellets were resuspended again with BugbusterTM at the same ratio as before. Then,
Lysozyme (10 mg/ml) was added with shaking at ratio of 100 µl per 1 gm of wet cells and the
suspension left to stand for 5 min at RT. 20 ml of diluted (1:10 in sterile water) BugbusterTM was
added to the suspension and gently shaken for 1 min, followed by centrifugation at 15000 g for
15 min at 4 °C. Pellets were washed three times with diluted solution of BugbusterTM (1:10 v/v in
water), same amount as before, and then centrifugation at 16000 g for 15 min at 4 °C was
performed between suspensions and re-pelleting. Finally, the pellets were solubilized in binding
buffer (20 mM phosphate pH 7.4, 20 mM imidazole, 0.5 M NaCl, 8 M urea), and centrifuged at
5000 g for 5 min at room temperature to remove debris.
Qualitative analysis by SDS-PAGE indicated that the core QconCAT was fractionated to high
purity at this point. Only 30S QconCATs expressed in LB and minimal medium were further
purified as outlined below.
Purification and analysis of QconCAT protein
The purification of 30S QconCATs protein from inclusion bodies was carried out using a 1 ml
HisTrapTM column (GE Healthcare). The column was washed with 10 ml water and then
equilibrated with 25 ml binding buffer using a flow rate at 0.5 ml/min. The supernatant
containing QconCAT protein was applied to the column at flow rate 0.25 ml/min. Nonspecifically bound protein was washed off using 25 ml binding buffer at flow rate 0.5 ml/min.
The bound protein was eluted from the column using 5 ml elution buffer (20 mM sodium
phosphate pH 7.4, 500 mM imidazole, 0.5 M NaCl, 8 M urea) at a flow rate of 0.25 ml/min,
collecting five 1 ml fractions. Proteins from each fraction were adsorbed onto StrataClean™
bead resin (Stratagene) and each suspension was analyzed by SDS-PAGE.
The eluent fractions containing His6-tagged protein were dialyzed against 100 volumes of 10
mM ammonium bicarbonate at 4 °C for 2 hrs and then with fresh buffer overnight. QconCAT
was purified by ultrafiltration device using Vivaspin centrifugal concentrator with 20,000
molecular weight cut off (Sartorius, UK).
128
Chapter 4
Determination of 30S QconCAT concentration using Bradford assay
The concentration of the 30S QconCAT protein in the pooled fractions after His6-tag protein
purification was measured by use of the Bradford Protein Assay as described previously in
chapter 3. The concentration of labelled 30S QconCAT was determined and found to exceed 20
mg purified 30S QconCAT per litre of culture medium.
Determination of 30S QconCAT concentration using an AQUA peptide as a standard
Another method used for measuring 30S QconCAT concentration involved using an AQUA
peptide. This approach is based on the addition of a known amount of AQUA peptide to the 30S
QconCAT sample. The unlabelled AQUA peptide, GGVNDNEEGFFSAR, was stored
lyophilized at -20°C, with a vial containing 0.7 mg of peptide. The peptide was thawed and
reconstituted in water to generate a working solution of 1µg/µl. Briefly, 700 µl of water was
added to the peptide vial. The vial was carefully mixed on a vortex mixer to fully dissolve the
peptide. The stock solution was further diluted to generate working solution of 0.5 pmole/µl.
For quantification of 30S QconCAT, the AQUA peptide was added to digested labelled material
in serial dilutions, and every dilution was analyzed in triplicate. Peptides were analyzed by
MALDI-ToF MS, and then confirmed by QToF LC-MS. Extracted ions chromatograms were
taken for the monoisotopic peak of the quantification peptide GGVNDNEEGFFSAR derived
from the 30S QconCAT protein. The absolute concentration of the [13C6]-R/K labelled 30S
QconCAT was determined by a serial dilution of the synthesized AQUA standard which was
then added to the labelled 30S QconCAT which had been digested with trypsin. This peptide
mixture was then analysed by MALDI-ToF MS and QToF LC-MS. Each of this serial dilutions
was carried out in triplicate. The L/H ratios were then determined and the L/H ratios were
calculated and used to quantify the amount of peptide released from the protein during
incubation with trypsin at 37 °C.
129
Chapter 4
Electrospray mass spectrometry of intact unlabelled core and labelled 30S QconCAT
A concentration 60 fmole/µl of QconCATs were prepared in 50 % acetonitrile/ 0.1 % formic acid
immediately before electrospray ionisation mass spectrometric analysis. Nanoflow capillaries
were used to introduce the samples into a Micromass quadrupole mass spectrometer operating in
positive ion mode using a nanoflow probe. Capillary voltages of 1.2–1.5 kV and cone voltages of
60–100 V typically were used. MaxEnt 1 processing was used to survey initially the spectra and
provide candidate species; reported m/z values for assigned proteins are calculated by
conventional deconvolution of the charge-state distributions (Figure 4.1).
Digestion protocols
Lys C/ tryptic digestion
QconCAT proteins (50 µg) were resuspended in 50 mM ammonium bicarbonate (pH 8.0) prior
to digestion. Proteins were reduced with (2.5 µl) 5 mM DTT at 60 °C for 30 min. After cooling
at room temperature, proteins were alkylated with (7.5 µl) 20 mM iodoacetamide, and the
mixture was incubated for 45 min in the dark at room temperature. Lys C or trypsin was added at
a ratio of 1:50 (w/w) and the enzymatic digestion was allowed to proceed at 30 °C or 37 °C for
18 h based on the enzyme used. The digestions were stopped by acidifying to pH 4 with TFA,
and the samples were loaded onto C18 ZipTips for desalting and concentration prior to MALDIToF MS analysis.
Digestion using RapiGestTM SF
This protocol was applied to core and 30S QconCAT and also to 70S ribosomal proteins
samples. The QconCATs (50 µg) were solubilized in 0.1% (w/v) RapiGest™ SF (Waters, UK)
solution prepared in 100 mM Tris pH 8.0 buffer containing 2 mM CaCl2, then reduced by the
addition of DDT (5 mM final concentration) for 30 min at 50° C, and alkylated by incubation
with iodoacetamide (final concentration of 15 mM) in the dark at room temperature for 30 min.
10 µl endopeptidase Lys C (0.1 µg/µl) was added, and the sample was incubated at 30°C with
130
Chapter 4
shaking for 4 h. For complete digestion, a further aliquot of Lys C was added and the sample
was incubated at 30 °C for 15-18 h with shaking. Adding aqueous HCl will irreversibly
inactivate trypsin 13 and also degrade the RapiGest surfactant. The RapiGest was precipitated by
addition of 250 mM HCl final concentration and then removed as a cloudy pellet by
centrifugation in a microfuge at 14000 rpm for 10 min at 4 °C. The supernatant was removed and
analyzed by mass spectrometry. Where further digestion with trypsin was required, trypsin (1
µg) was added to the supernatant and protein digested at 37 °C with shaking for 4 hrs; a further
aliquot of trypsin was then added and digestion completed overnight. Then, the supernatant was
collected and analysed by MALDI-ToF MS or RF-HPLC mass spectrometry based on the
complexity of the samples.
Digestion using urea denaturation, overnight digestion
QconCATs samples were digested in the presence of urea-solution (6M urea/2M thiourea), and
was performed as described elsewhere with minor modification.14 The proteins were evaporated
to 10 µl and then resupended in 40 µl urea-solution (in 10 mM Hepes pH 8.0) and incubated at
room temperature for 15 min. Proteins were reduced by addition of 5 µl 10 mM DTT (in 50 mM
ammonium bicarbonate pH 8.0) and incubation for 30 min at room temperature. Alkylation of
cysteine residues was subsequently performed by addition of 5.5 µl 55 mM iodoacetamide (in 50
mM ammonium bicarbonate pH 8.0) and incubation for 20 min in the dark. After reduction and
alkylation, endopeptidase Lys C was added in ratio of 1: 50 enzyme: protein followed by
incubation for 4 hrs at 30°C. To ensure complete proteolytic hydrolysis for peptide
quantification, another aliquot of Lys C was added after 4 hr incubation and the digest was
allowed to continuous at 30°C for 18 hrs. Before tryptic digestion, the resulting peptide mixtures
were diluted 4 times with 50 mM ammonium bicarbonate pH 7.9. Trypsin was added and
proteolysis was continued at 37°C for 4 hrs. Further to this another aliquot of trypsin was added
after the 4 hr incubation and the digest was allowed to continuous at 37° C for 18 hrs. Trypsin
activity was quenched by acidification using TFA or formic acid to a final concentration of 1%.
Samples were then concentrated by centrifugal evaporation to a total volume of 20 µl. Samples
were desalted using C18 ZipTips (millipore), and the eluted peptides were concentrated by
131
Chapter 4
centrifugal evaporation and dilution in 2% ACN, 0.1% formic acid or 0.1% TFA and then
analyzed by MALDI-ToF and RP-LC MSMS, respectively.
Guanidination
Peptides obtained by Lys C digestion were treated with 7 M ammonium hydroxide (10 µl) and
0.5 M O-methylisourea (6 µl) for 65 °C for 12 min. TFA (3 µl) was then added to stop the
reaction. Then sample was desalted using C18 ZipTips (Millipore, Watford, UK) before MALDIToF analysis.
MALDI-ToF Mass Spectrometry analysis
Experiments were performed on an Ultraflex II ToF/ToF mass spectrometer (Bruker Daltonics,
Germany) equipped with a nitrogen UV laser (337 nm) under the control of the software
FlexControlTM. MS spectra were analyzed with software FlexAnalysisTM 2.2. The ToF spectra
were recorded in the positive ion reflector mode over a mass range of 700-3000 m/z. The ion
acceleration voltage used was 25 kV. Spectra were stacked above each other. A solution of αcyano-4-hydroxycinnamic acid in 70% (vol/vol) ACN, 0.1% (vol/vol) TFA at concentration of
10 mg/ml was used as the matrix. The data were processed by software FlexAnalysisTM.
Isolation of total ribosomal proteins
The strain used in this study was E. coli K-12 (Biolabs, UK). The Cells were grown at 37 °C in
400 ml of LB with aeration until the A600 reached 0.2. At this point, gentamicin (final
concentration, 6 µg/ml) was added; no antibiotic was added to the control cultures. All
experiments were carried out in triplicate. Cells were harvested at mid-log growth phase (O.D600
of ~0.8 for control and ~0.6 for treated cultures), at 4200 g for 15 min, and the pellets washed
twice in ice-cold phosphate buffer saline (PBS) pH 7.4. Cells were resuspended in sterile
ribosome lysis buffer (RLB) containing tris(hydroxymethyl)-aminomethane (Tris) buffer, 20
mM, pH 7.5, 50 mM magnesium acetate, 100 mM ammonium chloride, 1.0 mM DDT and
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Chapter 4
ethylenediamine tetra-acetic acid (EDTA). The cells were sonically treated: ten repetitions of 15
sec bursts at 35% power followed by 45 sec pause to keep the extract cool. Deoxyribonuclease
was added directly to the lysate at a level of 2 µg/ml to degrade contaminant DNA and the
mixture incubated for 20 min at 4 °C. The mixture from the lysed cells was centrifuged at 30000
g in a SS-34 Sorval rotor for 30 min at 4 °C to remove debris and unbroken cells. This step was
repeated twice. To separate 70S ribosomes from the remaining cellular components, the clarified
supernatant was layered over a 1.1 M sucrose solution in the RLB and the 70S ribosomes were
pelleted by spinning at 100,000 g at 4 °C in a Ti 50.2 rotor (Beckman Coulter, Fullerton, CA) for
16 h. The ribosomal pellet was resuspended in a small volume (3 ml) of RLB. Salts and lowmolecular mass components were removed from the ribosome samples using a Slide-A-Lyzer
dialysis cassette (Thermo) comprising a 3500 kDa membrane against 10 mM ammonium
carbonate.
Determination of isolated ribosomal proteins concentration at 260/280 nm
The concentration and purity of isolated 70S ribosomal proteins were determined by measuring
the optical density (OD) at 260 and 280 nm in a Eppendorf Biophotometer (Helena Bioscience).
An OD260 of 1 of 70S represents 23 pmole/ml RNA. The OD260/OD280 ratio between 1.8 and 2
indicates high purity of nucleic acid. The 70S ribosomal protein samples from both control and
treated samples give ratio A260/280= 1.6-2.0 (C1-C3 and G1-G3) which confirms the presence of
other protein and this was confirmed by LC MSMS. Contaminants that absorb at 280 nm (e.g.
protein) will lower this ratio. The 70S ribosomal protein samples were then aliquoted and stored
at -80 °C for further use.
In-solution digestion of a mixture of unlabelled 70S ribosomal protein proteins and labelled
30S QconCAT
70S ribosomal proteins was spiked with 25 and 50 pmole labelled 30S QconCAT for tryptic and
Lys C digests, respectively, then the mixtures for control and treated samples were evaporated to
10 µl. The protein mixture then resuspended in 40 µl denature solution. The protocol was then
carried out as previously mention in the digestion using urea/thiourea protocol.
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LC–MS/MS operating conditions
The Lys C and tryptic peptide mixtures were separated by LC MS using a nanoACQUITY
chromatograph (Waters MS Technologies, Manchester, UK) coupled to an LTQ-Orbitrap mass
spectrometer XL (ThermoFisher Scientific, Bremen, Germany) with the manufacturer’s dynamic
nanospray source, fitted with a coated PicoTip Emitter 20-10 µm (New Objective, MA, USA),
with the voltage applied at the tip. The sample temperature was maintained at 10 °C, and 4 µl of
each peptide sample was injected initially onto a trapping column (C18, 180 µm x 20 mm,
Waters MS Technologies, Manchester, UK), using the partial loop mode of injection, at a flow
rate of 18 µl/min. Chromatographic separation was performed on a reversed-phase C18
analytical column (nanoACQUITY UPLC™ BEH C18 75 µm x 150 mm 1.7 µm column),
column temperature was set at 35 ºC and was developed at 300 nl/min. The aqueous mobile
phase (A) consisted of HPLC-grade water with 1% (v/v) formic acid; the organic phase (B) was
100% acetonitrile/1% (v/v) formic acid. The organic composition was increased gradually from
1% (v/v) to 50% (v/v) Buffer B over 30 min, followed by a rapid ramp to 85% buffer B over 1
min, and then a return to the initial conditions for re-equilibration prior to the next injection.
Mass spectrometry
The LTQ-Orbitrap XL was calibrated prior to use according to manufacturer’s instructions, and
was operated in data-dependent mode to automatically switch between full scan MS and MS/MS
acquisition. Full-scan MS spectra (m/z range 300-1600) were acquired operating at a resolution
(r) of 30,000 (all Orbitrap system resolution values are given at m/z 400). Instrument control was
through Xcalibur version 2.0.5/Tuneplus version 2.4SP1/configured with Waters Acquity driver
(build 1.0). For quantification analysis, data were acquired with a 30S QconCAT inclusion list
(i.e. Both Lys C and trypsin QconCAT m/z lists for labelled and unlabelled peptides included
and selected) directing collision-induced dissociation (CID) with helium as collision gas. This
approach was used to maximize the data points across the chromatographic peak, whilst
concomitantly acquiring tandem MS data for sequence verification. Dynamic exclusion was
enabled for 30s with a repeat count of two and an exclude duration width of 180 sec, and all
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product ion spectra were acquired in the LTQ. The Automatic Gain Control (AGC) feature was
used to control the number of ions in the linear trap, and was set to 1 x 106 charges for a full MS
scan, 1 x 104 for the LTQ (MSn), with ‘max’ injection times of 50 msec applied for the LTQ. All
Orbitrap scans consisted of 1 µscan.
Sample acquisitions were alternated with ‘buffer-only’ blank (defined as the starting mobile
phase) injections to ensure data analysis/quantification was not compromised by sample
carryover. Data analysis was carried out using Xcalibur 2.0.6 which supports the raw files from
LTQ-Orbitrap XL.
Raw LC-MSMS data analysis
Fragmented Lys C and tryptic peptides were identified MASCOT (version 2.1.0) software
platform (Matrix Science, London, U.K.). LTQ Orbitrap spectra from 30S QconCAT and
isolated ribosomal protein were searched against the E.coli-K12 database with taxonomy:
Escherichia coli, trypsin or Lys C with maximal 2 missed cleavages, carbamidomethyl (C) as
fixed modification, whereas labelled [13C6] K and [13C6] R, oxidized methionine were searched
as variable modifications. Peptide tolerance was set to 50 ppm with 2+, and 3+ peptide charges
and 0.8 Da for CID fragment ions.
Absolute quantification using QconCAT PrideWizard
Automated quantification of peptide pairs from ribosomal proteins and 30S QconCAT were
performed using QconCAT PrideWizard; an extension of the original PrideWizard, which was
developed to quantify iTRAQ labelled samples.15 The wizard provides a user interface to which
batches of spectra may be submitted. 30S QconCAT labelled peptides are then identified through
a Mascot MS/MS ion search. Protein hits are filtered such that only those that contain at least one
QconCAT labelled peptide, with rank 1 and a peptide expect score <5, are retained. Furthermore,
peptides are filtered such that the only ones quantified are unmodified (apart from the QconCAT
label) and are unique to a single protein.
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Chapter 4
In this study, analysis was performed on all replicates (a group of 36 Lys C and trypsin spectra
were analyzed using the QconCAT PrideWizard. Quantitation is then performed by firstly
generating an extracted ion chromatogram for the m/z value corresponding to the precursor ion
matching each labelled peptide. Where multiple matches occur against the same labelled peptide
in a given protein, the highest scoring one is considered for quantitation. Savitzky-Golay
smoothing16 is applied to the chromatogram, and the start and end retention time for the
chromatographic peak matching the peptide is determined, based on the retention time of the
fragmentation spectrum that supplied the peptide match. Each precursor scan within this
retention time window is extracted and analyzed with an implementation of the SILAC Analyzer
linear fit quantitation algorithm. This provides a light/heavy ratio, and standard error, for each
identified unlabelled/labelled peptide pair (see table 4.3 and 4.4). Ratios of unlabelled to labelled
peptide areas were calculated and these averaged across replicates. Manual analysis was also
performed for some peptides in which the wizard failed to measure theses peptides (See table 4.3
and 4.4). Absolute quantification is obtained by multiplying this ratio by the known amount of
the standard.
Results
The core QconCAT
We required a strategy that would enable us to measure the stoichiometries of ribosomal
proteins, and to measure the stoichiometries of other factors (especially protein synthesis factors)
relative to one another and relative to ribosomes. The sheer number of proteins to be assessed
makes a single QconCAT impractical. The approach we adopted was to develop a “core”
QconCAT consisting of signature peptides derived from six ribosomal proteins, L2, L4, L13, S4,
S7, S8 located in the core of their respective subunits. Signature peptides were identified
experimentally, by mass spectrometric analyses of digested ribosomal proteins.
An ideal signature peptide must show a high signal response in the mass spectrometer, low
susceptibility to modification (such as methionine oxidation) or missed cleavage and no posttranslational modification (PTM) listed in the SwissProt database, to minimize presence of
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variant forms. Because the ribosomal QconCATs are intended for use by groups outside the
specialist mass spectrometry community, detection by both ESI and MALDI mass spectrometry
was highly desirable.
The process of identification of signature peptides uncovered an immediate problem, confirmed
by in silico digestion of all the E. coli ribosomal proteins. Most ribosomal proteins are small and
basic, and many, including L13 and S7, give fewer than the preferred two proteotypic peptides
on tryptic digest, mainly because of miscleavage in regions with several basic residues. In silico
digestion with other readily available single proteolytic enzymes gave no improvement, but
suggested the strategy Lys C digestion, analyze, tryptic digestion, analyze. The digestion with
endoproteinase Lys C before tryptic digestion (reminiscent of the MudPIT experiment)16 not
only results in additional peptides available for analysis, but also directs the cleavage to lysine in
regions rich in basic residues, resulting in reduced missed cleavage.
The sequence of the core QconCAT is shown in Figure 4.2a. In addition to the ribosomal
peptides, the construct contains two sacrificial peptides which, at the DNA level, contain
restriction sites for the insertion of “cassettes” encoding groups of signature peptides. It also
contains two QCAL peptides,17 for very accurate absolute quantification of the QconCAT, and a
His6-tag for purification (see Table 4.5 and 4.6). Endopeptidase Lys C is generally believed to
cleave at KP motifs, but two of the chosen signature peptides contain a KPE motif, and there is
no evidence of these being substrates for the enzyme.
It was not strictly necessary to express the core QconCAT for this work. We did so for two
reasons. Firstly, a great deal of further work was dependent on the core QconCAT construct.
QconCATs generally become more difficult to express with increasing size; we reasoned that
any problems in expressing the core QconCAT were likely to be still worse when DNA cassettes
were ligated into it. Secondly the core QconCAT has potential value itself as a means of
quantifying ribosomes. The six proteins that can be quantified using this QconCAT are central
in the structures of their respective subunits and are assembled onto ribosomal RNA early. They
can therefore be used as proxies for whole ribosomes in quantification studies.
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Chapter 4
The core QconCAT was expressed in E coli in inclusion bodies (see Figure 4.2b). Isolation from
washed inclusion bodies yielded QconCAT that was pure by SDS PAGE, and affinity
purification using the His6-tag afforded no advantage. Digestion using endopeptidase Lys C
yielded the expected peptides with no evidence of missed cleavage (Figure 4.2c). A C-terminal
peptide at m/z 1961.9 contains the His6-tag and part of the sacrificial peptide KLPWR; all other
signals derive from Q-peptides intended for quantification.
Guanidination18 of lysine side-chains to yield homoarginine resulted in substantial improvements
in response factors for several of the peptides (Figure 4.2c), although Q4 (the QCAL peptide
GVNDNEEGFFSAK appears to be capable of a second (+42) modification. When the core
QconCAT was treated with trypsin, following Lys C digestion, all the tryptic peptides were
detected. Several different digestion protocols were compared and there was no evidence of
missed cleavage in the ribosomal Q-peptides, except a small peak due to mis-cleaved QCAL
peptide (GGVNDNEEGFFSARK) is present but, although we plan to edit the adjacent RK out of
future constructs, this does not compromise the utility of the QconCAT.
Digestion of the core QconCAT was carried out in the presence of RapiGestTM (Figure 4.2). As
the complexity of the protein sample increased, there was, however, a limitation of using
RapiGestTM as a means of denaturing the sample. This will be discussed later in this chapter.
138
Chapter 4
a
b
139
Chapter 4
c
Figure 4.1: Charge state distribution of the intact core QconCAT (b) in 50% acetonitrile:
1% Formic acid (Conc 100fmole/µl). The peak for each charge state is labelled. Spectra
(a) and (c) deconvoluted spectra of the m/z value shows that the peaks correspond to
species have mass of 21.7 and 72.4 kDa of core and 30S QconCATs, respectively.
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Chapter 4
Figure 4.2: The core QconCAT (a) sequence showing Lys C peptides in blue, tryptic
peptides in green, sacrificial peptides in orange (b) SDS-PAGE showing expression in
E. coli. M = molecular weight markers, T = total protein, S = supernatant after
removal of debris and inclusion bodies, IB = washed inclusion bodies. The 22 kDa
band represents the core QconCAT. (c) MALDI mass spectrum of Lys C digest of the
core QconCAT before (top) and after (bottom) guanidination (d) MALDI mass
spectrum of the sequential Lys C / tryptic digest of the core QconCAT. All peptides
are identified.
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Chapter 4
Figure 4.2 illustrates the extraordinary purity of the core QconCAT in inclusion bodies. The
conventional affinity chromatography was not required, indeed it contaminated the QconCAT
protein. This feature, together with the very high expression in inclusion bodies, has led to
another use for the core QconCAT. Now known as Qcore, it is being used as a fusion partner
with QconCATs that are difficult to prepare (fail to express or are rapidly degraded). A
QconCAT for quantification of liver enzymes has been prepared by fusion with Qcore, after the
failure of the conventional QconCAT and two reshuffles of its gene. (Achour and Barber,
unpublished data).
ATGGGCACGAAAACCTTTACCGCTAAACCGGAAACTGTGAAACGCGATTGGTACGTTGTGGA
M
G
T
K
T
F
T
A
K
P
E
T
V
K
R
D
W
Y
V
V
D
RP: L13
RP: L13
CGCCACTGGCAAATCTATGGCTCTGCGTCTGGCCAACGAGCTGAGCGACGCAGCGGAAAACA
A
T
G
K
S
M
A
L
R
L
A
N
E
L
S
D
A
A
E
N
RP: S7
AATTCGGTAGCGAACTGCTCGCCAAAGTCGAAGGCGATACCAAACCGGAACTGGAACTGACC
K
F
G
S
E
L
L
A
K
V
E
G
D
T
K
P
E
L
E
L
T
RP: S7
RP: S8
CTCAAATCTGATCTGTCTGCCGATATCAACGAACACCTGATCGTGGAACTGTACTCCAAAGGC
L
K
S
D
L
S
A
D
I
N
E
H
L
I
V
E
L
Y
S
K
G
RP: S4
GTGAACGACAACGAGGAAGGCTTCTTTTCCGCTAAACTCGAGCCCGGGCGTCTGGAATACGA
V
N
D
N
E
E
G
F
F
S
A
K
L
E
P
G
R
L
E
Y
D
Qcal
XhoI and SmaI
RP: L2
CCCGAACCGCTCCGCGGGTACCTACGTTCAGATCGTTGCACGTGACGCACAGTCTGCGCTGA
P
N
R
S
A
G
T
Y
V
Q
I
V
A
R
D
A
Q
S
A
L
RP: L2
RP: L4
CGGTGTCTGAGACTACCTTCGGTCGCGACTTCAACGAAGCGCTCGTCCACCAGGTTGTAGTG
T
V
S
E
T
T
F
G
R
D
F
N
E
A
L
V
H
Q
V
V
V
RP: L4
CGTACGCAGCTGGTGCACGTCTGGATAACGTAGTATACCGTGCGGTTGTAGAATCTATTCAG
A
Y
A A
G
A
R
L
D
N
V
V
Y
R
A
V
V
E
S
I
Q
RP: S4
RP: S8
CGTGGTGGTGTTAACGATAACGAAGAGGGTTTCTTCAGCGCTCGTAAGCTTCCATGGCGTCT
R
G
G V
N
D
N
E
E
G
F
F
S
A
R
K
L
P
W
R
Qcal
HindIII and NcoI
GGCTGCAGCGCTGGAATCACCACCACCACCACCAC
L
A
A
A
L
E
H
H
H
H
H
H
His6-tag
Figure 4.3: The DNA sequence of the synthetic gene (Core QconCAT), with relevant
cloning sites, is shown with black boxes above restriction enzymes used and the derived
amino acid sequence is shown below. The white blocked areas indicate the extent of the Lys
C and tryptic peptides and the ribosomal protein name indicated by RP. Peptides (blue boxe)
encode the initiator methione. The red boxes highlight the sequences of Qcal peptides for
quantification of QconCAT. His-tag (green box) for purification.
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The 30S ribosome QconCAT
Signature peptides for the remaining 18 small subunit proteins were identified experimentally,
two peptides for each protein. S6 modification protein (RimK) was also included, because it is
especially persistent in ribosomal preparations. Unfortunately, it was impossible to avoid
methionine residues in the QconCAT sequence due to the small ribosomal proteins size and the
presence of many basic residues that make the selection of suitable QconCAT peptide difficult,
Therefore, we endeavoured to ensure that the oxidation state of the Q-peptide is the same as the
analyte, since any discrepancy between the two would compromise the absolute quantification.
These signature peptides were inserted into the core QconCAT construct in two groups (Lys C
peptides and tryptic peptides; peptides which could be generated by either enzyme were classed
as tryptic) as shown in Figure 4.4a. The QconCAT protein (approximately 72 kDa) was
expressed in E coli inclusion bodies as shown in Figure 4.4b, and purified by affinity
chromatography using the His-tag (Figure 4.8b).
