Biology subjects

Todd, J. A.

Publications and source records attributed to Todd, J. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Expression of non-neuronal Tau in humans and mice

BackgroundTau, encoded by the microtubule-associated protein tau gene (MAPT), is traditionally associated with neuronal function in maintaining cytoskeletal stability and regulating intracellular transport. Its dysregulation is implicated in a range of neurodegenerative disorders. Beyond its well-established high levels of expression in neurons of the central and peripheral nervous systems, increasing evidence indicates that Tau is also expressed in non-neuronal cells/tissues such as muscle, kidney, testis, and pancreas, albeit at much lower levels than in neurons. However, the assessment of low levels of non-neuronal Tau requires that the specificities of the anti-Tau antibodies used are fully validated. MethodsThree previously validated anti-Tau antibodies (Tau-12, Tau-1, RD3) were used to test Tau expression levels in 33 mouse and 66 human tissues using Food and Drug Administration-quality tissue microarrays in immunocytochemistry and western blot analyses. ResultsThe presence of Tau was confirmed in salivary gland, kidney, skeletal muscle, heart, pancreas and oesophagus in both human and mouse tissues. ConclusionsDysregulation of Tau protein expression and alterations in its post-translational modification is a causative factor in neurodegenerative disease. We have demonstrated a wider Tau expression in other tissues and cells, outside the brain, which may be dysregulated in other diseases.

cell biology↗

bioRxiv2023

Validation of Tau Antibodies for Use in Western Blotting and Immunohistochemistry

BackgroundThe microtubule-associated protein Tau has attracted diverse and increasing research interest, with Tau being mentioned in the title/abstract of nearly 34,000 PubMed-indexed publications to date. To accelerate studies into Tau biology, the characterisation of its multiple proteoforms, including disease-relevant post-translational modifications (PTMs), and its role in neurodegeneration, a multitude of Tau-targeting antibodies have been developed, with hundreds of distinct antibody clones currently available for purchase. Nonetheless, concerns over antibody specificity and limited understanding of the performance of many of these reagents has hindered research. MethodsWe have employed a range of techniques in combination with samples of murine and human origin to characterise the performance and specificity of 53 commercially-available Tau antibodies by Western blot, and a subset of these, 35 antibodies, in immunohistochemistry. ResultsContinued expression of residual protein was found in presumptive Tau "knockout" human cells and further confirmed through mass-spectrometry proteomics, providing evidence of Tau isoforms generated by exon skipping. Importantly, many total and isoform-specific antibodies failed to detect this residual Tau, as well as Tau expressed at low, endogenous levels, thus highlighting the importance of antibody choice. Our data further reveal that the binding of several "total" Tau antibodies, which are assumed to detect Tau independently of post-translational modifications, was partially inhibited by phosphorylation. Many antibodies also displayed non-specific cross-reactivity, with some total and phospho-Tau antibodies cross-reacting with MAP2 isoforms, while the "oligomer-specific" T22 antibody detected monomeric Tau on Western blot. Regardless of their specificity, with one exception, the phospho-Tau antibodies tested were found to not detect the unphosphorylated protein. ConclusionsWe identify Tau antibodies across all categories (total, PTM-dependent and isoform-specific) that can be employed in Western blot and/or immunohistochemistry applications to reliably detect even low levels of Tau expression with high specificity. This is of particular importance for studying Tau in non-neuronal cells and peripheral tissues, as well as for the confident validation of knockout cells and/or animal models. This work represents an extensive resource that serves as a point of reference for future studies. Our findings may also aid in the re-interpretation of existing data and improve reproducibility of Tau research.

