Download Proteomic characterization of host response to viral infection

Institute of Biotechnology and
Department of Biosciences
Faculty of Biological and Environmental Sciences and
Helsinki Graduate Program in Biotechnology and Molecular Biology
University of Helsinki
Helsinki, Finland
Proteomic characterization of host response
to viral infection
Niina Lietzén
ACADEMIC DISSERTATION
To be presented for public examination with the permission of the Faculty of
Biological and Environmental Sciences of the University of Helsinki in the Auditorium
2402 (Telkänpönttö) at Viikki Biocenter 3, Viikinkaari 1, Helsinki, on May 16th at 12
o´clock noon.
Helsinki 2012
Supervisors
Docent Tuula Nyman
Institute of Biotechnology
University of Helsinki
Helsinki, Finland
Docent Sampsa Matikainen
Unit of Excellence in Immunotoxicology
Finnish Institute of Occupational Health
Helsinki, Finland
Thesis committee
Docent Jaana Vesterinen
Institute of Biomedicine
Biochemistry and Developmental Biology
University of Helsinki
Helsinki, Finland
Professor Juho Rousu
Department of Information and
Computer Science
Aalto University
Espoo, Finland
Reviewers
Docent Jaana Vesterinen
Institute of Biomedicine
Biochemistry and Developmental Biology
University of Helsinki
Helsinki, Finland
Docent Sampsa Hautaniemi
Institute of Biomedicine and
Genome-Scale Biology Research Program
Faculty of Medicine
University of Helsinki
Helsinki, Finland
Opponent
Professor Kris Gevaert
VIB Department of Medical Protein Research
and Department of Biochemistry
University of Ghent
Ghent, Belgium
Custos
Professor Kari Keinänen
Department of Biosciences
Faculty of Biological and Environmental Sciences
University of Helsinki
Helsinki, Finland
Cover figure: Cytoscape protein interaction network (Niina Lietzén)
ISBN 978-952-10-7955-9 (paperback)
ISBN 978-952-10-7956-6 (PDF)
ISSN 1799-7372
http://ethesis.helsinki.fi
Unigrafia
Helsinki 2012
TABLE OF CONTENTS
LIST OF ORIGINAL PAPERS
ABBREVIATIONS
ABSTRACT
1. INTRODUCTION..................................................................................................................... 1
1.1 Proteins and proteomics ................................................................................................. 1
1.2 Methods in proteomics ................................................................................................... 2
1.2.1 Sample prefractionation ........................................................................................... 3
1.2.2 Protein and peptide separation ................................................................................ 4
1.2.2.1 Gel-based methods ............................................................................................ 5
1.2.1.2 Chromatographic methods ................................................................................ 6
1.2.2 Mass spectrometry in proteomics ............................................................................. 8
1.2.2.1 Protein identification by mass spectrometry ...................................................... 9
1.2.3 Database search engines in protein identification ................................................... 11
1.2.4 Quantitative proteomics ......................................................................................... 12
1.2.5 Data analysis .......................................................................................................... 16
1.3 Innate immune system .................................................................................................. 18
1.3.1 Innate immune recognition of pathogens ............................................................... 19
1.3.2 Innate immune responses against viral infection .................................................... 21
1.3.3 Influenza A virus ..................................................................................................... 23
2. AIMS OF THE STUDY ............................................................................................................ 26
3. MATERIALS AND METHODS ................................................................................................. 27
3.1 Cells and stimulations.................................................................................................... 27
3.2 Subcellular fractionation and secretome analysis .......................................................... 27
3.3 Gel-based methods used in proteomic experiments ...................................................... 28
3.4 Quantitative analysis using iTRAQ.................................................................................. 29
3.5 Mass spectrometry ........................................................................................................ 30
3.6 Database searches......................................................................................................... 30
3.7 Protein classification, interaction networks and clustering analysis................................ 31
3.8 Immunological analyses................................................................................................. 32
3.9 Reagents ....................................................................................................................... 32
4. RESULTS .............................................................................................................................. 34
4.1 Proteomics is an efficient method to study innate immune responses in human
keratinocytes and macrophages .......................................................................................... 34
4.2 Mascot and Paragon give comparable protein identification results .............................. 36
4.3 Virus-induced responses in HaCaT keratinocytes and human primary macrophages ...... 37
4.3.1 Several inflammatory pathways are activated in human primary macrophages ...... 37
4.3.2 Viral infection triggers caspase-dependent apoptosis in human macrophages and
keratinocytes .................................................................................................................. 39
4.3.2.1 Prediction and identification of potential caspase cleavage targets from
proteomic data ............................................................................................................ 39
4.3.3 Influenza A virus infection and polyI:C transfection trigger significant protein
secretion from human primary macrophages .................................................................. 41
5. DISCUSSION ........................................................................................................................ 43
6. CONCLUSIONS AND FUTURE PERSPECTIVES ........................................................................ 49
ACKNOWLEDGEMENTS ........................................................................................................... 50
REFERENCES ........................................................................................................................... 51
LIST OF ORIGINAL PAPERS
This thesis is based on the following original articles that are referred to in the text by their
Roman numerals I-V.
I
Öhman T*, Lietzén N*, Välimäki E, Melchjorsen J, Matikainen S, Nyman TA (2010)
Cytosolic RNA recognition pathway activates 14-3-3 protein mediated signaling and
caspase-dependent disruption of cytokeratin network in human keratinocytes. Journal of
Proteome Research 9: 1549-1564.
- NL did the image analysis and comparison of 2D gels and participated in protein identification,
computational data analysis and writing the manuscript.
II Lietzén N, Öhman T*, Rintahaka J*, Julkunen I, Aittokallio T, Matikainen S#, Nyman TA#
(2011) Quantitative subcellular proteome and secretome profiling of influenza A virusinfected human primary macrophages. PLoS Pathogens 7: e1001340.
- NL was responsible of proteomic work, data analysis and writing the manuscript.
III Rintahaka J, Lietzén N, Öhman T, Nyman TA, Matikainen S (2011) Recognition of
cytoplasmic RNA results in cathepsin-dependent inflammasome activation and apoptosis in
human macrophages. The Journal of Immunology 186: 3085-3092.
- NL did the protein identifications and database searches for proteomic part of the work and
participated in making the manuscript.
IV Lietzén N*, Natri L*, Nevalainen OS, Salmi J, Nyman TA (2010) Compid: A new software
tool to integrate and compare MS/MS based protein identification results from Mascot and
Paragon. Journal of Proteome Research 9: 6795-6800.
- NL was responsible of testing the program and writing the manuscript. NL also participated in
developing the concept of the tool.
V
Piippo M, Lietzén N, Nevalainen OS, Salmi J, Nyman TA (2010) Pripper: prediction of
caspase cleavage sites from whole proteomes. BMC Bioinformatics 11: 320.
- NL was responsible of testing the tool and analyzing proteomic data with the tool. NL participated
in writing the manuscript.
*,# Authors with equal contribution
The original articles were reprinted with the permission of the original copyright holders.
ABBREVIATIONS
2-DE
two-dimensional gel electrophoresis
2D DIGE
two-dimensional differential gel electrophoresis
ESI
electrospray ionization
FDR
false discovery rate
FT-ICR
fourier transform-ion cyclotron resonance
GO
Gene Ontology
HILIC
hydrophilic interaction liquid chromatography
IFN
interferon
ICAT
isotope coded affinity tags
iTRAQ
isobaric tag for relative and absolute quantitation
LC
liquid chromatography
LIT
linear ion trap
MALDI
matrix assisted laser desorption ionization
MS
mass spectrometry
MS/MS
tandem mass spectrometry
m/z
mass-to-charge ratio
NLR
NOD-like receptor
PAMP
pathogen-associated molecular pattern
pI
isoelectric point
PMF
peptide mass fingerprint
polyI:C
polyinosic-polycytidylic acid
PRR
pattern recognition receptor
Q
quadrupole mass analyzer
RLR
RIG-I-like receptor
ROS
reactive oxygen species
RPLC
reversed-phase liquid chromatography
SCX
strong cation exchange chromatography
SDS-PAGE
sodium dodecyl sulfate polyacrylamide gel electrophoresis
SEC
size exclusion chromatography
SILAC
stable isotope labeling of amino acids in cell culture
TLR
toll-like receptor
TMT
tandem mass tag
TOF
time-of-flight mass analyzer
ABSTRACT
Proteomics is defined as large-scale study of proteins, and with current proteomic methods
thousands of proteins can be identified and quantified from a single experiment. Efficient
methods have also been developed for protein localization, posttranslational modification and
interaction studies. Mass spectrometry has an important role in proteomics and it is currently
used in almost all proteomic experiments to detect and characterize the proteins or peptides in a
sample. In addition, various bioinformatics tools have become increasingly important for
proteomics by improving data analysis and by helping in the biological interpretation of
complex proteomic data. The combination of proteomics and bioinformatics is nowadays an
important tool to study cellular signaling mechanisms under different conditions, for example
viral infection.
Viruses entering a host cell are first recognized by host´s innate immune receptors. This
recognition activates multiple signaling cascades resulting in antiviral immune responses,
inflammation and finally programmed cell death, apoptosis, of the infected cell. The detailed
mechanisms of host cell defense responses activated after viral infection are still partially
unknown. The aim of this project was to develop and utilize proteomic and bioinformatic
methods to characterize host responses to viral infection.
Three different proteomic approaches were used in this project to study virus-induced changes
in the proteomes of human epithelial cells and macrophages. First, cytosolic viral RNA-induced
responses in HaCaT keratinocytes were studied using cell fractionation, two-dimensional gel
electrophoresis and mass spectrometry (MS). Second, influenza A virus-induced changes in the
mitochondrial, cytoplasmic and nuclear cell fractions as well as in the secretomes of human
primary macrophages were characterized using iTRAQ labeling-based quantitative proteomics.
Third, cytosolic viral RNA-triggered protein secretion from human primary macrophages was
studied using qualitative high-throughput proteomics utilizing SDS-PAGE and liquid
chromatographic separations and MS. Various bioinformatics tools were also used to analyze
the protein identification and quantitation data. In addition, two computational tools, Compid
and Pripper, were developed to simplify the analysis of our proteomic data. Compid simplifies
the comparison of protein identification results from different database search engines and
Pripper enables the large-scale prediction of caspase cleavage products and their identification
from the collected MS data.
Our studies showed that both influenza A virus and cytosolic viral RNA trigger significant
changes in the proteomes of human primary macrophages and HaCaT keratinocytes. Virusinduced changes in the expression of 14-3-3 signaling proteins as well as rearrangement of host
cell cytoskeleton were detected in HaCaT keratinocytes. Caspase-3-dependent apoptosis was
detected in polyI:C transfected HaCaT keratinocytes as well as in influenza A virus infected and
polyI:C transfected human primary macrophages. Our studies with human primary macrophages
also showed that several inflammatory pathways, and especially the NLRP3 inflammasome, are
activated as a result of viral cytosolic RNA and influenza A virus infection. Additionally, we
showed that cathepsins, src tyrosine kinase and P2X7 receptor were involved in the
inflammasome activation. Finally, we showed that influenza A virus infection and polyI:C
transfection triggered extensive secretion of various different proteins. In conclusion, our
proteomic experiments have given an extensive view of cellular events activated in human
macrophages and keratinocytes after viral infection.
1. INTRODUCTION
Host cell defense responses against viruses are initiated immediately after viruses’ invasion to
the cell. Innate immune system is a complex network of interconnected biological pathways that
are responsible of organism’s first defence responses against viruses. Therefore, the study of
individual molecules or pathways is usually not sufficient to describe the effects of virus on host
cells (Gardy et al. 2009). Proteomics can be used to study protein expression levels,
localizations, posttranslational modifications and interactions in a cell at certain conditions
(Fields 2001). Various different methods have been developed for these purposes, and
especially with mass spectrometry (MS)-based proteomics, thousands of proteins can be
characterized in a single experiment (Geiger et al. 2012, Boisvert et al. 2012, Phanstiel et al.
2011). Proteomics has been used, for example, to study the expression and functions of viral
proteins (Shaw et al. 2008). Additionally, proteomics has been used to study interactions
between host and virus proteins (Naji et al. 2012) as well as virus-induced changes in host cell
proteomes (Vogels et al. 2011, Emmott et al. 2010b). Proteomic studies can give important
information about the cellular mechanisms activated by viral infection and could be utilized for
example to evaluate the pathogeneity of different viruses (Rasheed et al. 2009) or to develop
drugs and vaccines against viruses. In this Ph.D. project, different proteomic approaches were
utilized to study virus-induced events in human primary macrophages and epithelial cells.
1.1 PROTEINS AND PROTEOMICS
Proteins are the workhorses of a cell. They are the molecular instruments expressing genetic
information stored in DNA or RNA. Proteins are involved in almost all biological processes of a
cell. Gene expression and thus the level of proteins in a cell is constantly regulated by several
different processes. Rate of transcription, posttranscriptional processing and degradation of
mRNA as well as rate of translation, posttranslational modification, degradation and transport of
proteins all affect on the protein contents of a cell in certain conditions (Figure 1). The most
important factor affecting protein levels in a cell is the rate of translation (Schwanhäusser et al.
