Single-cell RNA-seq analysis reveals cell subsets and gene signatures associated with Rheumatoid Arthritis Disease Activity.

Rheumatoid arthritis (RA) management lean toward achieving remission or low-disease activity. In this study, we conducted single-cell RNA sequencing (scRNAseq) of peripheral blood mononuclear cells (PBMCs) from 36 individuals (18 RA patients and 18 matched controls, accounting for age, sex, race, and ethnicity), to identify disease-relevant cell subsets and cell type-specific signatures associated with disease activity. Our analysis revealed 18 distinct PBMC subsets, including an IFITM3 overexpressing Interferon-activated (IFN-activated) monocyte subset. We observed an increase in CD4+ T effector memory cells in patients with moderate to high disease activity (DAS28-CRP ≥ 3.2), and a decrease in non-classical monocytes in patients with low disease activity or remission (DAS28-CRP < 3.2). Pseudobulk analysis by cell type identified 168 differentially expressed genes between RA and matched controls, with a downregulation of pro-inflammatory genes in the gamma-delta T cells subset, alteration of genes associated with RA predisposition in the IFN-activated subset, and non-classical monocytes. Additionally, we identified a gene signature associated with moderate-high disease activity, characterized by upregulation of pro-inflammatory genes such as TNF, JUN, EGR1, IFIT2, MAFB, G0S2, and downregulation of genes including HLA-DQB1, HLA-DRB5, TNFSF13B. Notably, cell-cell communication analysis revealed an upregulation of signaling pathways, including VISTA, in both moderate-high and remission-low disease activity contexts. Our findings provide valuable insights into the systemic cellular and molecular mechanisms underlying RA disease activity.

Perturbations in podocyte transcriptome and biological pathways induced by FSGS associated circulating factors.

Background: Focal segmental glomerulosclerosis (FSGS) is frequently associated with heavy proteinuria and progressive renal failure requiring dialysis or kidney transplantation. However, primary FSGS also has a ~40% risk of recurrence of disease in the transplanted kidney (rFSGS). Multiple circulating factors have been identified to contribute to the pathogenesis of primary and rFSGS including soluble urokinase-type plasminogen activator receptor (suPAR) and patient-derived CD40 autoantibody (CD40autoAb). However, the downstream effector pathways specific to individual factors require further study. The tumor necrosis factor, TNF pathway activation by one or more circulating factors present in the sera of patients with FSGS has been supported by multiple studies. Methods: A human in vitro model was used to study podocyte injury measured as the loss of actin stress fibers. Anti-CD40 autoantibody was isolated from FSGS patients (recurrent and non-recurrent) and control patients with ESRD due to non-FSGS related causes. Two novel human antibodies-anti-uPAR (2G10) and anti-CD40 antibody (Bristol Meyer Squibb, 986090) were tested for their ability to rescue podocyte injury. Podocytes treated with patient derived antibody were transcriptionally profiled using whole human genome microarray. Results: Here we show that podocyte injury caused by sera from FSGS patients is mediated by CD40 and suPAR and can be blocked by human anti-uPAR and anti-CD40 antibodies. Transcriptomic studies to compare the molecules and pathways activated in response to CD40 autoantibody from rFSGS patients (rFSGS/CD40autoAb) and suPAR, identified unique inflammatory pathways associated with FSGS injury. Conclusions: We identified several novel and previously described genes associated with FSGS progression. Targeted blockade of suPAR and CD40 pathways with novel human antibodies showed inhibition of podocyte injury in FSGS.

Pan-vaccine analysis reveals innate immune endotypes predictive of antibody responses to vaccination.

Several studies have shown that the pre-vaccination immune state is associated with the antibody response to vaccination. However, the generalizability and mechanisms that underlie this association remain poorly defined. Here, we sought to identify a common pre-vaccination signature and mechanisms that could predict the immune response across 13 different vaccines. Analysis of blood transcriptional profiles across studies revealed three distinct pre-vaccination endotypes, characterized by the differential expression of genes associated with a pro-inflammatory response, cell proliferation, and metabolism alterations. Importantly, individuals whose pre-vaccination endotype was enriched in pro-inflammatory response genes known to be downstream of nuclear factor-kappa B showed significantly higher serum antibody responses 1 month after vaccination. This pro-inflammatory pre-vaccination endotype showed gene expression characteristic of the innate activation state triggered by Toll-like receptor ligands or adjuvants. These results demonstrate that wide variations in the transcriptional state of the immune system in humans can be a key determinant of responsiveness to vaccination.

The Immune Signatures data resource, a compendium of systems vaccinology datasets.

