Rheumatoid arthritis macrophages are primed for inflammation and display bioenergetic and functional alterations.

OBJECTIVES: Myeloid cells with a monocyte/macrophage phenotype are present in large numbers in the RA joint, significantly contributing to disease; however, distinct macrophage functions have yet to be elucidated. This study investigates the metabolic activity of infiltrating polarized macrophages and their impact on pro-inflammatory responses in RA. METHODS: CD14+ monocytes from RA and healthy control (HC) bloods were isolated and examined ex vivo or following differentiation into 'M1/M2' macrophages. Inflammatory responses and metabolic analysis ± specific inhibitors were quantified by RT-PCR, western blot, Seahorse XFe technology, phagocytosis assays and transmission electron microscopy along with RNA-sequencing (RNA-seq) transcriptomic analysis. RESULTS: Circulating RA monocytes are hyper-inflammatory upon stimulation, with significantly higher expression of key cytokines compared with HC (P < 0.05) a phenotype which is maintained upon differentiation into mature ex vivo polarized macrophages. This induction in pro-inflammatory mechanisms is paralleled by cellular bioenergetic changes. RA macrophages are highly metabolic, with a robust boost in both oxidative phosphorylation and glycolysis in RA along with altered mitochondrial morphology compared with HC. RNA-seq analysis revealed divergent transcriptional variance between pro- and anti-inflammatory RA macrophages, revealing a role for STAT3 and NAMPT in driving macrophage activation states. STAT3 and NAMPT inhibition results in significant decrease in pro-inflammatory gene expression observed in RA macrophages. Interestingly, NAMPT inhibition specifically restores macrophage phagocytic function and results in reciprocal STAT3 inhibition, linking these two signalling pathways. CONCLUSION: This study demonstrates a unique inflammatory and metabolic phenotype of RA monocyte-derived macrophages and identifies a key role for NAMPT and STAT3 signalling in regulating this phenotype.

Integration of the Transcriptome and Genome-Wide Landscape of BRD2 and BRD4 Binding Motifs Identifies Key Superenhancer Genes and Reveals the Mechanism of Bet Inhibitor Action in Rheumatoid Arthritis Synovial Fibroblasts.

Fibroblast-like synoviocytes (FLS), one of the main cell types of the rheumatoid arthritis (RA) synovium, possess phenotypic and molecular characteristics of transformed cells. JQ1, an inhibitor of the bromodomain and extra terminal domain family that includes BRD2, BRD3, BRD4, and BRDt, has shown efficacy in models of arthritis. We demonstrate that the active isomer of JQ1 but not its inactive isomer inhibits IL-1ß-induced RA-FLS activation and proliferation. To understand the mechanism of JQ1 action, we subjected JQ1-treated RA-FLS to transcriptional profiling and determined BRD2 and BRD4 cistromes by identifying their global chromatin binding sites. In addition, assay for transposable accessible chromatin by high throughput sequencing was employed to identify open and closed regions of chromatin in JQ1-treated RA-FLS. Through an integrated analysis of expression profiling, Brd2/Brd4 cistrome data, and changes in chromatin accessibility, we found that JQ1 inhibited key BRD2/BRD4 superenhancer genes, downregulated multiple crucial inflammatory pathways, and altered the genome-wide occupancy of critical transcription factors involved in inflammatory signaling. Our results suggest a pleiotropic effect of JQ1 on pathways that have shown to be individually efficacious in RA (in vitro, in vivo, and/or in humans) and provide a strong rationale for targeting BRD2/BRD4 for disease treatment and interception.

NetSets.js: a JavaScript framework for compositional assessment and comparison of biological networks through Venn-integrated network diagrams.

