Reciprocal Interactions Between the Gut Microbiome and Mammary Tissue Mast Cells Promote Metastatic Dissemination of HR+ Breast Tumors.

Establishing commensal dysbiosis, defined as an inflammatory gut microbiome with low biodiversity, before breast tumor initiation, enhances early dissemination of hormone receptor-positive (HR+) mammary tumor cells. Here, we sought to determine whether cellular changes occurring in normal mammary tissues, before tumor initiation and in response to dysbiosis, enhanced dissemination of HR+ tumors. Commensal dysbiosis increased both the frequency and profibrogenicity of mast cells in normal, non-tumor-bearing mammary tissues, a phenotypic change that persisted after tumor implantation. Pharmacological and adoptive transfer approaches demonstrated that profibrogenic mammary tissue mast cells from dysbiotic animals were sufficient to enhance dissemination of HR+ tumor cells. Using archival HR+ patient samples, we determined that enhanced collagen levels in tumor-adjacent mammary tissue positively correlated with mast cell abundance and HR+ breast cancer recurrence. Together, these data demonstrate that mast cells programmed by commensal dysbiosis activate mammary tissue fibroblasts and orchestrate early dissemination of HR+ breast tumors.

Comparative analyses of parasites with a comprehensive database of genome-scale metabolic models.

Protozoan parasites cause diverse diseases with large global impacts. Research on the pathogenesis and biology of these organisms is limited by economic and experimental constraints. Accordingly, studies of one parasite are frequently extrapolated to infer knowledge about another parasite, across and within genera. Model in vitro or in vivo systems are frequently used to enhance experimental manipulability, but these systems generally use species related to, yet distinct from, the clinically relevant causal pathogen. Characterization of functional differences among parasite species is confined to post hoc or single target studies, limiting the utility of this extrapolation approach. To address this challenge and to accelerate parasitology research broadly, we present a functional comparative analysis of 192 genomes, representing every high-quality, publicly-available protozoan parasite genome including Plasmodium, Toxoplasma, Cryptosporidium, Entamoeba, Trypanosoma, Leishmania, Giardia, and other species. We generated an automated metabolic network reconstruction pipeline optimized for eukaryotic organisms. These metabolic network reconstructions serve as biochemical knowledgebases for each parasite, enabling qualitative and quantitative comparisons of metabolic behavior across parasites. We identified putative differences in gene essentiality and pathway utilization to facilitate the comparison of experimental findings and discovered that phylogeny is not the sole predictor of metabolic similarity. This knowledgebase represents the largest collection of genome-scale metabolic models for both pathogens and eukaryotes; with this resource, we can predict species-specific functions, contextualize experimental results, and optimize selection of experimental systems for fastidious species.

Megasphaera in the Stool Microbiota Is Negatively Associated With Diarrheal Cryptosporidiosis.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Community standards to facilitate development and address challenges in metabolic modeling.

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.

Leveraging the effects of chloroquine on resistant malaria parasites for combination therapies.

BACKGROUND: Malaria is a major global health problem, with the Plasmodium falciparum protozoan parasite causing the most severe form of the disease. Prevalence of drug-resistant P. falciparum highlights the need to understand the biology of resistance and to identify novel combination therapies that are effective against resistant parasites. Resistance has compromised the therapeutic use of many antimalarial drugs, including chloroquine, and limited our ability to treat malaria across the world. Fortunately, chloroquine resistance comes at a fitness cost to the parasite; this can be leveraged in developing combination therapies or to reinstate use of chloroquine. RESULTS: To understand biological changes induced by chloroquine treatment, we compared transcriptomics data from chloroquine-resistant parasites in the presence or absence of the drug. Using both linear models and a genome-scale metabolic network reconstruction of the parasite to interpret the expression data, we identified targetable pathways in resistant parasites. This study identified an increased importance of lipid synthesis, glutathione production/cycling, isoprenoids biosynthesis, and folate metabolism in response to chloroquine. CONCLUSIONS: We identified potential drug targets for chloroquine combination therapies. Significantly, our analysis predicts that the combination of chloroquine and sulfadoxine-pyrimethamine or fosmidomycin may be more effective against chloroquine-resistant parasites than either drug alone; further studies will explore the use of these drugs as chloroquine resistance blockers. Additional metabolic weaknesses were found in glutathione generation and lipid synthesis during chloroquine treatment. These processes could be targeted with novel inhibitors to reduce parasite growth and reduce the burden of malaria infections. Thus, we identified metabolic weaknesses of chloroquine-resistant parasites and propose targeted chloroquine combination therapies.

Complex DNA structures trigger copy number variation across the Plasmodium falciparum genome.

