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Selection-driven gene loss in bacteria.

ABSTRACT: Gene loss by deletion is a common evolutionary process in bacteria, as exemplified by bacteria with small genomes that have evolved from bacteria with larger genomes by reductive processes. The driving force(s) for genome reduction remains unclear, and here we examined the hypothesis that gene loss is selected because carriage of superfluous genes confers a fitness cost to the bacterium. In the bacterium Salmonella enterica, we measured deletion rates at 11 chromosomal positions and the fitness effects of several spontaneous deletions. Deletion rates varied over 200-fold between different regions with the replication terminus region showing the highest rates. Approximately 25% of the examined deletions caused an increase in fitness under one or several growth conditions, and after serial passage of wild-type bacteria in rich medium for 1,000 generations we observed fixation of deletions that substantially increased bacterial fitness when reconstructed in a non-evolved bacterium. These results suggest that selection could be a significant driver of gene loss and reductive genome evolution.

SUBMITTER: Koskiniemi S

PROVIDER: S-EPMC3386194 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Selection-driven gene loss in bacteria.

Koskiniemi Sanna S Sun Song S Berg Otto G OG Andersson Dan I DI

PLoS genetics 20120628 6

Gene loss by deletion is a common evolutionary process in bacteria, as exemplified by bacteria with small genomes that have evolved from bacteria with larger genomes by reductive processes. The driving force(s) for genome reduction remains unclear, and here we examined the hypothesis that gene loss is selected because carriage of superfluous genes confers a fitness cost to the bacterium. In the bacterium Salmonella enterica, we measured deletion rates at 11 chromosomal positions and the fitness ...[more]

PMID: 22761588

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Project description:The hierarchical clustering and statistical techniques usually used to analyse microarray data do not inherently represent the underlying biology. Herein, a hybrid approach involving characteristics of both supervised and unsupervised learning is presented. This approach is based on template matching in which the interaction of the variables of inherent malignancy and the ability to express the malignant phenotype are modelled. Immortalised normal urothelial cells and bladder cancer cells of different malignancy were grown in conventional two-dimensional tissue culture and in three dimensions on extracellular matrices (ECMs) that were either permissive or restrictive for expression of the malignant phenotype. The transcriptome represents the effects of two variables--inherent malignancy and the modulatory effect of ECM. By assigning values to each of the biological variables of inherent malignancy and the ability to express the malignant phenotype, a template was constructed, which encapsulated the interaction between them. Gene expression correlating both positively and negatively with the template was observed, but when iterative correlations were carried out, the different models for the template converged on the same actual template. A subset of 21 genes was identified, which correlated with two a priori models or an optimised model above the 95% confidence limits identified in a bootstrap resampling with 5000 permutations of the data set. The correlation coefficients of expression of several genes were > 0.8. Analysis of upstream transcriptional regulatory elements (TREs) confirmed that these genes were not a randomly selected set of genes. Several TREs were identified as significantly over-expressed in the sample of 20 genes for which TREs were identified, and the high correlations of several genes were consistent with transcriptional co-regulation. The authors suggest that the template method can be used to identify a unique set of genes for further investigation.

Dataset Information

Selection-driven gene loss in bacteria.

Publications

Selection-driven gene loss in bacteria.

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