neuroscience↗

bioRxiv2022

Tau depletion in human neurons mitigates A beta-driven toxicity

BackgroundAlzheimers disease (AD) is an age-related neurodegenerative condition and the most common type of dementia, characterised by pathological accumulation of extracellular plaques and intracellular neurofibrillary tangles that mainly consist of amyloid-{beta} (A{beta}) and hyperphosphorylated tau aggregates, respectively. Previous studies in mouse models with a targeted knock-out of the microtubule-associated protein tau (Mapt) gene demonstrated that A{beta}-driven toxicity is tau-dependent. However, human cellular models with chronic tau lowering remain unstudied. MethodsIn this study, we generated stable tau-depleted human induced pluripotent stem cell (iPSC) isogenic panels from two healthy individuals using CRISPR-Cas9 technology. We then differentiated these iPSCs into cortical neurons in vitro in co-culture with primary rat cortical astrocytes before conducting electrophysiological and imaging experiments for a wide range of disease-relevant phenotypes. Both AD brain derived and recombinant A{beta} were used in this study to elicit toxic responses from the iPSC- derived cortical neurons. ResultsWe showed that tau depletion in human iPSC-derived cortical neurons caused considerable reductions in neuronal activity without affecting synaptic density. We also observed neurite outgrowth impairments in two of the tau-depleted lines used. We found axonal transport of mitochondria, mitochondrial function, and cortical neuron differentiation propensity remained unaffected regardless of tau expression levels. Finally, tau depletion protected neurons from adverse effects mitigating the impact of exogenous A{beta}-induced hyperactivity, deficits in retrograde axonal transport of mitochondria, and neurodegeneration. ConclusionsOur study established stable human iPSC isogenic panels with chronic tau depletion from two healthy individuals. Cortical neurons derived from these iPSC lines showed that tau is essential in A{beta}-driven hyperactivity, axonal transport deficits, and neurodegeneration, consistent with studies conducted in Mapt-/- mouse models. These findings highlight the protective effects of chronic tau lowering strategies in AD pathogenesis and reinforce the potential in clinical settings. The tau-depleted human iPSC models can now be applied at scale to investigate the involvement of tau in disease-relevant pathways and cell types.

neuroscience↗

bioRxiv2021

Thymocyte regulatory variant alters transcription factor binding and protects from type 1 diabetes in infants

We recently mapped a genetic susceptibility locus on chromosome 6q22.33 for type 1 diabetes diagnosed below age of 7 years near the gene Protein tyrosine phosphatase receptor type K (PTPRK) and the thymocyte selection associated gene (THEMIS). As the thymus plays a central role in shaping the T cell repertoire, we aimed to identify the most likely causal genetic factors behind the association using thymocyte genomic data. In four thymocyte populations we identified 253 DNA sequence motifs underlying histone modifications. The G insertion allele of rs138300818, associated with protection from diabetes, created thymocyte motifs for multiple histone modifications and thymocyte types. The insertion also disrupted a predicted RFX5/7 transcription factor binding site. RFX7 is abundantly expressed in thymus. Chromatin state and RNA sequencing data suggested strong transcription overlapping rs138300818 in fetal thymus, while eQTL and chromatin conformation data indicated that the rs138300818 insertion is associated with lower THEMIS expression. Taken together, our results support a role for thymic THEMIS gene expression and the rs138300818 variant in promoting the development and diagnosis of type 1 diabetes at an earlier age.

genetics↗

bioRxiv2021

HLA class II mediates type 1 diabetes risk by anti-insulin repertoire selection

Type 1 diabetes (T1D) is a common autoimmune disorder characterized by the destruction of insulin-secreting pancreatic {beta} cells [1], in which polymorphism of the human leukocyte antigen (HLA) class II region is the major genetic risk factor [2, 3, 4]. However, how variation in class II molecules alters T1D risk remains a longstanding question. Here we show how T1D risk due to HLA class II haplotype combinations [5] correlates with the frequency of negatively charged sequences in the CDR3{beta} region of CD4+ T cell receptor (TCR) repertoires purified from peripheral blood. These sequences are known to be common in receptors that bind insulin B:9-23 [6], the primary autoantigen in T1D. We also show the same effect in circulating activated CD4+ T cells from newly-diagnosed T1D cases, and in islet-infiltrating T cells from patients with active T1D. Furthermore, we demonstrate that the proportion of insulin-reactive CD4+ T cells present in islets is predicted by the frequency of these negatively charged CDR3{beta} amino acid sequences. Our results suggest diagnostic uses of T cell repertoire profiling in early detection of insulin autoimmunity, and inform ongoing efforts to improve tolerance induction to insulin and prevention of T1D [7].

