How Can Proteomics Help to Elucidate the Pathophysiological Crosstalk in Muscular Dystrophy and Associated Multi-System Dysfunction?

This perspective article is concerned with the question of how proteomics, which is a core technique of systems biology that is deeply embedded in the multi-omics field of modern bioresearch, can help us better understand the molecular pathogenesis of complex diseases. As an illustrative example of a monogenetic disorder that primarily affects the neuromuscular system but is characterized by a plethora of multi-system pathophysiological alterations, the muscle-wasting disease Duchenne muscular dystrophy was examined. Recent achievements in the field of dystrophinopathy research are described with special reference to the proteome-wide complexity of neuromuscular changes and body-wide alterations/adaptations. Based on a description of the current applications of top-down versus bottom-up proteomic approaches and their technical challenges, future systems biological approaches are outlined. The envisaged holistic and integromic bioanalysis would encompass the integration of diverse omics-type studies including inter- and intra-proteomics as the core disciplines for systematic protein evaluations, with sophisticated biomolecular analyses, including physiology, molecular biology, biochemistry and histochemistry. Integrated proteomic findings promise to be instrumental in improving our detailed knowledge of pathogenic mechanisms and multi-system dysfunction, widening the available biomarker signature of dystrophinopathy for improved diagnostic/prognostic procedures, and advancing the identification of novel therapeutic targets to treat Duchenne muscular dystrophy.

Integrating proteomics and metabolomics to elucidate the molecular network regulating of inosine monophosphate-specific deposition in Jingyuan chicken.

Inosine monophosphate (IMP) plays a significant role in meat taste, yet the molecular mechanisms controlling IMP deposition in muscle tissues still require elucidation. The present study systematically and comprehensively explores the molecular network governing IMP deposition in different regions of Jingyuan chicken muscle. Two muscle groups, the breast and leg, were examined as test materials. Using nontargeted metabolomic sequencing, we screened and identified 20 metabolites that regulate IMP-specific deposition. We maintained regular author and institution formatting, used clear, objective, and value-neutral language, and avoided biased or emotional language. We followed a consistent footnote style and formatting features and used precise word choice with technical terms where appropriate. Out of these, 5 were identified as significant contributors to the regulation of IMP deposition. We explained technical term abbreviations when first used and ensured a logical flow of information with causal connections between statements. The results indicate that PGM1, a key enzyme involved in synthesis, is higher in the breast muscle compared to the leg muscle, which may provide an explanation for the increased deposition of IMP in the breast muscle. We aimed for a clear structure with logical progression, avoided filler words, and ensured grammatical correctness. The activity of key enzymes (PKM2, AK1, AMPD1) involved in this process was higher in the breast muscle than in the leg muscle. In the case of IMP degradation metabolism, the activity of its participating enzyme (PurH) was lower in the breast muscle than in the leg muscle. These findings suggest that the increased deposition of IMP in Jingyuan chickens' breast muscle may result from elevated metabolism and reduced catabolism of key metabolites. In summary, a metaomic strategy was utilized to assess the molecular network regulation mechanism of IMP-specific deposition in various segments of Jingyuan chicken. These findings provide insight into genetic improvement and molecular breeding of meat quality traits for top-notch broilers.

A bespoke target selection tool to guide biomarker discovery in tubo-ovarian cancer.

