Laser capture microdissection of human pancreatic islets reveals novel eQTLs associated with type 2 diabetes

Multiple genetic variants at the SLC30A8 locus affect local super-enhancer activity and influence pancreatic β-cell survival and function

Variants at the SLC30A8 locus are associated with type 2 diabetes (T2D) risk. The lead variant, rs13266634, encodes an amino acid change, Arg325Trp (R325W), at the C-terminus of the secretory granule-enriched zinc transporter, ZnT8. Although this protein-coding variant was previously thought to be the sole driver of T2D risk at this locus, recent studies have provided evidence for lowered expression of SLC30A8 mRNA in protective allele carriers. In the present study, we examined multiple variants that influence SLC30A8 allele-specific expression. Epigenomic mapping has previously identified an islet-selective enhancer cluster at the SLC30A8 locus, hosting multiple T2D risk and cASE associations, which is spatially associated with the SLC30A8 promoter and additional neighboring genes. Here, we show that deletion of variant-bearing enhancer regions using CRISPR-Cas9 in human-derived EndoC-βH3 cells lowers the expression of SLC30A8 and several neighboring genes and improves glucose-stimulated insulin secretion. While downregulation of SLC30A8 had no effect on beta cell survival, loss of UTP23, RAD21, or MED30 markedly reduced cell viability. Although eQTL or cASE analyses in human islets did not support the association between these additional genes and diabetes risk, the transcriptional regulator JQ1 lowered the expression of multiple genes at the SLC30A8 locus and enhanced stimulated insulin secretion.

Bridging the Gap: Pancreas Tissue Slices From Organ and Tissue Donors for the Study of Diabetes Pathogenesis

Over the last two decades, increased availability of human pancreatic tissues has allowed for major expansions in our understanding of islet biology in health and disease. Indeed, studies of fixed and frozen pancreatic tissues, as well as efforts using viable isolated islets obtained from organ donors, have provided significant insights toward our understanding of diabetes. However, the procedures associated with islet isolation result in distressed cells that have been removed from any surrounding influence. The pancreas tissue slice technology was developed as an in situ approach to overcome certain limitations associated with studies on isolated islets or fixed tissue. In this Perspective, we discuss the value of this novel platform and review how pancreas tissue slices, within a short time, have been integrated in numerous studies of rodent and human islet research. We show that pancreas tissue slices allow for investigations in a less perturbed organ tissue environment, ranging from cellular processes, over peri-islet modulations, to tissue interactions. Finally, we discuss the considerations and limitations of this technology in its future applications. We believe the pancreas tissue slices will help bridge the gap between studies on isolated islets and cells to the systemic conditions by providing new insight into physiological and pathophysiological processes at the organ level.

ARTICLE HIGHLIGHTS

Human pancreas tissue slices represent a novel platform to study human islet biology in close to physiological conditions. Complementary to established technologies, such as isolated islets, single cells, and histological sections, pancreas tissue slices help bridge our understanding of islet physiology and pathophysiology from single cell to intact organ. Diverse sources of viable human pancreas tissue, each with distinct characteristics to be considered, are available to use in tissue slices for the study of diabetes pathogenesis.

Exploring the role of purinergic receptor P2RY1 in type 2 diabetes risk and pathophysiology: Insights from human functional genomics

OBJECTIVE

Human functional genomics has proven powerful in discovering drug targets for common metabolic disorders. Through this approach, we investigated the involvement of the purinergic receptor P2RY1 in type 2 diabetes (T2D).

METHODS

P2RY1 was sequenced in 9,266 participants including 4,177 patients with T2D. In vitro analyses were then performed to assess the functional effect of each variant. Expression quantitative trait loci (eQTL) analysis was performed in pancreatic islets from 103 pancreatectomized individuals. The effect of P2RY1 on glucose-stimulated insulin secretion was finally assessed in human pancreatic beta cells (EndoCβH5), and RNA sequencing was performed on these cells.

