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Comprehensive Studies On the Outer Membrane Subproteome of
Caulobacter crescentus Using Mass Spectrometry Based
Shotgun Proteomics
A dissertation presented
by
Yuan Cao
B.S., Peking University, Beijing, China, 2001
to
The Graduate Studies Council
Department of Chemistry
Brown University
in partial fulfillment of the requirements
for the degree of
Doctor of Philosophy
in the subject of
Chemistry
Brown University
Providence, Rhode Island
May 2013
© Copyright 2012 by Yuan Cao
All Rights Reserved
This dissertation by Yuan Cao is accepted in its present form by
the Department of Chemistry as satisfying the dissertation requirements for
the Degree of Doctor of Philosophy
Date ________________
______________________________
Dr. Carthene R. Bazemore-Walker, Director
Recommend to the Graduate Council
Date ________________
______________________________
Dr. J. William Suggs, Reader
Date ________________
______________________________
Dr. Arthur R. Salomon, Reader
Approved by the Graduate Council
Date ________________
______________________________
Dr. Peter M. Weber, Dean of the Graduate School
‐ iii ‐ Curriculum Vitae
Yuan Cao
Tel.: 401-865-9380
E-mail: [email protected]
Date of Birth: 11/11/1982
Education
·Department of Chemistry, Brown University
Ph. D. in Chemistry (05/2012)
Overall GPA 3.95
·College of Chemistry and Molecular Engineering, Peking University
B. S. in Chemistry (07/ 2005)
Overall GPA 3.50
Major GPA 3.66
·Dongzhimen High School, Beijing
Publications
y Cao, Yuan, Johnson, Helen M. and Bazemore-Walker, Carthene R. “Improved
Enrichment and Proteomics Identification of Outer Membrane Proteins from a
Gram-Negative Bacterium: Focus on Caulobacter crescentus”. Proteomics. 2011. doi:
10.1002/pmic.201100288.
y
Poston, Chloe N., Duong, Ellen, Cao, Yuan and Bazemore-Walker, Carthene R.
“Proteomic Analysis of Lipid Raft-enriched Membranes Isolated from Internal
Organelles”. Biochemistry and Biophysical Research Communications. 2011. doi:
10.1016/j.bbrc.2011.10.07.
y
Cao, Yuan and Bazemore-Walker, Carthene R. “Deletion of the HfsA Exopolysaccharide
Export Protein Changes the Composition of the Outer Membrane Proteome of
Caulobacter crescentus”. 2012. In preparation.
y
Cao, Yuan and Bazemore-Walker, Carthene R. “Global profiling of the cell surface
proteome of Caulobacter crescentus via biotinylation”. 2012. In preparation.
y
Hou, Sicong, Cao, Yuan, Xiong, Wei, Liu, Haichao and Kou, Yuan. “Site Requirements
for the Oxidative Coupling of Methane on SiO2-Supported Mn Catalysts”. Ind. Eng.
Chem. Res., 2006.
y
Hou, Sicong, Cao, Yuan, Xiong, Wei, Liu, Haichao and Kou, Yuan. “In Situ XRD Study
on Sodium Salt-Modified Mn/SiO2 Catalysts for the Oxidative Coupling of Methane”.
Chinese Journal of Catalysis, 2006.
‐ iv ‐ Curriculum Vitae
Teaching Experiences
·Teaching Assistantship of Chemistry 33 and Chemistry 50, Brown University
(09/2006-07/2011)
·Teaching Assistantship of Chemistry 166, Brown University (02/2012-05/2012)
Honors and Awards
·Bruker Daltonics Travel Grant Award, University of New Hampshire (June 2008)
·Fellowship Award, Brown University (01/2007-09/2007)
·Excellent Thesis Award, Peking University (06/2005)
‐ v ‐ Abstract of comprehensive studies on the outer membrane subproteome of Caulobacter
crescentus using mass spectrometry based shotgun proteomics, by Yuan Cao, Ph. D.,
Brown University, May, 2013.
Efforts to characterize proteins found in the outer membrane of gram-negative bacteria
have been steadily increasing due to the promise of expanding our understanding of fundamental
bacterial processes such as cell wall biogenesis as well as the promise of finding potential
vaccine- or drug-targets for virulent bacteria. Meanwhile, rapid development in mass
spectrometry based proteomics, especially quantitative analysis of targeted proteome, received
increased research interest as it provides information showing temporal and spatial change of a
particular protein within the proteome.
We have developed a mass spectrometry-compatible experimental strategy that resulted
in increased coverage of the outer membrane (OM) proteome of a model organism, Caulobacter
crescentus. The specificity of the OM enrichment step was improved by using detergent
solubilization, low-density cell culture, and a surface-layer deficient cell line. Additionally,
efficient gel-assisted digestion, high resolution RP/RP-MS/MS led to the identification of 234
proteins using strict identification criteria. Eighty-four of the detected proteins were predicted to
localize to the OM or extracellular space, which accounted for ~77% of the total relative
abundance in the OM fraction. In addition, biotinylation of bacterial surface-exposed proteins has
been applied as an alternative approach to characterize bacterial membrane subproteome. Labeled
proteins were purified by immobilized avidin and identified using LC/MS/MS. There were 91
putative surface-exposed membrane proteins identified in this work, out of which 63 (70%)
proteins were also identified in the previous OM fraction.
These comprehensive analytical approaches, which considers important experimental
variables not previously explored in published outer membrane protein studies, can be applied to
‐ vi ‐ other OM proteomic endeavors “as is” or with slight modification and should improve the largescale study of this especially challenging subproteome.
‐ vii ‐ Acknowledgements
First and foremost, I would like to express my deepest appreciation to my
supervisor, Professor Carthene R. Bazemore-Walker, for granting me the opportunity to
study in her research group and for her invaluable guidance, inspiration, encouragement
and advice during the course of my research. It has been a wonderful 5-year journey for
me and I have learned a significant amount from her and I am sure it will continue to
benefit my life and career.
I would like to thank the other members of my supervisory committee, Professor
J. William Suggs, Professor Arthur R. Salomon, Professor Dwight A. Sweigart and
Professor Peter M. Weber, for their active participation during my oral examinations,
their thorough reviews and comments on this thesis, and their valuable advice on my
research.
My deep gratitude goes to the people with whom I collaborated. I especially thank
Professor Jay. X. Tang and Research Assistant Professor Guanglai Li, from the
Department of Physics at Brown University for their guidance on C. crescentus cell
cultures and fluorescent microscopy. I am very grateful to Professor Yves Brun and Dr.
Gail Hardy from the Department of Microbiology at Indiana University for creating the
RsaA- and RsaA- + HfsA- mutant strains of C. crescentus which were used throughout all
three projects. Many thanks to Professor Arthur Salomon from Molecular Biology, Cell
Biology, & Biochemistry and Chemistry Department at Brown University and Dr. James
Cliffton from Rhode Island NSF/EPSCoR Center for Proteomics at Brown University for
instruction on running LTQ-FT and LTQ-Orbitrap Velos mass spectrometers. I also thank
‐ viii ‐ Dr. Tun-Li Shen for his professional training on the MALDI instrument used for
preliminary method development on gel-assisted digestion and solid phase extraction of
glycopeptides. I am also grateful to former undergraduates Seth Levin, Helen M. Johnson
at Brown University for their assistance in biological sample preparations.
I would like to thank many members in Professor Bazemore-Walker's research
group including Dr. Hongbo Gu and Dr. Chao Gong for their practical advice on
performing instruments and protocols. The friendship with them goes along with my
whole life. My appreciation also extends to other members in the group, in no particular
order, Chloe N. Poston, Michael Ellisor, Yiying Zhu, Zhuo Chen and Shuomin Yao for
their helpful discussions and assistance.
I would also like to thank the departmental staff in the general and purchasing
offices, the mailroom, and the electronics and machine shops for their kind help.
I especially thank my parents, Mr. Guangcai Cao and Mrs. Peizhen Lu for their
endless love, understanding, patience and encouragement over these years. I realize how
big a sacrifice they have made while I grew up, studied abroad and completed my degree.
I will be forever grateful for that.
And of course, I wish to express my gratitude to my girlfriend Yifan Zhang for
her admirable patience and support, and for tolerating my unreasoning and ignoring
behavior in the last intensive month of my Ph.D. study.
‐ ix ‐ Contents
Chapter 1 Introduction.............................................................................................. 1
1.1 Caulobacter crescentus....................................................................................... 2
1.2 Why Apply Proteomic Technologies for Characterizing Proteins from Complex
sBiological System?..................................................................................................... 4
1.3 MS-based Shotgun Proteomics ........................................................................... 6
Chapter 2 Improved Enrichment and Proteomic Identification of Outer
Membrane Proteins from a Gram-negative Bacterium: Focus on
Caulobacter crescentus ............................................................................................. 19
2.1 Introduction....................................................................................................... 20
2.2 Matrails and Mehtods ....................................................................................... 22
2.2.1 Bacterial Growth and Cell Lysis....................................................... 22
2.2.2 Enrichment of C. crescentus OMPs.................................................. 22
2.2.3 SDS-PAGE and Gel Staining............................................................ 23
2.2.4 In-gel Tryptic Digestion.................................................................... 24
2.2.5 Gel-assisted (Ga) Digestion .............................................................. 24
2.2.6 Wheat Germ Agglutinin (WGA) Western Blotting .......................... 25
2.2.7 High-pH Reversed-phase (RP) HPLC .............................................. 25
2.2.8 LC-MS/MS Analysis ........................................................................ 26
2.2.9 Database Searching........................................................................... 26
2.2.10 Spectral Counting and Statistical Analysis ..................................... 27
2.2.11 Bioinformatics................................................................................. 28
2.3 Results.............................................................................................................. 28
2.3.1 SLS Successfully Isolates OMs from C. crescentus......................... 28
2.3.2 Gel-assisted (Ga) Digestion coupled to RP/RP-MS/MS (2DLC)
Results in Increased C. crescentus OMP Identifications .......................... 33
‐ x ‐ 2.3.3 Low Density Culture Improves C. crescentus OMP Extraction
Specificity .................................................................................................. 37
2.3.4 Deletion of the Surface-Layer Protein Improves C. crescentus OM
Proteome Coverage.................................................................................... 38
2.3.5 Nearly 70% of the Expected C. crescentus OM/extracellular
Proteome is Detected Using the Optimized Procedure.............................. 41
2.4 Discussion ......................................................................................................... 47
2.5 References......................................................................................................... 53
Chapter 3 Identification of Putative Surface-Exposed Proteins of
C. crescentus Using a Biotinylation Approach ...................................................... 60
3.1 Introduction....................................................................................................... 61
3.2 Matrails and Mehtods ....................................................................................... 64
3.2.1 Bacterial Growth and In vivo Cell Surface Labeling........................ 64
3.2.2 Affinity Capture of Labeled Cell Surface Proteins/Peptides ............ 65
3.2.3 Streptavidin Western Blotting........................................................... 66
3.2.4 In-solution Tryptic Digestion............................................................ 67
3.2.5 nanoLC-MS/MS Analysis................................................................. 67
3.2.6 Protein Identification by Database Search........................................ 68
3.2.7 Bioinformatics................................................................................... 69
3.3 Results.............................................................................................................. 69
3.3.1 Visualization of C. crescentus Surface Proteins ............................... 69
3.3.2 Identifications of Biotinylated Proteins by LC-MS/MS: Protein-level
Analysis...................................................................................................... 71
3.3.3 Identifications of Biotinylated Proteins by LC-MS/MS: Peptide-level
Analysis...................................................................................................... 77
3.3.4 Functional Characterization of Biotinylated Proteins from RsaA- ... 81
‐ xi ‐ 3.4 Discussion ......................................................................................................... 84
3.5 References......................................................................................................... 92
‐ xii ‐ List of Figures
Figure 1.1: Schematic of C. crescentus cell cycle………..................................................3
Figure 1.2: General MS-based proteomics workflow.........................................................6
Figure 1.3: Schmetic diagram of QSTAR Elite (ESI-QTOF) mass spectrometer
(ABSciex) in our lab……………………………………..................................................10
Figure 1.4: The nomenclature and structure of sequence-specific fragmentation ions in
MS/MS………………………………………………………………………....................11
Figure 1.5: Identification of an outer membrane protein, RsaF using 2DLC-MS/ MS….14
Figure 2.1: Schematic diagram of OM enrichment procedure.........................................29
Figure 2.2: The OM fraction, which contains holdfast, is most successfully isolated using
SLS.....................................................................................................................................31
Figure 2.3: Na2CO3 does not effectively separate the OM fraction from IM fraction….32
Figure 2.4: Overview of the GeLC and Ga2DLC methods..............................................34
Figure 2.5: Graphical representation of the predicted/annotated subcellular distribution
of proteins identified in enriched OM fractions from C. crescentus using either the GeLC
or Ga2DLC method............................................................................................................36
Figure 2.6: Graphical representation of the predicted/annotated subcellular distribution
of proteins identified in enriched OM fractions from C. crescentus using either WT or
RsaA- cells.........................................................................................................................38
Figure 2.7: Bacterial growth profiles for WT and RsaA- C. crescentus cells..................40
Figure 2.8: Venn diagram illustrating the common and uniquely identified OMPs across
‐ xiii ‐ all four datasets..................................................................................................................42
Figure 2.9: The majority of the identified OMPs are conserved hypothetical/hypothetical
proteins, transport/binding proteins, or cell envelope proteins..........................................43
Figure 2.10: Distribution of proteins containing (A) α-helical or (B) β-barrel motifs
along with calculated (C) GRAVY values……………………………………………….45
Figure 2.11: Venn diagram illustrating the overlapping and uniquely identified proteins
using either the GeLC or gel-assisted methodologies........................................................50
Figure 2.12: Venn diagrams illustrating the common and uniquely identified proteins
between our studies and Dr. Phadke’s work......................................................................52
Figure 3.1: Schematic of biotinylation strategies for identification of surface-exposed
proteins using MS-based shotgun proteomics...................................................................63
Figure 3.2: Schematic diagram of biotinylation labeling and enrichment strategy……..64
Figure 3.3: Cell surface exposed proteins were successfully labeled by biotin reagents.70
Figure 3.4: Venn diagram representation of protein identifications in both protein-level
and peptide-level purified fractions (negative controls)....................................................72
Figure 3.5: A MS/MS spectrum of a biotinylated peptides assigning to the TonBdependent receptor, a typical OMP in C. crescentus………………………….................79
Figure 3.6: Venn diagrams illustrating the common and uniquely identified biontinylated
proteins using both Q-TOF and LTQ-Orbitrap LC-MS/MS..............................................80
Figure 3.7: Graphical representation of the predicted/annotated subcellular distribution
of biotinylated proteins identified a peptide-level purified fractions from C. crescentus
‐ xiv ‐ RsaA- using Q-TOF or LTQ-Orbitrap……………………...............................................81
Figure 3.8: Functional classification of total 91 putative surface-exposed proteins
identified using both Q-TOF and LTQ-Orbitrap method..................................................82
Figure 3.9: Distribution of biotinylated proteins with tranmembrane α-helices………..83
Figure 3.10: Venn diagrams illustrating the common and uniquely identified proteins
between biotinylation experiments and OM isolation experiments...................................88
Figure 3.11: Venn diagram representation of protein identifications in ‘protein-level’
purified biotin labeling fractions in RsaA-…………………………………………...…..73
Figure 3.12: Graphical representation of the predicted/annotated subcellular distribution
of biotinylated proteins identified in protein-level purified fractions from C. crescentus
RsaA- using Q-TOF……………………………………...................................................74
Figure 3.13: FtsZ forms a ring-structure to mediate the peptidoglycan (PG) synthesis
near midcell compartment during C. crescentus cell cycle…………………….………..91
‐ xv ‐ List of Tables
Table 2.1: Summary of proteins detected in C. crescentus OM fractions……………………...35
Table 3.1: Summary of detected biotinylated proteins in C. crescentus RsaA- fractions
using Q-TOF and LTQ-Orbitrap........................................................................................78
Table 3.2: Summary of C. crescentus proteins identified in both biotinylation
experiments and in OM isolation experiments…………………….....................................85
Table 3.3: Summary of C. crescentus proteins identified in protein-level biotinylation
experiments………………………………………………………………………....…………...75
‐ xvi ‐ Chapter1
INTRODUCTION
‐ 1 ‐ 1.1 Caulobacter crescentus
Caulobacter crescentus is an Gram-negative, alpha-proteobacterium first isolated
from fresh water [1]. One of the first features noticed about this organism was that after
cell division, the two daughter cells appeared different: only one end contained a stalked
appendage while the other end contained a single polar flagellum. This morphological
difference implied that each daughter cell was differentiated to serve a specific purpose.
