Transcriptome analysis describing new immunity and defense genes in peripheral blood mononuclear cells of rheumatoid arthritis patients

Drug Repurposing: A Leading Strategy for New Threats and Targets

Less than 6% of rare illnesses have an appropriate treatment option. Repurposed medications for new indications are a cost-effective and time-saving strategy that results in excellent success rates, which may significantly lower the risk associated with therapeutic development for rare illnesses. It is becoming a realistic alternative to repurposing "conventional" medications to treat joint and rare diseases considering the significant failure rates, high expenses, and sluggish stride of innovative medication advancement. This is due to delisted compounds, cheaper research fees, and faster development time frames. Repurposed drug competitors have been developed using strategic decisions based on data analysis, interpretation, and investigational approaches, but technical and regulatory restrictions must also be considered. Combining experimental and computational methodologies generates innovative new medicinal applications. It is a one-of-a-kind strategy for repurposing human-safe pharmaceuticals to treat uncommon and difficult-to-treat ailments. It is a very effective method for discovering and creating novel medications. Several pharmaceutical firms have developed novel therapies by repositioning old medications. Repurposing drugs is practical, cost-effective, and speedy and generally involves lower risks when compared to developing a new drug from the beginning.

Genome-wide identification of RNA modification-related single nucleotide polymorphisms associated with rheumatoid arthritis

BACKGROUND

RNA modification plays important roles in many biological processes, such as gene expression control. The aim of this study was to identify single nucleotide polymorphisms related to RNA modification (RNAm-SNPs) for rheumatoid arthritis (RA) as putative functional variants.

METHODS

We examined the association of RNAm-SNPs with RA in summary data from a genome-wide association study of 19,234 RA cases and 61,565 controls. We performed eQTL and pQTL analyses for the RNAm-SNPs to find associated gene expression and protein levels. Furthermore, we examined the associations of gene expression and circulating protein levels with RA using two-sample Mendelian randomization analysis methods.

RESULTS

A total of 160 RNAm-SNPs related to m⁶A, m¹A, A-to-I, m⁷G, m⁵C, m⁵U and m⁶Am modifications were identified to be significantly associated with RA. These RNAm-SNPs were located in 62 protein-coding genes, which were significantly enriched in immune-related pathways. RNAm-SNPs in important RA susceptibility genes, such as PADI2, SPRED2, PLCL2, HLA-A, HLA-B, HLA-DRB1, HLA-DPB1, TRAF1 and TXNDC11, were identified. Most of these RNAm-SNPs showed eQTL effects, and the expression levels of 26 of the modifiable genes (e.g., PADI2, TRAF1, HLA-A, HLA-DRB1, HLA-DPB1 and HLA-B) in blood cells were associated with RA. Circulating protein levels, such as CFB, GZMA, HLA-DQA2, IL21, LRPAP1 and TFF3, were affected by RNAm-SNPs and were associated with RA.

CONCLUSION

The present study identified RNAm-SNPs in the reported RA susceptibility genes and suggested that RNAm-SNPs may affect RA risk by affecting the expression levels of corresponding genes and proteins.

Systems biology approach delineates critical pathways associated with disease progression in rheumatoid arthritis

Rheumatoid Arthritis (RA) is a chronic systemic autoimmune disease leading to inflammation, cartilage cell death, synoviocyte proliferation, and increased and impaired differentiation of osteoclasts and osteoblasts leading to joint erosions and deformities. Transcriptomics, proteomics, and metabolomics datasets were analyzed to identify the critical pathways that drive the RA pathophysiology. Single nucleotide polymorphisms (SNPs) associated with RA were analyzed for the functional implications, clinical outcomes, and blood parameters later validated by literature. SNPs associated with RA were grouped into pathways that drive the immune response and cytokine production. Further gene set enrichment analysis (GSEA) was performed on gene expression omnibus (GEO) data sets of peripheral blood mononuclear cells (PBMCs), synovial macrophages, and synovial biopsies from RA patients showed enrichment of Th1, Th2, Th17 differentiation, viral and bacterial infections, metabolic signalling and immunological pathways with potential implications for RA. The proteomics data analysis presented pathways with genes involved in immunological signaling and metabolic pathways, including vitamin B12 and folate metabolism. Metabolomics datasets analysis showed significant pathways like amino-acyl tRNA biosynthesis, metabolism of amino acids (arginine, alanine aspartate, glutamate, glutamine, phenylalanine, and tryptophan), and nucleotide metabolism. Furthermore, our commonality analysis of multi-omics datasets identified common pathways with potential implications for joint remodeling in RA. Disease-modifying anti-rheumatic drugs (DMARDs) and biologics treatments were found to modulate many of the pathways that were deregulated in RA. Overall, our analysis identified molecular signatures associated with the observed symptoms, joint erosions, potential biomarkers, and therapeutic targets in RA. Communicated by Ramaswamy H. Sarma.

ReDisX, a machine learning approach, rationalizes rheumatoid arthritis and coronary artery disease patients uniquely upon identifying subpopulation differentiation markers from their genomic data

Diseases originate at the molecular-genetic layer, manifest through altered biochemical homeostasis, and develop symptoms later. Hence, symptomatic diagnosis is inadequate to explain the underlying molecular-genetic abnormality and individual genomic disparities. The current trends include molecular-genetic information relying on algorithms to recognize the disease subtypes through gene expressions. Despite their disposition toward disease-specific heterogeneity and cross-disease homogeneity, a gap still exists in describing the extent of homogeneity within the heterogeneous subpopulation of different diseases. They are limited to obtaining the holistic sense of the whole genome-based diagnosis resulting in inaccurate diagnosis and subsequent management. Addressing those ambiguities, our proposed framework, ReDisX, introduces a unique classification system for the patients based on their genomic signatures. In this study, it is a scalable machine learning algorithm deployed to re-categorize the patients with rheumatoid arthritis and coronary artery disease. It reveals heterogeneous subpopulations within a disease and homogenous subpopulations across different diseases. Besides, it identifies granzyme B (GZMB) as a subpopulation-differentiation marker that plausibly serves as a prominent indicator for GZMB-targeted drug repurposing. The ReDisX framework offers a novel strategy to redefine disease diagnosis through characterizing personalized genomic signatures. It may rejuvenate the landscape of precision and personalized diagnosis and a clue to drug repurposing.

Altered Levels of Negative Costimulatory Molecule V-Set Domain-Containing T-Cell Activation Inhibitor-1 (VTCN1) and Metalloprotease Nardilysin (NRD1) are Associated with Generalized Active Vitiligo

BACKGROUND

Vitiligo is characterized by depigmented macules on the skin caused due to autoimmune destruction of melanocytes. V-set domain-containing T-cell activation inhibitor-1 (VTCN1) is a negative costimulatory molecule that plays a vital role in suppressing autoimmunity and tuning immune response. Nardilysin (NRD1), a metalloproteinase, cleaves membrane-tethered VTCN1 resulting in the shedding of soluble-VTCN1 (sVTCN1). However, the role of VTCN1 and NRD1 in vitiligo pathogenesis is unexplored.

OBJECTIVES AND METHODS

This study was aimed to (i) Investigate the association of VTCN1 intronic polymorphisms (rs10923223 T/C and rs12046117 C/T) with vitiligo susceptibility in Gujarat population by using Polymerase Chain Reaction- Restriction Fragment Length Polymorphism (PCR-RFLP) (ii) Estimate VTCN1 & NRD1 transcript levels from peripheral blood mononuclear cells (PBMCs) and skin samples of vitiligo patients by real-time PCR, (iii) Estimate sVTCN1 and NRD1 protein levels from plasma by ELISA and (iv) Estimate VTCN1 protein levels in the skin samples of vitiligo patients by immunofluorescence.

RESULTS

The analysis revealed increased VTCN1 and NRD1 transcript levels in the skin (p = .039, p = .021 respectively), increased sVTCN1 and NRD1 levels (p  = .026, p = .015 respectively) in the plasma, and decreased VTCN1 protein levels (p = .0002) in the skin of vitiligo patients as compared to healthy controls. The genetic analysis revealed no significant association of VTCN1 intronic polymorphisms rs10923223 T/C and rs12046117 C/T with vitiligo susceptibility in Gujarat population (p = .359, p = .937, respectively).

CONCLUSIONS

The present study revealed altered VTCN1 and NRD1 expressions in the blood and skin of vitiligo patients, suggesting their potential role in the development and progression of Vitiligo.