RESULTS

Sequencing P2YR1 in 9,266 participants revealed 22 rare variants, seven of which were loss-of-function according to our in vitro analyses. Carriers, except one, exhibited impaired glucose control. Our eQTL analysis of human islets identified P2RY1 variants, in a beta-cell enhancer, linked to increased P2RY1 expression and reduced T2D risk, contrasting with variants located in a silent region associated with decreased P2RY1 expression and increased T2D risk. Additionally, a P2RY1-specific agonist increased insulin secretion upon glucose stimulation, while the antagonist led to decreased insulin secretion. RNA-seq highlighted TXNIP as one of the main transcriptomic markers of insulin secretion triggered by P2RY1 agonist.

CONCLUSION

Our findings suggest that P2RY1 inherited or acquired dysfunction increases T2D risk and that P2RY1 activation stimulates insulin secretion. Selective P2RY1 agonists, impermeable to the blood-brain barrier, could serve as potential insulin secretagogues.

Multiple genetic variants at the SLC30A8 locus affect local super-enhancer activity and influence pancreatic β-cell survival and function

Variants at the SLC30A8 locus are associated with type 2 diabetes (T2D) risk. The lead variant, rs13266634, encodes an amino acid change, Arg325Trp (R325W), at the C-terminus of the secretory granule-enriched zinc transporter, ZnT8. Although this protein-coding variant was previously thought to be the sole driver of T2D risk at this locus, recent studies have provided evidence for lowered expression of SLC30A8 mRNA in protective allele carriers. In the present study, combined allele-specific expression (cASE) analysis in human islets revealed multiple variants that influence SLC30A8 expression. Epigenomic mapping identified an islet-selective enhancer cluster at the SLC30A8 locus, hosting multiple T2D risk and cASE associations, which is spatially associated with the SLC30A8 promoter and additional neighbouring genes. Deletions of variant-bearing enhancer regions using CRISPR-Cas9 in human-derived EndoC-βH3 cells lowered the expression of SLC30A8 and several neighbouring genes, and improved insulin secretion. Whilst down-regulation of SLC30A8 had no effect on beta cell survival, loss of UTP23, RAD21 or MED30 markedly reduced cell viability. Although eQTL or cASE analyses in human islets did not support the association between these additional genes and diabetes risk, the transcriptional regulator JQ1 lowered the expression of multiple genes at the SLC30A8 locus and enhanced stimulated insulin secretion.

rs67047829 genotypes of ERV3-1/ZNF117 are associated with lower body mass index in the Polish population

There is now substantial evidence that zinc-finger proteins are implicated in adiposity. Aims were to datamine for high-frequency (near-neutral selection) pretermination-codon (PTC) single-nucleotide polymorphisms (SNPs; n = 141) from a database with > 550,000 variants and analyze possible association with body mass index in a large Polish sample (n = 5757). BMI was regressed (males/females together or separately) against genetic models. Regression for rs67047829 uncovered an interaction-independent association with BMI with both sexes together: mean ± standard deviation, kg/m2: [G];[G], 25.4 ± 4.59 (n = 3650); [G](;)[A], 25.0 ± 4.28 (n = 731); [A];[A], 23.4 ± 3.60 (n = 44); additive model adjusted for age and sex: p = 4.08 × 10-5; beta: - 0.0458, 95% confidence interval (CI) - 0.0732 : - 0.0183; surviving Bonferroni correction; for males: [G];[G], 24.8 ± 4.94 (n = 1878); [G](;)[A], 24.2 ± 4.31 (n = 386); [A];[A], 22.4 ± 3.69 (n = 23); p = 4.20 × 10-4; beta: - 0.0573, CI - 0.0947 : - 0.0199. For average-height males the difference between [G];[G] and [A];[A] genotypes would correspond to ~ 6 kg, suggesting considerable protection against increased BMI. rs67047829 gives a pretermination codon in ERV3-1 which shares an exonic region and possibly promoter with ZNF117, previously associated with adiposity and type-2 diabetes. As this result occurs in a near-neutral Mendelian setting, a drug targetting ERV3-1/ZNF117 might potentially provide considerable benefits with minimal side-effects. This result needs to be replicated, followed by analyses of splice-variant mRNAs and protein expression.

