Reactive Arthritis (ReA), a rare seronegative inflammatory arthritis, lacks exquisite classification under rheumatic autoimmunity. ReA is solely established using differential clinical diagnosis of the patient cohorts, where pathogenic triggers linked to enteric and urogenital microorganisms e.g.Salmonella,Shigella,Yersinia,Campylobacter,Chlamydiahave been reported. Inflammatory Bowel Disease (IBD), an idiopathic enteric disorder co-evolved and attuned to present gut microbiome dysbiosis, can be correlated to the genesis of enteropathic arthropathies like ReA. Gut microbes symbolically modulate immune system homeostasis and are elementary for varied disease patterns in autoimmune disorders. The gut-microbiota axis structured on the core host-microbe interactions execute an imperative role in discerning the etiopathogenesis of ReA and IBD. This study predicts the molecular signatures for ReA with co-evolved IBD through the enveloped host-microbe interactions and microbe-microbe ‘interspecies communication’, using synonymous gene expression data for selective microbes. We have utilized a combinatorial approach that have concomitant in-silico work-pipeline and experimental validation to corroborate the findings. In-silico analysis involving text mining, metabolic network reconstruction, simulation, filtering, host-microbe interaction, docking and molecular mimicry studies results in robust drug target/s and biomarker/s for co-evolved IBD and ReA. Cross validation of the target/s or biomarker/s was done by targeted gene expression analysis following a non-probabilistic convenience sampling. Studies were performed to substantiate the host-microbe disease network consisting of protein-marker-symptom/disease-pathway-drug associations resulting in possible identification of vital drug targets, biomarkers, pathways and inhibitors for IBD and ReA.Our study identified Na⁽⁺⁾/H⁽⁺⁾anti-porter (NHAA) and Kynureninase (KYNU) to be robust early and essential host-microbe interacting targets for IBD co-evolved ReA. Other vital host-microbe interacting genes, proteins, pathways and drugs include Adenosine Deaminase (ADA), Superoxide Dismutase 2 (SOD2), Catalase (CAT), Angiotensin I Converting Enzyme (ACE), carbon metabolism (folate biosynthesis) and methotrexate. These can serve as potential prognostic/theranostic biomarkers and signatures that can be extrapolated to stratify ReA and related autoimmunity patient cohorts for further pilot studies.

Article: In-silico analysis of multiepitope based vaccine targeting respiratory viruses SARS, MERS and SARS-CoV-2 - Minerva Biotechnology and Biomolecular Research 2022 September;34(3):97-113 - Minerva Medica - Journals

Inflammatory bowel disease (IBD) is a persistent idiopathic disorder responsible for intestinal inflammatory conditions. IBD exhibits several extraintestinal manifestations, the most common being arthropathy, that are important triggers and risk factors for adverse progression of the disease. Due to a lack of definite diagnostic criteria and treatment regime, a need arises for early diagnosis and management of IBD associated arthropathy to reduce its morbidity. The present study utilizes a machine learning approach for development and validation of a risk prediction and early diagnostic disease model for IBD associated arthropathy. A publically available IBD cases vs control dataset from University of Massachusetts Medical School’s institutional repository was taken and the data was filtered into 54 IBD individuals along with their demographic and clinical characteristics. The arthropathy characteristics were incorporated from literature in the IBD dataset. Data was randomly split into training (50%; n=27) and testing (50%; n=27) for development and validation of model using logistic regression. Out of 54 IBD cases, 21 had a higher risk of developing arthropathy. The area under the receiver operator curve for the validated model was 0.90 (95% CI 0.80–0.99; accuracy 96%). This disease model can aid in identification of high-risk individuals and for early diagnosis of arthropathy in IBD cases before reaching the imaging and invasive diagnostic stage. This model warrants for prospective case-control trials validation.