PCBP1 is associated with rheumatoid arthritis by affecting RNA products of genes involved in immune response in Th1 cells

Rheumatoid arthritis (RA) is an autoimmune disease characterized by persistent synovitis, in which T helper 1 (Th1) can promote the development of a pro-inflammatory microenvironment. Poly(rC)-binding protein 1 (PCBP1) has been identified as a promising biomarker of RA, while its molecular mechanisms in RA development are unknown. As a canonical RNA binding protein, we propose that PCBP1 could play roles in RA by affecting both expression and alternative splicing levels in Th1 cells. Here, microarray datasets (GSE15573 and GSE23561), including 102 peripheral blood mononuclear cell samples from 39 RA patients and 63 controls, were used to evaluate the PCBP1 expression changes in RA patients. High throughput sequencing data (GSE84702) of iron driven pathogenesis in Th1 cells were downloaded and reanalyzed, including two Pcbp1 deficiency samples and two control samples in Th1 cells. In addition, CLIP-seq data of PCBP1 in Jurkat T cells was also analyzed to investigate the regulatory mechanisms of PCBP1. We found PCBP1 were down-regulated in RA specimens compared with control. The result of differentially expressed genes (DEGs) showed that Pcbp1 silencing in Th1 cells affected the expression of genes involved in immune response pathway. Alternative splicing analysis also revealed that PCBP1-regulated alternative splicing genes (RASGs) were enriched in TNF-a/NF-κB signaling pathway, T cell activation, T cell differentiation and T cell differentiation associated immune response pathways, which were highly associated with RA. DEGs and RASGs by Pcbp1 deficiency in mice were validated in PBMCs specimens of RA patients by RT-qPCR. Investigation of the CLIP-seq data revealed PCBP1 preferred to bind to 3'UTR and intron regions. PCBP1-bound genes were also significantly associated with RASGs, identifying 102 overlapped genes of these two gene sets. These genes were significantly enriched in several immune response related pathways, including myeloid cell differentiation and positive regulation of NF-κB transcription factor activity. Two RA-related genes, PML and IRAK1, were screened from the above immune related pathways. These results together support our hypothesis that PCBP1 can regulate the expression of genes involved in immune response pathway, and can bind to and regulate the alternative splicing of immune response related genes in immune T cells, and ultimately participate in the molecular mechanism of RA, providing new research ideas and directions for clinical diagnosis and treatment.

RA-MAP, molecular immunological landscapes in early rheumatoid arthritis and healthy vaccine recipients

Rheumatoid arthritis (RA) is a chronic inflammatory disorder with poorly defined aetiology characterised by synovial inflammation with variable disease severity and drug responsiveness. To investigate the peripheral blood immune cell landscape of early, drug naive RA, we performed comprehensive clinical and molecular profiling of 267 RA patients and 52 healthy vaccine recipients for up to 18 months to establish a high quality sample biobank including plasma, serum, peripheral blood cells, urine, genomic DNA, RNA from whole blood, lymphocyte and monocyte subsets. We have performed extensive multi-omic immune phenotyping, including genomic, metabolomic, proteomic, transcriptomic and autoantibody profiling. We anticipate that these detailed clinical and molecular data will serve as a fundamental resource offering insights into immune-mediated disease pathogenesis, progression and therapeutic response, ultimately contributing to the development and application of targeted therapies for RA.

Drug repurposing for rheumatoid arthritis: Identification of new drug candidates via bioinformatics and text mining analysis

Rheumatoid arthritis (RA) is an autoimmune disease that results in the destruction of tissue by attacks on the patient by his or her own immune system. Current treatment strategies are not sufficient to overcome RA. In the present study, various transcriptomic data from synovial fluids, synovial fluid-derived macrophages, and blood samples from patients with RA were analysed using bioinformatics approaches to identify tissue-specific repurposing drug candidates for RA. Differentially expressed genes (DEGs) were identified by integrating datasets for each tissue and comparing diseased to healthy samples. Tissue-specific protein-protein interaction (PPI) networks were generated and topologically prominent proteins were selected. Transcription-regulating biomolecules for each tissue type were determined from protein-DNA interaction data. Common DEGs and reporter biomolecules were used to identify drug candidates for repurposing using the hypergeometric test. As a result of bioinformatic analyses, 19 drugs were identified as repurposing candidates for RA, and text mining analyses supported our findings. We hypothesize that the FDA-approved drugs momelotinib, ibrutinib, and sodium butyrate may be promising candidates for RA. In addition, CHEMBL306380, Compound 19a (CHEMBL3116050), ME-344, XL-019, TG100801, JNJ-26483327, and NV-128 were identified as novel repurposing candidates for the treatment of RA. Preclinical and further validation of these drugs may provide new treatment options for RA.

Targeting MyD88 Downregulates Inflammatory Mediators and Pathogenic Processes in PBMC From DMARDs-Naïve Rheumatoid Arthritis Patients

MyD88-dependent intracellular signalling cascades and subsequently NF-kappaB-mediated transcription lead to the dynamic inflammatory processes underlying the pathogenesis of rheumatoid arthritis (RA) and related autoimmune diseases. This study aimed to identify the effect of the MyD88 dimerization inhibitor, ST2825, as a modulator of pathogenic gene expression signatures and systemic inflammation in disease-modifying antirheumatic drugs (DMARDs)-naïve RA patients. We analyzed bulk RNA-seq from peripheral blood mononuclear cells (PBMC) in DMARDs-naïve RA patients after stimulation with LPS and IL-1β. The transcriptional profiles of ST2825-treated PBMC were analyzed to identify its therapeutic potential. Ingenuity Pathway Analysis was implemented to identify downregulated pathogenic processes. Our analysis revealed 631 differentially expressed genes between DMARDs-naïve RA patients before and after ST2825 treatment. ST2825-treated RA PBMC exhibited a gene expression signature similar to that of healthy controls PBMC by downregulating the expression of proinflammatory cytokines, chemokines and matrix metalloproteases. In addition, B cell receptor, IL-17 and IL-15 signalling were critically downregulated pathways by ST2825. Furthermore, we identified eight genes (MMP9, CXCL9, MZB1, FUT7, TGM2, IGLV1-51, LINC01010, and CDK1) involved in pathogenic processes that ST2825 can potentially inhibit in distinct cell types within the RA synovium. Overall, our findings indicate that targeting MyD88 effectively downregulates systemic inflammatory mediators and modulates the pathogenic processes in PBMC from DMARDs-naïve RA patients. ST2825 could also potentially inhibit upregulated genes in the RA synovium, preventing synovitis and joint degeneration.

NMR-Based Metabolomics Revealed the Underlying Inflammatory Pathology in Reactive Arthritis Synovial Joints

Reactive arthritis (ReA) is an aseptic synovitis condition that often develops 2-4 weeks after a distant (extra-articular) infection with Chlamydia, Salmonella, Shigella, Campylobacter, and Yersinia species. The metabolic changes in the synovial fluid (SF) may serve as indicative markers to both improve the diagnostic accuracy and understand the underlying inflammatory pathology of ReA. With this aim, the metabolic profiles of SF collected from ReA (n = 58) and non-ReA, i.e., rheumatoid arthritis (RA, n = 21) and osteoarthritis (OA, n = 20) patients, respectively, were measured using NMR spectroscopy and compared using orthogonal partial least-squares discriminant analysis (OPLS-DA). The discriminatory metabolic features were further evaluated for their diagnostic potential using the receiver operating characteristic (ROC) curve analysis. Compared to RA, two (alanine and carnitine), and compared to OA, six (NAG, glutamate, glycerol, isoleucine, alanine, and glucose) metabolic features were identified as diagnostic biomarkers. We further demonstrated the impact of ReA synovitis condition on the serum metabolic profiles through performing a correlation analysis. The Pearson rank coefficient (r) was estimated for 38 metabolites (profiled in both SF and serum samples obtained in pair from ReA patients) and was found significantly positive for 71% of the metabolites (r ranging from 0.17 to 0.87).

The impact of sex on susceptibility to systemic lupus erythematosus and rheumatoid arthritis; a bioinformatics point of view

The unknown etiology of systemic autoimmune diseases, such as Systemic Lupus Erythematosus (SLE) and Rheumatoid Arthritis (RA), with a remarkable predominance of female, have prompted many researchers for unveiling the precise molecular mechanisms involved in this gender bias. In fact, depending on hormones and transcribed genes from sex chromosomes, at least, the initial mechanisms involved in pathogenesis might differ largely. With the aim of elucidating the above mechanisms, we have tried to specify the differentially expressed genes (DEGs) extracted from microarray libraries from both female and male SLE and RA patients. Subsequently, the androgen and estrogen receptor elements (ARE and ERE) among differentially expressed transcription factors (TFs) and the DEGs located on X or Y chromosomes have been determined. Moreover, the pathways regarding the common DEGs in both sexes are enriched. Our data revealed several ARE and ERE-containing genes (LCN2, LTF, RPL31, RPL9, RPS17, RPS24, RPS27L, S100A8, ABCA1, HIST1H2BD, ISG15, MAFB, GNLY, EVL, and HDC) to be associated with the related autoimmune disease and sex. Also, two DEGs (KDM5D and RPS4Y1) in SLE patients were determined to be on Y chromosome with one had been proved to be associated with autoantigens in SLE. Altogether, our data showed a number of plausible pathways in both autoimmune conditions together with the relevance of several sex-related genes in the mentioned diseases pathogenesis.