Introduction: Cancers presenting at advanced stages inherently have poor prognosis. High grade serous carcinoma (HGSC) is the most common and aggressive form of tubo-ovarian cancer. Clinical tests to accurately diagnose and monitor this condition are lacking. Hence, development of disease-specific tests are urgently required. Methods: The molecular profile of HGSC during disease progression was investigated in a unique patient cohort. A bespoke data browser was developed to analyse gene expression and DNA methylation datasets for biomarker discovery. The Ovarian Cancer Data Browser (OCDB) is built in C# with a.NET framework using an integrated development environment of Microsoft Visual Studio and fast access files (.faf). The graphical user interface is easy to navigate between four analytical modes (gene expression; methylation; combined gene expression and methylation data; methylation clusters), with a rapid query response time. A user should first define a disease progression trend for prioritising results. Single or multiomics data are then mined to identify probes, genes and methylation clusters that exhibit the desired trend. A unique scoring system based on the percentage change in expression/methylation between disease stages is used. Results are filtered and ranked using weighting and penalties. Results: The OCDB's utility for biomarker discovery is demonstrated with the identified target OSR2. Trends in OSR2 repression and hypermethylation with HGSC disease progression were confirmed in the browser samples and an independent cohort using bioassays. The OSR2 methylation biomarker could discriminate HGSC with high specificity (95%) and sensitivity (93.18%). Conclusions: The OCDB has been refined and validated to be an integral part of a unique biomarker discovery pipeline. It may also be used independently to aid identification of novel targets. It carries the potential to identify further biomarker assays that can reduce type I and II errors within clinical diagnostics.

New horizons in the stormy sea of multimodal single-cell data integration.

While measurements of RNA expression have dominated the world of single-cell analyses, new single-cell techniques increasingly allow collection of different data modalities, measuring different molecules, structural connections, and intermolecular interactions. Integrating the resulting multimodal single-cell datasets is a new bioinformatics challenge. Equally important, it is a new experimental design challenge for the bench scientist, who is not only choosing from a myriad of techniques for each data modality but also faces new challenges in experimental design. The ultimate goal is to design, execute, and analyze multimodal single-cell experiments that are more than just descriptive but enable the learning of new causal and mechanistic biology. This objective requires strict consideration of the goals behind the analysis, which might range from mapping the heterogeneity of a cellular population to assembling system-wide causal networks that can further our understanding of cellular functions and eventually lead to models of tissues and organs. We review steps and challenges toward this goal. Single-cell transcriptomics is now a mature technology, and methods to measure proteins, lipids, small-molecule metabolites, and other molecular phenotypes at the single-cell level are rapidly developing. Integrating these single-cell readouts so that each cell has measurements of multiple types of data, e.g., transcriptomes, proteomes, and metabolomes, is expected to allow identification of highly specific cellular subpopulations and to provide the basis for inferring causal biological mechanisms.

Identification of Gene Co-Expression Networks Associated with Consensus Molecular Subtype-1 of Colorectal Cancer.

Colorectal cancer (CRC) is driven in part by dysregulated Wnt, Ras-Raf-MAPK, TGF-ß, and PI3K-Akt signaling. The progression of CRC is also promoted by molecular alterations and heterogeneous-yet interconnected-gene mutations, chromosomal instability, transcriptomic subtypes, and immune signatures. Genomic alterations of CRC progression lead to changes in RNA expression, which support CRC metastasis. An RNA-based classification system used for CRC, known as consensus molecular subtyping (CMS), has four classes. CMS1 has the lowest survival after relapse of the four CRC CMS phenotypes. Here, we identify gene signatures and associated coding mRNAs that are co-expressed during CMS1 CRC progression. Using RNA-seq data from CRC primary tumor samples, acquired from The Cancer Genome Atlas (TCGA), we identified co-expression gene networks significantly correlated with CMS1 CRC progression. CXCL13, CXCR5, IL10, PIK3R5, PIK3AP1, CCL19, and other co-expressed genes were identified to be positively correlated with CMS1. The co-expressed eigengene networks for CMS1 were significantly and positively correlated with the TNF, WNT, and ERK1 and ERK2 signaling pathways, which together promote cell proliferation and survival. This network was also aligned with biological characteristics of CMS1 CRC, being positively correlated to right-sided tumors, microsatellite instability, chemokine-mediated signaling pathways, and immune responses. CMS1 also differentially expressed genes involved in PI3K-Akt signaling. Our findings reveal CRC gene networks related to oncogenic signaling cascades, cell activation, and positive regulation of immune responses distinguishing CMS1 from other CRC subtypes.

Multi-Omics Approach to Elucidate Cerebrospinal Fluid Changes in Dogs with Intervertebral Disc Herniation.

