Biology subjects

Chiou, J.

Publications and source records attributed to Chiou, J..

27 records · Page 2Linked to original sources

bioRxiv2019

Nutrient regulation of the islet epigenome controls adaptive insulin secretion

Adaptation of the islet {beta}-cell insulin secretory response to changing insulin demand is critical for blood glucose homeostasis, yet the mechanisms underlying this adaptation are unknown. Here, we show that nutrient-stimulated histone acetylation plays a key role in adapting insulin secretion through regulation of genes involved in {beta}-cell nutrient sensing and metabolism. Nutrient regulation of the epigenome occurs at sites occupied by the chromatin-modifying enzyme Lsd1 in islets. We demonstrate that {beta}-cell-specific deletion of Lsd1 leads to insulin hypersecretion, aberrant expression of nutrient response genes, and histone hyperacetylation. Islets from mice adapted to chronically increased insulin demand exhibited similar epigenetic and transcriptional changes. Moreover, genetic variants associated with fasting glucose and type 2 diabetes are enriched at LSD1-bound sites in human islets, suggesting that interpretation of nutrient signals is genetically determined. Our findings reveal that adaptive insulin secretion involves Lsd1-mediated coupling of nutrient state to regulation of the islet epigenome.

physiology↗

bioRxiv2019

Single cell chromatin accessibility reveals pancreatic islet cell type- and state-specific regulatory programs of diabetes risk

Genetic risk variants for complex, multifactorial diseases are enriched in cis-regulatory elements. Single cell epigenomic technologies create new opportunities to dissect cell type-specific mechanisms of risk variants, yet this approach has not been widely applied to disease-relevant tissues. Given the central role of pancreatic islets in type 2 diabetes (T2D) pathophysiology, we generated accessible chromatin profiles from 14.2k islet cells and identified 13 cell clusters including multiple alpha, beta and delta cell clusters which represented hormone-producing and signal-responsive cell states. We cataloged 244,236 islet cell type accessible chromatin sites and identified transcription factors (TFs) underlying both lineage- and state-specific regulation. We measured the enrichment of T2D and glycemic trait GWAS for the accessible chromatin profiles of single cells, which revealed heterogeneity in the effects of beta cell states and TFs on fasting glucose and T2D risk. We further used machine learning to predict the cell type-specific regulatory function of genetic variants, and single cell co-accessibility to link distal sites to putative cell type-specific target genes. We localized 239 fine-mapped T2D risk signals to islet accessible chromatin, and further prioritized variants at these signals with predicted regulatory function and co-accessibility with target genes. At the KCNQ1 locus, the causal T2D variant rs231361 had predicted effects on an enhancer with beta cell-specific, long-range co-accessibility to the insulin promoter, and deletion of this enhancer reduced insulin gene and protein expression in human embryonic stem cell-derived beta cells. Our findings provide a cell type- and state-resolved map of gene regulation in human islets, illuminate likely mechanisms of T2D risk at hundreds of loci, and demonstrate the power of single cell epigenomics for interpreting complex disease genetics.

genomics↗

bioRxiv2019

TAO Kinase 3 Overexpression Is a Poor Prognosis Marker for Lung Adenocarcinoma and Predicts Paclitaxel Resistance

High expression of TAOK3 has been observed in several cancers, but very little about TAOK3-involved mechanisms has been reported. In this study, we investigated the role of TAOK3 in the therapeutic effect of paclitaxel treatment. Protein expression of TAOK3 was evaluated among 121 patients by using immunohistochemical staining. TAOK3 expression was estimated by analyzing the genes expression and its correlation with the IC50 of paclitaxel in cells. The expression of TAOK3 in lung cancer was highly correlated with poor overall survival, progression-free survival, and recurrence rate in lung cancer patients. The expression level of TAOK3 was positively correlated with paclitaxel resistance in the lung cancer cell lines. The depletion of TAOK3 could enhance the sensitivity of paclitaxel in lung cancer and vice versa. TAOK3 expression is associated with paclitaxel-resistance and could be a clinical predictor for disease recurrence and a potential therapeutic target in lung cancer.

cancer biology↗

bioRxiv2019

A network of microRNAs acts to promote cell cycle exit and differentiation of human pancreatic endocrine cells

