Embed Size (px)
Citation preview
PROTEOMIC ANALYSIS OF A DYNAMIC SALMONELLA-
HOST INTERACTOME
by
Lyda Mimi Brown
B.Sc., California State University, Chico, 2010
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
in
THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES
(Genome Science and Technology)
THE UNIVERSITY OF BRITISH COLUMBIA
(Vancouver)
December 2013
© Lyda Mimi Brown, 2013
ii
Abstract
Salmonella is capable of evading the host immune response by secreting
virulence factors (effectors) that enter the host and interfere with critical cell
signaling networks. Although many of the individual effector proteins have been well
characterized by traditional biochemical methods, a shift towards global strategies
would offer a system’s view of the intricate network of interactions (interactome)
between the host and pathogen.
Protein profiling across size exclusion chromatography with stable isotope
labeled amino acids in cell culture (PCP-SILAC) is a recent proteomic method
developed to characterize the composition of protein complexes with the advantages
of being quantitative, capable of monitoring dynamic changes, and being completely
tag and chemical cross-link free. This method was applied to study the dynamic
changes of host protein complexes as a result of Salmonella infection.
Analysis of dynamic PCP-SILAC proteins led to the hypothesis that host
translational machinery was being targeted by Salmonella during infection. To test
this hypothesis, a fluorescent assay was used to measure protein synthesis of cells
infected with WT Salmonella versus control (non-infected cells). Results from the
protein synthesis analysis showed a decrease in host translation, supporting our
hypothesis that Salmonella targets host translational machinery. I addition to this
novel finding, this work provides a rich resource of candidate host proteins that may
be involved with Salmonella’s pathogenesis.
iii
Preface
Research experiments involving Salmonella have been approved by Biosafety
Certificate # B12-0088
iv
Table of Contents
Abstract............................................................................................. ii!
Preface .............................................................................................iii!
Table of Contents................................................................................. iv!
List of Tables .................................................................................... viii!
List of Figures .....................................................................................ix!
List of Abbreviations ..............................................................................x!
Acknowledgements............................................................................. xiii!
Dedication........................................................................................ xiv!
Chapter 1: Introduction ......................................................................... 1!
1.1! Mapping the interactome ................................................................2!
1.1.1! Traditional mapping tools ..........................................................3!
1.2! Salmonella enterica ......................................................................4!
1.2.1! Classification .........................................................................4!
1.2.2! Human health.........................................................................5!
1.2.3! Pathogenesis ..........................................................................6!
1.2.4! Proteomics and the host pathogen frontier......................................8!
1.3! Quantitative mass spectrometry based proteomics..................................8!
1.3.1! The proteome.........................................................................8!
1.3.2! Instrumentation ......................................................................9!
1.3.3! Protein identification.............................................................. 10!
v
1.3.4! Quantitative techniques .......................................................... 12!
1.4! Proteomic techniques for analysis of protein complexes ......................... 13!
1.4.1! Tandem affinity purification (TAP).............................................. 15!
1.4.2! High-throughput protein complex profiling .................................... 17!
1.4.3! Chemical cross-linking in living systems........................................ 19!
1.4.4! Validation ........................................................................... 21!
Chapter 2: Materials and methods ...........................................................23!
2.1! General solutions and buffers ......................................................... 23!
2.2! Cell culture............................................................................... 23!
2.2.1! SILAC labeling....................................................................... 24!
2.3! Sample preparation ..................................................................... 24!
2.3.1! Salmonella infection............................................................... 24!
2.3.2! Cell harvesting...................................................................... 25!
2.3.3! Size exclusion chromatography of protein complexes ....................... 25!
2.3.4! In solution digestion of protein complexes .................................... 26!
2.3.5! StageTip purification .............................................................. 26!
2.4! Mass spectrometric methods .......................................................... 27!
2.4.1! LC-MS/MS ........................................................................... 27!
2.5! Bioinformatics and statistical analysis ............................................... 27!
2.5.1! Database searching and quantitation ........................................... 27!
2.5.2! Visualizing protein chromatograms with R .................................... 28!
2.5.3! Filtering protein chromatograms ................................................ 29!
2.5.4! Clustering protein chromatograms .............................................. 29!
vi
2.5.5! Protein complex enrichment analysis........................................... 29!
2.5.6! Differential protein changes ..................................................... 30!
2.5.7! GO analysis of changing proteins ................................................ 31!
2.6! S. Typhimurium strains................................................................. 31!
2.6.1! RFP gene transfection of S. Typhimurium...................................... 32!
2.6.2! Salmonella protein synthesis assay.............................................. 32!
Chapter 3: Results...............................................................................33!
3.1! Mapping a global interactome landscape with PCP-SILAC ........................ 35!
3.1.1! Applying PCP-SILAC to a host-pathogen system ............................... 39!
3.2! Identification of host proteins targeted during Salmonella invasion............ 40!
3.2.1! Preprocessing of Replicate Data ................................................. 41!
3.2.2! Identifying dynamic proteins (log fold change, z-test, t-test) ............. 42!
3.2.3! Integrating a phosphoproteomic Salmonella study ........................... 47!
3.2.4! GO enrichment analysis ........................................................... 48!
3.3! Host protein synthesis machinery targeted during Salmonella invasion........ 49!
Chapter 4: Discussion...........................................................................53!
4.1! Current Salmonella-host interactome ............................................... 53!
4.2! The identified dynamic host proteins during Salmonella invasion .............. 53!
4.2.1! PTRF/Cavin-1 ....................................................................... 54!
4.2.2! HSP27 ................................................................................ 55!
4.2.3! Cyclase Associated Protein-1..................................................... 57!
4.2.4! C-t-PAK2 ............................................................................. 58!
4.3! Host translation during Salmonella invasion ........................................ 58!
vii
4.3.1! Protein synthesis during Salmonella invasion.................................. 59!
4.3.2! HSP27 inhibits translation ........................................................ 60!
Chapter 5: Conclusion ..........................................................................62!
Bibliography ......................................................................................63!
Appendices........................................................................................72!
Appendix A List of Protein Complexes Enriched in PCP-SILAC dataset ................ 72!
Appendix B List of Dynamic Proteins in PCP-SILAC Salmonella Dataset Identified by
Z-test ............................................................................................ 85!
viii
List of Tables
Table 2.1 Salmonella strains used in this thesis ............................................ 31!
Table 3.1 Identification of dynamic proteins after Salmonella infection (Log FC ) ... 43!
Table 3.2 Identification of dynamic proteins after Salmonella infection (Z-Test) .... 44!
Table 3.3 Identification of dynamic proteins after Salmonella infection (T-Test) .... 45!
Table 3.4 Protein Synthesis of Salmonella strains tested .................................. 51!
Table 5.1 Protein Complex Enrichment Analysis Results .................................. 72!
Table 5.2 Identification of dynamic proteins after Salmonella infection (Z-Test) .... 85!
ix
List of Figures
Figure 1.1 Modular organization in origami and protein interactions ......................2!
Figure 1.2 Salmonella invasion ...................................................................6!
Figure 1.3 MS-based shotgun proteomics ..................................................... 12!
Figure 1.4 Mass spectrometry approaches for analyzing protein complexes............ 15!
Figure 3.1 Experimental workflow for PCP SILAC with Salmonella infection. .......... 33!
Figure 3.2 PCP-SILAC data analysis workflow ................................................ 34!
Figure 3.3 PCP-SILAC landscape interactome ................................................ 35!
Figure 3.4 Individual protein profiles ......................................................... 36!
Figure 3.5 Filtering protein profiles ........................................................... 37!
Figure 3.6 Heatmap of hierarchical clustered protein profiles of one replicate. ...... 38!
Figure 3.7 Aligning protein chromatograms with proteasome subunits .................. 41!
Figure 3.8 Heatmap of changing proteins identified by the z-test ....................... 42!
Figure 3.9 HSP27 PCP-SILAC profile ........................................................... 46!
Figure 3.10 Phosphoproteomic and PCP-SILAC venn diagram ............................. 47!
Figure 3.11 GO functional analysis of dynamic PCP-SILAC proteins with infection .... 48!
Figure 3.12 AHA protein synthesis assay ...................................................... 50!
Figure 3.13 Protein synthesis in HeLa cells infected with WT Salmonella and effector
knockout strains .................................................................................. 52!
x
List of Abbreviations
2DE Two-dimensional gel electrophoresis
ABC Ammonium bicarbonate
ABP Actin binding protein
AHA L-azidohomoalanine
ARAF Serine/threonine-protein kinase A-Raf
ARF ADP-ribosylating factor
CAP1 Adenylyl cyclase associated protein
CDC42 Cell division control protein 42 homolog
CID Collision-induced dissociation
Da Dalton
DNA Deoxyribonucleic Acid
DMEM Dulbecco’s Modified Eagle Medium
dFBS Dialyzed fetal bovine serum
emPAI Exponentially modified Protein Abundance Index
ESI Electrospray ionization
FPR False positive rates
g Gravity
GAP GTPase-activating protein
GEF Guanine nucleotide exchange factor
GO Gene Ontology
xi
HCD Higher-energy collisional dissociation
H/L Heavy/Light Ratio of proteins
HSP Heat shock protein
IL Interleukin
IPI International Protein Index
kDa Kilodalton
LB Luria’s Broth
LC-MS Liquid chromatography mass spectrometry
LTQ Linear trap quadrupole
MALDI Matrix-assisted laser/desorption ionization
MAPK Mitogen-activated protein kinase
MAPKK MAPK kinase
M cell Microfold cell
mRNA Messenger ribonucleic acid
M/L Medium to light ratio of proteins
MOI Multiplicity of infection
M/Z Mass-to-charge ratio
PAI Protein abundance indices
PAK P21-activated kinases
PAMP Pathogen associated molecular patterns
PBS phosphate buffered saline
PCP Protein correlation profiling
PI phosphatidylinositol
xii
PMN Polymorphonuclear
PPI Protein-protein-interaction
PTRF/Cavin1 Polymerase 1 and transcript release factor
RPM Revolutions per minute
RSD Relative standard deviation
RT Room temperature
SCV Salmonella containing vacuole
SDS Sodium dodecyl sulfate
SEC Size exclusion chromatography
SIF Salmonella-induced filament
SILAC Stable isotope labeling by amino acids in cell culture
Sip Salmonella inner protein
Sop Salmonella outer protein
SPI-1 Salmonella pathogenicity island 1
SPI-2 Salmonella pathogenicity island 2
StageTip Stop-and-go extraction tip
T3SS Type III secretion system
TAP Tandem affinity purification
TLR Toll-like receptors
WT Wild type
XIC Extracted ion current
xiii
Acknowledgements
Domo arigatou (thank you) to my supervisor Dr. Leonard Foster for his superb
guidance and providing me a challenging project that allowed me to develop skills in
multiple disciplines (biochemistry, cell biology, microbiology, and bioinformatics).
Thank you to all members of the Foster lab, especially Dr. Anders Kristensen and Dr.
Nat Brown for mentoring me.
I would like to thank my committee members, Dr. Paul Pavlidis and Dr. Jim
Kronstad for their helpful discussions and comments. I’m grateful for the generous
funding provided by the GSAT program.
Finally, I’d like to thank the SJC community, my family and friends for all of
their encouragement and support.
xiv
Dedication
To my parents and Mrs. Sugaya-Jones
1
Chapter 1: Introduction
This thesis presents a global analysis of host-pathogen protein interactions by
quantitatively monitoring cytosolic protein complex dynamics in response to
Salmonella infection. The research is interdisciplinary, bringing together
biochemistry, cell biology, and bioinformatic approaches to address a microbiology
question. The introduction begins with a brief description of the protein interactome,
illustrating the ‘big picture’ of this thesis. The section covering basic Salmonella
biology provides the motivation for this research as well as necessary background for
the host-pathogen system to be studied. The remainder of the introduction describes
quantitative mass spectrometry and compares the PCP-SILAC method to other
proteomic techniques for studying protein complexes.
2
A. Origami subunit, B. Origami cube, C. Origami octahedron D. Origami stellated icosahedron E.
Origami inverted icosahedron. F. Crystal structure of HSP27 (PDB: 3q9p) within a protein protein
interaction network map generated from the STRING database v. 9.02.82
1.1 Mapping the interactome
Figure 1.1 Modular organization in origami and protein interactions
Proteins inside a cell interact with each other forming modular protein
complexes that carry out complex biological functions.1,2 The relationship between
modular organization and complexity is illustrated above in Figure 1.1. A simple
origami subunit when assembled with other similar subunits, can form progressively
larger and very intricate looking shapes. The Sonobe origami models from top to
bottom include a cube (B. 6 subunits), stellated octahedron (C. 12 subunits), and
stellated icosahedrons (D. and E. 30 subunits each). Models D and E are both made
from the same number of subunits, however one fold was reversed in the subunits,
3
demonstrating the impact a small modification of a subunit can have on the overall
structure of the model. This point will come up again when discussing the caveats of
using protein tags to purify protein complexes.
1.1.1 Traditional mapping tools
Over the past four decades, charting of protein-protein interactions by
traditional reductionist techniques has been a slow and laborious process. The yeast
two hybrid (Y2H) system was the first high throughput approach for protein-protein
interaction studies. In this technique, a yeast transcription factor is split into two
segments and fused to two proteins (bait and prey). A protein interaction between
the bait and prey proteins reconstitutes the functionality of the transcription factor
by bringing them in close proximity, and produces a product that can be selected for.3
Characterizing these complexes by mass spectrometry (MS) based proteomics,
which is a high throughput method for studying cellular protein complements,
provides the most comprehensive maps of protein-protein interactions. In 2006, two
groups concurrently published the first genome-wide characterizations of protein
complexes in Saccharomyces cerevisiae, with an identification of more than 30,000
proteins organized into around 500 complexes at an estimated 70% coverage. 4,5
Databases have been set up to store data on protein protein interactions, with the
goal of providing users the most comprehensive interactome maps. An example of a
protein-protein interaction map is displayed in the right panel of Figure 1 with HSP27
as the query protein, shown in the middle with its protein crystal structure. The
colored nodes represent other proteins HSP27 interacts with and the type of evidence
for that interaction is notated by the color of the lines.
4
1.2 Salmonella enterica
1.2.1 Classification
In the mid 19th century, waves of hog cholera outbreaks were sweeping through
the southern states of America, devastating the swine industry. A veterinary
pathologist by the name of Dr. Salmon isolated a strain of bacteria, Salmonella
choleraesuis, from the intestine of an infected pig.6 Salmonellae are Gram-negative,
rod-shaped bacilli ranging in size from 2-5 x 0.7-1.5 !m. They are motile with flagella
distributed around its entire body. The optimal growth temperature is between 35-37
ºC. As facultative anaerobes they are metabolically capable of thriving in a variety of
environments.7
Phylogenetically, Salmonella belongs to the Enterobacteriacae family of
bacteria along with Escherichia coli and Yersenia pestis. From genomic comparisons,
it has been estimated that Salmonella and E. coli diverged from a common ancestor
around 100 million years ago.8 The Salmonella genus is split into two species:
Salmonella enterica and Salmonella bongori. Salmonella enterica is further divided
into six subspecies (I,II, IIIa, IIIb, IV, VI). Salmonella enterica subspecies enterica (I)
invade warm-blooded animals while the other subspecies primarily invade cold-
blooded animals (with rare occurrences of human infection). Based on Salmonella’s
diverse outer structural antigens, the subspecies have been serotyped into over 2,500
serovars. Many serovars have adapted to a particular host, for example Salmonella
Typhi only infects humans and Salmonella Dublin is restricted to cattle. In contrast,
Salmonella Typhimurium has a broad host range.9
5
1.2.2 Human health
Infections by the foodborne pathogen Salmonella (salmonellosis) cause a
spectrum of clinical diseases. Enteric fever (typhoid fever) is a systemic, long lasting
infection characterized by abdominal pain, fever, rashes and diarrhea after ingestion
of S. enterica serovars Typhi or Paratyphi. Cases occur most frequently in regions of
poor sanitation and have a mortality rate of 10-15% if left untreated.!"#$ Bacteremia
(infection of the bloodstream) is another life threatening disease that accounts for
about 8% of untreated salmonellosis cases.7 Gastroenteritis is a self-limited form of
salmonellosis caused by nontyphoidal serotypes such as S. enterica serovar Enteriditis
and S. enterica serovar Typhimurium. Symptoms of gastroenteritis occur 6-48 hours
after ingestion of contaminated food or water and include abdominal pain, vomiting,
and diarhhoea.9,11 Antibiotics are a common treatment for salmonellosis, which has
led to a rise of multidrug resistant strains.
Salmonella is transmitted through ingestion of contaminated foods (meats,
eggs, produce), contaminated water, and contact with animal carriers of Salmonella
(humans can also be chronic carriers, e.g., Typhoid Mary). A food inspection study of
ground meat reported that 26% of chicken, 18% of turkey, and 3% of beef tested
positive for Salmonella9. The current global estimates of salmonellosis are 16 million
enteric fever and 1.3 billion gastroenteritis cases per year with 3 million deaths.7 In
the United States, the incidence rate of nontyphoidal salmonellosis has doubled in the
last two decades.9 New drug targets for combating salmonellosis are in high demand
globally.
6
To invade epithelial cells, Salmonella must first make contact with the outer membrane through
reversible adhesion with membrane receptors. Salmonella uses a type three secretion system
(yellow line) to inject effector proteins into the host cell that interfere with host signaling
pathways. Host membrane ruffling allows Salmonella entry into the host cytosol within a
vacuole, where it can multiply hidden from host defense mechanisms.
1.2.3 Pathogenesis
On the outer surface, Salmonella express several fimbrial adhesins that help
deliver the bacterium to the intestinal epithelium10. Microfold cells (M cells) are
interspersed with intestinal epithelial cells at lymphoid follicles above Peyer’s
patches. They have an increased rate of pinocytosis, which allows Peyer’s patches to
sample foreign antigens from the lumen and prime developing lymphoblasts.
Salmonella exploits this immune surveillance function of the gut by targeting M cells
for infection.#%
Invasion begins by injecting bacterial virulence factors (effectors) into the
cell. The effectors hijack the host cell machinery, rearrange host cell architecture
and induce membrane ruffling at the point of contact. Salmonella gains entry into
the host cell within a membrane vacuole, the Salmonella containing vacuole (SCV).
During later stages of infection, Salmonella can replicate within the SCV and cause
inflammation leading to further dissemination within the host (see Figure 1.2).
Figure 1.2 Salmonella invasion
7
Salmonella triggers the host inflammatory response as a strategy to
outcompete resident microbiota of the human gut. One highly reactive and toxic
compound produced in large quantities by the gut microbiota is hydrogen sulfide
(H2S). The host quickly converts H2S to a more stable compound, thiosulphate (S2O32-).
