Plasmid Acquisition Alters Vancomycin Susceptibility in Clostridioides difficile.

The increasing incidence of primary and recurring Clostridioides difficile infections (CDI), which evade current treatment strategies, reflects the changing biology of C difficile. Here, we describe a putative plasmid-mediated mechanism potentially driving decreased sensitivity of C difficile to vancomycin treatment. We identified a broad host range transferable plasmid in a C difficile strain associated with lack of adequate response to vancomycin treatment. The transfer of this plasmid to a vancomycin-susceptible C difficile isolate decreased its susceptibility to vancomycin in vitro and resulted in more severe disease in a humanized mouse model. Our findings suggest plasmid acquisition in the gastrointestinal tract to be a possible mechanism underlying vancomycin treatment failure in patients with CDI, but further work is needed to characterize the mechanism by which plasmid genes determine vancomycin susceptibility in C difficile.

Mackinac: a bridge between ModelSEED and COBRApy to generate and analyze genome-scale metabolic models.

SUMMARY: Reconstructing and analyzing a large number of genome-scale metabolic models is a fundamental part of the integrated study of microbial communities; however, two of the most widely used frameworks for building and analyzing models use different metabolic network representations. Here we describe Mackinac, a Python package that combines ModelSEED's ability to automatically reconstruct metabolic models with COBRApy's advanced analysis capabilities to bridge the differences between the two frameworks and facilitate the study of the metabolic potential of microorganisms. AVAILABILITY AND IMPLEMENTATION: This package works with Python 2.7, 3.4, and 3.5 on MacOS, Linux and Windows. The source code is available from https://github.com/mmundy42/mackinac . CONTACT: mundy.michael@mayo.edu or soares.maria@mayo.edu.

MMinte: an application for predicting metabolic interactions among the microbial species in a community.

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, ß-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 the broader ecology of that system. 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 plays a strong role in determining the metabolism of individual microbes. We present two case studies that demonstrate the utility of this software. 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.

Genomic Comparisons of Lactobacillus crispatus and Lactobacillus iners Reveal Potential Ecological Drivers of Community Composition in the Vagina.

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.

Comparative functional genomics of Lactobacillus spp. reveals possible mechanisms for specialization of vaginal lactobacilli to their environment.

Lactobacilli are found in a wide variety of habitats. Four species, Lactobacillus crispatus, L. gasseri, L. iners, and L. jensenii, are common and abundant in the human vagina and absent from other habitats. These may be adapted to the vagina and possess characteristics enabling them to thrive in that environment. Furthermore, stable codominance of multiple Lactobacillus species in a single community is infrequently observed. Thus, it is possible that individual vaginal Lactobacillus species possess unique characteristics that confer to them host-specific competitive advantages. We performed comparative functional genomic analyses of representatives of 25 species of Lactobacillus, searching for habitat-specific traits in the genomes of the vaginal lactobacilli. We found that the genomes of the vaginal species were significantly smaller and had significantly lower GC content than those of the nonvaginal species. No protein families were found to be specific to the vaginal species analyzed, but some were either over- or underrepresented relative to nonvaginal species. We also found that within the vaginal species, each genome coded for species-specific protein families. Our results suggest that even though the vaginal species show no general signatures of adaptation to the vaginal environment, each species has specific and perhaps unique ways of interacting with its environment, be it the host or other microbes in the community. These findings will serve as a foundation for further exploring the role of lactobacilli in the ecological dynamics of vaginal microbial communities and their ultimate impact on host health.

Chimaeric load among sympatric social bacteria increases with genotype richness.

