Microbial genomic trait evolution is dominated by frequent and rare pulsed evolution
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Abstract
On the macroevolutionary timescale, does trait evolution proceed gradually or by rapid bursts (pulses) separated by prolonged periods of stasis or slow evolution? Although studies have shown pulsed evolution is prevalent in animals, our knowledge about the tempo and mode of evolution across the tree of life is very limited. This long-standing debate calls for a test in bacteria and archaea, the most ancient and diverse forms of life with unique population genetic properties (asexual reproduction, large population sizes, short generation times, high dispersal rates and extensive lateral gene transfers). Using a likelihood-based framework, we analyzed evolutionary patterns of four microbial genomic traits (genome size, genome GC%, 16S rRNA GC% and the nitrogen use in proteins) on a broad macroevolutionary timescale. Our model fitting of phylogenetic comparative data shows that pulsed evolution is not only present, but also prevalent and predominant in microbial genomic trait evolution. Interestingly, for the first time, we detected two distinct types of pulsed evolution (small frequent and large rare jumps) that are predicted by the punctuated equilibrium and quantum evolution theories. Our findings suggest that major bacterial lineages could have originated in quick bursts and pulsed evolution is a common theme across the tree of life despite the drastically different population genetic properties of bacteria, archaea and eukaryotes.
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References (39)
- doi:10.5531/sd.paleo.7 via crossref
- doi:10.1073/pnas.72.2.646 via crossref
- doi:10.1017/s0094837300012902 via crossref
- doi:10.1111/j.1558-5646.2007.00310.x via crossref
- doi:10.1073/pnas.1014503108 via crossref
- doi:10.1093/sysbio/syx028 via crossref
- doi:10.1073/pnas.1710920114 via crossref
- doi:10.1007/s00239-005-0255-4 via crossref
- doi:10.1073/pnas.0903507106 via crossref
- doi:10.1093/gbe/evz201 via crossref
- doi:10.1126/science.272.5269.1802 via crossref
- doi:10.1073/pnas.93.7.2873 via crossref
- doi:10.1093/oxfordjournals.molbev.a026208 via crossref
- doi:10.1016/s0092-8674(00)81985-6 via crossref
- doi:10.1093/nar/gkab776 via crossref
- doi:10.1038/nrmicro2670 via crossref
- doi:10.1038/ismej.2017.156 via crossref
- doi:10.2307/2408095 via crossref
- doi:10.1111/j.1558-5646.2010.00960.x via crossref
- doi:10.1126/science.1127573 via crossref
- doi:10.1371/journal.pone.0053539 via crossref
- doi:10.1093/gbe/evx026 via crossref
- doi:10.1086/510633 via crossref
- doi:10.1038/nrmicro2367 via crossref
- doi:10.1038/nmeth.2066 via crossref
- doi:10.1126/science.1133420 via crossref
- doi:10.1038/s41559-018-0625-0 via crossref
- doi:10.2307/2408147 via crossref
- doi:10.1038/nature09649 via crossref
- doi:10.1038/nature13805 via crossref
- doi:10.1126/science.1119966 via crossref
- doi:10.1093/nar/gkn668 via crossref
- doi:10.1093/bioinformatics/bts079 via crossref
- doi:10.1093/bioinformatics/btu033 via crossref
- doi:10.1371/journal.pone.0009490 via crossref
- doi:10.1086/284325 via crossref
- doi:10.1080/01490450303891 via crossref
- doi:10.1038/s41559-018-0644-x via crossref
- doi:10.1038/nature10516 via crossref
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