Le Shu

Exposure to the BPA-Substitute Bisphenol S Causes Unique Alterations of Germline Function.

Concerns about the safety of Bisphenol A, a chemical found in plastics, receipts, food packaging and more, have led to its replacement with substitutes now found in a multitude of consumer products. However, several popular BPA-free alternatives, such as Bisphenol S, share a high degree of structural similarity with BPA, suggesting that these substitutes may disrupt similar developmental and reproductive pathways. We compared the effects of BPA and BPS on germline and reproductive functions using the genetic model system Caenorhabditis elegans. We found that, similarly to BPA, BPS caused severe reproductive defects including germline apoptosis and embryonic lethality. However, meiotic recombination, targeted gene expression, whole transcriptome and ontology analyses as well as ToxCast data mining all indicate that these effects are partly achieved via mechanisms distinct from BPAs. These findings therefore raise new concerns about the safety of BPA alternatives and the risk associated with human exposure to mixtures.

Mergeomics: a web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration

BackgroundHuman diseases are commonly the result of multidimensional changes at molecular, cellular, and systemic levels. Recent advances in genomic technologies have enabled an outpour of omics datasets that capture these changes. However, separate analyses of these various data only provide fragmented understanding and do not capture the holistic view of disease mechanisms. To meet the urgent needs for tools that effectively integrate multiple types of omics data to derive biological insights, we have developed Mergeomics, a computational pipeline that integrates multidimensional disease association data with functional genomics and molecular networks to retrieve biological pathways, gene networks, and central regulators critical for disease development.ResultsTo make the Mergeomics pipeline available to a wider research community, we have implemented an online, user-friendly web server (http://mergeomics.research.idre.ucla.edu/). The web server features a modular implementation of the Mergeomics pipeline with detailed tutorials. Additionally, it provides curated genomic resources including tissue-specific expression quantitative trait loci, ENCODE functional annotations, biological pathways, and molecular networks, and offers interactive visualization of analytical results. Multiple computational tools including Marker Dependency Filtering (MDF), Marker Set Enrichment Analysis (MSEA), Meta-MSEA, and Weighted Key Driver Analysis (wKDA) can be used separately or in flexible combinations. User-defined summary-level genomic association datasets (e.g., genetic, transcriptomic, epigenomic) related to a particular disease or phenotype can be uploaded and computed real-time to yield biologically interpretable results, which can be viewed online and downloaded for later use.ConclusionsOur Mergeomics web server offers researchers flexible and user-friendly tools to facilitate integration of multidimensional data into holistic views of disease mechanisms in the form of tissue-specific key regulators, biological pathways, and gene networks.

Mergeomics: Multidimensional data integration to identify pathogenic perturbations to biological systems

BackgroundComplex diseases are characterized by multiple subtle perturbations to biological processes. New omics platforms can detect these perturbations, but translating the diverse molecular and statistical information into testable mechanistic hypotheses is challenging. Therefore, we set out to create a public tool that integrates these data across multiple datasets, platforms, study designs and species in order to detect the most promising targets for further mechanistic studies.ResultsWe developed Mergeomics, a computational pipeline consisting of independent modules that 1) leverage multi-omics association data to identify biological processes that are perturbed in disease, and 2) overlay the disease-associated processes onto molecular interaction networks to pinpoint hubs as potential key regulators. Unlike existing tools that are mostly dedicated to specific data type or settings, the Mergeomics pipeline accepts and integrates datasets across platforms, data types and species. We optimized and evaluated the performance of Mergeomics using simulation and multiple independent datasets, and benchmarked the results against alternative methods. We also demonstrate the versatility of Mergeomics in two case studies that include genome-wide, epigenome-wide and transcriptome-wide datasets from human and mouse studies of total cholesterol and fasting glucose. In both cases, the Mergeomics pipeline provided statistical and contextual evidence to prioritize further investigations in the wet lab. The software implementation of Mergeomics is freely available as a Bioconductor R package.ConclusionMergeomics is a flexible and robust computational pipeline for multidimensional data integration. It outperforms existing tools, and is easily applicable to datasets from different studies, species and omics data types for the study of complex traits.

