A Common Type 2 Diabetes Risk Variant Potentiates Activity of an Evolutionarily Conserved Islet Stretch Enhancer and Increases C2CD4A and C2CD4B Expression.

Genome-wide association studies (GWASs) and functional genomics approaches implicate enhancer disruption in islet dysfunction and type 2 diabetes (T2D) risk. We applied genetic fine-mapping and functional (epi)genomic approaches to a T2D- and proinsulin-associated 15q22.2 locus to identify a most likely causal variant, determine its direction of effect, and elucidate plausible target genes. Fine-mapping and conditional analyses of proinsulin levels of 8,635 non-diabetic individuals from the METSIM study support a single association signal represented by a cluster of 16 strongly associated (p < 10-17) variants in high linkage disequilibrium (r2 > 0.8) with the GWAS index SNP rs7172432. These variants reside in an evolutionarily and functionally conserved islet and ß cell stretch or super enhancer; the most strongly associated variant (rs7163757, p = 3 × 10-19) overlaps a conserved islet open chromatin site. DNA sequence containing the rs7163757 risk allele displayed 2-fold higher enhancer activity than the non-risk allele in reporter assays (p < 0.01) and was differentially bound by ß cell nuclear extract proteins. Transcription factor NFAT specifically potentiated risk-allele enhancer activity and altered patterns of nuclear protein binding to the risk allele in vitro, suggesting that it could be a factor mediating risk-allele effects. Finally, the rs7163757 proinsulin-raising and T2D risk allele (C) was associated with increased expression of C2CD4B, and possibly C2CD4A, both of which were induced by inflammatory cytokines, in human islets. Together, these data suggest that rs7163757 contributes to genetic risk of islet dysfunction and T2D by increasing NFAT-mediated islet enhancer activity and modulating C2CD4B, and possibly C2CD4A, expression in (patho)physiologic states.

V-SVA: an R Shiny application for detecting and annotating hidden sources of variation in single-cell RNA-seq data.

SUMMARY: Single-cell RNA-sequencing (scRNA-seq) technology enables studying gene expression programs from individual cells. However, these data are subject to diverse sources of variation, including 'unwanted' variation that needs to be removed in downstream analyses (e.g. batch effects) and 'wanted' or biological sources of variation (e.g. variation associated with a cell type) that needs to be precisely described. Surrogate variable analysis (SVA)-based algorithms, are commonly used for batch correction and more recently for studying 'wanted' variation in scRNA-seq data. However, interpreting whether these variables are biologically meaningful or stemming from technical reasons remains a challenge. To facilitate the interpretation of surrogate variables detected by algorithms including IA-SVA, SVA or ZINB-WaVE, we developed an R Shiny application [Visual Surrogate Variable Analysis (V-SVA)] that provides a web-browser interface for the identification and annotation of hidden sources of variation in scRNA-seq data. This interactive framework includes tools for discovery of genes associated with detected sources of variation, gene annotation using publicly available databases and gene sets, and data visualization using dimension reduction methods. AVAILABILITY AND IMPLEMENTATION: The V-SVA Shiny application is publicly hosted at https://vsva.jax.org/ and the source code is freely available at https://github.com/nlawlor/V-SVA. CONTACT: leed13@miamioh.edu or duygu.ucar@jax.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BiFET: sequencing Bias-free transcription factor Footprint Enrichment Test.

Transcription factor (TF) footprinting uncovers putative protein-DNA binding via combined analyses of chromatin accessibility patterns and their underlying TF sequence motifs. TF footprints are frequently used to identify TFs that regulate activities of cell/condition-specific genomic regions (target loci) in comparison to control regions (background loci) using standard enrichment tests. However, there is a strong association between the chromatin accessibility level and the GC content of a locus and the number and types of TF footprints that can be detected at this site. Traditional enrichment tests (e.g. hypergeometric) do not account for this bias and inflate false positive associations. Therefore, we developed a novel post-processing method, Bias-free Footprint Enrichment Test (BiFET), that corrects for the biases arising from the differences in chromatin accessibility levels and GC contents between target and background loci in footprint enrichment analyses. We applied BiFET on TF footprint calls obtained from EndoC-ßH1 ATAC-seq samples using three different algorithms (CENTIPEDE, HINT-BC and PIQ) and showed BiFET's ability to increase power and reduce false positive rate when compared to hypergeometric test. Furthermore, we used BiFET to study TF footprints from human PBMC and pancreatic islet ATAC-seq samples to show its utility to identify putative TFs associated with cell-type-specific loci.

