Ovarian Endometriosis Signatures Established through Discovery and

Article pubs.acs.org/jpr

Ovarian Endometriosis Signatures Established through Discovery and Directed Mass Spectrometry Analysis Anni P. Vehmas,† Dorota Muth-Pawlak,† Kaisa Huhtinen,‡,§,□ Taija Saloniemi-Heinonen,‡ Kimmo Jaakkola,∥ Teemu D. Laajala,⊥,# Heidi Kaprio,‡ Pia A. Suvitie,§ Tero Aittokallio,⊥,# Harri Siitari,∥ Antti Perheentupa,§ Matti Poutanen,‡,¶,○ and Garry L. Corthals*,†,△ †

Turku Centre for Biotechnology, ‡Department of Physiology, Institute of Biomedicine, ⊥Department of Mathematics and Statistics, and ¶Turku Center for Disease Modeling, University of Turku, Turku, Finland § Department of Obstetrics and Gynecology, Turku University Hospital, Turku, Finland ∥ VTT Technical Research Centre of Finland, Turku, Finland # Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland ○ Institute of Medicine, The Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden △ Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, The Netherlands S Supporting Information *

ABSTRACT: New molecular information on potential therapeutic targets or tools for noninvasive diagnosis for endometriosis are important for patient care and treatment. However, surprisingly few efforts have described endometriosis at the protein level. In this work we enumerate the proteins in patient endometrium and ovarian endometrioma by extensive and comprehensive analysis of minute amounts of cryosectioned tissues in a three-tiered mass spectrometric approach. Quantitative comparison of the tissues revealed 214 differentially expressed proteins in ovarian endometrioma and endometrium. These proteins are reported here as a resource of SRM (selected reaction monitoring) assays that are unique, standardized, and openly available. Pathway analysis of the proteome measurements revealed a potential role for Transforming growth factor β-1 in ovarian endometriosis development. Subsequent mRNA microarray analysis further revealed clear ovarian endometrioma specificity for a subset of these proteins, which was also supported by further in silico studies. In this process two important proteins emerged, Calponin-1 and EMILIN-1, that were additionally confirmed in ovarian endometrioma tissues by immunohistochemistry and Western blotting. This study provides the most comprehensive molecular description of ovarian endometriosis to date and researchers with new molecular methods and tools for high throughput patient screening using the SRM assays. KEYWORDS: endometriosis, tissue, AIMS, proteotypic peptides, mass spectrometry, quantitative proteomics, Western blot, immunohistochemistry, selected reaction monitoring, SRM

■

INTRODUCTION Endometriosis is a common medical condition that affects the lives of up to 10% of women in their reproductive years, with symptoms ranging from severe pain to delayed pregnancy and infertility.1 The disease is characterized by the presence of endometrial-like tissues outside the uterus, typically in the pelvic peritoneum and the ovaries. These ectopic endometrial lesions grow and develop in situ, often leading to inflammation and fibrosis formation.2 Ovarian endometriomas are benign cysts that are histologically defined by a thin lining of endometrioid mucosa.3 Even though the cysts are benign, there is an increased risk of endometrioma developing into a clear cell, low-grade serous, or endometrioid ovarian carcinoma.4 Laparoscopic surgery is the method of choice for the diagnosis and treatment of endometriosis. However, when © 2014 American Chemical Society

endometriomas are surgically removed, careful preservation of the healthy ovarian tissue needs to be undertaken to protect fertility and the normal functions of the ovary.5 There is a critical need for a straightforward and minimally invasive diagnostic test on endometrial tissue, urine, or serum, especially if it could replace laparoscopy as a means of definitive diagnosis of endometriosis. We have reasoned to begin the exploration of clinical markers in affected tissues, as they will be present at higher concentrations in tissues prior to their Special Issue: Proteomics of Human Diseases: Pathogenesis, Diagnosis, Prognosis, and Treatment Received: April 23, 2014 Published: August 6, 2014 4983

dx.doi.org/10.1021/pr500384n | J. Proteome Res. 2014, 13, 4983−4994

Journal of Proteome Research

Article

to a potential role for TGFB1 in ovarian endometriosis development.

secretion. The markers are then monitored with decreasing levels in proximal body fluids and, finally, subsequent amounts in serum or plasma.6,7 Nowadays mass spectrometry (MS) offers an attractive platform to both discover and screen these markers, as a result of both continued developments boosting the speed and sensitivity of MS instrumentation and the concomitant computational tools specifically developed to analyze these data. Furthermore, associated preparation methods, recently developed specifically for the analysis of small amounts of patient material,8−10 have enabled deep proteome analysis of disease-specific tissue regions.11,12 Nevertheless, in endometriosis, MS-based studies aimed at identifying markers or understanding the etiology of the disease have mostly used the endometrium as tissue material,13−21 missing the obvious detail of pathological ectopic tissues. To our knowledge, only very few proteomics studies have been performed on ovarian endometrioma to date.22,23 Considering the limited proteome information available for endometriosis tissues, newly developed MS approaches have the potential to discover more proteins to better define and classify the disease at the molecular level. The use of routine MS workflows employing data-dependent analysis (DDA) would have features that limit its value in this project due to three key aspects. First, discovery proteomics using precursor ion selection is a stochastic process that provides poor reproducibility. Second, in the highly complex samples found in disease proteomics, the sequencing events of MS instruments are considerably lower than the number of analytes to be sequenced, and therefore proteomes are only partially analyzed.24,25 This “under sampling” typically restricts MS analysis to only the most highly expressed proteins in a sample. Third, comprehensive discovery-based MS with multiple prefractionation steps requires sufficient sample material and hours of analysis time, which restricts the number of patients and cohorts that can be analyzed.24,25 Owing to these limitations, integrative approaches that use existing molecular information, including (e.g.) prior information from MS, genome-wide mRNA analysis, or metabolome data, would be extremely beneficial for endometriosis researchers and patients. The integrative approaches applied in MS should bypass discovery-based DDA workflows and ideally incorporate both discovery and targeted modes based on previous information from a molecular perspective. In this work we developed an integrative workflow for the analysis of endometriosis tissues that are by nature limited in sample quantity and present as minute tissue cryosections. The method we have used consists of three stages that integrate targeted and reiterative discovery proteomics. The steps include two rounds of targeted proteomics carried out sequentially, based on prior microarray activity analysis and exhaustive proteome analysis. Ultimately the results enabled the enumeration of proteins in ovarian endometriosis that revealed detailed molecular descriptions of the patient tissue proteomes. Additionally, quantitative analysis of the proteomes revealed tissue-specific differences that were used to construct a large resource of tissues and disease-specific SRM (selected reaction monitoring) assays, ultimately enabling rapid and subsequent analysis in orthogonal studies. This set of highly differentially expressed proteins in ovarian endometrioma was further investigated for their specificity and pathway activity. We found a clear activation of cytokine Transforming growth factor β-1 (TGFB1) regulatory network in proteins that are specifically deregulated in ovarian endometrioma, pointing

■

MATERIALS AND METHODS

Patients and Sample Collection

Patients were enrolled in the study through a written agreement, and the study protocol was approved by the Joint Ethics Committee of University of Turku and Turku University Central Hospital, Turku, Finland (Project 231/2004). Samples were acquired at Turku University Central Hospital and Lahti Central Hospital in Finland. The patient endometrium (PE, n = 7) and ovarian endometrioma (PO, n = 6) samples were obtained from 8 women during operative laparoscopy for endometriosis. From five patients, both PE and PO samples were acquired; from two patients we used the PE sample only, and from one patient only the PO sample. The patients were matched by age and menstrual cycle phase and received no hormonal medication. All tissue samples were washed in isotonic phosphate buffered saline (PBS) and snap frozen in liquid nitrogen within 5 min of collection. A histopathological verification of endometriosis was made on all samples. Sample Preparation for Proteome Measurements

