4.5 Article

Exploiting rRNA operon copy number to investigate bacterial reproductive strategies

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NATURE MICROBIOLOGY
卷 1, 期 11, 页码 -

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NATURE PORTFOLIO
DOI: 10.1038/NMICROBIOL.2016.160

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  1. Department of Energy Office of Science Graduate Fellowship Program (DOE SCGF) [DE-AC05-06OR23100]
  2. National Science Foundation's Long-Term Ecological Research Program [DEB 1027253]
  3. National Institutes of Health [GM0099549]
  4. Direct For Biological Sciences
  5. Division Of Environmental Biology [1027253] Funding Source: National Science Foundation

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The potential for rapid reproduction is a hallmark of microbial life, but microbes in nature must also survive and compete when growth is constrained by resource availability. Successful reproduction requires different strategies when resources are scarce and when they are abundant(1,2), but a systematic framework for predicting these reproductive strategies in bacteria has not been available. Here, we show that the number of ribosomal RNA operons (rrn) in bacterial genomes predicts two important components of reproduction-growth rate and growth efficiency-which are favoured under contrasting regimes of resource availability(3,4). We find that the maximum reproductive rate of bacteria doubles with a doubling of rrn copy number, and the efficiency of carbon use is inversely related to maximal growth rate and rrn copy number. We also identify a feasible explanation for these patterns: the rate and yield of protein synthesis mirror the overall pattern in maximum growth rate and growth efficiency. Furthermore, comparative analysis of genomes from 1,167 bacterial species reveals that rrn copy number predicts traits associated with resource availability, including chemotaxis and genome streamlining. Genome-wide patterns of orthologous gene content covary with rrn copy number, suggesting convergent evolution in response to resource availability. Our findings imply that basic cellular processes adapt in contrasting ways to long-term differences in resource availability. They also establish a basis for predicting changes in bacterial community composition in response to resource perturbations using rrn copy number measurements(5) or inferences(6,7).

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