A null model for microbial diversification PNAS PLUS Timothy J. Strauba,1 and Olga Zhaxybayevaa,b,2 aDepartment of Biological Sciences, Dartmouth College, Hanover, NH 03755; and bDepartment of Computer Science, Dartmouth College, Hanover, NH 03755 Edited by Eugene V. Koonin, National Institutes of Health, Bethesda, MD, and approved May 10, 2017 (received for review December 12, 2016) Whether prokaryotes (Bacteria and Archaea) are naturally orga- and selective sweeps, nascent ecologically differentiated groups nized into phenotypically and genetically cohesive units compara- are proposed to undergo up to five stages that eventually result ble to animal or plant species remains contested, frustrating in their speciation (11). Whatever the genetic and ecological attempts to estimate how many such units there might be, or to mechanisms, clustering patterns are suggested to be an “accu- identify the ecological roles they play. Analyses of gene sequences rate” (13), “quick,” and “easy” (14) way to delineate bacterial in various closely related prokaryotic groups reveal that sequence species and are widely interpreted as indicative of the operation diversity is typically organized into distinct clusters, and processes of evolutionary forces (e.g., refs. 12, 15, and 16). such as periodic selection and extensive recombination are under- However, a simple turnover of microbial populations due to stood to be drivers of cluster formation (“speciation”). However, their random diversification and extinction accompanied by ac- observed patterns are rarely compared with those obtainable with cumulation of neutral changes in genomic DNA sequence will simple null models of diversification under stochastic lineage birth also inevitably produce clusters in the genealogies of the enco- and death and random genetic drift. Via a combination of simula- ded genes (17). Intuitively, such clustering becomes apparent tions and analyses of core and phylogenetic marker genes, we show when one examines patterns on any tree-like diagram resulting Escherichia Neisseria that patterns of diversity for the genera , , and from the processes of birth and death, but it can also be shown Borrelia are generally indistinguishable from patterns arising un- mathematically (e.g., ref. 18). That purely stochastic processes der a null model. We suggest that caution should thus be taken in produce clusters raises the possibility that microdiverse clusters interpreting observed clustering as a result of selective evolution- observed in gene trees might not actually reflect the operation of ary forces. Unknown forces do, however, appear to play a role in genetic or ecological forces of speciation. Helicobacter pylori EVOLUTION , and some individual genes in all groups fail to In this study, we developed a statistical framework that sim- conform to the null model. Taken together, we recommend the ulates microbial diversity under a null model of birth–death and presented birth−death model as a null hypothesis in prokaryotic compares the created patterns to those observed in data col- speciation studies. It is only when the real data are statistically lected from actual bacterial populations, such as 16S rRNA different from the expectations under the null model that some genes, housekeeping genes frequently used in multilocus se- speciation process should be invoked. quence analyses (MLSA), and protein-coding gene families. We applied this framework to analyses of hundreds of genomes prokaryotic diversity | bacterial species | pathogen typing | genetic drift | neutral evolution within four bacterial groups: Escherichia spp., Borrelia spp., Neisseria spp., and Helicobacter pylori. The first three of these icrobiologists have long debated whether a coherent spe- Significance Mcies concept might apply to Bacteria (and the other pro- karyotic domain, Archaea). Without such a concept, ad hoc conventions, for instance grouping taxa into “operational taxo- When evolutionary histories of closely related microorganisms nomic units” (OTUs) delimited by regions of 16S ribosomal are reconstructed, the lineages often cluster into visibly rec- RNA (rRNA) genes showing at least 97% sequence identity, ognizable groups. However, we do not know if these clusters have been used for identification and quantification of pro- represent fundamental units of bacterial diversity, such as karyotic diversity (1). Sometimes, however, 16S rRNA or other “species,” nor do we know the nature of evolutionary and marker genes in environmental samples exhibit “microdiversity” ecological forces that are responsible for cluster formation. at this or more stringent levels, with an overabundance of closely Addressing these questions is crucial, both for describing bio- related sequences