Shared genetic regulatory networks for cardiovascular disease and type 2 diabetes in multiple populations of diverse ethnicities in the United States

Cardiovascular diseases (CVD) and type 2 diabetes (T2D) are closely interrelated complex diseases likely sharing overlapping pathogenesis driven by aberrant activities in gene networks. However, the molecular circuitries underlying the pathogenic commonalities remain poorly understood. We sought to identify the shared gene networks and their key intervening drivers for both CVD and T2D by conducting a comprehensive integrative analysis driven by five multi-ethnic genome-wide association studies (GWAS) for CVD and T2D, expression quantitative trait loci (eQTLs), ENCODE, and tissue-specific gene network models (both co-expression and graphical models) from CVD and T2D relevant tissues. We identified pathways regulating the metabolism of lipids, glucose, and branched-chain amino acids, along with those governing oxidation, extracellular matrix, immune response, and neuronal system as shared pathogenic processes for both diseases. Further, we uncovered 15 key drivers including HMGCR, CAV1, IGF1 and PCOLCE, whose network neighbors collectively account for approximately 35% of known GWAS hits for CVD and 22% for T2D. Finally, we cross-validated the regulatory role of the top key drivers using in vitro siRNA knockdown, in vivo gene knockout, and two Hybrid Mouse Diversity Panels each comprised of >100 strains. Findings from this in-depth assessment of genetic and functional data from multiple human cohorts provide strong support that common sets of tissue-specific molecular networks drive the pathogenesis of both CVD and T2D across ethnicities and help prioritize new therapeutic avenues for both CVD and T2D.

Sox5 regulates beta-cell phenotype and is reduced in type 2 diabetes

Type 2 diabetes (T2D) is characterized by insulin resistance and impaired insulin secretion, but the mechanisms underlying insulin secretion failure are not completely understood. Here, we show that a set of co-expressed genes, which is enriched for genes with islet-selective open chromatin, is associated with T2D. These genes are perturbed in T2D and have a similar expression pattern to that of dedifferentiated islets. We identify Sox5 as a regulator of the module. Sox5 knockdown induces gene expression changes similar to those observed in T2D and diabetic animals and has profound effects on insulin secretion, including reduced depolarization-evoked Ca2+-influx and β-cell exocytosis. SOX5 overexpression reverses the expression perturbations observed in a mouse model of T2D, increases the expression of key β-cell genes and improves glucose-stimulated insulin secretion in human islets from donors with T2D. We suggest that human islets in T2D display changes reminiscent of dedifferentiation and highlight SOX5 as a regulator of β-cell phenotype and function.

Multidimensional Integrative Genomics Approaches to Dissecting Cardiovascular Disease

Elucidating the mechanisms of complex diseases such as cardiovascular disease (CVD) remains a significant challenge due to multidimensional alterations at molecular, cellular, tissue, and organ levels. To better understand CVD and offer insights into the underlying mechanisms and potential therapeutic strategies, data from multiple omics types (genomics, epigenomics, transcriptomics, metabolomics, proteomics, microbiomics) from both humans and model organisms have become available. However, individual omics data types capture only a fraction of the molecular mechanisms. To address this challenge, there have been numerous efforts to develop integrative genomics methods that can leverage multidimensional information from diverse data types to derive comprehensive molecular insights. In this review, we summarize recent methodological advances in multidimensional omics integration, exemplify their applications in cardiovascular research, and pinpoint challenges and future directions in this incipient field.

Mergeomics: integration of diverse genomics resources to identify pathogenic perturbations to biological systems

Mergeomics is a computational pipeline (http://mergeomics.research.idre.ucla.edu/Download/Package/) that integrates multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It first identifies biological pathways and tissue-specific gene subnetworks that are perturbed by disease-associated molecular entities. The disease-associated subnetworks are then projected onto tissue-specific gene-gene interaction networks to identify local hubs as potential key drivers of pathological perturbations. The pipeline is modular and can be applied across species and platform boundaries, and uniquely conducts pathway/network level meta-analysis of multiple genomic studies of various data types. Application of Mergeomics to cholesterol datasets revealed novel regulators of cholesterol metabolism.

Bioinformatics Principles for Deciphering Cardiovascular Diseases

Cardiovascular diseases (CVD) are a group of highly prevalent and deadly diseases involving multiple risk factors, diverse cell types, tissues, organs, and multidimensional molecular perturbations. To effectively capture the multilevel complexity, big data science has emerged as a powerful strategy to investigate CVD, thus making bioinformatics an essential field in the modern data-rich era. Here we summarize the basic principles of bioinformatics and core algorithms and tools applicable to CVD by focusing on those relevant to the most commonly used omics data types including genomics, epigenomics, transcriptomics, metabolomics, proteomics, and microbiota. Multiomics integration concepts and tools are also introduced.

