Analysis of Pan-omics Data in Human Interactome Network (APODHIN).
ABSTRACT: Analysis of Pan-omics Data in Human Interactome Network (APODHIN) is a platform for integrative analysis of transcriptomics, proteomics, genomics, and metabolomics data for identification of key molecular players and their interconnections exemplified in cancer scenario. APODHIN works on a meta-interactome network consisting of human protein-protein interactions (PPIs), miRNA-target gene regulatory interactions, and transcription factor-target gene regulatory relationships. In its first module, APODHIN maps proteins/genes/miRNAs from different omics data in its meta-interactome network and extracts the network of biomolecules that are differentially altered in the given scenario. Using this context specific, filtered interaction network, APODHIN identifies topologically important nodes (TINs) implementing graph theory based network topology analysis and further justifies their role via pathway and disease marker mapping. These TINs could be used as prospective diagnostic and/or prognostic biomarkers and/or potential therapeutic targets. In its second module, APODHIN attempts to identify cross pathway regulatory and PPI links connecting signaling proteins, transcription factors (TFs), and miRNAs to metabolic enzymes via utilization of single-omics and/or pan-omics data and implementation of mathematical modeling. Interconnections between regulatory components such as signaling proteins/TFs/miRNAs and metabolic pathways need to be elucidated more elaborately in order to understand the role of oncogene and tumor suppressors in regulation of metabolic reprogramming during cancer. APODHIN platform contains a web server component where users can upload single/multi omics data to identify TINs and cross-pathway links. Tabular, graphical and 3D network representations of the identified TINs and cross-pathway links are provided for better appreciation. Additionally, this platform also provides few example data analysis of cancer specific, single and/or multi omics dataset for cervical, ovarian, and breast cancers where meta-interactome networks, TINs, and cross-pathway links are provided. APODHIN platform is freely available at http://www.hpppi.iicb.res.in/APODHIN/home.html.
Project description:OBJECTIVE:To uncover the microRNA (miRNA) interactome of the osteoarthritis (OA) pathophysiological process in the cartilage. METHODS:We performed RNA sequencing in 130 samples (n=35 and n=30 pairs for messenger RNA (mRNA) and miRNA, respectively) on macroscopically preserved and lesioned OA cartilage from the same patient and performed differential expression (DE) analysis of miRNA and mRNAs. To build an OA-specific miRNA interactome, a prioritisation scheme was applied based on inverse Pearson's correlations and inverse DE of miRNAs and mRNAs. Subsequently, these were filtered by those present in predicted (TargetScan/microT-CDS) and/or experimentally validated (miRTarBase/TarBase) public databases. Pathway enrichment analysis was applied to elucidate OA-related pathways likely mediated by miRNA regulatory mechanisms. RESULTS:We found 142 miRNAs and 2387 mRNAs to be differentially expressed between lesioned and preserved OA articular cartilage. After applying prioritisation towards likely miRNA-mRNA targets, a regulatory network of 62 miRNAs targeting 238 mRNAs was created. Subsequent pathway enrichment analysis of these mRNAs (or genes) elucidated that genes within the 'nervous system development' are likely mediated by miRNA regulatory mechanisms (familywise error=8.4×10-5). Herein NTF3 encodes neurotrophin-3, which controls survival and differentiation of neurons and which is closely related to the nerve growth factor. CONCLUSIONS:By an integrated approach of miRNA and mRNA sequencing data of OA cartilage, an OA miRNA interactome and related pathways were elucidated. Our functional data demonstrated interacting levels at which miRNA affects expression of genes in the cartilage and exemplified the complexity of functionally validating a network of genes that may be targeted by multiple miRNAs.