genomics↗

bioRxiv2021

Enhanced genetic analysis of type 1 diabetes by selecting variants on both effect size and significance, and by integration with autoimmune thyroid disease

For polygenic traits, associations with genetic variants can be detected over many chromosome regions, owing to the availability oflarge sample sizes. Most variants, however, have small effects on disease risk and, therefore, unravelling the causal variants, target genes, and biology of these variants is challenging. Here, we define the Bigger or False Discovery Rate (BFDR) as the probability that either a variant is a false-positive or a randomly drawn, true-positive association exceeds it in effect size. Using the BFDR, we identified 302 previously unreported signals with larger effect associations with type 1 diabetes and autoimmune thyroid disease. Out of 239 genome-wide significant signals in both diseases, only 66 (28%) show evidence for having a large effect using the BFDR, further demonstrating how using a combination of effect size and significance, rather than significance alone, is important in identifying SNPs and candidate genes for further investigation.

genetics↗

bioRxiv2021

Using de novo assembly to identify structural variation of complex immune system gene regions

Driven by the necessity to survive environmental pathogens, the human immune system has evolved exceptional diversity and plasticity, to which several factors contribute including inheritable structural polymorphism of the underlying genes. Characterizing this variation is challenging due to the complexity of these loci, which contain extensive regions of paralogy, segmental duplication and high copy-number repeats, but recent progress in long-read sequencing and optical mapping techniques suggests this problem may now be tractable. Here we assess this by using long-read sequencing platforms from PacBio and Oxford Nanopore, supplemented with short-read sequencing and Bionano optical mapping, to sequence DNA extracted from CD14+ monocytes and peripheral blood mononuclear cells from a single European individual identified as HV31. We use this data to build a de novo assembly of eight genomic regions encoding four key components of the immune system, namely the human leukocyte antigen, immunoglobulins, T cell receptors, and killer-cell immunoglobulin-like receptors. Validation of our assembly using k-mer based and alignment approaches suggests that it has high accuracy, with estimated base-level error rates below 1 in 10 kb, although we identify a small number of remaining structural errors. We use the assembly to identify heterozygous and homozygous structural variation in comparison to GRCh38. Despite analyzing only a single individual, we find multiple large structural variants affecting core genes at all three immunoglobulin regions and at two of the three T cell receptor regions. Several of these variants are not accurately callable using current algorithms, implying that further methodological improvements are needed. Our results demonstrate that assessing haplotype variation in these regions is possible given sufficiently accurate long-read and associated data; application of these methods to larger samples would provide a broader catalogue of germline structural variation at these loci, an important step toward making these regions accessible to large-scale genetic association studies.

bioinformatics↗

bioRxiv2020

Open Targets Genetics: An open approach to systematically prioritize causal variants and genes at all published GWAS trait-associated loci

Genome-wide association studies (GWAS) have identified many variants robustly associated with complex traits but identifying the gene(s) mediating such associations is a major challenge. Here we present an open resource that provides systematic fine-mapping and protein-coding gene prioritization across 133,441 published human GWAS loci. We integrate diverse data sources, including genetics (from GWAS Catalog and UK Biobank) as well as transcriptomic, proteomic and epigenomic data across many tissues and cell types. We also provide systematic disease-disease and disease-molecular trait colocalization results across 92 cell types and tissues and identify 729 loci fine-mapped to a single coding causal variant and colocalized with a single gene. We trained a machine learning model using the fine mapped genetics and functional genomics data using 445 gold standard curated GWAS loci to distinguish causal genes from background genes at the same loci, outperforming a naive distance based model. Genes prioritized by our model are enriched for known approved drug targets (OR = 8.1, 95% CI: [5.7, 11.5]). These results will be regularly updated and are publicly available through a web portal, Open Targets Genetics (OTG, http://genetics.opentargets.org), enabling users to easily prioritize genes at disease-associated loci and assess their potential as drug targets.

