scholarly journals Predictive network modeling in human induced pluripotent stem cells identifies key driver genes for insulin responsiveness

2020 ◽  
Vol 16 (12) ◽  
pp. e1008491
Author(s):  
Ivan Carcamo-Orive ◽  
Marc Y. R. Henrion ◽  
Kuixi Zhu ◽  
Noam D. Beckmann ◽  
Paige Cundiff ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Insulin Sensitivity ◽  
Disease Risk ◽  
Association Studies ◽  
Network Modeling ◽  
Genome Wide Association Studies ◽  
Driver Genes ◽  
Key Driver ◽  
Insulin Responsiveness ◽  
Induced Pluripotent

Insulin resistance (IR) precedes the development of type 2 diabetes (T2D) and increases cardiovascular disease risk. Although genome wide association studies (GWAS) have uncovered new loci associated with T2D, their contribution to explain the mechanisms leading to decreased insulin sensitivity has been very limited. Thus, new approaches are necessary to explore the genetic architecture of insulin resistance. To that end, we generated an iPSC library across the spectrum of insulin sensitivity in humans. RNA-seq based analysis of 310 induced pluripotent stem cell (iPSC) clones derived from 100 individuals allowed us to identify differentially expressed genes between insulin resistant and sensitive iPSC lines. Analysis of the co-expression architecture uncovered several insulin sensitivity-relevant gene sub-networks, and predictive network modeling identified a set of key driver genes that regulate these co-expression modules. Functional validation in human adipocytes and skeletal muscle cells (SKMCs) confirmed the relevance of the key driver candidate genes for insulin responsiveness.

scholarly journals Autocrine IGF2 programmes β-cell plasticity under conditions of increased metabolic demand

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ionel Sandovici ◽  
Constanze M. Hammerle ◽  
Sam Virtue ◽  
Yurena Vivas-Garcia ◽  
Adriana Izquierdo-Lahuerta ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Glucose Homeostasis ◽  
Association Studies ◽  
Susceptibility Gene ◽  
Chow Diet ◽  
Genome Wide Association Studies ◽  
Cell Plasticity ◽  
Β Cells ◽  
Β Cell ◽  
Pancreatic Β Cells

AbstractWhen exposed to nutrient excess and insulin resistance, pancreatic β-cells undergo adaptive changes in order to maintain glucose homeostasis. The role that growth control genes, highly expressed in early pancreas development, might exert in programming β-cell plasticity in later life is a poorly studied area. The imprinted Igf2 (insulin-like growth factor 2) gene is highly transcribed during early life and has been identified in recent genome-wide association studies as a type 2 diabetes susceptibility gene in humans. Hence, here we investigate the long-term phenotypic metabolic consequences of conditional Igf2 deletion in pancreatic β-cells (Igf2βKO) in mice. We show that autocrine actions of IGF2 are not critical for β-cell development, or for the early post-natal wave of β-cell remodelling. Additionally, adult Igf2βKO mice maintain glucose homeostasis when fed a chow diet. However, pregnant Igf2βKO females become hyperglycemic and hyperinsulinemic, and their conceptuses exhibit hyperinsulinemia and placentomegalia. Insulin resistance induced by congenital leptin deficiency also renders Igf2βKO females more hyperglycaemic compared to leptin-deficient controls. Upon high-fat diet feeding, Igf2βKO females are less susceptible to develop insulin resistance. Based on these findings, we conclude that in female mice, autocrine actions of β-cell IGF2 during early development determine their adaptive capacity in adult life.