The sample preparation was performed according to the direct tissue proteomics (DTP) protocol optimized for frozen tissue sections by Bagnato and co-workers.26 Briefly, 10-μm-thick sections were cut on a cryostat (Leica Microsystems, Wetzlar, Germany) from 7 endometrium (PE) and 6 ovarian endometrioma (PO) tissues at −18 °C. Adjacent sections were stained with a standard hematoxylin and eosin protocol, and the size of each section was measured with a stereo microscope (Zeiss SteREO Lumar V12, Zeiss, Wezlar, Germany). For direct tissue digestion,26,27 the glass slides with frozen sections were dipped in liquid nitrogen for 3 s, and the tissue sections were scraped to an Eppendorf tube with a cooled razor blade. A solution containing 12 ng/μL trypsin in 30% acetonitrile (ACN) and 100 mM NH4HCO3 was added to each section (5 ng of trypsin/mm2). No denaturant or chemicals for reduction and alkylation were added. After this, the tissues were spun to the bottom of the tube and incubated overnight (17 h) at 37 °C. After incubation, the samples were centrifuged for 45 min at 14,000g, and the supernatant was collected. The supernatants were transferred to fresh tubes in 2μL aliquots and lyophilized in a vacuum centrifuge (Hetovac; Heto Holten, Copenhagen, Denmark) after which they were stored in −70 °C until use. Accurate Inclusion Mass Screening (Stage 1)

Accurate inclusion mass screening (AIMS)28 refers to an approach where the mass spectrometer selects specific peptides for fragmentation typically from a complex sample matrix, such as tissues. For the AIMS work, two endometrium and two ovarian endometrioma tissue samples were mixed together in equimolar amounts. This was done to determine whether proteotypic peptides from proteins selected for their relevance for endometriosis by microarray data mining and shotgun proteomics in previous studies (manuscript in preparation; Table 1) could be identified in endometrium and ovarian endometrioma samples and, if so, to define their elution times for a scheduled inclusion (Figure 1; Stage 1). Skyline version 1.129 was used to create the list of proteotypic peptides (Figure 1; Stage 1a). First, in silico digestion of the proteins of interest was performed. To accept a 4984



Article

sites and to not include cysteine and methionine or ragged ends (KK, RR, KR, or RK). Additionally, only those peptides reported in the Peptide Atlas database (http://www. peptideatlas.org/, 2011-09-27) were chosen for the analysis. Finally the AIMS inclusion list was created for m/z for +2 and +3 charge states of peptides (Figure 1; Stage 1b). The AIMS inclusion list was divided into three equivalent AIMS runs, and the mixed sample was analyzed in three replicates in an Orbitrap Velos as described below. Those peptides that were not identified in the first three runs were further divided into four separate lists based on the precursor mass, and the mixed sample was analyzed four more times at four different mass ranges (m/z 230−480, m/z 480−650, m/z 650−900, and m/z 900−2,000) (Figure 1; Stage 1c).

Table 1. Proteins in Accurate Inclusion gene symbol

Uniprot accession

ACTN1 FLNA PDLIM3 CNN1 MYH11 MMP11 EMILIN1 PRELP BGN ASRGL1 CAPS sFRP2 SH3BGRL

P12814 P21333 Q53GG5 P51911 P35749 P24347 Q9Y6C2 P51888 P21810 Q7L266 Q13938 Q96HF1 O75368

CSRP1 DLX5 TRH LAMP5

P21291 P56178 P20396 Q9UJQ1

C20orf85

Q9H1P6

protein name Alpha-actinin-1 Filamin-A PDZ and LIM domain protein 3 Calponin-1 Myosin heavy chain 11 Matrix metalloproteinase-11 Elastin microfibril interface-located protein 1 Prolargin Biglycan L-Asparaginase Calcyphosin Secreted frizzled-related protein 2 SH3 domain-binding glutamic acid-rich-like protein 3 Cysteine- and glycine-rich protein 1 Distal-less homeobox 5 Prothyroliberin Lysosome-associated membrane glycoprotein 5 Uncharacterized protein C20orf85

Directed Mass Spectrometry (Stage 2)

The endometrium (+n = 7) and ovarian endometrioma (n = 6) samples were first analyzed in the Orbitrap Velos in datadependent acquisition mode (DDA, Figure 1; Stage 2a−c) as described below. To minimize the redundancy in the precursor ion identifications in the future sample analyses, a directed mass spectrometry approach was chosen.30 In the workflow, the spectrum files from DDA analysis of each sample were imported into Progenesis 4.0 (Nonlinear Dynamics, Newcastle upon Tyne, U.K.), where the area of each precursor ion was determined by an automatic feature detection algorithm. In the process, the strictest preset threshold for peak picking was chosen to include only high abundant ions and to filter out indiscriminant precursor ions from the data set. Finally, precursors that were not fragmented but eluted over a period of at least 20 s in the DDA analysis were exported to an inclusion list for the directed mass spectrometry experiment. Additionally, when transferred to the list, the retention time window for each precursor was expanded by 2 min to compensate for possible variations in retention time (Figure 1; Stage 2d). Combination of the Inclusion Lists from AIMS and the Directed Mass Spectrometry (Stage 3)

The AIMS (Figure 1; Stage 1d) and the directed mass spectrometry (Figure 1; Stage 2d) inclusion lists were merged to construct a unique inclusion list for each specimen (Figure 1; Stage 3a). Each tissue sample was then run for the second time in the Orbitrap Velos as described below, but fragmenting only the precursors included in this list (Figure 1; Stage 3b,c).

Figure 1. Overview of the MS study design. A three-stage approach was used. In Stage 1, an AIMS inclusion list of proteotypic peptide masses (m/z) was constructed based on 18 proteins of interest. A pooled sample was analyzed by the use of this list, resulting in 132 identified proteotypic peptides. In Stage 2, a DDA analysis was performed on all 13 tissue samples individually. In Stage 3, the m/z and retention times of 132 peptides of interest from Stage 1 and unidentified precursors from Stage 2 were combined to create an inclusion list for each individual sample. After this, all 13 tissue samples were analyzed for the second time by the use of this list. Next, the MS profiles and the unique peptide identifications from Stages 2 and 3 were used in a label-free quantitative analysis. At the final stage, the differentially expressed proteins in the label-free quantitative analysis were used in further qualification work and in the construction of a list of transitions for SRM for endometriosis. AIMS, accurate inclusion mass screening; MS/MS, tandem mass spectrometry; DDA, datadependent analysis; IHC, immunohistochemistry; WB, Western blot.

Mass Spectrometry Data Collection

Samples were analyzed by microcapillary liquid chromatography electrospray ionization−tandem mass spectrometry (μLC−ESI-MS/MS) on an ESI-hybrid Ion Trap-Orbitrap mass spectrometer (LTQ Orbitrap Velos; Thermo Fisher Scientific, Bremen, Germany). The mass spectrometer was coupled to an Easy Nano LC nanoliquid chromatography (LC) system (Thermo Fisher Scientific, Bremen, Germany). Sample loading, solvent delivery, and scan functions were controlled by Xcalibur software (version 2.1.0 SP1.1160, Thermo Fisher Scientific). A 250-ng sample of peptides in 5 μL of 1% HCOOH was injected into the LC system, where peptides were separated according to their hydrophobicity on a reversed-phase chromatography column. An in-house packed 2.5 cm long, 75 μm inner diameter trap column (Magic AQ C18 resin, 5 μm/ 200 Å, Bruker-Michrom, Billerica, MA, USA) was used for desalting and concentrating the sample, and a 15 cm long, 75

peptide sequence to the inclusion list, it was required to be unique for a protein and fully tryptic with no missed cleavage 4985