Vaccines are among the most cost-effective public health interventions for preventing infection-induced morbidity and mortality, yet much remains to be learned regarding the mechanisms by which vaccines protect. Systems immunology combines traditional immunology with modern 'omic profiling techniques and computational modeling to promote rapid and transformative advances in vaccinology and vaccine discovery. The NIH/NIAID Human Immunology Project Consortium (HIPC) has leveraged systems immunology approaches to identify molecular signatures associated with the immunogenicity of many vaccines. However, comparative analyses have been limited by the distributed nature of some data, potential batch effects across studies, and the absence of multiple relevant studies from non-HIPC groups in ImmPort. To support comparative analyses across different vaccines, we have created the Immune Signatures Data Resource, a compendium of standardized systems vaccinology datasets. This data resource is available through ImmuneSpace, along with code to reproduce the processing and batch normalization starting from the underlying study data in ImmPort and the Gene Expression Omnibus (GEO). The current release comprises 1405 participants from 53 cohorts profiling the response to 24 different vaccines. This novel systems vaccinology data release represents a valuable resource for comparative and meta-analyses that will accelerate our understanding of mechanisms underlying vaccine responses.

Physical Activity Associates With Lower Systemic Inflammatory Gene Expression in Rheumatoid Arthritis.

OBJECTIVE: While general population studies have shown inverse associations between physical activity and common inflammatory biomarkers, the effects of physical activity on inflammatory gene expression and signaling pathways in rheumatoid arthritis (RA) remain unknown. We aimed to determine whether physical activity independently associates with expression of inflammatory genes among people with RA. METHODS: This was a prospective observational study of adults with RA. Physical activity was measured by quantitative actigraphy over 7 consecutive days, and peripheral blood collected during the same time period was used for RNA sequencing followed by differential gene expression, pathway, and network analyses. RESULTS: Actigraphy and RNA sequencing data were evaluated in 35 patients. The cohort had a mean age of 56 (SD 12) years, and was 91% female, 31% White, 9% Black, 9% Asian, and 40% Hispanic. We found 767 genes differentially expressed (adjusted P < 0.1) between patients in the greatest vs lowest physical activity tertiles, after adjusting for sex, age, race, and ethnicity. The most active patients exhibited dose-dependent downregulation of several immune signaling pathways implicated in RA pathogenesis. These included CD40, STAT3, TREM-1, interleukin (IL)-17A, IL-8, Toll-like receptor, and interferon (IFN) signaling pathways. Upstream cytokine activation state analysis predicted reduced activation of tumor necrosis factor-α and IFN in the most active group. In sensitivity analyses, we adjusted for RA disease activity and physical function and found consistent results. CONCLUSION: Patients with RA who were more physically active had lower expression of immune signaling pathways implicated in RA pathogenesis, even after adjusting for disease activity, suggesting that physical activity may confer a protective effect in RA.

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.

Molecular Diversity of Clinically Stable Human Kidney Allografts.

Importance: Clinical decision and immunosuppression dosing in kidney transplantation rely on transplant biopsy tissue histology even though histology has low specificity, sensitivity, and reproducibility for rejection diagnosis. The inclusion of stable allografts in mechanistic and clinical studies is vital to provide a normal, noninjured comparative group for all interrogative studies on understanding allograft injury. Objective: To refine the definition of a stable allograft as one that is clinically, histologically, and molecularly quiescent using publicly available transcriptomics data. Design, Setting, and Participants: In this prognostic study, the National Center for Biotechnology Information Gene Expression Omnibus was used to search for microarray gene expression data from kidney transplant tissues, resulting in 38 studies from January 1, 2017, to December 31, 2018. The diagnostic annotations included 510 acute rejection (AR) samples, 1154 histologically stable (hSTA) samples, and 609 normal samples. Raw fluorescence intensity data were downloaded and preprocessed followed by data set merging and batch correction. Main Outcomes and Measures: The primary measure was area under the receiver operating characteristics curve from a set of feature selected genes and cell types for distinguishing AR from normal kidney tissue. Results: Within the 28 data sets, the feature selection procedure identified a set of 6 genes (KLF4, CENPJ, KLF2, PPP1R15A, FOSB, TNFAIP3) (area under the curve [AUC], 0.98) and 5 immune cell types (CD4+ T-cell central memory [Tcm], CD4+ T-cell effector memory [Tem], CD8+ Tem, natural killer [NK] cells, and Type 1 T helper [TH1] cells) (AUC, 0.92) that were combined into 1 composite Instability Score (InstaScore) (AUC, 0.99). The InstaScore was applied to the hSTA samples: 626 of 1154 (54%) were found to be immune quiescent and redefined as histologically and molecularly stable (hSTA/mSTA); 528 of 1154 (46%) were found to have molecular evidence of rejection (hSTA/mAR) and should not have been classified as stable allografts. The validation on an independent cohort of 6 months of protocol biopsy samples in December 2019 showed that hSTA/mAR samples had a significant change in graft function (r = 0.52, P < .001) and graft loss at 5-year follow-up (r = 0.17). A drop by 10 mL/min/1.73m2 in estimated glomerular filtration rate was estimated as a threshold in allograft transitioning from hSTA/mSTA to hSTA/mAR. Conclusions and Relevance: The results of this prognostic study suggest that the InstaScore could provide an important adjunct for comprehensive and highly quantitative phenotyping of protocol kidney transplant biopsy samples and could be integrated into clinical care for accurate estimation of subsequent patient clinical outcomes.

