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Integrating genetics with single-cell multiomic measurements across disease states identifies mechanisms of beta cell dysfunction in type 2 diabetes.

ABSTRACT: Dysfunctional pancreatic islet beta cells are a hallmark of type 2 diabetes (T2D), but a comprehensive understanding of the underlying mechanisms, including gene dysregulation, is lacking. Here we integrate information from measurements of chromatin accessibility, gene expression and function in single beta cells with genetic association data to nominate disease-causal gene regulatory changes in T2D. Using machine learning on chromatin accessibility data from 34 nondiabetic, pre-T2D and T2D donors, we identify two transcriptionally and functionally distinct beta cell subtypes that undergo an abundance shift during T2D progression. Subtype-defining accessible chromatin is enriched for T2D risk variants, suggesting a causal contribution of subtype identity to T2D. Both beta cell subtypes exhibit activation of a stress-response transcriptional program and functional impairment in T2D, which is probably induced by the T2D-associated metabolic environment. Our findings demonstrate the power of multimodal single-cell measurements combined with machine learning for characterizing mechanisms of complex diseases.

SUBMITTER: Wang G

PROVIDER: S-EPMC10550816 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Integrating genetics with single-cell multiomic measurements across disease states identifies mechanisms of beta cell dysfunction in type 2 diabetes.

Wang Gaowei G Chiou Joshua J Zeng Chun C Miller Michael M Matta Ileana I Han Jee Yun JY Kadakia Nikita N Okino Mei-Lin ML Beebe Elisha E Mallick Medhavi M Camunas-Soler Joan J Dos Santos Theodore T Dai Xiao-Qing XQ Ellis Cara C Hang Yan Y Kim Seung K SK MacDonald Patrick E PE Kandeel Fouad R FR Preissl Sebastian S Gaulton Kyle J KJ Sander Maike M

Nature genetics 20230525 6

Dysfunctional pancreatic islet beta cells are a hallmark of type 2 diabetes (T2D), but a comprehensive understanding of the underlying mechanisms, including gene dysregulation, is lacking. Here we integrate information from measurements of chromatin accessibility, gene expression and function in single beta cells with genetic association data to nominate disease-causal gene regulatory changes in T2D. Using machine learning on chromatin accessibility data from 34 nondiabetic, pre-T2D and T2D dono ...[more]

PMID: 37231096

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Project description:Both, pharmacological and genome-wide association studies suggest N-methyl-D-aspartate receptor (NMDAR) dysfunction and excitatory/inhibitory (E/I)-imbalance as a major pathophysiological mechanism of schizophrenia. The identification of shared fMRI brain signatures of genetically and pharmacologically induced NMDAR dysfunction may help to define biomarkers for patient stratification. NMDAR-related genetic and pharmacological effects on functional connectivity were investigated by integrating three different datasets: (A) resting state fMRI data from 146 patients with schizophrenia genotyped for the disease-associated genetic variant rs7191183 of GRIN2A (encoding the NMDAR 2 A subunit) as well as 142 healthy controls. (B) Pharmacological effects of the NMDAR antagonist ketamine and the GABA-A receptor agonist midazolam were obtained from a double-blind, crossover pharmaco-fMRI study in 28 healthy participants. (C) Regional gene expression profiles were estimated using a postmortem whole-brain microarray dataset from six healthy donors. A strong resemblance was observed between the effect of the genetic variant in schizophrenia and the ketamine versus midazolam contrast of connectivity suggestive for an associated E/I-imbalance. This similarity became more pronounced for regions with high density of NMDARs, glutamatergic neurons, and parvalbumin-positive interneurons. From a functional perspective, increased connectivity emerged between striato-pallido-thalamic regions and cortical regions of the auditory-sensory-motor network, while decreased connectivity was observed between auditory (superior temporal gyrus) and visual processing regions (lateral occipital cortex, fusiform gyrus, cuneus). Importantly, these imaging phenotypes were associated with the genetic variant, the differential effect of ketamine versus midazolam and schizophrenia (as compared to healthy controls). Moreover, the genetic variant was associated with language-related negative symptomatology which correlated with disturbed connectivity between the left posterior superior temporal gyrus and the superior lateral occipital cortex. Shared genetic and pharmacological functional connectivity profiles were suggestive of E/I-imbalance and associated with schizophrenia. The identified brain signatures may help to stratify patients with a common molecular disease pathway providing a basis for personalized psychiatry.

Dataset Information

Integrating genetics with single-cell multiomic measurements across disease states identifies mechanisms of beta cell dysfunction in type 2 diabetes.

Publications

Integrating genetics with single-cell multiomic measurements across disease states identifies mechanisms of beta cell dysfunction in type 2 diabetes.

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