Many prokaryote species are known to have fluid genomes, with different strains varying markedly in accessory gene content through the combined action of gene loss, gene gain via lateral transfer, as well as gene duplication. However, the evolutionary forces determining genome fluidity are not yet well understood. We here for the first time systematically analyse the degree to which this distinctive genomic feature differs between bacterial species. We find that genome fluidity is positively correlated with synonymous nucleotide diversity of the core genome, a measure of effective population size Ne. No effects of genome size, phylogeny or homologous recombination rate on genome fluidity were found. Our findings are consistent with a scenario where accessory gene content turnover is for a large part dictated by neutral evolution

A Knöppel

AO Kislyuk

B Haegeman

Elze Hesse

F Baumdicker

H Ochman

H Tettelin

HP Narra

JP Gogarten

M Lynch

M Touchon

M Vos

MF Polz

Michiel Vos

Nadia Andrea Andreani

RW Nowell

S Wielgoss

The ISME Journal

LetterThis is the author accepted manuscript. The final version is available from Springer Nature via the DOI in this record.Many prokaryote species are known to have fluid genomes, with different strains varying markedly in accessory gene content through the combined action of gene loss, gene gain via lateral transfer as well as gene duplication. However, the evolutionary forces determining genome fluidity are not yet well-understood. We here for the first time systematically analyse the degree to which this distinctive genomic feature differs between bacterial species. We find that genome fluidity is positively correlated with synonymous nucleotide diversity of the core genome, a measure of effective population size Ne. No effects of genome size, phylogeny or homologous recombination rate on genome fluidity were found. Our findings are consistent with a scenario where accessory gene content turnover is for a large part dictated by neutral evolutionThis work was supported by NERC grant NE/L013177/1 to MV and the Fondazione Ing. Aldo Gini and the PhD school of Veterinary Science of the University of Padova (NAA)