Article

μm inner diameter analytical column (PicoFrit, 15 μm, New Objective, Woburn, MA, USA) packed with the same C18 particles was used for peptide separation. A 75 min long gradient from 95% solvent A (98% H2O, 2% ACN, and 0.2% HCOOH) to 90% solvent B (95% ACN, 5% H2O, and 0.2% HCOOH) with a flow rate 0.3 μL/min was used for peptide elution. A 30 min long wash with a similar gradient with a full loop injection of solvent B was run between the samples. Mass scanning (MS1) was performed in a positive-ion mode in the Orbitrap mass analyzer, where a low resolution preview scan and subsequent survey scan (MS) with a resolution of 60,000 was performed. A high-resolution MS1 screening cycle was used in order to avoid coelution of peptides, overlapping features, and signal suppression.31 The automatic gain control in the Orbitrap was set to 106, and the mass range for analysis was from 300 to 2,000 m/z. Precursor ions were selected for fragmentation (MS/MS) by collision-induced dissociation (CID) in the ion trap mass analyzer, after which they were added to an exclusion list for 60s to prevent the reanalysis. In AIMS (Figure 1; Stage 1, see below) precursors were selected from a local parent mass list, where they were defined according to their m/z. For the last four AIMS analyses, also the screening range of the instrument was restricted as described previously. For DDA (Figure 1; Stage 2) the 15 most intense doubly or triply charged parent masses were automatically selected for fragmentation, whereas in the directed mass spectrometry experiments (Figure 1; Stage 3), the precursors were picked from a global mass list by the m/z and elution time window.

accepted normalization was 0.5−1.75. Statistical analysis was performed on unique features within Progenesis by using the Student’s t test (p-value), power estimations, and correction for multiple testing (q-value). SRM Transition List

The search result files from Proteome Discoverer were converted into spectral libraries and studied against the background proteome, UniProtKB/Swiss-Prot human database (20 335 sequences, 2011-09-05) in Skyline (v.1.4.0.4421). The background proteome was subjected to in silico trypsin digestion, and peptides that matched both the library and filter were picked. Peptides with potential ragged ends (KK, RR, KR, or RK), peptides with missing cleavage sites, and peptides including methionine, histidine, or amino acid combinations of asparagine-X-threonine and asparagine-X-serine or peptides that were shorter than 8 or longer than 25 amino acids were excluded from the list. For transitions, doubly and triply charged precursor ions were included; ion charge was set to “1”, and y-ions were visualized. Product ions from m/z larger than precursor (m/z) to the last ion −1 were included, and Nterminal to proline was added. Precursor m/z exclusion window was set to 2 Th and ion match tolerance for library to 1 Th. The four most intense ions from the library were picked from filtered product ions. In the peptide list, duplicate peptides were removed, and a minimum of four transitions per precursor was required. Additionally, those proteins without spectra in the library and proteins from the contamination database were removed. Finally, those proteins that were not found significantly different between the endometrium and ovarian endometrioma were filtered from the list.

Database Search for Peptide Spectral Data

The database search for the raw spectrum files was performed in Proteome Discoverer (version 1.3.0.339, Thermo Fisher Scientific) by using Mascot (Matrix Science, London, U.K.) and Sequest32 algorithms. The spectra were searched against UniProtKB/Swiss-Prot human database (20 335 sequences, 2011-09-05), which included common contaminants acquired from the contaminants database cRAP (the common Repository of Adventitious Proteins, 2011-04-03). Search parameters were the following: enzyme used was trypsin, decoy database search was performed, maximum two missed cleavage sites were allowed, accepted precursor mass tolerance was set to 5 ppm, and fragment mass tolerance to 0.5 Da. Moreover, the false discovery rate threshold was set to 1% by the Percolator algorithm.33,34 Only those protein matches that had at least one unique identified peptide and at least two peptide spectral matches were exported to Progenesis for labelfree quantitative analysis.

Pathway Analysis

The list of quantified proteins was exported from Progenesis and imported to Ingenuity Pathway Analysis system (IPA; Ingenuity Systems, www.ingenuity.com, version 192063, 201211-01). A q-value cutoff of 0.01 was used, resulting in 214 proteins to be included. The pathway analysis was performed by using the following settings: direct and indirect experimentally observed relationships were studied, and endogenous chemicals were included in the analysis. Briefly, the pathways recognized in the proteomics data were superimposed to all pathways found in the IPA, and the significance of enrichment was calculated by Fisher’s exact test. Specificity Measures

Those proteins found to be up-regulated in both proteomics and microarray data were subjected to specificity measures in two in silico databases, the In Silico Transcriptomics database (IST)35 and the Genevestigator Biomedical database.36 The purpose was to obtain further evidence for the expression of the proteins, especially in the ovarian endometrioma tissue. Therefore, we studied their expression in the uterus as compared with that in the ovary by applying the IST database (www.genesapiens.org) and further analyzed their expression in ovarian endometrioma as compared with healthy ovary by applying the Genevestigator biomedical package (www. genevestigator.com).

Label-Free Quantitation of Proteomics Data

Raw spectral data from all mass spectrometry runs (Figure 1; Stages 2 and 3) were imported to Progenesis 4.0 for feature detection and data analysis. The LC−MS maps were aligned to the mixed sample by manually placing 10−20 alignment vectors followed by the use of an automatic alignment algorithm. The feature detection was done by automatic peak picking in default sensitivity mode excluding charges of 4+ and larger. The peptide identifications were imported to Progenesis from Proteome Discoverer as described above. In Progenesis, the peptide-feature matches that had less than two MS/MS hits or had precursor mass tolerance higher than 5 ppm were removed. Also peptides originating from the contaminants database were removed at this stage. Moreover, the normalization was done against the identified peptide features, where the threshold for

Immunohistochemistry and Western Blot Analyses

Immunohistochemistry and Western blot validation experiments were performed for CNN1 and EMILIN1 as follows. Ovarian endometriosis sections from 4 patients were stained for CNN1 and EMILIN1. The paraffin sections were rehydrated, and antigen retrieval was done by incubating the sections in 10 4986



Article

mM Tris, 1 nM EDTA buffer, pH 9.0 for 20 min in a pressure cooker. The sections were blocked against nonspecific binding in 3% BSA-PBST (0.05% Tween-PBS) for 30 min at room temperature. Mouse monoclonal anti-human CNN1 (1:300 in 3% BSA-PBST; Dako M3556) and rabbit polyclonal antihuman EMILIN1 (1:1000 in 3% BSA-PBST; Sigma HPA002822) primary antibodies were used to bind CNN1 and EMILIN1, respectively, overnight at +4 °C. Endogenous peroxidase activity was blocked by incubating the sections with 1% H2O2−PBST for 20 min at room temperature, followed by 30 min of incubation with HRP-labeled polymer anti-mouse and anti-rabbit secondary antibodies for CNN1 and EMILIN1, respectively, according to the manufacturer’s instructions. After the substrate-chromogen reaction, the sections were background-stained with Mayer’s hematoxylin, dehydrated, and mounted. The sections were washed twice for 5 min between each step. For Western blotting, patient endometrium and ovarian endometriosis tissue samples from three patients each were homogenized by Ultra Turrax in RIPA buffer (150 MM TrisHCL, pH 7.4, 1% NP-40, 150 mM NaCl, 0.5% sodium deoxycholate, 1 mM EDTA, and 1 mM SDS) with protease inhibitors (Roche). Protein concentration of the lysates was measured by BCA (Pierce) according to manufacturer’s instructions. A 25-μg sample of protein/well were run through a 12.5% polyacrylamide gel and blotted onto nitrocellulose membranes. Mouse monoclonal anti-human CNN1 (1:300 in 5% milk-TBST; Dako M3556), rabbit polyclonal anti-human EMILIN1 (1:300 in 5% milk-TBST; Sigma HPA002822), and mouse monoclonal anti-human α-tubulin (1:10000 in 5% milkTBST; Sigma T5168) primary antibodies were used to detect CNN1, EMILIN1, and the loading control α-tubulin, respectively. Horseradish peroxidase-conjugated secondary antibodies were purchased from Dako. The signals were visualized by enhanced chemiluminescence (Thermo Scientific).

