Multi-dimensional analysis identified rheumatoid arthritis-driving pathway in human T cell

Computational deconvolution to estimate cell type-specific gene expression from bulk data

Computational deconvolution is a time and cost-efficient approach to obtain cell type-specific information from bulk gene expression of heterogeneous tissues like blood. Deconvolution can aim to either estimate cell type proportions or abundances in samples, or estimate how strongly each present cell type expresses different genes, or both tasks simultaneously. Among the two separate goals, the estimation of cell type proportions/abundances is widely studied, but less attention has been paid on defining the cell type-specific expression profiles. Here, we address this gap by introducing a novel method Rodeo and empirically evaluating it and the other available tools from multiple perspectives utilizing diverse datasets.

Distinct features between HLA-DR+ and HLA-DR- PD-1hi CXCR5- T peripheral helper cells in seropositive rheumatoid arthritis

OBJECTIVES

PD-1hi CXCR5- T peripheral helper (Tph) cells are newly identified pathogenic CD4 helper T cells in RA. We evaluated the usefulness of Tph cell subsets as biomarkers of RA.

METHODS

RA patients who visited our rheumatology department between May 2015 and September 2017 and met the 2010 ACR/EULAR classification criteria were included. We compared the correlation of DAS28-ESR between Tph cell subsets and 40 immune cell subsets. We also explored which subsets reflected the chronological changes in the disease activity after treatment.

RESULTS

Thirty-four seropositive RA patients, 11 seronegative RA patients and 34 healthy controls were included. Tph cell subsets that correlated with the DAS28-ESR were HLA-DR+ Tph cells (rs = 0.50, P = 0.002), HLA-DR- Tph cells (rs = 0.39, P = 0.03) and Tph1 cells (rs = 0.41, P = 0.02). Among the other 40 immune cell subsets, HLA-DR+ Th1-17 cells (rs = 0.38, P = 0.03), activated B cells (rs = -0.35, P = 0.04), plasma cells (rs = 0.43, P = 0.01) and CD14++ CD16+ monocytes (rs = 0.36, P = 0.04) correlated, but not strongly as HLA-DR+ Tph cells. However, MTX treatment reduced the proportion of HLA-DR+ Tph cells independently of the disease activity. In contrast, HLA-DR- Tph cells accurately reflected the change in the DAS28-ESR during MTX treatment.

CONCLUSION

HLA-DR+ Tph cells were decreased with MTX treatment, independent of the disease activity, while HLA-DR- Tph cells reflected the disease activity accurately during the treatment.

Genome-wide association study in a Korean population identifies six novel susceptibility loci for rheumatoid arthritis

OBJECTIVE

Genome-wide association studies (GWAS) in rheumatoid arthritis (RA) have discovered over 100 RA loci, explaining patient-relevant RA pathogenesis but showing a large fraction of missing heritability. As a continuous effort, we conducted GWAS in a large Korean RA case-control population.

METHODS

We newly generated genome-wide variant data in two independent Korean cohorts comprising 4068 RA cases and 36 487 controls, followed by a whole-genome imputation and a meta-analysis of the disease association results in the two cohorts. By integrating publicly available omics data with the GWAS results, a series of bioinformatic analyses were conducted to prioritise the RA-risk genes in RA loci and to dissect biological mechanisms underlying disease associations.

RESULTS

We identified six new RA-risk loci (SLAMF6, CXCL13, SWAP70, NFKBIA, ZFP36L1 and LINC00158) with pmeta<5×10-8 and consistent disease effect sizes in the two cohorts. A total of 122 genes were prioritised from the 6 novel and 13 replicated RA loci based on physical distance, regulatory variants and chromatin interaction. Bioinformatics analyses highlighted potentially RA-relevant tissues (including immune tissues, lung and small intestine) with tissue-specific expression of RA-associated genes and suggested the immune-related gene sets (such as CD40 pathway, IL-21-mediated pathway and citrullination) and the risk-allele sharing with other diseases.

CONCLUSION

This study identified six new RA-associated loci that contributed to better understanding of the genetic aetiology and biology in RA.

