Kahn, S. E., Hull, R. L. & Utzschneider, Ok. M. Mechanisms linking weight problems to insulin resistance and kind 2 diabetes. Nature 444, 840–846 (2006).
Halban, P. A. et al. β-cell failure in sort 2 diabetes: postulated mechanisms and prospects for prevention and therapy. Diabetes Care 37, 1751–1758 (2014).
Mahajan, A. et al. High quality-mapping sort 2 diabetes loci to single-variant decision utilizing high-density imputation and islet-specific epigenome maps. Nat. Genet. 50, 1505–1513 (2018).
Rai, V. et al. Single-cell ATAC-seq in human pancreatic islets and deep studying upscaling of uncommon cells reveals cell-specific sort 2 diabetes regulatory signatures. Mol. Metab. 32, 109–121 (2019).
Chiou, J. et al. Single-cell chromatin accessibility identifies pancreatic islet cell type- and state-specific regulatory applications of diabetes threat. Nat. Genet. 53, 455–466 (2021).
Ahlqvist, E., Prasad, R. B. & Groop, L. Subtypes of sort 2 diabetes decided from medical parameters. Diabetes 69, 2086–2093 (2020).
Redondo, M. J. et al. The medical penalties of heterogeneity inside and between completely different diabetes varieties. Diabetologia 63, 2040–2048 (2020).
Weitz, J., Menegaz, D. & Caicedo, A. Deciphering the complicated communication networks that orchestrate pancreatic islet operate. Diabetes 70, 17–26 (2020).
Vujkovic, M. et al. Discovery of 318 new threat loci for sort 2 diabetes and associated vascular outcomes amongst 1.4 million members in a multi-ancestry meta-analysis. Nat. Genet. 52, 680–691 (2020).
Mahajan, A. et al. Multi-ancestry genetic examine of sort 2 diabetes highlights the ability of various populations for discovery and translation. Nat. Genet. 54, 560–572 (2022).
Parker, S. C. J. et al. Chromatin stretch enhancer states drive cell-specific gene regulation and harbor human illness threat variants. Proc. Natl Acad. Sci. USA 110, 17921–17926 (2013).
Trynka, G. et al. Chromatin marks establish crucial cell varieties for nice mapping complicated trait variants. Nat. Genet. 45, 124–130 (2013).
Pasquali, L. et al. Pancreatic islet enhancer clusters enriched in sort 2 diabetes risk-associated variants. Nat. Genet. 46, 136–43 (2014).
Walker, J. T., Saunders, D. C., Brissova, M. & Powers, A. C. The human islet: mini-organ with mega-impact. Endocr. Rev. 42, bnab010 (2021).
Brissova, M. et al. Evaluation of human pancreatic islet structure and composition by laser scanning confocal microscopy. J. Histochem. Cytochem. 53, 1087–1097 (2005).
Dai, C. et al. Stress-impaired transcription issue expression and insulin secretion in transplanted human islets. J. Clin. Make investments. 126, 1857–1870 (2016).
Wigger, L. et al. Multi-omics profiling of residing human pancreatic islet donors reveals heterogeneous beta cell trajectories in the direction of sort 2 diabetes. Nat. Metab. 3, 1017–1031 (2021).
Camunas-Soler, J. et al. Patch-seq hyperlinks single-cell transcriptomes to human islet dysfunction in diabetes. Cell Metab. 31, 1017–1031.e4 (2020).
Shapira, S. N., Naji, A., Atkinson, M. A., Powers, A. C. & Kaestner, Ok. H. Understanding islet dysfunction in sort 2 diabetes via multidimensional pancreatic phenotyping: The Human Pancreas Evaluation Program. Cell Metab. 34, 1906–1913 (2022).
Albrechtsen, N. J. W. et al. The liver–α-cell axis and kind 2 diabetes. Endocr. Rev. 40, 1353–1366 (2019).
Wu, M. et al. Single-cell evaluation of the human pancreas in sort 2 diabetes utilizing multi-spectral imaging mass cytometry. Cell Rep. 37, 109919 (2021).
Dam, T. J. Pvan et al. CiliaCarta: an built-in and validated compendium of ciliary genes. PLoS ONE 14, e0216705 (2019).