■

Figure 2. Results from the accurate inclusion list construction in Stage 1. AIMS inclusion list was divided into three separate inclusion experiments. Shown here is the cumulative number of sequences identified in each inclusion experiment (FDR < 1%). The ratio of AIMS target sequences to all sequences was found to be approximately 0.2 in all three inclusion experiments. As a result, 129 target sequences were identified in the first three experiments by the use of AIMS.

experiments, a final scheduled inclusion list with the m/z and retention time window for the 132 peptide sequences was constructed, representing 12 proteins (Figure 1; Stage 1d). The specificity of the unscheduled inclusion was found to be low; only 20% of all peptides identified were found on the inclusion list (Figure 2). To acquire MS1 level precursor ion data and peptide identifications for each sample, a data-dependent analysis (DDA) was performed (Figure 1; Stage 2b,c). In the DDA, altogether 10,794 unique peptide sequences were identified among the 13 samples by applying the combined Mascot and Sequest searches. An average of 8,405 peptide features (5,732− 10,607 features/sample) was detected in each biological sample. Of these peptide features, an average of 5,634 peptide features (4,004−6,820 features/sample) had at least one MS/ MS event, whereas an average of 2,771 (1,692−3,806 features/ sample) peptide features remained unidentified (m/z, RT). The m/z and retention time of those features that were not selected for fragmentation were transferred to a directed mass spectrometry inclusion list for reanalysis. (Figure 1; Stage 2d) In Stage 3, which used the combined AIMS and directed mass spectrometry inclusion lists from Stage 1 and Stage 2 (Figure 1; Stage 3), we identified 4,143 peptide sequences. This increased the total amount of all identified unique peptides by 12%, when compared to the DDA. For the proteotypic peptides of the proteins of interest (Table 1), the increase of identified unique peptides was found to be higher, up to 19%. The use of the scheduled inclusion lists presented high specificity, as on average 70% of those peptides identified by the use of the

RESULTS

Proteomics Sample Preparation

The size of the 10-μm-thick tissue section used for digestion varied from 8.8 to 59.1 mm2, depending on the sample. By standardizing the amount of trypsin used according to the size of a section, we were able to extract from 24 to 93 μg of peptides from each sample, which resulted in a yield of peptides between 0.7 and 2.7 μg/mm2. The high yield of peptides was due to the simple sample handling protocol applied without tissue disruption, protein precipitation, or salt removal steps. MS Data Collection, AIMS, and Inclusion Lists

In order to minimize the instrument time used for each sample and to ensure the best possible proteome coverage, an AIMS approach was used (Figure 1; Stage 1, Table 1). After in silico digestion of the proteins, a list of 1,079 m/z values for the proteotypic peptides was acquired. These masses were used to create an inclusion list, which enabled specific selection of the m/z values for fragmentation in a mixed sample consisting of pooled peptides of two ovarian endometrioma and two endometrium tissue specimens. The first three MS analyses of the mixed sample resulted in 129 identified proteotypic peptides (Figure 2), and only three additional peptide sequences were identified when the previously unidentified m/z values were reanalyzed in restricted mass ranges (data not shown). Based on the identified peptides in these AIMS 4987



Article

Figure 3. Visual representation of the quantification results for the proteins of interest. Of the 18 proteins chosen for the accurate inclusion (Table 1), 12 were identified and quantified. Two of the proteins, CAPS and ASRGL1, show lower, whereas others present higher, expression levels in ovarian endometrioma compared to endometrium. For statistics, please refer to Supplemental Table 1. PE, patient endometrium; PO, ovarian endometrioma.

inclusion list had a unique sequence. The combined database searches comprised 11,559 unique peptide sequences.

For quantitation, a threshold of a minimum of three unique peptides per protein was set for the 7 PE and for the 6 PO samples to ensure the precision of the measurement. Moreover, thorough alignment of MS analyses according to the peptide elution times as well as the use of two database search algorithms and strict admittance criteria for identification were used to obtain accurate feature-peptide matches.38 The thresholds of q < 0.01, power >0.8, and fold change >1.5 were used to determine a significant quantitative difference between the endometrium and ovarian endometrioma tissues. By using these criteria, 214 proteins were found significantly

Label-Free Quantitation of Proteomics Data and SRM Transitions

After the import of the peptide spectral data to Progenesis (v.4.0), we acquired 7,234 peptide features linked to 1,516 proteins. The normalized median technical CV of the Orbitrap Velos system on the protein level was 6%, determined by four injections of the mixed sample. Data-dependent analysis of the 15 most intense precursor ions provided a minimum of 10 measurement points over an eluting peak.37 4988



Article

Figure 4. TGFB1-dependent signaling. On the basis of the pathway analysis of the 214 differentially expressed proteins between ovarian endometrioma and endometrium, 63 were reported to be regulated by TGFB1. The direction of TGFB1 regulation was as predicted for 42 of these proteins, i.e., 36 were up-regulated and 6 down-regulated by TGFB1. For 10 proteins, however, the direction of change was in conflict to that reported in literature for TGFB1. For the remaining 11 proteins, no data was available for the direction of the regulation.

different in the specimens (Supplemental Table 1). Among these, we found 12 of the proteins that were targeted in the accurate inclusion list, 10 of which were found of higher and 2 of lower abundance in the ovarian endometrioma (Figure 3). Of the 214 proteins, we could generate a SRM transition list of 168 endometrium and ovarian endometrioma proteins. This list includes 591 unique peptides with four transitions each (Supplemental Data).

1.28 × 10−21, z-score = 4.4). Of the identified 214 differentially expressed proteins between ovarian endometrioma and endometrium, 63 have been reported to be regulated by TGFB1 (Figure 4 and Supplemental Table 2). According to the literature-based prediction of the activation state performed by IPA, TGFB1 enhances the expression of most of these proteins; however, also down-regulation can be seen (Supplemental Table 2).

Pathway Analysis

Intersection of Microarray and Proteomics Results

On the basis of the IPA, the biological functions enriched in ovarian endometrioma included apoptosis (p = 1.91 × 10−9), proliferation of cells (p = 1.31 × 10−8), and metabolism of reactive oxygen species (p = 5.38 × 10−6). The pathways of ILK signaling (p = 3.7 × 10−9), caveolar-mediated endocytosis signaling (p = 5.85 × 10−7), and mitochondrial dysfunction (2.07 × 10−6) were also found to be enriched. Furthermore, one of the most interesting findings of the pathway analysis was the predicted activation of TGFB1 (Transforming growth factor β-1)-dependent signaling in ovarian endometrioma (p =

The results of the proteomics experiments were compared to microarray results performed on corresponding tissues.39 No genes were found significantly differentially expressed between the control and patient endometrium using linear mixed models by the statistical software R (q < 0.01, fold change >1.5). However, we did observe 3,378 differentially expressed mRNAs between patient endometrium versus ovarian endometrioma. Of the 214 proteins found to be changed in the proteomics experiment, 88 were also changed at the mRNA level (Figure 5, Supplemental Table 3). Nine of these proteins 4989



Article

Figure 5. Common identifiers in the microarray and proteomics data sets. Comparison of the 3,378 differentially expressed mRNAs and 214 proteins found to be changed in the proteomics experiments performed on corresponding tissues reveals 88 common gene identifiers. Nine of these proteins were also targeted in the AIMS experiment (*, Table 2).

were also targeted in the AIMS experiment (Figure 5, Supplemental Table 3). The results of the microarray and proteomics comparison are summarized in Figure 6.

