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Biomedical subjects

Edgar J Hernandez

Publications and source records attributed to Edgar J Hernandez.

2 recordsLinked to original sources

Rare variant analysis of whole genome sequenced juvenile idiopathic arthritis multiplex pedigrees identifies rare variants in NOD2 and ACVR1.

Juvenile idiopathic arthritis is a complex rheumatic disease that is influenced by environmental and genetic factors. Linkage and genome-wide association studies have identified genes that contribute to the risk of developing juvenile idiopathic arthritis but are limited in their ability to identify disease-risk variants of large effect. Penetrant, heritable risk variants can be detected in high-risk families, but such cases are uncommon due to the low prevalence of juvenile idiopathic arthritis. This study utilizes whole-genome sequencing of 23 multiplex families, the largest such cohort to date, to discover variants and genes relevant to JIA pathogenesis. Pathogenic variants in NOD2 associated with Blau syndrome, an ultra-rare Mendelian inflammatory disorder, are the most recurrent variants in the cohort, consistent with previous reports that milder presentations of Blau syndrome are oftentimes misdiagnosed as juvenile idiopathic arthritis. For the first time, however, rare variants in ACVR1 and SMAD6, integral components of the Bone Morphogenic Protein pathway, are found to be associated with juvenile idiopathic arthritis. Identified ACVR1 variants map to critical protein domains. AlphaFold modeling predicts that the ACVR1 interaction with its inhibitor OGT is disrupted by these variants, indicating that the patient-mutated protein has a gain-of-function phenotype. Drosophila melanogaster expressing either a wild-type or patient-mutated version of ACVR1 exhibit embryonic lethality, with the mutant exhibiting 1.4-fold greater lethality than wild-type. The combination of family-based cohorts for gene discovery, AI-based computational tools, and animal model studies for tests of variant function underscores shared disease pathogenesis between JIA and monogenic disorders of immunity and connective tissue.

Arthritis, Juvenile

ReGAIN: a bioinformatics platform for assessing probabilistic co-occurrence between resistance genes in bacterial pathogens.

MOTIVATION: Multidrug-resistant bacterial pathogens continue to rise globally, yet scalable methods are needed to infer how resistance determinants co-occur across pathogen populations and to quantify conditional dependencies underlying co-occurrence and shared genetic context. RESULTS: We present ReGAIN (Resistance Gene Association and Inference Network), an open-source platform that applies Bayesian network structure learning to infer probabilistic, conditional dependency relationships among antibiotic resistance, heavy metal tolerance, stress response, and virulence determinants in bacteria. In contrast to pairwise co-occurrence analyses, ReGAIN reports conditional probabilities, relative risks, and absolute risk differences with confidence intervals to prioritize candidate relationships for downstream prioritization. Applied across ESKAPEE pathogens, ReGAIN recapitulated established resistance gene relationships and identified additional candidate patterns consistent with co-selection and shared genetic context. Together, these results support scalable, reproducible population-wide analysis of resistance networks for surveillance, comparative genomics and epidemiology. AVAILABILITY: ReGAIN analyses are performed using Python v3.11.5 and R v4.4.1 and is available as open-source software through Bioconda at {https://anaconda.org/bioconda/regain-cli}. Source code and documentation can be found at {https://github.com/ERBringHorvath/regain_CLI}. All genomes used in this publication were downloaded from the National Center for Biotechnology Information database. Large supplementary tables and results data from the ESKAPEE pathogen example network analyses can be downloaded from https://figshare.com/articles/dataset/ReGAIN_command_line_software_and_supplemental_figures_/28959431.

Computational Biology