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      Gene loci associated with insulin secretion in islets from nondiabetic mice

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          Abstract

          Genetic susceptibility to type 2 diabetes is primarily due to β cell dysfunction. However, a genetic study to directly interrogate β cell function ex vivo has never been previously performed. We isolated 233,447 islets from 483 Diversity Outbred (DO) mice maintained on a Western-style diet, and measured insulin secretion in response to a variety of secretagogues. Insulin secretion from DO islets ranged greater than 1000-fold even though none of the mice were diabetic. The insulin secretory response to each secretagogue had a unique genetic architecture; some of the loci were specific for one condition, whereas others overlapped. Human loci that are syntenic to many of the insulin secretion quantitative trait loci (QTL) from mice are associated with diabetes-related SNPs in human genome-wide association studies. We report on 3 genes, Ptpn18 , Hunk , and Zfp148 , where the phenotype predictions from the genetic screen were fulfilled in our studies of transgenic mouse models. These 3 genes encode a nonreceptor type protein tyrosine phosphatase, a serine/threonine protein kinase, and a Kr ϋ ppel -type zinc-finger transcription factor, respectively. Our results demonstrate that genetic variation in insulin secretion that can lead to type 2 diabetes is discoverable in nondiabetic individuals.

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          Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach

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            Metabolic signaling in fuel-induced insulin secretion.

            The pancreatic islet β cell senses circulating levels of calorigenic nutrients to secrete insulin according to the needs of the organism. Altered insulin secretion is linked to various disorders such as diabetes, hypoglycemic states, and cardiometabolic diseases. Fuel stimuli, including glucose, free fatty acids, and amino acids, promote insulin granule exocytosis primarily via their metabolism in β cells and the production of key signaling metabolites. This paper reviews our current knowledge of the pathways involved in both positive and negative metabolic signaling for insulin secretion and assesses the role of established and candidate metabolic coupling factors, keeping recent developments in focus. Copyright © 2013 Elsevier Inc. All rights reserved.
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              Defining the consequences of genetic variation on a proteome-wide scale.

              Genetic variation modulates protein expression through both transcriptional and post-transcriptional mechanisms. To characterize the consequences of natural genetic diversity on the proteome, here we combine a multiplexed, mass spectrometry-based method for protein quantification with an emerging outbred mouse model containing extensive genetic variation from eight inbred founder strains. By measuring genome-wide transcript and protein expression in livers from 192 Diversity outbred mice, we identify 2,866 protein quantitative trait loci (pQTL) with twice as many local as distant genetic variants. These data support distinct transcriptional and post-transcriptional models underlying the observed pQTL effects. Using a sensitive approach to mediation analysis, we often identified a second protein or transcript as the causal mediator of distant pQTL. Our analysis reveals an extensive network of direct protein-protein interactions. Finally, we show that local genotype can provide accurate predictions of protein abundance in an independent cohort of collaborative cross mice.
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                Author and article information

                Journal
                Journal of Clinical Investigation
                American Society for Clinical Investigation
                0021-9738
                1558-8238
                October 1 2019
                October 1 2019
                October 1 2019
                September 9 2019
                September 9 2019
                October 1 2019
                : 129
                : 10
                : 4419-4432
                Article
                10.1172/JCI129143
                6763251
                31343992
                fdad8b95-76a3-4b43-af74-16f17faf6f17
                © 2019
                History

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