Smith, S. B. et al. Rfx6 directs islet formation and insulin manufacturing in mice and people. Nature 463, 775–780 (2010).
Patel, Ok. A. et al. Heterozygous RFX6 protein truncating variants are related to MODY with decreased penetrance. Nat. Commun. 8, 888 (2017).
Varshney, A. et al. Genetic regulatory signatures underlying islet gene expression and kind 2 diabetes. Proc. Natl Acad. Sci. USA 114, 2301–2306 (2017).
Walker, J. T. et al. Built-in human pseudoislet system and microfluidic platform demonstrates variations in G-protein-coupled-receptor signaling in islet cells. JCI Perception 5, e137017 (2020).
Viñuela, A. et al. Genetic variant results on gene expression in human pancreatic islets and their implications for T2D. Nat. Commun. 11, 4912 (2020).
Kahn, S. E., Zraika, S., Utzschneider, Ok. M. & Hull, R. L. The beta cell lesion in sort 2 diabetes: there needs to be a main useful abnormality. Diabetologia 52, 1003–1012 (2009).
Meier, J. J. & Bonadonna, R. C. Position of decreased β-cell mass versus impaired β-cell operate within the pathogenesis of sort 2 diabetes. Diabetes Care 36, S113–S119 (2013).
Cohrs, C. M. et al. Dysfunction of persisting β cells is a key function of early sort 2 diabetes pathogenesis. Cell Rep. 31, 107469 (2020).
McCarthy, M. I. Portray a brand new image of personalised drugs for diabetes. Diabetologia 60, 793–799 (2017).
Chandra, V. et al. RFX6 regulates insulin secretion by modulating Ca2+ homeostasis in human β cells. Cell Rep. 9, 2206–2218 (2014).
Piccand, J. et al. Rfx6 maintains the useful id of grownup pancreatic β cells. Cell Rep. 9, 2219–2232 (2014).
Choksi, S. P., Lauter, G., Swoboda, P. & Roy, S. Switching on cilia: transcriptional networks regulating ciliogenesis. Improvement 141, 1427–1441 (2014).
Piasecki, B. P., Burghoorn, J. & Swoboda, P. Regulatory issue X (RFX)-mediated transcriptional rewiring of ciliary genes in animals. Proc. Natl Acad. Sci. USA 107, 12969–12974 (2010).
Kurki, M. I. et al. FinnGen supplies genetic insights from a well-phenotyped remoted inhabitants. Nature 613, 508–518 (2023).
Iotchkova, V. et al. GARFIELD classifies disease-relevant genomic options via integration of useful annotations with affiliation indicators. Nat. Genet. 51, 343–353 (2019).
Gloyn, A. L. et al. Each islet issues: bettering the influence of human islet analysis. Nat. Metab. 4, 970–977 (2022).
Balamurugan, A. N., Chang, Y., Fung, J. J., Trucco, M. & Bottino, R. Versatile administration of enzymatic digestion improves human islet isolation end result from sub‐optimum donor pancreata. Am. J. Transplant. 3, 1135–1142 (2003).
Dai, C. et al. Age-dependent human β cell proliferation induced by glucagon-like peptide 1 and calcineurin signaling. J. Clin. Make investments. 127, 3835–3844 (2017).
Brissova, M. et al. α cell operate and gene expression are compromised in sort 1 diabetes. Cell Rep. 22, 2667–2676 (2018).
Brissova, M. et al. Islet microenvironment, modulated by vascular endothelial development factor-A signaling, promotes β cell regeneration. Cell Metab. 19, 498–511 (2014).
Brissova, M. et al. The Built-in Islet Distribution Program solutions the decision for improved human islet phenotyping and reporting of human islet traits in analysis articles. Diabetologia 62, 1312–1314 (2019).
Kayton, N. S. et al. Human islet preparations distributed for analysis exhibit quite a lot of insulin-secretory profiles. Am. J. Physiol. Endocrinol. Metab. 308, E592–E602 (2015).
Fitzmaurice, G. M., Laird, N. M. & Ware, J. H. Utilized Longitudinal Evaluation (Wiley, 2011).
