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Microb Genom. 2016 Nov 30;2(11):e000094. doi: 10.1099/mgen.0.000094. eCollection 2016 Nov.

Genome-scale rates of evolutionary change in bacteria.

Author information

1
1​Marie Bashir Institute of Infectious Diseases and Biosecurity, Charles Perkins Centre, School of Life and Environmental Sciences and Sydney Medical School, The University of Sydney, Sydney, NSW 2006, Australia.
2
2​Centre for Systems Genomics, The University of Melbourne, Melbourne, VIC 3010, Australia.
3
3​Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Melbourne, VIC 3010, Australia.
4
4​Institut Pasteur, Unité des Bactéries Pathogènes Entériques, Paris 75015, France.

Abstract

Estimating the rates at which bacterial genomes evolve is critical to understanding major evolutionary and ecological processes such as disease emergence, long-term host-pathogen associations and short-term transmission patterns. The surge in bacterial genomic data sets provides a new opportunity to estimate these rates and reveal the factors that shape bacterial evolutionary dynamics. For many organisms estimates of evolutionary rate display an inverse association with the time-scale over which the data are sampled. However, this relationship remains unexplored in bacteria due to the difficulty in estimating genome-wide evolutionary rates, which are impacted by the extent of temporal structure in the data and the prevalence of recombination. We collected 36 whole genome sequence data sets from 16 species of bacterial pathogens to systematically estimate and compare their evolutionary rates and assess the extent of temporal structure in the absence of recombination. The majority (28/36) of data sets possessed sufficient clock-like structure to robustly estimate evolutionary rates. However, in some species reliable estimates were not possible even with 'ancient DNA' data sampled over many centuries, suggesting that they evolve very slowly or that they display extensive rate variation among lineages. The robustly estimated evolutionary rates spanned several orders of magnitude, from approximately 10-5 to 10-8 nucleotide substitutions per site year-1. This variation was negatively associated with sampling time, with this relationship best described by an exponential decay curve. To avoid potential estimation biases, such time-dependency should be considered when inferring evolutionary time-scales in bacteria.

KEYWORDS:

bacteria; evolution; molecular clock; phylogeny; substitution rates; time-dependency

PMID:
28348834
PMCID:
PMC5320706
DOI:
10.1099/mgen.0.000094
[Indexed for MEDLINE]
Free PMC Article

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