Identifying the lungs as a susceptible site for allele-specific regulatory changes associated with type 1 diabetes risk

The relationship between SARS-CoV-2 infection and type 1 diabetes mellitus

Environmental factors, in particular viral infections, are thought to have an important role in the pathogenesis of type 1 diabetes mellitus (T1DM). The COVID-19 pandemic reinforced this hypothesis as many observational studies and meta-analyses reported a notable increase in the incidence of T1DM following infection with SARS-CoV-2 as well as an association between SARS-CoV-2 infection and the risk of new-onset T1DM. Experimental evidence suggests that human β-cells express SARS-CoV-2 receptors and that SARS-CoV-2 can infect and replicate in β-cells, resulting in structural or functional alterations of these cells. These alterations include reduced numbers of insulin-secreting granules, impaired pro-insulin (or insulin) secretion, and β-cell transdifferentiation or dedifferentiation. The inflammatory environment induced by local or systemic SARS-CoV-2 infection might result in a set of signals (such as pro-inflammatory cytokines) that lead to β-cell alteration or apoptosis or to a bystander activation of T cells and disruption of peripheral tolerance that triggers autoimmunity. Other mechanisms, such as viral persistence, molecular mimicry and activation of endogenous human retroviruses, are also likely to be involved in the pathogenesis of T1DM following SARS-CoV-2 infection. This Review addresses the issue of the involvement of SARS-CoV-2 infection in the development of T1DM using evidence from epidemiological, clinical and experimental studies.

Machine Learning Identifies Six Genetic Variants and Alterations in the Heart Atrial Appendage as Key Contributors to PD Risk Predictivity

Parkinson's disease (PD) is a complex neurodegenerative disease with a range of causes and clinical presentations. Over 76 genetic loci (comprising 90 SNPs) have been associated with PD by the most recent GWAS meta-analysis. Most of these PD-associated variants are located in non-coding regions of the genome and it is difficult to understand what they are doing and how they contribute to the aetiology of PD. We hypothesised that PD-associated genetic variants modulate disease risk through tissue-specific expression quantitative trait loci (eQTL) effects. We developed and validated a machine learning approach that integrated tissue-specific eQTL data on known PD-associated genetic variants with PD case and control genotypes from the Wellcome Trust Case Control Consortium. In so doing, our analysis ranked the tissue-specific transcription effects for PD-associated genetic variants and estimated their relative contributions to PD risk. We identified roles for SNPs that are connected with INPP5P, CNTN1, GBA and SNCA in PD. Ranking the variants and tissue-specific eQTL effects contributing most to the machine learning model suggested a key role in the risk of developing PD for two variants (rs7617877 and rs6808178) and eQTL associated transcriptional changes of EAF1-AS1 within the heart atrial appendage. Similarly, effects associated with eQTLs located within the Brain Cerebellum were also recognized to confer major PD risk. These findings were replicated in two additional, independent cohorts (the UK Biobank, and NeuroX) and thus warrant further mechanistic investigations to determine if these transcriptional changes could act as early contributors to PD risk and disease development.