Expression of Interferon-γ Receptor Genes in Peripheral Blood Mononuclear Cells Is Associated With Rheumatoid Arthritis and Its Radiographic Severity in African Americans

Time series analysis combined with transcriptome sequencing to explore characteristic genes and potential molecular mechanisms associated with ultrasound-guided microwave ablation of glioma

OBJECTIVE

This study aimed to explore marker genes and their potential molecular mechanisms involved in US-guided MWA for glioma in mice.

METHOD

The differentially expressed genes (DEGs1 and DEGs2) and lncRNAs (DELs1 and DELs2) were obtained between Non (glioma tissues without MWA) and T0 groups (0h after MWA), as well as between Non and T24 groups (24h after MWA). The down-regulation cluster genes (CONDOWNDEGs) and upregulation cluster genes (CONUPDEGs) were identified by time series analysis. Candidate genes were obtained by overlapping CONDOWNDEGs with downregulation DEGs (DOWNDEGs)1 and DOWNDEGs2, as well as CONUPDEGs with up-regulation DEGs (UPDEGs)1 and UPDEGs2. The expressions of immune checkpoints and inflammatory factors, gene set enrichment analysis (GSEA), and protein subcellular localization were performed. The eXpression2Kinases (X2K), GeneMANIA, transcription factor (TF), and competing endogenous (ce) RNA regulatory networks were conducted. The expression of marker genes was validated by quantitative real-time polymerase chain reaction (qRT-PCR).

RESULTS

Five marker genes (IL32, VCAM1, IL34, NFKB1 and CXCL13) were identified, which were connected with immune-related functions. Two immune checkpoints (CD96 and TIGIT) and six inflammatory factors played key roles in US-guided MWA for glioma. ceRNA regulatory networks revealed that miR-625-5p, miR-625-3p, miR-31-5p and miR-671-5p were associated with target genes. qRT-PCR indicated both IL32, VCAM1, and NFKB1 were potential markers under US-guided MWA-related time series analysis.

CONCLUSION

The use of US-guided MWA might be a practical method for influencing the function of target genes, regulating time frames to decrease inflammation, and stimulating immune responses in glioma therapy.

A Crowdsourcing Approach to Develop Machine Learning Models to Quantify Radiographic Joint Damage in Rheumatoid Arthritis

Importance

An automated, accurate method is needed for unbiased assessment quantifying accrual of joint space narrowing and erosions on radiographic images of the hands and wrists, and feet for clinical trials, monitoring of joint damage over time, assisting rheumatologists with treatment decisions. Such a method has the potential to be directly integrated into electronic health records.

Objectives

To design and implement an international crowdsourcing competition to catalyze the development of machine learning methods to quantify radiographic damage in rheumatoid arthritis (RA).

Design, Setting, and Participants

This diagnostic/prognostic study describes the Rheumatoid Arthritis 2-Dialogue for Reverse Engineering Assessment and Methods (RA2-DREAM Challenge), which used existing radiographic images and expert-curated Sharp-van der Heijde (SvH) scores from 2 clinical studies (674 radiographic sets from 562 patients) for training (367 sets), leaderboard (119 sets), and final evaluation (188 sets). Challenge participants were tasked with developing methods to automatically quantify overall damage (subchallenge 1), joint space narrowing (subchallenge 2), and erosions (subchallenge 3). The challenge was finished on June 30, 2020.

Main Outcomes and Measures

Scores derived from submitted algorithms were compared with the expert-curated SvH scores, and a baseline model was created for benchmark comparison. Performances were ranked using weighted root mean square error (RMSE). The performance and reproductivity of each algorithm was assessed using Bayes factor from bootstrapped data, and further evaluated with a postchallenge independent validation data set.

