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We read with great interest the recent article by Cheng et al that discussed the topic of gut microbiome modulation, which may promote sensitivity or resistance to chemotherapeutic agents or immune checkpoint inhibitors.1 Historically, the pancreas was considered to be a sterile organ; however, bacterial DNA has been detected in 76% of surgically resected pancreatic ductal adenocarcinoma (PDAC) samples, some of which had prior biliary instrumentation, and in 15% of normal pancreas controls.2 3 It is unknown how bacteria may colonise the pancreas, but some have postulated bacterial reflux into the pancreatic duct via the major/minor papilla as a possibility and by dysbiosis due to pancreatitis, obesity, smoking and diabetes, as it alters gut permeability.4 5 Intratumoral microbiome composition may also impact therapeutic drug sensitivity and disease survival.6 The presence of PDAC intratumoral Gammaproteobacteria has the potential to inactivate gemcitabine sensitivity via the bacterial enzyme cytidine deaminase.7 Conversely, Bifidobacterium is believed to promote beneficial effects on …

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.

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.

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.

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

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.

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.

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.