Inflammatory macrophages exacerbate neutrophil-driven joint damage through ADP/P2Y1 signaling in rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune disease that primarily affects the joints and is associated with excessive immune cell infiltration. However, the complex interactions between the immune cell populations in the RA synovium remain unknown. Here, we demonstrate that inflammatory macrophages in the synovium exacerbate neutrophil-driven joint damage in RA through ADP/P2Y₁ signaling. We show that extracellular ADP (eADP) and its receptors are obviously increased in synovial tissues of RA patients as well as collagen-induced arthritis (CIA) mice, and eADP enhances neutrophil infiltration into joints through macrophages producing the chemokine CXCL2, aggravating disease development. Accordingly, the arthritis mouse model had more neutrophils in inflamed joints following ADP injection, whereas P2Y₁ deficiency and pharmacologic inhibition restored arthritis severity to basal levels, suggesting a dominant role of ADP/P2Y₁ signaling in RA pathology. Moreover, cellular activity of ADP/P2Y₁-mediated CXCL2 production was dependent on the G_αq/Ca²⁺-NF-κB/NFAT pathway in macrophages. Overall, this study reveals a non-redundant role of eADP as a trigger in the pathogenesis of RA through neutrophil recruitment and disrupted tissue homeostasis and function.

Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis

There is an urgent need to identify biomarkers for diagnosis and disease activity monitoring in rheumatoid arthritis (RA). We leveraged publicly available microarray gene expression data in the NCBI GEO database for whole blood (N=1,885) and synovial (N=284) tissues from RA patients and healthy controls. We developed a robust machine learning feature selection pipeline with validation on five independent datasets culminating in 13 genes: TNFAIP6, S100A8, TNFSF10, DRAM1, LY96, QPCT, KYNU, ENTPD1, CLIC1, ATP6V0E1, HSP90AB1, NCL and CIRBP which define the RA score and demonstrate its clinical utility: the score tracks the disease activity DAS28 (p = 7e-9), distinguishes osteoarthritis (OA) from RA (OR 0.57, p = 8e-10) and polyJIA from healthy controls (OR 1.15, p = 2e-4) and monitors treatment effect in RA (p = 2e-4). Finally, the immunoblotting analysis of six proteins on an independent cohort confirmed two proteins, TNFAIP6/TSG6 and HSP90AB1/HSP90.

Identification of biological pathways specific to phases preceding rheumatoid arthritis development through gene expression profiling

The etiopathogenesis of rheumatoid arthritis is partially understood; however, it is believed to result from a multi-step process. The immune onset followed by pre-clinical phases will eventually lead to the development of symptomatic disease. We aim at identifying differentially expressed genes in order to highlight pathways involved in the pre-clinical stages of rheumatoid arthritis development. The study population consisted of first-degree relatives of patients with rheumatoid arthritis, known to have an increased risk of developing disease as compared to the general population. Whole transcriptome analysis was performed in four groups: asymptomatic without autoantibodies or symptoms associated with possible rheumatoid arthritis (controls); having either clinically suspect arthralgias, undifferentiated arthritis or autoimmunity associated with RA (pre-clinical stages of RA: Pcs-RA); having subsequently developed classifiable RA (pre-RA); and early untreated rheumatoid arthritis patients (RA). Differentially expressed genes were determined, and enrichment analysis was performed. Functional enrichment analysis revealed 31 pathways significantly enriched in differentially expressed genes for Pcs-RA, pre-RA and RA compared to the controls. Osteoclast pathway is among the seven pathways specific for RA. In Pcs-RA and in pre-RA, several enriched pathways include TP53 gene connections, such as P53 and Wnt signalling pathways. Analysis of whole transcriptome for phenotypes related to rheumatoid arthritis allows highlighting which pathways are requested in the pre-clinical stages of disease development. After validation in replication studies, molecules belonging to some of these pathways could be used to identify new specific biomarkers for individuals with impending rheumatoid arthritis.

Transcriptome-wide association study identifies susceptibility genes for rheumatoid arthritis

Objective

To identify rheumatoid arthritis (RA)-associated susceptibility genes and pathways through integrating genome-wide association study (GWAS) and gene expression profile data.

Methods

A transcriptome-wide association study (TWAS) was conducted by the FUSION software for RA considering EBV-transformed lymphocytes (EL), transformed fibroblasts (TF), peripheral blood (NBL), and whole blood (YBL). GWAS summary data was driven from a large-scale GWAS, involving 5539 autoantibody-positive RA patients and 20,169 controls. The TWAS-identified genes were further validated using the mRNA expression profiles and made a functional exploration.

Results

TWAS identified 692 genes with P_TWAS values < 0.05 for RA. CRIPAK (P^EL = 0.01293, P^TF = 0.00038, P^NBL = 0.02839, P^YBL = 0.0978), MUT (P^EL = 0.00377, P^TF = 0.00076, P^NBL = 0.00778, P^YBL = 0.00096), FOXRED1 (P^EL = 0.03834, P^TF = 0.01120, P^NBL = 0.01280, P^YBL = 0.00583), and EBPL (P^EL = 0.00806, P^TF = 0.03761, P^NBL = 0.03540, P^YBL = 0.04254) were collectively expressed in all the four tissues/cells. Eighteen genes, including ANXA5, AP4B1, ATIC (P_TWAS = 0.0113, downregulated expression), C12orf65, CMAH, PDHB, RUNX3 (P_TWAS = 0.0346, downregulated expression), SBF1, SH2B3, STK38, TMEM43, XPNPEP1, KIAA1530, NUFIP2, PPP2R3C, RAB24, STX6, and TLR5 (P_TWAS = 0.04665, upregulated expression), were validated with integrative analysis of TWAS and mRNA expression profiles. TWAS-identified genes functionally involved in endoplasmic reticulum organization, regulation of cytokine production, TNF signaling pathway, immune response-regulating signaling pathway, regulation of autophagy, etc.

Conclusion

We identified multiple candidate genes and pathways, providing novel clues for the genetic mechanism of RA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-021-02419-9.

Is gene expression among women with rheumatoid arthritis dysregulated during a postpartum flare?

Background

To evaluate our hypotheses that, when rheumatoid arthritis (RA) flares postpartum, gene expression patterns are altered compared to (a) healthy women, (b) RA women whose disease activity is low or in remission postpartum, and (c) pre-pregnancy expression profiles.

Methods

Twelve women with RA and five healthy women were included in this pilot study. RA disease activity and postpartum flare were assessed using the Clinical Disease Activity Index (CDAI). Total RNA from frozen whole blood was used for RNA sequencing. Differential gene expression within the same women (within-group) over time, i.e., postpartum vs. third trimester (T3) or pre-pregnancy (T0), were examined, using a significance threshold of q < 0.05 and fold-change ≥ 2.

Results

Nine of the women with RA experienced a flare postpartum (RA_Flare), while three had low disease activity or were in remission (RA_NoFlare) during that time frame. Numerous immune-related genes were differentially expressed postpartum (vs. T3) during a flare. Fold-changes in expression from T3 to postpartum were mostly comparable between the RA_Flare and healthy groups. At 3 months postpartum, compared to healthy women, several genes were significantly differentially expressed only among the RA_Flare women, and not among the RA_NoFlare women. Some of these genes were among those whose “normal” expression was significantly modulated postpartum, and the postpartum expression patterns were significantly altered during the RA flare. There were also some genes that were significantly differentially expressed in RA_Flare compared to both healthy and RA_NoFlare women, even though their expression was not significantly modulated postpartum. Furthermore, while postpartum expression profiles were similar to those at pre-pregnancy among healthy women, significant differences were found between those time points among the RA_Flare women.

Conclusions

The large majority of gene expression changes between T3 and 3 months postpartum among RA women who flared postpartum reflected normal postpartum changes also seen among healthy women. Nonetheless, during a postpartum flare, a set of immune-related genes showed dysregulated expression compared to healthy women and women with RA whose disease activity was low or in remission during the same time frame, while other genes demonstrated significant differences in expression compared to RA pre-pregnancy levels.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-021-02418-w.

Integrative analysis identifies potential causal methylation-mRNA regulation chains for rheumatoid arthritis

Genome-wide association studies have identified many genetic loci for rheumatoid arthritis (RA). However, causal factors underlying these loci were largely unknown. The aim of this study was to identify potential causal methylation-mRNA regulation chains for RA. We identified differentially expressed mRNAs and methylations and conducted summary statistic data-based Mendelian randomization (SMR) analysis to detect potential causal mRNAs and methylations for RA. Then causal inference test (CIT) was performed to determine if the methylation-mRNA pairs formed causal chains. We identified 11,170 mRNAs and 24,065 methylations that were nominally associated with RA. Among them, 197 mRNAs and 104 methylations passed the SMR test. According to physical positions, we defined 16 cis methylation-mRNA pairs and inferred 5 chains containing 4 methylations and 4 genes (BACH2, MBP, MX1 and SYNGR1) to be methylation→mRNA→RA causal chains. The effect of SYNGR1 expression in peripheral blood mononuclear cells on RA risk was found to be consistent in both the in-house and public data. The identified methylations located in CpG Islands that overlap promoters in the 5' region of the genes. The promoter regions showed long-range interactions with other enhancers and promoters, suggesting a regulatory potential of these methylations. Therefore, the present study provided a new integrative analysis strategy and highlighted potential causal methylation-mRNA chains for RA. Taking the evidences together, SYNGR1 promoter methylations most probably affect mRNA expressions and then affect RA risk.

