Integrative Multi-omics Analysis to Characterize Human Brain Ischemia

Stroke is a major cause of death and disability. A better comprehension of stroke pathophysiology is fundamental to reduce its dramatic outcome. The use of high-throughput unbiased omics approaches and the integration of these data might deepen the knowledge of stroke at the molecular level, depicting the interaction between different molecular units. We aimed to identify protein and gene expression changes in the human brain after ischemia through an integrative approach to join the information of both omics analyses. The translational potential of our results was explored in a pilot study with blood samples from ischemic stroke patients. Proteomics and transcriptomics discovery studies were performed in human brain samples from six deceased stroke patients, comparing the infarct core with the corresponding contralateral brain region, unveiling 128 proteins and 2716 genes significantly dysregulated after stroke. Integrative bioinformatics analyses joining both datasets exposed canonical pathways altered in the ischemic area, highlighting the most influential molecules. Among the molecules with the highest fold-change, 28 genes and 9 proteins were selected to be validated in five independent human brain samples using orthogonal techniques. Our results were confirmed for NCDN, RAB3C, ST4A1, DNM1L, A1AG1, A1AT, JAM3, VTDB, ANXA1, ANXA2, and IL8. Finally, circulating levels of the validated proteins were explored in ischemic stroke patients. Fluctuations of A1AG1 and A1AT, both up-regulated in the ischemic brain, were detected in blood along the first week after onset. In summary, our results expand the knowledge of ischemic stroke pathology, revealing key molecules to be further explored as biomarkers and/or therapeutic targets

DNA microarray integromics analysis platform

Background: The study of interactions between molecules belonging to different biochemical families (such as lipids and nucleic acids) requires specialized data analysis methods. This article describes the DNA Microarray Integromics Analysis Platform, a unique web application that focuses on computational integration and analysis of "multi-omics" data. Our tool supports a range of complex analyses, including - among others - low- and high-level analyses of DNA microarray data, integrated analysis of transcriptomics and lipidomics data and the ability to infer miRNA-mRNA interactions. Results: We demonstrate the characteristics and benefits of the DNA Microarray Integromics Analysis Platform using two different test cases. The first test case involves the analysis of the nutrimouse dataset, which contains measurements of the expression of genes involved in nutritional problems and the concentrations of hepatic fatty acids. The second test case involves the analysis of miRNA-mRNA interactions in polysaccharide-stimulated human dermal fibroblasts infected with porcine endogenous retroviruses. Conclusions: The DNA Microarray Integromics Analysis Platform is a web-based graphical user interface for "multi-omics" data management and analysis. Its intuitive nature and wide range of available workflows make it an effective tool for molecular biology research. The platform is hosted at https://lifescience.plgrid.pl

Integrative Multi-omics Analysis to Characterize Human Brain Ischemia

Stroke is a major cause of death and disability. A better comprehension of stroke pathophysiology is fundamental to reduce its dramatic outcome. The use of high-throughput unbiased omics approaches and the integration of these data might deepen the knowledge of stroke at the molecular level, depicting the interaction between different molecular units. We aimed to identify protein and gene expression changes in the human brain after ischemia through an integrative approach to join the information of both omics analyses. The translational potential of our results was explored in a pilot study with blood samples from ischemic stroke patients. Proteomics and transcriptomics discovery studies were performed in human brain samples from six deceased stroke patients, comparing the infarct core with the corresponding contralateral brain region, unveiling 128 proteins and 2716 genes significantly dysregulated after stroke. Integrative bioinformatics analyses joining both datasets exposed canonical pathways altered in the ischemic area, highlighting the most influential molecules. Among the molecules with the highest fold-change, 28 genes and 9 proteins were selected to be validated in five independent human brain samples using orthogonal techniques. Our results were confirmed for NCDN, RAB3C, ST4A1, DNM1L, A1AG1, A1AT, JAM3, VTDB, ANXA1, ANXA2, and IL8. Finally, circulating levels of the validated proteins were explored in ischemic stroke patients. Fluctuations of A1AG1 and A1AT, both up-regulated in the ischemic brain, were detected in blood along the first week after onset. In summary, our results expand the knowledge of ischemic stroke pathology, revealing key molecules to be further explored as biomarkers and/or therapeutic targets. Graphical abstract: [Figure not available: see fulltext.].This work has been funded by Instituto de Salud Carlos III (PI15/00354, PI18/00804), MINECO (MTM2015-64465-C2-1R) and GRBIO (2014-SGR-464) and co-financed by the European Regional Development Fund (FEDER). Neurovascular Research Laboratory takes part in the Spanish stroke research network INVICTUS + (RD16/0019/0021). L.R is supported by a pre-doctoral fellowship from the Instituto de Salud Carlos III (IFI17/00012).Peer reviewe

Hypothesis exploration with visualization of variance.

