1. Genome-scale rates of evolutionary change in bacteria
- Author
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Jane Hawkey, David J. Edwards, Simon Le Hello, Kathryn E. Holt, Mathieu Fourment, Edward C. Holmes, François-Xavier Weill, Sebastián Duchêne, Marie Bashir Institute for Infectious Diseases and Biosecurity, The University of Sydney, Centre for Systems Genomics, The University of MelbourneParkville, VIC, Australia., Department of Biochemistry and Molecular Biology, Centre National de Référence - National Reference Center Escherichia coli, Shigella et Salmonella (CNR-ESS), Institut Pasteur [Paris] (IP), This research was funded by the NHMRC (Australia Fellowship#AF30 to E. C. H., Career Development Fellowship #1061409 toK. E. H.). S. D. was supported by a McKenzie fellowship from the University of Melbourne.This paper was supported by the following grant(s):National Health and Medical Research Council 1061409.National Health and Medical Research Council GNT1037231., and Institut Pasteur [Paris]
- Subjects
0301 basic medicine ,0106 biological sciences ,Mechanisms of Evolution ,Evolutionary change ,Genome scale ,Bacterial genome size ,phylogeny ,Genome ,010603 evolutionary biology ,01 natural sciences ,Orders of magnitude (bit rate) ,Evolution, Molecular ,03 medical and health sciences ,Phylogenetics ,evolution ,Molecular clock ,Evolutionary dynamics ,030304 developmental biology ,Whole genome sequencing ,Genetics ,0303 health sciences ,biology ,Bacteria ,Models, Genetic ,molecular clock ,Genetic Variation ,General Medicine ,biology.organism_classification ,Biological Evolution ,030104 developmental biology ,Ancient DNA ,13. Climate action ,Evolutionary biology ,time-dependency ,Microbial Evolution and Epidemiology ,Mutation ,Human genome ,[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie ,substitution rates ,Genome, Bacterial ,Research Paper - 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 10−6 to 10−8 nucleotide substitutions site-1 year-1. This variation was largely attributable to sampling time, which was strongly negatively associated with estimated evolutionary rates, 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.
- Published
- 2016