Abstract 288: Novel Mechanisms of Non-coding Genomic Regulation Identified in Cardiac Disease-in-a-dish Models

2016 ◽  
Vol 119 (suppl_1) ◽  
Author(s):  
Aditya Kumar ◽  
Stephanie Thomas ◽  
Kirsten Wong ◽  
Kevin Tenerelli ◽  
Valentina Lo Sardo ◽  
...  
Keyword(s):  
Heart Attack ◽  
Connexin 43 ◽  
Disease Risk ◽  
Association Studies ◽  
Correlation Coefficients ◽  
Induced Pluripotent Stem Cell ◽  
Genome Wide Association Studies ◽  
Isogenic Line ◽  
Nucleotide Polymorphisms ◽  
Increased Risk

Genome-wide association studies have identified single nucleotide polymorphisms (SNPs) at gene loci that affect cardiovascular function, and while mechanisms in protein-coding loci are obvious, those in non-coding loci are difficult to determine. 9p21 is a recently identified locus associated with increased risk of coronary artery disease (CAD) and myocardial infarction. Associations have implicated SNPs in altering smooth muscle and endothelial cell properties but have not identified adverse effects in cardiomyocytes (CMs) despite enhanced disease risk. Using induced pluripotent stem cell-derived CMs from patients that are homozygous risk/risk (R/R) and non-risk/non-risk (N/N) for 9p21 SNPs and either CAD positive or negative, we assessed CM function when cultured on hydrogels capable of mimicking the fibrotic stiffening associated with disease post-heart attack, i.e. “heart attack-in-a-dish” stiffening from 11 kiloPascals (kPa) to 50 kPa. While all CMs independent of genotype and disease beat synchronously on soft matrices, R/R CMs cultured on dynamically stiffened hydrogels exhibited asynchronous contractions and had significantly lower correlation coefficients versus N/N CMs in the same conditions. Dynamic stiffening reduced connexin 43 expression and gap junction assembly in R/R CMs but not N/N CMs. To eliminate patient-to-patient variability, we created an isogenic line by deleting the 9p21 gene locus from a R/R patient using TALEN-mediated gene editing, i.e. R/R KO. Deletion of the 9p21 locus restored synchronous contractility and organized connexin 43 junctions. As a non-coding locus, 9p21 appears to repress connexin transcription, leading to the phenotypes we observe, but only when the niche is stiffened as in disease. These data are the first to demonstrate that disease-specific niche remodeling, e.g. a “heart attack-in-a-dish” model, can differentially affect CM function depending on SNPs within a non-coding locus.

scholarly journals Application of Genetic Studies to Flow Cytometry Data and Its Impact on Therapeutic Intervention for Autoimmune Disease

2021 ◽  
Vol 12 ◽  
Author(s):  
Valeria Orrù ◽  
Maristella Steri ◽  
Francesco Cucca ◽  
Edoardo Fiorillo
Keyword(s):  
Flow Cytometry ◽  
Autoimmune Disease ◽  
Autoimmune Diseases ◽  
Immune Cell ◽  
Disease Risk ◽  
Association Studies ◽  
Large Fraction ◽  
Surface Protein ◽  
Genome Wide Association Studies ◽  
Human Immune System

In recent years, systematic genome-wide association studies of quantitative immune cell traits, represented by circulating levels of cell subtypes established by flow cytometry, have revealed numerous association signals, a large fraction of which overlap perfectly with genetic signals associated with autoimmune diseases. By identifying further overlaps with association signals influencing gene expression and cell surface protein levels, it has also been possible, in several cases, to identify causal genes and infer candidate proteins affecting immune cell traits linked to autoimmune disease risk. Overall, these results provide a more detailed picture of how genetic variation affects the human immune system and autoimmune disease risk. They also highlight druggable proteins in the pathogenesis of autoimmune diseases; predict the efficacy and side effects of existing therapies; provide new indications for use for some of them; and optimize the research and development of new, more effective and safer treatments for autoimmune diseases. Here we review the genetic-driven approach that couples systematic multi-parametric flow cytometry with high-resolution genetics and transcriptomics to identify endophenotypes of autoimmune diseases for the development of new therapies.

scholarly journals Analysis of chromatin organization and gene expression in T cells identifies functional genes for rheumatoid arthritis