BackgroundThe Consortium for Neuropsychiatric Phenomics (CNP) at UCLA was an investigation into the biological bases of traits such as memory and response inhibition phenotypes-to explore whether they are linked to syndromes including ADHD, Bipolar disorder, and Schizophrenia. An aim of the consortium was in moving from traditional categorical approaches for psychiatric syndromes towards more quantitative approaches based on large-scale analysis of the space of human variation. It represented an application of phenomics-wide-scale, systematic study of phenotypes-to neuropsychiatry research.ResultsThis paper reports on a system for exploration of hypotheses in data obtained from the LA2K, LA3C, and LA5C studies in CNP. ViVA is a system for exploratory data analysis using novel mathematical models and methods for visualization of variance. An example of these methods is called VISOVA, a combination of visualization and analysis of variance, with the flavor of exploration associated with ANOVA in biomedical hypothesis generation. It permits visual identification of phenotype profiles-patterns of values across phenotypes-that characterize groups. Visualization enables screening and refinement of hypotheses about variance structure of sets of phenotypes.ConclusionsThe ViVA system was designed for exploration of neuropsychiatric hypotheses by interdisciplinary teams. Automated visualization in ViVA supports 'natural selection' on a pool of hypotheses, and permits deeper understanding of the statistical architecture of the data. Large-scale perspective of this kind could lead to better neuropsychiatric diagnostics

mvlearnR and Shiny App for multiview learning

The package mvlearnR and accompanying Shiny App is intended for integrating data from multiple sources or views or modalities (e.g. genomics, proteomics, clinical and demographic data). Most existing software packages for multiview learning are decentralized and offer limited capabilities, making it difficult for users to perform comprehensive integrative analysis. The new package wraps statistical and machine learning methods and graphical tools, providing a convenient and easy data integration workflow. For users with limited programming language, we provide a Shiny Application to facilitate data integration anywhere and on any device. The methods have potential to offer deeper insights into complex disease mechanisms. Availability and Implementation: mvlearnR is available from the following GitHub repository: https://github.com/lasandrall/mvlearnR. The web application is hosted on shinyapps.io and available at: https://multi-viewlearn.shinyapps.io/MultiView_Modeling

A multiomics disease progression signature of low‑risk ccRCC

Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer. Identification of ccRCC likely to progress, despite an apparent low risk at the time of surgery, represents a key clinical issue. From a cohort of adult ccRCC patients (n = 443), we selected low-risk tumors progressing within a 5-years average follow-up (progressors: P, n = 8) and non-progressing (NP) tumors (n = 16). Transcriptome sequencing, miRNA sequencing and proteomics were performed on tissues obtained at surgery. We identified 151 proteins, 1167 mRNAs and 63 miRNAs differentially expressed in P compared to NP low-risk tumors. Pathway analysis demonstrated overrepresentation of proteins related to “LXR/ RXR and FXR/RXR Activation”, “Acute Phase Response Signaling” in NP compared to P samples. Integrating mRNA, miRNA and proteomic data, we developed a 10-component classifier including two proteins, three genes and five miRNAs, effectively differentiating P and NP ccRCC and capturing underlying biological differences, potentially useful to identify “low-risk” patients requiring closer surveillance and treatment adjustments. Key results were validated by immunohistochemistry, qPCR and data from publicly available databases. Our work suggests that LXR, FXR and macrophage activation pathways could be critically involved in the inhibition of the progression of low-risk ccRCC. Furthermore, a 10-component classifier could support an early identification of apparently low-risk ccRCC patients.Peer reviewe