genetics↗

bioRxiv2020

Fine-mapping, trans-ancestral and genomic analyses identify causal variants, cells, genes and drug targets for type 1 diabetes

We report the largest and most ancestrally diverse genetic study of type 1 diabetes (T1D) to date (61,427 participants), yielding 152 regions associated to false discovery rate < 0.01, including 36 regions associated to genome-wide significance for the first time. Credible sets of disease-associated variants are specifically enriched in immune cell accessible chromatin, particularly in CD4+ effector T cells. Colocalization with chromatin accessibility quantitative trait loci (QTL) in CD4+ T cells identified five regions where differences in T1D risk and chromatin accessibility are potentially driven by the same causal variant. Allele-specific chromatin accessibility further refined the set of putative causal variants with functional relevance in CD4+ T cells and integration of whole blood expression QTLs identified candidate T1D genes, providing high-yield targets for mechanistic follow-up. We highlight rs72938038 in BACH2 as a candidate causal T1D variant, where the T1D risk allele leads to decreased enhancer accessibility and BACH2 expression in T cells. Finally, we prioritise potential drug targets by integrating genetic evidence, functional genomic maps, and immune protein-protein interactions, identifying 12 genes implicated in T1D that have been targeted in clinical trials for autoimmune diseases. These findings provide an expanded genomic landscape for T1D, including proposed genetic regulatory mechanisms of T1D-associated variants and genetic support for therapeutic targets for immune intervention.

genetics↗

bioRxiv2020

Divergent genetic effects for type 1 and type 2 diabetes at overlapping association signals

Aims/hypothesisGiven the potential shared aetiology between type 1 and type 2 diabetes, we aimed to identify any genetic regions associated with both diseases. For associations where there is a shared signal and the allele that increases risk to one disease also increases risk to the other, inference about shared aetiology could be made, with the potential to develop therapeutic strategies to treat or prevent both diseases simultaneously. Alternatively, if a genetic signal colocalises with divergent effect directions, it could provide valuable biological insight into how the association affects the two diseases differently. MethodsUsing publicly available type 2 diabetes summary statistics from a genomewide association study (GWAS) meta-analysis of European ancestry individuals (74,124 cases and 824,006 controls) and type 1 diabetes GWAS summary statistics from a meta-analysis of studies on individuals from the UK and Sardinia (7,467 cases and 10,218 controls), we identified all regions of 0.5 Mb that contained variants associated with both diseases (false discovery rate<0.01). In each region, we performed forward stepwise logistic regression to identify independent association signals, then examined colocalisation of each type 1 diabetes signal with each type 2 diabetes signal using coloc. Any association with a colocalisation posterior probability of [≥]0.9 was considered a genuine shared association with both diseases. ResultsOf the 81 association signals from 42 genetic regions that showed association with both type 1 and type 2 diabetes, four association signals colocalised between both diseases (posterior probability [≥]0.9): (i) chromosome 16q23.1, near Chymotripsinogen B1 (CTRB1) / Breast Cancer Anti-Estrogen Resistance Protein 1 (BCAR1), which has been previously identified; (ii) chromosome 11p15.5, near the Insulin (INS) gene; (iii) chromosome 4p16.3, near Transmembrane protein 129 (TMEM129), and (iv) chromosome 1p31.3, near Phosphoglucomutase 1 (PGM1). In each of these regions, the effect of genetic variants on type 1 diabetes was in the opposite direction to the effect on type 2 diabetes. Use of additional datasets also supported the previously identified colocalisation on chromosome 9p24.2, near the GLIS Family Zinc Finger Protein 3 (GLIS3) gene, in this case with a concordant direction of effect. Conclusions/interpretationThat four of five association signals that colocalise between type 1 diabetes and type 2 diabetes are in opposite directions suggests a complex genetic relationship between the two diseases. Research in ContextWhat is already known about this subject? O_LIOther than insulin, there are currently no treatments for both type 1 and type 2 diabetes. C_LIO_LIFindings that genetic variants near the GLIS3 gene increase risk of both type 1 and type 2 diabetes have indicated shared genetic mechanisms at the level of the pancreatic {beta} cell. C_LI What is the key question? O_LIBy examining chromosome regions associated with both diseases, are there any more variants that affect risk of both diseases and could support common mechanisms and repositioning of therapeutics between the diseases? C_LI What are the new findings? O_LIAt current sample sizes, there is evidence that five genetic variants in different chromosome regions impact risk of developing both diseases. C_LIO_LIHowever, four of these variants have the opposite direction of effect in type 1 diabetes compared to type 2 diabetes, with only one, near GLIS3, having a concordant direction of effect. C_LI How might this impact on clinical practise in the foreseeable future? O_LIGenetic findings have furthered research in type 1 and type 2 diabetes independently, and suggest therapeutic strategies. However, our current investigation into their shared genetics suggests that repositioning of current type 2 diabetes treatments into type 1 diabetes may not be straightforward. C_LI

