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de Raad, M.

Publications and source records attributed to de Raad, M..

2 recordsLinked to original sources

Enzyme promiscuity shapes evolutionary innovation and optimization

Evidence suggests that novel enzyme functions evolved from low-level promiscuous activities in ancestral enzymes. Yet, the evolutionary dynamics and physiological mechanisms of how such side activities contribute to systems-level adaptations are poorly understood. Furthermore, it remains untested whether knowledge of an organisms promiscuous reaction set ( underground metabolism) can aid in forecasting the genetic basis of metabolic adaptations. Here, we employ a computational model of underground metabolism and laboratory evolution experiments to examine the role of enzyme promiscuity in the acquisition and optimization of growth on predicted non-native substrates in E. coli K-12 MG1655. After as few as 20 generations, the evolving populations repeatedly acquired the capacity to grow on five predicted novel substrates-D-lyxose, D-2-deoxyribose, D-arabinose, m-tartrate, and monomethyl succinate-none of which could support growth in wild-type cells. Promiscuous enzyme activities played key roles in multiple phases of adaptation. Altered promiscuous activities not only established novel high-efficiency pathways, but also suppressed undesirable metabolic routes. Further, structural mutations shifted enzyme substrate turnover rates towards the new substrate while retaining a preference for the primary substrate. Finally, genes underlying the phenotypic innovations were accurately predicted by genome-scale model simulations of metabolism with enzyme promiscuity. Computational approaches will be essential to synthesize the complex role of promiscuous activities in applied biotechnology and in models of evolutionary adaptation.

systems biology

MAGI: A Bayesian-like method for metabolite, annotation, and gene integration

Metabolomics is a widely used technology for obtaining direct measures of metabolic activities from diverse biological systems. However, ambiguous metabolite identifications are a common challenge and biochemical interpretation is often limited by incomplete and inaccurate genome-based predictions of enzyme activities (i.e. gene annotations). Metabolite, Annotation, and Gene Integration (MAGI) generates a metabolite-gene association score using a biochemical reaction network. This is calculated by a method that emphasizes consensus between metabolites and genes via biochemical reactions. To demonstrate the potential of this method, we applied MAGI to integrate sequence data and metabolomics data collected from Streptomyces coelicolor A3(2), an extensively characterized bacterium that produces diverse secondary metabolites. Our findings suggest that coupling metabolomics and genomics data by scoring consensus between the two increases the quality of both metabolite identifications and gene annotations in this organism. MAGI also made biochemical predictions for poorly annotated genes that were consistent with the extensive literature on this important organism. This limited analysis suggests that using metabolomics data has the potential to improve annotations in sequenced organisms and also provides testable hypotheses for specific biochemical functions. MAGI is freely available for academic use both as an online tool at https://magi.nersc.gov and with source code available at https://github.com/biorack/magi.

bioinformatics