Various protocols were in-house developed, and previously published methods tested; however the DTP digestion protocol applied to frozen tissue sections26 was found to produce superior quality and reproducibility between patient tissues. Hence, the method was applied to endometrium and ovarian endometrioma tissues, resulting in almost 12,000 unique peptide identifications. In the simple sample preparation method, frozen tissue sections are incubated with trypsin solution containing acetonitrile, resulting in trypsin cleaving proteins directly at the tissue surface. The DTP method was originally developed for paraformaldehyde-fixed archival tissues,43 but soon after its applicability was demonstrated also in frozen sections.26 It was surprising to observe that the method was capable of extracting peptides efficiently and reproducibly, as methods often require tissue homogenization with denaturizing reagents and reduction and alkylation steps. In concurrence with previous reports,44,45 achieving an optimal number of identified peptides and also reducing the number of sample treatment steps is highly recommended, as this increases method reliability and repeatability, which is particularly important large-scale studies in clinical quantitative proteomics. In this work, we used AIMS successfully to guide the mass spectrometer to analyze peptides that were unique to the proteins of interest and available for analysis in the sample. The DDA of the tissue samples generated a minimal number of identifications for each analyzed sample (Figure 1). However, the complex tissue samples were analyzed without prefractionation, which left on average one-third of potential precursor ions unidentified in each sample. The targeted reanalysis (via inclusion lists) of both, the unidentified features in each sample, and the scheduled list of proteotypic peptides of the proteins of interest (Figure 1) increased the amount of all identified unique peptides by 12%. When the proteotypic peptides used for the AIMS experiment are examined independently, the number is even higher (19%). These numbers are consistent with those previously reported in Drosophila melanogaster lysates30 and in mouse serum,46 which clearly demonstrates that targeted analyses are very well suited to complement DDA in analyzing complex proteomes. Label-free quantification methods generally favor high abundant peptides, and due to the strict admittance criteria

Validation

Among the proteins found up-regulated in ovarian endometriosis, at both transcript and protein levels (Figure 6), in silico specificity analyzes were made. The criteria used were the following: specificity for (1) higher expression in uterus as compared with ovary (analyzed in the IST database35), (2) higher expression in ovarian endometrioma as compared with healthy ovary (analyzed in Genevestigator Biomedical database), and (3) regulated by TGFB1. According to these criteria, 14 proteins presented specificity to ovarian endometrioma and were TGFB1-activated (Table 2). Among those, two proteins, an actin interacting protein Calponin-1 (CNN1) and an extracellular matrix protein EMILIN-1, were studied further. Using Western blot analyses both were found to be upregulated in the majority of endometriosis samples as compared with the endometrium. EMILIN1 was detected in all 11 endometriosis specimens analyzed with variable intensity and CNN1 was detected in 8 out of 11 specimens (Figure 7A; a representative example of three specimens analyzed), and the proteins were localized in the stromal compartment of endometriotic lesions (Figure 7B). The differences in the expression levels of the proteins can be explained with the fact that in endometriosis, biological variation and histological differences between endometriosis cysts is a common phenomenon.40,41

■

DISCUSSION Despite the advances in the MS instrumentation, discovery proteomics still suffers from an inherent inability to deliver exhaustive analyses of complex proteomes and to operate at high resolution at high speed.24,25 Therefore, to comprehensively detail proteins present only in low amounts, reiterative analyses need to be performed to overcome the relatively slow sequencing cycles compared to the large number of peptides present. The slow speed of analysis also limits the number of samples that can be analyzed on a practical time scale. Targeted MS-based approaches on the other hand complement traditional shotgun methods and offer reliable and direct measurements of predetermined proteins of interest.42 4990



Article

Table 2. Ovarian Endometrioma Specificity and TGFB1Dependent Signalinga gene ID

b

BGN* MYLK CNN1* CSRP1* CALD1 TAGLN EMILIN1* MYH11* LTBP2 TPM2 CAV1 PLS3 TPM1 DES AEBP1 PDLIM3* AOC3 LMOD1 PTRF DCN DPYSL3 SDPR OGN PRELP* KCTD12 LXN MYL9 VAT1 LAMB2 LPP AKAP12 ADH1B AK1 COL12A1 OLFML1 COL14A1 PTGIS ISYNA1 MYH10 PGM1 SPTBN1

Figure 6. Flowchart describing the comparison of mRNA and protein level screening of patient endometrium and ovarian endometrioma. At the mRNA level, no transcripts were found significantly differentially expressed between the control and patient endometrium, but 3,378 differentially expressed mRNAs (q < 0.01, fold change >1.5) were found between patient endometrium and ovarian endometrioma. When these gene identifiers were compared to the 214 proteins found to be changed (q < 0.01, fold change >1.5), 88 common identifiers were observed. Of the common identifiers, 41 were found to be upregulated and 47 were down-regulated in ovarian endometrioma (Figure 5). When the up-regulated proteins were evaluated for specificity of endometrioma tissue and TGFB1 regulation, 14 promising candidates were found (Table 2). Finally, two of the candidates, CNN1 and EMILIN1 were chosen for WB and IHC validation. PE, patient endometrium; CE, control endometrium; PO, ovarian endometrioma; FC, fold change; WB, Western blot; IHC, immunohistochemistry.

uterine specificity in ISTc

ovarian endometrioma specificity in Genevestigatord

TGFB1 regulation in IPAe

no.

++ ++ ++ ++ ++ ++ ++ ++ +− ++ ++ ++ ++ ++ ++ ++ ++ ++ ++ ++ +− −− +− ++ +− +− ++ +− +− −− +− ++ −− −− −− +− −− −− +− −− −−

++ ++ ++ ++ ++ ++ +− +− ++ +− +− +− +− +− ++ ++ ++ ++ ++ +− ++ ++ ++ +− ++ ++ +− +− +− ++ +− −− −− +− +− −− +− +− −− +− −−

++ ++ ++ ++ ++ ++ ++ ++ ++ ++ ++ ++ ++ ++ −− −− −− −− −− + (+) + (+) ++ −− −− −− −− −− + (+) −− −− −− −− ++ −− −− −− −− −− −− −− −−

1 1 1 1 1 1 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 5 5 5 5 5 6 6 6 6 6 6 6 7

+ +, positive; (+) inconsistent pathway analysis results; + − no relation; − − negative. bAsterisk (∗) indicates those targeted by accurate inclusion. cUterine vs ovary; In Silico Transcriptomics database www.genesapiens.org. dOvarian endometrioma vs normal ovary; Genevestigator Biomedical www.genevestigator.com. eTGFB1 pathway regulation; Ingenuity Systems www.ingenuity.com. a

that were used for peptide identifications and features in this work, only 63% of all identified unique peptides could be quantified in Progenesis. This is demonstrated by the large difference in the number of deregulated genes and proteins (Figure 6) and gives an indication for the possible detection of additional low abundant proteins in these samples. However, despite the low share of peptides being quantified, the technical and biological variability of the data was found to be negligible. Finally, 214 proteins were found differentially expressed between endometrium and ovarian endometrioma in Progenesis (Supplemental Table 1), including 12 proteins from the AIMS experiments (Figure 3). The strict filtering of proteotypic peptides of these proteins enabled us to generate a list of high quality SRM transitions for 168 of the proteins. Excluded proteins did not have identified proteotypic peptides passing the strict filtering in Skyline; these are peptides with missed

cleavage sites, unfavorable length, or amino acid sequence or precursors presenting inadequate charge or fragmentation data. To our knowledge, only a small part of the proteins reported here has been previously described to be deregulated in ovarian endometriosis. Nevertheless, our findings of these proteins are very similar to those published previously. The up-regulation of transgelin (TAGLN) reported here has been measured also by others in peritoneal endometriosis,47 and its mRNA and protein expression has been measured recently in ovarian endometrioma.23,48 Additionally, the increased superoxide 4991



Article

all of these had the same direction of change in both omics data sets, thereby further validating our approach. In addition, these findings imply a central role for the 88 proteins/genes in ovarian endometrioma (Figure 5, Supplemental Table 3). To assemble a list of TGFB1-regulated ovarian endometriomaspecific proteins, we chose proteins with up-regulated profiles in both mRNA and protein level in ovarian endometrioma and studied their specificity to endometriosis in silico. As a result, 14 TGFB1-regulated ovarian endometriosis-specific proteins were found (Table 2). Two proteins, EMILIN1 and Calponin-1 were further used to confirm our findings by analyzing them with Western blot and immunohistochemistry (Figure 7). Indeed, we were able to show the up-regulation of EMILIN1 and CNN1 in ovarian endometrioma. Through these analyses, our MS-based approaches were confirmed by microarray as well as Western blot and immunohistochemistry, thereby further adding to the relevance of our findings. Importantly, in addition to the novel endometriosis-related proteins discovered, the high correlation of the proteome results with the microarray data set further confirmed that wellplanned and properly controlled MS experiments produce reproducible data and can reveal new insights into patientderived protein profiles and category-specific disease protein profiles from only minute amounts of patient tissue.