The asymmetric morphology during cell cycle makes C. crescentus an excellent model
for the study of bacterial cell morphogenesis [2].
C. crescentus begins life as a motile swarmer cell with a single polar flagellum
and polar pili. The swarmer cell grows and then differentiates into a stalked cell,
shedding its flagellum, retracting its pili, and synthesizing a nutrient-uptaking stalk at the
pole at the former site of the flagellum. The stalked cell then replicates its chromosome,
becoming a predivisional cell that synthesizes a new flagellum at the opposite pole from
the stalk and initiates cell constriction. Finally, cytokinesis occurs, producing one
swarmer progeny and one, slightly longer, stalked cell that each enters the cell cycle at
their respective stages (Fig. 1.1) [2, 3]. Stalked cells attach tightly to substrate surfaces
via the holdfast, an adhesive organelle located at the end of the stalk. The C. crescentus
holdfast is the strongest characterized bioadhesive to date (adhesion strength > 68
N/mm2) [4]. The ability to synchronize the cells and monitor change in the genome and
proteome as cells proceed through the cell cycle has made C. crescentus a perfect model
to identify gene and protein clusters on regulatory development in the cell.
‐ 2 ‐ Figure 1.1: Schematic of C. crescentus cell cycle. A C. crescentus cell begins its life as
a mobile, non-reproductive swarmer cell and eventually differentiates into a stationary,
fertile stalked cell. The second generation of the swarmer cell goes back to a new cell
cycle after predivision cell stage, while the mother stalk cell produces another daughter
swarmer cell as the black arrow indicates. Photo courtesy of Prof. Yves Brun from
Indiana University [5].
Bacteria display a diversified array of cell morphologies, from spheres, rods, and
helices to tapered, branched, and star shapes [6]. Both shape and size are important for
cell function, particularly with respect to diffusion and nutrient uptake [7]. Bacteria can
also make morphological transitions in response to changes in environmental conditions.
For example: the rod-shaped plant pathogen Sinorhizobium meliloti differentiates into Yshaped nitrogen-fixing cells in plant cells [8]. The spiral-shaped pathogen Helicobacter
pylori adopts a spherical shape in extended culture [9]. Uropathogenic E. coli cells
lengthen into long filaments as part of an immune evasion response [10]. These
morphological responses and maintenance of cell morphology indicate that sophisticated
control systems must exist to regulate cell morphogenesis. The cell envelope of C.
crescentus comprises the cytoplasmic membrane, a single peptidoglycan layer residing in
the periplasm, the outer membrane, and the S-layer in the order from inside to outside of
‐ 3 ‐ cells. The S-layer is a hexagonal protein lattice on the cell exterior that is attached
through the O-antigen portion of lipopolysaccharide [11]. The crescent-shaped
morphology of C. crescentus cells can be conceived of a section of a helical cell that is
shorter than one helical turn [12]. This suggestion is consensuses by the fact that
elongated C. crescentus cells produced either by blocking cell division or by culturing
cells for extended times (more than 10 days) display a clearly helical morphology [13,
14].
1.2 Why Apply Proteomic Technologies for Characterizing Proteins from Complex
Biological System?
Current research efforts often focus on describing the detailed complexity of
particular aspects of the total system in bacterial cell development and progression [1517]. It is beneficial to consider systems study, mapping multiple pathways and
interactions that regulate development processes in bacteria. To capture all interactions
and get a complete picture of what is really happening during pathogenesis progression
and cell cycle development, conventional tools to study single protein or pathway are
inadequate and large-scale, high throughput approaches are required to provide
complementary information [18-20].
Advances
in
new
high
throughput
technologies,
such
as
genomics,
transcriptomics and proteomics, led to the explosive growth of information and the
improved molecular tools for regulatory network, subcellular component and vaccine
development [15, 18, 19]. However, gene expression analysis can not accurately predict
protein expression and post-translational modifications (such as phosphorylation and
‐ 4 ‐ glycosylation) [20, 21]. The transcriptome represents a set of messenger RNA (mRNA)
produced by a given cell and can reflect the gene expression under specific conditions
[22]. However, the transcriptome is incompetent to completely reflect the fundamental
biology due to alternative splicing and post-translational modifications in response to
changes of external conditions [23, 24]. As the final products for gene expression,
proteins are directly responsible for the molecular functions that mediate most changes at
the cellular level in biological systems. Therefore, protein-directed studies using
advanced technologies are vitally important.
Proteomics is the simultaneous study of numerous proteins on a large scale by
high throughput analyses of cells and tissues [25]. The accelerating development of
proteomics field has been enabled by multiple progresses: completion of the majority of
genomes in eukaryotes and prokaryotes, advances in separation techniques on
proteins/peptides, crucial improvements in mass spectrometry technologies, and progress
in bioinformatics.
The high frequency, powerful selectivity and extreme sensitivity of mass
spectrometry (MS) makes MS-based proteomic methodologies a perfect alternative to
traditional biological methods for analyses of proteins in complex biological systems
[26]. The distinct advantage of proteomics and the advances in proteomic technologies
have offered tremendous opportunities for protein discovery and post-translation
modification analysis. Proteomics-based approaches have the potential to provide novel,
systems-level insight to understand molecular mechanisms of regulatory networks, and
also hold great promise for profiling subcellular proteomes in microbiology.
‐ 5 ‐ 1.3 MS-based Shotgun Proteomics
The proteomics-based strategy includes the incorporation of a number of
technologies in biochemistry, molecular biology, bioinformatics and bioanalytical
chemistry [27]. Although varieties of options exist within each stage of the proteomic
analysis, a typical approach includes the following elements: sample preparation,
protein/peptide separation, protein identification (MS/MS), and data mining (Fig. 1.2).
Figure 1.2: General MS-based proteomics workflow.
Sample preparation ― The first critical step of a proteomics-based study is
sample preparation. Proteins are isolated from biological samples and then solubilized for
separation. The task of sample preparation is to maximally solubilize sample proteins
under conditions which are compatible with down stream MS analysis without
‐ 6 ‐ introducing artificial modifications on proteins. There have been numerous studies that
discuss various approaches to sample preparation and solubilization of proteins for MSbased shotgun proteomics [28], such as the spin-filter method [29], the gel-assisted
method [30] and detergent/detergent-free methods [31]. The use of a protease and/or
phosphatase inhibitor cocktail is often included to inhibit protein degradation and
dephosphorylation. In addition, different combinations of reducing agents, chaotropic
agents and detergents are used to disrupt intra- and inter-protein interactions and
maximize protein solubilization [32].
Protein/peptide separation ― Two-dimensional polyacrylamide gel electrophoresis (2DGE) is the traditionally used technique for the separation of proteins in
complex mixtures because of its high resolution [33]. The technique combines isoelectric
focusing (IEF) in the first dimension and sodium dodecylsulfate polyacrylamide gel
electrophoresis (SDS-PAGE) in the second dimension. The separation power of 2DGE
originates from the fact that proteins are separated based on two different
physicochemical properties. Proteins are separated based on their different isoelectric
points in the first dimension and based on molecular weight in the second dimension.
Protein profiles are then quantitatively analyzed through 2-DE gel imaging software [34].
Mass spectrometry has been applied for identification of proteins following 2DGE
separation in recent decades [35, 36].
In addition to 2DGE based approaches, a number of different separation
methodologies have been developed as well. In general, these methodologies fall within
two categories: one dimensional (1D)-gel-based and gel-free approaches. Furthermore,
various methods aimed at reducing the proteome complexity prior to analysis have been
‐ 7 ‐ introduced. One-dimensional gel-based proteomics strategies use a single dimension of
electrophoretic separation at the protein level combined with advanced LC-MS/MS for
protein identification. A combination of SDS-PAGE and LC-MS/MS, the so called
GeLC-MS/MS approach, has been developed and used for numerous applications. For
instance, GeLC has been applied to analyses of the proteome in the human pituitary [37]
and pancreatic fluid in chronic pancreatitis [38].
A well-known example of a gel-free proteomics strategy is the multidimensional
protein identification technology (MudPIT) [39]. MudPIT involves multidimensional
chromatographic separation of complex mixtures at the peptide level. In brief, a whole
protein mixture is digested directly and the resulting peptides are separated by two
different types of liquid chromatography (LC), such as strong cation exchange, normal
phase and reversed-phase LC, coupled to tandem mass spectrometry. Compared to
2DGE, the MudPIT method is unbiased, meaning that proteins that are normally under
represented in 2D gels, including low-abundance proteins, proteins with extremes pI and
MW, and membrane proteins, can be probed with MudPIT [40].
Mass spectrometry ― Mass spectrometry (MS) is a powerful technology for the
identification of peptides, proteins, and their post-translation modifications. The
development of advanced mass spectrometry instrumentation that allowed highsensitivity, high throughput protein identification has been the most extraordinary event
in the expansion of proteomics [41]. Novel and improved instruments are being
continuously developed and push the power of mass spectrometry to higher levels.
‐ 8 ‐ MS is an analytical technique that measures the molecular weight of molecules
according to their mass-to-charge (m/z) ratio. A mass spectrometer is composed of three
key components: ion source, mass analyzer and detector. Analytes are converted into ions
in the gas phase using an ion source (Thanks to the two Mass Spectrometrists - John B.
Fenn and Koichi Tanaka – who shared the Nobel Prize with an NMR Spectroscopist in
2002.). The ions are then separated according to their m/z ratio in a mass analyzer and the
signal is detected by a compatible detector such as an electron multiplier. There are a
number of different types of mass spectrometers that include different combination of ion
sources and mass analyzers [42].
There are two broadly used ionization methods in proteomics: matrix-assisted
laser desorption ionization (MALDI) and electrospray ionization (ESI). In MALDI
peptides generally ionize through addition of a proton, forming a singly charged
molecular ion, [M+H]+ [43]. While ESI generates multi-protonated molecular ions
[M+H]n+, n ≥ 2 depending on the number of basic amino acids in the peptide sequence
[44]. Additionally, ESI can directly generate gas-phase ionized molecules from a liquid
solution. This feature enables the on-line coupling of ESI-based mass spectrometers with
HPLC (LC-MS/MS). In my thesis work, I used a tandem quadrupole (Q) time-of-flight
(TOF) mass spectrometer to generate fragment ion spectra of peptide identifications (Fig.
1.3). It is a hybrid orthogonal acceleration time-of-flight mass spectrometer which
enables automated accurate mass measurement of precursor and fragment ions to yield
high confidence in structural elucidation and database search results [45]. The nanospray
interface allows electrospray ionization to be performed at low flow rates (5 nL/min or
‐ 9 ‐ nanoflow), which dramatically increase the sensitivity of the analysis and is compatible
with on-line nanoflow LC systems.
Figure 1.3: Schmetic diagram of QSTAR Elite (ESI-QTOF) mass spectrometer
(ABSciex) in our lab.
Mass spectrometry measurements can be carried out in two general ways: a
single-stage mass spectrometry (MS) or as tandem mass spectrometry (MS/MS). An MS
experiment includes ionization of the analytes and measurement of the m/z ratios for the
parent ions. An MS/MS experiment is more complex than an MS experiment and
includes additional steps. The steps in an MS/MS experiment are ionization of the
molecules, selection of target ions (so-called precursor ions), activation of the selected
precursor ion through collisions (collision-induced dissociation or CID) with a target gas,
‐ 10 ‐ and dissociation of the precursor ion into a series of product ions. Product ions are used
for structure determination in the data mining process. In CID mode of MS/MS,
protonated peptide ions dissociate predominantly via cleavages along the peptide
backbone, theoretically generating two major ion series: the N-terminal ion series (b-ions)
and the C-terminal ion series (y-ions) [46]. In theory, every peptide bond could be
fragmented into b- and y- ions. Therefore the m/z patterns of the product ions can be used
to derive amino acid sequence information for the peptide under analysis (Fig. 1.4).
Figure 1.4: The nomenclature and structure of sequence-specific fragmentation ions
in MS/MS.
Database searching for protein identification ― Identification of proteins based
on MS or MS/MS has been enabled by the completion of protein sequence databases that
are being continuously updated and on the development of database search software
programs. There are generally two ways to identify a protein based on mass spectrometric
results, namely peptide mass fingerprinting (PMF) and MS/MS searching. The PMF
method is designed specifically for single MS spectra, which was often obtained on
MALDI-TOF MS results during the 1990s and early 2000s [47-49]. In general, the mass
list of precursor ions is compared in silico to the database containing the known protein,
‐ 11 ‐ which is theoretically digested into peptides. However, one m/z value of a precursor ion
probably matches to multiple peptide sequence, which will generate false positive
identifications. The second approach is based on a correlation of the MS/MS product ion
patterns, which correlates with the amino acid sequence of a protein in the target protein
database [50, 51]. This approach has become the gold standard for protein identification
in MS-based proteomics. Since a MS/MS spectrum contains the amino acid sequence
information of the peptide, rather than the peptide masses alone, these searches generate
more specific and discriminative results than PMF. In practice, during the database
search, the search engine uses computer programs to theoretically cut the known proteins
in a database (for example; the entire set of human proteins predicted from the genome)
into peptides according to the protease, and each peptide is further fragmented into
product ions depending on the MS/MS dissociation type. Experimental MS/MS spectra
are matched against theoretical fragment ion spectra for all the peptides in the databases
that have the same precursor ion mass within the experimental error. Peptides that turn
out to be the first hits along with the identification scores higher than the identity
threshold defined by the searching algorithm are generally considered as positive
matches. The matched peptides are sequence-linked to their corresponding proteins,
resulting in the identification of proteins. Many different algorithms for database
searching of MS/MS spectra have been developed. They include SEQUEST
(http://fields.scripps.edu/sequest/), MASCOT (http ://www. matrixscience. com/), X!
Tandem
(http://www.thegpm.org/TANDEM/), OMASS (http://pubchem.ncbi. nlm.nih.
gov/omssa/) and ProteinPilot (http://www.absciex.com/products/software/proteinpilotsoftware). As shown in Figure 1.5, the MS/MS spectrum matched to peptide
‐ 12 ‐ GALPTELIIGTFDK using two search algorithms against the C. crescentus database. The
in silico MS/MS spectrum generated from the outer membrane protein RsaF matched to
the experimental MS/MS spectrum very well. Thus, we were confident that we identified
this protein in our biological samples.