Shultz, L. D. et al. Human lymphoid and myeloid cell improvement in NOD/LtSz-scid IL2Rγnull mice engrafted with mobilized human hemopoietic stem cells. J. Immunol. 174, 6477–6489 (2005).
Dai, C. et al. Tacrolimus- and sirolimus-induced human β cell dysfunction is reversible and preventable. JCI Perception 5, e130770 (2020).
Dorrell, C. et al. Transcriptomes of the most important human pancreatic cell varieties. Diabetologia 54, 2832 (2011).
Saunders, D. C. et al. Ectonucleoside triphosphate diphosphohydrolase-3 antibody targets grownup human pancreatic β cells for in vitro and in vivo evaluation. Cell Metab. 29, 745–754.e4 (2019).
Dorrell, C. et al. Human islets comprise 4 distinct subtypes of β cells. Nat. Commun. 7, 11756 (2016).
Haliyur, R. et al. Human islets expressing HNF1A variant have faulty β cell transcriptional regulatory networks. J. Clin. Make investments. 129, 246–251 (2018).
Marzban, L., Park, Ok. & Verchere, C. B. Islet amyloid polypeptide and kind 2 diabetes. Exp. Gerontol. 38, 347–351 (2003).
Westermark, P., Andersson, A. & Westermark, G. T. Islet amyloid polypeptide, islet amyloid, and diabetes mellitus. Physiol. Rev. 91, 795–826 (2011).
Hart, N. J. et al. Cystic fibrosis–associated diabetes is attributable to islet loss and irritation. JCI Perception 3, e98240 (2018).
Noguchi, G. M. & Huising, M. O. Integrating the inputs that form pancreatic islet hormone launch. Nat. Metab. 1, 1189–1201 (2019).
Black, S. et al. CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Nat. Protoc. 16, 3802–3835 (2021).
Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Quick unfolding of communities in massive networks. J. Stat. Mech. Principle Exp. 2008, P10008 (2008).
Luhn, H. P. The automated creation of literature abstracts. IBM J. Res. Dev. 2, 159–165 (1958).
Schürch, C. M. et al. Coordinated mobile neighborhoods orchestrate antitumoral immunity on the colorectal most cancers invasive entrance. Cell 182, 1341–1359.e19 (2020).
Dobin, A. et al. STAR: ultrafast common RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Liao, Y., Smyth, G. Ok. & Shi, W. featureCounts: an environment friendly common objective program for assigning sequence reads to genomic options. Bioinformatics 30, 923–930 (2014).
Hartley, S. W. & Mullikin, J. C. QoRTs: a complete toolset for high quality management and knowledge processing of RNA-Seq experiments. BMC Bioinformatics 16, 224 (2015).
Wang, L. et al. Measure transcript integrity utilizing RNA-seq knowledge. BMC Bioinformatics 17, 58 (2016).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq knowledge with DESeq2. Genome Biol. 15, 550 (2014).
Risso, D., Ngai, J., Velocity, T. P. & Dudoit, S. Normalization of RNA-seq knowledge utilizing issue evaluation of management genes or samples. Nat. Biotechnol. 32, 896–902 (2014).
Lee, C., Patil, S. & Sartor, M. A. RNA-Enrich: a cut-off free useful enrichment testing technique for RNA-seq with improved detection energy. Bioinformatics 32, 1100–1102 (2016).
Supek, F., Bošnjak, M., Škunca, N. & Šmuc, T. REVIGO summarizes and visualizes lengthy lists of Gene Ontology phrases. PLoS ONE 6, e21800 (2011).
Shannon, P. et al. Cytoscape: a software program surroundings for built-in fashions of biomolecular interplay networks. Genome Res. 13, 2498–2504 (2003).
Zhou, Y. et al. Metascape supplies a biologist-oriented useful resource for the evaluation of systems-level datasets. Nat. Commun. 10, 1523 (2019).
Langfelder, P. & Horvath, S. WGCNA: an R package deal for weighted correlation community evaluation. BMC Bioinformatics 9, 559 (2008).