Figure 4.4: The 30S ribosomal QconCAT (a) sequence showing Lys-C peptides in blue,
tryptic peptides on green, sacrificial peptides in orange. KP sequences are underlined. (b)
SDS-PAGE showing expression in E. coli. M = molecular weight markers, T = total
protein, S = supernatant after removal of debris and inclusion bodies. The dense band at 72
kDa representing the unlabelled 30S QconCAT in LB medium is indicated.
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Chapter 4
CATATGGGCACGAAAACCTTTACCGCTAAACCGGAAACTGTGAAACGCGATTGGTACGTTGTGGAC
GCCACTGGCAAATCTATGGCTCTGCGTCTGGCCAACGAGCTGAGCGACGCAGCGGAAAACAAATT
CGGTAGCGAACTGCTCGCCAAAGTCGAAGGCGATACCAAACCGGAACTGGAACTGACCCTCAAAT
CTGATCTGTCTGCCGATATCAACGAACACCTGATCGTGGAACTGTACTCCAAAGGCGTGAACGACA
NdeI
ACGAGGAAGGCTTCTTTTCCGCTAAACTCGAGAAAGCGTTCGATCATCGTCTGATCGACCAGGCTA
CGGCAGAGATCGTGGAAACTGCCAAAATTTCCGAGCTGTCTGAAGGTCAGATCGACACCCTGCGTG
ATGAGGTCGCCAAAGCAATTATCAGCGATGTTAACGCGTCCGACGAAGACCGCTGGAACGCTGTTC
TGAAAATTCGCACGCTGCAGGGTCGCGTAGTTTCTGACAAAATCGTTCCGTCTCGCATCACCGGCA
CTCGTGCCAAATACCAGCGCCAGCTGGCTCGTGCAATCAAAGCGAACCTGACCGCTCAGATCAACA
AAGCCTTTAACGAGATGCAGCCGATCGTCGATCGTCAGGCAGCTAAACTGGAAAACAGCCTGGGC
XhoI and SmaI
GGCATTAAACCCGGGCGTCTGGAATACGACCCGAACCGCTCCGCGGGTACCTACGTTCAGATCGTT
GCACGTGACGCACAGTCTGCGCTGACGGTGTCTGAGACTACCTTCGGTCGCGACTTCAACGAAGCG
CTCGTCCACCAGGTTGTAGTTGCGTACGCAGCTGGTGCACGTCTGGATAACGTAGTATACCGTGCG
HindIII and NcoI
GTTGTAGAATCTATTCAGCGTGGTGGTGTTAACGATAACGAAGAGGGTTTCTTCAGCGCTCGTAAG
CTTAAAGGTGCTACCGTTGAACTGGCTGACGGCGTTGAAGGTTATCTGCGTGCTTTCCTGCCAGGTT
CTCTGGTAGACGTTCGTCCGGTTCGCGCAGGCGTGCATTTCGGCCATCAGACTCGTGCGGATATCG
ATTATAACACCTCCGAGGCGCACACCACTTATGGTGTAATTGGTGTGAAACTGGGCATTGTCAAAC
CGTGGAACAGCACTTGGTTCGCGAACACCAAAGCGTACGGTAGCACCAACCCGATCAACGTGGTC
CGTATCTTTAGCTTCACCGCGCTGACTGTCGTAGGTGATGGTAACGGCCGTTACACGGCTGCAATC
ACCGGCGCAGAGGGTAAATTCAACGATGCGGTAATCCGCTCTCTGGAACAGTACTTCGGTCGCGGC
GGTGGCATTTCTGGTCAGGCCGGTGCAATTCGCCTGGTCGACATTGTTGAACCGACGGAGAAAGCA
CTGAACGCAGCTGGTTTCCGTCAGGGCAACGCCCTGGGCTGGGCAACCGCTGGCGGTTCCGGTTTT
CGCGTTTACACGACTACCCCAAAACTGACGAACGGCTTTGAGGTCACGTCCTACATTGGTGGTGAA
GGCCACAACCTGCAGGAGCATTCCGTAATCCTGATCCGTATCGCTGGCATCAACATCCCAGACCAT
AAAGCGATTATCTCTGACGTGAACGCCAGCGACGAGGATCGTTATACCCAGCTGATCGAGCGTATC
GTGTCCGAATTCGGCCGCATTGCGCACTGGGTTGGTCAGGGTGCCACTATCAGCGATCGTGTGGGT
TTTTTCAACCCAATCGCGTCTGAGAAATCTATCGTGGTGGCTATTGAACGTCTGGGCGAGTTTGCGC
CAACCCGTCAGCACGTTCCAGTATTCGTGACTGACGAGATGGTGGGTCACAAAGCGGGTGTACTGG
CGGAAGTCCGCGAATTCTACGAAAAACCGACTACCGAACGTATCGGCACCGCAATCACTTTCTACG
BamHI
GTACTGCTGCACTGCGTGAAGCTCAGGGTTGCGACATTCGTCCATGGCGTCTGGCTGCAGCGCTGG
AACACCACCACCACCACCACTAATGATGGGGATCC
Figure 4.5: The DNA sequence of the synthetic gene (30S QconCAT), with restriction
sites shown in red letters and restriction enzymes indicated.
To optimise the expression of 30S QconCAT, the effect of induction time on 30S QconCAT
expression was explored. Samples of cell culture with BL21 harbouring 30S plasmid were
collected after 0, 1, 2, 3, 4 at 37 ºC or overnight induction at 20 ºC and 220 rpm agitation, and
the rest of the cell culture was left overnight. SDS-PAGE showed an increased 30S QconCAT
expression from 1 to 4 h (Figure 4.6a). Overnight induction did not improve the 30S QconCAT
expression further. The SDS-PAGE results showed degradation in the protein after overnight
induction. However, the best expression amount in LB medium was observed after 4 h induction.
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Chapter 4
Although the presence of 30S labelled and unlabelled QconCAT proteins were confirmed by
SDS-page, we still need to check the accuracy of the 30S QconCAT sequence. A small pieces of
SDS-PAGE was digested with Lys C and trypsin and the sequence confirmed by MALDI-ToF
MS (data not shown)
a
kDa
116
66
36
29
20
M
0
1
2
time (hr)
3
4
O/N
c
b
kDa
30S unlabelled
QconCAT
116
66
30S labelled
QconCAT
36
29
20
14
M
0
1
2
3
time (hr)
4
5
M
0
1
2
3
time (hr)
4
5
Figure 4.6: Expression of unlabelled 30S QconCAT protein in LB medium (a), unlabelled and
labelled 30S QconCAT in minimal medium (b and c) detected by Coomassie staining. From
left to right (a): protein standard (M), 30S QconCAT induced BL21 containing 30S QconCAT
plasmid. Arrow indicates the 72.7 kDa 30S unlabelled QconCAT band (a). The sizes of
protein standards are labelled on the left.
Characterisation 30S QconCAT-To investigate where 30S QconCAT resided in the host cells
after induced expression, the total and soluble fractions (obtained according to the centrifugation
described in experimental section) for both labelled and unlabelled 30S QconCATs in minimal
145
Chapter 4
media were analyzed by SDS-PAGE. The outcome showed that the majority of the 30S
QconCAT protein ends up in inclusion bodies, whereas the soluble fraction lacks of 30S
QconCAT protein (Figure 4.7). Unlike the core QconCAT (Figure 4.2b), however, the formation
of inclusion bodies did not purify the 30S QconCAT completely (Figure 4.7). Additional
purification was achieved using a his6-tag column. Both light and heavy 30S QconCAT were
homogeneous on 1D SDS-PAGE after this step and were used without any other further
purification.
MALDI-ToF mass spectrometry (Figure 4.8) indicated that 27 of the expected 36 tryptic peptides
were identified and there are several peptides not fully digested. To identify the rest of signature
peptides and reduce the number of peptide miscleavages for examples Q31+Q37, We examined
the digestion efficiency of 30S QconCAT under different protocols explained in the next section.
M
unlabelled
T
S
labelled
T
S
Figure 4.7: SDS-PAGE analysis of soluble (S) and total (T) fractions of labelled and
unlabelled 30S QconCAT expressed in minimal medium. The 30S QconCAT protein is not
found in the soluble fraction (S). Arrows indicate the 72.7 and 73.1 kDa 30S unlabelled and
labelled QconCAT protein, respectively.
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Chapter 4
a
b
M
SM
UM
W
1st
2nd
Figure 4.8: MALDI-ToF mass spectrum of purified 30S unlabelled QconCAT trypsin digestion
(a). After purification and cleavage of his-tag, the purity of produced 30S QconCAT was
checked by SDS-PAGE (b). Asterisks indicate the fragment of Lys C peptides. M = molecular
weight marker, SM = starting materials, UM = unbound materials, W=wash, 1st and 2nd = first
and second pooled purified 30S QconCAT fractions. In the SDS-PAGE (b), contaminates were
almost undetectable by either SDS-PAGE or mass spectrometry.
Optimization of digestion QconCATs
Because of our goal to use the QconCAT for absolute quantification, the incomplete QconCAT
and analyte digestion is an issue and protein will lead to an incorrect of the actual protein
concentration. In our study, the core and 30S QconCAT digestion conditions were optimized.
Therefore, the digestions were performed in three protocols commonly used for the digestion of
proteins. One protocol utilizing the RapiGestTM SF, other was performed with 6M urea/2M
thiourea and the third protocol was achieved without denaturant (see experimental procedures).
The protocol employing the urea was better than the other protocols tested in this study. The
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Chapter 4
comparison between the various protocols is based on numbers of identified QconCAT peptides
and the number of miscleavage. We also tested the value of guanidination as a method to
improve peptide ion yields in MALDI-ToF MS. Moreover, we applied a proof of principle
experiment of QconCAT for quantification of crude ribosomal protein samples challenged with
gentamicin.
Table 4.1 shows the number of peptides identifications from tryptic digestions of labelled 30S
QconCAT in presence of denaturants. The urea/thiourea protocol (Figure 4.9b) produced the
most total peptide identifications of 30S tryptic peptides of any protocol tested. The RapiGestTM
protocol produced the lowest peptides number. It is possible that the RapiGestTM protocol
(Figure 4.9a) did not yield as many peptides as the urea protocol due to the effects of peptide
losses from the acid precipitation step to remove the RapiGestTM. Furthermore, the RapiGestTM
introduced impurities in the spectrum, these could lead to complications such as obscuring of Q
peptides. Similarly, a mixture of labelled and unlabelled core QconCAT digested with Lys C in
presence of RapiGestTM produced all the peptides expected from core QconCAT but resulted in
high proportions of impurities identified in the spectrum (Figure 4.10). The protocol with no
denaturant (Figure 4.9c) yield a higher number of 30S QconCAT peptides (table 4.1), with more
miscleavage, comparing with RapiGestTM protocol and because we intend to use QconCAT for
quantification purpose its necessary to achieve a complete digestion. Therefore, the urea/thiourea
protocol was used in future experiments.
In the urea/thiourea trypsin protocol, 34 out of 36 Q peptides were seen by MALDI-ToF MS,
two peptides were missing, one of them is EAQGCDIR which is a Rim peptide (both Rim
peptide included in a 30S QconCAT sequence (see table 4.6) from protein sometimes found as
contaminant in ribosome preparations. There is a problem with this peptide in the plasmid
construct as there is a sacrificial peptide (PWR) followed peptide C-terminus EAQGCDIRPWR,
and trypsin is not able to cleave RP. The other missing peptide from tryptic digestion is
YTAAITGAEGK (S6). To identify this peptide, LC MSMS was performed (Figure 4.12 top).
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Chapter 4
Close inspection of each QconCAT peptide indicated that for most, the observed mass was as
expected. Two peptide in particular (IFSFTALTVVGDGNGR, [M+H]+ 1659.8
m/z and
LTNGFEVTSYIGGEGHNLQEHSVILIR [M+H]+ 2989.5 m/z) were notably different. One of
monoisotopic m/z 1660.6 and second at a monoisotopic m/z 2991.3 (Figure 4.9b) were absent
from Figure 4.9a and c, but were seen in spectrum b. The higher m/z peptides have been
generated from the peptide at m/z 1659.8 and 2989.5, the most probable explanation for the mass
increase was deamidation of the asparagine residue, which, by conversion to an aspartate residue,
would increase the mass by 1 Da (-NH2 to -OH). To confirm that MS/MS spectrum was
submitted for database searching with variable modification due to deamidation (+ 1Da) and
carbamylation (+ 43Da).
Figure 4.9: Comparison of three protocols for the trypsin digestion of 30S QconCAT. 30S
QconCAT digested with RapiGest™ (a), 6M urea/2M thiourea (b) and 50 mM NH4HCO3 (c)
were used. For details, see the Experimental section. ♦Peaks corresponding to impurities
formed in spectrum of RapiGest protocol.● fragments of Lys C peptide.
149
Chapter 4
The resulted data for those two peptides provided strong evidence for deamidations, with y- and
b-ion series increased by 1 Da following N-to-D substitutions. However, because the mass
change for a deamidated peptide is similar to that for a naturally occurring 13C isotope. Even so,
an irregular isotopic peak height pattern with an apparent mass shift of +1 Da was indicative of
deamidation. No sign of carbamylation was detected of any of QconCAT peptides.
Table 4.1: Number of peptide identifications for different enhancers of the 30S QconCAT tryptic digestion
by MALDI-ToF
Enhancers
No of detected peptides/overall number of 30S tryptic QconCAT peptides
6M urea/2M thiourea
RapiGest™
No enhancer
34/36
24/36
29/36
Figure 4.10: MALDI-ToF MS analysis of a tryptic digest of a mixture of labelled and
unlabelled core QconCAT in presence of RapiGestTM.
150
Chapter 4
Figure 4.11: MALDI-ToF MS spectra of unlabelled core QconCAT Lys C digest. Spectrum (a)
is the digestion with one Lys C dose, spectrum (b) is the digestion with double dose of Lys C.
Miscleaved peptides (Q2+Q3) disappeared in spectrum b. Q2 usually appears with very low
intensity as in figure 4.2. This problem has been solved either by using ESI or peptide
guanidination (see figure 4.2c).
Next, we evaluated the effect of double additions of enzyme (Lys C or trypsin enzyme) on the
peptide miscleavage, a separate set of experiments were performed to reduce the number of
miscleaved peptides. We digested core QconCAT with Lys C in presence of urea/thiourea into
two separate experiments, in one experiments Lys C (in ratio 1:50) was added and samples
digested for overnight and in other the sample was digested for 4 h and then another dose of Lys
C enzyme was added and the sample was digested for overnight (Figure 4.11b). The samples
containing double dose of Lys C give complete digestion compared to those with one dose of
Lys C (Figure 4.11a). Digestion with Lys C and trypsin yielded all expected signature peptides
for Lys C and most of signature peptides for trypsin, but a liquid chromatography step was
required for full detection.
151
Chapter 4
Detailed tables of all Lys C and tryptic 30S QconCAT peptides listed in table 4.5 and 4.6.
y10 y9 y8 y7 y6 y5 y4 y3 y2 y1
y5 2+
Y
Y—T--A--A—I—T--G--A--E--G--K
y2 -H2 O
b2 b3 b4
b2 -H2O b2
y9
y6
y3-H2O
y1
y7
y5
y2
b3
y3
y3
b4
y10
y4
y14 y13 y12 y11 y10 y9 y8 y7 y6 y5
y4
a2
y8
y2 y1
y4
I—F—S—F—T—A—L—T—V—V—G—D—G—N—G--R
y7
y6
b2
b2
y9
b3-H2 O
y8
y5
y10
y11
y12
y1
y2
y13
a2
y8 y7 y6
y5
y3
y14
y1
T—F—T—A—K—P—E—T—V--K
b2
b2
y3
y1-H2 O
y1
F
b3
b4 -H2O
b3
MH-NH3 +2 MH+2
y5
y8
2+
y92+
y8
y6
y7
Figure 4.12: Q-ToF CID mass spectra of the doubly protonated peptides ion, (top)
TYAAITGAEGK (S6), at m/z 544.24. (Middle) IFSFTALTVVGDGNGR (S5), at
m/z 830.42, and (bottom) TFTAKPETVK (L13), at m/z 567.34.
152
Chapter 4
Table 4.2: Summary of the number of peptides generated from Lys C and Trypsin digests of labelled 30S QconCAT
using LTQ Orbitrap MS. The urea/thiourea protocol and inclusion lists for both Lys C and trypsin 30S QconCAT
peptides were considered in this analysis.
Types of
Number of observed peptides without inclusion
Number of observed peptides with inclusion
digests
list/overall 30S QC peptides number
list/overall 30S QC peptides number
Lys C
15/16
16/16
Trypsin
26/36
35/36
Determination of ribosomal proteins stoichiometries challenged with gentamicin using 30S
QconCAT by LC MSMS (Proof-of-principle experiment)
With a QconCAT and a set of digestion protocols in place, we had the means of quantifying
ribosomal proteins in real samples. We therefore designed a proof of principle experiment to
demonstrate that the ribosomal QconCAT can be used for quantification of ribosomal proteins
samples. E.coli 70 S ribosomes were first isolated from the whole lysate of both control and
gentamicin-treated samples in biological triplicate and then spiked with a known amount 30S
labelled QconCAT, digested with Lys C and then trypsin to generate twelve samples (see
experimental section) and subjected to triplicate LC MS/MS analysis resulting 36 LC MS/MS
runs using an LTQ-Orbitrap XL mass spectrometer to obtain signal intensities for the 30S
ribosomal proteins and 30S QconCAT (light to heavy) for each peptide.
The absolute amounts of ribosomal proteins in both conditions were determined by comparison
with the known amount of 30S QconCAT spiked. Automated quantification of the mass
spectrometric data was carried using the QconCAT Pride Wizard. The Wizard takes Mascot
analysis as its starting point, thus peptides that are not detected by Mascot cannot be analyzed in
this way. Tables 4.3 and 4.4 show these data. Additional peptides were analyzed by manual
inspection of the MS data.
Methodology
Each of the 21 proteins of the 30S subunit gave quantitative data for at least one signature
peptide, and quantification showed good agreement between the technical triplicates (see
153
Chapter 4
standard errors in table 4.3 and 4.4). Generally where two peptides from the same protein were
detected, there was very close agreement between the two sets of measurements. There was
some signal overlap for some of the peptides, preventing their quantification. Clearly, this could
readily be overcome by using MRM measurements, however, the high resolution of an LTQOrbitrap was deemed sufficient for these fairly simple samples. For instance, the two peptides
derived from proteins S11 and S16 were in good agreement in the tryptic measurements (Table
4.4). An example is IRTLQGRVVSDK originally selected as a signature Lys C peptide for
ribosomal protein S17 (Table 4.3), which shows a good agreement with the other tryptic peptide
SIVVAIER (S17) of the same protein (Table 4.4).
However, ribosomal protein S6 was an exception in this case. The calculated peptide ratio for
this selected signature peptide FNDAVIR differs from peptide YTAAITGAEGK (see table 4.4),
both peptides checked manually, the inconsistency due to peak overlap of QconCAT peptide.
This peptide (YTAAITGAEGK) was excluded from this study.
Accuracy and precision of the data
As we can see from table 4.3 and 4.4, the technical replicated show excellent reproducibility with
standard errors of typically 3% and in the range 0.5-6%. Where two peptides are generated by
the same enzyme, the reproducibility of the analysis is most reassuring. For example, S7 has
peptides quantified at 842 ± 22 fmole and 850 ± 35 fmole. The difference in the means is small
compared with the standard error on a single measurement. For S18 and S20, the difference in
the means is just outside the standard error, but it is noticeable that in both these cases we were
dependant on at least one manual analysis of the data. The third indicator of the accuracy of these
measurements lies in a composition of Lys C generated peptides with tryptic peptides.
For proteins S7, S10, S13 and S20 these quantification is again the same irrespective of enzyme.
S8 and S17 show slightly 15 % discrepancies between the two samples. We conclude that the
two enzyme strategy does not introduce significant error into the measurement and provides
154
Chapter 4
further confirmation that the methodology is robust. The most important facet of the
quantification is, however, the comparison of the amounts of the different ribosomal proteins.
Structure of the ribosomes in the control samples
The ribosome samples used in this experiment were not purified. We were interested in total the
contents of the ribosome fraction, not in the structure or activity of purified ribosomes. The close
agreement in the quantities of ribosomal proteins in the samples is therefore of interest. These
results are shown in figure 4.13. Most of the proteins were present at approximately 900 fmole
±100 in the control samples. It is interesting that the three large subunit proteins analyzed
showed no significant difference in concentration from the small subunit proteins.
Notably, ribosomal proteins S1 and S2 are not identical with the rest of the sample (figure 4.13).
Several groups identified and characterized ribosomal proteins in various organisms including
E.coli, they found, the copy number of ribosomal protein S1 are lower than other proteins.
Similarly, the amounts of S2 were higher than other ribosomal protein. Our results are consistent
with their data.19
The very good accuracy and precision of our data, as evidenced by its consistency over different
peptides and different proteolytic enzymes, see table 4.3 and 4.4, suggests that the small
deviations from 1:1:1 etc stoichiometry observed here are real. If we assume that the central
proteins S7, S8, S4, L4, L13, L2 are stoichiometric with respect to ribosomal RNA, then any
protein at a significantly different concentration from these is not stoichiometric. S1, S2, S18
and S20 have concentrations in this sample which suggests that their binding to the ribosome is
less well regulated than that of the other proteins.
The effect of gentamicin on the bacterial ribosome
The result in figure 4.13 shows that the regulation of ribosomal protein synthesis has been
interrupted and the total amount of protein increased in gentamicin-treated sample. It possible to
155
Chapter 4
use absolute QconCAT to reveal the copy number of ribosomal protein. The copy number for S7
was calculated as an example; 4.10 × 104.
4000
fmole light ribosomal proteins
3500
3000
2500
2000
C
G
1500
1000
500
S1
S1
S2
S2
S3
S3
S4
S4
S5
S5
S6
S6
S7
S7
S8
S8
S9
S9
S10
S10
S11
S11
S12
S12
S13
S13
S14
S14
S15
S15
S16
S16
S17
S17
S18
S18
S19
S19
S20
S20
S21
S21
L13
L13
L2
L2
L4
L4
0
Ribosomal proteins
Figure 4.13: The graph shows the amount (fmole) of each ribosomal protein in control (C)
compared to gentamicin-treated samples. Use of error bars to present precision.
156
Chapter 4
Figure 4.14: LC-MS-based quantitation of a peptide (LGEFAPTR) generated from
S19 of ribosomal protein and was spiked with 600 fmol of labelled 30S QconCAT.
The upper panel represents the total ion chromatogram across the elution time of the
LC MS experiment. The middle panel shows a survey scan acquired. Zooming into
this spectrum, the lower panel highlights a doubly charged 30S QconCAT peptide
pair versions. The mass offset corresponds to the expected difference of 3.009 Da for
a peptide containing 13C6-R. This one of the peptide pair failed to quantify by
QconCAT Pride Wizard, and quantified manually (see table 4.4).
157
Chapter 4
Table 4.3: Manual and automated quantitation of Lys C peptides from control and gentamicin-treated 70S isolated
ribosomal proteins using 30S QconCAT
Protein
Peptide
m/z
z
Score
Control (C)
L/H)ratio±SE fmole light
Gentamicin (G)
L/H)ratio±SE fmole light
S2
LENSLGGIK*
468.77
2
45.2
1.325±0.031
1709±40
2.234±0.047
2882±61
S7
SMALR*LANELSDAAENK*
922.98
2
80.8
0.653±0.017
842±22
1.221±0.013
1575±17
S7
FGSELLAK*
435.75
2
49.4
0.659±0.027
850±35
1.112±0.022
1434±28
S8
VEGDTK*PELELTLK*
792.45
2
78.9
0.568±0.033
733±42
0.548±0.006
707±8
S10
AFDHR*LIDQATAEIVETAK*
1070.57
2
75.1
0.609±0.015
786±19
1.047±0.041
1351±53
S13
ISELSEGQIDTLR*DEVAK*
1008.03
2
82.7
0.657±0.022
848±29
1.077±0.077
1389±100
S14
AIISDVNASDEDR*WNAVLK*
1064.5
2
101.2
0.720±0.018
929±23
1.122 ±0.017
1447±22
S17
IR*TLQGR*VVSDK*
695.44
2
32.6
0.648±0.053
836±70
0.963±0.194
1242±250
S18
YQR*QLAR*AIK*
422.251
0.942±0.003
1215±4
0.835±0.109
1077±141
S18
IVPSR*ITGTR*AK*
433.755
3
15.2
0.855±0.049
1103±63
0.838±0.0914
1081±117
S20
AFNEMQPIVDR*QAAK*
865.45
2
19.2
0.940±0.038
1213±50
1.315±0.024
1696±31
S20
ANLTAQINK*
489.78
2
15.9
0.806±0.12
1040±4
1.20±0.01
1548±13
L13
TFTAK*PETVK*
567.33
2
49.3
0.667±0.081
860±104
1.039±0.078
1340±101
L13
R*DWYVVDATGK*
661.35
2
68.9
0.662±0.047
854±61
1.046±0.036
1349±46
2 25.73
158
Chapter 4
Table 4.4: Manual and automated quantitation of tryptic peptides from control and gentamicin-treated 70S isolated
ribosomal proteins using 30S QconCAT.
Protein
Peptide
m/z
z
Score
S1
AFLPGSLVDVR*PVR*
769.46
2
Quantitation
Control
Gentamicin-treated
(L/H)ratio±SE fmole light (L/H) ratio±SE fmole light
24.7
0.586±0.0415
352±25
1.01±0.065
606±39
S1
GATVELADGVEGYLR
778.40
2
45.5
OL
OL
OL
OL
S2
AGVHFGHQTR
-
2
-
OL
OL
OL
OL
S3
ADIDYNTSEAHTTYGVIGVK*
1080.5
2
99.4
1.68±0.023
1008±14
2.383±0.015
1430±9
S3
LGIVKPWNSTWFANTK
-
-
ND
ND
ND
ND
ND
S4
LDNVVYR*
442.74
2
-
1.02±0.006
612±4
1.79±0.084
1074±50
S4
SDLSADINEHLIVELYSK*
684.83
-
-
OL
OL
OL
OL
S5
AYGSTNPINVVR*
648.85
2
50.2
1.283±0.0186
770±11
2.234±0.060
1340±36
S5
IFSFTALTVVGDGNGR *
830.43
2
-
1.286±0.021
772±13
OL
OL
S6
FNDAVIR*
420.74
2
50.1
1.194±0.0542
716±33
1.810±0.027
1086±16
S6
YTAAITGAEGK*
544.29
2
41.4
4.068±0.180
2441±152
5.425±0.123
3255±74
S7
LANELSDAAENK*
640.82
2
77.8
1.371±0.083
823±50
2.28±0.121
1368±73
S8
AVVESIQR*
454.26
2
19.9
1.256±0.031
754 ±19
OL
OL
S9
GGGISGQAGAIR*
525.29
2
93.3
1.174±0.0548
704±33
2.252±0.0436
1351±26
S9
SLEQYFGR*
503.26
2
28.6
1.197±0.097
718±58
1.81±0.035
1086±21
S10
LIDQATAEIVETAK*
754.42
2
21.1
1.264±0.063
758±38
2.203±0.009
1322±5
S10
LVDIVEPTEK*
574.83
2
52.3
1.268±0.041
761±25
2.077±0.141
1246±85
S11
ALNAAGFR*
413.23
2
56.1
1.404±0.05671
842±34
2.302±0.17459
1381±105
S11
QGNALGWATAGGSGFR*
778.39
2
104.6
1.448±0.15499
869±93
2.387±0.09258
1446±56
S12
VYTTTPK*
408.20
2
30.2
1.193±0.0463
716±28
1.351±0.1083
811±65
S12
LTNGFEVTSYIGGEGHNLQEHSVILIR*
ND
-
ND
ND
ND
ND
ND
S13
IAGINIPDHK*
542.31
2
53.5
1.288±0.10581
773±63
2.375±0.10196
1425±61
S14
AIISDVNASDEDR*
MC
-
MC
MC
MC
MC
MC
S15
IVSEFGR*
407.23
2
39.1
1.317±0.018
790±11
2.45±0.054
1470±32
S15
YTQLIER*
464.75
2
57.1
1.301±0.0288
781±17
OL
OL
S16
VGFFNPIASEK*
607.83
2
64.1
1.657±0.0378
994±23
2.313±0.133
1388±80
S16
IAHWVGQGATISDR*
758.90
2
73.4
1.646±0.072
988±43
2.321±0.110
1393±66
S17
SIVVAIER*
446.78
2
54.9
1.219±0.004
732±3
2.114±0.0042
1268±3
159
Chapter 4
S19
LGEFAPTR*
448.74
2
26
1.296±0.090
778±54
1.9255±0.1755
1155±105
S19
QHVPVFVTDEMVGHK*
861.93
2
34.3
OL
OL
OL
OL
S20
AFNEMQPIVDR*
663.34
2
61.1
2.108±0.007
1265±4
2.937±0.079
1762±47
S21
AGVLAEVR*
410.75
2
18.5
1.042 ±0.109
625±66
0.882 ±0.223
529 ±134
S21
EFYEK*PTTER*
656.3
2
45.3
1.147±0.162
688 ±97
1.130±0.068
678±41
L2
SAGTYVQIVAR*
585.83
2
66.2
1.248 ±0.0355
749±21
2.029 ±0.0891
1217±53
L2
LEYDPNR*
456.73
2
42.2
OL
OL
OL
OL
L4
DAQSALTVSETTFGR*
794.90
2
93.7
1.566 ±0.070
940±42
2.416±0.0957
1449±57
L4
DFNEALVHQVVVAYAAGAR*
679.36
3
24.6
OL
OL
2.317±0.1999
1390±120
OL., overlapped. MC., miscleaved. ND., not detected. The ratio of the peptides highlighted in
red is calculated manually, while the peptides in black measured by using QconCAT Pride
Wizard. The values given in tables 4.3 and 4.4 are averages from three experiments in control
and gentamicin-treated samples. SE=Standard errors. Amount= ratio multiplied by amount of
QconCAT spiked (1290 fmole for Lys C and 600 fmole for trypsin digests).
