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      Lactobacillus rhamnosus colonisation antagonizes Candida albicans by forcing metabolic adaptations that compromise pathogenicity

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          Abstract

          Intestinal microbiota dysbiosis can initiate overgrowth of commensal Candida species – a major predisposing factor for disseminated candidiasis. Commensal bacteria such as Lactobacillus rhamnosus can antagonize Candida albicans pathogenicity. Here, we investigate the interplay between C. albicans, L. rhamnosus, and intestinal epithelial cells by integrating transcriptional and metabolic profiling, and reverse genetics. Untargeted metabolomics and in silico modelling indicate that intestinal epithelial cells foster bacterial growth metabolically, leading to bacterial production of antivirulence compounds. In addition, bacterial growth modifies the metabolic environment, including removal of C. albicans’ favoured nutrient sources. This is accompanied by transcriptional and metabolic changes in C. albicans, including altered expression of virulence-related genes. Our results indicate that intestinal colonization with bacteria can antagonize C. albicans by reshaping the metabolic environment, forcing metabolic adaptations that reduce fungal pathogenicity.

          Abstract

          Commensal bacteria such as Lactobacillus rhamnosus can inhibit the pathogenicity of the fungus Candida albicans. Here, Alonso-Roman et al. investigate the interplay between C. albicans, L. rhamnosus and intestinal epithelial cells, showing that changes in the metabolic environment, induced by the bacteria, trigger adaptations in C. albicans that reduce fungal pathogenicity.

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          REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms

          Outcomes of high-throughput biological experiments are typically interpreted by statistical testing for enriched gene functional categories defined by the Gene Ontology (GO). The resulting lists of GO terms may be large and highly redundant, and thus difficult to interpret. REVIGO is a Web server that summarizes long, unintelligible lists of GO terms by finding a representative subset of the terms using a simple clustering algorithm that relies on semantic similarity measures. Furthermore, REVIGO visualizes this non-redundant GO term set in multiple ways to assist in interpretation: multidimensional scaling and graph-based visualizations accurately render the subdivisions and the semantic relationships in the data, while treemaps and tag clouds are also offered as alternative views. REVIGO is freely available at http://revigo.irb.hr/.
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            MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights

            Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC–MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca . Graphical Abstract From raw data to statistical and functional insights using MetaboAnalyst 5.0.
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              What is flux balance analysis?

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                Author and article information

                Contributors
                bernhard.hube@hki-jena.de
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                9 June 2022
                9 June 2022
                2022
                : 13
                : 3192
                Affiliations
                [1 ]GRID grid.418398.f, ISNI 0000 0001 0143 807X, Department of Microbial Pathogenicity Mechanisms, , Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knoell-Institute, ; Jena, Germany
                [2 ]GRID grid.418398.f, ISNI 0000 0001 0143 807X, Systems Biology and Bioinformatics Unit, , Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knoell-Institute, ; Jena, Germany
                [3 ]Dynamic42 GmbH, Jena, Germany
                [4 ]GRID grid.418398.f, ISNI 0000 0001 0143 807X, Septomics Research Centre, , Leibniz Institute for Natural Product Research and Infection Biology – Hans-Knoell-Institute, ; Jena, Germany
                [5 ]GRID grid.194645.b, ISNI 0000000121742757, Department of Medicine and State Key Laboratory of Pharmaceutical Biotechnology, , University of Hong Kong, ; Hong Kong, China
                [6 ]GRID grid.9613.d, ISNI 0000 0001 1939 2794, Institute of Microbiology, , Friedrich Schiller University, ; Jena, Germany
                [7 ]GRID grid.418398.f, ISNI 0000 0001 0143 807X, Junior Research Group Adaptive Pathogenicity Strategies, , Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knoell-Institute, ; Jena, Germany
                Author information
                http://orcid.org/0000-0001-6106-724X
                http://orcid.org/0000-0003-0642-0068
                http://orcid.org/0000-0002-6028-0425
                http://orcid.org/0000-0002-9514-4634
                Article
                30661
                10.1038/s41467-022-30661-5
                9184479
                35680868
                1376cda1-323e-441d-a57f-91ffdde9c019
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 2 November 2021
                : 12 May 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft (German Research Foundation);
                Award ID: Project-ID 390713860
                Award ID: Project number 210879364
                Award ID: Project number 210879364
                Award ID: Project number 210879364
                Award ID: Project number 210879364
                Award ID: Project-ID 390713860
                Award ID: DFG project number 210879364
                Award ID: project no. 434385622 / GR 5617/1-1
                Award Recipient :
                Funded by: Infect ERA-NET Program FunComPath (BMBF 031L0001A) Centre for Sepsis Control and Care (CSCC, BMBF 01EO1002)
                Funded by: FundRef https://doi.org/10.13039/100004440, Wellcome Trust (Wellcome);
                Award ID: 215599_Z_19_Z
                Award ID: 215599_Z_19_Z
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                fungal systems biology,bacteria,fungal pathogenesis,microbial communities
                Uncategorized
                fungal systems biology, bacteria, fungal pathogenesis, microbial communities

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