As mentioned at the beginning, the database is the control reference for MS/MS
searching algorithms. Thus, it is very important to maintain and update the protein
database in order to keep accurate protein identifications. Several databases, such as
Genbank [52], RefSeq [53], SWISSPROT [54] and TrEMBL [55], are supported by
independent groups and can be accessed though the internet. They contain a
comprehensive and updated catalog of protein sequences.
‐ 13 ‐ Figure 1.5: Identification of an outer membrane protein, RsaF (gi|16125267|type I
secretion system outer membrane protein RsaF|OuterMembrane), using 2DLCMS/MS as described in chapter 2. (A) The MS/MS spectrum of peptide
GALPTELIIGTFDK. This peptide was identified by a Paragon confidence value >99%
(ProteinPilot) and a MASCOT score of 54. (B) Close-up view of the doubly protonated
tryptic peptide GALPTELIIGTFDK at m/z 737.8804 selected for MS/MS using
information dependent acquisition (IDA) mode at 59.84 min.
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‐ 18 ‐ Chapter2
IMPROVED ENRICHMENT AND PROTEOMIC IDENTIFICATION OF
OUTER MEMBRANE PROTEINS FROM A GRAM-NEGATIVE BACTERIUM:
FOCUS ON Caulobacter crescentus
‐ 19 ‐ 2.1 INTRODUCTION
The outer membrane (OM) of Gram-negative bacteria works in concert with the
periplasm (PERI) and the inner membrane (IM) to preserve the cell’s structural integrity,
protect the metabolic processes occurring within the cytoplasm, and anchor
‘transenvelope molecular machines’ such as flagella and protein secretion systems [1].
The biological importance of the OM is a direct result of the proteins localized to this
subcellular region. Nonspecific porins, selective channels, and high affinity receptors
allow the cell to sustain itself through proper nutrition and rid itself of harmful waste
material or toxic substances [2]. Deletion of specific OM proteins (OMPs) or a
compromise in OMP assembly negatively impacts cell growth and decreases the
chemical or heat resistance of this protective barrier [3-6]. When considering pathogenic
bacteria, OMPs also encompass virulence factors that are putative vaccine- or drugtargets [7, 8]. Identification and characterization of the complement of proteins found in
the OM under different environmental conditions or after ablation of a key component
promises to improve our understanding of fundamental bacterial processes.
Proteomic characterization of OMPs initially appears to be a relatively easy
endeavor. Analysis of a subset of the proteome guarantees an overall reduction in sample
complexity compared to global studies. The permutations in protein products due to
mRNA splicing is thought to be minimal in bacterial systems, and integral OMPs differ
from other integral membrane proteins in that they do not exhibit an overall hydrophobic
character [9]. Yet even with these advantages, most OMP studies fall far short of
detecting the entire repertoire of predicted OM protein products in their respective
systems. This could be due to a protein’s low basal level of expression or due to tight
‐ 20 ‐ control of the synthesis of a given protein until induced by external stimuli. However,
failure to detect all predicted OMPs could also be due to inadequate sample handling and
processing.
In addition to the MS detection step, OM proteomic studies are critically
dependent upon the specificity of the sample enrichment step [10-12], the efficiency of
the protein dissolution step [13, 14], and the resolving capacity of the separation step [1517]. Although the first study describing an isopycnic sucrose density gradient
centrifugation method to isolate Escherichia coli OMs appeared in 1968 [18], the search
for a fast, selective, and general OM purification technique continues [10]. Buffers,
detergents, or denaturing agents used to ensure complete solubilization of the OM
fraction can interfere with the subsequent separation and identification steps, so
solubilization solutions must be selected with care. Two-dimensional gel electrophoresis
(2DGE) coupled with MS is by far the classic separation/analysis technique for studies of
this type [11, 13, 19-27]. Alternative methods, such as one-dimensional SDS-PAGE
coupled with LC-MS/MS (or GeLC-MS) [12, 16, 28-30] and gel-free shotgun proteomics
[31-35] are increasingly being used.
In the work presented here, we refined an OMP isolation technique using
Caulobacter crescentus, a free-living gram-negative α-proteobacterium. It is an
established model system for studies of cell cycle control and polar morphogenesis [36]
and is expected to express a set of OMPs that differ from the more commonly studied γproteobacterium E. coli [13, 37, 38]. A previous study using C. crescentus identified 41
OMPs via 2DGE [13] and provides a good starting point for comparison. We first
qualitatively evaluated two popular methods for isolation of the OM fraction. We then
‐ 21 ‐ assessed the performance of two analytical techniques that integrate protein solubilization
with analysis: (i) GeLC-MS and (ii) RP/RP-MS/MS facilitated by gel-assisted digestion
[39, 40]. Finally, we improved the specificity of OM isolation by lowering the target cell
density prior to cell lysis and by using a mutant cell line that is devoid of a high
abundance contaminate protein.
2.2 MATERIALS AND METHODS
An overview of the isolation procedures described in sections 2.2.1 and 2.2.2 are shown
in Figure 1.
2.2.1 Bacterial Growth and Cell Lysis
Wildtype (WT) CB15 [41] or RsaA-negative (RsaA–) C. crescentus cells (kindly
provided by Prof. Yves Brun at Indiana University and referred to as YB991 in [42])
were first grown to the late exponential phase in a peptone yeast extract (PYE) medium.
Then, 300 mL fresh PYE medium was inoculated at a 1:30 dilution with bacterial cells.
The cells were cultured at 30 ˚C with constant shaking at 100 rpm until an OD600 of either
1.0 or 0.6 was reached. Cells were harvested by centrifugation at 4000g for 10 min at 4
˚C and washed three times with 50 mM ammonium bicarbonate (AMBIC, pH 8.0). Cells
were resuspended in 2 ml of 50 mM AMBIC (pH 8.0) with protease inhibitors (Complete
EDTA-free, Roche Diagnostics, Germany) before being disrupted by repeated
intermittent sonic oscillation (20 × 5 s). Cellular debris was removed by centrifugation at
12,000g for 20 min. Supernatant was collected and centrifuged at 100,000g for 40 min to
obtain a total membrane pellet.
2.2.2 Enrichment of C. crescentus OMPs
‐ 22 ‐ Sodium carbonate method: The OM fraction (Figure 1, P4 pellet) was obtained
from the total membrane pellet essentially as described previously [13]. Briefly, total
membranes were resuspended in 100 mM NaCO3, incubated on ice for 1 h, and
centrifuged at 100,000g for 40 min. The recovered pellet was analyzed by SDS-PAGE
and Western blotting without further processing. Protein concentrations were determined
using the Pierce BCA protein assay (Thermo Fisher Scientific, Rockfold, IL).
Sodium lauryl sarcosine (SLS) method: The SLS outer membrane fraction (Figure
1, P4 pellet) was obtained as described [26] with modification. Briefly, the total
membrane pellet was resuspended in 20 mM AMBIC (pH 8.0) containing 1.0% (wt/vol)
SLS (Affymetrix, Fremont, CA), incubated at room temperature for 30 min, and then
centrifuged at 100,000g for 40 min. Next, the SLS-insoluble pellet (Figure 1, P4 pellet)
was washed sequentially with 2.5 M sodium bromide and 100 mM sodium carbonate,
which further removes cytoplasmic and loosely associated proteins as noted before [12,
14]. The final OM pellet was analyzed by SDS-PAGE and Western blotting or processed
for gel-assisted digestion. The Pierce BCA protein assay was used to determine protein
concentrations.
2.2.3 SDS-PAGE and Gel Staining
OMP samples (50 μg) were mixed with lithium dodecyl sulfate (LDS) sample
buffer (Invitrogen Corp., Calsbad, CA) and DTT to final concentrations of 2% (wt/vol)
and 50 mM, respectively. OMPs were then denatured for 10 min at 70 °C and separated
using NuPAGE Novex 10% Bis-Tris gels (Invitrogen) according to the manufacturer’s
instructions. Essentially following established protocols, gels were either silver stained
‐ 23 ‐ [43] for visualization purposes only or Coomassie stained [44] to allow for downstream
LC-MS/MS analysis.
2.2.4 In-gel Tryptic Digestion
Coomassie blue stained gel lanes were excised into 18 bands of equal size (2 × 2
mm) and subjected to in-gel proteolysis with trypsin (Promega) and 0.1% (wt/vol) noctylglucoside (Sigma-Aldrich) according to the Katayama et al. protocol [45]. The
extracted peptides were reduced to near dryness and reconstituted in 50 μL with 0.1%
acetic acid for LC-MS/MS analysis.
2.2.5 Gel-assisted (Ga) Digestion
The gel-assisted method described by Han [39] was applied with minor
modifications. In brief, the OM pellet (50 μg) was solubilized in 50 μl of 6 M urea, 5 mM
EDTA and 2% (wt/vol) SDS in 50 mM AMBIC (pH 8.0) for 30 min at 37 °C. Proteins
were reduced using tris (2-carboxyethyl) phosphine (Thermo Fisher Scientific) at 5 mM
final concentration for 1 h at room temperature and alkylated with fresh iodoacetamide at
10 mM final concentration in the dark for 30 min at room temperature. The protein
solution was then directly incorporated into a gel by adding 20 μl of a 29:1 mixture of
40% (v/v) acrylamide solution/bis-acrylamide, 3 μl of 10% (wt/vol) ammonium
persulfate and 1 μl of TEMED (Amersham Bioscience, Piscataway, NJ). The
polymerized sample was then enzymatically digested with trypsin (Promega) and 0.1%
(wt/vol) n-octylglucoside (Sigma-Aldrich) via the method of Katayama [45]. Extracted
peptides were reduced to near dryness and reconstituted in 50 μL with 0.1% acetic acid
for offline HPLC separation.
‐ 24 ‐ 2.2.6 Wheat Germ Agglutinin (WGA) Western Blotting
Proteinaeous sample was electrophoretically transferred onto nitrocellulose
membranes (0.45 mm, Bio-Rad, Hercules, CA) at 4 ˚C for 90 min (17 mA/30V) using the
Invitrogen XCell II Blot Module. Membranes were first rinsed with wash buffer (25 mM
Tris-HCl (pH 7.0), 1% Tween 20, 1 mM CaCl2, 1 mM MgCl2, 0.15 M NaCl) and
subsequently blocked with the same buffer for 60 min. Incubation with biotinylatedWGA (10 μg/ml in wash buffer) was carried out overnight at 4 ˚C. Following this step,
the membrane was rinsed three times with wash buffer (5 min each). HRP-conjugated
streptavidin (diluted 1:1000 in wash buffer) was incubated with the membrane for 90
min. Then, the membrane was washed again (3×, 10 min each) in a buffer containing 25
mM Tris-HCl (pH 7.0), 1 mM CaCl2, 1 mM MgCl2, and 0.15 M NaCl. Finally, the blot
was visualized by ECL (SuperSignal® Chemiluminescent Substrate, Thermo Fisher
Scientific)
2.2.7 High-pH Reversed-phase (RP) HPLC
Peptide digests were loaded onto an Agilent ZORBAX 300Extend-C18 column
(150 × 2.1 mm i.d., 3.5 μm) using an Agilent 1200 binary HPLC system. Peptides were
initially separated at a flow rate of 0.5 ml/min using the following gradient: 0-70% B in 4
min; 70-100% B in 8 min. A modified gradient (0-50%B in 20 min; 50-85% B in 10 min;
85% B for 5 min) was used to fractionate peptides in the final experiment. Mobile phase
A consisted of 1% methanol in 20 mM ammonia (pH 10.5). Mobile phase B consisted of
a 90/10 mix of acetonitrile/1% methanol in 20 mM ammonia. The column temperature
was held at 30 ˚C and UV detection was performed at 214 nm. Fractions were collected
‐ 25 ‐ once per minute. Each fraction was concentrated by vacuum centrifugation and
reconstituted in 0.1% acetic acid for MS analysis.
2.2.8 LC-MS/MS Analysis
Peptides from the first dimension separation were analyzed by LC-MS/MS using
a Tempo MDLC™ system coupled to a QSTAR Elite™ hybrid quadrupole time-of-flight
mass spectrometer (ABSciex, Foster City, CA) operating in positive ionization mode.
Peptides were loaded at 3 μl/min onto home-built precolumns (75 μm ID, 5 cm length of
POROS 10R2; Applied Biosystems, Carlsbad, CA) and washed for 30 min with solvent
A (0.1% formic acid and 2% acetonitrile in water). The peptides were then eluted at a
flow rate of 100 nl/min onto analytical columns (50 μm ID, 10 cm length of Monitor
100Å-Spherical Silica C18; Column Engineering Inc., Ontario, CA) using the following
gradient: 0%-30% B in 40 min; 30%-60% B in 40 min; 60%-95% B in 30 min; 95% B
for 30 min. The gradient was modified slightly for the last experiment: 0%-30% B in 40
min; 30%-60% B in 40 min; 60%-75% B in 30 min; 75% B for 30 min. Solvent B
consisted of 0.1% formic acid and 2% water in acetonitrile. MS data were acquired in
information-dependent acquisition mode with Analyst QS 2.0 (ABSciex). MS cycles
were comprised of one full scan (m/z range = 300-2000, 1 sec accumulation) followed by
sequential MS/MS scans of the four most abundant ions (+2 to +4 charge state, minimum
ion count = 75, collision energy = 40, exclusion time = 20 sec, maximum accumulation
time = 2 sec).
2.2.9 Database Searching
Tandem mass spectrometry data from .WIFF files were directly analyzed by
ProteinPilot™ 2.0.1 (ABSciex) using the Paragon™ Algorithm [46] and the RefSeq C.
‐ 26 ‐ crescentus database (dated April 25, 2009) downloaded from NCBI. The search
parameters permitted the identification of tryptic peptides and cysteines modified by
iodoacetamide. A 95% confidence threshold for protein matches was used, which
corresponded to an unused protein score ≥ 1.3. The Pro Group™ algorithm within
ProteinPilot™ facilitated protein grouping and removal of redundancy. As a final filter,
two unique peptides at the 95% confidence level or greater were needed to confirm the
identity of each protein in each analysis. In addition, the actual false discovery rate (FDR)
at the peptide level was calculated using the target-decoy database search strategy [47]
and the percent FDR was < 2% for all experiments.
2.2.10 Spectral Counting and Statistical Analysis
In order to evaluate protein abundance using spectral counts, we conducted a
Mascot (version 2.2.0; Matrix Science, Boston, MA) search using the RefSeq C.
crescentus database (dated April 25, 2009) because the output from ProteinPilot was not
compatible with our spectral counting software, ProteoIQ (version 2.0.01; BioInquire,
Bogart, GA). The .WIFF files were converted to .mzXML format using the TransProteomic Pipeline (version 4.4.0) [48]. The .mzXML files were then converted to .MGF
files via MassMatrix MS File Conversion Tools (version 3.8; www.massmatrix.net). The
.MGF files were used for the Mascot search. Mascot parameters were as follows:
precursor ion mass tolerance and fragment ion tolerance were both set at 0.2 Da;
carboxyamidomethylation was chosen as a fixed modification; methionine oxidation was
chosen as a variable modification; trypsin was selected as the enzyme; and one missed
cleavage was specified. The Mascot search results (.dat files) were filtered with ProteoIQ
to generate a protein list nearly identical to the ProteinPilot results using the following
‐ 27 ‐ parameters: number of peptides: 2; Peptide Probability: 0.9; Protein Probability: 0.95;
Protein Group Probability: 0.95. Finally, ProteoIQ assigned total spectral counts to each
identified protein. Protein spectral count data were analyzed by the two-sided t-test for
paired samples using Microsoft Excel 2002 (Redmond, WA). P values of < 0.05 were
considered statistically significant.