Figure 7. CNN1 and EMILIN1 protein expression. (A) Western blot analysis indicated higher expression of CNN1 and EMILIN1 in PO as compared with PE. The proteins were localized in the stromal compartment of ovarian endometriosis lesions (B; CNN1 and C; EMILIN1). EP, epithelium; S, stroma.

dismutase [Cu-Zn] (SOD1) and extracellular superoxide dismutase [Cu-Zn] (SOD3) detectable in our data has been suggested to be a reaction to an increased endogenous oxidative stress in ovarian endometrioma also by others.49−51 In a recent study by Vouk et al.,52 the transcription of 172 genes was compared between ovarian endometriomas and endometrium of patients with myomas. In corroboration to their study, we observed that prostacyclin synthase (PTGIS), adipocyte enhancer-binding protein 1 (AEBP1), biglycan (BGN), caldesmon (CALD1), and collagen α-1(XIV) chain (COL14A1) were up-regulated and long-chain fatty-acid-CoA ligase 5 (ACSL5) and retinal dehydrogenase 2 (ALDH1A2) down-regulated in ovarian endometrioma. Very recently, in further agreement with our data, the protein expression of vimentin (VIM), COL14A1, collagen α-1 (VI) chain (COL6A1), and desmin (DES) have been reported in ovarian endometriomas.23 In conclusion, of the 214 proteins with changed expression levels identified in this work, only 42 have been reported previously in this context. Our ability to identify 172 new, interesting proteins (Supplemental Table 1) related to endometriosis, serves to demonstrate the power of the approach chosen in this work. In the pathway analysis of the differentially expressed proteins in ovarian endometrioma, Transforming growth factor β-1 (TGFB1) was identified as one of the main upstream regulators in the data set. Specifically, our results suggest that a TGFB1 has a role as an activator in ovarian endometrioma tissue (Figure 4, Supplemental Table 2). Interestingly, processes involving TGFB1 in endometriosis have recently been observed also by others.53−55 It was shown that TGFB1 is involved, e.g., in smooth muscle metaplasia, where fibroblasts differentiate into myofibroblasts similar to what occurs during wound healing56 or in stromal decidualization by progesterone and TGF-β, where the elongated fibroblast-like endometrial stromal cells transform into large decidual cells.57 Smooth muscle metaplasia has been reported in ovarian endometriosis,58 and the existence of myofibroplastic cells has been also verified in deep infiltrating endometriosis.53 Further studies are required to elucidate the role of TGFB1 and smooth muscle metaplasia in ovarian endometriosis. The comparison of endometrioma-deregulated proteins and mRNA microarray analysis in a larger patient population revealed that 88 proteins and their respective gene identifiers (Figure 6) could be identified with both methods. Interestingly,

■

CONCLUSION The results of this work demonstrate the feasibility of the combination of a semi-targeted approach that combines accurate inclusion mass screening (AIMS), inclusion lists, and LC−MS label-free quantitation in clinical proteomics. Minimally redundant LC−MS/MS methods were used to study minute amounts of ovarian endometrioma and endometrium tissues to focus on the proteome of the endometriosis of the ovary. In the process we found proteins that are specifically deregulated in ovarian endometrioma and in pathway analysis, the clear activation of cytokine TGFB1 regulatory network. This molecular profile gives us tools to study and understand the disease better but also ideas for possible endometriosis treatment and diagnosis. An equally important product of this work is the generation of a large resource of assays in the form of SRM transitions that are unique, standardized, and openly available. This collection of the tissue proteome results generates the most complete picture of the proteome of ovarian endometriosis to date.

■

ASSOCIATED CONTENT

S Supporting Information *

SRM transition list and supporting data. Data files are available at https://panoramaweb.org/labkey/project/ University%20of%20Turku/Endomet/begin.view. Supplemental tables of proteins showing differential expression between the two tissue types, the TGFB1 regulation network, and genes and corresponding proteins. This material is available free of charge via the Internet at http://pubs.acs.org.

■

AUTHOR INFORMATION

Corresponding Author

*Tel: +312 05255406. E-mail: [email protected]. Present Address □

Department of Pathology, University of Turku and Turku University Hospital, Turku, Finland.

4992



Article

Notes

(13) Fowler, P. A.; Tattum, J.; Bhattacharya, S.; Klonisch, T.; Hombach-Klonisch, S.; Gazvani, R.; Lea, R. G.; Miller, I.; Simpson, W. G.; Cash, P. An investigation of the effects of endometriosis on the proteome of human eutopic endometrium: a heterogeneous tissue with a complex disease. Proteomics 2007, 7 (1), 130−42. (14) Rai, P.; Kota, V.; Deendayal, M.; Shivaji, S. Differential proteome profiling of eutopic endometrium from women with endometriosis to understand etiology of endometriosis. J. Proteome Res. 2010, 9 (9), 4407−19. (15) Ding, X.; Wang, L.; Ren, Y.; Zheng, W. Detection of mitochondrial biomarkers in eutopic endometria of endometriosis using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. Fertil. Steril. 2010, 94 (7), 2528−30. (16) Stephens, A. N.; Hannan, N. J.; Rainczuk, A.; Meehan, K. L.; Chen, J.; Nicholls, P. K.; Rombauts, L. J.; Stanton, P. G.; Robertson, D. M.; Salamonsen, L. A. Post-translational modifications and proteinspecific isoforms in endometriosis revealed by 2D DIGE. J. Proteome Res. 2010, 9 (5), 2438−49. (17) Wang, L.; Zheng, W.; Ding, X. Y.; Yu, J. K.; Jiang, W. Z.; Zhang, S. Z. Identification biomarkers of eutopic endometrium in endometriosis using artificial neural networks and protein fingerprinting. Fertil. Steril. 2010, 93 (7), 2460−2. (18) Fassbender, A.; Simsa, P.; Kyama, C. M.; Waelkens, E.; Mihalyi, A.; Meuleman, C.; Gevaert, O.; Van de Plas, R.; de Moor, B.; D’Hooghe, T. M. TRIzol treatment of secretory phase endometrium allows combined proteomic and mRNA microarray analysis of the same sample in women with and without endometriosis. Reprod. Biol. Endocrinol. 2010, 8, 123. (19) Ding, X.; Wang, L.; Ren, Y.; Zheng, W. Differences in mitochondrial proteins in the eutopic endometrium of patients with adenomyosis and endometriosis identified using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. J. Int. Med. Res. 2010, 38 (3), 987−93. (20) Kyama, C. M.; Mihalyi, A.; Gevaert, O.; Waelkens, E.; Simsa, P.; Van de Plas, R.; Meuleman, C.; De Moor, B.; D’Hooghe, T. M. Evaluation of endometrial biomarkers for semi-invasive diagnosis of endometriosis. Fertil. Steril. 2011, 95 (4), 1338−43.e3. (21) Fassbender, A.; Verbeeck, N.; Bornigen, D.; Kyama, C. M.; Bokor, A.; Vodolazkaia, A.; Peeraer, K.; Tomassetti, C.; Meuleman, C.; Gevaert, O.; Van de Plas, R.; Ojeda, F.; De Moor, B.; Moreau, Y.; Waelkens, E.; D’Hooghe, T. M. Combined mRNA microarray and proteomic analysis of eutopic endometrium of women with and without endometriosis. Hum. Reprod. 2012, 27 (7), 2020−9. (22) Zhang, H.; Niu, Y.; Feng, J.; Guo, H.; Ye, X.; Cui, H. Use of proteomic analysis of endometriosis to identify different protein expression in patients with endometriosis versus normal controls. Fertil. Steril. 2006, 86 (2), 274−82. (23) Marianowski, P.; Szymusik, I.; Malejczyk, J.; Hibner, M.; Wielgos, M. Proteomic analysis of eutopic and ectopic endometriotic tissues based on isobaric peptide tags for relative and absolute quantification (iTRAQ) method. Neuroendocrinol. Lett. 2013, 34 (7), 717−21. (24) Rifai, N.; Gillette, M. A.; Carr, S. A. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat. Biotechnol. 2006, 24 (8), 971−83. (25) Domon, B.; Aebersold, R. Options and considerations when selecting a quantitative proteomics strategy. Nat. Biotechnol. 2010, 28 (7), 710−21. (26) Bagnato, C.; Thumar, J.; Mayya, V.; Hwang, S.; Zebroski, H.; Claffey, K.; Haudenschild, C.; Eng, J.; Lundgren, D.; Han, D. Proteomics analysis of human coronary atherosclerotic plaque: a feasibility study of direct tissue proteomics by liquid chromatography and tandem mass spectrometry. Mol. Cell. Proteomics 2007, 6 (6), 1088−102. (27) Han, M.; Hwang, S.; Roy, D.; Lundgren, D.; Price, J.; Ousman, S.; Fernald, G.; Gerlitz, B.; Robinson, W.; Baranzini, S.; Grinnell, B.; Raine, C.; Sobel, R.; Han, D.; Steinman, L. Proteomic analysis of active multiple sclerosis lesions reveals therapeutic targets. Nature 2008, 451 (7182), 1076−81.