2011). Therefore, the study of protein contents of a cell gives the most information about the
biological processes active in a cell at certain conditions.
1
Figure 1. Gene expression in eukaryotic cells is regulated at several different stages.
Proteome is the entire set of proteins expressed by a cell, tissue or organism at given time under
certain conditions (Wilkins et al. 1996). The concept of proteomics evolved in 1990s to describe
large-scale studies of proteins. In addition to protein levels, protein-protein interactions, protein
localization and posttranslational modifications influence the physiological state of a cell.
Therefore, the aim of proteomics is to identify all the proteins present in a sample, to quantify
them and to study their localizations, posttranslational modifications and interactions (Fields
2001). At present, thousands of proteins can be identified and quantified in a single proteomic
experiment (Geiger et al. 2012, Boisvert et al. 2012, Luber et al. 2010). Efficient methods have
also been developed for extensive studies of protein posttranslational modifications and
interactions (Phanstiel et al. 2011, Kim et al. 2011, Li et al. 2011, Rees et al. 2011). Thus,
proteomics can be used to study molecular mechanisms active in a variety of biological systems.
1.2 METHODS IN PROTEOMICS
There is a large variety of methods available for proteomic experiments nowadays. The method
of choice is often determined by the biological question, sample material, costs and
instrumentation available for the experiments. However, most of the modern proteomic
experiments utilize MS in the analysis of complex protein samples. MS-based proteomic
analyses can be roughly divided into two classes: bottom-up and top-down proteomics (Kelleher
et al. 1999). The most common approach at the moment is bottom-up proteomics which relies
on MS analysis of proteolytic peptides followed by protein inference using computational
methods. Despite the wide range of methods available, there is a common workflow for almost
all bottom-up proteomic experiments (Figure 2). In top-down proteomics, intact proteins are
2
injected into a mass spectrometer and fragmented there to characterize them. Top-down
proteomic experiments are still quite rare because of both technical challenges and the
difficulties in data analysis (Zhou et al. 2012).
Figure 2. General workflow for bottom-up proteomic experiments.
1.2.1 Sample prefractionation
Proteomic samples originate from various sources: for example from cell cultures, tissues or
biological fluids. There are often thousands or tens of thousands of distinct proteins in one
sample and concentration range between low- and high-abundant proteins can be several orders
of magnitude. Therefore, various prefractionation and enrichment methods are often needed to
improve the analysis of such complex samples.
Subcellular fractionation is often used in proteomics to simplify complex samples.
Mitochondrial, cytoplasmic, and nuclear fractions as well as other cell compartments can be
extracted from intact cells and studied separately (Andreyev et al. 2010, Qattan et al. 2010, Du
et al. 2010). Enrichment of proteins into different cell fractions may facilitate the detection of
low-abundant proteins (Du et al. 2010). Additionally, subcellular fractionation can give
important insights into cellular events since protein localization is often important for its
function (Qattan et al. 2010). Sucrose gradient density centrifugation, immunoaffinity
purification and free-flow electrophoresis are often used for cell fractionation in proteomic
experiments (Hartwig et al. 2009, Lee et al. 2010). In addition, different commercial kits have
been developed for the enrichment of specific subcellular organelles (Hartwig et al. 2009).
Regardless of the method used for cell fractionation, the enriched fractions usually contain
impurities from other cell compartments. On the other hand, proteins may also exist in multiple
subcellular compartments and those localizations may vary between different conditions (Qattan
et al. 2010, Boisvert et al. 2010, Lee et al. 2010). Also, database annotations of proteins´
subcellular locations are still incomplete and often show only one location per protein.
Therefore, it can be difficult to evaluate the quality and results of cell fractionation experiments
and care must be taken when reporting these results.
3
In addition to cell fractionation, several different affinity-based methods can be used to extract a
certain group of proteins from complex biological samples. In phosphoproteomics, several
different affinity enrichment methods like immunoaffinity enrichments, titanium dioxide
chromatography and immobilized metal ion affinity chromatography are used to extract
phosphoproteins or –peptides from complex protein mixtures (Thingholm et al. 2009). Affinity
enrichments are also used to study other types of posttranslational modifications like protein
glycosylation (Vandenborre et al. 2010) and ubiquitination (Hjerpe et al. 2009) as well as
protein-protein interactions (Rees et al. 2011).
1.2.2 Protein and peptide separation
After the initial prefractionation or enrichment steps, proteomic samples might still contain
hundreds or thousands of distinct proteins making direct MS analysis of these complex samples
challenging. When multiple peptides are introduced to the mass spectrometer simultaneously,
the instrument may not have enough time to fragment and analyze all of them. Additionally,
simultaneous ionization of multiple different peptides may result in signal suppression based on
different ionization properties of peptides causing signal losses for some of the peptides
(Horvatovich et al. 2010). Therefore, efficient separation of peptides prior to MS is important to
decrease the number of different peptides entering the mass spectrometer simultaneously and to
increase the dynamic range of analysis. Increased separation efficiency can also result in more
peptide identifications and thus more and better quality protein identifications per sample.
The most common separation methods used in proteomics are two-dimensional gel
electrophoresis (2-DE), sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDSPAGE) and liquid chromatography (LC). Complexity of samples introduced to the mass
spectrometer varies clearly between these methods (Figure 3). Simple peptide mixtures
originating from one or few proteins can be extracted from 2D gels and introduced to the mass
spectrometer. SDS-PAGE and LC separations, on the other hand, result in highly complex
peptide mixtures where links between original proteins and resulting peptides are mostly or
completely lost prior MS analyses. Therefore, each of these separation methods has its own
requirements for MS analysis and the following protein identification.
4
Figure 3. Protein/peptide separation methods commonly used in proteomics. A) 2-DE
separation of proteins results in protein spots containing only one or a few proteins. The spots
are excised from the gel individually followed by MS analysis. B) SDS-PAGE gives rough
separation of proteins and is often followed by LC separation prior MS analysis. C) In LC
separation-based approaches, the whole sample is digested into peptides prior separation. Thus,
all LC fractions analyzed by MS contain peptides from multiple proteins.
1.2.2.1 Gel-based methods
In the early days of proteomics, two-dimensional gel electrophoresis was the most important
method for protein separation (Wilkins et al. 1996, Görg et al. 2004). In 2-DE, proteins are first
separated based on their isoelectric points (pI) using isoelectric focusing followed by size-based
separation using SDS-PAGE. As a result, a two-dimensional map of protein spots is created in
the gel. The protein spots are then visualized using different staining methods, for example
fluorescent dyes (Ünlü et al. 1997, Berggren et al. 2000) or silver staining (O´Connel et al.
1997). Quantitation of protein spots is done based on the intensity of staining in each spot.
Finally, the protein spots of interest can be picked from the gel for identification, in-gel
digested, and the resulting peptides can be analyzed by MS.
2-DE aims at complete separation of proteins resulting in gels where each spot represents only
one protein. In a routine experiment, even 2000 protein spots can be separated in a single gel
providing adequate resolution for many proteomic experiments (Görg et al. 2004). 2-DE is a
valuable method to study for example protein degradation since protein fragments can be easily
5
detected based on their vertical positions in a gel (Bredemeyer et al. 2004). 2-DE can also be
used to visualize changes in protein posttranslational modifications since, for example, different
phosphorylation and glycosylation states of a protein can often be seen as a horizontal series of
spots in a 2D gel (Koponen et al. 2011, Di Michele et al. 2010). However, 2-DE has limitations
in the analysis of certain protein classes: the limited solubility of hydrophobic proteins hinders
their analysis with 2-DE (Rabilloud et al. 2010). Also, proteins with extreme pIs can often not
be detected in a 2D gel because of the limited pI range in isoelectric focusing.
Another method for protein separation in a gel is one-dimensional SDS-PAGE. It is a robust
method for protein separation and is more universal than 2-DE because it does not suffer from
limited solubility of hydrophobic proteins or limited protein pI range in isoelectric focusing. In
SDS-PAGE, proteins are separated in a gel based on their size, and only partial separation of
proteins in a complex mixture is achieved. SDS-PAGE is often used as a first separation step for
complex protein samples and is usually followed by slicing of the gel into pieces, in-gel
digestion of proteins in each gel piece and finally identification of the extracted peptides by LCMS/MS (Fang et al. 2010, Boisvert et al. 2012, Savijoki et al. 2011). For quantitative analysis,
SDS-PAGE separations can also be easily combined with protein labeling using stable isotopes
(Ong et al. 2003, Boisvert et al. 2012).
1.2.1.2 Chromatographic methods
In addition to gel-based separation, liquid chromatography (LC) is often used in proteomics to
simplify complex samples prior MS analysis. Since proteins are very diverse in their chemical
properties, it is usually easier to optimize an LC separation for a set of peptides with more
uniform characteristics (Motoyama and Yates 2008). Several different LC methods can be used
for peptide separation. Reversed-phase liquid chromatography (RPLC) separates peptides based
on their hydrophobicity, strong cation exchange chromatography (SCX) based on their ionic
character, hydrophilic interaction liquid chromatography (HILIC) based on peptide
hydrophilicity and size exclusion chromatography (SEC) based on peptide size. Unlike many
other LC methods, SEC separations can also be easily optimized for a heterogeneous mixure of
intact proteins and it is therefore used in proteomics to separate both peptides and proteins (Lee
et al. 2011, Wisniewski et al. 2010). Finally, affinity chromatography is used for peptide or
protein separation based on certain specific characteristics like posttranslational modifications
(Thingholm et al. 2009).
6
Digestion of protein mixtures into peptides increases sample complexity significantly resulting
in tens or hundreds of thousands of different peptides with a concentration range over several
orders of magnitude. With such complex samples, a single separation step does usually not give
sufficient separation. The power of multidimensional liquid chromatography in proteomics was
recognized in the beginning of 21st century when Washburn et al. used a combination of RPLC
and SCX to identify almost 1500 yeast proteins (Washburn et al. 2001). Since then,
multidimensional LC has been used extensively for various proteomic purposes (Table 1). For
maximal separation efficiency, retention mechanisms of each LC separation step should be as
independent from each other as possible. Orthogonality studies of Gilar et al. showed that fairly
good orthogonality for peptide separation can be achieved by various combinations of LC
methods (Gilar et al. 2005). However, RPLC is usually chosen as the last separation method due
to the compatibility of RPLC eluents with MS analysis. An alternative approach for
multidimensional chromatographic analyses is COFRADIC (combined fractional diagonal
chromatography), where peptides fractionated in the first dimension are enzymatically modified
based on a specific characteristic (e.g. N-terminal peptides) followed by second separation step
and peptide sorting (Gevaert et al. 2005). Here, the properties of peptides are modified instead
of chromatographic conditions to yield a good separation for a specific peptide class.
During the last 10 years, liquid chromatography has become an increasingly popular separation
technique in proteomics. Due to the very limited amount of sample available for most
experiments, development of efficient nanoscale LC separation methods has been important for
proteomics (Shen et al. 2002). Additionally, multidimensional LC separations are easily
automated, minimizing the amount of manual work in each analysis. Ultra high pressure LC
systems have shown an increase in separation performance as well as a decrease in separation
time, both features being important for high-throughput proteomic experiments (Nagaraj et al.
2012). Flexibility, variability and separation power of LC and its compatibility with many other
primary separation methods such as SDS-PAGE and peptide isoelectric focusing (Boisvert et al.
2012, Martins-de-Souza et al. 2009) have made LC a central method in proteomics.
7
Table 1. Examples of different multidimensional LC separations used in proteomic
experiments. pepSEC = size exclusion chromatography for peptides.
1st
dimension
HILIC
2nd
dimension
RPLC
pepSEC
study
reference
phosphoproteomics for estrogen-induced
transcriptional regulation
Wu et al. 2011
RPLC
biomarkers for hepatocellular carcinoma
Lee et al. 2011
RPLC
(pH 10)
RPLC
(pH 2)
human NK cell proteome
Dwivedi et al.
2008
SCX
RPLC
iTRAQ quantitation of Saccharomyces
cerevisiae proteome
Ross et al. 2004
SCX
RPLC
yeast proteome
Washburn et al.
2001
1.2.2 Mass spectrometry in proteomics
Mass spectrometry is a technique used to measure mass-to-charge ratios (m/z) and abundancies
of gas phase ions that are introduced into a mass spectrometer. The technique can be used to
analyze any molecule that can be converted into a sufficiently stable gas phase ion. In 1980s,
development of electrospray ionization (ESI) (Yamashita and Fenn 1984) and matrix assisted
laser desorption ionization (MALDI) (Karas et al. 1985) enabled the efficient ionization and
subsequent MS analysis of large biomolecules like proteins and peptides (Fenn et al. 1989,
Hillenkamp et al. 1990). A few years later, nanoESI was developed for the efficient analysis of
small sample amounts (Wilm & Mann 1996). These developments in biological mass
spectrometry started a new era of protein analysis and were also crucial for the development of
modern MS-based proteomics.