Results

The RA2-DREAM Challenge received a total of 173 submissions from 26 participants or teams in 7 countries for the leaderboard round, and 13 submissions were included in the final evaluation. The weighted RMSEs metric showed that the winning algorithms produced scores that were very close to the expert-curated SvH scores. Top teams included Team Shirin for subchallenge 1 (weighted RMSE, 0.44), HYL-YFG (Hongyang Li and Yuanfang Guan) subchallenge 2 (weighted RMSE, 0.38), and Gold Therapy for subchallenge 3 (weighted RMSE, 0.43). Bootstrapping/Bayes factor approach and the postchallenge independent validation confirmed the reproducibility and the estimation concordance indices between final evaluation and postchallenge independent validation data set were 0.71 for subchallenge 1, 0.78 for subchallenge 2, and 0.82 for subchallenge 3.

Conclusions and Relevance

The RA2-DREAM Challenge resulted in the development of algorithms that provide feasible, quick, and accurate methods to quantify joint damage in RA. Ultimately, these methods could help research studies on RA joint damage and may be integrated into electronic health records to help clinicians serve patients better by providing timely, reliable, and quantitative information for making treatment decisions to prevent further damage.

Molecular Modeling Insights into Upadacitinib Selectivity upon Binding to JAK Protein Family

Rheumatoid arthritis (RA) is a chronic disease characterized by bone joint damage and incapacitation. The mechanism underlying RA pathogenesis is autoimmunity in the connective tissue. Cytokines play an important role in the human immune system for signal transduction and in the development of inflammatory responses. Janus kinases (JAK) participate in the JAK/STAT pathway, which mediates cytokine effects, in particular interleukin 6 and IFNγ. The discovery of small molecule inhibitors of the JAK protein family has led to a revolution in RA therapy. The novel JAK inhibitor upadacitinib (Rinvoq^TM) has a higher selectivity for JAK1 compared to JAK2 and JAK3 in vivo. Currently, details on the molecular recognition of JAK1 by upadacitinib are not available. We found that characteristics of hydrogen bond formation with the glycine loop and hinge in JAKs define the selectivity. Our molecular modeling study could provide insight into the drug action mechanism and pharmacophore model differences in JAK isoforms.

Decreased Levels of STAT1 and Interferon-γ-Induced STAT1 Phosphorylation in Rheumatoid Arthritis CD4 and CD8 T Cells

Objective

We investigated whether a previously reported association of IFNGR expression with rheumatoid arthritis (RA) and its radiographic severity reflects differences in proximal interferon‐γ (IFN‐γ) signaling in T cells from patients with RA compared with healthy controls (HC).

Methods

Using phosphoflow cytometry, we compared IFN‐γ–stimulated signal transducer and activator of transcription 1 (STAT1) activation in CD4⁺ and CD8⁺ T‐cell populations from patients with RA and HC.

Results

Compared with controls, patients with RA had a higher proportion of CD4⁺ T cells, associated with expansion of the CD4⁺ effector memory subset. Several CD4⁺ T‐cell types exhibited reduced IFN‐γ–induced phosphoSTAT1^Y701 (pSTAT1^Y701) in patients with RA compared with HC. Engaging the T‐cell receptor (TCR) complex on CD4⁺ T cells during IFN‐γ stimulation abrogated the reduction in STAT1 activation in patients with RA but had no effect in HC. The phosphorylation of STAT1^S727 was similar in CD4⁺ T cells from patients with RA and HC. In contrast to CD4⁺ T cells, IFN‐γ–induced pSTAT1^Y701 levels in CD8⁺ T cells were equivalent or higher in patients with RA compared with HC. Total STAT1 levels (phosphorylated + unphosphorylated) were lower in CD4⁺ and CD8⁺ T cells from patients with RA compared with HC.

Conclusion

We report diminished IFN‐γ–induced pSTAT1^Y701 levels in CD4⁺ T cells in patients with RA, which were restored by TCR engagement. There were lower levels of total STAT1 in patients with RA compared with HC, but this likely does not explain diminished IFN‐γ–induced pSTAT1^Y701 levels in CD4⁺ T cells because activation in CD8⁺ T cells was higher or equivalent to that seen in HC. The enhanced IFNGR expression in patients with RA reported previously may reflect a compensatory mechanism to overcome deficiency in IFN‐γ responsiveness.

