Identification of multiple novel susceptibility genes associated with autoimmune thyroid disease

TRanscriptome ANalysis of StratifiEd CohorTs (TRANSECT) enables automated assessment of global gene regulation linked to disparate expression in user defined genes and gene sets

Publicly accessible expression data produced by large consortium projects like TCGA and GTEx are increasing in number and size at an unprecedented rate. Their utility cannot be underestimated given the diversity of valuable tools widely used to interrogate these data and the many discoveries of biological and clinical significance already garnered from these datasets. However, there remain undiscovered ways to mine these rich resources and a continuing need to provide researchers with easily accessible and user-friendly applications for complex or bespoke analyses. We introduce TRanscriptome ANalysis of StratifiEd CohorTs (TRANSECT), a bioinformatics application automating the stratification and subsequent differential expression analysis of cohort data to provide further insights into gene regulation. TRANSECT works by defining two groups within a cohort based on disparate expression of a gene or a gene set and subsequently compares the groups for differences in global expression. Akin to reverse genetics minus the inherent requirement of in vitro or in vivo perturbations, cell lines or model organisms and all the while working within natural physiological limits of expression, TRANSECT compiles information about global transcriptomic change and functional outcomes. TRANSECT is freely available as a command line application or online at https://transect.au.

Whole Transcriptome-wide Analysis Combined With Summary Data-Based Mendelian Randomization Identifies High-Risk Genes for Cholelithiasis Incidence

INTRODUCTION

Cholelithiasis is influenced by various factors, including genetic elements identified in genomewide association studies, but their biological functions are not fully understood.

METHODS

Analyzing data from the Finngen database with 37,041 cholelithiasis cases and 330,903 controls, this study combined SNP data from GTEx v8 and linkage disequilibriums data from the 1000 Genomes Project. Using the Transcriptomewide Association Studies FUSION protocol and summary data-based Mendelian randomization analysis, it investigated the relationship between gene expression and cholelithiasis, using colocalization tests and conditional analyses to explore causality.

RESULTS

The study identified genes associated with cholelithiasis in the liver and whole blood, such as LINC01595, TTC39B, and UGT1A3, with several showing colocalization traits. Notably, RP11-378A13.1 and adenosine deaminase acting on RNA (ADAR) were significantly associated with the disease in both tissues.

DISCUSSION

This research provides insights into the genetic underpinnings of cholelithiasis, highlighting the significant role of gene expression in its development. It establishes new gene associations and identifies potential genetic markers for the disease.

Association of MTNR1B Gene Polymorphisms with Body Mass Index in Hashimoto's Thyroiditis

Hashimoto's thyroiditis (HT) is an autoimmune disorder of the thyroid gland characterized by chronic inflammation, which in most cases results in hypothyroidism. The melatonin receptor MTNR1B is sporadically expressed in the thyroid gland. It modulates immune responses, and alterations in the melatonin-MTNR1B receptor signaling pathway may play a role in developing autoimmune diseases. Obesity worsens the severity and progression of some autoimmune diseases and reduces treatment efficacy. This study aimed to investigate the association of MTNR1B gene polymorphisms (rs10830963, rs1387153, and rs4753426) with HT with regards to the body mass index (BMI). Patients with HT were categorized into normal weight BMI ≤ 25 kg/m2 and overweight/obese BMI > 25 kg/m2 groups. This study included 115 patients with a clinical-, ultrasound-, and laboratory-confirmed diagnosis of HT (64 normal-weight patients and 51 overweight/obese patients) with a mean age of 43 ± 12 years. The results showed that specific MTNR1B polymorphisms are associated with obesity in HT patients. BMI was found to be associated with the rs10830963 polymorphism, and the G allele and GG genotype of the rs10830963 polymorphism were more common in overweight/obese HT patients. Furthermore, the results suggest that genetic factors associated with BMI play a role in developing HT and open new possibilities for personalized treatment approaches.