MOTIVATION: Venn diagrams are frequently used to compare composition of datasets (e.g. datasets containing list of proteins and genes). Network diagram constructed using such datasets are usually generated using 'list of edges', popularly known as edge-lists. An edge-list and the corresponding generated network are, however, composed of two elements, namely, edges (e.g. protein-protein interactions) and nodes (e.g. proteins). Researchers often use individual lists of edges and nodes to compare composition of biological networks using existing Venn diagram tools. However, specialized analysis workflows are required for comparison of nodes as well as edges. Apart from this, different tools or graph libraries are needed for visualizing any specific edges of interest (e.g. protein-protein interactions which are present across all networks or are shared between subset of networks or are exclusively present in a selected network). Further, these results are required to be exported in the form of publication worthy network diagram(s), particularly for small networks. RESULTS: We introduce a (server independent) JavaScript framework (called NetSets.js) that integrates popular Venn and network diagrams in a single application. A free to use intuitive web application (utilizing NetSets.js), specifically designed to perform both compositional comparisons (e.g. for identifying common/exclusive edges or nodes) and interactive user defined visualizations of network (for the identified common/exclusive interactions across multiple networks) using simple edge-lists is also presented. The tool also enables connection to Cytoscape desktop application using the Netsets-Cyapp. We demonstrate the utility of our tool using real world biological networks (microbiome, gene interaction, multiplex and protein-protein interaction networks). AVAILABILITYAND IMPLEMENTATION: http://web.rniapps.net/netsets (freely available for academic use). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Distinct stromal and immune cell interactions shape the pathogenesis of rheumatoid and psoriatic arthritis.

OBJECTIVES: Immune and stromal cell communication is central in the pathogenesis of rheumatoid arthritis (RA) and psoriatic arthritis (PsA), however, the nature of these interactions in the synovial pathology of the two pathotypes can differ. Identifying immune-stromal cell crosstalk at the site of inflammation in RA and PsA is challenging. This study creates the first global transcriptomic analysis of the RA and PsA inflamed joint and investigates immune-stromal cell interactions in the pathogenesis of synovial inflammation. METHODS: Single cell transcriptomic profiling of 178 000 synovial tissue cells from five patients with PsA and four patients with RA, importantly, without prior sorting of immune and stromal cells. This approach enabled the transcriptomic analysis of the intact synovial tissue and identification of immune and stromal cell interactions. State of the art data integration and annotation techniques identified and characterised 18 stromal and 14 immune cell clusters. RESULTS: Global transcriptomic analysis of synovial cell subsets identifies actively proliferating synovial T cells and indicates that due to differential λ and κ immunoglobulin light chain usage, synovial plasma cells are potentially not derived from the local memory B cell pool. Importantly, we report distinct fibroblast and endothelial cell transcriptomes indicating abundant subpopulations in RA and PsA characterised by differential transcription factor usage. Using receptor-ligand interactions and downstream target characterisation, we identify RA-specific synovial T cell-derived transforming growth factor (TGF)-ß and macrophage interleukin (IL)-1ß synergy in driving the transcriptional profile of FAPα+THY1+ invasive synovial fibroblasts, expanded in RA compared with PsA. In vitro characterisation of patient with RA synovial fibroblasts showed metabolic switch to glycolysis, increased adhesion intercellular adhesion molecules 1 expression and IL-6 secretion in response to combined TGF-ß and IL-1ß treatment. Disrupting specific immune and stromal cell interactions offers novel opportunities for targeted therapeutic intervention in RA and PsA.

MetagenoNets: comprehensive inference and meta-insights for microbial correlation networks.

Microbial association networks are frequently used for understanding and comparing community dynamics from microbiome datasets. Inferring microbial correlations for such networks and obtaining meaningful biological insights, however, requires a lengthy data management workflow, choice of appropriate methods, statistical computations, followed by a different pipeline for suitably visualizing, reporting and comparing the associations. The complexity is further increased with the added dimension of multi-group 'meta-data' and 'inter-omic' functional profiles that are often associated with microbiome studies. This not only necessitates the need for categorical networks, but also integrated and bi-partite networks. Multiple options of network inference algorithms further add to the efforts required for performing correlation-based microbiome interaction studies. We present MetagenoNets, a web-based application, which accepts multi-environment microbial abundance as well as functional profiles, intelligently segregates 'continuous and categorical' meta-data and allows inference as well as visualization of categorical, integrated (inter-omic) and bi-partite networks. Modular structure of MetagenoNets ensures logical flow of analysis (inference, integration, exploration and comparison) in an intuitive and interactive personalized dashboard driven framework. Dynamic choice of filtration, normalization, data transformation and correlation algorithms ensures, that end-users get a one-stop solution for microbial network analysis. MetagenoNets is freely available at https://web.rniapps.net/metagenonets.