genetics↗

bioRxiv2020

SARS-CoV-2 genomics beyond the consensus sequence: evidence for circulating mixed viral populations

Extensive global sampling and whole genome sequencing of the pandemic virus SARS-CoV-2 have enabled researchers to characterise its spread, and to identify mutations that may increase transmission or enable the virus to escape therapies or vaccines. Two important components of viral spread are how frequently variants arise within individuals, and how likely they are to be transmitted. Here, we characterise the within-host diversity of SARS-CoV-2, and the extent to which genetic diversity is transmitted, by quantifying variant frequencies in 1390 clinical samples from the UK, many from individuals in known epidemiological clusters. We show that SARS-CoV-2 infections are characterised by low levels of within-host diversity across the entire viral genome, with evidence of strong evolutionary constraint in Spike, a key target of vaccines and antibody-based therapies. Although within-host variants can be observed in multiple individuals in the same phylogenetic or epidemiological cluster, highly infectious individuals with high viral load carry only a limited repertoire of viral diversity. Most viral variants are either lost, or occasionally fixed, at the point of transmission, consistent with a narrow transmission bottleneck. These results suggest potential vaccine-escape mutations are likely to be rare in infectious individuals. Nonetheless, we identified Spike variants present in multiple individuals that may affect receptor binding or neutralisation by antibodies. Since the fitness advantage of escape mutations in highly-vaccinated populations is likely to be substantial, resulting in rapid spread if and when they do emerge, these findings underline the need for continued vigilance and monitoring.

genomics↗

bioRxiv2019

Peripheral tolerance to insulin is encoded by mimicry in the microbiome

How organisms achieve sustained peripheral tolerance throughout their lifetime, a correct immune discrimination between self and non-self, remains poorly understood. Host-microbiome interactions carry fundamental information that facilitates this process. We hypothesize that commensal microbes are under evolutionary pressure to develop epitopes that, when presented along with other antigens from their own bacterial community, lead to an overall tolerogenic self classification by the host immune system. Hosts, which have co-evolved with commensals, may rely on mimotopes, bacterial epitopes that are indistinguishable from key self epitopes, as a homeostatic feedback mechanism to establish and maintain tolerance. Using a probabilistic sequence model of peptide mimicry, we show that the gut microbiome contains a set of genes that are likely to trigger identical immune responses to insulin B 9-25, a widely distributed self epitope across tissues and the primary autoantigen in type 1 diabetes. Similarities in the antigen receptor sequences determined from CD4 T cells reacting to insulin epitopes and mimotopes provide experimental evidence for mimicry. All predicted high posterior probability mimotopes belong to the transketolase superfamily, an enzyme that allows efficient harvest of commensal-derived sugar polymers and dietary fibre, an advantage during host colonisation. Microbial transketolase upregulation during infant weaning coincides in time with the peak in autoantibody development against insulin. Abundance changes in bacterial genera that carry these mimotopes have also been observed to precede disease diagnosis. Our findings suggest gut dysbiosis followed by immune response to insulin mimotopes as a primary cause of type 1 diabetes, and may contribute towards unraveling similar causal patterns in a wide variety of disorders.

