In this in-depth review, we examine the worldwide epidemiology of SLE and summarize current knowledge on the influence of race/ethnicity on clinical manifestations, disease activity, damage ...accumulation and outcome in SLE. Susceptibility to SLE has a strong genetic component, and trans-ancestral genetic studies have revealed a substantial commonality of shared genetic risk variants across different genetic ancestries that predispose to the development of SLE. The highest increased risk of developing SLE is observed in black individuals (incidence 5- to 9-fold increased, prevalence 2- to 3-fold increased), with an increased risk also observed in South Asians, East Asians and other non-white groups, compared with white individuals. Black, East Asian, South Asian and Hispanic individuals with SLE tend to develop more severe disease with a greater number of manifestations and accumulate damage from lupus more rapidly. Increased genetic risk burden in these populations, associated with increased autoantibody reactivity in non-white individuals with SLE, may explain the more severe lupus phenotype. Even after taking into account socio-economic factors, race/ethnicity remains a key determinant of poor outcome, such as end-stage renal failure and mortality, in SLE. Community measures to expedite diagnosis through increased awareness in at-risk racial/ethnic populations and ethnically personalized treatment algorithms may help in future to improve long-term outcomes in SLE.
Although targeted biological treatments have transformed the outlook for patients with rheumatoid arthritis (RA), 40% of patients show poor clinical response, and there is an imperative to unravel ...the molecular pathways and mechanisms underlying non-response and disease progression. 5–20% of RA individuals do not respond to all current medications including biologic and targeted therapies, which suggests that distinct pathogenic processes underlie multi-drug refractoriness.
In this brief review we discuss advances from recent studies in precision medicine in rheumatoid arthritis.
Bulk RNA-Sequencing of synovial biopsies from RA individuals combined with histology and deep clinical phenotyping has revealed substantial insights into divergent pathogenic pathways which lead to disease progression and illuminated mechanisms underlying failure to response to specific treatments. Biopsy-driven randomised controlled trials, such as R4RA and the forthcoming STRAP trial, have enabled the development of machine learning predictive models for predicting response to different therapies.
In the Pathobiology of Early Arthritis Cohort (PEAC), gene expression analysis showed that individuals could be classified into three gene expression subgroups which correlated with histopathotypes defined by histological markers: pauci-immune fibroid pathotype characterised by fibroblasts and an absence of immune inflammatory cells; diffuse-myeloid pathotype characterised by macrophage influx; and the lympho-myeloid pathotype delineated by the presence of B cells, but typically containing a complex inflammatory infiltrate with ectopic lymphoid structure formation. In the R4RA biopsy-driven randomised controlled trial, patients were randomised to either rituximab or tocilizumab. Comprehensive analysis of synovial biopsies pre/post-treatment identified gene signatures of response associated with pathogenic pathways which could be tracked over time. A group of true refractory patients were identified who had failed anti-TNF prior to the study (it was an entry criterion) and then subsequently failed both trial biologics during the trial. RNA-Seq analysis and digital spatial profiling identified specific cell types including DKK3+ fibroblasts as being associated with the refractory state. We identified machine learning predictive models based on specific gene signatures which were able to predict future response to therapy as well as the refractory state.