Discussion
Many elegant mass spectrometric approaches exist for relative quantification (comparing the
amounts of a given protein in two different samples).
Rather few methods for absolute
quantification exist and these are required for determination of stoichiometries within a sample.
The QconCAT method is especially powerful when the same stoichiometries are to be
determined for many different samples.
A strategy for determining stoichiometries for ribosomal proteins and the factors which associate
with the ribosome has been developed. The strategy depends upon a core QconCAT into which
cassettes containing signature peptides for the proteins under study are inserted (at the DNA
level). The core QconCAT has been successfully prepared and one cassette (which encodes
signature peptides for the 30S ribosomal proteins) has been developed. Both QconCATs require
a two-enzyme digestion protocol, because ribosomal proteins are often small and basic and yield
few (in some cases no) proteotypic tryptic peptides.
Although endoproteinase Lys C, unlike trypsin, is generally believed to cleave at KP, the 30S
QconCAT contains five KP motifs, and only one of these is a substrate for the enzyme under the
160
Chapter 4
conditions used. Peptides containing KPE and KPT were identified during experiments on
ribosomal proteins as potential signature peptides and appear to be unscathed by enzyme
treatment. However, KPW (Q47) is cleavable and the SmaI restriction site present at the DNA
level encodes a KPG sequence, at which Lys C digestion was required and appeared to be
quantitative. These results suggest the need for a systematic study of KP cleavage by Lys C. We
established the 30S QconCATs can be used to quantify ribosomal protein samples based on our
proof-of -principle experiment. Further cassettes (the 50S ribosomal protein cassette) are
development in the following chapter.
Acknowledgements
We thank Professor Venki Ramakrishnan (MRC, Cambridge) for a gift of 30S ribosomal
subunits and the Higher Education Department of the Libyan Government for a studentship.
Table 4.5: The core ribosomal QconCAT showing origins of the proteolytic peptides
Peptide sequence
AnnotationProtein
Note
m/z
MGTK
Sacrificial peptide containing the N-terminus
TFTAKPETVK
RDWYVVDATGK
SMALRLANELSDAAENK
FGSELLAK
VEGDTKPELELTLK
SDLSADINEHLIVELYSK
GVNDNEEGFFSAK
1133.62
1321.65
1844.91
870.48
1583.85
2052.03
1419.62
Q2
Q3
Q6
Q1
Q5
Q7
Q4
LEYDPNR
912.43
SAGTYVQIVAR
1170.63
DAQSALTVSETTFGR
1588.77
DFNEALVHQVVVAYAAGAR2036.04
LDNVVYR
884.47
AVVESIQR
907.51
GGVNDNEEGFFSAR
1504.65
Q10
Q11
Q14
Q15
Q8
Q9
Q13
LEPGR
KLPWR
LAAALEHHHHHH
1409.69
Q12
L13
L13
S7
S7 Lys-terminated peptides released by Lys C
S8
S4
QCAL
Sacrificial peptide containing restriction sites, XhoI
and SmaI
L2
L2
L4
L4 Arg-terminated peptides released by trypsin
S4
S8
QCAL
Sacrificial peptide containing restriction sites, HindIII
and NcoI
His-tag
161
Chapter 4
Table 4.6: Additional signature peptides in the 30S ribosomal subunit QconCAT
Peptide sequence
m/z
Annotation Protein
Note
LENSLGGIK
930.52
Q16
S2
Lys-terminated peptides
ANLTAQINK
978.56
Q17
S20 released by Lys C
YQRQLARAIK
1264.79
Q18
S18
IVPSRITGTRAK
1316.85
Q19
S18
IRTLQGRVVSDK
1389.86
Q20
S17
AFNEMQPIVDRQAAK
1729.90
Q21
S20
AIISDVNASDEDRWNAVLK
2128.10
Q22
S14
AFDHRLIDQATAEIVETAK
2140.14
Q23
S10
ISELSEGQIDTLRDEVAK
2015.06
Q24
S13
IVSEFGR
813.45
Q25
S15 Trypsin-terminated peptides
AGVLAEVR
820.49
Q26
S21 released by Trypsin
ALNAAGFR
825.46
Q27
S11
FNDAVIR
840.46
Q28
S6
SIVVAIER
892.55
Q29
S17
LGEFAPTR
896.49
Q30
S19
YTQLIER
928.51
Q31
S15
SLEQYFGR
1005.50
Q32
S9
GGGISGQAGAIR
1049.57
Q33
S9
AGVHFGHQTR
1115.58
Q34
S2
AYGSTNPINVVR
1296.70
Q35
S5
EFYEKPTTER
1311.66
Q36
S21
AIISDVNASDEDR
1410.68
Q37
S14
IGTAITFYGTAALR
1460.82
Q38
Rim K
EAQGCDIR
891.39
Q39
RimK
IAHWVGQGATISDR
1516.79
Q40
S16
AFLPGSLVDVRPVR
1537.92
Q41
S1
QGNALGWATAGGSGFR
1555.80
Q42
S11
GATVELADGVEGYLR
1555.80
Q43
S1
IFSFTALTVVGDGNGR
1659.87
Q44
S5
LTNGFEVTSYIGGEGHNLQEHSVILIR 2989.54
Q45
S12
LVDIVEPTEK
1148.65
Q46
S10 Lysine terminated peptides
released by trypsin
LGIVKPWNSTWFANTK
1874.03
Q47
S3
ADIDYNTSEAHTTYGVIGVK
2160.05
Q48
S3
YTAAITGAEGK
1087.57
Q49
S6
VYTTTPK
815.46
Q50
S12
IAGINIPDHK
1083.62
Q51
S13
VGFFNPIASEK
1214.65
Q52
S16
QHVPVFVTDEMVGHK
1728.88
Q53
S19
162
Chapter 4
References
1. Wilson, D. N.; Nierhaus, K. H., The weird and wonderful world of bacterial ribosome
regulation. Critical Reviews in Biochemistry and Molecular Biology 2007, 42, 187-219.
2. Poehlsgaard, J.; Douthwaite, S., The bacterial ribosome as a target for antibiotics. Nature
Reviews Microbiology 2005, 3, 870-881.
3. Wimberly, B. T.; Brodersen, D. E.; Clemons, W. M.; Morgan-Warren, R. J.; Carter, A. P.;
Vonrhein, C.; Hartsch, T.; Ramakrishnan, V., Structure of the 30S ribosomal subunit. Nature
2000, 407, 327-339.
4. Ban, N.; Nissen, P.; Hansen, J.; Moore, P. B.; Steitz, T. A., The complete atomic structure of
the large ribosomal subunit at 2.4 Å resolution. Science 2000, 289, 905-920.
5. Schluenzen, F.; Tocilj, A.; Zarivach, R.; Harms, J.; Gluehmann, M.; Janell, D.; Bashan, A.;
Bartels, H.; Agmon, I.; Franceschi, F. o.; Yonath, A., Structure of functionally activated small
ribosomal subunit at 3.3 Å resolution. Cell 2000, 102, 615-623.
6. Usary, J.; Champney, W. S., Erythromycin inhibition of 50S ribosomal subunit formation in
Escherichia coli cells. Molecular Microbiology 2001, 40, 951-962.
7. Mehta, R.; Champney, W. S., 30S ribosomal subunit assembly is a target for inhibition by
aminoglycosides in Escherichia coli. Antimicrobial Agents and Chemotherapy 2002, 46, 15461549.
8. Ong, S. E.; Blagoev, B.; Kratchmarova, I.; Kristensen, D. B.; Steen, H.; Pandey, A.; Mann,
M., Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate
approach to expression proteomics. Molecular & Cellular Proteomics 2002, 1, 376-386.
9. Multiplex protein quantitation using iTRAQ® reagents–8plex available at
http://www3.appliedbiosystems.com/cms/groups/psm_marketing/documents/generaldocuments/c
ms_049786.pdf
10. Kirkpatrick, D. S.; Gerber, S. A.; Gygi, S. P., The absolute quantification strategy: a general
procedure for the quantification of proteins and post-translational modifications. Methods 2005,
35, 265-273.
11. Beynon, R. J.; Doherty, M. K.; Pratt, J. M.; Gaskell, S. J., Multiplexed absolute
quantification in proteomics using artificial QCAT proteins of concatenated signature peptides.
Nature Methods 2005, 2, 587-589.
12. http://prospector.ucsf.edu/prospector/mshome.htm
13. Wierenga, S. K.; Zocher, M. J.; Mirus, M. M.; Conrads, T. P.; Goshe, M. B.; Veenstra, T. D.,
A method to evaluate tryptic digestion efficiency for high-throughput proteome analyses. Rapid
Communications in Mass Spectrometry 2002, 16, 1404-1408.
14. Mann, M., RP-In solution digest for mass spectrometry (MS) Analysis. RUBICON Protocols
Database, 1-2.
15. Siepen, J.; Swainston, N.; Jones, A.; Hart, S.; Hermjakob, H.; Jones, P.; Hubbard, S., An
informatic pipeline for the data capture and submission of quantitative proteomic data using
iTRAQTM. Proteome Science 2007, 5, 1-9.
16. Savitzky, A.; Golay, M. J. E., Smoothing and differentiation of data by simplified least
squares procedures. Analytical Chemistry 1964, 36, 1627-1639.
17. Washburn, M. P.; Wolters, D.; Yates, J. R., Large-scale analysis of the yeast proteome by
multidimensional protein identification technology. Nature Biotechnology 2001, 19, 242-247.
163
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18. Eyers, C.; Simpson, D.; Wong, S.; Beynon, R.; Gaskell, S., QCAL—a novel standard for
assessing instrument conditions for proteome analysis. Journal of the American Society for Mass
Spectrometry 2008, 19, 1275-1280.
19. Warwood, S.; Mohammed, S.; Cristea, I. M.; Evans, C.; Whetton, A. D.; Gaskell, S. J.,
Guanidination chemistry for qualitative and quantitative proteomics. Rapid Communications in
Mass Spectrometry 2006, 20, 3245-3256.
20. Van Knippenberg, P. H.; Hooykaas, P. J. J.; Van Duin, J., The stoichiometry of E. coli 30S
ribosomal protein S1 on in vivo and in vitro polyribosomes. FEBS Letters 1974, 41, 323-326.
164
Chapter 5
A tool for the quantification of 50S ribosomal proteins-the E.coli
50S ribosomal QconCAT
Introduction
The bacterial ribosome comprises 30S and 50S ribonucleoprotein subunits, which together
compose the 2.5-MDa 70S ribosome. The 50S subunit consists of 23S RNA, 5S RNA and
about 30 proteins; the 30S subunit consists of 16S RNA and about 20 proteins. In addition,
several protein factors act on the ribosome at various stages of translation. The ribosome
contains a number of binding sites for known antibiotics and is an attractive target for seven
different classes of antibiotics used clinically, including arguably the safest (macrolides) and
most potent (aminoglycosides) known.1 The groundbreaking structural study of the ribosome
have been rewarded with the 2009 Nobel Prize for Chemistry.2-4 To generate as complete as
possible picture of the dynamic ribosomal protein changes during its assembly and interaction
of ribosome with other proteins. The exact ribosomal protein stoichiometries within this
particular protein complex are the key to understand these conditions and especially the
effects of drugs, including the macrolides and aminoglycosides on this process.
Several different approaches and techniques are available for measuring protein expression
levels include MS-based quantification. Most of these approaches are based on the
introduction of stable isotopes. This is performed by metabolic,5 chemical or enzymatic
labeling.6 In addition, some label-free methods (e.g. spectral count)7,8 are available.
For stoichiometric measurements, the determination of the absolute amount of proteins is
necessary. This is achieved by either providing standard peptides (AQUATM)9,10 or by labelfree approaches. There are however limitations for the absolute quantification using standard
peptides. First, the addition of standard peptides to a proteome digest provides quantification
of only single or few proteins of the sample. Complex studies would require the synthesis of
several standard peptides per protein at significant cost, and would have to be quantified
individually. When the stoichiometries of the same proteins are to be measured repeatedly,
the QconCAT11 approach is more streamlined, especially if multiple proteins are to be
quantified.
165
Chapter 5
In brief, a synthetic gene is produced and expressed in Escherichia coli in a medium
containing heavy lysine and arginine, resulting in the expression of a fully labelled protein in
which peptides of interest are concatenated.12 After purification and concentration
determination, the artificial protein (QconCAT) is mixed with a complex mixture of analyte
proteins, digested to yield both the stable-isotope labelled and the natural peptides from the
analyte. The quantity of standard added can then be used for absolute quantification of the
analyte. We aim to prepare a flexible 50S QconCAT to perform absolute quantification study
of 50S ribosomal proteins and to quantify the isolated 50S ribosomal proteins in response to
the presence of sub-lethal concentrations of azithromycin.
Experimental procedures
All the experimental procedures for 50S QconCAT are performed in the same way as for the
30S QconCAT (chapter 4), these including transformation, expression in LB and minimal
media, purification of heavy and light QconCAT, determination the concentration of 50S
QconCAT using AQUA and Bradford assay, guanidination of 50S QconCAT lysine peptides,
peptide desalting, digestion using urea denaturation, Isolation of 70S ribosomal proteins from
whole E.coli lysate (in this case azithromycin was used as drug in concentration 6 µg/ml),
LC–MS/MS operating conditions, mass spectrometry (For quantification analysis, data were
acquired with a 50S QconCAT inclusion lists) and raw LC-MSMS data analysis. Some
experimental procedures are slightly different, theses mentioned in details in the following
sections.
Selection and Construction of QconCATs
A key stage in the design of a QconCAT is the selection of the appropriate proteotypic
peptides to act as quantification standards. 50S QconCAT was constructed by random
concatenation of the 74 selected Lys C and tryptic peptides (see table 5.3 and 5.4 for the list
of peptides). These peptides were selected according to their uniqueness of sequence among
the E.coli ribosomal proteins and their detectability in MALDI-ToF and ESI LC-MS/MS
analysis. Because of the anticipated molecular weight of the recombinant 50S QconCAT, a
restriction site was incorporated midway through the construct and translated to a small linker
peptide, thus different peptides for each of the target proteins were separated. This would
166
Chapter 5
facilitate subcloning if expression failed. The subsequent amino acid sequences were flanked
with
a
leader
N-terminal
sequence
(MGTK-)
and
a
C-terminal
sequence
(-
LAAALEHHHHHH). The DNA construct for 50S QconCAT was produced by PolyQuant
GmbH (http://www.polyquant.com) (Germany). The assembled gene was inserted into a
pET21a vector along with a gene for ampicillin resistance. Recombinant C-terminally His6tagged 50S QconCATs were expressed in E.coli strain BL21 (λDE3) cells.
Determination of isolated 70S ribosomal proteins concentration at 260/280 nm
The concentration and purity of isolated 70S ribosomal proteins were determined by
measuring the optical density (OD) at 260 in a Eppendorf Biophotometer (Helena
Bioscience). An OD260 of 1 of 70S represents 23 pmole/ml RNA. The OD260/OD280 ratio
between 1.8 and 2 indicates high purity of nucleic acid. The 70S ribosomal protein samples
from both control and azithromycin-treated samples give ratio A260/280= between 1.0-1.4
which confirms the presence of other protein and this was confirmed by LC MSMS.
Contaminants that absorb at 280 nm (e.g. protein) will lower this ratio. The 70S ribosomal
protein samples were then aliquoted and stored at -80 °C for further use.
Monitored complete digestion of 50S QconCAT and ribosomal proteins
For monitoring complete digestion, known amounts of the recombinant isotopically labelled
50S QconCAT protein were mixed with the ribosome samples. The samples were reduced,
alkylated, and digested with Lys C/trypsin using optimized procedure (urea/thiourea
protocol) used for 30S QconCAT. The samples were digested for 4h at 30 oC and 37oC,
respectively. After which the digests were incubated with additional enzyme for 24h to
ensure complete digestion. After the addition of enzyme, proteolysis was monitored until no
residual undigested protein could be detected as assessed by SDS-PAGE, and this was further
confirmed mass spectrometrically where the end point was defined as the time point where
the ion intensity ratios of analyte to QconCAT had stabilized. Following digestion, the
resultant peptide mixture was analyzed in by LC-MS/MS. A complete list of 50S QconCAT
peptides identified are listed in tables 5.1 and 5.2.
167
Chapter 5
Digestion using urea denaturation, overnight digestion
This digestion protocol has applied for 50S QconCAT alone and for a mixture 70S ribosomal
proteins and 50S QconCAT. 70S ribosomal proteins samples in 50 µl of 10 mM ammonium
bicarbonate were spiked with 500 fmole labelled 50S QconCAT then the mixtures for control
and azithromycin-treated samples in triplicate were evaporated until 25 µl. The samples were
incubated with 25 µl 6M urea/2M thiourea (prepared in 10mM Hepes pH 8.0) and kept at
room temperature for 15 min. Then the protocol was carried out as previously mentioned
(urea/thiourea protocol) in chapter 5
MALDI-ToF mass spectrometry analysis
Both versions of 50S QconCAT analysis was performed on an Ultraflex IITM ToF/ToF mass
spectrometer (Bruker, Bremen, Germany). The samples were mixed (1:1) with a matrix
consisting of a mixture of saturated solution of a-cyano-4-hydroxycinnamic acid (CHCA) and
2,5-Dihydroxy benzoic acid (DHB) prepared in 70% acetonitrile/0.1% TFA. Aliquots of
samples (1 µL) were spotted onto the MALDI sample target plate. Data were acquired using
FlexControlTM version 3.0 (Bruker, Bremen, Germany) in reflectron mode for positive ions.
Signals from 100 to 1000 laser shots were summed for MS analysis per mass spectrum.
Peptide masses were acquired over a range of m/z 700 to 3000 with the laser operating at a
frequency of 200 Hz. Data analysis was performed using Flex analysisTM software (Bruker,
Bremen, Germany).
Results and discussion
Signature peptides for the remaining 30 large subunit proteins were identified experimentally,
two peptides for each protein. These signature peptides were inserted into the core QconCAT
construct in two groups (Lys-C peptides and tryptic peptides as shown in Figure 5.1. The 50S
QconCAT protein (approximately 99 kDa) was expressed in E coli inclusion bodies as shown
in Figure 5.2, and purified by affinity chromatography using the His6-tag.
168
Chapter 5
Figure 5.1: The 50S ribosomal QconCAT sequence showing Lys-C peptides in
blue, tryptic peptides in green, sacrificial peptides containing restriction enzymes in
orange and His6-tag in black.
169
Chapter 5
Start codon
CATATGGGCACGAAAACCTTCACCGCAAAACCAGAAACCGTCAAACGTGATTGGTACGTCGTTGATGCCAC
TGGTAAATCTATGGCTCTGCGTCTGGCGAACGAACTGTCTGACGCAGCGGAAAACAAATTTGGTTCTGAGC
TGCTGGCTAAAGTTGAAGGTGATACCAAACCAGAACTGGAACTGACCCTGAAATCTGACCTCTCCGCTGAT
ATCAACGAACACCTGATCGTGGAGCTGTACAGCAAAGGCGTTAACGATAACGAGGAAGGTTTCTTCTCTGC
Xhol
GAAACTCGAGAAAGTGCTGGGCGGTTCTCACCGCCGCTATGCAGGTGTTGGCGACATTATCAAAGAACTGC
GTCGCGTGGTTGAGCCACTGATCACCCTGGCCAAACAGGTACGTCGCGACGTGGCACGCGTTAAACAGCGC
GATGTGGCGACGGGTGGTCGCGTCGACCGTTTCAACAAAGTAGATCGTGTTCGTGGCCTGGACATCACCAT
CACCACTACTGCGAAAGCACCGGTCGTAGTGCCGGCCGGTGTTGATGTGAAAGCGGTGCGTGGTGCCACTG
GTCTGGGCCTGAAATTTGGCGTTAGCGCAGCTGCAGCGGTAGCAGTTGCTGCAGGTCCGGTAGAAGCTGCG
GAGGAAAAACTGTTCGGCTCCATCGGCACCCGTGACATCGCGGACGCAGTGACGGCAGCTGGTGTTGAAGT
TGCCAAAATTTCTCGTGCACAGCTGCAGGAAATCGCGCAGACCAAAGCGGCAAACATTATCGGTATCCAGA
TCGAATTCGCGAAAGGCAACGTTGAGTATTGGGTTGCGCTGATCCAGCCGGGTAAACAGGACGTACCGTCT
TTCCGTCCAGGCGACACCGTGGAAGTTAAACTGTACTACCTGCGTGAACGCACCGGCAAAATTCTGGCGGA
CATCGCTGTCTTCGACAAAGTGGCGTTCACCGCGCTGGTGGAAAAAATTGGTGTTCCGTTTGTGGATGGCG
GTGTCATCAAAGTAAGCCAGGCTCTCGATATCCTGACGTATACTAACAAACTGTTCGAAGTTGAAGTTGAAG
TTGTCAACACCCTGGTCGTTAAAGTTCTGCGTGCACCGCACGTCAGCGAGAAAGAGGCTGCGATTCAGGTA
TCTAACGTCGCGATTTTCAACGCGGCTACTGGTAAAATTCGTCGCGACGATGAAGTCATCGTACTCACGGG
TAAAGAAGCGCCACTGGCCATCGAGCTGGACCACGACAAATTTCCAGCGATCATCTACGGCGGCAAAGCAG
GCGGTTCCACTCGTAACGGCCGCGACAGCGAAGCTAAAGGCATTGATACTGTACTGGCAGAACTGCGTGCG
CGTGGTGAGAAAATCACCCAGACCCGCTCCGCGATCGGTCGCCTGCCGAAACTGGTTTCCTCTGCTGGCAC
CGGTCACTTCTACACCACCACCAAACGTACCTTTCAGCCGTCTGTGCTGAAACGCCACCTGCGCCCAAAACA
TGCGAACCTGCGTCACATTCTGACTAAACGTGATGGCGTTATCCGTGTGATCTGCTCTGCGGAACCGAAAA
Smal
TCTTCGTGGATGAAGGCCCGAGCATGAAAGCTCCCGGGAAACTGGAATACGATCCGAACCGCTCCGCAGGT
ACTTATGTTCAATCGTTGCTCGTGACGCTCAGTCCGCTCTGACCGTGTCCGAAACGACCTTCGGTCGTGACT
TTAACGAAGCACTGGTTCATCAGGTTGTAGTAGCGTACGCAGCGGGTGCCCGTCTGGATAACGTGGTATAC
CGTGCTGTCGTGGAAAGCATTCAGCGTGGTGGTGTTAACGACAACGAGGAAGGTTTCTTCAGCGCACGTAA
Hindlll
GCTTCGTGGTGCGACCGTTCTGCCACACGGTACCGGTCGCGTAGTGGGCCAGCTCGGCCAGGTTCTGGGCC
CACGCATTTTTACTGAAGATGGTGTTTCTATCCCAGTGACCGTCATTGAAGTTGAAGCCAACCGCGTTACCG
TTCAGTCTCTGGATGTGGTTCGTGATGGTTACGCAGACGGTTGGGCCCAGGCTGGTACTGCACGCCTGGCT
GAAGTTCTGGCTGCAGCCAACGCGCGTGCCGCAGCGTTCGAGGGTGAACTGATCCCAGCCTCTCAGATTGA
TCGCCTGGCAACCCTGCCAACTTATGAGGAAGCCATCGCTCGTGGTCTGCCGATTCCGGTTGTAATCACGG
TTTACGCTGACCGTGTGATCCTGGCCGGCGAGGTGACCACTCCGGTGACGGTTCGTCTGTTCAACGAACTG
GGTCCGCGTCTGCAGGAACTGGGTGCCACGCGTGTACAGGCGCTGGCTGACGCCGCTCGTGTAAGCGAAG
GCCAGACCGTGCGTTTTGGCGGCGAATCCGTACTGGCTGGCTCTATTATCGTTCGTTCCCACGATGCTCTG
ACCGCGGTAACCAGCCTGTCTGTTGACAAATTCCTGCCGAACCTGCATTCTCACCGTGGCCTGGCTCAGGG
TACCGACGTGTCTTTCGGTTCTTTCGGCCTGAAATCCGTAGAGGAACTGAACACTGAACTGCTGAACCTGC
GCGTGGCATGATCAACGCGGTCTCTTTCATGGTGAAACACCACATCACTGCTGACGGCTATTATCGCTTTAA
CATTCCGGGTTCTAAATTCGATCCAGTTGTACGTGAACAGATCATTTTCCCGGAAATCGATTACGACAAAGC
Ncol
TCCATGGCGTCTCGCCGCGGCTCTGGAACACCACCATCATCACCATTAATGATAGGGATCC
Figure 5.2: The DNA sequence of the synthetic gene (50S QconCAT), with restriction
sites shown in red letters and restriction enzymes shown in green. Start codon in blue
Expression of 50S QconCAT
The 50S plasmid was successfully expressed into the E. coli BL21 strain supplement with
ampicillin. The expression of labelled (Figure 5.3) and unlabelled 50S QconCAT (data not
shown) were carried out for 5 h. SDS-PAGE both showed an increased 50S QconCAT
expression from 2 to 5 h. However, the best target protein/total protein ratio was observed
between 4 to 5 h inductions. 50S QconCAT labelled and unlabelled were subjected to in-gel
170
Chapter 5
digestion with Lys-C and trypsin, sequentially. The peptide digests were analyzed by
MALDI-ToF and nano-LCMS/MS.
50S QconCAT
116kDa
99kDa
65kDa
24kDa
20kDa
14kDa
1
2
3
time (hr)
4
5
Figure 5.3: Expression of 50S QconCAT protein over 5 h was detected using
Coomassie staining. The size of 50S QconCAT is labelled on the right.
