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Lactobacillus crispatus and Lactobacillus iners are common inhabitants of the healthy human vagina. These two species are closely related and are thought to perform similar ecological functions in the vaginal environment. Temporal data on the vaginal microbiome have shown that nontransient instances of cooccurrence are uncommon, while transitions from an L. iners -dominated community to one dominated by L. crispatus , and vice versa, occur often. This suggests that there is substantial overlap in the fundamental niches of these species. Given this apparent niche overlap, it is unclear how they have been maintained as common inhabitants of the human vagina. In this study, we characterized and compared the genomes of L. iners and L. crispatus to gain insight into possible mechanisms driving the maintenance of this species diversity. Our results highlight differences in the genomes of these two species that may facilitate the partitioning of their shared niche space. Many of the identified differences may impact the protective benefits provided to the host by these two species. IMPORTANCE The microbial communities that inhabit the human vagina play a critical role in the maintenance of vaginal health through the production of lactic acid and lowering the environmental pH. This precludes the growth of nonindigenous organisms and protects against infectious disease. The two most common types of vaginal communities are dominated by either Lactobacillus iners or Lactobacillus crispatus , while some communities alternate between the two over time. We combined ecological theory with state-of-the-art genome analyses to characterize how these two species might partition their shared niche space in the vagina. We show that the genomes of L. iners and L. crispatus differ in many respects, several of which may drive differences in their competitive abilities in the vagina. Our results provide insight into factors that drive the complicated temporal dynamics of the vaginal microbiome and demonstrate how closely related microbial species partition shared fundamental niche space.

Interest in the human microbiome is at an all time high. The number of human microbiome studies is growing exponentially, as are reported associations between microbial communities and disease. However, we have not been able to translate the ever-growing amount of microbiome sequence data into better health. To do this, we need a practical means of transforming a disease-associated microbiome into a health-associated microbiome. This will require a framework that can be used to generate predictions about community dynamics within the microbiome under different conditions, predictions that can be tested and validated. In this review, using the gut microbiome to illustrate, we describe two classes of model that are currently being used to generate predictions about microbial community dynamics: ecological models and metabolic models. We outline the strengths and weaknesses of each approach and discuss the insights into the gut microbiome that have emerged from modeling thus far. We then argue that the two approaches can be combined to yield a community metabolic model, which will supply the framework needed to move from high-throughput omics data to testable predictions about how prebiotic, probiotic, and nutritional interventions affect the microbiome. We are confident that with a suitable model, researchers and clinicians will be able to harness the stream of sequence data and begin designing strategies to make targeted alterations to the microbiome and improve health.

Predator-prey interactions presumably play major roles in shaping the composition and dynamics of microbial communities. However, little is understood about the population biology of such interactions or how predation-related parameters vary or correlate across prey environments. Myxococcus xanthus is a motile soil bacterium that feeds on a broad range of other soil microbes that vary greatly in the degree to which they support M. xanthus growth. In order to decompose predator-prey interactions at the population level, we quantified five predation-related parameters during M. xanthus growth on nine phylogenetically diverse bacterial prey species. The horizontal expansion rate of swarming predator colonies fueled by prey lawns served as our measure of overall predatory performance, as it incorporates both the searching (motility) and handling (killing and consumption of prey) components of predation. Four other parameters – predator population growth rate, maximum predator yield, maximum prey kill, and overall rate of prey death – were measured from homogeneously mixed predator-prey lawns from which predator populations were not allowed to expand horizontally by swarming motility. All prey species fueled predator population growth. For some prey, predator-specific prey death was detected contemporaneously with predator population growth, whereas killing of other prey species was detected only after cessation of predator growth. All four of the alternative parameters were found to correlate significantly with predator swarm expansion rate to varying degrees, suggesting causal inter-relationships among these diverse predation measures. More broadly, our results highlight the importance of examining multiple parameters for thoroughly understanding the population biology of microbial predation.

