The DOI matches its Crossref record; 170 of the 170 references it cites were found in a registry or an index.
Why do bacteria use so many enzymes to scavenge hydrogen peroxide?
Open accessAbstract
Hydrogen peroxide (H2O2) is continuously formed by the autoxidation of redox enzymes in aerobic cells, and it also enters from the environment, where it can be generated both by chemical processes and by the deliberate actions of competing organisms. Because H2O2 is acutely toxic, bacteria elaborate scavenging enzymes to keep its intracellular concentration at nanomolar levels. Mutants that lack such enzymes grow poorly, suffer from high rates of mutagenesis, or even die. In order to understand how bacteria cope with oxidative stress, it is important to identify the key enzymes involved in H2O2 degradation. Catalases and NADH peroxidase (Ahp) are primary scavengers in many bacteria, and their activities and physiological impacts have been unambiguously demonstrated through phenotypic analysis and through direct measurements of H2O2 clearance in vivo. Yet a wide variety of additional enzymes have been proposed to serve similar roles: thiol peroxidase, bacterioferritin comigratory protein, glutathione peroxidase, cytochrome c peroxidase, and rubrerythrins. Each of these enzymes can degrade H2O2 in vitro, but their contributions in vivo remain unclear. In this review we examine the genetic, genomic, regulatory, and biochemical evidence that each of these is a bona fide scavenger of H2O2 in the cell. We also consider possible reasons that bacteria might require multiple enzymes to catalyze this process, including differences in substrate specificity, compartmentalization, cofactor requirements, kinetic optima, and enzyme stability. It is hoped that the resolution of these issues will lead to an understanding of stress resistance that is more accurate and perceptive.
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Metadata
- Authors
- Mishra, S., & Imlay, J.
- Venue
- Archives of Biochemistry and Biophysics
- Publisher
- Elsevier BV
- Published
- 2012-09
- Volume
- 525
- Issue
- 2
- Pages
- 145-160
- ISSN
- 0003-9861 (ISSN-L)
- OpenAlex
- W2058964632
- Cited by
- 411
- References
- 170
- FWCI
- 9.99
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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