Sensitivity, variability and resolution of MS are nowadays utilized in many fields of proteomics,
for example in protein identification and quantitation (Boisvert et al. 2012, Bluemlein et al.
2011, Luber et al. 2010), characterization of posttranslational modifications (Kim et al. 2011,
Rajimakers et al. 2010) and study of protein complexes and protein-protein interactions (Li et al.
2011, Rees et al. 2011). Different types of instruments have been built to meet the requirements
for different types of proteomic experiments (Table 2). Quadrupole mass analyzers (Q), time-of8
flight (TOF) analyzers, fourier transform ion cyclotron resonance instruments (FT-ICR), ion
traps and various hybrid instruments have been utilized in proteomics, each of them having their
own advantages and disadvantages (Domon and Aebersold 2006). One of the newest inventions,
Orbitrap mass analyzer with high mass accuracy and high resolution (Makarov 2000, Hu et al.
2005), has pushed the boundaries of proteomics by increasing the amount of information that
can be collected from the complex samples.
Table 2. Recent examples of proteomic experiments utilizing different mass analyzers.
large-scale
proteome
characterization
quantitative
proteomics
LTQ-Orbitrap
Geiger et al. 2012,
Nagaraj et al. 2011,
Trost et al. 2009
Boisvert et al. 2012,
Monetti et al. 2011,
Luber et al. 2010
Q-TOF
Savijoki et al. 2011,
Dwivedi et al. 2008
Bewley et al. 2011
mass
analyzer
TOF-TOF
FT-ICR
Pounds et al. 2008
QQQ
posttranslational
modification
analysis
Kim et al. 2011,
Monetti et al. 2011,
Lemeer et al. 2012
Rajimakers et al. 2010,
Lemeer et al. 2012
Holland et al. 2011
Holland et al. 2011,
Lemeer et al. 2012
Collier et al. 2010
Wang et al. 2011
Bluemlein et al. 2011
1.2.2.1 Protein identification by mass spectrometry
In bottom-up proteomics, mass spectrometer is used to detect and identify peptides rather than
proteins (Kelleher et al. 1999). Protein identifications are then retrieved based on peptide
identification data using different computational tools. MS-based peptide identification and
protein inference can be done by peptide mass fingerprinting (PMF) or by utilizing tandem mass
spectrometry (MS/MS).
9
In PMF analyses, all peptides from one or a few proteins are ionized and introduced to the mass
spectrometer simultaneously. Mass spectrometer measures all the masses of ionized peptides,
and this combination of peptide masses is considered to be a characteristic “fingerprint” of a
protein that can be searched for (Figure 4A) (Gevaert and Vandekerckhove 2000). In PMF,
protein identifications are based solely on unique sets of peptide masses and no information
about peptide sequences is collected. Therefore, several peptide masses have to be detected for
each protein to be able to uniquely assign these masses to a certain protein. PMF analyses are
typically used for samples that contain peptides from only one or a few proteins. Therefore, 2DE separation of proteins followed by MALDI-TOF analyses of protein spots is the most
common workflow for PMF analyses.
Figure 4. Principles of protein identification based on A) PMF and B) MS/MS analyses.
In MS/MS-based analyses, m/z-values of the ionized peptides are measured first, followed by
fragmentation of selected m/z-values and detection of the resulting fragment ions (Figure 4B)
(Domon and Aebersold 2006). Fragment ion masses contain information about peptide sequence
and thus, both intact peptide masses and sequence information can be retrieved resulting in
more reliable protein identifications. MS/MS analyses are used especially in shotgun proteomic
experiments where complex proteomic samples are first digested followed by LC separation and
ESI-MS/MS analysis of the resulting peptides.
10
1.2.3 Database search engines in protein identification
High-throughput LC-MS/MS experiments can produce even one million mass spectra per
experiment making manual data interpretation impossible. Therefore, various database search
engines have been developed to process raw MS data. Sequence searching is the most common
method for MS-based protein identification in proteomics. Mascot (Perkins et al. 1999), Sequest
(Eng et al. 1994), X!Tandem (Craig et al. 2004) and Paragon (Shilov et al. 2007) are commonly
used sequence database search engines for proteomic purposes. In sequence searching, data
analysis consists of two consecutive steps: peptide identification and protein inference (Deutsch
et al. 2008, Nesvizhskii 2007). In the first step, in silico digestion of all proteins in the protein
sequence database is performed. The peptides are created and studied based on user-defined
criteria like enzyme specificity, mass tolerance and potential posttranslational modifications.
Most database search engines try first to find matches between in silico peptides and
experimental data based on intact peptide masses. In silico fragment ion spectra are then created
for candidate peptides. After this, a list of potential peptide-spectrum matches is created and
qualities of each match are evaluated based on different scoring schemes. In the second step of
analysis, peptide identifications are grouped to yield protein identifications. Most database
search engines have their own grouping algorithms that handle peptide identification data in
slightly different ways resulting in partially different protein identification results for the same
set of data. However, a common principle in most search engines is to try to find a minimum set
of proteins that can explain all the identified peptides. Sequence searching is a suitable method
to study already sequenced organisms since only peptides and proteins whose sequence is
present in a database can be detected (Nesvizhskii 2007).
Another type of search engines identifies peptides based on spectral matching of previously
observed and identified MS/MS spectra with the collected MS/MS spectra (Lam 2011, Craig et
al. 2006, Lam et al. 2007). At present, vast numbers of MS/MS spectra are stored in different
data repositories, and it is possible to build extensive spectral libraries from these data (Lam et
al. 2011). Comparison between spectral matching search engine SpectraST and sequence
database search engine Sequest showed that spectral matching can be a faster and more accurate
method for peptide identification than traditional sequence database search engines (Lam et al.
2007). However, quality of MS/MS spectra included in libraries and limitations in library
coverage have to be considered when identifying peptides based on spectral matching.
11
A third type of search engines are de novo sequencing algorithms which try to read the peptide
sequence directly from MS/MS spectra (Deutsch et al. 2008, Nesvizhskii 2007). This method
requires no prior knowledge of peptide sequences that are identified. However, de novo
sequencing is a computationally heavy process and requires good-quality MS/MS spectra for
peptide identification. Additionally, problems with protein inference may appear with complex
samples.
Since protein identifications can be performed in numerous different ways using various tools, it
is important to be able to evaluate the quality of protein identifications retrieved from a search
engine. Most database search engines like Mascot, Sequest and Paragon use a statistical scoring
mechanism to assess the reliability of protein identifications. In addition, false discovery rates
(FDRs) based on for example target-decoy searches are often used to evaluate the reliability of
identifications (Elias and Gygi 2007). In target-decoy strategies, database searches are
performed against a composite database of target protein sequences and decoy sequences, the
reversed or randomized counterparts of the target protein sequences. Based on the assumption
that an incorrect peptide assignment is equally likely to originate from a target or a decoy
database, the number of decoy identifications can be used to estimate the total number of
incorrect assignments. However, the identities of these false positive assignments can not be
determined.
1.2.4 Quantitative proteomics
In proteomics, it is often necessary to study changes in protein levels in different conditions.
Although proteomic methods for both relative and absolute quantitation have been developed,
absolute quantitation is rarely performed in proteomic experiments (Elliott et al. 2009).
Peptide´s physicochemical properties affect its ionization efficiency and thus the detected signal
intensity in a mass spectrometer. Therefore, a reference ion with known concentration is always
required to determine the absolute amount of interesting peptides in a sample. This requirement
of reference compounds limits the use of absolute quantitation in proteomics. Instead, most
proteomic experiments utilize relative quantitation to study changes in protein levels between
different samples. This relative quantitation of proteins can be achieved using either gel-based
or MS-based quantitation methods.
12
In gel-based quantitative proteomics, proteins are usually separated using 2-DE and quantitation
is performed based on protein spots detected from the gel. Gels from different samples can be
matched and quantitation done based on the intensities of corresponding protein spots in distinct
gels. Here, fluorescent dyes (Berggren et al. 2000, Ünlü et al. 1997) or silver staining (Chevallet
et al. 2006) are often used for spot detection. Two samples can also be labeled with different
fluorophores and separated in the same gel using two-dimensional differential gel
electrophoresis (2D DIGE) (Ünlü et al. 1997). Then, relative quantitation is done based on the
intensities of the different fluorophores in the same spot. The development of 2D DIGE has
improved the quantitation accuracy as well as sensitivity of gel-based quantitative proteomics
(Marouga et al. 2005). However, dynamic range of different staining methods used in 2-DE is
limited compared to the huge differences in protein abundances in real biological samples
(Rabilloud et al. 2010). Sensitivity of staining methods is also sometimes limited hindering the
detection of low abundance proteins from the gel and thus their identification and quantitation.
In addition, complete separation of proteins is required for quantitative analysis because if more
than one protein is present in a single spot, quantitation data cannot be assigned to either of
them.
In MS-based quantitative proteomics, different labeling methods or label-free approaches can be
used for protein quantitation. Some of the most common labeling strategies in proteomics are
stable isotope labeling of amino acids in cell culture (SILAC) (Ong et al. 2002), isotope-coded
affinity tags (ICAT) (Gygi et al. 1999), isobaric tag for relative and absolute quantitation
(iTRAQ) (Ross et al. 2004) and tandem mass tags (TMT) (Dayon et al. 2008).
In SILAC-based quantitation, protein labeling occurs in cell culture when heavy or light
isotopes of common amino acids like arginine and lysine are incorporated metabolically into
proteins (Figure 5) (Ong et al. 2002). The labeling is done prior any treatment of the samples
minimizing technical variations in sample preparation and analysis. If both lysine and arginine
are used in labeling, at least one amino acid in each tryptic peptide should be labeled resulting in
the detection of multiple labeled peptides per protein. A mass shift of a few Daltons is detected
between the differentially labeled forms of a peptide and relative quantitation of peptides is then
performed by comparing MS peak areas of these differentially labeled forms of each peptide.
Although SILAC is often used to compare only two or three parallel samples, 5plex SILAC
experiments have also been published (Molina et al. 2009). However, multiplexing increases the
SILAC sample complexity significantly making the MS analysis more difficult. Finally, for
adequate incorporation of labels, viable cell lines that can be cultured long enough are required.
13
Figure 5. SILAC-, ICAT- and iTRAQ-based quantitation in proteomics. In each of these
methods, labeling of proteins/peptides is done at a different stage (first coloured boxes). In
SILAC and ICAT experiments, quantitation is based on MS data whereas in iTRAQ,
quantitation is based on MS/MS data.
ICAT is a protein labeling method where “heavy” or “light” biotinylated tags are attached to
cysteine residues of proteins (Figure 5) (Gygi et al. 1999, Hansen et al. 2003). The proteins are
then digested and labeled peptides are enriched using affinity chromatography. Since cysteine is
a rare amino acid, enrichment of labeled peptides simplifies the sample mixture significantly. In
traditional ICAT method, differentially labeled forms of each peptide show an 8 Da mass
difference in MS spectra (Gygi et al. 1999) whereas in the newer, cleavable ICAT method, the
corresponding mass difference is 9 Da (Hansen et al. 2003). Quantitation in ICAT can be
performed by comparing the peak areas of differentially labeled peptides. Due to the low
number of cysteine-containing peptides, ICAT quantitation of a protein is often based on only
one or two peptides making the results prone to errors.
iTRAQ and TMT are chemical labeling methods where isobaric tags are attached to peptides
after protein digestion (Figure 5)(Ross et al. 2004, Dayon et al. 2008). Both tags are structurally
very similar and react with free amino groups of peptides, i.e. N-termini and lysine residues.
Each isobaric tag contains a cleavable reporter ion group with a specific mass. Differentially
labelled forms of each peptide can be distinguished from each other only after peptide
14
fragmentation in mass spectrometer when the reporter ion groups are cleaved from the peptide.
The quantitation is then performed based on the reporter ion peak areas in MS/MS spectra. Both
iTRAQ and TMT are multiplexed methods allowing the analysis of four (4plex iTRAQ), six
(6plex TMT) or even eight (8plex iTRAQ) samples in parallel (Ross et al. 2004, Dayon et al.
2008, Pierce et al. 2008). The structure of an iTRAQ label and the principle of iTRAQ-based
quantitation are shown in Figure 6.
Figure 6. 4plex iTRAQ labeling. A) Structure of an iTRAQ label and attachment of the label
into peptide. B) Differentially labelled forms of one peptide elute simultaneously from RPLC
and have the same total mass but can be separated based on MS/MS spectra.
Label-free quantitation can be performed based on peptide peak areas in MS spectra
(Bondarenko et al. 2002) or based on spectral counts at MS/MS level (Liu et al. 2004). In both
approaches, all the samples are analyzed individually and data analysis and comparison is done
computationally after MS analyses. Thus, an unlimited number of samples can be compared
with each other. In signal intensity-based measurements, peptide peaks from different runs are
matched based on retention times and peptide masses (Bondarenko et al. 2002). Relative
quantitation of peptides and subsequently proteins is then performed based on differences in
peptide peak areas between runs. Technical reproducibility of LC-MS/MS analyses and minimal
overlap of peptides are extremely important because quantitation relies completely on matching
the MS data between runs. Spectral counting, on the other hand, is based on the idea that in
MS/MS experiments performed using data-dependent acquisition more abundant peptides will
15
be selected for fragmentation more often (Liu et al. 2004).The method can be refined by taking
into consideration for example the number of detectable tryptic peptides for a protein
(Rappsilber et al. 2002) and the properties of these peptides (Lu et al. 2007). Both spectral
counting- and peptide peak intensity-based label-free quantitation methods have been
successfully applied in large-scale proteomic studies (Luber et al. 2010, Mosley et al. 2009, Old
et al. 2005).