Herniation of the intervertebral disc (IVDH) is the most common cause of neurological and intervertebral disc degeneration-related diseases. Since the disc starts to degenerate before it can be observed by currently available diagnostic methods, there is an urgent need for novel diagnostic approaches. To identify molecular networks and pathways which may play important roles in intervertebral disc herniation, as well as to reveal the potential features which could be useful for monitoring disease progression and prognosis, multi-omics profiling, including high-resolution liquid chromatography-mass spectrometry (LC-MS)-based metabolomics and tandem mass tag (TMT)-based proteomics was performed. Cerebrospinal fluid of nine dogs with IVDH and six healthy controls were used for the analyses, and an additional five IVDH samples were used for proteomic data validation. Furthermore, multi-omics data were integrated to decipher a complex interaction between individual omics layers, leading to an improved prediction model. Together with metabolic pathways related to amino acids and lipid metabolism and coagulation cascades, our integromics prediction model identified the key features in IVDH, namely the proteins follistatin Like 1 (FSTL1), secretogranin V (SCG5), nucleobindin 1 (NUCB1), calcitonin re-ceptor-stimulating peptide 2 precursor (CRSP2) and the metabolites N-acetyl-D-glucosamine and adenine, involved in neuropathic pain, myelination, and neurotransmission and inflammatory response, respectively. Their clinical application is to be further investigated. The utilization of a novel integrative interdisciplinary approach may provide new opportunities to apply innovative diagnostic and monitoring methods as well as improve treatment strategies and personalized care for patients with degenerative spinal disorders.

Integrative Analyses of Multilevel Omics Reveal Preneoplastic Breast to Possess a Molecular Landscape That is Globally Shared with Invasive Basal-Like Breast Cancer (Running Title: Molecular Landscape of Basal-Like Breast Cancer Progression).

To characterize molecular changes accompanying the stepwise progression to breast cancer and to identify functional target pathways, we performed miRNA and RNA sequencing using MCF10A cell lines based model system that replicates the multi-step progression involving normal, preneoplastic, ductal carcinoma in situ, and invasive carcinoma cells, where the carcinoma most resemble the basal-like subgroup of human breast cancers. These analyses suggest that 70% of miRNA alterations occurred during the initial progression from normal to a preneoplastic stage. Most of these early changes reflected a global upregulation of miRNAs. This was consistent with a global increase in the miRNA-processing enzyme DICER, which was upregulated as a direct result of loss of miRNA let-7b-5p. Several oncogenic and tumor suppressor pathways were also found to change early, prior to histologic stigmata of cancer. Our finding that most genomic changes in the progression to basal-like breast cancer occurred in the earliest stages of histologic progression has implications for breast cancer prevention and selection of appropriate control tissues in molecular studies. Furthermore, in support of a functional significance of let-7b-5p loss, we found its low levels to predict poor disease-free survival and overall survival in breast cancer patients.

Meta-proteomics for the discovery of protein biomarkers of beef tenderness: An overview of integrated studies.

This meta-proteomics review focused on proteins identified as candidate biomarkers of beef tenderness by comparing extreme groups of tenderness using two-dimensional electrophoresis (2-DE) associated with mass spectrometry (MS). We reviewed in this integromics study the results of 12 experiments that identified protein biomarkers from two muscles, Longissimus thoracis (LT) and Semitendinosus (ST), of different types of cattle: young bulls, steers or cows from beef breeds (Charolais, Limousin, Blond d'Aquitaine), hardy breed (Salers) or mixed breed (PDO Maine-Anjou). Comparative proteomics of groups differing in their tenderness evaluated by instrumental Warner-Bratzler shear force (WBSF) or by sensory analysis using trained panelists, revealed 61 proteins differentially abundant (P < 0.05) between tender and tough groups. A higher number of discriminative proteins was observed for LT (50 proteins) compared to ST muscle (28 proteins). The Gene Ontology annotations showed that the proteins of structure and contraction, protection against oxidative stress and apoptosis, energy metabolism, 70 family HSPs and proteasome subunits are more involved in LT tenderness than in ST. Amongst the list of candidate biomarkers of tenderness some proteins such as HSPB1 are common between the two muscles whatever the evaluation method of tenderness, but some relationships with tenderness for other proteins (MYH1, TNNT3, HSPB6) were inversed. Muscle specificities were revealed in this meta-proteomics. For example, Parvalbumin (PVALB) appeared as a robust biomarker in ST muscle whatever the evaluation method of tenderness. HSPA1B seems to be a robust candidate for LT tenderness (with WBSF) regardless the animal type. Some gender specificities were further identified including similarities between cows and steers (MSRA and HSPA9) in contrast to bulls. The comparison of the 12 proteomic studies revealed strong dissimilarities to identify generic biomarkers of beef tenderness. This integrative analysis allowed better understanding of the biological processes involved in beef tenderness in two muscles and their variations according to the main factors underlying this quality. It allowed also proposing for the first time a comprehensive list of candidate biomarkers to be evaluated deeply to validate their relationships with tenderness on a large number of cattle and breeds.

