Masaryk University
Faculty of Science
PROTEOMICS IN MOLECULAR ONCOLOGY
AND IN BIOTECHNOLOGY
Mgr. Pavel Bouchal, Ph.D.
Habilitation thesis
Brno 2016
2
Acknowledgements
First of all, I would like to express my sincere thanks to all my students, to all colleagues and
collaborators for any contribution to our research summarized here.
To Rudolf Nenutil for his clinical pathologist’s point of view supervision and to Bořivoj Vojtěšek and
Dalibor Valík for the opportunity of participation in RECAMO centre at Masaryk Memorial Cancer
Institute.
To all international collaborators for their supervision and unique know-how they contributed to the
papers. Special thanks to Ruedi Aebersold for the opportunity to spend more than 5 months in highly
stimulating environment of his lab at ETH Zurich and for his paragon of highly generous, naturally
respected scientific personality.
Many thanks to my wife Pavla and our kids Marta and Ondřej for their patience and understanding
during preparation of my thesis and of my work in general.
In 2016, this work was supported by Czech Science Foundation (project 14-19250S), by MEYS –NPS I LO1413,
and by MH CZ –DRO (MMCI, 00209805).
3
TABLE OF CONTENTS
1 Preface ............................................................................................................................................................ 4
2 Introduction .................................................................................................................................................... 6
3 Proteomics, a tool for complex protein analysis............................................................................................. 7
3.1 Two-dimensional gel electrophoresis-mass spectrometry based proteomics...................................... 7
3.2 Surface enhanced laser desorption ionization time-of-flight mass spectrometry ................................ 8
3.3 Shotgun proteomics .............................................................................................................................. 9
3.4 Targeted proteomics ........................................................................................................................... 10
3.5 Top-down vs. bottom-up proteomics.................................................................................................. 12
4 Aims of the thesis.......................................................................................................................................... 13
5 Proteomics in molecular oncology................................................................................................................ 14
5.1 Proteomics based identification and characterization of protein biomarkers associated with breast
cancer metastasis.............................................................................................................................................. 14
5.2 Verification and clinical validation of breast cancer biomarkers using targeted proteomics ............. 24
5.3 Breast cancer classification via proteomics......................................................................................... 27
5.4 Identification of biomarkers for non-invasive cancer detection and therapy monitoring.................. 30
6 Proteomics in biotechnological applications................................................................................................. 33
6.1 Proteomics in studying metabolism regulation and cell stress response in denitrifying bacteria ...... 33
6.2 Proteomics in studying sulfur oxidizing bacteria................................................................................. 37
7 Conclusions ................................................................................................................................................... 40
8 References..................................................................................................................................................... 41
9 Supplements: List of publications included in the thesis .............................................................................. 47
4
1 PREFACE
This habilitation thesis is based on scientific publications to which I have contributed as the
corresponding author, first author and co-author. The articles were published between 2006 and
2016. All these publications are based on quantitative proteomics as a key tool to answer complex
biological questions. With an original background in bacterial proteomics, I have been extending my
research to the field of cancer research since 2004 and have established research direction of our
group focused mainly on breast cancer metastasis, its biomarkers and mechanisms potentially
targetable by the treatment. Proteomics remained a major research approach and, along with a
development of proteomics technology, it has involved all key principles from gel based proteomics
to the latest high-throughput and targeted proteomic tools. Proteomic tools have been
complemented by additional cellular and molecular biology approaches.
After a general introduction to proteomics techniques, the thesis is divided into two blocks
focused on proteomics in molecular oncology and biotechnology, respectively. Each block is further
subdivided into 2-4 parts containing overview of the literature, problem definition and overview of
our contribution to the field. The molecular oncology block covers the identification of potential
metastatic biomarkers in breast cancer, their clinical validation using targeted proteomics,
proteomics based cancer classification and chapter on biomarkers for cancer detection and noninvasive
therapy monitoring. The biotechnology block is focused on applications of proteomics in
metabolism control and stress response mechanisms in denitrifying bacteria and for studying the
metabolism of sulfur oxidizing bacteria with biotechnological application.
The habilitation thesis is based on 18 publications in journals with impact factor (Supplement
J1-J18). In addition, we have reviewed some more details in additional student’s publications in
journals indexed in Scopus and I add some of them to the thesis to provide a complete picture of our
work (Supplements S1-S5), together with two published book chapters (Supplements C1-C2).
5
On August 25, 2016, I am author or co-author of 24 published papers in journals with impact
factor, 13 papers in journals indexed in Scopus, 8 additional papers in other peer-reviewed journals,
2 chapters in books and 71 conference contributions.
6
2 INTRODUCTION
Proteins are the key structural, functional and catalytic units that form molecular complexes, cell
organelles, cells and organs. In medicine, number of proteins play a crucial role as disease biomarkers
that are associated with prognosis (prognostic biomarkers) and treatment decisions (predictive
biomarkers) [1]. Central dogma of molecular biology defines how the genes encode the genetic
information, how the genes are expressed and how the transcripts serve as information templates
for proteins [2, 3]. There are number of regulation mechanisms and factors that define the level of
proteins in the cells: (i) regulation of transcription, (ii) regulation of translation, (iii) transcript
stability, (iv) posttranslational modifications of proteins, and (v) protein degradation (Fig. 1). Due to
these reasons, correlation of transcripts and proteins is not perfect. It is estimated that it does not
generally exceed ~40 % although higher percentage of agreement was shown for steady-state [4].
Most of high-throughput biology studies have been performed on transcript level (Affymetrix arrays,
RNA-Sequencing), however, imperfect protein-transcript correlation makes the analysis of proteins
essential. The reason is obvious: proteotype (a complex proteome profile) reflects the phenotype
much better than genotype or transcriptome profile (Fig. 1).
Fig. 1. Central dogma of molecular biology (yellow box) defines the mechanisms of gene expression via
transcripts to proteins. Proteins (and not genes or transcripts) as the true structural molecules and regulatory
effectors that form the phenotypes.
7
3 PROTEOMICS, A TOOL FOR COMPLEX PROTEIN ANALYSIS
Proteomics represents a methods toolbox to study proteins in a large scale. These methods have
largely developed in recent years: Classical biochemical methods are able to bring new information
on individual proteins and regulation of their levels. Development of mass spectrometry (MS) based
technologies has provided new opportunities to study the complex protein composition of cells,
tissues and biological fluids in their complexity with high accuracy and both sample and analyte
throughput in last two decades. We have provided a complete overview of proteomics approaches in
molecular medicine in a recently published book chapter [5] (Supplement C1).
3.1 TWO-DIMENSIONAL GEL ELECTROPHORESIS-MASS SPECTROMETRY BASED PROTEOMICS
The classical, namely in the past broadly used proteomics approach is based on twodimensional
gel electrophoresis (2-DE) that allows separation of hundreds to thousands proteins
according to isoelectric point (pI) and molecular weight [6, 7] (Fig. 2). The individual proteins, their
isoforms and posttranslational variants separated as spots can be quantified using image analysis.
Selected spots can be digested with a protease (typically trypsin), resulting peptides can be extracted
from the gel, ionized and fragmented in mass spectrometer (MS). Both ionized peptides (precursor
ions) and peptide fragments (product ions) are measured in MS either in matrix assisted laser
desorption-ionization time-of-flight mass spectrometry (MALDI-TOF MS) mode, or using liquid
chromatography on-line connected to mass spectrometry with electrospray ionization (LC-ESI-MS).
While the 2-DE method itself was published in 1975 [8], it was more broadly adopted after 1994
when the term “proteome” was defined by Marc Wilkins as a complex protein composition of cell,
tissue or biological fluid [9] and when soft mass spectrometry ionization methods, MALDI-TOF MS
and ESI-MS became available for routine peptide and protein identification. The current advantage of
2-DE based approach is that whole proteins (including their isoforms and posttranslational variants)
and not their surrogate peptides are analyzed and quantified. However, since separation power of
gels is not perfect, latest highly sensitive mass spectrometers are able to identify more than one
8
protein in each protein spot in the gel in some cases. This may lead to loss of link between protein
identification and quantification and motivates the researchers to use more advanced methods.
We previously reviewed 2-DE-MS approach in [6] and used in 7 rather earlier articles involved
in this thesis (Supplements J5, J13, J14, J16, J17, J18 and C2).
3.2 SURFACE ENHANCED LASER DESORPTION IONIZATION TIME-OF-FLIGHT MASS
SPECTROMETRY
Surface enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF MS)
is a relatively simple method that involves protein pre-separation on chips of various chemistries and
detection and quantification of binding proteins in MALDI-TOF MS spectra [10]. This very highthroughput
method enables measurement of up to few hundreds of samples per day in which whole
undigested proteins are detected and quantified. The major disadvantage originates from low mass
resolution of the MALDI-TOF mass analyzer. As a result, no more than few hundreds of polypeptides
can be screened in single analysis, based on sample complexity. Another key disadvantage of SELDITOF
MS approach is that identity of protein peaks is basically unknown and multidimensional
fractionations on individual basis are required to obtain the protein identity of the peaks [11].
Fig. 2. Two-dimensional proteome map
of breast cancer tissue. Each protein, its
isoform or posttranslational variant is
potentially visible as single spot,
depending on its concentration and
separation power of the gel. Protein
quantification is based on image
analysis. Proteins can be identified via
mass spectrometry.
9
We used SELDI-TOF MS approach in 5 earlier articles involved in this thesis (Supplements J8,
J9, J10, J11, J12).
3.3 SHOTGUN PROTEOMICS
Along with development in mass spectrometry technology, 2-DE-MS and SELDI-TOF have been
replaced for many applications with more powerful liquid chromatography - mass spectrometry
approach based on peptide sequencing and quantitation, called shotgun proteomics. Shortly,
peptides obtained by digestion of proteins in complex samples using protease are typically separated
with one- or multidimensional chromatography prior to tandem mass spectrometry (MS/MS)
analysis. MS has been developing in term of sensitivity, resolution and speed to increase number of
proteins that can be detected and quantified in single analysis. Orbitrap and quadrupole-time-offlight
(qTOF) are currently the most powerful mass analyzers used for untargeted proteomics
analyses [12-14]. Shotgun proteomics currently allows identification and quantification of thousands
proteins from complex sample. Quantification is ensured by labelling via isobaric tags for relative and
absolute quantification (iTRAQ) [15], tandem mass tags (TMT) [16], dimethyl labelling [17], or stable
isotope labelling in cell culture (SILAC) [18]. Alternatively, label-free quantification in MS1 mode can
be used [19]. Detection of post-translational modifications (such as phosphorylation, glycosylation) is
also possible from tandem mass spectra. Disadvantage of the approach originates from the fact that
intact molecular weight of the whole proteins is not measured. It results in limited protein sequence
coverage by identified peptides, loss of labile PTMs, and ambiguity of the origin for redundant
peptide sequences [20]. This limits the information on isoforms and status of posttranslational
modifications in the intact proteins.
We used shotgun proteomics in four articles involved in this thesis (Supplements J2, J3, J5 and
J15).
10
Fig. 3. LC-MS proteomics analysis of proteolytic peptides. Proteins are digested with protease (e.g., trypsin) to
peptides, optionally fractionated and injected to capillary LC column on-line connected to mass spectrometer.
Here, m/z and signal intensities of peptides and their fragmentation products are measured in data dependent
(“shotgun“ proteomics) mode: E.g., 20 most intense precursor ions eluted at the particular time from the
column are fragmented and their MS/MS spectra are obtained. Peptides and proteins are identified by
database search of resulting MS/MS data. Alternatively, data acquisition can be performed in targeted or data
independent modes, as explained in Fig. 4.
3.4 TARGETED PROTEOMICS
All above described approaches have been typically used for screening as many proteins as
possible between biological states to find biologically important differentially regulated proteins and
potential disease biomarkers. Sample throughput of discovery proteomics based on 2-DE or
multidimensional LC-MS/MS is however relatively low. To validate such proteins in control
experiments and in independent sets of samples, selected reaction monitoring (SRM) is a suitable
approach with higher sample throughput. SRM has been well established in the analysis of small
molecules and later on it was adopted for analysis of peptides and proteins -see Fig. 4 for basic
principle. Development of SRM assays typically requires MS/MS spectra of peptides, from which the
best combinations of precursor peptide ions and their fragments (called transitions) are selected and
used in SRM assays for protein quantification. Isotopically labelled peptides are often used as internal
standards for relative and/or absolute quantitation [21]. SRM assays are typically measured using
triple-quadrupole mass spectrometer, however, alternative approaches on high-resolution mass
11
spectrometers such as pseudo-SRM (p-SRM or MRMHR
) [22] or parallel reaction monitoring (PRM)
[23, 24] are now available.
Fig. 4. Principles of targeted proteomics analysis. Peptides obtained by protein trypsin digestion are separated
by liquid chromatography on-line connected to mass spectrometer (similarly to Fig. 3), in which data is
obtained in different acquisition modes: A: In triple quadrupole (QqQ) mass spectrometer operating in SRM
mode, precursor ions selected in Q1 (mass filter typically set to unit resolution, 0.7 Da) are fragmented in
collision cell (Q2) to product ions. The third quadrupole (Q3) filters only selected product ions used for
quantification. B: In mass spectrometer working in DIA mode (e.g., SWATH), the first analyzer selects ions using
broader m/z windows (e.g., 25 Da wide SWATH windows). Selected precursor ions are fragmented in collision
cell and all product ions are analyzed in high resolution mass analyzer [25].
