11,089 research outputs found
Detecting pathogenic variants in autoimmune diseases using highâthroughput sequencing
Sequencing the first human genome in 2003 took 15 years and cost 1000 in a single day. These advances have allowed the generation of huge volumes of highâquality human sequence data used to construct increasingly large catalogs of both populationâlevel and diseaseâcausing variation. The existence of such databases, coupled with a highâquality human reference genome, means we are able to interrogate and annotate all types of genetic variation and identify pathogenic variants for many diseases. Increasingly, sequencingâbased approaches are being used to elucidate the underlying genetic cause of autoimmune diseases, a group of roughly 80 polygenic diseases characterized by abnormal immune responses where healthy tissue is attacked. Although sequence data generation has become routine and affordable, significant challenges remain with no goldâstandard methodology to identify pathogenic variants currently available. This review examines the latest methodologies used to identify pathogenic variants in autoimmune diseases and considers available sequencing options and subsequent bioinformatic methodologies and strategies. The development of reliable and robust sequencing and analytic workflows to detect pathogenic variants is critical to realize the potential of precision medicine programs where patient variant information is used to inform clinical practice
Analysis of gut microbiota in rheumatoid arthritis patients. Disease-related dysbiosis and modifications induced by etanercept
A certain number of studies were carried out to address the question of how dysbiosis could affect the onset and development of rheumatoid arthritis (RA), but little is known about the reciprocal influence between microbiota composition and immunosuppressive drugs, and how this interaction may have an impact on the clinical outcome. The aim of this study was to characterize the intestinal microbiota in a groups of RA patients treatment-naïve, under methotrexate, and/or etanercept (ETN). Correlations between the gut microbiota composition and validated immunological and clinical parameters of disease activity were also evaluated. In the current study, a 16S analysis was employed to explore the gut microbiota of 42 patients affected by RA and 10 healthy controls. Disease activity score on 28 joints (DAS-28), erythrocyte sedimentation rate, C-reactive protein, rheumatoid factor, anti-cyclic citrullinated peptides, and dietary and smoking habits were assessed. The composition of the gut microbiota in RA patients free of therapy is characterized by several abnormalities compared to healthy controls. Gut dysbiosis in RA patients is associated with different serological and clinical parameters; in particular, the phylum of Euryarchaeota was directly correlated to DAS and emerged as an independent risk factor. Patients under treatment with ETN present a partial restoration of a beneficial microbiota. The results of our study confirm that gut dysbiosis is a hallmark of the disease, and shows, for the first time, that the anti-tumor necrosis factor alpha (TNF-α) ETN is able to modify microbial communities, at least partially restoring a beneficial microbiota
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Transposable Elements, Inflammation, and Neurological Disease.
Transposable Elements (TE) are mobile DNA elements that can replicate and insert themselves into different locations within the host genome. Their propensity to self-propagate has a myriad of consequences and yet their biological significance is not well-understood. Indeed, retrotransposons have evaded evolutionary attempts at repression and may contribute to somatic mosaicism. Retrotransposons are emerging as potent regulatory elements within the human genome. In the diseased state, there is mounting evidence that endogenous retroelements play a role in etiopathogenesis of inflammatory diseases, with a disposition for both autoimmune and neurological disorders. We postulate that active mobile genetic elements contribute more to human disease pathogenesis than previously thought
Intestinal microbiota influences non-intestinal related autoimmune diseases
Indexación: Scopus.The human body is colonized by millions of microorganisms named microbiota that interact with our tissues in a cooperative and non-pathogenic manner. These microorganisms are present in the skin, gut, nasal, oral cavities, and genital tract. In fact, it has been described that the microbiota contributes to balancing the immune system to maintain host homeostasis. The gut is a vital organ where microbiota can influence and determine the function of cells of the immune system and contributes to preserve the wellbeing of the individual. Several articles have emphasized the connection between intestinal autoimmune diseases, such as Crohn's disease with dysbiosis or an imbalance in the microbiota composition in the gut. However, little is known about the role of the microbiota in autoimmune pathologies affecting other tissues than the intestine. This article focuses on what is known about the role that gut microbiota can play in the pathogenesis of non-intestinal autoimmune diseases, such as Grave's diseases, multiple sclerosis, type-1 diabetes, systemic lupus erythematosus, psoriasis, schizophrenia, and autism spectrum disorders. Furthermore, we discuss as to how metabolites derived from bacteria could be used as potential therapies for non-intestinal autoimmune diseases. © 2018 Opazo, Ortega-Rocha, Coronado-Arråzola, Bonifaz, Boudin, Neunlist, Bueno, Kalergis and Riedel.https://www.frontiersin.org/articles/10.3389/fmicb.2018.00432/ful
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Mother-child histocompatibility and risk of rheumatoid arthritis and systemic lupus erythematosus among mothers.
The study objective was to test the hypothesis that having histocompatible children increases the risk of rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE), possibly by contributing to the persistence of fetal cells acquired during pregnancy. We conducted a case control study using data from the UC San Francisco Mother Child Immunogenetic Study and studies at the Inova Translational Medicine Institute. We imputed human leukocyte antigen (HLA) alleles and minor histocompatibility antigens (mHags). We created a variable of exposure to histocompatible children. We estimated an average sequence similarity matching (SSM) score for each mother based on discordant mother-child alleles as a measure of histocompatibility. We used logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals. A total of 138 RA, 117 SLE, and 913 control mothers were analyzed. Increased risk of RA was associated with having any child compatible at HLA-B (OR 1.9; 1.2-3.1), DPB1 (OR 1.8; 1.2-2.6) or DQB1 (OR 1.8; 1.2-2.7). Compatibility at mHag ZAPHIR was associated with reduced risk of SLE among mothers carrying the HLA-restriction allele B*07:02 (nâ=â262; OR 0.4; 0.2-0.8). Our findings support the hypothesis that mother-child histocompatibility is associated with risk of RA and SLE
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A Haystack Heuristic for Autoimmune Disease Biomarker Discovery Using Next-Gen Immune Repertoire Sequencing Data.