The Causal Relationship Between Gut Microbiomes, Inflammatory Mediators, and Traumatic Brain Injury in Europeans: Evidence from Genetic Correlation and Functional Mapping Annotation Analyses

Background: The gut microbiome (GM) has been reported to play a role in traumatic brain injury (TBI). To investigate the causal relationship between GMs, inflammatory mediators, and TBI, a comprehensive Mendelian randomization (MR) analysis was conducted. Methods: We utilized Genome-Wide Association Study (GWAS) summary statistics to examine the causal relationships between GM and TBI. To assess the potential causal associations between GM and TBI, we employed the inverse-variance-weighted, MR-Egger, and weighted median methods. Mediation analysis was used to assess the possible mediating factors. Several sensitivity analyses methods were implemented to verify the stability of the results. Additionally, we utilized FUMA GWAS to map single-nucleotide polymorphisms to genes and conduct transcriptomic MR analysis. Results: We identified potential causal relationships between nine bacterial taxa and TBI. Notably, class Methanobacteria, family Methanobacteriaceae, and order Methanobacteriales (p = 0.0003) maintained a robust positive correlation with TBI. This causal association passed false discovery rate (FDR) correction (FDR < 0.05). Genetically determined 1 inflammatory protein, 30 immune cells and 3 inflammatory factors were significantly causally related to TBI. None of them mediated the relationship between GMs and TBI. The outcome of the sensitivity analysis corroborated the findings. Regarding the mapped genes of significant GMs, genes such as CLK4, MTRF1, NAA16, SH3BP5, and ZNF354A in class Methanobacteria showed a significant causal correlation with TBI. Conclusions: Our study reveals the potential causal effects of nine GMs, especially Methanogens on TBI, and there was no link between TBI and GM through inflammatory protein, immune cells, and inflammatory factors, which may offer fresh insights into TBI biomarkers and therapeutic targets through specific GMs.

Multi-omics data integration reveals novel genes related to autoimmune hypothyroidism in the brain: A molecular basis for the brain-thyroid axis

BACKGROUND

The mechanisms underlying the complex relationship between autoimmune hypothyroidism and neurological disorders remain unclear. We conducted a comprehensive analysis of associations between alternative splicing, transcriptomics, and proteomics data and autoimmune hypothyroidism.

METHODS

Splicing-wide association studies (SWAS), proteome-wide association studies (PWAS), and transcriptome-wide association studies (TWAS) were used to identify genes and proteins that regulate autoimmune hypothyroidism within the brain axis. We performed TWAS on GTEx V8 thyroid tissue data to identify autoimmune hypothyroidism-associated thyroid axis genes. A FUSION analysis of overlapping genes in the brain and thyroid axes and brain splicing weights was conducted to determine the influence of alternative splicing in the brain on thyroid tissue gene expression.

RESULTS

SWAS identified 223 alternative splicing events, TWAS identified 270 genes, and PWAS revealed five genes (FDPS, PPIL3, PEX6, MMAB, and ALDH2) encoding proteins associated with autoimmune hypothyroidism. Neuroimaging analyses revealed distinct brain-imaging phenotypes associated with these five genes. TWAS of thyroid tissue identified four genes (FDPS, PPIL3, MMAB, and ALDH2) associated with the brain axis related to thyroid tissue. A FUSION analysis indicated that alternative splicing changes in ALDH2 in brain tissue influenced its expression in thyroid tissue.

CONCLUSION

Integrating brain splicing, proteomic, and transcriptomic data supports the association between specific genes and proteins in the brain and autoimmune hypothyroidism. Additionally, ALDH2 alternative splicing in brain tissue influences its thyroid tissue expression. These findings provide new insights into the molecular basis of autoimmune hypothyroidism, facilitating future pathogenesis research.

A scoping review of statistical methods to investigate colocalization between genetic associations and microRNA expression in osteoarthritis

Background

Genetic colocalization analysis is a statistical method that evaluates whether two traits (e.g., osteoarthritis [OA] risk and microRNA [miRNA] expression levels) share the same or distinct genetic association signals in a locus typically identified in genome-wide association studies (GWAS). This method is useful for providing insights into the biological relevance of genetic association signals, particularly in intergenic regions, which can help to elucidate disease mechanisms in OA and other complex traits.

Objectives

To review the existing literature on genetic colocalization methods, assess their suitability for studying OA, and investigate their capacity to integrate miRNA data, while bearing in view their statistical assumptions.

Design

We followed scoping review methodology and used Covidence software for data management. Search terms for colocalization, GWAS, and genetic or statistical models were used in the databases MEDLINE and EMBASE, searched till March 4, 2024.

Results

Our search returned 546 peer-reviewed papers, of which 96 were included following title/abstract and full-text screening. Based on both cumulative and annual publication counts, the most cited method for colocalization analysis was coloc. Four papers examined OA-related phenotypes, and none examined miRNA. An approach to colocalization analysis using miRNA was postulated based on further hand-searching.

Conclusions

Colocalization analysis is a largely unexplored method in OA. Many of the approaches to colocalization analysis identified in this review, including the integration of GWAS and miRNA data, may help to elucidate genetic and epigenetic factors implicated in OA and other complex traits.

