Type 2 diabetes risk alleles in PAM impact insulin release from human pancreatic β-cells

Thomsen, Soren K.; Raimondo, Anne; Hastoy, Benoît; Sengupta, Shahana; Dai, Xiao-Qing; Bautista, Austin; Censin, Jenny C; Payne, Anthony; Umapathysivam, Mahesh M.; Spigelman, Aliya F; Barrett, Amy; Groves, Christopher J.; Beer, Nicola L.; Fox, Jocelyn E. Manning; McCarthy, Mark I.; Clark, Anne; Mahajan, Anubha; Rorsman, Patrik; MacDonald, Patrick E.; Gloyn, Anna L.

doi:10.1038/s41588-018-0173-1

Cited by 66 publications

(81 citation statements)

References 69 publications

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“…Given that robust β-cell heterogeneity and state transitions have not been reported for stem-cell derived β-like cells (Veres et al, 2019), it is likely that in vitro differentiation protocols will need to be further optimized to produce a full range of dynamic β-cell characteristics. Interestingly, many of the genes that are differentially expressed in Ins2(GFP) HIGH β-cells are known to play roles in type 2 diabetes susceptibility, including common alleles of the MODY/neonatal diabetes genes Pdx1, Neurod1, Nkx6.1, Abcc8, Slc2a2, as well as Slc30a8 and Pam genome-wide association previously identified by genome-wide association (Thomsen et al, 2018). It will also be interesting to examine the frequency of β-cell states in the context of type 1 diabetes, given that pro-insulin, Slc30a8 and Chga are auto-antigens (Nakayama et al, 2005;Stadinski et al, 2010;Wenzlau et al, 2007).…”

Section: Discussionmentioning

confidence: 99%

“…4D). Gfp-high cells also had reduced expression of several genes linked to insulin production and secretion, such as multiple subunits of the 40S and 60S ribosomes, eukaryotic translation initiation factor 3 subunit C (Eif3c), eukaryotic translation elongation factor 1 alpha 1 (Eef1a1), peptidylglycine alphaamidating monooxygenase (Pam), chromogranin B (Chgb), heat shock 70kDa protein 9 (Hspa9; Mortalin), G-protein subunit gamma 5 (Gng5), regulator of G-protein signaling 4 (Rgs4), synaptotagmin 4 (Syt4) (Huang et al, 2018;Thomsen et al, 2018) (Fig. 4D).…”

Section: Profiling β-Cell States With Single-cell Rna Sequencingmentioning

confidence: 99%

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Dynamic Ins2 gene activity defines β-cell maturity states

Modi

Chu

Skovsø

et al. 2019

Preprint

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Word Count: 230 Word Count: 4037 Reference Count: 54 Figure Count: 5 AbstractHeterogeneity within specific cell types is common and increasingly apparent with the advent of single-cell transcriptomics. Transcriptional and functional cellular specialization has been described for insulin-secreting β-cells of the endocrine pancreas, including so-called extreme β-cells exhibiting >2 fold higher insulin gene activity. However, it is not yet clear whether β-cell heterogeneity is stable or reflects dynamic cellular states. We investigated the temporal kinetics of endogenous insulin gene activity using live-cell imaging, with complementary experiments employing FACS and single-cell RNA sequencing, in β-cells from Ins2 GFP knock-in mice. In vivo staining and FACS analysis of islets from Ins2 GFP mice confirmed that at a given moment, ~25% of β-cells exhibited significantly higher activity at the conserved insulin gene Ins2(GFP) HIGH . Live-cell imaging captured on and off 'bursting' behaviour in single β-cells that lasted hours to days. Single cell RNA sequencing determined that Ins2(GFP) HIGH βcells were enriched for markers of β-cell maturity and had reduced expression of anti-oxidant genes. Ins2(GFP) HIGH β-cells were also significantly less viable at all glucose concentrations and in the context of ER stress. Collectively, our results demonstrate that the heterogeneity of extreme insulin production, observed in mouse and human β-cells, can be accounted for by dynamic states of insulin gene activity. Our observations define a previously uncharacterized form of β-cell plasticity. Understanding the dynamics of insulin production has relevance for understanding the pathobiology of diabetes and for regenerative therapy research.