Genomics of Islet (Dys)function and Type 2 Diabetes.

Pancreatic islet dysfunction and beta cell failure are hallmarks of type 2 diabetes mellitus (T2DM) pathogenesis. In this review, we discuss how genome-wide association studies (GWASs) and recent developments in islet (epi)genome and transcriptome profiling (particularly single cell analyses) are providing novel insights into the genetic, environmental, and cellular contributions to islet (dys)function and T2DM pathogenesis. Moving forward, study designs that interrogate and model genetic variation [e.g., allelic profiling and (epi)genome editing] will be critical to dissect the molecular genetics of T2DM pathogenesis, to build next-generation cellular and animal models, and to develop precision medicine approaches to detect, treat, and prevent islet (dys)function and T2DM.

Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes.

Blood glucose levels are tightly controlled by the coordinated action of at least four cell types constituting pancreatic islets. Changes in the proportion and/or function of these cells are associated with genetic and molecular pathophysiology of monogenic, type 1, and type 2 (T2D) diabetes. Cellular heterogeneity impedes precise understanding of the molecular components of each islet cell type that govern islet (dys)function, particularly the less abundant delta and gamma/pancreatic polypeptide (PP) cells. Here, we report single-cell transcriptomes for 638 cells from nondiabetic (ND) and T2D human islet samples. Analyses of ND single-cell transcriptomes identified distinct alpha, beta, delta, and PP/gamma cell-type signatures. Genes linked to rare and common forms of islet dysfunction and diabetes were expressed in the delta and PP/gamma cell types. Moreover, this study revealed that delta cells specifically express receptors that receive and coordinate systemic cues from the leptin, ghrelin, and dopamine signaling pathways implicating them as integrators of central and peripheral metabolic signals into the pancreatic islet. Finally, single-cell transcriptome profiling revealed genes differentially regulated between T2D and ND alpha, beta, and delta cells that were undetectable in paired whole islet analyses. This study thus identifies fundamental cell-type-specific features of pancreatic islet (dys)function and provides a critical resource for comprehensive understanding of islet biology and diabetes pathogenesis.

The human milk oligosaccharide 2'-fucosyllactose modulates CD14 expression in human enterocytes, thereby attenuating LPS-induced inflammation.

BACKGROUND: A major cause of enteric infection, Gram-negative pathogenic bacteria activate mucosal inflammation through lipopolysaccharide (LPS) binding to intestinal toll-like receptor 4 (TLR4). Breast feeding lowers risk of disease, and human milk modulates inflammation. OBJECTIVE: This study tested whether human milk oligosaccharides (HMOSs) influence pathogenic Escherichia coli-induced interleukin (IL)-8 release by intestinal epithelial cells (IECs), identified specific proinflammatory signalling molecules modulated by HMOSs, specified the active HMOS and determined its mechanism of action. METHODS: Models of inflammation were IECs invaded by type 1 pili enterotoxigenic E. coli (ETEC) in vitro: T84 modelled mature, and H4 modelled immature IECs. LPS-induced signalling molecules co-varying with IL-8 release in the presence or absence of HMOSs were identified. Knockdown and overexpression verified signalling mediators. The oligosaccharide responsible for altered signalling was identified. RESULTS: HMOSs attenuated LPS-dependent induction of IL-8 caused by ETEC, uropathogenic E. coli, and adherent-invasive E. coli (AIEC) infection, and suppressed CD14 transcription and translation. CD14 knockdown recapitulated HMOS-induced attenuation. Overexpression of CD14 increased the inflammatory response to ETEC and sensitivity to inhibition by HMOSs. 2'-fucosyllactose (2'-FL), at milk concentrations, displayed equivalent ability as total HMOSs to suppress CD14 expression, and protected AIEC-infected mice. CONCLUSIONS: HMOSs and 2'-FL directly inhibit LPS-mediated inflammation during ETEC invasion of T84 and H4 IECs through attenuation of CD14 induction. CD14 expression mediates LPS-TLR4 stimulation of portions of the 'macrophage migration inhibitory factors' inflammatory pathway via suppressors of cytokine signalling 2/signal transducer and activator of transcription 3/NF-κB. HMOS direct inhibition of inflammation supports its functioning as an innate immune system whereby the mother protects her vulnerable neonate through her milk. 2'-FL, a principal HMOS, quenches inflammatory signalling.