Fine mapping-based multi-omics analysis interprets the gut-lung axis function of SGLT2 inhibitors

Background

Currently, Sodium-glucose cotransporter 2 (SGLT2) inhibitors demonstrate additional effects beyond glucose control on the gut microbiota and circulating metabolites. The gut microbiota and metabolites have been found to be useful in elucidating potential biological mechanisms of pulmonary diseases. Therefore, our study aims to investigate the effects of gut microbiota and metabolites mediating SGLT2 inhibition in 10 pulmonary diseases through Mendelian randomization (MR) research.

Methods

We conducted a two-sample, two-step MR study to assess the association between SGLT2 inhibition and 10 pulmonary diseases and to investigate the mediating effects of gut microbiota and metabolite. Gene-fine mapping and annotation of mediators by FUMA and Magma analyses were performed, and causal associations of mapped genes with diseases were assessed by muti-omics MR analyses. Possible side effects of SGLT2 inhibition were assessed by PheWAS analysis.

Results

SGLT2 inhibition was linked to a reduced risk of T2DM, Interstitial lung disease (ILD), Pneumoconiosis, Pulmonary tuberculosis, and Asthma(OR=0.457, 0.054, 0.002, 0.280, 0.706). The family Enterobacteriaceae and order Enterobacteriales were associated with SGLT2 inhibition and ILD(95% CI:0.079-0.138). The family Alcaligenaceae and X-12719 were linked to pneumoconiosis (95% CI: 0.042-0.120, 0.050-0.099). The genus Phascolarctobacterium was connected to pulmonary tuberculosis (95% CI: 0.236-0.703).The degree of unsaturation (Fatty Acids), ratio of docosahexaenoic acid to total fatty acids, and 4-androsten-3beta,17beta-diol disulfate 2, were associated with asthma(95% CI: 0.042-0.119, 0.039-0.101, 0.181-0.473). Furthermore, Fuma and Magma analyses identified target genes for the four diseases, and proteomic MR analysis revealed six overlapping target genes in asthma. PheWAS analysis also highlighted potential side effects of SGLT2 inhibition.

Conclusions

This comprehensive study strongly supports a multi-omics association between SGLT2 inhibition and reduced risk of interstitial lung disease, tuberculosis, pneumoconiosis, and asthma. Four identified gut microbiota, four metabolites, sixteen metabolic pathways, and six target genes appear to play a potential role in this association. The results of the comprehensive phenome-wide association analysis also identified the full effect of SGLT2 inhibitors.

Associations between immune cell traits and autoimmune thyroid diseases: a bidirectional two-sample mendelian randomization study

Autoimmune thyroid diseases (AITDs), mainly including Graves' disease (GD) and Hashimoto's thyroiditis (HT), are common autoimmune disorders characterized by abnormal immune responses targeting the thyroid gland. We conducted a bidirectional two-sample MR analysis using the largest dataset of peripheral immune cell phenotypes from Sardinia, and the AITD dataset from the 10th round of the FinnGen and the UK Biobank project. Instrumental variables (IVs) were rigorously selected based on the three assumptions of MR and analyzed using the Wald ratio, inverse-variance weighted (IVW), MR-Egger, and weighted median methods. Additionally, sensitivity analyses were performed using Cochrane's Q, the Egger intercept, the MR-PRESSO, and the leave-one-out (LOO) method to ensure the robustness of the results. The Steiger test was utilized to identify and exclude potential reverse causation. The results showed that 3, 3, and 11 immune cell phenotypes were significantly associated with the risk of AITD. In GD, the proportion of naive CD4-CD8- (DN) T cells in T cells and the proportion of terminally differentiated CD4+T cells in T cells showed the strongest inducing and protective effects, respectively. In HT, lymphocyte count and CD45 on CD4+T cells showed the strongest inducing and protective effects, respectively. In autoimmune hypothyroidism, CD127 CD8+T cell count and terminally differentiated DN T cell count exhibited the strongest inducing and protective effects, respectively. Through MR analysis, our study provides direct genetic evidence of the impact of immune cell traits on AITD risk and lays the groundwork for potential therapeutic and diagnostic target discovery.

Epigenome-wide Association Study Shows Differential DNA Methylation of MDC1, KLF9, and CUTA in Autoimmune Thyroid Disease

CONTEXT

Autoimmune thyroid disease (AITD) includes Graves disease (GD) and Hashimoto disease (HD), which often run in the same family. AITD etiology is incompletely understood: Genetic factors may account for up to 75% of phenotypic variance, whereas epigenetic effects (including DNA methylation [DNAm]) may contribute to the remaining variance (eg, why some individuals develop GD and others HD).

