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      Antidepressants can induce mutation and enhance persistence toward multiple antibiotics

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          Antibiotic resistance is a global threat to public health and associated with the overuse of antibiotics. Although non-antibiotic drugs occupy 95% of the drug market, their impact on the emergence and spread of antibiotic resistance remains unclear. Here we demonstrate that antidepressants, one of the most frequently prescribed drugs, can induce antibiotic resistance and persistence. Such effects are associated with increased reactive oxygen species, enhanced stress signature responses, and stimulation of efflux pump expression. Mathematical modeling also supported a role for antidepressants in the occurrence of antibiotic-resistant mutants and persister cells. Considering the high consumption of antidepressants (16,850 kg annually in the United States alone), our findings highlight the need to re-evaluate the antibiotic-like side effects of antidepressants.

          Abstract

          Antibiotic resistance is an urgent threat to global health. Antidepressants are consumed in large quantities, with a similar pharmaceutical market share (4.8%) to antibiotics (5%). While antibiotics are acknowledged as the major driver of increasing antibiotic resistance, little attention is paid to the contribution of antidepressants in this process. Here, we demonstrate that antidepressants at clinically relevant concentrations induce resistance to multiple antibiotics, even following short periods of exposure. Antibiotic persistence was also enhanced. Phenotypic and genotypic analyses revealed the enhanced production of reactive oxygen species following exposure to antidepressants was directly associated with increased resistance. An enhanced stress signature response and stimulation of efflux pump expression were also associated with increased resistance and persistence. Mathematical modeling also predicted that antidepressants would accelerate the emergence of antibiotic-resistant bacteria, and persister cells would help to maintain the resistance. Overall, our findings highlight the antibiotic resistance risk caused by antidepressants.

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          Most cited references84

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

            Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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              The PRIDE database and related tools and resources in 2019: improving support for quantification data

              Abstract The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world’s largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.
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                Author and article information

                Contributors
                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                23 January 2023
                31 January 2023
                23 July 2023
                : 120
                : 5
                : e2208344120
                Affiliations
                [1] aAustralian Centre for Water and Environmental Biotechnology, The University of Queensland , Brisbane, QLD 4072, Australia
                [2] bSchool of Environmental Science and Engineering, Tiangong University , Tianjin 300387, China
                [3] cSchool of Biological Sciences, The University of Queensland , Brisbane, QLD 4072, Australia
                [4] dSchool of Chemistry and Molecular Biosciences, The University of Queensland , Brisbane, QLD 4072, Australia
                Author notes
                2To whom correspondence may be addressed. Email: jianhua.guo@ 123456uq.edu.au .

                Edited by James Tiedje, Michigan State University, East Lansing, MI; received May 17, 2022; accepted December 13, 2022

                1Y.W. and Z. Yu contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-4755-4620
                https://orcid.org/0000-0001-5352-2126
                https://orcid.org/0000-0003-4863-9260
                https://orcid.org/0000-0002-4732-9175
                Article
                202208344
                10.1073/pnas.2208344120
                9945972
                36689653
                7b364b19-23ab-4c9f-baca-80f6acc75fae
                Copyright © 2023 the Author(s). Published by PNAS.

                This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                : 17 May 2022
                : 13 December 2022
                Page count
                Pages: 12, Words: 8372
                Funding
                Funded by: Department of Education and Training | Australian Research Council (ARC), FundRef 501100000923;
                Award ID: DP220101526
                Award Recipient : Yue Wang Award Recipient : Zhigang Yu Award Recipient : Pengbo Ding Award Recipient : Ji Lu Award Recipient : Likai Mao Award Recipient : Lyman Ngiam Award Recipient : Zhiguo Yuan Award Recipient : Jan Engelstaedter Award Recipient : Mark A. Schembri Award Recipient : Jianhua Guo
                Categories
                research-article, Research Article
                microbio, Microbiology
                423
                Biological Sciences
                Microbiology

                antibiotic resistance,mutation,persistence,antidepressants

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