Characterisation of the expressed 50S QconCAT
To investigate where 50S QconCAT reside in the host cells after induced expression, the total
and soluble cytosolic fractions were analyzed by SDS-PAGE. The outcome of this analysis
showed that the majority of the 50S QconCAT protein ends up in inclusion bodies (Figure
5.4).
Purification of labelled 50S QconCAT
Inclusion bodies containing 50S QconCAT were isolated from the rest of the lysed host cell
by centrifugation, and purified using His6-trap column. The 50S QconCAT mostly was
detected in fractions 1 and 2 (Lane 4 and 5) and appeared to be pure enough with molecular
weight around 99 kDa as expected (Figure 5.5)
171
Chapter 5
M
1
2
3
4
116kDa
50S QconCAT
65kDa
24kDa
Figure 5.4: SDS-PAGE analysis of total and soluble fraction of 50S QconCAT
labelled and unlabelled by Coomassie staining. Both 50S QconCAT protein are
not found in the soluble fraction. From left to right: M=protein marker, 1 and
2=total fraction of labelled and unlabelled protein, respectively. 3 and 4=Soluble
fraction of labelled, and unlabelled protein.
M
1
2
3
4
5
6
7
8
9
116kDa
84kDa
45kDa
29kDa
20kDa
Figure 5.5: The purity of 50S labelled QconCAT was checked by SDS-PAGE.
Lane M=protein Marker, Lane 1=starting materials (inclusion bodies), Lane 2=
unbound materials, Lane 3=wash, Lane 4-9=eluted bound materials.
172
Chapter 5
Absolute quantification of 50S QconCAT by MALDI-ToF (peak area)
MALDI ToF mass spectrometry technique was applied to obtain signal intensities for AQUA
and endogenous peptide of 50S QconCAT. For absolute quantification, same amounts of
digested 50S QconCAT were supplemented with various amounts of AQUA peptide. Peak
area ratios of endogenous and AQUA peptides were obtained from multiple MS spectra (for
an example spectrum see Figure 5.6).
Protein concentration was calculated from average peptide ratios of three technical replicates
and three spots from each digestion. 50S QconCAT concentration was determined by
comparing peptide ratios (Figure 5.6). They are displayed by the ratio AQUA peptide
(GGVNDNEEGFFSAR unlabelled) to the identical peptide of 50S QconCAT in labelled
form. The concentration of the 50S QconCAT protein in the pooled fractions after buffer
exchange was measured also using Bradford assay by the JANWAY spectrophotometer, and
found to be 0.19 mg/ml. The AQUA peptide quantification result using the same 50S
QconCAT sample read a concentration of 0.15 mg/ml.
100
GGVNDNEEGFFSAR
GGVNDNEEGFFSAR
A(Light) / A(area)= 0.9
m/z
Intens.
Area
1498.673
17163
6308
1504.691
18586
6998
%
0
Figure 5.6: MALDI ToF spectrum for GGVNDNEEGFFSAR and the
corresponding unlabelled AQUA peptide GGVNDNEEGFFSAR, The calculated
ratio of the endogenous and the AQUA peptide is 0.9.
173
Chapter 5
Digestion of 50S QconCAT by Lys C and then trypsin
Following buffer exchange, reduction of cysteines, proteolysis by both Lys C and then
trypsin was performed. The 50S QconCAT digests were then analyzed by MALDI-ToF
(Figure 5.7A) and LC MS/MS.
* Q30
L
* Q7
L
TP
L
2393.261
* Q16
L
2277.156
L *
Q6
2315.241
2063.055
1851.975
* Q11
* Q15
TP
L
1925.96
2697.38
2273.22
Q7L
2832.48
2235.14
TP
TP
2436.30
Q11L
TP
Q16L
2309.23
Q6L
1987.99
1742.95
1685.85
Q20L
Q15L
2052.03
Q29L
1617.87
1531.88
1393.76
1816.04
1645.94
1497.83
1444.06
1316.73
1214.70
1147.64
1087.62
Q19L
Q18L
Q8L
Q5L
Q2L+Q3L
1054.63
Q4L
Q30L+Q31L
Q36L Q24L
971.52
Q3L
Q10L
Q28L
Q9L Q12
L
Q17L
APWRLAAALEHHHHHH
Q14L
Q23L
Q13L
Q33L
Q34L
Q22L
824.54
870.46
L
L
L
L
50S_QC_H_Lys C_tube1_spot1_20%_laser_2500shots_220110\0_A14\1\1SRef
Q21L
Q1L
L
* Q20
A
%
* Q9
L
0
100
Q37L
* Q12
1819.066
1667.910
1617.804
* Q29
L
* Q8
2030.007
L
1435.799
L
L
* Q35
L
* Q5
* Q28
1727.905
L
* Q4
1567.897
* Q10
L
1461.633
L
* Q18
1398.696
1025.581
* Q31
1198.694
L
* Q14
L
* Q2
1292.811
861.046
* Q1
L
L
* Q30 + * Q31
L
L
912.503
%
L
* Q21 * Q34
L
* Q17
L
L
* Q3
* Q24
* Q19
L
L
L
1096.668
* Q23
50S_QC_Lys C_derivatized_tube2expression_Heavey_240111\0_A15\1\1SRef
* Q25
1358.771
* Q36
1486.135
1129.657
* Q22
L
* Q13
1248.705
B
1539.870
100
0
800
1000
1200
1400
1600
1800
2000
2200
2400
2600
2800
m/z
Figure 5.7: MALDI-ToF mass spectra of 50S QconCAT labelled Lys C digest. The
lower spectrum is that of the underivatized digest; the upper spectrum is that obtained
following guanidination. TP indicates a trypsin peptide fragments. Red labelling
peptides indicate a remarkable improvement in the intensity of Lys C terminated
peptides after quanidination.
39 out of 40 Lys-C peptides were identified by MALDI-ToF. One Lys-C peptide was missing
AGGSTRNGRDSEAK (Q32L). Both peptides derived from protein L25, EAPLAIELDHDK
(Q30L) and FPAIIYGGK (Q31L) was seen as miscleaved peptides. Similarly, TFTAKPETVK
(Q2L) and RDWYVVDATGK (Q3L). Some peptides show very low intensity. An easy
solution to this problem is the conversion of lysine to homoarginine residues using Omethylisourea as the derivatizing reagent. This
174
Chapter 5
derivatization reaction occurs in one rapid step (12 minutes) (Figure 5.7B). All the Lys-C and
tryptic peptides from the MALDI-ToF analysis of 50S labelled QconCAT were confirmed by
LC MS/MS sequencing (For a complete list of identified labelled 50S QconCAT peptides and
Q15T
1126.7
1693.9
1453.8
1737.0
1714.1
2079.1
2237.2
2058.1
2309.3
2352.3
LC
2117.1
1988.1
1810.0
1853.1
LC
2216.2
1631.9
1532.0
Q3T
1515.8
1431.8
LC
1668.9
1364.8
1321.7
LC
1293.9
1206.8
1087.7
LC
1060.1
Q30T
969.6
1
927.5
♦
LC
LC
Q27T
1014.6
1012.6
2
Q26T
Q11T
Q13T
899.6
Q1T
1410.8
1271.8
994.6
Q19T
LC
1575.0
951.6
LC
Q22T
4
♦
Q4T
Q12T
Q28T
3
Q10T
2036.1
Q24T
Q18T
Q14T
1496.9
5
Q9T
1228.8
1169.7
6
2194.2
x104
856.5
Intens. [a.u.]
their MASCOT score, see Table 5.1 and 5.2).
0
800
1000
1200
1400
1600
1800
2000
2200
m/z
Figure 5.8: MALDI-ToF mass spectrum of 50S labelled QconCAT, digested with
trypsin. Only some 50S QconCAT tryptic peptides were labelled on the spectrum. LC
represented fragments of Lys C generated peptides. Other unannotated peaks also
correspond to fragments of LysC peptides.
175
Peptide
Q1L
Q23L
Q14L
Q36L
Q22L
Q40L
Q2L
Q25L
Q27L
Q13L
Q24L
Q21L
Q3L
Q30L
Q31L
Q18L
Q4L
Q34L
Q25L
Q17L
Q29L
Q35L
Q19L
Q5L
Protein
name
S7
L20
L7/L12
L34
L20
L22
L13
L22
L23
L6
L21
L19
L13
L25
L25
L15
Qcal
L30
L22
L11
L24
L33
L16
S8
(m/z)
435.75
492.30
527.84
544.34
555.83
564.79
567.33
572.78
574.35
578.85
603.86
658.89
661.35
678.86
483.27
697.41
710.33
729.96
736.41
749.44
766.47
788.41
790.43
792.45
Observed
(m/z)
869.50
982.58
1053.6
1086.6
1109.6
1127.5
1132.6
1143.5
1146.6
1155.6
1205.7
1315.7
1320.6
1355.7
964.54
1392.8
1418.6
1457.9
1470.8
1496.8
1530.9
1574.8
1578.8
1582.8
Exp.
2
2
2,3
2
2
2
2
2
2
2
2
2
2
2,3
2
2
2
2,3
2,3
2,3
2,3
2,3
2,3
2,3
z
Error
0.0052
0.0087
0.0092
0.0074
0.0073
0.0070
0.0073
0.0066
0.0017
0.0023
0.0056
0.0081
0.0063
0.0052
0.004
0.0043
0.0080
0.0064
0.0084
0.0072
0.0060
0.0043
0.0093
0.0068
Mass
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
miss
50.71
64.81
49.99
40.15
78.25
55.31
27.78
46.52
40.53
67.91
44.12
14.29
68.15
64.41
30.00
91.55
70.65
24.98
115.9
66.14
58.61
110.0
31.91
77.95
score
value
8.50E-06
3.30E-07
1.00E-05
9.70E-05
1.50E-08
2.90E-06
0.0017
2.20E-05
8.90E-05
1.60E-07
3.90E-05
0.037
1.50E-07
3.60E-07
0.001
7.00E-10
8.60E-08
0.0032
2.60E-12
2.40E-07
1.40E-06
9.80E-12
0.00064
1.60E-08
Expect.
Sequence
FGSELLAK
VAFTALVEK
AVRGATGLGLK
RTFQPSVLK
ILADIAVFDK
IFVDEGPSMK
TFTAKPETVK
IFVDEGPSMK
VLRAPHVSEK
APVVVPAGVDVK
IGVPFVDGGVIK
LYYLRERTGK
RDWYVVDATGK
EAPLAIELDHDK
FPAIIYGGK
AANIIGIQIEFAK
GVNDNEEGFFSAK
ITQTRSAIGRLPK
VSQALDILTYTNK
ISRAQLQEIAQTK
IRRDDEVIVLTGK
LVSSAGTGHFYTTTK
GNVEYWVALIQPGK
VEGDTKPELELTLK
AGGSTRNGRDSEAK highlighted in red indicates that peptide not detected based on the Mascot output.
modification
Label:13C(6) (K)
Label:13C(6) (K)
Label:13C(6) (K);
Label:13C(6) (K);
Label:13C(6) (K)
Label:13C(6) (K)
2 Label:13C(6) (K)
Label:13C(6) (K);
Label:13C(6)(K);
Label:13C(6) (K)
Label:13C(6) (K)
Label:13C(6) (K); 2
Label:13C(6) (K);
Label:13C(6) (K)
Label:13C(6) (K)
Label:13C(6) (K)
Label:13C(6) (K)
Label:13C(6) (K); 2
Label:13C(6) (K)
Label:13C(6) (K);
Label:13C(6) (K); 2
Label:13C(6) (K)
Label:13C(6) (K)
2 Label:13C(6) (K)
Peptide _Variable
Table 5.1: LC-MS/MS Mascot results from a single LC-MS/MS analysis of a Lys-C digest using a LTQ-Orbitrap mass spectrometer XL. The peptide
176
Chapter 5
Q39L
Q33L
Q9L
Q20L
Q12L
Q28L
Q6L
Q15L
Q7L
Q16L
Q8L
Q11L
Q26L
Q37L
Q38L
Q10L
Q32L
809.46
549.33
828.0
843.45
593.03
905.49
922.98
1011.0
1026.5
1137.1
908.5
871.5
861.9
824.5
607.4
622.8
711.8
1616.9
1644.9
1654.0
1684.8
1776.0
1808.9
1843.9
2020.0
2051.0
2272.2
1816.0
1742.9
1722.9
412.7
1214.7
1243.6
1423.6
2
3
2
2
3
2
2,3
2
2,3
2
2,3
2,3
2
2
2
2
-
0.0046
0.0167
0.0062
0.0062
0.0112
0.0099
0.0053
0.01
0.0106
0.0091
0.0167
0.0112
0.0073
0.0081
0.0046
-0.133
-
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
-
13.97
14.07
37.17
58.34
16.10
107.6
97.65
58.17
28.18
43.92
14.06
16.10
27.70
14.29
13.99
3
-
0.04
0.039
0.00019
1.50E-06
0.025
2.00E-11
1.70E-10
1.50E-06
0.0017
4.10E-05
0.040
0.025
0.002
0.039
0.06
0.5
-
RDGVIRVICSAEPK
GIDTVLAELRARGEK
ELRRVVEPLITLAK
QDVPSFRPGDTVEVK
VDRVRGLDITITTTAK
EAAIQVSNVAIFNAATGK
SMALRLANELSDAAENK
FGVSAAAAVAVAAGPVEAAEE
SDLSADINEHLIVELYSK
LFGSIGTRDIADAVTAAGVEVA
VLGGSHRRYAGVGDIIK
QRDVATGGRVDRFNK
LFEVEVEVVNTLVVK
RHLRPK
HANLRHILTK
QVRRDVARVK
AGGSTRNGRDSEAK
name
L33
L31
S4
L18
S8
L36
L2
L18
L34
Protein
Q30T
Q29T
Q5T
Q19T
Q6T
Q34T
Q1T
Q20T
Q33T
Peptide
(m/z)
369.717
384.723
442.752
447.263
454.271
455.245
456.734
460.768
463.278
Observed
(m/z)
737.419
767.433
883.491
892.513
906.528
908.477
911.454
919.522
924.542
Experimental
2
2
2
2
2
2
2
2
2
z
Error
0.0027
0.0057
0.0053
0.0057
0.005
0.0039
0.0102
0.0041
0.0046
Mass
0
0
0
0
0
0
0
0
0
Miss
21.10
36.10
35.93
40.78
45.22
27.58
21.68
49.61
29.14
Score
Value
0.0077
0.00024
0.00026
8.40E-05
3.00E-05
0.0017
0.0068
1.10E-05
0.0012
Expectation
FDPVVR
FNIPGSK
LDNVVYR
LQELGATR
AVVESIQR
VICSAEPK
LEYDPNR
VQALADAAR
TFQPSVLK
Sequence
Modification
Label:13C(6)
(R)
Label:13C(6) (R)
Label:13C(6) (R)
Label:13C(6) (R)
Label:13C(6) (R)
Label:13C(6) (K)
Label:13C(6) (R)
Label:13C(6) (R)
Label:13C(6) (K)
Peptide-variable-
Label:13C(6) (K); 2
Label:13C(6) (K); 2
Label:13C(6) (K); 2
Label:13C(6) (K);
Label:13C(6) (K); 2
Label:13C(6) (K)
Label:13C(6) (K);
Label:13C(6) (K)
Label:13C(6) (K)
Label:13C(6) (K);
Label:13C(6) (K);
Label:13C(6) (K); 3
Label:13C(6) (K)
Label:13C(6) (K); 2
Label:13C(6) (K);
Label:13C(6) (K); 3
-
Table 5.2: LC-MS/MS Mascot results from a single LC-MS/MS analysis of a trypsin digest using a LTQ-Orbitrap mass spectrometer XL
L36
L28
L17
L19
L5
L24
S7
L7/L12
S4
L9
L14
L31
L23
L35
L35
L29
L27
177
Chapter 5
L14
L17
L1
L9
L2
L30
L32
L27
L6
L10
L15
L11
L32
L4
L16
L29
L1
L10
L4
L3
L3
L21
L5
L28
Q32T
Q18T
Q8T
Q13T
Q2T
Q27T
Q28T
Q22T
Q12T
Q15T
Q17T
Q16T
Q23T
Q3T
Q25T
Q26T
Q9T
Q14T
Q4T
Q10T
Q11T
Q21T
Q31T
Q24T
471.2759
476.273
536.306
552.828
585.832
601.822
619.803
705.910
722.833
727.403
730.948
759.956
775.415
794.903
795.422
824.968
614.852
847.450
679.363
1097.57
561.333
441.232
758.377
374.19
940.537
950.532
1070.59
1103.64
1169.65
1201.63
1237.59
1409.80
1443.65
1452.79
1459.88
1517.89
1548.81
1587.79
1588.83
1647.92
1227.69
1692.88
2035.06
2194.18
1120.65
881.458
1515.74
1119.567
2
2
2
2
2
2
2
2
2
2
2
2
2
2,3
2
2
2
2,3
3
2,3
2
2
2
2
0.0048
0.0043
0.0063
0.0027
0.0004
0.0006
0.0008
0.009
0.0064
0.0088
0.0109
0.0066
0.0082
0.009
0.0113
0.0083
0.0489
0.0079
0.009
0.0375
0.0017
0.0091
0.031
-0.026
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
47.73
59.24
45.33
73.51
104.5
71.75
25.94
88.68
94.77
96.16
75.85
54.72
91.93
80.92
115.4
94.83
44.61
85.15
33.46
30.15
41.19
41.05
43.97
1
1.70E-05
1.20E-06
2.90E-05
4.50E-08
3.50E-11
6.70E-08
0.0025
1.40E-09
3.30E-10
3.10E-10
2.60E-08
3.40E-06
1.10E-09
8.10E-09
5.00E-12
3.30E-10
3.50E-05
7.20E-09
0.0009
0.0011
7.60E-05
7.90E-05
4.00E-05
0.87
YAGVGDIIK
LFNELGPR
GATVLPHGTGR
LAEVLAAANAR
SAGTYVQIVAR
GMINAVSFMVK
HHITADGYYR
FGGESVLAGSIIV
DGYADGWAQA
LATLPTYEEAIAR
VILAGEVTTPVT
GLPIPVVITVYAD
SHDALTAVTSLS
DAQSALTVSETT
GLAQGTDVSFGS
SVEELNTELLNL
VVGQLGQVLGP
AAAFEGELIPAS
DFNEALVHQVV
IFTEDGVSIPVTVI
VTVQSLDVVR
VSEGQTVR
EQIIFPEIDYDK
FLPNLHSHR
Label:13C(6) (K)
Label:13C(6) (R)
Label:13C(6) (R)
Label:13C(6) (R)
Label:13C(6) (R)
Label:13C(6) (K)
Label:13C(6) (R)
Label:13C(6) (R)
Label:13C(6) (R)
Label:13C(6) (R)
Label:13C(6) (R)
Label:13C(6) (R)
Label:13C(6) (K)
Label:13C(6) (R)
Label:13C(6) (K)
Label:13C(6) (R)
Label:13C(6) (R)
Label:13C(6) (R)
Label:13C(6) (R)
Label:13C(6) (R)
Label:13C(6) (R)
Label:13C(6) (R)
Label:13C(6) (R)
Label:13C(6) (R)
178
Chapter 5
Chapter 5
Determination of 50S ribosomal proteins stoichiometries challenged with
azithromycin using 50S QconCAT by LC MSMS (Peak intensity)
In this experiment, a QconCAT technique has been applied to obtain signal intensities
for endogenous and 50S QconCAT signature peptides of the ribosomal proteins
complex. For absolute quantification, a known amount of labelled 50S QconCAT was
added to 70S ribosomal proteins samples isolated from whole E.coli cell lysate,
control and azithromycin treated samples. Biological triplicates from both treated and
control were prepared and the mixture was digested by the Lys-C and then trypsin to
generate twelve samples (see experimental section) and the resulting peptide mixtures
were analyzed in triplicate using Orbitrap MS with 50S inclusion list of both Lys C
and trypsin peptides resulting in 36 LC MS/MS runs. Unfortunately, we could not
measure the ratios of peptide pairs. This is because of the amount of 50S QconCAT
spiked was not enough to produce a reasonable light to heavy ratio (Figure 5.8 top) or
the concentration of ribosomal proteins from which the QconCAT peptides were
derived was too high. So the signature peptides are at too low concentration to be
seen. Previous experiment of 50S QconCAT by MALDI-ToF and LC MSMS
confirmed all of QconCAT peptides although the peak intensity for some of them was
low. In this experiment, Manual detection confirmed the concentration of 50S
QconCAT was very low to be detected. Further experiment has been planned to
prepare a new batch of samples with increased concentration of the 50S QconCAT.
179
Chapter 5
50sLc1_110709101012 #1659 RT: 23.38 AV: 1 NL: 1.30E6
T: FTMS + p NSI Full ms [300.00-1600.00]
785.40
100
90
785.90
80
Relative Abundance
70
60
50
40
786.40
30
20
786.91
10
784.45 784.95
0
787.00
787.41
785.72
785
786
787
788.41
787.91
788.91
788
m/z
790.54
789.41
789
Light peptide
790
791.04
791
Heavey peptide
50sLc1_110709101012 #1650 RT: 23.27 AV: 1 NL: 2.92E3
+2
T: ITMS + c NSI d Full ms2 [email protected] [205.00-1585.00] y13
679.50
100
90
80
MH-NH3 +2
776.56
Relative Abundance
70
y10
1112.47
60
50
40
767.67
y8
954.47
30
y12 +2
635.99
20
10
y4
450.24
y3
y2
248.23
y5
613.36
y10 +2
y6
760.36
729.10
b9
810.36
y7
y9
y11
897.45
1055.41
1183.52
y
1221.46 121322.59
556.79
349.23
865.53
0
300
400
500
600
700
800
y13
1357.63
900
1037.65
1000
1100
1200
1300
b14
1423.66
y14
1400
m/z
Figure 5.8: LTQ-Orbitrap mass spectra of peptide LVSSAGTGHFYTTTK
(Q35L) and its [13C6]-R/K labelled counterpart (top) and fragment ions resulting
from light version of Q35L (bottom).
180
Chapter 5
Table 5.3: The 50S ribosomal QconCAT Lys C peptides. The peptide numbers from Q1L-Q41L are indicated
along with the ribosomal protein from which the Q-peptides are derived.
12
Peptide Sequence
C-R/K 13C-R/K
unlabelled Labelled Annotation Protein
[M+H]+ [M+H]+
Note
Peptides encoded the initiator
methione, N-terminal sacrificial
sequence and spacer sequence
MGTK
FGSELLAK
864.50
870.50
Q1L
S7
TFTAKPETVK
1121.6
1133.6
Q2L
L13
RDWYVVDATGK
1309.6
1321.6
Q3L
L13
GVNDNEEGFFSAK
1413.6
1419.6
Q4L
Qcal
VEGDTKPELELTLK
1571.8
1583.8
Q5L
S8
SMALRLANELSDAAENK
1832.9
1844.9
Q6L
S7
SDLSADINEHLIVELYSK
2046.1
2052.1
Q7L
S4
Lys-terminated core peptides
released by Lys-C
Sacrificial peptide containing
restriction site, XhoI
LEK
VLGGSHRRYAGVGDIIK
1798.1
1816.1
Q8L
L14
ELRRVVEPLITLAK
1637.1
1655.0
Q9L
L17
QVRRDVARVK
1226.8
1250.8
Q10L
L29
QRDVATGGRVDRFNK
1718.9
1742.9
Q11L
L31
VDRVRGLDITITTTAK
1759.1
1777.0
Q12L
L5
APVVVPAGVDVK
1150.7
1156.7
Q13L
L6
AVRGATGLGLK
1042.6
1054.6
Q14L
L7/L12
FGVSAAAAVAVAAGPVEAAEEK 2015.1
2021.1
Q15L
L7/L12
LFGSIGTRDIADAVTAAGVEVAK 2261.3
2273.3
Q16L
L9
ISRAQLQEIAQTK
1485.8
1497.8
Q17L
L11
AANIIGIQIEFAK
1387.8
1393.8
Q18L
L15
GNVEYWVALIQPGK
1573.8
1579.8
Q19L
L16
QDVPSFRPGDTVEVK
1673.9
1685.8
Q20L
L19
LYYLRERTGK
1298.7
1316.7
Q21L
L19
ILADIAVFDK
1104.6
1110.6
Q22L
L20
VAFTALVEK
977.50
983.58
Q23L
L20
IGVPFVDGGVIK
1200.7
1206.7
Q24L
L21
VSQALDILTYTNK
1465.7
1471.7
Q25L
L22
LFEVEVEVVNTLVVK
1716.9
1722.9
Q26L
L23
VLRAPHVSEK
1135.7
1147.6
Q27L
L23
EAAIQVSNVAIFNAATGK
1803.9
1809.9
Q28L
L24
IRRDDEVIVLTGK
1513.9
1531.9
Q29L
L24
EAPLAIELDHDK
1350.7
1356.7
Q30L
L25
FPAIIYGGK
965.56
971.56
Q31L
L25
AGGSTRNGRDSEAK
1405.7
1423.7
Q32L
L27
GIDTVLAELRARGEK
1627.9
1645.9
Q33L
L28
ITQTRSAIGRLPK
1440.9
1458.9
Q34L
L30
LVSSAGTGHFYTTTK
1569.8
1575.8
Q35L
L33
RTFQPSVLK
1075.6
1087.6
Q36L
L34
RHLRPK
806.57
824.57
Q37L
L35
HANLRHILTK
1202.7
1214.7
Q38L
L35
181
Chapter 5
RDGVIRVICSAEPK
1599.9
1617.9
Q39L
L36
IFVDEGPSMK
1122.5
1128.5
Q40L
L22
APWRLAAALEHHHHHH
1919.9
1925.9
Q41
His-tag
182
Chapter 5
Table 5.4: The 50S ribosomal QconCAT trypsin peptides. The peptide numbers from Q1T-Q35T are indicated
along with the ribosomal protein from which the Q-peptides are derived.
12
C-R/K 13C-R/K
Peptide Sequence
Protein
Note
unlabelled Labelled Annotation
[M+H]+ [M+H]+
Sacrificial peptide
APGK
containing restriction site,
SmaI
LEYDPNR
906.40
912.40
Q1T
L2
SAGTYVQIVAR
1164.6
1170.6
Q2T
L2
DAQSALTVSETTFGR
1582.7
1588.7
Q3T
L4
DFNEALVHQVVVAYAAGAR
2030.1
2036.1
Q4T
L4
LDNVVYR
878.49
884.49
Q5T
S4
AVVESIQR
901.53
907.53
Q6T
S8
GGVNDNEEGFFSAR
1498.7
1504.7
Q7T
Qcal
Sacrificial peptide
containing restriction site,
HindIII
KLR
GATVLPHGTGR
1065.6
1071.6
Q8T
L1
VVGQLGQVLGPR
1222.7
1228.7
Q9T
L1
IFTEDGVSIPVTVIEVEANR
2188.2
2194.2
Q10T
L3
VTVQSLDVVR
1115.6
1121.6
Q11T
L3
DGYADGWAQAGTAR
1438.6
1444.6
Q12T
L6
LAEVLAAANAR
1098.6
1104.6
Q13T
L9
AAAFEGELIPASQIDR
1687.8
1693.8
Q14T
L10
LATLPTYEEAIAR
1447.7
1453.7
Q15T
L10
GLPIPVVITVYADR
1512.8
1518.8
Q16T
L11
VILAGEVTTPVTVR
1454.8
1460.8
Q17T
L15
LFNELGPR
945.50
951.5
Q18T
L17
LQELGATR
887.50
893.5
Q19T
L18
VQALADAAR
914.50
920.5
Q20T
L18
VSEGQTVR
875.40
881.4
Q21T
L21
FGGESVLAGSIIVR
1404.8
1410.8
Q22T
L27
SHDALTAVTSLSVDK
1543.8
1549.8
Q23T
L32
FLPNLHSHR
1120.6
1126.6
Q24T
L28
GLAQGTDVSFGSFGLK
1583.8
1589.8
Q25T
L16
SVEELNTELLNLLR
1642.9
1648.9
Q26T
L29
GMINAVSFMVK
1196.6
1202.6
Q27T
L30
HHITADGYYR
1232.6
1238.6
Q28T
L32
FNIPGSK
762.40
768.4
Q29T
L31
FDPVVR
732.40
738.4
Q30T
L33
EQIIFPEIDYDK
1509.7
1515.7
Q31T
L5
YAGVGDIIK
935.50
941.5
Q32T
L14
TFQPSVLK
919.50
925.5
Q33T
L34
VICSAEPK
846.40
852.4
Q34T
L36
183
Chapter 5
References
1. Poehlsgaard, J.; Douthwaite, S., The bacterial ribosome as a target for antibiotics.
Nature Reviews Microbiology 2005, 3, 870-881.