1.2.5 Data analysis
Database searches of large proteomic datasets result in lists containing thousands of protein
identifications. In addition, quantitative data and information about protein posttranslational
modifications are often included in these lists. It is extremely difficult to deduce potentially
relevant biological processes by the manual inspection of collected data. Thus, numerous
bioinformatics tools have been developed to help data interpretation.
Functional classification of the identified proteins is often one of the first data analysis steps
after database searches. Gene Ontology (GO) database comprises of a well-standardized set of
biological processes, molecular functions and cellular compartments associated to different gene
products (Ashburner et al. 2000). Currenly, more than 500 000 gene products from several
different organisms are annotated in the database. These annotations are often utilized in the
initial characterizations of proteomic datasets. AmiGO is the official GO database browsing tool
that can be used for example to retrieve GO annotations for a single protein or for simple
visualizations of the database´s hierarchical structure (Carbon et al. 2009). GO analyses of large
proteomic datasets can be performed using several different bioinformatic tools, such as
GeneTrail (Backes et al. 2007) and GOMiner (Zeeberg et al. 2003). These tools utilize different
statistical methods to find GO categories that are over- or underrepresented in the dataset of
interest compared to a reference dataset such as the genome of the selected organism (Backes et
al. 2007, Zeeberg et al. 2003). GO database is manually annotated, and each annotation has an
evidence code describing the type of evidence supporting the annotation (Dimmer et al. 2008).
This allows the user to evaluate the reliability of the GO analysis results. Although GO
classification is a valuable tool in proteomics, it does not provide detailed mechanistic
information about the cellular events. Therefore, pathway analyses and protein-protein
interaction analyses are used to retrieve more detailed information about the cellular events.
16
Pathway analyses can be used to study the role of the identified proteins in well-defined
biomolecular reactions. The Kyoto Encyclopedia of Genes and Genomes (KEGG) PATHWAY
database contains several maps of biochemical pathways, especially metabolic pathways (Ogata
et al. 1999). Reactome is another large biological pathway database, which is focused especially
on human proteins (Joshi-Tope et al. 2005). Pathway databases contain information about
physical and functional interactions between proteins and the annotations in these databases are
usually manually curated (Malik et al. 2010). KEGG and REACTOME pathway databases can
be mined directly using protein identification data from proteomic experiments. Additionally,
some of the GO classification tools, such as GeneTrail and PANTHER, can be used for overand underrepresentation analysis of biological pathways (Backes et al. 2007, Thomas et al.
2003). Also some additional bioinformatics tools, such as ExPlain, have been developed for
biological pathway analyses (Zubarev et al. 2008).
Protein-protein interactions can provide important information about the functional complexes
and intermolecular associations in a cell. Protein-protein interaction databases contain both
experimentally determined and computationally predicted information about physical and
functional interactions between proteins (Malik et al. 2010). Various tools such as String
(Jensen et al. 2009), PINA (Wu et al. 2009) and Cytoscape (Shannon et al. 2003) mine the
existing protein interaction data stored in these databases and can be used to create proteinprotein interaction networks based on these data. PINA and Cytoscape are also capable of
integrating functional data into protein-protein interaction networks making them extremely
useful and efficient tools in proteomic data analyses (Wu et al. 2009, Shannon et al. 2003).
Cytoscape can also be used to incorporate quantitative data into networks and the numerous
visualization and analysis possibilities available with Cytoscape make it the most
comprehensive visualization tool available for complex proteomic data (Shannon et al. 2003).
Since the reliability of annotations in protein-protein interaction databases varies widely, it is
important to be aware of the quality of interaction data used in the networks. Filtering out lowquality interactions prior analyses can also simplify the interpretation of the data.
Combinations of Gene Ontology classification, pathway analyses and protein-protein interaction
networks are often used when analyzing complex proteomic data. These computational analyses
can help building biological hypotheses based on proteomic data. However, different functional
experiments are required to verify these hypotheses.
17
1.3 INNATE IMMUNE SYSTEM
Immune system comprises of various cells and molecules that work to protect an organism
against pathogens. It can be divided into two categories: innate immune system and adaptive
immune system. Innate immune system is organism´s first line of defence against pathogens. It
is responsible for the early detection of invading pathogens and launching of the first immune
reactions to eliminate the pathogen (Murphy et al. 2008). The conserved defence mechanisms of
innate immune system are triggered immediately after the pathogen has been detected and they
protect the organism until the adaptive immune responses against the pathogen have been
developed. In addition, innate immune system is required for the development of adaptive
immune responses that are fully activated only several days after infection. Adaptive immune
responses are targeted specifically against the detected pathogen and are thus more efficient
than the unspecific innate immune responses. In addition, adaptive immune system is capable of
generating immunological memory that ensures faster and more efficient adaptive immune
responses if the same pathogen is re-encountered later.
When organisms encounter with a pathogen, epithelial cells of skin and mucous form the first
physical barrier between the pathogen and internal parts of an organism (Medzhitov 2007,
Kupper and Fuhlbrigge 2004). Infections can occur only when the pathogen passes this physical
barrier. Epithelial cells recognize invading pathogens with their pattern-recognition receptors
(PRRs) resulting in the secretion of antimicrobial peptides, chemokines and cytokines. These
molecules function as signals of infection to immune cells. Different types of immune cells are
activated as a result of infection (Janeway and Medzhitov 2002, Murphy et al. 2008).
Macrophages and dendritic cells are important phagocytes present in most tissues. They are
activated at early stages of infection when pathogens cross the epithelial barrier. One of their
important tasks is to engulf and digest pathogens. In addition, they orchestrate immune
responses by inducing inflammation and secreting cytokines and chemokines that activate other
immune cells and recruit them to the site of infection. Finally, macrophages and dendritic cells
can also function as antigen presenting cells helping the development of adaptive immune
responses. Other types of phagocytes working in the innate immune system are neutrophils,
eosinophils and basophils that can be activated by inflammatory cytokines and chemokines
(Janeway and Medzhitov 2002).
18
1.3.1 Innate immune recognition of pathogens
Host defence against invading pathogens is initiated when host cells recognize specific
microbial components called pathogen-associated molecular patterns (PAMPs) (Janeway and
Medzhitov 2002, Akira et al. 2006). PAMPs contain various structures that are essential for
microbes, for example viral RNA, DNA and bacterial lipopolysaccharide. Host PRRs are
germline-encoded receptors designed to differentiate between self and non-self structures and
thus to detect foreign microbial structures invading the cell. Several distinct PRRs are found in
mammals, each with their own specificities and roles in the innate immune recognition of
pathogens.
Toll-like receptors (TLRs) are the best characterized group of PRRs. They are integral
membrane glycoproteins with extracellular domains for the recognition of PAMPs and
intracellular domains for signaling (Kumar et al. 2011). Human TLRs 1, 2, 4, 5 and 6 are
expressed on cell surface whereas TLRs 3, 7, 8 and 9 are found from endolysosomal
membranes. Endolysosomal TLRs are specialized for the recognition of viral nucleic acids
(TLR3 for viral dsRNA, TLR7/8 for viral ssRNA and TLR9 for viral DNA) whereas cell
surface TLRs recognize mostly bacterial and fungal structures. Recognition of PAMPs by TLRs
results in the recruitment of different adaptor molecules such as MyD88 and TRIF. This initiates
signaling events resulting in the activation of transcription factors, for example NF-B, IRF3/7
and MAP kinases, and the production of proinflammatory cytokines and type I interferons
(Figure 7).
Another group of PRRs is RIG-I-like receptors (RLRs) found in the cytoplasm of host cells
(Kumar et al. 2011). These receptors recognize viral RNA in the cytoplasm of infected cells.
There are three receptors belonging to this family: RIG-I and MDA-5, both recognizing
different types of viral RNA, and LGP2 which is a positive regulator of RIG-I- and MDA-5mediated signaling. When RIG-I or MDA-5 are activated with viral RNA, they interact with
mitochondrial-antiviral signaling protein (MAVS) located on mitochondrial outer membrane
and peroxisomes (Figure 7) (Dixit et al. 2010). Interactions with peroxisomal MAVS result in
rapid interferon-independent expression of interferon-stimulated genes whereas mitochondrial
MAVS activates type I interferon production with slower kinetics. In addition, activation of
RLRs results in the production of pro-inflammatory cytokines.
19
NOD-like receptors (NLRs) are the third main group of PRRs sensing a wide range of ligands in
the cytoplasm (Kumar et al. 2011). NOD1 and 2, for example, are cytoplasmic receptors for
bacterial cell wall structures. NOD1- and NOD2-mediated recognition of PAMPS results in the
activation of NF-B or MAP kinases inducing the production of proinflammatory cytokines.
Another group of NLRs are the inflammasome components, whose activation results in the
assembly of a protein complex called the inflammasome (Martinon et al. 2002). Inflammasome
activation results in caspase-1 cleavage followed by the activation of proinflammatory cytokines
IL-1 and IL-18 (Figure 7). One of the inflammasome components, NLRP3, is activated by
bacterial and viral RNA as well as some endogenous danger signals (e.g. danger signal proteins)
and environmental pollutants like asbestos. In addition, NLRP1 and NLRC4 are known
inflammasome components recognizing various structures.
Figure 7. Innate immune recognition of viral nucleic acids results in the production of
interferons and pro-inflammatory cytokines. MAPK = MAP kinases.
20
1.3.2 Innate immune responses against viral infection
Innate immune responses against viral infection are initiated when host PRRs recognize the
invading virus. The most important innate immune responses against viral infection are antiviral
responses, inflammation and apoptosis of the infected cells.
Interferons (IFNs) are proteins produced and secreted by virus infected cells. They are antiviral
agents helping infected cells to fight against invading viruses. From the three different classes of
IFNs (type I, II and III interferons) type I IFNs (interferon and ) are the most central in innate
immune responses against viral infection (Randall and Goodbourn 2008). Production of type I
IFNs is initiated after host cell´s TLRs and RLRs have recognized the invading virus. TLRs 3, 7
and 9 as well as RIG-I and MDA-5 each activate type I IFN production via different signaling
pathways resulting in the activation of interferon regulatory factors IRF3 and/or IRF7 (Akira et
al. 2006).
IFN produced by the infected cells are secreted and can be detected by interferon receptors
on the surface of infected and neighboring cells (Randall and Goodbourn 2008). Activation of
these IFN receptors initiates the production of interferon-stimulated genes. There are hundreds
of interferon-stimulated genes involved in for example host cell transcription and translation,
immune modulation, signaling and apoptosis (de Veer et al. 2001). The proteins encoded by
these genes are the primary effectors of antiviral immune responses.
Inflammatory responses triggered by viral infection aim at recruitment of leukocytes to the sites
of infection and to the elimination of infectious agents. Macrophages residing in infected tissues
are important triggerers of inflammatory responses (Medzhitov 2008). Inflammatory responses
are initially triggered by PRRs when they recognize PAMPs or specific virulence factors.
Additionally, at later phases of infection, inflammation can be induced by endogenous danger
signal proteins like HMGB1 and S100A9 secreted by infected cells (Bianchi 2007). Activation
of inflammatory pathways results in the production of proinflammatory cytokines (e.g. TNF-
and IL-1) and chemokines that activate other immune cells and attract them to the site of
infection as well as production of proteolytic enzymes like caspases and matrix
metalloproteinases for host defence (Medzhitov et al. 2008).
21
Transcriptional and MAP kinase-mediated activation of pro-inflammtory cytokines can occur
via various different pathways depending on the virus (Takeuchi and Akira 2010). TLRs 7 and 9
use MyD88-dependent pathways and TLR3 a TRIF-dependent pathway to activate NF-B
transcription factor and thus the production of proinflammatory cytokines. RIG-I/MAVS
interaction activates pathways resulting in, for example, NF-B activation.
To complete the work of TLRs and RLRs in activating transcription of pro-inflammatory
cytokines, a multiprotein complex called the inflammasome functions to activate
proinflammatory cytokines IL-1 and IL-18 (Martinon et al. 2002). Inflammasomes consist of a
cytoplasmic receptor, an adaptor protein ASC (apoptosis-associated speck-like protein
containing a CARD) and caspase-1. There are different types of inflammasomes recognizing
different viruses, for example NLRP3 inflammasome recognizing RNA viruses like influenza A
virus and AIM2 inflammasome recognizing DNA viruses (Martinon et al. 2009, Hornung et al.
2009). These inflammasomes work as caspase-1 activating platforms (Martinon et al. 2002,
2009). The active caspase-1 can then cleave inactive pro-IL-1 and pro-IL-18 into their active
forms which are secreted from the cell to induce inflammation (Pirhonen et al. 1999, 2001).