Nitric oxide radicals and reactive oxygen species released during inflammation
oxidizes thiosulphate into tetrathionate (S4O62-), a compound which Salmonella has
the unique ability to reduce as an energy source.#&"#'
Virulence genes, acquired by horizontal gene transfer, are clustered into two
locations on the bacterial chromosome that are referred to as Salmonella
Pathogenicity Islands I and II (SPI-1 and SPI-2). SPI1 encodes effectors for invasion of
host cell and SPI2 effectors are for replication within the SCV.#$,#( Salmonella injects
effectors into the host by a needle complex, referred to as a type three secretion
system (T3SS). Effectors flagged for secretion contain a signal near their N-terminal
which can bind to chaperone proteins to aid in their delivery.8
Downstream targets have been traced for a handful of Salmonella effectors. A
burst of SipA effectors translocated into the host, (10^3 SipA /bacterium) helps
rearrange host architecture by binding to actin filaments.16 SipA also attracts
polymorphonuclear cells (PMN ) to sites of infection by activating PKC alpha signalling
pathway to turn on expression for chemokine, IL-815
Membrane ruffling of the host cell is a reversible process mediated by
monomeric G proteins involved with cell morphology and motility. These Rho family G
proteins (Cdc41, Rac1, Rho) are hijacked temporally by the effectors SopE, a guanine
nucleotide exchange factor, SptP, a GTPase activating protein, and SopB, an inositol
8
polyphosphate phosphatase. Once inside the host cell, SopE has a shorter half life
than the others, which then allows SptP an open window to reverse the actions of
SopB and SopE15,17
1.2.4 Proteomics and the host pathogen frontier
Developments in the proteomic field have expanded the scope of host-
pathogen interaction studies, providing an unbiased global view of the proteome
under different conditions. A mass spectrometry analysis of the Salmonella
secretome identified six new T3SS effectors, and estimated Salmonella Typhimurium
to have as many as 300 T3SS effectors.15 The first steps towards elucidating
proteome-wide phosphorylation and ubiquination signaling pathways of host pathogen
systems have been taken using purification enrichment methods.8,18 Assembling a
coherent story from different types of proteomic studies is challenging, and will
require a systems biology model with additional layers of omic data. The
development of host-pathogen interaction models will be key to designing novel
antimicrobial therapies that target a specific pathogen and doesn’t disrupt the host’s
microflora.10
1.3 Quantitative mass spectrometry based proteomics
1.3.1 The proteome
The genomics revolution laid the foundation for a number of other ‘omic fields,
including proteomics (the study of the proteome). Before the completion of the
human genome project, the proteomes of simple model organisms such as
9
Mycoplasma genitallium were mapped. In these early studies, the proteome was
referred to as, “The total protein complement able to be encoded by a given
genome”.19 Technological advances allowed scientists to dig deeper into the vast
proteome of a number of model organisms, cataloguing proteins in a high-throughput
mode. The original definition of a proteome quickly become outdated as it does not
reflect the dynamic and complex behaviour of proteins in a living organism. Today the
proteome is defined as the protein complement of a specified cell type in reference
to time and includes splice isoforms as well as any modifications20.
1.3.2 Instrumentation
Proteomes were traditionally characterized by protein microarrays and protein
staining with two-dimensional gel electrophoresis (2DE); the latter is a technique that
resolves a complex protein sample in two dimensions based on each protein’s size and
isolectric point. These technologies, especially 2DE, suffered from poor
reproducibility and were limited to characterizing the most abundant proteins. In the
late 1980s, breakthrough technological advances in adapting mass spectrometry to
biomolecules overcame the limitations of competing technologies and pushed mass
spectrometry to the forefront of the proteomic field.
A mass spectrometer is a versatile instrument that generates electrical and
magnetic fields to control movements of gas phase ions under vacuum. The velocity of
an ion in an electromagnetic field is directly proportional to an ion’s charge state and
inversely proportional to its mass. The three major components of a mass
spectrometer are an ionization source, a mass analyzer, and a detector. Ions
generated by the ionization source are spatially separated by an analyzer that
10
separates them based on their mass-to-charge (M/Z) ratios. Selected ions are then
passed towards the detector where they are registered. A mass spectrum is a plot of
ion intensities versus M/Z (measured in Thompsons) which can be used to calculate
the mass of a molecule.21
1.3.3 Protein identification
Whole genome sequencing projects unlocked the codes necessary for proteome
identification by mass spectrometry as theoretical masses could be calculated and
stored in a searchable database. Proteins in their native state are challenging to
analyze by mass spectrometry because of their size and polarity. Site-specific
proteases can reduce these barriers by breaking proteins down into peptides.
Proteomic analysis by mass spectrometry began in the 1980s with the development of
two novel soft-ionization techniques. Matrix assisted laser desorption ionization
(MALDI) sublimates and ionizes peptides from a solid crystalline state. The second
and more widely used technique is electro spray ionization (ESI), which vaporizes and
ionizes peptides into multiply charged ions (typically cations since the initial solution
is acidic). ESI is well suited for high throughput analyses of proteomes since it can be
coupled to upstream peptide separation by nano flow liquid chromatography.22
Hybrid mass spectrometers designed for proteomics contain multiple mass
analyzers to extract additional information from peptide ions. The first mass analyzer
has the highest resolution and selects a specific M/Z (parent ion) from the mixture of
peptides eluting from the liquid chromatography column at that time. The parent ion
is accelerated into a chamber filled with inert gas, causing the peptide to fragment
along the peptide backbone, a process termed collision induced dissociation (CID).
11
Cells are harvested and complex protein mixtures are digested into peptides by proteases. Peptides are
separated by reverse phase chromatography, ionized, and analyzed inside a mass spectrometer.
Peptides to be sequenced are isolated, fragmented, and analyzed by a parallel mass spectrometer. Raw
MS data are processed by software for identification and quantitation of proteins
Fragment ions are measured (tandem MS or MS/MS) and provide sequence information
to help identify the peptide. In the downstream data analysis, protein identifications
are made by algorithms that search the protein sequence database for peptides that
match the experimental MS and MS/MS spectra. An overview of the proteomic
workflow is illustrated in Figure 1.3.
Figure 1.3 MS-based shotgun proteomics
12
1.3.4 Quantitative techniques
It is often necessary to add a quantitative dimension to a proteomic analysis.
Biochemical properties such as length, side chains, post translational modifications
(PTM) and charge state can all contribute to differences in ionization efficiency of a
peptide.23 Thus, due to the stochastic nature of peptide ionization, signal intensity is
not always an accurate measure of protein abundance.
Label-free methods have a high dynamic range, are global, and offer
quantitation accuracies comparable to protein staining (>30 % relative standard
devation)22,23. The two most common label-free methods for calculating protein
abundance are integration of peptide precursor peak signals and counting of peptide
fragment spectra. In the first approach high resolution MS and reproducible
chromatography are critically important for the analysis. Integration of precursor
peaks generates an extracted ion currents (XIC) for a peptide, which can then be
compared to other XIC of the same peptide in different samples. Protein abundance
roughly correlates with the amount of MS/MS fragment spectra generated. This led to
the development of a protein abundance index (PAI), which is the count of observed
spectra divided by the number of theoretically observable peptides for a protein. A
few variants exist, such as the exponentially modified PAI, emPAI, which takes the PAI
as an exponent of base 10.24
The use of stable isotopes (e.g., 1H vs. 2H, 12C vs. 13C, 15N, or 18O) to
differentially label samples allows a direct measure of the relative abundances of
proteins/peptides between the light (naturally occurring) and heavy (isotopically
enriched) forms. In the 1970’s labeling methods based upon the concept of stable
13
isotope dilution were applied in clinical chemistry and pharmacokinetics.25 Today,
stable isotope labels are widely used in proteomics and a number of techniques have
been developed to incorporate the labels into the proteomic workflow.
Proteomic samples be labeled at the protein level or downstream at the
peptide level through metabolic, enzymatic, or chemical reactions. Quantitative
accuracies are generally higher when samples are labeled at the protein versus
peptide level since mixing of the two samples occurs further upstream in the
proteomic workflow and can reduce experimental error. Stable isotope labeling by
amino acids in cell culture (SILAC) incorporates heavy amino acids into proteins at the
time they are synthesized by the ribosome. Cell culture media deficient in essential
amino acids are supplemented with isotopic analogues (most commonly arginine and
lysine are chosen since together they are found in virtually all tryptic peptides). After
five to ten generations, labeled proteins in a cell population have nearly complete
incorporation of the heavy or medium amino acids and have a characteristic Lys4, Arg
6 (medium) or Lys 8, Arg10 (heavy) shift in the MS. Relative comparisons can be made
about the proteome by identifying and quantifying isotope pairs.26 Absolute
quantification can be reached by spiking in labeled internal standards, which is
typically reserved for targeted studies due to technical difficulties in generating such
standards in high-throughput.
1.4 Proteomic techniques for analysis of protein complexes
Two inherent properties of signaling complexes that make them challenging to
work with are their transient nature and low abundance. These bottlenecks have
14
driven the development of a variety of novel proteomic techniques, which can broadly
be classified as being either tag or chemical cross-link based. These developments
will be framed in context of their strengths and weaknesses.
In a standard proteomic workflow, one has to enrich the proteins of
interest. For signaling complexes, the goal is to purify the target structure with a
minimal amount of contamination, as well as maintaining the interactions of the
native binding partners.
Finding this balance is how the two main classes of strategies are divided in
this paper, as depicted in Figure 1.7. One class utilizes epitope tags as handles to
enrich complexes and thereby aid in identifying the components. When such affinity
purification is coupled with quantitative proteomic techniques, the stoichiometry of
the protein complex subunits can be accurately determined. The second class uses
chemical cross-linking with mass spectrometry, which adds a spatial organization of
Figure 1.4 Mass spectrometry approaches for analyzing protein complexes
Affinity purification of tagged proteins and chemical cross-linking to stabilize complexes are two
common approaches for studying protein complexes, their advantages are highlighted in dark gray
15
the signaling complexes. By combining both classes of strategies, signaling complexes
can be reconstructed and their functions predicted.
1.4.1 Tandem affinity purification (TAP)
Epitope-tagging is a widely used method for isolation of protein complexes out
of cell extracts. In this approach, a bait protein is genetically fused with an epitope
tag expressed in the host cell. The interacting partners of a complex can
subsequently be captured by binding of the epitope tag to an affinity matrix. For
example, a protein fused to Protein A, which is an epitope derived from
Staphylococcus aureus, can be purified with an anti-Protein A antibody.
Although one-step affinity purification can be an effective strategy for isolation
of multiprotein complexes, non-specific binding can be quite common. To achieve
higher purity, a dual purification strategy termed tandem affinity purification (TAP)
was developed by Rigaut27. The original TAP-tag construct system included protein A
and a calmodulin binding peptide as tandem tags with a tobacco etch virus protease
cleavage site in between the tags. The tag cassette has the flexibility of being fused
to either the N or C-terminal ends of a target protein to obtain the optimal expression
of the fused protein in a host cell.
The first purification step of the fusion protein and its associated components
involves binding of Protein A to an IgG matrix. After gentle washing, purified
complexes are released by the tobacco etch virus enzyme cleavage while
nonspecifically bound proteins are left behind. In the second purification step, the
eluate of the first affinity step is incubated with calmodulin-coated beads in the
16
presence of calcium. Following another wash step, the fusion protein and its binding
partners are specifically released via calcium chelation. Again, proteins that interact
nonspecifically with the support matrix are left behind.28
A wave of new tandem affinity tag combinations are currently available
commercially. The choice of tag combination is heavily influenced by the
biochemistry of the model organism being studied. For example, higher eukaryotic
cells naturally express high levels of calmodulin and calmodulin-binding proteins and
that can interfere with the binding of the calmodulin binding peptide tag to the
resins. For this reason, mammals and plants generally use alternative tags such as the
streptavidin-binding peptide tag (GS-TAP tag). Another important consideration is the
size of the tag. Bulky tags are more likely to interfere with the biological function of
the tagged protein, such as protein folding and recruitment to protein complexes.
The tags taking advantage of the high biotin and streptavidin binding affinities are
much smaller compared to the original TAP tag.29
Epitope tags and their cognate antibodies have an advantage over traditional
immunoprecipitations using antibodies against the protein of interest itself in that
they avoid the need for specific antibodies for every target protein of interest. This
method has been widely used in both targeted and large-scale analysis of protein
complexes. Some disadvantages include the time it takes, issues with expressing the
tags at physiological concentrations, tagging artifacts mentioned earlier, and being
limited to complexes with high affinity.29 To address these drawbacks, Kristensen of
the Foster lab developed a new tag free method for protein complex analysis that is
faster and adds a quantitative dimension.
17
1.4.2 High-throughput protein complex profiling
Protein complexes are now being analyzed on a global scale by using tag-free
approaches that separate complex mixtures of endogenous protein complexes into a
set of fractions by gentle, non-denaturing techniques like size exclusion
chromatography (SEC), ion-exchange chromatography (IEX), and blue native
polyacrylamide gel electrophoresis (BN-PAGE). Fractions are analyzed by mass
spectrometry, and then characteristic profile plots of comigrating proteins are
clustered to reconstruct protein complexes.
Initial ‘tagless’ studies of protein complexes suffered from poor yields, for
example a study from 2007 of cytosolic E. coli protein complexes used a three step
chromatography separation (IEX, HIC, and SEC) and identified 103 proteins and 13
protein complexes30. Last year Emili’s group published a study with extensive IEX and
sucrose fractionation (1,163 fractions) for a reported 13,993 cocomplex interactions,
3,006 human proteins with an estimated 21.5% FDR31. These numbers reflect
improvements in chromatography and mass spectrometry over the past five years.
Blue native PAGE gels are able to resolve large labile membrane protein
complexes of mitochondria, up to 30 MDa. Heide et al. identified 464 mitochondrial
proteins and assigned new members to a previously characterized protein complex32.
Membrane complexes require careful sample handling, since solubilizing proteins from
the membrane can lead to disassembly of the protein complex. Digitonin has had the
most success in recent years for this application.
18
Using a technique called protein correlation profiling across SEC with SILAC
(PCP-SILAC), dynamic changes of cytosolic protein complexes in response to stimuli
can now be monitored. This method takes less than two percent of the time of
conventional AP-MS approaches for profiling protein complexes, thus opening the door
for testing a wide variety of stimuli33.
In the PCP-SILAC experimental workflow, triplex SILAC cells (light, medium,
and heavy) are grown, and heavy labeled cells are treated with a compound or
infection. Cell lysates are separated into 50 fractions by high-performance liquid
chromatography with a SEC having an optimal resolution between 150 Kda and 2 Mda.
After fractionation, light samples are pooled together and added to each of the
medium/heavy fractions, serving as internal standards for quantification. Each
fraction mixture is then tryptic digested and analyzed by tandem mass spectrometry.
Hierarchical clustering of the chromatograms led to the identification of 291
complexes based on an empirically determined distance threshold. Kristensen et al.
were able to identify the chaperonin complex, a complex that hasn't been identified
with the TAG strategy. The resolution of these profiles allows the distinction
between assembled and non-assembled proteasome complexes.33 Stimulation by EGF
for 20 minutes led to an increase or decrease of association to complexes for 351
proteins.
The main advantage of using SILAC for high throughput protein complex
profiling is the ability to globally measure dynamic responses of the cell to stimuli.
Lamond’s group recently published a paper using a similar SEC approach for
separating protein complexes and identified nearly double the number of proteins
19
compared to PCP-SILAC, however they used spectral counting for MS quantization and
therefore analyzed a static protein interactome34. A limitation of SILAC is the
prerequisite of a compatible cell line that can be fully labeled by the isotope fed
media.
1.4.3 Chemical cross-linking in living systems
Up to this point, the focus of this section has been on isolating protein
complexes with biochemical techniques. The second class of techniques involves
chemical cross- linking strategies. The idea is to use a cross-linker to capture a
snapshot of the cell. The chemical bonds formed from the cross-linker help stabilize
the complex and allows more stringent washing conditions to lower background
binding.
There are a variety of cross-linkers available with different spacer length and
functional groups. Formaldehyde is an attractive option because it cross-links only
closely associated proteins, has a high permeability towards cell membranes, and is
cheap. Currently, the field is still at the very early stages of using formaldehyde for
protein-protein interaction studies. A study conducted by the Kast group address
some important issues with formaldehyde and provide optimized experimental
conditions for integrin Beta 1 (B1). In a basic workflow, cells are treated with
formaldehyde, lysed and protein complexes are precipitated by antibodies. One
concern was that formaldehyde might disrupt the epitope tag and prevent
precipitation. They tested this hypothesis by precipitating Integrin B1 cross-linked
complexes that have epitopes with varying numbers of amino acids that can be
20
modified by formaldehyde. They concluded that under the cross-linking conditions
they tested, this was not a problem. They provided optimal experimental conditions,
such as formaldehyde concentration, length of incubation and temperature for
running the gels. They demonstrated with gels that formaldehyde complexes were
preserved if samples were only incubated at 65 degrees, whereas most of the cross-
links were reversed at 99 degrees Celsius35.
A novel cross-linker that has potential applications for protein complex studies
is a photocleavable protein interaction reporter that provides both identification of
interacting proteins and spatial details about the binding domain. The structure
contains two reactive groups next to photocleavable groups and a reporter with an
affinity tag. Directing UV light onto the cross-linked complex allows fragmentation
into two peptides and the reporter.
In a study demonstrating the proof of this concept, Zhang et al. cross-linked E.
coli proteins in vivo prior to lysing the cells and digesting the proteins into peptides.
An avidin-biotin affinity purification was used to enrich the cross-linked products.
Photocleavage was performed by a UV laser that was focused on the sample in a
capillary. Samples were then analyzed by tandem MS and identified with in house
software. They identified 114 inter and intra protein interactions, 38 which had been
previously reported in the E. coli interaction database.36
The attractive aspects of chemical cross-linking for the analysis of multiprotein
complexes are the spatial information about where proteins bind and an increased
purity of the sample. The trade offs are spectra that are more complex requiring
21
sophisticated software for data analysis and the time to work out all the optimal
conditions, such as concentration of cross-linker reagent.
1.4.4 Validation
Currently there is not a strict standardized method for validating protein
complexes. In these types of large-scale studies, individual interactions are typically
not quality controlled or validated. Therefore, the results almost certainly contain
false positive interactions arising from spurious interactions or false negatives arising
from missed interactions. Generally within a paper studying protein complexes, the
authors will make some reference as to how their identifications compare to the
known interactors from a literature-curated reference of interactions, such as the
CORUM database37.
Although a number of signaling complexes have been successfully identified
and studied by proteomic techniques, a comprehensive analysis of signaling
complexes is currently unattainable due to several bottlenecks. First, the proteins
comprising most signaling complexes are typically expressed near the lower end of
the abundance range, making them harder to identify. Typically MS identification of
signaling complexes start from 108 cells. For dynamic or low abundant protein
complexes, scaling up would require hundreds of flasks of monolayer cells to be
cultured. Second, the interactions holding signaling complexes together can be weak
and thus may not always survive affinity purification. Cross-linking can help alleviate
this problem to a degree, but when functional groups of the proteins being cross-
linked are not positioned correctly in the interacting interface, a cross-link can't be
22
made. Third, their assembly and disassembly occurs rapidly. For example, T cell
receptors recruit different proteins within 15 s of receptor activation which can't be
captured by the current methods.38 To overcome these obstacles in the short term,
proteomic studies can complement their data with alternative technologies, such as
electron microscopy and cellular electron tomograms. 39
23
Chapter 2: Materials and methods
2.1 General solutions and buffers
ABC buffer
50 mM ammonium bi-carbonate (NH4HCO3, ABC) in water (pH8.0). Stored at RT.
Reduction buffer
10 mM dithiothreitol (DTT) in 50 mM ABC buffer. Stored in small aliquots at -20 C.
Alkylation buffer
55 mM iodoacetamide in 50 mM ABC buffer. Stored in small aliquots at -20 C in the
dark.
Buffer A (starting mobile phase for LC-MS/MS)
0.5% acetic acid in water. Stored at RT.
Buffer B (ending mobile phase for LC-MS/MS)
0.5% acetic acid in water, 80% acetonitrile in water. Stored at RT.