Project description:Although insulin resistance (IR) is a key pathophysiologic condition underlying various metabolic disorders, impaired cellular glucose uptake is one of many manifestations of metabolic derangements in the human body. To study the systems-wide molecular changes associated with obesity-dependent IR, we integrated information on plasma proteins and microRNAs in eight obese insulin-resistant (OIR, HOMA-IR > 2.5) and nine lean insulin-sensitive (LIS, HOMA-IR < 1.0) normoglycemic males. Of 374 circulating miRNAs we profiled, 65 species increased and 73 species decreased in the OIR compared to the LIS subjects, suggesting that the overall balance of the miRNA secretome is shifted in the OIR subjects. We also observed that 40 plasma proteins increased and 4 plasma proteins decreased in the OIR subjects compared to the LIS subjects, and most proteins are involved in metabolic and endocytic functions. We used an integrative -omics analysis framework called iOmicsPASS to link differentially regulated miRNAs with their target genes on the TargetScan map and the human protein interactome. Combined with tissue of origin information, the integrative analysis allowed us to nominate obesity-dependent and obesity-independent protein markers, along with potential sites of post-transcriptional regulation by some of the miRNAs. We also observed the changes in each -omics platform that are not linked by the TargetScan map, suggesting that proteins and microRNAs provide orthogonal information for the progression of OIR. In summary, our integrative analysis provides a network of elevated plasma markers of OIR and a global shift of microRNA secretome composition in the blood plasma.
Project description:<h4>Background</h4>Recent DNA/RNA sequencing and other multi-omics technologies have advanced the understanding of the biology and pathophysiology of AD, yet there is still a lack of disease-modifying treatments for AD. A new approach to integration of the genome, transcriptome, proteome, and human interactome in the drug discovery and development process is essential for this endeavor.<h4>Methods</h4>In this study, we developed AlzGPS (Genome-wide Positioning Systems platform for Alzheimer's Drug Discovery, https://alzgps.lerner.ccf.org ), a comprehensive systems biology tool to enable searching, visualizing, and analyzing multi-omics, various types of heterogeneous biological networks, and clinical databases for target identification and development of effective prevention and treatment for AD.<h4>Results</h4>Via AlzGPS: (1) we curated more than 100 AD multi-omics data sets capturing DNA, RNA, protein, and small molecule profiles underlying AD pathogenesis (e.g., early vs. late stage and tau or amyloid endophenotype); (2) we constructed endophenotype disease modules by incorporating multi-omics findings and human protein-protein interactome networks; (3) we provided possible treatment information from ~?3000 FDA approved/investigational drugs for AD using state-of-the-art network proximity analyses; (4) we curated nearly 300 literature references for high-confidence drug candidates; (5) we included information from over 1000?AD clinical trials noting drug's mechanisms-of-action and primary drug targets, and linking them to our integrated multi-omics view for targets and network analysis results for the drugs; (6) we implemented a highly interactive web interface for database browsing and network visualization.<h4>Conclusions</h4>Network visualization enabled by AlzGPS includes brain-specific neighborhood networks for genes-of-interest, endophenotype disease module networks for omics-of-interest, and mechanism-of-action networks for drugs targeting disease modules. By virtue of combining systems pharmacology and network-based integrative analysis of multi-omics data, AlzGPS offers actionable systems biology tools for accelerating therapeutic development in AD.
Project description:The group of inborn errors of metabolism (IEM) displays a marked heterogeneity and IEM can affect virtually all functions and organs of the human organism; however, IEM share that their associated proteins function in metabolism. Most proteins carry out cellular functions by interacting with other proteins, and thus are organized in biological networks. Therefore, diseases are rarely the consequence of single gene mutations but of the perturbations caused in the related cellular network. Systematic approaches that integrate multi-omics and database information into biological networks have successfully expanded our knowledge of complex disorders but network-based strategies have been rarely applied to study IEM. We analyzed IEM on a proteome scale and found that IEM-associated proteins are organized as a network of linked modules within the human interactome of protein interactions, the IEM interactome. Certain IEM disease groups formed self-contained disease modules, which were highly interlinked. On the other hand, we observed disease modules consisting of proteins from many different disease groups in the IEM interactome. Moreover, we explored the overlap between IEM and non-IEM disease genes and applied network medicine approaches to investigate shared biological pathways, clinical signs and symptoms, and links to drug targets. The provided resources may help to elucidate the molecular mechanisms underlying new IEM, to uncover the significance of disease-associated mutations, to identify new biomarkers, and to develop novel therapeutic strategies.