Andreani, NA

Hesse, E

Vos, M

Open Research Exeter

Prokaryote genome fluidity is dependent on effective population sizeNadia Andrea Andreani1,2,3, Elze Hesse4 and Michiel Vos2*1= Department of Comparative Biomedicine and Food Science (BCA), University of Padova, Agripolis, Viale dell’Università 16, 35020 Legnaro, Italy 2= European Centre for Environment and Human Health, University of Exeter Medical School, University of Exeter, Penryn Campus, TR10 9FE, UK 3= current address= Istituto Zooprofilattico della Lombardia e dell'Emilia Romagna, reparto Substrati Cellulari e Immunologia Cellulare, via Bianchi, 9, 25124, Brescia, Italy4= Department of Biosciences, University of Exeter, Penryn Campus, TR10 9FE, UK*corresponding author: m.vos@exeter.ac.ukArticle Type: Short FormatRunning Title: Genome fluidity meta-analysisKey words: accessory genome, genome fluidity, gene content turnover, gene repertoire, evolution, meta-analysisSubject Category: Evolutionary GeneticsFunding Information: This work was supported by NERC grant NE/L013177/1 to MV and the Fondazione Ing. Aldo Gini and the PhD school of Veterinary Science of the University of Padova (NAA).AbstractMany prokaryote species are known to have fluid genomes, with different strains varying markedly in accessory gene content through the combined action of gene loss, gene gain via lateral transfer as well as gene duplication. However, the evolutionary forces determining genome fluidity are not yet well-understood. We here for the first time systematically analyse the degree to which this distinctive genomic feature differs between bacterial species. We find that genome fluidity is positively correlated with synonymous nucleotide diversity of the core genome, a measure of effective population size Ne. No effects of genome size, phylogeny or homologous recombination rate on genome fluidity were found. Our findings are consistent with a scenario where accessory gene content turnover is for a large part dictated by neutral evolution.Results and DiscussionMany bacterial species have been shown to exhibit extensive variation in gene repertoires, where a set of core genes shared by all strains are supplemented with a set of accessory genes that are only present in a subset of strains (Gogarten et al 2002, Ochman et al 2000, Tettelin et al 2005). Although accessory genome analyses are routinely performed in prokaryote genomics studies, whether certain genome characteristics are associated with particularly low or high genome fluidity has not been systematically tested. We here make use of the increasing availability of whole genome sequences to for the first time perform a meta-analysis to 1) gauge the extent to which genome fluidity varies among different species and 2) test which genome characteristics best explain genome fluidity. Methods to quantify pan genome diversity are generally sensitive to the absence of rare accessory genes from genome samples. We therefore use the φ measure of genome fluidity that has been shown to be robust to sample size (Kislyuk et al 2011) (Supplemental Methods). This measure of genomic fluidity is defined as the ratio of unique gene families to the sum of gene families in pairs of genomes averaged over randomly chosen genome pairs from within a group of sampled genomes. Because it is vital to reliably score gene presence/absence and most available genomes are not sequenced to completion, we first verified that good quality (<150 contigs) non-closed genomes resulted in fluidity estimates comparable to those based on closed genomes (linear regression, R2=0.70, p<0.001; Fig. S1). Genome fluidity could be calculated for 90 free-living species for which five or more genomic datasets were available (three Archaea and 87 Bacteria belonging to 15 major taxonomic groups, Table S1). Only a single species was selected per genus in order to minimize phylogenetic bias. As estimates for individual species are dependent on genome selection and to a degree on the specifics of bioinformatics processing, they are not to be taken as absolutes and we will refrain from highlighting individual species, analysing broad patterns only.Genome fluidity φ was plotted against synonymous nucleotide diversity of the core genome (πsyn) on a natural log scale for all species (Fig. 1), which showed a significant positive relationship (linear regression: ln()=-1.39(0.12)+0.27(0.03)*ln(); a: t=-11.61*** and b: t=8.59***, adjusted R2=0.45). No genetically monomorphic species with high gene content variation or species with diverse core genomes but limited variation in accessory gene content were found. The same analysis was performed for the genera Pseudomonas and Streptococcus for which multiple species genome sets are available (Supplemental Tables S2-S3). All estimates of φ for these two genera were found to lie inside the 95% prediction interval of the relationship depicted in Fig. 1 (Fig. S2), adding to the generality of our finding. A linear mixed effect model was used with phylogenetic grouping included (group-dependent random intercepts) to test for the effect of genome size in addition to π (fixed effects) (Table 1). This analysis was limited to the 77 species belonging to the broad Proteobacteria and Terrabacteria classifications. No effect of phylogeny or genome size (ranging from 0.9 to 10.2 Mb) on genome fluidity was found, but the positive relation with evolutionary divergence of the core genome remained highly significant (Table 1). Interestingly, the intercept of the relationship of φ with π is significantly different from zero (Table 1), indicating that accessory genomes diverge before SNPs appear