CD147 Expressed on Memory CD4+ T Cells Limits Th17 Responses in Patients With Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a common autoimmune disease in which T helper-type 17 (Th17) cells have been critically involved. CD147 is a T cell activation-associated molecule and is involved in T cell development. However, it remains unclear whether CD147 participates in Th17 responses in RA patients. In this study, we demonstrated that in both the RA and healthy controls (HC) groups, CD147 expression on CD4⁺ T cells was increased in CCR6⁺ and CD161⁺ subsets, and was associated with IL-17 production. Ligation of CD147 with its monoclonal antibody (mAb) strongly inhibited Th17 responses, and knock down of CD147 expression on CD4⁺ Tm cells specifically enhanced Th17 responses, triggered by coculture with in vitro activated monocytes from HC. Further functional studies showed that anti-CD147 mAb decreased the activation of AKT, mTORC1 and STAT3 signaling, which is known to enhance Th17 responses. Ligation of CD147 with its mAb on CD4⁺ Tm cells specifically reduced Th17 responses induced by in vitro or in vivo activated monocytes from RA patients. In collagen-induced arthritis model, anti-CD147 mAb treatment reduced the Th17 levels and severity of arthritis in vivo. These data suggest that CD147 plays a negative role in regulating human Th17 responses. Anti-CD147 mAb can limit the extraordinary proliferation of Th17 cells and may be a new therapeutic option in RA.

Nanoparticle-siRNA: A potential strategy for rheumatoid arthritis therapy?

Rheumatoid arthritis (RA) is a common clinical inflammatory disease of the autoimmune system manifested by persistent synovitis, cartilage damage and even deformities. Despite significant progress in the clinical treatment of RA, long-term administration of anti-rheumatic drugs can cause a series of problems, including infections, gastrointestinal reactions, and abnormal liver and kidney functions. The emergence of RNA interference (RNAi) drugs has brought new hope for the treatment of RA. Designing a reasonable vector for RNAi drugs will greatly expand the application prospects of RNAi. Nanoparticles as a promising drug carrier provide reliable support for RNAi drugs. The review summarizes the pathogenesis of RA as a possible target for small interference RNA (siRNA) design. At the same time, the review also analyzes the nanoparticles used in siRNA carriers in recent years, laying the foundation and prospect for the next step in the development of intelligent nanocarriers.

Systemic Investigation of Promoter-wide Methylome and Genome Variations in Gout

Current knowledge of gout centers on hyperuricemia. Relatively little is known regarding the pathogenesis of gouty inflammation. To investigate the epigenetic background of gouty inflammation independent of hyperuricemia and its relationship to genetics, 69 gout patients and 1455 non-gout controls were included. Promoter-wide methylation was profiled with EPIC array. Whole-genome sequencing data were included for genetic and methylation quantitative trait loci (meQTL) analyses and causal inference tests. Identified loci were subjected to co-methylation analysis and functional localization with DNase hypersensitivity and histone marks analysis. An expression database was queried to clarify biologic functions of identified loci. A transcription factor dataset was integrated to identify transcription factors coordinating respective expression. In total, seven CpG loci involved in interleukin-1β production survived genetic/meQTL analyses, or causal inference tests. None had a significant relationship with various metabolic traits. Additional analysis suggested gouty inflammation, instead of hyperuricemia, provides the link between these CpG sites and gout. Six (PGGT1B, INSIG1, ANGPTL2, JNK1, UBAP1, and RAPTOR) were novel genes in the field of gout. One (CNTN5) was previously associated with gouty inflammation. Transcription factor mapping identified several potential transcription factors implicated in the link between differential methylation, interleukin-1β production, and gouty inflammation. In conclusion, this study revealed several novel genes specific to gouty inflammation and provided enhanced insight into the biological basis of gouty inflammation.

Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls

This study evaluated the use of multiomics data for classification accuracy of rheumatoid arthritis (RA). Three approaches were used and compared in terms of prediction accuracy: (1) whole-genome prediction (WGP) using SNP marker information only, (2) whole-methylome prediction (WMP) using methylation profiles only, and (3) whole-genome/methylome prediction (WGMP) with combining both omics layers. The number of SNP and of methylation sites varied in each scenario, with either 1, 10, or 50% of these preselected based on four approaches: randomly, evenly spaced, lowest p value (genome-wide association or epigenome-wide association study), and estimated effect size using a Bayesian ridge regression (BRR) model. To remove effects of high levels of pairwise linkage disequilibrium (LD), SNPs were also preselected with an LD-pruning method. Five Bayesian regression models were studied for classification, including BRR, Bayes-A, Bayes-B, Bayes-C, and the Bayesian LASSO. Adjusting methylation profiles for cellular heterogeneity within whole blood samples had a detrimental effect on the classification ability of the models. Overall, WGMP using Bayes-B model has the best performance. In particular, selecting SNPs based on LD-pruning with 1% of the methylation sites selected based on BRR included in the model, and fitting the most significant SNP as a fixed effect was the best method for predicting disease risk with a classification accuracy of 0.975. Our results showed that multiomics data can be used to effectively predict the risk of RA and identify cases in early stages to prevent or alter disease progression via appropriate interventions.