Biology subjects

Robida, A.

Publications and source records attributed to Robida, A..

4 recordsLinked to original sources

bioRxiv2026

Isolation of oxygen-dependent nicotine- and pseudooxynicotine-metabolizing enzymes

NicA2 is a flavin-bound amine dehydrogenase from Pseudomonas putida S16 that converts nicotine to the pharmacologically inactive N-methylmyosmine. In animal models of nicotine addiction, injection of NicA2 can decrease nicotine-seeking behavior 10-fold. Accordingly, NicA2-related enzymes have been investigated as smoking-cessation therapeutics. However, efficient catalysis by NicA2 in Pseudomonas putida relies on electron transfer to CycN, a cytochrome c, and not directly to O2. Impractically high amounts of NicA2 are thus necessary to achieve a pharmacological effect in the absence of CycN. Directed evolution has improved the ambient-O2 value of kcat from 0.007 s-1 to 1 s-1 for NicA2, but further improvements have been challenging. Here, we identify a strain of Peribacillus frigoritolerans NIC8 which encodes two flavin amine oxidoreductases, Ncox and Pnox. In the presence of oxygen, Ncox and Pnox act on nicotine and pseudooxynicotine respectively with apparent kcat values of 7.7 s-1 and 3.9 s-1. Transient kinetics establishes bimolecular rate constants of 51100 M-1s-1 and 81000 M-1s-1 for the half-reactions between Ncox and O2 and between Pnox and O2 respectively, consistent with Ncox and Pnox being bona-fide oxidases. Transcriptomics shows enhanced expression of Ncox and Pnox under nicotine-dependent growth as well as supporting the identification of downstream enzymes. Phylogenetic analysis suggests that Ncox and Pnox arose out of repurposing of homologous enzymes found in Bacillus species. The enzymes we describe may be useful for the development of nicotine addiction therapeutics and for bioconversion of nicotine in waste streams.

biochemistry↗

bioRxiv2026

Modeling Microbiome Modulation of Tumor Metabolic Networks to Predict Synergistic Therapies

Differences in microbiome composition profoundly influence drug response, yet methods to model the metabolic interplay between tumors, microbes, and therapeutics remain limited. We present a generalizable framework combining machine-learning and genome-scale metabolic modeling to prioritize combination therapies for colorectal cancer (CRC) in the presence of Fusobacterium nucleatum (Fn) and other pathogenic, probiotic, and commensal microbes. Trained on 6,514 drug combinations in microbe-free CRC cell lines, the model predicted synergistic combinations in both microbe-free and microbe-associated contexts and generalized to immunotherapy-associated conditions. Predictions were validated using an asymmetric co-culture system that mimics the colons normoxic-anaerobic gradient, confirming synergistic combinations in HCT116 cells with Fn, including drugs not typically used in CRC therapy. Mechanistic analysis and targeted pharmacological perturbations revealed phospho-inositol metabolism and cysteine transport as key determinants of Fn-dependent drug synergy. Together, this work introduces a scalable, microbiome-aware framework to enable discovery of context-specific combination therapies.

bioinformatics↗

bioRxiv2024

Ultra-high-throughput screening of antimicrobial combination therapies using a two-stage transparent machine learning model

Here, we present M2D2, a two-stage machine learning (ML) pipeline that identifies promising antimicrobial drug combinations, which are crucial for combating drug resistance. M2D2 addresses key challenges in drug combination discovery by predicting drug synergies using computationally generated drug-protein interaction data, thereby circumventing the need for expensive omics data. The model improves the accuracy of drug target identification using high-throughput experimental and computational methods via feedback between ML stages. M2D2s transparent framework provides mechanistic insights into drug interactions and was benchmarked against chemogenomics, transcriptomics, and metabolomics datasets. We experimentally validated M2D2 using high-throughput screening of 946 combinations of Food and Drug Administration (FDA)- approved drugs and antibiotics against Escherichia coli. We discovered synergy between a cerebrovascular drug and a widely used penicillin antibiotic and validated predicted mechanisms of action using genome-wide CRISPR inhibition screens. M2D2 offers a transparent ML tool for rapidly designing combination therapies and guides repurposing efforts while providing mechanistic insights.

systems biology↗

bioRxiv2023

Data-Driven Screening to Infer Metabolic Modulators of the Cancer Epigenome

Metabolites such as acetyl-CoA and citrate play an important moonlighting role by influencing the levels of histone post-translational modifications (PTMs) and regulating gene expression. This cross talk between metabolism and epigenome impacts numerous biological processes including development and tumorigenesis. However, the extent of moonlighting activities of cellular metabolites in modulating the epigenome is unknown. We developed a data-driven screen to discover moonlighting metabolites by constructing a histone PTM-metabolite interaction network using global chromatin profiles, metabolomics, and epigenetic drug sensitivity data from over 600 cell lines. Our ensemble statistical learning approach uncovered metabolites that are predictive of histone PTM levels and epigenetic drug sensitivity. We experimentally validated synergistic and antagonistic interactions between histone deacetylase and demethylase inhibitors with epigenetic metabolites kynurenic acid, pantothenate, and 1-methylnicotinamide. We apply our approach to track metaboloepigenetic interactions during the epithelial-mesenchymal transition. Overall, our data-driven approach unveils a broader range of metaboloepigenetic interactions than anticipated from previous studies, with implications for reversing aberrant epigenetic alterations and enhancing epigenetic therapies through diet.

systems biology↗