Search

Aims/hypothesis Type 2 diabetes is characterised by islet amyloid and toxic oligomers of islet amyloid polypeptide (IAPP). We posed the questions, (1) does IAPP toxicity induce an islet response comparable to that in humans with type 2 diabetes, and if so, (2) what are the key transcriptional drivers of this response? Methods The islet transcriptome was evaluated in five groups of mice: beta cell specific transgenic for (1) human IAPP, (2) rodent IAPP, (3) human calpastatin, (4) human calpastatin and human IAPP, and (5) wild-type mice. RNA sequencing data was analysed by differential expression analysis and gene co-expression network analysis to establish the islet response to adaptation to an increased beta cell workload of soluble rodent IAPP, the islet response to increased expression of oligomeric human IAPP, and the extent to which the latter was rescued by suppression of calpain hyperactivation by calpastatin. Rank-rank hypergeometric overlap analysis was used to compare the transcriptome of islets from human or rodent IAPP transgenic mice vs humans with prediabetes or type 2 diabetes. Results The islet transcriptomes in humans with prediabetes and type 2 diabetes are remarkably similar. Beta cell overexpression of soluble rodent or oligomer-prone human IAPP induced changes in islet transcriptome present in prediabetes and type 2 diabetes, including decreased expression of genes that confer beta cell identity. Increased expression of human IAPP, but not rodent IAPP, induced islet inflammation present in prediabetes and type 2 diabetes in humans. Key mediators of the injury responses in islets transgenic for human IAPP or those from individuals with type 2 diabetes include STAT3, NF-κB, ESR1 and CTNNB1 by transcription factor analysis and COL3A1, NID1 and ZNF800 by gene regulatory network analysis. Conclusions/interpretation Beta cell injury mediated by IAPP is a plausible mechanism to contribute to islet inflammation and dedifferentiation in type 2 diabetes. Inhibition of IAPP toxicity is a potential therapeutic target in type 2 diabetes. Graphical abstract

ObjectiveCROP-Seq combines gene silencing using CRISPR interference (CRISPRi) with single-cell RNA sequencing (scRNA-Seq) to conduct a functional reverse genetic screen of novel gene targets associated with adipocyte differentiation or function, with single-cell transcriptomes as the readout.MethodsWe created a human preadipocyte SGBS cell line with stable expression of KRAB-dCas9 for CRISPRi-mediated gene knock-down. This line was transduced with a lentiviral library of sgRNAs targeting 6 genes of interest (3 sgRNAs / gene, 18 sgRNAs), 6 positive control genes (3 sgRNAs / gene, 18 sgRNAs), and non-targeting control sgRNAs (4 sgRNAs). Transduced cells were selected and differentiated, and individual cells were captured using microfluidics at day 0, 4 and 8 of adipogenic differentiation. Next, expression and sgRNA libraries were created and sequenced. Bioinformatic analysis of resulting scRNA-Seq expression data was used to determine the effects of gene knock-down and the dysregulated pathways, and to predict cellular phenotypes.ResultsSingle-cell transcriptomes obtained from SGBS cells following CRISPRi recapitulate different states of differentiation from preadipocytes to adipocytes. We confirmed successful knock-down of targeted genes. Transcriptome-wide changes were observed for all targeted genes, with over 400 differentially expressed genes identified per gene at least at one timepoint. Knock-down of known adipogenesis regulators PPARG and CEBPB inhibited adipogenesis. Gene set enrichment analyses revealed molecular processes for adipose tissue differentiation and function for novel genes. MAFF knock-down led to a downregulation of transcriptional response to proinflammatory cytokine TNF-α in preadipocytes. TIPARP knock-down resulted in an increase in the expression of a beiging marker UCP1 at D8 of adipogenesis.ConclusionsThe CROP-Seq system in SGBS cells can determine the consequences of target gene knock-down at the transcriptome level. This powerful, hypothesis-free tool can identify novel regulators of adipogenesis, preadipocyte and adipocyte function associated with metabolic disease.HighlightsCRISPR interference screen coupled with single-cell RNA sequencing (CROP-Seq)Parallel screening of 12 genes in human SGBS adipocytes and preadipocytesUncovered novel regulators of adipogenesis and adipocyte functionGraphical abstract

