The response of Escherichia coli to the alkylating agents chloroacetaldehyde and styrene oxide.

DNA damage is ubiquitous and can arise from endogenous or exogenous sources. DNA-damaging alkylating agents are present in environmental toxicants as well as in cancer chemotherapy drugs and are a constant threat, which can lead to mutations or cell death. All organisms have multiple DNA repair and DNA damage tolerance pathways to resist the potentially negative effects of exposure to alkylating agents. In bacteria, many of the genes in these pathways are regulated as part of the SOS reponse or the adaptive response. In this work, we probed the cellular responses to the alkylating agents chloroacetaldehyde (CAA), which is a metabolite of 1,2-dichloroethane used to produce polyvinyl chloride, and styrene oxide (SO), a major metabolite of styrene used in the production of polystyrene and other polymers. Vinyl chloride and styrene are produced on an industrial scale of billions of kilograms annually and thus have a high potential for environmental exposure. To identify stress response genes in E. coli that are responsible for tolerance to the reactive metabolites CAA and SO, we used libraries of transcriptional reporters and gene deletion strains. In response to both alkylating agents, genes associated with several different stress pathways were upregulated, including protein, membrane, and oxidative stress, as well as DNA damage. E. coli strains lacking genes involved in base excision repair and nucleotide excision repair were sensitive to SO, whereas strains lacking recA and the SOS gene ybfE were sensitive to both alkylating agents tested. This work indicates the varied systems involved in cellular responses to alkylating agents, and highlights the specific DNA repair genes involved in the responses.

Prediction of distal residue participation in enzyme catalysis.

A scoring method for the prediction of catalytically important residues in enzyme structures is presented and used to examine the participation of distal residues in enzyme catalysis. Scores are based on the Partial Order Optimum Likelihood (POOL) machine learning method, using computed electrostatic properties, surface geometric features, and information obtained from the phylogenetic tree as input features. Predictions of distal residue participation in catalysis are compared with experimental kinetics data from the literature on variants of the featured enzymes; some additional kinetics measurements are reported for variants of Pseudomonas putida nitrile hydratase (ppNH) and for Escherichia coli alkaline phosphatase (AP). The multilayer active sites of P. putida nitrile hydratase and of human phosphoglucose isomerase are predicted by the POOL log ZP scores, as is the single-layer active site of P. putida ketosteroid isomerase. The log ZP score cutoff utilized here results in over-prediction of distal residue involvement in E. coli alkaline phosphatase. While fewer experimental data points are available for P. putida mandelate racemase and for human carbonic anhydrase II, the POOL log ZP scores properly predict the previously reported participation of distal residues.