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      Targeting Bacterial Gyrase with Cystobactamid, Fluoroquinolone, and Aminocoumarin Antibiotics Induces Distinct Molecular Signatures in Pseudomonas aeruginosa

      research-article
      a , a , b , c , a , d ,
      mSystems
      American Society for Microbiology
      antibiotics, Pseudomonas aeruginosa, mode of action, gyrase, metabolomics, RNA sequencing, DNA gyrase

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          ABSTRACT

          The design of novel antibiotics relies on a profound understanding of their mechanism of action. While it has been shown that cellular effects of antibiotics cluster according to their molecular targets, we investigated whether compounds binding to different sites of the same target can be differentiated by their transcriptome or metabolome signatures. The effects of three fluoroquinolones, two aminocoumarins, and two cystobactamids, all inhibiting bacterial gyrase, on Pseudomonas aeruginosa at subinhibitory concentrations could be distinguished clearly by RNA sequencing as well as metabolomics. We observed a strong (2.8- to 212-fold) induction of autolysis-triggering pyocins in all gyrase inhibitors, which correlated with extracellular DNA (eDNA) release. Gyrase B-binding aminocoumarins induced the most pronounced changes, including a strong downregulation of phenazine and rhamnolipid virulence factors. Cystobactamids led to a downregulation of a glucose catabolism pathway. The study implies that clustering cellular mechanisms of action according to the primary target needs to take class-dependent variances into account.

          IMPORTANCE Novel antibiotics are urgently needed to tackle the growing worldwide problem of antimicrobial resistance. Bacterial pathogens possess few privileged targets for a successful therapy: the majority of existing antibiotics as well as current candidates in development target the complex bacterial machinery for cell wall synthesis, protein synthesis, or DNA replication. An important mechanistic question addressed by this study is whether inhibiting such a complex target at different sites with different compounds has similar or differentiated cellular consequences. Using transcriptomics and metabolomics, we demonstrate that three different classes of gyrase inhibitors can be distinguished by their molecular signatures in P. aeruginosa. We describe the cellular effects of a promising, recently identified gyrase inhibitor class, the cystobactamids, in comparison to those of the established gyrase A-binding fluoroquinolones and the gyrase B-binding aminocoumarins. The study results have implications for mode-of-action discovery approaches based on target-specific reference compounds, as they highlight the intraclass variability of cellular compound effects.

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          Most cited references73

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          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
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            featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

            Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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              Gene Expression Omnibus: NCBI gene expression and hybridization array data repository.

              R. Edgar (2002)
              The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                mSystems
                mSystems
                msystems
                mSystems
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2379-5077
                13 July 2021
                Jul-Aug 2021
                13 July 2021
                : 6
                : 4
                : e00610-21
                Affiliations
                [a ] Department of Chemical Biology, Helmholtz Centre for Infection Researchgrid.7490.a, , Braunschweig, Germany
                [b ] Institute of Molecular Bacteriology, Twincore, Centre for Clinical and Experimental Infection Research, Hannover, Germany
                [c ] Department of Molecular Bacteriology, Helmholtz Centre for Infection Researchgrid.7490.a, , Braunschweig, Germany
                [d ] German Centre for Infection Research (DZIF), Braunschweig, Germany
                University of California, Berkeley
                Author notes

                Citation Franke R, Overwin H, Häussler S, Brönstrup M. 2021. Targeting bacterial gyrase with cystobactamid, fluoroquinolone, and aminocoumarin antibiotics induces distinct molecular signatures in Pseudomonas aeruginosa. mSystems 6:e00610-21. https://doi.org/10.1128/mSystems.00610-21.

                Author information
                https://orcid.org/0000-0002-5407-5521
                https://orcid.org/0000-0001-6141-9102
                https://orcid.org/0000-0002-8971-7045
                Article
                mSystems00610-21
                10.1128/mSystems.00610-21
                8407119
                34254824
                04cb976c-9e62-4631-9130-4a302aafcea0
                Copyright © 2021 Franke et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 18 May 2021
                : 22 June 2021
                Page count
                supplementary-material: 4, Figures: 5, Tables: 2, Equations: 0, References: 74, Pages: 16, Words: 10515
                Funding
                Funded by: Helmholtz Association (亥姆霍兹联合会致力), FundRef https://doi.org/10.13039/501100009318;
                Award ID: VH-GS-202
                Award Recipient : Award Recipient : Award Recipient :
                Categories
                Research Article
                antimicrobial-chemotherapy, Antimicrobial Chemotherapy
                Custom metadata
                July/August 2021

                antibiotics,pseudomonas aeruginosa,mode of action,gyrase,metabolomics,rna sequencing,dna gyrase

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