2.2.11 Bioinformatics
Two algorithms were used to predict each protein’s location in the cell: Proteome
Analyst 3.0 [49] and PSORTb 2.0 [50]. The predictions made by each program had to
agree in order for a given protein to be assigned to a subcellular location. Otherwise, the
protein was assigned to the “unknown” category.
Less than 10% of the identified
proteins were categorized as such and the vast majority of these were proteins predicted
by both algorithms to reside in more than one cellular compartment.
2.3. RESULTS
2.3.1 SLS Successfully Isolates OMs from C. crescentus
To develop a rapid and robust OMP enrichment procedure (Fig. 2.1), we
compared the two reagents most often used to isolate OMPs from whole cell lysates of
gram-negative bacteria: sodium carbonate at pH 11 and sodium lauryl sarcosinate (SLS).
Sodium carbonate opens and linearizes sealed membrane vesicles [51]. This allows for
the removal of cytoplasmic and membrane-associated components and leaves only
integral membrane proteins as pelletable material. SLS is an ionic detergent that
preferentially solubilizes the inner membrane of gram-negative bacteria [52]; and after
‐ 28 ‐ differential centrifugation, the outer membrane is left as an insoluble pellet. In both cases,
the protein pellet can be dissolved in the researcher’s buffer of choice.
Figure 2.1. Schematic diagram of OM enrichment procedure. The OM fraction was
obtained from C. crescentus whole cell lysates using differential centrifugation and SLS
or Na2CO3 solubilization.
First, we qualitatively assessed the effectiveness of OM enrichment using Na2CO3
or SLS via SDS-PAGE followed by silver staining. OMs were isolated at least three
separate times from independently grown aliquots of cells using each method and the
results were similar in each case. The isolated OMPs are strikingly different: intense
‐ 29 ‐ bands observed in the OM fraction isolated using SLS are absent in the OM fraction
obtained using sodium carbonate (Fig. 2.2A). This is problematic since some of the bands
contain major known or predicted OMPs (as assessed by subsequent MS analysis). For
example, the 75 kDa band contains TonB-dependent receptors (CC3500, CC2194) and
flagellar hook-associated protein FlaN (CC0899). The band at 55 kDa consists of OmpA
(CC3494) and flageller hook protein FlgE (CC0902), and HfaB (CC 2629) and a putative
outer membrane protein (CC2094) are found in the region between 35-45 kDa. Moreover,
the SLS method gives a distinct profile of proteins in each subcellular fraction that is not
observed when using sodium carbonate. In fact, the IM and OM fractions obtained using
sodium carbonate have nearly identical banding patterns using 10% SDS-PAGE
suggesting an inefficient purification of the OM from the IM.
‐ 30 ‐ Figure 2.2. The OM fraction, which contains holdfast, is most successfully isolated
using SLS. (A) SDS-PAGE analysis of subcellular fractions isolated using either SLS or
Na2CO3. Equal amounts of IMs or OMs (5 µg) were separated by SDS-PAGE gels and
silver-stained. The experiment was performed at least 3 times with similar results. (B)
Western blot analysis to determine the presence of holdfast in IM and OM fractions
isolated using SLS. Equal amounts of IMs and OMs (10 µg) were separated using SDSPAGE and probed with biotinylated-WGA and HRP-strepavidin. The experiment was
performed at least 2 times with similar results.
Next, we assayed for the presence of an OM component in each subcellular
fraction in order to further evaluate the ability of Na2CO3 and SLS to generate a purified
OM pellet. The adhesin of C. crescentus (called the holdfast) is located on the tip of its
stalk [41] and is partially composed of GlcNAc polymers [53, 54]. We took advantage of
this distinct feature and developed an immunoblot procedure using HRP-conjugated
streptavidin and biotinylated wheat germ agglutinin (WGA), a GlcNAc-specific lectin
that recognizes the holdfast [53, 54]. A high molecular weight band representing the
holdfast is found in the OM fraction isolated using SLS (Fig. 2.2B). As expected, this
‐ 31 ‐ band is absent from the IM fraction recovered using SLS. The band at 60 kD, found
predominantly in the IM fraction, is characteristic of peptidoglycan material that can
contain GlcNAc. On the other hand, both IM and OM fractions obtained using sodium
carbonate contain holdfast material, demonstrating that this reagent does not effectively
separate the OM from the IM (Fig. 2.3).
Figure 2.3. Na2CO3 does not effectively separate the OM fraction from the IM
fraction. SDS-PAGE analysis of subcellular fractions isolated using Na2CO3. Equal
amounts of IMs or OMs (10 µg) were separated by SDS-PAGE. Western blot analysis
was used to determine the presence of holdfast in either fraction. The holdfast was
detected using biotinylated-WGA, HRP-strepavidin, and ECL.
Together, the SDS-PAGE and immunoblot analyses demonstrate that our SLS
method, in contrast to the traditional Na2CO3 technique, successfully isolates OMs from
C. crescentus. The OMPs for all subsequent experiments described in this work were
isolated using SLS.
‐ 32 ‐ 2.3.2 Gel-assisted (Ga) Digestion coupled to RP/RP-MS/MS (2DLC) Results in
Increased C. crescentus OMP Identifications
To determine the most effective analytical method for analysis of C. crescentus
OMPs, we compared (i) SDS-PAGE combined with RP-LC-MS/MS (the “GeLC”
approach; workflow shown in Fig. 2.4A) with (ii) gel-assisted digestion [39, 40] coupled
with RP/RP-MS/MS (referred to as the “Ga2DLC” approach; workflow shown in Figure
2.4B). These two techniques were chosen for comparison because GeLC is increasingly
employed in studies of this type [12, 16, 28-30] while 2DLC is superior in performance
and fractionation efficiency [55]. Additionally, gel-assisted digestion results in enhanced
digestion efficiency of membrane proteins when compared to various in-solution
digestion protocols [39] and was incorporated into the Ga2DLC workflow to overcome
this anticipated problem. One technical replicate was performed per analytical approach
for each biological sample.
‐ 33 ‐ Figure 2.4. Overview of the GeLC and Ga2DLC methods. (A) In the GeLC method,
OMP pellets are separated by SDS-PAGE after solubilization in LDS buffer. Each gel
lane is cut into 18 bands, which are enzymatically digested separately and analyzed by
LC-MS/MS. (B) In the Ga2DLC method, OMP pellets are co-polymerized with
acrylamide solution after solubilization in a strong denaturing solution. The ‘plug’ of gel
is then washed with buffer, enzymatically digested, and fractionated offline at pH 10.
Each high pH fraction is analyzed by LC-MS/MS.
We first assessed the solubility of the OM fraction in the sample buffer typically
used for each analytical technique. We noted visually that the solubilization of the OMP
pellet was only partially effective using the traditional SDS-PAGE sample reducing
buffer because precipitate was still clearly observable even after heating at 70 °C for 10
min according to the standard procedure. As a consequence, this sample was centrifuged
and the supernatant was taken for further processing using the GeLC approach (Fig.
2.4A). In contrast, the OMP pellet completely dissolved in the detergent mixture
specified for gel-assisted digestion (see Materials and Methods) during the recommended
‐ 34 ‐ 30 min incubation at 37 °C. The solubilized protein solution was then processed
according to the Ga2DLC workflow (Fig. 2.4B).
We then compared the performance of GeLC to Ga2DLC by examining four
parameters for each approach: (1) total number of peptides identified; (2) total number of
unique proteins identified; (3) total number of OM/extracellular proteins identified; and
(4) total number of spectral counts attributed to OM/extracellular proteins. The Ga2DLC
approach resulted in a significant improvement in the number of total peptides identified
without a corresponding enhancement in the total number of unique proteins identified
(Table 2.1, column 2 versus column 1). Upon closer inspection of the data however, a
noteworthy improvement in the number of identified OM/extracellular proteins using
Ga2DLC (54 vs. 43) can be discerned. Furthermore, the difference in total spectral counts
(Fig.2.5, C and D) assigned to the OM/extracellular protein category increased from 34%
to 48% in a statistically significant manner (p = 0.0021 using the Student’s t-test).
Table 2.1. Summary of proteins detected in C. crescentus OM fractions across all
conditions.
OD600 1.0
OD600 0.6
GeLC
(n=1)
Ga2DLC
(n=1)
WT
(n=2)
RsaA(n=2)
Number of identified peptides
2647
3154
2518
5927
Number of unique proteins
179
182
99
234
Number of extracellular proteins
15
13
13
21
Number of OMPs
28
41
29
63
Number of PERI proteins
12
9
10
38
Number of IMPs
9
12
6
16
Number of CYT proteins
61
56
20
45
Number of proteins with unspecified location
54
51
21
51
‐ 35 ‐ Overall, gel-assisted digestion allowed for improved solubilization of the OMP
pellets through the use of very strong denaturants and facilitated the analysis of the OMP
fraction by providing a procedure that effectively removed the harsh reagents prior to
LC-MS/MS. The remaining studies described below were conducted using our
multiplexed Ga2DLC approach in combination with SLS isolation of OMPs.
Figure 2.5. Graphical representation of the predicted/annotated subcellular
distribution of proteins identified in enriched OM fractions from C. crescentus using
either the GeLC or Ga2DLC method. Proteins were distributed in each pie chart
according to their annotated or predicted subcellular location based on the number of
proteins IDs per category for either the (A) GeLC method or (B) Ga2DLC method. The
total number of proteins (n) identified in each experiment is shown below each pie chart.
Proteins were distributed in each pie chart according to their annotated or predicted
subcellular location based on relative abundance from spectral count data for either the
(C) GeLC method or (D) Ga2DLC method. The total number of spectral counts (SpC) for
each experiment is shown below each pie chart.
‐ 36 ‐ 2.3.3 Low Density Culture Improves C. crescentus OMP Extraction Specificity
In the work described above, the bacteria were grown in a fixed volume to an
OD600 of 1.0. This is approximately 109-1010 cells/mL. Though not an experimental
parameter explicitly considered in studies of this type, we postulated that this high
density cell culture condition is stressful to the cells and could lead to morphological
changes and/or premature cell lysis. Both scenarios could contribute to the co-purification
of proteins from cellular compartments other than the OM and increase the “noise” in our
proteomic experiments. Therefore, we investigated the effect that a reduction in cell
density would have on the specificity of the OM enrichment step.
OMPs were isolated from two independent batches of C. crescentus cells grown
to an OD600 of 0.6. This optical density roughly correlates to a cell density of 108
cells/mL. The OMP fractions were then analyzed using the Ga2DLC approach. Ninetynine proteins were identified (Table 1, column 3). This low-density protocol improved
the percentage of OM/extracellular protein identifications (Fig. 2.6A) with a concomitant
increase in the OM/extracellular protein group abundance as accessed via spectral count
data (Fig. 2.6C). The quantitative data in Figure 2.6C also indicated that this protocol
resulted in a low level of contamination from other cellular locations (IM, 6%; CYT, 6%;
PERI, 8%). The contribution by the unknown category (16%) to total spectral counts is
considered acceptable since many of these proteins may actually reside in the OM
although the predication programs are not yet able to specifically determine localization.
Overall, the low-density cell culture protocol improved the specificity of OMP isolation
due to a reduction in the co-purification of proteins located in other subcellular
compartments.
‐ 37 ‐ Figure 2.6. Graphical representation of the predicted/annotated subcellular
distribution of proteins identified in enriched OM fractions from C. crescentus (low
density cell culture) using either WT or RsaA– cells. Proteins were distributed in each
pie chart according to their annotated or predicted subcellular location based on the
number of proteins IDs per category for either the (A) WT cells or (B) RsaA– cells. The
total number of proteins (n) identified in each experiment is shown below each pie chart.
Proteins were distributed in each pie chart according to their annotated or predicted
subcellular location based on relative abundance from spectral count data for either the
(C) WT cells or (D) RsaA– cells The total number of spectral counts (SpC) for each
experiment is shown below each pie chart.
2.3.4 Deletion of the Surface-Layer Protein Improves C. crescentus OM Proteome
Coverage
The surface layer (S-layer) - the outermost region of a bacterium that interacts
with its environment - is usually composed of one protein that can contribute up to 15%
of protein mass, and serves various species-dependent functions [56, 57]. For C.
crescentus, the S-layer is thought to act as a shield from invasive pathogens and the harsh
environment [58] and is exclusively composed of the RsaA protein that can account for
‐ 38 ‐ an incredible 31% of the total protein mass [59]. In the previous experiments, we found
that RsaA was consistently one of the top 10 most abundant proteins in each sample
based on spectral counts (data not shown). Peptides derived from RsaA eluted across
numerous fractions during the first dimension separation (>10 offline fractions) and, in
some instances, eluted throughout the entire second dimension LC-MS/MS run as well
(data not shown). Furthermore, we detected RsaA in 13 of the 18 gel bands analyzed by
LC-MS/MS during the GeLC analysis. So, SDS-PAGE does not offer any special
analytical advantage in this case. Unfortunately, RsaA is of concern for our proteomic
study because it is possible that this highly abundant protein interfered with the detection
of proteins found at lower concentrations and consequently, limited the dynamic range of
our multiplex strategy. Additionally, the RsaA protein most likely compromised our
OMP isolation attempts since it has been documented to interfere with the efficiency of
OMP isolation [42]. To overcome these issues, a C. crescentus S-layer mutant (RsaA-)
[42] that lacks the protein was used for further OMP characterization. In our hands, this
mutant is phenotypically indistinguishable from the WT strain as assessed by growth (Fig.
2.7).
‐ 39 ‐ Figure 2.7. Bacterial growth profiles for WT and RsaA- C. crescentus cells. Each
experiment was performed in duplicate.
OMPs were isolated from two independent cultures of RsaA- grown to an OD600
of 0.6. Proteomic analyses of the OMP samples yielded 234 unique proteins (Table 2.1,
column 4). The eighty-four OM/extracellular proteins identified represented only 36% of
all protein identifications but accounted for 77% of the normalized spectral counts (Fig.
2.6, B and D). Individual contributions from each of the other subcellular locations were
≤ 10% based on the semi-quantitative data (Fig. 2.6D). As anticipated, RsaA was the only
OM/extracellular protein identified in WT cells that was not detected in the mutant cell
line. A few other non-OM proteins (23 proteins) were found in WT C. crescentus and not
in RsaA- cells (data not shown). They were of low abundance and can be regarded as
trivial impurities. Most importantly, the low-density cell culture procedure resulted in a
two-fold increase in OM/extracellular protein identifications when RsaA- cells were
analyzed instead of WT cells (Table 2.1, columns 3 and 4).
