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Chapron, A.

Publications and source records attributed to Chapron, A..

3 recordsLinked to original sources

Origins and breadth of pairwise epistasis in an α-helix of β-lactamase TEM-1

Epistasis affects genome evolution together with our ability to predict individual mutation effects. The mechanistic basis of epistasis remains, however, largely unknown. To quantify and better understand interactions between fitness-affecting mutations, we focus on a 11 amino-acid -helix of the protein {beta}-lactamase TEM-1, and build a comprehensive library of more than 15,000 double mutants. Analysis of the growth rates of these mutants shows pervasive epistasis, which can be largely explained by a non-linear two-state model, where inactivating, destabilizing, neutral, or stabilizing mutations additively contribute to the phenotype. Hence, most epistatic interactions can be predicted by a non-linear model informed by single-point mutational measurements only. Deviations from the two-state model are consistently found for few pairs of residues, in particular when they are in contact. This result, as well as single-point mutation parameters, can be quantitatively found back through direct-coupling-analysis-based statistical models inferred from homologous sequence data. Our results thus shed light on the existence and the origins of the multiple determinants of the epistatic landscape, even at the level of small structural components of a protein, and suggest that the corresponding constraints shape the entire {beta}-lactamase family.

evolutionary biology↗

Bridging the gap between in silico and in vivo: modeling opioid disposition in a kidney proximal tubule microphysiological system

BackgroundOpioid overdose, dependence, and addiction are a major public health crisis. Patients with chronic kidney disease (CKD) are at high risk of opioid overdose, therefore novel methods that provide accurate prediction of kidney clearance (CLr) and systemic disposition of opioids in CKD patients can facilitate the optimization of therapeutic regimens. MethodsWe conducted prediction of kidney clearance and systemic disposition of morphine and its active metabolite morphine-6-glucuronide (M6G) in CKD patients using a vascularized human proximal tubule microphysiological system (VPT-MPS) coupled with a parent-metabolite full body physiologically-based pharmacokinetic (PBPK) model. ResultsThe VPT-MPS, populated with a human umbilical vein endothelial cell (HUVEC) channel and an adjacent human primary proximal tubular epithelial cells (PTEC) channel, successfully demonstrated secretory transport of morphine and M6G from the HUVEC channel into the PTEC channel in a time-dependent manner; transporter inhibitors decreased translocation by 74.3% and 63.6%, respectively. The in vitro data generated by VPT-MPS were incorporated into a mechanistic kidney model and parent-metabolite full body PBPK model to predict CLr and systemic disposition of morphine and M6G. The model successfully predicted CLr within 1.5-fold, and the plasma concentration-time profiles of morphine and M6G in both healthy subjects and CKD patients, with absolute average fold error values <1.5. ConclusionsA microphysiological system together with mathematical modeling successfully predicted kidney clearance and systemic disposition of opioids in CKD patients and healthy subjects.

pharmacology and toxicology↗

The molecular basis of genetic interaction diversity in a metabolic pathway

Our ability to predict the impact of mutations on traits relevant for disease and evolution remains severely limited by the dependence of their effects on the genetic background and environment. Even when molecular interactions between genes are known, it is unclear how these translate to organism-level interactions between alleles. We therefore characterized the interplay of genetic and environmental dependencies in determining fitness by quantifying ~4,000 fitness interactions between expression variants of two metabolic genes, in different environments. We detect a remarkable variety of environment-dependent interactions, and demonstrate they can be quantitatively explained by a mechanistic model accounting for catabolic flux, metabolite toxicity and expression costs. Complex fitness interactions between mutations can therefore be predicted simply from their simultaneous impact on a few connected molecular phenotypes.

genetics↗