Data independent acquisition (DIA) methods represent multiplexed variant of SRM. One of
the most commonly used approach, “sequential window acquisition of all theoretical fragment ion
spectra mass spectrometry” (SWATH-MS) was published in 2012 by Gillet et al. [26]. In SWATH, not
individual precursor ions but the whole effective mass range is being selected for fragmentation in
sequential SWATH windows (Fig. 4), resulting in quantitative information on all detectable peptides
in the sample, called digital fingerprints of the proteome. DIA typically enables consistent
12
quantitation of several thousands of proteins per ~2 h run with analytical characteristics comparable
to SRM and data structure similar to SRM. One of the key advantages of SWATH is that the targets
(peptides and proteins) are defined for targeted extraction of quantitative information in postacquisition
step while for SRM they have to be defined in acquisition method prior to the analysis.
Although DIA is slightly less sensitive than SRM, it is evident that digital proteome maps obtained by
DIA proteomics could serve for verification of protein levels via data extraction from publicly
available datasets in the future, not requiring new data acquisition [25, 27].
We recently reviewed the principles of SRM in [28] (Supplement J6) and other targeted
proteomics methods in [29] (Supplement S4). Two additional articles that involve targeted
proteomics [25, 30] (Supplements J5, J7) are part of this thesis.
3.5 TOP-DOWN VS. BOTTOM-UP PROTEOMICS
As mentioned above, “old-fashioned” 2-DE-MS [31] an SELDI-TOF MS have been mostly
replaced by modern shotgun and targeted proteomics with better proteome coverage and
quantification accuracy. However, shotgun and targeted proteomics methods analyze tryptic
peptides (bottom-up approach), while 2-DE and SELDI-TOF MS quantify whole undigested proteins
(top-down approach). Top-down approach dramatically improves information on splice variants and
posttranslational modifications of intact proteins. Moreover, it bypasses the need for proteolysis that
typically increases the sample complexity and measurement irreproducibility due to proteolytic
inhibition phenomena by molecules present in the clinical samples. This is a key advantage over most
of current proteomics approaches based on shotgun proteomics [11]. Unfortunately, obtaining highthroughput
top-down proteomics data is still technically difficult and limited to specialized research
labs with an advanced experience and ultrahigh mass accuracy instruments, nevertheless, highthroughput
top-down proteomics represents highly promising approach for the future [32].
One article in this thesis [11] (Supplement J9) is focused specifically on top-down proteomics
field.
13
4 AIMS OF THE THESIS
Proteomics in molecular oncology
1. Identification, verification and validation of potential protein biomarkers associated with
lymph node metastasis in breast cancer with major focus on low-grade tumors of luminal A
subtype.
2. Verification of the above breast cancer biomarkers using targeted proteomics.
3. Classification of breast cancer tissues based on proteomic profiles and comparison of the
results with transcript profiling.
4. Identification of biomarkers for cancer detection and non-invasive therapy monitoring.
Proteomics in biotechnological applications
1. Large-scale studying the mechanism of denitrification regulation via FNR-family transcription
regulators and redox stress response in denitrifying bacteria.
2. Analysis of proteome dynamics of sulfur oxidizing bacteria in response to various growth
conditions.
14
5 PROTEOMICS IN MOLECULAR ONCOLOGY
5.1 PROTEOMICS BASED IDENTIFICATION AND CHARACTERIZATION OF PROTEIN
BIOMARKERS ASSOCIATED WITH BREAST CANCER METASTASIS
Breast cancer is the most common form of cancer in women worldwide. Currently, cancer
metastasis and not the presence of primary tumor in the patient are the main reasons for patient
mortality [33]. From the molecular point of view, cancer is multifactorial and multistage process that
emerges as a consequence of multiple genetic aberrations. The same applies to cancer metastasis:
The metastatic characteristics of cancer cells may be predisposed or acquired during disease
development and are governed by a number of genetic and biochemical mechanisms [33, 34].
From the mechanistic point of view, metastasis formation involves dissemination of cancer
cells from a primary tumor site to distant organs and tissues [35, 36] and their adaptation to novel
tissue microenvironments [37] (Fig. 5). This process is called metastatic cascade and metastases are
its terminal products [37, 38].
The first step of metastatic cascade represents the escape of cancer cells from primary tumor
[38]. By overexpression of the proteases by both the cancer cells and the cells of the tumor
environment, cancer cells break the basement membrane (Fig. 5b) and enter the stroma [37].
Stromal cells enhance aggressive behavior (invasiveness and migration potential) of cancer cells by
production of cytokines and growth factors. These factors, together with hypoxia in tumor
microenvironment, can trigger epithelial-to-mesenchymal transition (EMT) [39, 40]. During this
process cancer cells remodel their cytoskeleton, lose cell-cell adhesion, lose epithelial phenotype and
markers (e.g., E-cadherin) and gain mesenchymal phenotype and produce its markers (e.g., Ncadherin,
vimentin). This leads to increased invasion and motility of these cells. Various signaling
pathways like Wnt, Notch, NF-κB, TGF-β and other are also involved in EMT [41].
15
Fig. 5. Metastatic cascade is the process that consists of local invasion of cells from primary tumors through
basement membrane and stromal cell layers (a-b); their intravasation into blood or lymph vessels (c); arrest at
distant tissues and organs (d); extravasation into parenchyma of these tissues and organs (e); settlement and
neoplastic growths of metastatic tumors (f) [37, 42, 43].
Next, locally invasive cancer cells penetrate into lumina of lymphatic or blood vessels. This
process called intravasation is strongly influenced by tumor associated blood vessels [37], tumor cells
and tumor associated macrophages which produce factors contributing to remodeling of
extracellular matrix (ECM) [38] (Fig. 5c). New blood vessels formed during tumor progression are very
penetrable that makes the intravasation easier [37]. After intravasation, circulating tumor cell (CTCs)
must survive in spite of variety of stresses in the bloodstream before they achieve distant organ sites
[37]. First of all, CTCs avoid specific type of apoptosis known as anoikis [40] which is triggered by
16
disruption of the integrin-dependent adhesion to ECM components. CTCs overcome degradation by
innate immune system [37] by formation of metastatic thrombi between cancer cells and blood
platelets [44]. Survived CTCs are transported by blood into distant organ sites. Whether the capture
of CTCs in distant sites is passive [37] or active process [40] (Fig. 1d) must be first elucidated. Not all
the cancer cells that successfully escape from their native tissue survive in circulation and grab in
capillaries are able to extravasate or, in other words, to overcome physical barrier of endothelial cells
and pericite layers by production of factors that induce vascular hyperpermeability and facilitate
extravasation of tumor cells (Fig. 5f) [37, 38]. The great challenge for extravasated and thus
disseminated carcinoma cells is to survive in secondary loci. The microenvironment at the metastatic
site is usually different from that in primary tumor [37], but it has been proposed that primary
tumors produce factors that contribute to appropriate environment in metastatic locus [40]. Even if
the disseminated cancer cells survive, they are still not guaranteed to proliferate. Majority of
extravasated cancer cells thus remain in long-termed dormancy as microcolonies and only small
amounts are able to form macroscopic metastases [37].
In general, metastatic cascade is diverse but also highly inefficient process, especially in the
later steps. The fact that metastases are the main cause of death in patients with solid malignancies
[35] predicts the pro-metastatic pathways as sources of important therapeutics and diagnostics
targets that become subjects of further studies [37, 43].
In clinical practice, both traditional and molecular prognostic markers have been used for
risk-group discrimination and determination of metastatic potential. Traditional prognostic markers
in breast cancer involve age at diagnosis, tumor size and grade, lymph node status and the presence
of distant metastasis. Tumor size is a potent prognostic factor predicting higher probability of
metastatic behavior for larger tumors. More differentiated tumors (e.g., grade 1) have low
dissemination potential in general, while less differentiated, more proliferative high grade tumors
(e.g., grade 3) form metastases much more frequently. Low grade breast tumor cells spread
predominantly via lymph vessels and lymph nodes are therefore the first site of tumor cell
17
dissemination prior to their eventual spread into distant organs such as lung or bone [45]. Molecular
prognostic markers involve hormonal receptors (estrogen receptor (ER), progesterone receptor (PR)),
Her-2/neu receptor and expression panels like Oncotype DX and MammaPrint. Also, the American
Society for Clinical Oncology (ASCO) has recommended urokinase plasminogen activator (PLAU) and
urokinase plasminogen activator inhibitor (SERPINE1) as indicative factors for metastatic potential in
breast cancer [46, 47], however their use in clinical practice has not been generally accepted [46].
There is thus a clinical need to find novel biomarkers that would identify patients with high risk of
metastasis development who should receive more personalized therapy [48].
Currently available markers are not sufficient for precise risk-group or individual assessment
specifically in low grade luminal-A tumors, whose general prognosis is very favorable, resulting in
treatment by less aggressive adjuvant therapy and no chemotherapy. However, a low percentage of
these tumors develop early lymph node metastases. The molecular mechanism of this phenomenon
is not known and current clinical practice lacks the means for predicting its occurrence. New
knowledge is thus essential for identifying biomarkers that can identify high risk individuals within
the predominantly low risk population of patients with low grade breast cancers. These high risk
patients should then receive more intensive follow-up and could be considered for more aggressive
therapy, which cannot be achieved currently in view of the detrimental effects of therapy on the
majority of patients who will not show benefit. In addition, understanding the mechanisms of
metastasis of low grade breast cancer may lead to the identification of new therapeutic targets.
Our contribution to the field
The field of metastatic cascade, mechanisms, metastatic biomarkers, and contribution of
proteomics to their investigation was reviewed in an invited paper published in special issue
“Reviews 2014” of the journal PROTEOMICS [43] (Supplement J1). We have shown that both 2-DE
and shotgun proteomics has enabled the identification of number of metastasis-associated proteins
and potential biomarkers in cancer tissues, microdissected cells, model systems, and secretomes.
18
Proteomics has also contributed to functional description of such targets, either as a part of
functional studies, or by interactome analysis. It is thus evident that current proteomics has a great
potential to provide functionally meaningful data in the field [43]. We also published another
complementary review more focused on the method approaches, including overview of methods to
interactome analysis and identification of cell surface proteins related to metastasis [49]
(Supplement S1).
Our first experimental study in the field was a pilot study performed and published in an
international collaboration with dr. Spiros Garbis at Academy of Athens, Greece in 2009 [50]
(Supplement J2). We firstly used a quantitative iTRAQ-2LC-MS/MS based proteomic method for the
analysis of breast cancer tissue and its lymph node metastasis. We identified several differentially
regulated proteins up-regulated in metastatic primary tumor and/or lymph node metastasis relative
to the non-metastatic tumor type, namely: heme binding protein 1, group of interferon induced
proteins, carbonic anhydrase 2 and parathymosin. On the other hand, some proteins involved in
biosynthesis of lipids were down-regulated. The most important findings were verified also on
transcript level. Our analysis confirmed applicability of iTRAQ-2DLC-MS/MS analysis in breast cancer
biomarker discovery [50]. This method paper was cited 53x (Web of Science, August 2016). After
publication, it was selected as the best publication in the field of proteomics in 2008-9 in the Czech
Republic by Czech Society of Biochemistry and Molecular Biology in 2010 and received Arnold
Beckman Prize.
A subsequent large study on clinically-pathologically representative set of 96 breast cancer
patient tissues was performed and published in the journal Molecular and Cellular Proteomics in
2015 [48]. To identify markers for this group of patients, we employed a combined proteomics and
transcriptomics to 24 lymph node positive and 24 lymph node negative grade 1 luminal A primary
breast tumors. Another group of 48 high-grade tumors (luminal B, triple negative, Her-2 subtypes)
was also analyzed to investigate marker specificity for grade 1 luminal A tumors (see Fig. 6 for the
19
workflow). The total of 4405 proteins were identified using an iTRAQ-2DLC-MS/MS proteomics
(FDR<5%). The expression of top 65 differentially expressed together with 30 previously identified
and control markers were analyzed also on transcript level. We identified a panel of proteins
associated with lymph node metastasis in luminal A grade breast cancer and determined their
specificity: Increased levels of carboxypeptidase B1 (CPB1), PDZ and LIM domain protein 2 (PDLIM2)
and ring finger protein 25 (RNF25) were associated specifically with lymph node positive grade 1
tumors, whereas stathmin (STMN1) and thymosin beta 10 (TMSB10) associated with aggressive
tumor phenotype also in high grade tumors at both protein and transcript level. Central transcription
regulator of NF-κB pathway, p65/RELA, TRAF3 interacting protein 1 (TRAF3IP2) and 14-3-3η (YWHAH)
were associated with lymph node status without delimited specificity. Protein level validation for
CPB1 using immunohistochemical analysis on tissue microarrays showed similar differences.