Laser capture microdissection for biomedical research: towards high-throughput, multi-omics, and single-cell resolution

Spatial omics technologies have become powerful methods to provide valuable insights into cells and tissues within a complex context, significantly enhancing our understanding of the intricate and multifaceted biological system. With an increasing focus on spatial heterogeneity, there is a growing need for unbiased, spatially resolved omics technologies. Laser capture microdissection (LCM) is a cutting-edge method for acquiring spatial information that can quickly collect regions of interest (ROIs) from heterogeneous tissues, with resolutions ranging from single cells to cell populations. Thus, LCM has been widely used for studying the cellular and molecular mechanisms of diseases. This review focuses on the differences among four types of commonly used LCM technologies and their applications in omics and disease research. Key attributes of application cases are also highlighted, such as throughput and spatial resolution. In addition, we comprehensively discuss the existing challenges and the great potential of LCM in biomedical research, disease diagnosis, and targeted therapy from the perspective of high-throughput, multi-omics, and single-cell resolution.

Type 2 Diabetes Mellitus and its comorbidity, Alzheimer's disease: Identifying critical microRNA using machine learning

MicroRNAs (miRNAs) are critical regulators of gene expression in healthy and diseased states, and numerous studies have established their tremendous potential as a tool for improving the diagnosis of Type 2 Diabetes Mellitus (T2D) and its comorbidities. In this regard, we computationally identify novel top-ranked hub miRNAs that might be involved in T2D. We accomplish this via two strategies: 1) by ranking miRNAs based on the number of T2D differentially expressed genes (DEGs) they target, and 2) using only the common DEGs between T2D and its comorbidity, Alzheimer's disease (AD) to predict and rank miRNA. Then classifier models are built using the DEGs targeted by each miRNA as features. Here, we show the T2D DEGs targeted by hsa-mir-1-3p, hsa-mir-16-5p, hsa-mir-124-3p, hsa-mir-34a-5p, hsa-let-7b-5p, hsa-mir-155-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-129-2-3p, and hsa-mir-146a-5p are capable of distinguishing T2D samples from the controls, which serves as a measure of confidence in the miRNAs' potential role in T2D progression. Moreover, for the second strategy, we show other critical miRNAs can be made apparent through the disease's comorbidities, and in this case, overall, the hsa-mir-103a-3p models work well for all the datasets, especially in T2D, while the hsa-mir-124-3p models achieved the best scores for the AD datasets. To the best of our knowledge, this is the first study that used predicted miRNAs to determine the features that can separate the diseased samples (T2D or AD) from the normal ones, instead of using conventional non-biology-based feature selection methods.

Identification of key genes and pathways associated with diabetes of the exocrine pancreas

This study aimed to identify potential essential genes and pathways in diabetes of the exocrine pancreas (DEP) and explore possible molecular mechanisms. The array dataset GSE76895 was downloaded from the Gene Expression Omnibus database. Pancreatic tissue samples from 20 Diabetes of the exocrine pancreas and 32 nondiabetic individuals were selected for analysis. GEO2R analyzed differentially expressed genes (DEGs) in the 2 groups. Gene ontology annotation, Kyoto Encyclopedia of Genes Genomes and Reactome pathway enrichment analyses and Gene Set Enrichment Analysis were performed in this study. Protein-protein interaction (PPI) networks were constructed using Cytoscape software, and core networks were identified using MCODE plugins. A total of 62 genes, including 59 up-regulated and 3 down-regulated genes, were differentially expressed in DEP samples compared with nondiabetic patients. PPI network with 53 nodes and 138 edges was established. HLA-DRA is identified as the central gene of the PPI network and maybe a marker gene for DEP. Furthermore, up-regulated DEGs are mainly enriched in pathways related to the immune system and infection. The results of this study suggest that HLA-DRA and immune system pathways may play essential roles in DEP.