The Mergeomics web server is a flexible online tool for multi-omics data integration to derive biological pathways, networks, and key drivers important to disease pathogenesis and is based on the open source Mergeomics R package. The web server takes summary statistics of multi-omics disease association studies (GWAS, EWAS, TWAS, PWAS, etc.) as input and features four functions: Marker Dependency Filtering (MDF) to correct for known dependency between omics markers, Marker Set Enrichment Analysis (MSEA) to detect disease relevant biological processes, Meta-MSEA to examine the consistency of biological processes informed by various omics datasets, and Key Driver Analysis (KDA) to identify essential regulators of disease-associated pathways and networks. The web server has been extensively updated and streamlined in version 2.0 including an overhauled user interface, improved tutorials and results interpretation for each analytical step, inclusion of numerous disease GWAS, functional genomics datasets, and molecular networks to allow for comprehensive omics integrations, increased functionality to decrease user workload, and increased flexibility to cater to user-specific needs. Finally, we have incorporated our newly developed drug repositioning pipeline PharmOmics for prediction of potential drugs targeting disease processes that were identified by Mergeomics. Mergeomics is freely accessible at http://mergeomics.research.idre.ucla.edu and does not require login., Graphical Abstract Graphical AbstractMergeomics uses single omics or multi-omics data to produce pathway- and network-level mechanistic understanding of disease and identify potential therapeutic targets.

Drug development has been hampered by a high failure rate in clinical trials due to our incomplete understanding of drug functions across organs and species. Therefore, elucidating species- and tissue-specific drug functions can provide insights into therapeutic efficacy, potential adverse effects, and interspecies differences necessary for effective translational medicine. Here, we present PharmOmics, a drug knowledgebase and analytical tool that is hosted on an interactive web server. Using tissue- and species-specific transcriptome data from human, mouse, and rat curated from different databases, we implemented a gene-network-based approach for drug repositioning. We demonstrate the potential of PharmOmics to retrieve known therapeutic drugs and identify drugs with tissue toxicity using

Sex differences in physiology and disease in mammals result from the effects of three classes of factors that are inherently unequal in males and females: reversible (activational) effects of gonadal hormones, permanent (organizational) effects of gonadal hormones, and cell-autonomous effects of sex chromosomes, as well as genes driven by these classes of factors. Often, these factors act together to cause sex differences in specific phenotypes, but the relative contribution of each and the interactions among them remain unclear. Here, we used the Four Core Genotypes (FCG) mouse model with or without hormone replacement to distinguish the effects of each class of sex-biasing factors on transcriptome regulation in liver and adipose tissues. We found that the activational hormone levels have the strongest influence on gene expression, followed by the organizational gonadal sex effect and, lastly, sex chromosomal effect, along with interactions among the three factors. Tissue specificity was prominent, with a major impact of estradiol on adipose tissue gene regulation, and of testosterone on the liver transcriptome. The networks affected by the three sex-biasing factors include development, immunity and metabolism, and tissue-specific regulators were identified for these networks. Furthermore, the genes affected by individual sex-biasing factors and interactions among factors are associated with human disease traits such as coronary artery disease, diabetes, and inflammatory bowel disease. Our study offers a tissue-specific account of the individual and interactive contributions of major sex-biasing factors to gene regulation that have broad impact on systemic metabolic, endocrine, and immune functions.