Data from the Farmgate-to-Meat Continuum Including Omics-Based Biomarkers to Better Understand the Variability of Beef Tenderness: An Integromics Approach.

This study is based on an integromic approach of 71 young bulls' data from the farmgate-to-meat continuum including omics-based biomarkers, to understand beef tenderness variability in two muscle cuts that differ by their contractile and metabolic properties. By the means of chemometrics using partial least-squares (PLS) and principal component regressions (PCR), important variables from a list of 49 that characterize four levels of the continuum (rearing factors-carcass-muscle-meat) were identified to explain tenderness of Longissimus thoracis (LT) and Semitendinosus (ST) muscles evaluated by a sensory panel and instrumental Warner-Bratzler shear force (WBSF). The PLS and PCR analyses validated 16 and 15 variables for LT and 12 and 14 for ST from the whole continuum to explain sensory tenderness and WBSF, respectively. Among the explanatory variables in the four models and in line with the role of apoptosis in tenderness determinism, HSP70-1A/B (a heat shock protein) was retained to explain beef tenderness irrespective of muscle and evaluation method. Similarly, dressing percentage from the carcass level was another robust predictor but in a muscle-dependent direction manner. HSP20, ENO3, and MyHC-I as three muscle protein biomarkers and dry matter intake (DMI) as a rearing factor were involved in three models to explain beef tenderness. This study highlighted also that several variables were muscle-specific irrespective of the evaluation method of tenderness. For LT muscle, six variables including three carcass traits (fatness score, fat carcass %, and muscle carcass %), two muscle biomarkers (HSP70-8 and MyHC-IIx/b), and one meat quality trait (pH3h) were found. For ST muscle, five variables were validated from two rearing factors (average daily gain and feed efficiency) and three structural protein biomarkers (α-actin, MyBP-H, and CapZ-ß). Finally, for WBSF only, lactate dehydrogenase chain B (LDH-B) was retained positively for LT and negatively for ST muscles. Overall, this trial showed that tenderness of LT and ST muscle cuts is influenced by variables belonging to the whole continuum with relationships that depend on both the muscle type and the evaluation method. It further highlighted the potential of integromic/chemometric approaches on the farmgate-to-meat continuum data to better understand the sophisticated biological processes that orchestrate the conversion of muscle into meat and tenderness determinism.

From Genomics to Omics Landscapes of Parkinson's Disease: Revealing the Molecular Mechanisms.

Molecular mechanisms of Parkinson's disease (PD) have already been investigated in various different omics landscapes. We reviewed the literature about different omics approaches between November 2005 and November 2017 to depict the main pathological pathways for PD development. In total, 107 articles exploring different layers of omics data associated with PD were retrieved. The studies were grouped into 13 omics layers: genomics-DNA level, transcriptomics, epigenomics, proteomics, ncRNomics, interactomics, metabolomics, glycomics, lipidomics, phenomics, environmental omics, pharmacogenomics, and integromics. We discussed characteristics of studies from different landscapes, such as main findings, number of participants, sample type, methodology, and outcome. We also performed curation and preliminary synthesis of multiple omics data, and identified overlapping results, which could lead toward selection of biomarkers for further validation of PD risk loci. Biomarkers could support the development of targeted prognostic/diagnostic panels as a tool for early diagnosis and prediction of progression rate and prognosis. This review presents an example of a comprehensive approach to revealing the underlying processes and risk factors of a complex disease. It urges scientists to structure the already known data and integrate it into a meaningful context.