Pancreatic endocrine cell differentiation is orchestrated by transcription factors that operate in a gene regulatory network to activate endocrine lineage genes and repress lineage-inappropriate genes. MicroRNAs (miRNAs) are important modulators of gene expression, yet their role in endocrine cell differentiation has not been explored system-wide. Here we characterize miRNA-regulatory networks active in human endocrine cell differentiation by combining small RNA sequencing, miRNA overexpression experiments, and network modeling approaches. This analysis identifies Let-7g, Let-7a, miR-200a, and miR-375 as endocrine-enriched miRNAs with high impact on driving endocrine differentiation-associated gene expression changes. These miRNAs target different sets of transcription factors, which converge on a network of genes involved in cell cycle regulation. When expressed in human embryonic stem cell-derived pancreatic progenitors these miRNAs induce cell cycle exit and promote endocrine cell differentiation. Our study delineates the role of miRNAs in human endocrine cell differentiation and identifies miRNAs that could facilitate endocrine cell reprogramming.

developmental biology↗

bioRxiv2019

Common DNA sequence variation influences 3-dimensional conformation of the human genome

The 3-dimensional (3D) conformation of chromatin inside the nucleus is integral to a variety of nuclear processes including transcriptional regulation, DNA replication, and DNA damage repair. Aberrations in 3D chromatin conformation have been implicated in developmental abnormalities and cancer. Despite the importance of 3D chromatin conformation to cellular function and human health, little is known about how 3D chromatin conformation varies in the human population, or whether DNA sequence variation between individuals influences 3D chromatin conformation. To address these questions, we performed Hi-C on Lymphoblastoid Cell Lines (LCLs) from 20 individuals. We identified thousands of regions across the genome where 3D chromatin conformation varies between individuals and found that this conformational variation is often accompanied by variation in gene expression, histone modifications, and transcription factor (TF) binding. Moreover, we found that DNA sequence variation influences several features of 3D chromatin conformation including loop strength, contact insulation, contact directionality and density of local cis contacts. We mapped hundreds of Quantitative Trait Loci (QTLs) associated with 3D chromatin features and found evidence that some of these same variants are associated at modest levels with other molecular phenotypes as well as complex disease risk. Our results demonstrate that common DNA sequence variants can influence 3D chromatin conformation, pointing to a more pervasive role for 3D chromatin conformation in human phenotypic variation than previously recognized.

genomics↗

bioRxiv2018

Clustering of Type 2 Diabetes Genetic Loci by Multi-Trait Associations Identifies Disease Mechanisms and Subtypes

BackgroundType 2 diabetes (T2D) is a heterogeneous disease for which 1) disease-causing pathways are incompletely understood and 2) sub-classification may improve patient management. Unlike other biomarkers, germline genetic markers do not change with disease progression or treatment. In this paper we test whether a germline genetic approach informed by physiology can be used to deconstruct T2D heterogeneity. First, we aimed to categorize genetic loci into groups representing likely disease mechanistic pathways. Second, we asked whether the novel clusters of genetic loci we identified have any broad clinical consequence, as assessed in four independent cohorts of individuals with T2D.\n\nMethods and FindingsIn an effort to identify mechanistic pathways driven by established T2D genetic loci, we applied Bayesian nonnegative matrix factorization clustering to genome-wide association results for 94 independent T2D genetic loci and 47 diabetes-related traits. We identified five robust clusters of T2D loci and traits, each with distinct tissue-specific enhancer enrichment based on analysis of epigenomic data from 28 cell types. Two clusters contained variant-trait associations indicative of reduced beta-cell function, differing from each other by high vs. low proinsulin levels. The three other clusters displayed features of insulin resistance: obesity-mediated (high BMI, waist circumference), \"lipodystrophy-like\" fat distribution (low BMI, adiponectin, HDL-cholesterol, and high triglycerides), and disrupted liver lipid metabolism (low triglycerides). Increased cluster GRSs were associated with distinct clinical outcomes, including increased blood pressure, coronary artery disease, and stroke risk. We evaluated the potential for clinical impact of these clusters in four studies containing participants with T2D (METSIM, N=487; Ashkenazi, N=509; Partners Biobank, N=2,065; UK Biobank N=14,813). Individuals with T2D in the top genetic risk score decile for each cluster reproducibly exhibited the predicted cluster-associated phenotypes, with ~30% of all participants assigned to just one cluster top decile.\n\nConclusionOur approach identifies salient T2D genetically anchored and physiologically informed pathways, and supports use of genetics to deconstruct T2D heterogeneity. Classification of patients by these genetic pathways may offer a step toward genetically informed T2D patient management.

