Multi-omics Biomarker Pipeline Reveals Elevated Levels of Protein-glutamine Gamma-glutamyltransferase 4 in Seminal Plasma of Prostate Cancer Patients

Advances in mass spectrometry for metabolomics: Strategies, challenges, and innovations in disease biomarker discovery

Mass spectrometry (MS) plays a crucial role in metabolomics, especially in the discovery of disease biomarkers. This review outlines strategies for identifying metabolites, emphasizing precise and detailed use of MS techniques. It explores various methods for quantification, discusses challenges encountered, and examines recent breakthroughs in biomarker discovery. In the field of diagnostics, MS has revolutionized approaches by enabling a deeper understanding of tissue-specific metabolic changes associated with disease. The reliability of results is ensured through robust experimental design and stringent system suitability criteria. In the past, data quality, standardization, and reproducibility were often overlooked despite their significant impact on MS-based metabolomics. Progress in this field heavily depends on continuous training and education. The review also highlights the emergence of innovative MS technologies and methodologies. MS has the potential to transform our understanding of metabolic landscapes, which is crucial for disease biomarker discovery. This article serves as an invaluable resource for researchers in metabolomics, presenting fresh perspectives and advancements that propels the field forward.

Identification and Quantification of Human Relaxin Proteins by Immunoaffinity-Mass Spectrometry

The human relaxins belong to the Insulin/IGF/Relaxin superfamily of peptide hormones, and their physiological function is primarily associated with reproduction. In this study, we focused on a prostate tissue-specific relaxin RLN1 (REL1_HUMAN protein) and a broader tissue specificity RLN2 (REL2_HUMAN protein). Due to their structural similarity, REL1 and REL2 proteins were collectively named a 'human relaxin protein' in previous studies and were exclusively measured by immunoassays. We hypothesized that the highly selective and sensitive immunoaffinity-selected reaction monitoring (IA-SRM) assays would reveal the identity and abundance of the endogenous REL1 and REL2 in biological samples and facilitate the evaluation of these proteins for diagnostic applications. High levels of RLN1 and RLN2 transcripts were found in prostate and breast cancer cell lines by RT-PCR. However, no endogenous prorelaxin-1 or mature REL1 were detected by IA-SRM in cell lines, seminal plasma, or blood serum. The IA-SRM assay of REL2 demonstrated its undetectable levels (<9.4 pg/mL) in healthy control female and male sera and relatively high levels of REL2 in maternal sera across different gestational weeks (median 331 pg/mL; N = 120). IA-SRM assays uncovered potential cross-reactivity and nonspecific binding for relaxin immunoassays. The developed IA-SRM assays will facilitate the investigation of the physiological and pathological roles of REL1 and REL2 proteins.

Multiplexed quantitative proteomics in prostate cancer biomarker development

Prostate cancer (PCa) is the most common non-skin cancer among men in the United States. However, the widely used protein biomarker in PCa, prostate-specific antigen (PSA), while useful for initial detection, its use alone cannot detect aggressive PCa and can lead to overtreatment. This chapter provides an overview of PCa protein biomarker development. It reviews the state-of-the-art liquid chromatography-mass spectrometry-based proteomics technologies for PCa biomarker development, such as enhancing the detection sensitivity of low-abundance proteins through antibody-based or antibody-independent protein/peptide enrichment, enriching post-translational modifications such as glycosylation as well as information-rich extracellular vesicles, and increasing accuracy and throughput using advanced data acquisition methodologies. This chapter also summarizes recent PCa biomarker validation studies that applied those techniques in diverse specimen types, including cell lines, tissues, proximal fluids, urine, and blood, developing novel protein biomarkers for various clinical applications, including early detection and diagnosis, prognosis, and therapeutic intervention of PCa.

Development of a predictive model to distinguish prostate cancer from benign prostatic hyperplasia by integrating serum glycoproteomics and clinical variables

BACKGROUND

Prostate Cancer (PCa) represents the second leading cause of cancer-related death in men. Prostate-specific antigen (PSA) serum testing, currently used for PCa screening, lacks the necessary sensitivity and specificity. New non-invasive diagnostic tools able to discriminate tumoral from benign conditions and aggressive (AG-PCa) from indolent forms of PCa (NAG-PCa) are required to avoid unnecessary biopsies.