Additional transcript level validation in independent dataset of 343 patients was successful for CPB1
(p=0.00155), PDLIM2 (p=0.02027) and RELA (p=0.00015). Moreover, statistically significant
connections with patient survival were identified in another public dataset (N=1678). Our findings
indicated unique pro-metastatic mechanisms in grade 1 tumors that can include up-regulation of
CPB1 and activation of NF-κB pathway [48]. At the time of publishing, this was the largest proteomics
study on clinicopathologically representative set of breast cancer. Later on, additional studies were
published by leading teams in highly impacted journals such as Nature [51] (by Clinical Proteomics
Technology for Analysis of Cancer (CPTAC) consortium), Nature Communications [52] (by Matthias
Mann group) or in Journal of Proteome Research [53] (by Emma Nimeus and Ruedi Aebersold teams)
[54], however, none of them have focused on specifically metastatic biomarkers in luminal A tumors.
20
.
Fig. 6. Experimental design of a proteomic-transcriptomics study focused on identification of biomarkers of
lymph node metastasis in low-grade breast cancer [48]. The design of proteomics experiment allowed analysis
of 24 samples (each pooled from 4 patients’ lysates, 24x4=96 patients involved); these 24 samples are
represented by 24 rectangles in three rows corresponding to three iTRAQ 8-plex experiments (E1, E2, E3). Each
pooled sample in each of three 8-plex experiments was labeled by one of eight iTRAQ labels (113, 114, 115,
116, 117, 118, 119, 121). For more details and more detailed legend see Supplement J3.
Results of our study led to hypothesis whether NF-κB activation in metastatic tumors could
be targetable via NF-κB modulating anti-metastatic therapeutic strategy. Interestingly, it was
21
presented during San Antonio Breast Cancer Symposium (Dec 8-12, 2015) that receptor activator of
nuclear factor-kappa B (RANK) inhibitor denosumab improves disease-free survival for
postmenopausal patients with hormone receptor positive breast cancer according to the results of
the phase III ABCSG-18 clinical trial. Moreover, the data suggest that this treatment should be
offered to all patients with hormone receptor positive breast cancer who are receiving adjuvant
aromatase inhibitor therapy, irrespective of their bone health status [55]. Although this data does
not allow making conclusions on possible effect on metastasis development, it indicates that NF-κB
blocking is an effective approach in early breast cancer treatment, supporting findings of our recent
combined proteomics-transcriptomics study [48].
One of the proteins identified in our above mentioned study [48] was PDLIM2. It is a member
of the actinin-associated LIM family of proteins that play essential roles in cytoskeleton organization,
cell differentiation and have been associated with oncogenesis. Josef Maryáš et al. have reviewed
the current knowledge on PDLIM2 role in cancer development [56] (Supplement S2). Shortly, it is
known that PDLIM2 regulates stability and activity of several transcription factors, e. g. NF- κB or
STAT, and its deregulation is associated with several malignancies. Interestingly, PDLIM2 expression
has been connected with both tumor suppression and tumorigenesis: PDLIM2 levels are
epigenetically suppressed in different cancers due to Mystique promoter hypermetylation that
blocks its transcription, moreover, PDLIM2 re-expression is able to inhibit tumorigenicity and induces
tumor cell death both in vitro and in vivo, which suggest potential tumor suppressor role of PDLIM2.
On the other hand, PDLIM2 is highly expressed in cancer cell lines derived from metastatic cancer
and its expression is associated with tumor progression and metastasis formation, indicating prooncogenic
role of PDLIM2. In our review, we hypothesized a dual model of this protein function in
early and late stage of tumorigenesis that could explain the contradictory observations [56]. Our
latest experimental results show its pro-metastatic role by influencing migration, invasion and EMT
of breast cancer cells (Maryas J. et al., in preparation), fully supporting our previous expression
results in clinical cohorts.
22
Fig. 7. Overview of transgelin and transgelin-2 expression in relation to the important processes in cancer. It
seems that transgelin and transgelin-2 levels change conversely during tumor development. Whilst transgelin
expression is detected mainly in the cells of tumor stroma, where its level increases together with stroma
activation and decreases together with dedifferentiation of the tumor cells, transgelin-2 is restricted to the
tumor cells where its level increases together with dedifferentiation of the tumors and thus with a tumor
progression. On the other hand results of some studies indicate that transgelin level positively correlates with
the metastatic potential of the tumor cells [58].
Our research has also focused on the group of cytoskeletal proteins, transgelins. Transgelin was
firstly identified as protein associated with lymph node metastasis of breast cancer in very early
phase of our research by Jarmila Sobotkova using 2-DE [57]. Transgelin is an abundant protein of
smooth muscle cells, where its role has been primarily studied. As a protein affecting dynamics of the
actin cytoskeleton via stabilization of actin filaments, transgelin is both directly and indirectly
involved in many cancer-related processes such as migration, proliferation, differentiation or
apoptosis. Monika Dvořáková proceeded in this research direction, preparing a comprehensive
review on the role of transgelins in cancer [58] (Supplement J4). We summarized contradictory
23
observations in the literature and reviewed the role of transgelin in tumors (see also Fig. 7),
concluding that expression and biological role of transgelin seems to differ among various types of
tumor cells and stroma and possibly change during tumor progression. This first comprehensive
review on transgelin function in cancer has received around 20 manuscript requests and 10 citations
from other authors since 2014.
Our experimental work on transgelin association with lymph node metastatis in breast
cancer was summarized in a paper published in Journal of Proteomics in 2016 [30] (Supplement J5).
Initial transgelin discovery in the cohort of 12 patients using 2-DE-MS was followed by a combined
proteomics and transcriptomics validation in the set of 96 breast cancer tissues. The validation data
has confirmed transgelin connection with the lymph node metastasis and, moreover,
immunohistochemical staining specified that transgelin is expressed mostly in tumor stroma. These
results entered the research into the topical field of carcinoma associated fibroblasts [59, 60] as
discussed in the paper. Our data on transgelin homologue, transgelin-2, show that it was mainly
expressed by epithelial cancer cells and its levels were increased in metastatic and poorly
differentiated tumors [30] (Supplement J5). Subsequent functional analyses of BT549 and PMC42
breast cancer cell lines with silenced transgelin expression vs. control cell lines indicate connection of
transgelin with cell migration and apoptosis in breast cancer (Dvořáková M. et al., in preparation).
Important step in studying a pro-metastatic role of identified proteins is analysis of their
contribution to cell migration and invasiveness. There are several in vitro methods available: They
include simple assays such as scratch–wound assay and methods based on chemotaxis (e.g., Dunn‘s
chamber). More complex methods that enable studying both cell migration and invasiveness include
systems based on Boyden chamber such as Transwell migration/invasion test, xCELLigence system,
and confocal microscopy based approaches. In vivo methods are represented mainly by mice model.
Petra Kovaříková has used in vitro methods in her diploma thesis [61] to study the role of
carboxypeptidase B1 (CPB1) in migration and invasiveness. Based on her experience, we reviewed
24
principles and summarized pros and cons of the methods in this toolbox [62] (Supplement S3).
5.2 VERIFICATION AND CLINICAL VALIDATION OF BREAST CANCER BIOMARKERS
USING TARGETED PROTEOMICS
As shown above, proteomics holds a potential to find novel biomarkers for detection, diagnosis
and prognosis of diseases including cancer. To overcome potential method issues such as peptide coelution
in LC-MS, it is recommended to verify proteomics findings using an independent method.
More importantly, to overcome biological issues related to genetic, inter-sample and intra-sample
(e.g. intra-tissue) heterogeneity, it is essential to validate the selected biomarker candidates in large
independent sets of samples to confirm their clinical relevance. For these purposes, the availability of
targeted methods for protein quantification with high sample throughput is very important.
Immunoaffinity-based approaches such as immunohistochemistry (IHC) for tissues, or enzyme-linked
immunosorbent assay (ELISA) for blood samples are not available for all biomarker candidates,
especially for the new ones, and their development on-purpose is expensive and time consuming.
Targeted proteomics represents a reasonable alternative, also because of faster method
development. Many studies [27, 63-68] have proved good correlation of gold standard of targeted
proteomics, SRM, with immunoaffinity-based methods suggesting that SRM is suitable for biomarker
quantification and can complement traditional methods in cancer research. More recently, targeted
data extraction from SWATH-MS datasets could serve for biomarker verification in publically
available datasets not requiring new data acquisition in the future (reviewed in [27]). In a current
literature, establishment of targeted proteomics assays, generation of assay databases such as SRM
Atlas [69] and method application for routine protein determination in biological samples has been
solved in most aspects [25]. This fact contributed to selection of targeted proteomics as “Method of
the year 2012” by the journal Nature Methods [70].
Our contribution to the field
We reviewed the technical principles of selected reaction monitoring (SRM) and current
25
status of knowledge essential to assay development and targeted proteomics method building in
[28](Supplement J6). We explained that the instrumentation for SRM-based targeted proteomics
basically consists of a nano-flow liquid chromatograph coupled with (nano)electrospray ionization
and a triple-quadrupole mass spectrometer. We described the functionality of the quadrupoles in
protein applications, showed our first results on model samples, discussed the applications in
medicine and outlined future directions in the field. The principles of targeted proteomics
approaches on high-resolution mass spectrometers (pseudo-SRM, SWATH, HRM) were summarized
elsewhere [29](Supplement S4), highlighting the differences to SRM by the use of time-of-flight
detector (TOF) instead the third quadrupole (Q3) and its consequences. The aim of the article was
also to show the potential and model applications of targeted proteomics to the clinicians involved in
cancer research where it serves for studying the biologically important proteins and their role in
cancer development, progression and metastasis.
The outputs of targeted proteomics research, both SRM-based and DIA-based, in the field of
solid tumors were comprehensively overviewed by Vendula Pernikářová et al. [71] (Supplement J7).
We have shown that targeted proteomics has been successfully used in a high number of studies
answering clinical questions on solid malignancies, namely breast, colorectal, prostate, ovarian,
endometrial, pancreatic, hepatocellular, lung, bladder and other cancers. We summarized that 62
potential biomarkers of breast cancer were collectively validated using targeted proteomics by 16
studies, 29 for colorectal cancer by 9 studies, 9 for prostate cancer by 11 studies, 27 for ovarian
cancer by 7 studies, 2 for endometrial cancer by 3 studies, 14 for pancreatic cancer by 7 studies, 16
for hepatocellular cancer by 8 studies, 33 for lung cancer by 11 studies and 8 for bladder cancer by 2
studies. The potential biomarkers successfully quantified using targeted proteomics through large
amount of studies are Erbb2/HER2, lactate dehydrogenase A and pyruvate kinase (PK) in breast
cancer, S100A9 protein in colorectal cancer, prostate serum antigen (PSA) in prostate cancer, PK in
endometrial cancer, a-1-acid glycoprotein, ceruloplasmin (CERU) and a-fetoprotein in hepatocellular
cancer and serum amyloid A in lung cancer. Moreover, proteins clusterin, CERU, aldolase A,
26
osteopontin, fibronectin and inter-a trypsin inhibitor heavy chain H3 (ITIH3) were quantified
throughout many types of malignancies [71]. By overviewing verified biomarker candidates
successfully quantified by targeted proteomics in the field, our review has directed the readers who
plan to design their own hypothesis-driven experiments to appropriate sources of methods and
knowledge.
We firstly experimentally used targeted proteomics to verify differences in levels of
transgelins in breast cancer tissues. We developed a SRM assay with mass differential tags for
Fig. 8. Differential expression of
transgelin (TAGLN)/transgelin-2
(TAGLN2) according to lymph node
status (part a) and tumor grade (part b).
Using iTRAQ-proteomics, targeted
proteomics and transcriptomics, we
determined significant increase of both
proteins in lymph node positive (N1) vs.
lymph node negative (N0) tumors and
significant decrease of transgelin and
increase of transgelin-2 level in grade 3
(G3) tumors in comparison with grade 1
(G1) tumors. Transgelin mRNA level is
expressed as normalized absolute
number of mRNA copies in log scale
[30].
27
relative and absolute quantification (mTRAQ) labelling (mTRAQ-SRM) for transgelin and transgelin-2
proteins and analyzed the clinically-pathologically representative set of 96 breast cancer tissues. Our
SRM results confirmed transgelin connection with the lymph node metastasis observed by shotgun
proteomics and transcriptomics and showed that both proteins have been reversely connected to
tumor grade and tumor cell differentiation [30] (Supplement J5). Similar mTRAQ-SRM analyses have
been performed for additional 11 proteins (AGR2, ANXA1, STMN1, COF1, PAIRBP1, TSP2, POSTN,
ACTN4, RNF25, GELS, BGH3) in the same set of 96 breast cancer tissues, highlighting a panel of
validated proteins connected to several aspects of breast cancer aggressiveness (estrogen receptor
status, tumor grade and lymph node status) that are of further interest in terms of diagnostics and/or
more precise breast cancer treatment ([72], under review).
