Population expansions shared among coexisting bacterial lineages are revealed by genetic evidence

Introduction

The present-day distribution of biodiversity is a consequence of the evolutionary dynamics of populations and the history of the regions in which they occur (Lomolino, Riddle & Brown, 2006). Lineage diversification results from interactions between the intrinsic biological constraints of organisms and extrinsic environmental factors (Dawson, 2012). The demographic patterns of coexisting populations have often been analyzed in the context of the history of a region to better understand how the evolution of resident lineages has been affected by their environment (Lessa, Cook & Patton, 2003; Carnaval & Moritz, 2008; Ramírez-Barahona & Eguiarte, 2013; Chan, Schanzenbach & Hickerson, 2014; Hope et al., 2014). The analysis of genetic traits through the application of coalescent theory has provided evidence of the impacts of past geological and environmental events on the demographic history of resident species, such as the effect of Quaternary glaciations upon the distribution of animal species in North America (Lessa, Cook & Patton, 2003; Hope et al., 2014).

It is clear that patterns of biodiversity across phylogenetically distinct taxa may be influenced by shared historical factors (Chan, Schanzenbach & Hickerson, 2014; Hope et al., 2014). Despite the importance of microorganisms in ecosystems (Allison & Martiny , 2008; Strickland et al., 2009), the scale of the impact of historical factors remains poorly understood and little is known about the population dynamics of natural microbial populations, which has greatly hindered our understanding of the processes determining diversity. Most studies on microbial population dynamics have analyzed demographic patterns of bacterial pathogens, undertaken primarily to understand disease epidemics and population expansion events of human pathogens (Pybus et al., 2001; Wirth et al., 2007; Tazi et al., 2010). Studies of demographic trends in natural bacterial populations have been scant (Guttman, Morgan & Wang, 2008), due in part to limited sampling of populations from different lineages at similar temporal and geographic scales.

In this context, the Cuatro Cienegas Basin (CCB) harbors high levels of bacterial diversity, arguably due to environmental variation (e.g., salinity) across the aquatic systems within the basin (Cerritos et al., 2011). However, there is no conclusive evidence that identifies environmental conditions or geographic distance as predictors for the presence of certain bacterial groups (Escalante et al., 2008; Rebollar et al., 2012). Thus the influence of shared historical factors in population dynamics could be a plausible explanation for the observed diversity in CCB. A collection of coexisting bacterial isolates from the aquatic systems in this area has been built for over a decade (Cerritos et al., 2011; Rebollar et al., 2012; Rodríguez-Verdugo et al., 2012) and represents a unique opportunity to investigate the historical population patterns of coexisting bacterial lineages in a natural setting.

In the present work, we tested the hypothesis that a shared history in the CCB region has left common genetic signatures across phylogenetically diverse microbial populations. Using population genetics and coalescent-based approaches, we assessed the population history of lineages from two closely related genera of Firmicutes, Bacillus and Exiguobacterium, as well as lineages of Gammaproteobacteria from the genus Pseudomonas. The selected lineages are all common in the natural setting of the CCB aquatic system, and we anticipated that the genetic variation present among coexisting lineages would reveal a signature of common historical dynamics independent of phylogenetic relationships. Multilocus sequence typing (MLST) markers were used to (1) perform comparative phylogenetic analyses, (2) describe the genetic structure of bacterial populations, (3) calculate descriptive parameters of genetic diversity, (4) perform neutrality tests, and (5) conduct historical demography reconstructions.

Consistent with our hypothesis, we identified a common trend of expansion in the studied populations over a similar time frame that occurred independently of phylogenetic relationships. These results provide empirical evidence that analyzing coexisting bacterial lineages in natural environments can advance our understanding of bacterial evolution, beyond medical or health-related species.

Materials & Methods

Results

To investigate the influence of a common habitat in the genetic variation of bacterial populations, we analyzed MLST data from a collection of isolates across three bacterial genera (Bacillus, Exiguobacterium and Pseudomonas) sampled from water bodies in the geographic region of CCB, Mexico.

The phylogenetic history of CCB lineages

Phylogenetic reconstructions based on MLST and 16S rRNA sequences were used to assign isolates to monophyletic lineages (Figs. 1 and S1 respectively). MLST phylogenies identified at least two lineages within each genus. We named the lineages according to genus: “B” for Bacillus, “E” for Exiguobacterium, and “P” for Pseudomonas.

Among Bacillus isolates, we identified two clusters, B1 and B2 (Fig. 1A); their closest relatives were B. aquimaris, B. vietnamensis, B. marisflavi, and B. coahuilensis (Fig. S1A). Exiguobacterium isolates clustered into three well-defined groups: E1, E2, and E3 (Fig. 1B). For clusters E1 and E2, the most closely related species was E. aurantiacum, and E. profundum was the closest relative of cluster E3. Pseudomonas isolates were divided into three groups: P1, P2, and P3 (Fig. 1C). P1 included the majority of isolates and, according to 16S rRNA sequences, was closely related to P. oryzihabitans. P2 isolates were closely related to P. otitidis and P. aeruginosa, while P3 had recently been described as a new species, P. cuatrocienegasensis (Escalante et al., 2009) (Fig. S1C).

