Biology subjects

Badenoch, B.

Publications and source records attributed to Badenoch, B..

3 recordsLinked to original sources

bioRxiv2026

Multi-Tissue Metabolomic Signatures of Five Longevity Interventions Converge on Ergothioneine and Lipid Remodeling in Male UM-HET3 Mice

The pace of aging can be delayed by mutations, dietary manipulations, and drugs, yet the metabolic mechanisms underlying longevity interventions remain poorly understood. Here we present a multi-tissue metabolomic analysis of male UM-HET3 mice treated from 4 to 12 months of age with five validated longevity interventions: rapamycin, acarbose, 17-estradiol, canagliflozin, or caloric restriction. Using a feature-stabilized XGBoost pipeline applied to seven tissues, we show that metabolomic profiles can identify treated mice as likely recipients of a lifespan-extending intervention well before survival differences emerge. A leave-one-intervention-out procedure confirmed that models trained on any four interventions successfully classified mice from a fifth, unseen intervention, implying shared metabolic alterations across mechanistically distinct treatments. The most influential metabolites -- defined as the minimum set explaining 50% of cumulative model gain -- differed substantially across tissues. Only ergothioneine, a dietary antioxidant, ranked highly in more than two tissues: it was elevated by all five interventions in plasma and brain, and by four of five in muscle. Enrichment analyses further identified coordinated remodeling of lipid classes in plasma, perigonadal fat, and kidney. These findings reveal tissue-specific metabolic reprogramming shared across mechanistically distinct longevity interventions and, pending validation against interventions that do not extend lifespan, suggest a path toward metabolomic screening of candidate anti-aging drugs.

Molecular Biology↗

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↗

bioRxiv2025

Discrimination of Normal from Slow-Aging Mice by Plasma Metabolomic and Proteomic Features

Tests that can predict whether a drug is likely to extend mouse lifespan could speed up the search for anti-aging drugs. We have applied a machine learning algorithm, XGBoost regression, to seek sets of plasma metabolites that can discriminate control mice from mice treated with an anti-aging diet (caloric restriction) or any of four anti-aging drugs. When the model is trained on any four of these five interventions, it predicts significantly higher lifespan extension in mice exposed to the intervention which was not included in the training set. Plasma peptide data sets also succeed at this task. Models trained on drug-treated normal mice also discriminate long-lived mutant mice from their respective controls, and models trained on males can discriminate drug-treated from control females. Triglycerides are over-represented among the most influential features in the regression models. Triglycerides with longer fatty acid chains tend to be higher in the slow-aging mice, while triglycerides with shorter fatty acid chains tend to decrease. Plasma metabolite patterns may help to select the most promising anti-aging drugs in mice or in humans, and may give new leads into physiological and enzymatic targets relevant to discovery of new anti-aging drugs.

bioinformatics↗