2019 ◽  
Author(s):  
Jing Yang ◽  
Amanda McGovern ◽  
Paul Martin ◽  
Kate Duffus ◽  
Xiangyu Ge ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
T Cells ◽  
Complex Disease ◽  
Target Genes ◽  
Disease Risk ◽  
Association Studies ◽  
Dna Interaction ◽  
Genome Wide Association Studies ◽  
Causal Genes

AbstractGenome-wide association studies have identified genetic variation contributing to complex disease risk. However, assigning causal genes and mechanisms has been more challenging because disease-associated variants are often found in distal regulatory regions with cell-type specific behaviours. Here, we collect ATAC-seq, Hi-C, Capture Hi-C and nuclear RNA-seq data in stimulated CD4+ T-cells over 24 hours, to identify functional enhancers regulating gene expression. We characterise changes in DNA interaction and activity dynamics that correlate with changes gene expression, and find that the strongest correlations are observed within 200 kb of promoters. Using rheumatoid arthritis as an example of T-cell mediated disease, we demonstrate interactions of expression quantitative trait loci with target genes, and confirm assigned genes or show complex interactions for 20% of disease associated loci, including FOXO1, which we confirm using CRISPR/Cas9.

scholarly journals A Network-based Deep Learning Framework Catalyzes GWAS and Multi-Omics Findings to Biology and Drug Repurposing for Alzheimer's Disease

2021 ◽  
Author(s):  
Jielin Xu ◽  
Yuan Hou ◽  
Yadi Zhou ◽  
Ming Hu ◽  
Feixiong Cheng
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Disease Risk ◽  
Association Studies ◽  
Open Chromatin ◽  
Genome Wide Association Studies ◽  
Risk Genes ◽  
Learning Framework ◽  
Protein Interactome

Human genome sequencing studies have identified numerous loci associated with complex diseases, including Alzheimer's disease (AD). Translating human genetic findings (i.e., genome-wide association studies [GWAS]) to pathobiology and therapeutic discovery, however, remains a major challenge. To address this critical problem, we present a network topology-based deep learning framework to identify disease-associated genes (NETTAG). NETTAG is capable of integrating multi-genomics data along with the protein-protein interactome to infer putative risk genes and drug targets impacted by GWAS loci. Specifically, we leverage non-coding GWAS loci effects on expression quantitative trait loci (eQTLs), histone-QTLs, and transcription factor binding-QTLs, enhancers and CpG islands, promoter regions, open chromatin, and promoter flanking regions. The key premises of NETTAG are that the disease risk genes exhibit distinct functional characteristics compared to non-risk genes and therefore can be distinguished by their aggregated genomic features under the human protein interactome. Applying NETTAG to the latest AD GWAS data, we identified 156 putative AD-risk genes (i.e., APOE, BIN1, GSK3B, MARK4, and PICALM). We showed that predicted risk genes are: 1) significantly enriched in AD-related pathobiological pathways, 2) more likely to be differentially expressed regarding transcriptome and proteome of AD brains, and 3) enriched in druggable targets with approved medicines (i.e., choline and ibudilast). In summary, our findings suggest that understanding of human pathobiology and therapeutic development could benefit from a network-based deep learning methodology that utilizes GWAS findings under the multimodal genomic analyses.

scholarly journals CONVERGENT AGING LOCI: PATHWAYS THAT TRANSCEND INDIVIDUAL DISEASES

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S221-S221
Author(s):  
Luke C Pilling ◽  
Luigi Ferrucci ◽  
David Melzer
Keyword(s):  
Disease Risk ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Telomere Shortening ◽  
Chronic Disease Risk ◽  
Trade Offs ◽  
Age Related ◽  
Genome Wide ◽  
Artery Disease

