Ketogenic diet therapy for pediatric epilepsy is associated with alterations in the human gut microbiome that confer seizure resistance in mice.

The gut microbiome modulates seizure susceptibility and the anti-seizure effects of the ketogenic diet (KD) in animal models, but whether these relationships translate to KD therapies for human epilepsy is unclear. We find that the clinical KD alters gut microbial function in children with refractory epilepsy. Colonizing mice with KD-associated microbes promotes seizure resistance relative to matched pre-treatment controls. Select metagenomic and metabolomic features, including those related to anaplerosis, fatty acid ß-oxidation, and amino acid metabolism, are seen with human KD therapy and preserved upon microbiome transfer to mice. Mice colonized with KD-associated gut microbes exhibit altered hippocampal transcriptomes, including pathways related to ATP synthesis, glutathione metabolism, and oxidative phosphorylation, and are linked to susceptibility genes identified in human epilepsy. Our findings reveal key microbial functions that are altered by KD therapies for pediatric epilepsy and linked to microbiome-induced alterations in brain gene expression and seizure protection in mice.

Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse.

Mouse models have been used extensively to study human coronary artery disease (CAD) or atherosclerosis and to test therapeutic targets. However, whether mouse and human share similar genetic factors and pathogenic mechanisms of atherosclerosis has not been thoroughly investigated in a data-driven manner. We conducted a cross-species comparison study to better understand atherosclerosis pathogenesis between species by leveraging multiomics data. Specifically, we compared genetically driven and thus CAD-causal gene networks and pathways, by using human GWAS of CAD from the CARDIoGRAMplusC4D consortium and mouse GWAS of atherosclerosis from the Hybrid Mouse Diversity Panel (HMDP) followed by integration with functional multiomics human (STARNET and GTEx) and mouse (HMDP) databases. We found that mouse and human shared >75% of CAD causal pathways. Based on network topology, we then predicted key regulatory genes for both the shared pathways and species-specific pathways, which were further validated through the use of single cell data and the latest CAD GWAS. In sum, our results should serve as a much-needed guidance for which human CAD-causal pathways can or cannot be further evaluated for novel CAD therapies using mouse models.

Role of Matrix Gla Protein in Transforming Growth Factor-ß Signaling and Nonalcoholic Steatohepatitis in Mice.

BACKGROUND & AIMS: Nonalcoholic steatohepatitis (NASH) is a complex disease involving both genetic and environmental factors in its onset and progression. We analyzed NASH phenotypes in a genetically diverse cohort of mice, the Hybrid Mouse Diversity Panel, to identify genes contributing to disease susceptibility. METHODS: A "systems genetics" approach, involving integration of genetic, transcriptomic, and phenotypic data, was used to identify candidate genes and pathways in a mouse model of NASH. The causal role of Matrix Gla Protein (MGP) was validated using heterozygous MGP knockout (Mgp+/-) mice. The mechanistic role of MGP in transforming growth factor-beta (TGF-ß) signaling was examined in the LX-2 stellate cell line by using a loss of function approach. RESULTS: Local cis-acting regulation of MGP was correlated with fibrosis, suggesting a causal role in NASH, and this was validated using loss of function experiments in 2 models of diet-induced NASH. Using single-cell RNA sequencing, Mgp was found to be primarily expressed in hepatic stellate cells and dendritic cells in mice. Knockdown of MGP expression in stellate LX-2 cells led to a blunted response to TGF-ß stimulation. This was associated with reduced regulatory SMAD phosphorylation and TGF-ß receptor ALK1 expression as well as increased expression of inhibitory SMAD6. Hepatic MGP expression was found to be significantly correlated with the severity of fibrosis in livers of patients with NASH, suggesting relevance to human disease. CONCLUSIONS: MGP regulates liver fibrosis and TGF-ß signaling in hepatic stellate cells and contributes to NASH pathogenesis.

A single-cell CRISPRi platform for characterizing candidate genes relevant to metabolic disorders in human adipocytes.

CROP-Seq combines gene silencing using CRISPR interference with single-cell RNA sequencing. Here, we applied CROP-Seq to study adipogenesis and adipocyte biology. Human preadipocyte SGBS cell line expressing KRAB-dCas9 was transduced with a sgRNA library. Following selection, individual cells were captured using microfluidics at different timepoints during adipogenesis. Bioinformatic analysis of transcriptomic data was used to determine the knockdown effects, the dysregulated pathways, and to predict cellular phenotypes. Single-cell transcriptomes recapitulated adipogenesis states. For all targets, over 400 differentially expressed genes were identified at least at one timepoint. As a validation of our approach, the knockdown of PPARG and CEBPB (which encode key proadipogenic transcription factors) resulted in the inhibition of adipogenesis. Gene set enrichment analysis generated hypotheses regarding the molecular function of novel genes. MAFF knockdown led to downregulation of transcriptional response to proinflammatory cytokine TNF-α in preadipocytes and to decreased CXCL-16 and IL-6 secretion. TIPARP knockdown resulted in increased expression of adipogenesis markers. In summary, this powerful, hypothesis-free tool can identify novel regulators of adipogenesis, preadipocyte, and adipocyte function associated with metabolic disease.NEW & NOTEWORTHY Genomics efforts led to the identification of many genomic loci that are associated with metabolic traits, many of which are tied to adipose tissue function. However, determination of the causal genes, and their mechanism of action in metabolism, is a time-consuming process. Here, we use an approach to determine the transcriptional outcome of candidate gene knockdown for multiple genes at the same time in a human cell model of adipogenesis.

Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse.

Mouse models have been used extensively to study human coronary artery disease (CAD) or atherosclerosis and to test therapeutic targets. However, whether mouse and human share similar genetic factors and pathogenic mechanisms of atherosclerosis has not been thoroughly investigated in a data-driven manner. We conducted a cross-species comparison study to better understand atherosclerosis pathogenesis between species by leveraging multiomics data. Specifically, we compared genetically driven and thus CAD-causal gene networks and pathways, by using human GWAS of CAD from the CARDIoGRAMplusC4D consortium and mouse GWAS of atherosclerosis from the Hybrid Mouse Diversity Panel (HMDP) followed by integration with functional multiomics human (STARNET and GTEx) and mouse (HMDP) databases. We found that mouse and human shared >75% of CAD causal pathways. Based on network topology, we then predicted key regulatory genes for both the shared pathways and species-specific pathways, which were further validated through the use of single cell data and the latest CAD GWAS. In sum, our results should serve as a much-needed guidance for which human CAD-causal pathways can or cannot be further evaluated for novel CAD therapies using mouse models.

Genome-wide association meta-analysis of spontaneous coronary artery dissection identifies risk variants and genes related to artery integrity and tissue-mediated coagulation.

Spontaneous coronary artery dissection (SCAD) is an understudied cause of myocardial infarction primarily affecting women. It is not known to what extent SCAD is genetically distinct from other cardiovascular diseases, including atherosclerotic coronary artery disease (CAD). Here we present a genome-wide association meta-analysis (1,917 cases and 9,292 controls) identifying 16 risk loci for SCAD. Integrative functional annotations prioritized genes that are likely to be regulated in vascular smooth muscle cells and artery fibroblasts and implicated in extracellular matrix biology. One locus containing the tissue factor gene F3, which is involved in blood coagulation cascade initiation, appears to be specific for SCAD risk. Several associated variants have diametrically opposite associations with CAD, suggesting that shared biological processes contribute to both diseases, but through different mechanisms. We also infer a causal role for high blood pressure in SCAD. Our findings provide novel pathophysiological insights involving arterial integrity and tissue-mediated coagulation in SCAD and set the stage for future specific therapeutics and preventions.

Conserved multi-tissue transcriptomic adaptations to exercise training in humans and mice.

Physical activity is associated with beneficial adaptations in human and rodent metabolism. We studied over 50 complex traits before and after exercise intervention in middle-aged men and a panel of 100 diverse strains of female mice. Candidate gene analyses in three brain regions, muscle, liver, heart, and adipose tissue of mice indicate genetic drivers of clinically relevant traits, including volitional exercise volume, muscle metabolism, adiposity, and hepatic lipids. Although ∼33% of genes differentially expressed in skeletal muscle following the exercise intervention are similar in mice and humans independent of BMI, responsiveness of adipose tissue to exercise-stimulated weight loss appears controlled by species and underlying genotype. We leveraged genetic diversity to generate prediction models of metabolic trait responsiveness to volitional activity offering a framework for advancing personalized exercise prescription. The human and mouse data are publicly available via a user-friendly Web-based application to enhance data mining and hypothesis development.

Relative contributions of sex hormones, sex chromosomes, and gonads to sex differences in tissue gene regulation.

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 last, 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.

PharmOmics: A species- and tissue-specific drug signature database and gene-network-based drug repositioning tool.

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 in silico performance assessment. We further validated predicted drugs for nonalcoholic fatty liver disease in mice. By combining tissue- and species-specific in vivo drug signatures with gene networks, PharmOmics serves as a complementary tool to support drug characterization and network-based medicine.

IAPP-induced beta cell stress recapitulates the islet transcriptome in type 2 diabetes.

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.

Found in translation-core network preservation across liver diseases and species.

Esmaili et al.1 conducted co-expression network analysis to uncover core homeostatic modules critical to a broad spectrum of liver diseases in mouse and human. Perturbation state of core modules may underlie disease stages across species and serve as therapeutic targets.

Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics.

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.

Gene networks and pathways for plasma lipid traits via multitissue multiomics systems analysis.

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.

Systems toxicogenomics of prenatal low-dose BPA exposure on liver metabolic pathways, gut microbiota, and metabolic health in mice.

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 5 µg/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.

Multi-Tissue Multi-Omics Nutrigenomics Indicates Context-Specific Effects of Docosahexaenoic Acid on Rat Brain.

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.

Differential metabolic and multi-tissue transcriptomic responses to fructose consumption among genetically diverse mice.