that could be interpreted as resulting from diversity and for rapid and unambiguous identification of speciation-like ecological and genetic processes (2–4). For in- microorganisms, including pathogens. Multiple competing stance, in an early pivotal study of marine vibrios, Acinas et al. (5) scenarios of ecological diversification have been previously observed “a large predominance of closely related taxa in this proposed. Here we show that simple cell death and division community” and concluded that “such microdiverse clusters arise over time could also explain the observed clustering. We argue by selective sweeps and persist because competitive mechanisms that testing for the signatures of such “neutral” patterns are too weak to purge diversity from within them.” should be considered a null hypothesis in any microbial Two models have been most widely invoked to explain the classification analysis. evolutionary forces behind the formation of such clusters. Under the ecotype (periodic selection) model alluded to above, the Author contributions: T.J.S. and O.Z. designed research; T.J.S. performed research; T.J.S. and O.Z. analyzed data; and T.J.S. and O.Z. wrote the paper. progeny of the most-fit genotype in a clonally evolving bacterial population takes over the population, resulting in a selective The authors declare no conflict of interest. sweep that purges diversity at all loci (6). In this case, micro- This article is a PNAS Direct Submission. diversity would be the consequence of neutral mutations occur- Data deposition: Gene families are deposited to FigShare, available at https://dx.doi.org/ 10.6084/m9.figshare.4731946. Note that this is a derived dataset. The actual sequence ring between sweeps (6, 7). Under the competing recombination data were taken from PATRIC database records, descriptions of which are supplied in model, the frequent exchange of material between genes within Dataset S1. ecologically differentiated bacterial populations effects gene se- 1Present address: Genomic Center for Infectious Diseases, Broad Institute, Cambridge, MA quence similarity clustering (8, 9). Perhaps a combination of 02142. these forces is in place, or different diversification modes act on 2To whom correspondence should be addressed. Email: [email protected]. different bacterial groups, even if they coexist in the same en- This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. vironment (10–12). Under the selective forces of recombination 1073/pnas.1619993114/-/DCSupplemental. www.pnas.org/cgi/doi/10.1073/pnas.1619993114 PNAS Early Edition | 1of10 Downloaded by guest on September 27, 2021 analyzed groups have been routinely subdivided into clusters Selection of Bacterial Groups and Compilation of Gene Families. To designated as species and recognized as such in Bergey’s Manual, test the null hypothesis, we examined four bacterial groups the authoritative classification compendium (15). However, for (Escherichia spp., Neisseria spp., Borrelia spp., and Helicobacter these three groups, the majority of ubiquitous gene families pylori) that had at least 100 genomes in public access (to ensure formed clusters indistinguishable from those simulated under the statistical power) and represented a range of lifestyles (to capture null model in 1 to 5% DNA sequence divergence interval, and potentially different selective pressures that could have affected therefore no speciation processes need be invoked to explain the their diversification) (Table 1 and Dataset S1). Escherichia is a observed diversity. γ-proteobacterial genus comprised of six described species and a few yet unclassified strains (20–24). The analyzed 620 Escherichia Results and Discussion genomes span representatives from environmental isolates to A Null Model of Microbial Diversification. As a null model, we commensals and disease-causing strains of Escherichia coli, propose a diversification scenario in which a bacterial group does Escherichia albertii, Escherichia fergusonii, Escherichia hermanii, not experience evolutionary and ecological forces that either and the unclassified Escherichia spp. Members of β-proteobacte- purge or promote diversity. Under the proposed model, the ge- rial genus Neisseria colonize the mucosal and dental surfaces of nealogy of diversifying bacterial populations (hereafter referred many animals, and two of its 21 currently named species (25), to as lineages) is influenced only by a random process of ex- Neisseria meningitidis and Neisseria gonorrhoeae, cause disease in tinction (death) or divergence into two new lineages (birth). The humans (26). Neisseria cluster into “species groups” that do not net birth–death rate is constant, and recombination is absent. match perfectly to named species (27), and the