‐ 40 ‐ 2.3.5 Nearly 70% of the Expected C. crescentus OM/extracellular Proteome is
Detected Using the Optimized Procedure
The genome of C. crescentus contains 3,763 predicted proteins [38] and 140 of
these were predicted to be OMPs based on a number of structural features [13]. In our
optimization experiments described herein, we used consensus predictions from
Proteome Analyst 3.0 [49] and PSORTb 2.0 [50] to categorize the location of our
identified proteins. These are two of the most sensitive and most accurate algorithms
currently available for global prediction of subcellular location [60]. So, we also decided
to use these two programs to reevaluate the C. crescentus genome and recalculate the
number of expected OM and extracellular proteins. Our analysis indicates that 99 and 105
OMPs exist in the C. crescentus proteome as predicted by Proteome Analyst and
PSORTb, respectively. The list decreases to 96 after removing those hits generated by
only one program. Proteome Analyst and PSORTb also indicated that 44 and 33
extracellular gene products are present within C. crescentus, respectively, with 29
predictions in common. This bioinformatic survey results in a high confidence
(consensus) list of 125 predicted OM/extracellular proteins. A total of 234 proteins were
identified using our optimized procedure and 84 of these proteins are predicted or known
OM/extracellular proteins across four experiments (Fig. 2.8). Therefore, we have
identified nearly 70% of the expected OM/extracellular proteome for C. crescentus using
our optimized procedure.
‐ 41 ‐ Figure 2.8. Venn diagram illustrating the common and uniquely identified OMPs across
all four datasets.
Reconstruction of the extracellular, OM, and PERI regions is still difficult for
prediction algorithms to do since they are trained on partial data sets [60]. As
demonstrated in this work and others [10, 12], discrepancies in predictions exist between
subcellular localization tools. So, inconsistencies between in silico predictions and actual
locations are expected to exist as well [32]. With that in mind, if we consider the cell
envelope (extracellular, OM, PERI, IM) as one unit then 59% of our protein
identifications and 86% of the total spectral counts are annotated or predicted to the cell
envelope region.
Categorization of the 234 proteins according to their JCVI (J. Craig Venter
Institute) cellular role category showed that the majority of the proteins are conserved
hypothetical/hypothetical proteins (59 proteins, 25% of protein identifications) with no
known function and nineteen of these are predicted OMPs. The three cellular categories
‐ 42 ‐ represented by the greatest number of proteins were: transport/binding proteins (14%);
protein synthesis (9%); and cell envelope (8%). The transport/binding protein
classification was largely comprised of TonB-dependent receptors (23 proteins), the
second largest class of proteins encoded by the C. crescentus genome [38]. The proteins
found in the protein synthesis group have unknown locations or presumably originate
from the CYT and IM. The cell envelope category, which contains important structural
proteins and proteins required for the biogenesis of the cell envelope, consisted of 15
OMPs, 3 PERI proteins and 1 protein of unknown subcellular localization (Fig. 2.9).
Figure 2.9. The majority of the identified OMPs are conserved hypothetical/
hypothetical proteins, transport/binding proteins, or cell envelope proteins. The
JCVI cellular role was used to classify the 234 proteins identified in experiment 4.
Predicted subcellular locations for the proteins are indicated in the figure legend.
‐ 43 ‐ Due to the limitations of bacterial subcellular predication programs mentioned
above, we subjected the 234 identified proteins to additional bioinformatic examination
in order to assess their overall OMP-like character. Although integral OMPs typically
adopt β-barrel conformations, recent structural analysis has revealed that they can make
use of α-helices as well [61]. So, the number of α-helical or β-barrel membrane-spanning
domains within each protein was predicted using TMHMM 2.0[62] and BOMP [63],
respectively. In Figure 2.10, we see that 88 of the 234 identified proteins (38%) contain
one or more transmembrane helical domains. Evidence for this predicted secondary
structure was found in > 50% of the identified OMPs (37 of 66 proteins). The other
predicted cellular locations have proteins that putatively contain α-helices as well: 21
PERI proteins; 13 “unknown” proteins; 10 IMPs; 6 extracellular proteins; and 1 CYT
protein. Figure 5B shows the 28 proteins forecast to contain β-barrels. They are all
predicted/known OMPs. The 16 β-barrel OMP predictions classified as highly reliable
(based on BOMP categories 4 and 5) describe 14 TonB-dependent receptor family
proteins, one OmpA-related protein, and one efflux system protein. All 16 of these
proteins have a known role as transport and binding proteins.
‐ 44 ‐ Figure 2.10. Distribution of proteins containing (A) α-helical (88 proteins) or (B) βbarrel (28 proteins) motifs along with calculated (C) GRAVY values. This
information was acquired in an effort to further assess the membrane-like qualities of the
234 identified proteins although single feature prediction programs have been shown to
be less reliable than global prediction programs.
In addition, a GRAVY score for each protein was determined using the
ProtParam software on the ExPASY server [64]. As mentioned earlier, bacterial OMPs
are distinguished by their overall hydrophilicity similar to CYT proteins [9]. As shown in
Figure 2.10, the majority of the 234 identified proteins (174 proteins) have negative
‐ 45 ‐ GRAVY values indicating that these proteins are not hydrophobic. As anticipated, nearly
all of the OMPs (52 out of 63 identified in this study) and CYT proteins (36 out of 45
identified in this study) are hydrophilic. Again, proteins thought to reside in other
subcellular regions have negative GRAVY values as well (34 “unknown” proteins; 29
PERI proteins; 14 extracellular proteins; and 9 IMPs).
Our analyses also allowed for the detection of components of three known
macromolecular complexes that span the cell envelope: (1) the β-barrel assembly
machine (BAM); (2) the holdfast transenvelope apparatus; and (3) the flagella. BAM is
responsible for the proper folding and targeting of OMPs to the outer membrane of gramnegative bacteria [5]. In C. crescentus, the core unit is composed of four proteins (BamA,
BamB, BamD, and BamE) [3, 65] and at least one additional protein (Pal) that secures the
Bam complex to the peptidoglycan layer [65]. We identified BamA (CC1915),
lipoprotein BamB (CC1653) (a PERI protein) and lipoprotein Pal (CC3229). The
polysaccharide portion of the holdfast adhesion ‘organelle’ in C. crescentus is transported
to the cell surface by a transenvelope complex composed of HfsA, HfsB, and HfsD [66].
Additionally, the holdfast is anchored to the stalk of C. crescentus by HfaB and other
proteins [67]. We detected IMP HfsA (CC2431), PERI protein HfsD (CC2432), and
OMP HfaB (CC2629). Interestingly, HfsD has been experimentally confirmed to localize
to the OM region [68, 69] despite the contradictory PERI prediction generated by
Proteome Analyst and PSORTb in our study. We also identified numerous flagellar
proteins: four external flagellins (FljL (CC1460), FljM (CC0792), FljN (CC0793), FljK
(CC1461)); two hook-filament junction proteins (FlgL (CC0898), FlgK (CC0899)); the
flagellar hook assembly protein FlgD (CC0901); the flagellar basal body rod protein
‐ 46 ‐ FlgG (CC2064); the flagellar basal body P-ring protein FlgI (CC2582); and the flagellar
hook length determination protein FlbG (CC0900).
2.4. DISCUSSION
The goal of this study was to develop an optimized protocol for the improved
coverage of the OM proteome of a model gram-negative bacterium, C. crescentus. We
were able to do this by examining the effect of modifying several important parameters in
the OMP sample isolation and processing pipeline including: (1) the protein extraction
method; (2) the protein solubilization approach; (3) the mode of sample fractionation; (4)
the cell growth conditions; and (4) the presence of a highly abundant protein. As we
evaluated each variable, we continually improved the enrichment and detectability of the
OMPs. Using the final optimized procedure, we generated a sample that overwhelmingly
contained OM/extracellular proteins – 77% based on spectral counts – and less than 10%
contamination from each of the other subcellular regions. Importantly, nearly 70% (84
proteins) of the expected OM/extracellular C. crescentus proteome was detected.
In agreement with our expectation, this study showed that our optimized SLS
method is far superior to the traditional Na2CO3 approach for the selective isolation of
OMPs from C. crescentus. Numerous techniques have been used to isolate OMPs for
proteomic studies of various gram-negative bacteria including sucrose density gradient
centrifugation [10, 11, 18], differential detergent solubility [32, 52, 70], and washes with
chaotropic agents [12, 14] and/or alkaline buffers [13, 27]. We evaluated two of the more
commonly employed reagents for this type of work because they each have rapid and
‐ 47 ‐ fairly generic protocols across cell types. After obtaining SLS-insoluble OMs, we washed
them with solutions containing NaBr and Na2CO3. These two reagents have been used in
other membrane extraction protocols to effectively eliminate membrane-associated
components [12, 14] and most likely improved the performance of our method: The OM
protein profile was distinctly different from the IM protein profile. In contrast, the
Na2CO3 wash of total membranes left integral membrane proteins from both the IM and
the OM in the insoluble fraction (as postulated before [27]) in addition to leaving some of
each membrane in the supernatant as well (Fig. 2.3). Sodium carbonate has certainly been
used to generate impressive data in past OMP studies that relied on 2DGE [13, 27, 71].
However, these early OMP-focused efforts most likely benefited from the inherent bias
of the 2DGE analysis technique utilized at the time. OMPs are typically hydrophilic
proteins [72] and readily dissolve in IEF solution [27]; so, these proteins are not excluded
by 2DGE en mass. Conversely, many inner membrane proteins are very hydrophobic and
are under-represented after 2DGE separation [73]. Therefore, although adequate for a
2DGE study, an isolation step using Na2CO3 would not be the best choice for an unbiased
MS-based OMP study as indicated by our results.
This study also revealed that our multiplexed approach, referred to as Ga2DLC,
resulted in a substantial improvement in OMP identifications in particular in addition to
an increase in peptide identifications per protein on average when compared to GeLC.
There are two important reasons why these results were obtained. One, we improved the
efficiency of the protein dissolution step by using the gel-assisted digestion buffer. We
found that OMPs dissolved completely in this buffer but did not completely solubilize in
the SDS sample-reducing buffer. Although GeLC has been successfully utilized for many
‐ 48 ‐ different types of challenging samples [74-76], its general protocol has not been
optimized for analysis of membrane proteins. So, its poorer performance for OMP
analysis was an anticipated outcome. Second, we increased the resolving capacity of the
overall analytical technique by using RP chromatography in the first dimension
separation instead of SDS-PAGE. Our results concur with a previous study demonstrating
that two-dimensional fractionation schemes using peptide-level high pH RP-HPLC in the
first dimension have higher fractionation efficiencies than those using SDS-PAGE for
protein-level separation in the first dimension [55]. Admittedly, exhaustive replicate
analyses were not conducted in our proof-of-principle evaluation of GeLC versus
Ga2DLC. Nevertheless, we show that the Ga2DLC approach detected more
OM/extracellular proteins than the GeLC method and it nearly identified all of the OMPs
– 25 out of 28 – found in the GeLC analysis (Fig. 2.11).
‐ 49 ‐ Figure 2.11. Venn diagram illustrating the overlapping and uniquely identified
proteins using either the GeLC or gel-assisted methodologies. (A) Twenty-five OM
proteins were observed using both techniques. (B) One-hundred twenty-four total
proteins were observed in both experiments.
The results from our final set of experiments proved for the first time that careful
control of cell growth conditions and use of an S-layer deletion mutant [42] could further
enhance OM enrichment and OMP characterization. C. crescentus morphology and
physiology is certainly sensitive to its surroundings [77-80]. It is likely that
overpopulation of the cell flask led to a toxic environment causing not only cell death, but
also changes in protein expression and secretion due to cell stress. Indeed, high-density
cell culture affects the abundance and diversity of proteins found in other bacterial
internal and extracellular proteomes [81-83]. So, mitigation of this undesired situation by
lowering cell density allowed normal cellular equilibrium to be established, which
resulted in a decrease in co-purified non-OMPs. Ablation of RsaA even more markedly
‐ 50 ‐ facilitated OMP isolation and detection. Not only were the relative amounts of non-OMP
identifications reduced but RsaA removal allowed for the more efficient capture of a
greater number of OMPs and/or allowed for the detection of less abundant OMPs
previously obscured by RsaA. Our strategy using the RsaA– cells is similar to that used
by researchers working to discover novel biomarkers in bodily fluids. Depletion of highly
abundant proteins allows access to the numerous low abundance proteins of potential
biological significance [84]. This genetic approach, or alternatively low-pH extraction
[85], could be used to remove the S-layer protein in other gram-negative bacteria.
Overall, we suggest that careful control of cell density and the removal of the bacterial Slayer is of critical importance for “outer membrane proteomics” projects given the gain in
OMP purity and identifications we achieved by doing so.
The pooled results from all of the experiments described in this study allowed us
to detect 98 out of the 125, or nearly 80%, of the predicted OM/extracellular proteins
(Fig. 2.9). This is the largest coverage of the C. crescentus OM/extracellular proteome to
date. The overlap in terms of protein identifications between each optimization was
considerable (Fig. 2.9). However, the final experimental conditions clearly resulted in
improved identification of OMPs. An initial analysis of the C. crescentus outer
membrane proteome using 2DGE and MALDI-TOF MS was conducted by Phadke et al.
in 2001 [13]. They detected 54 proteins and 41 of these were OMPs as designated by
their prediction programs. As a point of comparison, 25 of the 41 “Phadke OMPs” are
contained within our comprehensive list of 98 OM/extracellular C. crescentus proteins
(Fig. 2.12). We should note that our protein list is the result of combining data from four
separate experiments with replicate analyses. It is unclear how many experiments and/or
‐ 51 ‐ replicates were conducted by Phadke et al. We also acknowledge that our study benefits
from highly sensitive, modern mass spectrometry instrumentation not available ten years
ago.
Figure 2.12. Venn diagrams illustrating the common and uniquely identified
proteins between our studies and Dr. Phadke’s work. (A) Twenty-five OM proteins
were observed in both studies. (B) Thirty-three total proteins were observed in both
studies.
In sum, we have demonstrated that not only is the choice of protein extraction
method and analytical approach important for “outer membrane proteomics,” at least two
other seemingly under-appreciated variables (i.e. cell culture conditions and presence of
S-layer protein) also make a significant difference in data quality. Furthermore, our
multiplexed approach to protein analysis using gel-assisted digestion in conjunction with
‐ 52 ‐ RP/RP-MS/MS (or Ga2DLC) performs better than GeLC-MS most likely due to both
superior separation efficiency and improved sample solubilization.
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‐ 59 ‐ Chapter3
IDENTIFICATION OF PUTATIVE SURFACE-EXPOSED PROTEINS OF
C. crescentus USING A BIOTINYLATION APPROACH
‐ 60 ‐ 3.1 INTRODUCTION
The surface proteins of Gram-negative bacteria are key players in the physical
interactions between the cell and its environment [1]. Surface proteins recognize host
molecules during pathogenic encounters, which induce a immunogenic response from the
pathogen such as immune evasion [2] and adhesion [3]. Thus, these functional surface
proteins provide a number of candidates for antibiotic development as well as direct
vaccine production [4, 5]. At present, there are significant efforts to discover novel
vaccines for protection against diseases using either genomic [6, 7] or proteomic [8, 9]
strategies. In chapter 2, we developed an OM isolation and separation technique
compatible with nano-flow LC-MS/MS shotgun proteomics to successfully identify 234
proteins. The results contained 36% of the predicted OM/EX proteins which covered
almost 70% of the expected OM/EX products from the C. crescentus genome [10].
However, 22% of the identified proteins failed to be assigned to a specific subcellular
location and several known OMPs were erroneously assigned to other regions [10]. This
is most likely due to the inconsistency in performance of current subcellular localization
predictors [11] and their incompetence to localize ‘anchorless’ proteins with no signal
sequence residues [12]. Thus, we want to explore an alternative method to directly label
surface-exposed membrane proteins in order to comprehensively map the outer
membrane proteome of C. crescentus.
