Comprehensive quantitative analysis of ovarian and breast cancer tumor peptidomes

Peptidomics: A Review of Clinical Applications and Methodologies

Improvements in both liquid chromatography (LC) and mass spectrometry (MS) instrumentation have greatly enhanced proteomic and small molecule metabolomic analysis in recent years. Less focus has been on the improved capability to detect and quantify small bioactive peptides, even though the exact sequences of the peptide species produced can have important biological consequences. Endogenous bioactive peptide hormones, for example, are generated by the targeted and regulated cleavage of peptides from their prohormone sequence. This process may include organ specific variants, as proglucagon is converted to glucagon in the pancreas but glucagon-like peptide-1 (GLP-1) in the small intestine, with glucagon raising, whereas GLP-1, as an incretin, lowering blood glucose. Therefore, peptidomics workflows must preserve the structure of the processed peptide products to prevent the misidentification of ambiguous peptide species. The poor in vivo and in vitro stability of peptides in biological matrices is a major factor that needs to be considered when developing methods to study them. The bioinformatic analysis of peptidomics data sets requires the inclusion of specific post-translational modifications, which are critical for the function of many bioactive peptides. This review aims to discuss and contrast the various extraction, analytical, and bioinformatics approaches used for human peptidomics studies in a multitude of matrices.

Peptidomics-Driven Strategy Reveals Peptides and Predicted Proteases Associated With Oral Cancer Prognosis

Protease activity has been associated with pathological processes that can lead to cancer development and progression. However, understanding the pathological unbalance in proteolysis is challenging because changes can occur simultaneously at protease, their inhibitor, and substrate levels. Here, we present a pipeline that combines peptidomics, proteomics, and peptidase predictions for studying proteolytic events in the saliva of 79 patients and their association with oral squamous cell carcinoma (OSCC) prognosis. Our findings revealed differences in the saliva peptidome of patients with (pN+) or without (pN0) lymph-node metastasis and delivered a panel of ten endogenous peptides correlated with poor prognostic factors plus five molecules able to classify pN0 and pN+ patients (area under the receiver operating characteristic curve > 0.85). In addition, endopeptidases and exopeptidases putatively implicated in the processing of differential peptides were investigated using cancer tissue gene expression data from public repositories, reinforcing their association with poorer survival rates and prognosis in oral cancer. The dynamics of the OSCC-related proteolysis were further explored via the proteomic profiling of saliva. This revealed that peptidase/endopeptidase inhibitors exhibited reduced levels in the saliva of pN+ patients, as confirmed by selected reaction monitoring-mass spectrometry, while minor changes were detected in the level of saliva proteases. Taken together, our results indicated that proteolytic activity is accentuated in the saliva of patients with OSCC and lymph-node metastasis and, at least in part, is modulated by reduced levels of salivary peptidase inhibitors. Therefore, this integrated pipeline provided better comprehension and discovery of molecular features with implications in the oral cancer metastasis prognosis.

Recent Technological Advances in the Mass Spectrometry-based Nanomedicine Studies: An Insight from Nanoproteomics

Nanoscience becomes one of the most cutting-edge research directions in recent years since it is gradually matured from basic to applied science. Nanoparticles (NPs) and nanomaterials (NMs) play important roles in various aspects of biomedicine science, and their influences on the environment have caused a whole range of uncertainties which require extensive attention. Due to the quantitative and dynamic information provided for human proteome, mass spectrometry (MS)-based quantitative proteomic technique has been a powerful tool for nanomedicine study. In this article, recent trends of progress and development in the nanomedicine of proteomics were discussed from quantification techniques and publicly available resources or tools. First, a variety of popular protein quantification techniques including labeling and label-free strategies applied to nanomedicine studies are overviewed and systematically discussed. Then, numerous protein profiling tools for data processing and postbiological statistical analysis and publicly available data repositories for providing enrichment MS raw data information sources are also discussed.

Probing the endogenous peptidomes of cancer for biomarkers: A new endeavor

The life span of cancer patients can be prolonged with appropriate therapies if detected early. Mass screening for early detection of cancer, however, requires sensitive and specific biomarkers obtainable from body fluids such as blood or urine. To date, most biomarker discovery programs focus on the proteome rather than the endogenous peptidome. It has been long-established that tumor cells and stromal cells produce tumor resident proteases (TRPs) to remodel the surrounding tumor microenvironment in support of tumor progression. In fact, proteolytic products of TRPs have been shown to correlate with malignant behavior. Being of low molecular weight, these unique peptides can pass through the endothelial barrier of the vasculature into the bloodstream. As such, the cancer peptidome has increasingly become a focus for biomarker discovery. In this review, we discuss on the various aspects of the peptidome in cancer biomarker research.