Saeedi, P. et al. World and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: outcomes from the Worldwide Diabetes Federation Diabetes Atlas, ninth version. Diabetes Res. Clin. Pract. 157, 107843 (2019).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray research. Nucleic Acids Res. 43, e47 (2015).
Naba, A. et al. The matrisome: in silico definition and in vivo characterization by proteomics of regular and tumor extracellular matrices. Mol. Cell Proteomics 11, M111.014647 (2012).
Breuer, Ok. et al. InnateDB: methods biology of innate immunity and past—latest updates and persevering with curation. Nucleic Acids Res. 41, D1228–D1233 (2013).
Kolberg, L., Raudvere, U., Kuzmin, I., Vilo, J. & Peterson, H. gprofiler2–an R package deal for gene checklist useful enrichment evaluation and namespace conversion toolset g:Profiler. F1000research 9, ELIXIR–709 (2020).
Chen, J. et al. The trans-ancestral genomic structure of glycemic traits. Nat. Genet. 53, 840–860 (2021).
Bailey, T. L. et al. MEME Suite: instruments for motif discovery and looking. Nucleic Acids Res. 37, W202–W208 (2009).
Weirauch, M. T. et al. Willpower and inference of eukaryotic transcription issue sequence specificity. Cell 158, 1431–1443 (2014).
Das, S. et al. Subsequent-generation genotype imputation service and strategies. Nat. Genet. 48, 1284–1287 (2016).
Loh, P.-R. et al. Reference-based phasing utilizing the Haplotype Reference Consortium panel. Nat. Genet. 48, 1443–1448 (2016).
Auton, A. et al. A worldwide reference for human genetic variation. Nature 526, 68–74 (2015).
Li, H. & Durbin, R. Quick and correct quick learn alignment with Burrows–Wheeler remodel. Bioinformatics 25, 1754–1760 (2009).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Orchard, P., Kyono, Y., Hensley, J., Kitzman, J. O. & Parker, S. C. J. Quantification, dynamic visualization, and validation of bias in ATAC-seq knowledge with ataqv. Cell Syst. 10, 298–306.e4 (2020).
Lun, A. T. L. et al. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing knowledge. Genome Biol. 20, 63 (2019).
Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing utilizing pure genetic variation. Nat. Biotechnol. 36, 89–94 (2018).
Yang, S. et al. Decontamination of ambient RNA in single-cell RNA-seq with DecontX. Genome Biol. 21, 57 (2020).
R Core Group. R: A Language and Surroundings for Statistical Computing. http://www.R-project.org/ (R Basis for Statistical Computing, 2020).
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic knowledge throughout completely different situations, applied sciences, and species. Nat. Biotechnol. 36, 411 (2018).
Stuart, T. et al. Complete integration of single-cell knowledge. Cell 177, 1888–1902.e21 (2019).
Hao, Y. et al. Built-in evaluation of multimodal single-cell knowledge. Cell 184, 3573–3587.e29 (2021).
Thibodeau, A. et al. AMULET: a novel learn count-based technique for efficient multiplet detection from single nucleus ATAC-seq knowledge. Genome Biol. 22, 252 (2021).
Speir, M. L. et al. UCSC Cell Browser: visualize your single-cell knowledge. Bioinformatics 37, 4578–4580 (2021).
Sande, B. Vde et al. A scalable SCENIC workflow for single-cell gene regulatory community evaluation. Nat. Protoc. 15, 2247–2276 (2020).
Quinlan, A. R. BEDTools: the Swiss‐military instrument for genome function evaluation. Curr. Protoc. Bioinform. 47, 11.12.1–11.12.34 (2014).
Zhang, Y. et al. Mannequin-based evaluation of ChIP-seq (MACS). Genome Biol. 9, R137–R137 (2008).
Kent, W. J., Zweig, A. S., Barber, G., Hinrichs, A. S. & Karolchik, D. BigWig and BigBed: enabling shopping of huge distributed datasets. Bioinformatics 26, 2204–2207 (2010).
Grant, C. E., Bailey, T. L. & Noble, W. S. FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017–1018 (2011).
Kheradpour, P. & Kellis, M. Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments. Nucleic Acids Res. 42, 2976–2987 (2014).