METHODS

In this work, 32 formerly N-glycosylated peptides were quantified by PRM (parallel reaction monitoring) in 163 serum samples (79 from PCa patients and 84 from individuals affected by benign prostatic hyperplasia (BPH)) in two technical replicates. These potential biomarker candidates were prioritized through a multi-stage biomarker discovery pipeline articulated in: discovery, LC-PRM assay development and verification phases. Because of the well-established involvement of glycoproteins in cancer development and progression, the proteomic analysis was focused on glycoproteins enriched by TiO2 (titanium dioxide) strategy.

RESULTS

Machine learning algorithms have been applied to the combined matrix comprising proteomic and clinical variables, resulting in a predictive model based on six proteomic variables (RNASE1, LAMP2, LUM, MASP1, NCAM1, GPLD1) and five clinical variables (prostate dimension, proPSA, free-PSA, total-PSA, free/total-PSA) able to distinguish PCa from BPH with an area under the Receiver Operating Characteristic (ROC) curve of 0.93. This model outperformed PSA alone which, on the same sample set, was able to discriminate PCa from BPH with an AUC of 0.79. To improve the clinical managing of PCa patients, an explorative small-scale analysis (79 samples) aimed at distinguishing AG-PCa from NAG-PCa was conducted. A predictor of PCa aggressiveness based on the combination of 7 proteomic variables (FCN3, LGALS3BP, AZU1, C6, LAMB1, CHL1, POSTN) and proPSA was developed (AUC of 0.69).

CONCLUSIONS

To address the impelling need of more sensitive and specific serum diagnostic tests, a predictive model combining proteomic and clinical variables was developed. A preliminary evaluation to build a new tool able to discriminate aggressive presentations of PCa from tumors with benign behavior was exploited. This predictor displayed moderate performances, but no conclusions can be drawn due to the limited number of the sample cohort. Data are available via ProteomeXchange with identifier PXD035935.

Redefining serological diagnostics with immunoaffinity proteomics

Serological diagnostics is generally defined as the detection of specific human immunoglobulins developed against viral, bacterial, or parasitic diseases. Serological tests facilitate the detection of past infections, evaluate immune status, and provide prognostic information. Serological assays were traditionally implemented as indirect immunoassays, and their design has not changed for decades. The advantages of straightforward setup and manufacturing, analytical sensitivity and specificity, affordability, and high-throughput measurements were accompanied by limitations such as semi-quantitative measurements, lack of universal reference standards, potential cross-reactivity, and challenges with multiplexing the complete panel of human immunoglobulin isotypes and subclasses. Redesign of conventional serological tests to include multiplex quantification of immunoglobulin isotypes and subclasses, utilize universal reference standards, and minimize cross-reactivity and non-specific binding will facilitate the development of assays with higher diagnostic specificity. Improved serological assays with higher diagnostic specificity will enable screenings of asymptomatic populations and may provide earlier detection of infectious diseases, autoimmune disorders, and cancer. In this review, we present the major clinical needs for serological diagnostics, overview conventional immunoassay detection techniques, present the emerging immunoassay detection technologies, and discuss in detail the advantages and limitations of mass spectrometry and immunoaffinity proteomics for serological diagnostics. Finally, we explore the design of novel immunoaffinity-proteomic assays to evaluate cell-mediated immunity and advance the sequencing of clinically relevant immunoglobulins.

Germ cell-specific proteins AKAP4 and ASPX facilitate identification of rare spermatozoa in non-obstructive azoospermia

Non-obstructive azoospermia (NOA), the most severe form of male infertility, could be treated with intra-cytoplasmic sperm injection, providing spermatozoa were retrieved with the microdissection testicular sperm extraction (mTESE). We hypothesized that testis- and germ cell-specific proteins would facilitate flow cytometry-assisted identification of rare spermatozoa in semen cell pellets of NOA patients, thus enabling non-invasive diagnostics prior to mTESE. Data mining, targeted proteomics, and immunofluorescent microscopy identified and verified a panel of highly testis-specific proteins expressed at the continuum of germ cell differentiation. Late germ cell-specific proteins AKAP4_HUMAN and ASPX_HUMAN (ACRV1 gene) revealed exclusive localization in spermatozoa tails and acrosomes, respectively. A multiplex imaging flow cytometry assay facilitated fast and unambiguous identification of rare but morphologically intact AKAP4⁺/ASPX⁺/Hoechst⁺ spermatozoa within debris-laden semen pellets of NOA patients. While the previously suggested markers for spermatozoa retrieval suffered from low diagnostic specificity, the multi-step gating strategy and visualization of AKAP4⁺/ASPX⁺/Hoechst⁺ cells with elongated tails and acrosome-capped nuclei facilitated fast and unambiguous identification of the mature intact spermatozoa. AKAP4⁺/ASPX⁺/Hoechst⁺ assay may emerge as a non-invasive test to predict retrieval of morphologically intact spermatozoa by mTESE, thus improving diagnostics and treatment of severe forms of male infertility.

