Profile of differential promoter activity by nucleotide substitution at GWAS signals for multiple sclerosis

A network-based approach reveals long non-coding RNAs associated with disease activity in lupus nephritis: key pathways for flare and potential biomarkers to be used as liquid biopsies

Objective

A blood-based biomarker is needed to assess lupus nephritis (LN) disease activity, minimizing the need for invasive kidney biopsies. Long non-coding RNAs (lncRNAs) are known to regulate gene expression, appear to be stable in human plasma, and can serve as non-invasive biomarkers.

Methods

Transcriptomic data of whole blood samples from 74 LN patients and 20 healthy subjects (HC) were analyzed to identify differentially expressed (DE) lncRNAs associated with quiescent disease and flares. Weighted gene co-expression network analysis (WGCNA) was performed to uncover lncRNAs with a central role (hub lncRNAs) in regulating key biological processes that drive LN disease activity. The association of hub lncRNAs with disease activity was validated using RT-qPCR on an independent cohort of 15 LN patients and 9 HC. cis- and trans-targets of validated lncRNAs were explored in silico to examine potential mechanisms of their action.

Results

There were 444 DE lncRNAs associated with quiescent disease and 6 DE lncRNAs associated with flares (FDR <0.05). WGCNA highlighted IFN signaling and B-cell activity/adaptive immunity as the most significant processes contributing to nephritis activity. Four disease-activity-associated lncRNAs, namely, NRIR, KLHDC7B-DT, MIR600HG, and FAM30A, were detected as hub genes and validated in an independent cohort. NRIR and KLHDC7B-DT emerged as potential key regulators of IFN-mediated processes. Network analysis suggests that FAM30A and MIR600HG are likely to play a central role in the regulation of B-cells in LN through cis-regulation effects and a competing endogenous RNA mechanism affecting immunoglobulin gene expression and the IFN-λ pathway.

Conclusions

The expression of lncRNAs NRIR, KLHDC7B-DT, FAM30A, and MIR600HG were associated with disease activity and could be further explored as blood-based biomarkers and potential liquid biopsy on LN.

A New Risk Variant for Multiple Sclerosis at 11q23.3 Locus Is Associated with Expansion of CXCR5+ Circulating Regulatory T Cells

Genome-wide association studies and meta-analysis have contributed to the identification of more than 200 loci associated with multiple sclerosis (MS). However, a proportion of MS heritability remains unknown. We aimed to uncover new genetic variants associated with MS and determine their functional effects. For this, we resequenced the exons and regulatory sequences of 14 MS risk genes in a cohort of MS patients and healthy individuals (n = 1070) and attempted to validate a selection of signals through genotyping in an independent cohort (n = 5138). We identified three new MS-associated variants at C-X-C motif chemokine receptor 5 (CXCR5), Ts translation elongation factor, mitochondrial (TSFM) and cytochrome P450 family 24 subfamily A member 1 (CYP24A1). Rs10892307 resulted in a new signal at the CXCR5 region that explains one of the associations with MS within the locus. This polymorphism and three others in high linkage disequilibrium mapped within regulatory regions. Of them, rs11602393 showed allele-dependent enhancer activity in the forward orientation as determined by luciferase reporter assays. Immunophenotyping using peripheral blood mononuclear cells from MS patients associated the minor allele of rs10892307 with increased percentage of regulatory T cells expressing CXCR5. This work reports a new signal for the CXCR5 MS risk locus and points to rs11602393 as the causal variant. The expansion of CXCR5+ circulating regulatory T cells induced by this variant could cause its MS association.

Prediction of Alzheimer's Disease-Associated Genes by Integration of GWAS Summary Data and Expression Data

Alzheimer's disease (AD) is the most common cause of dementia. It is the fifth leading cause of death among elderly people. With high genetic heritability (79%), finding the disease's causal genes is a crucial step in finding a treatment for AD. Following the International Genomics of Alzheimer's Project (IGAP), many disease-associated genes have been identified; however, we do not have enough knowledge about how those disease-associated genes affect gene expression and disease-related pathways. We integrated GWAS summary data from IGAP and five different expression-level data by using the transcriptome-wide association study method and identified 15 disease-causal genes under strict multiple testing (α < 0.05), and four genes are newly identified. We identified an additional 29 potential disease-causal genes under a false discovery rate (α < 0.05), and 21 of them are newly identified. Many genes we identified are also associated with an autoimmune disorder.

Genetic Analysis of Rare Human Variants of Regulators of G Protein Signaling Proteins and Their Role in Human Physiology and Disease

Regulators of G protein signaling (RGS) proteins modulate the physiologic actions of many neurotransmitters, hormones, and other signaling molecules. Human RGS proteins comprise a family of 20 canonical proteins that bind directly to G protein-coupled receptors/G protein complexes to limit the lifetime of their signaling events, which regulate all aspects of cell and organ physiology. Genetic variations account for diverse human traits and individual predispositions to disease. RGS proteins contribute to many complex polygenic human traits and pathologies such as hypertension, atherosclerosis, schizophrenia, depression, addiction, cancers, and many others. Recent analysis indicates that most human diseases are due to extremely rare genetic variants. In this study, we summarize physiologic roles for RGS proteins and links to human diseases/traits and report rare variants found within each human RGS protein exome sequence derived from global population studies. Each RGS sequence is analyzed using recently described bioinformatics and proteomic tools for measures of missense tolerance ratio paired with combined annotation-dependent depletion scores, and protein post-translational modification (PTM) alignment cluster analysis. We highlight selected variants within the well-studied RGS domain that likely disrupt RGS protein functions and provide comprehensive variant and PTM data for each RGS protein for future study. We propose that rare variants in functionally sensitive regions of RGS proteins confer profound change-of-function phenotypes that may contribute, in newly appreciated ways, to complex human diseases and/or traits. This information provides investigators with a valuable database to explore variation in RGS protein function, and for targeting RGS proteins as future therapeutic targets.

Rare high-impact disease variants: properties and identifications

Although many genome-wide association studies have been performed, the identification of disease polymorphisms remains important. It is now suspected that many rare disease variants induce the association signal of common variants in linkage disequilibrium (LD). Based on recent development of genetic models, the current study provides explanations of the existence of rare variants with high impacts and common variants with low impacts. Disease variants are neither necessary nor sufficient due to gene–gene or gene–environment interactions. A new method was developed based on theoretical aspects to identify both rare and common disease variants by their genotypes. Common disease variants were identified with relatively small odds ratios and relatively small sample sizes, except for specific situations in which the disease variants were in strong LD with a variant with a higher frequency. Rare disease variants with small impacts were difficult to identify without increasing sample sizes; however, the method was reasonably accurate for rare disease variants with high impacts. For rare variants, dominant variants generally showed better Type II error rates than recessive variants; however, the trend was reversed for common variants. Type II error rates increased in gene regions containing more than two disease variants because the more common variant, rather than both disease variants, was usually identified. The proposed method would be useful for identifying common disease variants with small impacts and rare disease variants with large impacts when disease variants have the same effects on disease presentation.