RNA-sequencing of synovial biopsies has enabled substantial progress in understanding disease endotypes in RA and identifying synovial gene signatures which predict prognosis and future response to treatment.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Patients with rheumatoid arthritis (RA) receive highly targeted biologic therapies without previous knowledge of target expression levels in the diseased tissue. Approximately 40% of patients do not ...respond to individual biologic therapies and 5-20% are refractory to all. In a biopsy-based, precision-medicine, randomized clinical trial in RA (R4RA; n = 164), patients with low/absent synovial B cell molecular signature had a lower response to rituximab (anti-CD20 monoclonal antibody) compared with that to tocilizumab (anti-IL6R monoclonal antibody) although the exact mechanisms of response/nonresponse remain to be established. Here, in-depth histological/molecular analyses of R4RA synovial biopsies identify humoral immune response gene signatures associated with response to rituximab and tocilizumab, and a stromal/fibroblast signature in patients refractory to all medications. Post-treatment changes in synovial gene expression and cell infiltration highlighted divergent effects of rituximab and tocilizumab relating to differing response/nonresponse mechanisms. Using ten-by-tenfold nested cross-validation, we developed machine learning algorithms predictive of response to rituximab (area under the curve (AUC) = 0.74), tocilizumab (AUC = 0.68) and, notably, multidrug resistance (AUC = 0.69). This study supports the notion that disease endotypes, driven by diverse molecular pathology pathways in the diseased tissue, determine diverse clinical and treatment-response phenotypes. It also highlights the importance of integration of molecular pathology signatures into clinical algorithms to optimize the future use of existing medications and inform the development of new drugs for refractory patients.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Abstract
Motivation
Clustering patient omic data is integral to developing precision medicine because it allows the identification of disease subtypes. A current major challenge is the integration ...multi-omic data to identify a shared structure and reduce noise. Cluster analysis is also increasingly applied on single-omic data, for example, in single cell RNA-seq analysis for clustering the transcriptomes of individual cells. This technology has clinical implications. Our motivation was therefore to develop a flexible and effective spectral clustering tool for both single and multi-omic data.
Results
We present Spectrum, a new spectral clustering method for complex omic data. Spectrum uses a self-tuning density-aware kernel we developed that enhances the similarity between points that share common nearest neighbours. It uses a tensor product graph data integration and diffusion procedure to reduce noise and reveal underlying structures. Spectrum contains a new method for finding the optimal number of clusters (K) involving eigenvector distribution analysis. Spectrum can automatically find K for both Gaussian and non-Gaussian structures. We demonstrate across 21 real expression datasets that Spectrum gives improved runtimes and better clustering results relative to other methods.
Availability and implementation
Spectrum is available as an R software package from CRAN https://cran.r-project.org/web/packages/Spectrum/index.html.
Supplementary information
Supplementary data are available at Bioinformatics online.
Systemic Autoimmune Rheumatic Diseases, including Rheumatoid Arthritis, Systemic Lupus Erythematosus and Sjogren’s syndrome, are characterised by a loss of immune tolerance and chronic inflammation. ...There is marked heterogeneity in clinical and molecular phenotypes in each condition, and the aetiology of these is unclear. NF-κB is an inducible transcription factor that is critical in the physiological inflammatory response, and which has been implicated in chronic inflammation. Genome-wide association studies have linked risk alleles related to the NF-κB pathway to the pathogenesis of multiple Systemic Autoimmune Rheumatic Diseases. This review describes how cell- and pathway-specific NF-κB activation contribute to the spectrum of clinical phenotypes and molecular pathotypes in rheumatic disease. Potential clinical applications are explored, including therapeutic interventions and utilisation of NF-κB as a biomarker of disease subtypes and treatment response.
•NF-κB-related genetic variants have been identified in most Systemic Autoimmune Rheumatic Diseases.•Dysregulated NF-κB is present in most SARDs.•Cell-specific NF-κB dysregulation may influence the site of inflammation.•Cell- and pathway-specific NF-κB activity corresponds with RA synovial pathotypes.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Accumulation of lactate in the tissue microenvironment is a feature of both inflammatory disease and cancer. Here, we assess the response of immune cells to lactate in the context of chronic ...inflammation. We report that lactate accumulation in the inflamed tissue contributes to the upregulation of the lactate transporter SLC5A12 by human CD4+ T cells. SLC5A12-mediated lactate uptake into CD4+ T cells induces a reshaping of their effector phenotype, resulting in increased IL17 production via nuclear PKM2/STAT3 and enhanced fatty acid synthesis. It also leads to CD4+ T cell retention in the inflamed tissue as a consequence of reduced glycolysis and enhanced fatty acid synthesis. Furthermore, antibody-mediated blockade of SLC5A12 ameliorates the disease severity in a murine model of arthritis. Finally, we propose that lactate/SLC5A12-induced metabolic reprogramming is a distinctive feature of lymphoid synovitis in rheumatoid arthritis patients and a potential therapeutic target in chronic inflammatory disorders.