Correction to: NetConfer: a web application for comparative analysis of multiple biological networks.

An amendment to this paper has been published and can be accessed via the original article.

NetConfer: a web application for comparative analysis of multiple biological networks.

BACKGROUND: Most biological experiments are inherently designed to compare changes or transitions of state between conditions of interest. The advancements in data intensive research have in particular elevated the need for resources and tools enabling comparative analysis of biological data. The complexity of biological systems and the interactions of their various components, such as genes, proteins, taxa, and metabolites, have been inferred, represented, and visualized via graph theory-based networks. Comparisons of multiple networks can help in identifying variations across different biological systems, thereby providing additional insights. However, while a number of online and stand-alone tools exist for generating, analyzing, and visualizing individual biological networks, the utility to batch process and comprehensively compare multiple networks is limited. RESULTS: Here, we present a graphical user interface (GUI)-based web application which implements multiple network comparison methodologies and presents them in the form of organized analysis workflows. Dedicated comparative visualization modules are provided to the end-users for obtaining easy to comprehend, insightful, and meaningful comparisons of various biological networks. We demonstrate the utility and power of our tool using publicly available microbial and gene expression data. CONCLUSION: NetConfer tool is developed keeping in mind the requirements of researchers working in the field of biological data analysis with limited programming expertise. It is also expected to be useful for advanced users from biological as well as other domains (working with association networks), benefiting from provided ready-made workflows, as they allow to focus directly on the results without worrying about the implementation. While the web version allows using this application without installation and dependency requirements, a stand-alone version has also been supplemented to accommodate the offline requirement of processing large networks.

CD40L-Dependent Pathway Is Active at Various Stages of Rheumatoid Arthritis Disease Progression.

The inflammatory CD40-CD40L pathway is implicated in various autoimmune diseases, but the activity status of this pathway in various stages of rheumatoid arthritis (RA) progression is unknown. In this study, we used gene signatures of CD40L stimulation derived from human immature dendritic cells and naive B cells to assess the expression of CD40-downstream genes in synovial tissues from anti-citrullinated protein Ab-positive arthralgia, undifferentiated arthritis (UA), early RA, and established RA cohorts in comparison with healthy donors. Interestingly, the expression of CD40LG and active full-length CD40 was increased in the disease tissues, whereas that of a dominant-negative CD40 isoform was decreased. Gene set variation analysis revealed that CD40L-responsive genes in immature dendritic cells and naive B cells were significantly enriched in synovial tissues from UA, early RA, and established RA patients. Additionally, CD40L-induced naive B cell genes were also significantly enriched in synovial tissues from arthralgia patients. In our efforts to characterize downstream mediators of CD40L signaling, we have identified GPR120 and KDM6B as novel components of the pathway. In conclusion, our data suggest that therapeutic CD40-CD40L blocking agents may prove efficacious not only in early and established RA, but also in inhibiting the progression of the disease from arthralgia or UA to RA.

EnsembleSeq: a workflow towards real-time, rapid, and simultaneous multi-kingdom-amplicon sequencing for holistic and resource-effective microbiome research at scale.