OBJECTIVE

This work aimed to identify differentially methylated positions (DMPs) and differentially methylated regions (DMRs) comparing GD to HD.

METHODS

Whole-blood DNAm was measured across the genome using the Infinium MethylationEPIC array in 32 Australian patients with GD and 30 with HD (discovery cohort) and 32 Danish patients with GD and 32 with HD (replication cohort). Linear mixed models were used to test for differences in quantile-normalized β values of DNAm between GD and HD and data were later meta-analyzed. Comb-p software was used to identify DMRs.

RESULTS

We identified epigenome-wide significant differences (P < 9E-8) and replicated (P < .05) 2 DMPs between GD and HD (cg06315208 within MDC1 and cg00049440 within KLF9). We identified and replicated a DMR within CUTA (5 CpGs at 6p21.32). We also identified 64 DMPs and 137 DMRs in the meta-analysis.

CONCLUSION

Our study reveals differences in DNAm between GD and HD, which may help explain why some people develop GD and others HD and provide a link to environmental risk factors. Additional research is needed to advance understanding of the role of DNAm in AITD and investigate its prognostic and therapeutic potential.

The association between epilepsy and COVID-19: analysis based on Mendelian randomization and FUMA

Objective

A multitude of observational studies have underscored a substantial comorbidity between COVID-19 and epilepsy. This study was aimed at establishing a conclusive causal link between these two conditions.

Methods

We employed Mendelian randomization (MR) to evaluate the causal link between COVID-19 and epilepsy, as well as its focal and generalized subtypes. The GWAS for epilepsy and its subtypes database were abstracted from both FinnGen consortium and ILAE. Additionally, we leveraged functional mapping and annotation (FUMA) to integrate information from genome-wide association studies (GWAS) results.

Results

The MR analyses revealed that genetic liability to COVID-19 infection conferred a causal effect on epilepsy [FinnGen: OR: 1.5306; 95% confidence interval (CI): 1.1676-2.0062, PFDR (false discovery rate) = 0.0076; ILAE: OR: 1.3440; 95% CI: 1.0235-1.7649, PFDR = 0.0429], and generalized epilepsy (FinnGen: OR: 2.1155; 95% CI: 1.1734-3.8139, PFDR = 0.0327; ILAE: OR: 1.1245; 95% CI: 1.0444-1.2108, PFDR = 0.0114). Genetic liability to COVID-19 hospitalization conferred a causal effect on epilepsy (FinnGen: OR: 1.0934; 95% CI: 1.0097-1.1841, PFDR = 0.0422; ILAE: OR: 1.7381; 95% CI: 1.0467-2.8862, PFDR = 0.0451), focal epilepsy (ILAE: OR: 1.7549; 95% CI: 1.1063-2.7838, PFDR = 0.0338), and generalized epilepsy (ILAE: OR: 1.1827; 95% CI: 1.0215-1.3693, PFDR = 0.0406). Genetic liability to COVID-19 severity conferred a causal effect on epilepsy (FinnGen consortium: OR: 1.2454; 95% CI: 1.0850-1.4295, PFDR = 0.0162; ILAE: OR: 1.2724; 95% CI: 1.0347-1.5647, PFDR = 0.0403), focal epilepsy (FinnGen: OR: 1.6818; 95% CI: 1.1478-2.4642, PFDR = 0.0231; ILAE: OR: 1.6598; 95% CI: 1.2572-2.1914, PFDR = 0.0054), and generalized epilepsy (FinnGen: OR: 1.1486; 95% CI: 1.0274-1.2842, PFDR = 0.0335; ILAE: OR: 1.0439; 95% CI: 1.0159-1.0728, PFDR = 0.0086). In contrast, no causal linkage of epilepsy on COVID-19 was observed. Further, FUMA analysis identified six overlapping genes, including SMEK2, PNPT1, EFEMP1, CCDC85A, VRK2, and BCL11A, shared between COVID-19 and epilepsy. Tissue-specific expression analyses revealed that the disease-gene associations of COVID-19 were significantly enriched in lung, ovary, and spleen tissue compartments, while being significantly enriched in brain tissue for epilepsy.

Conclusion

Our study demonstrates that COVID-19 can be a contributing factor to epilepsy, but we found no evidence that epilepsy contributes to COVID-19.