2.2 Cell culture
HeLa cells (American Type Culture Collection) were cultured in a humidified
incubator at 37 °C in the presence of 5% CO2 with Dulbecco’s Modified Eagle’s Medium
(DMEM) (Caisson Laboratories Inc.) supplemented with 10% (v/v) dialyzed fetal bovine
serum (dFBS) (Invitrogen), 2 mM glutamine (Thermo Fisher Scientific), and 100 U/mL
penicillin/streptomycin antibiotics (Thermo Fisher Scientific).
24
2.2.1 SILAC labeling
For SILAC labeling of HeLa cells, DMEM media lacking arginine and lysine were
enriched by adding the following: (1) L-arginine (22 mg/L) and L-lysine (38 mg/L)
(Sigma-Aldrich, Oakville, ON) for “light” labelled cells, (2) 13C6 L-Arginine (22 mg/L)
and D4 L-Lysine (38 mg/L) (Cambridge Isotope Laboratories, Andover, MA) for
“medium” labeled cells, and (3) 13C6 15N4 L-Arginine (22 mg/L) and 13C6 15N2 L-Lysine
(46 mg/L) (Cambridge Isotope Laboratories, Andover, MA) for “heavy” labeled cells.
HeLa cells were split at a 1:4 dilution into the three SILAC media formulations and
passaged five times for complete replacement of labeled amino acids into proteins.
Arginine and lysine are the most common amino acids for labeling cells in proteomic
experiments because the protease trypsin cleaves at the carboxy-termini of arginine
and lysine (thus producing ideal peptides for quantitation)26,40.
2.3 Sample preparation
2.3.1 Salmonella infection
Prior to Salmonella infection, all cell cultures (five 15 cm dishes per SILAC
population) were serum starved for 20 h by washing cells with phosphate buffer saline
(PBS) two times and plating cells in SILAC DMEM containing no antibiotics or fetal
bovine serum. An overnight culture of wild-type Salmonella enterica serovar
Typhimurium strain SL1344, was subcultured (1:33) for 3 h at 35 °C. The Salmonella
inoculum was prepared by pelleting the bacteria at 10,000 relative centrifugal force
(rcf) for 2 min at RT and resuspending cells in antibiotic-free DMEM at a multiplicity
25
of infection (MOI) of 200. Heavy labeled cells were incubated with the Salmonella
inoculum for 20 min at 37 °C.
2.3.2 Cell harvesting
After infection, cells were immediately placed on ice. Cells were washed three
times with cold PBS and harvested with a scraper. Harvested cells having the same
SILAC label were pooled, pelleted for 4 min at 550 rcf at 4 °C and resuspended in 2
mL of size exclusion chromatography (SEC) mobile phase (50 mM KCl, 50 mM
NaCH3COO, pH 7.2) containing complete protease inhibitor cocktail without EDTA
(Roche) and additional phosphatase inhibitors (5 mM Na4P2O7, 0.5 mM pervanadate).
Cells were lysed by 200 strokes with a Dounce homogenizer and concentrated with a
spin column (100 kDa MW cutoff, Sartorius Stedim).
2.3.3 Size exclusion chromatography of protein complexes
Cell lysates from the heavy labeled cells (Salmonella infected) were combined
with the medium labeled cells right before separation by size exclusion
chromatography. Samples were loaded onto a 600 x 7.8 mm BioSep4000 Column
(Phenomenex) and separated into 80 fractions by a 1200 Series semi-preparative HPLC
(Agilent Technologies, Santa Clara, CA) at a flow rate of 0.5 mL/min at 8°C. The
fractions from the light SILAC populations served as an internal standard and were
separated by SEC independently of the medium/heavy samples. The fractions to be
analyzed by MS (first 45 fractions) were pooled together and spiked into each of the
corresponding medium/heavy fractions at a volume of 1:1.
26
2.3.4 In solution digestion of protein complexes
Sodium deoxycholate was added to each fraction to a final concentration of
1.0% (v/v) then each sample was boiled for 5 min. Protein samples were reduced for
30 min at RT in 10 mM dithiothreitol (DTT) solution followed by alkylation for 20 min
by 55 mM iodacetamide (IAA) in the dark at RT. Sequence grade trypsin (Promega;
protein:enzyme concentration 50:1) was added to each sample and incubated
overnight at 37 °C. Peptides were acidified to pH < 3 with acetic acid and cholic acid
was pelleted by spinning at 16,000 rcf for 10 min.
2.3.5 StageTip purification
Stop-and-go-extraction tips (StageTips)41 were self-made by punching out two
small disks of C18 Empore material (3M) using a 22G syringe and packing them at the
end of a 200 !L pipette tip. The StageTips were conditioned with methanol and
equilibrated with Buffer A. The in-solution peptides were acidified to pH < 3 with
acetic acid. Peptides were loaded onto the column with Buffer A by centrifugaton at a
maximum speed of less than 5,000 rpm. Peptides were washed once with Buffer B,
and eluted from the column with 30 !L of Buffer B directly into a HPLC autosampler
plate. Samples were concentrated in a vacuum concentrator and resuspended in 10 !L
Buffer A.
27
2.4 Mass spectrometric methods
2.4.1 LC-MS/MS
Peptides from each sample were separated by a 180 min gradient (5-35%
acetonitrile in 0.5% aqueous acetic acid) using an in-house packed C-18 analytical
column (200 mm length 75 !m I.D.), packed with 3.0 !m-diameter ReproSil-Pur C-18-
AQ beads (Dr. Maisch, www.Dr-Maisch.com). Peptides were eluted from the column
and electrosprayed into a linear-trapping quadrupole – Orbitrap mass spectrometer
(LTQ-Orbitrap Velos; Thermo Fisher). The LTQ-Orbitrap was operated with the
following settings: one full precursor scan in the Orbitrap (resolution 60,000; 350-
1,600 Th). The top ten most intense peptide ions were selected for simultaneous
fragmentation by collision-induced dissociation and top five by HCD (resolution 7500)
in each cycle in the LTQ. The LTQ was operated with the following settings: minimum
signal intensity 1000 counts, singly charged ions were excluded, and parent ions were
excluded from MS/MS for the next 30 sec.
2.5 Bioinformatics and statistical analysis
2.5.1 Database searching and quantitation
The acquired spectra were analyzed by the MaxQuant software (v1.1.1.36)42.
Isotope clusters and SILAC doublets/triplets were extracted from the RAW data files
and quantified. A database of the most recent host-pathogen protein sequences were
compiled from the human International Protein Index protein sequence database (IPI
28
human v3.68) and UniProt/Swiss-Prot (11/7/2011) Salmonella Typhimurium (total
89753 protein sequences). Max Quant’s Andromeda43 algorithm was used to identify
proteins with the following search parameters: carbamidomethylation of cysteine as a
fixed modification; oxidation of methionine, acetylation of protein N-terminal and
SILAC labeling as variable modifications; trypsin/P cleavage with a maximum of 2
missed cleavages, 0.5 Da mass tolerance for MS/MS. False discovery rates were
estimated by searching against a reversed sequence concatenated target-decoy
database44. A maximum false discovery rate of 1.0% at both the protein and peptide
level were accepted for protein identifications.
2.5.2 Visualizing protein chromatograms with R
R (version 2.11.1) scripts were used to plot thousands of M/L and H/L protein
profiles to serve as a visual reference and for inspecting the raw data quickly.
Plotting the individual profiles together in a 10x4 grid reduced the pages needed to
display all the profiles of one replicate. In generating the ‘landscape’ view, plotting
all the protein profiles together using the default settings is not visually informative
as the result is one thick line. To overcome this, the transparency (alpha channel) was
adjusted. This is the two digits appended at the end of a hexadecimal color code. For
example, in Figure 3.3 the line color was set to col="#00000025". By adjusting the
transparency, thousands of profiles can be plotted together, and features of the
profile landscape can be seen.
29
2.5.3 Filtering protein chromatograms
Chromatogram (M/L) ratio profiles across the 45 fractions were filtered in a
two-step algorithm. 1.) Data for a given protein was retained where there were
quantified ratios in at least three consecutive SEC fractions and 2.) at least one of the
three data points was greater than a specified minimum threshold value (0.1-.2).
2.5.4 Clustering protein chromatograms
The R package ‘gplots’ (version 2.11.3) was used to hierarchically cluster
filtered protein profiles and display them in a heatmap. The CORUM database of
protein complexes was used for annotating the heatmap with well characterized
protein complexes.37
2.5.5 Protein complex enrichment analysis
The COMPLex Enrichment Analysis Tool (COMPLEAT)45 contains a comprehensive
resource of human protein complexes (3,638 compiled from literature sources and
6,251 predicted for a combined total of 9,293 human protein complexes). The tool
was developed to analyze high-throughput genomic and proteomic datasets without
the need of preselecting hits. A complex score (ciqm) is calculated by mapping the
data to protein complexes, sorting the data from highest to lowest, then calculating
the interquartile mean of the data. A p-value is also computed by comparing the ciqm
score to 1000 random complexes of the same size.
30
Protein complex enrichment was performed with COMPLEAT by analyzing each
fraction of PCP-SILAC separately. Protein complexes were included in the list of
enriched complexes if they were in at least two of the three biological replicates
using a p-value threshold of 0.05.
2.5.6 Differential protein changes
Three independent biological replicates of PCP SILAC were generated, and
all ratios were converted to log2 values. The protein profiles of two abundant
complexes (proteasome and 14-3-3) were plotted in excel for all replicates and peaks
were aligned manually by shifting the whole dataset left or right in increments of one
fraction. Differential protein changes were calculated by three methods: fold
change, Z-test, and linear modeling with a moderated t-test.
Fold Change H/M ratios of 1.5-fold change (FC) were identified. Proteins
having at least two replicates with H/M ratios above this FC threshold cutoff were
considered significant.
Z-Test H/M ratios across three replicates were tested in each fraction
independently with the null hypothesis that the average H/M raio was equal to 0 using
Microsoft Excel’s two-tailed Z-test function. Multiple hyppthesis testing was
accounted for using a FDR method and q value threshold of 0.05.
Linear Modeling M/L and H/L ratios for three replicates were tested in the R
environment with the limma package46. Data were fitted to a simple linear model and
tested with Student’s t test, the null hypothesis that the average ratio is 0. The
empirical Bayes moderated t-test was applied to each t statistic. Multiple hypothesis
31
testing was accounted for across protein chromatograms using Storey and Tibshirani
FDR method and q value cut-off of 0.05.47
2.5.7 GO analysis of changing proteins
Dynamic proteins were investigated further with DAVID Bioinformatics Database
(DAVID Bioinformatics Resource v 6.7)48,49. Calculations of GO term over-
representation was performed with IPI identifiers comparing dynamic proteins
(identified by the z-test, q-value threshold of 0.05) to the entire list of identified
proteins as background. A p-value threshold of 0.01 was used for the analysis.
2.6 S. Typhimurium strains
Table 2.1 Salmonella strains used in this thesis Strains
S. enterica sv. Typhimurium
Genotype Source or
reference
SL1344 wild-type, SmR Boyle et al.50
"sopB SL1344 SL1344 sopB- Boyle et al.50
"sptP SL1344 SL1344 sptP- Boyle et al.50
"InvA SL1344 SL1344 invA- Boyle et al.50
"sopD SL1344 SL1344 sopD- Boyle et al.50
"sipA"sopE"sopE2 SL1344 SL1344, sipA-,sopE-, sopE2-, Boyle et al.50
RFP SL1344 wild-type, SmR, AmpR Zheng, YL51
RFP "sopB SL1344 SL1344 sopB-, AmpR This work
RFP "sptP SL1344 SL1344 sptP-, AmpR This work
RFP "InvA SL1344 SL1344 invA-, AmpR This work
RFP "sopD SL1344 SL1344 sopD-, AmpR This work
RFP "sipA"sopE"sopE2 SL1344 SL1344, sipA-,sopE-, sopE2-, AmpR This work
32
2.6.1 RFP gene transfection of S. Typhimurium
S. Typhimurium SLI1344 and mutants (see Table 2.1) were grown in liquid LB at
37°C for 2.5 hrs with shaking. Salmonella were pelleted at 4000 g for 10 min and
resuspended in 250 !L of 15 % ice cold glycerol solution. Electrocompetent Salmonella
(45 uL/transformation) were mixed with 0.2 uL RFP plasmid in a prechilled cuvette.
Salmonella were electroporated using a Gene Pulser apparatus (BioRad) following the
manufacture’s instructions. Electroporation was done at 2500 V and 200 # resistance.
2.6.2 Salmonella protein synthesis assay
HeLa cells were seeded in 96-well tissue culture plates and grown overnight.
RFP expressing Salmonella strains (listed in Table 2.1) were used to infect HeLa cells
at an MOI of 50 for 30 minutes at 37°C. Protein synthesis was measured by AHA
incorporation (50 !M AHA) using Click-iT AHA Alexa Fluor 488 Protein Synthesis HCS
Assay (Invitrogen) following the manufacturer’s instructions.
33
Three populations of SILAC HeLa cells are grown. Heavy cells are infected with Salmonella and
medium cells act as a control. Cells are harvested, lysed with a Dounce homogenizer, and the
lysate is spun at high speed to remove cellular debris. Protein complexes enriched away from
most monomeric proteins using a 100,000 molecular weight cutoff filter. Equal volumes of
medium and heavy protein complexes are mixed and fractionated by SEC. Light cells serve as an
internal standard that are spiked into each H/M fraction before analysis by LC-MS/MS.
Chapter 3: Results
Global interactome studies have traditionally been investigated with Y2H and
TAP-TAG technologies, costly methods that require extensive labor for cloning and
expressing thousands of tagged proteins. Recently, a quantitative proteomics
approach for interactome studies was designed to be fast, affordable, and avoid tags
or chemical cross link agents. This proteomic interactome method, PCP-SILAC, takes
advantage of the high resolving power of the latest size exclusion chromatography
technology for large biomolecules and the accurate and quantitative properties of LC-
MS/MS SILAC technology, (a schematic of the experimental workflow for PCP-SILAC is
shown below in Figure 3.1).
Figure 3.1 Experimental workflow for PCP SILAC with Salmonella infection.
34
Raw MS/MS sequence data from 50 SEC fractions are processed with Max Quant for identification and
quantitation of proteins. Protein profiles are generated from the M/L ratios. Data are filtered in excel
to remove noise and visualized with Excel and R. Dynamic complexes are identified statistically with
H/M ratios.
Similar to other high throughput genomic technologies, PCP-SILAC generates an
incredibly large amount of data in a single replicate (e.g., 66.3 GB of MS/MS data).
Creating software for analyzing this type of data is currently one of the bottlenecks of
the field, and was a major component in the development of the PCP-SILAC method.
The data analysis workflow of a PCP-SILAC experiment is depicted in Figure 3.2 with a
visual representation of generating protein profiles from quantitative mass
spectrometry data across multiple fractions and downstream preprocessing,
visualization, and statistical analysis.
Figure 3.2 PCP-SILAC data analysis workflow
35
Generated by plotting all protein profiles of one replicate with R.
3.1 Mapping a global interactome landscape with PCP-SILAC
Three independent biological replicates of PCP SILAC Salmonella resulted in
the identification and quantification of 4,049 human HeLa proteins and identification
of 21 Salmonella proteins at a 1.0% FDR. The PCP SILAC interactome landscape
reveals the complex topology of cytosolic protein complexes on a global scale, as
shown below in Figure 3.3 with all the protein profiles of one replicate plotted
together. Protein complexes are separated by size (the heavier complexes eluting
from the SEC column in the early fractions). Co-eluting proteins of a protein complex
give rise to a characteristic Gaussian peak that can be distinguished by its center,
width, and height. There are a handful of prominent peaks that represent the most
abundant cytosolic complexes (e.g. the proteasome centered around fraction 23).
The number of protein complexes a protein is associated with can be visualized by
plotting the protein profiles individually, as shown in Figure 3.4.
Figure 3.3 PCP-SILAC landscape interactome
36
Profiles of 40 randomly selected proteins from one replicate are plotted (M/L ratio y-axis and SEC
fraction x-axis). The number of peaks in a profile represent the number of protein complexes that
protein is associated with (as detected by PCP-SILAC).
Figure 3.4 Individual protein profiles
37
In the data analysis workflow, one of the early steps that required fine-tuning
was the filter step. During the PCP-SILAC Salmonella experiments, it was noted that
in one of the practice replicates, too much internal standard was added to the
fractions resulting in quantified ratios that were much lower than the threshold value
applied in the paper that developed SEC-PCP-SILAC method33. A range of minimum
threshold values (0.1-2.0) were evaluated, as judged by the number of protein
profiles that remained after the two filtering steps, as shown in Figure 3.5. It appears
that a 0.6 threshold was too stringent; therefore the optimal value is between 0.2-
0.4. The relative weight of the two filtering steps was calculated and diagramed in
Figure 3.5 B. With this dataset, filtering the data for a set of 3 consecutive data
points (mini clustering), had a much greater relative impact on the number of
proteins filtered out, when compared to the threshold step. The cluster filter step
can be adjusted in the future to see if using a less stringent cluster criteria (such as a
larger cluster with holes), would allow more profiles to be characterized.
Figure 3.5 Filtering protein profiles
A B
38
Hierarchically clustering the
protein profiles was one method to
visually investigate the protein
complexes. Many of the protein
complexes identified by this clustering
technique are curated in protein
interacton databases like CORUM. One
protein complex that was identified
from the clustering was the eukaryotic
translation initiation factor 3 protein
complex (eIF3). This complex has
been described by previous reports as
being a versatile scaffold for
translation initiation complexes and is
a known interactor of mTOR6. Proteins
that cluster near a protein complex
can be investigated further as a
candidate member of th at complex.
Figure 3.6 Heatmap of hierarchical clustered protein profiles of one replicate.
Fraction
39
Classifying protein complexes by hierarchically clustering protein profiles works
well for proteins that comigrate as only one protein complex and have a single
Gaussian peak. The method’s performance declines when proteins comigrate in more
than one protein complex because the clustering is based on similarities across the
whole chromatogram. For this reason, hierarchically clustering provides a limited
scope of the interactome captured in a PCP-SILAC experiment. To provide a more
global analysis of the interactome, an alternative approach for identifying protein
complexes on a per fraction basis was applied to the PCP-SILAC dataset.
The COMPLEAT tool performs protein complex enrichment analysis on
submitted proteomic datasets using a comprehensive resource of human protein
complexes. A p-value measure of significance is calculated for each protein complex
that is mapped from the dataset by comparing the score to a 1000 randomly
generated protein complexes of the same size. Using a p-value cutoff of 0.05 and
criteria of being in at least two of the three replicates, the PCP-SILAC dataset was
enriched with 346 protein complexes (the full list is provided in Appendix A). Of the
enriched protein complexes identified, $ were from literature sources and % are
predicted protein complexes.
3.1.1 Applying PCP-SILAC to a host-pathogen system
One of the benefits of SILAC is the simplicity of multiplexing an experiment and
quantitatively measuring dynamic changes of the proteomic interactome landscape.
In the Nature Methods paper of PCP-SILAC, Kristensen et al. demonstrated the ability
of PCP-SILAC to detect temporal shifts of protein architecture after EGF stimulation.
40
In this study we applied PCP-SILAC to study the initial host-pathogen
interactions of Salmonella with HeLa cells. Light, medium, and heavy SILAC labeled
cells were fully incorporated by growing the cells for five generations. Heavy labeled
cells were infected with WT Salmonella enterica at a MOI of 100 for 30 minutes at 37
degrees Celsius. Light, medium and heavy cells were immediately harvested and
protein complexes analyzed by PCP-SILAC. Identifying and characterizing the changes
between medium and heavy (infected vs. non-infected) cell populations was the focus
of the remainder of this thesis.