Prenatal Bisphenol A Exposure in Mice Induces Multi-tissue Multi-omics Disruptions Linking to Cardiometabolic Disorders

The health impacts of endocrine disrupting chemicals (EDCs) remain debated and their tissue and molecular targets are poorly understood. Here we leveraged systems biology approaches to assess the target tissues, molecular pathways, and gene regulatory networks associated with prenatal exposure to the model EDC Bisphenol A (BPA). Prenatal BPA exposure led to scores of transcriptomic and methylomic alterations in the adipose, hypothalamus, and liver tissues in mouse offspring, with cross-tissue perturbations in lipid metabolism as well as tissue-specific alterations in histone subunits, glucose metabolism and extracellular matrix. Network modeling prioritized main molecular targets of BPA, including Pparg, Hnf4a, Esr1, and Fasn. Lastly, integrative analyses identified the association of BPA molecular signatures with cardiometabolic phenotypes in mouse and human. Our multi-tissue, multi-omics investigation provides strong evidence that BPA perturbs diverse molecular networks in central and peripheral tissues, and offers insights into the molecular targets that link BPA to human cardiometabolic disorders. Author summary The inability to pinpoint the mechanistic underpinnings of environmentally-induced diseases likely stems from the pleiotropic effects of chemicals such as BPA on diverse tissues and molecular space (transcriptome, epigenome, etc.). This makes it challenging to fully dissect their health impact and merits a call for modern big data approaches to examine environmental factors. Our data-driven study is the first unbiased, multi-tissue multiomic systems biology investigation of the molecular circuitry and mechanisms underlying offspring response to prenatal BPA exposure. Importantly, the incorporation of network-based modeling allows us to capture novel players in the regulation of BPA activities in vivo, and the integration with human disease association datasets helps bridge the molecular pathways affected by BPA with diverse human diseases. In doing so, our study provides compelling molecular evidence that developmental BPA exposure significantly perturbs metabolic and endocrine systems in the offspring, and supports BPA as one of the environmental factors involved in the developmental origins of health and disease (DOHaD).

Differential Metabolic and Multi-tissue Transcriptomic Responses to Fructose Consumption among Genetically Diverse Mice

The escalating prevalence of metabolic syndrome (MetS) poses significant risks to type 2 diabetes mellitus, cardiovascular diseases, and non-alcoholic fatty liver disease. High fructose intake has emerged as an environmental risk for MetS and the associated metabolic diseases. To examine inter-individual variability in MetS susceptibility in response to fructose consumption, here we fed three inbred mouse strains, namely C57BL/6J (B6), DBA (DBA) and FVB/NJ (FVB) with 8% fructose in drinking water for 12 weeks. We found that fructose-fed DBA mice had significantly higher amount of body weight, adiposity, and glucose intolerance starting from the 4th week of fructose feeding compared to the control group, while B6 and FVB showed no differences in these phenotypes over the course of fructose feeding. In addition, elevated insulin levels were found in fructose-fed DBA and FVB mice, and cholesterol levels were uniquely elevated in B6 mice. To explore the molecular underpinnings of the observed distinct phenotypic responses among strains, we applied RNA sequencing to investigate the effect of fructose on the transcriptional profiles of liver and hypothalamus tissues, revealing strain- and tissue-specific patterns of transcriptional and pathway perturbations. Strain-specific liver pathways altered by fructose include fatty acid and cholesterol metabolic pathways for B6 and PPAR signaling for DBA. In hypothalamus tissue, only B6 showed significantly enriched pathways such as protein folding, pancreatic secretion, and fatty acid beta-oxidation. Using network modeling, we predicted potential strain-specific key regulators of fructose response such as Fgf21 (DBA) and Lss (B6) in liver, and Fmod (B6) in hypothalamus. We validated strain-biased responses of Fgf21 and Lss to fructose in primary hepatocytes. Our findings support that fructose perturbs different tissue networks and pathways in genetically diverse mice and associates with distinct features of metabolic dysfunctions. These results highlight individualized molecular and metabolic responses to fructose consumption and may help guide the development of personalized strategies against fructose-induced MetS.