immunology↗

bioRxiv2019

An integrated platform to systematically identify causal variants and genes for polygenic human traits.

Genome-wide association studies (GWAS) have identified over 150,000 links between common genetic variants and human traits or complex diseases. Over 80% of these associations map to polymorphisms in non-coding DNA. Therefore, the challenge is to identify disease-causing variants, the genes they affect, and the cells in which these effects occur. We have developed a platform using ATAC-seq, DNaseI footprints, NG Capture-C and machine learning to address this challenge. Applying this approach to red blood cell traits identifies a significant proportion of known causative variants and their effector genes, which we show can be validated by direct in vivo modelling.

genetics↗

bioRxiv2019

The origin and diversification of pteropods predate past perturbations in the Earth’s carbon cycle

Pteropods are a group of planktonic gastropods that are widely regarded as biological indicators for assessing the impacts of ocean acidification (OA). Their thin aragonitic shells are highly sensitive to acute changes in ocean chemistry. However, to gain insight into their potential to adapt to current climate change, we need to accurately reconstruct their evolutionary history and assess their responses to past changes in Earths carbon cycle. Here, we resolve the phylogeny and timing of pteropod evolution with a phylogenomic dataset incorporating 21 new species and new fossil evidence. In agreement with traditional taxonomy, we recovered the first molecular support for a division between sea butterflies (Thecosomata: mucus-web feeders) and sea angels (Gymnosomata: active predators). Molecular dating demonstrated that these two lineages diverged in the early Cretaceous, and that all main pteropod clades, including shelled, partially-shelled and unshelled groups, diverged in the mid to late Cretaceous. Hence, these clades originated prior to and subsequently survived major global change events, including the Paleocene Eocene Thermal Maximum (PETM), which is the closest analogue to modern-day ocean acidification and warming. Our findings indicate that aragonitic calcifiers have been resilient to extreme perturbations in the Earths carbon cycle over evolutionary timescales.

evolutionary biology↗

bioRxiv2019

Simultaneous mRNA and protein quantification at the single-cell level delineates trajectories of CD4+ T-cell differentiation

The transcriptomic and proteomic characterisation of CD4+ T cells at the single-cell level has been performed traditionally by two largely exclusive types of technologies: single cell RNA-sequencing (scRNA-seq) technologies and antibody-based cytometry. Here we demonstrate that the simultaneous targeted quantification of mRNA and protein expression in single-cells provides a high-resolution map of human primary CD4+ T cells, and identified precise trajectories of Th1, Th17 and regulatory T-cell (Treg) differentiation in blood and tissue. Furthermore, the sensitivity provided by this massively-parallel multi-omics approach revealed novel insight into the mechanism of expression of CD80 and CD86 on the surface of activated CD4+ Tregs and demonstrate their potential to identify recently activated T cells in circulation. This transcriptomic and proteomic hybrid technology provides a cost-effective solution to dissect the heterogeneity of immune cell populations, including more precise and detailed descriptions of the differentiation and activation of circulating and tissue-resident cells in response to therapies and in stratification of patients.

immunology↗

bioRxiv2019

Specific type 1 diabetes risk genes underpin age-at-diagnosis and indicate joint defects in immunity, beta-cell fragility and responses to viral infections in early-onset disease.