Embracing an integromic approach to tissue biomarker research in cancer: Perspectives and lessons learned.

Modern approaches to biomedical research and diagnostics targeted towards precision medicine are generating 'big data' across a range of high-throughput experimental and analytical platforms. Integrative analysis of this rich clinical, pathological, molecular and imaging data represents one of the greatest bottlenecks in biomarker discovery research in cancer and other diseases. Following on from the publication of our successful framework for multimodal data amalgamation and integrative analysis, Pathology Integromics in Cancer (PICan), this article will explore the essential elements of assembling an integromics framework from a more detailed perspective. PICan, built around a relational database storing curated multimodal data, is the research tool sitting at the heart of our interdisciplinary efforts to streamline biomarker discovery and validation. While recognizing that every institution has a unique set of priorities and challenges, we will use our experiences with PICan as a case study and starting point, rationalizing the design choices we made within the context of our local infrastructure and specific needs, but also highlighting alternative approaches that may better suit other programmes of research and discovery. Along the way, we stress that integromics is not just a set of tools, but rather a cohesive paradigm for how modern bioinformatics can be enhanced. Successful implementation of an integromics framework is a collaborative team effort that is built with an eye to the future and greatly accelerates the processes of biomarker discovery, validation and translation into clinical practice.

Integrated analysis of microRNAs, transcription factors and target genes expression discloses a specific molecular architecture of hyperdiploid multiple myeloma.

Multiple Myeloma (MM) is a malignancy characterized by the hyperdiploid (HD-MM) and the non-hyperdiploid (nHD-MM) subtypes. To shed light within the molecular architecture of these subtypes, we used a novel integromics approach. By annotated MM patient mRNA/microRNA (miRNA) datasets, we investigated mRNAs and miRNAs profiles with relation to changes in transcriptional regulators expression. We found that HD-MM displays specific gene and miRNA expression profiles, involving the Signal Transducer and Activator of Transcription (STAT)3 pathway as well as the Transforming Growth Factor-beta (TGFß) and the transcription regulator Nuclear Protein-1 (NUPR1). Our data define specific molecular features of HD-MM that may translate in the identification of novel relevant druggable targets.

PICan: An integromics framework for dynamic cancer biomarker discovery.

Modern cancer research on prognostic and predictive biomarkers demands the integration of established and emerging high-throughput technologies. However, these data are meaningless unless carefully integrated with patient clinical outcome and epidemiological information. Integrated datasets hold the key to discovering new biomarkers and therapeutic targets in cancer. We have developed a novel approach and set of methods for integrating and interrogating phenomic, genomic and clinical data sets to facilitate cancer biomarker discovery and patient stratification. Applied to a known paradigm, the biological and clinical relevance of TP53, PICan was able to recapitulate the known biomarker status and prognostic significance at a DNA, RNA and protein levels.

Perspectives of integrative cancer genomics in next generation sequencing era.

The explosive development of genomics technologies including microarrays and next generation sequencing (NGS) has provided comprehensive maps of cancer genomes, including the expression of mRNAs and microRNAs, DNA copy numbers, sequence variations, and epigenetic changes. These genome-wide profiles of the genetic aberrations could reveal the candidates for diagnostic and/or prognostic biomarkers as well as mechanistic insights into tumor development and progression. Recent efforts to establish the huge cancer genome compendium and integrative omics analyses, so-called "integromics", have extended our understanding on the cancer genome, showing its daunting complexity and heterogeneity. However, the challenges of the structured integration, sharing, and interpretation of the big omics data still remain to be resolved. Here, we review several issues raised in cancer omics data analysis, including NGS, focusing particularly on the study design and analysis strategies. This might be helpful to understand the current trends and strategies of the rapidly evolving cancer genomics research.