Targeting ERRα promotes cytotoxic effects against acute myeloid leukemia through suppressing mitochondrial oxidative phosphorylation.

Acute myeloid leukemia (AML) is an aggressive blood cancer with poor clinical outcomes. Emerging data suggest that mitochondrial oxidative phosphorylation (mtOXPHOS) plays a significant role in AML tumorigenesis, progression, and resistance to chemotherapies. However, how the mtOXPHOS is regulated in AML cells is not well understood. In this study, we investigated the oncogenic functions of ERRα in AML by combining in silico, in vitro, and in vivo analyses and showed ERRα is a key regulator of mtOXPHOS in AML cells. The increased ERRα level was associated with worse clinical outcomes of AML patients. Single cell RNA-Seq analysis of human primary AML cells indicated that ERRα-expressing cancer cells had significantly higher mtOXPHOS enrichment scores. Blockade of ERRα by pharmacologic inhibitor (XCT-790) or gene silencing suppressed mtOXPHOS and increased anti-leukemic effects in vitro and in xenograft mouse models.

Single Cell Analysis of Blood Mononuclear Cells Stimulated Through Either LPS or Anti-CD3 and Anti-CD28.

Immune cell activation assays have been widely used for immune monitoring and for understanding disease mechanisms. However, these assays are typically limited in scope. A holistic study of circulating immune cell responses to different activators is lacking. Here we developed a cost-effective high-throughput multiplexed single-cell RNA-seq combined with epitope tagging (CITE-seq) to determine how classic activators of T cells (anti-CD3 coupled with anti-CD28) or monocytes (LPS) alter the cell composition and transcriptional profiles of peripheral blood mononuclear cells (PBMCs) from healthy human donors. Anti-CD3/CD28 treatment activated all classes of lymphocytes either directly (T cells) or indirectly (B and NK cells) but reduced monocyte numbers. Activated T and NK cells expressed senescence and effector molecules, whereas activated B cells transcriptionally resembled autoimmune disease- or age-associated B cells (e.g., CD11c, T-bet). In contrast, LPS specifically targeted monocytes and induced two main states: early activation characterized by the expression of chemoattractants and a later pro-inflammatory state characterized by expression of effector molecules. These data provide a foundation for future immune activation studies with single cell technologies (https://czi-pbmc-cite-seq.jax.org/).

Tet2 Controls the Responses of ß cells to Inflammation in Autoimmune Diabetes.

ß cells may participate and contribute to their own demise during Type 1 diabetes (T1D). Here we report a role of their expression of Tet2 in regulating immune killing. Tet2 is induced in murine and human ß cells with inflammation but its expression is reduced in surviving ß cells. Tet2-KO mice that receive WT bone marrow transplants develop insulitis but not diabetes and islet infiltrates do not eliminate ß cells even though immune cells from the mice can transfer diabetes to NOD/scid recipients. Tet2-KO recipients are protected from transfer of disease by diabetogenic immune cells.Tet2-KO ß cells show reduced expression of IFNγ-induced inflammatory genes that are needed to activate diabetogenic T cells. Here we show that Tet2 regulates pathologic interactions between ß cells and immune cells and controls damaging inflammatory pathways. Our data suggests that eliminating TET2 in ß cells may reduce activating pathologic immune cells and killing of ß cells.

AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data.

Detecting multiplets in single nucleus (sn)ATAC-seq data is challenging due to data sparsity and limited dynamic range. AMULET (ATAC-seq MULtiplet Estimation Tool) enumerates regions with greater than two uniquely aligned reads across the genome to effectively detect multiplets. We evaluate the method by generating snATAC-seq data in the human blood and pancreatic islet samples. AMULET has high precision, estimated via donor-based multiplexing, and high recall, estimated via simulated multiplets, compared to alternatives and identifies multiplets most effectively when a certain read depth of 25K median valid reads per nucleus is achieved.

(Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes.

