There is an emerging paradigm that the human microbiome is central to many aspects of health and may have a role in preventing enteric infection. Entamoeba histolytica is a major cause of amebic diarrhea in developing countries. It colonizes the colon lumen in close proximity to the gut microbiota. Interestingly, not all individuals are equally susceptible to E. histolytica infection. Therefore, as the microbiota is highly variable within individuals, we sought to determine if a component of the microbiota could regulate susceptibility to infection. In studies utilizing a murine model, we demonstrated that colonization of the gut with the commensal Clostridia-related bacteria known as segmented filamentous bacteria (SFB) is protective during  E. histolytica infection. SFB colonization in this model was associated with elevated cecal levels of interleukin 17A (IL-17A), dendritic cells, and neutrophils. Bone marrow-derived dendritic cells (BMDCs) from SFB-colonized mice had higher levels of IL-23 production in response to stimulation with trophozoites. Adoptive transfer of BMDCs from an SFB+ to an SFB− mouse was sufficient to provide protection against E. histolytica. IL-17A induction during BMDC transfer was necessary for this protection. This work demonstrates that intestinal colonization with a specific commensal bacterium can provide protection during amebiasis in a murine model. Most importantly, this work demonstrates that the microbiome can mediate protection against an enteric infection via extraintestinal effects on bone marrow-derived dendritic cells.

The diversity and number of species present within microbial communities create the potential for a multitude of interspecies metabolic interactions. Here, we develop, apply, and experimentally test a framework for inferring metabolic mechanisms associated with interspecies interactions. We perform pairwise growth and metabolome profiling of co-cultures of strains from a model mouse microbiota. We then apply our framework to dissect emergent metabolic behaviors that occur in co-culture. Based on one of the inferences from this framework, we identify and interrogate an amino acid cross-feeding interaction and validate that the proposed interaction leads to a growth benefit in vitro. Our results reveal the type and extent of emergent metabolic behavior in microbial communities composed of gut microbes. We focus on growth-modulating interactions, but the framework can be applied to interspecies interactions that modulate any phenotype of interest within microbial communities.

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Standardization of data and models facilitates effective communication, especially in computational systems biology. However, both the development and consistent use of standards and resources remain challenging. As a result, the amount, quality, and format of the information contained within systems biology models are not consistent and therefore present challenges for widespread use and communication. Here, we focused on these standards, resources, and challenges in the field of constraint‐based metabolic modeling by conducting a community‐wide survey. We used this feedback to (i) outline the major challenges that our field faces and to propose solutions and (ii) identify a set of features that defines what a “gold standard” metabolic network reconstruction looks like concerning content, annotation, and simulation capabilities. We anticipate that this community‐driven outline will help the long‐term development of community‐inspired resources as well as produce high‐quality, accessible models within our field. More broadly, we hope that these efforts can serve as blueprints for other computational modeling communities to ensure the continued development of both practical, usable standards and reproducible, knowledge‐rich models.

BackgroundMalaria remains a major public health burden and resistance has emerged to every antimalarial on the market, including the frontline drug, artemisinin. Our limited understanding of Plasmodium biology hinders the elucidation of resistance mechanisms. In this regard, systems biology approaches can facilitate the integration of existing experimental knowledge and further understanding of these mechanisms.ResultsHere, we developed a novel genome-scale metabolic network reconstruction, iPfal17, of the asexual blood-stage P. falciparum parasite to expand our understanding of metabolic changes that support resistance. We identified 11 metabolic tasks to evaluate iPfal17 performance. Flux balance analysis and simulation of gene knockouts and enzyme inhibition predict candidate drug targets unique to resistant parasites. Moreover, integration of clinical parasite transcriptomes into the iPfal17 reconstruction reveals patterns associated with antimalarial resistance. These results predict that artemisinin sensitive and resistant parasites differentially utilize scavenging and biosynthetic pathways for multiple essential metabolites, including folate and polyamines. Our findings are consistent with experimental literature, while generating novel hypotheses about artemisinin resistance and parasite biology. We detect evidence that resistant parasites maintain greater metabolic flexibility, perhaps representing an incomplete transition to the metabolic state most appropriate for nutrient-rich blood.ConclusionUsing this systems biology approach, we identify metabolic shifts that arise with or in support of the resistant phenotype. This perspective allows us to more productively analyze and interpret clinical expression data for the identification of candidate drug targets for the treatment of resistant parasites.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3905-1) contains supplementary material, which is available to authorized users.

Standardization of data and models facilitates effective communication, especially in computational systems biology. However, both the development and consistent use of standards and resources remains challenging.As a result, the amount, quality, and format of the information contained within systems biology models are not consistent and therefore present challenges for widespread use and communication. Here, we focused on these standards, resources, and challenges in the field of metabolic modeling by conducting a community-wide survey. We used this feedback to (1) outline the major challenges that our field faces and to propose solutions and (2) identify a set of features that defines what a "gold standard" metabolic network reconstruction looks like concerning content, annotation, and simulation capabilities. We anticipate that this community-driven outline will help the long-term development of community-inspired resources as well as produce high-quality, accessible models. More broadly, we hope that these efforts can serve as blueprints for other computational modeling communities to ensure continued development of both practical, usable standards and reproducible, knowledge-rich models. KEYWORDS

Construction and analysis of genome-scale metabolic models (GEMs) is a well-established systems biology approach that can be used to predict metabolic and growth phenotypes. The ability of GEMs to produce mechanistic insight into microbial ecological processes makes them appealing tools that can open a range of exciting opportunities in microbiome research.

The role of the immune system in microbial translocation must be clarified. In these studies, the effect of blood transfusion-related immunosuppression on translocation was investigated in a burn animal model previously known to increase the gut's permeability to 14C-radiolabeled Escherichia coli. In a first experiment, Balb/c mice underwent transfusion (T) with 0.2 mL per mouse of allogeneic C3H/HeJ mouse blood 5 days prior to undergoing 30-percent burn injury (B) and simultaneous gavage (G) with 10(9) E. coli bacteria labeled with 14C. An additional six groups of Balb/c mice underwent different combinations of T, B, and G procedures (TG, BG, TB, T, B, G). Survival rate was recorded for all groups on Day 10. This experiment suggested that B and T, to a lesser extent, were the factors affecting survival, although the combination of T, B, and G clearly showed a synergistic effect on mortality. In a second experiment, 18 Balb/c mice belonging to TBG, BG, TG, and G groups were sacrificed 1, 4, and 24 hours after burn or gavage. The residual radioactivity and the percentage of viable bacteria were computed for mesenteric lymph nodes, spleen, liver, lungs, blood, and peritoneal fluid. Statistical analysis of the radionuclide counts recognized B as the only variable able to enhance the magnitude of 14C E. coli translocation. The percentage of viable bacteria showed that T and, more moderately, B were the factors leading to the failure of bacterial clearance in the tissues.