Population structure

Populations defined by pairwise F_ST values corresponded with sampling-site pairs with non-significant F_ST values (Table S3). We named the resulting populations indicating lineage and sampling site as: B1_C, B1_HPa, B1_M, B2_C, B2_MPa, E1_CHPa, E1_M, E2_CH, E2_M, E3_CM, P1_C, P2_C, and P3_C. Further exploration of structure within lineages was performed with BAPS and showed clusters that are shared among sites within lineages. In all cases, differences in frequency distribution of haplotypes (clusters) correspond with the population structure that we identified through pairwise F_ST (Fig. 2). Therefore, we delimited populations based on F_ST estimates since it incorporates both the geographic and genetic components in the definition of populations.

Population history: population genetics and coalescent approaches

Results of the genetic diversity analyses and neutrality tests (including Fu’s F_s; Fu, 1997) are presented in Table S4. Neutrality tests suggested expansion events but were inconclusive. In our data set, most values for Tajima’s D and Fu & Li’s tests were negative, although non-significant, which is suggestive of expansion events of populations. However, Fu’s F_s, a test that was explicitly designed to evaluate population expansions, was not significant in most cases. Overall, these classic population genetics estimates did not allow for strong conclusions about historical population events.

To further explore the influence of historical processes in shaping the diversity of these bacterial populations, we used a coalescent approach. Extended Bayesian Skyline Plot (EBSP) analyses of populations (genetic pools) showed signals of expansion in 9 of the 13 populations, of which 4 have statistical support for at least one change in population size (Fig. 3; for statistical support values of the population size changes see Table S4). Within Bacillus, an expansion (although not statistically supported) was detected in one population (B1_HPa) that included most B1 isolates. Plots for the other two B1 populations (B1_M and B1_C) showed constant population size across generations (Fig. 3). The B2 plots showed statistical support for change in population size in at least one population (B2_MPa) and constant population size in another (B2_C). Within Exiguobacterium, there was evidence of expansion in one of two E1 populations (E1_M), in one of the two E2 populations (E2_CH) and a trend of expansion (not statistically supported) in the only E3 population (E3_CM). Finally, the plots for all Pseudomonas populations displayed signals of expansion, although only P2_C was statistically supported. Overall, a trend of expansion was observed for the studied populations independent of phylogenetic relationships or sampling sites. Moreover, the skyline plots revealed a common time frame for the start of expansions (20,000 to 30,000 generations ago, Fig. 3), regardless of lineage- or locus-specific parameters.

Discussion

Diversity patterns in nature are a consequence of the evolutionary dynamics of populations and the history of the regions in which populations occur (Lomolino, Riddle & Brown, 2006). A powerful approach to unveil the influence of historical factors is to analyze current genetic variation to reconstruct the demographic history of populations (Lessa, Cook & Patton, 2003; Hope et al., 2014). In microorganisms, genetic studies looking at historical demography have mainly focused on host-associated bacteria to gain insight in disease epidemics (Mhedbi-Hajri et al., 2013; Wirth et al., 2006; Comas et al., 2013; Holt et al., 2013). However, little is known about population dynamics of non-host associated and naturally occurring bacteria.

When analyzing bacterial populations caution should be taken in the delimitation of populations, which has to consider key aspects such as the degree of genetic and ecological diversity. It is also important to consider the molecular resolution at which populations are defined (Kopac & Cohan, 2011). The more variable loci studied, the more populations may be identified. In our case, few and highly conserved loci may result in a coarse population delimitation. Nonetheless, to include both genetic and ecological aspects in the delimitation of populations, we defined individual populations for each lineage by analyzing genetic structure across sampling sites. The analysis showed that populations defined by the F_ST statistic include different sampling sites with contrasting environmental conditions (Rebollar et al., 2012), which is in accordance with previous work where no evidence of geographic or environmental barriers to dispersal exists (Escalante et al., 2008; Rebollar et al., 2012). We explored the existence of subpopulations within the F_ST-defined populations following a Bayesian approach implemented in BAPS (Tang et al., 2009), and we found more clusters that subdivided even further the populations defined with F_ST estimates. However, genetic diversity within the identified populations (π = 0.00032–0.04698 and Watterson’s θ = 0.00073–0.047; Table S4) is in the range of values reported for natural bacterial populations (Roberts & Cohan, 1995; Vos & Velicer, 2006), meaning that the observed diversity is within the range of normal polymorphism in bacterial populations. Overall, F_ST, BAPS, and diversity estimates support our population delimitation and indicate that we are not including unusually divergent individuals into the studied populations which might confound the population genetics and expansion estimations. Given that classic population genetics estimates did not allow for strong conclusions about historical population events, we used a coalescent approach to further explore the influence of historical processes. From this approach, we found evidence showing that common historical events were population expansions that occurred in 9 out of 13 populations (Fig. 3). Even more, it is noteworthy that at least one population of each genus presented evidence of expansion that was statistically supported. In clonal and free-living populations of bacteria, evolutionary models predict that populations experience strong fluctuations, either by selective sweeps or by metapopulation dynamics (Fraser et al., 2009; Kopac & Cohan, 2011; Shapiro & Polz, 2014). The results obtained in this study can be explained by these models since the analyzed lineages come from a natural setting and present no evidence of recombination (Table S2; Roberts & Cohan, 1995; Spiers, Buckling & Rainey, 2000; Rebollar et al., 2012). Without recombination, clonal clusters emerge by random mutation, but can quickly drift to extinction unless they have a major selective advantage, in which case their presence could suggest the emergence of new ecologically distinct populations (Shapiro & Polz, 2014) or ecotypes (Kopac & Cohan, 2011). Nonetheless, given that we do not have conclusive evidence that identifies environmental conditions or geographic distance as predictors for the presence of certain bacterial groups or populations (Escalante et al., 2008; Rebollar et al., 2012), the influence of shared historical factors in population dynamics could be a plausible explanation for the observed population expansion events in CCB. Given that we used just a few loci to delimit the studied populations, it should be noted here that we cannot rule out the possibility that the observed expansions may be a reflection of the emergence of closely related populations (undetectable to our molecular markers), and not population expansions in size. This ambiguity could be resolved if more variable loci, even complete genomes, are analyzed. Despite the alternative interpretations of the results, we consider notable the fact that we identified the same type of events (either diversification or population expansions) in most populations in the same time frame.