Diminished cytokine-induced Jak/STAT signaling is associated with rheumatoid arthritis and disease activity

Rheumatoid arthritis (RA) is a systemic and incurable autoimmune disease characterized by chronic inflammation in synovial lining of joints. To identify the signaling pathways involved in RA, its disease activity, and treatment response, we adapted a systems immunology approach to simultaneously quantify 42 signaling nodes in 21 immune cell subsets (e.g., IFNα→p-STAT5 in B cells) in peripheral blood mononuclear cells (PBMC) from 194 patients with longstanding RA (including 98 patients before and after treatment), and 41 healthy controls (HC). We found multiple differences between patients with RA compared to HC, predominantly in cytokine-induced Jak/STAT signaling in many immune cell subsets, suggesting pathways that may be associated with susceptibility to RA. We also found that high RA disease activity, compared to low disease activity, was associated with decreased (e.g., IFNα→p-STAT5, IL-10→p-STAT1) or increased (e.g., IL-6→STAT3) response to stimuli in multiple cell subsets. Finally, we compared signaling in patients with established, refractory RA before and six months after initiation of methotrexate (MTX) or TNF inhibitors (TNFi). We noted significant changes from pre-treatment to post-treatment in IFNα→p-STAT5 signaling and IL-10→p-STAT1 signaling in multiple cell subsets; these changes brought the aberrant RA signaling profiles toward those of HC. This large, comprehensive functional signaling pathway study provides novel insights into the pathogenesis of RA and shows the potential of quantification of cytokine-induced signaling as a biomarker of disease activity or treatment response.

Type 1 helper T cells generate CXCL9/10-producing T-bet+ effector B cells potentially involved in the pathogenesis of rheumatoid arthritis

Efficacy of B-cell depletion therapy highlights the antibody-independent effector functions of B cells in rheumatoid arthritis (RA). Given type 1 helper T (Th1) cells abundant in synovial fluid (SF) of RA, we have determined whether Th1 cells could generate novel effector B cells. Microarray and qPCR analysis identified CXCL9/10 transcripts as highly expressed genes upon BCR/CD40/IFN-γ stimulation. Activated Th1 cells promoted the generation of CXCL9/10-producing T-bet⁺ B cells. Expression of CXCL9/10 was most pronounced in CXCR3⁺ switched memory B cells. Compared with peripheral blood, SFRA enriched highly activated Th1 cells that coexisted with abundant CXCL9/10-producing T-bet⁺ B cells. Intriguingly, anti-IFN-γ antibody and JAK inhibitors significantly abrogated the generation of CXCL9/10-producing T-bet⁺ B cells. B cell derived CXCL9/10 significantly facilitated the migration of CD4⁺ T cells. These findings suggest that Th1 cells generate the novel CXCL9/10-producing T-bet⁺ effector B cells that could be an ideal pathogenic B cell target for RA therapy.

Genetic influences on susceptibility to rheumatoid arthritis in African-Americans

Large meta-analyses of rheumatoid arthritis (RA) susceptibility in European (EUR) and East Asian (EAS) populations have identified >100 RA risk loci, but genome-wide studies of RA in African-Americans (AAs) are absent. To address this disparity, we performed an analysis of 916 AA RA patients and 1392 controls and aggregated our data with genotyping data from >100 000 EUR and Asian RA patients and controls. We identified two novel risk loci that appear to be specific to AAs: GPC5 and RBFOX1 (PAA < 5 × 10-9). Most RA risk loci are shared across different ethnicities, but among discordant loci, we observed strong enrichment of variants having large effect sizes. We found strong evidence of effect concordance for only 3 of the 21 largest effect index variants in EURs. We used the trans-ethnic fine-mapping algorithm PAINTOR3 to prioritize risk variants in >90 RA risk loci. Addition of AA data to those of EUR and EAS descent enabled identification of seven novel high-confidence candidate pathogenic variants (defined by posterior probability > 0.8). In summary, our trans-ethnic analyses are the first to include AAs, identified several new RA risk loci and point to candidate pathogenic variants that may underlie this common autoimmune disease. These findings may lead to better ways to diagnose or stratify treatment approaches in RA.