Bacterial communities are often concomitantly present with numerous microorganisms in the human body and other natural environments. Amplicon-based microbiome studies have generally paid skewed attention, that too at a rather shallow genus level resolution, to the highly abundant bacteriome, with interest now forking toward the other microorganisms, particularly fungi. Given the generally sparse abundance of other microbes in the total microbiome, simultaneous sequencing of amplicons targeting multiple microbial kingdoms could be possible even with full multiplexing. Guiding studies are currently needed for performing and monitoring multi-kingdom-amplicon sequencing and data capture at scale. Aiming to address these gaps, amplification of full-length bacterial 16S rRNA gene and entire fungal internal-transcribed spacer (ITS) region was performed for human saliva samples (n = 96, including negative and positive controls). Combined amplicon DNA libraries were prepared for nanopore sequencing using a major fraction of 16S molecules and a minor fraction of ITS amplicons. Sequencing was performed in a single run of an R10.4.1 flow cell employing the latest V14 chemistry. An approach for real-time monitoring of the species saturation using dynamic rarefaction was designed as a guiding determinant of optimal run time. Real-time saturation monitoring for both bacterial and fungal species enabled the completion of sequencing within 30 hours, utilizing less than 60% of the total nanopores. Approximately 5 million high quality (HQ) taxonomically assigned reads were generated (~4.2 million bacterial and 0.7 million fungal), providing a wider (beyond bacteriome) snapshot of human oral microbiota at species-level resolution. Among the more than 400 bacterial and 240 fungal species identified in the studied samples, the species of Streptococcus (e.g., Streptococcus mitis and Streptococcus oralis) and Candida (e.g., Candida albicans and Candida tropicalis) were observed to be the dominating microbes in the oral cavity, respectively. This conformed well with the previous reports of the human oral microbiota. EnsembleSeq provides a proof-of-concept toward the identification of both fungal and bacterial species simultaneously in a single fully multiplexed nanopore sequencing run in a time- and resource-effective manner. Details of this workflow, along with the associated codebase, are provided to enable large-scale application for a holistic species-level microbiome study. IMPORTANCE: Human microbiome is a sum total of a variety of microbial genomes (including bacteria, fungi, protists, viruses, etc.) present in and on the human body. Yet, a majority of amplicon-based microbiome studies have largely remained skewed toward bacteriome as an assumed proxy of the total microbiome, primarily at a shallow genus level. Cost, time, effort, data quality/management, and importantly lack of guiding studies often limit progress in the direction of moving beyond bacteriome. Here, EnsembleSeq presents a proof-of-concept toward concomitantly capturing multiple-kingdoms of microorganisms (bacteriome and mycobiome) in a fully multiplexed (96-sample) single run of long-read amplicon sequencing. In addition, the workflow captures dynamic tracking of species-level saturation in a time- and resource-effective manner.

LXR modulation blocks prostaglandin E2 production and matrix degradation in cartilage and alleviates pain in a rat osteoarthritis model.

Osteoarthritis (OA), the most common arthritic condition in humans, is characterized by the progressive degeneration of articular cartilage accompanied by chronic joint pain. Inflammatory mediators, such as cytokines and prostaglandin E(2) (PGE(2)) that are elevated in OA joints, play important roles in the progression of cartilage degradation and pain-associated nociceptor sensitivity. We have found that the nuclear receptor family transcription factors Liver X Receptors (LXRalpha and -beta) are expressed in cartilage, with LXRbeta being the predominant isoform. Here we show that genetic disruption of Lxrbeta gene expression in mice results in significantly increased proteoglycan (aggrecan) degradation and PGE(2) production in articular cartilage treated with IL-1beta, indicating a protective role of LXRbeta in cartilage. Using human cartilage explants, we found that activation of LXRs by the synthetic ligand GW3965 significantly reduced cytokine-induced degradation and loss of aggrecan from the tissue. Furthermore, LXR activation dramatically inhibited cytokine-induced PGE(2) production by human osteoarthritic cartilage as well as by a synovial sarcoma cell line. These effects were achieved at least partly by repression of the expression of ADAMTS4, a physiological cartilage aggrecanase, and of cyclooxygenase-2 and microsomal prostaglandin E synthase-1, key enzymes in the PGE(2) synthesis pathway. Consistent with our in vitro observations, oral administration of GW3965 potently alleviated joint pain in a rat meniscal tear model of osteoarthritis.