2. Wimberly, B. T.; Brodersen, D. E.; Clemons, W. M.; Morgan-Warren, R. J.; Carter,
A. P.; Vonrhein, C.; Hartsch, T.; Ramakrishnan, V., Structure of the 30S ribosomal
subunit. Nature 2000, 407, 327-339.
3. Schluenzen, F.; Tocilj, A.; Zarivach, R.; Harms, J.; Gluehmann, M.; Janell, D.;
Bashan, A.; Bartels, H.; Agmon, I.; Franceschi, F.; Yonath, A., Structure of
functionally activated small ribosomal subunit at 3.3 Å resolution. Cell 2000, 102,
615-623.
4. Ban, N.; Nissen, P.; Hansen, J.; Moore, P. B.; Steitz, T. A., The complete atomic
structure of the large ribosomal subunit at 2.4 Å resolution. Science 2000, 289, 905920.
5. Ong, S.-E.; Mann, M., Mass spectrometry-based proteomics turns quantitative.
Nature Chemical Biology 2005, 1, 252-262.
6. Gygi, S. P.; Rist, B.; Gerber, S. A.; Turecek, F.; Gelb, M. H.; Aebersold, R.,
Quantitative analysis of complex protein mixtures using isotope-coded affinity tags.
Nature Biotechnology 1999, 17, 994-999.
7. Gilchrist, A.; Au, C. E.; Hiding, J.; Bell, A. W.; Fernandez-Rodriguez, J.;
Lesimple, S.; Nagaya, H.; Roy, L.; Gosline, S. J. C.; Hallett, M.; Paiement, J.;
Kearney, Robert E.; Nilsson, T.; Bergeron, J. J. M., Quantitative proteomics analysis
of the secretory pathway. Cell 2006, 127, 1265-1281.
8. Pratt, J. M.; Simpson, D.; Doherty, M.; Rivers, J.; Gaskell, S. J.; Beynon, R. J.,
Multiplexed absolute quantification for proteomics using concatenated signature
peptides encoded by QconCAT genes. Nature Protocols 2006, 1, 1029-1043.
9. Beynon, R. J.; Doherty, M. K.; Pratt, J. M.; Gaskell, S. J., Multiplexed absolute
quantification in proteomics using artificial QCAT proteins of concatenated signature
peptides. Nature Methods 2005, 2, 587-589.
10. Washburn, M. P.; Wolters, D.; Yates, J. R., Large-scale analysis of the yeast
proteome by multidimensional protein identification technology. Nature
Biotechnology 2001, 19, 242-247.
11. Gerber, S. A.; Rush, J.; Stemman, O.; Kirschner, M. W.; Gygi, S. P., Absolute
quantification of proteins and phosphoproteins from cell lysates by tandem MS.
Proceedings of the National Academy of Sciences of the United States of America
2003, 100, 6940-6945.
12. Kirkpatrick, D. S.; Gerber, S. A.; Gygi, S. P., The absolute quantification strategy:
a general procedure for the quantification of proteins and post-translational
modifications. Methods 2005, 35, 265-273.
184
Chapter 6
The Effects of Gentamicin on the Proteomes of Aerobic and
Oxygen-limited Escherichia coli
DECLARATION
This chapter consists of a piece of work in preparation for publication – Zubida M. Al-Majdoub,
Abiola Owoseni, Simon J. Gaskell and Jill Barber, The Effects of Gentamicin on the Proteomes
of Aerobic and Oxygen-limited Escherichia coli. It is produced in the form intended for
submission to J. Med. Chem. I am first author. Abiola Owoseni prepared oxygen-limited E.coli
cultures and carried out the FASP protocol. I prepared the aerobic E.coli cultures, carried out
FASP protocol and all the mass spectrometric analysis for both aerobic and oxygen-limited. I
prepared the article for publication. It has been edited by Dr Jill Barber and Professor Simon
Gaskell.
185
Chapter 6
The Effects of Gentamicin on the Proteomes of Aerobic and
Oxygen-limited Escherichia coli
Zubida M. Al-Majdoub, ‡,§ Abiola Owoseni, ‡,§ Simon J. Gaskell,║ Jill Barber,*‡,§
‡
Michael Barber Centre for Mass Spectrometry, Manchester Interdisciplinary Biocentre
§
School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Manchester, M13
9PT
║
Queen Mary, University of London, Mile End Road, London, E1 4NS
ABSTRACT
The key role of the bacterial ribosome makes it an important target for antibacterial agents.
Indeed, a large number of clinically useful antibiotics target this complex translational
ribonucleoprotein machinery. Unfortunately, the development of resistant bacterial strains has
compromised the effectiveness of most classes of antibacterial agent, including the classes that
target the ribosome.
Combinations of two or more drugs can be used to help overcome
resistance, and in certain circumstances their action may be synergistic. In this study we have
used proteomic techniques to establish the effects of gentamicin on the proteomes of aerobic and
oxygen-limited Escherichia coli. Ribosomal proteins L1, L10 and S2 were found to be upregulated in both conditions and we postulate that these are candidate drug targets for the
development of synergistic combinations with gentamicin.
1
*Corresponding Author; Phone: +44 (0)161 275 2369. Fax: +44 (0) 161 275 2396. E-mail: [email protected]
186
Chapter 6
INTRODUCTION
Gentamicin (Figure 6.1) is a clinically useful antibiotic of the aminoglycoside class. It
was discovered in 1963, a product of the fungus Micromonospora purpurea. 1 In common with
other aminoglycoside antibiotics, such as streptomycin and kanamycin, it is chemically stable
and very water-soluble. 2
Figure 6.1: Gentamicin
Gentamicin binds specifically to the A site of 16S ribosomal RNA thereby inhibiting bacterial
protein synthesis.
3
This results in a bactericidal effect on most pathogenic bacteria, whether
Gram-positive or Gram-negative. Gentamicin acts similarly to other aminoglycoside antibiotics,
such as neomycin and kanamycin. They are transported across cell membranes in an oxygendependent process and strict anaerobes are therefore expected to be resistant to gentamicin and
this resistance has been reported.4,5 In human medicine, gentamicin is a first-choice drug for the
treatment of serious aerobic bacterial infections caused by pathogenic Gram negative bacilli such
as E.coli and Klebsiella; indeed aminoglycosides are the most commonly-used drugs worldwide
in the treatment of Gram-negative infections.6
Gentamicin concentrates in the urine, so, despite the relatively anaerobic environment of the
urinary tract, it is often used for the treatment of urinary tract infections as well as for respiratory
tract infections, septicaemia and intra-abdominal infections.5 Therefore, it is worth to study not
only the effect of gentamicin on aerobic, gram-negative E.coli bacteria but also the effect of
gentamicin on anaerobic or oxygen-limited environment. Gentamicin distributes poorly into the
plasma because it is very hydrophilic and unable to cross membranes readily, and oral
administration is ineffective for the same reason; gentamicin is therefore administered by
187
Chapter 6
intravenous or intramuscular injection. Gentamicin, like other aminoglycosides may be
inactivated by aminoglycoside-modifying enzymes 7, or it may fail to accumulate because of
efflux pumps 8; however, aminoglycoside resistance is actually comparatively rare in clinical
isolates. More important in limiting the use of gentamicin and other aminoglycosides, is their
toxicity to both kidneys and ears. Nephrotoxicity is found in up to 20% of cases after long
periods of therapy, drug metabolites accumulate and these can cause severe side effects 8.
Gentamicin also affects the vestibular cells of the ear causing irreversible hearing loss
6,9
. It is
therefore common for the serum levels of gentamicin to be carefully monitored during therapy.
Antibiotic combinations are nearly always advantageous in combating bacterial resistance. A
bacterial cell is much less likely to develop resistance to two or more antibiotics than to a single
compound. However, some antibiotic combinations have the additional advantage of being
synergistic and this is especially advantageous when one of the components is a toxic drug, like
gentamicin. Gentamicin is often used in combination with benzyl penicillin in the treatment of
Enterococcal, Enterobacterial and
Staphylococcal infections,10
synergy having been
demonstrated in vitro.
Bacteria show adaptive responses to antibiotic challenge, upregulating some proteins that help
combat antibacterial action.11 Proteomic studies based on two-dimensional gel electrophoresis
have been used to identify markers of antibiotic treatment12 and limited mass spectrometric
studies have been performed on antibiotic-treated bacteria.13 In this study we exploit the
powerful and sensitive approach of mass spectrometry based proteomics to describe the adaptive
response of E. coli to gentamicin treatment, with the ultimate aim of developing novel
synergistic antibiotic combinations in which gentamicin is one partner.
RESULTS
Bacterial Growth Conditions
The workflow we used was adapted from the work of Champney and Foster on the effect of antiribosomal drugs on bacterial ribosome assembly.14 Antibiotic was added when the bacteria
entered exponential phase (A600 0.2 for aerobic cultures, 0.15 for oxygen-limited cultures) and
the bacteria were harvested after two doublings. The amount of antibiotic was chosen to permit
188
Chapter 6
two doublings without a significant change in growth rate. Both aerobic and oxygen-limited E.
coli were challenged with gentamicin. Oxygen-limitation was achieved by growing the bacteria
in sealed tubes containing resazurin indicator.
The optical densities of the cultures were
estimated by visual comparison with McFarland standards
15
made up in the same type of tube.
Correlation of measured optical density with estimates was previously shown to be ± 5%.
Growth curves for aerobic and oxygen-limited E. coli are shown in Figure 6.2. On the basis of
these data, a drug concentration of 6 µg ml-1 was chosen for aerobic E coli and 0.5 µg ml-1 for
oxygen-limited E. coli. These concentrations of antibiotic could be used without significant
effects on the growth rates of the bacteria over the range A600 0.2 – 0.8 (aerobic) or 0.15 – 0.6
(oxygen-limited). The minimum inhibitory concentration (MIC) of gentamicin to aerobic E.coli
cells is normally around 4 µg mL-1.16
1.8
0.6
Control
1.6
Control
Gentamicin
Optical density at 600 nm
Optical density at 600 nm
Gentamicin
0.5
1.4
1.2
1
0.8
0.6
0.4
0.4
0.3
0.2
0.1
0.2
0
A
0
0
0.5
1
1.5
2
Genta micin exposure (hr)
2.5
3
3.5
B
0
1
2
3
Gentamicin exposure (hr)
4
5
Figure 6.2: Effect of gentamicin on the growth rate of E. coli K12 in (A) aerobic conditions,
exposed to 6 µg mL-1 gentamicin and (B) oxygen-limited conditions, exposed to 0.5 µg mL-1
gentamicin. Error bars are so small that they do not extend beyond the boundaries of the
symbol.
Preparation of control and drug-treated E. coli
E coli triplicate cultures (controls and drug-treated, aerobic and oxygen-limited) were prepared
as described in the Experimental Section. The resazurin indicator changed colour from blue to
pink when the oxygen-limited cultures reached an A600 of approximately 0.3. Although
pathogenic E. coli are sometimes described as anaerobic, and inhabit parts of the human body
where oxygen is limited, the urinary tract (for example) is not a strictly anaerobic environment.
189
Chapter 6
This is why we made no efforts to eliminate oxygen completely, only to limit the concentration
in the cultures.
Isolation of protein fraction, generation of peptides and LC-MS/MS
Proteins from gentamicin-treated and control samples were isolated using the FASP (filter aided
sample preparation) protocol,17 which was adapted in several minor ways to the use of bacterial
cultures (see Experimental Section). Peptides suitable for LC-MS/MS analysis were generated
using trypsin in the case of oxygen-limited cultures. For aerobic E. coli, endopeptidase Lys-C
digests as well as tryptic digests were analyzed and the data pooled, increasing the number of
protein hits. This was judged unnecessary for the oxygen-limited cultures where (presumably
because the proteome is not dominated to the same degree by proteins necessary for exponential
growth) tryptic digests gave greater numbers of confident identities. The use of an Amazon iontrap mass spectrometer allowed both CID (collision induced dissociation) and ETD (electron
transfer dissociation) data to be collected. The data were analyzed using an in-house Mascot
server for identification of peptides and proteins. Peptide samples were not fractionated prior to
LC-MS/MS because preliminary experiments had indicated that gentamicin perturbs the
proteome quite significantly. Our objective was to identify drug targets for potential synergizers;
many candidates could be identified without probing deeply into the proteome.
Proteins (peptides) Sample (drug Proteins (peptides)
detected
treated)
detected
C1a
133 (503)
G6A
112 (359)
C2a
143 (433)
G6B
120 (374)
Aerobic E. coli
C3a
116 (453)
G6C
117 (376)
C1ol
112 (359)
G0.5A
124 (374)
Oxygen-limited
C2ol
120 (374)
G0.5B
131 (426)
E. coli
C3ol
117 (376)
G0.5C
151 (560)
Table 6.1: Number of proteins and peptides from each sample identified by LC-MS/MS
Sample (control)
The number of proteins identified and the corresponding peptides is shown in Table 6.1. Table
6.2 shows the distribution of peptides detected from each proteolytic enzyme / ionization method
for a typical (aerobic control) dataset. Although the classical tryptic digest with CID as the
190
Chapter 6
ionization method yielded the most information, each of the other experiments contributed
additional data.
Enzyme / Ionization
Number of proteins Number of peptides Number of residues
Method
Trypsin / CID
90
312
4518
Trypsin / ETD
55
196
2816
LysC / CID
90
270
4267
LysC / ETD
59
204
3089
Total
133
503
7102
Table 6.2: Numbers of proteins, peptides and residues detected by different enzyme / ionization
method
The Proteomes of Aerobic and Oxygen-Limited E. coli
Label-free quantification has been achieved in several ways in proteomic studies. These include
the emPAI score, the average peptide score of the highest scoring three peptides and peptide
counting. In this work, we were not concerned with precise quantification of proteins within a
sample; we aimed only to identify proteins that were up- or down- regulated.
A semi-
quantitative method is therefore sufficient. In addition, label-free quantification is largely free
from dynamic range issues suffered by isotopic-labelling methods. Methods involving isotopic
labeling, whether chemical or metabolic, are useful for the detection of relatively small
differences in concentration of a component of two different samples. The power of techniques
such as iTRAQ and SILAC lies in the detection of doublets corresponding to labelled and
unlabelled peptide. The power of techniques such as iTRAQ lies in the measurement and ensuing
quantitation of iTRAQ reporter ions occurs after fragmentation of the precursor ion. While in
SILAC, lies in the detection of doublets corresponding to labelled and unlabelled peptide.
Treatment of a culture with a barely-sublethal dose of antibiotic is likely to cause major changes
in the concentrations of the most interesting proteins spanning many orders of magnitude.
Doublets are not then detected and the increase in complexity of data analysis through the
presence of isotopic label is a high price to pay. We considered 6 parameters that indicate the
relative abundance of a particular protein: the emPAI score (as calculated by Mascot for the
trypsin-CID data), the number of peptides detected for a protein and the closely-related
percentage of total peptides detected, the number of residues detected and the closely-related
191
Chapter 6
percentage sequence coverage, and the average peptide score of the most intense three peptide
signals. We considered only proteins for which at least two peptides were detected in all three
replicates of one or both datasets (ie aerobic or oxygen-limited) and peptide detection was
defined as a Mascot score of at least 15 for unique peptides. In this way 85 proteins were
selected for comparison. For each of the six parameters, we calculated the means and standard
deviations of the replicates and the differences between the means in terms of the standard
deviation (σ). The percentage of peptides detected was judged the most attractive parameter in
this study. Unlike emPAI, it uses both Lys C and tryptic peptides, both ETD and CID data. The
use of a percentage rather than the total number of peptides compensates for small changes in
sample concentration. The data were therefore sorted initially by this parameter. A 5σ difference
in the mean percentage peptides, together with a 3σ difference in all the other parameters
conforms to the standards set by particle physicists to overcome the “Look elsewhere effect”.18
These proteins are overwhelmingly likely to be present at different concentrations in the aerobic
and oxygen-limited samples and are termed “green category”. Yellow and orange category
proteins are also likely to be up- or down- regulated on oxygen limitation, but the standard of
proof is lower (see table 6.3).
The Effect of Gentamicin on Aerobic E.coli
The effect of gentamicin on the aerobic E. coli proteome was now assessed similarly. 69
proteins were selected for comparison, and Table 4 shows the proteins most clearly up- or downregulated in the presence of gentamicin.
The Effect of Gentamicin on Oxygen-limited E.coli
The effect of gentamicin on the oxygen-limited E. coli proteome was also assessed. 83 proteins
were selected for comparison, and Table 6.6 shows the proteins most clearly up- or downregulated in the presence of gentamicin.
192
Chapter 6
Protein
Function
Green category, up-regulated on oxygen-depletion
ATPA
ATP synthase α subunit
DEOC
Deoxyribose-phosphate aldolase
MBHM
Hydrogenase-2 large chain precursor
ODP1
Pyruvate dehydrogenase E1 component
ATPB
ATP synthase β subunit
PTA
Phosphate acetyltransferase
CLPB
Chaperone protein clpB
CH60
60 kDa chaperonin
GLDA
Glycerol dehydrogenase
SYK2
Lysyl-tRNA synthetase, heat inducible
TDCE
Keto-acid formate acetyltransferase
Yellow category up-regulated on oxygen-depletion
KPYK2
Pyruvate kinase II
2,3,4,5-tetrahydropyridine-2,6-dicarboxylate NDAPD
succinyltransferase
FRDB
Fumarate reductase iron-sulfur subunit
WRBA
Flavoprotein wrbA
ASPA
Aspartate ammonia-lyase
PTNAB
PTS system mannose-specific EIIAB component
UDP
Uridine phosphorylase
FRDA
Fumarate reductase flavoprotein subunit
Orange category up-regulated on oxygen-depletion
USPG
Universal stress protein G
KPYK1
Pyruvate kinase I
YGEY
Uncharacterized protein ygeY
Green category down-regulated on oxygen-depletion
FKBP-type peptidyl-prolyl cis-trans isomerase
SLYD
slyD
Periplasmic oligopeptide-binding protein
OPPA
precursor
TPX
Thiol peroxidase
Yellow category down-regulated on oxygen-depletion
IDH
Isocitrate dehydrogenase [NADP]
D-galactose-binding periplasmic protein
DGAL
precursor
SUCD
Succinyl-CoA ligase [ADP-forming] α subunit
YJGF
UPF0076 protein yjgF
PGK
Phosphoglycerate kinase
CSPE
Cold shock-like protein cspE
YAHO
UPF0379 protein yahO precursor
RRF
Ribosome recycling factor
FABD
Malonyl CoA-acyl carrier protein transacylase
Orange category down-regulated on oxygen-depletion
MALE
Maltose-binding periplasmic protein precursor
CSPC
Cold shock-like protein cspC
OSMY
Osmotically-inducible protein Y precursor
EFTS
Elongation Factor Ts
Mean no
Mass (Da) peptides
(aerobic)
Mean no
peptides (O2
limited)
Difference
mean %
peptides (σ)
55416
27944
62908
99948
50351
77466
95697
57464
39087
57847
86166
0
0
0
0
0.3
0
0
1.7
0
0
0
7
2
2
4.6
9
5.7
5.7
7.3
2.3
6.3
8.7
31
16
16
15
13
11
9
7
5
5
5
51553
1.3
6
5
30044
0.3
2.7
5
27732
20832
52950
35026
27313
66500
0.3
2
2.7
0.7
0.3
1
2.7
5
8.3
5.3
3
5.7
5
5
4
4
4
3
15925
51039
45288
0.3
0.7
0
2.7
2.7
3.7
5
5
4.1
21182
2
0
11
60975
15.3
4.3
6
17995
5.7
0.7
5
46070
6.3
0
12
35690
3.3
0
7
30044
13660
41264
7459
9889
20683
32682
3.3
3.7
15.7
4
2
5.7
3.3
0
0
8
0
1
0.7
0.3
7
7
6
6
6
5
5
43360
7398
21061
30518
11.3
4
3.7
10.3
6.3
0
0
3.2
9
9
7
5
193
Chapter 6
THIO
ACP
RBSB
Thioredoxin-1
11913
3.3
0.7
5
Acyl carrier protein
8634
2.3
1
4.2
D-ribose-binding periplasmic protein precursor 30931
9.3
3.3
3.8
Thiol:disulfide interchange protein dsbA
DSBA
23204
3
0
3.7
precursor
Table 6.3: Major differences in the proteomes of aerobic and oxygen-limited E. coli. Green category: minimum of
5σ difference in % peptides, 3 σ in all other categories. Yellow category: minimum of 5σ difference in % peptides,
3 σ in all but one of the other categories, or minimum of 3σ difference in all categories. Orange category:
minimum of 5σ difference in % peptides, 3 σ in all but two of the other categories, or minimum of 3σ difference in
all categories except one.
Protein
Function
Mean no
Mass (Da) peptides
(control)
Mean no
peptides
(treated)
Difference
mean %
peptides (σ)
Green category, up-regulated on gentamicin treatment
RL1
50S ribosomal protein L1
24714
0
4.3
14.6
OMPX
Outer membrane protein X precursor
18648
0
2.7
9.2
Yellow category up-regulated on gentamicin treatment
RS1
30S ribosomal protein S1
61235
0
6.3
15.8
RS2
30S ribosomal protein S2
26784
0
3.3
7.5
CLPB
Chaperone protein clpB
95697
0
4
5.9
Orange category up-regulated on gentamicin treatment
DNAK
Chaperone protein dnaK
69130
18.7
28.3
10.4
RL9
50S ribosomal protein L9
15759
0
3.7
8.3
CH60
60 kDa chaperonin
57464
1.7
11.3
6
RL10
50S ribosomal protein L10
17757
0
5.7
4.1
AHPC
Alkyl hydroperoxide reductase subunit C
20862
4.7
7
4.0
Yellow category down-regulated on gentamicin treatment
ASPG-2 L-asparaginase 2 precursor
36942
7
0.3
7.2
2,3-bisphosphoglycerate-independent
GPMI
56272
4
0.3
5.9
phosphoglycerate mutase
GRCA
Autonomous glycyl radical cofactor
14332
6.7
1
5.6
TREC
Trehalose-6-phosphate hydrolase
64082
2.3
0
4.2
Orange category down-regulated on gentamicin treatment
MALE
Maltose-binding periplasmic protein precursor
43360
11.3
3.3
5.7
CSPC
Cold shock-like protein cspC
7398
4
2
5.6
IDH
Isocitrate dehydrogenase [NADP]
46070
6.3
3
5.2
PGK
Phosphoglycerate kinase
41264
15.7
8
5
FABD
Malonyl CoA-acyl carrier protein transacylase
32682
3.3
0.3
4.0
RAIA
Ribosome-associated inhibitor A
12777
2.7
0
3.8
PFLB
Formate acetyltransferase 1
85588
13.3
1
3.3
Table 6.4: Major differences in the proteomes of control and gentamicin-treated aerobic E. coli. Green category:
minimum of 5σ difference in % peptides, 3 σ in all other categories. Yellow category: minimum of 5σ difference
in % peptides, 3 σ in all but one of the other categories, or minimum of 3σ difference in all categories. Orange
category: minimum of 5σ difference in % peptides, 3 σ in all but two of the other categories, or minimum of 3σ
difference in all categories except one.
194
Chapter 6
For the example L1 (table 6.5), it can be seen that the six parameters all gave similar (though not
identical) values for σ. There is no very clear consensus in the literature as to which of these
values would be expected to give the most accurate and reproducible results, which is why we
initially considered all six (and report all of them in the Supporting information). We have
chosen to present the difference in the mean percentage peptides (MPP), which is broadly
representative of all the possible parameters considered.
Protein description
emPAI
Diffmean (σ)
TotRes
Diffmean (σ)
No_peps
Diffmean (σ)
%Peps
Diffmean (σ)
Seq_cov
Diffmean (σ)
Av3PepSco
Diffmean (σ)
L1
5.23
4.53
3.56
5.3
4.52
3.19
Table 6.5 shows an example of ribosomal protein L1 protein with the values of six parameters considered. (σ) is the
differences between the means of the replicates in terms of the standard deviation (σ).
This parameter is preferred (albeit slightly) over the alternatives because (a) emPAI, though
fairly widely used, does not take account of all the data; we have measured two digests and
recorded two dissociation techniques and the data cannot readily be combined (b) total residues,
although seemingly sensible as a method of estimating quantity, gives (empirically) a few
unexpected outliers in the data (see Supporting information); this parameter does not have
extensive literature precedent. (c) sequence coverage is closely related and is normalised for the
size of the protein, but shows the same outliers. (d) The average top 3 peptide scores is a
parameter with good literature precedent; our concern here was that small, basic proteins, such as
ribosomal proteins have atypical peptides and very few of them; in some cases a ribosomal
protein has fewer than three theoretical peptides. (e) Number of peptides has good literature
precedent; where samples containing identical numbers of total peptides are compared, this
parameter will be the same as our preferred MPP. MPP has the advantage of being normalised
for small differences in sample concentration.
DISCUSSION
We have used a very simple proteomic strategy to identify potential drug targets for synergizers
with gentamicin. The FASP protocol is a simple, but powerful, method of isolating whole
195
Chapter 6
proteomes. Trypsin, however, is not always the best enzyme for digestion and analysis of small,
basic proteins, and we therefore analyzed both Lys C and tryptic digests, facilitating detection of
ribosomal proteins. It would be possible to use stable-isotope labeling19-21 and extensive
chromatography to gain detailed, quantitative insights into the effects of drugs on bacterial
proteomes. We set out, however, to understand gross effects, because it is these that are most
likely to assist the drug discovery process. A label-free mass spectrometric approach allowed
complete flexibility in the medium and conditions used for bacterial growth. Label-free methods
have normally been used to derive intra-proteomic quantifications;22-24 it becomes progressively
more difficult to achieve accuracy as the range of protein size, hydrophobicity and isoelectric
point increases (see, for example, the very low concentration of elongation factor Tu given in ref
25).
The proteins of the translation machinery include both small, basic ribosomal proteins and acidic
proteins, such as EF-Tu, and are therefore especially vulnerable. Our use of label-free methods
is, however, much more conservative. We have compared only like-with-like. We have made
no attempt to quantify, only to ascertain whether a protein is up- or down- regulated, and we
have required a high standard of proof over a range of data analysis methods.