Biotinylation has been demonstrated to be an effective technique for profiling cell
surface proteomes since the low molecular weight biotin tag does not change the
conformation of proteins and has high specificity for avidin purification [13-16]. The
bacterial membrane is impermeable to selective labeling regents, such as Sulfo-N ‐ 61 ‐ hydrosuccinimide (NHS)-long chain (LC) biotin and Sulfo-NHS-LC-LC biotin [17].
These biotin reagents derivatize primary amines at the ε-amino group of lysine residues
and N-termini of proteins which are exposed to the extracellular space. Therefore, the
labeled peptides should originate from the extracellular domains of transmembrane
proteins situated within the outer membrane [18]. This labeling method has been applied
to both eukaryotic and prokaryotic cell systems, such as ovarian cancer [19], human
mesenchymal stem cells [20] and Leptospira interrogans [21]. In those studies, the
enriched labeled proteins were analyzed using MS-based shotgun proteomic analysis.
This strategy generally is based on labeling cell surface proteins with biotin derivatives,
capturing proteins on immobilized avidins, elution and digestion of isolated proteins,
separation of resulting peptides, and identification of peptides via RPLC-MS/MS (Fig.
3.1).
Here, we described a strategy for large scale and selective identification of cell
surface proteins of C. crescentus using a biotinylation labeling and enrichment strategy
coupled with high resolution LC-MS/MS. The approach allowed us to indentify 91
proteins, of which almost 90% had membrane characteristics. Our study used the RsaA
gene deletion (RsaA-) mutant strains of C. crescentus described previously in chapter 2.
To our knowledge, this study provided the first global proteomics profile of surface
exposed proteins of C. crescentus. We have identified a large number of membrane
proteins with different biological functions, which would provide a catalogue for
potential vaccine development. Future application of this biotin-labeling approach in
combination with quantitative proteomics labeling, such as stable isotope labeling would
‐ 62 ‐ assist in the determination of the expression level of cell surface proteins on C.
crescentus.
Figure 3.1: Schematic of biotinylation strategies for identification of surface exposed
proteins using MS-based shotgun proteomics. The Sulfo-NHS-LC biotin (or other
impermeable biotin reagents) is used to label the outer portion of membrane proteins.
Intact cells undergo surface biotinylation and cell lysis. Labeled proteins are then
captured on immobilized streptavidin substrates. Purified proteins could be either
digested on bead or eluted prior to proteolytic digestion. Eluted peptides are analyzed by
LC-MS/MS. Modified from [22].
‐ 63 ‐ 3.2 MATERIALS AND METHODS
An overview of the cell surface labeling and enrichment strategy described in section
3.2.1 and 3.2.2 are shown in Figure 3.2.
Figure 3.2. Schematic diagram of biotinylation labeling and enrichment strategy. The avidin
enrichment method was conducted on both the protein and peptide levels.
3.2.1 Bacterial Growth and In vivo Cell Surface Labeling
Two biological replicates of RsaA-negative (RsaA-) C. crescentus cells were first
grown to the late exponential phase in 10 mL peptone yeast extract (PYE) medium. Then,
200 mL fresh PYE medium was inoculated with 10 mL cell cultures. The cells were
cultured at 30 ˚C with constant shaking at 100 rpm until an OD600 of 0.6 was reached.
‐ 64 ‐ Cells were labeled by EZ-linkTM Sulfo-NHS-LC-LC Biotin (Pierce, Rockford, IL)
followed by a previously described procedure with minor modifications [23]. In general,
cells were harvested by centrifugation at 4000g for 10 min at 4 ˚C and washed three times
with ice-cold phosphate buffered saline (PBS, 10 mM NaH2PO4/Na2HPO4, pH 7.4, 138
mM NaCl, 2.7 mM KCl) supplemented with 1 mM CaCl2, 1 mM MgCl2. Cells were then
resuspended in the same ice-cold PBS to adjust OD600 to approximately 2.0 value. After
resuspension, each cell cultures were spited into two aliquots, where one was ready for in
vivo biotinylation labeling, and the other half was served as negative control for further
comparison. The ‘biotinylation’ cells were incubated with 0.2 mM final concentration of
Sulfo-NHS-LC-LC Biotin for 1 h at room temperature with gentle rotation. The extra
residue of Sulfo-NHS-LC-LC Biotin were quenched by the addition of Tris-HCl (pH 7.5)
to a final concentration of 50 mM and incubated for 10min at room temperature. After
three time of wash with 50 mM Tris-HCl (pH 7.5), cells were resuspended in 50 mM
AMBIC (pH 7.5) with protease inhibitors (Complete EDTA-free, Roche Diagnostics). In
the meantime, the ‘negative control’ cells were incubated with nano-pure water instead of
the biotin reagent and underwent the same procedures. For the preparation of cell lysates,
both labeled and control cells were disrupted by repeated intermittent sonic oscillation
(20 × 5 s). Cellular debris was removed by centrifugation at 10,000g for 10 min.
Supernatant was collected and stored for further analysis. The Pierce BCA protein assay
was used to determine protein concentrations.
3.2.2 Affinity Capture of Labeled Cell Surface Proteins/Peptides
The biotinylated fractions were purified through avidin cartridge column
(ICATTM kit, AB SCIEX, Framingham, MA). We tested the isolation efficiency of the
‐ 65 ‐ avidin cartridge column on both biotinylate proteins and peptides (Fig. 3.2), which meant
that half of the labeling fractions were isolated by avidin column first and digested by
trypsin (the tryptic digestion protocol was described in 3.2.3.), the other half fractions
were carried for trypsin digestion first followed by avidin purification. The purification
procedure by avidin cartridge was followed as the manufacture suggested. In brief, the
avidin cartridge was activated sequentially using 2 mL of Affinity Buffer-Elute (30%
ACN and 0.4% TFA) and Affinity Buffer-Load (2× PBS, pH 7.5). 100 µg of labeling
proteins/peptides were combined with 500 µL Affinity Buffer-Load and loaded onto the
avidin column at slow injection speed (1 drop/5 seconds). After loading, the column was
washed by 2 mL of Affinity Buffer-Load, Affinity Buffer-Wash (1× PBS, pH 7.5) and
nano-pure water sequentially. Finally the biotinylated proteins/peptides were eluted off
the column using 1 mL of Affinity Buffer-Elute and collected in Eppendof tubes. After
the elutions were reduced to near dryness, the labeled peptides were reconstituted in 50
μL with 0.1% acetic acid for LC-MS/MS analysis, while the labeled proteins were first
reconstituted in 50 mL AMBIC (pH 8.0) followed by tryptic digestion, nearly dried by
centrivap and then reconstituted in 50 μL with 0.1% acetic acid for LC-MS/MS analysis.
3.2.3 Streptavidin Western Blotting
Biotinylated proteins (50 µg) were resolved in SDS-PAGE gels as described in
chapter 2. Proteinaeous samples were then electrophoretically transferred onto
nitrocellulose membranes (0.45 mm, Bio-Rad, Hercules, CA) at 4 ˚C for 90 min (17
mA/30V) using the Invitrogen XCell II Blot Module. Membranes were first rinsed with
wash buffer (25 mM Tris-HCl (pH 7.0), 1% Tween 20, 1 mM CaCl2, 1 mM MgCl2, 0.15
‐ 66 ‐ M NaCl) and subsequently blocked with the same buffer for 60 min. HRP-conjugated
streptavidin (diluted 1:1000 in wash buffer) was incubated with the membrane for 90
min. Then, the membrane was washed again (3×, 10 min each) in a buffer containing 25
mM Tris-HCl (pH 7.0), 1 mM CaCl2, 1 mM MgCl2, and 0.15 M NaCl. Finally, the blot
was visualized by ECL (SuperSignal® Chemiluminescent Substrate, Thermo Fisher
Scientific)
3.2.4 In-solution Tryptic Digestion
The biotinylated proteins were reduced with 5 mM TCEP for 1 h at room
temperature and alkylated with fresh IAA at 10 mM final concentration in the dark for 30
min at room temperature. The extra IAA was further quenched by 5 mM TCEP for 30
min at room temperature. Proteins were then digested with trypsin at an enzyme:
substrate ratio of 1: 50 (w/w) at 37 ˚C overnight.
3.2.5 nanoLC-MS/MS Analysis
LC-MS/MS analysis was performed using both QSTAR Elite QTOF mass
spectrometer (AB Sciex, Foster City, CA) equipped with a Tempo nanoLC system and
LTQ-Orbitrap velos (Thermo Fisher Scientific, Waltham, MA) equipped with a Agilent
1200 LC system (Agilent Technologies, Santa Clara, CA). Sample was loaded onto a precolumn (75µm × 3cm) packed with 5um Monitor C18 particles (Column Engineering.
Ontario, CA) and then eluted at a flow rate of 100 nL/min (Tempo nanoLC) or 200
nL/min (Agilent 1200 LC system) onto analytical columns (75 μm ID, 10 cm length of 3
µm Monitor 100Å-Spherical Silica C18; Column Engineering Inc., Ontario, CA) using
the following gradient: 0%-30% B in 60 min; 30%-60% B in 40 min; 60%-70% B in 20
‐ 67 ‐ min; 70% B for 10 min. Solvent B consisted of 0.1% formic acid and 2% water in ACN.
The ionization voltage was set at 1.8 kV. Precursor ions were scanned over the mass
range from 400-2000 and MS/MS spectra were acquired for selected ions under
automatic collision energy. For QSATR, MS data were acquired in informationdependent acquisition mode with Analyst QS 2.0 (ABSciex). MS cycles were comprised
of one full scan (m/z range = 400-2000, 2 sec accumulation) followed by sequential
MS/MS scans of the four most abundant ions (+2 to +4 charge state, minimum ion count
= 100, exclusion time = 15 sec, maximum accumulation time = 2 sec). For LTQ-Orbitrap
velos, the MS survey scan was performed in the FT cell recording a window between 400
and 2000 m/z. The resolution was set to 60 000, and the automatic gain control was set
to 106 ions. Minimum MS signal for triggering MS/MS was set to 1000, and m/z values
triggering MS/MS were put on an exclusion list for 120 s. Top 10 MS/MS spectra were
acquired with a data-dependent automatic switch per survey scan (+2 to +4 charge state).
3.2.6 Protein Identification by Database Search
Tandem mass spectrometry data from .WIFF and .RAW files were converted to
.mzXML format using the Trans-Proteomic Pipeline (version 4.4.0). The .mzXML files
were then converted to .MGF files via MassMatrix MS File Conversion Tools (version
3.8; www.massmatrix.net). The .MGF files were used for the Mascot search against the
RefSeq C. crescentus database (dated April 25, 2009) downloaded from NCBI. Mascot
parameters were as follows: precursor ion mass tolerance were set at 50 ppm (QSTAR) or
20 ppm (LTQ-Orbitrap) and fragment ion tolerance were set at 50 ppm (QSTAR) or 0.5
Da (LTQ-Orbitrap); carboxyamidomethylation was chosen as a fixed modification;
methionine oxidation, sulfo-NHS-LC-LC biotin (K/N-term) were chosen as a variable
‐ 68 ‐ modification; trypsin was selected as the enzyme; and two missed cleavage was
specified. For peptide and protein identifications in peptide-level enrichments, the search
results were processed as follows. (i) The candidate peptides were screened with the
Mascot score higher than 28 as well as y- or b- ions ≥ 4. (ii) The MS/MS signal
corresponding to the labeled lysine and N-terminal were manually examined to ensure the
presence of sulfo-NHS-LC-LC biotin modifications. (iii) Proteins identified without
biotin modifications were excluded regardless of their scores (not applicable for ‘proteinlevel isolation’ and ‘negative control’ samples). For peptide and protein identifications in
protein-level enrichments, only step (i) was applied and each protein was identified from
two unique peptides.
3.2.7 Bioinformatics
Two algorithms were used to predict each protein’s location in the cell: Proteome
Analyst 3.0 and PSORTb 2.0. The predictions made by each program had to agree in
order for a given protein to be assigned to a subcellular location. Otherwise, the protein
was assigned to the “unknown” category. For functional analysis on each protein,
TMHMM Server version 2.0 (www.cbs.dtu.dk/services/TMHMM/) was used to predict
the occurrence of transmembrane helices. DAVID Bioinformatics Resources V6.7 [24]
was used to extract biological functions (http://david.abcc.ncifcrf.gov/conversion.jsp).
3.3 RESULTS
3.3.1 Visualization of C. crescentus Surface Proteins
‐ 69 ‐ Most bacterial adhesins are membrane proteins containing extracellular domains
that specifically interact with host cell ligands [25]. We used a membrane-impermeable
biotinylation reagent (Sulfo-NHS-LC-LC Biotin) to specifically biotinylate the free
amines in order to detect surface-exposed proteins. After lysis of the labeled cells, debris
was removed by centrifugation. The recovered proteins were resolved on SDS-PAGE
gels and visualized with Coomassie blue staining and streptavidin western blotting.
Figure 3.3: Cell surface exposed proteins were successfully labeled by biotin
reagents. (A) SDS-PAGE analysis of both negative control and biotinylated samples.
Equal amounts of cell lysate (5 µg) were separated by SDS-PAGE gels and coomassie
blue stained. (B) Western blot analysis to determine the presence of biotinylated proteins
in labeled RsaA fractions. Equal amounts of cell lysate (20 µg) were separated using
SDS-PAGE and probed with HRP-streptavidin. The experiment was performed 2 times
with similar results. The experiment was performed 2 times with similar results.
Both non-labeled negative control and the biotinylated cell lysates showed almost
identical protein complexity (Fig. 3.3B). The biotinylated proteins were less complex
‐ 70 ‐ than the total whole cell lysate by comparing the protein band patterns of both results
(Fig. 3.3 A and B). However, there were still around 50 bands of labeled proteins in the
single gel lane from the WGA western blotting. The non-biotinylated sample only
contained 4 positive protein bands (Fig. 3.3 A), which suggested that there was few nonspecific binding proteins interacting with the streptavidin. Four proteins were detected in
the negative control replicates: molecular chaperone GroEL (CC_0685); alanine
dehydrogenase (CC_3574); glycine dehydrogenase subunit 1 (CC_3353) and
hypothetical protein (CC_1275). The theoretical molecular weights of these proteins
correspond to the ~ 40 kDa and 30 kDa, which correlate with the western data. The top
two gel bands may come from an oligomer of those three proteins or simply fail to be
detected through LC-MS.
3.3.2 Identifications of Biotinylated Proteins by LC-MS/MS: Protein-level Analysis
Most studies on bacterial surface proteins using biotinylation labeling
technologies involve an avidin affinity purification step at the protein-level instead of
isolating peptides in order to increase the sequence coverage of the identified proteins
[15, 21, 23]. However, a batch purification process was used in those studies. Nonbiotinylated proteins were washed away using high concentrations of salts or detergents
and the avidin beads were thrown away afterwards. In contrast, a reusable avidin column
was used with milder wash conditions in our work.
In order to evaluate the enrichment specificity of the avidin cartridge column on
both labeled proteins and peptides, the ‘negative control’ fractions (protein/peptide
isolations) were analyzed by LC-MS/MS at the beginning. There were four non-labeled
‐ 71 ‐ proteins identified in the peptide-level isolation of negative control fractions after
combining two biological replicates, whereas the protein-level negative controls were
possessed of 184 proteins (Fig. 3.4).
Figure 3.4: Venn diagram representation of protein identifications in both proteinlevel and peptide-level purified fractions (negative controls). Two biological
replicates were run on both samples.