5.3 BREAST CANCER CLASSIFICATION VIA PROTEOMICS
As mentioned above in part 5.1, breast cancer is a disease relatively well characterized on
molecular level [52]. Despite progress in early diagnosis and therapy, many patients however
develop fatal disease. Breast cancer is currently classified into five subtypes or immunophenotypes
based on cell proliferation/tumor grade, estrogen receptor (ER), progesterone receptor (PR) and
HER2 status. The immunophenotypes can be defined as luminal A (ER+, HER2-, low proliferation),
luminal B (ER+, HER2-, high proliferation), luminal B HER2 positive (ER+, HER2+, high proliferation),
HER2 subtype (ER-, HER2+, high proliferation) and triple negative (ER-, PR-, HER2-, high proliferation)
[73]. This classification serves to guide decisions for first line therapy which, however, fails in a
substantial proportion of cases due to cancer recurrence, therapy resistance and/or metastasis.
Development of advanced, generalized disease despite first line therapy indicates that the current
classification of tumors based on immunophenotyping may not fully reflect the genetic, inter- and
intra-tumor heterogeneity. The search for better tumor classifiers nowadays concentrates on the
application of omics approaches able to analyze thousands of genes, gene transcripts or proteins in
28
one experiment. Proteins are the direct effector molecules in the cell and their direct measurement
is therefore preferable over transcript measurements (expression arrays, RNA sequencing) [74].
Our contribution to the field
In our early study published in 2008 [75] (Supplement J8), we analyzed a set of 105 tissue
lysates of breast carcinomas using SELDI-TOF MS on IMAC 30 ProteinChip Arrays to study their
classification based on proteomic profiles. Cluster analysis of protein spectra was performed to
identify protein patterns potentially related to established clinicopathological variables and/or tumor
markers. Unsupervised hierarchical clustering of 130 peaks detected in spectra from breast cancer
tissue lysates provided six clusters of peaks and five groups of patients differing significantly in tumor
type, nuclear grade, presence of hormonal receptors, mucin 1 and cytokeratin 5/6 or cytokeratin 14
(Fig. 9). These tumor groups resembled closely luminal types A and B, basal and HER2-like
carcinomas. Our results showed similar clustering of tumors to those provided by cDNA expression
profiles of breast carcinomas (Fig. 9). This fact testified the validity of proteomic approach in such a
type of study. As proteomics provides different information from cDNA expression profiles, the
results suggested the technique's potential to supplement and expand our knowledge of breast
cancer, to identify novel biomarkers and to produce clinically useful classifications of breast
carcinomas [75]. This paper was highlighted as highly accessed by the Breast Cancer Research journal
upon publication.
29
In 2013 we published a paper “Intact protein profiling in breast cancer biomarker discovery:
protein identification issue and the solutions based on 3D protein separation, bottom-up and topdown
mass spectrometry” [11] (Supplement J9) focused on a new protocol based on 3DLC-MS/MS
with top-down proteomics approach. The new method enabled to obtain an identity of protein peak
of m/z=3357 that correlated with estrogen receptor positivity in breast cancer tissues in the previous
study [75] as an intact non-tryptic peptide originating from HNRNP A2/B1 protein. Importantly, we
summarized our experience on multidimensional separation techniques for identification of intact
proteins screened using SELDI-TOF MS and discussed aspects of future development of this general
concept towards high-throughput, top-down based protein profiling at high resolution.
Another paper in the field of proteomics classification of breast cancer using SWATH-MS
method is currently in preparation in collaboration with Prof. Ruedi Aebersold at Institute of
Molecular Systems Biology at ETH Zürich, Switzerland [74].
Fig. 9. Result of hierarchical
clustering in the form of a
heat map of peak values.
Rows represent 105
individual patients and
columns represent 130
peaks used for the analysis.
The value of the peak is
indicated by the color
intensity. Unsupervised
hierarchical clustering
revealed two (not labeled),
three (labeled A, B, C), five
(labeled I to V) and six
(labeled 1 to 6) groups of
patients [75].
30
5.4 IDENTIFICATION OF BIOMARKERS FOR NON-INVASIVE CANCER DETECTION AND
THERAPY MONITORING
Number of proteomics studies has utilized tissue samples, cell cultures and in vivo model
systems in the past to identify potential biomarker candidates of cancer. To enable non-invasive
diagnostics based on secreted tissue markers, it is essential to analyze the profiles of these proteins
also in bodily fluids such as plasma and serum. In principle, the marker proteins secreted by the
cancer tissue might enter the blood where they could be potentially determined as serum markers of
the disease [76]. This determination of high clinical need is, however, significantly complicated (i) by
protein dilution in 5-6 L of human blood decreasing the marker concentration and increasing
requirements on method sensitivity for serum/plasma quantification, (ii) by another tissues that may
secrete the same protein into blood [77]. Another issue is the presence of contaminants of nonprotein
nature that may affect the mass profile of the sample. This is especially important for MSonly
based techniques where the identity of the peaks is unknown, such as SELDI-TOF MS. Moreover,
some of these contaminants may originate from blood collection devices [78].
Our contribution to the field
Clot-activating blood-sampling devices are routinely used in clinical laboratories for serum
chemistry and immunoassay testing because they provide serum within a short period of time and
may reduce turnaround times. However, it has not been sufficiently demonstrated that these tubes
are free from interferences. In our study [79] (Supplement J10), we collected 2 fasting blood
specimens from each of 20 healthy female volunteers. Specimens were collected in 2 types of tubes:
Microvette Sarstedt, type neutral, denoted “white”, and Microvette Sarstedt Serum Gel Clotting
activator, denoted “brown”.
31
Fig. 10. Typical SELDI/TOF MS spectra of blood samples collected into plain tube (A and A1-cutout) and gel
clotting activator containing tube (B and B1-cutout). The peaks with area increasing after blood collection into
gel clotting activator containing tube are marked with arrows in spectra cutouts (B1 vs. A1)[79].
Within the mass range analyzed (3000–20 000 m/z) by SELDI-TOF MS, 34 peak clusters were
found. In the brown sample group, we detected 2 peaks, m/z 3957.3 and 4283.6, with intensities
whose mean normalized peak areas were >40-fold higher than those of the white sample group.
These peaks were situated next to 2 peaks with m/z 3885.2 and 4211.2, the signal of which was
decreased in some spectra in the brown group (Fig. 10). It seems that more rapid coagulation process
in clot-activator tubes may be associated with more extensive proteolysis in the specimen,
potentially leading to greater protein digestion that is subsequently detected by mass spectrometry.
Non-biological changes, observed repeatedly in the low-molecular-weight serum proteome profiles,
raised the question of whether serum is the specimen of choice for major protein- and/or peptidetype
clinical analytes such as hormones and tumor markers. In summary, we have shown that clot
activator-containing collection tubes may lead to pre-analytical artifacts in proteomic studies. In our
experience, these tubes can be effectively substituted with Li-heparin plasma tubes for chemistry
analytes or plain serum tubes used for immunoassay and specimen banking [79].
In the study by Holcakova et al. [80] (Supplement J11), we applied SELDI-TOF MS technology
32
to study tissues from renal cell carcinomas (RCC) in comparison to healthy controls. We found that
different biomarkers are identified from tissues than those previously identified in serum, and that
serum markers are often not produced by the tumors themselves at detectable levels, reflecting the
non-specific nature of many circulating biomarkers. We detected and characterized αB-crystallin as
an overexpressed protein in RCC tissues and showed differential expression by
immunohistochemistry. We conclude that SELDI-TOF is more useful for the identification of
biomarkers that are synthesized by tumor tissues than for the identification of serum biomarkers and
identifies a separate set of markers. We suggest that SELDI-TOF should be used to screen human
cancer tissues to identify potential tissue-specific proteins and simpler and more sensitive techniques
can then be applied to determine their validity as biomarkers in biological fluids [80].
Proteomics can be also tested for identification of proteins that are connected with levels of
administered drugs. We complemented an initial pharmacokinetic study of a new anticancer agent
(OC-6-43)-bis(acetato)(1-adamantylamine)amminedichloroplatinum (IV) (LA-12) by proteomic
screening of rat plasma. The objective of our proteomics study [81] (Supplement J12) was to identify
new LA-12 target proteins that serve as markers of LA-12 treatment, response and therapy
monitoring. Proteomic profiles were measured by SELDI-TOF MS in 72 samples of rat plasma
randomized according to LA-12 dose and time from administration. Correlation of 92 peak clusters
with platinum concentration was evaluated using Spearman analysis. We identified Retinol-binding
protein 4 (RBP4) whose level correlated with LA-12 level in treated rats. Similar results were
observed in randomly selected patients involved in Phase I clinical trials. RBP4 induction was in
agreement with known RBP4 regulation by amantadine and cisplatin. Since retinol metabolism is
disrupted in many cancers and inversely associates with malignancy, this data identified a potential
novel mechanism for the action of LA-12 and other similar anti-cancer drugs [81].
33
6 PROTEOMICS IN BIOTECHNOLOGICAL APPLICATIONS
6.1 PROTEOMICS IN STUDYING METABOLISM REGULATION AND CELL STRESS
RESPONSE IN DENITRIFYING BACTERIA
Paracoccus denitrificans is a non-fermentative, facultatively autotrophic soil bacterium often
studied in the field of bioenergetics, particularly due to the resemblance of its aerobic respiratory
chain to that of mitochondria. It has a potential of biotechnological application in nitrate removal in
waterworks, in waste water treatment and in agriculture. This bacterium responds to a decreasing
level of oxygen and the presence of nitrate in the environment by a switch from aerobic to a
denitrification growth mode. The first step of denitrification is the reduction of nitrate to nitrite,
which is then sequentially reduced via nitric oxide (NO) and nitrous oxide to dinitrogen gas [82].
P. denitrificans is one of the organisms that expresses all four key enzymes of this process, i.e. the
nitrate, nitrite, nitric oxide and nitrous oxide reductases [83]. Expression of these enzymes is tightly
controlled on the transcription level not only globally according to an energetic hierarchy but also on
the level of the individual genes. It ensures a proper balance in the concentration and activity of the
reductases since the intermediate compounds of denitrification, nitrite and nitric oxide, are cytotoxic
[84]. The major players in this regulatory network are three members of the FNR (fumarate and
nitrate reductase regulatory protein) family of transcription regulators, which upon activation by
their corresponding signals bind to specific sites (FNR boxes) in target promoters upstream of the σ
factor binding site to stabilize the RNA-polymerase transcription initiation complex (reviewed in
[85]). One of the paralogues is FnrP which has a [4Fe-4S] cluster for oxygen sensing [86], the second
is NNR, which has a heme for NO sensing [87] and the third one is NarR which is poorly characterized
and likely to be a nitrite and nitrate sensor [88, 89]. All three have dedicated tasks in gene regulation
and cannot take over each other’s role [90]. In response to oxygen deprivation, FnrP controls the
expression of the nar gene cluster encoding nitrate reductase, the cco-gene cluster encoding a cbb3type
oxidase for respiration at low oxygen concentrations and the ccp gene encoding cytochrome c
peroxidase. NNR specifically controls the expression of the gene clusters encoding the nitrite (nirS),
34
and nitric oxide (norCB) reductases and, to a certain extent, nitrous oxide (nosZ) reductase. NarR is
required for the transcription of the nar gene cluster in an unknown interplay with the FnrP protein
[88, 91]. These properties have been deduced from a number of studies on each of these
transcriptional activators, but knowledge on the interplay between these regulators along with their
position in the complete regulatory network are scarce [94].
P. denitrificans is also a useful model for studying redox stress in bacterial cells. Living in an
oxygenated environment brings about the possibility of a non-enzymatic one-electron reduction of
molecular oxygen to the superoxide anion (O2
),
a primary source of several other potentially
dangerous oxygen metabolites known as reactive oxygen species (ROS). A useful tool for studying the
mechanisms that cells employ to protect themselves from ROS is to provoke a cellular response by
application of an exogenous ROS source. Aerobically growing P. denitrificans elaborate a respiratory
chain closely resembling that of mitochondria with respect to the presence of the respiratory
complexes I–IV and a ubiquinone homologue with ten isoprene units in the side chain (UQ-10).
Besides the mitochondrial-type cytochrome aa3 terminal oxidase, additional oxidases branch from
the respiratory chain at the level of ubiquinone (cytochrome ba3 oxidase) and cytochrome(s) c
(cytochrome cbb3 oxidase). In the absence of oxygen, energy is gained from denitrification catalyzed
by denitrifying enzymes under control of the FNR proteins as described above. We recently
determined the structure of a cytoplasmic flavoprotein termed FerB and studied its function [92]: In
in vitro experiments it reduced ubiquinones (UQ-0, UQ-10) and superoxide and in vivo it provided
partial protection against methyl viologen-induced lipid peroxidation [93]. We thus proposed that
FerB may represent a prokaryotic counterpart of the well-known mammalian antioxidant enzyme
NAD(P)H:quinone oxidoreductase 1 (NQO1) that plays a crucial role in defense against oxidative
stress and hypothesized a similar role for FerB.
Our contribution to the field
In our first related paper involved in this thesis [94] (Supplement J13), we analyzed the
35
protein composition of four P. denitrificans strains (wild type, FnrP-, NNR- and NarR-mutant strains)
grown aerobically, semiaerobically and semiaerobically in the presence of nitrate to discover the
global role of FNR-family transcription regulators using proteomics, with data validation at the
transcript and genome levels. Expression profiles were acquired using two-dimensional gel
electrophoresis for 737 protein spots, in which 640 proteins were identified using mass
spectrometry. The annotated 2-D proteome map provided the most comprehensive coverage of P.
denitrificans proteome available to-date and can be accessed on-line at http://www.mpiibberlin.mpg.de/2D-PAGE/
(see [95], Supplement C2, for details).