3.2 Identification of host proteins targeted during Salmonella
invasion
Three biological replicates of PCP-SILAC were performed testing Salmonella
infection versus noninfected conditions of two separate populations of labeled HeLa
cells. Before statistical analysis of control versus infected cells could be performed,
data from the replicates needed to be preprocessed.
41
"!
#!
$!
%!
&!
'!
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47
0
1
2
3
4
5
6
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47
"!
#!
$!
%!
&!
'!
#! %! '! (! )! ##!#%!#'!#(!#)!$#!$%!$'!$(!$)!%#!%%!%'!%(!%)!&#!&%!&'!&(!
"!
#!
$!
%!
&!
'!
#! %! '! (! )! ##!#%!#'!#(!#)!$#!$%!$'!$(!$)!%#!%%!%'!%(!%)!&#!&%!&'!
0
1
2
3
4
5
6
#! %! '! (! )! ##!#%!#'!#(!#)!$#!$%!$'!$(!$)!%#!%%!%'!%(!%)!&#!&%!&'!
"!
#!
$!
%!
&!
'!
#! %! '! (! )! ##!#%!#'!#(!#)!$#!$%!$'!$(!$)!%#!%%!%'!%(!%)!&#!&%!&'!
3.2.1 Preprocessing of Replicate Data
Protein profiles of the proteasome and 14-3-3 complex subunits were used to
align replicate data since they are abundant complexes of the cytosol and are well
annotated in the literature. Three alignments are shown below in Figure 3.7.
Figure 3.7 Aligning protein chromatograms with proteasome subunits
HC2
LMPX
PSC5
42
3.2.2 Identifying dynamic proteins (log fold change, z-test, t-test)
Filtered replicate data from the aligned protein profiles were processed with
three complementary techniques of identifying changing proteins in response to
Salmonella infection. In the first approach we set a fold change threshold of 1.5 for
the H/M ratios, which resulted in a list of 15 proteins. We next applied a Z-test
(location test) to the protein H/M ratios, based on the null hypothesis that host
proteins that were not dynamically affected by Salmonella infection would have the
same protein profiles in the two conditions, and a ratio of 1. The Z-test resulted in
the identification of 226 proteins with a q-value threshold of 0.05 shown below in
Figure 3.8 as a heatmap. The final approach was to fit linear models to the M/L and
H/L ratios and apply an empirical Bayes moderated t-test to the data with multiple
hypothesis testing accounted for by a FDR method. This led to six proteins being
flagged as significant meeting a q value threshold of 0.05. (Results of dynamic
proteins are listed on the following pages in Tables 3.1-3.3).
Figure 3.8 Heatmap of changing proteins identified by the z-test
43
Table 3.1 Identification of dynamic proteins after Salmonella infection (threshold log FC )
Gene Name IPI Identifier Protein Name H2BFD IPI00646240 Histone H2B
H2AFC IPI00291764 Histone H2A
H4/A IPI00453473 Histone H4
HNRNPH1 IPI00479191 Heterogenous nuclear
ribonucleoprotein BASP1 IPI00299024 Brain abundant, membrane
attached signal protein1 CDC47 IPI00291764 CDC47 Homolog
BM28 IPI00184330 DNA replication licensing factor
RPL23 IPI00010153 60S Ribosomal Protein
BAG3 IPI00641582 BAG family chaperone regulator
HK1 IPI00903226 Hexokinase1
CAP43 IPI00022078 N-myc downstream regulated 1
HSP60 IPI00784154 60 kDa heat shock protein
GCN1L1 IPI00001159 Translational activator GCN1
HSP27 IPI00025512 28 kDa heat shock protein
PTRF IPI00176903 Cavin-1 Polymerase I and
transcript release factor SEC Fraction
44
Table 3.2 Identification of dynamic proteins after Salmonella infection (z-test)
Gene Name
IPI Identifier Protein Name Function q-value
HSP27 IPI00025512 28 kDa heat shock protein Regulate Actin Dynamics 1.54 E-08
PAK2 IPI00419979 C-t-PAK2 Regulate Cytoskeleton Dynamics
4.31 E-02
ASNS IPI00306960 Asparagine—tRNA ligase Protein Translation 7.87 E-04
CAP1 IPI00939159 Adenylyl cyclase-associated protein1 Regulate Actin Dynamics 2.03 E-02
GCN1L1 IPI00001159 Translational activator GCN1 Protein Translation 3.30 E-04
RPL23 IPI00010153 60S ribosomal protein L17 Protein Translation 6.43 E-03
HSP60 IPI00784154 60 kDa heat shock protein Protein Folding 8.92 E -07
IQGAP1 IPI00009342 Ras GTPase-activating like protein Regulation of GTPase Activity 2.09 E -04
BASP1 IPI00299024 22 kDa neuronal tissue-enriched acidic protein Regulation of Transcription 2.15 E -06
AHNAK IPI00021812 Desmoyokin Regulate Actin Dynamics 7.28 E -09
AHNAK2 IPI00856045 Protein AHNAK2 Regulate Actin Dynamics 1.85 E -04
NHERF IPI00003527 Ezrin-radixin-moesin-binding phosphoprotein Regulate Actin Dynamics 4.71 E -04
EIFA IPI00025491 Eukaryotic initiation factor 4A Protein Translation 2.08 E -02
GLRX3 IPI00008552 PKC-interacting cousin of thioredoxin Cell Redox Homeostasis 8.34 E -04
CAPZA1 IPI00005969 F-actin capping protein Regulate Actin Dynamics 1.92 E -06
* Table continued in Appendix B
45
Table 3.3 Identification of dynamic proteins after Salmonella infection (t-test)
Overlap Gene Name Protein Name Function Log2 Fold Change (H/M)
q-value Reference
*** PTRF Cavin-1 Polymerase I and transcript release factor
Caveole biogenesis 2.13 1.03 E-06 52,53
** ASNS Asparagine—tRNA ligase
Protein Translation -1.74 5.59 E-03
*** HSP27 28 kDa heat shock protein
Regulate Actin Dynamics
-2.19 1.50 E-02 54
** PAK2 C-t-PAK2 Regulate Cytoskeleton Dynamics
-2.52 3.17 E-02 55,56
** CAP1 Adenylyl cyclase-associated protein1
Regulate Actin Dynamics
-1.86 4.42 E-02 57-59
* PSMB3 Proteasome Chain 13
Protein Degradation -1.36 3.51 E-03
* Indicates the number of tests the gene was identified as a dynamic protein.
46
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0 5 10 15 20 25 30 35 40 45
All three methods detected 28 kDa heat shock protein (HSP27) as being
dynamic in response to Salmonella infection. The average M/L and H/L ratios of
HSP27 are plotted below in Figure 3.9. Comparing the two plots, there is a distinct
shift of the H/L peak center to the right of the M/L peak center, suggesting
disassembly or rearrangement of protein complexes that include HSP27 as a subunit.
A high proportion of HSP27’s fractions 16/46 were identified by the Z-test as
significantly changing (q-value < 0.0.5).
Figure 3.9 HSP27 PCP-SILAC profile
SEC Fraction
HSP27
log2
(Ra
tio
)
Avg M/L
Avg H/L
47
3.2.3 Integrating a phosphoproteomic Salmonella study
A previous study in our lab examined the dynamic changes of the
human host phosphoproteome as a time course experiment of early (1-30 min)
Salmonella Typhimurium infection. Phosphorylation is an important protein
modification for regulating the dynamic assembly and disassembly of protein
complexes, so we were interested in finding the overlap between the changing
proteins identified in the phosphoproteomic study and this study. The results of this
analysis (70 protein overlap) are shown below in Figure 3.12 as a Venn Diagram.
Figure 3.10 Phosphoproteomic and PCP-SILAC venn diagram
48
3.2.4 GO enrichment analysis
The DAVID functional analysis tool was used for GO enrichment analysis of the
dynamic proteins identified by the Z test. Although this list may contain relatively
more false positives than the other two methods, it has a higher sensitivity and
captures dynamic proteins to a greater depth. The list of candidates identified by the
Z test is large enough for GO analysis (the other candidate lists identified by fold
change and t-test were too small with 15 and 6 candidate proteins). The GO term
‘translation’ was enriched five fold more in Salmonella infected cells compared to the
background of proteins identified in the experiment. A second term that was
significantly enriched was ‘Negative regulation of ubiquitin-protein ligase activity’. A
bar graph of the enrichment analysis is shown above in Figure 3.11.
Figure 3.11 GO functional analysis of dynamic PCP-SILAC proteins with infection
49
3.3 Host protein synthesis machinery targeted during
Salmonella invasion
We were intrigued by the GO Analysis results as recent reports have found host
translational machinery being targeted in other host-pathogen systems but hasn’t
been reported in Salmonella. To test the hypothesis that host translational
machinery is targeted by Salmonella during infection, we measured protein synthesis
using a fluorescent assay as an alternative to the traditional radioactive methionine
approach.
In this assay, L-azidohomoalanine (AHA) acts as an analogue for methionine and
is incorporated into cells during active protein synthesis. After fixing cells, Click-it
chemistry is used to label AHA with a fluorescent marker (AlexaFluor488) to measure
nascent protein synthesis. The AHA assay was tested in HeLa cells using a range of
cycloheximide (inhibitor of protein synthesis) treatment concentrations. A dose
response curve along with corresponding images of green (AHA) and blue (nuclei)
channels of cycloheximide treated and untreated cells is shown below in Figure 3.12.
50
Figure 3.12 AHA protein synthesis assay
We next used the AHA assay to measure protein synthesis in HeLa cells infected
with red fluorescent protein (RFP) expressing Salmonella, WT. Based on three
biological replicates, we saw a significant decrease of protein synthesis of Salmonella
infected cells compared to the non-infected control (2-tailed T-test, p-value 0.01).
The PCP-SILAC Salmonella data identified cytosolic host protein complexes involved
with protein translation as being dynamic in response to Salmonella infection, and the
D 200 !M Cycloheximide
A
A. Chemical structure of L-methionine and L-azidohomoalanine. AHA Assay with HeLa cells, AHA (50 !M)
B. Dose response of cycloheximide, an inhibitor of protein translation C. Image of AHA fluorescence in
untreated HeLa cells and D. HeLa cells treated with 200 !M cycloheximide for 30 minutes.
!"
#!"
$!"
%!"
&!"
'!!"
'#!"
'$!"
()" ($" (*" (#" ('" !" '" #" *"
!"#$%&#'()
*#)+,*-'!.!'
/0&12'3-4/05#6,7,8#'9:;<'
'=0+#'$#+>0)+#'0?'4-4/05#6,7,8#''
C Untreated
B
51
results from the AHA protein synthesis experiments reveal that the host translational
machinery is being down regulated.
Table 3.4 Protein synthesis of Salmonella strains tested
Click-iT AHA Signal Intensity
Strains
R1 R2 R3
Average Std Dev T-test p-values
Control (No Salmonella)
355.8 359.6 407.7 374.4 28.94 WT vs Control
RFP WT SL1344 270.3 284.8 306.2 287.1 18.0 0.01 WT vs ! RFP "sopB SL1344 243.8 298.3 302.9 281.7 32.9 0.81 RFP "sptP SL1344 272.5 303.1 362.3 312.6 45.6 0.42 RFP "InvA SL1344 226.8 221.1 285.8 251.5 47.8 0.29 RFP "sopD SL1344 246.6 263.6 285.9 265.4 19.7 0.23 RFP "sipA "sopE "sopE2 SL1344
315.5 283.5 288.9 296.0 17.1 0.57
To further investigate our hypothesis that Salmonella targets host cell
translational machinery during infection, we tested five additional Salmonella strains
that had one or more SPI-1 effector(s) knocked out, testing a total of seven effectors.
All of the mutant knockout strains we tested did not show a significant difference
compared to wildtype (two tailed t-test with p-values greater than 0.05). This
suggests that protein synthesis is not being regulated by the SPI-1 effector knockouts
tested. Results of AHA protein synthesis experiments are listed in Table 3.4 and
plotted in Figure 3.14.
52
Figure 3.13 Protein synthesis in HeLa cells infected with WT Salmonella and effector
knockout strains
53
Chapter 4: Discussion
4.1 Current Salmonella-host interactome
The interface of evolving host-pathogen systems has typically been viewed
from a reductionist perspective. These type of reductionist studies have identified 62
Salmonella host protein interactions to support our current understanding of
Salmonella's complex pathogenesis60. High throughput technologies have allowed
scientists to capture global dynamics within the host during infection.18 The results of
PCP-SILAC Salmonella reported here make it the first global host-Salmonella protein
interactome study and provides novel insight into the shifts of cytosolic protein
complex architecture occurring in the host. PCP-SILAC is currently being adapted for
the analysis membrane protein complexes, which will fill in a critical component of
Salmonella’s host-pathogen interactome involved in cell signaling.
4.2 The identified dynamic host proteins during Salmonella
invasion
We have identified a subset of dynamic proteins from our PCP-SILAC dataset
that were significantly changing during Salmonella infection, suggesting the protein
complexes involving these proteins have functional roles during Salmonella invasion.
Since this is the first global host-pathogen interactome study of Salmonella, we
applied three different techniques to determine which proteins have dynamic protein
54
profiles. The overlap of candidate dynamic proteins is high between these
techniques, providing more confidence that the candidates are not false positives.
The candidate proteins identified by the T-test and overlapped with the other lists
are discussed in further detail below:
4.2.1 PTRF/Cavin-1
The plasma membrane of most mammalian cell types contains small (50-100
nm) flask-shape lipid invaginations known as caveole61. Caveole are a subclass of lipid
rafts that are enriched in cholesterol and typically contain caveolin, a hallmark
protein for caveole62. These dynamic membrane structures are hot spots for signaling
proteins and have many important trafficking functions including lipid storage,
endocytosis, and cell signaling63,64.
Caveolin was the first (and until recently only) marker for caveole. Further
investigation into the composition revealed another class of proteins associated with
caveole- the cavins52,53. PTRF/Cavin-1 is a cytosolic phosphoprotein originally named
after its role in regulating the activity of RNA transcription complexes, Polymerase I
Transcript Release Factor (PTRF). A dual function was demonstrated for PTRF/Cavin-
1 with its involvement in caveole biogenesis63,65.
Lipid rafts are often targeted by pathogens as a source to hijack host signaling
circuitry66. Previous work in our lab has shown that caveolin 1 is necessary for
Salmonella invasion in HeLa cells51 and similar findings were reported in endothelial M
cells67. Further support that Salmonella targets caveole to gain entry into the host is
provided by results here with PCP-SILAC, as PTRF/Cavin-1 was identified to have a
55
log2 fold change of 2.13 (interestingly, this was the only significant protein identified
with a positive fold change). Immunofluorescence studies suggest PTRF/Cavin-1
assembly with caveole is regulated by mitogenic serine/threonine kinase ARAF168, a
kinase shown to be regulated during Salmonella invasion18.
4.2.2 HSP27
Heat shock proteins (HSP) are ubiquitously expressed in all cell types and
function cooperatively in a network to maintain cellular balance during stressful
conditions. The chaperone activity of HSP helps prevent misfolded or denatured
proteins from aggregating (a problem associated with aging and neurodegenerative
diseases like Alzheimers). Small heat shock proteins (HSPB) are 12-42 kDa, and have a
conserved #-crystallin domain69.
HSPB1 (HSP27), a small heat shock protein, has a number of reported
regulatory roles including controlling the cellular redox state, protein folding, protein
degradation, cytoskeleton dynamics, and anti-apoptotic activity70. HSP27 is an
abundant protein in the cytosol and occurs in small complexes as dimers and large
complexes as oligomers, depending on cellular conditions. Structural dynamics of
HSP27 are regulated by the cellular redox and phosphorylation status of HSP2771,72.
Under heat stress, HSP27 can be translocated to the nucleus and cytoskeleton70.
Two recent Salmonella phosphoproteomic studies by Rogers and Imami focused
on global signaling dynamics of early and late stages of Salmonella infection in HeLa
cells. Imami reported that Salmonella directly targets HSP27 during late stages of
infection with SPI2 effector SteC (the only serine/threonine kinase encoded in the
56
Salmonella genome)54. In vitro and in vivo quantitative MS/MS experiments revealed
six sites on HSP27 dynamically phosphorylated by SteC, (S9, S15, S43/S49/S50/Y54,
S82, T174/S176, S199). Rogers Salmonella phosphorylation study of early infection
(timecourse of 2-30 min) identified two sites on HSP27 dynamically regulated (S15 and
S82) at 10 and 20 minutes18. The natural host kinase of HSP27 has been reported to
phosphorylate HSP27 at three sites (S15, S78, and S82). Additional multiply-
phosphorylation of HSP27 during late stages of Salmonella infection could be
explained by the host actin remodeling activity of HSP27. Confocal microscopy
provides support for this hypothesis, (actin condensation is seen in labeled F-actin
Salmonella SteC+ but not Salmonella SteC- infected cells)54.
A SILAC study investigating host proteins involved with Salmonella replication
during late stages of Salmonella infection reported that HSP27 was significantly
enriched in a Golgi fraction. The SCV has previously been observed to localize in the
Golgi region during SCV maturation, but the mechanism for SCV maturation remains
unkown73. PCP-SILAC protein profiles of HSP27 shows two peaks, a large broad peak
and a small sharp peak in the low MW region, possibly corresponding to different size
oligomers of HSP27. The protein profiles show a shift towards the lower MW species
during Salmonella infection, implying HSP27 disassembly, (Figure 3.9). This provides
the first structural evidence of HSP27 being targeted during early Salmonella
infection.
57
4.2.3 Cyclase Associated Protein-1
Dynamic changes of cell morphology are shaped by the underlying actin
cytoskeleton that forms a complex network within the cytosol. Rapid remodeling of
the actin cytoskeleton is a tightly controlled process regulated by actin binding
proteins that respond to internal and external stimuli. One family of actin binding
proteins that coordinate actin dynamics with cell signaling pathways are the highly
conserved adenylyl cyclase associated proteins (CAP). CAPs help regulate cell
polarity, cell motility, and endocytosis74,75.
In cells, actin is an ATPase that exists either in a monomeric globular state (G-
actin) or a polymeric filamentous state (F-actin). The rate-limiting steps of actin
disassembly are severing actin filaments and recycling of ADP-G-Actin to ATP-G-Actin.
Recent experiments show that CAP1 is a bifunctional protein that helps catalyze both
of these rate-limiting steps. CAP1 binds to actin in a 1:1 stoichiometry and self
associates to form a 600 kDa hexameric complex76. The C-terminal end of CAP1
recycles ADP-G-Actin to ATP-G-Actin while the N-terminal end enhancess cofilin-
mediated filament severing rates57-59.
A genome wide RNAi screen of host proteins affecting SopE mediated
Salmonella invasion identified CAP1 as one of their top hits that enhanced invasion
efficiency77. PCP-SILAC also identified CAP1 as a protein with dynamic protein
complexes during Salmonella invasion. Further investigations of CAP1 protein
complex dynamics will be necessary to understand how Salmonella manipulates the
actin cytoskeleton.
58
4.2.4 C-t-PAK2
The p21-activated kinases (PAKs) are a family of serine/threonine kinases that
control diverse biological processes including cytoskeleton dynamics and apoptosis.
They are present in the cytoplasm as homodimers in a trans-inhibited conformation
and become activated by external stimuli downstream of small GTPases, RAC and
CDC42. PAKs have a growing list of binding targets, making them a versatile class of
signaling enzymes55.