The protein-level analysis led to the identification of 196 and 205 proteins from
each of the two biological replicates (Fig. 3.11A). In total, 233 proteins were
characterized from 672 unique peptide identifications after combining the two biological
replicates. Analysis of the peptide matches resulted in protein identification substantiated
by the identification of non-biotinylated tryptic peptides. In our analysis, only 10 proteins
(4.3 % of total proteins) were identified from MS/MS spectra of peptides possessing a
biotin label. Interestingly, these 10 proteins are all predicted to reside in the cell
envelope. MS analysis of the control samples yielded 184 protein identifications. After
removing these 184 “background binding” proteins from the 233 protein list, 66 proteins
remained and considered to be true biotinylated proteins (Fig. 3.11B). Almost 91% (167
‐ 72 ‐ proteins) of proteins identified from the control experiments were also presente in the
biotinylated samples, which indicats a high-level of non-specific binding between
proteins in this sample and the avidin beads acquired from the manufacturer.
Figure 3.11: Venn diagram representation of protein identifications in ‘proteinlevel’ purified biotin labeling fractions in RsaA-. Two biological replicates were
performed. A) 233 total proteins were characterized from two combined biotinylated
fractions. B) 66 proteins were considered as true positive biotin labeling proteins, since
they were uniquely identified in the biotinylated RsaA- fractions while other 167 proteins
were identified in both biotinylated fractions and negative controls.
‐ 73 ‐ Figure 3.12: Graphical representation of the predicted/annotated subcellular
distribution of biotinylated proteins identified in protein-level purified fractions
from C. crescentus RsaA- using Q-TOF method. Proteins were distributed in each pie
chart according to their annotated or predicted subcellular location based on the number
of proteins IDs per category. The percentage of biotinylated proteins identified in each
experiment is shown below each pie chart.
To evaluate the specificity of the biotinylation procedure for membrane proteins,
we annotated the subcellular localization of each identified protein using PSORTb and
Proteome Analyst as describe in chapter 2. This analysis allowed us to categorize proteins
as extracellular proteins, outer membrane proteins, periplasmic proteins, inner membrane
proteins, cytoplasmic proteins and proteins with unknown localization (if both predictors
failed to identify cellular localizations or the prediction results from the two software
were contradictory). In these protein-level avidin purifications, the 66 biotin-labeled
proteins included 2 EX proteins (3%), 13 OM proteins (20%), 14 periplasmic proteins
(21%), 11 IM proteins (17%), 15 cytoplasmic proteins (22%) and 11 protein with
unknown subcellular localization (17%) (Fig. 3.12, Table. 3.3). Compared to the peptidelevel avidin purifications (Fig. 3.7, Table. 3.2) (to be described in the next section), the
protein-level enrichments provided three-fold higher cytoplasmic protein identifications
‐ 74 ‐ (22% vs 7%) and two-fold higher inner membrane protein identifications (17% vs 8%),
respectively. We believe that these were caused by the excessive non-specific interactions
between the protein sample and the avidin column. In spite of having negative control
samples, it should be noted that there remains uncertainty in the confidence of
determining these 66 proteins as cell-surface proteins since biotinylated peptides were not
detected. In the future, other types of immobilized avidin reagents followed by batch
enrichment techniques [26, 27] should be used in order to specifically isolate biotinylated
proteins in order to increase peptide coverage for protein identifications.
Table 3.3 Summary of C. crescentus proteins identified in protein-level biotinylation
experimentsa.
Accession
Numbers
gi|16127499
gi|16127480
gi|16127277
gi|16125392
gi|16125140
gi|16125096
gi|16125017
gi|16124592
gi|16124303
gi|16124263
gi|16124419
gi|16124418
gi|16126907
gi|16124692
gi|16124519
gi|16126783
gi|16125142
gi|16127463
gi|16126575
gi|16126387
gi|16126105
gi|16125414
gi|16125261
gi|16124728
gi|16124621
gi|16124577
gi|16125024
gi|16124372
Descriptions
suppressor protein SuhB
aldolase, class II
polymerase sigma factor RpoD
reductase
4-phosphate synthase/GTP cyclohydrolase I
hypothetical protein
protein CheW
synthetase, beta subunit
pyrophosphatase
protein S20
hypothetical protein
hypothetical protein
dipeptidase, putative
protein CheYII
nucleosidase
aminopeptidase, putative
aminopeptidase-related protein
proton channel family protein
proton channel family protein
transporter, ATP-binding protein
transporter, ATP-binding protein
aminotransferase
secretion system, membrane protein RsaE
c reductase, cytochrome b
synthase F0, B subunit
transport protein ExbD
esterase, putative
hypothetical protein
‐ 75 ‐ Localizationa
CYT
CYT
CYT
CYT
CYT
CYT
CYT
CYT
CYT
CYT
CYT
CYT
CYT
CYT
CYT
EX
EX
IM
IM
IM
IM
IM
IM
IM
IM
IM
IM
IM
Gene
Locus
CC_3269
CC_3250
CC_3047
CC_1140
CC_0887
CC_0843
CC_0764
CC_0337
CC_0047
CC_0007
CC_0164
CC_0163
CC_2672
CC_0437
CC_0264
CC_2544
CC_0889
CC_3233
CC_2336
CC_2148
CC_1862
CC_1162
CC_1009
CC_0473
CC_0366
CC_0322
CC_0771
CC_0117
gi|16127730
gi|16127724
gi|16127406
gi|16127376
gi|16127243
gi|16126213
gi|16125994
gi|16125716
gi|16125351
gi|16125290
gi|16125235
gi|16125177
gi|16124465
gi|16127734
gi|16127281
gi|16127056
gi|16126816
gi|16126778
gi|16126570
gi|16126393
gi|16126239
gi|16126229
gi|16126157
gi|16125396
gi|16125227
gi|16125112
gi|16124617
gi|16127587
gi|16127130
gi|16126561
gi|16126409
gi|16126164
gi|16126162
gi|16125289
gi|16125262
gi|16124953
gi|16124949
gi|16124555
TonB receptor
OmpA family protein
ferredoxin/flavodoxin oxidoreductase family
protein
TonB receptor
TonB receptor
TonB receptor
TonB receptor
single-strand binding protein
TonB receptor
hypothetical protein
TonB receptor
hypothetical protein
TonB receptor
M13 family protein
domain family protein
PmbA family protein
M16 family
hypothetical protein
protein HU
dipeptidyl peptidase IV
M23/M37 family
oligopeptidase family protein
hypothetical protein
aminopeptidase, putative
hypothetical protein
ABC transporter, periplasmic sugar-binding
protein
ABC transporter, periplasmic phosphonatesbinding protein
hypothetical protein
carbamoyl phosphate synthase, large subunit
hypothetical protein
carboxylase, beta subunit, putative
uridylate kinase
diphosphate synthase
hypothetical protein
GDP-mannose 4,6-dehydratase
hypothetical protein
hypothetical protein
dipeptidase, putative
a
OM
OM
CC_3500
CC_3494
OM
OM
OM
OM
OM
OM
OM
OM
OM
OM
OM
PERI
PERI
PERI
PERI
PERI
PERI
PERI
PERI
PERI
PERI
PERI
PERI
CC_3176
CC_3146
CC_3013
CC_1970
CC_1750
CC_1468
CC_1099
CC_1038
CC_0983
CC_0925
CC_0210
CC_3504
CC_3051
CC_2824
CC_2578
CC_2539
CC_2331
CC_2154
CC_1996
CC_1986
CC_1914
CC_1144
CC_0975
PERI
CC_0859
PERI
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
CC_0362
CC_3357
CC_2900
CC_2322
CC_2170
CC_1921
CC_1919
CC_1037
CC_1010
CC_0700
CC_0696
CC_0300
The 66 biotinylated proteins were characterized from RsaA- biotinylation
experiments filtered by negative control results. b Protein subcellular localizations were
predicted by Proteome Analyst and PsortB.
‐ 76 ‐ The above results clearly show a high-level of nonspecific binding between our
avidin column and our protein sample. Therefore, decided to pursue a peptide-level
strategy instead. This strategy is described in the next section.
3.3.3 Identifications of Biotinylated Proteins by LC-MS/MS: Peptide-level Analysis
The LC-MS/MS analysis of the avidin-purified biotinylated peptide fractions
generated 1,024 (Q-TOF) and 2,133 (LTQ-Orbitrap) MS/MS spectra in a single 2.5-h
analysis. These spectra were assigned to 136 (Q-TOF) and 242 (LTQ-Orbitrap) unique
peptides by the Mascot search of Refseq C. crescentus CB15 database. Among those, 105
peptides (77.2%) were biotin-labeled in Q-TOF method and 196 peptides (80.9%) were
biotin-labeled in LTQ-Orbitrap method (Table 3.1, Fig. 3.6). A typical MS/MS spectrum
matched to a biotinylated peptide from TonB-dependent receptor is shown in Fig. 3.5.
Thus, all MS/MS spectra were carefully inspected using the criteria described in the
method section in order to ensure the existence of labeled biotin reagents on peptide
matches. The 105 (Q-TOF) and 196 (LTQ-Orbitrap) peptides were attributed to 61 and
91 unique labeled proteins, correspondingly. The ratio of biotin-labeled peptide/protein
was 1.7 in Q-TOF and 2.1 in LTQ-Orbitrap result. Although, LTQ-Orbitrap analysis
increased the MS spectra by 1.1 fold, number of unique peptides by 0.9 fold and number
of proteins by 0.5 fold, respectively, the biotinylated peptide coverage of each protein
only increased by 0.2 fold. These results clearly show that the low abundant proteins
were able to be identified by LTQ-Orbitrap duo to its higher frequent MS duty cycles and
higher number of parent ion selections than those in Q-TOF, but the ratio of biotinylated
peptide per protein remained at similar level in both methods. Further investigation
‐ 77 ‐ showed significant overlap between both MS methods, in which all 61 labeled proteins
from Q-TOF were also identified in 91 proteins from LTQ-Orbitrap (data were not
shown.)
Table 3.1 Summary of detected biotinylated proteins in C. crescentus RsaAfractions using Q-TOF and LTQ-Orbitrapa.
Q-TOF
LTQ-Orbitrap
BioRep1 & (2) Total
c
BioRep1 & (2) Total
Number of identified biotinylated peptides
81 (91)
105
152 (160)
196
Number of proteins
50 (56)
61
69 (73)
91
Number of extracellular proteins
1 (1)
1
3 (3)
4
Number of OMPs
8 (9)
10
15 (18)
19
22 (27)
30
33 (35)
41
Number of IMPs
3 (3)
4
2 (4)
6
Number of CYT proteins
2 (5)
5
4 (4)
5
Number of proteins with unspecified
location
8 (8)
11
11 (14)
16
b
Number of PERI proteins
a
The protein identifications in the table were from the (peptide level) enrichment
fractions by avidin columns. Since the (protein level) enrichment fractions had significant
non-specific binding contaminants from non-labeled proteins, all MS/MS data were
processed using the peptide-level fractions. b The biotinylated peptides were unique
peptides. c The total number of proteins/peptides are the combined results from two
biological replicates.
‐ 78 ‐ Figure 3.5: A MS/MS spectrum of a biotinylated peptides assigning to the TonBdependent receptor, a typical OMP in C. crescentus. The arrow annotations indicate
how the Sulfo-NHS-LC-LC biotin was interpreted by mass shift of 452.2 on the lysine
residue (labeled with the star) in both series of b and y ion fragments.
Among the total of 61 biotin-labeled protein identified from Q-TOF, (Table 3.1,
Fig. 3.7), 11 (18%) were predicted as OM/EX proteins. 30 (49%) are predicted as PERI
proteins which was almost half of the total identified proteins. 4 (7%) were predicted as
IM proteins. Finally, 5 (8%) and 11 (18%) were predicted as CYT proteins and proteins
with unknown localizations, respectively. Thus, at least 76% of the identified proteins
was predicted located in the cell envelop. Correspondingly, the LTQ-Orbitrap results
(they could also be considered as total combined results from both methods, since the
LTQ-Orbitrap contained all protein identifications from Q-TOF.) represented 23 (25%)
OM/EX proteins, 41 (45%) PERI proteins, 6 (7%) IM proteins, 5 (5%) CYT proteins and
‐ 79 ‐ 16 (18%) proteins with unknown localizations. In total, At least 77% of cell envelop
proteins were identified using both MS analysis. The under-representation of highly
abundant cytoplasmic proteins (8%) including 2 elongation factor proteins, 1 translation
factor protein, 1 ribosomal protein and 1 RNA synthesize protein indicated that the
biotinylation procedure did not cause cell lysis. A possible explanation is that these
cytoplasmic proteins were secreted out into the cell membrane during the biotin-labeling
process [28].
Figure 3.6: Venn diagrams illustrating the common and uniquely identified
biontinylated proteins between two biological replicates using both Q-TOF and
LTQ-Orbitrap LC-MS/MS.
‐ 80 ‐ Figure 3.7: Graphical representation of the predicted/annotated subcellular
distribution of biotinylated proteins identified in peptide-level purified fractions
from C. crescentus RsaA- using Q-TOF or LTQ-Orbitrap method. Proteins were
distributed in each pie chart according to their annotated or predicted subcellular location
based on the number of proteins IDs per category for either the (A) Q-TOF or (B) LTQOrbitrap method. The percentage of biotinylated proteins identified in each experiment is
shown below each pie chart.
3.3.4 Functional Characterization of Biotinylated Proteins from RsaA-.
The molecular functions of the total 91 potential surface-exposed proteins in this
study were classified according to the DAVID functional annotation clustering tool.
Functional annotation clustering [29] in DAVID generated the functional classification
by measuring the relationships among all Gene Ontology (GO) annotation terms on the
basis of the degree of their correlations with our protein lists to cluster into similar
annotation groups. This reduced the redundancy of different GO terms associating with
similar biological functions. In general, each functional cluster had one Enrichment Score
representing the geometric mean value of all p-values of each annotation term in this
group, which the p-value of the functional annotation was calculated by a Fisher’s exact
test to compare the degree of significance on each functional annotation in our results to
those in the total bacteria genome. The classification stringency was set at ‘highest’ level
‐ 81 ‐ and a higher Enrichment Score of the functional cluster indicated the annotation members
in this group played more important roles. Enrichment Score 1.3 correlated to non-log
number of 0.05. Thus, the annotation clusters with score ≥ 1.3 were considered as
significant in the study.
Figure 3.8: Functional classification of total 91 putative surface-exposed proteins
identified using both Q-TOF and LTQ-Orbitrap method. The Enrichment Score of
each cluster was labeled on the corresponding pie charts. The annotation terms were
according to the GO databases.
Among the total six functional groups, four groups were significantly enriched (≥
1.3) in the results including ion transporter, peptidase activity, cell membrane and protein
binding. Metal ion transporters was the top 1 score functional cluster consisting of 27
proteins (30.0% of total identified proteins). This group included various types of zinc
transporters, iron transporters, transition metal ion transporters and metallopeptidase. The
second ranking cluster was peptidase group, which consisted of 17 proteins (18.7%),
including serine-type peptidase and PDZ proteins. A subgroup of serine peptidase [30]
and PDZ proteins [31] were often associating with membrane domain to regulate
‐ 82 ‐ membrane transporters. Another two major categories were cell membrane proteins and
protein binding partners which included 17 (18.7%) and 12 (13.1%) proteins,
respectively. 11 conserved hypothetical proteins were not assigned into the functional
clusters which accounts for approximately 10% of total identified proteins.
Figure 3.9: Distribution of biotinylated proteins with multiple tranmembrane αhelices.