Mass spectrometry-based peptidome profiling of human serous ovarian cancer tissues

BACKGROUND

Bioactive peptides existing in vivo have been considered as an important class of natural medicines for the treatment of diseases. Peptidome analysis of tissues and biofluids had provided important information about the differentially expressed bioactive peptides in vivo.

METHODS

Here, we analyzed the peptidome of serous ovarian cancer tissue samples and normal ovarian epithelial tissue samples by mass spectrometry and further investigated the possible bioactive peptides that were differentially expressed.

RESULTS

We identified 634 differentially expressed peptides, 508 of these peptides were highly abundant in serous ovarian cancer tissues, a result consistent with higher protease activity in ovarian cancer patients. The difference in preferred cleavage sites between the serous ovarian cancer tissues and normal ovarian epithelium indicated the characteristic peptidome of ovarian cancer and the nature of cancer-associated protease activity. Interestingly, KEGG pathway analysis of the peptide precursors indicated that the differentially regulated pathways in ovarian cancer are highly consistent with the pathways discovered in other cancers. Besides, we found that a proportion of the differentially expressed peptides are similar to the known immune-regulatory peptides and anti-bacterial peptides. Then we further investigated the function of the two down-regulated peptides in ovarian cancer cells and found that peptide P1DS significantly inhibited the invasion and migration of OVCAR3 and SKOV3 ovarian cancer cells.

CONCLUSIONS

Our results are the first to identify the differentially expressed peptides between the serous ovarian cancer tissue and the normal ovarian epithelium. Our results indicate that bioactive peptides involved in tumorigenesis are existed in vivo.

Characterization of the Ovarian Tumor Peptidome

Aberrant degradation of proteins is associated with many pathological states, including cancers. Mass spectrometric analysis of the tumor peptidome has the potential to provide biological insights on proteolytic processing in cancer. However, attempts to use the tumors peptidome information in cancer research have been fairly limited to date, largely due to the lack of effective approaches for robust peptidomics identification and quantification, and the prevalence of confounding factors and biases associated with sample handling and processing. To address this need, we have recently developed an effective and robust analytical platform as well as a novel informatics approach for comprehensive analyses of tissue peptidomes. The ability of this new peptidomics pipeline for high-throughput, comprehensive, and quantitative peptidomics analysis, as well as the suitability of clinical ovarian tumor samples with postexcision delay limited to less than 60min before freezing for peptidomics analysis, has been demonstrated. These initial analyses set a stage for further determination of molecular details and functional significance of the peptidomic activities in ovarian cancer.

Surveying the sequence diversity of model prebiotic peptides by mass spectrometry

The rise of peptides with secondary structures and functions would have been a key step in the chemical evolution which led to life. As with modern biology, amino acid sequence would have been a primary determinant of peptide structure and activity in an origins-of-life scenario. It is a commonly held hypothesis that unique functional sequences would have emerged from a diverse soup of proto-peptides, yet there is a lack of experimental data in support of this. Whereas the majority of studies in the field focus on peptides containing only one or two types of amino acids, here we used modern mass spectrometry (MS)-based techniques to separate and sequence de novo proto-peptides containing broader combinations of prebiotically plausible monomers. Using a dry-wet environmental cycling protocol, hundreds of proto-peptide sequences were formed over a mere 4 d of reaction. Sequence homology diagrams were constructed to compare experimental and theoretical sequence spaces of tetrameric proto-peptides. MS-based analyses such as this will be increasingly necessary as origins-of-life researchers move toward systems-level investigations of prebiotic chemistry.

Peptidomics Analysis of Transient Regeneration in the Neonatal Mouse Heart

Neonatal mouse hearts have completely regenerative capability after birth, but the ability to regenerate rapidly lost after 7 days, the mechanism has not been clarified. Previous studies have shown that mRNA profile of adult mouse changed greatly compared to neonatal mouse. So far, there is no research of peptidomics related to heart regeneration. In order to explore the changes of proteins, enzymes, and peptides related to the transient regeneration, we used comparative petidomics technique to compare the endogenous peptides in the mouse heart of postnatal 1 and 7 days. In final, we identified 236 differentially expressed peptides, 169 of which were upregulated and 67 were downregulated in the postnatal 1 day heart, and also predicted 36 functional peptides associated with transient regeneration. The predicted 36 candidate peptides are located in the important domains of precursor proteins and/or contain the post-transcriptional modification (PTM) sites, which are involved in the biological processes of cardiac development, cardiac muscle disease, cell proliferation, necrosis, and apoptosis. In conclusion, for the first time, we compared the peptidomics profiles of neonatal heart between postnatal 1 day and postnatal 7 day. This study provides a new direction and an important basis for the mechanism research of transient regeneration in neonatal heart. J. Cell. Biochem. 118: 2828-2840, 2017. © 2017 Wiley Periodicals, Inc.

