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Adaptability of non-genetic diversity in bacterial chemotaxis.

Nicholas W Frankel | William Pontius | Yann S Dufour | Junjiajia Long | Luis Hernandez-Nunez | Thierry Emonet
eLife | 2014

Bacterial chemotaxis systems are as diverse as the environments that bacteria inhabit, but how much environmental variation can cells tolerate with a single system? Diversification of a single chemotaxis system could serve as an alternative, or even evolutionary stepping-stone, to switching between multiple systems. We hypothesized that mutations in gene regulation could lead to heritable control of chemotactic diversity. By simulating foraging and colonization of E. coli using a single-cell chemotaxis model, we found that different environments selected for different behaviors. The resulting trade-offs show that populations facing diverse environments would ideally diversify behaviors when time for navigation is limited. We show that advantageous diversity can arise from changes in the distribution of protein levels among individuals, which could occur through mutations in gene regulation. We propose experiments to test our prediction that chemotactic diversity in a clonal population could be a selectable trait that enables adaptation to environmental variability.

Pubmed ID: 25279698

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Associated grants

  • Agency: NIGMS NIH HHS, United States
    Id: R01 GM106189
  • Agency: NIGMS NIH HHS, United States
    Id: T32 GM007223
  • Agency: NIGMS NIH HHS, United States
    Id: 1R01GM106189

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FundRef (tool)

RRID:SCR_003218

Funder Registry and associated funding metadata allows everyone to have transparency into research funding and its outcomes. Open registry of persistent identifiers for grant-giving organizations around the world.

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ChEA (tool)

RRID:SCR_005403

Data analysis service for gene-list enrichment analysis against a manual database. It allows users to input lists of mammalian gene symbols for which the program computes over-representation of transcription factor targets from the ChIP-X database. The database integrates interaction data from ChIP-chip, ChIP-seq, ChIP-PET and DamID studies and contains 189,933 interactions, manually extracted from 87 publications, describing the binding of 92 transcription factors to 31,932 target genes.

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