The Rheumatoid Arthritis Gene Expression Signature Among Women Who Improve or Worsen During Pregnancy: A Pilot Study

OBJECTIVE

To assess whether gene expression signatures associated with rheumatoid arthritis (RA) before pregnancy differ between women who improve or worsen during pregnancy, and to determine whether these expression signatures are altered during pregnancy when RA improves or worsens.

METHODS

Clinical data and blood samples were collected before pregnancy (T0) and at the third trimester (T3) from 11 women with RA and 5 healthy women. RA disease activity was assessed using the Clinical Disease Activity Index (CDAI). At each timepoint, RA-associated gene expression signatures were identified using differential expression analysis of RNA sequencing profiles between women with RA and healthy women.

RESULTS

Of the women with RA, 6 improved by T3 (RA_improved), 3 worsened (RA_worsened),and 2 were excluded. At T0, mean CDAI scores were similar in both groups (RA_improved 11.2 ± 9.8; RA_worsened 13.8 ± 6.7; Wilcoxon rank-sum test: P = 0.6). In the RA_improved group, 89 genes were differentially expressed at T0 (q < 0.05 and fold change ≥ 2) compared to healthy women. When RA improved at T3, 65 of 89 (73%) of these genes no longer displayed RA-associated expression. In the RA_worsened group, a largely different RA gene expression signature (429 genes) was identified at T0. When RA disease activity worsened at T3, 207 of 429 (48%) genes lost their differential expression, while an additional 151 genes became newly differentially expressed.

CONCLUSION

In our pilot dataset, pre-pregnancy RA expression signatures differed between women who subsequently improved or worsened during pregnancy, suggesting that inherent genomic differences may influence how pregnancy affects disease activity. Further, these RA signatures were altered during pregnancy as disease activity changed.

RNA Identification of PRIME Cells Predicting Rheumatoid Arthritis Flares

BACKGROUND

Rheumatoid arthritis, like many inflammatory diseases, is characterized by episodes of quiescence and exacerbation (flares). The molecular events leading to flares are unknown.

METHODS

We established a clinical and technical protocol for repeated home collection of blood in patients with rheumatoid arthritis to allow for longitudinal RNA sequencing (RNA-seq). Specimens were obtained from 364 time points during eight flares over a period of 4 years in our index patient, as well as from 235 time points during flares in three additional patients. We identified transcripts that were differentially expressed before flares and compared these with data from synovial single-cell RNA-seq. Flow cytometry and sorted-blood-cell RNA-seq in additional patients were used to validate the findings.

RESULTS

Consistent changes were observed in blood transcriptional profiles 1 to 2 weeks before a rheumatoid arthritis flare. B-cell activation was followed by expansion of circulating CD45-CD31-PDPN+ preinflammatory mesenchymal, or PRIME, cells in the blood from patients with rheumatoid arthritis; these cells shared features of inflammatory synovial fibroblasts. Levels of circulating PRIME cells decreased during flares in all 4 patients, and flow cytometry and sorted-cell RNA-seq confirmed the presence of PRIME cells in 19 additional patients with rheumatoid arthritis.

CONCLUSIONS

Longitudinal genomic analysis of rheumatoid arthritis flares revealed PRIME cells in the blood during the period before a flare and suggested a model in which these cells become activated by B cells in the weeks before a flare and subsequently migrate out of the blood into the synovium. (Funded by the National Institutes of Health and others.).

A new computational drug repurposing method using established disease-drug pair knowledge

MOTIVATION

Drug repurposing is a potential alternative to the classical drug discovery pipeline. Repurposing involves finding novel indications for already approved drugs. In this work, we present a novel machine learning-based method for drug repurposing. This method explores the anti-similarity between drugs and a disease to uncover new uses for the drugs. More specifically, our proposed method takes into account three sources of information: (i) large-scale gene expression profiles corresponding to human cell lines treated with small molecules, (ii) gene expression profile of a human disease and (iii) the known relationship between Food and Drug Administration (FDA)-approved drugs and diseases. Using these data, our proposed method learns a similarity metric through a supervised machine learning-based algorithm such that a disease and its associated FDA-approved drugs have smaller distance than the other disease-drug pairs.

RESULTS

We validated our framework by showing that the proposed method incorporating distance metric learning technique can retrieve FDA-approved drugs for their approved indications. Once validated, we used our approach to identify a few strong candidates for repurposing.

AVAILABILITY AND IMPLEMENTATION

The R scripts are available on demand from the authors.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

BioMethyl: an R package for biological interpretation of DNA methylation data

Motivation

The accumulation of publicly available DNA methylation datasets has resulted in the need for tools to interpret the specific cellular phenotypes in bulk tissue data. Current approaches use either single differentially methylated CpG sites or differentially methylated regions that map to genes. However, these approaches may introduce biases in downstream analyses of biological interpretation, because of the variability in gene length. There is a lack of approaches to interpret DNA methylation effectively. Therefore, we have developed computational models to provide biological interpretation of relevant gene sets using DNA methylation data in the context of The Cancer Genome Atlas.

Results

We illustrate that Biological interpretation of DNA Methylation (BioMethyl) utilizes the complete DNA methylation data for a given cancer type to reflect corresponding gene expression profiles and performs pathway enrichment analyses, providing unique biological insight. Using breast cancer as an example, BioMethyl shows high consistency in the identification of enriched biological pathways from DNA methylation data compared to the results calculated from RNA sequencing data. We find that 12 out of 14 pathways identified by BioMethyl are shared with those by using RNA-seq data, with a Jaccard score 0.8 for estrogen receptor (ER) positive samples. For ER negative samples, three pathways are shared in the two enrichments with a slight lower similarity (Jaccard score = 0.6). Using BioMethyl, we can successfully identify those hidden biological pathways in DNA methylation data when gene expression profile is lacking.

Availability and implementation

BioMethyl R package is freely available in the GitHub repository (https://github.com/yuewangpanda/BioMethyl).

Supplementary information

Supplementary data are available at Bioinformatics online.

Can joint fluid metabolic profiling (or "metabonomics") reveal biomarkers for osteoarthritis and inflammatory joint disease?: A systematic review

Aims

Metabolic profiling is a top-down method of analysis looking at metabolites, which are the intermediate or end products of various cellular pathways. Our primary objective was to perform a systematic review of the published literature to identify metabolites in human synovial fluid (HSF), which have been categorized by metabolic profiling techniques. A secondary objective was to identify any metabolites that may represent potential biomarkers of orthopaedic disease processes.

Methods

A systematic review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines using the MEDLINE, Embase, PubMed, and Cochrane databases. Studies included were case series, case control series, and cohort studies looking specifically at HSF.

Results

The primary analysis, which pooled the results from 17 published studies and four meeting abstracts, identified over 200 metabolites. Seven of these studies (six published studies, one meeting abstract) had asymptomatic control groups and collectively suggested 26 putative biomarkers in osteoarthritis, inflammatory arthropathies, and trauma. These can broadly be categorized into amino acids plus related metabolites, fatty acids, ketones, and sugars.

Conclusion

The role of metabolic profiling in orthopaedics is fast evolving with many metabolites already identified in a variety of pathologies. However, these results need to be interpreted with caution due to the presence of multiple confounding factors in many of the studies. Future research should include largescale epidemiological metabolic profiling studies incorporating various confounding factors with appropriate statistical analysis to account for multiple testing of the data.

Cite this article:Bone Joint Res. 2020;9(3):108–119.

Individualized Drug Repositioning For Rheumatoid Arthritis Using Weighted Kolmogorov-Smirnov Algorithm

Background

Existing drugs are far from enough for investigators and patients to administrate the therapy of rheumatoid arthritis. Drug repositioning has drawn broad attention by reusing marketed drugs and clinical candidates for new uses.

Purpose

This study attempted to predict candidate drugs for rheumatoid arthritis treatment by mining the similarities of pathway aberrance induced by disease and various drugs, on a personalized or customized basis.

Methods

We firstly measured the individualized pathway aberrance induced by rheumatoid arthritis based on the microarray data and various drugs from CMap database, respectively. Then, the similarities of pathway aberrances between RA and various drugs were calculated using a Kolmogorov–Smirnov weighted enrichment score algorithm.