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Chapter 6
Protein
Function
Mean no
Mass (Da) peptides
(control)
Mean no
peptides
(treated)
Difference
mean %
peptides (σ)
Green category, up-regulated on gentamicin treatment
RL10
50S ribosomal protein L10
17757
0
2.3
10
RS8
30S ribosomal protein S8
14175
0
2.3
10
RS10
30S ribosomal protein S10
11728
0
2.7
8.7
RL1
50S ribosomal protein L1
24714
1
7.3
5.3
Yellow category up-regulated on gentamicin treatment
RS2
30S ribosomal protein S2
26784
0
3.3
3.5
Orange category up-regulated on gentamicin treatment
LCDI
Lysine decarboxylase, inducible
81607
0
7
6
OMPA
Outer membrane protein A precursor
37292
1.7
5
4
DPS
DNA protection during starvation protein
18684
0
2.3
3.8
Green category, down-regulated on gentamicin treatment
DEOC
Deoxyribose-phosphate aldolase
27944
2
0
15.8
MBHM
Hydrogenase-2 large chain precursor
62908
2
0
15.8
WRBA
Flavoprotein wrbA
20832
5
0.3
12.7
ATPA
ATP synthase α subunit
55416
7
2
11.2
2,3,4,5-tetrahydropyridine-2,6-dicarboxylate NDAPD
30044
2.7
0
8.7
succinyltransferase
ATPB
ATP synthase β subunit
50351
9
3
8.3
EFG
Elongation factor G
77704
12.3
5.6
8
GLDA
Glycerol dehydrogenase
39087
2.3
0
5.4
Yellow category down-regulated on gentamicin treatment
USPG
Universal stress protein G
15925
2.7
0.3
4.9
UDP
Uridine phosphorylase
27313
3
0
4.8
Orange category down-regulated on gentamicin treatment
FRDB
Fumarate reductase iron-sulfur subunit
27732
2.7
0.7
4.3
YGEY
Uncharacterized protein ygeY
45288
3.67
0
4.1
GLPK
Glycerol kinase
56480
4.7
1.3
3.6
Table 6.6: Major differences in the proteomes of control and gentamicin-treated oxygen-limited E. coli. Green category:
minimum of 5σ difference in % peptides, 3 σ in all other categories. Yellow category: minimum of 5σ difference in %
peptides, 3 σ in all but one of the other categories, or minimum of 3σ difference in all categories. Orange category:
minimum of 5σ difference in % peptides, 3 σ in all but two of the other categories, or minimum of 3σ difference in all
categories except one.
We were interested in the effect of gentamicin on both aerobic and oxygen-depleted E. coli
because the drug is used clinically in varying degrees of oxygenation. Clearly the proteomes of
the organism in these two states are different, as shown in Table 6.3. Oxygen depletion appears
to have heat shock-like characteristics, with CH60, CLPB and SYK2 up-regulated and CSPE and
CSPC down-regulated. ATP synthase, which is significantly up-regulated on oxygen-depletion,
is also up-regulated as a part of the alkaline response.26
197
Chapter 6
Name of operon
Proteins in operon
SPC
S10
L11
α
Str
L35
trmD
S15
L10
S20
L14, L24, L5, S14, S8, L6, L18, S5, L30, L15, SecY and L36
S10, L3, L4, L23, L2, S19, L22, S3, L16, L29 and S17
L11, L1
S13, S11, S4, RpoA, L17
S12, S7, EF-G, EF-Tu
IF3, L35, L20
S16, rim, trmD, L19
S15, pnp (polynucleotide phosphorylase)
L10, L12, RpoB, RpoC
S20
Table 6.7: The E. coli ribosomal proteins and their operons
Regulatory
Protein
S8
L4
L1
S4
S7
L20
S15
L10, L12
S20
Effects of gentamicin treatment include the up-regulation of outer membrane proteins, OMPA in
the aerobic cultures and OMPX in the oxygen-limited cultures. Changes in membrane structure
in response to antibiotic insult are expected.
28-32
Down-regulated proteins tend to be house-
keeping proteins, with no very consistent patterns emerging. The striking feature in both aerobic
and oxygen-limited cultures, is, however, the up-regulation of ribosomal proteins.
Ribosomal
proteins are contained in a small number of operons in the E. coli genome, as shown in table 6.
Although there is some level of co-regulation, certain individual proteins, such as L1, are able to
auto-regulate apparently without effects on expression of proteins in the same operon.27 Our
results do not suggest operon-specific up-regulation of ribosomal proteins.
Rather, total
ribosomal protein is up-regulated and certain individual proteins, especially the larger proteins
that generate several proteotypic peptides on trypsin treatment, are up-regulated at the high
standard of proof we have applied.
Ribosomal proteins may therefore represent attractive drug targets for use in combination with
gentamicin. The cell responds to gentamicin-injury by up-regulation of the protein-synthesis
machinery. Inhibition of a component of this machinery is likely to disrupt ribosomal assembly,
and our data suggest that ribosomal L1, which functions as a regulator of ribosomal protein
synthesis, is a candidate target. Ribosomal S2 and L10 are also significantly upregulated and
L10 has regulatory function. In addition S1, S8, S10 and L9 are up-regulated in either aerobic or
oxygen-limited conditions, and more complex experiments may indicate general up-regulation in
response to gentamicin. In our study, we analyze the total proteome of the E. coli cell, not just
198
Chapter 6
the ribosomal sub-proteome. We are therefore confident that ribosomal proteins accumulate
because they are up-regulated, not just because they fail to assemble into functional ribosomes.
The up-regulation is not uniform for all ribosomal proteins, and quantitative proteomic methods
will undoubtedly illuminate this aspect further. We propose that the regulatory ribosomal
proteins L1, L10 and S2 have been identified as candidate drug targets for synergizers with
gentamicin.
Evaluation of ribosomal proteins L1 and L10 as drug targets
Calvin Chan33 carried out validation of the candidate drug targets L1 and L10 using virtual
screening. The aim of virtual screening is to identify bioactive compounds through
computational means, by employing knowledge about the protein target (structure-based virtual
screening). Structure-based virtual screening which was used for evaluation, utilizes the threedimensional (3D) structure of the biological target (determined either experimentally through Xray crystallography or NMR, or computationally through homology modelling) to dock the
candidate molecules and rank them based on their predicted binding affinity between the
compound and the protein.
The ribosomal proteins (L1 and L10) were prepared for virtual screening as following; the RNA
binding sites on the proteins in ribosome were visualized and identified using Molecular
Operating Environment software (MOE). The 3D structural model of these proteins gerenated by
Hermes software that used to visualize Genetic Optimization for ligand docking (GOLD) results
in three dimensional (3D). Zinc database containing natural product library and known
marketing drug library was used for docking ribosomal protein L1 and L10 into the active sites.
Chan`s result shows that protein residue Asn164, Asn167, Ile169 and His171 are identified to be
the RNA binding sites on protein L1 using MOE, in which His171 has a strongest interaction;
His171 was shown to have potential to bind prostaglandins. 10 binding ligands (members of
prostaglandin family including Prostaglandin E1 alcholol, Prostaglandin E2, Beraprost) were
identified as potential ligands for L1. Prostaglandins are mediators and have a variety of strong
199
Chapter 6
physiological effects such as pain production and blood clotting. No antimicrobial effect has
been detected for prostaglandins, and this is not an expected result.
Figure 6.3: Chemical structure of Mupirocin (pseudomonic acid A)
However, one of the ligands identified with potential to bind to ribosomal protein L1, is
Mupirocin (Figure 6.3) which inhibits isoleucyl-tRNA synthetases. Synergy between gentamicin
and Mupirocin has not been reported, but it is interesting to test the synergy as a sequential
blockage could occur and they act on different sequential targets in the same pathway.
EXPERIMENTAL SECTION
Strain and Materials
E.coli-K12 from Biolabs was used in all experiments. Reagents were obtained from SigmaAldrich (Poole, UK) unless otherwise stated. Mass spectrometry grade lysyl endpeptidase (LysC) was purchased from Wako (Osaka, Japan). Protease inhibitor cocktail was purchased from
Roche. Bugbuster and Benzonase were from Novagen. Gentamicin was supplied by Sigma and
prepared freshly in sterile water before each experiment.
Aerobic growth of E.coli cells and cell lysis
E. coli K-12 cells were grown at 37 °C in 100 mL of Luria broth (LB) medium with aeration
until the A600 reached 0.2. At this point, gentamicin was added to a final concentration of 6 µg
200
Chapter 6
mL-1; no antibiotic was added to the control cultures. The cultures were then incubated for a
further 3 h at 37°C. The cells were collected at OD600 0.8 and completely resuspended in
BugBusterTM Protein Extraction Reagent, using 5 mL BugBuster per gram of wet cell paste. 200
µL Protease inhibitor solution (prepared by dissolving a tablet in 2 mL sterile water), was added,
followed by 1 µL (25 units) of Benzonase per mL of BugBuster reagent added. The cells were
incubated with gentle mixing for 20 min at room temperature. Lysates were clarified by
centrifugation at 16,000 g for 20 min at 4°C. The supernatants (clarified cell lysate) were
transferred to fresh tubes, and frozen at –20°C until needed. All experiments were carried out in
triplicate.
Anaerobic culture of E.coli cells
1 mL of 0.001% resazurin indicator solution was added to 300 mL of sterile LB broth culture
medium and the medium was agitated until a homogeneous blue colouration was obtained. 30
mL universal bottles were filled to the brim to displace undissolved oxygen. Then, 10 µl of an
overnight E.coli culture were transferred into the medium and each universal bottle was
immediately covered with a sterile rubber stoppers. The culture was then incubated at 37oC and
60 rpm and the growth rate of the cells was monitored by visual comparison with McFarland
standards. When OD600 of about 0.15 was achieved, the cells were treated by injecting
gentamicin to a final concentration of 0.5 µg mL-1. Controls were untreated. At the end of the
experiment, when an OD600 of 0.6 was obtained, the absorbance was measured to confirm the
estimations made. Cell lysis was carried out as above.
MS and MS/MS analysis on the Amazon ion trap
For the analysis of peptide mixtures, liquid chromatography was performed using an Ultimate
3000 nano-HPLC system (Dionex, Surrey, UK) comprising a WPS-3000 well-plate micro auto
sampler, a FLM-3000 flow manager and column compartment, a UVD-3000 UV detector for
chromatogram acquisition usually set at 214 nm, an LPG-3600 dual-gradient micro-pump, and
an SRD-3600 solvent rack controlled by Hystar (Bruker Daltonics) and Trap software. Samples
201
Chapter 6
were concentrated on a trapping column (Dionex, 300 µm x 0.1 cm) at a flow rate of 30 µLmin-1.
For the separation with a C18 Pepmap column (75 µm х 15 cm, Dionex), was used as generated
by a cap-flow splitter cartridge (1/1000), the column oven set to 30 °C to maintain a constant
temperature, a flow rate of 300 nL min-1 was applied. At this flow rate, typical column pressure
was around 120 bar, master pressure was 200 bar and loading pressure was 65 bar. Peptides were
eluted by the application of a 90 min linear gradient: solvent A (95% H2O, 5% acetonitrile, 0.1%
formic acid), 0%–95% Solvent B (95% acetonitrile, 5% water, 0.1% formic acid). The LC was
interfaced directly with ion trap mass spectrometer (Amazon; Bruker Daltonics) utilizing fused
silica PicoTipTM emitter (New Objective, Woburn, MA, USA) using a capillary voltage of 17002200 V and nano-ESI mode.
The typical setting on the ion trap was tuned as: dry gas temperature was set to 150 °C, dry gas
was set to 6 L min-1, the scan mode was set to standard enhanced with a m/z range of 200-3000
with a speed of 8100 Th s-1. Ions were accumulated in the trap until the ion charge count (ICC)
reaches 200,000 with a maximum accumulation time of 200 ms. Sample tables were inserted into
HyStarTM (Bruker, Bremen, Germany) which incorporates Esquire control version 6.2 (for the
control of the mass spectrometer) and Chromeleon version 6.8 (for the control of the LC system).
In MS/MS analysis, up to three precursor ions were selected per cycle with active exclusion (1.5
min) for both CID and ETD, excluding singly charged ions. CID fragmentation was achieved
using helium gas and a 30%–200% collision energy sweep with amplitude 1.20 (ions are ejected
from the trap as soon as they fragment). For electron-transfer dissociation experiments, the
source temperature was set 60 with ionization energy 80 eV and emission current 3 µA. The ion
trap instrument was calibrated monthly using Bruker tunes mix.
Data analysis
Protein identification was performed by searching the raw data using the Mascot search
algorithm on an in-house Mascot server (version 2.2.06, Matrix Science) against E. coli-K12
database. The mascot generic file (mgf file) contain the m/z values of the precursor ions, the m/z
202
Chapter 6
values of the correspondent fragment ions, the charge states of these ions and their respective
signal intensities.
The MASCOT score refers to -10·log (p), where p is the likelihood that an observed event is
random. Scores over 15 indicate identity and confidence levels are p<0.05 for each peptide.
Tandem mass spectra were processed using DataAnalysisTM 3.4 software (Bruker Daltonics).
Two searches were performed in for CID and ETD, using the E.coli database with the following
parameters: Maximum missed cleavages: 2; Carbamidomethyl (C) as fixed modification; Nacetyl (Protein) and Oxidation (M) as variable modifications. Peptide tolerance 0.8 Da, fragment
tolerance 0.6 Da, instrument type: ESI-TRAP (for CID) and ETD-TRAP (for ETD), respectively,
for the two separate datasets. The peptides analyzed included all possible charge states of those
Lys C and tryptic peptides, where 300 ≤ m/z ≤ 1800 and the peptides contained at least 5 amino
acids. We also used the reverse database functionality in Mascot (Decoy) and these values were
less than 3 %.
203
Chapter 6
References
1. Weinstein, M.; Luedemann, G.; Oden, E.; Wagman, G.; Rosselet, J.; Marquez, J.; Coniglio,
C.; Charney, W.; Herzog, H.; Black, J., Gentamicin, a new antibiotic complex from
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2. Shakil, S.; Khan, R.; Zarrilli, R.; Khan, A. U., Aminoglycosides versus bacteria- A description
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7. Houang, E. T.; Greenwood, D., Aminoglycoside cross-resistance patterns of gentamicinresistant bacteria. Journal of Clinical Pathology 1977, 30, 738-744.
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11. Basak, J.; Chatterjee, S. N., Induction of adaptive response by nitrofurantoin against
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15. McFarland Standards, http://en.wikipedia.org/wiki/McFarland_standards.
16. Paisley, J. W.; Washington, J. A. II, Synergistic activity of gentamicin with trimethoprim or
sulfamethoxazole-trimethoprim against Escherichia coli and Klebsiella pneumoniae.,
Antimicrobial Agents and Chemotherapy 1978, 14, 656-658.
17. Wisniewski, J. R.; Zougman, A.; Nagaraj, N.; Mann, M., Universal sample preparation
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19. Julka, S.; Regnier, F., Quantification in proteomics through stable isotope coding: A review.,
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20. Ong, S. E.; Foster, L. J.; Mann, M., Mass spectrometric-based approaches in quantitative
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22. Patel, V. J.; Thalassinos, K.; Slade, S. E.; Connolly, J. B.; Crombie, A.; Murrell, J. C.;
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23. Bantscheff, M.; Schirle, M.; Sweetman, G.; Rick, J.; Kuster, B., Quantitative mass
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24. Mann, M., Comparative analysis to guide quality improvements in proteomics. Nature
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25. Ishihama, Y.; Schmidt, T.; Rappsilber, J.; Mann, M.; Hartl, F. U.; Kerner, M.; Frishman, D.
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26. Kasimoglu, E.; Park, S. J.; Malek, J.; Tseng, C. P.; Gunsalus, R. P., Transcriptional
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28. De Mot, R.; Vanderleyden, J., The C-terminal sequence conservation between OmpA-related
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29. Koebnik, R.; Krämer, L., Membrane assembly of circularly permuted variants of the E. coli
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Chapter 6
SUPPORTING INFORMATION:
The Effects of Gentamicin on the Proteomes of Aerobic and
Oxygen-limited Escherichia coli
Zubida M. Al-Majdoub, Abiola Owoseni, Simon J. Gaskell, Jill Barber
*
Running Title: Changing proteomes by gentamicin
*To whom correspondence should be addressed: E-mail: [email protected]
Phone: +44 (0)161 275 2369. Fax: +44 (0) 161 275 2396.
Contents
SI-1: Sample preparation for mass spectrometry………………………………………....207
SI-2: Peptide desalting………………………………………………………………........208
SI-3: Key to data in Excel spreadsheet, raw data and analyzed data…………..................209
SI-4: Figure shows CID and ETD MS/MS spectra of the [M + 2H]+2 ion (m/z 695.91) of a
peptide derived from the ribosomal protein L1....................................................................212
206
Chapter 6
SI-1: Sample preparation for mass spectrometry
An Ultracel-10 filtration device (Millipore) with a 10,000 Da molecular mass cutoff was
washed twice with 200 µL sterile water, and then the protein extract was added to the filter
column and centrifuged for 30 min at 13,000 rpm. Then the 400 µL of freshly prepared 8 M
urea in 0.1 M Tris-HCl pH 8.5 was added twice to the filter. The devices were centrifuged at
14,000 rpm for 40 min at room temperature (RT). The proteins were reduced by the addition
of 10 mM Dithiothreitol (100 µL) in 8 M urea/0.1 M Tris-HCl, pH 8.5 following 15 minute
incubation with mixing at 600 rpm at RT. Then 200 µL of 50 mM iodoacetamide in 8 M
urea/0.1 M Tris-HCl, pH 8.5 which was stored in the dark was added to the sample to prevent
cysteine residues from reforming disulfide bonds and was shaken at 600 rpm in thermomixer
for 1 min and incubated without mixing for 5 min followed by centrifugation at 14,000 rpm
for 30 min. The resulting concentrates were diluted with 200 µL 8 M urea/0.1 M Tris-HCl,
pH 8.0 and concentrated again at 14,000 rpm for 30 min. 210 µL 8 M urea/0.1 M Tris-HCl,
pH 8.0 was then added to the filter to dilute the concentrated protein sample and this was
collected in a new collection tube. 40 µL of protein lysate containing about 120 µg proteins
was transferred into the spin filter with another set of collection tubes. 40 µL 8 M urea / 0.1
M Tris-HCl pH 8.0 was added to dilute and thus prevent evaporation of sample protein
solution. 20 µL (2.5 µg) of enzyme Lys-C solution was added in the filter. This was shaken at
600 rpm for 1 min before incubating the sample overnight (16-18 h) at 30 ˚C. The samples
were then centrifuged at 14,000 rpm, 20 min and 100 µl of 0.5 M sodium chloride solution
was added to the filter and centrifuged at 14,000 rpm for 20min. 20 µL of the Lys C digest
was transferred into a sterile small eppendorf tube and was diluted by the factor of four with
50 mM ammonium bicarbonate solution. 2 µL of trypsin enzyme was added to protein
solution in the mass ratio 1:50. The sample was incubated overnight at 37 ˚C. The peptides
have low molecular weight so they can pass through the filter holes when centrifuged at
14,000 rpm for 20 min. Peptides are now in the collection tubes ready for desalting.
207
Chapter 6
SI-2: Peptide Desalting.
The peptides were desalted using a C18 ZipTip (Millipore, Bedford, MA). Briefly, the tip
was equilibrated by 4 times aspirating and then dispensing 50% methanol, followed by 4
times aspirating and then dispensing 0.1 % formic acid. The peptides were bound to the
column by aspirating then dispensing the peptide mixture for 10-15 cycles. The salts were
removed by washing with 0.1 % formic acid 5 times. The peptides were eluted by aspirating
and then dispensing 0.1% formic acid/ 70% acetonitrile for 10 cycles. The peptides were
dried down and re-suspended in 2% acetonitrile /0.1% formic acid and analyzed by LC
MS/MS.
208
Chapter 6
SI-3: Key to Excel File containing Raw and Analyzed Data
Sheet name
C1LCZcid
C1LCZetd
C1TZetd
C1TZcid
C2TZetd
C2TZcid
C2LCetd
C2LCcid
C3LCZetd
C3LCZcid
C3TZetd
C3TZcid
G6ALCcid
G6ALCetd
G6ATcid
G6ATetd
G6BLCcid
G6BLCetd
G6BTcid
G6BTetd
G6CLCcid
G6CLCetd
Description
Raw data from Mascot, control 1, aerobic E. coli, digestion with
endopeptidase LysC, fragmentation method CID
Raw data from Mascot, control 1, aerobic E. coli, digestion with
endopeptidase LysC, fragmentation method ETD
Raw data from Mascot, control 1, aerobic E. coli, digestion with
trypsin, fragmentation method ETD
Raw data from Mascot, control 1, aerobic E. coli, digestion with
trypsin, fragmentation method CID
Raw data from Mascot, control 2, aerobic E. coli, digestion with
trypsin, fragmentation method ETD
Raw data from Mascot, control 2, aerobic E. coli, digestion with
trypsin, fragmentation method CID
Raw data from Mascot, control 2, aerobic E. coli, digestion with
endopeptidase LysC, fragmentation method ETD
Raw data from Mascot, control 2, aerobic E. coli, digestion with
endopeptidase LysC, fragmentation method CID
Raw data from Mascot, control 3, aerobic E. coli, digestion with
endopeptidase LysC, fragmentation method ETD
Raw data from Mascot, control 3, aerobic E. coli, digestion with
endopeptidase LysC, fragmentation method CID
Raw data from Mascot, control 3, aerobic E. coli, digestion with
trypsin, fragmentation method ETD
Raw data from Mascot, control 3, aerobic E. coli, digestion with
trypsin, fragmentation method CID
Raw data from Mascot, gentamicin-treated sample A, aerobic E.
coli, digestion with Lys-C, fragmentation method CID
Raw data from Mascot, gentamicin-treated sample A, aerobic E.
coli, digestion with Lys-C, fragmentation method ETD
Raw data from Mascot, gentamicin-treated sample A, aerobic E.
coli, digestion with trypsin, fragmentation method CID
Raw data from Mascot, gentamicin-treated sample A, aerobic E.
coli, digestion with trypsin, fragmentation method ETD
Raw data from Mascot, gentamicin-treated sample B, aerobic E.
coli, digestion with Lys-C, fragmentation method CID
Raw data from Mascot, gentamicin-treated sample B, aerobic E.
coli, digestion with Lys-C, fragmentation method ETD
Raw data from Mascot, gentamicin-treated sample B, aerobic E.
coli, digestion with trypsin, fragmentation method CID
Raw data from Mascot, gentamicin-treated sample B, aerobic E.
coli, digestion with trypsin, fragmentation method ETD
Raw data from Mascot, gentamicin-treated sample C, aerobic E.
coli, digestion with Lys-C, fragmentation method CID
Raw data from Mascot, gentamicin-treated sample C, aerobic E.
coli, digestion with Lys-C, fragmentation method ETD
209
Chapter 6
G6CTcid
G6CTetd
C1Tcid
C1Tetd
C2Tcid
C2Tetd
C3Tcid
C3Tetd
G0.5ATcid2
G0.5ATetd
G0.5BTcid
G0.5BTetd
G0.5CTcid
G0.5CTetd
C1All
C2All
C3TAll
G0.5AAll
G0.5Ball
G0.5CAll
C1ZAll
C2ZAAll
C3ZAll
G6AAll
G6Ball
G6CAll
C1Summ
C2Summ
Raw data from Mascot, gentamicin-treated sample C, aerobic E.
coli, digestion with trypsin, fragmentation method CID
Raw data from Mascot, gentamicin-treated sample C, aerobic E.
coli, digestion with trypsin, fragmentation method ETD
Raw data from Mascot, control 1, oxygen-limited E. coli, digestion
with trypsin, fragmentation method CID
Raw data from Mascot, control 1, oxygen-limited E. coli, digestion
with trypsin, fragmentation method ETD
Raw data from Mascot, control 2, oxygen-limited E. coli, digestion
with trypsin, fragmentation method CID
Raw data from Mascot, control 2, oxygen-limited E. coli, digestion
with trypsin, fragmentation method ETD
Raw data from Mascot, control 3, oxygen-limited E. coli, digestion
with trypsin, fragmentation method CID
Raw data from Mascot, control 3, oxygen-limited E. coli, digestion
with trypsin, fragmentation method ETD
Raw data from Mascot, gentamicin-treated sample A, oxygenlimited E. coli, digestion with trypsin, fragmentation method CID
Raw data from Mascot, gentamicin-treated sample A, oxygenlimited E. coli, digestion with trypsin, fragmentation method ETD
Raw data from Mascot, gentamicin-treated sample B, oxygenlimited E. coli, digestion with trypsin, fragmentation method CID
Raw data from Mascot, gentamicin-treated sample B, oxygenlimited E. coli, digestion with trypsin, fragmentation method ETD
Raw data from Mascot, gentamicin-treated sample C, oxygenlimited E. coli, digestion with trypsin, fragmentation method CID
Raw data from Mascot, gentamicin-treated sample C, oxygenlimited E. coli, digestion with trypsin, fragmentation method ETD
Summary of all peptides from control 1, oxygen-limited E. coli
Summary of all peptides from control 2, oxygen-limited E. coli
Summary of all peptides from control 3, oxygen-limited E. coli
Summary of all peptides from gentamicin-treated sample A,
oxygen-limited E. coli
Summary of all peptides from gentamicin-treated sample B,
oxygen-limited E. coli
Summary of all peptides from gentamicin-treated sample C,
oxygen-limited E. coli
Summary of all peptides from control sample 1, aerobic E. coli
Summary of all peptides from control sample 2, aerobic E. coli
Summary of all peptides from control sample 3, aerobic E. coli
Summary of all peptides from gentamicin-treated sample A,
aerobic E. coli
Summary of all peptides from gentamicin-treated sample B,
aerobic E. coli
Summary of all peptides from gentamicin-treated sample C,
aerobic E. coli
Summary of all proteins from control 1, oxygen-limited E. coli
Summary of all proteins from control 2, oxygen-limited E. coli
210
Chapter 6
C3Summ
G0.5ASumm
Summary of all proteins from control 3, oxygen-limited E. coli
Summary of all proteins from gentamicin-treated sample A,
oxygen-limited E. coli
G0.5BSumm
Summary of all proteins from gentamicin-treated sample B,
oxygen-limited E. coli
G0.5CSumm
Summary of all proteins from gentamicin-treated sample C,
oxygen-limited E. coli
C1ZSumm
Summary of all proteins from control 1, aerobic E. coli
C2ZSumm
Summary of all proteins from control 2, aerobic E. coli
C3ZSumm
Summary of all proteins from control 3, aerobic E. coli
G6ASumm
Summary of all proteins from gentamicin-treated sample A,
aerobic E. coli
G6BSumm
Summary of all proteins from gentamicin-treated sample B,
aerobic E. coli
G6CSumm
Summary of all proteins from gentamicin-treated sample C,
aerobic E. coli
AnaerCvsG
Summary of statistically significant proteins with quantification
data comparing oxygen-limited controls against gentamicin-treated
Aer CvsG
Summary of statistically significant proteins with quantification
data comparing aerobic controls against gentamicin-treated
AerVsAnaer
Summary of statistically significant proteins with quantification
data comparing aerobic controls against oxygen-limited controls
AerobicEffectofGent
Difference in quantitative parameters between control and
gentamicin-treated aerobic E. coli
AnaerobicEffectofGent Difference in quantitative parameters between control and
gentamicin-treated oxygen-limited E. coli
EffectofOxygenLimitation Difference in quantitative parameters between aerobic and
oxygen-limited E. coli in the absence of gentamicin
Note: CD contains the data attached with this thesis
211
Chapter 6
Intens.
[%]
A
y8
y4
100
+MS2(696.0), 60.2min #2832
871.5
444.3
y10 y9 y8 y7 y6 y5 y4 y3 y2 y1
80
QYD IN EAIALLK
60
b 4-NH3
503.2
y5
475.2
y6
628.4
557.4
b6-NH3
746.3
y2
40
b8-NH3
260.2
930.4
y3
y7
373.3
b11-NH3
y10
b7-NH3
1227.6
1099.5
817.3
b 9-NH3
20
1001.4
675.4
y1
299.2
147.1
y9
347.7
0
200
Intens.
[%]
400
600
800
1000
1200
m/z
+MS2(ETD 696.0), 60.3min #2833
B
z10 z9 z8 z7 z6 z5 z4
100
1390.7
z2
QY D I N E A I A L L K
c11 c10
80
[M+2H]2+
696.4
60
40
z4
429.3
z4++
219.1z2
317.1 373.3
z7
c10
1147.6
856.5
613.4
20
z
1034.4 10
z8
z6
c11
1262.6
z9
964.5
742.4
z5
542.2
1715.1
1568.8
1655.5
0
200
400
600
800
1000
1200
1400
1600
m/z
Figure SI-4: CID (top) and ETD (bottom) MS/MS spectra of the [M + 2H]+2
ion (m/z 695.91) of a peptide derived from the ribosomal protein L1. (A)
Matched b- and y-ions are indicated in the spectra as well as the peptide
sequence. (B) Matched z+1 and z+2, and c ions are indicated in the spectra and
the peptide sequence.