The total productivity of social groups can be determined by interactions among their constituents. Chimaeric load--the reduction of group productivity caused by antagonistic within-group heterogeneity--may be common in heterogeneous microbial groups due to dysfunctional behavioural interactions between distinct individuals. However, some instances of chimaerism in social microbes can increase group productivity, thus making a general relationship between chimaerism and group-level performance non-obvious. Using genetically similar strains of the soil bacterium Myxococcus xanthus that were isolated from a single centimetre-scale patch of soil, we tested for a relationship between degree of chimaerism (genotype richness) and total group performance at social behaviours displayed by this species. Within-group genotype richness was found to correlate negatively with total group performance at most traits examined, including swarming in both predatory and prey-free environments and spore production during development. These results suggest that interactions between such neighbouring strains in the wild will tend to be mutually antagonistic. Negative correlations between group performance and average genetic distance among group constituents at three known social genes were not found, suggesting that divergence at other loci that govern social interaction phenotypes is responsible for the observed chimaeric load. The potential for chimaeric load to result from co-aggregation among even closely related neighbours may promote the maintenance and strengthening of kin discrimination mechanisms, such as colony-merger incompatibilities observed in M. xanthus. The findings reported here may thus have implications for understanding the evolution and maintenance of diversity in structured populations of soil microbes.

Decomposing predation: testing for parameters that correlate with predatory performance by a social bacterium.

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 interrelationships 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.

Amplification of Femtograms of Bacterial DNA Within 3 h Using a Digital Microfluidics Platform for MinION Sequencing.

Whole genome sequencing is emerging as a promising tool for the untargeted detection of a broad range of microbial species for diagnosis and analysis. However, it is logistically challenging to perform the multistep process from sample preparation to DNA amplification to sequencing and analysis within a short turnaround time. To address this challenge, we developed a digital microfluidic device for rapid whole genome amplification of low-abundance bacterial DNA and compared results with conventional in-tube DNA amplification. In this work, we chose Corynebacterium glutamicum DNA as a bacterial target for method development and optimization, as it is not a common contaminant. Sequencing was performed in a hand-held Oxford Nanopore Technologies MinION sequencer. Our results show that using an in-tube amplification approach, at least 1 pg starting DNA is needed to reach the amount required for successful sequencing within 2 h. While using a digital microfluidic device, it is possible to amplify as low as 10 fg of C. glutamicum DNA (equivalent to the amount of DNA within a single bacterial cell) within 2 h and to identify the target bacterium within 30 min of MinION sequencing-100× lower than the detection limit of an in-tube amplification approach. We demonstrate the detection of C. glutamicum DNA in a mock community DNA sample and characterize the limit of bacterial detection in the presence of human cells. This approach can be used to identify microbes with minute amounts of genetic material in samples depleted of human cells within 3 h.

Spiteful Interactions in a natural population of the bacterium Xenorhabdus bovienii.

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.

Model of personalized postprandial glycemic response to food developed for an Israeli cohort predicts responses in Midwestern American individuals.

BACKGROUND: Controlled glycemic concentrations are associated with a lower risk of conditions such as cardiovascular disease and diabetes. Models commonly used to guide interventions to control the glycemic response to food have low efficacy, with recent clinical guidelines arguing for the use of personalized approaches. OBJECTIVE: We tested the efficacy of a predictive model of personalized postprandial glycemic response to foods that was developed with an Israeli cohort and that takes into consideration food components and specific features, including the microbiome, when applied to individuals from the Midwestern US. DESIGN: We recruited 327 individuals for this study. Participants provided information regarding lifestyle, dietary habits, and health, as well as a stool sample for characterization of their gut microbiome. Participants were connected to continuous glucose monitors for 6 d, and the glycemic response to meals logged during this time was computed. The ability of a model trained using meals logged by the Israeli cohort to correctly predict glycemic responses in the Midwestern cohort was assessed and compared with that of a model trained using meals logged by both cohorts. RESULTS: When trained on the Israeli cohort meals only, model performance for predicting responses of individuals in the Midwestern cohort was better (R = 0.596) than that observed for models taking into consideration the carbohydrate (R = 0.395) or calorie content of the meals alone (R = 0.336). Performance increased (R = 0.618) when the model was trained on meals from both cohorts, likely because of the observed differences in age distribution, diet, and microbiome. CONCLUSIONS: We show that the modeling framework described in Zeevi et al. for an Israeli cohort is applicable to a Midwestern population, and outperforms commonly used approaches for the control of blood glucose responses. The adaptation of the model to the Midwestern cohort further enhances performance and is a promising means for designing effective nutritional interventions to control glycemic responses to foods. This trial was registered at clinicaltrials.gov as NCT02945514.