BACKGROUND: Pancreatic Islets of Langerhans are heterogeneous tissues consisting of multiple endocrine cell types that carry out distinct yet coordinated roles to regulate blood glucose homeostasis. Islet dysfunction and specifically failure of the beta cells to secrete adequate insulin are known precursors to type 2 diabetes (T2D) onset. However, the exact genetic, (epi)genomic, and environmental mechanisms that contribute to islet failure, and ultimately to T2D pathogenesis, require further elucidation. SCOPE OF REVIEW: This review summarizes efforts and advances in dissection of the complex genetic underpinnings of islet function and resilience in T2D pathogenesis. In this review, we will highlight results of the latest T2D genome-wide association study (GWAS) and discuss how these data are being combined with clinical measures in patients to uncover putative T2D subtypes and with functional (epi)genomic studies in islets to understand the genetic programming of islet cell identity, function, and adaptation. Finally, we discuss new and important opportunities to address major knowledge gaps in our understanding of islet (dys)function in T2D risk and progression. MAJOR CONCLUSIONS: Genetic variation exerts clear effects on the islet epigenome, regulatory element usage, and gene expression. Future (epi)genomic comparative analyses between T2D and normal islets should incorporate genetics to distinguish patient-specific from disease-specific differences. Incorporating genotype information into future analyses and studies will also enable more precise insights into the molecular genetics of islet deficiency and failure in T2D risk, and should ultimately contribute to a stratified view of T2D and more precise treatment strategies. Islet cellular heterogeneity continues to remain a challenge for understanding the associations between islet failure and T2D development. Further efforts to obtain purified islet cell type populations and determine the specific genetic and environmental effects on each will help address this. Beyond observation of islets at steady state conditions, more research of islet stress and stimulation responses are needed to understand the transition of these tissues from a healthy to diseased state. Together, focusing on these objectives will provide more opportunities to prevent, treat, and manage T2D.

Multiomic Profiling Identifies cis-Regulatory Networks Underlying Human Pancreatic ß Cell Identity and Function.

EndoC-ßH1 is emerging as a critical human ß cell model to study the genetic and environmental etiologies of ß cell (dys)function and diabetes. Comprehensive knowledge of its molecular landscape is lacking, yet required, for effective use of this model. Here, we report chromosomal (spectral karyotyping), genetic (genotyping), epigenomic (ChIP-seq and ATAC-seq), chromatin interaction (Hi-C and Pol2 ChIA-PET), and transcriptomic (RNA-seq and miRNA-seq) maps of EndoC-ßH1. Analyses of these maps define known (e.g., PDX1 and ISL1) and putative (e.g., PCSK1 and mir-375) ß cell-specific transcriptional cis-regulatory networks and identify allelic effects on cis-regulatory element use. Importantly, comparison with maps generated in primary human islets and/or ß cells indicates preservation of chromatin looping but also highlights chromosomal aberrations and fetal genomic signatures in EndoC-ßH1. Together, these maps, and a web application we created for their exploration, provide important tools for the design of experiments to probe and manipulate the genetic programs governing ß cell identity and (dys)function in diabetes.

Detection of correlated hidden factors from single cell transcriptomes using Iteratively Adjusted-SVA (IA-SVA).

Single cell RNA-sequencing (scRNA-seq) precisely characterizes gene expression levels and dissects variation in expression associated with the state (technical or biological) and the type of the cell, which is averaged out in bulk measurements. Multiple and correlated sources contribute to gene expression variation in single cells, which makes their estimation difficult with the existing methods developed for batch correction (e.g., surrogate variable analysis (SVA)) that estimate orthogonal transformations of these sources. We developed iteratively adjusted surrogate variable analysis (IA-SVA) that can estimate hidden factors even when they are correlated with other sources of variation by identifying a set of genes associated with each hidden factor in an iterative manner. Analysis of scRNA-seq data from human cells showed that IA-SVA could accurately capture hidden variation arising from technical (e.g., stacked doublet cells) or biological sources (e.g., cell type or cell-cycle stage). Furthermore, IA-SVA delivers a set of genes associated with the detected hidden source to be used in downstream data analyses. As a proof of concept, IA-SVA recapitulated known marker genes for islet cell subsets (e.g., alpha, beta), which improved the grouping of subsets into distinct clusters. Taken together, IA-SVA is an effective and novel method to dissect multiple and correlated sources of variation in scRNA-seq data.

Type 2 Diabetes-Associated Genetic Variants Regulate Chromatin Accessibility in Human Islets.