Results

Using this method, we identified 4 crucial pathways involved in rheumatoid arthritis development and predicted 9 underlying candidate drugs for rheumatoid arthritis treatment. Some candidates with current indications to treat other diseases might be repurposed to treat rheumatoid arthritis and complement the drug group for rheumatoid arthritis.

Conclusion

This study predicts candidate drugs for rheumatoid arthritis treatment through mining the similarities of pathway aberrance induced by disease and various drugs, on a personalized or customized basis. Our framework will provide novel insights in personalized drug discovery for rheumatoid arthritis and contribute to the future application of custom therapeutic decisions.

Phosphorylation-related SNPs influence lipid levels and rheumatoid arthritis risk by altering gene expression and plasma protein levels

Objectives

Phosphorylation-related single-nucleotide polymorphisms (phosSNPs) are missense SNPs that may influence protein phosphorylation. The aim of this study was to evaluate the effect of phosSNPs on lipid levels and RA.

Methods

We examined the association of phosSNPs with lipid levels and RA in large-scale genome-wide association studies (GWAS) and performed random sampling and fgwas analyses to determine whether the phosSNPs associated with lipid levels and RA were significantly enriched. Furthermore, we performed QTL analysis and Mendelian randomization analysis to obtain additional evidence to be associated with the identified phosSNPs and genes.

Results

We found 483 phosSNPs for lipid levels and 243 phosSNPs for RA in the GWAS loci (P &lt; 1.0 × 10−5). SNPs associated with high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, Total cholesterol (TC) and RA were significantly enriched with phosSNPs. Almost all of the identified phosSNPs showed expression quantitative trait loci (eQTL) effects. A total of 48 protein QTLs and 9 metabolite QTLs were found. The phosSNP rs3184504 (p.Trp262Arg) at SH2B3 was significantly associated with RA, SH2B3 expression level, and plasma levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, TC, hypoxanthine and 80 proteins, including beta-2-microglobulin. SH2B3 was differentially expressed between RA cases and controls in peripheral blood mononuclear cells and synovial tissues. Mendelian randomization analysis showed that SH2B3 expression level was significantly associated with TC level and RA. Plasma beta-2-microglobulin level was causally associated with high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, TC levels and RA.

Conclusion

The findings suggested that phosSNPs may play important roles in lipid metabolism and the pathological mechanisms of RA. PhosSNPs may influence lipid levels and RA risk by altering gene expression and plasma protein levels.

Analysis of gene expression in rheumatoid arthritis and related conditions offers insights into sex-bias, gene biotypes and co-expression patterns

The era of next-generation sequencing has mounted the foundation of many gene expression studies. In rheumatoid arthritis research, this has led to the discovery of important candidate genes which offered novel insights into mechanisms and their possible roles in the cure of the disease. In the last years, data generation has outstripped data analysis and while many studies focused on specific aspects of the disease, a global picture of the disease is not yet accomplished. Here, we analyzed and compared a collection of gene expression information from healthy individuals and from patients suffering under different arthritis conditions from published studies containing the following clinical conditions: early and established rheumatoid arthritis, osteoarthritis and arthralgia. We show comprehensive overviews of this data collection and give new insights specifically on gene expression in the early stage, into sex-dependent gene expression, and we describe general differences in expression of different biotypes of genes. Many genes that are related to cytoskeleton changes (actin filament related genes) are differently expressed in early rheumatoid arthritis in comparison to healthy subjects; interestingly, eight of these genes reverse their expression ratio significantly between men and women compared early rheumatoid arthritis and healthy subjects. There are some slighter changes between men and woman between the conditions early and established rheumatoid arthritis. Another aspect are miRNAs and other gene biotypes which are not only promising candidates for diagnoses but also change their expression grossly in average at rheumatoid arthritis and arthralgia compared to the healthy condition. With a selection of intersecting genes, we were able to generate simple classification models to distinguish between healthy and rheumatoid arthritis as well as between early rheumatoid arthritis to other arthritides based on gene expression.

Gene expression profiling meta-analysis reveals novel gene signatures and pathways shared between tuberculosis and rheumatoid arthritis

Tuberculosis (TB) is among the leading causes of death by infectious diseases. An epidemiological association between Mycobacterium tuberculosis infection and autoimmune diseases like rheumatoid arthritis (RA) has been reported but it remains unclear if there is a causal relationship, and if so, which molecular pathways and regulatory mechanisms contribute to it. Here we used a computational biology approach by global gene expression meta-analysis to identify candidate genes and pathways that may link TB and RA. Data were collected from public expression databases such as NCBI GEO. Studies were selected that analyzed mRNA-expression in whole blood or blood cell populations in human case control studies at comparable conditions. Six TB and RA datasets (41 active TB patients, 33 RA patients, and 67 healthy controls) were included in the downstream analysis. This approach allowed the identification of deregulated genes that had not been identified in the single analysis of TB or RA patients and that were co-regulated in TB and RA patients compared to healthy subjects. The genes encoding TLR5, TNFSF10/TRAIL, PPP1R16B/TIMAP, SIAH1, PIK3IP1, and IL17RA were among the genes that were most significantly deregulated in TB and RA. Pathway enrichment analysis revealed 'T cell receptor signaling pathway', 'Toll-like receptor signaling pathway,' and 'virus defense related pathways' among the pathways most strongly associated with both diseases. The identification of a common gene signature and pathways substantiates the observation of an epidemiological association of TB and RA and provides clues on the mechanistic basis of this association. Newly identified genes may be a basis for future functional and epidemiological studies.

Identification of gene expression predictors of occupational benzene exposure

BACKGROUND

Previously, using microarrays and mRNA-Sequencing (mRNA-Seq) we found that occupational exposure to a range of benzene levels perturbed gene expression in peripheral blood mononuclear cells.

OBJECTIVES

In the current study, we sought to identify gene expression biomarkers predictive of benzene exposure below 1 part per million (ppm), the occupational standard in the U.S.

METHODS

First, we used the nCounter platform to validate altered expression of 30 genes in 33 unexposed controls and 57 subjects exposed to benzene (<1 to ≥5 ppm). Second, we used SuperLearner (SL) to identify a minimal number of genes for which altered expression could predict <1 ppm benzene exposure, in 44 subjects with a mean air benzene level of 0.55±0.248 ppm (minimum 0.203ppm).

RESULTS

nCounter and microarray expression levels were highly correlated (coefficients >0.7, p<0.05) for 26 microarray-selected genes. nCounter and mRNA-Seq levels were poorly correlated for 4 mRNA-Seq-selected genes. Using negative binomial regression with adjustment for covariates and multiple testing, we confirmed differential expression of 23 microarray-selected genes in the entire benzene-exposed group, and 27 genes in the <1 ppm-exposed subgroup, compared with the control group. Using SL, we identified 3 pairs of genes that could predict <1 ppm benzene exposure with cross-validated AUC estimates >0.9 (p<0.0001) and were not predictive of other exposures (nickel, arsenic, smoking, stress). The predictive gene pairs are PRG2/CLEC5A, NFKBI/CLEC5A, and ACSL1/CLEC5A. They play roles in innate immunity and inflammatory responses.

CONCLUSIONS

Using nCounter and SL, we validated the altered expression of multiple mRNAs by benzene and identified gene pairs predictive of exposure to benzene at levels below the US occupational standard of 1ppm.

Knowledge-Based Neuroendocrine Immunomodulation (NIM) Molecular Network Construction and Its Application

Growing evidence shows that the neuroendocrine immunomodulation (NIM) network plays an important role in maintaining and modulating body function and the homeostasis of the internal environment. The disequilibrium of NIM in the body is closely associated with many diseases. In the present study, we first collected a core dataset of NIM signaling molecules based on our knowledge and obtained 611 NIM signaling molecules. Then, we built a NIM molecular network based on the MetaCore database and analyzed the signaling transduction characteristics of the core network. We found that the endocrine system played a pivotal role in the bridge between the nervous and immune systems and the signaling transduction between the three systems was not homogeneous. Finally, employing the forest algorithm, we identified the molecular hub playing an important role in the pathogenesis of rheumatoid arthritis (RA) and Alzheimer’s disease (AD), based on the NIM molecular network constructed by us. The results showed that GSK3B, SMARCA4, PSMD7, HNF4A, PGR, RXRA, and ESRRA might be the key molecules for RA, while RARA, STAT3, STAT1, and PSMD14 might be the key molecules for AD. The molecular hub may be a potentially druggable target for these two complex diseases based on the literature. This study suggests that the NIM molecular network in this paper combined with the forest algorithm might provide a useful tool for predicting drug targets and understanding the pathogenesis of diseases. Therefore, the NIM molecular network and the corresponding online tool will not only enhance research on complex diseases and system biology, but also promote the communication of valuable clinical experience between modern medicine and Traditional Chinese Medicine (TCM).