Population expansions in our data were identified to occur during the same time frame (≅20,000 generations ago). Our estimation of time to the most recent population change is calculated in number of generations. Although generation times may vary considerably among bacterial lineages (Mason, 1934; Errington, Powell & Thompson, 1965), laboratory observations of growth rates of the lineages included in this study do not suggest major differences in generation times between them, which supports the synchrony argument. In this study, we used previously reported estimates for Firmicutes (Bacillus and Exiguobacterium) and Pseudomonas to parameterize the coalescent simulations. We used the same substitution rate for Bacillus and Exiguobacterium since little information is available for Exiguobacterium and also because Maughan (2007) did not detect a considerable rate of heterogeneity between sporulating and non-sporulating Firmicutes. In this respect, it should be acknowledged that values of substitution rates are critical for the estimation of the temporal scale for the demographic events that result from ESPB analyses (Ho et al., 2011) and ideally should be directly estimated for the studied sample. We are aware that this could impact the temporal scales of our skyline plots, and be of particular relevance when comparing lineages with different substitution rates, but we do not have any reason to doubt the substitution rates reported in the literature. Further work should try to directly estimate substitution rates for the studied lineages to minimize the possibility of different time scales for the expansion events.

Despite the fact that expansions are expected in bacterial populations, the time of such expansions may differ among lineages because they depend on specific traits such as mutation rate, life history, and population structure (Avise, 2000; Avise, 2009; Kuo & Ochman, 2009; Maughan, 2007; Woolfit & Bromham, 2003). The synchrony of expansions in coexisting populations of different lineages are not expected to be observed unless shared environmental and historical factors have a major influence on population dynamics obscuring evidence of lineage specific adaptation, as has been observed in different organisms including “human associated” pathogens (Ornelas et al., 2013; Comas et al., 2013; Falush et al., 2003). The synchrony of population expansions supports the argument that current genetic variation may be the result of a major regional event over all populations.

Currently, we do not have the necessary information or samples from the appropriate temporal and spatial scales to determine the environmental changes associated with the historical events that influenced population dynamics of the studied lineages. However, we can propose an approximate timespan in which these demographic events occurred and hypothesize on the environmental causes behind our observations. Most estimates of generation times for bacteria have been determined in laboratory conditions, which may be considerably shorter than generation times in the wild (for Escherichia coli see Pierucci, 1972; Ochman, Elwyn & Moran, 1999). Thus, we estimated the approximate absolute time of population expansions based on generation times for aquatic bacteria estimated in conditions that are similar to their natural habitat conditions (approximately between 75 and 300 generations per year; Jannasch, 1969; Hendricks , 1972). We selected a generation time at the lower limit (75 gen/yr), because it is possible that the bacteria in our study may grow slowly, given the extreme limitation of nutrients, in particular phosphorous, found in the CCB aquatic system (Souza et al., 2006; Souza et al., 2012; Souza et al., 2008). Thus, if the synchronic event occurred 200–300 years ago for most populations, it is tempting to suggest that the population expansions we observed reflect changes resulting from the recent human activity in the CCB area. In particular, an increase in anthropogenic activity during the late eighteenth and nineteenth centuries has been reported, primarily centered around agriculture and ranching (Alessio-Robles, 1946) which coincides with the time frame of expansions found here.

Conclusions

This study investigates historical population events of coexisting but phylogenetically distant bacterial lineages. To our knowledge, this is the first report of a synchronous signature of population expansion that occurred independently of phylogenetic relationships, which is in accordance with our initial hypothesis. Our findings provide strong evidence for the potential of comparative population genetics to address questions about the influence of shared historical factors in the population evolutionary processes of naturally occurring non-host associated bacteria. This information may be important for natural resource management in the context of the ecosystem services that microorganisms provide.

Supplemental Information