Environmental insults and compensative responses: when microbiome meets cancer.

Tumor microenvironment has recently been ascribed a new hallmark-the polymorphic microbiome. Accumulating evidence regarding the tissue specific territories of tumor-microbiome have opened new and interesting avenues. A pertinent question is regarding the functional consequence of the interface between host-microbiome and cancer. Given microbial communities have predominantly been explored through an ecological perspective, it is important that the foundational aspects of ecological stress and the fight to 'survive and thrive' are accounted for tumor-micro(b)environment as well. Building on existing evidence and classical microbial ecology, here we attempt to characterize the ecological stresses and the compensative responses of the microorganisms inside the tumor microenvironment. What insults would microbes experience inside the cancer jungle? How would they respond to these insults? How the interplay of stress and microbial quest for survival would influence the fate of tumor? This work asks these questions and tries to describe this underdiscussed ecological interface of the tumor and its microbiota. It is hoped that a larger scientific thought on the importance of microbial competition sensing vis-à-vis tumor-microenvironment would be stimulated.

Transcriptomic profiling of programmed cell death 1 (PD-1) expressing T cells in early rheumatoid arthritis identifies a decreased CD4 + PD-1 + signature post-treatment.

Programmed cell death protein 1 (PD-1)-expressing T cells are expanded in individuals with established rheumatoid arthritis (RA). However, little is known about their functional role in the pathogenesis of early RA. To address this, we investigated the transcriptomic profiles of circulating CD4+ and CD8+ PD-1+ lymphocytes from patients with early RA (n = 5) using fluorescence activated cell sorting in conjunction with total RNA sequencing. Additionally, we assessed for alterations in CD4+PD-1+ gene signatures in previously published synovial tissue (ST) biopsy data (n = 19) (GSE89408, GSE97165) before and after six-months of triple disease modifying anti-rheumatic drug (tDMARD) treatment. Comparisons of gene signatures between CD4+PD-1+ vs. PD-1- cells identified significant upregulation of genes including CXCL13 and MAF, and in pathways including Th1 and Th2, cross talk between dendritic cells and NK cells, B cell development and antigen presentation. Gene signatures from early RA ST before and after six-month tDMARD treatment revealed downregulation of the CD4+PD-1+ signatures following treatment, identifying a mechanism through which tDMARDs exert their effect by influencing T cell populations. Furthermore, we identify factors associated with B cell help that are enhanced in the ST compared with PBMCs, highlighting their importance in driving synovial inflammation.

Liver X receptor-retinoid X receptor (LXR-RXR) heterodimer cistrome reveals coordination of LXR and AP1 signaling in keratinocytes.

Liver X receptors (LXRs) play a critical role in regulating lipid synthesis and transport in numerous tissues. In the skin, activation of LXR induces keratinocyte differentiation and improves epidermal permeability barrier homeostasis. To elucidate the mechanism of LXR action in skin, we mapped its cistrome by identifying LXRß-RXRα binding sites using ChIP-on-chip in normal human epidermal keratinocytes (NHEKs). The cistrome was integrated with transcription data to obtain a global view of LXR action in keratinocyte biology. Here, we identify 2035 LXRß-RXRα binding sites containing 4794 LXR response elements in NHEKs and show the presence of consensus heterodimer active regions in genes involved in keratinocyte lipid transport/synthesis and terminal differentiation. Bioinformatics analysis of the cistrome revealed an enrichment of AP1 cis-regulatory motifs in the vicinity of the LXRß-RXRα binding sites. Importantly, we have demonstrated a direct interaction between LXR and Jun/Fos, indicating that the cooperation between LXR and AP1 may orchestrate keratinocyte differentiation. Finally, we corroborated these results by genome-wide mapping of the c-Fos and c-Jun cistromes in NHEKs, demonstrating that 77% of all the LXRß-RXRα binding regions show the presence of AP1 motifs at adjacent locations. Our findings provide new insight into the mechanism of LXR action in keratinocyte differentiation, lipid production and barrier formation, further strengthening the validation of LXR as a potential therapeutic target for skin disorders including skin aging, psoriasis, and atopic dermatitis.