in the core genome. This finding supports the emerging view that changes in gene content occur at high rates relative to mutation in bacteria (Nowell et al 2014, Touchon et al 2009, Vos et al 2015, Wielgoss et al 2016). The uptake and loss of accessory genes is in part mediated via recombination of flanking homologous sequences (Polz et al 2013). To test whether the flexibility of the accessory genome is dependent on the rate of homologous recombination in the core genome, we compared φ estimates and r/m estimates (the probability that a nucleotide is changed as the result of recombination relative to point mutation) for 26 species that also featured in a meta-analysis of homologous recombination rate (Vos and Didelot 2009). No significant relationship was detected (linear regression: =0.13(0.01)+0.01(0.01)•ln(r/m), a:t=9.78*** and b:t=0.54NS, adjusted R2=-0.03; Table S4), confirming results of a previous analysis (Narra and Ochman 2006). The φ estimate only provides a general indication of genome fluidity as it ignores genome rearrangements or plasmids, and we cannot exclude the fact that elevated or decreased levels of genome fluidity are associated with some of the many Phyla that could not be included in this analysis due to a lack of data. These caveats aside, the positive relationship of genome fluidity with synonymous diversity is highly significant. The synonymous nucleotide diversity equals two times the product of the mutation rate µ and effective population size Ne for haploid species. As variation in prokaryote mutation rate is believed to be relatively small (Lynch 2010), syn can be taken as a proxy for Ne. Large effective population size is expected to result in generally higher levels of genetic diversity due to neutral evolution (Kimura 1984). The result of our cross-species meta-analysis is therefore consistent with the expectation that large Ne species exhibit greater accessory genome variation. A variety of studies have suggested that many gene content changes have only minor effects on fitness and are effectively neutral (Baumdicker et al 2012, Gogarten and Townsend 2005, Haegeman and Weitz 2012, Knöppel et al 2014), although it is clear that a proportion of gene gains and losses will be significantly deleterious or beneficial. In order to gain a full understanding of selection on the accessory genome, it will be vital to collect data on the distribution of fitness effects of gene content changes (Vos et al 2015).AcknowledgmentsThis work was supported by NERC grant NE/L013177/1 to MV and the Fondazione Ing. Aldo Gini and the PhD school of Veterinary Science of the University of Padova to NAA. We thank Adam Eyre-Walker, Haiwei Luo and Joshua Weitz for helpful discussion. Conflict of InterestThe authors declare no conflict of interest.Supplementary Information (SI)The Supplementary Information contains Supplementary Methods, four Tables and two Figures.ReferencesBaumdicker F, Hess WR, Pfaffelhuber P (2012). The infinitely many genes model for the distributed genome of bacteria. Genome Biol Evol 4: 443-456.Gogarten JP, Doolittle WF, Lawrence JG (2002). Prokaryotic evolution in light of gene transfer. Mol Biol Evol 19: 2226-2238.Gogarten JP, Townsend JP (2005). Horizontal gene transfer, genome innovation and evolution. Nature Rev Microbiol 3: 679-687.Haegeman B, Weitz JS (2012). A neutral theory of genome evolution and the frequency distribution of genes. BMC Genomics 13: 196.Kimura M (1984). The neutral theory of molecular evolution. Cambridge University Press.Kislyuk AO, Haegeman B, Bergman NH, Weitz JS (2011). Genomic fluidity: an integrative view of gene diversity within microbial populations. BMC Genomics 12: 32.Knöppel A, Lind PA, Lustig U, Näsvall J, Andersson DI (2014). Minor fitness costs in an experimental model of horizontal gene transfer in bacteria. Mol Biol Evol: msu076.Lynch M (2010). Evolution of the mutation rate. Trends Genet 26: 345-352.Narra, H P, Ochman, H (2006). Of what use is sex to bacteria? Curr Biol 16: 705-710.Nowell RW, Green S, Laue BE, Sharp PM (2014). The extent of genome flux and its role in the differentiation of bacterial lineages. Genome Biol Evol 6: 1514-1529.Ochman H, Lawrence JG, Groisman EA (2000). Lateral gene transfer and the nature of bacterial innovation. Nature 405: 299-304.Polz MF, Alm EJ, Hanage WP (2013). Horizontal gene transfer and the evolution of bacterial and archaeal population structure. Trends Genet 29: 170-175Tettelin H, Masignani V, Cieslewicz MJ, Donati C, Medini D, Ward NL et al (2005). Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial “pan-genome”. Proc Natl Acad Sci 102: 13950-13955.Touchon M, Hoede C, Tenaillon O, Barbe V, Baeriswyl S, Bidet P et al (2009). Organised genome dynamics in the Escherichia coli species results in highly diverse adaptive paths. Plos Genet 5: e1000344.Vos M, Didelot X (2009). A comparison of homologous recombination rates in bacteria and archaea. ISME J 3: 199-208.Vos M, Hesselman MC, te Beek TA, van Passel MW, Eyre-Walker A (2015). Rates of Lateral Gene Transfer in Prokaryotes: High but Why? Trends Microbiol 23: 598-605.Wielgoss S, Didelot X, Chaudhuri RR, Liu X, Weedall GD, Velicer GJ et al (2016). A barrier to homologous recombination between sympatric strains of the cooperative soil bacterium Myxococcus xanthus. ISME J. doi: 10.1038/ismej.2016.34LegendFigure 1. The genome fluidity statistic φ as a function of synonymous core genome nucleotide variation π for 90 free-living prokaryote species on a ln-ln scale. White dots: Proteobacteria, black dots: Terrabacteria (Actinobacteria, Firmicutes and Cyanobacteria), grey dots: other taxa. 7