Our results revealed several types of regulation under the conditions tested (Fig. 11): (1)
FnrP-controlled regulation of the nitrous oxide reductase, UspA and OmpW confirmed at protein,
transcript and DNA level (position of FNR boxes). (2) Proteins regulated via additional regulators,
including proteins involved in NNR and NarR regulons: nitrate reductase β-subunit, TonB-dependent
receptors, nitrite reductase, a TenA-type transcription regulator, and an unknown protein with an
alpha/beta hydrolase fold. (3) Proteins whose expression was affected mainly by the growth
condition. This group contains a SSU ribosomal protein S305 /σ54 modulation protein, and two shortchain
reductase–dehydrogenase proteins [94]. After the publication, FnrP-controlled regulation of
nitrous oxide reductase reported in this study attracted further attention in the group Asa Fostergard
and Linda Bergaust at Norvegian University of Life Sciences who followed our proteomics screening
by more targeted experiments and reported that nitrous oxide reductase regulation also responds to
pH [96], oxygen and nitric oxide via both FnrP and NNR transcription regulators [97].
Our previous proteomics study [98] showed that P. denitrificans cells undergo changes in protein
composition upon exposure to azide, a known activator of the FNR-type transcription factor NarR.
One of the most prominent protein species inducible by azide is a Fe/Mn-family superoxide
dismutase (SOD) [99](Supplement J14). Azide induced SOD at protein, mRNA transcript and enzyme
activity levels in the aerobically growing cells. Since SOD expression remains unaffected in the fnrP,
36
nnr and narR mutant strains, we postulated a mechanism independent of the known FNR-type
regulators but involving a redox signal arising from the respiratory chain [99].
Fig. 11. Expression profiles of proteins whose gene promoters possess FNR box sequences. Downregulation
in FnrP mutant in the cases of nitrous oxide reductase, UspA and OmpW proteins indicate direct regulation via
FnrP protein; expression profiles of these proteins are highly correlative [94].
Based on our previous work [92], we hypothesized that FerB in P. denitrificans may play a
putative role in defense against oxidative stress. To further explore this hypothesis, we compared
protein variations upon methyl viologen treatment in wild-type and FerB mutant strains by a
quantitative proteomics based on iTRAQ-3DLC-MS/MS approach. This comprehensive analysis
resulted in identification of 24948 peptides representing 2627 proteins (FDR<0.01) in P. dentirificans
wild type and ferB mutant strains (54% coverage of the theoretical proteome) grown in the presence
or absence of methyl viologen as an oxidative stressor (see [100], Supplement S5 for dataset details).
The proteins showing the most prominent increase in abundance were assigned to carbon fixation
and sulfur assimilatory pathways. By employing these proteins as indirect markers, oxidative stress
37
was found to be 15 % less severe in the wild-type than in the FerB-deficient mutant cells. Oxidative
stress altered the levels of proteins whose expression is dependent on the transcriptional factor
FnrP. The observed down-regulation of the fnrP regulon members, most notably that of nitrous oxide
reductase, was tentatively explained by an oxidative degradation of the [4Fe-4S] center of FnrP
leading to a protein form which no longer activates transcription. While the level of FerB remained
relatively constant, two proteins homologous to FerB accumulated during oxidative stress. When we
expressed their genes in Escherichia coli, neither of the protein products contained a bound flavin,
whereas they both had a high activity of flavin reductase, one preferentially utilizing NADH and the
other NADPH [71] (Supplement J15).
6.2 PROTEOMICS IN STUDYING SULFUR OXIDIZING BACTERIA
Acidophilic microorganisms such as Acidithiobacillus sp.are chemolithotrophs that can oxidize
ferrous iron and inorganic sulfur compounds. These bacteria are involved in formation of acid mine
drainage and have strong potential in biotechnology, namely in biohydrometallurgy [101, 102]. Both
iron- and sulfur-oxidizing cells play a different role in oxidation of metal sulfides. Specific proteins are
expressed during iron or sulfur metabolism, and proteomic analysis can provide the quantitative and
qualitative markers typical of iron or sulfur oxidation under both aerobic and anaerobic conditions.
Since the genome of A. ferrooxidans was fully sequenced (http://www.tigr.org), there was a
challenge to perform complex proteomic analyses of this environmentally and industrially important
microbe [103].
Our contribution to the field
A first comparative analysis of the proteome of A. ferrooxidans cells with immobilized pH
gradients and mass spectrometry protein identification was performed by us and published in the
journal PROTEOMICS in 2006 [103](Supplement J16). A newly developed protocol involved also
improved protein reduction, MALDI-MS and LC-MS/MS protein identification based on full genome
sequence information. This approach resulted in more than 1300 protein spots displayed in broad
38
and basic pH ranges, the best A. ferrooxidans proteome resolution at the time of publication (Fig. 12).
A comparative image analysis of cells grown on elemental sulfur and ferrous iron revealed that the
proteome was significantly influenced by the growth type, and allowed for the detection of many
physiologically important proteins. Among them were sulfate adenylyltransferase and sulfide
dehydrogenase, which are involved in sulfate assimilation and sulfide metabolism, respectively.
Many other proteins were related to important processes like cell attachment and electron
transport. Co-migration of phosphate and sulfate transport proteins was also observed [103].
Fig. 12. Representative 2-DE proteome maps of Acidithiobacillus ferrooxidans CCM 4253 grown on ferrous iron
(A, B) and elemental sulfur (C, D). Total cell lysate was analyzed using broad range (3–10 NL; A, C) and basic (6–
11; B, D) pH gradients. At least five-fold differentially regulated protein spots between both growth modes
were selected for MS identification; these spots are marked with spot numbers [103].
39
The first proteomic screening of total cell lysates of anaerobically incubated A. ferrooxidans
resulted in the detection of 1599 protein spots in the master two-dimensional electrophoresis gel
[104] (Supplement J17). A set of 59 more abundant and 49 less abundant protein spots that changed
their protein abundances in an anaerobiosis-dependent manner was identified and compared to
iron- and sulfur-grown cells, respectively. Proteomic analysis detected a significant increase in
abundance under anoxic conditions of electron transporters, such as rusticyanin (rus) and
cytochrome c552, involved in the ferrous iron oxidation pathway. Therefore we suggested the
incorporation of rus-operon encoded proteins in the anaerobic respiration pathway. Two sulfur
metabolism proteins were identified, pyridine nucleotide-disulfide oxidoreductase and sulfidequinone
reductase. The important transcription regulator, ferric uptake regulation protein, was
anaerobically more abundant. The anaerobic expression of several proteins involved in cell envelope
formation indicated a gradual adaptation to elemental sulfur oxidation.
In contrast to iron-oxidizing A. ferrooxidans, A. ferrooxidans from a stationary phase elemental
sulfur-oxidizing culture exhibited a lag phase in pyrite oxidation, which is similar to its behavior
during ferrous iron oxidation [105] (Supplement J18). The ability of elemental sulfur-oxidizing A.
ferrooxidans to immediately oxidize ferrous iron or pyrite without a lag phase was only observed in
bacteria obtained from growing cultures with elemental sulfur. However, these cultures that shifted
to ferrous iron oxidation showed a low rate of ferrous iron oxidation while no growth was observed.
We used 2-DE-MS approach for a quantitative proteomic analysis of the adaptation process when
bacteria were switched from elemental sulfur to ferrous iron. A comparison of total cell lysates
revealed 39 proteins whose increase or decrease in abundance was related to this phenotypic
switching. However, only a few proteins were closely related to iron and sulfur metabolism. Reversetranscription
quantitative PCR was used to further characterize the bacterial adaptation process. The
expression profiles of selected genes primarily involved in the ferrous iron oxidation indicated that
phenotypic switching is a complex process that includes the activation of genes encoding a
membrane protein, maturation proteins, electron transport proteins and their regulators.
40
Nowadays, we have implemented label-free LC-MS proteomics in the analysis of acidophiles.
Pilot data confirmed identification of new unique proteins whose expression was specifically
dependent on substrates and culture conditions. Ongoing analyses indicate promising results that
may lead to unravelling of the novel metabolic pathways under extreme conditions.
7 CONCLUSIONS
Proteomics is a systems biology tool to analyze proteins in a large scale. It was established in
mid-nineties and involves gel-based, shotgun and targeted mass spectrometry approaches. Shotgun
proteomics was shown to be well applicable in cancer biomarker discovery field to identify proteins
associated with lymph node metastasis in breast cancer. We have identified carboxypeptidase B1
and NF-κB associated proteins as the key proteins connected with lymph node metastasis in luminal
A breast cancer and validated them on transcript level and in independent datasets [48]. Based on
these results, we hypothesized NF-κB pathway as a target of anti-metastatic therapy. To-date, this
was indirectly confirmed by preliminary data of ABCSG-18 trial which showed that NF-κB blocking via
denosumab is an effective therapeutic approach in hormone receptor positive early breast cancer
[55]. Transgelin [30] was identified, validated and further characterized as additional potential
marker for lymph node status in breast cancer. Tumor classification of breast cancer mostly
recapitulated current clinicopathologically relevant subtypes and it is evidently effective to provide
more comprehensive clinically useful classifications of this heterogeneous disease [75]. Although
retinol binding protein 4 [81] was identified as potential marker for LA-12 treatment monitoring,
proteomics identification of clinically applicable serum or plasma biomarkers remains challenging
[79][80][71]. Proteomics has been successfully used in studies on metabolic regulation, redox stress
and response to extreme growth condition in bacteria with potential biotechnological applications.
41
8 REFERENCES
[1] Rifai N, Gillette MA, Carr SA. Protein biomarker discovery and validation: the long and uncertain
path to clinical utility. Nat Biotechnol. 2006;24:971-83.
[2] Crick F. Central dogma of molecular biology. Nature. 1970;227:561-3.
[3] Crick FH. On protein synthesis. Symp Soc Exp Biol. 1958;12:138-63.
[4] Schwanhausser B, Busse D, Li N, Dittmar G, Schuchhardt J, Wolf J, et al. Global quantification of
mammalian gene expression control. Nature. 2011;473:337-42.
[5] Bouchal P. Proteomika v molekulární medicíně. In: Slaby O, editor. Molekulární medicína. Praha:
Galén; 2015. p. 148-55.
[6] Bouchal P, Kucera I. Two-dimensional electrophoresis in proteomics: Principles and applications.
Chem Listy. 2002;97:29-36.
[7] Gorg A, Weiss W, Dunn MJ. Current two-dimensional electrophoresis technology for proteomics.
Proteomics. 2004;4:3665-85.
[8] O'Farrell PH. High resolution two-dimensional electrophoresis of proteins. J Biol Chem.
1975;250:4007-21.
[9] Wilkins M. Proteomics data mining. Expert review of proteomics. 2009;6:599-603.
[10] Tang N, Tornatore P, Weinberger SR. Current developments in SELDI affinity technology. Mass
Spectrom Rev. 2004;23:34-44.
[11] Bouchal P, Dvorakova M, Scherl A, Garbis SD, Nenutil R, Vojtesek B. Intact protein profiling in
breast cancer biomarker discovery: protein identification issue and the solutions based on 3D protein
separation, bottom-up and top-down mass spectrometry. Proteomics. 2013;13:1053-8.
[12] Scigelova M, Makarov A. Advances in bioanalytical LC-MS using the Orbitrap mass analyzer.
Bioanalysis. 2009;1:741-54.
[13] Scigelova M, Makarov A. Orbitrap mass analyzer--overview and applications in proteomics.
Proteomics. 2006;6 Suppl 2:16-21.
[14] Beck S, Michalski A, Raether O, Lubeck M, Kaspar S, Goedecke N, et al. The Impact II, a Very
High-Resolution Quadrupole Time-of-Flight Instrument (QTOF) for Deep Shotgun Proteomics. Mol
Cell Proteomics. 2015;14:2014-29.
[15] DeSouza L, Diehl G, Rodrigues MJ, Guo J, Romaschin AD, Colgan TJ, et al. Search for cancer
markers from endometrial tissues using differentially labeled tags iTRAQ and cICAT with
multidimensional liquid chromatography and tandem mass spectrometry. J Proteome Res.
2005;4:377-86.
[16] Dayon L, Hainard A, Licker V, Turck N, Kuhn K, Hochstrasser DF, et al. Relative quantification of
proteins in human cerebrospinal fluids by MS/MS using 6-plex isobaric tags. Anal Chem.
2008;80:2921-31.
[17] Boersema PJ, Raijmakers R, Lemeer S, Mohammed S, Heck AJ. Multiplex peptide stable isotope
dimethyl labeling for quantitative proteomics. Nat Protoc. 2009;4:484-94.
[18] Ong SE, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, 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;1:376-86.
[19] Bantscheff M, Schirle M, Sweetman G, Rick J, Kuster B. Quantitative mass spectrometry in
proteomics: a critical review. Anal Bioanal Chem. 2007;389:1017-31.