Exploring the role of metabolism in cancer and cardiac settings

Metabolism supports all aspects of cellular function. As such, dysregulated control of the enzymes and signalling pathways coordinating metabolism is common in diseases such as cancer and heart disease. Despite the critical role of metabolism in these diseases and their global burden, their complexity has limited the development of therapeutics that directly target their metabolism. Therefore, this thesis explored the role of extrinsic and intrinsic metabolic dysfunction and characterised examples of metabolism’s potential role in future therapeutics. This work utilised examples of diabetic cardiomyopathy (DCM) to illustrate pathology downstream of extrinsic metabolic dysfunction occurring with insulin resistance in type II diabetes mellitus (T2DM), which remodels the heart independent of adjacent vascular disease. Given that the pathological processes underlying DCM are poorly characterised, and no therapeutics are currently available, this work explored the utility of 2D monolayer, and 3D engineered heart tissue human-induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) models to characterise human-relevant DCM pathology in vitro and explore the trade-offs between model complexity and practicality. A multi-omics approach showed that both models demonstrated the expected shift to fatty acid metabolism and blunting of insulin signalling. The 3D model exhibited a more mature phenotype but showed increased variability. Moreover, phenotypes emerging in animal models of DCM were identified, such as deregulated CD36 trafficking and blunted hypoxia signalling. Examples were given for the intrinsic metabolic dysregulation that supports growth and survival in cancer. Transcriptomics from tumour biopsies was integrated with positron emission tomography (PET) measures for metabolite radiotracer uptake to infer this dysregulation in patients. We examined the utility of dynamic modelling of PET measures using the glucose radiotracer 2-deoxy-2 [18F]-fluoro-D-glucose (FDG). A robust correlation-based pathway approach established that simple dynamic models, which incorporate tracer perfusion and uptake across biological compartments, outperform static measures such as the standardised uptake value (SUV) and complex multi-compartmental models that overfit the data. They associated with the glycolysis term, absent from static findings, identified the most significant pathways, and showed robust detection of inflammatory signals. Dynamic approaches also more robustly established the pathways, such as oxidative phosphorylation, associated with metformin treatment, a metabolic drug under investigation for repurposing in cancer therapy. We used this established methodology to investigate an emerging radiotracer for glutamine uptake, 18F-Fluciclovine, evaluating its ability to characterise glutamine metabolism and its utility in the clinical setting for breast cancer. Immunohistochemistry and metabolomics confirmed that 18F-Fluciclovine was taken up by the glutamine transporter ASCT2 as a proxy for glutamine. 18F-Fluciclovine uptake was associated with pyrimidine metabolism, a biosynthetic pathway incorporating glutamine to supply proliferating cells with nucleotides. Clustering patients by pyrimidine metabolism expression captured the differences in oncogenic signalling, proliferation, and survival, highlighting pyrimidine metabolism’s role in pathology. Our radiogenomics analysis shows potential for metabolic drugs, like metformin, to treat cancer and cardiovascular disease concurrently. However, current cancer therapies like doxorubicin (DOX) lead to cardiotoxicity. Therefore, we used a rodent model of DOX cardiotoxicity to investigate the metabolic drug 5-aminoimidazole-4-carboxamide riboside (AICAR), previously shown to restore cardiac function. Transcriptomics explored a potential mechanism for this protection, confirming that DOX remodels the heart through substrate metabolism deregulation and fibrosis. AICAR, which activates AMP-activated protein kinase (AMPK), restored glycolytic gene expression and heme metabolism, indicative of reduced mitochondrial ferroptosis. A set of ‘rescue genes’ altered by DOX and restored by AICAR were identified, including evidence for alleviating DOX-induced MYC signalling that may mediate heart remodelling. This thesis used omics-based approaches to highlight metabolism’s role in human health and disease, characterising examples of intrinsic metabolic dysfunction, as seen in cancer, and extrinsic metabolic dysfunction, as in diabetic cardiomyopathy. The research employed in vitro, in vivo, and in situ measures to evaluate metabolic changes and their therapeutic implications, highlighting the potential of radiogenomics and hiPSC-CM models in advancing our understanding and treatment of metabolic diseases and the repurposing of metabolic drugs to target cancer and mitigate the cardiotoxicity that follows current treatment strategies

Age-associated Impairment of the Mucus Barrier Function is Associated with Profound Changes in Microbiota and Immunity

Aging significantly increases the vulnerability to gastrointestinal (GI) disorders but there are few studies investigating the key factors in aging that affect the GI tract. To address this knowledge gap, we used 10-week- and 19-month-old litter-mate mice to investigate microbiota and host gene expression changes in association with ageing. In aged mice the thickness of the colonic mucus layer was reduced about 6-fold relative to young mice, and more easily penetrable by luminal bacteria. This was linked to increased apoptosis of goblet cells in the upper part of the crypts. The barrier function of the small intestinal mucus was also compromised and the microbiota were frequently observed in contact with the villus epithelium. Antimicrobial Paneth cell factors Ang4 and lysozyme were expressed in significantly reduced amounts. These barrier defects were accompanied by major changes in the faecal microbiota and significantly decreased abundance of Akkermansia muciniphila which is strongly and negatively affected by old age in humans. Transcriptomics revealed age-associated decreases in the expression of immunity and other genes in intestinal mucosal tissue, including decreased T cell-specific transcripts and T cell signalling pathways. The physiological and immunological changes we observed in the intestine in old age, could have major consequences beyond the gut.</p

STATegra: Multi-Omics Data Integration - A Conceptual Scheme With a Bioinformatics Pipeline

Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. Therefore, it is an unmet need to conceptualize how to integrate such data and implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining available multi-omic anlaysis tools (machine learning component analysis, non-parametric data combination, and a multi-omics exploratory analysis) in a step-wise manner. While in several studies, we have previously combined those integrative tools, here, we provide a systematic description of the STATegra framework and its validation using two The Cancer Genome Atlas (TCGA) case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma (SKCM) cases, we demonstrate an enhanced capacity of the framework (and beyond the individual tools) to identify features and pathways compared to single-omics analysis. Such an integrative multi-omics analysis framework for identifying features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor packag