Alteration of circulating microbiome and its associated regulation role in rheumatoid arthritis: Evidence from integration of multiomics data.
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ABSTRACT:
SUBMITTER: Mo XB
PROVIDER: S-EPMC7668190 | biostudies-literature | 2020 Nov
REPOSITORIES: biostudies-literature
Publications
Alteration of circulating microbiome and its associated regulation role in rheumatoid arthritis: Evidence from integration of multiomics data.
Mo Xing-Bo XB Dong Chen-Yue CY He Pei P Wu Long-Fei LF Lu Xin X Zhang Yong-Hong YH Deng Hong-Wen HW Deng Fei-Yan FY Lei Shu-Feng SF
Clinical and translational medicine 20201101 7
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Project description:Rheumatoid Arthritis (RA) has been increasingly associated with perturbations to the microbial communities that reside in and on the body (the microbiome), in both human and animal studies. To date, such studies have mainly focused on the microbial communities that inhabit the gut and oral cavity. Mounting evidence suggests that microbial DNA can be detected in the blood circulation using a range of molecular methods. This DNA may represent an untapped pool of biomarkers that have the potential to report on changes to the microbiome of distant sites (e.g., example, the gut and oral cavity). To this end, through amplification and sequencing of the bacterial 16S rRNA variable region four, we evaluated the presence and identity of microbial DNA in blood samples obtained from RA patients (both prior to and 3 months following the instigation of treatment) in comparison to a small number of healthy control subjects and samples obtained from patients with ankylosing spondylitis (AS) and psoriatic arthritis (PA). Bacterial-derived DNA was identified in the majority of our patient samples. Taxonomic classification revealed that the microbiome community in RA was distinct from AS, PA, and the healthy state. Through analysis of paired patient samples obtained prior to and 3 months following treatment (V0 vs. V3), we found the microbiome to be modulated by treatment, and in many cases, this shift reduced the distance between these samples and the healthy control samples, suggesting a partial normalization following treatment in some patients. This effect was especially evident in seronegative arthritis patients. Herein, we provide further evidence for the existence of a blood microbiome in health and identify specific taxa modulated in disease and following treatment. These blood-derived signatures may have significant utility as disease biomarkers and suggest this area warrants further investigation.
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Project description:Rheumatoid arthritis (RA) is an autoimmune and inflammatory disease for which there is a lack of therapeutic options. Genome-wide association studies (GWASs) have identified over 100 genetic loci associated with RA susceptibility; however, the most causal risk genes (RGs) associated with, and molecular mechanism underlying, RA remain unknown. In this study, we collected 95 RA-associated loci from multiple GWASs and detected 87 candidate high-confidence risk genes (HRGs) from these loci via integrated multiomics data (the genome-scale chromosome conformation capture data, enhancer-promoter linkage data, and gene expression data) using the Bayesian integrative risk gene selector (iRIGS). Analysis of these HRGs indicates that these genes were indeed, markedly associated with different aspects of RA. Among these, 36 and 46 HRGs have been reported to be related to RA and autoimmunity, respectively. Meanwhile, most novel HRGs were also involved in the significantly enriched RA-related biological functions and pathways. Furthermore, drug repositioning prediction of the HRGs revealed three potential targets (ERBB2, IL6ST, and MAPK1) and nine possible drugs for RA treatment, of which two IL-6 receptor antagonists (tocilizumab and sarilumab) have been approved for RA treatment and four drugs (trastuzumab, lapatinib, masoprocol, and arsenic trioxide) have been reported to have a high potential to ameliorate RA. In summary, we believe that this study provides new clues for understanding the pathogenesis of RA and is important for research regarding the mechanisms underlying RA and the development of therapeutics for this condition.
