213
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Host-Microbe-Drug-Nutrient Screen Identifies Bacterial Effectors of Metformin Therapy

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Summary

          Metformin is the first-line therapy for treating type 2 diabetes and a promising anti-aging drug. We set out to address the fundamental question of how gut microbes and nutrition, key regulators of host physiology, affect the effects of metformin. Combining two tractable genetic models, the bacterium E. coli and the nematode C. elegans, we developed a high-throughput four-way screen to define the underlying host-microbe-drug-nutrient interactions. We show that microbes integrate cues from metformin and the diet through the phosphotransferase signaling pathway that converges on the transcriptional regulator Crp. A detailed experimental characterization of metformin effects downstream of Crp in combination with metabolic modeling of the microbiota in metformin-treated type 2 diabetic patients predicts the production of microbial agmatine, a regulator of metformin effects on host lipid metabolism and lifespan. Our high-throughput screening platform paves the way for identifying exploitable drug-nutrient-microbiome interactions to improve host health and longevity through targeted microbiome therapies.

          Video Abstract

          Graphical Abstract

          Highlights

          • A high-throughput method for investigating host-microbe-drug-nutrient interactions

          • Metformin host effects are regulated by a bacterial nutrient signaling pathway

          • Metabolic modeling of human gut microbiomes links metformin to microbial agmatine

          • Metformin-bacterial interactions engage host lipid metabolism to extend lifespan

          Abstract

          Looking at the effects of diet, microbiome, and host biology on drug responsiveness highlights pathways contributing to metformin’s effects on lifespan.

          Related collections

          Most cited references58

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
                Bookmark

                Author and article information

                Contributors
                Journal
                Cell
                Cell
                Cell
                Cell Press
                0092-8674
                1097-4172
                05 September 2019
                05 September 2019
                : 178
                : 6
                : 1299-1312.e29
                Affiliations
                [1 ]MRC London Institute of Medical Sciences, Du Cane Road, London W12 0NN, UK
                [2 ]Institute of Clinical Sciences, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0NN, UK
                [3 ]Institute of Structural and Molecular Biology, University College London and Birkbeck, London WC1E 6BT, UK
                [4 ]Institute for Experimental Medicine, Kiel University, 24105 Kiel, Germany
                [5 ]Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, 24105 Kiel, Germany
                [6 ]Molecular and Functional Neurobiology, Department of Biology, KU Leuven, 3000 Leuven, Belgium
                [7 ]UCL Cancer Institute, University College London, London WC1E 6JD, UK
                [8 ]Laboratory of Genetic Metabolic Diseases, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, the Netherlands
                [9 ]Department of Computer Science, University College London, London WC1E 6BT, UK
                [10 ]Department of Internal Medicine I, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
                [11 ]Institute of Epidemiology, Christian Albrechts University Kiel, 24105 Kiel, Germany
                Author notes
                []Corresponding author c.kaleta@ 123456iem.uni-kiel.de
                [∗∗ ]Corresponding author f.cabreiro@ 123456lms.mrc.ac.uk
                [12]

                These authors contributed equally

                [13]

                Senior author

                [14]

                Lead Contact

                Article
                S0092-8674(19)30891-8
                10.1016/j.cell.2019.08.003
                6736778
                31474368
                a4f90e05-7a62-4a7b-be6d-07680efe65cd
                © 2019 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 29 October 2018
                : 8 July 2019
                : 2 August 2019
                Categories
                Article

                Cell biology
                aging,c. elegans,drosophila,humans,type-2 diabetes,metabolic modeling,crp signaling,metformin,microbiome,diet

                Comments

                Comment on this article