Utility of DNA, RNA, Protein, and Functional Approaches to Solve Cryptic Immunodeficiencies

PURPOSE

We report a female infant identified by newborn screening for severe combined immunodeficiencies (NBS SCID) with T cell lymphopenia (TCL). The patient had persistently elevated alpha-fetoprotein (AFP) with IgA deficiency, and elevated IgM. Gene sequencing for a SCID panel was uninformative. We sought to determine the cause of the immunodeficiency in this infant.

METHODS

We performed whole-exome sequencing (WES) on the patient and parents to identify a genetic diagnosis. Based on the WES result, we developed a novel flow cytometric panel for rapid assessment of DNA repair defects using blood samples. We also performed whole transcriptome sequencing (WTS) on fibroblast RNA from the patient and father for abnormal transcript analysis.

RESULTS

WES revealed a pathogenic paternally inherited indel in ATM. We used the flow panel to assess several proteins in the DNA repair pathway in lymphocyte subsets. The patient had absent phosphorylation of ATM, resulting in absent or aberrant phosphorylation of downstream proteins, including γH2AX. However, ataxia-telangiectasia (AT) is an autosomal recessive condition, and the abnormal functional data did not correspond with a single ATM variant. WTS revealed in-frame reciprocal fusion transcripts involving ATM and SLC35F2 indicating a chromosome 11 inversion within 11q22.3, of maternal origin. Inversion breakpoints were identified within ATM intron 16 and SLC35F2 intron 7.

CONCLUSIONS

We identified a novel ATM-breaking chromosome 11 inversion in trans with a pathogenic indel (compound heterozygote) resulting in non-functional ATM protein, consistent with a diagnosis of AT. Utilization of several molecular and functional assays allowed successful resolution of this case.

Perspectives on Systems Modeling of Human Peripheral Blood Mononuclear Cells

Human peripheral blood mononuclear cells (PBMCs) are the key drivers of the immune responses. These cells undergo activation, proliferation and differentiation into various subsets. During these processes they initiate metabolic reprogramming, which is coordinated by specific gene and protein activities. PBMCs as a model system have been widely used to study metabolic and autoimmune diseases. Herein we review various omics and systems-based approaches such as transcriptomics, epigenomics, proteomics, and metabolomics as applied to PBMCs, particularly T helper subsets, that unveiled disease markers and the underlying mechanisms. We also discuss and emphasize several aspects of T cell metabolic modeling in healthy and disease states using genome-scale metabolic models.

Computational Systems Biology Approach for the Study of Rheumatoid Arthritis: From a Molecular Map to a Dynamical Model

In this work we present a systematic effort to summarize current biological pathway knowledge concerning Rheumatoid Arthritis (RA). We are constructing a detailed molecular map based on exhaustive literature scanning, strict curation criteria, re-evaluation of previously published attempts and most importantly experts' advice. The RA map will be web-published in the coming months in the form of an interactive map, using the MINERVA platform, allowing for easy access, navigation and search of all molecular pathways implicated in RA, serving thus, as an on line knowledgebase for the disease. Moreover the map could be used as a template for Omics data visualization offering a first insight about the pathways affected in different experimental datasets. The second goal of the project is a dynamical study focused on synovial fibroblasts' behavior under different initial conditions specific to RA, as recent studies have shown that synovial fibroblasts play a crucial role in driving the persistent, destructive characteristics of the disease. Leaning on the RA knowledgebase and using the web platform Cell Collective, we are currently building a Boolean large scale dynamical model for the study of RA fibroblasts' activation.

Comprehensive analysis of long non-coding RNA and mRNA expression profiles in rheumatoid arthritis

Abnormal expression of long non-coding RNA (lncRNA) has been demonstrated to be involved in a variety of human diseases. However, the role of lncRNA remains largely unknown in rheumatoid arthritis (RA). The present study aimed to investigate whether lncRNA are differentially expressed in RA. Differentially expressed lncRNA and mRNA in peripheral blood mononuclear cells from individuals with RA and healthy controls were detected using a human lncRNA microarray containing 30,586 lncRNA and 26,109 coding transcripts. Several candidate lncRNA and mRNA in 24 paired samples were verified by reverse transcription-quantitative polymerase chain reaction analysis. Bioinformatics analyses (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes) were used to evaluate signaling pathways and biological functions influenced by the differentially expressed mRNA. A total of 5,045 lncRNA (upregulated, 2,410; downregulated, 2,635) and 3,289 mRNA (upregulated, 1,403; downregulated, 1,886) were differentially expressed in patients with RA (fold change >2; P<0.05). The majority of abnormal lncRNA were from intergenic spacer regions (42%), natural antisense (19%) and intronic antisense (15%) to protein-coding loci. lncRNA target prediction indicated the presence of 135 potential lncRNA-mRNA target pairs for the 85 aberrant lncRNA and 109 aberrant mRNA. Significantly enriched (P<0.05) signaling pathways based on deregulated mRNA were mostly implicated in bile secretion, T cell receptor signaling pathway and systemic lupus erythematosus. In summary, to the best of our knowledge, the present study executed global expression profiling of lncRNA and mRNA involved in RA for the first time. These results may provide important insights regarding lncRNA in RA pathogenesis and provide potential therapeutic targets.

Cytokine Imbalance as a Common Mechanism in Both Psoriasis and Rheumatoid Arthritis

Psoriasis (PS) and rheumatoid arthritis (RA) are immune-mediated inflammatory diseases. Previous studies showed that these two diseases had a common pathogenesis, but the precise molecular mechanism remains unclear. In this study, RNA sequencing of peripheral blood mononuclear cells was employed to explore both the differentially expressed genes (DEGs) of 10 PS and 10 RA patients compared with those of 10 healthy volunteers and the shared DEGs between these two diseases. Bioinformatics network analysis was used to reveal the connections among the shared DEGs and the corresponding molecular mechanism. In total, 120 and 212 DEGs were identified in PS and RA, respectively, and 31 shared DEGs were identified. Bioinformatics analysis indicated that the cytokine imbalance relevant to key molecules (such as extracellular signal-regulated kinase 1/2 (ERK1/2), p38 mitogen-activated protein kinase (MAPK), tumor necrosis factor (TNF), colony-stimulating factor 3 (CSF3), interleukin- (IL-) 6, and interferon gene (IFNG)) and canonical signaling pathways (such as the complement system, antigen presentation, macropinocytosis signaling, nuclear factor-kappa B (NF-κB) signaling, and IL-17 signaling) was responsible for the common comprehensive mechanism of PS and RA. Our findings provide a better understanding of the pathogenesis of PS and RA, suggesting potential strategies for treating and preventing both diseases. This study may also provide a new paradigm for illuminating the common pathogenesis of different diseases.

Gene-Based Genome-Wide Association Analysis in European and Asian Populations Identified Novel Genes for Rheumatoid Arthritis

Objective

Rheumatoid arthritis (RA) is a complex autoimmune disease. Using a gene-based association research strategy, the present study aims to detect unknown susceptibility to RA and to address the ethnic differences in genetic susceptibility to RA between European and Asian populations.

Methods

Gene-based association analyses were performed with KGG 2.5 by using publicly available large RA datasets (14,361 RA cases and 43,923 controls of European subjects, 4,873 RA cases and 17,642 controls of Asian Subjects). For the newly identified RA-associated genes, gene set enrichment analyses and protein-protein interactions analyses were carried out with DAVID and STRING version 10.0, respectively. Differential expression verification was conducted using 4 GEO datasets. The expression levels of three selected ‘highly verified’ genes were measured by ELISA among our in-house RA cases and controls.

Results

A total of 221 RA-associated genes were newly identified by gene-based association study, including 71‘overlapped’, 76 ‘European-specific’ and 74 ‘Asian-specific’ genes. Among them, 105 genes had significant differential expressions between RA patients and health controls at least in one dataset, especially for 20 genes including 11 ‘overlapped’ (ABCF1, FLOT1, HLA-F, IER3, TUBB, ZKSCAN4, BTN3A3, HSP90AB1, CUTA, BRD2, HLA-DMA), 5 ‘European-specific’ (PHTF1, RPS18, BAK1, TNFRSF14, SUOX) and 4 ‘Asian-specific’ (RNASET2, HFE, BTN2A2, MAPK13) genes whose differential expressions were significant at least in three datasets. The protein expressions of two selected genes FLOT1 (P value = 1.70E-02) and HLA-DMA (P value = 4.70E-02) in plasma were significantly different in our in-house samples.

Conclusion

Our study identified 221 novel RA-associated genes and especially highlighted the importance of 20 candidate genes on RA. The results addressed ethnic genetic background differences for RA susceptibility between European and Asian populations and detected a long list of overlapped or ethnic specific RA genes. The study not only greatly increases our understanding of genetic susceptibility to RA, but also provides important insights into the ethno-genetic homogeneity and heterogeneity of RA in both ethnicities.

