Mapping of the gene network that regulates glycan clock of ageing.

Glycans are an essential structural component of immunoglobulin G (IgG) that modulate its structure and function. However, regulatory mechanisms behind this complex posttranslational modification are not well known. Previous genome-wide association studies (GWAS) identified 29 genomic regions involved in regulation of IgG glycosylation, but only a few were functionally validated. One of the key functional features of IgG glycosylation is the addition of galactose (galactosylation), a trait which was shown to be associated with ageing. We performed GWAS of IgG galactosylation (N=13,705) and identified 16 significantly associated loci, indicating that IgG galactosylation is regulated by a complex network of genes that extends beyond the galactosyltransferase enzyme that adds galactose to IgG glycans. Gene prioritization identified 37 candidate genes. Using a recently developed CRISPR/dCas9 system we manipulated gene expression of candidate genes in the in vitro IgG expression system. Upregulation of three genes, EEF1A1, MANBA and TNFRSF13B, changed the IgG glycome composition, which confirmed that these three genes are involved in IgG galactosylation in this in vitro expression system.

Development and Replication of a Genome-Wide Polygenic Risk Score for Chronic Back Pain.

Chronic back pain (CBP) is a complex heritable trait and a major cause of disability worldwide. We developed and validated a genome-wide polygenic risk score (PRS) for CBP using a large-scale GWAS based on UK Biobank participants of European ancestry (N = 265,000). The PRS showed poor overall predictive ability (AUC = 0.56 and OR = 1.24 per SD, 95% CI: 1.22-1.26), but individuals from the 99th percentile of PRS distribution had a nearly two-fold increased risk of CBP (OR = 1.82, 95% CI: 1.60-2.06). We validated the PRS on an independent TwinsUK sample, obtaining a similar magnitude of effect. The PRS was significantly associated with various ICD-10 and OPCS-4 diagnostic codes, including chronic ischemic heart disease (OR = 1.1, p-value = 4.8 × 10-15), obesity, metabolism-related traits, spine disorders, disc degeneration, and arthritis-related disorders. PRS and environment interaction analysis with twelve known CBP risk factors revealed no significant results, suggesting that the magnitude of G × E interactions with studied factors is small. The limited predictive ability of the PRS that we developed is likely explained by the complexity, heterogeneity, and polygenicity of CBP, for which sample sizes of a few hundred thousand are insufficient to estimate small genetic effects robustly.

Association Between Human Gut Microbiome and N-Glycan Composition of Total Plasma Proteome.

Being one of the most dynamic entities in the human body, glycosylation of proteins fine-tunes the activity of the organismal machinery, including the immune system, and mediates the interaction with the human microbial consortium, typically represented by the gut microbiome. Using data from 194 healthy individuals, we conducted an associational study to uncover potential relations between the gut microbiome and the blood plasma N-glycome, including N-glycome of immunoglobulin G. While lacking strong linkages on the multivariate level, we were able to identify associations between alpha and beta microbiome diversity and the blood plasma N-glycome profile. Moreover, for two bacterial genera, namely, Bilophila and Clostridium innocuum, significant associations with specific glycans were also shown. The study's results suggest a non-trivial, possibly weak link between the total plasma N-glycome and the gut microbiome, predominantly involving glycans related to the immune system proteins, including immunoglobulin G. Further studies of glycans linked to microbiome-related proteins in well-selected patient groups are required to conclusively establish specific associations.

Identification of tissue-specific and common methylation quantitative trait loci in healthy individuals using MAGAR.

BACKGROUND: Understanding the influence of genetic variants on DNA methylation is fundamental for the interpretation of epigenomic data in the context of disease. There is a need for systematic approaches not only for determining methylation quantitative trait loci (methQTL), but also for discriminating general from cell type-specific effects. RESULTS: Here, we present a two-step computational framework MAGAR ( https://bioconductor.org/packages/MAGAR ), which fully supports the identification of methQTLs from matched genotyping and DNA methylation data, and additionally allows for illuminating cell type-specific methQTL effects. In a pilot analysis, we apply MAGAR on data in four tissues (ileum, rectum, T cells, B cells) from healthy individuals and demonstrate the discrimination of common from cell type-specific methQTLs. We experimentally validate both types of methQTLs in an independent data set comprising additional cell types and tissues. Finally, we validate selected methQTLs located in the PON1, ZNF155, and NRG2 genes by ultra-deep local sequencing. In line with previous reports, we find cell type-specific methQTLs to be preferentially located in enhancer elements. CONCLUSIONS: Our analysis demonstrates that a systematic analysis of methQTLs provides important new insights on the influences of genetic variants to cell type-specific epigenomic variation.