Nonalcoholic fatty liver disease (NAFLD) has presented itself as a significant health burden, with increasing prevalence globally and evidence of a sexually dimorphic disease course. Particularly, it is of critical importance to understand the latter stage of disease, nonalcoholic steatohepatitis (NASH) with fibrosis, which is more prevalent and severe in males, in order to provide better prevention, treatment, and biomarker options to reduce the health burden. Our mechanistic understanding of NASH progression is far from elucidated, and thus, we present a multi-omics pathway and network analysis utilizing genomic, transcriptomic, and lipidomic data from the Hybrid Mouse Diversity Panel (HMDP) to uncover key biological processes and key driver genes causal to liver fibrosis and to dissect sex differences. Analyzing these multi-omics datasets by sex, we present mechanistic differences important to fibrosis development between males and females. We find that males present with greater enrichment of immune processes as well as abnormalities in protein and lipid metabolism, whereas females present with significant abnormalities linked with carbohydrate metabolism processes, suggesting differential causal mechanisms of fibrosis progression between sexes. Comparing males and females, we also find consistent processes such as extracellular matrix development, clearly vital for fibrosis to develop. Using gene regulatory network analysis of the liver tissue, we further identified key network genes driving the sex-specific processes as well as those consistent between sexes that are important for liver fibrosis. Our multi-omics integrative studies offer comprehensive understanding of NASH in a sex-specific manner. Disclosure M. Blencowe: None. A. Lusis: None. F. Norheim: None. Z. Saleem: None. N. S. Hsu: None. S. Hui: None. C. Pan: None. K. Chella krishnan: None. C. Edillor: None. X. Yang: None. Funding National Institutes of Health (R01DK117850)

Genome-wide association studies (GWASs) have implicated ∼380 genetic loci for plasma lipid regulation. However, these loci only explain 17–27% of the trait variance, and a comprehensive understanding of the molecular mechanisms has not been achieved. In this study, we utilized an integrative genomics approach leveraging diverse genomic data from human populations to investigate whether genetic variants associated with various plasma lipid traits, namely, total cholesterol, high and low density lipoprotein cholesterol (HDL and LDL), and triglycerides, from GWASs were concentrated on specific parts of tissue-specific gene regulatory networks. In addition to the expected lipid metabolism pathways, gene subnetworks involved in “interferon signaling,” “autoimmune/immune activation,” “visual transduction,” and “protein catabolism” were significantly associated with all lipid traits. In addition, we detected trait-specific subnetworks, including cadherin-associated subnetworks for LDL; glutathione metabolism for HDL; valine, leucine, and isoleucine biosynthesis for total cholesterol; and insulin signaling and complement pathways for triglyceride. Finally, by using gene-gene relations revealed by tissue-specific gene regulatory networks, we detected both known (e.g., APOH, APOA4, and ABCA1) and novel (e.g., F2 in adipose tissue) key regulator genes in these lipid-associated subnetworks. Knockdown of the F2 gene (coagulation factor II, thrombin) in 3T3-L1 and C3H10T1/2 adipocytes altered gene expression of Abcb11, Apoa5, Apof, Fabp1, Lipc, and Cd36; reduced intracellular adipocyte lipid content; and increased extracellular lipid content, supporting a link between adipose thrombin and lipid regulation. Our results shed light on the complex mechanisms underlying lipid metabolism and highlight potential novel targets for lipid regulation and lipid-associated diseases.

Bisphenol A (BPA) is an industrial plasticizer widely found in consumer products, and exposure to BPA during early development has been associated with the prevalence of various cardiometabolic diseases including obesity, metabolic syndrome, type 2 diabetes, and cardiovascular diseases. To elucidate the molecular perturbations underlying the connection of low-dose prenatal BPA exposure to cardiometabolic diseases, we conducted a multi-dimensional systems biology study assessing the liver transcriptome, gut microbial community, and diverse metabolic phenotypes in both male and female mouse offspring exposed to 5μg/kg/day BPA during gestation. Prenatal exposure to low-dose BPA not only significantly affected liver genes involved in oxidative phosphorylation, PPAR signaling and fatty acid metabolism, but also affected the gut microbial composition in an age- and sex-dependent manner. Bacteria such as those belonging to the S24-7 and Lachnospiraceae families were correlated with offspring phenotypes, differentially expressed liver metabolic genes such as Acadl and Dgat1, and key drivers identified in our gene network modeling such as Malat1 and Apoa2. This multiomics study provides insight into the relationship between gut bacteria and host liver genes that could contribute to cardiometabolic disease risks upon low-dose BPA exposure.

