Proteo Mi

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Institute of Biotechnology and

Department of BiosciencesFaculty 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 Auditorium2402 (Telkänpönttö) at Viikki Biocenter 3, Viikinkaari 1, Helsinki, on May 16th at 12

oćlock noon.

Helsinki 2012

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Supervisors

Docent Tuula Nyman Docent Sampsa Matikainen

Institute of Biotechnology Unit of Excellence in Immunotoxicology

University of Helsinki Finnish Institute of Occupational HealthHelsinki, Finland Helsinki, Finland

Thesis committee

Docent Jaana Vesterinen Professor Juho Rousu

Institute of Biomedicine Department of Information and

Biochemistry and Developmental Biology Computer ScienceUniversity of Helsinki Aalto University

Helsinki, Finland Espoo, Finland

Reviewers

Docent Jaana Vesterinen Docent Sampsa HautaniemiInstitute of Biomedicine Institute of Biomedicine and

Biochemistry and Developmental Biology Genome-Scale Biology Research ProgramUniversity of Helsinki Faculty of Medicine

Helsinki, Finland University of Helsinki

Helsinki, Finland

Opponent

Professor Kris GevaertVIB Department of Medical Protein Research

and Department of Biochemistry

University of GhentGhent, 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

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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

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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

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LIST OF ORIGINAL PAPERS

This thesis is based on the following original articles that are referred to in the text by theirRoman 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 virus-infected 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 ofcytoplasmic 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 softwaretool 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 indeveloping 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 participatedin writing the manuscript.

*,# Authors with equal contribution

The original articles were reprinted with the permission of the original copyright holders.

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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 speciesRPLC 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

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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. Efficientmethods 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.

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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. Virus-

induced 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.

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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.

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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 undercertain 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 andinteractions (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

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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.

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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 (SDS-

PAGE) and liquid chromatography (LC). Complexity of samples introduced to the mass

spectrometer varies clearly between these methods (Figure 3). Simple peptide mixturesoriginating 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.

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Figure 3. Protein/peptide separation methods commonly used in proteomics. A) 2-DEseparation 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 (OĆonnel 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

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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 LC-

MS/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 basedon 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).

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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.

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Table 1. Examples of different multidimensional LC separations used in proteomic

experiments. pepSEC = size exclusion chromatography for peptides.

1st

dimension

2nd

dimension

study reference

HILIC RPLC phosphoproteomics for estrogen-induced

transcriptional regulation

Wu et al. 2011

pepSEC 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-of-

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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.

mass

analyzer

large-scale

proteome

characterization

quantitative

proteomics

posttranslational

modification

analysis

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

Kim et al. 2011,Monetti et al. 2011,

Lemeer et al. 2012

Q-TOFSavijoki et al. 2011,

Dwivedi et al. 2008

Bewley et al. 2011 Rajimakers et al. 2010,

Lemeer et al. 2012

TOF-TOF Holland et al. 2011Holland et al. 2011,

Lemeer et al. 2012

FT-ICR Pounds et al. 2008 Collier et al. 2010 Wang et al. 2011

QQQ 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).

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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, 2-

DE 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.

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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.

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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.

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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 arginineare 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.

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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

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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

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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.

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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 over-

and 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 protein-

protein 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 low-

quality 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.

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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 efficientthan 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).

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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 integralmembrane 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-5-mediated 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.

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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.

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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 dangersignal 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).

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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 Dixit2010). 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 aspartate-

specific 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.

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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

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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 proteomicdata 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

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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).

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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)

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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

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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 (OĆonnel 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.

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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 technicalreplicates 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 nanoLC-

ESI-MS/MS.

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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 surveyscan 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-4-

hydroxycinnamic acid was used as the crystallization matrix. Mass spectra for MALDI analyses

were acquired using an Ultraflex TOF/TOF instrument (Bruker Daltonik) in positive ionreflector 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).

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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.

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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 II, III

cathepsin B inhibition II, IIIcathepsin D siRNA III

confocal microscopy I

ELISA I, II, III

P2X7 receptor inhibition IIP2X7 receptor siRNA II

quantitative RT-PCR I, III

src tyrosine kinase inhibition IIwestern blotting 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.

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Table 4. Antibodies used in the publications.

Antibody Manufacturer Publication

14-3-3 Santa Cruz Biotechnology I

-tubulin Cell Signaling I-amyloid Santa Cruz Biotechnology II

Actin Santa Cruz Biotechnology II

Annexin A1 Santa Cruz Biotechnology IIAPOE Santa Cruz Biotechnology II

ASC Millipore III

Bid Cell Signaling IIICaspase-1 p10 Santa Cruz Biotechnology II, III

Caspase-1 p20 Sigma-Aldrich IIICaspase-3 (H-277) Santa Cruz Biotechnology I

Caspase-3, cleaved (#9661) Cell Signaling II, IIICathepsin B Calbiochem II, IIICathepsin D Santa Cruz Biotechnology II, III

Cathepsin Z Santa Cruz Biotechnology IICytochrome c Santa Cruz Biotechnology II

Cytokeratin 18 Santa Cruz Biotechnology I

eIF4A Santa Cruz Biotechnology IGalectin-3 Santa Cruz Biotechnology II

GAPDH Santa Cruz Biotechnology II

Histone H1 Santa Cruz Biotechnology II

HMGB1 Santa Cruz Biotechnology IIHSP27 Santa Cruz Biotechnology IHSP90 Cell Signaling I, II

IFIT3 BD Transduction Laboratories II

IL-18 Pirhonen et al. 1999 II, IIIInfluenza A virus (H3N2) provided by prof. Ilkka Julkunen II

LAMP-1 Santa Cruz Biotechnology IIP2X7 receptor Santa Cruz Biotechnology II

pan14-3-3 Santa Cruz Biotechnology I

phospho-Ser 14-3-3 binding motif Cell Signaling I

S100-A9 Santa Cruz Biotechnology IIVDAC1 Santa Cruz Biotechnology II

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4. RESULTS

4.1 PROTEOMICS IS AN EFFICIENT METHOD TO STUDY INNATE IMMUNERESPONSES 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

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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-gel-

based 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 theintracellular 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

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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), wereused 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, 41-

65%. 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.

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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.

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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,

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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 macrophagesand 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 ouriTRAQ 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

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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 high-

throughput 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.

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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 ofsecretory 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).

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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.

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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 virus-

induced 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 signal-

to-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

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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.

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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 influenza-

induced 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 caspase-

mediated 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

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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 beenassociated 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 p22 phox (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 oxidase-

mediated 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 importantrole 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.

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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.

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Figure 14. Cellular processes potentially related with influenza A virus-induced inflammasomeactivity. 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) P2X7 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.

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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 expertsis required.

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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 ofOccupational Health and the Department of Information Technology, University of Turku. TheInstitute 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 varietyof 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 SampsaHautaniemi, 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 couldnń have hoped for a better teacher and lab partner. I am also thankful toJohanna 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 theirexpertise 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 thankthe 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

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