Prokaryote genome fluidity is dependent on effective population size

Crossref

Andreani, Nadia

Hesse, Elze

Vos, Michiel

University of Lincoln Institutional Repository

OPENSHORT COMMUNICATIONProkaryote genome fluidity is dependent on effectivepopulation sizeNadia Andrea Andreani1,2,4, Elze Hesse3 and Michiel Vos21Department of Comparative Biomedicine and Food Science (BCA), University of Padova, Padua, Italy;2European Centre for Environment and Human Health, University of Exeter Medical School, University ofExeter, Penryn, UK and 3Department of Biosciences, University of Exeter, Penryn, UKMany prokaryote species are known to have fluid genomes, with different strains varying markedly inaccessory gene content through the combined action of gene loss, gene gain via lateral transfer, aswell as gene duplication. However, the evolutionary forces determining genome fluidity are not yetwell understood. We here for the first time systematically analyse the degree to which this distinctivegenomic feature differs between bacterial species. We find that genome fluidity is positivelycorrelated with synonymous nucleotide diversity of the core genome, a measure of effectivepopulation size Ne. No effects of genome size, phylogeny or homologous recombination rate ongenome fluidity were found. Our findings are consistent with a scenario where accessory genecontent turnover is for a large part dictated by neutral evolution.The ISME Journal advance online publication, 31 March 2017; doi:10.1038/ismej.2017.36Results and discussionMany bacterial species have been shown to exhibitextensive variation in gene repertoires, where a set ofcore genes shared by all strains are supplementedwith a set of accessory genes that are only present ina subset of strains (Ochman et al., 2000; Gogartenet al., 2002; Tettelin et al., 2005). Although accessorygenome analyses are routinely performed in prokar-yote genomics studies, whether certain genomecharacteristics are associated with particularly lowor high genome fluidity has not been systematicallytested. We here make use of the increasing avail-ability of whole-genome sequences to, for the firsttime, perform a meta-analysis to (1) gauge the extentto which genome fluidity varies among differentspecies and (2) test which genome characteristicsbest explain genome fluidity.Methods to quantify pan-genome diversity aregenerally sensitive to the absence of rare accessorygenes from genome samples. We therefore use the φmeasure of genome fluidity that has been shown tobe robust to sample size (Kislyuk et al., 2011)(Supplementary Methods). This measure of genomicfluidity is defined as the ratio of unique gene familiesto the sum of gene families in pairs of genomesaveraged over randomly chosen genome pairs fromwithin a group of sampled genomes. Because it isvital to reliably score gene presence/absence andmost available genomes are not sequenced tocompletion, we first verified that good quality(o150 contigs) non-closed genomes resulted in flui-dity estimates comparable to those based on closedgenomes (linear regression, R2 = 0.70, Po0.001;Supplementary Figure S1). Genome fluidity couldbe calculated for 90 free-living species for which fiveor more genomic data sets were available (3 archaeaand 87 bacteria belonging to 15 major taxonomicgroups, Supplementary Table S1). Only a singlespecies was selected per genus to minimize phylo-genetic bias. As estimates for individual species aredependent on genome selection and to a degree onthe specifics of bioinformatics processing, they arenot to be taken as absolutes and we will refrain fromhighlighting individual species, analysing broadpatterns only.Genome fluidity φ was plotted against synon-ymous nucleotide diversity of the core genome (πsyn)on a natural log scale for all species (Figure 1), whichshowed a significant positive relationship (linearregression: ln(φ) =− 1.39(0.12)+0.27(0.03) × ln(π);a: t=− 11.61*** and b: t=8.59***, adjustedR2 = 0.45). No genetically monomorphic species withhigh gene content variation or species with diversecore genomes but limited variation in accessory genecontent were found. The same analysis wasperformed for the genera Pseudomonas and Strepto-coccus for which multiple species genome setsare