Type 2 diabetes (T2D) is a complex disorder in which both genetic and environmental risk factors contribute to islet dysfunction and failure. Genome-wide association studies (GWAS) have linked single nucleotide polymorphisms (SNPs), most of which are noncoding, in >200 loci to islet dysfunction and T2D. Identification of the putative causal variants and their target genes and whether they lead to gain or loss of function remains challenging. Here, we profiled chromatin accessibility in pancreatic islet samples from 19 genotyped individuals and identified 2,949 SNPs associated with in vivo cis-regulatory element use (i.e., chromatin accessibility quantitative trait loci [caQTL]). Among the caQTLs tested (n = 13) using luciferase reporter assays in MIN6 ß-cells, more than half exhibited effects on enhancer activity that were consistent with in vivo chromatin accessibility changes. Importantly, islet caQTL analysis nominated putative causal SNPs in 13 T2D-associated GWAS loci, linking 7 and 6 T2D risk alleles, respectively, to gain or loss of in vivo chromatin accessibility. By investigating the effect of genetic variants on chromatin accessibility in islets, this study is an important step forward in translating T2D-associated GWAS SNP into functional molecular consequences.

Alpha TC1 and Beta-TC-6 genomic profiling uncovers both shared and distinct transcriptional regulatory features with their primary islet counterparts.

Alpha TC1 (αTC1) and Beta-TC-6 (ßTC6) mouse islet cell lines are cellular models of islet (dys)function and type 2 diabetes (T2D). However, genomic characteristics of these cells, and their similarities to primary islet alpha and beta cells, are undefined. Here, we report the epigenomic (ATAC-seq) and transcriptomic (RNA-seq) landscapes of αTC1 and ßTC6 cells. Each cell type exhibits hallmarks of its primary islet cell counterpart including cell-specific expression of beta (e.g., Pdx1) and alpha (e.g., Arx) cell transcription factors (TFs), and enrichment of binding motifs for these TFs in αTC1/ßTC6 cis-regulatory elements. αTC1/ßTC6 transcriptomes overlap significantly with the transcriptomes of primary mouse/human alpha and beta cells. Our data further indicate that ATAC-seq detects cell-specific regulatory elements for cell types comprising ≥ 20% of a mixed cell population. We identified αTC1/ßTC6 cis-regulatory elements orthologous to those containing type 2 diabetes (T2D)-associated SNPs in human islets for 33 loci, suggesting these cells' utility to dissect T2D molecular genetics in these regions. Together, these maps provide important insights into the conserved regulatory architecture between αTC1/ßTC6 and primary islet cells that can be leveraged in functional (epi)genomic approaches to dissect the genetic and molecular factors controlling islet cell identity and function.

Human Milk Components Modulate Toll-Like Receptor-Mediated Inflammation.

Toll-like receptor (TLR) signaling is central to innate immunity. Aberrant expression of TLRs is found in neonatal inflammatory diseases. Several bioactive components of human milk modulate TLR expression and signaling pathways, including soluble toll-like receptors (sTLRs), soluble cluster of differentiation (sCD) 14, glycoproteins, small peptides, and oligosaccharides. Some milk components, such as sialyl (α2,3) lactose and lacto-N-fucopentaose III, are reported to increase TLR signaling; under some circumstances this might contribute toward immunologic balance. Human milk on the whole is strongly anti-inflammatory, and contains abundant components that depress TLR signaling pathways: sTLR2 and sCD14 inhibit TLR2 signaling; sCD14, lactadherin, lactoferrin, and 2'-fucosyllactose attenuate TLR4 signaling; 3'-galactosyllactose inhibits TLR3 signaling, and ß-defensin 2 inhibits TLR7 signaling. Feeding human milk to neonates decreases their risk of sepsis and necrotizing enterocolitis. Thus, the TLR regulatory components found in human milk hold promise as benign oral prophylactic and therapeutic treatments for the many gastrointestinal inflammatory disorders mediated by abnormal TLR signaling.

multiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome Profiles.

Clustering is carried out to identify patterns in transcriptomics profiles to determine clinically relevant subgroups of patients. Feature (gene) selection is a critical and an integral part of the process. Currently, there are many feature selection and clustering methods to identify the relevant genes and perform clustering of samples. However, choosing an appropriate methodology is difficult. In addition, extensive feature selection methods have not been supported by the available packages. Hence, we developed an integrative R-package called multiClust that allows researchers to experiment with the choice of combination of methods for gene selection and clustering with ease. Using multiClust, we identified the best performing clustering methodology in the context of clinical outcome. Our observations demonstrate that simple methods such as variance-based ranking perform well on the majority of data sets, provided that the appropriate number of genes is selected. However, different gene ranking and selection methods remain relevant as no methodology works for all studies.