Limits...
Adaptability of non-genetic diversity in bacterial chemotaxis.

Frankel NW, Pontius W, Dufour YS, Long J, Hernandez-Nunez L, Emonet T - Elife (2014)

Bottom Line: 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 propose experiments to test our prediction that chemotactic diversity in a clonal population could be a selectable trait that enables adaptation to environmental variability.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, United States.

ABSTRACT
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.

Show MeSH
Genetic control of phenotypic diversity.(A) Clustering genes on multicistronic operons constrains the ratios in protein abundance. (B) Protein expression of core chemotaxis proteins CheRBYZ are shown relative to the mean level in wildtype cells. Two thousand cells are plotted. Light blue: mean levels of the proteins CheRBYZAW and receptors are equal to the mean levels in wildtype cells, which we take to be 140, 240, 8200, 3200, 6700, 6700, 15,000 mol./cell, respectively (Li and Hazelbauer, 2004); the extrinsic noise scaling parameter, ω, is 0.26 and the intrinsic noise scaling parameter, η, is 0.125, which are both equal to wildtype levels (Figure 2—figure supplement 1). Dark blue: same but with ω = 0.8, which is greater than wildtype level. Note the substantial variability around the mean even in the case of wildtype noise levels (light blue). (C) Clockwise bias and adaptation time of individuals in (A). (D) Changes in the strength of individual RBSs will independently change the mean levels of individual proteins. (E and F) Light blue: gene expression of cells with same population parameters as in A, light blue. Pink: mean levels of CheR changed to twice wildtype mean. (G) Promoter sequences can be inherently more or less noisy, resulting in amplification or attenuation of the variability of total protein amounts without affecting protein ratios. (H and I) Pink: gene expression of cells with same population parameters as in (E), pink. Red: ω reduced from 0.26 to 0.1.DOI:http://dx.doi.org/10.7554/eLife.03526.017
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4210811&req=5

fig6: Genetic control of phenotypic diversity.(A) Clustering genes on multicistronic operons constrains the ratios in protein abundance. (B) Protein expression of core chemotaxis proteins CheRBYZ are shown relative to the mean level in wildtype cells. Two thousand cells are plotted. Light blue: mean levels of the proteins CheRBYZAW and receptors are equal to the mean levels in wildtype cells, which we take to be 140, 240, 8200, 3200, 6700, 6700, 15,000 mol./cell, respectively (Li and Hazelbauer, 2004); the extrinsic noise scaling parameter, ω, is 0.26 and the intrinsic noise scaling parameter, η, is 0.125, which are both equal to wildtype levels (Figure 2—figure supplement 1). Dark blue: same but with ω = 0.8, which is greater than wildtype level. Note the substantial variability around the mean even in the case of wildtype noise levels (light blue). (C) Clockwise bias and adaptation time of individuals in (A). (D) Changes in the strength of individual RBSs will independently change the mean levels of individual proteins. (E and F) Light blue: gene expression of cells with same population parameters as in A, light blue. Pink: mean levels of CheR changed to twice wildtype mean. (G) Promoter sequences can be inherently more or less noisy, resulting in amplification or attenuation of the variability of total protein amounts without affecting protein ratios. (H and I) Pink: gene expression of cells with same population parameters as in (E), pink. Red: ω reduced from 0.26 to 0.1.DOI:http://dx.doi.org/10.7554/eLife.03526.017

Mentions: Since phenotypic selection can alter variability in protein abundance (Mora and Walczak, 2013), we asked the question of whether selection on genetic regulatory features of the chemotaxis network could serve as an adaptive mechanism capable of shaping diversity in protein abundance, and thus phenotypes, to resolve trade-offs. Such features include the organization of the genes on the chromosome and the sequences of ribosomal binding sites (RBSs) and promoter regions. Selection for individuals with mutations in these features would give rise to adaptation of the distribution without changing highly-conserved network proteins. In our model of gene expression, such alterations were realized through changes in the levels of extrinsic and intrinsic noise and the mean expression level of each protein. We first varied these parameters individually to investigate their effects on phenotypic diversity (Figure 6).10.7554/eLife.03526.017Figure 6.Genetic control of phenotypic diversity.