Integrative analysis for identification of shared markers from various functional cells/tissues for rheumatoid arthritis

Rheumatoid arthritis (RA) is a systemic autoimmune disease. So far, it is unclear whether there exist common RA-related genes shared in different tissues/cells. In this study, we conducted an integrative analysis on multiple datasets to identify potential shared genes that are significant in multiple tissues/cells for RA. Seven microarray gene expression datasets representing various RA-related tissues/cells were downloaded from the Gene Expression Omnibus (GEO). Statistical analyses, testing both marginal and joint effects, were conducted to identify significant genes shared in various samples. Followed-up analyses were conducted on functional annotation clustering analysis, protein-protein interaction (PPI) analysis, gene-based association analysis, and ELISA validation analysis in in-house samples. We identified 18 shared significant genes, which were mainly involved in the immune response and chemokine signaling pathway. Among the 18 genes, eight genes (PPBP, PF4, HLA-F, S100A8, RNASEH2A, P2RY6, JAG2, and PCBP1) interact with known RA genes. Two genes (HLA-F and PCBP1) are significant in gene-based association analysis (P = 1.03E-31, P = 1.30E-2, respectively). Additionally, PCBP1 also showed differential protein expression levels in in-house case-control plasma samples (P = 2.60E-2). This study represented the first effort to identify shared RA markers from different functional cells or tissues. The results suggested that one of the shared genes, i.e., PCBP1, is a promising biomarker for RA.

Genome-wide association studies and gene expression profiles of rheumatoid arthritis: An analysis

OBJECTIVES

The molecular mechanism of rheumatoid arthritis (RA) remains elusive. We conducted a protein-protein interaction network-based integrative analysis of genome-wide association studies (GWAS) and gene expression profiles of RA.

METHODS

We first performed a dense search of RA-associated gene modules by integrating a large GWAS meta-analysis dataset (containing 5539 RA patients and 20 169 healthy controls), protein interaction network and gene expression profiles of RA synovium and peripheral blood mononuclear cells (PBMCs). Gene ontology (GO) enrichment analysis was conducted by DAVID. The protein association networks of gene modules were generated by STRING.

RESULTS

For RA synovium, the top-ranked gene module is HLA-A, containing TAP2, HLA-A, HLA-C, TAPBP and LILRB1 genes. For RA PBMCs, the top-ranked gene module is GRB7, consisting of HLA-DRB5, HLA-DRA, GRB7, CD63 and KIT genes. Functional enrichment analysis identified three significant GO terms for RA synovium, including antigen processing and presentation of peptide antigen via major histocompatibility complex class I (false discovery rate (FDR) = 4.86 × 10 - 4), antigen processing and presentation of peptide antigen (FDR = 2.33 × 10 - 3) and eukaryotic translation initiation factor 4F complex (FDR = 2.52 × 10 - 2).

CONCLUSION

This study reported several RA-associated gene modules and their functional association networks.Cite this article: X. Xiao, J. Hao, Y. Wen, W. Wang, X. Guo, F. Zhang. Genome-wide association studies and gene expression profiles of rheumatoid arthritis: an analysis. Bone Joint Res 2016;5:314-319. DOI: 10.1302/2046-3758.57.2000502.

Longitudinal Transcriptome Analysis Reveals a Sustained Differential Gene Expression Signature in Patients Treated for Acute Lyme Disease

Lyme disease is a tick-borne illness caused by the bacterium Borrelia burgdorferi, and approximately 10 to 20% of patients report persistent symptoms lasting months to years despite appropriate treatment with antibiotics. To gain insights into the molecular basis of acute Lyme disease and the ensuing development of post-treatment symptoms, we conducted a longitudinal transcriptome study of 29 Lyme disease patients (and 13 matched controls) enrolled at the time of diagnosis and followed for up to 6 months. The differential gene expression signature of Lyme disease following the acute phase of infection persisted for at least 3 weeks and had fewer than 44% differentially expressed genes (DEGs) in common with other infectious or noninfectious syndromes. Early Lyme disease prior to antibiotic therapy was characterized by marked upregulation of Toll-like receptor signaling but lack of activation of the inflammatory T-cell apoptotic and B-cell developmental pathways seen in other acute infectious syndromes. Six months after completion of therapy, Lyme disease patients were found to have 31 to 60% of their pathways in common with three different immune-mediated chronic diseases. No differential gene expression signature was observed between Lyme disease patients with resolved illness to those with persistent symptoms at 6 months post-treatment. The identification of a sustained differential gene expression signature in Lyme disease suggests that a panel of selected human host-based biomarkers may address the need for sensitive clinical diagnostics during the “window period” of infection prior to the appearance of a detectable antibody response and may also inform the development of new therapeutic targets.

Lyme disease is the most common tick-borne infection in the United States, and some patients report lingering symptoms lasting months to years despite antibiotic treatment. To better understand the role of the human host response in acute Lyme disease and the development of post-treatment symptoms, we conducted the first longitudinal gene expression (transcriptome) study of patients enrolled at the time of diagnosis and followed up for up to 6 months after treatment. Importantly, we found that the gene expression signature of early Lyme disease is distinct from that of other acute infectious diseases and persists for at least 3 weeks following infection. This study also uncovered multiple previously undescribed pathways and genes that may be useful in the future as human host biomarkers for diagnosis and that constitute potential targets for the development of new therapies.

Loss of Peripheral Protection in Pancreatic Islets by Proteolysis-Driven Impairment of VTCN1 (B7-H4) Presentation Is Associated with the Development of Autoimmune Diabetes

Ag-specific activation of T cells is an essential process in the control of effector immune responses. Defects in T cell activation, particularly in the costimulation step, have been associated with many autoimmune conditions, including type 1 diabetes (T1D). Recently, we demonstrated that the phenotype of impaired negative costimulation, due to reduced levels of V-set domain-containing T cell activation inhibitor 1 (VTCN1) protein on APCs, is shared between diabetes-susceptible NOD mice and human T1D patients. In this study, we show that a similar process takes place in the target organ, as both α and β cells within pancreatic islets gradually lose their VTCN1 protein during autoimmune diabetes development despite upregulation of the VTCN1 gene. Diminishment of functional islet cells' VTCN1 is caused by the active proteolysis by metalloproteinase N-arginine dibasic convertase 1 (NRD1) and leads to the significant induction of proliferation and cytokine production by diabetogenic T cells. Inhibition of NRD1 activity, alternatively, stabilizes VTCN1 and dulls the anti-islet T cell responses. Therefore, we suggest a general endogenous mechanism of defective VTCN1 negative costimulation, which affects both lymphoid and peripheral target tissues during T1D progression and results in aggressive anti-islet T cell responses. This mechanism is tied to upregulation of NRD1 expression and likely acts in two synergistic proteolytic modes: cell-intrinsic intracellular and cell-extrinsic systemic. Our results highlight an importance of VTCN1 stabilization on cell surfaces for the restoration of altered balance of immune control during T1D.

Integrated Analyses of Gene Expression Profiles Digs out Common Markers for Rheumatic Diseases

OBJECTIVE

Rheumatic diseases have some common symptoms. Extensive gene expression studies, accumulated thus far, have successfully identified signature molecules for each rheumatic disease, individually. However, whether there exist shared factors across rheumatic diseases has yet to be tested.

METHODS

We collected and utilized 6 public microarray datasets covering 4 types of representative rheumatic diseases including rheumatoid arthritis, systemic lupus erythematosus, ankylosing spondylitis, and osteoarthritis. Then we detected overlaps of differentially expressed genes across datasets and performed a meta-analysis aiming at identifying common differentially expressed genes that discriminate between pathological cases and normal controls. To further gain insights into the functions of the identified common differentially expressed genes, we conducted gene ontology enrichment analysis and protein-protein interaction analysis.

RESULTS

We identified a total of eight differentially expressed genes (TNFSF10, CX3CR1, LY96, TLR5, TXN, TIA1, PRKCH, PRF1), each associated with at least 3 of the 4 studied rheumatic diseases. Meta-analysis warranted the significance of the eight genes and highlighted the general significance of four genes (CX3CR1, LY96, TLR5, and PRF1). Protein-protein interaction and gene ontology enrichment analyses indicated that the eight genes interact with each other to exert functions related to immune response and immune regulation.

CONCLUSION

The findings support that there exist common factors underlying rheumatic diseases. For rheumatoid arthritis, systemic lupus erythematosus, ankylosing spondylitis and osteoarthritis diseases, those common factors include TNFSF10, CX3CR1, LY96, TLR5, TXN, TIA1, PRKCH, and PRF1. In-depth studies on these common factors may provide keys to understanding the pathogenesis and developing intervention strategies for rheumatic diseases.

