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New and emerging molecular techniques are expanding understanding of the pathophysiology of spondyloarthritis (SpA). Genome-wide association studies identified novel pathways in antigen processing and presentation as well as helper T cell type 17 (T H 17) immunity associated with SpA. Immune cell profiling techniques have supported T H 17 immune responses and increasingly are revealing intestinal mucosal immune cells as associated with disease. Emerging technologies in epigenetics, transcriptomics, microbiome, and proteomics/metabolomics are adding to these, refining disease pathways and potentially identifying biomarkers for diagnosis and treatment responses. This review describes many of the new molecular techniques that are being utilized to investigate SpA., Competing Interests: Disclosure The authors have nothing to disclose., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Mucosal surfaces are a unique symbiotic environment between a host and a vast and diverse ecology of microbes. These microbes have great immunomodulatory potential with respect to the host organism. Indeed, the mucosal immune system strikes a delicate balance between tolerance of commensal organisms and overt inflammation to ward off pathogens. Disruptions of the microbial ecology at mucosal surfaces has been described in a vast number of different human disease processes including many forms of arthritis, and the resulting implications are still being understood to their fullest. Herein, we review the current state of knowledge in microbe-host interactions as it relates to the development of arthritis through bacterial translocation, bacterial metabolite production, education of the immune response, and molecular mimicry., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Interactions between the microbiota and distal gut are important for the maintenance of a healthy intestinal barrier; dysbiosis of intestinal microbial communities has emerged as a likely contributor to diseases that arise at the level of the mucosa. Intraepithelial lymphocytes (IELs) are positioned within the epithelial barrier, and in the small intestine they function to maintain epithelial homeostasis. We hypothesized that colon IELs promote epithelial barrier function through the expression of cytokines in response to interactions with commensal bacteria. Profiling of bacterial 16S ribosomal RNA revealed that candidate bacteria in the order Bacteroidales are sufficient to promote IEL presence in the colon that in turn produce interleukin-6 (IL-6) in a MyD88 (myeloid differentiation primary response 88)-dependent manner. IEL-derived IL-6 is functionally important in the maintenance of the epithelial barrier as IL-6 -/- mice were noted to have increased paracellular permeability, decreased claudin-1 expression, and a thinner mucus gel layer, all of which were reversed by transfer of IL-6 +/+ IELs, leading to protection of mice in response to Citrobacter rodentium infection. Therefore, we conclude that microbiota provide a homeostatic role for epithelial barrier function through regulation of IEL-derived IL-6.

The association of inflammatory arthritis with intestinal pathology extends back more than 100 years. This association is now supported by epidemiologic studies demonstrating an elevated prevalence of inflammatory bowel disease in spondyloarthritis and vice versa, compared with the general population. Genetic and intestinal microbiome studies have further linked these diseases. Although diabetes and nonalcoholic fatty liver disease disproportionately affect individuals with psoriatic arthritis, diseases of the esophagus, stomach, pancreas, and liver are not particularly common in spondyloarthritis. Clinicians should be aware of the differential diagnosis and the appropriate diagnostic tools available when evaluating digestive and hepatic disorders in spondyloarthritis., (Published by Elsevier Inc.)

Multiple genetic and environmental factors have been associated with an increased risk for rheumatoid arthritis (RA). Of these, the strongest associations have been seen with female sex, a family history of RA, the genetic factor the "shared epitope," and exposure to tobacco smoke. There is also renewed interest in mucosal inflammation and microbial factors as contributors to the development of RA. However, the identification of a "preclinical" period of RA that can be defined as local or systemic autoimmunity as measured by autoantibodies and other biomarkers prior to the development of clinically apparent synovitis suggests that the risk factors for RA are acting long prior to first clinical evidence of IA. As such, a major challenge to the field will be to investigate the full spectrum of the development of RA, from initiation and propagation of autoimmunity during preclinical RA and transition to clinically apparent synovitis and classifiable RA, to determine which genetic and environmental factors are important at each stage of disease development. Understanding the exact role and timing of action of risk factors for RA is especially important given the advent of prevention trials in RA, and the hope that a full understanding of genetic and environmental factors in RA could lead to effective preventive interventions., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Objective: With the ARX data anonymization tool structured biomedical data can be de-identified using syntactic privacy models, such as k-anonymity. Data is transformed with two methods: (a) generalization of attribute values, followed by (b) suppression of data records. The former method results in data that is well suited for analyses by epidemiologists, while the latter method significantly reduces loss of information. Our tool uses an optimal anonymization algorithm that maximizes output utility according to a given measure. To achieve scalability, existing optimal anonymization algorithms exclude parts of the search space by predicting the outcome of data transformations regarding privacy and utility without explicitly applying them to the input dataset. These optimizations cannot be used if data is transformed with generalization and suppression. As optimal data utility and scalability are important for anonymizing biomedical data, we had to develop a novel method., Methods: In this article, we first confirm experimentally that combining generalization with suppression significantly increases data utility. Next, we proof that, within this coding model, the outcome of data transformations regarding privacy and utility cannot be predicted. As a consequence, existing algorithms fail to deliver optimal data utility. We confirm this finding experimentally. The limitation of previous work can be overcome at the cost of increased computational complexity. However, scalability is important for anonymizing data with user feedback. Consequently, we identify properties of datasets that may be predicted in our context and propose a novel and efficient algorithm. Finally, we evaluate our solution with multiple datasets and privacy models., Results: This work presents the first thorough investigation of which properties of datasets can be predicted when data is anonymized with generalization and suppression. Our novel approach adopts existing optimization strategies to our context and combines different search methods. The experiments show that our method is able to efficiently solve a broad spectrum of anonymization problems., Conclusion: Our work shows that implementing syntactic privacy models is challenging and that existing algorithms are not well suited for anonymizing data with transformation models which are more complex than generalization alone. As such models have been recommended for use in the biomedical domain, our results are of general relevance for de-identifying structured biomedical data., (Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.)

The etiology of most systemic autoimmune diseases remains unknown. There is often a preclinical period of systemic autoimmunity prior to the onset of clinically classifiable disease; established and emerging data suggest that dysregulated immune interactions with commensal microbiota may play a role in the initial generation of autoimmunity in this preclinical period. This article reviews potential mechanisms by which alterations of healthy microbiota may induce autoimmunity as well as mucosal microbial associations with autoimmune diseases. If mucosal microbiota lead to the development of autoimmunity, these mucosal sites, microorganisms, and immunologic mechanisms can be targeted to prevent the onset of systemic autoimmune disease., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Sensitive biomedical data is often collected from distributed sources, involving different information systems and different organizational units. Local autonomy and legal reasons lead to the need of privacy preserving integration concepts. In this article, we focus on anonymization, which plays an important role for the re-use of clinical data and for the sharing of research data. We present a flexible solution for anonymizing distributed data in the semi-honest model. Prior to the anonymization procedure, an encrypted global view of the dataset is constructed by means of a secure multi-party computing (SMC) protocol. This global representation can then be anonymized. Our approach is not limited to specific anonymization algorithms but provides pre- and postprocessing for a broad spectrum of algorithms and many privacy criteria. We present an extensive analytical and experimental evaluation and discuss which types of methods and criteria are supported. Our prototype demonstrates the approach by implementing k-anonymity, ℓ-diversity, t-closeness and δ-presence with a globally optimal de-identification method in horizontally and vertically distributed setups. The experiments show that our method provides highly competitive performance and offers a practical and flexible solution for anonymizing distributed biomedical datasets., (Copyright © 2013 Elsevier Inc. All rights reserved.)