Diabetes risk loci-associated pathways are shared across metabolic tissues

Aims/hypothesis

Numerous genome-wide association studies have been performed to understand the influence of genetic variation on type 2 diabetes etiology. Many identified risk variants are located in non-coding and intergenic regions, which complicates understanding of how genes and their downstream pathways are influenced. An integrative data approach will help to understand the mechanism and consequences of identified risk variants.

Methods

In the current study we use our previously developed method CONQUER to overlap 403 type 2 diabetes risk variants with regulatory, expression and protein data to identify tissue-shared disease-relevant mechanisms.

Results

One SNP rs474513 was found to be an expression-, protein- and metabolite QTL. Rs474513 influenced LPA mRNA and protein levels in the pancreas and plasma, respectively. On the pathway level, in investigated tissues most SNPs linked to metabolism. However, in eleven of the twelve tissues investigated nine SNPs were linked to differential expression of the ribosome pathway. Furthermore, seven SNPs were linked to altered expression of genes linked to the immune system. Among them, rs601945 was found to influence multiple HLA genes, including HLA-DQA2, in all twelve tissues investigated.

Conclusion

Our results show that in addition to the classical metabolism pathways, other pathways may be important to type 2 diabetes that show a potential overlap with type 1 diabetes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08587-5.

Identification of Type 2 Diabetes Based on a Ten-Gene Biomarker Prediction Model Constructed Using a Support Vector Machine Algorithm

Background

Type 2 diabetes is a major health concern worldwide. The present study is aimed at discovering effective biomarkers for an efficient diagnosis of type 2 diabetes.

Methods

Differentially expressed genes (DEGs) between type 2 diabetes patients and normal controls were identified by analyses of integrated microarray data obtained from the Gene Expression Omnibus database using the Limma package. Functional analysis of genes was performed using the R software package clusterProfiler. Analyses of protein-protein interaction (PPI) performed using Cytoscape with the CytoHubba plugin were used to determine the most sensitive diagnostic gene biomarkers for type 2 diabetes in our study. The support vector machine (SVM) classification model was used to validate the gene biomarkers used for the diagnosis of type 2 diabetes.

Results

GSE164416 dataset analysis revealed 499 genes that were differentially expressed between type 2 diabetes patients and normal controls, and these DEGs were found to be enriched in the regulation of the immune effector pathway, type 1 diabetes mellitus, and fatty acid degradation. PPI analysis data showed that five MCODE clusters could be considered as clinically significant modules and that 10 genes (IL1B, ITGB2, ITGAX, COL1A1, CSF1, CXCL12, SPP1, FN1, C3, and MMP2) were identified as “real” hub genes in the PPI network using algorithms such as Degree, MNC, and Closeness. The sensitivity and specificity of the SVM model for identifying patients with type 2 diabetes were 100%, with an area under the curve of 1 in the training as well as the validation dataset.

Conclusion

Our results indicate that the SVM-based model developed by us can facilitate accurate diagnosis of type 2 diabetes.

Coronary Heart Disease in Type 2 Diabetes Mellitus: Genetic Factors and Their Mechanisms, Gene-Gene, and Gene-Environment Interactions in the Asian Populations

Asians are more susceptible to type 2 diabetes mellitus (T2D) and its coronary heart disease (CHD) complications than the Western populations, possibly due to genetic factors, higher degrees of obesity, insulin resistance, and endothelial dysfunction that could occur even in healthy individuals. The genetic factors and their mechanisms, along with gene-gene and gene-environment interactions associated with CHD in T2D Asians, are yet to be explored. Therefore, the objectives of this paper were to review the current evidence of genetic factors for CHD, summarize the proposed mechanisms of these genes and how they may associate with CHD risk, and review the gene-gene and gene-environment interactions in T2D Asians with CHD. The genetic factors can be grouped according to their involvement in the energy and lipoprotein metabolism, vascular and endothelial pathology, antioxidation, cell cycle regulation, DNA damage repair, hormonal regulation of glucose metabolism, as well as cytoskeletal function and intracellular transport. Meanwhile, interactions between single nucleotide polymorphisms (SNPs) from different genes, SNPs within a single gene, and genetic interaction with environmental factors including obesity, smoking habit, and hyperlipidemia could modify the gene's effect on the disease risk. Collectively, these factors illustrate the complexities of CHD in T2D, specifically among Asians.