ScopeWe explored the influence of DHA on cardiometabolic and cognitive phenotypes, and multiomic alterations in the brain under two metabolic conditions to understand context-specific nutritional effects.Methods and ResultsRats were randomly assigned to a DHA-rich or a control chow diet while drinking water or high fructose solution, followed by profiling of metabolic and cognitive phenotypes and the transcriptome and DNA methylome of the hypothalamus and hippocampus. DHA reduced serum triglyceride and improved insulin resistance and memory exclusively in the fructose-consuming rats. In hippocampus, DHA affected genes related to synapse functions in the chow group but immune functions in the fructose group; in hypothalamus, DHA altered immune pathways in the chow group but metabolic pathways in the fructose group. Network modeling revealed context-specific regulators of DHA effects, includingKlf4andDusp1for chow condition andLum, Fn1, andCol1a1for fructose condition in hippocampus, as well asCyr61, JunB, Ier2, andPitx2under chow condition andHcar1, Cdh1, andOsr1under fructose condition in hypothalamus.ConclusionDHA exhibits differential influence on epigenetic loci, genes, pathways, and metabolic and cognitive phenotypes under different dietary contexts, supporting population stratification in DHA studies to achieve precision nutrition.

Single-cell multi-omics technologies are rapidly evolving, prompting both methodological advances and biological discoveries at an unprecedented speed. Gene regulatory network modeling has been used as a powerful approach to elucidate the complex molecular interactions underlying biological processes and systems, yet its application in single-cell omics data modeling has been met with unique challenges and opportunities. In this review, we discuss these challenges and opportunities, and offer an overview of the recent development of network modeling approaches designed to capture dynamic networks, within-cell networks, and cell–cell interaction or communication networks. Finally, we outline the remaining gaps in single-cell gene network modeling and the outlooks of the field moving forward.

Genome-wide association studies (GWAS) have implicated ∼380 genetic loci for plasma lipid regulation. However, these loci only explain 17-27% of the trait variance and a comprehensive understanding of the molecular mechanisms has not been achieved. In this study, we utilized an integrative genomics approach leveraging diverse genomic data from human populations to investigate whether genetic variants associated with various plasma lipid traits, namely total cholesterol (TC), high and low density lipoprotein cholesterol (HDL and LDL), and triglycerides (TG), from GWAS were concentrated on specific parts of tissue-specific gene regulatory networks. In addition to the expected lipid metabolism pathways, gene subnetworks involved in ‘interferon signaling’, ‘autoimmune/immune activation’, ‘visual transduction’, and ‘protein catabolism’ were significantly associated with all lipid traits. Additionally, we detected trait-specific subnetworks, including cadherin-associated subnetworks for LDL, glutathione metabolism for HDL, valine, leucine and isoleucine biosynthesis for TC, and insulin signaling and complement pathways for TG. Finally, utilizing gene-gene relations revealed by tissue-specific gene regulatory networks, we detected both known (e.g. APOH, APOA4, and ABCA1) and novel (e.g. F2 in adipose tissue) key regulator genes in these lipid-associated subnetworks. Knockdown of the F2 gene (Coagulation Factor II, Thrombin) in 3T3-L1 and C3H10T1/2 adipocytes reduced gene expression of Abcb11, Apoa5, Apof, Fabp1, Lipc, and Cd36, reduced intracellular adipocyte lipid content, and increased extracellular lipid content, supporting a link between adipose thrombin and lipid regulation. Our results shed light on the complex mechanisms underlying lipid metabolism and highlight potential novel targets for lipid regulation and lipid-associated diseases.

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.