Background.The explosive growth of microbiome research has yielded great quantities of data. These data provide us with many answers, but raise just as many questions. 16S rDNA—the backbone of microbiome analyses—allows us to assess α-diversity, β-diversity, and microbe-microbe associations, which characterize the overall properties of an ecosystem. However, we are still unable to use 16S rDNA data to directly assess the microbe-microbe and microbe-environment interactions that determine that system's broader ecology. Thus, properties such as competition, cooperation, and nutrient conditions remain insufficiently analyzed. Here, we apply predictive community metabolic models of microbes identified with 16S rDNA data to probe the ecology of microbial communities.Results.We developed a methodology for the large-scale assessment of microbial metabolic interactions (MMinte)from 16S rDNA data. MMinte assesses the relative growth rates of interacting pairs of organisms within a community metabolic network and whether that interaction has a positive or negative effect. Moreover, MMinte's simulations take into account the nutritional environment, which play a strong role in determining the metabolism of individual microbes. We present two case studies that demonstrate this software's utility. In the first, we show how diet influences the nature of the microbe-microbe interactions. In the second, we use MMinte's modular feature set to better understand how the growth of Desulfovibrio piger is affected by, and affects the growth of, other members in a simplified gut community under metabolic conditions suggested to be determinant for their dynamics.Conclusion.By applying metabolic models to commonly available sequence data, MMinte grants the user insight into the metabolic relationships between microbes, highlighting important features that may relate to ecological stability, susceptibility, and cross-feeding. These relationships are at the foundation of a wide range of ecological questions that impact our ability to understand problems such as microbially-derived toxicity in colon cancer.

An individual behaves spitefully when it harms itself in the act of harming other individuals. One of the clearest potential examples of spite is the costly production and release of toxins called bacteriocins. Bacteriocins are toxins produced by bacteria that can kill closely related strains of the same species. Theoretical work has predicted that bacteriocin-mediated interactions could play an important role in maintaining local genetic and/or species diversity, but these interactions have not been studied at biologically relevant scales in nature. Here we studied toxin production and among-strain inhibitions in a natural population of Xenorhabdus bovienii. We found genetic differences and inhibitions between colonies that were collected only a few meters apart. These results suggest that spite exists in natural populations of bacteria.

Swimming and diving abilities of two syntopic species of water shrews, Neomys anomalus and Neomys fodiens, were tested in aquaria using video recordings taken from three views (lateral distant, lateral close and dorsal). The frequency and total duration of diving, as well as the mean duration of diving and floating bouts, were significantly higher in N. fodiens than in N. anomalus. Frequency of paddling during surface swimming was lower in N. fodiens than in N. anomalus. N. fodiens dived mainly for long distances at the bottom of the aquarium and performed a wider range of dive profiles than N. anomalus, which preferred rather short and shallow dives. The two species differed also significantly in their fineness ratios (describing how streamlined their bodies are) when diving. When swimming, N. fodiens had a relatively wider body and performed narrower movements with its tail than N. anomalus. These results show quantitatively and qualitatively for the first time that N. fodiens is more proficient at swimming and diving than N. anomalus. The results also help to explain the inter-specific differences in efficiency of underwater foraging, and support the idea of segregation of ecological niches of these species based on their different foraging modes (diving vs. wading).

Although vaginal microbial communities of some healthy women have high proportions of Atopobium vaginae, the genus Atopobium is more commonly associated with bacterial vaginosis, a syndrome associated with an increased risk of adverse pregnancy outcomes and the transmission of sexually transmitted diseases. Genetic differences within Atopobium species may explain why single species can be associated with both health and disease. We used 16S rRNA gene sequences from previously published studies to explore the taxonomic diversity of the genus Atopobium in vaginal microbial communities of healthy women. Although A. vaginae was the species most commonly found, we also observed three other Atopobium species in the vaginal microbiota, one of which, A. parvulum, was not previously known to reside in the human vagina. Furthermore, we found several potential novel species of the genus Atopobium and multiple phylogenetic clades of A. vaginae. The diversity of Atopobium found in our study, which focused only on samples from healthy women, is greater than previously recognized, suggesting that analysis of samples from women with BV would yield even more diversity. Classification of microbes only to the genus level may thus obfuscate differences that might be important to better understand health or disease.