Assessment of a Personalized Approach to Predicting Postprandial Glycemic Responses to Food Among Individuals Without Diabetes.

Importance: Emerging evidence suggests that postprandial glycemic responses (PPGRs) to food may be influenced by and predicted according to characteristics unique to each individual, including anthropometric and microbiome variables. Interindividual diversity in PPGRs to food requires a personalized approach for the maintenance of healthy glycemic levels. Objectives: To describe and predict the glycemic responses of individuals to a diverse array of foods using a model that considers the physiology and microbiome of the individual in addition to the characteristics of the foods consumed. Design, Setting, and Participants: This cohort study using a personalized predictive model enrolled 327 individuals without diabetes from October 11, 2016, to December 13, 2017, in Minnesota and Florida to be part of a study lasting 6 days. The study measured anthropometric variables, described the gut microbial composition, and assessed blood glucose levels every 5 minutes using a continuous glucose monitor. Participants logged their food and activity information for the duration of the study. A predictive model of individualized PPGRs to a diverse array of foods was trained and applied. Main Outcomes and Measures: Glycemic responses to food consumed over 6 days for each participant. The predictive model of personalized PPGRs considered individual features, including the microbiome, in addition to the features of the foods consumed. Results: Postprandial response to the same foods varied across 327 individuals (mean [SD] age, 45 [12] years; 78.0% female). A model predicting each individual's responses to food that considers several individual factors in addition to food features had better overall performance (R = 0.62) than current standard-of-care approaches using nutritional content alone (R = 0.34 for calories and R = 0.40 for carbohydrates) to control postprandial glycemic levels. Conclusions and Relevance: Across the cohort of adults without diabetes who were examined, a personalized predictive model that considers unique features of the individual, such as clinical characteristics, physiological variables, and the microbiome, in addition to nutrient content was more predictive than current dietary approaches that focus only on the calorie or carbohydrate content of foods. Providing individuals with tools to manage their glycemic responses to food based on personalized predictions of their PPGRs may allow them to maintain their blood glucose levels within limits associated with good health.

Community metabolic modeling approaches to understanding the gut microbiome: Bridging biochemistry and ecology.

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.

Fine-scale analysis of 16S rRNA sequences reveals a high level of taxonomic diversity among vaginal Atopobium spp.

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.

Comparative analysis of microbial community of novel lactic acid fermentation inoculated with different undefined mixed cultures.

Three undefined mixed cultures (activated sludge) from different municipal wastewater treatment plants were used as seeds in a novel lactic acid fermentation process fed with potato peel waste (PPW). Anaerobic sequencing batch fermenters were run under identical conditions to produce predominantly lactic acid. Illumina sequencing was used to examine the 16S rRNA genes of bacteria in the three seeds and fermenters. Results showed that the structure of microbial communities of three seeds were different. All three fermentation products had unique community structures that were dominated (>96%) by species of the genus Lactobacillus, while members of this genus constituted <0.1% in seeds. The species of Lactobacillus sp. differed among the three fermentations. Results of this study suggest the structure of microbial communities in lactic acid fermentation of PPW with undefined mixed cultures were robust and resilient, which provided engineering prospects for the microbial utilization of carbohydrate wastes to produce lactic acid.

A shift from magnitude to sign epistasis during adaptive evolution of a bacterial social trait.

Although the importance of epistasis in evolution has long been recognized, remarkably little is known about the processes by which epistatic interactions evolve in real time in specific biological systems. Here, we have characterized how the epistatic fitness relationship between a social gene and an adapting genome changes radically over a short evolutionary time frame in the social bacterium Myxococcus xanthus. We show that a highly beneficial effect of this social gene in the ancestral genome is gradually reduced--and ultimately reversed into a deleterious effect--over the course of an experimental adaptive trajectory in which a primitive form of novel cooperation evolved. This reduction and reversal of a positive social allelic effect is driven solely by changes in the genetic context in which the gene is expressed as new mutations are sequentially fixed during adaptive evolution, and explicitly demonstrates a significant evolutionary change in the genetic architecture of an ecologically important social trait.

Temperate bacterial viruses as double-edged swords in bacterial warfare.