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Section: Discussionmentioning

confidence: 99%

Section: Profiling β-Cell States With Single-cell Rna Sequencingmentioning

confidence: 99%

Dynamic Ins2 gene activity defines β-cell maturity states

Modi

Chu

Skovsø

et al. 2019

Preprint

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“…The authors then focused on one such candidate, ZMIZ1, and showed that its upregulation reduced glucose-induced insulin secretion due to the impairment of insulin exocytosis. In another study on the T2D-associated locus PAM, insulin secretion was found to be significantly reduced in islets from carriers that were heterozygous for the low-frequency rs35658696 risk variant, compared to matched controls (n=16 in each group) [109]. The measurements of exocytosis from dispersed human islets revealed that beta cells from risk variant carriers showed altered insulin granule release dynamics under glucose stimulatory conditions.…”

Section: Omics Studies Of Human Isletsmentioning

confidence: 96%

“…Network analyses identified additional transcription factors that were potential drivers of the dysregulated beta cell gene signatures in T2D. Access to a large biobank of human islets with next-generation sequencing and phenotypic characterization data also facilitated the targeted studies of diabetesassociated genetic variants in islets [109][110][111]. For instance, RNA sequencing and high density genotyping were performed on 118 human islet samples (from Oxford and Edmonton) to generate expression quantitative trait locus (eQTL) data [111].…”

Section: Omics Studies Of Human Isletsmentioning

confidence: 99%

Human Islet Isolation and Distribution Efforts for Clinical and Basic Research

Ng¹,

Wei²,

Koh³

et al. 2019

obm transplant

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The ability to routinely and reproducibly obtain purified human islets has facilitated substantial progress in providing a safe and reliable treatment option for adult patients of type 1 diabetes. The availability of human islets for basic research has also significantly improved the understanding of the biology of human islets, and consequently the pathophysiology of diabetes. Presently, about 70 human islet isolation centers are known to exist around the world, in addition to multiple coordinated human islet distribution programs, that facilitate the exchange of knowledge and sharing of this precious resource. This review summarizes the steps involved in the isolation and dissemination of human islets, and discusses key considerations with respect to the major challenges faced during this

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“…The studies outlined above, and other similar studies not discussed, [56][57][58] describe how genetic loci have led to a new understanding of the etiology of complex traits and share commonalities in design ( Fig. 1).…”

Section: The Role That Human Neuroimaging Of Gross Brain Structure Camentioning

confidence: 99%

Mapping causal pathways from genetics to neuropsychiatric disorders using genome‐wide imaging genetics: Current status and future directions

Stein

2019

Psychiatry Clin Neurosci

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Imaging genetics aims to identify genetic variants associated with the structure and function of the human brain. Recently, collaborative consortia have been successful in this goal, identifying and replicating common genetic variants influencing gross human brain structure as measured through magnetic resonance imaging. In this review, we contextualize imaging genetic associations as one important link in understanding the causal chain from genetic variant to increased risk for neuropsychiatric disorders. We provide examples in other fields of how identifying genetic variant associations to disease and multiple phenotypes along the causal chain has revealed a mechanistic understanding of disease risk, with implications for how imaging genetics can be similarly applied. We discuss current findings in the imaging genetics research domain, including that common genetic variants can have a slightly larger effect on brain structure than on risk for disorders like schizophrenia, indicating a somewhat simpler genetic architecture. Also, gross brain structure measurements share a genetic basis with some, but not all, neuropsychiatric disorders, invalidating the previously held belief that they are broad endophenotypes, yet pinpointing brain regions likely involved in the pathology of specific disorders. Finally, we suggest that in order to build a more detailed mechanistic understanding of the effects of genetic variants on the brain, future directions in imaging genetics research will require observations of cellular and synaptic structure in specific brain regions beyond the resolution of magnetic resonance imaging. We expect that integrating genetic associations at biological levels from synapse to sulcus will reveal specific causal pathways impacting risk for neuropsychiatric disorders.

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Type 2 diabetes risk alleles in PAM impact insulin release from human pancreatic β-cells

Cited by 66 publications

References 69 publications

Dynamic Ins2 gene activity defines β-cell maturity states

Dynamic Ins2 gene activity defines β-cell maturity states

Human Islet Isolation and Distribution Efforts for Clinical and Basic Research

Mapping causal pathways from genetics to neuropsychiatric disorders using genome‐wide imaging genetics: Current status and future directions

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