ObjectiveImmunohistological analyses of pancreata from patients with type 1 diabetes suggest a stratification of islet pathology of both B and T lymphocyte islet inflammation common in children diagnosed at <7 years (<7 group), whereas B cells are rare in those diagnosed age [≥]13 ([≥]13 group). Based on these observations, we sought to identify differences in genetic susceptibility between these age-at-diagnosis groups, to inform on the aetiology of the most aggressive form of type 1 diabetes that initiates in the first years of life.\n\nResearch Design and MethodsUsing multinomial logistic regression models, we tested if known type 1 diabetes loci (17 within the HLA region and 55 non-HLA regions) had significantly stronger effect sizes in the <7 group compared to the [≥]13 group, using genotype data from 27,075 individuals (18,488 controls, 3,109 cases diagnosed at <7, 3,754 at 7-13 and 1,724 at [≥]13).\n\nResultsSix HLA haplotypes/classical alleles and seven non-HLA regions, one of which functions specifically in beta cells (GLIS3), and the other six likely affecting key T cell (IL2RA, IL10, SIRPG, IKZF3, THEMIS), thymus (THEMIS) and B cell development/functions (IKZF3, IL10) or in both immune and beta cells (CTSH) had stronger effects in the <7 group.\n\nConclusionsIn newborn children with the greatest load of certain risk alleles, dysregulated response of immune and beta cells to environmental stresses such as virus infection, combine to cause a rapid loss of insulin production, driving down the age of type 1 diabetes diagnosis.

genetics↗

bioRxiv2019

The impact of pro-inflammatory cytokines on the β-cell regulatory landscape provides new insights into the genetics of type 1 diabetes

Early stages of type 1 diabetes (T1D) are characterized by local autoimmune inflammation and progressive loss of insulin-producing pancreatic {beta} cells. We show here that exposure to pro-inflammatory cytokines unmasks a marked plasticity of the {beta}-cell regulatory landscape. We expand the repertoire of human islet regulatory elements by mapping stimulus-responsive enhancers linked to changes in the {beta}-cell transcriptome, proteome and 3D chromatin structure. Our data indicates that the {beta} cell response to cytokines is mediated by the induction of novel regulatory regions as well as the activation of primed regulatory elements pre-bound by islet-specific transcription factors. We found that T1D-associated loci are enriched of the newly mapped cis-regulatory regions and identify T1D-associated variants disrupting cytokine-responsive enhancer activity in human {beta} cells. Our study illustrates how {beta} cells respond to a pro-inflammatory environment and implicate a role for stimulus-response islet enhancers in T1D.

genomics↗

bioRxiv2018

Dexi disruption depletes gut microbial metabolites and accelerates autoimmune diabetes

Non-coding genetic variants in the CLEC16A gene on human chromosome 16p13.13 are associated with risk of autoimmune diseases, including type 1 diabetes and multiple sclerosis. In this region, we previously identified DEXI, a candidate causal gene of unknown function, which alters the risk of type 1 diabetes, where the T1D predisposing allele is associated with lower DEXI expression. Here, we demonstrate by CRISPR mutagenesis in vivo and deep phenotyping that disrupted Dexi expression accelerates diabetes in the non-obese diabetic (NOD) mouse, a spontaneous model of autoimmune pancreatic beta-cell destruction. Mutant mice have increased serum IgM and IgA concentrations compared to wild-type NOD mice, as well as changes in both the gut microbiome and molecular metabolites associated with microbial metabolism. These findings suggest that the mechanism by which DEXI alters diabetes risk involves the composition and function of the microbiome and its impact on host metabolites. Such metabolites, including short chain fatty acids such as butyrate, have been shown to alter the activity of the immune cells involved in beta-cell destruction and susceptibility of the beta cells to autoimmune attack.\n\nOne Sentence Summary: Disruption of the Dexi gene leads to accelerated diabetes in the non-obese diabetic (NOD) mouse, accompanied by changes in serum immunoglobulins, gut microbiome and microbial metabolites.

genetics↗