Drug development has been hampered by a high failure rate in clinical trials due to efficacy or safety issues not predicted by preclinical studies in model systems. A key contributor is our incomplete understanding of drug functions across organ systems and species. Therefore, elucidating species- and tissue-specific actions of drugs can provide systems level insights into therapeutic efficacy, potential adverse effects, and interspecies differences that are necessary for more effective translational medicine. Here, we present a comprehensive drug knowledgebase and analytical tool, PharmOmics, comprised of genomic footprints of drugs in individual tissues from human, mouse, and rat transcriptome data from GEO, ArrayExpress, TG-GATEs, and DrugMatrix. Using multi-species and multi-tissue gene expression signatures as molecular indicators of drug functions, we developed gene network-based approaches for drug repositioning. We demonstrate the potential of PharmOmics to predict drugs for new disease indications and validated two predicted drugs for non-alcoholic fatty liver disease in mice. We also examined the potential of PharmOmics to identify drugs related to hepatoxicity and nephrotoxicity. By combining tissue- and species-specific in vivo drug signatures with biological networks, PharmOmics serves as a complementary tool to support drug characterization.

Author(s): Blencowe, Montgomery | Advisor(s): Yang, Xia | Abstract: Type 1 diabetes (T1D) is a complex disease, involving a genetic predisposition that interacts with environmental triggers, leading to the loss of insulin producing beta cells in the pancreas. However, the molecular cascades underlying T1D are poorly understood and remain to be explored. We hypothesize that genetic risk factors of T1D perturb tissue-specific biological pathways and gene networks, which ultimately leads to the pathogenic end point in beta cells. We sought to identify the gene networks and key regulators for T1D by conducting a comprehensive, data-driven multi-omics analysis that integrates human genome-wide association studies (GWAS) of T1D, tissue-specific genetic regulation of gene expression in the form of expression quantitative trait loci (eQTLs), and tissue-specific gene network models using a computational pipeline Mergeomics. Our integrative genomics approach revealed immune pathways such as adaptive immune system, cytokines and inflammatory response, ZAP70 translocation, primary immunodeficiency and immunoregulatory interactions between a lymphoid and non-lymphoid cell, across various tissues. We also identified tissue-specific signals such as regulation of complement cascade in adipose tissue, macrophages, and monocytes, NOTCH signaling in adipose tissue and macrophages, protein folding, calcium signaling chemotaxis and lysosomal pathways in the pancreas, adipose, and monocytes, and viral infection in macrophages and monocytes. Network modelling of these pathways highlights a number of key regulator genes such as GBP1, USP18, STAT1, RPL17, HLA genes (HLA-A,-B,-C, and –G), and immunomodulatory genes (LCK, VAV1, ZAP-70), each of which has suggestive roles in the pathophysiology of T1D or other autoimmune disorders. Together, our integrative genomics approach offers comprehensive insights into the tissue-specific molecular networks and regulators as well potential between-tissue interactions underlying T1D, with potential for guiding future development of therapeutic strategies targeting the disease.

Scope The influence of docosahexaenoic acid (DHA) on cardiometabolic and cognitive phenotypes, and multi-omic alterations in the brain under two metabolic conditions is explored to understand context-specific nutritional effects. Methods and results Rats are randomly assigned to a DHA-rich or a control chow diet while drinking water or high fructose solution, followed by profiling of metabolic and cognitive phenotypes and the transcriptome and DNA methylome of the hypothalamus and hippocampus. DHA reduces serum triglyceride and improves insulin resistance and memory exclusively in the fructose-consuming rats. In hippocampus, DHA affects genes related to synapse functions in the chow group but immune functions in the fructose group; in hypothalamus, DHA alters immune pathways in the chow group but metabolic pathways in the fructose group. Network modeling reveals context-specific regulators of DHA effects, including Klf4 and Dusp1 for chow condition and Lum, Fn1, and Col1a1 for fructose condition in hippocampus, as well as Cyr61, JunB, Ier2, and Pitx2 under chow condition and Hcar1, Cdh1, and Osr1 under fructose condition in hypothalamus. Conclusion DHA exhibits differential influence on epigenetic loci, genes, pathways, and metabolic and cognitive phenotypes under different dietary contexts, supporting population stratification in DHA studies to achieve precision nutrition.