available (Supplementary Tables S2 and S3).Correspondence: M Vos, European Centre for Environment andHuman Health, University of Exeter Medical School, University ofExeter, ESI Building, Penryn Campus, Exeter TR10 9EZ, UK.E-mail: m.vos@exeter.ac.uk4Current address: Istituto Zooprofilattico della Lombardia edell'Emilia Romagna, reparto Substrati Cellulari e ImmunologiaCellulare, via Bianchi 8, Brescia 25124, Italy.Received 23 August 2016; revised 2 February 2017; accepted 9February 2017The ISME Journal (2017), 1–3www.nature.com/ismejAll estimates of φ for these two genera were found tolie inside the 95% prediction interval of the relation-ship depicted in Figure 1 (Supplementary Figure S2),adding to the generality of our finding. A linearmixed-effects model was used with phylogeneticgrouping included (group-dependent random inter-cepts) to test for the effect of genome size in additionto πsyn (fixed effects) (Table 1). This analysis waslimited to the 77 species belonging to the broadProteobacteria and Terrabacteria classifications. Noeffect of phylogeny or genome size (ranging from 0.9to 10.2Mb) on genome fluidity was found, but thepositive relation with evolutionary divergence of thecore genome remained highly significant (Table 1).Interestingly, the intercept of the relationship of φwith πsyn is significantly different from zero (Table 1),indicating that accessory genomes diverge beforesingle-nucleotide polymorphisms appear in the coregenome. This finding supports the emerging viewthat changes in gene content occur at high ratesrelative to mutation in bacteria (Touchon et al., 2009;Nowell et al., 2014; Vos et al., 2015; Wielgoss et al.,2016). The uptake and loss of accessory genes is inpart mediated via recombination of flanking homo-logous sequences (Polz et al., 2013). To test whetherthe flexibility of the accessory genome is dependenton the rate of homologous recombination in thecore genome, we compared φ estimates and r/mestimates (the probability that a nucleotide ischanged as the result of recombination relative topoint mutation) for 26 species that also featured in ameta-analysis of homologous recombination rate(Vos and Didelot, 2009). No significant relationshipwas detected (linear regression: φ=0.13(0.01)+0.01(0.01) × ln(r/m), a: t=9.78*** and b: t=0.54NS,adjusted R2 =− 0.03; Supplementary Table S4), con-firming results of a previous analysis (Narra andOchman, 2006).The φ estimate only provides a general indicationof genome fluidity as it ignores genome rearrange-ments or plasmids, and we cannot exclude the factthat elevated or decreased levels of genome fluidityare associated with some of the many phyla thatcould not be included in this analysis due to a lack ofdata. These caveats aside, the positive relationship ofgenome fluidity with synonymous diversity is highlysignificant. The synonymous nucleotide diversityequals two times the product of the mutation rate μand effective population size Ne for haploid species.As variation in prokaryote mutation rate is believedto be relatively small (Lynch, 2010), πsyn can be takenas a proxy for Ne. Large effective population size isexpected to result in generally higher levels ofgenetic diversity due to neutral evolution (Kimura,1984). The result of our cross-species meta-analysisis therefore consistent with the expectation that largeNe species exhibit greater accessory genome varia-tion. A variety of studies have suggested that manygene content changes have only minor effects onfitness and are effectively neutral (Gogarten andTownsend, 2005; Baumdicker et al., 2012; Haegemanand Weitz, 2012; Knöppel et al., 2014), although it isclear that a proportion of gene gains and losses willbe significantly deleterious or beneficial. To gain afull understanding of selection on the accessorygenome, it will be vital to collect data on thedistribution of fitness effects of gene content changes(Vos et al., 2015).Figure 1 The genome fluidity statistic φ as a function ofsynonymous core genome nucleotide variation π for 90 free-living prokaryote species on a ln-ln scale. White dots: Proteobac-teria, black dots: Terrabacteria (Actinobacteria, Firmicutes andCyanobacteria), grey dots: other taxa.Table 1 Results of the linear mixed-effects model testing