Adaptability of non-genetic diversity in bacterial chemotaxis.

Frankel NW, Pontius W, Dufour YS, Long J, Hernandez-Nunez L, Emonet T - Elife (2014)

Genetic control of phenotypic diversity.(A) Clustering genes on multicistronic operons constrains the ratios in protein abundance. (B) Protein expression of core chemotaxis proteins CheRBYZ are shown relative to the mean level in wildtype cells. Two thousand cells are plotted. Light blue: mean levels of the proteins CheRBYZAW and receptors are equal to the mean levels in wildtype cells, which we take to be 140, 240, 8200, 3200, 6700, 6700, 15,000 mol./cell, respectively (Li and Hazelbauer, 2004); the extrinsic noise scaling parameter, ω, is 0.26 and the intrinsic noise scaling parameter, η, is 0.125, which are both equal to wildtype levels (Figure 2—figure supplement 1). Dark blue: same but with ω = 0.8, which is greater than wildtype level. Note the substantial variability around the mean even in the case of wildtype noise levels (light blue). (C) Clockwise bias and adaptation time of individuals in (A). (D) Changes in the strength of individual RBSs will independently change the mean levels of individual proteins. (E and F) Light blue: gene expression of cells with same population parameters as in A, light blue. Pink: mean levels of CheR changed to twice wildtype mean. (G) Promoter sequences can be inherently more or less noisy, resulting in amplification or attenuation of the variability of total protein amounts without affecting protein ratios. (H and I) Pink: gene expression of cells with same population parameters as in (E), pink. Red: ω reduced from 0.26 to 0.1.DOI:http://dx.doi.org/10.7554/eLife.03526.017
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4210811&req=5

fig6: Genetic control of phenotypic diversity.(A) Clustering genes on multicistronic operons constrains the ratios in protein abundance. (B) Protein expression of core chemotaxis proteins CheRBYZ are shown relative to the mean level in wildtype cells. Two thousand cells are plotted. Light blue: mean levels of the proteins CheRBYZAW and receptors are equal to the mean levels in wildtype cells, which we take to be 140, 240, 8200, 3200, 6700, 6700, 15,000 mol./cell, respectively (Li and Hazelbauer, 2004); the extrinsic noise scaling parameter, ω, is 0.26 and the intrinsic noise scaling parameter, η, is 0.125, which are both equal to wildtype levels (Figure 2—figure supplement 1). Dark blue: same but with ω = 0.8, which is greater than wildtype level. Note the substantial variability around the mean even in the case of wildtype noise levels (light blue). (C) Clockwise bias and adaptation time of individuals in (A). (D) Changes in the strength of individual RBSs will independently change the mean levels of individual proteins. (E and F) Light blue: gene expression of cells with same population parameters as in A, light blue. Pink: mean levels of CheR changed to twice wildtype mean. (G) Promoter sequences can be inherently more or less noisy, resulting in amplification or attenuation of the variability of total protein amounts without affecting protein ratios. (H and I) Pink: gene expression of cells with same population parameters as in (E), pink. Red: ω reduced from 0.26 to 0.1.DOI:http://dx.doi.org/10.7554/eLife.03526.017
Mentions: Since phenotypic selection can alter variability in protein abundance (Mora and Walczak, 2013), we asked the question of whether selection on genetic regulatory features of the chemotaxis network could serve as an adaptive mechanism capable of shaping diversity in protein abundance, and thus phenotypes, to resolve trade-offs. Such features include the organization of the genes on the chromosome and the sequences of ribosomal binding sites (RBSs) and promoter regions. Selection for individuals with mutations in these features would give rise to adaptation of the distribution without changing highly-conserved network proteins. In our model of gene expression, such alterations were realized through changes in the levels of extrinsic and intrinsic noise and the mean expression level of each protein. We first varied these parameters individually to investigate their effects on phenotypic diversity (Figure 6).10.7554/eLife.03526.017Figure 6.Genetic control of phenotypic diversity.

Bottom Line: 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 propose experiments to test our prediction that chemotactic diversity in a clonal population could be a selectable trait that enables adaptation to environmental variability.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, United States.

ABSTRACT
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.

Show MeSH