Cellular responses to reactive oxygen species are predicted from molecular mechanisms

Abstract

Catalysis using iron–sulfur clusters and transition metals can be traced back to the last universal common ancestor. The damage to metalloproteins caused by reactive oxygen species (ROS) can prevent cell growth and survival when unmanaged, thus eliciting an essential stress response that is universal and fundamental in biology. Here we develop a computable multiscale description of the ROS stress response inEscherichia coli, called OxidizeME. We use OxidizeME to explain four key responses to oxidative stress: 1) ROS-induced auxotrophy for branched-chain, aromatic, and sulfurous amino acids; 2) nutrient-dependent sensitivity of growth rate to ROS; 3) ROS-specific differential gene expression separate from global growth-associated differential expression; and 4) coordinated expression of iron–sulfur cluster (ISC) and sulfur assimilation (SUF) systems for iron–sulfur cluster biosynthesis. These results show that we can now develop fundamental and quantitative genotype–phenotype relationships for stress responses on a genome-wide basis.

In vitro Antioxidant Activities and Effect of Hydroethanolic and Aqueous Extracts of Terminalia avicennioides (Combretaceae) on Salmonella

“…This oxidative stress state is the direct cause of various pathological conditions such as aging and cancer and the indirect cause of the peroxidation of lipids in foodstuffs. In any case, the risk is increased with the accumulation of these molecules in the body resulting in a radical chain reaction that degrades vital biological molecules, namely DNA, lipids, proteins and carbohydrates [5]. Plants species belonging to the Combretaceae family have been tested for their antimicrobial activities against some pathogenic microorganisms that are prone to drug resistance [6].…”

Programmed trade-offs in protein folding networks

“…The present work also highlights both current opportunity and challenge for structural proteomics and systems biology in general, and for genome-scale models of protein folding networks in particular. The potential benefit of integrating structural data into genome-scale models is undisputed (81,82) and rapidly establishing itself as an exciting new frontier of modeling large-scale biochemical networks (83,84). A main challenge in truly bridging the scales from the atomistic to the cellular levels lies in incomplete availability of structural data as well as the development of appropriate structure representations.…”

Synthesizing Systems Biology Knowledge from Omics Using Genome‐Scale Models

“…[ 56 ] ME models compute up to 85% protein mass in Escherichia coli . [ 57 ] ME models are now available for three organisms: Thermotoga maritima , [ 58 ] E. coli , [ 54a,54b ] and Clostridium ljungdahlii . [ 59 ] Proteomic data has been used to calibrate a ME‐model of E. coli , decreasing prediction errors of growth rate and metabolic fluxes by 69% and 14%, [ 54c ] and to validate proteomes predicted by a ME‐model updated with machine learning‐based enzyme turnover rates.…”

“…Recently, ME‐models were extended to predict cellular response to three stresses: thermal (FoldME), [ 56 ] oxidative (OxidizeME), [ 57 ] and acid (AcidifyME). [ 60 ] By mechanistically reconstructing key molecular responses to each stress, the models successfully predicted phenotypic response (change in growth rate) and differential expression in various growth conditions (i.e., media, supplements, etc.)…”