High Throughput LC-MS Platform for Large Scale Screening of Bioactive Polar Lipids in Human Plasma and Serum

Lipids play a key role in many biological processes, and their accurate measurement is critical to unraveling the biology of diseases and human health. A high throughput HILIC-based (LC-MS) method for the semiquantitative screening of over 2000 lipids, based on over 4000 MRM transitions, was devised to produce an accessible and robust lipidomic screen for phospholipids in human plasma/serum. This methodology integrates many of the advantages of global lipid analysis with those of targeted approaches. Having used the method as an initial "wide class" screen, it can then be easily adapted for a more targeted analysis and quantification of key, dysregulated lipids. Robustness was assessed using 1550 continuous injections of plasma extracts onto a single column and via the evaluation of columns from 5 different batches of stationary phase. Initial screens in positive (239 lipids, 431 MRM transitions) and negative electrospray ionization (ESI) mode (232 lipids, 446 MRM transitions) were assessed for reproducibility, sensitivity, and dynamic range using analysis times of 8 min. The total number of lipids monitored using these screening methods was 433 with an overlap of 38 lipids in both modes. A polarity switching method for accurate quantification, using the same LC conditions, was assessed for intra- and interday reproducibility, accuracy, dynamic range, stability, carryover, dilution integrity, and matrix interferences and found to be acceptable. This polarity switching method was then applied to lipids important in the stratification of human prostate cancer samples.

Rational Design and Development of SARS-CoV-2 Serological Diagnostics by Immunoprecipitation-Targeted Proteomics

Current design of serological tests utilizes conservative immunoassay approaches and is focused on fast and convenient assay development, throughput, straightforward measurements, and affordability. Limitations of common serological assays include semiquantitative measurements, cross-reactivity, lack of reference standards, and no differentiation between human immunoglobulin subclasses. In this study, we suggested that a combination of immunoaffinity enrichments with targeted proteomics would enable rational design and development of serological assays of infectious diseases, such as COVID-19. Immunoprecipitation-targeted proteomic assays allowed for sensitive and specific measurements of NCAP_SARS2 protein with a limit of detection of 313 pg/mL in serum and enabled differential quantification of anti-SARS-CoV-2 antibody isotypes (IgG, IgA, IgM, IgD, and IgE) and individual subclasses (IgG1-4 and IgA1-2) in plasma and saliva. Simultaneous evaluation of the numerous antigen–antibody subclass combinations revealed a receptor-binding domain (RBD)-IgG1 as a combination with the highest diagnostic performance. Further validation revealed that anti-RBD IgG1, IgG3, IgM, and IgA1 levels were significantly elevated in convalescent plasma, while IgG2, IgG4, and IgA2 were not informative. Anti-RBD IgG1 levels in convalescent (2138 ng/mL) vs negative (95 ng/mL) plasma revealed 385 ng/mL as a cutoff to detect COVID-19 convalescent plasma. Immunoprecipitation-targeted proteomic assays will facilitate improvement and standardization of the existing serological tests, enable rational design of novel tests, and offer tools for the comprehensive investigation of immunoglobulin subclass cooperation in immune response.

Prostate cancer in omics era

Recent advances in omics technology have prompted extraordinary attempts to define the molecular changes underlying the onset and progression of a variety of complex human diseases, including cancer. Since the advent of sequencing technology, cancer biology has become increasingly reliant on the generation and integration of data generated at these levels. The availability of multi-omic data has transformed medicine and biology by enabling integrated systems-level approaches. Multivariate signatures are expected to play a role in cancer detection, screening, patient classification, assessment of treatment response, and biomarker identification. This review reports current findings and highlights a number of studies that are both novel and groundbreaking in their application of multi Omics to prostate cancer.