A randomized placebo-controlled study of noninvasive cortical electrostimulation in the treatment of fibromyalgia patients.

OBJECTIVE: The aim of this multicenter study was to evaluate the efficacy, safety, and tolerability of noninvasive cortical electrostimulation in the management of fibromyalgia (FM). DESIGN: A prospective, randomized, double-blind, placebo-controlled design was used. Setting. Subjects received therapy at two different outpatient clinical locations. PATIENTS: There were 77 subjects meeting the American College of Rheumatology 1990 classification criteria for FM. Intervention. Thirty-nine (39) active treatment (AT) FM patients and 38 placebo controls received 22 applications of either noninvasive cortical electrostimulation or a sham therapy over an 11-week period. OUTCOME MEASURES: The primary outcome measures were the number of tender points (TePs) and pressure pain threshold (PPT). Secondary outcome measures were responses to the Fibromyalgia Impact Questionnaire (FIQ), Symptom Checklist-90 (SCL-90), Beck Depression Inventory-II, and a novel sleep questionnaire, all evaluated at baseline and at the end of treatment. RESULTS: Intervention provided significant improvements in TeP measures: compared with placebo, the AT patients improved in the number of positive TePs (-7.4 vs -0.2, P<0.001) and the PPT (19.6 vs -3.2, P<0.001). Most secondary outcomes also improved more in the AT group: total FIQ score (-15.5 vs -5.6, P=0.03), FIQ pain (-2.0 vs -0.6, P=0.03), FIQ fatigue (-2.0 vs -0.4, P=0.02), and FIQ refreshing sleep (-2.1 vs -0.7, P=0.02); and while FIQ function improved (-1.0 vs -0.2), the between-group change had a 14% likelihood of occurring due to chance (P=0.14). There were no significant side effects observed. CONCLUSIONS: Noninvasive cortical electrostimulation in FM patients provided modest improvements in pain, TeP measures, fatigue, and sleep; and the treatment was well tolerated. This form of therapy could potentially provide worthwhile adjunctive symptom relief for FM patients.

MarkerML - Marker Feature Identification in Metagenomic Datasets Using Interpretable Machine Learning.

Identification of environment specific marker-features is one of the key objectives of many metagenomic studies. It aims to identify such features in microbiome datasets that may serve as markers of the contrasting or comparable states. Hypothesis testing and black-box machine learnt models which are conventionally used for identification of these features are generally not exhaustive, especially because they generally do-not provide any quantifiable relevance (context) of/between the identified features. We present MarkerML web-server, that seeks to leverage the emergence of interpretable machine learning for facilitating the contextual discovery of metagenomic features of interest. It does so through a comprehensive and automated application of the concept of Shapley Additive Explanations in companionship to the compositionality accounted hypothesis testing for the multi-variate microbiome datasets. MarkerML not only helps in identification of marker-features, but also enables insights into the role and inter-dependence of the identified features in driving the decision making of the supervised machine learnt model. Generation of high quality and intuitive visualizations spanning prediction effect plots, model performance reports, feature dependency plots, Shapley and abundance informed cladograms (Sungrams), hypothesis tested violin plots along-with necessary provisions for excluding the participant bias and ensuring reproducibility of results, further seek to make the platform a useful asset for the scientists in the field of microbiome (and even beyond). The MarkerML web-server is freely available for the academic community at https://microbiome.igib.res.in/markerml/.

Tracking mutational semantics of SARS-CoV-2 genomes.