It has been argued that bacterial cells may use their temperate viruses as biological weapons. For instance, a few bacterial cells among a population of lysogenic cells could release the virus and kill susceptible non-lysogenic competitors, while their clone mates would be immune. Because viruses replicate inside their victims upon infection, this process would amplify their number in the arena. Sometimes, however, temperate viruses spare recipient cells from death by establishing themselves in a dormant state inside cells. This phenomenon is called lysogenization and, for some viruses such as the λ virus, the probability of lysogenization increases with the multiplicity of infection. Therefore, the amplification of viruses leads to conflicting predictions about the efficacy of temperate viruses as biological weapons: amplification can increase the relative advantage of clone mates of lysogens but also the likelihood of saving susceptible cells from death, because the probability of lysogenization is higher. To test the usefulness of viruses as biological weapons, we performed competition experiments between lysogenic Escherichia coli cells carrying the λ virus and susceptible λ-free E. coli cells, either in a structured or unstructured habitat. In structured and sometimes in unstructured habitats, the λ virus qualitatively behaved as a "replicating toxin". However, such toxic effect of λ viruses ceased after a few days of competition. This was due to the fact that many of initially susceptible cells became lysogenic. Massive lysogenization of susceptible cells occurred precisely under the conditions where the amplification of the virus was substantial. From then on, these cells and their descendants became immune to the λ virus. In conclusion, if at short term bacterial cells may use temperate viruses as biological weapons, after a few days only the classical view of temperate bacterial viruses as parasitic agents prevails.

Influence of vaginal bacteria and D- and L-lactic acid isomers on vaginal extracellular matrix metalloproteinase inducer: implications for protection against upper genital tract infections.

UNLABELLED: We evaluated levels of vaginal extracellular matrix metalloproteinase inducer (EMMPRIN) and matrix metalloproteinase (MMP-8) in vaginal secretions in relation to the composition of vaginal bacterial communities and D- and L-lactic acid levels. The composition of vaginal bacterial communities in 46 women was determined by pyrosequencing the V1 to V3 region of 16S rRNA genes. Lactobacilli were dominant in 71.3% of the women, followed by Gardnerella (17.4%), Streptococcus (8.7%), and Enterococcus (2.2%). Of the lactobacillus-dominated communities, 51.5% were dominated by Lactobacillus crispatus, 36.4% by Lactobacillus iners, and 6.1% each by Lactobacillus gasseri and Lactobacillus jensenii. Concentrations of L-lactic acid were slightly higher in lactobacillus-dominated vaginal samples, but most differences were not statistically significant. D-Lactic acid levels were higher in samples containing L. crispatus than in those with L. iners (P<0.0001) or Gardnerella (P=0.0002). The relative proportion of D-lactic acid in vaginal communities dominated by species of lactobacilli was in concordance with the proportions found in axenic cultures of the various species grown in vitro. Levels of L-lactic acid (P<0.0001) and the ratio of L-lactic acid to D-lactic acid (P=0.0060), but not concentrations of D-lactic acid, were also correlated with EMMPRIN concentrations. Moreover, vaginal concentrations of EMMPRIN and MMP-8 levels were highly correlated (P<0.0001). Taken together, the data suggest the relative proportion of L- to D-lactic acid isomers in the vagina may influence the extent of local EMMPRIN production and subsequent induction of MMP-8. The expression of these proteins may help determine the ability of bacteria to transverse the cervix and initiate upper genital tract infections. IMPORTANCE: A large proportion of preterm births (>50%) result from infections caused by bacteria originating in the vagina, which requires that they traverse the cervix. Factors that influence susceptibility to these infections are not well understood; however, there is evidence that matrix metalloproteinase (MMP-8) is known to alter the integrity of the cervix. In this work, we show that concentrations of vaginal extracellular matrix metalloproteinase inducer (EMMPRIN) are influenced by members of the vaginal microbial community and concentrations of D- or L-lactic acid isomers in vaginal secretions. Elevated levels of D-lactic acid and the ratio of D- to L-lactic acid influence EMMPRIN concentrations as well as MMP-8 levels. Thus, isomers of lactic acid may function as signaling molecules that alter host gene expression and influence risk of infection-related preterm birth.