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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.

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., Author summary As antimicrobial resistance is on the rise, the need for compounds with novel targets or mechanisms-of-action [MOA] are of the utmost importance from the standpoint of public health. A major bottleneck in drug discovery is the ability to rapidly screen candidate compounds for precise MOA activity as current approaches are expensive, time consuming, and are difficult to implement in high-throughput. To alleviate this bottleneck in drug discovery, we developed a human interpretable artificial intelligence classification framework that can be used to build highly accurate and flexible predictive models. In this study, we investigated antimicrobial MOA through the transcriptional responses of Escherichia coli challenged with 41 known antibiotic compounds, 9 crude extracts, and a recently discovered (circa 2019) compound, darobactin, with novel MOA activity. We implemented a highly stringent Leave Compound Out Cross-Validation procedure to stress-test our predictive models by simulating the scenario of observing novel compounds. Furthermore, we developed a versatile feature selection algorithm, Clairvoyance, that we apply to our hierarchical ensemble of classifiers framework to build high performance explainable machine-learning models. Although the methods in this study were developed and stress-tested to predict the primary MOA from transcriptomic responses in E. coli, we designed these methods for general application to any classification problem and open-sourced the implementations in our Soothsayer Python package. We further demonstrate the versatility of these methods by deploying them on recent Mycobacterium smegmatis metabolomic and E. coli transcriptomics datasets to predict MOA with high accuracy.

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., 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.

International audience; In the context of scientometrics and bibliometrics, several research elds are dealing with bibliographic data. In this paper, we will explore how the combination of OLAP analysis (Online Analytical Processing) and information networks could be an interesting issue. In Business Intelligence, OLAP is a technology supported by data warehousing systems. It provides tools for analyzing data according to multiple dimensions and multiple hierarchical levels. At the same time, several information networks (co-authors network, citations network, institutions network, etc.) can be built based on bibliographic databases. Originally, OLAP was introduced to analyze structured data. However, in this paper, we wonder if, by combining OLAP and information networks, we can provide a new way of analyzing bibliographic data. OLAP should be able to handle information networks and be also useful for monitoring, browsing and analyzing the content and the structure of bibliographic networks. The goal of this survey paper is to review previous work on OLAP and information networks dealing with bibliographic data. We also propose a comparison between traditional OLAP and OLAP on information networks and discuss the challenges OLAP faces regarding bibliographic networks.