Large-scale DNA sequencing of immunological repertoires offers an opportunity for the discovery of novel biomarkers for autoimmune disease. Available bioinformatics techniques however, are not adequately suited for elucidating possible biomarker candidates from within large immunosequencing datasets due to unsatisfactory scalability and sensitivity. Here, we present the Haystack Heuristic, an algorithm customized to computationally extract disease-associated motifs from next-generation-sequenced repertoires by contrasting disease and healthy subjects. This technique employs a local-search graph-theory approach to discover novel motifs in patient data. We apply the Haystack Heuristic to nine million B-cell receptor sequences obtained from nearly 100 individuals in order to elucidate a new motif that is significantly associated with multiple sclerosis. Our results demonstrate the effectiveness of the Haystack Heuristic in computing possible biomarker candidates from high throughput sequencing data and could be generalized to other datasets
Nutrition and Rheumatoid Arthritis in the âOmicsâ Era
Modern high-throughput âomicsâ science tools (including genomics, transcriptomics, pro teomics, metabolomics and microbiomics) are currently being applied to nutritional sciences to
unravel the fundamental processes of health effects ascribed to particular nutrients in humans and
to contribute to more precise nutritional advice. Diet and food components are key environmental
factors that interact with the genome, transcriptome, proteome, metabolome and the microbiota,
and this life-long interplay defines health and diseases state of the individual. Rheumatoid arthritis
(RA) is a chronic autoimmune disease featured by a systemic immune-inflammatory response, in
genetically susceptible individuals exposed to environmental triggers, including diet. In recent years
increasing evidences suggested that nutritional factors and gut microbiome have a central role in RA
risk and progression. The aim of this review is to summarize the main and most recent applications
of âomicsâ technologies in human nutrition and in RA research, examining the possible influences of
some nutrients and nutritional patterns on RA pathogenesis, following a nutrigenomics approach.
The opportunities and challenges of novel âomics technologiesâ in the exploration of new avenues in
RA and nutritional research to prevent and manage RA will be also discussed
The challenge of early diagnosis of autoimmune lymphoproliferative syndrome in children with suspected autoinflammatory/autoimmune disorders
OBJECTIVES: To test the usefulness of an extended panel of lymphocyte subsets in combination with Oliveira's diagnostic criteria for the identification of autoimmune lymphoproliferative syndrome (ALPS) in children referred to a paediatric rheumatology centre. METHODS: Patients referred from 2015 to 2018 to our rheumatology unit for an autoimmune or autoinflammatory condition were retrospectively analysed. Oliveira's required criteria [chronic lymphoproliferation and elevated double-negative T (DNT)] were applied as first screening. Flow cytometry study included double-negative CD4-CD8-TCR\u3b1\u3b2+ T lymphocytes (DNT), CD25+CD3+, HLA-DR+CD3+ T cells, B220+ T cells and CD27+ B cells. Data were analysed with a univariate logistic regression analysis, followed by a multivariate analysis. Sensitivity and specificity of the Oliveira's required criteria were calculated. RESULTS: A total of 264 patients were included in the study and classified as: (i) autoimmune diseases (n\u2009=\u200926); (ii) juvenile idiopathic arthritis (JIA) (35); (iii) monogenic systemic autoinflammatory disease (27); (iv) periodic fever, aphthous stomatitis, pharyngitis and adenitis syndrome (100); (v) systemic undefined recurrent fever (45); (vi) undetermined-systemic autoinflammatory disease (14); or (vii) ALPS (17). Oliveira's required criteria displayed a sensitivity of 100% and specificity of 79%. When compared with other diseases the TCR\u3b1\u3b2+B220+ lymphocytes were significantly increased in ALPS patients. The multivariate analysis revealed five clinical/laboratory parameters positively associated to ALPS: splenomegaly, female gender, arthralgia, elevated DNT and TCR\u3b1\u3b2+B220+ lymphocytes. CONCLUSIONS: Oliveira's required criteria are useful for the early suspicion of ALPS. TCR\u3b1\u3b2+B220+ lymphocytes should be added in the diagnostic work-up of patients referred to the paediatric rheumatology unit for a suspected autoimmune or autoinflammatory condition, providing a relevant support in the early diagnosis of ALPS
Genetic data: The new challenge of personalized medicine, insights for rheumatoid arthritis patients
Rapid advances in genotyping technology, analytical methods, and the establishment of large cohorts for population genetic studies have resulted in a large new body of information about the genetic basis of human rheumatoid arthritis (RA). Improved understanding of the root pathogenesis of the disease holds the promise of improved diagnostic and prognostic tools based upon this information. In this review, we summarize the nature of new genetic findings in human RA, including susceptibility loci and gene-gene and gene-environment interactions, as well as genetic loci associated with sub-groups of patients and those associated with response to therapy. Possible uses of these data are discussed, such as prediction of disease risk as well as personalized therapy and prediction of therapeutic response and risk of adverse events. While these applications are largely not refined to the point of clinical utility in RA, it seems likely that multi-parameter datasets including genetic, clinical, and biomarker data will be employed in the future care of RA patients
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