Bioinformatics Analysis Identifies Potential Ferroptosis Key Gene in Type 2 Diabetic Islet Dysfunction

BACKGROUND

Islet β cells dysfunction (IBCD) is a cortical component in pathogenesis of type 2 diabetic mellitus (T2DM). However, the relationship of ferroptosis and IBCD remains unknown. This study was aimed to screen potential ferroptosis key genes to reveal latent physiological and pathological process of IBCD in T2DM.

METHODS

Firstly, T2DM key genes were screened by combining with differentially expressed genes (DEGs) analysis and WGCNA. Then, ferroptosis-related genes (FRGs) in IBCD of T2DM were identified by taking the intersection between T2DM key genes and FRGs. Finally, T2DM-FRGs were validated in another T2DM dataset as well as islet single-cell RNA sequencing dataset and the miRNA regulated T2DM-FRG was predicted by using four miRNA databases.

RESULTS

89 T2DM key genes were identified between DEGs and WGCNA. Then, 3 T2DM-FRGs were screened by taking the intersection of T2DM key genes and FRGs, namely ITGA6, MGST1 and ENO2. At last, MGST1 were validated as the T2DM-FRG in another T2DM islet issues dataset and islet single-cell RNA sequencing dataset.

CONCLUSION

MGST1 may be the potential ferroptosis key gene of IBCD in T2DM.

Scientific Advances in Diabetes: The Impact of the Innovative Medicines Initiative

Diabetes Mellitus is one of the World Health Organization's priority diseases under research by the first and second programmes of Innovative Medicines Initiative, with the acronyms IMI1 and IMI2, respectively. Up to October of 2019, 13 projects were funded by IMI for Diabetes & Metabolic disorders, namely SUMMIT, IMIDIA, DIRECT, StemBANCC, EMIF, EBiSC, INNODIA, RHAPSODY, BEAT-DKD, LITMUS, Hypo-RESOLVE, IM2PACT, and CARDIATEAM. In general, a total of €447 249 438 was spent by IMI in the area of Diabetes. In order to prompt a better integration of achievements between the different projects, we perform a literature review and used three data sources, namely the official project's websites, the contact with the project's coordinators and co-coordinator, and the CORDIS database. From the 662 citations identified, 185 were included. The data collected were integrated into the objectives proposed for the four IMI2 program research axes: (1) target and biomarker identification, (2) innovative clinical trials paradigms, (3) innovative medicines, and (4) patient-tailored adherence programmes. The IMI funded projects identified new biomarkers, medical and research tools, determinants of inter-individual variability, relevant pathways, clinical trial designs, clinical endpoints, therapeutic targets and concepts, pharmacologic agents, large-scale production strategies, and patient-centered predictive models for diabetes and its complications. Taking into account the scientific data produced, we provided a joint vision with strategies for integrating personalized medicine into healthcare practice. The major limitations of this article were the large gap of data in the libraries on the official project websites and even the Cordis database was not complete and up to date.

Multi-omics profiling of living human pancreatic islet donors reveals heterogeneous beta cell trajectories towards type 2 diabetes

Most research on human pancreatic islets is conducted on samples obtained from normoglycaemic or diseased brain-dead donors and thus cannot accurately describe the molecular changes of pancreatic islet beta cells as they progress towards a state of deficient insulin secretion in type 2 diabetes (T2D). Here, we conduct a comprehensive multi-omics analysis of pancreatic islets obtained from metabolically profiled pancreatectomized living human donors stratified along the glycemic continuum, from normoglycemia to T2D. We find that islet pools isolated from surgical samples by laser-capture microdissection display remarkably more heterogeneous transcriptomic and proteomic profiles in patients with diabetes than in non-diabetic controls. The differential regulation of islet gene expression is already observed in prediabetic individuals with impaired glucose tolerance. Our findings demonstrate a progressive, but disharmonic, remodelling of mature beta cells, challenging current hypotheses of linear trajectories toward precursor or transdifferentiation stages in T2D. Furthermore, through integration of islet transcriptomics with preoperative blood plasma lipidomics, we define the relative importance of gene coexpression modules and lipids that are positively or negatively associated with HbA1c levels, pointing to potential prognostic markers.