The Crosstalk of Pathways Involved in Immune Response Maybe the Shared Molecular Basis of Rheumatoid Arthritis and Type 2 Diabetes

Rheumatoid arthritis (RA) and Type 2 diabetes (T2D) are both systemic diseases linked with altered immune response, moderate mortality when present together. The treatment for both RA and T2D are not satisfied, partly because of the linkage between them has not yet been appreciated. A comprehensive study for the potential associations between the two disorders is needed. In this study, we used RNA sequencing to explore the differently expressed genes (DEGs) in peripheral blood mononuclear cells (PBMC) of 10 RA and 10 T2D patients comparing with 10 healthy volunteers (control). We used bioinformatics analysis and the Ingenuity Pathways Analysis (IPA) to predict the commonalities on signaling pathways and molecular networks between those two diseases. 212 DEGs in RA and 114 DEGs in T2D patients were identified compared with healthy controls, respectively. 32 DEGs were shared between the two comparisons. The top 10 shared pathways interacted in cross-talking networks, regulated by 5 shared predicted upstream regulators, leading to the activated immune response were explored, which was considered as partly of the association mechanism of this two disorders. These discoveries would be considered as new understanding on the associations between RA and T2D, and provide novel treatment or prevention strategy.

Impact of exercise stress testing on diagnostic gene expression in patients with obstructive and nonobstructive coronary artery disease

A blood-based gene expression test can diagnose obstructive coronary artery disease (CAD). The test is sensitive to inflammatory and immune processes associated with atherosclerosis. Acute exercise engages short-term inflammatory pathways, and exercise stress testing may affect results of gene expression testing during the same diagnostic workup. The objective of this study was to evaluate the effect of exercise on diagnostic gene expression testing. Ten patients with obstructive CAD (≥50% stenosis) and 10 with no/minimal CAD (≤20% stenosis) were identified by angiography. Blood samples for gene expression were obtained at baseline, peak exercise, 30 to 60 minutes after testing, and 24 to 36 hours after testing. Core-lab gene expression analysis yielded raw gene expression scores (GES) for each time point. Linear models were used to estimate changes in GES, adjusting for CAD status and other covariates. GES increased during peak exercise across both genders, with no significant differences as a function of CAD status. The overall adjusted mean GES increase at peak exercise was 0.29 (95% confidence interval 0.22 to 0.36; p <0.001). GES after exercise were not significantly different from baseline. The change in gene expression levels during peak exercise may reflect a transient inflammatory response to acute exercise that may be independent of patient gender or CAD status. In conclusion, CAD GES increase at peak exercise testing and rapidly return to baseline. Such may reflect a transient inflammatory response to acute exercise independent of gender or extent of CAD.

CD4 T-cell transcriptome analysis reveals aberrant regulation of STAT3 and Wnt signaling pathways in rheumatoid arthritis: evidence from a case-control study

INTRODUCTION

Rheumatoid arthritis (RA) is a systemic autoimmune disease in which T cells play a pivotal role in the pathogenesis. Knowledge in terms of the CD4 T-cell transcriptome in RA is limited. The aim of this study was to examine the whole-genome transcription profile of CD4 T cells in RA by comparing patients with RA to healthy controls.

METHODS

Peripheral blood CD4 T cells were isolated from 53 RA patients with active disease and 45 healthy individuals; 13 cases and 10 controls were enrolled in microarray analysis. The remaining 40 cases and 35 controls were recruited as an independent cohort for the validation study. Bioinformatics was performed on Gene Ontology (GO), gene-gene interaction networks, and pathway analysis. The gene modules, by combining the results from GO, gene networks, and pathway analysis, were selected for further validation.

RESULTS

The CD4 T cells showed 1,496 differentially expressed (DE) genes in RA patients relative to healthy individuals. GO analysis revealed that the DE genes were enriched in immune response, T-cell response, apoptosis process, and Wnt receptor signaling. Pathway analysis also identified that 'Wnt signaling pathway' was differentially regulated between two groups (P=2.78×10(-10)). By gene-gene network analysis, we found that the DE genes were enriched in T-cell receptor (TCR), JAK-STAT signaling, and Wnt signaling pathway. By gene module analysis, we found that a number of DE genes overlapped in the three different analyses. In total, 23 genes were selected for further validation, and nine genes were confirmed. Of these, four genes (SOCS3, CBL, IFNAR1, and PIK3CA) were involved in STAT3 (signal transducer and activator of transcription 3) signaling, and three genes (CBL, KLF9, and CSNK2A1) were involved in the Wnt signaling pathway. Additionally, several zinc finger transcription factors (ZEB1, ZNF292, and ZNF644) were confirmed.

CONCLUSIONS

We report here the first case-control study of the CD4 T-cell transcriptome profile in RA. Our data provide evidence that CD4 T cells from patients with RA have abnormal functional networks in STAT3 signaling and Wnt signaling. Our results also suggest that the aberrant expression of several zinc finger transcription factors (ZEB1, ZNF292, and ZNF644) may be potential pathogenic factors for RA.

The shared crosstalk of multiple pathways involved in the inflammation between rheumatoid arthritis and coronary artery disease based on a digital gene expression profile

Rheumatoid arthritis (RA) and coronary artery disease (CAD) are both complex inflammatory diseases, and an increased prevalence of CAD and a high rate of mortality have been observed in RA patients. But the molecular mechanism of inflammation that is shared between the two disorders is unclear. High-throughput techniques, such as transcriptome analysis, are becoming important tools for genetic biomarker discovery in highly complex biological samples, which is critical for the diagnosis, prognosis, and treatment of disease. In the present study, we reported one type of transcriptome analysis method: digital gene expression profiling of peripheral blood mononuclear cells of 10 RA patients, 10 CAD patients and 10 healthy people. In all, 213 and 152 differently expressed genes (DEGs) were identified in RA patients compared with normal controls (RA vs. normal) and CAD patients compared with normal controls (CAD vs. normal), respectively, with 73 shared DEGs between them. Using this technique in combination with Ingenuity Pathways Analysis software, the effects on inflammation of four shared canonical pathways, three shared activated predicted upstream regulators and three shared molecular interaction networks were identified and explored. These shared molecular mechanisms may provide the genetic basis and potential targets for optimizing the application of current drugs to more effectively treat these diseases simultaneously and for preventing one when the other is diagnosed.

Shared signatures between rheumatoid arthritis, systemic lupus erythematosus and Sjögren's syndrome uncovered through gene expression meta-analysis

Introduction

Systemic lupus erythematosus (SLE), rheumatoid arthritis (RA) and Sjögren’s syndrome (SjS) are inflammatory systemic autoimmune diseases (SADs) that share several clinical and pathological features. The shared biological mechanisms are not yet fully characterized. The objective of this study was to perform a meta-analysis using publicly available gene expression data about the three diseases to identify shared gene expression signatures and overlapping biological processes.

Methods

Previously reported gene expression datasets were selected and downloaded from the Gene Expression Omnibus database. Normalization and initial preprocessing were performed using the statistical programming language R and random effects model–based meta-analysis was carried out using INMEX software. Functional analysis of over- and underexpressed genes was done using the GeneCodis tool.

Results

The gene expression meta-analysis revealed a SAD signature composed of 371 differentially expressed genes in patients and healthy controls, 187 of which were underexpressed and 184 overexpressed. Many of these genes have previously been reported as significant biomarkers for individual diseases, but others provide new clues to the shared pathological state. Functional analysis showed that overexpressed genes were involved mainly in immune and inflammatory responses, mitotic cell cycles, cytokine-mediated signaling pathways, apoptotic processes, type I interferon–mediated signaling pathways and responses to viruses. Underexpressed genes were involved primarily in inhibition of protein synthesis.

Conclusions

We define a common gene expression signature for SLE, RA and SjS. The analysis of this signature revealed relevant biological processes that may play important roles in the shared development of these pathologies.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-014-0489-x) contains supplementary material, which is available to authorized users.

Nardilysin-dependent proteolysis of cell-associated VTCN1 (B7-H4) marks type 1 diabetes development

T-cell responses directed against insulin-secreting pancreatic β-cells are the key events highlighting type 1 diabetes (T1D). Therefore, a defective control of T-cell activation is thought to underlie T1D development. Recent studies implicated a B7-like negative costimulatory protein, V-set domain-containing T-cell activation inhibitor-1 (VTCN1), as a molecule capable of inhibiting T-cell activation and, potentially, an important constituent in experimental models of T1D. Here, we unravel a general deficiency within the VTCN1 pathway that is shared between diabetes-prone mice and a subset of T1D patients. Gradual loss of membrane-tethered VTCN1 from antigen-presenting cells combined with an increased release of soluble VTCN1 (sVTCN1) occurs in parallel to natural T1D development, potentiating hyperproliferation of diabetogenic T cells. Mechanistically, we demonstrate that the loss of membrane-tethered VTCN1 is linked to proteolytic cleavage mediated by the metalloproteinase nardilysin. The cleaved sVTCN1 fragment was detected at high levels in the peripheral blood of 53% T1D patients compared with only 9% of the healthy subjects. Elevated blood sVTCN1 levels appeared early in the disease progression and correlated with the aggressive pace of disease, highlighting the potential use of sVTCN1 as a new T1D biomarker, and identifying nardilysin as a potential therapeutic target.