Extracellular Vesicle Proteome in Prostate Cancer: A Comparative Analysis of Mass Spectrometry Studies

Molecular diagnostics based on discovery research holds the promise of improving screening methods for prostate cancer (PCa). Furthermore, the congregated information prompts the question whether the urinary extracellular vesicles (uEV) proteome has been thoroughly explored, especially at the proteome level. In fact, most extracellular vesicles (EV) based biomarker studies have mainly targeted plasma or serum. Therefore, in this study, we aim to inquire about possible strategies for urinary biomarker discovery particularly focused on the proteome of urine EVs. Proteomics data deposited in the PRIDE archive were reanalyzed to target identifications of potential PCa markers. Network analysis of the markers proposed by different prostate cancer studies revealed moderate overlap. The recent throughput improvements in mass spectrometry together with the network analysis performed in this study, suggest that a larger standardized cohort may provide potential biomarkers that are able to fully characterize the heterogeneity of PCa. According to our analysis PCa studies based on urinary EV proteome presents higher protein coverage compared to plasma, plasma EV, and voided urine proteome. This together with a direct interaction of the prostate gland and urethra makes uEVs an attractive option for protein biomarker studies. In addition, urinary proteome based PCa studies must also evaluate samples from bladder and renal cancers to assess specificity for PCa.

Biochemical Characterisation of Human Transglutaminase 4

Transglutaminases are protein-modifying enzymes involved in physiological and pathological processes with potent therapeutic possibilities. Human TG4, also called prostate transglutaminase, is involved in the development of autoimmune and tumour diseases. Although rodent TG4 is well characterised, biochemical characteristics of human TG4 that could help th e understanding of its way of action are not published. First, we analysed proteomics databases and found that TG4 protein is present in human tissues beyond the prostate. Then, we studied in vitro the transamidase activity of human TG4 and its regulation using the microtitre plate method. Human TG4 has low transamidase activity which prefers slightly acidic pH and a reducing environment. It is enhanced by submicellar concentrations of SDS suggesting that membrane proximity is an important regulatory event. Human TG4 does not bind GTP as tested by GTP-agarose and BODIPY-FL-GTPγS binding, and its proteolytic activation by dispase or when expressed in AD-293 cells was not observed either. We identified several potential human TG4 glutamine donor substrates in the AD-293 cell extract by biotin-pentylamine incorporation and mass spectrometry. Several of these potential substrates are involved in cell–cell interaction, adhesion and proliferation, suggesting that human TG4 could become an anticancer therapeutic target.

Beyond liquid biopsy: Toward non-invasive assays for distanced cancer diagnostics in pandemics

Liquid biopsy technologies have seen a significant improvement in the last decade, offering the possibility of reliable analysis and diagnosis from several biological fluids. The use of these technologies can overcome the limits of standard clinical methods, related to invasiveness and poor patient compliance. Along with this there are now mature examples of lab-on-chips (LOC) which are available and could be an emerging and breakthrough technology for the present and near-future clinical demands that provide sample treatment, reagent addition and analysis in a sample-in/answer-out approach. The possibility of combining non-invasive liquid biopsy and LOC technologies could greatly assist in the current need for minimizing exposure and transmission risks. The recent and ongoing pandemic outbreak of SARS-CoV-2, indeed, has heavily influenced all aspects of life worldwide. Ordinary tasks have been forced to switch from “in presence” to “distanced”, limiting the possibilities for a large number of activities in all fields of life outside of the home. Unfortunately, one of the settings in which physical distancing has assumed noteworthy consequences is the screening, diagnosis and follow-up of diseases.

In this review, we analyse biological fluids that are easily collected without the intervention of specialized personnel and the possibility that they may be used -or not-for innovative diagnostic assays. We consider their advantages and limitations, mainly due to stability and storage and their integration into Point-of-Care diagnostics, demonstrating that technologies in some cases are mature enough to meet current clinical needs.