Project description:Cholangiopathies are a diverse group of progressive diseases whose primary cell targets are cholangiocytes. To identify shared pathogenesis and molecular connectivity among the three main human cholangiopathies (biliary atresia [BA], primary biliary cholangitis [PBC], and primary sclerosing cholangitis [PSC]), we built a comprehensive platform of published data on gene variants, gene expression, and functional studies and applied network-based analytics in the search for shared molecular circuits. Mining the data platform with largest connected component and interactome analyses, we validated previously reported associations and identified essential and hub genes. In addition to disease-specific modules, we found a substantial overlap of disease neighborhoods and uncovered a group of 34 core genes that are enriched for immune processes and abnormal intestine/hepatobiliary mouse phenotypes. Within this core, we identified a gene subcore containing signal transduction and activator of transcription 3, interleukin-6, tumor necrosis factor, and forkhead box P3 prominently placed in a regulatory connectome of genes related to cellular immunity and fibrosis. We also found substantial gene enrichment in the advanced glycation endproduct/receptor for advanced glycation endproducts (RAGE) pathway and showed that RAGE activation induced cholangiocyte proliferation. Conclusion: Human cholangiopathies share pathways enriched by immunity genes and a molecular connectome that links different pathogenic features of BA, PBC, and PSC. (Hepatology 2018;67:676-689).
Project description:The increasing availability of multi-omic platforms poses new challenges to data analysis. Joint visualization of multi-omics data is instrumental in better understanding interconnections across molecular layers and in fully utilizing the multi-omic resources available to make biological discoveries. We present here PaintOmics 3, a web-based resource for the integrated visualization of multiple omic data types onto KEGG pathway diagrams. PaintOmics 3 combines server-end capabilities for data analysis with the potential of modern web resources for data visualization, providing researchers with a powerful framework for interactive exploration of their multi-omics information. Unlike other visualization tools, PaintOmics 3 covers a comprehensive pathway analysis workflow, including automatic feature name/identifier conversion, multi-layered feature matching, pathway enrichment, network analysis, interactive heatmaps, trend charts, and more. It accepts a wide variety of omic types, including transcriptomics, proteomics and metabolomics, as well as region-based approaches such as ATAC-seq or ChIP-seq data. The tool is freely available at www.paintomics.org.
Project description:Integration of multiple profiling data and construction of functional gene networks may provide additional insights into the molecular mechanisms of complex diseases. Osteoporosis is a worldwide public health problem, but the complex gene-gene interactions, post-transcriptional modifications and regulation of functional networks are still unclear. To gain a comprehensive understanding of osteoporosis etiology, transcriptome gene expression microarray, epigenomic miRNA microarray and methylome sequencing were performed simultaneously in 5 high hip BMD (Bone Mineral Density) subjects and 5 low hip BMD subjects. SPIA (Signaling Pathway Impact Analysis) and PCST (Prize Collecting Steiner Tree) algorithm were used to perform pathway-enrichment analysis and construct the interaction networks. Through integrating the transcriptomic and epigenomic data, firstly we identified 3 genes (FAM50A, ZNF473 and TMEM55B) and one miRNA (hsa-mir-4291) which showed the consistent association evidence from both gene expression and methylation data; secondly in network analysis we identified an interaction network module with 12 genes and 11 miRNAs including AKT1, STAT3, STAT5A, FLT3, hsa-mir-141 and hsa-mir-34a which have been associated with BMD in previous studies. This module revealed the crosstalk among miRNAs, mRNAs and DNA methylation and showed four potential regulatory patterns of gene expression to influence the BMD status. In conclusion, the integration of multiple layers of omics can yield in-depth results than analysis of individual omics data respectively. Integrative analysis from transcriptomics and epigenomic data improves our ability to identify causal genetic factors, and more importantly uncover functional regulation pattern of multi-omics for osteoporosis etiology.