genetics↗

bioRxiv2018

Shared genetic contribution to type 1 and type 2 diabetes risk

The role of shared genetic risk in the etiology of type 1 diabetes (T1D) and type 2 diabetes (T2D) and the mechanisms of these effects is unknown. In this study, we generated T1D association data of 15k samples imputed into the HRC reference panel which we compared to T2D association data of 159k samples imputed into 1000 Genomes. The effects of genetic variants on T1D and T2D risk at known loci and genome-wide were positively correlated, which we replicated using data from the UK Biobank and clinically-defined diabetes in the WTCCC. Increased risk of T1D and T2D was correlated with higher fasting insulin and fasting glucose level and decreased birth weight, among T1D- and T2D-specifc correlations, and T1D and T2D associated variants were enriched in regulatory elements for pancreatic, insulin resistance (adipose, CD19+ B cell), and developmental (CD184+ endoderm) cell types. We fine-mapped causal variants at known T1D and T2D loci and found evidence for co-localization at five signals, four of which had same direction of effect, including CENPW and GLIS3. Shared risk variants at GLIS3 and other signals were associated with measures of islet function, while CENPW was associated with early growth, and we identified shared risk variants at GLIS3 in islet accessible chromatin with allelic effects on islet regulatory activity. Our findings support shared genetic risk involving variants affecting islet function as well as insulin resistance, growth and development in the etiology of T1D and T2D.

genetics↗

bioRxiv2018

Pancreatic islet chromatin accessibility and conformation defines distal enhancer networks of type 2 diabetes risk

The gene targets of enhancer activity in pancreatic islets are largely unknown, impeding discovery of islet regulatory networks involved in type 2 diabetes (T2D) risk. We mapped chromatin state, accessibility and conformation using ChIP-seq, ATAC-seq and Hi-C in human pancreatic islets, which we integrated with T2D genetic fine-mapping and islet expression QTL data. Active islet regulatory elements preferentially interacted with other active elements, often at distances over 1MB, and we identified target genes for thousands of distal islet enhancers. A third of T2D risk signals mapped in islet enhancers, and target genes regulated by these signals were specifically involved in processes related to protein transport and secretion. Among implicated target genes of T2D islet enhancer signals with no prior known role in islet function, we demonstrated that reduced IGF2BP2 activity in mouse islets leads to impaired glucose-stimulated insulin secretion. These results link distal islet enhancer regulation of protein secretion and transport to genetic risk of T2D, and highlight the utility of high-throughput chromatin conformation maps to uncover the gene regulatory networks of complex disease.

genomics↗

bioRxiv2018

A Survey of Orbitrap All Ion Fragmentation Analysis Assessed by an R MetaboList Package to Study Small-molecule Metabolites

Leukemia cell and melanoma tumor tissue extracts were studied for small (mostly m/z <250) polar metabolites by LC-ESI-HRMSn analysis powered by a hybrid Quadrupole-Orbitrap. MS data was simultaneously acquired in fast polarity switching mode operating in MS1 and MS/MS (All Ion Fragmentation, AIF) full-scan analyses at high mass resolution. Positive assignments were achieved by AIF analysis considering at least two characteristic transitions of metabolites. A targeted metabolite profiling was achieved by the relative quantification of 18 metabolites through spiking their respective deuterated counterparts. Manual data processing of MS1 and AIF scans were compared for accurate determination of natural metabolites and their deuterated analogs by chromatographic alignment and peak area integration. Evaluation of manual and automated (MetaboList R package) AIF data processing yielded comparable results. The versatility of AIF analysis also enabled the untargeted metabolite profiling of leukemia and melanoma samples in which 22 and 53 compounds were respectively identified outside those studied by labeling. The main limitation of the method was that low abundance metabolites with scan rates below 8 scans/peak could not be accurately quantified by AIF analysis. Combination of AIF analysis with MetaboList R package represents an opportunity to move towards automated, faster and more global metabolomics approaches supported by an entirely flexible open source automated data processing platform freely available from Comprehensive R Archive Network (CRAN, https://CRAN.R-project.org/package=MetaboList).

bioinformatics↗