[20] Yates JR, Ruse CI, Nakorchevsky A. Proteomics by mass spectrometry: approaches, advances,
and applications. Annu Rev Biomed Eng. 2009;11:49-79.
[21] Gallien S, Duriez E, Domon B. Selected reaction monitoring applied to proteomics. J Mass
Spectrom. 2011;46:298-312.
[22] Nakamura K, Hirayama-Kurogi M, Ito S, Kuno T, Yoneyama T, Obuchi W, et al. Large-scale
multiplex absolute protein quantification of drug-metabolizing enzymes and transporters in human
intestine, liver, and kidney microsomes by SWATH-MS: Comparison with MRM/SRM and HRMRM/PRM.
Proteomics. 2016.
42
[23] Gallien S, Duriez E, Crone C, Kellmann M, Moehring T, Domon B. Targeted proteomic
quantification on quadrupole-orbitrap mass spectrometer. Mol Cell Proteomics. 2012;11:1709-23.
[24] Peterson AC, Russell JD, Bailey DJ, Westphall MS, Coon JJ. Parallel reaction monitoring for high
resolution and high mass accuracy quantitative, targeted proteomics. Mol Cell Proteomics.
2012;11:1475-88.
[25] Pernikarova V, Bouchal P. Targeted proteomics of solid cancers: from quantification of known
biomarkers towards reading the digital proteome maps. Expert review of proteomics. 2015;12:651-
67.
[26] Gillet LC, Navarro P, Tate S, Rost H, Selevsek N, Reiter L, et al. Targeted data extraction of the
MS/MS spectra generated by data-independent acquisition: a new concept for consistent and
accurate proteome analysis. Mol Cell Proteomics. 2012;11:O111 016717.
[27] Liu Y, Huttenhain R, Collins B, Aebersold R. Mass spectrometric protein maps for biomarker
discovery and clinical research. Expert review of molecular diagnostics. 2013;13:811-25.
[28] Faktor J, Struharova I, Fucikova A, Hubalek M, Vojtesek B, Bouchal P. Mass-Spectrometry
Quantification of Protein Biomarkers by Selected Reaction Monitoring. Chem Listy. 2011;105:846-50.
[29] Faktor J, Michalova E, Bouchal P. [p SRM, SWATH and HRM - targeted proteomics approaches on
TripleTOF 5600+ mass spectrometer and their applications in oncology research]. Klinicka onkologie :
casopis Ceske a Slovenske onkologicke spolecnosti. 2014;27 Suppl 1:S110-5.
[30] Dvorakova M, Jerabkova J, Prochazkova I, Lenco J, Nenutil R, Bouchal P. Transgelin is
upregulated in stromal cells of lymph node positive breast cancer. J Proteomics. 2016;132:103-11.
[31] Rabilloud T. Two-dimensional gel electrophoresis in proteomics: old, old fashioned, but it still
climbs up the mountains. Proteomics. 2002;2:3-10.
[32] Toby TK, Fornelli L, Kelleher NL. Progress in Top-Down Proteomics and the Analysis of
Proteoforms. Annu Rev Anal Chem (Palo Alto Calif). 2016;9:499-519.
[33] Ross JS, Hortobagyi G.N. (Eds). Molecular Oncology of Breast Cancer. Sadbury, MA, USA: Jones
and Barlett Publishers; 2005. p. 500
[34] Cress AE, Nagle, R.B. Cell Adhesion and Cytoskeletal Molecules in Metastasis. In: Ablin RJ, Jiang,
W.G., editor. Cancer Metastasis - Biology and Treatment. Dodrecht, The Netherlands: Springer; 2006.
p. 198.
[35] Chambers AF, Groom AC, MacDonald IC. Dissemination and growth of cancer cells in metastatic
sites. Nat Rev Cancer. 2002;2:563-72.
[36] Le XF, Merchant O, Bast RC, Calin GA. The Roles of MicroRNAs in the Cancer Invasion-Metastasis
Cascade. Cancer Microenviron. 2010;3:137-47.
[37] Valastyan S, Weinberg RA. Tumor metastasis: molecular insights and evolving paradigms. Cell.
2011;147:275-92.
[38] Patel LR, Camacho DF, Shiozawa Y, Pienta KJ, Taichman RS. Mechanisms of cancer cell metastasis
to the bone: a multistep process. Future Oncol. 2011;7:1285-97.
[39] Penet MF, Chen Z, Bhujwalla ZM. MRI of metastasis-permissive microenvironments. Future
Oncol. 2011;7:1269-84.
[40] Signal transduction in cancer metastasis: Springer; 2010.
[41] Jing Y, Han Z, Zhang S, Liu Y, Wei L. Epithelial-Mesenchymal Transition in tumor
microenvironment. Cell Biosci. 2011;1:29.
[42] Le XF, Merchant O, Bast RC, Calin GA. The Roles of MicroRNAs in the Cancer Invasion-Metastasis
Cascade. Cancer microenvironment : official journal of the International Cancer Microenvironment
Society. 2010;3:137-47.
[43] Maryas J, Faktor J, Dvorakova M, Struharova I, Grell P, Bouchal P. Proteomics in investigation of
cancer metastasis: functional and clinical consequences and methodological challenges. Proteomics.
2014;14:426-40.
[44] Bendas G, Borsig L. Cancer cell adhesion and metastasis: selectins, integrins, and the inhibitory
potential of heparins. Int J Cell Biol. 2012;2012:676731.
[45] Mansel RE, Fodstad, O., Jiang, W.G. Metastasis of Breast Cancer. In: Ablin RJ, Jiang, W.G., editor.
Cancer Metastasis - Biology and Treatment. Dodrecht, The Netherlands: Springer; 2007. p. 435.
43
[46] Harris L, Fritsche H, Mennel R, Norton L, Ravdin P, Taube S, et al. American Society of Clinical
Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J Clin
Oncol. 2007;25:5287-312.
[47] Stephens RW, Brunner N, Janicke F, Schmitt M. The urokinase plasminogen activator system as a
target for prognostic studies in breast cancer. Breast Cancer Res Treat. 1998;52:99-111.
[48] Bouchal P, Dvorakova M, Roumeliotis T, Bortlicek Z, Ihnatova I, Prochazkova I, et al. Combined
Proteomics and Transcriptomics Identifies Carboxypeptidase B1 and Nuclear Factor kappaB (NFkappaB)
Associated Proteins as Putative Biomarkers of Metastasis in Low Grade Breast Cancer. Mol
Cell Proteomics. 2015;14:1814-30.
[49] Faktor J, Dvorakova M, Maryas J, Struharova I, Bouchal P. Identification and characterisation of
pro-metastatic targets, pathways and molecular complexes using a toolbox of proteomic
technologies. Klinicka onkologie : casopis Ceske a Slovenske onkologicke spolecnosti. 2012;25 Suppl
2:2S70-7.
[50] Bouchal P, Roumeliotis T, Hrstka R, Nenutil R, Vojtesek B, Garbis SD. Biomarker discovery in lowgrade
breast cancer using isobaric stable isotope tags and two-dimensional liquid chromatographytandem
mass spectrometry (iTRAQ-2DLC-MS/MS) based quantitative proteomic analysis. J Proteome
Res. 2009;8:362-73.
[51] Mertins P, Mani DR, Ruggles KV, Gillette MA, Clauser KR, Wang P, et al. Proteogenomics
connects somatic mutations to signalling in breast cancer. Nature. 2016;534:55-62.
[52] Tyanova S, Albrechtsen R, Kronqvist P, Cox J, Mann M, Geiger T. Proteomic maps of breast
cancer subtypes. Nat Commun. 2016;7:10259.
[53] Sjostrom M, Ossola R, Breslin T, Rinner O, Malmstrom L, Schmidt A, et al. A Combined Shotgun
and Targeted Mass Spectrometry Strategy for Breast Cancer Biomarker Discovery. J Proteome Res.
2015;14:2807-18.
[54] Olsson N, Carlsson P, James P, Hansson K, Waldemarson S, Malmstrom P, et al. Grading breast
cancer tissues using molecular portraits. Mol Cell Proteomics. 2013;12:3612-23.
[55] Gnant M. Denosumab Improves Disease-free Survival for Postmenopausal Patients With HRpositive
Breast Cancer. San Antonio, TX: American Association for Cancer Research; 2015.
[56] Maryas J, Bouchal P. [PDLIM2 and Its Role in Oncogenesis--Tumor Suppressor or Oncoprote?].
Klinicka onkologie : casopis Ceske a Slovenske onkologicke spolecnosti. 2015;28 Suppl 2:2S40-6.
[57] Sobotkova J. Biomarkers of early metastases into lymphatic ganglia in "low-grade" mammal
carcinoma [Master thesis]. Brno, Czech Republic: Masaryk University; 2008.
[58] Dvorakova M, Nenutil R, Bouchal P. Transgelins, cytoskeletal proteins implicated in different
aspects of cancer development. Expert review of proteomics. 2014;11:149-65.
[59] Cirri P, Chiarugi P. Cancer associated fibroblasts: the dark side of the coin. Am J Cancer Res.
2011;1:482-97.
[60] Augsten M. Cancer-associated fibroblasts as another polarized cell type of the tumor
microenvironment. Front Oncol. 2014;4:62.
[61] Kovarikova P. Carboxypeptidase B1 and its role in tumor biology. Brno, Czech Republi: Masaryk
university; 2015.
[62] Kovarikova P, Michalova E, Knopfova L, Bouchal P. [Methods for studying tumor cell migration
and invasiveness]. Klinicka onkologie : casopis Ceske a Slovenske onkologicke spolecnosti. 2014;27
Suppl 1:S22-7.
[63] Sprung RW, Martinez MA, Carpenter KL, Ham AJ, Washington MK, Arteaga CL, et al. Precision of
multiple reaction monitoring mass spectrometry analysis of formalin-fixed, paraffin-embedded
tissue. J Proteome Res. 2012;11:3498-505.
[64] Hembrough T, Thyparambil S, Liao WL, Darfler MM, Abdo J, Bengali KM, et al. Application of
selected reaction monitoring for multiplex quantification of clinically validated biomarkers in
formalin-fixed, paraffin-embedded tumor tissue. J Mol Diagn. 2013;15:454-65.
[65] Lin D, Alborn WE, Slebos RJ, Liebler DC. Comparison of protein immunoprecipitation-multiple
reaction monitoring with ELISA for assay of biomarker candidates in plasma. J Proteome Res.
2013;12:5996-6003.
44
[66] Broker ME, Lalmahomed ZS, Roest HP, van Huizen NA, Dekker LJ, Calame W, et al. Collagen
peptides in urine: a new promising biomarker for the detection of colorectal liver metastases. PLoS
One. 2013;8:e70918.
[67] Ahn YH, Lee JY, Lee JY, Kim YS, Ko JH, Yoo JS. Quantitative analysis of an aberrant glycoform of
TIMP1 from colon cancer serum by L-PHA-enrichment and SISCAPA with MRM mass spectrometry. J
Proteome Res. 2009;8:4216-24.
[68] Ahn YH, Shin PM, Kim YS, Oh NR, Ji ES, Kim KH, et al. Quantitative analysis of aberrant protein
glycosylation in liver cancer plasma by AAL-enrichment and MRM mass spectrometry. Analyst.
2013;138:6454-62.
[69] Kusebauch U, Campbell DS, Deutsch EW, Chu CS, Spicer DA, Brusniak MY, et al. Human
SRMAtlas: A Resource of Targeted Assays to Quantify the Complete Human Proteome. Cell.
2016;166:766-78.
[70] Method of the Year 2012. Nat Methods. 2013;10:1.
[71] Pernikarova V, Sedlacek V, Potesil D, Prochazkova I, Zdrahal Z, Bouchal P, et al. Proteomic
responses to a methyl viologen-induced oxidative stress in the wild type and FerB mutant strains of
Paracoccus denitrificans. J Proteomics. 2015;125:68-75.
[72] Procházková I, Lenčo J, Fučíková A, Dresler J, Čápková L, Nenutil R, et al. Combined mTRAQ-SRM
and transcriptomics verification of biomarker candidates associated with breast cancer
aggressiveness. Submitted.
[73] Lam SW, Jimenez CR, Boven E. Breast cancer classification by proteomic technologies: current
state of knowledge. Cancer Treat Rev. 2014;40:129-38.
[74] Bouchal P, Schubert OT, Faktor J, Capkova L, Imrichova H, Zoufalova K, et al. Breast cancer
classification based on proteotypes obtained by SWATH mass spectrometry. In preparation.
[75] Brozkova K, Budinska E, Bouchal P, Hernychova L, Knoflickova D, Valik D, et al. Surface-enhanced
laser desorption/ionization time-of-flight proteomic profiling of breast carcinomas identifies
clinicopathologically relevant groups of patients similar to previously defined clusters from cDNA
expression. Breast Cancer Res. 2008;10:R48.
[76] Kim YJ, Sertamo K, Pierrard MA, Mesmin C, Kim SY, Schlesser M, et al. Verification of the
biomarker candidates for non-small-cell lung cancer using a targeted proteomics approach. J
Proteome Res. 2015;14:1412-9.
[77] Cottingham K. Clinical proteomics: are we there yet? Anal Chem. 2003;75:472A-6A.