212
Chapter 7
A proteomic analysis for discovery of drug targets in Escherichia
coli
Zubida M Al-majdoub,*aJill Barber*a
*Michael Barber Centre for Mass Spectrometry, Manchester Interdisciplinary Biocentre
a
School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Manchester,
M13 9PT
Abstract
The effect of sublethal concentrations of azithromycin on E.coli was investigated using a
label-free proteomic approach. Four ribosomal proteins (RL6, RL11, RS2, and RL25) and
ACON2 were up-regulated in response to azithromycin treatment while several housekeeping proteins were down-regulated. These results suggest that ribosomal proteins may be
drug targets for synergizers with azithromycin.
Introduction
Azithromycin is a 15-membered azalide antibiotic, derived from erythromycin, that has been
widely used for the treatment of bacterial infections.1,2 (Figure 7.1).
Figure 7.1: Structure of azithromycin
Azithromycin has an improved in vitro spectrum of activity and higher potency3 than
erythromycin A and is much less sensitive to acid. Azithromycin has good activity against
213
Chapter 7
aerobic gram-positive microorganisms such as staphylococci and streptococci, anaerobic
clostridium species, and certain important gram-negative organisms such as Haemophilus
influenza5,6, Chlamydia trachomatis and some members of the enterobacteriaeceae family.4
Different types of infection are caused by these organisms, including respiratory tract
infections, gastric ulcers and soft tissue infections. It has also been established that
azithromycin, especially in combination with chloroquine, is effective in malaria
prophylaxis.7 Azithromycin concentrates in macrophages and fibroblasts, and it may be that it
is brought to the site of inflammation by phagocytes.
Because of its extensive uptake and high tissue concentration, azithromycin has long half-life
in the body: between two and four days. A course of therapy can therefore be as little as
single daily doses over three days.8 The high tissue affinity of azithromycin is thought to be
due to the presence of two tertiary amine groups in its structure which can be protonated.9
Azithromycin inhibits bacterial translation by binding to the large (50S) ribosomal subunit. It
also disturbs 50S ribosome assembly10,11 preventing the formation of a functional 70S
ribosome - the incomplete ribosomal particle is subsequently degraded in growing cells.12,13
With the increasing of drug resistance, there is an urgent need to identify targets for the
development of novel drugs, or to extend the use of old drugs. Bacterial combinations offer
advantages over single drugs, because the risk of resistance developing is much reduced.
Combinations can be synergistic, antagonistic or simply additive in their effects; synergy is
preferable, especially where one or both partners has a poor therapeutic index or significant
side-effects. We are interested in extending the use of existing antibacterial agents by
developing synergizers (compounds that act synergistically with these agents), and we are
therefore interested in proteins involved in the adaptive response to antibiotic treatment.
Some of these proteins are expected to be potential drug targets.
We have recently shown that several ribosomal proteins (L1, L9, L10, S1, S2, S8 and S10)
are up-regulated in E.coli in response to gentamicin treatment. Gentamicin targets the 30S
ribosomal subunit. We now describe the response of the same organism to treatment with
azithromycin.
214
Chapter 7
Experimental section
Materials and Reagents Trypsin (sequencing grade) and completeTM Mini EDTA-free
protease inhibitor cocktail were obtained from Roche Diagnostics (U.K.). Mass spectrometry
grade lysyl endpeptidase (Lys C) was purchased from Wako (Osaka, Japan). BugBusterTM
protein extraction reagent and benzonase were purchased from Novagen. All other chemicals
and solvents (HPLC grade) were purchased from Sigma-Aldrich Company Ltd. (Dorset,
U.K.) unless noted otherwise.
Microorganism E. coli-K12 was supplied through Biolabs and used throughout the study.
Antimicrobial agent
Azithromycin was obtained as powder from Sigma-Aldrich. A stock solution of 2 mg/mL
was prepared in ethanol on the same day of the experiments, 300 µL which is equivalent to
600 µg added to each flask containing 100 mL cultures.
Effect of azithromycin on growth inhibition of E.coli-K12
E. coli cells were grown overnight in 5 mL LB broth (10 g/L tryptone, 10 g/ L NaCl and 5
g/L yeast extract) at 37 ºC, agitated at 220 rpm and an aliquot used to inoculate 50 mL LB to
A600 0.1. The resulting cultures were shaken at 37 °C to A600=0.2, then azithromycin solution
added to a final concentration of 6 µg /mL. Control and treated cultures were shaken at 37 ºC
for 2.5 hr.
Cell lysis, FASP protocol, peptides desalting, mass spectrometric analysis and Mascot
database search were all performed as the same procedures as described in chapter 6
Results
E.coli cultures
The antibiotic concentration chosen for these experiments (6 µg/mL) was low enough not to
have a significant effect on the E. coli growth rate (Figure 7.2). The MIC of azithromycin has
been measured elsewhere14 as 2 µg/mL, azithromycin was added at the start of the
exponential phase (OD600 0.2) and cultures were grown until the control samples achieved
215
Chapter 7
OD600 0.8; treated samples obtained OD600 0.7 at this time. Cells were then harvested for
proteomic analysis. Both controls and treated samples were triplicates.
Response of E. coli to azithromycin treatment
The protein fractions of E. coli control and azithromycin-treated samples were subject to
digestion using endopeptidase Lys C and trypsin and then to LC-MS/MS using CID and ETD
fragmentation in order to improve coverage. There was no pre-fractionation because we were
interested at this stage only in the most clearly up- and down-regulated proteins. Our results
confirmed this: only 244 and 217 from control and treated samples proteins were identified.
0.9
Control
0.8
Azithromycin
0.7
0.6
A600
0.5
0.4
0.3
0.2
0.1
0
0
50
100
time (min)
150
200
Figure 7.2: Growth curve of E.coli-K12 in the presence of sub-lethal concentration of
azithromycin at 37°C. Each value represents the mean optical density (OD600) readings from
3 cultures. Error bars are so small that they do not extend beyond the boundaries of the
symbol.
Label-free quantification has been achieved by a number of different methods. These include
the emPAI score, the average peptide score of the highest scoring three peptides and peptide
counting. In this work, we aimed only to identify proteins that were up- or down- regulated.
We considered 3 parameters that indicate the relative abundance of a particular protein: the
number of peptides detected for a protein and the closely-related percentage of total peptides
detected, and the average peptide score of the most intense three peptide signals.
As discussed in chapter 6, the difference in mean percentage peptides (MPP) was the
preferred parameter for determining whether there was a significant difference in protein
concentration in the two sets of samples.
216
Chapter 7
The results of this analysis have been statistically summarized in supplemental information.
We considered only proteins for which at least two peptides were detected in all three
replicates both conditions and peptide detection was defined as a Mascot score of at least 15
for unique peptides. Therefore, 68 proteins examined for a purpose of comparison. The
proteins identified in this study were named according to the E.coli-K12 database. Notably,
we have previously employed FASP approach with label-free quantitative method of proteins
in E.coli challenged with gentamicin (chapter 6) and we were able to identify drug targets. In
this study, the proteome analysis was done at mid-log phase (A600 of 0.8) when there was no
discernible difference in growth between control and treated samples.
Characterization of differentially expressed proteins using FASP and label-free
approaches
Table 7.1 show the proteins most significantly up- and down- regulated on treatment with
azithromycin. Ribosomal L6 and L11 show a remarkable up-regulation in treated cells, this
assumption based on the calculation of the means and standard deviations of the replicates
and the differences between the means in terms of the standard deviation (σ) (supplemental
material). The percentage of peptides detected was judged the most attractive parameter in
this study. A >3σ difference in the mean percentage peptides, considered to be significant.
The most significantly down-regulated proteins (Formate acetyltransferase 1 and Malate
dehydrogenase) are involved in energy generation (Table 7.1). Malate dehydrogenase
catalyses the reversible oxidation of malate to oxaloacetate. Aconitate hydratase 2 (ACON2),
an enzyme that serves as a protective buffer against oxidative stress that accompanies aerobic
growth, by acting as a sink for reactive oxygen species, was hugely up-regulated.
Phosphoenolpyruvate carboxykinase [ATP] (PPCK) were down-regulated. Three enzymes in
transport and metabolism was found to be down-regulated such as L-asparaginase 2 precursor
(ASPG2), these enzymes being responsible for amino acid, glucose and carbohydrate
transport and metabolism. Glycerophosphoryl diester phosphodiesterase precursor (GLPQ),
enzyme involved in glycerol metabolism was down-regulated. Exposure to azithromycin led
to the decrease of aminoacyl-histidine dipeptidase (PEPD), this enzyme involved in peptide
catabolic process. Changes in all of these proteins indicated modulations in the tricarboxylic
217
Chapter 7
acid (TCA) cycle and in glycolysis/ gluconeogenesis.
Discussion
Our interest was in discovering changes in ribosomal proteins and ribosome-associated
factors after azithromycin treatment. From our findings, It can be suggested that azithromycin
statistically up-regulate a number of proteins involved in translation especially ribosomal
proteins L6, L11 and S2. The up-regulation of S2 was also observed with E.coli cells
challenged with gentamicin. These ribosomal proteins (L6, L11 and S2) could be considered
as candidate drug-targets and could produce a synergistic effect with azithromycin.
Future directions of this study is the validation of these candidate drug targets (L6, L11 and
S2) using virtual screening. The purpose of virtual screening is to identify bioactive
compounds that bind to ribosomal proteins L6, L11 and S2 and could produce a synergistic
effect with azithromycin.
References
1. Williams, J. D.; Sefton, A. M., Comparison of macrolide antibiotics. Journal of
Antimicrobial Chemotherapy 1993, 31, 11-26.
2. Zuckerman, J. M., The newer macrolides: azithromycin and clarithromycin. Infectious
disease clinics of North America 2000, 14, 449-462.
3. Retsema, J.; Girard, A.; Schelkly, W.; Manousos, M.; Anderson, M.; Bright, G.; Borovoy,
R.; Brennan, L.; Mason, R., Spectrum and mode of action of azithromycin (CP-62,993), a
new 15-membered-ring macrolide with improved potency against gram-negative organisms.
Antimicrob. Agents Chemother. 1987, 31,1939-1947.
4. Whitman, M. S.; Tunkel, A. R., Azithromycin and Clarithromycin: Overview and
comparison with erythromycin. Infection Control and Hospital Epidemiology 1992, 13, 357368.
5. Hopkins, S., Clinical safety and tolerance of azithromycin in children. Journal of
Antimicrobial Chemotherapy 1993, 31,111-117.
6. Gladue, R. P.; Bright, G. M.; Isaacson, R. E.; Newborg, M. F., In vitro and in vivo uptake
of azithromycin (CP-62,993) by phagocytic cells: possible mechanism of delivery and release
at sites of infection. Antimicrobial Agents Chemotherapy 1989, 33, 277-282.
7. Dunne, M. W.; Singh, N.; Shukla, M.; Valecha, N.; Bhattacharyya, P. C.; Dev, V.; Patel,
K.; Mohapatra, M. K.; Lakhani, J.; Benner, R.; Lele, C.; Patki, K., A Multicenter., Study of
azithromycin, alone and in combination with chloroquine, for the treatment of acute
uncomplicated plasmodium falciparum malaria in india. Journal of Infectious Diseases 2005,
191, 1582-1588.
218
Chapter 7
8. Lode, H., The pharmacokinetics of azithromycin and their clinical significance. European
Journal of Clinical Microbiology & Infectious Diseases 1991, 10, 807-812.
9. Ball, P., The future role and importance of macrolides. Journal of Hospital Infection 1991,
19, 47-59.
10. MacKenzie, F. M.; Gould, I. M., The post-antibiotic effect. Journal of Antimicrobial
Chemotherapy 1993, 32, 519-537.
11. Sharma, K. K .; Sangraula, H.; Mediratta, P. K., Some new concepts in antibacterial drug
therapy. Indian Pharmacological Society: 2002, 34, 10-20.
12. Champney, W. S.; Burdine, R., 50S ribosomal subunit synthesis and translation are
equivalent targets for erythromycin inhibition in Staphylococcus aureus. Antimicrobial
Agents Chemotherapy 1996, 40, 1301-1303.
13. Chittum, H. S.; Champney, W. S., Erythromycin inhibits the assembly of the large
ribosomal subunit in growing Escherichia coli cells. Current Microbiology 1995, 30, 273279.
14. Gordillo, M. E.; Singh, K. V.; Murray, B. E., In vitro activity of azithromycin against
bacterial enteric pathogens. Antimicrobial Agents Chemotherapy 1993, 37, 1203-1205.
219
Chapter 7
Locus
annotated function
Protein
Mass
(Da)
Mean number
peptides
Control Treated
Mean number
% peptides
Mean average
peptides Score
Diffmean
Diffmean
Diffmean
Control Treated
Control Treated
(s)(σ)
(s)(σ)
(s)(σ)
Up-regulated proteins
50S ribosomal protein
RL6_ECOLI
18949 0.33
3
6.51
0.08 0.92
L6
50S ribosomal protein
RL11_ECOLI
14923 0.33
2
4.07
0.08 0.61
L11
30S ribosomal protein
RS2_ECOLI
26784 1.67
3
3.24
0.43 0.92
S2
ACON2_ECOLI Aconitate hydratase 2 94009 1.67 3.33
2.86
0.47 1.01
50S ribosomal protein
RL25_ECOLI
10687 0.67
2
1.64
0.15 0.61
L25
Down-regulated proteins
PFLB_ECOLI
Formate
85588 13.67
2
3.85
3.54 0.58
acetyltransferase 1
Phosphoenolpyruvate 59891
PPCK_ECOLI
3
0.33
6.51
0.82
0.1
carboxykinase [ATP]
Glycerophosphoryl
GLPQ_ECOLI
diester
40874
3.67
2
4.07
0.97 0.61
phosphodiesterase
precursor
MDH_ECOLI Malate dehydrogenase 32488 2.67
1
4.07
0.7
0.3
L-asparaginase 2
36942 4.33 0.33
ASPG2_ECOLI
3.45
1.12
0.1
precursor
PEPD_ECOLI Aminoacyl-histidine 53110
6
3.33
3.26
1.6
1.01
dipeptidase
Glucose-specific
PTGA_ECOLI
18240
phosphotransferase
4.33
3
3.24
1.2
0.92
enzyme IIA component
6-phosphogluconate
51563
4.33
2
2.84
1.16 0.62
6PGD_ECOLI
dehydrogenase,
decarboxylating
SYGB_ECOLI
Glycyl-tRNA
76936
3.33
1
2.16
0.86 0.28
synthetase beta subunit
Table 7.1: Major differences in the proteomes of control and azithromycin-treated E. coli.
peptides are considered to be significant.
6.46
3.97
44
3.3
4.82
2.87
71.7
2.95
4.9
82.2 175.03
1.56
3
27.97 55.67
0.82
2.3
8.8
59.67
2.8
8.97
297.9
87
11.03
4
73.8
11.27
2.76
4.5
115.9 76.43
1.77
13.33
121.87 76.3
5.18
5.1
194.4 19.83
4.59
3.11
311.23 167.77
1.89
0.82
242.1 172.5
1
2.08
183.6 87.53
3.53
2.64
79.1
1.52
36.57
Minimum of 3σ difference in %
220
Chapter 7
Supporting information
Key to Excel File containing Raw and Analyzed Data. CD attached with the thesis
contains Excel file
Sheet name
C1LC
C1LE
C1TC
C1TE
C2TE
C2TC
C2LE
C2LC
C3LE
C3LC
C3TE
C3TC
A1LC
A1LE
A1TC
A1TE
A2LC
A2LE
A2TC
A2TE
A3LC
Description
Raw data from Mascot, control 1, E. coli, digestion with
endopeptidase LysC, fragmentation method CID
Raw data from Mascot, control 1, E. coli, digestion with
endopeptidase LysC, fragmentation method ETD
Raw data from Mascot, control 1, E. coli, digestion with trypsin,
fragmentation method CID
Raw data from Mascot, control 1, E. coli, digestion with trypsin,
fragmentation method ETD
Raw data from Mascot, control 2, E. coli, digestion with trypsin,
fragmentation method ETD
Raw data from Mascot, control 2, E. coli, digestion with trypsin,
fragmentation method CID
Raw data from Mascot, control 2, E. coli, digestion with
endopeptidase LysC, fragmentation method ETD
Raw data from Mascot, control 2, E. coli, digestion with
endopeptidase LysC, fragmentation method CID
Raw data from Mascot, control 3, E. coli, digestion with
endopeptidase LysC, fragmentation method ETD
Raw data from Mascot, control 3, E. coli, digestion with
endopeptidase LysC, fragmentation method CID
Raw data from Mascot, control 3, E. coli, digestion with trypsin,
fragmentation method ETD
Raw data from Mascot, control 3, E. coli, digestion with trypsin,
fragmentation method CID
Raw data from Mascot, azithromycin-treated sample 1, E. coli,
digestion with Lys-C, fragmentation method CID
Raw data from Mascot, azithromycin-treated sample 1, E. coli,
digestion with Lys-C, fragmentation method ETD
Raw data from Mascot, azithromycin-treated sample 1, E. coli,
digestion with trypsin, fragmentation method CID
Raw data from Mascot, azithromycin-treated sample 1, E. coli,
digestion with trypsin, fragmentation method ETD
Raw data from Mascot, azithromycin-treated sample 2, E. coli,
digestion with Lys-C, fragmentation method CID
Raw data from Mascot, azithromycin-treated sample 2, E. coli,
digestion with Lys-C, fragmentation method ETD
Raw data from Mascot, azithromycin-treated sample 2, E. coli,
digestion with trypsin, fragmentation method CID
Raw data from Mascot, azithromycin-treated sample 2, E. coli,
digestion with trypsin, fragmentation method ETD
Raw data from Mascot, azithromycin-treated sample 3, E. coli,
221
Chapter 7
A3LE
A3TC
A3TE
C1All
C2All
C3TAll
A1All
A2all
A3All
C1Summ
C2Summ
C3Summ
A1Summ
A2Summ
A3Summ
CvsG
CvsG_significant
Controls
Azithromycin
digestion with Lys-C, fragmentation method CID
Raw data from Mascot, azithromycin-treated sample 3, E. coli,
digestion with Lys-C, fragmentation method ETD
Raw data from Mascot, azithromycin-treated sample 3, E. coli,
digestion with trypsin, fragmentation method CID
Raw data from Mascot, azithromycin-treated sample 3, E. coli,
digestion with trypsin, fragmentation method ETD
Summary of all peptides from control 1, E. coli
Summary of all peptides from control 2, E. coli
Summary of all peptides from control 3, E. coli
Summary of all peptides from azithromycin-treated sample 1, E.
coli
Summary of all peptides from azithromycin-treated sample 2, E.
coli
Summary of all peptides from azithromycin-treated sample 3, E.
coli
Summary of all proteins from control 1, E. coli
Summary of all proteins from control 2, E. coli
Summary of all proteins from control 3, E. coli
Summary of all proteins from azithromycin-treated sample 1, E.
coli
Summary of all proteins from azithromycin-treated sample 2, E.
coli
Summary of all proteins from azithromycin-treated sample 3, E.
coli
Summary of proteins with quantification data comparing controls
against azithromycin-treated
Summary of statistically significant proteins with quantification
data comparing controls against azithromycin-treated
Difference in quantitative parameters of control E. coli
Difference in quantitative parameters of azithromycin-treated E.
coli
222
Chapter 8
A proteomic approach to the identification of drug targets in
Staphylococcus epidermidis
Zubida M Al-majdoub,*aJill Barber*a
*Michael Barber Centre for Mass Spectrometry, Manchester Interdisciplinary Biocentre
a
School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Manchester,
M13 9PT
Abstract
A proteomic approach was used to identify changes in protein concentration in
Staphylococcus epidermidis in response to gentamicin treatment. The most significant upregulated proteins were EBH and Q5HMY6, which are both involved in pathogenesis. This
contrasts with gentamicin treatment of Escherichia coli, in which ribosomal proteins are upregulated. Proteins EBH and Q5HMY6 are proposed as potential drug targets.
Introduction
Staphylococcus epidermidis (S. epidermidis) is increasingly recognized as true pathogen. Its
infections range from normal skin and soft tissue infections to serious infections such as
endocarditis in which S. epidermidis is the single most common cause1. Many reports show S.
epidermidis to cause infections of the bone, ear and urinary tract and meningitis.2,3
Figure 8.1: Structure of Gentamicin
The infections are usually long lasting and hard to eradicate. S. epidermidis comprises a
major part of the normal flora on human skin4 but becomes pathogenic when associated with
implanted body devices and can also cause disease in immunocompromised patients such as
cancer patients and neonates.5,6 The main cause of S. epidermidis is due to high uses of
223
Chapter 8
implant devices such as joint prostheses and central venous catheters.7 Antibiotic therapy of
S. epidermidis infections has become more difficult as a result of developing resistance over
the past years,8 especially since S. epidermidis can carry multiple antibiotic resistances by a
plasmid mediated mechanism.9,10 Most pathogenic strains of S. epidermidis show resistance
to chloramphenicol, clindamycin, macrolides, and tetracyclines. In general, penicillins,
cephalosporins, vancomycin and aminoglycosides are effective in treating infections caused
by staphylococci. 11
In this study we investigated the effect of gentamicin against S. epidermidis. Gentamicin
belongs to the aminoglycoside group of antibiotics and is a natural product, first isolated
1963 from Micromonospora.12 Gentamicin is especially effective in treatment of infections
caused by Gram negative bacteria but also important in treating staphylococcal infections,
especially hospital-acquired infections.
Aminoglycosides are known to inhibit bacterial protein synthesis, by binding to 16S rRNA of
the small ribosomal subunit, and interfering with translocation and with the high-fidelity
incorporation of amino acids13. In 2002, Mehta and Champney observed that
aminoglycosides can bind to a precursor of the 30S subunit and inhibit the ribosomal
assembly process.14 Resistance to aminoglycosides is common and is usually due to specific
aminoglycoside
inactivating
enzymes:
acetyltransferases,
adenylyltranferases
and
phosphotransferases.12 Antibiotic combinations are advantageous in overcoming bacterial
resistance, and synergistic combinations are especially beneficial when one partner is
gentamicin, a very effective but rather toxic drug. For this reason we have sought to identify
target proteins whose inhibitors will be synergizers with gentamicin. In previous work, we
have shown that E.coli responds to gentamicin challenge by up-regulating the ribosomal
proteins S2 and L10. Here we use similar proteomics-based methodology to explore the
effect of gentamicin on the proteome of a Gram-positive bacterium.
Results and discussion
Cells of the Gram positive bacterium, S. epidermidis were grown at 37 ºC in a Luria-broth
(LB) medium. As the cultures entered exponential growth (OD600 0.2), gentamicin was added
224
Chapter 8
to a final, sub-lethal concentration (0.5 µg/ mL). Cultures were monitored until the untreated
control and treated samples achieved two doublings (OD600 0.8) (Figure 8.2). The sub-lethal
concentration of gentamicin was chosen such that the growth rate of the cultures was not
significantly affected by the addition of gentamicin, and is based on preliminary experiments
described in the Supplementary Information. S. epidermidis is much more sensitive than
E.coli to gentamicin. For E. coli we used 6 µg/mL gentamicin.
The proteome of Staphylococcus epidermidis cytosol
The FASP (Filter-aided sample preparation) protocol15 was applied to triplicate control and
triplicate gentamicin-treated cultures of S. epidermidis. In this protocol, the protein fraction is
digested first with endopeptidase Lys-C and then with trypsin. We analysed both digests
separately by LC-MS/MS using alternating collision induced dissociation (CID) and electron
transfer dissociation (ETD) fragmentation methods. ETD helped to fragment peptides
resistant to CID; in general this method is better for fragmentation of larger peptides.
Figure 8.2: The effect of sublethal concentration of gentamicin
(0.5µg/ml) of Staphylococcus epidermidis. Error bars are so small that
they do not extend beyond the boundaries of the symbol
The resulting spectra were searched against the Staphylococcus epidermidis database using
the Mascot search engine. Table 8.1 shows that a slightly smaller number of peptides was
identified by ETD than by CID but the total number of peptides identified was increased by
225
Chapter 8
using both fragmentation techniques. Various methods of label-free quantification by mass
spectrometry have been explored in the literature. In our previous study about the effect of
gentamicin on E.coli, six parameters were considered for label-free semi-quantitative
measurements. In this work, we aim only to determine reliably whether a protein is up- or
down-regulated, not to make accurate estimates of the extent of the up- or down-regulation.
Thus we considered only three measurements (those judged most useful): the number of
peptides observed for each protein, the percentage of the total number of observed peptides,
and the average peptide score of the three most intense peptide signals. For each parameter
the mean and standard deviation of the three replicate cultures was calculated (Supplementary
information).
As discussed in chapter 6, the difference in mean percentage peptides was the preferred
parameter for determining whether there was a significant difference in protein concentration
in the two sets of samples. We required a minimum of 2 peptides per protein in all three
replicates of either control or treated samples, and peptide detection was defined as a Mascot
score of at least 15 for unique peptides. By these criteria, 327 and 298 proteins were detected
in treated and control samples, respectively (see supplementary information).
Table 8.1: Overall results in protein/peptide identification of control sample from S. epidermidis lysate.
Enzyme/dissociation method
Lys C/CID
Lys C/ETD
Try/CID
Try/ETD
Total
Number of proteins
87
75
81
80
174
Number of peptides
146
126
127
117
384
Number of residues
1599
1522
1442
1355
2693
The most significant changes were observed in 16 proteins, of which 3 were up-regulated and
13 down-regulated in treated samples (table 8.2). The proteins down-regulated on gentamicin
treatment include those involved in translation (EFG, EFTS and EFTU), energy metabolism
(G3P1, ALF1, KAD, PGK and Q5HND3). The chaperone protein, DNAK, and OHRL1,
which is also involved in the stress response, are down-regulated as are protein with
transferase activity (PTA and PTGA). PTA is an enzyme that catalyzes the conversion of
Acetyl coenzyme A (acetyl-CoA) and phosphate to coenzyme A (CoA) and acetyl phosphate
which serves as a high energy phosphate enzyme. While, PTGA catalyzes the
phosphorylation of incoming sugar and involved in glucose transport. Moreover,
226
Chapter 8
carbohydrate degradation proteins (Lyases) such as DEOC and HPS were down-regulated.
We have observed 3 proteins which were markedly up-regulated. One of these is an
uncharacterized protein (Q5HNF8) but the other two are both involved in pathogenesis.
Extra-cellular matrix-binding protein, Ebh, is a protein required by Staphylococcus organisms
to adhere to host extracellular matrix. It specifically binds to fibronectin, and Ebh is produced
as result of infections. The second protein that was up-regulated is Delta-hemolysin
(Q5HMY6), an exoprotein with a detergent effect on the membranes of various cell types
resulting in rapid cell lysis. The involvement of these proteins in pathogenesis suggests that
they could be potential drug targets. Their up-regulation in response to gentamicin treatment
makes them especially attractive. If up-regulation of Ebh and delta-hemolysin forms part the
adaptive response to gentamicin treatment, we may hypothesize that inhibitors of these
proteins may act synergistically with gentamicin.
These results were quite unexpected when compared with the effect of gentamicin on the E.
coli proteome. In the Gram negative organism, proteins necessary for survival are
upregulated, but in the Gram positive organism we see proteins necessary for pathogenesis
upregulated. We are now seeking to model these up-regulated proteins in silico and to
explore the possibility of using them as drug targets.
Acknowledgements
This work was supported by Libyan government. We thank Andrew Mcbain for the gift of
Staphylococcus epidermidis.