The authors declare no competing financial interest.

■ ■

ACKNOWLEDGMENTS We thank the CBT Proteomics Facility for the technical assistance and Professor Per Andrén for helpful discussions. REFERENCES

(1) Abbas, S.; Ihle, P.; Koster, I.; Schubert, I. Prevalence and incidence of diagnosed endometriosis and risk of endometriosis in patients with endometriosis-related symptoms: findings from a statutory health insurance-based cohort in Germany. Eur. J. Obstet. Gynecol. Reprod. Biol. 2012, 160 (1), 79−83. (2) Kennedy, S.; Bergqvist, A.; Chapron, C.; D’Hooghe, T.; Dunselman, G.; Greb, R.; Hummelshoj, L.; Prentice, A.; Saridogan, E. ESHRE guideline for the diagnosis and treatment of endometriosis. Hum. Reprod. 2005, 20 (10), 2698−704. (3) Giudice, L. C. Clinical practice. Endometriosis. N. Engl. J. Med. 2010, 362 (25), 2389−98. (4) Pearce, C. L.; Templeman, C.; Rossing, M. A.; Lee, A.; Near, A. M.; Webb, P. M.; Nagle, C. M.; Doherty, J. A.; Cushing-Haugen, K. L.; Wicklund, K. G.; Chang-Claude, J.; Hein, R.; Lurie, G.; Wilkens, L. R.; Carney, M. E.; Goodman, M. T.; Moysich, K.; Kjaer, S. K.; Hogdall, E.; Jensen, A.; Goode, E. L.; Fridley, B. L.; Larson, M. C.; Schildkraut, J. M.; Palmieri, R. T.; Cramer, D. W.; Terry, K. L.; Vitonis, A. F.; Titus, L. J.; Ziogas, A.; Brewster, W.; Anton-Culver, H.; Gentry-Maharaj, A.; Ramus, S. J.; Anderson, A. R.; Brueggmann, D.; Fasching, P. A.; Gayther, S. A.; Huntsman, D. G.; Menon, U.; Ness, R. B.; Pike, M. C.; Risch, H.; Wu, A. H.; Berchuck, A.; Consortium, O. C. A. Association between endometriosis and risk of histological subtypes of ovarian cancer: a pooled analysis of case-control studies. Lancet Oncol. 2012, 13 (4), 385−94. (5) Raffi, F.; Metwally, M.; Amer, S. The impact of excision of ovarian endometrioma on ovarian reserve: a systematic review and meta-analysis. J. Clin. Endocrinol. Metab. 2012, 97 (9), 3146−54. (6) Sedlaczek, P.; Frydecka, I.; Gabryś, M.; Van Dalen, A.; Einarsson, R.; Harłozińska, A. Comparative analysis of CA125, tissue polypeptide specific antigen, and soluble interleukin-2 receptor alpha levels in sera, cyst, and ascitic fluids from patients with ovarian carcinoma. Cancer 2002, 95 (9), 1886−93. (7) Gramolini, A.; Peterman, S.; Kislinger, T. Mass spectrometrybased proteomics: a useful tool for biomarker discovery? Clin. Pharmacol. Ther. 2008, 83 (5), 758−60. (8) Kislinger, T.; Cox, B.; Kannan, A.; Chung, C.; Hu, P.; Ignatchenko, A.; Scott, M. S.; Gramolini, A. O.; Morris, Q.; Hallett, M. T.; Rossant, J.; Hughes, T. R.; Frey, B.; Emili, A. Global survey of organ and organelle protein expression in mouse: combined proteomic and transcriptomic profiling. Cell 2006, 125 (1), 173−86. (9) Addona, T. A.; Shi, X.; Keshishian, H.; Mani, D. R.; Burgess, M.; Gillette, M. A.; Clauser, K. R.; Shen, D.; Lewis, G. D.; Farrell, L. A.; Fifer, M. A.; Sabatine, M. S.; Gerszten, R. E.; Carr, S. A. A pipeline that integrates the discovery and verification of plasma protein biomarkers reveals candidate markers for cardiovascular disease. Nat. Biotechnol. 2011, 29 (7), 635−43. (10) Waanders, L. F.; Chwalek, K.; Monetti, M.; Kumar, C.; Lammert, E.; Mann, M. Quantitative proteomic analysis of single pancreatic islets. Proc. Natl. Acad. Sci. U.S.A. 2009, 106 (45), 18902−7. (11) Nirmalan, N. J.; Hughes, C.; Peng, J.; McKenna, T.; Langridge, J.; Cairns, D. A.; Harnden, P.; Selby, P. J.; Banks, R. E. Initial development and validation of a novel extraction method for quantitative mining of the formalin-fixed, paraffin-embedded tissue proteome for biomarker investigations. J. Proteome Res. 2011, 10 (2), 896−906. (12) Mangé, A.; Chaurand, P.; Perrochia, H.; Roger, P.; Caprioli, R. M.; Solassol, J. Liquid chromatography-tandem and MALDI imaging mass spectrometry analyses of RCL2/CS100-fixed, paraffin-embedded tissues: proteomics evaluation of an alternate fixative for biomarker discovery. J. Proteome Res. 2009, 8 (12), 5619−28. 4993