[78] Banks RE, Stanley AJ, Cairns DA, Barrett JH, Clarke P, Thompson D, et al. Influences of blood
sample processing on low-molecular-weight proteome identified by surface-enhanced laser
desorption/ionization mass spectrometry. Clin Chem. 2005;51:1637-49.
[79] Pilny R, Bouchal P, Borilova S, Ceskova P, Zaloudik J, Vyzula R, et al. Surface-enhanced laser
desorption ionization/time-of-flight mass spectrometry reveals significant artifacts in serum obtained
from clot activator-containing collection devices. Clin Chem. 2006;52:2115-6.
[80] Holcakova J, Hernychova L, Bouchal P, Brozkova K, Zaloudik J, Valik D, et al. Identification of
alphaB-crystallin, a biomarker of renal cell carcinoma by SELDI-TOF MS. Int J Biol Markers.
2008;23:48-53.
[81] Bouchal P, Jarkovsky J, Hrazdilova K, Dvorakova M, Struharova I, Hernychova L, et al. The new
platinum-based anticancer agent LA-12 induces retinol binding protein 4 in vivo. Proteome Sci.
2011;9.
[82] Zumft WG. Cell biology and molecular basis of denitrification. Microbiol Mol Biol Rev.
1997;61:533-616.
[83] Berks BC, Ferguson SJ, Moir JWB, Richardson DJ. Enzymes and associated electron transport
systems that catalyse the respiratory reduction of nitrogen oxides and oxyanions. Biochim Biophys
Acta. 1995;1232:97-173.
[84] Baker SC, Ferguson SJ, Ludwig B, Page MD, Richter OM, van Spanning RJM. Molecular genetics of
the genus Paracoccus: metabolically versatile bacteria with bioenergetic flexibility. Microbiol Mol Biol
Rev. 1998;62:1046-78.
45
[85] Green J, Crack JC, Thomson AJ, LeBrun NE. Bacterial sensors of oxygen. Current opinion in
microbiology. 2009;12:145-51.
[86] Hutchings MI, Crack JC, Shearer N, Thompson BJ, Thomson AJ, Spiro S. Transcription factor FnrP
from Paracoccus denitrificans contains an iron-sulfur cluster and is activated by anoxia: identification
of essential cysteine residues. Journal of bacteriology. 2002;184:503-8.
[87] Lee YY, Shearer N, Spiro S. Transcription factor NNR from Paracoccus denitrificans is a sensor of
both nitric oxide and oxygen: isolation of nnr* alleles encoding effector-independent proteins and
evidence for a haem-based sensing mechanism. Microbiology. 2006;152:1461-70.
[88] Wood NJ, Alizadeh T, Bennett S, Pearce J, Ferguson SJ, Richardson DJ, et al. Maximal expression
of membrane-bound nitrate reductase in Paracoccus is induced by nitrate via a third FNR-like
regulator named NarR. J Bacteriol. 2001;183:3606-13.
[89] Van Spanning RJM, De Boer APN, Reijnders WNM, Westerhoof HV, Stouthamer AH, van der Oost
J. FnrP and NNR of Paracoccus denitrificans are both members of the FNR family of transcriptional
activators but have distinct roles in respiratory adaptation in response to oxygen limitation. Mol
Microbiol. 1997;12:893-907.
[90] Van Spanning RJM, De Boer APN, Reijnders WNM, Spiro S, Westerhoof HV, Stouthamer AH, et al.
Nitrite and nitric oxide reduction in Paracoccus denitrificans is under the control of NNR, a regulatory
protein that belongs to the FNR family of transcriptional activators. FEBS Lett. 1995;360:151-4.
[91] Veldman R, Reijnders WNM, van Spanning RJM. Specificity of FNR-type regulators in Paracoccus
denitrificans. Biochem Soc Trans. 2006;34:94-6.
[92] Sedlacek V, Klumpler T, Marek J, Kucera I. The structural and functional basis of catalysis
mediated by NAD(P)H:acceptor Oxidoreductase (FerB) of Paracoccus denitrificans. PloS one.
2014;9:e96262.
[93] Sedlacek V, Ptackova N, Rejmontova P, Kucera I. The flavoprotein FerB of Paracoccus
denitrificans binds to membranes, reduces ubiquinone and superoxide, and acts as an in vivo
antioxidant. The FEBS journal. 2015;282:283-96.
[94] Bouchal P, Struharova I, Budinska E, Sedo O, Vyhlidalova T, Zdrahal Z, et al. Unraveling an FNR
based regulatory circuit in Paracoccus denitrificans using a proteomics-based approach. Biochim
Biophys Acta. 2010;1804:1350-8.
[95] Bouchal P, Stein R, Zdrahal Z, Jungblut PR, Kucera I. Database for Studies on Energetic
Metabolism of the Denitrifying Bacterium Paracoccus denitrificans. In: Leung H-CE, T.-K. M, R.J. F,
editors. Integrative proteomics. Rijeka, Croatia: InTech; 2012. p. 141-56.
[96] Bergaust L, Mao Y, Bakken LR, Frostegard A. Denitrification response patterns during the
transition to anoxic respiration and posttranscriptional effects of suboptimal pH on nitrous
[corrected] oxide reductase in Paracoccus denitrificans. Appl Environ Microbiol. 2010;76:6387-96.
[97] Bergaust L, van Spanning RJ, Frostegard A, Bakken LR. Expression of nitrous oxide reductase in
Paracoccus denitrificans is regulated by oxygen and nitric oxide through FnrP and NNR. Microbiology.
2012;158:826-34.
[98] Bouchal P, Precechtelova P, Zdrahal Z, Kucera I. Protein composition of Paracoccus denitrificans
cells grown on various electron acceptors and in the presence of azide. Proteomics. 2004;4:2662-71.
[99] Bouchal P, Vyhlidalova T, Struharova I, Zdrahal Z, Kucera I. Fe/Mn superoxide dismutaseencoding
gene in Paracoccus denitrificans is induced by azide and expressed independently of the
FNR-type regulators. Folia Microbiol. 2011;56:13-7.
[100] Pernikarova V, Sedlacek V, Potesil D, Prochazkova I, Zdrahal Z, Bouchal P, et al. Proteome-wide
dataset generated by iTRAQ-3DLCMS/MS technique for studying the role of FerB protein in oxidative
stress in Paracoccus denitrificans. Data Brief. 2015;4:390-4.
[101] Baker BJ, Banfield JF. Microbial communities in acid mine drainage. FEMS Microbiol Ecol.
2003;44:139-52.
[102] Johnson DB, Hallberg KB. The microbiology of acidic mine waters. Res Microbiol. 2003;154:466-
73.
46
[103] Bouchal P, Zdrahal Z, Helanova S, Janiczek O, Hallberg KB, Mandl M. Proteomic and
bioinformatic analysis of iron- and sulfur-oxidizing Acidithiobacillus ferrooxidans using immobilized
pH gradients and mass spectrometry. Proteomics. 2006;6:4278-85.
[104] Kucera J, Bouchal P, Cerna H, Potesil D, Janiczek O, Zdrahal Z, et al. Kinetics of anaerobic
elemental sulfur oxidation by ferric iron in Acidithiobacillus ferrooxidans and protein identification by
comparative 2-DE-MS/MS. Antonie Van Leeuwenhoek. 2012;101:561-73.
[105] Kucera J, Bouchal P, Lochman J, Potesil D, Janiczek O, Zdrahal Z, et al. Ferrous iron oxidation by
sulfur-oxidizing Acidithiobacillus ferrooxidans and analysis of the process at the levels of transcription
and protein synthesis. Antonie Van Leeuwenhoek. 2013;103:905-19.
47
9 SUPPLEMENTS: LIST OF PUBLICATIONS INCLUDED IN THE THESIS
Articles published in journals with impact factor
[J1] MARYÁŠ, Josef, Jakub FAKTOR, Monika DVOŘÁKOVÁ, Iva STRUHÁROVÁ, Peter GRELL and Pavel
BOUCHAL. Proteomics in investigation of cancer metastasis: Functional and clinical
consequences and methodological challenges. Proteomics, Wiley VCH, 2014, vol. 14, No. 4-5, p.
426-440. ISSN 1615-9853. doi:10.1002/pmic.201300264. IF=4.132
http://onlinelibrary.wiley.com/doi/10.1002/pmic.201300264/abstract
Contribution to the paper: Overall idea, supervision of whole manuscript preparation process
and writing
[J2] BOUCHAL, Pavel, Theodoros ROUMELIOTIS, Roman HRSTKA, Rudolf NENUTIL, Bořivoj VOJTĚŠEK
and Spiros D. GARBIS. Biomarker discovery in low-grade breast cancer using isobaric stable
isotope tags and two-dimensional liquid chromatography-tandem mass spectrometry (iTRAQ-
2DLC-MS/MS) based quantitative proteomic analysis. Journal of Proteome Research,
Washington, USA: ACS, 2009, vol. 8, No. 1, p. 362-373. ISSN 1535-3893.
doi:10.1021/pr800622b. IF=5.675
http://pubs.acs.org/doi/abs/10.1021/pr800622b
Contribution to the paper: First idea of biological question and method combination,
development of tissue lysis method, proteomics and transcriptomics data interpretation,
manuscript writing
[J3] BOUCHAL, Pavel, Monika DVOŘÁKOVÁ, Theodoros ROUMELIOTIS, Zbyněk BORTLÍČEK, Ivana
IHNATOVÁ, Iva PROCHÁZKOVÁ, JTC HO, Josef MARYÁŠ, Hana IMRICHOVÁ, Eva BUDINSKÁ,
Rostislav VYZULA, Spiros D. GARBIS, Bořivoj VOJTĚŠEK and Rudolf NENUTIL. Combined
Proteomics and Transcriptomics Identifies Carboxypeptidase B1 and Nuclear Factor kappa B
(NF-kappa B) Associated Proteins as Putative Biomarkers of Metastasis in Low Grade Breast
Cancer. MOLECULAR & CELLULAR PROTEOMICS, BETHESDA: AMER SOC BIOCHEMISTRY
MOLECULAR BIOLOGY INC, 2015, vol. 14, No. 7, p. 1814-1830. ISSN 1535-9476.
doi:10.1074/mcp.M114.041335. IF= 7.254
http://www.mcponline.org/content/14/7/1814.short?rss=1
Contribution to the paper: Supervision of sample preparation for proteomics, contribution to
proteomics data analysis, proteomics data interpretation, selection of targets for
transcriptomics validation and qRT-PCR array design, transcriptomics data interpretation,
selection of targets for immunohistochemistry and interpretation of results, analysis of patient
survival data, paper writing
[J4] DVOŘÁKOVÁ, Monika, Rudolf NENUTIL and Pavel BOUCHAL. Transgelins, cytoskeletal proteins
implicated in different aspects of cancer development. Expert Review of Proteomics, 2014, Vol.
11, No. 2, p. 149-165. ISSN 1478-9450. doi:10.1586/14789450.2014.860358. IF=3.896
http://www.tandfonline.com/doi/full/10.1586/14789450.2014.860358
Contribution to the paper: First idea, supervision of paper writing
[J5] DVOŘÁKOVÁ, Monika, Jarmila JEŘÁBKOVÁ, Iva PROCHÁZKOVÁ, Juraj LENČO, Rudolf NENUTIL
and Pavel BOUCHAL. Transgelin is upregulated in stromal cells of lymph node positive breast
cancer. Journal of Proteomics, Amsterdam: ELSEVIER SCIENCE BV, 2016, Vol. 132, No. 11, p.
103-111. ISSN 1874-3919. doi: 10.1016/j.jprot.2015.11.025. IF=3.888
http://www.sciencedirect.com/science/article/pii/S1874391915301986
Contribution to the paper: Supervision of 2-DE-based analysis and interpretation, design of
validation phase, supervision of paper writing
[J6] FAKTOR, Jakub, Iva STRUHÁROVÁ, Alena FUČÍKOVÁ, Martin HUBÁLEK, Bořivoj VOJTĚŠEK a Pavel
BOUCHAL. Kvantifikace proteinových biomarkerů pomocí hmotnostní spektrometrie pracující v
režimu monitorování vybraných reakcí. Chemické listy, Praha: Česká společnost chemická,
2011, Vol. 105, No. 11, p. 846-850. ISSN 0009-2770. IF=0.717
48
http://www.chemicke-listy.cz/docs/full/2011_11_846-850.pdf
Contribution to the paper: Overall idea, supervision of whole manuscript preparation process
and writing
[J7] PERNIKÁŘOVÁ, Vendula and Pavel BOUCHAL. Targeted proteomics of solid cancers: from
quantification of known biomarkers towards reading the digital proteome maps. Expert Review
of Proteomics, 2015, Vol. 12, No. 6, p. 651-667. ISSN 1478-9450.
doi:10.1586/14789450.2015.1094381. IF=2.896
http://www.tandfonline.com/doi/abs/10.1586/14789450.2015.1094381?journalCode=ieru20
Contribution to the paper: Overall idea, supervision of whole manuscript preparation process
and writing, “Expert commentary” part writing
[J8] BROŽKOVÁ, Kristýna, Eva BUDINSKÁ, Pavel BOUCHAL, Lenka HERNYCHOVÁ, Dana
KNOFLÍČKOVÁ, Dalibor VALÍK, Rostislav VYZULA, Bořivoj VOJTĚŠEK and Rudolf NENUTIL.