Project description:Experimental studies have demonstrated several effects of statins in acute coronary syndrome (ACS) that may extend their clinical benefit beyond the lipid profile modification itself. However, the precise underlying mechanism remains to be elucidated. microRNAs (miRNAs) serve significant roles in the pathophysiology of atherosclerotic plaque progression. The present study investigated the protective role of statins in patients with unstable angina (UA) by regulating the circulating miRNA network. miRNA array results demonstrated that there were 21 differentially expressed miRNAs in non‑statin‑treated patients with UA (n=8) compared with non‑coronary artery disease controls (n=8), and 33 differentially expressed miRNAs in statin‑treated patients with UA (n=8) compared with non‑statin patients. TargetScan and miRanda programs were used to predict miRNAs target genes. miRNAs target genes in vascular endothelial cells and monocytes were clustered based on the CGAP SAGE library via the Database for Annotation, Visualization and Integrated Discovery (DAVID) platform, and miRNA target genes in platelets were clustered based on a UP tissue‑specific library via the DAVID platform. The PANTHER database via DAVID platform was used to perform signaling pathway analysis. The miRNA‑gene/pathway network was visualized by Cytoscape software. Bioinformatic analysis suggested that statin‑induced miRNAs functions were primarily enriched in angiogenesis, integrin and platelet derived growth factor signaling pathways in UA patients. In endothelial cells and platelets, statin‑induced miRNAs primarily targeted the integrin signaling pathway, and in monocytes primarily targeted cytoskeletal regulation by the Rho GTPase signaling pathway. These results revealed that statins may serve systematic protective roles in UA patients by influencing the circulating miRNA regulatory network. Further studies are required to verify the functions of statin‑induced miRNAs in endothelial cells, platelets and monocytes.
Project description:Over two-thirds of melanomas have activating mutations in B-Raf, leading to constitutive activation of the B-Raf/MKK/ERK signaling pathway. The most prevalent mutation, B-RafV600E, promotes cancer cell behavior through mechanisms that are still incompletely defined. Here, we used a sensitive microarray profiling platform to compare microRNA (miRNA) expression levels between primary melanocytes and B-RafV600E-positive melanoma cell lines, and between melanoma cells treated in the presence and absence of an MKK1/2 inhibitor. We identified a network of >20 miRNAs deregulated by B-Raf/MKK/ERK in melanoma cells, the majority of which modulate the expression of key cancer regulatory genes and functions. Importantly, miRNAs within the network converge on protein regulation and cancer phenotypes, suggesting that these miRNAs might function combinatorially. We show that miRNAs augment effects on protein repression and cell invasion when co-expressed, and gene-specific latency and interference effects between miRNAs were also observed. Thus, B-Raf/MKK/ERK controls key aspects of cancer cell behavior and gene expression by modulating a network of miRNAs with cross-regulatory functions. The findings highlight the potential for complex interactions between coordinately regulated miRNAs within a network.
Project description:microRNAs (miRNAs) regulate gene expression at post-transcriptional level by repressing target RNA molecules. Competition to bind miRNAs tends in turn to correlate their targets, establishing effective RNA-RNA interactions that can influence expression levels, buffer fluctuations and promote signal propagation. Such a potential has been characterized mathematically for small motifs both at steady state and away from stationarity. Experimental evidence, on the other hand, suggests that competing endogenous RNA (ceRNA) crosstalk is rather weak. Extended miRNA-RNA networks could however favour the integration of many crosstalk interactions, leading to significant large-scale effects in spite of the weakness of individual links. To clarify the extent to which crosstalk is sustained by the miRNA interactome, we have studied its emergent systemic features in silico in large-scale miRNA-RNA network reconstructions. We show that, although generically weak, system-level crosstalk patterns (i) are enhanced by transcriptional heterogeneities, (ii) can achieve high-intensity even for RNAs that are not co-regulated, (iii) are robust to variability in transcription rates, and (iv) are significantly non-local, i.e. correlate weakly with miRNA-RNA interaction parameters. Furthermore, RNA levels are generically more stable when crosstalk is strongest. As some of these features appear to be encoded in the network's topology, crosstalk may functionally be favoured by natural selection. These results suggest that, besides their repressive role, miRNAs mediate a weak but resilient and context-independent network of cross-regulatory interactions that interconnect the transcriptome, stabilize expression levels and support system-level responses.