Artificial Gene Amplification in Escherichia coli Reveals Numerous Determinants for Resistance to Metal Toxicity.
Kenric J HoeglerMichael H HechtPublished in: Journal of molecular evolution (2018)
When organisms are subjected to environmental challenges, including growth inhibitors and toxins, evolution often selects for the duplication of endogenous genes, whose overexpression can provide a selective advantage. Such events occur both in natural environments and in clinical settings. Microbial cells-with their large populations and short generation times-frequently evolve resistance to a range of antimicrobials. While microbial resistance to antibiotic drugs is well documented, less attention has been given to the genetic elements responsible for resistance to metal toxicity. To assess which overexpressed genes can endow gram-negative bacteria with resistance to metal toxicity, we transformed a collection of plasmids overexpressing all E. coli open reading frames (ORFs) into naive cells, and selected for survival in toxic concentrations of six transition metals: Cd, Co, Cu, Ni, Ag, Zn. These selections identified 48 hits. In each of these hits, the overexpression of an endogenous E. coli gene provided a selective advantage in the presence of at least one of the toxic metals. Surprisingly, the majority of these cases (28/48) were not previously known to function in metal resistance or homeostasis. These findings highlight the diverse mechanisms that biological systems can deploy to adapt to environments containing toxic concentrations of metals.
Keyphrases
- escherichia coli
- genome wide
- induced apoptosis
- human health
- oxidative stress
- microbial community
- cell proliferation
- genome wide identification
- working memory
- copy number
- risk assessment
- minimally invasive
- signaling pathway
- pseudomonas aeruginosa
- cell death
- gene expression
- endoplasmic reticulum stress
- climate change
- klebsiella pneumoniae
- drinking water
- gram negative
- oxide nanoparticles
- health risk assessment
- genome wide analysis
- highly efficient