Identification of rheumatoid arthritis causal genes using functional genomics

Over the past decade, genome-wide association studies have contributed a wealth of knowledge to our understanding of polygenic disorders such as rheumatoid arthritis. As the size of sample cohorts has improved so too have the computational and experimental methods used to robustly define variants associated with disease susceptibility. The challenge now remains to translate these findings into improved understanding of disease aetiology and patient care. Whilst much of the focus of translating the findings of genome-wide association studies has been on global analysis of all variants identified, careful functional study of individual disease susceptibility loci will be required in order to refine our understanding of how individual variants contribute to disease risk. Here, we present the argument behind such an approach and describe some of the novel tools being used to investigate risk loci. This includes the use of chromosomal conformation capture techniques and modifications of the CRISPR-Cas9 system, with several examples of their implementation being described.

Rheumatoid Arthritis in Sickle-Cell Population: Pathophysiologic Insights, Clinical Evaluation and Management

The advent of hydroxyurea and advanced medical care, including immunizations has led to improved survival among patients with Sickle Cell Disease (SCD). This prolonged survival however, introduces a chronic inflammatory disorder, Rheumatoid Arthritis (RA), which presents at a relatively older age and is rarely reported among SCD patients. In this review, we highlight the epidemiological association of SCD-RA and discuss the underlying common pathogenetic mechanisms, such as endothelial dysfunction, the role of inflammatory cytokines and oxidative stress. We also point to the difficulties in ascertaining the clinical diagnosis of RA in SCD patients. Finally, we provide rationale for therapeutic options available for RA and the challenges in the management of these patients with agents that are known to increase the risk of infection and immunosuppression such as steroids, disease modifying anti-rheumatic drugs and biologics.

Genomic risk prediction of aromatase inhibitor-related arthralgia in patients with breast cancer using a novel machine-learning algorithm

Many breast cancer (BC) patients treated with aromatase inhibitors (AIs) develop aromatase inhibitor‐related arthralgia (AIA). Candidate gene studies to identify AIA risk are limited in scope. We evaluated the potential of a novel analytic algorithm (NAA) to predict AIA using germline single nucleotide polymorphisms (SNP) data obtained before treatment initiation. Systematic chart review of 700 AI‐treated patients with stage I‐III BC identified asymptomatic patients (n = 39) and those with clinically significant AIA resulting in AI termination or therapy switch (n = 123). Germline DNA was obtained and SNP genotyping performed using the Affymetrix UK BioBank Axiom Array to yield 695,277 SNPs. SNP clusters that most closely defined AIA risk were discovered using an NAA that sequentially combined statistical filtering and a machine‐learning algorithm. NCBI PhenGenI and Ensemble databases defined gene attribution of the most discriminating SNPs. Phenotype, pathway, and ontologic analyses assessed functional and mechanistic validity. Demographics were similar in cases and controls. A cluster of 70 SNPs, correlating to 57 genes, was identified. This SNP group predicted AIA occurrence with a maximum accuracy of 75.93%. Strong associations with arthralgia, breast cancer, and estrogen phenotypes were seen in 19/57 genes (33%) and were functionally consistent. Using a NAA, we identified a 70 SNP cluster that predicted AIA risk with fair accuracy. Phenotype, functional, and pathway analysis of attributed genes was consistent with clinical phenotypes. This study is the first to link a specific SNP/gene cluster to AIA risk independent of candidate gene bias.