Single-Cell RNA Sequencing of Tocilizumab-Treated Peripheral Blood Mononuclear Cells as an in vitro Model of Inflammation.

COVID-19 has posed a significant threat to global health. Early data has revealed that IL-6, a key regulatory cytokine, plays an important role in the cytokine storm of COVID-19. Multiple trials are therefore looking at the effects of Tocilizumab, an IL-6 receptor antibody that inhibits IL-6 activity, on treatment of COVID-19, with promising findings. As part of a clinical trial looking at the effects of Tocilizumab treatment on kidney transplant recipients with subclinical rejection, we performed single-cell RNA sequencing of comparing stimulated PBMCs before and after Tocilizumab treatment. We leveraged this data to create an in vitro cytokine storm model, to better understand the effects of Tocilizumab in the presence of inflammation. Tocilizumab-treated cells had reduced expression of inflammatory-mediated genes and biologic pathways, particularly amongst monocytes. These results support the hypothesis that Tocilizumab may hinder the cytokine storm of COVID-19, through a demonstration of biologic impact at the single-cell level.

Gene Expression Meta-Analysis Reveals Concordance in Gene Activation, Pathway, and Cell-Type Enrichment in Dermatomyositis Target Tissues.

OBJECTIVE: We conducted a comprehensive gene expression meta-analysis in dermatomyositis (DM) muscle and skin tissues to identify shared disease-relevant genes and pathways across tissues. METHODS: Six publicly available data sets from DM muscle and two from skin were identified. Meta-analysis was performed by first processing data sets individually then cross-study normalization and merging creating tissue-specific gene expression matrices for subsequent analysis. Complementary single-gene and network analyses using Significance Analysis of Microarrays (SAM) and Weighted Gene Co-expression Network Analysis (WGCNA) were conducted to identify genes significantly associated with DM. Cell-type enrichment was performed using xCell. RESULTS: There were 544 differentially expressed genes (FC ≥ 1.3, q < 0.05) in muscle and 300 in skin. There were 94 shared upregulated genes across tissues enriched in type I and II interferon (IFN) signaling and major histocompatibility complex (MHC) class I antigen-processing pathways. In a network analysis, we identified eight significant gene modules in muscle and seven in skin. The most highly correlated modules were enriched in pathways consistent with the single-gene analysis. Additional pathways uncovered by WGCNA included T-cell activation and T-cell receptor signaling. In the cell-type enrichment analysis, both tissues were highly enriched in activated dendritic cells and M1 macrophages. CONCLUSION: There is striking similarity in gene expression across DM target tissues with enrichment of type I and II IFN pathways, MHC class I antigen-processing, T-cell activation, and antigen-presenting cells. These results suggest IFN-γ may contribute to the global IFN signature in DM, and altered auto-antigen presentation through the class I MHC pathway may be important in disease pathogenesis.

Reporters of TCR signaling identify arthritogenic T cells in murine and human autoimmune arthritis.

How pathogenic cluster of differentiation 4 (CD4) T cells in rheumatoid arthritis (RA) develop remains poorly understood. We used Nur77-a marker of T cell antigen receptor (TCR) signaling-to identify antigen-activated CD4 T cells in the SKG mouse model of autoimmune arthritis and in patients with RA. Using a fluorescent reporter of Nur77 expression in SKG mice, we found that higher levels of Nur77-eGFP in SKG CD4 T cells marked their autoreactivity, arthritogenic potential, and ability to more readily differentiate into interleukin-17 (IL-17)-producing cells. The T cells with increased autoreactivity, nonetheless had diminished ex vivo inducible TCR signaling, perhaps reflective of adaptive inhibitory mechanisms induced by chronic autoantigen exposure in vivo. The enhanced autoreactivity was associated with up-regulation of IL-6 cytokine signaling machinery, which might be attributable, in part, to a reduced amount of expression of suppressor of cytokine signaling 3 (SOCS3)-a key negative regulator of IL-6 signaling. As a result, the more autoreactive GFPhi CD4 T cells from SKGNur mice were hyperresponsive to IL-6 receptor signaling. Consistent with findings from SKGNur mice, SOCS3 expression was similarly down-regulated in RA synovium. This suggests that despite impaired TCR signaling, autoreactive T cells exposed to chronic antigen stimulation exhibit heightened sensitivity to IL-6, which contributes to the arthritogenicity in SKG mice, and perhaps in patients with RA.