Pathogens have evolved strategies to rewire host-signaling networks by
mimicking host proteins with their effector proteins. Enterohaemorrhagic (EHEC)
Escherichia coli and Salmonella both use similar strategies to hijack host machinery
and invade the host cell. An EHEC E. coli host-pathogen study by Selyunin provided
structural evidence of a novel host-pathogen complex. E. coli uses a T3SS effector,
EspG, as a scaffold to recruit host signaling proteins PAK2 and ARF to the Golgi
apparatus, a subcellular location previously not associated PAK256. Several
Salmonella effectors are known to modulate the activity of CDC42 and RAC by
mimicking the regulators. Data from PCP-SILAC identified PAK2 as a dynamic protein
(log2 fold change of -2.52), suggesting Salmonella also fine-tunes cytoskeleton
dynamics during invasion by rewiring host signaling networks.
4.3 Host translation during Salmonella invasion
Cells are challenged with dynamic environments that require orchestrated
protein turnover in order to adapt to the changing conditions. Protein translation is
an essential process that allows the cell to generate any protein encoded in its
genome, including proteins for defense against invading pathogens. In response,
59
pathogens have evolved mechanisms to control host translation as a way to suppress
the host’s ability to combat the pathogen and divert more nutrients for intracellular
pathogen growth78.
Experimental and computational studies of Salmonella’s nutritional landscape
revealed that Salmonella can access at least 31 chemically diverse host nutrients for
growth79. Among these host nutrients are amino acids (e.g., arginine, lysine,
threonine, glutamate, and serine), which suggests that by inhibiting host protein
synthesis, Salmonella can gain more nutrients for growth.
Legionella pneumophilia is a Gram-negative bacterium that typically resides
inside amoebe, but can also inhabit mammalian lung tissue causing pneumonia and
Legionnaires disease. Similar to Salmonella, Legionella pneumophilia uses a
specialized secretion apparatus to translocate effector molecules into the host cytosol
during invasion. Host-pathogen studies of Legionella pnemophilia have reported a
global decrease in host translation as a result of five effectors that bind and modify
host elongation factors. They claim this is a strategy to suppress the host signaling
response80.
4.3.1 Protein synthesis during Salmonella invasion
The host translational machinery was identified by PCP-SILAC experiments to
be changing composition (assembling/disassembling) in response to early Salmonella
enterica serovar Typhimurium infection. Further experiments with a fluorescence-
based assay to measure protein synthesis confirmed that protein translation was
globally being suppressed during Salmonella (WT) infection. The same assay was also
used to test mutant Salmonella strains with six effector proteins knocked out. None
60
of the tested strains showed statistically significant change of protein synthesis
compared to WT, suggesting the tested effector knockouts are not responsible for the
host protein translation inhibition phenotype. Further experiments are needed to
determine the mechanism of protein translation inhibition of host from Salmonella
infection.
It should be noted there was a high variability in some of the assays. AHA Click-
it protein synthesis assay was developed for high content screening (HCS) with 96 well
plates. This allows the assay to be easily be scaled up to test thousands of different
compound treatments. This high throughput comes at a cost, and working at smaller
scales makes minor technical variation such as pipeting errors or dust particles falling
into the wells important to control. Variability of this assay could be greatly reduced
by using a robotic system for dispensing fluids (experiments performed in this thesis
were done manually with a multi-channel pipette). Sensitivity of this assay could also
be improved by increasing the concentration of AHA reagent.
4.3.2 HSP27 inhibits translation
HSP27 was previously described because it was one of our top candidates
identified by PCP-SILAC as being dynamic in response to Salmonella infection.
Phosphoproteomic studies also identified this protein as being regulated at both early
and late stages of Salmonella infection and its function in context to Salmonella
infection has mainly been attributed to regulating actin dynamics. Another role for
HSP27 during heat shock has been inhibition of protein translation by binding to eIF4G
and facilitating dissociation of cap-initiation complexes81. This multifunctional protein
61
could potentially offer more insight into how Salmonella targets host translation
during Salmonella infection.
62
Chapter 5: Conclusion
SEC-PCP-SILAC was applied to a host-pathogen system for the first time to
study dynamic protein interactions of cytosolic protein complexes during early
Salmonella infection. This global analysis was performed in triplicate profiling 4,049
human proteins from HeLa cells, representing 346 distinct protein complexes.
Proteins that were members of protein complexes that were changing in response to
stimuli were identified by three different methods leading to the hypothesis that host
translational machinery was being targeted by Salmonella during infection.
A fluorescent assay was used to measure protein synthesis of cells infected
with WT and effector knockout strains of Salmonella. There was a significant
decrease of host protein translation in cells infected with WT Salmonella versus the
non-infected control (2-tailed T-test, p-value 0.01) supporting our hypothesis that
Salmonella targets host translational machinery. The effector knockout strains tested
did not show a significant difference of protein translation compared to WT. Further
mechanistic studies will be needed to determine the effector(s) responsible for this
host response. This work provides a rich resource of candidate host proteins that may
be involved with Salmonella’s pathogenesis and provides the first snapshot of global
cytosolic protein complexes during Salmonella infection.
63
Bibliography
1. Hartwell LH, Hopfield JJ, Leibler S, Murray AW. From molecular to modular cell
biology. Nature. 1999;402(6761 Suppl):C47-52.
2. Han JJ, Bertin N, Hao T, et al. Evidence for dynamically organized modularity in
the yeast protein-protein interaction network. Nature. 2004;430(6995):88-93.
3. Fields S, Song O. A novel genetic system to detect protein-protein interactions.
Nature. 1989;340(6230):245-246.
4. Krogan NJ, Cagney G, Yu H, et al. Global landscape of protein complexes in the
yeast saccharomyces cerevisiae. Nature. 2006;440(7084):637-643.
5. Gavin A, Aloy P, Grandi P, et al. Proteome survey reveals modularity of the yeast
cell machinery. Nature. 2006;440(7084):631-636.
6. Smith T. The hog-cholera group of bacteria. U S Bur Anim Ind Bull. 1894;6:6-40.
7. Pui CF, Wong WC, Chai LC, et al. Salmonella: A foodborne pathogen. International
Food Research Journal. 2011;18:465-473.
8. Ramos-Morales F. Impact of salmonella enterica type III secretion system effectors
on the eukaryotic host cell. ISRN Cell Biol. 2012.
9. Miller S, Pegues D. SalmonellaSpecies, IncludingSalmonellaTyphi. In: Mandell,
douglas, and bennett’s principles and practice of infectious diseases. Seventh ed.
Churchill Livingstone; 2009:2636-650.
10. Ohl ME, Miller SI. Salmonella: A model for bacterial pathogenesis. Annu Rev Med.
2001;52(1):259-274.
64
11. Haraga A, Ohlson MB, Miller SI. Salmonellae interplay with host cells. Nat Rev
Microbiol. 2008;6(1):53-66.
12. Jones BD. Host responses to pathogenic salmonella infection. Genes Dev.
1997;11(6):679-687.
13. Winter SE, Thiennimitr P, Winter MG, et al. Gut inflammation provides a
respiratory electron acceptor for salmonella. Nature. 2010;467(7314):426-429.
14. Thiennimitr P, Winter SE, Baumler AJ. Salmonella, the host and its microbiota.
Curr Opin Microbiol. 2012;15(1):108-114.
15. Porwollik S. Salmonella :From genome to function. Wymondham: Caister
Academic Press; 2011:300, A1.
16. Schlumberger MC, Müller AJ, Ehrbar K, et al. Real-time imaging of type III
secretion: Salmonella SipA injection into host cells. Proceedings of the National
Academy of Sciences of the United States of America. 2005;102(35):12548-12553.
17. Reis R, Horn F. Enteropathogenic escherichia coli, samonella, shigella and
yersinia: Cellular aspects of host-bacteria interactions in enteric diseases. Gut
Pathogens. 2010;2(1):8.
18. Rogers LD, Brown NF, Fang Y, Pelech S, Foster LJ. Phosphoproteomic analysis of
salmonella-infected cells identifies key kinase regulators and SopB-dependent host
phosphorylation events. Science signaling. 2011;4(191):rs9.
19. Wasinger VC, Cordwell SJ, Cerpa-Poljak A, et al. Progress with gene-product
mapping of the mollicutes: Mycoplasma genitalium. Electrophoresis. 1995;16(7):1090-
1094.
65
20. de Hoog CL, Mann M. Proteomics. Annu Rev Genomics Hum Genet. 2004;5:267-
293.
21. Canas B, Lopez-Ferrer D, Ramos-Fernandez A, Camafeita E, Calvo E. Mass
spectrometry technologies for proteomics. Brief Funct Genomic Proteomic.
2006;4(4):295-320.
22. Aebersold R, Mann M. Mass spectrometry-based proteomics. Nature.
2003;422(6928):198-207.
23. Schulze WX, Usadel B. Quantitation in mass-spectrometry-based proteomics. Annu
Rev Plant Biol. 2010;61:491-516.
24. Ishihama Y, Schmidt T, Rappsilber J, et al. Protein abundance profiling of the
escherichia coli cytosol. BMC Genomics. 2008;9:102-2164-9-102.
25. Pickup JF, McPherson K. Theoretical considerations in stable isotope dilution mass
spectrometry for organic analysis. Anal Chem. 1976;48(13):1885-1890.
26. Ong SE, Blagoev B, Kratchmarova I, et al. Stable isotope labeling by amino acids in
cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol
Cell Proteomics. 2002;1(5):376-386.
27. Rigaut G, Shevchenko A, Rutz B, Wilm M, Mann M, Seraphin B. A generic protein
purification method for protein complex characterization and proteome exploration.
Nat Biotechnol. 1999;17(10):1030-1032.
28. Gavin AC, Maeda K, Kuhner S. Recent advances in charting protein-protein
interaction: Mass spectrometry-based approaches. Curr Opin Biotechnol.
2011;22(1):42-49.
66
29. Collins MO, Choudhary JS. Mapping multiprotein complexes by affinity purification
and mass spectrometry. Curr Opin Biotechnol. 2008;19(4):324-330.
30. Dong M, Yang LL, Williams K, et al. A "tagless" strategy for identification of stable
protein complexes genome-wide by multidimensional orthogonal chromatographic
separation and iTRAQ reagent tracking. J Proteome Res. 2008;7(5):1836-1849.
31. Havugimana PC, Hart GT, Nepusz T, et al. A census of human soluble protein
complexes. Cell. 2012;150(5):1068-1081.
32. Heide H, Bleier L, Steger M, et al. Complexome profiling identifies TMEM126B as a
component of the mitochondrial complex I assembly complex. Cell Metab.
2012;16(4):538-549.
33. Kristensen AR, Gsponer J, Foster LJ. A high-throughput approach for measuring
temporal changes in the interactome. Nat Methods. 2012;9(9):907-909.
34. Kirkwood KJ, Ahmad Y, Larance M, Lamond AI. Characterisation of native protein
complexes and protein isoform variation using size-fractionation based quantitative
proteomics. Mol Cell Proteomics. 2013.
35. Klockenbusch C, Kast J. Optimization of formaldehyde cross-linking for protein
interaction analysis of non-tagged integrin beta1. J Biomed Biotechnol.
2010;2010:927585.
36. Zhang H, Tang X, Munske GR, Tolic N, Anderson GA, Bruce JE. Identification of
protein-protein interactions and topologies in living cells with chemical cross-linking
and mass spectrometry. Mol Cell Proteomics. 2009;8(3):409-420.
67
37. Ruepp A, Waegele B, Lechner M, et al. CORUM: The comprehensive resource of
mammalian protein complexes--2009. Nucleic Acids Res. 2010;38(Database
issue):D497-501.
38. Yang W, Steen H, Freeman MR. Proteomic approaches to the analysis of
multiprotein signaling complexes. Proteomics. 2008;8(4):832-851.
39. Kühner S, van Noort V, Betts MJ, et al. Proteome organization in a genome-
reduced bacterium. Science. 2009;326(5957):1235-1240.
40. Ong SE, Kratchmarova I, Mann M. Properties of 13C-substituted arginine in stable
isotope labeling by amino acids in cell culture (SILAC). J Proteome Res. 2003;2(2):173-
181.
41. Rappsilber J, Mann M, Ishihama Y. Protocol for micro-purification, enrichment,
pre-fractionation and storage of peptides for proteomics using StageTips. Nat
Protocols. 2007;2(8):1896-1906.
42. Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized
p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat
Biotechnol. 2008;26(12):1367-1372.
43. Cox J, Neuhauser N, Michalski A, Scheltema RA, Olsen JV, Mann M. Andromeda: A
peptide search engine integrated into the MaxQuant environment. J Proteome Res.
2011;10(4):1794-1805.
44. Elias JE, Gygi SP. Target-decoy search strategy for increased confidence in large-
scale protein identifications by mass spectrometry. Nat Meth. 2007;4(3):207-214.
45. Vinayagam A, Hu Y, Kulkarni M, et al. Protein complex-based analysis framework
for high-throughput data sets. Sci Signal. 2013;6(264):rs5.
68
46. Smyth GK. Linear models and empirical bayes methods for assessing differential
expression in microarray experiments. Stat Appl Genet Mol Biol. 2004;3:Article3.
47. Storey JD, Tibshirani R. Statistical significance for genomewide studies.
Proceedings of the National Academy of Sciences. 2003;100(16):9440-9445.
48. Huang da W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: Paths
toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res.
2009;37(1):1-13.
49. Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of
large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44-57.
50. Boyle EC, Brown NF, Finlay BB. Salmonella enterica serovar typhimurium effectors
SopB, SopE, SopE2 and SipA disrupt tight junction structure and function. Cell
Microbiol. 2006;8(12):1946-1957.
51. Zheng Y. Dynamic composition of membrane microdomains. [Doctoral
dissertation]. University of British Columbia; 2012.
52. Aboulaich N, Vainonen JP, Strålfors P, Vener AV. Vectorial proteomics reveal
targeting, phosphorylation and specific fragmentation of polymerase I and transcript
release factor (PTRF) at the surface of caveolae in human adipocytes. Biochem J.
2004;383(2):237-248.
53. Hill MM, Bastiani M, Luetterforst R, et al. PTRF-cavin, a conserved cytoplasmic
protein required for caveola formation and function. Cell. 2008;132(1):113-124.
54. Imami K, Bhavsar AP, Yu H, et al. Global impact of salmonella pathogenicity island
2-secreted effectors on the host phosphoproteome. Mol Cell Proteomics.
2013;12(6):1632-1643.
69
55. Bokoch GM. BIOLOGY OF THE P21-ACTIVATED KINASES. Annu Rev Biochem.
2003;72(1):743-781.
56. Selyunin AS, Sutton SE, Weigele BA, et al. The assembly of a GTPase-kinase
signalling complex by a bacterial catalytic scaffold. Nature. 2011;469(7328):107-111.
57. Balcer HI, Goodman AL, Rodal AA, et al. Coordinated regulation of actin filament
turnover by a high-molecular-weight Srv2/CAP complex, cofilin, profilin, and Aip1.
Curr Biol. 2003;13(24):2159-2169.
58. Chaudhry F, Breitsprecher D, Little K, Sharov G, Sokolova O, Goode BL.
Srv2/cyclase-associated protein forms hexameric shurikens that directly catalyze
actin filament severing by cofilin. Mol Biol Cell. 2013;24(1):31-41.
59. Brieher W. Mechanisms of actin disassembly. Mol Biol Cell. 2013;24(15):2299-
2302.
60. Schleker S, Sun J, Raghavan B, et al. The current salmonella-host interactome.
Proteomics Clin Appl. 2012;6(1-2):117-133.
61. Galbiati F, Razani B, Lisanti MP. Emerging themes in lipid rafts and caveolae. Cell.
2001;106(4):403-411.
62. Matveev S, Li X, Everson W, Smart EJ. The role of caveolae and caveolin in
vesicle-dependent and vesicle-independent trafficking. Adv Drug Deliv Rev.
2001;49(3):237-250.
63. Briand N, Dugail I, Le Lay S. Cavin proteins: New players in the caveolae field.
Biochimie. 2011;93(1):71-77.
64. Hayer A, Stoeber M, Bissig C, Helenius A. Biogenesis of caveolae: Stepwise
assembly of large caveolin and cavin complexes. Traffic. 2010;11(3):361-382.
70
65. Chadda R, Mayor S. PTRF triggers a cave in. Cell. 2008;132(1):23-24.
66. Manes S, del Real G, Martinez-A C. Pathogens: Raft hijackers. Nat Rev Immunol.
2003;3(7):557-568.
67. Lim JS, Choy HE, Park SC, Han JM, Jang IS, Cho KA. Caveolae-mediated entry of
salmonella typhimurium into senescent nonphagocytotic host cells. Aging Cell.
2010;9(2):243-251.
68. Pelkmans L, Zerial M. Kinase-regulated quantal assemblies and kiss-and-run
recycling of caveolae. Nature. 2005;436(7047):128-133.
69. Aquilina JA, Shrestha S, Morris AM, Ecroyd H. Structural and functional aspects of
hetero-oligomers formed by the small heat shock proteins alphaB-crystallin and
HSP27. J Biol Chem. 2013;288(19):13602-13609.
70. Mymrikov EV, Seit-Nebi AS, Gusev NB. Large potentials of small heat shock
proteins. Physiol Rev. 2011;91(4):1123-1159.
71. Garrido C. Size matters: Of the small HSP27 and its large oligomers. Cell Death
Differ. 2002;9(5):483-485.
72. Mymrikov EV, Seit-Nebi AS, Gusev NB. Heterooligomeric complexes of human small
heat shock proteins. Cell Stress Chaperones. 2012;17(2):157-169.
73. Vogels MW, van Balkom BW, Heck AJ, et al. Quantitative proteomic identification
of host factors involved in the salmonella typhimurium infection cycle. Proteomics.
2011;11(23):4477-4491.
74. Lee S, Dominguez R. Regulation of actin cytoskeleton dynamics in cells. Mol Cells.
2010;29(4):311-325.
71
75. Hubberstey AV, Mottillo EP. Cyclase-associated proteins: CAPacity for linking
signal transduction and actin polymerization. FASEB J. 2002;16(6):487-499.
76. Quintero-Monzon O, Jonasson EM, Bertling E, et al. Reconstitution and dissection
of the 600-kDa Srv2/CAP complex: Roles for oligomerization and cofilin-actin binding
in driving actin turnover. J Biol Chem. 2009;284(16):10923-10934.
77. Misselwitz B, Dilling S, Vonaesch P, et al. RNAi screen of salmonella invasion
shows role of COPI in membrane targeting of cholesterol and Cdc42. Mol Syst Biol.
2011;7.
78. Mohr I, Sonenberg N. Host translation at the nexus of infection and immunity. Cell
Host & Microbe. 2012;12(4):470-483.
79. Steeb B, Claudi B, Burton NA, et al. Parallel exploitation of diverse host nutrients
EnhancesSalmonellaVirulence. PLoS Pathog. 2013;9(4):e1003301.
80. Fontana MF, Banga S, Barry KC, et al. Secreted bacterial effectors that inhibit
host protein synthesis are critical for induction of the innate immune response to
virulent legionella pneumophila. PLoS Pathog. 2011;7(2):e1001289.
81. Cuesta R, Laroia G, Schneider RJ. Chaperone hsp27 inhibits translation during heat
shock by binding eIF4G and facilitating dissociation of cap-initiation complexes. Genes
Dev. 2000;14(12):1460-1470.
82. Szklarczyk D, Franceschini A, Kuhn M, et al. The STRING database in 2011:
Functional interaction networks of proteins, globally integrated and scored. Nucleic
Acids Res. 2011;39(Database issue):D561-8.