Natural language processing (NLP) algorithms process linguistic data in order to discover the associated word semantics and develop models that can describe or even predict the latent meanings of the data. The applications of NLP become multi-fold while dealing with dynamic or temporally evolving datasets (e.g., historical literature). Biological datasets of genome-sequences are interesting since they are sequential as well as dynamic. Here we describe how SARS-CoV-2 genomes and mutations thereof can be processed using fundamental algorithms in NLP to reveal the characteristics and evolution of the virus. We demonstrate applicability of NLP in not only probing the temporal mutational signatures through dynamic topic modelling, but also in tracing the mutation-associations through tracing of semantic drift in genomic mutation records. Our approach also yields promising results in unfolding the mutational relevance to patient health status, thereby identifying putative signatures linked to known/highly speculated mutations of concern.

Can machines learn the mutation signatures of SARS-CoV-2 and enable viral-genotype guided predictive prognosis?

MOTIVATION: Continuous emergence of new variants through appearance/accumulation/disappearance of mutations is a hallmark of many viral diseases. SARS-CoV-2 variants have particularly exerted tremendous pressure on global healthcare system owing to their life threatening and debilitating implications. The sheer plurality of variants and huge scale of genomic data have added to the challenges of tracing the mutations/variants and their relationship to infection severity (if any). RESULTS: We explored the suitability of virus-genotype guided machine-learning in infection prognosis and identification of features/mutations-of-interest. Total 199,519 outcome-traced genomes, representing 45,625 nucleotide-mutations, were employed. Among these, post data-cleaning, Low and High severity genomes were classified using an integrated model (employing virus genotype, epitopic-influence and patient-age) with consistently high ROC-AUC (Asia:0.97 ± 0.01, Europe:0.94 ± 0.01, N.America:0.92 ± 0.02, Africa:0.94 ± 0.07, S.America:0.93 ± 03). Although virus-genotype alone could enable high predictivity (0.97 ± 0.01, 0.89 ± 0.02, 0.86 ± 0.04, 0.95 ± 0.06, 0.9 ± 0.04), the performance was not found to be consistent and the models for a few geographies displayed significant improvement in predictivity when the influence of age and/or epitope was incorporated with virus-genotype (Wilcoxon p_BH < 0.05). Neither age or epitopic-influence or clade information could out-perform the integrated features. A sparse model (6 features), developed using patient-age and epitopic-influence of the mutations, performed reasonably well (>0.87 ± 0.03, 0.91 ± 0.01, 0.87 ± 0.03, 0.84 ± 0.08, 0.89 ± 0.05). High-performance models were employed for inferring the important mutations-of-interest using Shapley Additive exPlanations (SHAP). The changes in HLA interactions of the mutated epitopes of reference SARS-CoV-2 were then subsequently probed. Notably, we also describe the significance of a 'temporal-modeling approach' to benchmark the models linked with continuously evolving pathogens. We conclude that while machine learning can play a vital role in identifying relevant mutations and factors driving the severity, caution should be exercised in using the genotypic signatures for predictive prognosis.

Attenuation of the BTLA/HVEM Regulatory Network in the Circulation in Primary Sjögren's Syndrome.

Primary Sjögren's syndrome (SjS) is an inflammatory autoimmune disorder which targets the lacrimal and salivary glands, resulting in glandular dysfunction. Currently, the immune drivers of SjS remain poorly understood and peripheral biomarkers of disease are lacking. The present study therefore sought to investigate the immune cell constituents of the SjS peripheral blood, and to assess the role of the BTLA/HVEM/CD160 co-stimulatory network by characterizing expression within the periphery. Peripheral blood mononuclear cells (PBMCs) were isolated from whole blood of n = 10 patients with SjS and n = 10 age- and sex-matched healthy control donors. Cells were divided and stained with three panels of antibodies, allowing assessment of T, B, and myeloid cell subsets, and measurement of BTLA, HVEM, and CD160 surface expression by flow cytometry. We identified distinct alterations in proportions of peripheral T, B, and myeloid cell types in SjS compared with healthy controls. Expression of BTLA/CD160/HVEM and frequency of BTLA/CD160/HVEM-expressing cells were significantly altered in peripheral SjS lymphocytes. The proportion of T cells co-expressing BTLA/HVEM and CD160/HVEM were significantly reduced in SjS. We found decreased BTLA and HVEM levels on peripheral B and T cells of SjS patients, and decreased BTLA/HVEM and CD160/HVEM co-expression, demonstrating dysregulation of the BTLA/HVEM axis in the peripheral blood of SjS patients. These results indicate the potential of targeting the BTLA-HVEM axis for the treatment of SjS.