If viral infection cannot be resolved through antiviral and inflammatory immune responses,
programmed cell death, apoptosis, is activated to eliminate the infected cell (Lamkanfi and Dixit
2010). Apoptosis is a caspase-dependent, non-inflammatory form of programmed cell death
(Zimmermann et al. 2001, Ting et al. 2008). It can be initiated intracellularily by the release of
cytochrome c or other apoptogenic proteins from mitochondrial intermembrane space into the
cytosol or via cell-death receptors on cell surface. Initiation of apoptotic events results in the
activation of several apoptotic caspases.
Caspases are the most central effector proteins activated during apoptosis. They are a group of
cysteine proteases that are synthetized as inactive zymogens and can be activated by proteolytic
cleavage of the protein (Crawford and Wells 2011). Caspases mediate their effects via aspartatespecific cleavage of their target proteins. Caspase-3 is one of the central molecules in apoptosis
having several hundreds of known target proteins, and its activation is often held as a hallmark
for apoptosis. In addition to caspase-3, several other caspases (caspase-2, -6, -7, -8, -9 and -10)
are involved in apoptotic signalling. In addition to the apoptotic caspases, the human caspase
family also includes the inflammatory caspases-1, -4 and -5 as well as caspase-14 involved in
cellular remodelling.
22
Programmed cell death is an innate immune response that host cells can use to inhibit viral
replication and thus to prevent the spread of virus in the infected organism (Best 2008,
Lamkanfi and Dixit 2010). However, several viruses have evolved mechanisms to interfere with
host cell death pathways (Lamkanfi and Dixit 2010, Kaminskyy and Zhivotovsky 2010). Some
viruses have found ways to modulate the activity of caspases, central molecules in apoptosis.
Other viruses, such as some herpes viruses, can inhibit apoptosis by encoding proteins that are
homologous to cellular anti-apoptotic proteins of Bcl-2 family. Finally, apoptosis is not always
beneficial for host. HIV-1 virus, for example, triggers apoptosis in infected host immune cells
like dendritic cells and macrophages hindering development of proper immune responses
(Kaminskyy and Zhivotovsky 2010).
In addition to apoptosis, necrosis and pyroptosis can also be considered as forms of
programmed cell death (Lamkanfi and Dixit 2010). However, the mechanisms related with these
pro-inflammatory modes of cell death are still rather unclear.
1.3.3 Influenza A virus
Influenza A viruses are negative-stranded RNA viruses belonging to the Orthomyxovirus
family. They are highly pathogenic respiratory viruses capable of infecting avian and
mammalian species. Annual epidemics of influenza A virus cause severe illnesses in millions of
people worldwide. The severity of the infections varies from mild symptoms to severe illness
and even death. During the last century, influenza A viruses have triggered four pandemics
causing morbidity and mortality around the world.
Influenza A virus genome consists of 8 RNA segments that encode 11 distinct proteins
(Ludwig et al. 2003) (Figure 8). Each of these proteins has been studied extensively to elucidate
their roles in influenza A virus pathogeneity. NS1 protein of influenza A virus has been often
associated with viruses pathogenity. This protein interferes with RIG-I-mediated type I IFN
production in many different ways. NS1 protein, for example, blocks RIG-I ubiquitination
which is important for RIG-I/MAVS interaction (Gack et al. 2009). Another example of
influenza A virus protein interfering with host immune system is matrix protein 2 (M2) which
blocks autophagosome fusion in the infected cells (Gannagé et al. 2009). Finally, extensive
interactions between influenza A virus and host cell proteins are likely to affect the
23
consequences of infection. Influenza-induced innate immune responses in host cells can be
triggered by endosomal TLR3 and TLR7 recognizing viral dsRNA and ssRNA as well as by
RIG-I recognizing viruses cytosolic RNA (Wu et al. 2011). The virus replicates in host cell
nucleus and for replication, it has to hijack several host nuclear factors (Josset et al. 2008, König
et al. 2010, Karlas et al. 2010). Therefore, several influenza A virus proteins interact with host
cell nuclear machinery affecting for example nuclear structure and host cell splicing machinery.
Several other interactions between influenza A virus and host cells have also been reported and
they might have an impact on the state of host cells after viral infection (Shapira et al. 2009).
Figure 8. Sturcture of influenza A virus particle. PB1 = polymerase basic protein 1, PB2 =
polymerase basic protein 2, PA = polymerase acidic protein. Viruses NS1 protein is important
for replication but is not included in virus particles. (Adjusted from Ludwig et al. 2003)
Only few studies have utilized proteomic methods to study influenza A virus-induced changes
in host cell proteomes. In 2006, Baas et al. utilized MS-based proteomics to study influenza A
virus-induced changes in macaque lung tissues resulting in 3548 protein identifications (Baas et
al. 2006). Although the proteomic study was not quantitative, comparisons between proteomic
data and parallel mRNA-level studies indicated inconsistencies between protein and mRNA
levels of some proteins in the lung tissues. This shows the importance of protein level data in
studies of cells physiological state. Later on, few 2-DE-based (Liu et al. 2008, Vester et al.
2009, van Diepen et al. 2010) and MS-based (Coombs et al. 2010, Emmott et al. 2010a)
quantitative proteomic studies of host responses against influenza virus infection have been
performed. However, most of these studies have resulted in the identification of only few
differentially expressed proteins and the large-scale analyses about influenza-host-interplay
have been performed using genome-wide screening techniques (e.g. Hao et al. 2008, Shapira et
24
al. 2009, Karlas et al. 2010, König et al. 2010). Therefore, more in-depth quantitative proteomic
analyses and data interpretation would be needed to find cellular signalling pathways that are
potentially activated by influenza A virus infection. Successful proteomic studies of host-virus
interplay have already been published for other viruses showing the potential of this technique
(Emmott et al. 2010b, Naji et al. 2012).
25
2. AIMS OF THE STUDY
Macrophages and keratinocytes have an important role in the activation innate immune
responses after viral infection. The aim of this study was to develop and utilize proteomic and
bioinformatic methods to characterize host responses to viral infection. The more detailed aims
were:
-
To characterize cytosolic viral RNA-induced innate immune responses in HaCaT
keratinocytes (I)
-
To set up a quantitative subcellular proteomics workflow and to utilize it for global
characterization of influenza A virus-induced changes in human primary macrophages (II)
-
To study virus-induced protein secretion from human primary macrophages (II, III)
-
To develop a computational tool to help the comparison and analysis of protein
identification results from different database search engines (IV)
-
To develop a computational tool for large-scale caspase cleavage site predictions and to
utilize it for high-throughput mapping of potential caspase targets based on mass spectral
data (V)
26
3. MATERIALS AND METHODS
3.1 CELLS AND STIMULATIONS
In this project, two different types of human cells, HaCaT keratinocytes (I, IV) and human
primary macrophages (II, III), were used. HaCaT keratinocytes (American Type Culture
Collection) were cultured in DMEM (Dulbecco´s Modified Eagle Medium) supplemented with
10% Fetal Calf Serum, L-glutamate and antibiotics (I, IV). Human primary macrophages were
differentiated from blood monocytes obtained from leukocyte-rich buffy coats of healthy blood
donors (Pirhonen et al. 1999). Differentiation was done by maintaining the monocytes in
Macrophage serum-free medium supplemented with 10 ng/ml of Granulocyte-macrophage
colony-stimulating factor (GM-CSF) and antibiotics. After five days of culturing the
macrophages were used in the experiments. Each macrophage sample was a pool of separately
cultured and stimulated cells from three different blood donors.
To study viral RNA triggered immune responses in macrophages and HaCaT keratinocytes,
cells were transfected with polyinosic-polycytidylic acid (polyI:C) (I, III) or infected with
influenza A virus (II). PolyI:C is a mimetic of dsRNA and it has been used to mimic RNA virus
infections. Transfections were done using 10 μg/ml of polyI:C (Sigma-Aldrich). Lipofectamin
2000 (Invitrogen) was used as a transfection reagent. PolyI:C transfected cells were studied at
different timepoints between 1h and 18h. For viral infections of human macrophages, human
pathogenic influenza A virus strain Udorn/72/H3N2 was used with viral dose of 2,56
hemagglutination U/ml. The cells were studied at 6h, 9h, 12h and 18h post-infection. In
addition, other RNA viruses, influenza A virus strain Beijing/353/89/H3N2 (II), vesicular
stomatitis virus (I, III) and encephalomyocarditis virus (I, III) were used for infections to
confirm parts of the results.
3.2 SUBCELLULAR FRACTIONATION AND SECRETOME ANALYSIS
For subcellular fractionations, approximately 10 million cells were used. Mitochondrial and
cytoplasmic fractions of HaCaT keratinocytes (I, IV) and macrophages (II) were isolated by
QProteome Mitochondria Isolation Kit (Qiagen). After isolation, cytoplasmic fractions were
further purified using 2-D Clean-Up Kit (GE Healthcare). Nuclear fractions of macrophages
27
were isolated using QProteome Nuclear Protein Isolation Kit (Qiagen) (II). The resulting
soluble and insoluble nuclear protein fractions were combined before analysis. The enrichment
of mitochondrial, cytoplasmic and nuclear proteins in corresponding fractions was confirmed
using Western blots (II).
To study influenza A virus or polyI:C induced changes in protein secretion, macrophage growth
media were collected and analyzed (II, III). The cells grown in complete Macrophage-SFM
medium were washed three times with PBS after which the cells were stimulated in RPMI
growth media supplemented with 1 mM HEPES, L-glutamine and antibiotics (GIBCO). The
growth media were collected and concentrated with Amicon Ultra centrifugal filter devices
(Millipore) with 10 000 nominal molecular weight cutoff. The concentrated media were either
used directly for western blot analyses or purified with 2-D Clean-Up Kit (GE Healthcare) for
proteomic analyses.
3.3 GEL-BASED METHODS USED IN PROTEOMIC EXPERIMENTS
Two-dimensional gel electrophoresis (2-DE) was used for the separation of mitochondrial and
cytoplasmic protein fractions of HaCaT keratinocytes (I). 11 cm pI 4-7 IPG-strips (Bio-Rad)
were used as the first dimension and Criterion Tris-HCl 8–16% precast gels (Bio-Rad) as the
second dimension. The gels were stained using SYPRO Ruby protein gel stain (Bio-Rad or
Sigma-Aldrich) according to manufacturer´s instructions. Spot detection, matching and
intensity-based quantitation were done using Image Master 2D Platinum version 6.0 (GE
Healthcare). Spots with at least 2-fold difference in expression between control and polyI:C
transfected samples were considered differentially expressed and were picked for mass spectral
analysis. Finally, protein spots in the gels were visualized using silver staining (O´Connel et al.
1997).
Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) was used for protein
separation in the analysis of polyI:C transfected macrophage growth media (III) and
mitochondrial protein fraction of HaCaT keratinocytes (IV). In addition, SDS-PAGE was used
as a protein separation method in western blot analyses (I, II, III). With intracellular fractions,
equal amounts of protein were loaded on the gel. For secretomes, equal amounts of media were
taken for the analyses.
28
3.4 QUANTITATIVE ANALYSIS USING ITRAQ
4plex iTRAQ (AB Sciex) labeling was used for relative quantitation of proteins in influenza A
virus infected macrophages (II). Changes in mitochondrial, cytoplasmic and nuclear proteomes
and secretomes of influenza A virus infected macrophages were studied as a function of time.
The infected cells were studied at three different timepoints and protein amounts in these
samples were compared with uninfected control cells (Figure 9). With intracellular fractions,
equal amounts of protein from each sample were taken for the analyses based on silver stained
gels. For secretome analyses, equal amounts of cells were taken for the analyses and the whole
samples were labeled. Cysteine reduction, alkylation and protein in-solution digestion was done
for each sample followed by iTRAQ labeling of the resulting peptides. Digestion and labeling
were done according to manufacturer´s instructions.
Figure 9. Labeling of the samples for iTRAQ analyses. Two biological and two technical
replicates of each sample were analyzed.
After labeling, the peptide mixtures were prefractionated by SCX. The SCX separations were
performed with Ettan HPLC system (Amersham Biosciences) using a PolySULFOETHYL A
column (200 x 2,1 mm, PolyLC). The LC was operated at 0,2 ml/min and 20 mM KH2PO4
buffer (pH 3) was used with a gradient of 0-0,4 M KCl in 35 min. The eluting sample was
collected in 1 min fractions and the fractions containing peptides were analyzed using nanoLCESI-MS/MS.