72
Appendices
Appendix A List of Protein Complexes Enriched in PCP-SILAC dataset
Table 5.1 Protein Complex Enrichment Analysis Results
Complex
ID Size Type Complex Name Gene IDs
HC1180 3 Literature CAND1-CUL4A-RBX1 complex 8451 9978 55832
HC1459 3 Literature Ubiquitin E3 ligase (DDB1, CUL4A, RBX1) 8451 9978 1642
HC1790 3 Literature EIF3 complex (EIF3B, EIF3J, EIF3I) 8662 8668 8669
HC198 3 Literature RFC core complex 5984 5985 5982
HC2064 3 Literature ubiquitin-dependent protein catabolic process 8451 8450 79016
HC2542 3 Literature methionyl glutamyl tRNA synthetase complex 2058 4141 9255
HC2579 3 Literature p54(nrb)-PSF-matrin3 complex 4841 9782 6421
HC2611 3 Literature HSP90-CDC37-LRRK2 complex 3326 120892 11140
HC3292 3 Literature putative complex without known function 56647 9349 84193
HC4 3 Literature TOP1-PSF-P54 complex 4841 6421 7150
HC465 3 Literature COG5-COG6-COG7 subcomplex 57511 91949 10466
HC609 3 Literature CAND1-CUL4B-RBX1 complex 9978 8450 55832
HC794 3 Literature APC-IQGAP1-Cdc42 complex 324 998 8826
HC92 3 Literature COG2-COG3-COG4 subcomplex 83548 25839 22796
HC1035 4 Literature Tetrameric COG subcomplex 57511 84342 91949 10466
HC1645 4 Literature EIF3 core complex (EIF3A, EIF3B, EIF3G, EIF3I) 8662 8668 8666 8661
HC2090 4 Literature CDC37-HSP90AA1-HSP90AB1-MAP3K11 complex 3320 3326 4296 11140
HC2237 4 Literature Cul4B-RING ubiquitin ligase complex 51514 9978 1642 8450
HC2245 4 Literature glycogen catabolic process 5836 64210 5837 5834
HC2575 4 Literature actin filament capping 830 832 829 93661 Complex
ID Size Type Complex Name Gene IDs
73
HC3024 4 Literature protein K48-linked ubiquitination 8454 997 54926 8453
HC3108 4 Literature Ubiquitin E3 ligase (CDT1, DDB1, CUL4A, RBX1) 8451 9978 1642 81620
HC5426 4 Predicted SRP-dependent cotranslational protein targeting to membrane 682 51027 6155 6129
HC763 4 Literature 9S-cytosolic aryl hydrocarbon (Ah) receptor non-ligand activated complex 3320 196 3326 9049
HC9556 4 Predicted COPI coating of Golgi vesicle 22908 375 9276 11316
HC9836 4 Predicted putative complex without known function 56647 832 50618 5764
HC1088 5 Literature ribonucleoside monophosphate biosynthetic process 5636 5635 5631 5634 221823
HC1542 5 Literature eIF2B 8892 8893 8890 8891 1967
HC1815 5 Literature EIF3 complex (EIF3A, EIF3B, EIF3G, EIF3I, EIF3C) 8662 8663 8668 8666 8661
HC2450 5 Literature COG1-COG8-COG5-COG6-COG7 subcomplex 9382 57511 84342 91949 10466
HC2673 5 Literature tRNA-splicing ligase complex 51637 79074 283742 1653 51493
HC2832 5 Literature PCNA-RFC2-5 complex 5984 5111 5983 5985 5982
HC2851 5 Literature Ubiquitin E3 ligase (DDB1, DDB2, CUL4A, CUL4B, RBX1) 1643 8451 9978 1642 8450
HC2930 5 Literature EIF3 complex (EIF3A, EIF3B, EIF3G, EIF3I, EIF3J) 8662 8668 8666 8669 8661
HC3056 5 Literature COPI coating of Golgi vesicle 200205 55510 1314 9276 168400
HC3061 5 Literature Ubiquitin E3 ligase (DET1, DDB1, CUL4A, RBX1, COP1) 64326 8451 55070 9978 1642
HC3813 5 Predicted putative complex without known function 10484 7534 10539 57019 65220
HC49 5 Literature putative complex without known function 25940 147965 51637 283742 1653
HC5736 5 Predicted nucleotide-binding domain, leucine rich repeat containing receptor signaling pathway 1457 10808 5970 3326 7316
HC5962 5 Predicted M/G1 transition of mitotic cell cycle 7431 60 310 7316 6117
HC6954 5 Predicted RNA processing 1655 3187 56916 6613 10521
HC7 5 Literature COP9 signalosome complex (CSN) 10920 9318 2873 2671 10987
HC7079 5 Predicted translational elongation 10368 1937 10598 1891 1936
HC7272 5 Predicted putative complex without known function 10484 10539 57019 65220 552900
HC7763 5 Predicted putative complex without known function 3178 3608 56257 10528 3609
HC8050 5 Predicted G2/M transition of mitotic cell cycle 9857 1780 10121 10540 55860
HC900 5 Literature COG1-COG8-COG2-COG3-COG4 subcomplex 9382 83548 25839 22796 84342
HC9599 5 Predicted putative complex without known function 3178 56252 7916 10521 1434
HC99 5 Literature RFC complex (activator A 1 complex) 5984 5981 5983 5985 5982
HC101 6 Literature PCNA-CHL12-RFC2-5 complex 5984 5111 5983 5985 5982 63922
HC1573 6 Literature dynactin complex 10671 11258 10120 10121 10540 55860
HC1860 6 Literature BRD4-RFC complex 5984 5981 5983 23476 5985 5982 Complex
ID Size Type Complex Name Gene IDs
HC2037 6 Literature alpha DNA polymerase:primase complex 23649 5558 5422 92797 4172 5557
HC2479 6 Literature eukaryotic translation initiation factor 2B complex 1965 8892 8893 8890 8891 1967
74
HC305 6 Literature cullin deneddylation 64708 9318 10980 50813 51138 8533
HC3194 6 Literature DNA replication factor C complex 5984 5111 5981 5983 5985 5982
HC3233 6 Literature putative complex without known function 90861 10539 654483 57019 51155 552900
HC3486 6 Literature Ubiquitin E3 ligase (AHR, ARNT, DDB1, TBL3, CUL4B, RBX1) 9978 196 405 10607 1642 8450
HC3991 6 Predicted termination of G-protein coupled receptor signaling pathway 10971 2869 7531 6011 801 5957
HC4021 6 Predicted RNA splicing 22938 22803 23435 9768 5394 988
HC4171 6 Predicted protein targeting 2885 7534 324 6711 7316 7532
HC4303 6 Predicted nucleobase-containing compound metabolic process 56647 1806 9349 84193 5873 7316
HC4332 6 Predicted nuclear-transcribed mRNA catabolic process, nonsense-mediated decay 56647 9349 84193 7316 84305 6194
HC4376 6 Predicted oligodendrocyte development 8892 1965 8894 8890 8891 55572
HC5265 6 Predicted RNA biosynthetic process 2778 6154 409 9584 988 6207
HC5507 6 Predicted nucleobase-containing compound metabolic process 56647 51552 1806 9349 84193 7316
HC569 6 Literature dynactin complex 10120 9857 10121 10540 25999 55860
HC5950 6 Predicted RNA metabolic process 3178 1655 56252 7916 202559 10521
HC6043 6 Predicted putative complex without known function 56647 1806 9349 326624 84193 7316
HC6715 6 Predicted putative complex without known function 56647 1806 9349 84193 7316 5243
HC7447 6 Predicted endosome transport 4733 790 51389 1819 55681 7316
HC7559 6 Predicted nuclear mRNA splicing, via spliceosome 3190 3178 140890 7001 8761 6421
HC764 6 Literature MCM complex 4173 4175 4171 4176 4174 4172
HC7809 6 Predicted ribonucleoprotein complex assembly 11218 85015 158747 1653 23603 7316
HC7861 6 Predicted nuclear mRNA splicing, via spliceosome 3190 3178 140890 6428 26986 8761
HC8095 6 Predicted putative complex without known function 56647 201475 1806 9349 84193 7316
HC829 6 Literature BASC (Ab 81) complex (BRCA1-associated genome surveillance complex) 472 5984 672 4292 5981 5982
HC8495 6 Predicted ribonucleoside monophosphate biosynthetic process 23065 51643 5635 5631 7316 5634
HC9199 6 Predicted axon guidance 10963 3320 10728 3326 9049 7316
HC9301 6 Predicted positive regulation of proteasomal ubiquitin-dependent protein catabolic process 10210 4841 6613 6421 7341 7150
HC9364 6 Predicted translational elongation 1460 10728 1937 1917 1936 1933
HC9503 6 Predicted RNA processing 3178 51253 56252 8106 10521 3276
HC1260 7 Literature PALS1-Par3-aPKC-14-3-3 zeta complex 7534 64398 7531 117583 7529 56288 5584
HC1602 7 Literature Mcm2-7 4173 4175 4171 4176 4174 254394 4172
Complex
ID Size Type Complex Name Gene IDs
HC1605 7 Literature SCF-CDC4 complex 6500 8454 10910 55294 997 54926 8453
HC1870 7 Literature translational elongation 81570 10985 1937 1915 1917 1936 1933
HC1887 7 Literature positive regulation of cell cycle arrest 5527 5529 5526 5525 5528 51629 55972
75
HC2053 7 Literature Coatomer complex 22818 1314 372 1315 9276 11316 22820
HC2161 7 Literature transcription-coupled nucleotide-excision repair 5984 5981 5983 79915 5985 5982 63922
HC2549 7 Literature Pol epsilon 1655 55510 79009 5426 1662 10521 168400
HC2722 7 Literature DNA unwinding involved in replication 4173 4175 4171 4176 79892 4174 4172
HC323 7 Literature emerin C24 4173 79595 4175 2010 3192 4171 708
HC380 7 Literature Cul4A-RING ubiquitin ligase complex 51514 1161 8451 9978 1642 26133 51185
HC4104 7 Predicted mRNA metabolic process 6193 56647 1806 9349 6125 84193 7316
HC5431 7 Predicted actin filament capping 60 5521 832 11344 4703 829 4131
HC5467 7 Predicted RNA processing 3181 3178 1655 56252 202559 10521 7150
HC5564 7 Predicted COPI coating of Golgi vesicle 22938 1314 372 7316 1315 9276 988
HC5782 7 Predicted positive regulation of transcription from RNA polymerase II promoter 2033 1387 1655 8202 8648 10499 10521
HC5832 7 Predicted cellular amino acid catabolic process 4733 790 501 51389 1819 158078 7316
HC596 7 Literature RC complex 5984 5981 5558 5985 5982 5422 5557
HC6198 7 Predicted regulation of Ras protein signal transduction 9411 998 2286 5911 7316 10564 10565
HC6981 7 Predicted actin filament capping 1719 3636 830 832 11344 7316 93661
HC7001 7 Predicted actin filament capping 1719 60 3636 832 11344 829 4131
HC7564 7 Predicted nucleotide biosynthetic process 5636 5598 5635 5631 5723 7316 5634
HC7577 7 Predicted ubiquitin-dependent protein catabolic process 4738 8451 10980 9978 10987 8450 1642
HC7626 7 Predicted nucleobase-containing compound metabolic process 56647 9945 1806 9349 84193 7316 55505
HC7934 7 Predicted neuron development 2932 10413 1500 999 1499 5663 1495
HC8455 7 Predicted nerve growth factor receptor signaling pathway 7534 51727 4303 7249 9759 7531 4140
HC9142 7 Predicted actin filament capping 1719 56623 830 832 11344 7316 93661
HC9355 7 Predicted regulation of protein serine/threonine kinase activity 10963 3320 6885 5536 3326 7316 11140
HC9403 7 Predicted hippocampus development 7534 7248 2308 55711 9759 7531 4140
HC1131 8 Literature CCT complex (chaperonin containing TCP1 complex) 7203 908 22948 10576 10574 6950 10694 10575
HC2544 8 Literature Coatomer-Arf1 complex 22818 1314 11316 375 372 1315 9276 22820
HC4343 8 Predicted protein ubiquitination 51514 8451 9978 79016 8450 80344 1642 55832
HC4525 8 Predicted tRNA aminoacylation for protein translation 2058 3735 55613 5859 9255 7965 66036 1506
Complex
ID Size Type Complex Name Gene IDs
HC494 8 Literature Conserved oligomeric Golgi (COG) complex 9382 83548 25839 57511 22796 84342 91949 10466
HC5054 8 Predicted cullin deneddylation 8454 10920 10980 51138 9318 10987 8533 7316
HC5553 8 Predicted cullin deneddylation 10920 8451 10980 50813 51138 10987 8533 1642
HC6786 8 Predicted cullin deneddylation 8454 64708 10980 51138 9318 2873 8533 8453
HC7137 8 Predicted ubiquitin-dependent protein catabolic process 8451 8882 9978 8450 3312 80344 1642 55832
76
HC8321 8 Predicted transmembrane receptor protein tyrosine kinase signaling pathway 3667 8503 7534 2316 3480 7531 7532 9846
HC1356 9 Literature BRAF-RAF1-14-3-3 complex 673 5894 10971 7534 7531 7533 2810 7532 7529
HC1360 9 Literature regulation of translational initiation in response to stress 8893 8894 1968 8872 1967 1965 8892 8890 8891
HC1442 9 Literature tRNA aminoacylation for protein translation 2058 5917 3376 5859 1615 4141 9255 7965 9521
HC2495 9 Literature CCT complex (chaperonin containing TCP1 complex), testis specific 7203 908 22948 10576 10574 6950 10693 10694 10575
HC3100 9 Literature COP9 signalosome complex 10920 9318 50813 51138 8533 10987 64708 10980 2873
HC4835 9 Predicted cullin deneddylation 51138 9318 8533 2516 7534 64708 10980 2873 1642
HC5061 9 Predicted cullin deneddylation 51138 9318 8533 8453 64708 10980 7982 2353 7316
HC7099 9 Predicted insulin receptor signaling pathway 8503 3667 7534 7531 2308 3480 7249 7532 9846
HC8974 9 Predicted tRNA aminoacylation for protein translation 5917 4141 2058 3735 9255 3376 7965 5859 1615
HC9559 9 Predicted cullin deneddylation 51138 9318 8533 8453 3216 64708 10980 7982 7316
HC2308 10 Literature chaperonin-containing T-complex 22948 10576 6950 908 7203 10574 10693 150160 10694 10575
HC5632 10 Predicted protein ubiquitination 26043 51514 8451 9978 79016 8450 80344 8452 1642 55832
HC5640 10 Predicted protein ubiquitination 26043 8451 6923 9978 7428 8453 8450 122769 1642 55832
HC7047 10 Predicted cullin deneddylation 51138 9318 9978 8533 8453 8452 8454 64708 10980 2873
HC91 10 Literature L2DTL 51514 3308 5111 9318 51138 8533 10987 10980 2873 1642
HC975 10 Literature Golgi transport complex 23256 25839 57511 22796 84342 91949 2802 9382 83548 10466
HC9806 10 Predicted protein targeting 3799 3831 10971 7534 7531 7533 7532 23367 7316 7529
HC1026 11 Literature eIF3 8663 8668 23277 6294 8669 728689 8662 9667 8666 79811 8661
HC1649 11 Literature Ksr1 complex (Ksr1, Mek, 14-3-3), unstimulated 8844 5604 283455 10971 7534 7531 7533 5605 7532 7529 2810
HC2402 11 Literature COPI 22818 51226 1314 11316 26286 372 26958 22820 9276 1315 84364
HC2446 11 Literature B-Ksr1-MEK-MAPK-14-3-3 complex 8844 5604 283455 10971 7534 7531 5594 7533 5605 7532 7529
HC2675 11 Literature Arp2/3 protein complex 81873 653857 10095 10092 10096 10093 10097 10109 10552 57180 10094
Complex
ID Size Type Complex Name Gene IDs
HC4267 11 Predicted cullin deneddylation 51138 9318 9978 8533 8453 8450 8454 64708 10980 2873 1642
HC5608 11 Predicted transcription-coupled nucleotide-excision repair 9125 5984 23476 5111 5983 5985 142 2547 5981 5982 988
HC7230 11 Predicted COPI coating of Golgi vesicle 22938 1314 10972 11316 54732 372 9276 22820 1315 7316 988
HC953 11 Literature Multisynthetase complex 2058 3735 5917 51520 3376 5859 1615 4141 9255 7965 9521
HC3651 12 Predicted tRNA aminoacylation for protein translation 5917 2058 3735 10492 3376 51528 5859 1615 57520 4141 9255 7965
HC5280 12 Predicted tRNA aminoacylation for protein translation 6723 5917 2058 3735 3376 5859 1615 4141 9255 832 7965
77
124944
HC6407 12 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
22938 5708 5707 5719 5706 51377 5701 5705 5700 7316 988 5704
HC7815 12 Predicted tRNA aminoacylation for protein translation 5917 2058 3735 4800 3376 5859 1615 153443 4141 9255 7965 708
HC3121 13 Literature CSA complex 10920 1161 8451 9318 50813 51138 9978 8533 10987 64708 10980 2873 1642
HC3598 13 Literature DDB2 complex 10920 1643 8451 9318 50813 51138 9978 8533 10987 64708 10980 2873 1642
HC3926 13 Predicted SRP-dependent cotranslational protein targeting to membrane 6130 5036 6154 79877 6139 645683 6155 6132 6129 6142 6152 6135 25873
HC4172 13 Predicted SRP-dependent cotranslational protein targeting to membrane 54606 6154 6139 6160 6142 9789 29889 6123 6157 7316 100130892 6147 25873
HC6112 13 Predicted SRP-dependent cotranslational protein targeting to membrane 1355 11224 645683 9908 6155 9045 84154 6142 6123 4736 6157 7316 25873
HC8243 13 Predicted SRP-dependent cotranslational protein targeting to membrane 6137 6130 5036 6154 11224 6139 645683 6208 6155 6132 6152 6135 25873
HC2582 14 Literature CSA-POLIIa complex 10920 1161 8451 9318 50813 51138 9978 8533 10987 64708 10980 2873 5430 1642
HC3679 14 Predicted ubiquitin-dependent protein catabolic process 8451 51138 9318 9978 8533 253832 8453 8450 8452 8454 64708 10980 1642 55832
HC3985 14 Predicted SRP-dependent cotranslational protein targeting to membrane 6137 51042 5036 6154 11224 6139 6208 6155 6132 166378 6152 6124 6135 25873
HC4018 14 Predicted SRP-dependent cotranslational protein targeting to membrane 6137 5036 6154 6141 6181 6139 6155 6132 6152 6175 7316 64963 6135 25873
HC4199 14 Predicted SRP-dependent cotranslational protein targeting to membrane 51065 6210 83858 6205 6208 6193 6156 140032 6203 7316 55781 6189 6207 6218
HC4331 14 Predicted translational initiation 1981 8668 8663 5313 9960 8669 8665 8662 10289 8666 8667 8661 7316 4189
HC4405 14 Predicted SRP-dependent cotranslational protein targeting to membrane 6154 6139 6205 6160 6208 6155 6129 6128 9045 6156 23521 100505503 6135 25873
HC5796 14 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 6477 5706 51377 9908 5701 10213 5713 5705 9861 83940 5700 5704
HC5823 14 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 55005 5706 51377 5702 5713 5705 9861 5715 5700 31 5704
Complex
ID Size Type Complex Name Gene IDs
HC6332 14 Predicted SRP-dependent cotranslational protein targeting to membrane 54606 6154 3157 6230 6139 6205 6208 7555 6156 6142 6152 7316 25873 6147
HC6413 14 Predicted SRP-dependent cotranslational protein targeting to membrane 6137 6154 6133 8872 6139 6160 6128 9045 6156 6157 23521 10528 6135 25873
HC6624 14 Predicted SRP-dependent cotranslational protein targeting to membrane 11224 441502 6139 6205 6208 6223 6156 6142 51121 23521 7316 25873 6207 6218
HC7469 14 Predicted SRP-dependent cotranslational protein targeting to membrane 6137 5036 6154 6141 6181 6139 6160 6208 6155 6132 6129 6128 6135 25873
HC7535 14 Predicted SRP-dependent cotranslational protein targeting to membrane 6165 6159 54606 6154 11224 6181 6161 6139 6160 7555 6142 9051 25873 6147
HC7601 14 Predicted SRP-dependent cotranslational protein targeting to membrane 6137 5036 6154 6141 6181 6139 80060 6160 6155 6132 7316 64963 6135 25873
78
HC8029 14 Predicted cullin deneddylation 10920 8451 8065 50813 51138 9318 56254 10987 8533 8454 10980 2873 7316 1642
HC8600 14 Predicted translational initiation 6059 27335 1981 51386 8668 8669 8664 1974 10480 8665 8662 10289 8666 8667
HC8923 14 Predicted SRP-dependent cotranslational protein targeting to membrane 6159 54606 6154 11224 6161 6139 6160 7555 6173 6142 6152 9051 25873 6147