Serologic Biomarkers of Progression Toward Diagnosis of Rheumatoid Arthritis in Active Component Military Personnel.

OBJECTIVE: To identify a panel of serum biomarkers that could specifically identify imminent cases of rheumatoid arthritis (RA) before diagnosis. METHODS: Serum samples were collected at 4 time points from active component US military personnel, including 157 anti-citrullinated protein antibody (ACPA)-seropositive and 50 ACPA-seronegative RA subjects, 100 reactive arthritis (ReA) subjects, and 76 healthy controls. The cohorts were split into 2 phases, with samples tested on independent proteomic platforms for each phase. Classification models of RA diagnosis based on samples obtained within 6 months prior to diagnosis were developed both in univariate analyses and by multivariate random forest modeling of training sample sets and testing sample sets from each phase. RESULTS: Increases in serum analytes, including C-reactive protein levels, serum amyloid A, and soluble programmed cell death 1 (PD-1), were observed in seropositive RA subjects at the time point closest to diagnosis, up to several years before diagnosis. Only a small fraction of RA subjects had levels above the 95th percentile of healthy control levels until the time period within 6 months of diagnosis. For classification of RA diagnosis using samples obtained within 6 months prior to diagnosis, soluble PD-1 provided superior specificity compared to ReA cases (>89%), with a sensitivity of 48% for RA classification. An 8-analyte model provided superior sensitivity (69%), with comparable specificity relative to ReA (>82%). CONCLUSION: Our findings demonstrate that imminent RA diagnosis could be classified with high specificity, relative to healthy controls and ReA cases, using a panel of cytokines measured in serum samples collected within 6 months before actual diagnosis.

Differential expansion of T peripheral helper cells in early rheumatoid arthritis and osteoarthritis synovium.

OBJECTIVES: Programmed cell death protein 1 (PD-1)-expressing T cells are implicated in the pathogenesis of autoimmune inflammatory diseases such as rheumatoid arthritis. A subset of CXCR5- T cells, termed T peripheral helper (Tph) cells, which drive B cell differentiation, have been identified in ectopic lymphoid structures in established rheumatoid arthritis synovial tissue. Here, we aimed to characterise these in treatment-naïve, early rheumatoid arthritis to determine whether these cells accumulate prior to fully established disease. METHODS: Fresh dissociated tissue and peripheral blood mononuclear cell (PBMC) suspensions were stained with Zombie UV, followed by anti-CD45RO, PD-1, CD3, ICOS, CD8, CD4, CD20, CXCR5, TIGIT and CD38 antibodies prior to analysis. For histology, rheumatoid arthritis synovial sections were prepared for Opal multispectral immunofluorescence with anti-CD45RO, CD20, PD-1 and CXCR5 antibodies. Images were acquired on the Perkin Elmer Vectra V.3.0 imaging system and analysed using InForm Advanced Image Analysis software. RESULTS: Flow cytometry revealed T cell infiltration in the rheumatoid arthritis synovium with differential expression of PD-1, CD45RO, ICOS, TIGIT and CD38. We observed a higher frequency of PD1hiCXCR5- Tph in rheumatoid arthritis synovial tissue and PBMCs versus controls, and no significant difference in T follicular helper cell frequency. Microscopy identified a 10-fold increase of Tph cells in early rheumatoid arthritis synovial follicular and diffuse regions, and identified Tph adjacent to germinal centre B cells. CONCLUSIONS: These data demonstrate that PD-1hi Tph cells are present in early rheumatoid arthritis, but not osteoarthritis synovium, and therefore may provide a target for treatment of patients with early rheumatoid arthritis.