An integrated proteomic and glycoproteomic approach uncovers differences in glycosylation occupancy from benign and malignant epithelial ovarian tumors

Background

Epithelial ovarian carcinomas encompass a heterogeneous group of diseases with a poor 5-year survival rate. Serous carcinoma is the most common type. Most FDA-approved serum tumor markers are glycoproteins. These glycoproteins on cell surface or shed into the bloodstream could serve as therapeutic targets as well as surrogates of tumor. In addition to glycoprotein expressions, the analysis of protein glycosylation occupancy could be important for the understanding of cancer biology as well as the identification of potential glycoprotein changes in cancer. In this study, we used an integrated proteomics and glycoproteomics approach to analyze global glycoprotein abundance and glycosylation occupancy for proteins from high-grade ovarian serous carcinoma (HGSC) and serous cystadenoma, a benign epithelial ovarian tumor, by using LC–MS/MS-based technique.

Methods

Fresh-frozen ovarian HGSC tissues and benign serous cystadenoma cases were quantitatively analyzed using isobaric tags for relative and absolute quantitation for both global and glycoproteomic analyses by two dimensional fractionation followed by LC–MS/MS analysis using a Orbitrap Velos mass spectrometer.

Results

Proteins and N-linked glycosite-containing peptides were identified and quantified using the integrated global proteomic and glycoproteomic approach. Among the identified N-linked glycosite-containing peptides, the relative abundances of glycosite-containing peptide and the glycoprotein levels were compared using glycoproteomic and proteomic data. The glycosite-containing peptides with unique changes in glycosylation occupancies rather than the protein expression levels were identified.

Conclusion

In this study, we presented an integrated proteomics and glycoproteomics approach to identify changes of glycoproteins in protein expression and glycosylation occupancy in HGSC and serous cystadenoma and determined the changes of glycosylation occupancy that are associated with malignant and benign tumor tissues. Specific changes in glycoprotein expression or glycosylation occupancy have the potential to be used in the discrimination between benign and malignant epithelial ovarian tumors and to improve our understanding of ovarian cancer biology.

Electronic supplementary material

The online version of this article (doi:10.1186/s12014-017-9152-2) contains supplementary material, which is available to authorized users.

Characterization of the Low-Molecular-Weight Human Plasma Peptidome

The human plasma proteome represents an important secreted sub-proteome. Proteomic analysis of blood plasma with mass spectrometry is a challenging task. The high complexity and wide dynamic range of proteins as well as the presence of several proteins at very high concentrations complicate the profiling of the human plasma proteome. The peptidome (or low-molecular-weight fraction, LMF) of the human plasma proteome is an invaluable source of biological information, especially in the context of identifying plasma-based markers of disease. Peptides are generated by active synthesis and proteolytic processing, often yielding proteolytic fragments that mediate a variety of physiological and pathological functions. As such, degradomic studies, investigating cleavage products via peptidomics and top-down proteomics in particular, have warranted significant research interest. However, due to their molecular weight, abundance, and solubility, issues with identifying specific cleavage sites and coverage of peptide fragments remain challenging. Peptidomics is currently focused toward comprehensively studying peptides cleaved from precursor proteins by endogenous proteases. This protocol outlines a standardized rapid and reproducible procedure for peptidomic profiling of human plasma using centrifugal ultrafiltration and mass spectrometry. Ultrafiltration is a convective process that uses anisotropic semipermeable membranes to separate macromolecular species on the basis of size. We have optimized centrifugal ultrafiltration (cellulose triacetate membrane) for plasma fractionation with respect to buffer and solvent composition, centrifugal force, duration, and temperature to facilitate recovery >95% and enrichment of the human plasma peptidome. This method serves as a comprehensive and facile process to enrich and identify a key, underrepresented sub-proteome of human blood plasma.

Strategies in functional proteomics: Unveiling the pathways to precision oncology

Personalised strategies in cancer care are required to overcome the therapeutic challenges posed by variability between patients and disease subsets. To this end, enhanced precision tools must be developed to describe the molecular drivers of malignant proliferation. Such tools must also identify druggable targets and biomarkers in order to provide essential information regarding drug development and therapeutic outcome. Here we discuss how proteomics-based approaches provide a set of viable methodologies capable of delivering quantitative information throughout the main stages of personalised oncology and a ratiometric platform that delivers systems-wide methods for drug evaluation.