Understanding how individuals react differently to the same treatment is a major concern in precision medicine. Metabolic challenges such as the one posed by high fructose intake are important determinants of disease mechanisms. We embarked on studies to determine how fructose affects differential metabolic dysfunctions across genetically dissimilar mice, namely, C57BL/6 J (B6), DBA/2 J (DBA) and FVB/NJ (FVB), by integrating physiological and gene regulatory mechanisms. We report that fructose has strain-specific effects, involving tissue-specific gene regulatory cascades in hypothalamus, liver, and white adipose tissues. DBA mice showed the largest numbers of genes associated with adiposity, congruent with their highest susceptibility to adiposity gain and glucose intolerance across the three tissues. In contrast, B6 and FVB mainly exhibited cholesterol phenotypes, accompanying the largest number of adipose genes correlating with total cholesterol in B6, and liver genes correlating with LDL in FVB mice. Tissue-specific network modeling predicted strain-and tissue-specific regulators such as Fgf21 (DBA) and Lss (B6), which were subsequently validated in primary hepatocytes. Strain-specific fructose-responsive genes revealed susceptibility for human diseases such that genes in liver and adipose tissue in DBA showed strong enrichment for human type 2 diabetes and obesity traits. Liver and adipose genes in FVB were mostly related to lipid traits, and liver and adipose genes in B6 showed relevance to most cardiometabolic traits tested. Our results show that fructose induces gene regulatory pathways that are tissue specific and dependent on the genetic make-up, which may underlie interindividual variability in cardiometabolic responses to high fructose consumption.

Network Modeling Approaches and Applications to Unravelling Non-Alcoholic Fatty Liver Disease.

Non-alcoholic fatty liver disease (NAFLD) is a progressive condition of the liver encompassing a range of pathologies including steatosis, non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma. Research into this disease is imperative due to its rapid growth in prevalence, economic burden, and current lack of FDA approved therapies. NAFLD involves a highly complex etiology that calls for multi-tissue multi-omics network approaches to uncover the pathogenic genes and processes, diagnostic biomarkers, and potential therapeutic strategies. In this review, we first present a basic overview of disease pathogenesis, risk factors, and remaining knowledge gaps, followed by discussions of the need and concepts of multi-tissue multi-omics approaches, various network methodologies and application examples in NAFLD research. We highlight the findings that have been uncovered thus far including novel biomarkers, genes, and biological pathways involved in different stages of NAFLD, molecular connections between NAFLD and its comorbidities, mechanisms underpinning sex differences, and druggable targets. Lastly, we outline the future directions of implementing network approaches to further improve our understanding of NAFLD in order to guide diagnosis and therapeutics.

Integrative Genomics Analysis Unravels Tissue-Specific Pathways, Networks, and Key Regulators of Blood Pressure Regulation.

Blood pressure (BP) is a highly heritable trait and a major cardiovascular disease risk factor. Genome wide association studies (GWAS) have implicated a number of susceptibility loci for systolic (SBP) and diastolic (DBP) blood pressure. However, a large portion of the heritability cannot be explained by the top GWAS loci and a comprehensive understanding of the underlying molecular mechanisms is still lacking. Here, we utilized an integrative genomics approach that leveraged multiple genetic and genomic datasets including (a) GWAS for SBP and DBP from the International Consortium for Blood Pressure (ICBP), (b) expression quantitative trait loci (eQTLs) from genetics of gene expression studies of human tissues related to BP, (c) knowledge-driven biological pathways, and (d) data-driven tissue-specific regulatory gene networks. Integration of these multidimensional datasets revealed tens of pathways and gene subnetworks in vascular tissues, liver, adipose, blood, and brain functionally associated with DBP and SBP. Diverse processes such as platelet production, insulin secretion/signaling, protein catabolism, cell adhesion and junction, immune and inflammation, and cardiac/smooth muscle contraction, were shared between DBP and SBP. Furthermore, "Wnt signaling" and "mammalian target of rapamycin (mTOR) signaling" pathways were found to be unique to SBP, while "cytokine network", and "tryptophan catabolism" to DBP. Incorporation of gene regulatory networks in our analysis informed on key regulator genes that orchestrate tissue-specific subnetworks of genes whose variants together explain ~20% of BP heritability. Our results shed light on the complex mechanisms underlying BP regulation and highlight potential novel targets and pathways for hypertension and cardiovascular diseases.

Network modeling of single-cell omics data: challenges, opportunities, and progresses.

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.

Translating GWAS Findings to Novel Therapeutic Targets for Coronary Artery Disease.

The success of genome-wide association studies (GWAS) has significantly advanced our understanding of the etiology of coronary artery disease (CAD) and opens new opportunities to reinvigorate the stalling CAD drug development. However, there exists remarkable disconnection between the CAD GWAS findings and commercialized drugs. While this could implicate major untapped translational and therapeutic potentials in CAD GWAS, it also brings forward extensive technical challenges. In this review we summarize the motivation to leverage GWAS for drug discovery, outline the critical bottlenecks in the field, and highlight several promising strategies such as functional genomics and network-based approaches to enhance the translational value of CAD GWAS findings in driving novel therapeutics.