Chromatin 3D interaction analysis of the STARD10 locus unveils FCHSD2 as a regulator of insulin secretion

Using chromatin conformation capture, we show that an enhancer cluster in the STARD10 type 2 diabetes (T2D) locus forms a defined 3-dimensional (3D) chromatin domain. A 4.1-kb region within this locus, carrying 5 T2D-associated variants, physically interacts with CTCF-binding regions and with an enhancer possessing strong transcriptional activity. Analysis of human islet 3D chromatin interaction maps identifies the FCHSD2 gene as an additional target of the enhancer cluster. CRISPR-Cas9-mediated deletion of the variant region, or of the associated enhancer, from human pancreas-derived EndoC-βH1 cells impairs glucose-stimulated insulin secretion. Expression of both STARD10 and FCHSD2 is reduced in cells harboring CRISPR deletions, and lower expression of STARD10 and FCHSD2 is associated, the latter nominally, with the possession of risk variant alleles in human islets. Finally, CRISPR-Cas9-mediated loss of STARD10 or FCHSD2, but not ARAP1, impairs regulated insulin secretion. Thus, multiple genes at the STARD10 locus influence β cell function.

A Multi-omic Integrative Scheme Characterizes Tissues of Action at Loci Associated with Type 2 Diabetes

Resolving the molecular processes that mediate genetic risk remains a challenge because most disease-associated variants are non-coding and functional characterization of these signals requires knowledge of the specific tissues and cell-types in which they operate. To address this challenge, we developed a framework for integrating tissue-specific gene expression and epigenomic maps to obtain "tissue-of-action" (TOA) scores for each association signal by systematically partitioning posterior probabilities from Bayesian fine-mapping. We applied this scheme to credible set variants for 380 association signals from a recent GWAS meta-analysis of type 2 diabetes (T2D) in Europeans. The resulting tissue profiles underscored a predominant role for pancreatic islets and, to a lesser extent, adipose and liver, particularly among signals with greater fine-mapping resolution. We incorporated resulting TOA scores into a rule-based classifier and validated the tissue assignments through comparison with data from cis-eQTL enrichment, functional fine-mapping, RNA co-expression, and patterns of physiological association. In addition to implicating signals with a single TOA, we found evidence for signals with shared effects in multiple tissues as well as distinct tissue profiles between independent signals within heterogeneous loci. Lastly, we demonstrated that TOA scores can be directly coupled with eQTL colocalization to further resolve effector transcripts at T2D signals. This framework guides mechanistic inference by directing functional validation studies to the most relevant tissues and can gain power as fine-mapping resolution and cell-specific annotations become richer. This method is generalizable to all complex traits with relevant annotation data and is made available as an R package.

Persistent or Transient Human β Cell Dysfunction Induced by Metabolic Stress: Specific Signatures and Shared Gene Expression with Type 2 Diabetes

Pancreatic β cell failure is key to type 2 diabetes (T2D) onset and progression. Here, we assess whether human β cell dysfunction induced by metabolic stress is reversible, evaluate the molecular pathways underlying persistent or transient damage, and explore the relationships with T2D islet traits. Twenty-six islet preparations are exposed to several lipotoxic/glucotoxic conditions, some of which impair insulin release, depending on stressor type, concentration, and combination. The reversal of dysfunction occurs after washout for some, although not all, of the lipoglucotoxic insults. Islet transcriptomes assessed by RNA sequencing and expression quantitative trait loci (eQTL) analysis identify specific pathways underlying β cell failure and recovery. Comparison of a large number of human T2D islet transcriptomes with those of persistent or reversible β cell lipoglucotoxicity show shared gene expression signatures. The identification of mechanisms associated with human β cell dysfunction and recovery and their overlap with T2D islet traits provide insights into T2D pathogenesis, fostering the development of improved β cell-targeted therapeutic strategies.