References
1. Dismukes, W. E.; Karchmer, A. W.; Buckley, M. J.; Austen, W. G.; Swartz, M. N.,
Prosthetic valve endocarditis: Analysis of 38 cases. Circulation 1973, 48, 365-377.
2. Huebner, M. D. J.; Goldmann, M. D. D. A., Coagulase-negative staphylococci: Role as
pathogens. Annual Review of Medicine 1999, 50, 223-236.
3. Miele, P. S.; Kogulan, P. K.; Levy, C. S.; Goldstein, S.; Marcus, K. A.; Smith, M. A.;
Rosenthal, J.; Croxton, M.; Gill, V. J.; Lucey, D. R., Seven cases of surgical native valve
endocarditis caused by coagulase-negative staphylococci: An underappreciated disease.
American Heart Journal 2001, 142, 571-576.
4. Rupp, M. E.; Archer, G. L., Coagulase-negative staphylococci: Pathogens associated with
medical progress. Clinical Infectious Diseases 1994, 19, 231-245.
227
Chapter 8
5. Kloos, W. E.; Bannerman, T. L., Update on clinical significance of coagulase-negative
staphylococci. Clinical Microbiology Reviews 1994, 7, 117-140.
6. Gemmell, C, G., Coagulase negative staphylococci and their role in infection, Sussman,
M., edn., Academic press, London, UK, 2001.
7. Barie, P. S., Antibiotic-resistant gram-positive cocci: Implications for surgical practice.
World Journal of Surgery 1998, 22, 118-126.
8. Yu, V. L.; Zuravleff, J. J.; Bornholm, J.; Archer, G., In-vitro synergy testing of triple
antibiotic combinations against Staphylococcus epidermidis isolates from patients with
endocarditis. Journal of Antimicrobial Chemotherapy 1984, 14, 359-366.
Protein-name
Protein
mass
Mean number
peptides
Mean number
% peptides
Cont- Treat Diffmean Cont- Treat Diffmean Cont- Treat Diffmean
rol
ed
(s)(σ)
rol
ed
(s)(σ)
rol
ed
(s)(σ)
17
2.33
9.98
4.3 0.98
7.38
560.4 30.73
2.54
3
5
0
7.04
1.26
0
7.0
323.4 0
2.9
EFTS
Elongation factor Ts
32605
DEOC
Deoxyribose-phosphate aldolase
23651
OHRL1
15457
2.67
0
6.51
0.69
0
4.31
HPS
Organic hydroperoxide resistance
protein-like 1
3-hexulose-6-phosphate synthase
22387
6.67
0
6.18
1.66
0
12.77
PTA
Phosphate acetyltransferase
35193
2.33
0
5.68
0.58
0
11.6
EBH
1050109
0.67
3.67
5.17
0.16 1.54
EFG
Extracellular matrix-binding
protein ebh*
Elongation factor G
77113
7.33
0
4.99
1.82
EFTU
Elongation factor Tu
43188
9.33 3.67
4.19
Q5HN
D3
Q5HM
Y6
PGK
Transaldolase
25793
2
0.33
4.07
Delta-hemolysin*
2819
0
5
4.1
Phosphoglycerate kinase
42827
0
2.43
0
2.95
220.8 0
7
4.23 38.03
1.99
2.34 1.54
2.86
630.4 111.6
3
2.17
0.51 0.13
2.24
72.87 3.6
1.34
5.23
3.82
2.63 0.17
5.72
0.33
0.67
3.26
2.86
0.75 0.13
2.92 0.3
2.7
3.45
2.84
1.15 0.13
3.29
24178
32973
Glyceraldehyde-3-phosphate
dehydrogenase 1
PTGA Glucose-specific phosphotransferase
enzyme IIA component
Q5HN Putative uncharacterized protein*
36282
4.67 0.33
18124
5.33
0
13
1.34
15928
1
6
0
0.26 2.56
0
0
1.22
312.1
3
2.09
10.67 0.33
3
12
13.8
124.3
3
585
7.91
0
0
Adenylate kinase
KAD
ALF1 Fructose-bisphosphate aldolase class 1
G3P1
Mean average
peptides Score
0
1.74
383.0
7
348.4 1.67
7
309.5 1.77
588.6 6.17
3.54
1.89
22.33
205.5 2.1
7
288.7 0
2.29
8.21
46.87 316.3
2.64
2.96
2.06
2.43
F8
Table 8.3: Most significant up-regulated (*) and down-regulated proteins of control and
gentamicin-treated Staphylococcus epidermidis. (σ)=standard deviation, minimum of 5σ
difference in % peptides,
considered to be significant changes.
228
Chapter 8
Supplementary Information for:
A proteomic approach to the identification of drug targets in
Staphylococcus epidermidis
Zubida M Al-majdoub,*aJill Barber*a
*Michael Barber Centre for Mass Spectrometry, Manchester Interdisciplinary Biocentre
School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Manchester,
M13 9PT
a
Table of Contents
SI-1 Growth of Staphylococcus epidermidis cells and cell lysis………………………...230
SI-2 Detergent removal and protein Lys C/ trypsin digestion……………………….......231
SI-3 Sample analysis by Amazon ion trap ESI MS coupled with LC MS/MS…….........232
SI-4 Database search……………………………………………………………………..233
SI-5 Key to data in Excel spreadsheet, raw data and analyzed data……..........................233
229
Chapter 8
SI-1 Growth of Staphylococcus epidermidis cells and cell lysis
Staphylococcus epidermidis wildtype bacteria were kindly provided by Dr Andrew McBain,
University of Manchester (UK). The sub-lethal concentration was determined by treating the
cells with different concentrations of gentamicin (0.1, 0.5, 1.0, 1.5, 2.5 and 4.0 µg/mL) and
growth was monitored until the untreated control reached stationary phase (Figure 3). Growth
of the S. epidermidis was severely inhibited in the presence of gentamicin at 1.0, 1.5, 2.5 and
4.0 µg/mL. There was not much difference between growth without antibiotic and growth at
0.1 µg/mL. The MIC of gentamicin in different strains of S. epidermidis was determined in
the range of 0.0063-0.05 µg/mL8. The 0.5 µg/mL concentration caused a non-significant
reduction in the growth rate and did not inhibit growth completely over OD range between
0.2-0.8 (Figure 2). This concentration was considered as sublethal and was examined for
target identification.
Stationary phase cultures of S. epidermidis were used to inoculate 50 mL of Luria Broth (LB)
medium to an initial A600 of 0.1. These cultures were grown until OD600 reached 0.2.
Gentamicin solution was added to a final sublethal concentration 0.5 µg/mL to the cultures,
which were further incubated with shaking at 37 ºC to mid-exponential phase (OD600 0.8).
Control and treated cultures (both in triplicate) were immediately harvested by centrifugation
at 4200 xg for 10 min, and the cell pellets were stored at -80 °C.
Pellets were thawed and then drained to remove as much liquid as possible. Cell pellets were
resuspended in BugBuster at room temperature using 5 ml of reagent per 1 g wet weight
cells. 200 µL protease inhibitor solution (prepared by dissolving a water-soluble protease
inhibitor tablet from RocheTM in 2 mL sterile water) was added to the suspensions, then 1 µL
(25 units) of Benzonase per mL of BugBuster reagent was added. The cell suspension was
incubated on a shaking platform at a slow setting for 25 min at room temperature. Insoluble
cell debris was removed by centrifugation at 16,000 g for 20 min at 4 °C. Soluble extracts
were transferred to fresh tubes. Samples were subjected to FASP (filter aided sample
preparation) protocol for proteomic analysis.
230
Chapter 8
4
Absorbance at 600 nm
3.5
3
2.5
2
1.5
1
0.5
0
0
50
100
150
200
time (min)
250
300
350
Figure 3: Comparison of different gentamicin concentrations on
the growth curve of Staphylococcus epidermidis
Color
Gentamicin conc
µg/mL
Turquoise
4.0
Yellow
2.5
Fuchsia
1.5
Red
1.0
Orange
0.5
Brown
0.1
Blue
0
SI-2 Detergent removal and protein Lys C/ trypsin digestion
Supernatants were processed by FASP protocol using an AmiconTM filtration device
(Millipore) with a 10,000 Da molecular mass cut-off. The filters were washed twice with 200
µL sterile water and centrifuged for 10 min each time then the protein extract (60 µL) was
added to the filter column which was centrifuged in a Microfuge for 30 min at 13,000 rpm.
Then 400 µL of freshly prepared 8 M urea in 0.1 M Tris-HCl, pH 8.5 was added to the filter.
The devices were centrifuged at 14,000 rpm for 40 min at room temperature. The addition of
urea buffer and centrifugation was repeated once. Cysteine residues on proteins were reduced
by the addition of 10 mM dithiothreitol (100 µL) in 8 M urea/0.1 M Tris-HCl, pH 8.5
231
Chapter 8
followed by 15 min incubation with mixing at 600 rpm at RT. Then 200 µL of 50 mM
iodoacetamide in 8 M urea/0.1 M Tris-HCl, pH 8.5 was added. The mixture was shaken at
600 rpm on a thermomixer for 1 min then incubated in the dark without mixing for 5 min and
centrifuged at 14,000 rpm for 30 min. The resulting concentrates were diluted with 200 µL 8
M urea/0.1 M Tris-HCl, pH 8.0 and concentrated again at 14,000 rpm for 30 min. 210 µL 8
M urea/0.1 M Tris-HCl, pH 8.0 was then added to the filter to dilute the concentrated protein
sample and then the filter was inverted into the collection tube and centrifuged for 2 min at
2000 rpm. Only 40 µL of protein lysate containing about 120 µg proteins was transferred into
the spin filter with a new set of collection tubes. 40 µL 8 M urea/0.1 M Tris-HCl, pH 8.0 was
added to dilute and thus prevent evaporation of sample protein solution. 25 µL (2.5 µg) of
enzyme Lys-C solution (0.1 µg/ µL) was added in the filter. This was shaken at 600 rpm for 1
min then incubated overnight (16-18 h) at 30 ˚C. The samples were then centrifuged at
14,000 rpm for 20 min and 100 µl of 0.5 M sodium chloride solution was added to the filter
which was centrifuged at 14,000 rpm for 20 min. 20 µL (~20 µg) of the Lys-C digest was
transferred into a sterile small Eppendorf tube and was diluted by a factor of four with 50
mM ammonium bicarbonate solution. 4 µL of trypsin enzyme (20 µg/200 µL) was added to
protein solution in the mass ratio 1:50. Following overnight digestion at 37 ˚C, peptides were
collected by centrifugation of the filter unit at 14,000 rpm for 20 min. Lys-C and tryptic
peptides were then cleaned separately using C18 Ziptips (Millipore, UK). The eluted peptide
mixtures (20 µL) in 70% acetonitrile/0.1 % Formic acid was dried completely and
subsequently reconstituted in 40 µL HPLC loading buffer (2 % acetonitrile and 0.1 % formic
acid) for LC MS/MS analysis.
SI-3 Sample Analysis by Amazon ion trap ESI MS coupled with LC MS/MS
All experiments were performed on an UltiMate® 3000 RSLCnano system (Dionex,
Germany) connected to an Amazon™ ion trap mass spectrometer (Bruker Daltonics, Bremen,
Germany). A RSLC nano system was equipped with C18 5 µm, 5-mm х 300- µm precolumn
and Acclaim PepMap RSLC C18, 2 µm, 15-cm х 75- µm analytical reversed phase nano
column. Mobile phase A was water with 0.1% formic acid, and mobile phase B was 0.1 %
formic acid in acetonitrile. The peptides were separated with a gradient of 4 % to 90 %
acetonitrile over 87 minutes, followed by a 10 minute wash at 90% acetonitrile, then 2
232
Chapter 8
minutes equilibration at 2% acetonitrile in preparation for the next sample for a total run-time
of 99 minutes. The mass spectrometer used was a spherical ion trap. Samples were introduced
through a distal-coated fused silica PicoTip emitter using a capillary voltage of 2 kV with
drying gas of 6 L per minute. The source temperature was set to 150 ºC. The mass range was
scanned with a scan rate of 8100 amu per second. The mass range applied to detection was
200-1800 m/z. Ions were accumulated in the trap until the ion charge count (ICC) reached
20000 with a maximum accumulation time of 200 ms. Ionized peptides eluting from the
capillary column were selected for CID, ETD and alternating CID/ETD fragmentation.
.
SI-4 Database search
MS/MS spectra were processed using Data Analysis 3.4 software and searched using
MASCOT 2.2.06 to extract and search CID and ETD data. CID and ETD data in .mgf format
were searched simultaneously. Lys-C and trypsin digest data were searched against
S.epidermidis database. The peptide digests searches were as the following: (1) Lys-C or
trypsin specificity based on the type of digest, missed cleavage 2, charges up to +2 and +3,
global modification of carbamidomethyl on cysteine with variable modification of oxidation
on methionine, and acetyl (protein N-term), and no restrictions on protein mass. The peptide
tolerance was set at 0.8 Da and MS/MS tolerance at ± 0.6 Da. for MS/MS, individual ion cut
off score was set as 15 (p < 0.05). The false discovery rate was calculated using a decoy
database (MASCOT) and was less than 5 %.
The data are arranged in the Excel sheet as shown. CD attached with this thesis contains
excel files.
Sheet name
C1LCcid
C1LCetd
C1Tetd
C1Tcid
C2Tetd
Description
Raw data from Mascot, control 1, Staphylococcus epidermidis,
digestion with endopeptidase LysC, fragmentation method CID
Raw data from Mascot, control 1, Staphylococcus epidermidis,
digestion with endopeptidase LysC, fragmentation method ETD
Raw data from Mascot, control 1, Staphylococcus epidermidis,
digestion with trypsin, fragmentation method ETD
Raw data from Mascot, control 1, Staphylococcus epidermidis,
digestion with trypsin, fragmentation method CID
Raw data from Mascot, control 2, Staphylococcus epidermidis,
digestion with trypsin, fragmentation method ETD
233
C2Tcid
C2LCetd
C2LCcid
C3LCetd
C3LCcid
C3Tetd
C3Tcid
G0.5ALCcid
G0.5ALCetd
G0.5ATcid
G0.5ATetd
G0.5BLCcid
G0.5BLCetd
G0.5BTcid
G0.5BTetd
G0.5CLCcid
G0.5CLCetd
G0.5CTcid
G0.5CTetd
Chapter 8
Raw data from Mascot, control 2, Staphylococcus epidermidis,
digestion with trypsin, fragmentation method CID
Raw data from Mascot, control 2, Staphylococcus epidermidis,
digestion with endopeptidase LysC, fragmentation method ETD
Raw data from Mascot, control 2, Staphylococcus epidermidis,
digestion with endopeptidase LysC, fragmentation method CID
Raw data from Mascot, control 3, Staphylococcus epidermidis,
digestion with endopeptidase LysC, fragmentation method ETD
Raw data from Mascot, control 3, Staphylococcus epidermidis,
digestion with endopeptidase LysC, fragmentation method CID
Raw data from Mascot, control 3, Staphylococcus epidermidis,
digestion with trypsin, fragmentation method ETD
Raw data from Mascot, control 3, Staphylococcus epidermidis,
digestion with trypsin, fragmentation method CID
Raw data from Mascot, gentamicin-treated sample A,
Staphylococcus epidermidis digestion with Lys-C, fragmentation
method CID
Raw data from Mascot, gentamicin-treated sample A,
Staphylococcus
epidermidis,
digestion
with
Lys-C,
fragmentation method ETD
Raw data from Mascot, gentamicin-treated sample A,
Staphylococcus
epidermidis,
digestion
with
trypsin,
fragmentation method CID
Raw data from Mascot, gentamicin-treated sample A,
Staphylococcus
epidermidis,
digestion
with
trypsin,
fragmentation method ETD
Raw data from Mascot, gentamicin-treated sample B,
Staphylococcus
epidermidis,
digestion
with
Lys-C,
fragmentation method CID
Raw data from Mascot, gentamicin-treated sample B,
Staphylococcus
epidermidis,
digestion
with
Lys-C,
fragmentation method ETD
Raw data from Mascot, gentamicin-treated sample B,
Staphylococcus
epidermidis,
digestion
with
trypsin,
fragmentation method CID
Raw data from Mascot, gentamicin-treated sample B,
Staphylococcus
epidermidis,
digestion
with
trypsin,
fragmentation method ETD
Raw data from Mascot, gentamicin-treated sample C,
Staphylococcus
epidermidis,
digestion
with
Lys-C,
fragmentation method CID
Raw data from Mascot, gentamicin-treated sample C,
Staphylococcus
epidermidis,
digestion
with
Lys-C,
fragmentation method ETD
Raw data from Mascot, gentamicin-treated sample C,
Staphylococcus
epidermidis,
digestion
with
trypsin,
fragmentation method CID
Raw data from Mascot, gentamicin-treated sample C,
Staphylococcus
epidermidis,
digestion
with
trypsin,
234
Chapter 8
C1All
C2All
C3All
G0.5AAll
G0.5BAll
G0.5CAll
C1Summ
C2Summ
C3Summ
G0.5ASumm
G0.5BSumm
G0.5CSumm
CvsG
Controlall
Gentall
ContvsGent
fragmentation method ETD
Summary of all peptides from control 1, Staphylococcus
epidermidis.
Summary of all peptides from control 2, Staphylococcus
epidermidis.
Summary of all peptides from control 3, Staphylococcus
epidermidis.
Summary of all peptides from gentamicin-treated sample A,
Staphylococcus epidermidis.
Summary of all peptides from gentamicin-treated sample B,
Staphylococcus epidermidis.
Summary of all peptides from gentamicin-treated sample C,
Staphylococcus epidermidis.
Summary of all proteins from control 1, Staphylococcus
epidermidis.
Summary of all proteins from control 2, Staphylococcus
epidermidis.
Summary of all proteins from control 3, Staphylococcus
epidermidis
Summary of all proteins from gentamicin-treated sample A,
Staphylococcus epidermidis
Summary of all proteins from gentamicin-treated sample B,
Staphylococcus epidermidis
Summary of all proteins from gentamicin-treated sample C,
Staphylococcus epidermidis
Summary of statistically significant proteins with quantification
data comparing controls against gentamicin-treated
Summary of statistically significant proteins with quantification
data of all controls samples
Summary of statistically significant proteins with quantification
data of all gentamicin-treated samples
Most significant difference in quantitative parameters between
control and gentamicin-treated
235
Chapter 9
9.0-Conclusions
Up and down regulation of proteins is one of indicative changes that occur as a result of
disease progression. Measuring these changes in protein concentration is of great interest for
drug targets and biomarkers discovery. Relative quantification of proteins (diseased vs.
”normal”) measures fold-change while absolute quantification determines protein abundance
in terms of tangible concentrations. The first goal of the work presented in this thesis was to
use ribosomal QconCATs and label-free approaches for determination of ribosomal protein
stoichiometry within a ribosome. To satisfy this purpose, proteomics strategies, in general,
and mass spectrometry, in particular, were used.
In chapter 1, an introduction about the importance of ribosome as is an essential component
of the bacterial cell, responsible for all its protein synthesis. Moreover, the structure, function
conservations of ribosomal proteins and the effect of several antibiotics on ribosomes were
discussed in the same chapter. Also we are given a general overview of typical workflows
used for protein/peptide identification and characterization in mass spectrometry based
proteomics. Next, liquid chromatography mass spectrometry is described where general
peptide pre-fractionation and purification techniques are mentioned, for instance, as an
approach to reduce sample complexity and increase peptide identification. Different mass
spectrometers and parts of the instruments are described, as well as different fragmentation
techniques. In addition, a general description of the data analysis used for the generated mass
spectra of the protein and proteolytic peptides. Last, relative and absolute quantification
based proteomics approaches are described.
Chapter 2 describes the common experimental methods used within the work. Generally,
during this work proteomic methods have been used to quantify ribosomal proteins. Methods
using isotopic labels and label-free methods have been used, and quantification has been
relative or absolute depending upon the circumstances.
In chapter 3, we evaluate different approaches to reduce the 70S ribosomal protein
complexity and increase peptide identification. The identification of Q-peptides for the
bacterial ribosomal QconCAT involved the use of many methods for separating both proteins
and peptides. These methods included SDS-electrophoresis, OFFgel and RNA extraction
methods.
236
Chapter 9
Most importantly, we evaluated a digestion strategy based on sequential Lys C/trypsin
digestion on small basic ribosomal proteins. Lys C is a useful protease aiding in the increase
number of identified peptides suitable as signature peptides. The combined enzyme strategy
was successful with the small basic proteins and allowed the generation and detection of
suitable signature peptides for designing QconCATs. The use of Lys C reduced the number
of ragged ends in the enzyme-generated peptides. We further showed that it is possible to
separate and fractionate Lys C and tryptic generated peptides of ribosomal proteins sample by
OFFgel under various protocols. Prefractionation using OFFgel techniques increased the
number of proteins identified dramatically. Overall, the combinations of all the above
approaches enabled us to generate a list of signature peptides for 30S and 50S ribosomal
protein QconCAT.
Chapter 4 concerned the development of methodology for the sensitive and accurate
quantification of bacterial 30S and 50S ribosomal proteins. The bacterial ribosome is
arguably the single most important drug target for antibacterial action. Thousands of
scientists worldwide are active in trying to understand its assembly, function, structural
biology and its complexes with a plethora of other proteins, estimated currently at nearly 200.
Its structural studies attracted the 2009 Nobel Prize for Chemistry. Ribosome function, in
particular the conformational changes accompanying the arrival of different factors on the
L7/L12 stalk, have not been fully elucidated. Intermediates in translocation are still being
discovered. Clearly a huge molecular structure such as the ribosome needs to be assembled,
and the assembly is complex. Multiple pathways contribute to ribosome assembly. All of
these investigations will be aided by a better understanding of stoichiometry of ribosomal
protein.
Therefore, chapter 4 describes the development and validation of QconCAT (the 30S
QconCAT) for determining ribosomal stoichiometry in E. coli. An important extension to
QconCAT technology was developed during this work. The choice of conditions for
complete digestion strongly affected the results for the quantification; therefore, I have
optimized the digestion protocols by using different denaturing conditions and changing
protease concentration. Moreover, the 30S QconCAT has been used to study changes in
ribosomal protein expression under the influence of gentamicin and this compared with
untreated samples.
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Future work with ribosomal QconCAT should involve LC-coupled MRM experiments. Peak
overlaps that we encountered in the quantification experiments caused by co-eluting peptides
and can be neglected by choosing several MRM transitions specific for a QconCAT and
endogenous precursor. Operating the first and the third quadrupole as a mass filters
guarantees that only the chosen MRM transitions are monitored and co-eluting peptides do
not influence the quantitative signal, and any undetected peptide will be observed by this
method.
In chapter 5, the signature peptides of 50S ribosomal proteins identified in chapter 3 were
then used to design 50S QconCAT proteins for absolute quantification of 50S ribosomal
proteins stoichiometry. The identification of 50S QconCAT peptides was achieved through
RP-LC-MS/MS using an LTQ-Orbitrap MS. For quantitative studies, control and
azithromycin-treated ribosomal protein samples were mixed with known amount of 50S
QconCAT and then analyzing LC MS/MS. All 50S QconCATs peptides showed insufficient
intensity for quantitative analysis. Further work is required to quantify 50S ribosomal
proteins in control and azithromycin-treated samples. The determination of 50S ribosomal
proteins stoichiometry will require the use of more amounts of 50S QconCAT and MRM
technique.
As the incidence of antibiotic resistant infections is on the rise, while the discovery of
effective treatments is on a sharp decline, we continue to search for measures to combat
prevalent bacteria, such as E.coli and S. epidermidis.
In chapter 6, we decided to approach the problem of antibiotic resistance directly via finding
drug targets that work as synergy with existing antibiotics. We focused on gentamicin
because use of this excellent antibiotic is restricted by its narrow therapeutic index. The
ribosome is a particularly well-validated antibacterial target. Ribosomes are the target for
about half of the total number of antibiotics characterized to date. Our goal was to exploit
such anti-ribosomal drugs such as gentamicin to see if we can identify a synergistic protein
targets. Before these targets could be evaluated however, it was necessary to conduct several
studies such as sensitivity test and proteomic method with mass spectrometry. In this chapter,
label-free approaches combined with mass spectrometry was valuable in identifying a drug
targets through the capacity to analysis protein expression changes of E.coli under aerobic
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and oxygen-limited conditions bacteria following exposure to the sub-lethal concentration of
gentamicin, this approach easily adapted and do not require any expensive reagents. Since
gentamicin is a class of translational inhibitor, one might expect to see changes in proteins
involved in translation. The most striking features in both aerobic and oxygen-limited
cultures are up-regulation of ribosomal proteins such as L1, L10, and S2.
Ribosomal proteins L1, L10 and S2 were in both cases up-regulated, suggesting that these
might be drug targets for compounds synergistic with gentamicin. Validation study was done
for ribosomal proteins L1 and L10 by our collaborators; 10 binding ligands were identified as
potential ligands for L1. One of small molecule identified is Mupirocin, this drug binds to
ribosomal protein L1. Further work is required to test the synergistic effect of Mupirocin with
gentamicin.
Chapter 7 described how biological techniques combined with mass spectrometric strategy
were used to address the identification a novel drug targets after challenging the E.coli
cultures with azithromycin. Before proteomic studies, the sub-lethal concentration of the
azithromycin was determined which allows change of proteome without effect on the growth
of E.coli bacteria. Exposure of cultured E.coli to azithromycin, an inhibitor of protein
synthesis, caused an increase in abundance of both L6 and L11 ribosomal proteins suggesting
that these might be drug targets for compounds synergistic with azithromycin.
Chapter 8, gram-positive organisms are causing more and more serious infections than ever
before in surgical patients and the efficacy of most current antibiotics for treatment of S.
epidermidis infections has become quite limited due to the occurrence of multiple resistant
strains. Therefore, in order to overcome these problems, a proteomic analysis was achieved to
study the effect of sub-lethal concentration of gentamicin on the proteome of S. epidermidis.
In this study, an increase in extra-cellular matrix-binding protein (Ebh) and Delta-hemolysin
(Q5HMY6) expression were observed. These results were quite unexpected when compared
with the effect of gentamicin on the E. coli proteome. In the Gram negative organism,
proteins necessary for translation are up-regulated, but in the Gram positive organism we see
proteins necessary for pathogenesis increased in abundance. Further studies need to be done
such as molecular modelling for theses nominated drug targets to evaluate these findings.
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The future work can be done for chapter 6, 7 and 8 is molecular modelling of ribosomal L1,
L10, S2, Ebh and Q5HMY6 with a view to identifying small molecule inhibitors of these
proteins. Also identify small molecule inhibitors for L11 and L6. The hope is that they will be
synergistic with gentamicin and azithromycin, respectively, validating this approach to drug
discovery. Several binding ligands have been identified for ribosomal protein L1 and L10 and
the aim is to test the synergistic effect of these ligands with gentamicin. It would also be
possible to improve our mass spectrometric measurements, by explore other label-free
approach to achieve greater accuracy. However, much more important than accuracy is
throughput. We want to make many measurements for a single drug-organism combination.
Taken as a whole, this thesis describes novel methods for quantification of ribosomal
proteins, and explores the insights deriving from these proteomic methodologies. Although
ribosomes undergoing X-ray diffraction studies are presumed to have stoichiometric
quantities of ribosomes, our results show that not all ribosomes are perfect. S1 is downregulated, S2 is up-regulated and the very high precision of our data leads us to believe that
there are small variations in stoichiometry of other ribosomal proteins as well.
The treatment of bacteria with anti-ribosomal drugs leads to changes in protein expression
and this too can be monitored using proteomics methodology. Gentamicin and azithromycin
treatment led to upregulation of ribosomal proteins in Gram negative bacteria. Surprisingly,
the treatment of a Gram-positive organism with gentamicin led to upregulation of proteins
involved in pathogenesis.
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