No abstract

Background Some microbiome composition can be associated with negative outcomes, including among others, obesity, disease and the failure to respond to treatment. Microbiota manipulation or supplementation have been argued to restore the microbiome associated with a healthy condition. Fecal Microbiota Transplantation (FMT) is among the most popular microbiome intervention procedures. Current practices are to choose the transplanted microbiome based on the donor phenotype, and not based on the expected recipient phenotype. However, the two differ drastically. We here propose an algorithm to predict the expected outcome of FMT from the donor phenotype, and optimize the FMT for different required outcomes. Results We here show, using multiple microbiome properties, that the donor and recipient phenotypes differ widely, and propose a tool to predict the recipient phenotype after the FMT using only the donors' microbiome and when available demographics for transplants from humans to either antibiotic treated mice, or other humans. We then extend the method to optimize the best-planned transplant (bacterial cocktails) by combining the predictor and a genetic algorithm (GA). We validate the predictor using a de-novo FMT experiment highlighting the possibility to choose transplants that optimize an array of required goals. We further show that a limited number of taxa is enough to produce an optimal FMT. Conclusions Over the shelf FMT require recipient independent optimized FMT selection. Such a transplant can be from an optimal donor, or from a cultured set of microbes. We have here shown the feasibility of both types of donations in antibiotic treated mice and for transplants between humans.

The effect of microbes on their human host is often mediated through changes in metabolite concentrations. As such, multiple tools have been proposed to predict metabolitc profiles from microbial taxa frequencies, assuming a direct relation between the gut microbiome composition and blood metabolite concentrations. However, the microbiome-metabolite relation may depend on host demographics or condition. We show that the relation between microbiome and metabolites is best predicted at the log concentration level. We further develop LOCATE (Latent Of miCrobiome And meTabolites rElations), a machine learning (ML) tool based on latent representation which predicts the log normalized metabolites composition based on the log normalized microbiome composition. LOCATE has a higher overall accuracy than all current state-of-the-art predictors in both 16S rRNA gene and shotgun gene sequencing. The accuracy of LOCATE and all other predictors significantly decreases when predicting on one dataset and testing on a different dataset, or on a different condition in the same dataset, especially in 16S rRNA gene sequence based data. We propose an intermediate representation between the microbiome and the metabolite concentrations and show that this representation can be used to predict the host phenotype better than either the microbiome or the metabolome. This representation is strongly correlated with host demographics, including age, gender and diet and can be used to improve ML predictions of host phenotypes in comparison with either microbiome or metabolome using a large microbiome sample combined with a small number of metabolome samples (~ 50)

Identification of youth at risk for post-traumatic pathology is critical for public health, medicine, and social policy but research has not yet succeeded in pinpointing biomarkers that can distinguish the post-traumatic from the resilient profile in contexts of trauma. As trauma alters the microbiome with lasting effects on the host, the current longitudinal, multi-measure, cross-species study sought to outline the microbial signature of post-traumatic stress disorder (PTSD). We followed a unique trauma-exposed cohort for 15 years, from early childhood to adolescence, repeatedly assessing post-traumatic symptomatology. Gut microbiome composition and diversity characterized post-traumatic pathology, distinguished youth with PTSD from resilient individuals, and mediated the continuity of post-traumatic disorder. Mother-child microbial synchrony was reduced in cases of PTSD, suggesting that diminished microbial concordance among family members may index susceptibility to post-traumatic illness. Germ-free mice transplanted with PTSD microbiomes compared with those receiving resilient microbiomes exhibited anxious behavior. Our findings provide causative evidence that the microbial trauma profile is at least partially responsible for the trauma-related phenotype and highlight microbial underpinnings of resilience. Our results suggest that the microbial ecology may serve as additional biological memory of early life stress and underscore the potential for microbiome-related diagnosis and treatment following trauma exposure.

BackgroundProgesterone is a steroid hormone produced by the ovaries, involved in pregnancy progression and necessary for successful gestation. We have previously shown that progesterone affects gut microbiota composition and leads to increased relative abundance of Bifidobacterium.ResultsIn non-pregnant female GF mice, levels of progesterone were significantly higher than in SPF mice of the same status. However, no significant differences were observed between GF and SPF males. Females treated with progesterone gained more weight than females treated with a placebo. In contrast to female mice, males treated with progesterone did not gain significantly more weight than males treated with a placebo. Progesterone supplementation led to microbial changes in females but not in males (16S rRNA sequencing). Accordingly, the weight gain observed in female mice treated with progesterone was fully transferable to both male and female germ-free mice via fecal transplantation.ConclusionsWe demonstrate that bacteria play a role in regulating progesterone levels in a female-specific manner. Furthermore, weight gain and metabolic changes associated with progesterone may be mediated by the gut microbiota.