Pharmacodynamic endpoints as clinical trial objectives to answer important questions in oncology drug development

Analyzing the molecular interplay between malignancies and therapeutic agents is rarely a straightforward process, but we hope that this special issue of Seminars has highlighted the clinical value of such endeavors as well as the relevant theoretical and practical considerations. Here, we conclude with both an overview of the various high-value applications of clinical pharmacodynamics (PD) in developmental therapeutics and an outline of the framework for incorporating PD analyses into the design of clinical trials. Given the increasingly recognized importance of determining and administering the biologically effective dose (BED) and schedule of targeted agents, we explain how clinical PD biomarkers specific to the agent mechanism of action (MOA) can be used for the development of pharmacodynamics-guided biologically effective dosage regimens (PD-BEDR) to maximize the efficacy and minimize the toxicity of targeted therapies. In addition, we discuss how MOA-based PD biomarker analyses can be used both as patient selection diagnostic tools and for designing novel drug combinations targeting the specific mutational signature of a given malignancy. We also describe the role of PD analyses in clinical trials, including for MOA confirmation and dosage regimen optimization during phase 0 trials as well as for correlating molecular changes with clinical efficacy when establishing proof-of-concept in phase I/II trials. Finally, we outline the critical technological developments that are needed to enhance the quality and quantity of future clinical PD data collection, broaden the types of molecular questions that can be answered in the clinic, and, ultimately, improve patient outcomes.

Intact Cell MALDI-TOF MS on Sperm: A Molecular Test For Male Fertility Diagnosis

Currently, evaluation of sperm quality is primarily based on in vitro measures of sperm function such as motility, viability and/or acrosome reaction. However, results are often poorly correlated with fertility, and alternative diagnostic tools are therefore needed both in veterinary and human medicine. In a recent pilot study, we demonstrated that MS profiles from intact chicken sperm using MALDI-TOF profiles could detect significant differences between fertile/subfertile spermatozoa showing that such profiles could be useful for in vitro male fertility testing. In the present study, we performed larger standardized experimental procedures designed for the development of fertility- predictive mathematical models based on sperm cell MALDI-TOF MS profiles acquired through a fast, automated method. This intact cell MALDI-TOF MS-based method showed high diagnostic accuracy in identifying fertile/subfertile males in a large male population of known fertility from two distinct genetic lineages (meat and egg laying lines). We additionally identified 40% of the m/z peaks observed in sperm MS profiles through a top-down high-resolution protein identification analysis. This revealed that the MALDI-TOF MS spectra obtained from intact sperm cells contained a large proportion of protein degradation products, many implicated in important functional pathways in sperm such as energy metabolism, structure and movement. Proteins identified by our predictive model included diverse and important functional classes providing new insights into sperm function as it relates to fertility differences in this experimental system. Thus, in addition to the chicken model system developed here, with the use of appropriate models these methods should effectively translate to other animal taxa where similar tests for fertility are warranted.

Proteomics of ovarian cancer: functional insights and clinical applications

In the past decade, there has been an increasing interest in applying proteomics to assist in understanding the pathogenesis of ovarian cancer, elucidating the mechanism of drug resistance, and in the development of biomarkers for early detection of ovarian cancer. Although ovarian cancer is a spectrum of different diseases, the strategies for diagnosis and treatment with surgery and adjuvant therapy are similar across ovarian cancer types, increasing the general applicability of discoveries made through proteomics research. While proteomic experiments face many difficulties which slow the pace of clinical applications, recent advances in proteomic technology contribute significantly to the identification of aberrant proteins and networks which can serve as targets for biomarker development and individualized therapies. This review provides a summary of the literature on proteomics' contributions to ovarian cancer research and highlights the current issues, future directions, and challenges. We propose that protein-level characterization of primary lesion in ovarian cancer can decipher the mystery of this disease, improve diagnostic tools, and lead to more effective screening programs.

