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Spontaneous Mutations of a Model Heterotrophic Marine Bacterium

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Spontaneous Mutations of a Model Heterotrophic Marine Bacterium The ISME Journal (2017) 11, 1713–1718 © 2017 International Society for Microbial Ecology All rights reserved 1751-7362/17 www.nature.com/ismej SHORT COMMUNICATION Spontaneous mutations of a model heterotrophic marine bacterium Ying Sun1, Kate E Powell2, Way Sung3, Michael Lynch3, Mary Ann Moran2 and Haiwei Luo1,4 1Simon F. S. Li Marine Science Laboratory, School of Life Sciences and Partner State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong, China; 2Department of Marine Sciences, University of Georgia, Athens, GA, USA; 3Department of Biology, Indiana University, Bloomington, IN, USA and 4Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China Heterotrophic marine bacterioplankton populations display substantive genomic diversity that is commonly explained to be the result of selective forces imposed by resource limitation or interactions with phage and predators. Here we use a mutation-accumulation experiment followed by whole-genome sequencing of mutation lines to determine an unbiased rate and molecular spectrum of spontaneous mutations for a model heterotrophic marine bacterium in the globally important Roseobacter clade, Ruegeria pomeroyi DSS-3. We find evidence for mutational bias towards deletions over insertions, and this process alone could account for a sizable portion of genome size diversity among roseobacters and also implies that lateral gene transfer and/or selection must also play a role in maintaining roseobacters with large genome sizes. We also find evidence for a mutational bias in favor of changes from A/T to G/C nucleobases, which explains widespread occurrences of G/C-enriched Roseobacter genomes. Using the calculated mutation rate of 1.39 × 10 − 10 per base per generation, we implement a ‘mutation-rate clock’ approach to date the evolution of roseobacters by assuming a constant mutation rate along their evolutionary history. This approach gives an estimated date of Roseobacter genome expansion in good agreement with an earlier fossil-based estimate of ~ 250 million years ago and is consistent with a hypothesis of a correlated evolutionary history between roseobacters and marine eukaryotic phytoplankton groups. The ISME Journal (2017) 11, 1713–1718; doi:10.1038/ismej.2017.20; published online 21 March 2017 The ability of heterotrophic marine bacterioplankton which selection can work, but mutation itself may also lineages to drive critical transformations in global respond to environmental changes such as acquisition carbon and nutrient cycles is commonly attributed to of antibiotic resistance enhanced solely by increased their biochemical interactions with organic matter in mutation rate (Long et al., 2016). Despite their dilute waters and transient micro-environments (Azam significant role in evolutionary dynamics and micro- and Malfatti, 2007; Giovannoni, 2016). These adaptive bial adaptation, how the rate and spectrum of strategies are backed up by substantive bacterioplank- spontaneous mutations contribute to genetic diversity ton genomic diversity manifested as large variations in has not been assessed for any ecologically dominant metabolic and regulatory pathways, genome size and G marine bacterioplankton lineage. +C content (Swan et al., 2013; Giovannoni et al., 2014; Mutation-accumulation (MA) experiments followed Luo and Moran, 2015). While it is often acknowledged by whole-genome sequencing (WGS) of derived MA that this diversity is the result of a long and complex lines are being used to determine spontaneous evolutionary history through interactions between mutations of both prokaryotic and eukaryotic organ- natural selection and mutation among other mechan- isms (Sung et al., 2012; Dillon et al., 2016). This isms, only selection has been subject to intensive approach allows all but lethal mutations to accumu- discussions (Morris et al., 2012; Giovannoni et al., late and thus is considered an approximately 2014; Luo and Moran, 2015). Mutation is often unbiased method for mutation determination (Eyre- appreciated as a way to provide raw materials on Walker and Keightley, 2007). Here we apply the MA/ WGS strategy to characterize the genomic pattern of Correspondence: H Luo, Simon F. S. Li Marine Science Labora- spontaneous mutations arising in the model hetero- tory, School of Life Sciences and Partner State Key Laboratory of trophic marine bacterium Ruegeria pomeroyi DSS-3 Agrobiotechnology, The Chinese University of Hong Kong, Hong (Moran et al., 2004), a member of the alphaproteo- Kong, China. bacterial Roseobacter clade. Roseobacters constitute a E-mail: [email protected] Received 17 July 2016; revised 6 November 2016; accepted substantial proportion of marine bacterioplankton 23 December 2016; published online 21 March 2017 communities (5–20%), and are among the major Roseobacter genomic mutations Y Sun et al 1714 Figure 1 The genomic locations of the base-substitution mutations and insertion/deletion mutations in the 48 independent