Endocrine and Metabolic Insights from Pancreatic Surgery

Although it is well established that diabetes can also develop as a result of diseases or maneuvers on the exocrine pancreas, the complex relationship between glucose disorders and underlying pancreatic disease is still debated. There is evidence that several features linked to pancreatic diseases can modify endocrine and metabolic conditions before and after surgery. However, pancreatic surgery provides a rare opportunity to correlate in vivo endocrine and metabolic pathways with ex vivo pancreatic samples, to examine the endocrine and metabolic effects of acute islet removal, and finally to clarify the pathogenesis of diabetes. This approach could therefore represent a unique method to shed light on the molecular mechanisms, predicting factors, and metabolic consequences of insulin resistance, islet plasticity, β cell failure, and type 2 diabetes.

Molecular and functional profiling of human islets: from heterogeneity to human phenotypes

Efforts to phenotype pancreatic islets have contributed tremendously to our present understanding of endocrine function and diabetes. A continued evolution in approaches to study islet physiology is important given the need to establish reference points for mature islet functionality, understanding biological variation amongst individuals and cells, and the ongoing appreciation of the role for islets in diabetes susceptibility. Recent efforts in islet biology have focused on technological improvements in imaging, molecular profiling and data analysis, along with a push for enhanced transparency and reporting. The integration of these approaches within a classical islet physiology framework, and approaches to link these data with in vivo human phenotypes, will be critical as we move towards a better understanding of islet function in health and disease. Here we discuss what we feel are important issues and useful approaches to consider as we move forward as a field in islet and beta cell phenotyping. Graphical abstract.

Pancreatic Islet Transcriptional Enhancers and Diabetes

PURPOSE OF REVIEW

Common genetic variants that associate with type 2 diabetes risk are markedly enriched in pancreatic islet transcriptional enhancers. This review discusses current advances in the annotation of islet enhancer variants and their target genes.

RECENT FINDINGS

Recent methodological advances now allow genetic and functional mapping of diabetes causal variants at unprecedented resolution. Mapping of enhancer-promoter interactions in human islets has provided a unique appreciation of the complexity of islet gene regulatory processes and enabled direct association of noncoding diabetes risk variants to their target genes. The recently improved human islet enhancer annotations constitute a framework for the interpretation of diabetes genetic signals in the context of pancreatic islet gene regulation. In the future, integration of existing and yet to come regulatory maps with genetic fine-mapping efforts and in-depth functional characterization will foster the discovery of novel diabetes molecular risk mechanisms.

Metabolically phenotyped pancreatectomized patients as living donors for the study of islets in health and diabetes

BACKGROUND

The availability of human pancreatic islets with characteristics closely resembling those present in vivo is instrumental for ex vivo studies in diabetes research.

SCOPE OF REVIEW

In this review we propose metabolically phenotyped surgical patients as a novel source of pancreatic tissue for islet research. Laser Capture Microdissection from snap frozen surgical specimens is a relatively simple, reproducible and scalable method to isolate islets of highest purity for many types of "omics" analyses. Fresh pancreatic tissue slices enable the functional characterization of living islet cells in situ through dynamic experiments. Access to complete medical history and laboratory values for each donor offers the opportunity of direct correlations with different "omics" data and detailed metabolic profiling prior to pancreas surgery. Peripheral blood samples complete the picture of each patient and represent a platform for pursuit of biomarkers with uniquely comprehensive background information in regard to the donor's islet cells.

MAJOR CONCLUSIONS

Living donors provide the scientific community with a steady and abundant supply of excellent material to study islets closest to their in situ environment, thus advancing our understanding of their physiology in health and diseases.

T2D Risk Genes: Exome Sequencing Goes Straight to the Source

Genome-wide association studies have identified hundreds of genomic variants associated with human T2D risk, but translating such findings to clinically useful information has proved challenging. A new study in Nature (Flannick et al., 2019) breaks this gridlock, using direct exome sequencing to identify functional coding variants, providing critical complementary gene-level information.