Comparative transcriptomic analysis of porcine peripheral blood reveals differentially expressed genes from the cytokine-cytokine receptor interaction pathway related to health status

While some research has looked into the host genetic response in pigs challenged with specific viruses or bacteria, few studies have explored the expression changes of transcripts in the peripheral blood of sick pigs that may be infected with multiple pathogens on farms. In this study, the architecture of the peripheral blood transcriptome of 64 Duroc sired commercial pigs, including 18 healthy animals at entry to a growing facility (set as a control) and 23 pairs of samples from healthy and sick pen mates, was generated using RNA-Seq technology. In total, 246 differentially expressed genes were identified to be specific to the sick animals. Functional enrichment analysis for those genes revealed that the over-represented gene ontology terms for the biological processes category were exclusively immune activity related. The cytokine-cytokine receptor interaction pathway was significantly enriched. Nine functional genes from this pathway encoding members (as well as their receptors) of the interleukins, chemokines, tumor necrosis factors, colony stimulating factors, activins, and interferons exhibited significant transcriptional alteration in sick animals. Our results suggest a subset of novel marker genes that may be useful candidate genes in the evaluation and prediction of health status in pigs under commercial production conditions.

Dense Genotyping of Immune-Related Regions Identifies Loci for Rheumatoid Arthritis Risk and Damage in African Americans

Over 100 risk loci for rheumatoid arthritis (RA) have been identified in individuals of European and Asian descent, but the genetic basis for RA in African Americans is less well understood. We genotyped 610 African Americans with autoantibody positive RA and 933 African American controls on the ImmunoChip (iChip) array. Using multivariable regression we evaluated the association between iChip markers and the risk of RA and radiographic severity. The single nucleotide polymorphism (SNP) rs1964995 (OR = 1.97, p = 1.28 × 10^-15) near HLA-DRB1 was the most strongly associated risk SNP for RA susceptibility; SNPs in AFF3, TNFSF11, and TNFSF18 loci were suggestively associated (10^-4 < p < 3.1 × 10^-6). Trans-ethnic fine mapping of AFF3 identified a 90% credible set containing previously studied variants including rs9653442, rs7608424, and rs6712515 as well as the novel candidate variant rs11681966; several of these likely influence AFF3 gene expression level. Variants in TNFRSF9, CTLA4, IL2RA, C5/TRAF1, and ETS1 - but no variants within the major histocompatibility complex - were associated with RA radiographic severity. Conditional regression and pairwise linkage disequilibrium (LD) analyses suggest that additional pathogenic variants may be found in ETS1 and IL2RA beyond those found in other ethnicities. In summary, we use the dense genotyping of the iChip array and unique LD structure of African Americans to validate known risk loci for RA susceptibility and radiographic severity, and to better characterize the associations of AFF3, ETS1, and IL2RA.

Capture Hi-C identifies a novel causal gene, IL20RA, in the pan-autoimmune genetic susceptibility region 6q23

BACKGROUND

The identification of causal genes from genome-wide association studies (GWAS) is the next important step for the translation of genetic findings into biologically meaningful mechanisms of disease and potential therapeutic targets. Using novel chromatin interaction detection techniques and allele specific assays in T and B cell lines, we provide compelling evidence that redefines causal genes at the 6q23 locus, one of the most important loci that confers autoimmunity risk.

RESULTS

Although the function of disease-associated non-coding single nucleotide polymorphisms (SNPs) at 6q23 is unknown, the association is generally assigned to TNFAIP3, the closest gene. However, the DNA fragment containing the associated SNPs interacts through chromatin looping not only with TNFAIP3, but also with IL20RA, located 680 kb upstream. The risk allele of the most likely causal SNP, rs6927172, is correlated with both a higher frequency of interactions and increased expression of IL20RA, along with a stronger binding of both the NFκB transcription factor and chromatin marks characteristic of active enhancers in T-cells.

CONCLUSIONS

Our results highlight the importance of gene assignment for translating GWAS findings into biologically meaningful mechanisms of disease and potential therapeutic targets; indeed, monoclonal antibody therapy targeting IL-20 is effective in the treatment of rheumatoid arthritis and psoriasis, both with strong GWAS associations to this region.