Jolma, A. et al. DNA-binding specificities of human transcription components. Cell 152, 327–339 (2013).
Chinwalla, A. T. et al. Preliminary sequencing and comparative evaluation of the mouse genome. Nature 420, 520–562 (2002).
Bailey, T. L. & Elkan, C. Becoming a mix mannequin by expectation maximization to find motifs in biopolymers. Proc. Int. Conf. Intell. Syst. Mol. Biol. 2, 28–36 (1994).
Bailey, T. L. DREME: motif discovery in transcription issue ChIP–seq knowledge. Bioinformatics 27, 1653–1659 (2011).
Bailey, T. L., Johnson, J., Grant, C. E. & Noble, W. S. The MEME suite. Nucleic Acids Res. 43, W39–W49 (2015).
Bowden, J., Smith, G. D. & Burgess, S. Mendelian randomization with invalid devices: impact estimation and bias detection via Egger regression. Int. J. Epidemiol. 44, 512–525 (2015).
Bowden, J., Smith, G. D., Haycock, P. C. & Burgess, S. Constant estimation in Mendelian randomization with some invalid devices utilizing a weighted median estimator. Genet. Epidemiol. 40, 304–314 (2016).
Ye, T., Shao, J. & Kang, H. Debiased inverse-variance weighted estimator in two-sample summary-data Mendelian randomization. Ann. Stat. 49, 2079–2100 (2021).
Verbanck, M., Chen, C.-Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complicated traits and illnesses. Nat. Genet. 50, 693 (2018).
Yavorska, O. O. & Burgess, S. MendelianRandomization: an R package deal for performing Mendelian randomization analyses utilizing summarized knowledge. Int. J. Epidemiol. 46, 1734–1739 (2017).
Sudlow, C. et al. UK Biobank: an open entry useful resource for figuring out the causes of a variety of complicated illnesses of center and outdated age. PLoS Med. 12, e1001779 (2015).
McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).
Loh, P.-R. et al. Environment friendly Bayesian combined mannequin evaluation will increase affiliation energy in massive cohorts. Nat. Genet. 47, 284–290 (2015).
Bonner-Weir, S. & O’Brien, T. D. Islets in sort 2 diabetes: in honor of Dr. Robert C. Turner. Diabetes 57, 2899–2904 (2008).
Sakuraba, H. et al. Diminished beta-cell mass and expression of oxidative stress-related DNA harm within the islet of Japanese sort II diabetic sufferers. Diabetologia 45, 85–96 (2002).
Butler, A. E. et al. β-cell deficit and elevated β-cell apoptosis in people with sort 2 diabetes. Diabetes 52, 102–110 (2003).
Rahier, J., Guiot, Y., Goebbels, R. M., Sempoux, C. & Henquin, J. C. Pancreatic β‐cell mass in European topics with sort 2 diabetes. Diabetes Obes. Metab. 10, 32–42 (2008).
Talchai, C., Xuan, S., Lin, H. V., Sussel, L. & Accili, D. Pancreatic β cell dedifferentiation as a mechanism of diabetic β cell failure. Cell 150, 1223–1234 (2012).
Masters, S. L. et al. Activation of the NLRP3 inflammasome by islet amyloid polypeptide supplies a mechanism for enhanced IL-1β in sort 2 diabetes. Nat. Immunol. 11, 897–904 (2010).
Westwell-Roper, C. Y., Ehses, J. A. & Verchere, C. B. Resident macrophages mediate islet amyloid polypeptide–induced islet IL-1β manufacturing and β-cell dysfunction. Diabetes 63, 1698–1711 (2014).
Nair, G. & Hebrok, M. Islet formation in mice and males: classes for the era of useful insulin-producing β-cells from human pluripotent stem cells. Curr. Opin. Genet. Dev. 32, 171–180 (2015).
Arrojo e Drigo, R. et al. New insights into the structure of the islet of Langerhans: a centered cross-species evaluation. Diabetologia 58, 2218–2228 (2015).
Unger, R. H. & Cherrington, A. D. Glucagonocentric restructuring of diabetes: a pathophysiologic and therapeutic makeover. J. Clin. Make investments. 122, 4–12 (2012).