29
3.5 MASS SPECTROMETRY
NanoLC-ESI-MS/MS analyses of tryptic peptides were performed with Ultimate 3000 nanoLC
system (Dionex) combined with QStar Elite hybrid quadrupole time-of-flight mass spectrometer
(AB Sciex) (I, II, III, IV). In the analyses, the sample was first injected into ProteCol C18
trapping column (0,15x10mm; 3 μm; 120Å)(SGE) and the column was washed with 0,1%
trifluoroacetic acid (TFA) for 10 min. After that, the peptides were separated in a PepMap100
C18 analytical column (0,075x150mm; 5μm; 100Å) (LC Packings/Dionex) using a linear
gradient of 0-40% acetonitrile in 0,1% formic acid. The length of the gradient varied between
20 min and 120 min depending on the sample. Mass spectral analyses were performed on
positive ion mode. MS data were acquired using Analyst 2.0 software (AB Sciex) and an
information-dependent acquisition method. The method consisted of a 0,5 s TOF-MS survey
scan of m/z 400-1400 followed by MS/MS scans of two most abundant ions with charge states
+2 to +4. Once an ion was selected for fragmentation, it was put on an exclusion list for 60 s.
MALDI-MS was used for the identification of the most intense protein spots from 2D gels (I).
Prior MALDI analyses proteins from each spot were in-gel digested with trypsin and the
resulting peptide samples were desalted using μC18 Zip Tips (Millipore). -cyano-4hydroxycinnamic acid was used as the crystallization matrix. Mass spectra for MALDI analyses
were acquired using an Ultraflex TOF/TOF instrument (Bruker Daltonik) in positive ion
reflector mode. The collected spectra were processed using FlexAnalysis version 3.0 (Bruker
Daltonik).
3.6 DATABASE SEARCHES
Two different database search engines, Mascot (Matrix Science) (PMF: publicly available
Mascot; MS/MS: in-house Mascot version 2.2) (I, III, IV, V) and Paragon from ProteinPilot
version 2.0.1 (AB Sciex) (II, IV), were used for protein identifications based on MS data.
Searches were done against NCBI (I, IV) or UniProt/SwissProt (II, III, IV, V) databases. Similar
search parameters were used in all the searches. False discovery rates for the identifications
were calculated using a target-decoy strategy based on searches against concatenated normal
and reversed protein sequence databases (Elias & Gygi 2007).
30
For iTRAQ-based quantitative analyses, only Paragon was used (II). Raw MS data from both
technical replicates of an iTRAQ sample set were processed together to improve the quality of
quantitation data. Quality of quantitations was also improved by manually excluding all the
MS/MS spectra with all reporter ion peak hights below 10 counts from quantitation. Finally,
proteins with more than 1.5-fold difference (intracellular fractions) or more than 3-fold
difference (secretomes) between control and infected sample were considered differentially
expressed.
3.7 PROTEIN CLASSIFICATION, INTERACTION NETWORKS AND CLUSTERING
ANALYSIS
After retrieving a list of protein identifications (and quantitation data) from a database search
engine, additional data analysis steps were performed to help interpreting the results. Three
different types of data analyses were done: protein classification based on their functions and
localizations, protein-protein interaction network studies and expression profile-based clustering
analyses. Functional classification and subcellular localization studies of proteins were done
mainly based on their Gene Ontology annotations using GeneTrail (Backes et al. 2007) (II).
Protein expression profiles were studied and clustering of similarily behaving proteins was
performed using Chipster (Kallio et al. 2011). Protein-protein interaction networks for
interesting groups of proteins were created using String (Jensen et al. 2009) (II).
In more detailed analyses of the published secretome data (II, III), different forms of protein
secretion were studied using ExoCarta (Mathivanan et al. 2012) and SignalP (Petersen et al.
2011). ExoCarta is a manually curated database of exosomal proteins, lipids and RNA
(Mathaiavan et al. 2009, 2012). Protein entries in ExoCarta database were compared with
protein identifications from the secretomes of human primary macrophages (II, III) to detect
potentially exosomal proteins from our datasets. SignalP is a prediction tool for signal peptides
based on protein sequences (Nielsen et al. 1997, Petersen et al. 2011). It was used to find
potential signal peptide containing proteins from our secretome data (II, III) and thus to estimate
the contribution of classical, signal peptide-based protein secretion in our secretome datasets.
31
3.8 IMMUNOLOGICAL ANALYSES
Immunological methods used in this study are listed in Table 3 and introduced in more detail in
the original publications.
Table 3. Immunological methods used in the publications.
Method
Publication
APOPercentage apoptosis assay
cathepsin B inhibition
cathepsin D siRNA
confocal microscopy
ELISA
P2X7 receptor inhibition
P2X7 receptor siRNA
quantitative RT-PCR
src tyrosine kinase inhibition
western blotting
II, III
II, III
III
I
I, II, III
II
II
I, III
II
I, II, III
3.9 REAGENTS
Several different antibodies were used to verify the proteomic data and to further characterize
the innate immune responses in the experiments. All the antibodies used in the publications are
listed in Table 4.
32
Table 4. Antibodies used in the publications.
Antibody
Manufacturer
14-3-3 -tubulin
-amyloid
Actin
Annexin A1
APOE
ASC
Bid
Caspase-1 p10
Caspase-1 p20
Caspase-3 (H-277)
Caspase-3, cleaved (#9661)
Cathepsin B
Cathepsin D
Cathepsin Z
Cytochrome c
Cytokeratin 18
eIF4A
Galectin-3
GAPDH
Histone H1
HMGB1
HSP27
HSP90
IFIT3
IL-18
Influenza A virus (H3N2)
LAMP-1
P2X7 receptor
pan14-3-3
phospho-Ser 14-3-3 binding motif
S100-A9
VDAC1
Santa Cruz Biotechnology
Cell Signaling
Santa Cruz Biotechnology
Santa Cruz Biotechnology
Santa Cruz Biotechnology
Santa Cruz Biotechnology
Millipore
Cell Signaling
Santa Cruz Biotechnology
Sigma-Aldrich
Santa Cruz Biotechnology
Cell Signaling
Calbiochem
Santa Cruz Biotechnology
Santa Cruz Biotechnology
Santa Cruz Biotechnology
Santa Cruz Biotechnology
Santa Cruz Biotechnology
Santa Cruz Biotechnology
Santa Cruz Biotechnology
Santa Cruz Biotechnology
Santa Cruz Biotechnology
Santa Cruz Biotechnology
Cell Signaling
BD Transduction Laboratories
Pirhonen et al. 1999
provided by prof. Ilkka Julkunen
Santa Cruz Biotechnology
Santa Cruz Biotechnology
Santa Cruz Biotechnology
Cell Signaling
Santa Cruz Biotechnology
Santa Cruz Biotechnology
33
Publication
I
I
II
II
II
II
III
III
II, III
III
I
II, III
II, III
II, III
II
II
I
I
II
II
II
II
I
I, II
II
II, III
II
II
II
I
I
II
II
4. RESULTS
4.1 PROTEOMICS IS AN EFFICIENT METHOD TO STUDY INNATE IMMUNE
RESPONSES IN HUMAN KERATINOCYTES AND MACROPHAGES
Combination of proteomics and bioinformatics has emerged as a valuable approach to study
cellular signaling mechanisms (Choudhary et al. 2010). In this project, three different proteomic
methods were utilized to study virus-induced changes in human primary macrophages and
HaCaT keratinocytes (Figure 10).
Figure 10. Proteomic methods used in publications I-III. A) 2-DE and MS were used in
publication (I). B) iTRAQ-based quantitative subcellular proteomics and secretome analysis
were performed in publication (II). C) In publication (III), secretomes were analyzed using
SDS-PAGE separation of proteins followed by in-gel digestion and LC-MS/MS analysis of the
resulting peptides.
In our simplest proteomic approach, we used SDS-PAGE combined with LC-MS/MS analyses
to study secretomes of polyI:C transfected human primary macrophages (III). PolyI:C is a
mimic of viral dsRNA and can be used to model viral infections. This study resulted in the
34
identification of 595 distinct proteins secreted from human primary macrophages. Although the
method was not quantitative, clear differences between protein identifications from the
secretomes of Lipofectamine-treated control cells and polyI:C transfected cells were detected.
For example, several Ras-related proteins and vacuolar ATPases were identified only from the
secretomes of polyI:C transfected cells indicating that the detection of cytoplasmic dsRNA
affects vesicular trafficking in host cells. In addition, the secretion of cysteine protease
inhibitors cystatin-A and B were detected only from the secretomes of polyI:C transfected
macrophages.
The effects of cytoplasmic dsRNA recognition on intracellular host proteomes was studied
using quantitative 2-DE combined with mass spectrometry (I). Mitochondrial and cytoplasmic
protein fractions of untreated and polyI:C transfected human HaCaT keratinocytes were
separated using 2-DE and differential expression of proteins was studied based on these gels.
This study resulted in protein identifications from 176 differentially expressed protein spots.
The value of subcellular fractionation was clearly seen since only 15% of protein identifications
were common for mitochondrial and cytoplasmic fractions. Comparisons of 2D gels from
control and transfected cells showed clear polyI:C-induced changes in 14-3-3 protein
expression. In addition, 2D gel analysis showed that polyI:C transfection triggered significant
fragmentation of cytoskeletal proteins such as cytokeratins. Although similar cytoskeleton
fragmentation has probably been present in influenza A virus infected human primary
macrophages (Öhman et al. 2009) it could not be detect in our iTRAQ analyses using non-gelbased separation methods.
In publication (II), we used non-gel-based quantitative subcellular proteomics to study influenza
A virus-induced changes in mitochondrial, cytoplasmic and nuclear fractions and secretomes of
human primary macrophages. Our quantitative subcellular proteomic experiments resulted in
altogether 3477 protein identifications including 1321 differentially expressed proteins in the
intracellular fractions and 544 upregulated proteins in the secretomes. Non-gel-based
proteomics is considered to be the most universal method for proteomic analyses. Accordingly,
comparisons between protein identifications from mitochondrial and cytoplasmic fractions in
publications (I) and (II) show that 22% of proteins identified in the non-gel-based experiment
fall outside the pI and molecular weight range of our 2D gels and would therefore not have
been identified using 2-DE. On the other hand, comparisons of proteomic data from SDS-PAGE
(III) and iTRAQ (II) secretome experiments showed that these two approaches give comparable
results and could be used to support and complement each other. Bioinformatics analyses of our
35
iTRAQ data indicated that influenza A virus infection results in extensive changes in host cell
nuclear proteomes and distracts the mitochondrial structure. Finally, our data analyses revealed
several signs about activation of antiviral responses, apoptosis and various inflammatory
mechanisms in influenza A virus infected cells.
In conclusion, our experiments show that quantitative subcellular proteomics and secretome
analysis is an efficient approach to study virus-induced protein-level changes in host cells.
Spatial (I, II, III) and temporal (II, III) information about multiple proteins can be collected in a
single experiment. Finally, analysis of proteomic data using various bioinformatics tools helps
creating hypotheses about biological mechanisms activated as a result of viral infection (I, II).
4.2 MASCOT AND PARAGON GIVE COMPARABLE PROTEIN IDENTIFICATION
RESULTS
In MS-based proteomic experiments, protein identification and quantitation relies on database
search engines. Several different database search engines have been developed for proteomic
purposes, providing both confirmatory and complementary information (Searle et al. 2008, Yu
et al. 2010). Two different database search engines, Mascot (I, III, V) and Paragon (II), were
used in our proteomic experiments. A computational tool, Compid, was developed to simplify
the comparison of Mascot and Paragon protein identification results and it was used to confirm
that the results from both database search engines were comparable (IV).
Compid can be used for peptide and protein level comparisons between two sets of raw data
from database search engines and also to regroup these protein identification results. Our
comparisons of Mascot and Paragon protein identification results with Compid showed that
there are some differences in the results from these two database search engines. The biggest
differences were detected on peptide level where only 20% of the peptide identifications were
common for both search algorithms. Here, the most important reasons for low overlap were
probably the low-quality MS/MS spectra resulting in low-confidence peptide spectrum matches.
The overlap between Mascot and Paragon protein identifications was significantly better, 4165%. The differences in protein identifications were partially caused by the more efficient
protein grouping algorithm in Paragon. In addition, the size and redundancy of protein sequence
database had a clear impact on the overlap between Mascot and Paragon database search results.
36
However, most of the good quality protein identifications were common for Mascot and
Paragon showing that these database search algorithms gave comparable protein identification
results.
4.3 VIRUS-INDUCED RESPONSES IN HACAT KERATINOCYTES AND HUMAN
PRIMARY MACROPHAGES
Our studies showed that both influenza A virus infection and polyI:C transfection trigger
significant changes in the proteomes of human primary macrophages (II, III) and HaCaT
keratinocytes (I). Activation of antiviral immune responses was seen as the upregulation of
several interferon-inducible proteins in influenza A virus infected human primary macrophages
(II). Different mechanisms of inflammation, such as inflammasome activation and secretion of
inflammatory danger signal molecules, were also detected (II, III). Viral stimulation clearly
activated caspase-mediated apoptosis in HaCaT keratinocytes (I) and human primary
macrophages (II, III). Finally, influenza A virus and polyI:C triggered significant protein
secretion from human primary macrophages (II, III).