To further interpret the membrane features of our surface-labeled proteins,
transmembrane α-helices, a distinguishing characteristic of integral membrane proteins,
were inferred from primary structure of identified proteins (Fig. 3.9). Among 70
predicted cell envelope proteins, 67 proteins (95.7) had at least 1 transmembrane helice.
Since almost 50% of the identified proteins were predicted as periplasmic proteins, it
suggested that those periplasmic proteins could also have surface-exposed protein
segments reacting to the biotin reagent as well as OM/EX proteins. Interestingly, all 16
‐ 83 ‐ proteins with unknown localizations were in possession of at least 1 transmembrane
domain which indicated that they would be associated or localized on the bacterial
membranes. The proteins with TM domains ≥ 5 were from IMPs and OMPs. Only 8
proteins had no membrane domains containing 5 predicted cytoplasmic proteins, 1 IM
protein and 2 predicted periplasmic proteins.
3.4 DISCUSSION
The strategy described above demonstrated that the combination of cell surface
labeling with Sulfo-NHS-LC-LC biotin, purification of labeled peptides with avidin
affinity chromatography and high throughout LC-MS/MS analysis was a powerful
method to characterize cell surface subproteome of Gram-negative bacteria. We were
able to identify 83 (total 91) putative or potential cell surface proteins using this method.
In order to further validate the isolation specificity of membrane proteins using
the biotinylation strategy, we compared these results with the combined four OM
isolation results described in chapter 2 (Table 3.2, Fig. 3.9). There were a number of
membrane proteins identified in both two methods. Out of the 91 potential surfaceexposed proteins, 63 proteins were also identified in the OM isolations, which accounts
for 69.2% of the total identifications. After closely investigating the distribution of
subcellular localizations between the commonly identified proteins and uniquely
identified proteins only in biotinylation fractions, it indicated that the overlap between
proteins identified in both experiments depended on their predicted subcellular
localizations. Thus, out of the 63 common proteins, 23.8% were predicted OMPs,
‐ 84 ‐ whereas only 14.3% of unique biotin-labeled proteins were OMPs (Fig. 3.10). The
difference of distributions laid out in other subcellular localizations as well, which EX
proteins were 4.8% over 7.1%, periplasmic proteins were 43.2% over 50.0%, IMPs were
4.8% over 10.7% and cytoplasmic proteins were 8.0% over 0% out of the commonly
identified proteins over uniquely identified proteins in biotinylation fractions. In addition,
the OM isolation strategy obviously provided 2.2 fold more protein identifications (201
extra proteins) than the biotinylation method. However, the biotin-labeling approach
labels the lysine residues in the protein regions exposed to the cell surface, the bias for
enrichment was inherent. The 2DLC separation method would potentially be applied
prior to MS/MS analysis for the biotinylated samples as well in order to boost up the
number of protein identifications.
Table 3.2 Summary of C. crescentus proteins identified in both biotinylation
experiments and in OM isolation experiments.
Accession
Numbers
gi|16124614
Gene
Locus
Localization
b
Biotinylation
OM
fraction
CC_0359
CYT
×
×
gi|16124757
synthase, putative
RNA polymerase, beta
subunit
CC_0502
CYT
×
×
gi|16124838
transaminase, putative
CC_0584
CYT
×
×
gi|16124994
factor Tu family protein
CC_0741
CYT
×
×
gi|16127430
elongation factor G
CC_3200
CYT
×
×
gi|16124543
CC_0288
EX
×
×
gi|16125142
hypothetical protein
aminopeptidase-related
protein
CC_0889
EX
×
gi|16125963
hypothetical protein
CC_1719
EX
×
gi|16126783
aminopeptidase, putative
CC_2544
EX
×
gi|16124298
CC_0042
IM
×
gi|16125261
initiation factor IF-2
secretion system,
membrane protein RsaE
CC_1009
IM
×
gi|16125433
histidine kinase
CC_1181
IM
×
gi|16126400
carboxypeptidase
CC_2161
IM
×
gi|16126858
malic enzyme
cytochrome C,
membrane-bound
CC_2622
IM
×
CC_2935
IM
×
gi|16127165
Descriptions
‐ 85 ‐ ×
×
×
×
gi|16124343
conserved hypothetical
protein
CC_0088
OM
×
×
gi|16124419
hypothetical protein
CC_0164
OM
×
×
gi|16124465
TonB-dependent receptor
CC_0210
OM
×
×
gi|16124833
TonB-dependent receptor
alkaline
metalloproteinase,
putative
CC_0579
OM
×
×
CC_0746
OM
×
×
CC_0806
OM
×
×
gi|16125096
efflux system protein
conserved hypothetical
protein
CC_0843
OM
×
gi|16125177
hypothetical protein
CC_0925
OM
×
×
gi|16125235
TonB-dependent receptor
CC_0983
OM
×
×
gi|16125290
hypothetical protein
CC_1038
OM
×
gi|16124999
gi|16125059
gi|16125584
TPR domain protein
CC_1335
OM
×
gi|16125994
TonB-dependent receptor
CC_1750
OM
×
×
gi|16126213
TonB-dependent receptor
CC_1970
OM
×
×
gi|16126433
TonB-dependent receptor
CC_2194
OM
×
×
gi|16127243
TonB-dependent receptor
CC_3013
OM
×
×
gi|16127376
TonB-dependent receptor
CC_3146
OM
×
×
gi|16127724
OmpA family protein
CC_3494
OM
×
×
gi|16127730
TonB-dependent receptor
CC_3500
OM
×
×
gi|16127731
hypothetical protein
CC_3501
OM
×
gi|16124371
CC_0116
PERI
×
×
gi|16124617
hypothetical protein
ABC transporter,
periplasmic binding
protein
CC_0362
PERI
×
×
gi|16124649
hypothetical protein
CC_0394
PERI
×
×
gi|16124950
hypothetical protein
CC_0697
PERI
×
×
gi|16124952
hypothetical protein
CC_0699
PERI
×
gi|16125073
Cgr1 family protein
CC_0820
PERI
×
gi|16125236
M16 family
CC_0984
PERI
×
gi|16125396
aminopeptidase, putative
CC_1144
PERI
×
×
gi|16125531
CC_1282
PERI
×
×
gi|16125754
serine protease
conserved hypothetical
protein
CC_1507
PERI
×
×
gi|16126023
Thij family protein
CC_1779
PERI
×
gi|16126137
rotamase family protein
CC_1894
PERI
×
gi|16126157
CC_1914
PERI
×
gi|16126229
hypothetical protein
oligopeptidase family
protein
CC_1986
PERI
×
gi|16126239
M23/M37 family
CC_1996
PERI
×
gi|16126393
peptidase IV
CC_2154
PERI
×
gi|16126437
M1 family protein
CC_2198
PERI
×
‐ 86 ‐ ×
×
gi|16126468
hypothetical protein
CC_2229
PERI
×
×
gi|16126496
hypothetical protein
CC_2257
PERI
×
×
gi|16126700
phosphatase, putative
CC_2461
PERI
×
×
gi|16126778
hypothetical protein
CC_2539
PERI
×
×
gi|16126816
M16 family
CC_2578
PERI
×
×
gi|16126820
flagellar P-ring protein
CC_2582
PERI
×
×
gi|16126873
M16 family
CC_2638
PERI
×
×
gi|16126990
protease HtrA
CC_2758
PERI
×
×
gi|16127041
M20/M25/M40 family
CC_2809
PERI
×
gi|16127072
hypothetical protein
pilus assembly protein
CpaD
CC_2840
PERI
×
×
CC_2944
PERI
×
×
CC_2987
PERI
×
×
CC_3051
PERI
×
×
gi|16127174
gi|16127217
gi|16127281
gi|16127622
CC_3392
PERI
×
gi|16127665
hypothetical protein
carboxyl-terminal
protease
CC_3435
PERI
×
gi|16127671
hypothetical protein
CC_3441
PERI
×
gi|16127674
CC_3444
PERI
×
gi|16127719
hypothetical protein
penicillin-binding protein
AmpH, putative
CC_3489
PERI
×
×
gi|16127734
M13 family protein
CC_3504
PERI
×
×
gi|16127782
hypothetical protein
CC_3552
PERI
×
gi|16127814
M16 family
CC_3584
PERI
×
×
gi|16127896
hypothetical protein
CC_3666
PERI
×
×
gi|16127978
hypothetical protein
CC_3748
PERI
×
gi|16124752
protein L7/L12
CC_0497
Unknown
×
gi|16124949
hypothetical protein
CC_0696
Unknown
×
gi|16125357
beta-glucosidase
CC_1105
Unknown
×
gi|16125497
hypothetical protein
CC_1245
Unknown
×
×
gi|16125516
protein L15
CC_1267
Unknown
×
×
gi|16125631
aminotransferase class I
CC_1382
Unknown
×
gi|16125812
CC_1565
Unknown
×
×
CC_1972
Unknown
×
×
gi|16126217
phosphatase D
conserved hypothetical
protein
DNA topoisomerase IV,
subunit B
CC_1974
Unknown
×
×
gi|16126250
hypothetical protein
CC_2007
Unknown
×
gi|16126500
phosphoglycerate mutase
CC_2261
Unknown
×
×
gi|16126750
protein S4
CC_2511
Unknown
×
×
gi|16126770
hydrolase family protein
conserved hypothetical
protein
CC_2531
Unknown
×
×
CC_2536
Unknown
×
×
gi|16126215
gi|16126775
hypothetical protein
PDZ domain family
protein
‐ 87 ‐ ×
×
gi|16127055
CC_2823
Unknown
×
gi|16127130
PmbA family protein
carbamoyl phosphate
synthase, large subunit
CC_2900
Unknown
×
×
gi|16127941
hypothetical protein
CC_3711
Unknown
×
×
a
The 91 biotinylated protein identifications were correlated against the OM isolation
results which contains 292 proteins from all four experiments (chapter 2). b Protein
subcellular localizations were predicted by Proteome Analyst and PsortB.
Figure 3.10: Venn diagrams illustrating the common and uniquely identified
proteins between biotinylation experiments and OM isolation experiments. Pie charts
represented the subcellular locations of the uniquely indentified 28 proteins or the
commonly identified 63 proteins from the biotinylation experiments. The 292 proteins
were identified from OM isolations in the four experiments described in Chapter 2.
Among the total 28 uniquely identified proteins in biotin-labeled fractions,
putative aminopeptidase (CC_2544), TPR domain protein (CC_1335), rotamase family
protein (CC_1894) and hypothetical proteins (CC_3392, CC_3444, CC_3552) had been
‐ 88 ‐ previously characterized in a stalk isolated fractions [32, 33]. The hypothetical protein
(CC_1038) was confirmed as an outer microtubule [34]. M23/M37 family protein
(CC_1996) was localized at the base of the stalk at specific stages associating to the
peptidoglycan during the cell cycle of C. crescentus [35]. In addition, in a genomic
localization analysis using N-/C-terminal fluorescent protein fusions, five predicted
periplasmic proteins (CC_1996, CC_2944, CC_3051, CC_3435 and CC_3441) from our
biotin-labeled experiments were detected to either localized at ‘pole’ position of cells or
appeared ‘patchy/spotty’ patterns as cytoskeletal proteins involving in uncharacterized
filament systems [36].
Even though, our biotin-labeling results showed considerable specificity on cell
envelope proteins, we thought the potential permeation of the labeling reagent though
outer membrane of C. crescentus may still exist. In the total 91 putative surface-exposed
proteins, there were low amount of cytoplasmic proteins and IMPs accounting for 12% of
total protein identifications. The periplasmic proteins were the majority portion (45%) in
the sample pool, of which about 95% had TM domains. It was still not clear that all
periplasmic proteins had surface-exposed segments through outer membranes. As a
membrane-impermeable chemical, Sulfo-NHS-LC biotin had been suggested that it could
enter though the periplasm through OM porins [23], because the molecular weight of this
reagent is 557 Da, which is below the exclusion limit of 600-800 Da of outer membrane
porin channels in E. coli [37]. In this study, we used Sulfo-NHS-LC-LC biotin with
higher molecular weight of 670 Da and longer space arm (30.5 Å) to reduce the
permeation of membranes of C. crescentus. However, our labeling and isolation strategy
did not yield membrane proteins completely free of contamination. To fully overcome
‐ 89 ‐ this issue, polyethylene glycol (PEG) polymers would be substituted for LC arms on the
biotin labeling reagents to prevent permeation [38, 39].
In recent decade, it has been clearly demonstrated that the spatial positioning of
proteins play a key role in controlling the cell cycle of both prokaryotic and eukaryotic
systems [40, 41] thanks to the development of powerful imagining technologies and
genome engineering. Numerous proteins localize to specific subcellular locations in
bacteria, and this localization is essential for many important physiological and
behavioral processes. Next to temporal changes, spatial control of gene expressions is
utilized to coordinate chromosome replication [42] and segregation though cell division
[43, 44] and polar organelle development [45]. As examples in C. crescentus, the
localization of the tubulin-like FtsZ determines where cytokinesis will take place in
bacteria [46]. The localization of FtsZ changes from being dispersed from specific polar
to being positioned near midcell where cell division occurs [47] (Fig. 3.13), which
simultaneously initiate the peptidoglycan synthesis at predivision cell stage [48].
Additionally, FtsZ ring presents a high mobility until it is stabilized at the future polar
site of the daughter swarmer cell [49]. Moreover, many essential proteins that regulate
the progression of the cell cycle and cell differentiation in C. crescentus such as DivK,
PleC, and DivJ temporally localize to specific subcellular positions throughout the cell
cycle, and this localization is important for their functions as well [50, 51].
‐ 90 ‐ Figure 3.13: FtsZ forms a ring-structure to mediate the peptidoglycan (PG)
synthesis near midcell compartment during C. crescentus cell cycle [48]. FtsZ
assembles into a ring structure and immigrates from one side of pole to the midcell
position, where it mediates the PG synthesis by coordinating with MurG and MreB and
thereby redirecting the localization of the PG precursor synthesis near midcell. At the end
of cell cycle, MurG and MreB dissociates from the constriction site of FtsZ ring to
disperse the PG precursor along the side wall of the daughter cells.
Despite the impressive fluorescent analytical tools, analyses of the spatial
distribution of proteins in bacteria remain difficult and often suffer from great
uncertainty. One recent study of spatial process on the entire genome of C. crescentus
inserted fluorescent mCherry fusions into each gene sequence in order to globally image
the spatial distribution of proteins [36]. However, only 3,184 entry vectors (85%) were
successfully isolated from the total 3,763 predicted genes, and more regrettably only 289
(8% of whole genome) proteins were detectable using fluorescent microscopy. Besides
the low recovery of mCherry fusions, the extreme labor involvement is a primary
limitation for this technology as well. Therefore, our SLS containing differential
ultracentrifugation approach (chapter 2) and biotinylation of cell surface proteome
approach (chapter 3) offer a novel high-throughput methodology for rapid mapping of the
‐ 91 ‐ spatial blueprint of the entire proteome in C. crescentus. The differential
ultracentrifugation method not only isolates outer membranes, but also inner membranes
and the cytoplasm. If we could perform the osmotic separation for periplasm in a parallel
experiment [52], we could survey all possible subcellular compartments simultaneously.
We believe this proof-of-principle experiment offers a powerful proteomic platform for
global studies of spatial distribution. Eventually, this will advance our knowledge on how
proteome localization responds to growth conditions or pharmacological treatments in
bacterial systems.
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