Antimalarial resistance is a major obstacle in the eradication of the human malaria parasite, Plasmodium falciparum. Genome amplifications, a type of DNA copy number variation (CNV), facilitate overexpression of drug targets and contribute to parasite survival. Long monomeric A/T tracks are found at the breakpoints of many Plasmodium resistance-conferring CNVs. We hypothesize that other proximal sequence features, such as DNA hairpins, act with A/T tracks to trigger CNV formation. By adapting a sequence analysis pipeline to investigate previously reported CNVs, we identified breakpoints in 35 parasite clones with near single base-pair resolution. Using parental genome sequence, we predicted the formation of stable hairpins within close proximity to all future breakpoint locations. Especially stable hairpins were predicted to form near five shared breakpoints, establishing that the initiating event could have occurred at these sites. Further in-depth analyses defined characteristics of these 'trigger sites' across the genome and detected signatures of error-prone repair pathways at the breakpoints. We propose that these two genomic signals form the initial lesion (hairpins) and facilitate microhomology-mediated repair (A/T tracks) that lead to CNV formation across this highly repetitive genome. Targeting these repair pathways in P. falciparum may be used to block adaptation to antimalarial drugs.

Inferring Metabolic Mechanisms of Interaction within a Defined Gut Microbiota.

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.

Influential Parameters for the Analysis of Intracellular Parasite Metabolomics.

Metabolomics is increasingly popular for the study of pathogens. For the malaria parasite Plasmodium falciparum, both targeted and untargeted metabolomics have improved our understanding of pathogenesis, host-parasite interactions, and antimalarial drug treatment and resistance. However, purification and analysis procedures for performing metabolomics on intracellular pathogens have not been explored. Here, we purified in vitro-grown ring-stage intraerythrocytic P. falciparum parasites for untargeted metabolomics studies; the small size of this developmental stage amplifies the challenges associated with metabolomics studies as the ratio between host and parasite biomass is maximized. Following metabolite identification and data preprocessing, we explored multiple confounding factors that influence data interpretation, including host contamination and normalization approaches (including double-stranded DNA, total protein, and parasite numbers). We conclude that normalization parameters have large effects on differential abundance analysis and recommend the thoughtful selection of these parameters. However, normalization does not remove the contribution from the parasite's extracellular environment (culture media and host erythrocyte). In fact, we found that extraparasite material is as influential on the metabolome as treatment with a potent antimalarial drug with known metabolic effects (artemisinin). Because of this influence, we could not detect significant changes associated with drug treatment. Instead, we identified metabolites predictive of host and medium contamination that could be used to assess sample purification. Our analysis provides the first quantitative exploration of the effects of these factors on metabolomics data analysis; these findings provide a basis for development of improved experimental and analytical methods for future metabolomics studies of intracellular organisms.IMPORTANCE Molecular characterization of pathogens such as the malaria parasite can lead to improved biological understanding and novel treatment strategies. However, the distinctive biology of the Plasmodium parasite, including its repetitive genome and the requirement for growth within a host cell, hinders progress toward these goals. Untargeted metabolomics is a promising approach to learn about pathogen biology. By measuring many small molecules in the parasite at once, we gain a better understanding of important pathways that contribute to the parasite's response to perturbations such as drug treatment. Although increasingly popular, approaches for intracellular parasite metabolomics and subsequent analysis are not well explored. The findings presented in this report emphasize the critical need for improvements in these areas to limit misinterpretation due to host metabolites and to standardize biological interpretation. Such improvements will aid both basic biological investigations and clinical efforts to understand important pathogens.

Novel Plasmodium falciparum metabolic network reconstruction identifies shifts associated with clinical antimalarial resistance.

BACKGROUND: 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. RESULTS: 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. CONCLUSION: 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.

A Multifaceted Role for Myd88-Dependent Signaling in Progression of Murine Mammary Carcinoma.

Previous data obtained in our laboratory suggested that there may be constitutive signaling through the myeloid differentiation primary response gene 88 (Myd88)-dependent signaling cascade in murine mammary carcinoma. Here, we extended these findings by showing that, in the absence of an added Toll-like receptor (TLR) agonist, the myddosome complex was preformed in 4T1 tumor cells, and that Myd88 influenced cytoplasmic extracellular signal-regulated kinase (Erk)1/Erk2 levels, nuclear levels of nuclear factor-kappaB (NFκB) and signal transducer and activator of transcription 5 (STAT5), tumor-derived chemokine (C-C motif) ligand 2 (CCL2) expression, and in vitro and in vivo tumor growth. In addition, RNA-sequencing revealed that Myd88-dependent signaling enhanced the expression of genes that could contribute to breast cancer progression and genes previously associated with poor outcome for patients with breast cancer, in addition to suppressing the expression of genes capable of inhibiting breast cancer progression. Yet, Myd88-dependent signaling in tumor cells also suppressed expression of genes that could contribute to tumor progression. Collectively, these data revealed a multifaceted role for Myd88-dependent signaling in murine mammary carcinoma.