4.3.1 Several inflammatory pathways are activated in human primary macrophages
NLRP3 inflammasome is a caspase-1-activating platform that has an important role in
inflammatory responses triggered by influenza A virus infection (Allen et al. 2009, Thomas et
al. 2009). Although inflammasome activation is currently under extensive research, the exact
mechanisms leading to its activation have remained unresolved. Two out of three NLRP3
inflammasome components, caspase-1 and ASC, were identified from the cytoplasmic fraction
of our iTRAQ data (II). In addition, caspase-1 activation and secretion of mature IL-18 were
detected in influenza A virus infected and polyI:C transfected human primary macrophages
indicating virus-induced activation of the inflammasome.
Lysosome destabilization and especially leakage of lysosomal protease cathepsin B has often
been associated with NLRP3 inflammasome activation (Hornung et al. 2008, Allen et al. 2009).
At 6h post-infection, our proteomic data showed the upregulation of several lysosomal proteins
in the cytoplasmic fraction (II). Since this could be a sign of lysosomal rupture in the infected
cells (II), we studied whether lysosomal cathepsins are involved in inflammasome activation.
37
Both ELISA (II, III) and western blot (II) analyses of IL-18 showed that cathepsin B inhibitor
Ca-074 Me abolished IL-18 secretion from the stimulated cells almost completely. Additionally,
cathepsin D siRNA decreased the secretion of IL-18 from polyI:C transfected cells (III). Finally,
western blot analyses showed that secretion of caspase-1 p10 fragment was abolished by
cathepsin B inhibition (II) indicating a role for cathepsins in inflammasome activation upstream
of caspase-1 activation.
In addition to cytosolic cathepsin B, several other intracellular signals have been suggested to
trigger inflammasome activation. To study other possible routes for inflammasome activation, a
protein-protein interaction network was created from all inflammation-related proteins
identified from the intracellular fractions of influenza A virus infected macrophages (II). This
protein-protein interaction network showed that plasma membrane ATP receptor P2X7 is
directly associated with all NLRP3 inflammasome components. Therefore, we studied the role
of P2X7 receptor in influenza A virus infection triggered inflammasome activation. Inhibition of
P2X7 receptor with a specific inhibitor, AZ11645373, as well as P2X7 receptor siRNA
experiments resulted in a clear decrease in IL-18 secretion from human primary macrophages
implying that P2X7 receptor has a regulatory role in inflammasome activation after influenza A
virus infection.
NADPH oxidase complex has also been previously related with NLRP3 inflammasome activity
(Dostert et al. 2008). Two subunits of NADPH oxidase complex, CYBA and CYBB, were
overexpressed in cytoplasmic fraction of influenza A virus infected human primary
macrophages at 6 h post-infection (II). In addition, src tyrosine kinase, an upstream regulator of
NADPH complex (Giannoni et al. 2010), was identified from influenza A virus infected
macrophages (II). Our functional studies showed that IL-18 secretion from influenza A virus
infected macrophages was completely abolished by the inhibition of src tyrosine kinase with
PP2 (II). Thus, in addition to cytosolic cathepsin B and P2X7 receptor activity, also src tyrosin
kinase activity has a role in the regulation of inflammasome activity in influenza A virus
induced human primary macrophages.
In addition to inflammasome activation, also other signs of proinflammatory responses were
detected in influenza A virus infected and polyI:C transfected human primary macrophages (II,
III). Several inflammatory proteins like macrophage migration inhibitory factor (MIF), S100-A9
and HMGB1 were secreted from influenza A virus infected or polyI:C transfected macrophages
(II, III). Finally, multiple inflammation-related proteins such as allograft inflammatory factor,
38
S100-A8 and S100-A9 were differentially expressed in the intracellular fractions of influenza A
virus infected macrophages (II).
4.3.2 Viral infection triggers caspase-dependent apoptosis in human macrophages
and keratinocytes
Caspase-3 activation is a classical sign of apoptosis. Our studies showed several different signs
of caspase-dependent apoptosis in the influenza A virus and polyI:C stimulated macrophages
and keratinocytes (I, II, III). Western blot analyses of influenza A virus infected (II) and polyI:C
transfected (III) macrophages showed the activation of caspase-3 in stimulated cells. In addition,
classical signs of mitochondrial apoptosis: translocation of Bax from cytosol onto mitochondria
as well as leakage of cytochrome c from mitochondria into cytosol, were clearly seen in our
iTRAQ data (II). Cathepsin B inhibition experiments showed that caspase-3 activation in
influenza A virus infected and polyI:C transfected macrophages was largely dependent on
cathepsin B activity (II, III). Finally, based on our kinetics experiments, caspase-3 activation
was shown to precede inflammasome activation and it occurred already few hours after
infection (III).
Initiation of apoptosis causes clear changes in the morphology of dying cells (Crawford and
Wells 2011). A clear rearrangement of cytoskeletal proteins was seen in polyI:C transfected
HaCaT keratinocytes (I). Several fragments of cytokeratins and other structural proteins were
identified from the stimulated cells, especially from the mitochondrial fraction. In addition,
polyI:C transfection-induced changes in the subcellular distribution of cytokeratin 18 were seen
in the confocal microscopy images of HaCaT keratinocytes. Caspase-3 inhibition experiments
showed that the cleavage of cytokeratin-18 was caspase-dependent and therefore related with
apoptosis of the transfected cells.
4.3.2.1 Prediction and identification of potential caspase cleavage targets from
proteomic data
Determination of caspase target proteins can give important insights into the execution
mechanisms of programmed cell death. Therefore, a computational tool, Pripper, was developed
to predict potential caspase cleavage motifs based on protein sequences and known caspase
39
cleavage sites (V). Pripper was the first prediction tool capable of processing multiple protein
sequences or even entire proteomes simultaneously making it extremely useful for proteomic
applications. In addition, Pripper can create caspase cleavage product databases based on the
predictions. These databases can be downloaded directly into a database search engine and
utilized to identify potential caspase targets from MS-based proteomic experiments in a highthroughput way. Workflow for caspase target prediction based on Pripper and the following
database searches is described in Figure 11.
Figure 11. Workflow for caspase cleavage site and caspase target prediction in proteomics by
using Pripper.
We utilized Pripper to search our proteomic data from publications (I) and (II) for potential
caspase cleavage targets. The analysis of MS data from polyI:C transfected HaCaT
keratinocytes resulted in the identification of one cytokeratin-18 fragment as a potential caspase
cleavage product (V). This was an additional confirmation for caspase-dependent cytokeratin
fragmentation in polyI:C transfected HaCaT cells. In addition, Pripper analysis of proteomic
data from publication (II) resulted in the identification of several cytoskeleton-associated
proteins (e.g. actin) as potential caspase targets (V). This indicates that influenza A virus
infection of human primary macrophages also resulted in caspase-dependent cytoskeletal
rearrangement, although it was not detected from the proteomic data. Finally, the analysis of our
iTRAQ data using a caspase cleavage product database resulted in the identification of a
potential novel caspase cleavage motif in leukosialin. However, additional biological
experiments are required to confirm these caspase cleavage sites.
40
4.3.3 Influenza A virus infection and polyI:C transfection trigger significant protein
secretion from human primary macrophages
Proteomic methods were used to study changes in protein secretion followed by influenza A
virus infection and polyI:C transfection of human primary macrophages (II, III). Our analyses
showed that both stimulations trigger significant protein secretion. GeneTrail analysis of the
identified proteins showed that the secreted proteins are associated with several different
cellular processes including gene expression, signaling and cell death (data not shown).
Comparison of protein identifications in publication (III) and secretome data in publication (II)
showed that only 30% of the protein identifications were common for both experiments.
However, the functional roles of proteins identified in both experiments were very similar
(Figure 12A). DAMP (damage-associated molecular pattern) proteins are endogenous proteins
with various intracellular functions, acting as danger signals when secreted out of cells after
stress or injury (Bianchi 2007). Several DAMPs (e.g. S100-A8, S100-A9, Galectin-3, HMGB1,
HMGB2) were secreted from polyI:C transfected (III) and especially from infeluenza A virus
infected (II) human primary macrophages. Secretion of several Ras-related proteins as well as
other proteins involved in vesicle trafficking was also triggered by both stimulations. Finally,
several lysosomal proteins, for example cathepsins and lysosomal hydrolases were identified
from the secretomes of influenza A virus infected (II) and polyI:C transfected (III)
macrophages.
Classically, proteins are directed for secretion via ER/Golgi-pathway by signal peptides
included in protein sequences (Nickel et al. 2010). However, based on SignalP (Petersen et al.
2011) analyses, only 16% of proteins identified from our secretomes contain a signal sequence
(Figure 12B). This indicates that virus-induced human macrophages use other secretory routes
to direct proteins out of cells. In addition to signal peptide-mediated protein secretion, proteins
can be directed out of the cells via unconventional secretion mechanisms including plasma
membrane-located channels, budding of plasma membrane microvesicles, exocytosis of
secretory lysosomes and release of exosomes from multivesicular bodies (Théry et al. 2009,
Nickel et al. 2010). Exosomes have been associated with cell-to-cell signaling related with, for
example, immune responses. We used ExoCarta to determine the contribution of potentially
exosomal proteins for our secretome data. More than 50% of the identified proteins were
included in ExoCarta database (Figure 12B) suggesting that virus infection induces significant
protein secretion via exosomes. Additionally, comparison of our secretome data with commonly
identified exosomal proteins showed that most of the proteins that are generally identified from
exosomes were also present in our data (Figure 13).
41
Figure 12. Functionally similar proteins are secreted from influenza A virus infected and
polyI:C transfected human primary macrophages. A) Protein abundances in selected Gene
Ontology classes are shown for secretomes of Influenza A virus- (II) and polyI:C-stimulated
(III) macrophages. B) ExoCarta and SignalP classifications of proteins identified from
secretomes of influenza A virus infected (II) and polyI:C transfected (III) macrophages.
Figure 13. Proteins that are typically identified from purified exosomes (Adjusted from Théry
et al. 2009 and Mathivanan et al. 2010). All the proteins that were identified from secretomes of
influenza A virus infected or polyI:C transfected human primary macrophages are marked with
bold letters.
42
5. DISCUSSION
Proteomics is a powerful approach to characterize system-wide changes in protein expression.
Several different methods have been developed for protein localization studies (Hartwig et al.
2009, Lee et al. 2010), quantitative analyses (Coombs 2011, Neilson et al. 2011),
characterization of posttranslational modifications (Thingholm et al. 2009, Kim et al. 2011,
Hjerpe et al. 2009) and mapping of protein-protein interactions (Rees et al. 2011, Li et al. 2011).
Methods used in proteomic experiments have developed enormously during the last decades.
Mass spectrometry is one of the fastest developing areas of proteomics. The new orbitrap
instruments (Makarov 2000, Hu et al. 2005) have increased the amounts of data that can be
collected in proteomic experiments significantly and introduction of new fragmentation
techniques such as electron transfer dissociation has improved for example the analysis of
protein posttranslational modifications (Zubarev et al. 1998, Kim and Pandey 2012). These new
developments enable wider applicability of proteomic methods in solving biological questions
thereby opening new possibilities for proteomic studies. The expectations for proteomic
experiments have therefore constantly increased making it essential for scientists to keep up
with the latest inventions. In this project, two different quantitative subcellular proteomic
approaches and quantitative and qualitative secretome analyses were utilized to study virusinduced changes in human primary macrophages and HaCaT keratinocytes.
With modern mass spectral techniques, huge amounts of raw data can be collected from a single
proteomic experiment making manual interpretation and validation of these data impossible.
Therefore, different database search engines are used to identify and quantify proteins based the
collected MS data (Nesvizhskii 2007). However, differences in database search algorithms often
result in slightly different protein identification results from the same dataset (Elias et al. 2005,
Searle et al. 2008, Savijoki et al. 2011). Especially low quality MS/MS spectra with low signalto-noise ratio, minor peptide fragmentation or overlapping fragments from multiple distinct
peptides are often problematic for database search engines (Salmi et al. 2009). The resulting
low-confidence peptide-spectrum matches often differ between database search engines and
have a higher probability for being false positive identifications. Large differences in low
confidence peptide identifications were also seen in our comparisons between Mascot and
Paragon. To overcome this problem, several strategies for filtering out low quality MS/MS
spectra and low confidence peptide identifications have been developed (e.g. Keller et al. 2002,
Helsens et al. 2008, Salmi et al. 2006). Additionally, parallel use of different database search
engines can be used to increase the confidence of protein identifications as well as to increase
43
the number of proteins identified (Yu et al. 2010, Searle et al. 2008). For example, we have
utilized Compid to combine Mascot and Paragon protein identifications resulting in a slight
increase in the number of protein identifications and therefore higher coverage of Lactobacillus
rhamnosus proteomes (Savijoki et al. 2011).
After retrieving a list of protein identification, quantitation and modification data, further
bioinformatics tools are required to perceive the potentially underlying molecular mechanisms.
The repertoire of data analysis tools has expanded enormously and several tools for protein
classification, pathway analysis and interaction studies as well as for various specific
applications are currently available. In this project, we have used Gene Ontology-based protein
classification, protein expression profiling and protein-protein interaction studies to characterize
the collected data. Computationally built networks of proteins and their physical and functional
relationships are useful when associating proteomic data with cell-wide processes (Goh et al.