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Project description:BackgroundRheumatoid arthritis (RA) is an autoimmune disease characterized by tumor-like hyperplasia and inflammation of the synovium, which causes synovial cell invasion into the bone and cartilage. In RA pathogenesis, various molecules in effector cells (i.e., immune cells and mesenchymal cells) are dysregulated by genetic and environmental factors. Synovial fibroblasts (SFs), the most abundant resident mesenchymal cells in the synovium, are the major local effectors of the destructive joint inflammation and exert their effects through the pathogenic production of molecules such as interleukin-6.Main bodyTo date, more than 100 RA susceptibility loci have been identified in genome-wide association studies (GWASs), and finding novel therapeutic targets utilizing genome analysis is considered a promising approach because some candidate causal genes identified by GWASs have previously been established as therapeutic targets. For further exploration of RA-responsible cells and cell type-specific therapeutic targets, integrated analysis (or functional genome analysis) of the genome and intermediate traits (e.g., transcriptome and epigenome) is crucial.ConclusionThis review builds on the existing knowledge regarding the epigenomic abnormalities in RASFs and discusses the recent advances in single-cell analysis, highlighting the prospects of SFs as targets for safer and more effective therapies against RA.
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Project description:Here we report 16s rRNA data in patients with Rheumatoid Artrhitis as compared with patients with sarcoidosis and healthy volunteers. Our data shows that the BAL microbiota of RA patients was significantly less diverse and abundant when compared to healthy controls, but similar to sarcoidosis patients. This distal airways dysbiosis was attributed to the reduced presence of several genus (i.e., Actynomyces and Burkhordelia) as well as reported periodontopathic taxa, including Treponema, Prevotella and Porphyromonas. While multiple clades correlated with local and systemic levels of autoantibodies, the genus Pseudonocardia and various related OTUs were the only taxa overrepresented in RA BAL and correlated with higher disease activity and erosions.
2017-01-31 | GSE84608 | GEO
Project description:SummaryData integration workflows for multiomics data take many forms across academia and industry. Efforts with limited resources often encountered in academia can easily fall short of data integration best practices for processing and combining high-content imaging, proteomics, metabolomics, and other omics data. We present Phenonaut, a Python software package designed to address the data workflow needs of migration, control, integration, and auditability in the application of literature and proprietary techniques for data source and structure agnostic workflow creation.Availability and implementationSource code: https://github.com/CarragherLab/phenonaut, Documentation: https://carragherlab.github.io/phenonaut, PyPI package: https://pypi.org/project/phenonaut/.
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Project description:Abstract To pave the road towards precision medicine in cancer, patients with similar biology ought to be grouped into same cancer subtypes. Utilizing high-dimensional multiomics datasets, integrative approaches have been developed to uncover cancer subtypes. Recently, Graph Neural Networks have been discovered to learn node embeddings utilizing node features and associations on graph-structured data. Some integrative prediction tools have been developed leveraging these advances on multiple networks with some limitations. Addressing these limitations, we developed SUPREME, a node classification framework, which integrates multiple data modalities on graph-structured data. On breast cancer subtyping, unlike existing tools, SUPREME generates patient embeddings from multiple similarity networks utilizing multiomics features and integrates them with raw features to capture complementary signals. On breast cancer subtype prediction tasks from three datasets, SUPREME outperformed other tools. SUPREME-inferred subtypes had significant survival differences, mostly having more significance than ground truth, and outperformed nine other approaches. These results suggest that with proper multiomics data utilization, SUPREME could demystify undiscovered characteristics in cancer subtypes that cause significant survival differences and could improve ground truth label, which depends mainly on one datatype. In addition, to show model-agnostic property of SUPREME, we applied it to two additional datasets and had a clear outperformance.