mutation accumulation (MA) lines of Ruegeria pomeroyi DSS-3. From outer to inner rings scaled to genome size: (1)–(3) The three outermost rings represent gene density (gray), G/C content (orange) and A/T content (green), respectively, calculated in non-overlapping 1 kb blocks. If the value of the 1 kb block is above the genome-wide mean, the 1 kb block is colored; (4) locus tag of genes with mutations; (5) position of each base-substitution mutation (A/T → G/C (red), G/C → A/T (blue) and A ↔ TorG↔ C (grey)), as well as insertion/deletions (green) in MA lines, with each ring representing the genome of an individual MA line. The chromosome (a) and megaplasmid (b) of DSS-3 are not plotted in proportion to their number of nucleotides in order to make the features of the latter visible. drivers of global carbon and sulfur cycles (Luo and df = 1; Supplementary Table S2), and the same trend Moran, 2014; Voget et al., 2015). There is a consider- was observed in other bacterial MA/WGS analyses able diversity among the clade members in genome (Lee et al., 2012; Sung et al., 2015; Dillon et al., size, genome content and base composition. 2016). One explanation is that mismatch repair may Our MA experiment allowed accumulation of be more active in coding regions, though a minor role mutations over 5,386 cell divisions in 80 indepen- of selection in eliminating coding mutations cannot dent MA lines initiated from a single founder colony be excluded (Long et al., 2014; Dillon et al., 2016). of R. pomeroyi DSS-3 and passed through a single- The data derived from this MA/WGS procedure cell bottleneck every two days. By sequencing have important implications for understanding mar- genomes of 48 randomly selected lines at the end ine Roseobacter evolution. First, the base changes − of the MA experiment and using a robust consensus gave an average mutation rate (μ) of 1.39 × 10 10 per method shown to achieve a low false-positive base per generation. Direct, unbiased estimates of rate (Sung et al., 2012, 2015), we identified 161 natural mutation rates have been determined for base-substitution mutations over these sequenced only a handful of bacteria thus far, and this is among lines (Figure 1 and Supplementary Table S1). The the first determinations for a marine bacterial species ratio of nonsynonymous to synonymous mutations (see also Dillon et al., 2016). Previous studies have did not significantly differ from the ratio of non- measured rates that vary from 1.28 × 10 − 10 per base synonymous to synonymous sites in the DSS-3 per generation (for Bacillus anthracis) to 9.78 × 10 − 9 genome (χ2 = 1.66, P = 0.20, df = 1; Supplementary (for Mesoplasma florum) (Sung et al., 2012). Our Table S2), which is evidence for minimal selective estimate for DSS-3 is at the lower end of this range. elimination of deleterious mutations during the MA On the basis of this mutation rate, a genome size of process, confirming faithful representation of muta- 4 601 048 bp, and an average growth rate of 45 tional pattern for DSS-3. Base-substitution mutation generations per year inferred based on a well- (BSM) was slightly biased toward intergenic regions articulated linear relationship between growth rate compared to coding regions (χ2 = 11.73, P = 0.0006, and rRNA/rDNA ratio in a laboratory culture of The ISME Journal Roseobacter genomic mutations Y Sun et al 1715 DSS-3 and measures of this ratio in field populations reciprocal exchanges of metabolites between phyto- (Lankiewicz et al., 2016), it takes ~ 35 years for a plankton and roseobacters such as organic matter Roseobacter cell in the surface ocean to gain one and vitamins (Luo et al., 2013; Luo and Moran, 2014; base-substitution mutation. If the DSS-3 natural Durham et al., 2015). However, the substantial population has an effective population size of evolutionary distance between roseobacters and 3.0 × 108 (Supplementary Methods; Supplementary cyanobacteria suggests that this estimate may have Table S3), an average of 8.7 × 106 mutations are large uncertainties (Nei et al., 2001; Smith and expected to arise in the population each year. The Peterson, 2002; Bromham and Penny, 2003). Further- frequency of selectively advantageous mutations more, new evidence from ultraclean Archaean from this pool of spontaneous mutations is likely to samples argues against the validity of the previous be extremely low (Eyre-Walker and Keightley, 2007) cyanobacterial fossils used in various analyses but may make an important contribution to the (French et al., 2015), including the Roseobacter evolution of this lineage. dating study. A second implication of the MA/WGS data For these reasons, it is useful to have an approach emerges from evidence of a nucleobase bias in that does not rely on fossils and instead makes use of spontaneous mutations. The 161 base-substitution an unbiased measure of the DSS-3 mutation rate. mutations included 61 from G/C to A/T and 73 Here the molecular sequence divergence time (T) from A/T to G/C. Correcting for the genomic base was derived from mutation rate (μ; per base per composition (A/T:G/C = 0.56:1), a significantly generation) of R.
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