HC8998 14 Predicted SRP-dependent cotranslational protein targeting to membrane 6137 5036 6154 11224 6181 6139 6208 6155 6132 6152 6124 6157 6135 25873
HC9038 14 Predicted SRP-dependent cotranslational protein targeting to membrane 1654 6154 6210 6139 6205 6208 6129 9045 6156 5521 7316 23521 100505503 6135
HC9451 14 Predicted SRP-dependent cotranslational protein targeting to membrane 6122 54606 6154 11224 6139 6160 3843 7555 6129 6142 6152 9051 25873 6147
HC9472 14 Predicted SRP-dependent cotranslational protein targeting to membrane 6154 6133 79080 6139 645683 6129 6229 6156 6142 6157 7316 23521 6135 25873
HC9582 14 Predicted SRP-dependent cotranslational protein targeting to membrane 6801 6154 6133 8872 6139 6160 6128 9045 6156 23521 7316 10528 6135 25873
HC9618 14 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 9868 5707 5684 5719 4677 5706 5709 64747 5702 5701 10999 5705 5700
HC3669 15 Predicted ubiquitin-dependent protein catabolic process 4738 26043 8065 8451 9978 10987 8453 8450 54165 8452 8454 10980 9040 27231 1642
HC3675 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 51377 5688 10213 5713 5682 5705 9861 55768 5700 7415 5704
HC3710 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 51377 5709 10213 5713 26003 5717 5705 9861 5700 5718 5704 6189
HC3789 15 Predicted SRP-dependent cotranslational protein targeting to membrane 6159 54606 6154 6181 6161 6201 6139 6160 6144 6128 6173 6142 6157 25873 6147
HC3831 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
56893 6184 5707 79152 5719 7979 5701 5713 6782 5710 5705 29978 5700 5886 11047
HC3833 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
6301 5707 5719 5706 471 7979 10213 5713 5716 9861 9097 5710 9987 5700 7316
HC3892 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
6517 5707 5719 5706 5714 5701 5713 5716 9861 9097 5705 80227 5700 5704 60681
HC3909 15 Predicted SRP-dependent cotranslational protein targeting to membrane 6209 6159 6154 11224 6161 6139 6160 6144 4113 7555 6152 7316 6228 25873 6147
Complex
ID Size Type Complex Name Gene IDs
HC3910 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
6184 5707 5719 5706 51377 5701 10213 5713 6185 776 5705 9861 5700 5711 5704
HC3942 15 Predicted SRP-dependent cotranslational protein targeting to membrane 6209 6154 6187 6133 6166 6139 6160 6208 9045 6193 6142 6157 641293 6135 25873
HC3955 15 Predicted SRP-dependent cotranslational protein targeting to membrane 6137 51042 6154 6133 6139 6160 6129 6128 9045 6156 6157 23521 10528 6135 25873
HC4005 15 Predicted SRP-dependent cotranslational protein targeting to membrane 6209 6159 11224 6161 6201 6160 6144 6155 6128 6173 51121 6157 7316 6135 25873
HC4027 15 Predicted translational initiation 1981 8668 8663 9960 8669 8665 8662 1983 4942 10289 8666 8667 8661 7316 388
HC4282 15 Predicted SRP-dependent cotranslational protein targeting to membrane 6122 54606 6154 6187 6161 6139 6144 7555 10969 6157 7316 2248 7341 6189 6147
HC4345 15 Predicted regulation of cellular protein metabolic process 1981 8668 8663 5313 9960 8669 8665 1983 8662 1982 10289 8666 8667 8661 7316
79
HC4366 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 51377 7879 5701 2 10213 5682 5705 9861 5700 5886 5704
HC4398 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
8916 5707 5719 5701 5713 6782 55008 5710 5705 29978 5700 7316 29979 5886 11047
HC4451 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 51377 5709 5702 10213 5713 5717 5705 9861 5700 5704 5718
HC4457 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5743 5707 5719 5706 5887 8647 5701 5713 5716 10299 9861 5705 5700 7316 5704
HC4466 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 55679 7979 5701 5713 55795 9861 3611 9097 5705 80227 5700 5718
HC4527 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 55147 5707 5719 5706 5701 5713 5716 9861 9097 5705 80227 5700 5704 7277
HC4542 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 471 7979 5714 5701 10213 5713 5716 9861 9097 5710 8644 5700
HC4605 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 51377 5709 5702 10213 5713 5717 5705 9861 5715 5700 5718 5704
HC4633 15 Predicted regulation of translational initiation 27335 1981 51386 8663 8668 8669 8664 1974 3646 8665 8662 10289 8666 8661 8667
HC4662 15 Predicted SRP-dependent cotranslational protein targeting to membrane 6137 6159 6154 11224 6161 6201 6166 6139 6160 6144 6155 6128 6157 6135 25873
HC4788 15 Predicted ubiquitin-dependent protein catabolic process 5707 51138 9318 5719 5706 6138 9097 10980 27063 5705 2873 5700 7316 5704 11047
HC4856 15 Predicted SRP-dependent cotranslational protein targeting to membrane 9349 6133 6134 6210 6160 6128 9045 6193 6156 1806 6125 7316 23521 6135 10528
HC4890 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 7979 5701 5713 5716 5717 9861 5705 80227 5700 5704 134510
HC4932 15 Predicted regulation of translational initiation 1981 164781 8668 8663 9960 8669 9790 8665 5339 1983 8662 10289 8666 8661 8667
HC4985 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 5701 10213 5713 10477 5705 9861 115992 5700 51366 1347 5704
HC5059 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
55163 56893 5707 5719 5701 5713 6782 5710 5705 29978 5700 7316 5886 22983 11047
Complex
ID Size Type Complex Name Gene IDs
HC5104 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
112858 5707 5719 5706 5701 5713 5716 4869 5717 9861 9097 5705 80227 5700 5704
HC5135 15 Predicted SRP-dependent cotranslational protein targeting to membrane 6137 4738 6159 6154 6133 6161 6201 6139 6160 6144 6155 6128 6188 6135 25873
HC5206 15 Predicted regulation of translational initiation 1981 8894 317649 8668 8663 8669 6204 1965 1983 8662 1982 10289 8666 8667 8661
HC5239 15 Predicted SRP-dependent cotranslational protein targeting to membrane 6209 6159 6154 11224 6161 441502 6201 6139 6160 6144 6155 6128 7316 25873 6218
HC5302 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
56893 5707 5719 5706 23592 5701 5713 6782 5710 5705 29978 5700 7316 5886 11047
HC5383 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 7979 5702 5701 5713 5716 9861 9097 5705 5715 80227 5700 5704
HC5457 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5211 5706 55210 5716 10980 9861 9097 5705 80227 5700 7316 5704 55660
HC5465 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 51377 5701 10213 5713 5682 5717 5705 9861 548593 5700 5704
80
HC5468 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 7979 5701 5713 5716 55795 9861 9097 5705 80227 5700 5704 5718
HC5480 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 5701 5713 5705 9097 5710 29978 5700 219988 7316 3304 5886 11047
HC5496 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 7979 5701 5713 9097 9861 5710 5705 80227 5700 26287 5704 11047
HC5505 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 7979 5701 5713 5716 9861 9097 5710 5705 8644 2752 5700
HC5546 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 7979 5714 5701 5713 5716 9861 9097 5705 80227 5700 54973 29901
HC5593 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 7979 5887 1622 5701 5713 158 5710 7314 5705 5700 7316 5886
HC5595 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 7979 5701 5713 7805 5716 9690 9861 9097 5705 5700 7316 5704
HC5637 15 Predicted regulation of translation 1981 8668 8663 8669 1974 8672 1973 8665 8662 1977 1982 26986 8666 8661 8667
HC5639 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 51138 9318 5719 5706 5688 5701 81570 5716 5682 10980 9097 2873 5700 5704
HC5660 15 Predicted SRP-dependent cotranslational protein targeting to membrane 6137 6154 6133 6139 6160 6144 6129 6128 9045 6156 6157 7316 23521 6135 10528
HC5680 15 Predicted SRP-dependent cotranslational protein targeting to membrane 57584 6210 6201 6205 645683 6208 6155 6156 6142 6203 7316 23521 25873 6189 6218
HC5686 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
159 5708 5707 5719 5706 60678 5701 5713 5716 9861 9097 5710 5700 7316 5704
HC5805 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
56893 5707 5719 5706 7979 5701 5713 152559 6782 5710 5705 29978 5700 5886 11047
HC5930 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
56893 5707 23549 5719 5701 5713 54495 6782 5710 5705 29978 5700 7316 5886 11047
HC5936 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 7979 5701 5713 5716 9861 9097 5705 375449 80227 5700 5704 7277
Complex
ID Size Type Complex Name Gene IDs
HC5957 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 83858 5713 5716 9861 9097 5710 8565 5700 7316 5704 11047
HC5992 15 Predicted ATP catabolic process 5707 5719 5706 59345 5709 5701 5713 5716 10190 9861 9097 5705 80227 5700 5704
HC6010 15 Predicted regulation of translational initiation 6059 1981 8668 8663 9086 9960 8669 8665 1983 8662 9669 10289 8666 8667 8661
HC6044 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
6224 5707 92840 5719 5706 5701 10213 5713 5716 9861 9097 5710 5700 7316 11047
HC6115 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 6908 51377 5701 10213 5713 5682 9330 5705 9861 5700 5704
HC6134 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5147 5707 5719 5706 5701 5713 5716 9861 9097 5705 84952 80227 5700 5704
HC6154 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 51377 5709 5887 10213 5713 5717 3838 5705 9861 5700 5718 5704
HC6160 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
56893 5707 5719 5701 5713 6782 29978 5705 5710 284273 5700 7316 5886 11047 51132
HC6228 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 6477 5706 51377 5701 10213 5713 83940 5705 9861 23170 5700 5704
81
HC6230 15 Predicted SRP-dependent cotranslational protein targeting to membrane 6210 6201 6205 645683 6208 6155 6156 55741 6203 7316 23521 6135 25873 6189 6218
HC6254 15 Predicted SRP-dependent cotranslational protein targeting to membrane 6159 54606 6154 11224 6161 54663 6201 6139 6160 6144 7555 6128 7316 6147 25873
HC6288 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5719 5706 5713 5716 6472 9097 9861 5710 5705 10289 5700 8667 7316 5704
HC6290 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
3692 5707 5684 5692 5683 5688 10213 5695 5682 26137 3275 5700 5685 5686 5691
HC6324 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
56893 5707 5719 23409 5701 5713 6782 5710 5705 29978 5700 7316 293 5886 11047
HC6344 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 51377 5709 5701 10213 5713 5682 5717 5705 9861 5700 5704
HC6355 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 7979 5709 5701 5713 5716 9861 9097 5705 2783 80227 5700 5704
HC6379 15 Predicted regulation of translational initiation 1981 8668 8663 9960 8669 8665 8662 1983 7763 1982 10289 79811 8666 8667 8661
HC6392 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
55147 5707 5719 5706 7979 5701 5713 5716 9861 9097 5705 80227 5700 5704 6117
HC6401 15 Predicted regulation of cellular protein metabolic process 1981 8668 8663 9960 8669 6472 8665 1983 8662 10289 8666 8667 8661 7316 4189
HC6435 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 6397 5707 5719 5706 51377 55207 10213 5713 5682 5705 9861 5700 5686 5704
HC6454 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 2058 5707 118980 5719 5706 5701 5713 5716 9861 9097 5710 5700 7316 5704
HC6481 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 5713 5716 353 9861 9097 5710 5705 9114 5700 5704 11047
HC6578 15 Predicted protein targeting 9156 5509 60598 51305 5298 10971 7534 7531 3777 7533 10298 5501 7529 2810 7532
Complex
ID Size Type Complex Name Gene IDs
HC6623 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 51138 9318 5719 5706 7979 5701 81570 5716 10980 9097 7347 5705 2873 5700
HC6629 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 3861 5701 6613 5713 6612 5716 9861 9097 80227 5700 5704
HC6643 15 Predicted SRP-dependent cotranslational protein targeting to membrane 11224 6133 6201 6166 6139 6160 6208 6144 6223 6142 51121 6157 7316 25873 6218
HC6652 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
51520 5708 5707 5719 5706 83858 10213 5713 5716 9861 9097 5710 5700 7316 11047
HC6750 15 Predicted regulation of translational initiation 1981 3692 8668 8663 9960 8669 83607 6782 8662 1983 10289 8666 8667 8661 7316
HC6793 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 51377 5701 7157 10213 5713 29117 5717 5705 9861 5700 5704
HC6841 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 5701 5713 5716 9861 9097 5710 5705 80227 5700 5704 11047
HC6876 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5423 5707 5719 5706 7979 5714 5701 7374 5713 5716 9861 9097 80227 5700 11047
HC6901 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 471 5701 5713 5716 9861 9097 4200 5710 5705 5700 7316 5704
HC6921 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
2730 5707 5719 5706 5701 10213 5713 55871 5716 9861 9097 5705 10289 80227 5700
82
HC7005 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
56893 5479 5707 5719 7979 23350 5701 5713 6782 5710 5705 29978 5700 5886 11047
HC7044 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
221496 5707 5719 5706 5701 5713 6782 5710 9097 5705 29978 5700 7316 5886 11047
HC7063 15 Predicted SRP-dependent cotranslational protein targeting to membrane 6209 6159 6154 6161 6201 6139 6160 6144 6613 6155 6128 642 7316 6135 6218
HC7091 15 Predicted SRP-dependent cotranslational protein targeting to membrane 6202 6201 28987 6205 5295 6613 6204 6229 6193 9790 6188 6203 7316 6207 6218
HC7191 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 414301 7979 5714 5701 10213 5713 5716 9861 9097 80227 5700 11047
HC7279 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 51377 706 5701 10213 5713 5705 9861 1155 5700 5711 5704
HC7307 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 7979 5701 5713 11066 9861 3611 9097 5705 5700 1642 5704 5718
HC7452 15 Predicted translational initiation 1981 8668 8663 9960 8669 94134 8665 6894 8662 10289 8666 57599 8661 8667 7316
HC7474 15 Predicted SRP-dependent cotranslational protein targeting to membrane 6122 164781 54606 6154 6139 6160 6208 6132 7555 6129 6152 6157 7341 25873 6147
HC7493 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 4286 5701 10213 5713 5716 9861 9097 3101 80227 5700 7316 5718
HC7580 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 51377 5701 5713 5716 9861 5710 9097 5705 5700 7316 5704
HC7608 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
10681 5719 5706 4597 5702 5701 5713 5716 9861 9097 5710 5705 5700 7316 5704
HC7615 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5692 5719 5706 5683 5688 3009 5695 5705 9861 79888 5700 1347 5686 5704
Complex
ID Size Type Complex Name Gene IDs
HC7642 15 Predicted regulation of translational initiation 1981 8894 8668 8663 9960 8669 1965 8665 1983 8662 1982 10289 8666 8667 8661
HC7675 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 5701 5713 5716 5717 79751 9861 9097 5705 80227 5700 7316 5704
HC7784 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
3305 56893 5707 5719 7979 23259 5701 5713 6782 5710 5705 29978 5700 5886 11047
HC7798 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
3308 5707 5719 5706 57533 5709 5701 5713 5716 9861 9097 5705 80227 5700 5704
HC7882 15 Predicted posttranscriptional regulation of gene expression 1981 8668 8663 9960 8669 8665 6949 1983 8662 10289 8666 8667 57599 8661 1459
HC7907 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 5701 10213 5713 5716 9861 9097 5705 80227 5700 7316 5704 11047
HC7949 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
94081 5708 5707 5719 5706 79876 5701 5713 5716 9861 9097 5705 80227 5700 5704
HC7978 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5518 5706 51377 26173 5701 10213 5713 5705 9861 5700 8717 5704
HC8006 15 Predicted regulation of translational initiation 1981 3308 8668 8663 2302 9960 8669 51605 8665 1983 8662 10289 8666 8661 8667
HC8040 15 Predicted SRP-dependent cotranslational protein targeting to membrane 6202 27000 6205 9908 6223 9652 6193 6156 6123 6203 7316 51182 23521 6207 6218
HC8078 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 79228 5719 5706 3419 7979 5701 5713 5716 9861 9097 5710 5705 5700 5704
83
HC8173 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
56893 5707 5719 5706 7979 5701 5713 5716 9861 5710 5705 80227 5700 5704 5886
HC8236 15 Predicted posttranscriptional regulation of gene expression 1981 3692 8668 8663 9960 8669 3036 22824 1983 8662 10289 8666 8661 8667 7316
HC8267 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 7979 5714 5701 5713 5716 5717 9861 9097 5705 80227 5700 5704
HC8342 15 Predicted translational initiation 1981 8668 8663 9960 8669 84640 8665 6206 8662 10289 8666 8667 57599 8661 7316
HC8389 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 7979 5701 6613 5713 5716 10056 9861 9097 5710 5705 5700
HC8454 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 204 5707 5719 5706 5701 5713 5716 9861 9097 5710 11212 5700 7316 5704
HC8499 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5684 23037 5719 5706 51377 5709 226 10213 5713 5717 5705 9861 5700 5704
HC8532 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 5701 5713 5716 9097 9861 5710 5705 29978 5700 5704 5886 11047
HC8693 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 51377 5701 10213 5713 79029 5682 166378 5705 9861 5700 5704