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Project description:IntroductionThe aim of this study was to examine whether circulating levels of the pro-inflammatory glycoprotein tenascin-C (TNC) are elevated in musculoskeletal disorders including rheumatoid arthritis (RA) and to assess in RA whether levels are related to clinical disease status and/or patient response to treatment.MethodsTNC in serum or plasma was quantified by ELISA. Samples from 4 cohorts of RA patients were examined and compared to normal human subjects and to patients with other inflammatory diseases.ResultsCirculating TNC levels were significantly raised in patients with RA, as well as patients with systemic lupus erythematosus, idiopathic inflammatory myositis, psoriatic arthritis and ankylosing spondylitis, whilst patients with Sjogren's syndrome displayed levels similar to healthy controls. The highest levels of TNC were observed in RA patients with late stage disease. In early disease TNC levels correlated positively with ultrasound determined erosion scores. Treatment of early RA patients with infliximab plus methotrexate (MTX) resulted in a transient decrease in circulating TNC over the first year of therapy. In contrast, TNC levels increased over time in RA patients receiving MTX alone. In patients treated with infliximab plus MTX, baseline TNC levels significantly correlated with tender joint counts (TJC) at 18 and 54 weeks after initiation of infliximab therapy.ConclusionsRaised circulating TNC levels are detected in specific inflammatory diseases. Levels are especially high in RA where they may act as a biomarker of bone erosion and a predictor of the effect of infliximab on RA patient joint pain.
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Project description:ObjectiveTo investigate the association between obesity-related traits and risk of rheumatoid arthritis (RA).MethodsWe conducted genetic correlation analysis and a 2-sample Mendelian randomization (MR) study, using genome-wide genetic data based on >850,000 individuals of European ancestry. Summary statistics were collected from the largest genome-wide association study conducted to date for body mass index (BMI; n = 806,810), waist-to-hip ratio (WHR; n = 697,734), WHR adjusted for BMI (WHRadjBMI; n = 694,649), and RA (ncase = 14,361, ncontrol = 43,923). We conducted cross-trait linkage disequilibrium score regression and ρ-HESS analyses to quantify genetic correlation between pairs of traits (causal overlap). For each obesity-related exposure, we utilized independent, genome-wide significant single-nucleotide polymorphisms (P < 5 × 10-9 ) as instruments to perform MR analysis (causal relationship). We interrogated the causal relationship both in the general population and in a sex-specific manner and calculated odds ratios (ORs) and 95% confidence intervals (95% CIs). Sensitivity analyses were performed to validate MR model assumptions.ResultsDespite a negligible overall genetic correlation between the 3 obesity-related traits and RA, we found significant local genetic correlations at several regions on chromosome 6 (positions 28-29M, 30-35M, and 50-52M), highlighting a shared genetic basis. We further observed an increased risk of RA per SD increment (4.8 kg/m2 ) in genetically predicted BMI (OR 1.22 [95% CI 1.09-1.37]). The effect was consistent across sensitivity analyses and comparable between sexes (OR 1.22 [95% CI 1.04-1.44] in male subjects and 1.19 [95% CI 1.04-1.36] in female subjects). However, we did not find evidence supporting a causal role of either WHR (OR 0.98 [95% CI 0.84-1.14]) or WHRadjBMI (OR 0.90 [95% CI 0.79-1.04]) in RA.ConclusionGenetically predicted BMI significantly increases RA risk. Future studies are needed to understand the biologic mechanisms underlying this link.
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Project description:Rheumatoid arthritis (RA) and periodontitis are suggested to be closely linked based on microbial dysbiosis, but limited subgingival bacteria have been proven in the pathogenesis of RA. We enrolled 30 RA patients and 25 controls and divided them into three groups with matched age, gender, and diabetes statuses: group AM (all of the matched participants), group PD (periodontally diseased), and group PH (periodontally healthy). Their subgingival microbial composition was determined by V3-V4 16S rRNA gene sequencing. Significant differences in subgingival microbial clustering between the RA patients and controls were observed in groups AM and PD. Among the taxa enriched in RA, Aminipila butyrica and Peptococcus simiae were the only two species displaying positive correlation to the level of anti-citrullinated protein antibodies (ACPAs) in both of the groups. Surprisingly, the median of relative abundances of A. butyrica and P. simiae were 0% in the controls of group PD. Furthermore, a gene encoding arginine deiminase with the capability to produce citrulline was addressed in the complete genome sequence of A. butyrica. This is the first study to elucidate the important roles of A. butyrica and P. simiae as periodontal bacteria leading to RA possibly through the induction of ACPA production.
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