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.

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. IMPORTANCE Entamoeba histolytica is the causative agent of amebiasis, an infectious disease that contributes significantly to morbidity and mortality due to diarrhea in the developing world. We showed in a murine model that colonization with the commensal members of the Clostridia known as SFB provides protection against E. histolytica and that dendritic cells from SFB-colonized mice alone can recapitulate protection. Understanding interactions between enteropathogens, commensal intestinal bacteria, and the mucosal immune response, including dendritic cells, will help in the development of effective treatments for this disease and other infectious and inflammatory diseases. The demonstration of immune-mediated protection due to communication from the microbiome to the bone marrow represents an emerging field of study that will yield unique approaches to the development of these treatments.

The protozoan parasites in the Cryptosporidium genus cause both acute diarrheal disease and subclinical (ie, nondiarrheal) disease. It is unclear if the microbiota can influence the manifestation of diarrhea during a Cryptosporidium infection. To characterize the role of the gut microbiota in diarrheal cryptosporidiosis, the microbiome composition of both diarrheal and surveillance Cryptosporidium-positive fecal samples from 72 infants was evaluated using 16S ribosomal RNA gene sequencing. Additionally, the microbiome composition prior to infection was examined to test whether a preexisting microbiome profile could influence the Cryptosporidium infection phenotype. Fecal microbiome composition was associated with diarrheal symptoms at 2 timepoints. Megasphaera was significantly less abundant in diarrheal samples compared with subclinical samples at the time of Cryptosporidium detection (log₂ [fold change] = –4.3; P = 10^–10) and prior to infection (log₂ [fold change] = –2.0; P = 10^–4); this assigned sequence variant was detected in 8 children who had diarrhea and 30 children without diarrhea. Random forest classification also identified Megasphaera abundance in the pre- and postexposure microbiota as predictive of a subclinical infection. Microbiome composition broadly, and specifically low Megasphaera abundance, was associated with diarrheal symptoms prior to and at the time of Cryptosporidium detection. This observation suggests that the gut microenvironment may play a role in determining the severity of a Cryptosporidium infection. Clinical Trials Registration. NCT02764918.

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.

Malaria 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. Here, 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. Using 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.