The Toxoplasma secreted effector TgWIP modulates dendritic cell motility by activating host tyrosine phosphatases Shp1 and Shp2.

The obligate intracellular parasite Toxoplasma gondii causes life-threatening toxoplasmosis to immunocompromised individuals. The pathogenesis of Toxoplasma relies on its swift dissemination to the central nervous system through a 'Trojan Horse' mechanism using infected leukocytes as carriers. Previous work found TgWIP, a protein secreted from Toxoplasma, played a role in altering the actin cytoskeleton and promoting cell migration in infected dendritic cells (DCs). However, the mechanism behind these changes was unknown. Here, we report that TgWIP harbors two SH2-binding motifs that interact with tyrosine phosphatases Shp1 and Shp2, leading to phosphatase activation. DCs infected with Toxoplasma exhibited hypermigration, accompanying enhanced F-actin stress fibers and increased membrane protrusions such as filopodia and pseudopodia. By contrast, these phenotypes were abrogated in DCs infected with Toxoplasma expressing a mutant TgWIP lacking the SH2-binding motifs. We further demonstrated that the Rho-associated kinase (Rock) is involved in the induction of these phenotypes, in a TgWIP-Shp1/2 dependent manner. Collectively, the data uncover a molecular mechanism by which TgWIP modulates the migration dynamics of infected DCs in vitro.

Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach.

To better combat the expansion of antibiotic resistance in pathogens, new compounds, particularly those with novel mechanisms-of-action [MOA], represent a major research priority in biomedical science. However, rediscovery of known antibiotics demonstrates a need for approaches that accurately identify potential novelty with higher throughput and reduced labor. Here we describe an explainable artificial intelligence classification methodology that emphasizes prediction performance and human interpretability by using a Hierarchical Ensemble of Classifiers model optimized with a novel feature selection algorithm called Clairvoyance; collectively referred to as a CoHEC model. We evaluated our methods using whole transcriptome responses from Escherichia coli challenged with 41 known antibiotics and 9 crude extracts while depositing 122 transcriptomes unique to this study. Our CoHEC model can properly predict the primary MOA of previously unobserved compounds in both purified forms and crude extracts at an accuracy above 99%, while also correctly identifying darobactin, a newly discovered antibiotic, as having a novel MOA. In addition, we deploy our methods on a recent E. coli transcriptomics dataset from a different strain and a Mycobacterium smegmatis metabolomics timeseries dataset showcasing exceptionally high performance; improving upon the performance metrics of the original publications. We not only provide insight into the biological interpretation of our model but also that the concept of MOA is a non-discrete heuristic with diverse effects for different compounds within the same MOA, suggesting substantial antibiotic diversity awaiting discovery within existing MOA.

Mechanism-of-Action Classification of Antibiotics by Global Transcriptome Profiling.

Antimicrobial resistance (AMR) is an ever-growing public health problem worldwide. The low rate of antibiotic discovery coupled with the rapid spread of drug-resistant bacterial pathogens is causing a global health crisis. To facilitate the drug discovery processes, we present a large-scale study of reference antibiotic challenge bacterial transcriptome profiles, which included 37 antibiotics across 6 mechanisms of actions (MOAs) and provide an economical approach to aid in antimicrobial dereplication in the discovery process. We demonstrate that classical MOAs can be sorted based upon the magnitude of gene expression profiles despite some overlap in the secondary effects of antibiotic exposures across MOAs. Additionally, using gene subsets, we were able to subdivide broad MOA classes into subMOAs. Furthermore, we provide a biomarker gene set that can be used to classify most antimicrobial challenges according to their canonical MOA. We also demonstrate the ability of this rapid MOA diagnostic tool to predict and classify the expression profiles of pure compounds and crude extracts to their expression profile-associated MOA class.

The Toxoplasma secreted effector TgWIP modulates dendritic cell motility by activating host tyrosine phosphatases Shp1 and Shp2.

The obligate intracellular parasite Toxoplasma gondii causes life-threatening toxoplasmosis to immunocompromised individuals. The pathogenesis of Toxoplasma relies on its swift dissemination to the central nervous system through a 'Trojan Horse' mechanism using infected leukocytes as carriers. Previous work found TgWIP, a protein secreted from Toxoplasma, played a role in altering the actin cytoskeleton and promoting cell migration in infected dendritic cells (DCs). However, the mechanism behind these changes was unknown. Here, we report that TgWIP harbors two SH2-binding motifs that interact with tyrosine phosphatases Shp1 and Shp2, leading to phosphatase activation. DCs infected with Toxoplasma exhibited hypermigration, accompanying enhanced F-actin stress fibers and increased membrane protrusions such as filopodia and pseudopodia. By contrast, these phenotypes were abrogated in DCs infected with Toxoplasma expressing a mutant TgWIP lacking the SH2-binding motifs. We further demonstrated that the Rho-associated kinase (Rock) is involved in the induction of these phenotypes, in a TgWIP-Shp1/2 dependent manner. Collectively, the data uncover a molecular mechanism by which TgWIP modulates the migration dynamics of infected DCs in vitro.

The metabolic footprint of Clostridia and Erysipelotrichia reveals their role in depleting sugar alcohols in the cecum.

BACKGROUND: The catabolic activity of the microbiota contributes to health by aiding in nutrition, immune education, and niche protection against pathogens. However, the nutrients consumed by common taxa within the gut microbiota remain incompletely understood. METHODS: Here we combined microbiota profiling with an un-targeted metabolomics approach to determine whether depletion of small metabolites in the cecum of mice correlated with the presence of specific bacterial taxa. Causality was investigated by engrafting germ-free or antibiotic-treated mice with complex or defined microbial communities. RESULTS: We noted that a depletion of Clostridia and Erysipelotrichia from the gut microbiota triggered by antibiotic treatment was associated with an increase in the cecal concentration of sugar acids and sugar alcohols (polyols). Notably, when we inoculated germ-free mice with a defined microbial community of 14 Clostridia and 3 Erysipelotrichia isolates, we observed the inverse, with a marked decrease in the concentrations of sugar acids and polyols in cecal contents. The carbohydrate footprint produced by the defined microbial community was similar to that observed in gnotobiotic mice receiving a cecal microbiota transplant from conventional mice. Supplementation with sorbitol, a polyol used as artificial sweetener, increased cecal sorbitol concentrations in antibiotic-treated mice, which was abrogated after inoculation with a Clostridia isolate able to grow on sorbitol in vitro. CONCLUSIONS: We conclude that consumption of sugar alcohols by Clostridia and Erysipelotrichia species depletes these metabolites from the intestinal lumen during homeostasis. Video abstract.