Mass spectrometry-based proteomics in basic and translational research of SARS-CoV-2 coronavirus and its emerging mutants

Molecular diagnostics of the coronavirus disease of 2019 (COVID-19) now mainly relies on the measurements of viral RNA by RT-PCR, or detection of anti-viral antibodies by immunoassays. In this review, we discussed the perspectives of mass spectrometry-based proteomics as an analytical technique to identify and quantify proteins of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and to enable basic research and clinical studies on COVID-19. While RT-PCR and RNA sequencing are indisputably powerful techniques for the detection of SARS-CoV-2 and identification of the emerging mutations, proteomics may provide confirmatory diagnostic information and complimentary biological knowledge on protein abundance, post-translational modifications, protein-protein interactions, and the functional impact of the emerging mutations. Pending advances in sensitivity and throughput of mass spectrometry and liquid chromatography, shotgun and targeted proteomic assays may find their niche for the differential quantification of viral proteins in clinical and environmental samples. Targeted proteomic assays in combination with immunoaffinity enrichments also provide orthogonal tools to evaluate cross-reactivity of serology tests and facilitate development of tests with the nearly perfect diagnostic specificity, this enabling reliable testing of broader populations for the acquired immunity. The coronavirus pandemic of 2019-2021 is another reminder that the future global pandemics may be inevitable, but their impact could be mitigated with the novel tools and assays, such as mass spectrometry-based proteomics, to enable continuous monitoring of emerging viruses, and to facilitate rapid response to novel infectious diseases.

Mapping Isoform Abundance and Interactome of the Endogenous TMPRSS2-ERG Fusion Protein by Orthogonal Immunoprecipitation-Mass Spectrometry Assays

TMPRSS2-ERG gene fusion, a molecular alteration found in nearly half of primary prostate cancer cases, has been intensively characterized at the transcript level. However limited studies have explored the molecular identity and function of the endogenous fusion at the protein level. Here, we developed immunoprecipitation-mass spectrometry assays for the measurement of a low-abundance T1E4 TMPRSS2-ERG fusion protein, its isoforms, and its interactome in VCaP prostate cancer cells. Our assays quantified total ERG (∼27,000 copies/cell) and its four unique isoforms and revealed that the T1E4-ERG isoform accounted for 52 ± 3% of the total ERG protein in VCaP cells, and 50 ± 11% in formalin-fixed paraffin-embedded prostate cancer tissues. For the first time, the N-terminal peptide (methionine-truncated and N-acetylated TASSSSDYGQTSK) unique for the T1/E4 fusion was identified. ERG interactome profiling with the C-terminal, but not the N-terminal, antibodies identified 29 proteins, including mutually exclusive BRG1- and BRM-associated canonical SWI/SNF chromatin remodeling complexes. Our sensitive and selective IP-SRM assays present alternative tools to quantify ERG and its isoforms in clinical samples, thus paving the way for development of more accurate diagnostics of prostate cancer.

Plasma Metabolomics Reveals Metabolic Profiling For Diabetic Retinopathy and Disease Progression

BACKGROUNDS

Diabetic retinopathy (DR), the main retinal vascular complication of DM, is the leading cause of visual impairment and blindness among working-age people worldwide. The aim of this study was to investigate the difference of plasma metabolic profiles in patients with DR to better understand the mechanism of this disease and disease progression.

METHODS

We used ultrahigh-performance liquid Q-Exactive mass spectrometry and multivariate statistical analyses to conduct a comprehensive analysis of plasma metabolites in a population with DR and proliferative DR (PDR). A risk score based on the level of the selected metabolite was established and evaluated using the least absolute shrinkage and selection operator regularization logistic regression (LASSO-LR) based machine learning model.

RESULTS

22 differentially expressed metabolites which belonged to different metabolic pathway were identified and confirmed to be associated with the occurrence of DR. A risk score based on the level of the selected metabolite pseudouridine was established and evaluated to strongly associated with the occurrence of DR. Four circulating plasma metabolites (pseudouridine, glutamate, leucylleucine and N-acetyltryptophan) were identified to be differentially expressed between patients with PDR and other patients, and a risk score formula based on these plasma metabolites was developed and assessed to be significantly related to PDR.

CONCLUSIONS

Our work highlights the possible use of the risk score assessment based on the plasma metabolites not only reveal in the early diagnosis of DR and PDR but also assist in enhancing current therapeutic strategies in the clinic.

Cohort profile: the Alberta Prostate Cancer Research Initiative (APCaRI) Registry and Biorepository facilitates technology translation to the clinic through the use of linked, longitudinal clinical and patient-reported data and biospecimens from men in Alberta, Canada

PURPOSE

The Alberta Prostate Cancer Research Initiative (APCaRI) Registry and Biorepository was established in 2014 by the APCaRI to facilitate the collection of clinical and patient-reported data, biospecimen, to measure prostate cancer outcomes and to support the development and clinical translation of innovative technologies to better diagnose and predict outcomes for patients with prostate cancer.