2012, Albert 2005). However, one of the problems with proteomics is the incompleteness of the
collected data. For example low abundance regulatory proteins present in the samples often
remain undetected creating challenges for data analysis. Zubarev et al. have tried to solve this
problem with a method called key-node analysis where the presence of undetected regulatory
proteins in a pathway can be hypothesized based on the identification of their target proteins
(Zubarev et al. 2008). Another challenge in proteomic data analysis is the incompleteness of
several biological databases (Goh et al. 2012). Here, the best results can probably be achieved
by combinig data from several parallel databases. Finally, the analysis of proteomic data usually
results in extremely complex networks that are difficult to interpret. Therefore, the users often
focus only on a certain part of the network and filter out the rest of the data limiting the results.
Filtering out low quality data and incorporation of different types of data into one network can
result in more easily interpretable networks and help, for example, in finding novel associations
between protein clusters.
In addition to using general classification and network tools, we created a new tool, Pripper, to
study caspase-mediated protein fragmentation. Caspases are a family of proteins involved in the
regulation and execution of various immune processes like apoptosis and inflammation
(Crawford and Wells 2011). Caspase cleavage of a target protein may result in direct activation
or deactivation of the target protein or may alter protein functions by for example affecting their
localization. Therefore, identification of caspase targets may give important information about
executionary mechanisms in for example apoptosis. Although there are several tools available
for the prediction of caspase cleavage sites (Backes et al. 2005, Wee et al. 2009, Ayyash et al.
44
2012), Pripper was the first tool enabling the direct utilization of the collected MS data for
large-scale caspase target predictions. These analyses resulted in the identification of several
potential caspase targets, especially structural proteins. Together with our proteomic and
immunological data, this indicates that caspases have an important role in virus-induced
modulation of cell cytoskeleton.
Combination of proteomic and bioinformatic analyses has been used to study host defense
responses against different viruses (Zheng et al. 2011). Influenza A virus is a respiratory virus
infecting annually millions of people worldwide. Although influenza A viruses have been
studied widely for several decades, only few large-scale proteomic experiments of influenzainduced host responses have been published (Baas et al. 2006, Coombs et al. 2010, Emmott et
al. 2010a). Virus infection usually results in the activation of various antiviral and inflammatory
pathways and finally death of the infected host cell (Akira et al. 2006, Lamkanfi and Dixit
2010). We detected clear upregulation of several interferon-inducible proteins in the
cytoplasmic fraction of influenza A virus infected human primary macrophages showing the
activation of antiviral immune responses. Activation of inflammatory responses and caspasemediated apoptosis were also clearly seen in our data. One of the inflammatory responses
triggered by influenza A virus, as well as some other RNA viruses and cytosolic RNA, is the
activation of the NLRP3 inflammasome in the infected cells (Kanneganti et al. 2006). This
inflammasome activation is crucial for the development of proper innate immune responses
after influenza A virus infection (Allen et al. 2009, Thomas et al. 2009). However, the exact
mechanisms leading to NLRP3 inflammasome activation are still unknown.
Lysosomal rupture (Hornung et al. 2008, Allen et al. 2009), cytosolic cation imbalance (Qu et
al. 2007, Ichinohe et al. 2010) and ROS production (Dostert et al. 2008, Allen et al. 2009) have
often been related with inflammasome activation. Our data indicated that influenza A virus
infection causes lysosomal damage in influenza A virus infected and polyI:C transfected human
primary macrophages. Inhibition of lysosomal cathepsin B has been shown to block
inflammasome activation and IL-1 secretion to some extent (Hornung et al. 2008, Allen et al.
2009). This was also seen in our data, since both caspase-1 activation as well as IL-18 cleavage
and secretion were clearly blocked in influenza A virus and polyI:C stimulated macrophages
treated with cathepsin B inhibitor. Additionally, cathepsin D siRNA decreased the secretion of
IL-18 significantly. Cathepsins B and D can also cause significant increase in mitochondrial
ROS production (Zhao et al. 2003), providing another possible link between lysosome rupture
and inflammasome activation. Additionally, caspase-3 activation in human primary
45
macrophages was shown to be partially cathepsin dependent indicating that lysosomal rupture
might also result in activation of apoptotic pathways at early stages of infection.
Phagocytosis of microbial components and particles results in ROS generation and has been
associated with, for example, monosodium urate- and asbestos-triggered inflammasome
activation (Dostert et al. 2008). NADPH oxidase mediates ROS production in phagocytosis, and
knockdown of NADPH oxidase subunit p22phox (CYBA) has been shown to decrease IL-1
secretion. Src tyrosine kinases regulate NADPH oxidase-mediated ROS generation (Giannoni et
al. 2010) and SRC has been shown to localize into dsRNA containing endosomes and associate
with TLR3 (Johnsen et al. 2006). In addition, lysosomal protein LAMP-1 was localized in these
endosomes suggesting a potential link between src tyrosine kinase activity and lysosomes. We
showed that inhibition of src tyrosine kinase blocked IL-18 secretion from influenza A virus
infected human primary macrophages indicating that src tyrosine kinase- and NADPH oxidasemediated ROS has an important role in NLRP3 inflammasome activation. A recent publication
showed that src tyrosine kinase might regulate extracellular vesicle formation (Choi et al. 2012)
indicating that src tyrosine kinase-mediated inflammasome activation might also be involved in
exosomal protein secretion. Finally, ROS-dependent interaction between thioredoxin interacting
protein TXNIP and NLRP3 has been shown to result in NLRP3 inflammasome activation
creating a potential link between ROS generation and inflammasome activation (Zhou et al.
2010).
Additionally, activation of P2X7 receptor by ATP has been associated with ROS production,
and it also increases K + efflux out of the cells (Pfeiffer et al. 2007). In our studies, inhibition of
P2X7 receptor as well as silencing of P2X7 receptor expression decreased the amount of IL-18
secretion from influenza A virus infected human primary macrophages. Similar signs of of P2X7
receptor-related inflammasome activation after influenza A virus infection have also been
shown previously (Ichinohe et al. 2010). Influenza A viruses M2 ion channel has an important
role in influenza-induced inflammasome activation. M2 ion channel is capable of transporting
H+ out of the vesicles in trans-Golgi network affecting the intracellular cation balance. These
data indicate that also intracellular cation imbalance might have an important role in
inflammasome activation.
Extensive inflammatory responses can be destructive for host organism. A recent publication
showed that NLRP3 inflammasome activity can be regulated via autophagosomes (Shi et al.
46
2012). Autophagy is a specialized system for degradation of intracellular substances.
Inflammasome activation triggers autophagosome formation via RalB protein activation. The
formed autophagosomes limit inflammasome activity by engulfing inflammasomes. Proteins
and organelles that are sequestered into autophagosomes can be delivered to lysosomes for
degradation (Behrends et al. 2010) or autophagosomes can fuse with the plasma membrane and
release their contents into extracellular space in membrane-enclosed exosomes (Manjithaya and
Subramani 2011). In a recent study, proteomics was used to study autophagosome-associated
proteins in human breast cancer cells after various stimuli (Dengjel et al. 2012). 80 proteins
identified in this study were also present in our secretome datasets implying that
autophagosome-related protein secretion is one of the secretory mechanisms activated in human
primary macrophages after viral infection. Based on current data, inflammasome activating and
regulating processes are speculated in Figure 14.
NLRP3 inflammasome activity has been related with unconventional protein secretion (Qu et al.
2007, Keller et al. 2008). Keller et al. have shown that caspase-1 activation affects the secretion
of several proteins, for example galectin-3 and peroxiredoxin-1, that are secreted via
nonclassical route (Keller et al. 2008). In addition, P2X7 receptor can stimulate exosome
release and IL-1 secretion in an inflammasome-depentent manner (Qu et al. 2007). Finally,
inflammasome components as well as active forms of IL-1 and IL-18 are secreted from
activated cells via nonclassical mechanisms (Martinon et al. 2002, Qu et al. 2007).
Microvesicles have been shown to transfer functional, signaling competent receptors from one
cell to another (Mack et al. 2000). Exosomes are probably the most studied group of
microvesicles that can be used to transmit signals between cells. Our secretome data showed
that several exosome-related proteins, lysosomal proteins and vesicle-related proteins as well as
inflammasome components Caspase-1 and ASC are secreted from influenza A virus infected
and polyI:C transfected human primary macrophages. The unconventionally secreted proteins
also included several DAMPs, for example galectin-3, S100-A8 and S100-A9. This indicates
that inflammasome activation-related unconventional protein secretion and especially secretion
of inflammasome components might have an important role in cell-to-cell signaling after viral
infection.
47
Figure 14. Cellular processes potentially related with influenza A virus-induced inflammasome
activity. 1) Src tyrosine kinase- and NADPH oxidase-mediated ROS is involved in
inflammasome activation during phagocytosis. 2) Lysosomal rupture and cytosolic cathepsins B
and D are associated with ROS and inflammasome activation. 3) P2X 7 receptor activation
results in K+ efflux and participates in inflammasome activation 4) Influenza A virus protein
M2 facilitates H+ efflux from Golgi to cytosol activating the inflammasome 5) Inflammasome
activation triggers autophagocytosis of intracellular proteins (e.g. inflammasome components)
therefore inhibiting inflammasome activation. Autophagocytosis might be followed by either
degradation of infammasome components or by their secretion from autophagosomes.
48
6. CONCLUSIONS AND FUTURE PERSPECTIVES
In this project we have combined proteomics, bioinformatics and immunological studies to
characterize virus-induced changes in human primary macrophages and HaCaT keratinocytes.
We show that influenza A virus infection triggers significant changes in intracellular proteomes
of human primary macrophages. The production of several antiviral proteins is initiated as a
result of infection. In addition, several inflammatory pathways, especially NLRP3
inflammasome activation, are activated in influenza A virus infected and polyI:C transfected
human primary macrophages. Inflammasome activation in human macrophages was regulated
by cathepsins, src tyrosine kinase and P2X7 receptor activities and was followed by the
initiation of apoptotic events only few hours post stimulation. Caspase-3-dependent apoptosis
was detected in polyI:C transfected HaCaT keratinocytes as well as in influenza A virus infected
and polyI:C transfected human primary macrophages. Caspase activation resulted in, for
example, cytokeratin-18 cleavage and changes in 14-3-3 protein expression in HaCaT
keratinocytes. Finally, we show that influenza A virus infection and polyI:C transfection trigger
extensive secretion of various different proteins such as inflammatory, vesicle-related and
danger signal proteins from human primary macrophages which might have an important role in
cell-to-cell signaling after viral infection.
In conclusion, our studies show that quantitative subcellular proteomics and secretome analysis
is a powerful tool for system-wide studies of cellular antiviral defense responses. Cellular
signaling events are often regulated by protein posttranslational modifications such as
phosphorylation and ubiquitination and especially protein ubiquitination has been recently
associated with various innate immune signaling events. Therefore, large-scale studies of
protein phosphorylation and ubiquitination could give deeper insights into regulation of host
cell innate immune responses. Finally, to be able to utilize the full potential of large-scale
proteomic studies, close collaboration between proteomics, bioinformatics and biology experts
is required.
49
ACKNOWLEDGEMENTS
This project was carried out at the Institute of Biotechnology, University of Helsinki in close
collaboration with the Unit of Excellence in Immunotoxicology, Finnish Institute of
Occupational Health and the Department of Information Technology, University of Turku. The
Institute of Biotechnology is acknowledged for providing excellent facilities for the research.
The work was funded by the Academy of Finland and the Helsinki Graduate Program in
Biotechnology and Molecular Biology (GPBM). GPBM is also thanked for providing a variety
of interesting courses for PhD studies.
I am extremely grateful to my supervisors Docent Tuula Nyman and Docent Sampsa Matikainen
for all the guidance and help during these years. It has been a pleasure to work under your
supervision. Your research enthusiasm and expertise have been important for the progress of this
project.
I would like to thank the reviewers of this thesis, Docent Jaana Vesterinen and Docent Sampsa
Hautaniemi, for their valuable comments. Docent Jaana Vesterinen and Professor Juho Rousu
are thanked for the advises and discussions in the thesis committee meetings.
I am grateful to Docent Nisse Kalkkinen, the former head of the Protein Chemisry Research
Group, for the opportunity to work in his well-equipped laboratory.
I express my gratitude to all the coauthors of the publications. I would especially like to thank
Tiina Öhman: I couldn´n have hoped for a better teacher and lab partner. I am also thankful to
Johanna Kerminen for all the hard work in immunology as well as all the encouraging words.
Olli Nevalainen and Jussi Salmi are thanked for the fruitful collaboration and for sharing their
expertise in bioinformatics with us. Tero Aittokallio is thanked for the valuable help in the
analysis of complex proteomic data.
I would also like to thank all the former and current colleagues in the Protein Chemistry Group
and at FIOH for creating such a pleasant working environment. I would especially like to thank
the other occupants of “Lastenhuone”, Pia, Juho and Wojciech, for all the humour and
refreshing discussions.
Finally, I would like to thank my family, friends and Kalle for their love, patience and support.
Helsinki, April 2012
50
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