Article

(28) Jaffe, J. D.; Keshishian, H.; Chang, B.; Addona, T. A.; Gillette, M. A.; Carr, S. A. Accurate inclusion mass screening: a bridge from unbiased discovery to targeted assay development for biomarker verification. Mol. Cell. Proteomics 2008, 7 (10), 1952−62. (29) MacLean, B.; Tomazela, D. M.; Shulman, N.; Chambers, M.; Finney, G. L.; Frewen, B.; Kern, R.; Tabb, D. L.; Liebler, D. C.; MacCoss, M. J. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 2010, 26 (7), 966−8. (30) Schmidt, A.; Gehlenborg, N.; Bodenmiller, B.; Mueller, L. N.; Campbell, D.; Mueller, M.; Aebersold, R.; Domon, B. An integrated, directed mass spectrometric approach for in-depth characterization of complex peptide mixtures. Mol. Cell. Proteomics 2008, 7 (11), 2138− 50. (31) Sun, W.; Wu, S.; Wang, X.; Zheng, D.; Gao, Y. An analysis of protein abundance suppression in data dependent liquid chromatography and tandem mass spectrometry with tryptic peptide mixtures of five known proteins. Eur. J. Mass Spectrom. 2005, 11 (6), 575−80. (32) Eng, J. K.; McCormack, A. L.; Yates, J. R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 1994, 5 (11), 976−89. (33) Spivak, M.; Weston, J.; Bottou, L.; Kall, L.; Noble, W. S. Improvements to the percolator algorithm for Peptide identification from shotgun proteomics data sets. J. Proteome Res. 2009, 8 (7), 3737− 45. (34) Brosch, M.; Yu, L.; Hubbard, T.; Choudhary, J. Accurate and sensitive peptide identification with Mascot Percolator. J. Proteome Res. 2009, 8 (6), 3176−81. (35) Kilpinen, S.; Autio, R.; Ojala, K.; Iljin, K.; Bucher, E.; Sara, H.; Pisto, T.; Saarela, M.; Skotheim, R. I.; Björkman, M.; Mpindi, J. P.; Haapa-Paananen, S.; Vainio, P.; Edgren, H.; Wolf, M.; Astola, J.; Nees, M.; Hautaniemi, S.; Kallioniemi, O. Systematic bioinformatic analysis of expression levels of 17,330 human genes across 9,783 samples from 175 types of healthy and pathological tissues. Genome Biol. 2008, 9 (9), R139. (36) Hruz, T.; Laule, O.; Szabo, G.; Wessendorp, F.; Bleuler, S.; Oertle, L.; Widmayer, P.; Gruissem, W.; Zimmermann, P. Genevestigator v3: a reference expression database for the meta-analysis of transcriptomes. Adv. Bioinf. 2008, 2008, 420747. (37) Radulovic, D.; Jelveh, S.; Ryu, S.; Hamilton, T. G.; Foss, E.; Mao, Y.; Emili, A. Informatics platform for global proteomic profiling and biomarker discovery using liquid chromatography-tandem mass spectrometry. Mol. Cell. Proteomics 2004, 3 (10), 984−97. (38) Bondarenko, P. V.; Chelius, D.; Shaler, T. A. Identification and relative quantitation of protein mixtures by enzymatic digestion followed by capillary reversed-phase liquid chromatography-tandem mass spectrometry. Anal. Chem. 2002, 74 (18), 4741−9. (39) Hiissa, J.; Elo, L. L.; Huhtinen, K.; Perheentupa, A.; Poutanen, M.; Aittokallio, T. Resampling reveals sample-level differential expression in clinical genome-wide studies. OMICS 2009, 13 (5), 381−96. (40) Falconer, H.; D’Hooghe, T.; Fried, G. Endometriosis and genetic polymorphisms. Obstet. Gynecol. Surv. 2007, 62 (9), 616−28. (41) Asch, E.; Levine, D. Variations in appearance of endometriomas. J. Ultrasound Med. 2007, 26 (8), 993−1002. (42) Hüttenhain, R.; Soste, M.; Selevsek, N.; Röst, H.; Sethi, A.; Carapito, C.; Farrah, T.; Deutsch, E. W.; Kusebauch, U.; Moritz, R. L.; Niméus-Malmström, E.; Rinner, O.; Aebersold, R. Reproducible quantification of cancer-associated proteins in body fluids using targeted proteomics. Sci. Transl. Med. 2012, 4 (142), No. 142ra94. (43) Hwang, S.; Thumar, J.; Lundgren, D.; Rezaul, K.; Mayya, V.; Wu, L.; Eng, J.; Wright, M.; Han, D. Direct cancer tissue proteomics: a method to identify candidate cancer biomarkers from formalin-fixed paraffin-embedded archival tissues. Oncogene 2007, 26 (1), 65−76. (44) Krüger, T.; Lehmann, T.; Rhode, H. Effect of quality characteristics of single sample preparation steps in the precision and coverage of proteomic studiesa review. Anal. Chim. Acta 2013, 776, 1−10.

(45) Piehowski, P. D.; Petyuk, V. A.; Orton, D. J.; Xie, F.; Moore, R. J.; Ramirez-Restrepo, M.; Engel, A.; Lieberman, A. P.; Albin, R. L.; Camp, D. G.; Smith, R. D.; Myers, A. J. Sources of technical variability in quantitative LC−MS proteomics: human brain tissue sample analysis. J. Proteome Res. 2013, 12 (5), 2128−37. (46) Whiteaker, J. R.; Lin, C.; Kennedy, J.; Hou, L.; Trute, M.; Sokal, I.; Yan, P.; Schoenherr, R. M.; Zhao, L.; Voytovich, U. J.; Kelly-Spratt, K. S.; Krasnoselsky, A.; Gafken, P. R.; Hogan, J. M.; Jones, L. A.; Wang, P.; Amon, L.; Chodosh, L. A.; Nelson, P. S.; McIntosh, M. W.; Kemp, C. J.; Paulovich, A. G. A targeted proteomics-based pipeline for verification of biomarkers in plasma. Nat. Biotechnol. 2011, 29 (7), 625−34. (47) Kyama, C. M.; T’Jampens, D.; Mihalyi, A.; Simsa, P.; Debrock, S.; Waelkens, E.; Landuyt, B.; Meuleman, C.; Fulop, V.; Mwenda, J. M.; D’Hooghe, T. M. ProteinChip technology is a useful method in the pathogenesis and diagnosis of endometriosis: a preliminary study. Fertil. Steril. 2006, 86 (1), 203−9. (48) Dos Santos Hidalgo, G.; Meola, J.; Rosa, E. S. J. C.; Paro de Paz, C. C.; Ferriani, R. A. TAGLN expression is deregulated in endometriosis and may be involved in cell invasion, migration, and differentiation. Fertil. Steril. 2011, 96 (3), 700−3. (49) Ota, H.; Igarashi, S.; Hatazawa, J.; Tanaka, T. Immunohistochemical assessment of superoxide dismutase expression in the endometrium in endometriosis and adenomyosis. Fertil. Steril. 1999, 72 (1), 129−34. (50) Oner-Iyidoğan, Y.; Koçak, H.; Gürdöl, F.; Korkmaz, D.; Buyru, F. Indices of oxidative stress in eutopic and ectopic endometria of women with endometriosis. Gynecol. Obstet. Invest. 2004, 57 (4), 214− 7. (51) Ngo, C.; Chereau, C.; Nicco, C.; Weill, B.; Chapron, C.; Batteux, F. Reactive oxygen species controls endometriosis progression. Am. J. Pathol. 2009, 175 (1), 225−34. (52) Vouk, K.; Smuc, T.; Guggenberger, C.; Ribic-Pucelj, M.; Sinkovec, J.; Husen, B.; Thole, H.; Houba, P.; Thaete, C.; Adamski, J.; Rizner, T. L. Novel estrogen-related genes and potential biomarkers of ovarian endometriosis identified by differential expression analysis. J. Steroid Biochem. Mol. Biol. 2011, 125 (3−5), 231−42. (53) van Kaam, K. J.; Schouten, J. P.; Nap, A. W.; Dunselman, G. A.; Groothuis, P. G. Fibromuscular differentiation in deeply infiltrating endometriosis is a reaction of resident fibroblasts to the presence of ectopic endometrium. Hum. Reprod. 2008, 23 (12), 2692−700. (54) Barcena de Arellano, M. L.; Gericke, J.; Reichelt, U.; Okuducu, A. F.; Ebert, A. D.; Chiantera, V.; Schneider, A.; Mechsner, S. Immunohistochemical characterization of endometriosis-associated smooth muscle cells in human peritoneal endometriotic lesions. Hum. Reprod. 2011, 26 (10), 2721−30. (55) Sohler, F.; Sommer, A.; Wachter, D. L.; Agaimy, A.; Fischer, O. M.; Renner, S. P.; Burghaus, S.; Fasching, P. A.; Beckmann, M. W.; Fuhrmann, U.; Strick, R.; Strissel, P. L. Tissue remodeling and nonendometrium-like menstrual cycling are hallmarks of peritoneal endometriosis lesions. Reprod Sci. 2012, 20 (1), 85−102. (56) Gabbiani, G. The myofibroblast in wound healing and fibrocontractive diseases. J. Pathol. 2003, 200 (4), 500−3. (57) Kim, M. R.; Park, D. W.; Lee, J. H.; Choi, D. S.; Hwang, K. J.; Ryu, H. S.; Min, C. K. Progesterone-dependent release of transforming growth factor-β1 from epithelial cells enhances the endometrial decidualization by turning on the Smad signalling in stromal cells. Mol. Hum. Reprod. 2005, 11 (11), 801−8. (58) Fukunaga, M. Smooth muscle metaplasia in ovarian endometriosis. Histopathology 2000, 36 (4), 348−52.

4994


Ovarian Endometriosis Signatures Established through Discovery and

Recommend Documents