Biology subjects

Shetty, A. J.

Publications and source records attributed to Shetty, A. J..

3 recordsLinked to original sources

bioRxiv2025

Mechanism-Guided Engineering of Fluorinase Unlocks EfficientNucleophilic Biofluorination

The fluorinase enzyme, the only known biocatalyst forming stable carbon-fluorine bonds, operates with extremely low efficiency, catalyzing one reaction every 2-12 minutes. This severely limits its utility for sustainable biofluorination, and its sluggish activity remains poorly understood. We suppressed its aggregation through directed mutagenesis and elucidated the kinetic mechanism using a novel mathematical framework that fits complex kinetic and oligomerization data. This analysis revealed that >80% of enzyme molecules are inactive under standard conditions due to two dead-end pathways. The designed W50F+A279R mutant preferentially formed hexamers and displayed enhanced catalytic efficiency in this oligomeric state. When coupled with mechanism-based optimization of the reaction medium, including enzymatic removal of the inhibitory product, the catalytic turnover rate reached 12.5 {+/-} 2.1 min-{superscript 1}, representing [~]60-fold increase compared with previously reported turnover rates of the wild-type enzyme. Our work provides a mechanistic blueprint for fluorinase enhancement and a generalizable mathematical framework for analyzing kinetics of multimeric enzymes.

biochemistry↗

bioRxiv2025

Differential Expression and Microsystem Physiology Reveal Predominant and Drug Reversible CFTR-Related Defects in Idiopathic Pancreatitis

Pancreatitis is a potentially fatal and difficult to control exocrine-tissue defect with no FDA approved therapies. Variants of a chloride/bicarbonate transporter cystic fibrosis transmembrane conductance regulator (CFTR) pose multi-fold increased risk of pancreatitis accounting for up to 40% of the patients with idiopathic pancreatitis. However, the relationship between the duct-restricted CFTR-function and total exocrine tissue defect during pancreatitis remains less known and animal models do not translate well to human disease. To overcome this challenge, we developed a robust and highly durable iPSC-derived model system of pancreatic ductal tissues from an idiopathic pancreatitis patient with a common pancreatitis-associated CFTR variant. In the patient line termed PANx, we found deficient CFTR function and a distinct gene expression signature for ductal tissue pancreatitis marked by aberrant mucin production, inflammatory cytokines and cystic neoplasms. By applying clinically used CFTR-modulator drug ivacaftor, we observed a remarkable restoration of deficient CFTR-mediated fluid secretion as well as upto 40% reversal of the differential gene signature for PANx including the reduction in mucinous neoplasms and immunogenic cytokines such as IL-11, CCL20 and CXCL8. We further employed a microsystem device to model hyperamylasemia, a diagnostic feature of acute pancreatitis attack, due to a ductal reaction causing acinar injury. The key mucinous signature was validated in primary pancreatitis ductal tissues with a CFTR variant. Overall, we unraveled new layers of CFTR-related pathology in pancreatitis to help us better understand the early course of this debilitating condition. The test methods and model systems discovered in this study will significantly expedite the discovery of diagnostic and therapeutic tools for treating idiopathic pancreatitis. For the first time, we provided molecular and physiologic evidence supporting the benefit of CFTR modulator drug ivacaftor in human CFTR-related pancreatitis.

pathology↗

bioRxiv2022

Genome-wide prediction of dominant and recessive neurodevelopmental disorder risk genes

Despite great progress in the identification of neurodevelopmental disorder (NDD) risk genes, there are thousands that remain to be discovered. Computational tools that provide accurate gene-level predictions of NDD risk can significantly reduce the costs and time needed to prioritize and discover novel NDD risk genes. Here, we first demonstrate that machine learning models trained solely on single-cell RNA-sequencing data from the developing human cortex can robustly predict genes implicated in autism spectrum disorder (ASD), developmental and epileptic encephalopathy (DEE), and developmental delay (DD). Strikingly, we find differences in gene expression patterns of genes with monoallelic and biallelic inheritance patterns. We then integrate these expression data with 300 orthogonal features in a semi-supervised machine learning framework (mantis-ml) to train inheritance-specific models for ASD, DEE, and DD. The models have high predictive power (AUCs: 0.84 to 0.95) and top-ranked genes were up to two-fold (monoallelic models) and six-fold (biallelic models) more enriched for high-confidence NDD risk genes than genic intolerance metrics. Across all models, genes in the top decile of predicted risk genes were 60 to 130 times more likely to have publications strongly linking them to the phenotype of interest in PubMed compared to the bottom decile. Collectively, this work provides highly robust novel NDD risk gene predictions that can complement large-scale gene discovery efforts and underscores the importance of incorporating inheritance into gene risk prediction tools (https://nddgenes.com).

genetics↗