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•Lactate induces the expression of SLC5A12 on human CD4+ T cells in the inflamed tissue•Lactate promotes CD4+ T cell IL17 production via PKM2/STAT3 signaling and FA synthesis•Lactate inhibits CD4+ T cell motility via increased FA synthesis and reduced glycolysis•SLC5A12 blockade ameliorates the disease severity in a murine model of arthritis
With buildup in the inflamed tissue, lactate can exacerbate the inflammatory response. Pucino et al. report that lactate induces expression of its own transporter, SLC5A12, driving lactate uptake into CD4+ T cells and resulting in increased IL17 production via PKM2/STAT3 signaling and enhanced fatty acid synthesis. They provide evidence that targeting SLC5A12 may ameliorate chronic inflammatory disorders.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Biomarkers are needed for predicting the effectiveness of disease modifying antirheumatic drugs (DMARDs). Here, using functional lipid mediator profiling and deeply phenotyped patients with early ...rheumatoid arthritis (RA), we observe that peripheral blood specialized pro-resolving mediator (SPM) concentrations are linked with both DMARD responsiveness and disease pathotype. Machine learning analysis demonstrates that baseline plasma concentrations of resolvin D4, 10S, 17S-dihydroxy-docosapentaenoic acid, 15R-Lipoxin (LX)A
and n-3 docosapentaenoic-derived Maresin 1 are predictive of DMARD responsiveness at 6 months. Assessment of circulating SPM concentrations 6-months after treatment initiation establishes that differences between responders and non-responders are maintained, with a decrease in SPM concentrations in patients resistant to DMARD therapy. These findings elucidate the potential utility of plasma SPM concentrations as biomarkers of DMARD responsiveness in RA.
Abstract
Motivation
While many pipelines have been developed for calling genotypes using RNA-sequencing (RNA-Seq) data, they all have adapted DNA genotype callers that do not model biases specific to ...RNA-Seq such as allele-specific expression (ASE).
Results
Here, we present Bayesian beta-binomial mixture model (BBmix), a Bayesian beta-binomial mixture model that first learns the expected distribution of read counts for each genotype, and then deploys those learned parameters to call genotypes probabilistically. We benchmarked our model on a wide variety of datasets and showed that our method generally performed better than competitors, mainly due to an increase of up to 1.4% in the accuracy of heterozygous calls, which may have a big impact in reducing false positive rate in applications sensitive to genotyping error such as ASE. Moreover, BBmix can be easily incorporated into standard pipelines for calling genotypes. We further show that parameters are generally transferable within datasets, such that a single learning run of less than 1 h is sufficient to call genotypes in a large number of samples.
Availability and implementation
We implemented BBmix as an R package that is available for free under a GPL-2 licence at https://gitlab.com/evigorito/bbmix and https://cran.r-project.org/package=bbmix with accompanying pipeline at https://gitlab.com/evigorito/bbmix_pipeline.
UBE2L3 is associated with increased susceptibility to numerous autoimmune diseases, but the underlying mechanism is unexplained. By using data from a genome-wide association study of systemic lupus ...erythematosus (SLE), we observed a single risk haplotype spanning UBE2L3, consistently aligned across multiple autoimmune diseases, associated with increased UBE2L3 expression in B cells and monocytes. rs140490 in the UBE2L3 promoter region showed the strongest association. UBE2L3 is an E2 ubiquitin-conjugating enzyme, specially adapted to function with HECT and RING-in-between-RING (RBR) E3 ligases, including HOIL-1 and HOIP, components of the linear ubiquitin chain assembly complex (LUBAC). Our data demonstrate that UBE2L3 is the preferred E2 conjugating enzyme for LUBAC in vivo, and UBE2L3 is essential for LUBAC-mediated activation of NF-κB. By accurately quantifying NF-κB translocation in primary human cells from healthy individuals stratified by rs140490 genotype, we observed that the autoimmune disease risk UBE2L3 genotype was correlated with basal NF-κB activation in unstimulated B cells and monocytes and regulated the sensitivity of NF-κB to CD40 stimulation in B cells and TNF stimulation in monocytes. The UBE2L3 risk allele correlated with increased circulating plasmablast and plasma cell numbers in SLE individuals, consistent with substantially elevated UBE2L3 protein levels in plasmablasts and plasma cells. These results identify key immunological consequences of the UBE2L3 autoimmune risk haplotype and highlight an important role for UBE2L3 in plasmablast and plasma cell development.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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