PARTICIPANTS

Men suspected with prostate cancer and referred to Urology centres in Alberta were enrolled in the APCaRI 01 study, while men with a prior prostate cancer diagnosis participated in the APCaRI 03 study from 1 July 2014 to 30 June 2019. The APCaRI Registry and Biorepository links biospecimens and data from a wide representation of patients drawn from an Alberta population of more than 4 million.

FINDINGS TO DATE

From 1 July 2014 to 30 June 2019, total APCaRI 01 and 03 study recruitment was 3754 men; 142 (4%) of these men withdrew in full, 65 men (2%) withdrew biospecimens and 123 men (3%) died of any cause. Over this same time, 8677 patient-reported outcome measure (PROM) surveys and 7368 biospecimens were collected and are available from the registry and biorepository, respectively. The data entry error rate was 0.8% and 0.95% for critical and non-critical values, respectively, and 1.8% for patient-reported surveys.

FUTURE PLANS

The APCaRI Registry and Biorepository will collect longitudinal data and PROM surveys until 2024, patient outcomes up to 25 years after recruitment and biospecimen storage for up to 25 years. The APCaRI cohorts will continue to provide data and samples to researchers conducting retrospective studies. The richness of the data and biospecimens will complement many different research questions, ultimately to improve the quality of care for men with prostate cancer.

Dysregulation of Key Proteins Associated with Sperm Motility and Fertility Potential in Cancer Patients

Cancer has adverse effects on male reproductive health. Conventional semen analysis does not explain the molecular changes in the spermatozoa of cancer patients. Currently, proteomics is being widely used to identify the fertility-associated molecular pathways affected in spermatozoa. The objective of this study was to evaluate the sperm proteome of patients with various types of cancer. Cryopreserved semen samples from patients (testicular cancer, n = 40; Hodgkin’s disease, n = 32; lymphoma, n = 20; leukemia, n = 17) before starting therapy were used for proteomic analysis, while samples from fertile donors (n = 19) were included as controls. The proteomic profiling of sperm was carried out by liquid chromatography-tandem mass spectrometry, and differentially expressed proteins involved in the reproductive processes were validated by Western blotting. Bioinformatic analysis revealed that proteins associated with mitochondrial dysfunction, oxidative phosphorylation, and Sirtuin signaling pathways were dysregulated in cancer patients, while oxidative phosphorylation and tricarboxylic acid cycle were predicted to be deactivated. Furthermore, the analysis revealed dysregulation of key proteins associated with sperm fertility potential and motility (NADH:Ubiquinone oxidoreductase core subunit S1, superoxide dismutase 1, SERPINA5, and cytochrome b-c1 complex subunit 2) in the cancer group, which were further validated by Western blot. Dysfunctional molecular mechanisms essential for fertility in cancer patients prior to therapy highlight the potential impact of cancer phenotype on male fertility.

An overview of advances in multi-omics analysis in prostate cancer

Prostate cancer (PCa) is a deadly disease for men, and studies of all types of omics data are necessary to promote precision medicine. The maturity of sequencing technology, the improvements of computer processing power, and the progress achieved in omics analysis methods have improved research efficiency and saved research costs. The occurrence and development of PCa is due to multisystem and multilevel pathological changes. Although omics research at a single level is important, this approach often has limitations. In contrast, the combined analysis of multiple types of omics data can better analyze PCa changes as a whole, thus ensuring the validity of research results to the greatest extent. This paper introduces the applications of single omics in PCa and then summarizes research progress in the combined analysis of two or more types of omics data, so as to systematically and comprehensively analyze the necessity of combined analysis of multiple omics data in PCa.

Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools

In recent years, high-throughput sequencing technologies provide unprecedented opportunity to depict cancer samples at multiple molecular levels. The integration and analysis of these multi-omics datasets is a crucial and critical step to gain actionable knowledge in a precision medicine framework. This paper explores recent data-driven methodologies that have been developed and applied to respond major challenges of stratified medicine in oncology, including patients' phenotyping, biomarker discovery, and drug repurposing. We systematically retrieved peer-reviewed journals published from 2014 to 2019, select and thoroughly describe the tools presenting the most promising innovations regarding the integration of heterogeneous data, the machine learning methodologies that successfully tackled the complexity of multi-omics data, and the frameworks to deliver actionable results for clinical practice. The review is organized according to the applied methods: Deep learning, Network-based methods, Clustering, Features Extraction, and Transformation, Factorization. We provide an overview of the tools available in each methodological group and underline the relationship among the different categories. Our analysis revealed how multi-omics datasets could be exploited to drive precision oncology, but also current limitations in the development of multi-omics data integration.