Surface-enhanced laser desorption/ionization time-of-flight proteomic profiling of breast
carcinomas identifies clinicopathologically relevant groups of patients similar to previously
defined clusters from cDNA expression. Breast Cancer Research, London, Great Britain: Biomed
Central Ltd., 2008, Vol. 10, No. 3, p. R48. ISSN 1465-5411. doi:10.1186/bcr2101. IF=2.880
http://breast-cancer-research.biomedcentral.com/articles/10.1186/bcr2101
Contribution to the paper: Method contribution to SELDI-TOF MS study, identification of
selected protein peaks
[J9] BOUCHAL, Pavel, Monika DVOŘÁKOVÁ, Alexander SCHERL, Spiros D. GARBIS, Rudolf NENUTIL
and Bořivoj VOJTĚŠEK. Intact protein profiling in breast cancer biomarker discovery: Protein
identification issue and the solutions based on 3D protein separation, bottom-up and top-down
mass spectrometry. Proteomics, Weinheim: Wiley-VCH, 2013, Vol. 13, No. 7, p. 1053-1058. ISSN
1615-9853. doi:10.1002/pmic.201200121. IF=4.505
http://onlinelibrary.wiley.com/doi/10.1002/pmic.201200121/abstract
Contribution to the paper: Experimental design, method development, data interpretation and
paper writing
[J10] PILNÝ, Radomír, Pavel BOUCHAL, Šárka BOŘILOVÁ, Pavla ČEŠKOVÁ, Jan ŽALOUDÍK, Rostislav
VYZULA, Bořivoj VOJTĚŠEK and Dalibor VALÍK. Surface-enhanced laser desorptionionization/time-of-flight
mass spectrometry reveals significant artifacts in serum obtained from
clot activator-containing collection devices. Clinical Chemistry, Washington, USA: Amer. Assoc.
Clinical Chemistry, 2006, Vol. 52, No. 11, p. 2015-2016. ISSN 0009-9147. IF=7.717
http://clinchem.aaccjnls.org/content/52/11/2115
Contribution to the paper: Method contribution to SELDI-TOF MS study, data analysis,
interpretation and paper writing
[J11] HOLČÁKOVÁ, Jitka, Lenka HERNYCHOVÁ, Pavel BOUCHAL, Kristýna BROŽKOVÁ, Jan ŽALOUDÍK,
Dalibor VALÍK, Rudolf NENUTIL and Bořivoj VOJTĚŠEK. Identification of alphaB-crystallin, a
biomarker of renal cell carcinoma by SELDI-TOF MS. The International Journal of Biological
Markers, Itálie: Wichtig editore, 2008, Vol. 23, No. 1, p. 48-53. ISSN 0393-6155. IF=0.966
http://www.biological-markers.com/article/identification-of-alphab-crystallin--a-biomarker-of-
renal-cell-carcinoma-by-seldi-tof-ms-art001227
Contribution to the paper: Method contribution to SELDI-TOF MS study, data analysis and
interpretation
[J12] BOUCHAL, Pavel, Jiří JARKOVSKÝ, Kristýna HRAZDILOVÁ, Monika DVOŘÁKOVÁ, Iva
STRUHÁROVÁ, Lenka HERNYCHOVÁ, Jiří DAMBORSKÝ, Petr SOVA and Bořivoj VOJTĚŠEK. The
new platinum-based anticancer agent LA-12 induces retinol binding protein 4 in vivo. Proteome
Science, BioMed Central, 2011, Vol. 9, No. 68. ISSN 1477-5956. doi:10.1186/1477-5956-9-68.
IF=2.488
http://proteomesci.biomedcentral.com/articles/10.1186/1477-5956-9-68
Contribution to the paper: SELDI-TOF MS data analysis and interpretation, supervision of protein
identification, supervision of immunochemical validation, paper writing
49
[J13] BOUCHAL, Pavel, Iva STRUHÁROVÁ, Eva BUDINSKÁ, Ondrej ŠEDO, Tereza VYHLÍDALOVÁ, Zbyněk
ZDRÁHAL, Rob VAN SPANNING and Igor KUČERA. Unraveling an FNR based regulatory circuit in
Paracoccus denitrificans using a proteomics-based approach. Biochimica et Biophysica Acta Proteins
and Proteomics, Amsterdam, The Netherlands: Elsevier, 2010, Vol. 1804, No. 6, p.
1309-1316. ISSN 1570-9639. doi:10.1016/j.bbapap.2010.01.016. IF=2.480
http://www.sciencedirect.com/science/article/pii/S1570963910000312
Contribution to the paper: Bacterial cultivation, protein extraction, supervision of 2-DE and
image analysis, proteomics and transcriptomics data interpretation, paper writing
[J14] BOUCHAL, Pavel, Tereza VYHLÍDALOVÁ, Iva STRUHÁROVÁ, Zbyněk ZDRÁHAL and Igor KUČERA.
Fe/Mn superoxide dismutase in Paracoccus denitrificans is induced by azide and expressed
independently on FNR-type regulators. Folia Microbiologica, Dordrecht, The Netherlands:
Springer, 2011, Vol. 56, No. 1, p. 13-17. ISSN 0015-5632. doi:10.1007/s12223-011-0007-3.
IF=0.978
http://link.springer.com/article/10.1007%2Fs12223-011-0007-3
Contribution to the paper: supervision of proteomics and transcriptomics analysis and enzyme
activity determination, manuscript writing
[J15] PERNIKÁŘOVÁ, Vendula, Vojtěch SEDLÁČEK, David POTĚŠIL, Iva PROCHÁZKOVÁ, Zbyněk
ZDRÁHAL, Pavel BOUCHAL and Igor KUČERA. Proteomic responses to a methyl viologeninduced
oxidative stress in the wild type and FerB mutant strains of Paracoccus denitrificans.
Journal of Proteomics, Amsterdam: ELSEVIER SCIENCE BV, 2015, Vol. 125, July, p. 68-75. ISSN
1874-3919. doi:10.1016/j.jprot.2015.05.002. IF=3.888
http://www.sciencedirect.com/science/article/pii/S1874391915002201
Contribution to the paper: iTRAQ-3DLC method optimization, supervision of proteomics analysis,
MS data analysis and interpretation, contribution to manuscript preparation
[J16] BOUCHAL, Pavel, Zbyněk ZDRÁHAL, Šárka HELÁNOVÁ, Oldřich JANICZEK, Kevin B. HALLBERG
and Martin MANDL. Proteomic and bioinformatic analysis of iron- and sulfur-oxidizing
Acidithiobacillus ferrooxidans using immobilized pH gradients and mass spectrometry.
Proteomics, Weinheim: Wiley-VCH, 2006, Vol. 6, No. 15, p. 4278-4285. ISSN 1615-9853.
IF=6.088
http://onlinelibrary.wiley.com/doi/10.1002/pmic.200500719/abstract
Contribution to the paper: Sample preparation development, 2-DE proteomics optimization,
most of the interpretation, paper writing
[J17] KUČERA, Jiří, Pavel BOUCHAL, Hana ČERNÁ, David POTĚŠIL, Oldřich JANICZEK, Zbyněk ZDRÁHAL
and Martin MANDL. Kinetics of anaerobic elemental sulfur oxidation by ferric iron in
Acidithiobacillus ferrooxidans and protein identification by comparative 2-DE-MS/MS. ANTONIE
VAN LEEUWENHOEK INTERNATIONAL JOURNAL OF GENERAL AND MOLECULAR
MICROBIOLOGY, DORDRECHT (NETHERLANDS): Springer, 2012, Vol. 101, No. 3, p. 561-573. ISSN
0003-6072. doi:10.1007/s10482-011-9670-2. IF=1.673
http://link.springer.com/article/10.1007/s10482-011-9670-2
Contribution to the paper: Supervision of 2-DE proteomics analysis, contribution to proteomics
data interpretation
[J18] KUČERA, Jiří, Pavel BOUCHAL, Jan LOCHMAN, David POTĚŠIL, Oldřich JANICZEK, Zbyněk
ZDRÁHAL and Martin MANDL. Ferrous iron oxidation by sulfur-oxidizing Acidithiobacillus
ferrooxidans and analysis of the process at the levels of transcription and protein synthesis.
Antonie van Leeuwenhoek International Journal of General and Molecular Microbiology,
50
Dordrecht: Springer, 2013, Vol. 103, No. 4, p. 905-919. ISSN 0003-6072. doi:10.1007/s10482-
012-9872-2. IF=2.091
http://link.springer.com/article/10.1007/s10482-012-9872-2
Contribution to the paper: Supervision of 2-DE proteomics analysis, contribution to proteomics
data interpretation
Note: Impact factors correspond to the time of publishing
Articles published in journals indexed in Scopus database
[S1] FAKTOR, Jakub, Monika DVOŘÁKOVÁ, Josef MARYÁŠ, Iva STRUHÁROVÁ and Pavel BOUCHAL.
Identification and characterization of pro-metastatic targets, pathways and molecular
complexes using a toolbox of proteomic technologies. Klinická onkologie, Praha: Česká lékařská
společnost J.E.Purkyně, 2012, Vol. 25, Suppl. 2, p. 2S70-2S77. ISSN 0862-495X. doi:
10.14735/amko20122S70
http://www.linkos.cz/casopis-klinicka-onkologie/hledani-clanku/skupina/a/zobrazit/ids/4135/
Contribution to the paper: Overall idea, supervision of whole manuscript preparation process
and writing
[S2] MARYÁŠ, Josef a Pavel BOUCHAL. PDLIM2 and its role in oncogenesis: Tumor supressor or
oncoprotein? Klinická onkologie, Brno: Česká lékařská společnost J. E. Purkyně, 2015, Vol. 28,
Suppl. 2, p.2S40-46. ISSN 0862-495X.
http://www.linkos.cz/casopis-klinicka-onkologie/hledani-clanku/skupina/a/zobrazit/ids/4749/
Contribution to the paper: Initial idea on PDLIM2 studying, supervision of the writing
[S3] KOVAŘÍKOVÁ, Petra, Eva MICHALOVÁ, Lucia KNOPFOVÁ a Pavel BOUCHAL. Metody studia
buněčné migrace a invazivity nádorových buněk. Klinická onkologie, Brno: Česká lékařská
společnost J. E. Purkyně, 2014, Vol. 27, Suppl. 1, p. 22-27. ISSN 0862-495X.
http://www.linkos.cz/casopis-klinicka-onkologie/hledani-clanku/skupina/a/zobrazit/ids/4471/
Contribution to the paper: Overall idea, supervision of the manuscript preparation process and
writing
[S4] FAKTOR, Jakub, Eva MICHALOVÁ a Pavel BOUCHAL. p- SRM, SWATH a HRM – cílené
proteomické přístupy na hmotnostním spektrometru TripleTOF 5600+ a jejich aplikace v
onkologickém výzkumu. Klinická onkologie, Brno: Česká lékařská společnost J. E. Purkyně, 2014,
Vol. 27, Suppl. 1, p. 110-115. ISSN 0862-495X.
http://www.linkos.cz/casopis-klinicka-onkologie/hledani-clanku/skupina/a/zobrazit/ids/4499/
Contribution to the paper: Overall idea, supervision of the manuscript preparation process and
writing
[S5] PERNIKÁŘOVÁ, Vendula, Vojtěch SEDLÁČEK, David POTĚŠIL, Iva PROCHÁZKOVÁ, Zbyněk
ZDRÁHAL, Pavel BOUCHAL and Igor KUČERA. Proteome-wide dataset generated by iTRAQ-
3DLCMS/MS technique for studying the role of FerB protein in oxidative stress in Paracoccus
denitrificans. Data in Brief, Amsterdam: Elsevier, 2015, Vol. 4, September, p. 390-394. ISSN
2352-3409. doi:10.1016/j.dib.2015.06.015.
http://www.sciencedirect.com/science/article/pii/S2352340915001109
Contribution to the paper: iTRAQ-3DLC method optimization, supervision of proteomics analysis,
MS data analysis and interpretation, contribution to manuscript preparation
Book chapters
[C1] BOUCHAL, Pavel. Proteomika v molekulární medicíně. In Ondřej Slabý (Ed.): Molekulární
medicína. Praha: Galén, 2015. p. 148-155, 8 pages. ISBN 978-80-7492-121-6.
[C2] BOUCHAL, Pavel, Robert STEIN, Zbyněk ZDRÁHAL, Peter R. JUNGBLUT and Igor KUČERA. 2DPAGE
Database for Studies on Energetic Metabolism of the Denitrifying Bacterium Paracoccus
51
denitrificans. In Proteomics / Book 2. Rijeka, Croatia: In Tech, 2012. p. 141-156, 16 pages.
Integrative Proteomics. ISBN 978-953-51-0070-6.
http://www.intechopen.com/books/integrative-proteomics/2d-page-database-for-studies-on-
energetic-metabolism-of-the-denitrifying-bacterium-paracoccus-denitr
Contribution to the paper: Dataset preparation for database, paper writing