An Optimized Informatics Pipeline for Mass Spectrometry-Based Peptidomics

The comprehensive MS analysis of the peptidome, the intracellular and intercellular products of protein degradation, has the potential to provide novel insights on endogenous proteolytic processing and its utility in disease diagnosis and prognosis. Along with the advances in MS instrumentation and related platforms, a plethora of proteomics data analysis tools have been applied for direct use in peptidomics; however, an evaluation of the currently available informatics pipelines for peptidomics data analysis has yet to be reported. In this study, we began by evaluating the results of several popular MS/MS database search engines, including MS-GF+, SEQUEST, and MS-Align+, for peptidomics data analysis, followed by identification and label-free quantification using the well-established accurate mass and time (AMT) tag and newly developed informed quantification (IQ) approaches, both based on direct LC-MS analysis. Our results demonstrated that MS-GF+ outperformed both SEQUEST and MS-Align+ in identifying peptidome peptides. Using a database established from MS-GF+ peptide identifications, both the AMT tag and IQ approaches provided significantly deeper peptidome coverage and less missing data for each individual data set than the MS/MS methods, while achieving robust label-free quantification. Besides having an excellent correlation with the AMT tag quantification results, IQ also provided slightly higher peptidome coverage. Taken together, we propose an optimized informatics pipeline combining MS-GF+ for initial database searching with IQ (or AMT tag) approaches for identification and label-free quantification for high-throughput, comprehensive, and quantitative peptidomics analysis. Graphical Abstract ᅟ.

Limitations of Mass Spectrometry-Based Peptidomic Approaches

Mass spectrometry-based peptidomic approaches are powerful techniques to detect and identify the peptide content of biological samples. The present study investigated the limitations of peptidomic approaches using trimethylammonium butyrate isotopic tags to quantify relative peptide levels and Mascot searches to identify peptides. Data were combined from previous studies on human cell lines or mouse tissues. The combined databases contain 2155 unique peptides ranging in mass from 444 to 8765 Da, with the vast majority between 1 and 3 kDa. The amino acid composition of the identified peptides generally reflected the frequency in the Eukaryotic proteome with the exception of Cys, which was not present in any of the identified peptides in the free-SH form but was detected at low frequency as a disulfide with Cys residues, a disulfide with glutathione, or as S-cyanocysteine. To test if the low detection rate of peptides smaller than 500 Da, larger than 3 kDa, or containing Cys was a limitation of the peptidomics procedure, tryptic peptides of known proteins were processed for peptidomics using the same approach used for human cell lines and mouse tissues. The identified tryptic peptides ranged from 516 to 2418 Da, whereas the theoretical digest ranged from 217 to 7559 Da. Peptides with Cys were rarely detected and, if present, the Cys was usually modified S-cyanocysteine. Additionally, peptides with mono- and di-iodo Tyr and His were identified. Taken together, there are limitations of peptidomic techniques, and awareness of these limitations is important to properly use and interpret results. Graphical Abstract ᅟ.

Serum peptidome profiling analysis for the identification of potential biomarkers in cervical intraepithelial neoplasia patients

Cervical intraepithelial neoplasia (CIN) is a precancerous disease of cervical squamous cell carcinoma. We Used Mass Spectrometry based peptidome profile study to predict the transformation of CIN1, which is the primary stage of this lesion. . Serum samples of 34 Cervical squamous cell carcinoma patients, 31 healthy controls, and 29 CIN1 samples were analyzed. Peptides were purified by WCX magnetic beads (Bioyong), and analyzed by MALDI TOF (Bruker). Raw data were analyzed by BioExplorer software (Bioyong). The results showed 14 mass peaks with significant differences. The diagnosis model is established by analyzing peptide profiles of 15 SCC patients and 20 healthy women serum, with a sensitivity of 100% and specificity of 100.00%. In validation set, the SCC diagnosis model also had good performance with a sensitivity of 80%, a specificity of 100%. In addition, this model could predict 29 CIN1 patients with accuracy of 55.17%. These results would provide a new method to predict the trend of CIN1 and take effective measures for high risk group timely.

Proteomics in cancer biomarkers discovery: challenges and applications

With the introduction of recent high-throughput technologies to various fields of science and medicine, it is becoming clear that obtaining large amounts of data is no longer a problem in modern research laboratories. However, coherent study designs, optimal conditions for obtaining high-quality data, and compelling interpretation, in accordance with the evidence-based systems biology, are critical factors in ensuring the emergence of good science out of these recent technologies. This review focuses on the proteomics field and its new perspectives on cancer research. Cornerstone publications that have tremendously helped scientists and clinicians to better understand cancer pathogenesis; to discover novel diagnostic and/or prognostic biomarkers; and to suggest novel therapeutic targets will be presented. The author of this review aims at presenting some of the relevant literature data that helped as a step forward in bridging the gap between bench work results and bedside potentials. Undeniably, this review cannot include all the work that is being produced by expert research groups all over the world.