Abstract Thousands of loci across the genome have been identified for specific diseases in genome-wide association studies (GWAS), yet very few are associated with lifespan itself. We hypothesized that specific biological pathways transcend individual diseases and affect health and lifespan more broadly. Using the published results for the most recent GWAS for 10 key age-related diseases (including coronary artery disease, type-2 diabetes, and several cancers) we identified 22 loci with a strong genetic association with at least three of the diseases. These multi-trait aging loci include known genes affecting multiple diverse health end points, such as CDKN2A/B (9p21.3) and APOE. There are also novel multi-trait genes including SH2B3 and CASC8, likely involved in hallmark pathways of aging biology, including telomere shortening and inflammation. Several of these loci involve trade-offs between chronic disease risk and cancer.

scholarly journals A selective inference approach for false discovery rate control using multiomics covariates yields insights into disease risk

2020 ◽  
Vol 117 (26) ◽  
pp. 15028-15035 ◽  
Author(s):  
Ronald Yurko ◽  
Max G’Sell ◽  
Kathryn Roeder ◽  
Bernie Devlin
Keyword(s):  
Gene Expression ◽  
Multiple Testing ◽  
Disease Risk ◽  
Association Studies ◽  
Auxiliary Information ◽  
Primary Data ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Hypothesis Tests ◽  
Selective Inference

To correct for a large number of hypothesis tests, most researchers rely on simple multiple testing corrections. Yet, new methodologies of selective inference could potentially improve power while retaining statistical guarantees, especially those that enable exploration of test statistics using auxiliary information (covariates) to weight hypothesis tests for association. We explore one such method, adaptiveP-value thresholding (AdaPT), in the framework of genome-wide association studies (GWAS) and gene expression/coexpression studies, with particular emphasis on schizophrenia (SCZ). Selected SCZ GWAS associationPvalues play the role of the primary data for AdaPT; single-nucleotide polymorphisms (SNPs) are selected because they are gene expression quantitative trait loci (eQTLs). This natural pairing of SNPs and genes allow us to map the following covariate values to these pairs: GWAS statistics from genetically correlated bipolar disorder, the effect size of SNP genotypes on gene expression, and gene–gene coexpression, captured by subnetwork (module) membership. In all, 24 covariates per SNP/gene pair were included in the AdaPT analysis using flexible gradient boosted trees. We demonstrate a substantial increase in power to detect SCZ associations using gene expression information from the developing human prefrontal cortex. We interpret these results in light of recent theories about the polygenic nature of SCZ. Importantly, our entire process for identifying enrichment and creating features with independent complementary data sources can be implemented in many different high-throughput settings to ultimately improve power.

scholarly journals A systems genomics approach to uncover the molecular properties of cancer genes

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Felix Grassmann ◽  
Yudi Pawitan ◽  
Kamila Czene
Keyword(s):  
Cancer Survival ◽  
Association Studies ◽  
Gc Content ◽  
Molecular Properties ◽  
Molecular Profile ◽  
Genome Wide Association Studies ◽  
Cancer Genes ◽  
Driver Genes ◽  
Cancer Driver ◽  
Genome Wide

Abstract Genes involved in cancer are under constant evolutionary pressure, potentially resulting in diverse molecular properties. In this study, we explore 23 omic features from publicly available databases to define the molecular profile of different classes of cancer genes. Cancer genes were grouped according to mutational landscape (germline and somatically mutated genes), role in cancer initiation (cancer driver genes) or cancer survival (survival genes), as well as being implicated by genome-wide association studies (GWAS genes). For each gene, we also computed feature scores based on all omic features, effectively summarizing how closely a gene resembles cancer genes of the respective class. In general, cancer genes are longer, have a lower GC content, have more isoforms with shorter exons, are expressed in more tissues and have more transcription factor binding sites than non-cancer genes. We found that germline genes more closely resemble single tissue GWAS genes while somatic genes are more similar to pleiotropic cancer GWAS genes. As a proof-of-principle, we utilized aggregated feature scores to prioritize genes in breast cancer GWAS loci and found that top ranking genes were enriched in cancer related pathways. In conclusion, we have identified multiple omic features associated with different classes of cancer genes, which can assist prioritization of genes in cancer gene discovery.

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