Seminal Plasma Proteome as an Indicator of Sperm Dysfunction and Low Sperm Motility in Chickens

Molecular mechanisms underlying sperm motility have not been fully explained, particularly in chickens. The objective was to identify seminal plasma proteins associated with chicken sperm motility by comparing the seminal plasma proteomic profile of roosters with low sperm motility (LSM, n = 4) and high sperm motility (HSM, n = 4). Using a label-free MS-based method, a total of 522 seminal plasma proteins were identified, including 386 (∼74%) previously reported and 136 novel ones. A total of 70 differentially abundant proteins were defined, including 48 more-abundant, 15 less-abundant, and seven proteins unique to the LSM group (specific proteins). Key secretory proteins like less-abundant adhesion G-protein coupled receptor G2 (ADGRG2) and more-abundant serine peptidase inhibitor Kazal-type 2 (SPINK2) in the LSM suggested that the corresponding secretory tissues played a crucial role in maintaining sperm motility. Majority (80%) of the more-abundant and five specific proteins were annotated to the cytoplasmic domain which might be a result of higher plasma membrane damage and acrosome dysfunction in LSM. Additionally, more-abundant mitochondrial proteins were detected in LSM seminal plasma associated with lower spermatozoa mitochondrial membrane potential (ΔΨm) and ATP concentrations. Further studies showed that the spermatozoa might be suffering from oxidative stress, as the amount of spermatozoa reactive oxygen species (ROS) were largely enhanced, seminal malondialdehyde (MDA) concentrations were increased, and the seminal plasma total antioxidant capacity (T-AOC) were decreased. Our study provides an additional catalogue of chicken seminal plasma proteome and supports the idea that seminal plasma could be as an indicator of spermatozoa physiology. More-abundant of acrosome, mitochondria and sperm cytoskeleton proteins in the seminal plasma could be a marker of sperm dysfunction and loss of motility. The degeneration of spermatozoa caused by the reduced seminal T-AOC and enhanced oxidative stress might be potential determinants of low sperm motility. These results could extend our understanding of sperm motility and sperm physiology regulation.

Label-free peptide quantification coupled with in silico mapping of proteases for identification of potential serum biomarkers in gastric adenocarcinoma patients

OBJECTIVES

We aimed to identify serum level variations in protein-derived peptides between patients diagnosed with gastric adenocarcinoma (GAC) and non-cancer persons (control) to detect the activity changes of proteases and explore the auxiliary diagnostic value in the context of GAC physiopathology.

METHODS

The label-free quantitative peptidome approach was applied to identify variants in serum levels of peptides that can differentiate GAC patients from the control group. Peptide sequences were submitted against Proteasix tool predicting proteases potentially involved in their generation. The activity change of proteases was subsequently estimated based on the peptides with significantly altered relative abundance. In turn, activity change prediction of proteases was correlated with relevant protease expression data from the literature.

RESULTS

A total of 191 peptide sequences generated by the cleavage of 36 precursor proteins were identified. Using the label-free quantification approach, 33 peptides were differentially quantified (adjusted fold change ≥ 1.5 and p-value < 0.05) in which 19 were up-regulated and 14 were down-regulated in GAC samples. Of these peptides, fibrinopeptide A was significantly decreased and its phosphorylated form ADpSGEGDFLAEGGGVR was upregulated in GAC samples. Activity change prediction yielded 10 proteases including 6 Matrix Metalloproteinases (MMPs), Thrombin, Plasmin, and kallikreins 4 and 14. Among predicted proteases in our analysis, MMP-7 was presented as a more promising biomarker associated with useful assays of clinical practice for GAC diagnosis.

CONCLUSION

Our experimental results demonstrate that the serum levels of peptides were significantly differentiated in GAC physiopathology. The hypotheses built on protease regulation could be used for further investigations to measure proteases and their activity levels that have been poorly studied for GAC diagnosis.