the additive effects of genome size and synonymous core genome diversity (πsyn,ln-transformed) on accessory genome fluidity (φ, ln-transformed) with random intercepts fitted for each broad phylogenetic group (that is,Proteobacteria and Terrabacteria)Parameter estimate± s.e.a F-testIntercept −1.64±0.18***, t=−8.87Genome size −0.02±0.04NS, t=− 0.42 F1,4 = 0.18, P=0.67πsyn 0.17±0.04***, t=4.05 F1,4 = 15.42, Po0.001Phylogenetic group o1% of total varianceAbbreviation: NS, not significant.aNote: significance of parameter estimates are based on Wald’s t-test, ***Po0.001.The most parsimonious model was arrived at by sequentially deleting terms and comparing model fits using F-tests of likelihood ratios.Genome fluidity meta-analysisNA Andreani et al2The ISME JournalConflict of InterestThe authors declare no conflict of interest.AcknowledgementsThis work was supported by NERC grant NE/L013177/1 toMV, and the Fondazione Ing. Aldo Gini and the PhDschool of Veterinary Science of the University of Padova toNAA. We thank Adam Eyre-Walker, Haiwei Luo andJoshua Weitz for helpful discussion.ReferencesBaumdicker F, Hess WR, Pfaffelhuber P. (2012). Theinfinitely many genes model for the distributedgenome of bacteria. Genome Biol Evol 4: 443–456.Gogarten JP, Doolittle WF, Lawrence JG. (2002). Prokar-yotic evolution in light of gene transfer. Mol Biol Evol19: 2226–2238.Gogarten JP, Townsend JP. (2005). Horizontal gene trans-fer, genome innovation and evolution. Nat Rev Micro-biol 3: 679–687.Haegeman B, Weitz JS. (2012). A neutral theory of genomeevolution and the frequency distribution of genes. BMCGenomics 13: 196.Kimura M (1984). The Neutral Theory of MolecularEvolution. Cambridge University Press: Cambridge, UK.Kislyuk AO, Haegeman B, Bergman NH, Weitz JS. (2011).Genomic fluidity: an integrative view of gene diversitywithin microbial populations. BMC Genomics 12: 32.Knöppel A, Lind PA, Lustig U, Näsvall J, Andersson DI(2014). Minor fitness costs in an experimental model ofhorizontal gene transfer in bacteria. Mol Biol Evol 31:1220–1227.Lynch M. (2010). Evolution of the mutation rate. TrendsGenet 26: 345–352.Narra HP, Ochman H. (2006). Of what use is sex tobacteria? Curr Biol 16: 705–710.Nowell RW, Green S, Laue BE, Sharp PM. (2014).The extent of genome flux and its role in thedifferentiation of bacterial lineages. Genome Biol Evol6: 1514–1529.Ochman H, Lawrence JG, Groisman EA. (2000). Lateralgene transfer and the nature of bacterial innovation.Nature 405: 299–304.Polz MF, Alm EJ, Hanage WP. (2013). Horizontalgene transfer and the evolution of bacterial andarchaeal population structure. Trends Genet 29:170–175.Tettelin H, Masignani V, Cieslewicz MJ, Donati C, MediniD, Ward NL et al. (2005). Genome analysis of multiplepathogenic isolates of Streptococcus agalactiae: impli-cations for the microbial “pan-genome”. Proc NatlAcad Sci 102: 13950–13955.Touchon M, Hoede C, Tenaillon O, Barbe V, Baeriswyl S,Bidet P et al. (2009). Organised genome dynamics inthe Escherichia coli species results in highly diverseadaptive paths. PLoS Genet 5: e1000344.Vos M, Didelot X. (2009). A comparison of homologousrecombination rates in bacteria and archaea. ISME J 3:199–208.Vos M, Hesselman MC, te Beek TA, van Passel MW, Eyre-Walker A. (2015). Rates of lateral gene transfer inprokaryotes: high but why? Trends Microbiol 23:598–605.Wielgoss S, Didelot X, Chaudhuri RR, Liu X, Weedall GD,Velicer GJ et al. (2016). A barrier to homologousrecombination between sympatric strains of the coop-erative soil bacterium Myxococcus xanthus. ISME J 10:2468–2477.This work is licensed under a CreativeCommons Attribution 4.0 InternationalLicense. The images or other third party material inthis article are included in the article’s CreativeCommons license, unless indicated otherwise in thecredit line; if the material is not included under theCreative Commons license, users will need to obtainpermission from the license holder to reproduce thematerial. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/© The Author(s) 2017Supplementary Information accompanies this paper on The ISME Journal website (http://www.nature.com/ismej)Genome fluidity meta-analysisNA Andreani et al3The ISME Journal

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