HC8708 15 Predicted regulation of translational initiation 1981 8894 8668 8663 1968 8669 1965 1983 8662 1982 10289 8666 11171 8667 8661
HC8716 15 Predicted translational initiation 1981 8668 8663 9960 8669 10209 26019 1973 8665 1983 8662 1982 8666 8667 8661
HC8732 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 5701 5713 5716 79751 9861 1080 9097 80227 5700 7316 5704
Complex
ID Size Type Complex Name Gene IDs
HC8746 15 Predicted SRP-dependent cotranslational protein targeting to membrane 6165 6159 54606 6154 11224 6161 6139 6160 6144 7555 6142 9051 8634 25873 6147
HC8768 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
1013 5707 5719 1410 5706 51377 10213 5713 5717 506 5705 9861 5710 5700 5704
HC8796 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 51377 5701 10213 5713 5682 10999 5705 9861 8678 5700 5704
HC8843 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
1591 5708 5707 5719 5706 51377 5709 5701 10213 5713 9330 5705 9861 5700 5704
HC8912 15 Predicted regulation of translational initiation 1981 8894 8668 8663 1968 8669 1964 1965 1983 8662 9669 10289 8666 8661 8667
HC8951 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 51377 5887 5701 10213 5713 5682 5717 5705 9861 5700 5704
HC8955 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 5701 5713 9520 5716 9861 9097 5705 80227 5700 7316 5704
HC9024 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 2806 5706 471 7979 5714 5701 10213 5713 5716 9861 9097 5710 5700
HC9057 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
22938 5708 5707 5719 5706 51377 5702 10213 5713 5717 166378 5705 9861 5700 5704
HC9074 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 10555 5719 5706 5701 5713 5716 9861 9097 5710 80227 5700 7316 5704
HC9080 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 51377 5709 5701 10213 5713 5717 5705 9861 5700 5718 5704
HC9110 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 3376 5719 5706 5702 5713 5716 7534 5710 9861 9097 5705 10289 5700 5704
84
HC9162 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
56893 5707 5719 8913 7979 5701 5713 6782 5710 5705 29978 5700 11052 5886 11047
HC9260 15 Predicted translational initiation 6059 1981 8668 8663 9960 8669 8665 6206 8662 1983 10289 8666 8667 8661 7316
HC9261 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
128 5707 5719 55005 5706 51377 5702 7157 10213 5713 5717 5705 9861 5700 5704
HC9267 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 3419 3421 5701 5713 5716 9861 9097 5710 5705 80227 5700 5704
HC9335 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 3326 5701 5713 29978 5705 5710 5700 7316 27145 5704 5886 11047
HC9342 15 Predicted SRP-dependent cotranslational protein targeting to membrane 6137 6130 6159 6154 6161 6166 6139 6144 6155 6128 6157 7341 6135 6189 6147
HC9377 15 Predicted SRP-dependent cotranslational protein targeting to membrane 6202 6154 6133 6139 6132 6129 6128 6188 6157 7316 23521 6147 7341 6207 10528
HC9410 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
56893 5707 5719 51499 5701 5713 27166 6782 5710 5705 29978 5700 7316 5886 11047
HC9424 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 23503 51377 5709 10213 5713 5717 5705 9861 5700 988 5704 5718
HC9429 15 Predicted SRP-dependent cotranslational protein targeting to membrane 6202 654364 6217 6205 6208 6223 55226 6156 6193 9790 7316 100505503 25873 6207 55781
HC9480 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
56893 5707 5719 5706 5701 5713 29978 5705 5710 2882 5700 7316 5704 5886 11047
Complex
ID Size Type Complex Name Gene IDs
HC9568 15 Predicted SRP-dependent cotranslational protein targeting to membrane 54606 6154 11224 6217 6139 6205 6208 10061 7555 6223 6142 7316 25873 6218 6147
HC9586 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
1069 5707 5719 5706 7979 5887 5701 5713 5716 9861 9097 5705 80227 5700 5704
HC9595 15 Predicted SRP-dependent cotranslational protein targeting to membrane 6122 23481 6209 6159 54606 6154 6161 6139 6160 6613 6157 7316 7341 6207 6147
HC9625 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
56893 6184 5707 79152 5719 5706 5701 5713 5710 5705 29978 5700 5704 5886 11047
HC9637 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 5702 5701 7157 10213 5682 5717 7486 5705 5700 5711 5704
HC9661 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 7979 5701 5713 5716 9690 9861 9097 5705 80227 5700 5704
HC9670 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
8803 5707 8802 5719 5706 5701 10213 5713 5716 9861 9097 80227 5700 7316 5704
HC9721 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
56893 5707 5719 7979 5701 51661 5713 6782 5710 5705 29978 5700 5886 1760 11047
HC9758 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 5713 5716 6232 9861 5705 80227 5700 7316 11047 134510 5704
HC9760 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 5719 5706 5701 5713 29978 9097 5710 5705 55008 5700 7316 5704 5886 11047
HC9765 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5707 7317 5719 5706 51377 5709 10213 5713 5717 776 9097 5705 9861 5700 5704
HC9775 15 Predicted SRP-dependent cotranslational protein targeting to membrane 54606 1355 79080 80060 9908 6169 23560 84154 6142 29889 6123 6157 7316 25873 6147
HC9811 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 51377 5887 5701 10213 5713 5710 9861 55768 5700 7415 5704
85
HC9850 15 Predicted posttranscriptional regulation of gene expression 1981 2730 8668 8663 9960 8669 3312 8665 1983 8662 1982 10289 8666 8667 8661
HC9869 15 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 5713 5716 6232 9861 23148 5710 5705 5700 7316 11047 134510
HC6909 16 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5708 5707 5719 5706 51377 5701 10213 5713 5716 5710 9861 9097 5705 5700 7316 5704
HC4255 18 Predicted DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest
5702 5713 5716 5710 7316 5704 5708 5707 5719 5706 51377 5701 10213 9861 9097 5705 5700 5711
HC1404 20 Literature PA700 complex 5702 5713 5716 5710 5715 5704 5708 5707 5719 5706 5709 5714 5701 10213 5717 5705 9861 5700 5711 5718
HC2273 31 Literature 40S ribosomal subunit, cytoplasmic
6202 6224 6209 6187 6230 6235 6204 6193 6229 6203 3921 6189 6218 6191 6234 6217 6210 6201 6233 6208 6205 6194 6223 6227 6206 2197 6188 6222 6231 6228 6207
Appendix B List of Dynamic Proteins in PCP-SILAC Salmonella Dataset Identified by Z-test
Table 5.2 Identification of dynamic proteins after Salmonella infection (Z-Test)
Gene
name IPI Protein name
Gene
name IPI Protein name
PTRF IPI00176903 Cavin -1, Polymerase 1 and transcript release factor PAIRBP1 IPI00410693 PAI1 RNA-binding protein
HSPA5 IPI00003362 Heat Shock 70kDa Protein 5(Glucose-regulated protein, 78kDa) PSMC4 IPI00020042 26S protease regulatory subunit 6B
IMPDH2 IPI00291510 Inosine 5'-monophosphate dehydrogenase 2 MPD IPI00022745 Mevalonate decarboxylase
PDIA6 IPI00299571 Protein disulfide isomerase P5 PSMC5 IPI00023919 26S protease regulatory subunit 8
MCM3 IPI00013214 Minichromosome maintenance complex component 3 PSMD14 IPI00024821
26S proteasome non-ATPase regulatory subunit 14
CCT5 IPI00010720 Chaperonin containing TCP1, subunit 5 eIF3S10 IPI00029012 Eukaryotic translation initiation factor 3 subunit 10
CCT6 IPI00027626 Chaperonin containing TCP1, subunit 6a HIP IPI00032826 Hsc70-interacting protein
CDC46 IPI00018350 CDC46 homolog ADRM1 IPI00033030 110 kDa cell membrane glycoprotein
CDC47 IPI00299904 CDC47 homolog eIF3S12 IPI00033143 Eukaryotic translation iniation factor 3 subunit 12
86
CCT4 IPI00302927 Chaperonin containing TCP1, subunit 4 eIF3M IPI00102069 Eukaryotic translation iniation factor 3 subunit M
LAMBR IPI00413108 37 kDa laminin receptor precursor GRIPAP1 IPI00873904 GRIP1-associated protein 1
CCT7 IPI00018465 Chaperonin containing TCP1, subunit 7 PSMD12 IPI00185374 26S proteasome non-ATPase regulatory subunit 12
ACTB IPI00021440 Actin, cytplasmic 2 FKBP4 IPI00219005 51 kDa FK506-binding protein MCM6 IPI00031517 DNA-replication licensing factor MCM6 RPL22 IPI00219153 60S ribosomal protein L22 BM28 IPI00184330 DNA-replication licensing factor MCM2 RPL6 IPI00790342 60S ribosomal protein L6
CCT3 IPI00553185 Chaperonin containing TCP1, subunit 3 SMC1 IPI00291939 Structural maintenance of chromosomes protein 1A
DNAJ2 IPI00012535 DaJ homolog subfamily A member IWS1 IPI00296432 IWS1-like protein
HBP IPI00022228 High density lipoprotein-binding protein PSMD1 IPI00299608
26S proteasome non-ATPase regulatory subunit 1
Gene
name IPI Protein name
Gene
name IPI Protein name
GRP94 IPI00027230 94 kDa glucose-regulated protein RBAP46 IPI00395865 Histone acetyltransferase type B subunit 2
SQSTM1 IPI00179473 Sequestosome 1 MATR3 IPI00789551 Matrin-3
HNRNPA1 IPI00215965 Heterogeneous nuclear ribonucleoprotein A1 HSPC117 IPI00550689 tRNA-splicing ligase RtcB homolog
CCT1 IPI00290566 Chaperonin containing TCP1, subunit 1 LAP3 IPI00419237 Cytosol aminopeptidase CCT2 IPI00297779 Chaperonin containing TCP1, subunit 2 EIF3EIP IPI00465233 Eukaryotic initiation factor 3
HNRNPH1 IPI00479191 Heterogeneous nuclear ribonucleoprotein PSMD13 IPI00549672
26S proteasome non-ATPase regulatory subunit 13
CCT8 IPI00784090 Chaperonin containing TCP1, subunit 8 EIF3F IPI00654777 highly similar to Eukaryotic translation iniation factor 3 subunit 5
GRP170 IPI00000877 170 kDa glucose-regulated protein IGF2BP3 IPI00658000 IGF-II mRNA-binding protein 3
ERP57 IPI00025252 Disulfide isomerase ER-60 EIF3B IPI00719752 Eukaryotic translation iniation factor 3 subunit 9
AUF1 IPI00028888 AU-rich element RNA-binding protein TPR IPI00742682 TPR protein SND1 IPI00140420 100 kDa coactivator UBE2O IPI00783378 Ubiquitin carrier protein O RPS19 IPI00215780 40S ribosomal protein S19 HEXC IPI00477231 Beta-hexosaminidase RPS9 IPI00221088 40S ribosomal protein S9 SPTA2 IPI00844215 Alpha-II spectrin HSPA1 IPI00304925 Heat shock 70 kDa protein 1/2 DDX9 IPI00844578 ATP-dependent RNA helicase A ABBP1 IPI00334587 APOBEC1-binding protein 1 HK1 IPI00903226 Hexokinase type I ACAC IPI00396015 ACC-alpha EF1G IPI00937615 Eukaryotic translation elongation factor 1
87
gamma
KAP1 IPI00438229 KRAB-associated protein 1 PSMB6 IPI00000811 Macropain delta chain
MYL6 IPI00796366 highly similar to Myosin light polypeptide 6 KYNU IPI00003818 Kyneureninase
KPNA2 IPI00002214 Importin subunit alpha-2 FLN IPI00333541 Actin-binding protein 280
HNRNPR IPI00011937 Heterogeneous nuclear ribonucleoprotein R PFD6 IPI00005657 Prefoldin subunit 6
RPS18 IPI00013296 40S ribosomal protein S18 RPS2 IPI00013485 40S ribosomal protein S2 PSF IPI00010740 100 kDa DNA-pairing protein DDX5 IPI00017617 DEAD box protein 5 TPM4 IPI00010779 Tropomyosin alpha-4 chain
RPS14 IPI00026271 40S ribosomal protein S14 PDXDC1 IPI00384689 Pyridoxal-dependent decarboxylase domain-containing protein 1
Gene
name IPI Protein name
Gene
name IPI Protein name
KIAA1153 IPI00099311 tRNA(adenine-N(1)-)-methyltransferase PGDH3 IPI00011200 D-3 phosphoglycerate dehydrogenase
GFAT IPI00217952 D-fructose-6-phosphate amidotransferase 1 PSMD3 IPI00011603
26S proteasome non-ATPase regulatory subunit 3
RPS16 IPI00221092 40S ribosomal protein S16 AIMP2 IPI00011916 Aminoacyl tRNA synthase complex-interacting multifunctional protein 2
FUS IPI00260715 75 kDa DNA-pairing protein H2AFC IPI00291764 Histone H2A type 1 ALY IPI00328840 Transcriptional coactivator Aly/REF DEK IPI00020021 Protein DEK H4/A IPI00453473 Histone H4 LMN1 IPI00021405 70 kDa lamin NPM IPI00549248 Nucleolar phosphoprotein B23 EIF5 IPI00022648 Eukaryotic translation initation factor 5 H2BFD IPI00646240 Histone H2B FARS IPI00031820 Phenylalanine-tRNA ligase alpha chain
GNB2L1 IPI00848226 Cell proliferation-inducing gene 21 protein CAPL IPI00032313 Calvasculin
HNRNPU IPI00883857 Heterogeneous nuclear ribonucleoprotein U PSMD11 IPI00105598
26S proteasome non-ATPase regulatory subunit 11
HSP73 IPI00003865 Heat shock 70 kDa protein 8 EZR IPI00843975 Ezrin TMOD3 IPI00005087 Tropomodulin-3 PLS3 IPI00216694 Plastin-3
ARPC18 IPI00005160 Actin-related protein 2/3 complex subunit 1B TPM3 IPI00218319 Tropomyosin alpha-4 chain
SPTB2 IPI00005614 Beta-II spectrin GAPD IPI00219018 Glyceraldehyde-3-phosphate dehydrogenase
RPS10 IPI00008438 40S ribosomal protein S10 EIF3S2 IPI00012795 Eukaryotic translation initiation factor 3 subunit
RPS20 IPI00012493 40S ribosomal protein S20 EIF3G IPI00290460 Eukaryotic initiation factor 3 RNA-binding
88
subunit
RPS25 IPI00012750 40S ribosomal protein S25 TUBA1 IPI00007750 Alpha-tubulin 1
EIF3C IPI00016910 Eukaryotic translation initiation factor 3 subunit CBX3 IPI00297579 Chromobox protein homolog
RPS21 IPI00017448 40S ribosomal protein S21 EIF5B IPI00299254 Eukaryotic translation initation factor 5B ARP3 IPI00028091 Actin-like protein 3 FARSB IPI00300074 Phenylalanine-tRNA ligase beta chain RPS8 IPI00216587 40S ribosomal protein S8 RPL13A IPI00304612 60S ribosomal protein L13A RPS4 IPI00217030 40S ribosomal protein S4 CACYBP IPI00395627 Calcyclin-binding protein
HPG2 IPI00396378 Heterogeneous nuclear ribonucleoprotein A2/B1 RPL26 IPI00433834 60S ribosomal protein L26
Gene
name IPI Protein name
Gene
name IPI Protein name
RPS3A IPI00419880 40S ribosomal protein S3A UCHL5 IPI00642374 Ubiquitin carboxyl-terminal hydrolase RPS15 IPI00479058 40S ribosomal protein S15 ASNS IPI00554777 Asparagine synthetase
FUBP2 IPI00479786 Far upstream element-binding protein 2 RPL14 IPI00555744 60S ribosomal protein L14
RPS24 IPI00915363 40S ribosomal protein S24 NME2 IPI00604590 Nucleoside diphosphate kinase PSMD5 IPI00002134 26S protease subunit S5 basic CAPRIN1 IPI00783872 Caprin-1
CRDBP IPI00008557 Coding region determinant-binding protein CSDE1 IPI00844264 Cold shock domain-containing protein E1
ERP70 IPI00009904 Endoplasmic reticulum resident protein 70 PSMD2 IPI00012268
26S proteasome non-ATPase regulatory subunit
PSMC1 IPI00011126 26S protease regulatory subunit 4 SSB IPI00009032 Sjoegren syndrome type B antigen RPS3 IPI00011253 40S ribosomal protein S3 COPS7B IPI00009301 COP9 signalosome complex subunit 7b
EIF3E IPI00013068 Eukaryotic translation iniation factor 3 subunit 6 EEF1E1 IPI00003588
Eukaryotic translation elongation factor 1 epsilon
ACTN4 IPI00013808 Alpha-actinin-4 PSMC6 IPI00926977 26S protease regulatory subunit 10B MM1 IPI00015361 C-Myc-binding protein Mm-1 PSMB7 IPI00003217 Macropain chain Z
CAS IPI00022744 Cellular apoptosis susceptibility protein EFTUD2 IPI00003519
Elongation factor Tu GTP-binding domain-containing protein
NSEP1 IPI00031812 CCAAT-binding transcription factor 1 subunit PSME3 IPI00005260 Proteasome activator complex subunit 4
DBC1 IPI00182757 Deleted in breast cancer gene 1 protein ACO1 IPI00008485 Cytoplasmic aconitate hydrolase
MSN IPI00219365 Membrane-organizing extension protein 1 ATP6A1 IPI00007682 Vacuolar ATPase Isoform VA68
PSME3 IPI00219445 11S regulator complex subunit gamma CAPE IPI00007927 Chromosome-associated protein E
89
RBS13 IPI00221089 40S ribosomal protein 13 PAB1 IPI00008524 Polyadenylate-binding protein 1 RBS15A IPI00221091 40S ribosomal protein S15A MYH9 IPI00019502 Cellular myosin heavy chain RBS17 IPI00221093 40S ribosomal protein S17 G22P2 IPI00220834 86 kDa subunit of Ku antigen
CRM1 IPI00298961 Chromosome region maintenance 1 protein homolog CAP43 IPI00022078 Differentation-related gene 1 protein
HGRG8 IPI00306043 CLL-associated antigen KW-14 TLP46 IPI00171438 Thioredoxin domain-containing protein 5
CAPZA2 IPI00026182 F-actin-capping protein subunit alpha-Z RPL12 IPI00024933 60S ribosomal protein L12
Gene
name IPI Protein name
Gene
name IPI Protein name
DBP1 IPI00396435 ATP-dependent RNA helicase 46 CAPN4 IPI00025084 Calcium-activated neutral proteinase small subunit
ANX2 IPI00418169 Annexin A2 FAS IPI00026781 S-malonyltransferase
BAG3 IPI00641582 BAG family molecular chaperone regulator 3 CLIP1 IPI00013455
CAP-Gly domain-containing linker protein 1
EIFS3 IPI00647650 highly similar to Eukaryotic translation initiation factor 3 subunit 3 PSMD6 IPI00014151
26S proteasome non-ATPase regulatory subunit 6
RPS28 IPI00719622 40S ribosomal protein 28 G2AN IPI00383581 Alpha-glucosidase 2 FLNB IPI00900293 Filamin B NKEFB IPI00027350 Natural killer cell-enhancing factor B
PSME2 IPI00943181 Putative uncharacterized protein PSME2 PSMB2 IPI00028006 Macropain subunit C7-I
DNCLI1 IPI00007675 Cytoplasmic dynein 1 light intermediate chain 1 PUS7 IPI00044761 Pseudouridylate synthase 7 homolog
RPS5 IPI00008433 40S ribosomal protein 5 LARS IPI00103994 Leucine-tRNA ligase RPS12 IPI00013917 40S ribosomal protein 12 PTB IPI00183626 Polypyrimidine tract binding protein 1 PLEC1 IPI00014898 Plectin-1 LARP IPI00185919 La-related protein 1 PSMC3 IPI00018398 26S protease regulatory subunit 6A EEF2 IPI00186290 Elongation factor 2 PSMD7 IPI00019927 26S protease regulatory subunit 6B H1F5 IPI00217468 Histone H1.5 PSMC2 IPI00021435 26S protease regulatory subunit 7 G6PD IPI00216008 Glucose-6-phosphate 1-dehydrogenase RPS6 IPI00021840 40S ribosomal protein S6 RBAP48 IPI00328319 Chromatin assembly factor 1 subunit C RPS11 IPI00025091 40S ribosomal protein S11 IMP48 IPI00398009 Importin-4
H1F2 IPI00217465 Histone H1.2 ISOC1 IPI00304082 Isochorismatase domain-containing protein
LAMB2 IPI00294879 Laminin B2 chain
