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      Acinetobacter baumannii Can Survive with an Outer Membrane Lacking Lipooligosaccharide Due to Structural Support from Elongasome Peptidoglycan Synthesis

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          ABSTRACT

          Gram-negative bacteria resist external stresses due to cell envelope rigidity, which is provided by two membranes and a peptidoglycan layer. The outer membrane (OM) surface contains lipopolysaccharide (LPS; contains O-antigen) or lipooligosaccharide (LOS). LPS/LOS are essential in most Gram-negative bacteria and may contribute to cellular rigidity. Acinetobacter baumannii is a useful tool for testing these hypotheses as it can survive without LOS. Previously, our group found that strains with naturally high levels of penicillin binding protein 1A (PBP1A) could not become LOS deficient unless the gene encoding it was deleted, highlighting the relevance of peptidoglycan biosynthesis and suggesting that high PBP1A levels were toxic during LOS deficiency. Transposon sequencing and follow-up analysis found that axial peptidoglycan synthesis by the elongasome and a peptidoglycan recycling enzyme, ElsL, were vital in LOS-deficient cells. The toxicity of high PBP1A levels during LOS deficiency was clarified to be due to a negative impact on elongasome function. Our data suggest that during LOS deficiency, the strength of the peptidoglycan specifically imparted by elongasome synthesis becomes essential, supporting that the OM and peptidoglycan contribute to cell rigidity.

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          SignalP 5.0 improves signal peptide predictions using deep neural networks

          Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish between various types of signal peptides. We present a deep neural network-based approach that improves SP prediction across all domains of life and distinguishes between three types of prokaryotic SPs.
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            Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection

            We have systematically made a set of precisely defined, single-gene deletions of all nonessential genes in Escherichia coli K-12. Open-reading frame coding regions were replaced with a kanamycin cassette flanked by FLP recognition target sites by using a one-step method for inactivation of chromosomal genes and primers designed to create in-frame deletions upon excision of the resistance cassette. Of 4288 genes targeted, mutants were obtained for 3985. To alleviate problems encountered in high-throughput studies, two independent mutants were saved for every deleted gene. These mutants—the ‘Keio collection'—provide a new resource not only for systematic analyses of unknown gene functions and gene regulatory networks but also for genome-wide testing of mutational effects in a common strain background, E. coli K-12 BW25113. We were unable to disrupt 303 genes, including 37 of unknown function, which are candidates for essential genes. Distribution is being handled via GenoBase (http://ecoli.aist-nara.ac.jp/).
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              MicrobeJ, a tool for high throughput bacterial cell detection and quantitative analysis

              Single cell analysis of bacteria and subcellular protein localization dynamics has shown that bacteria have elaborate life cycles, cytoskeletal protein networks, and complex signal transduction pathways driven by localized proteins. The volume of multi-dimensional images generated in such experiments and the computation time required to detect, associate, and track cells and subcellular features pose considerable challenges, especially for high-throughput experiments. Therefore, there is a need for a versatile, computationally efficient image analysis tool capable of extracting the desired relationships from images in a meaningful and unbiased way. Here we present MicrobeJ, a plug-in for the open-source platform ImageJ. MicrobeJ provides a comprehensive framework to process images derived from a wide variety of microscopy experiments with special emphasis on large image sets. It performs the most common intensity and morphology measurements as well as customized detection of poles, septa, fluorescent foci, and organelles, determines their sub-cellular localization with sub-pixel resolution, and tracks them over time. Because a dynamic link is maintained between the images, measurements, and all data representations derived from them, the editor and suite of advanced data presentation tools facilitates the image analysis process and provides a robust way to verify the accuracy and veracity of the data.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                mBio
                mBio
                mbio
                mBio
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2150-7511
                30 November 2021
                Nov-Dec 2021
                30 November 2021
                : 12
                : 6
                : e03099-21
                Affiliations
                [a ] Department of Infectious Diseases, College of Veterinary Medicine, University of Georgiagrid.213876.9, , Athens, Georgia, USA
                [b ] Department of Molecular Biology and Laboratory for Molecular Infection Medicine Sweden, Umeå Centre for Microbial Research, Umeå University, Umeå, Sweden
                [c ] Department of Microbiology, College of Art and Sciences, University of Georgiagrid.213876.9, , Athens, Georgia, USA
                KUMC
                Author notes

                Marta Nieckarz and Victor Pinedo contributed equally to this work. Author order was determined alphabetically by last name.

                Author information
                https://orcid.org/0000-0001-5995-718X
                https://orcid.org/0000-0001-6134-1800
                Article
                mBio03099-21 mbio.03099-21
                10.1128/mBio.03099-21
                8630537
                34844428
                d2f64dbe-8719-4d50-8d43-a6b8a9ab0bea
                Copyright © 2021 Simpson et al.

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

                History
                : 20 October 2021
                : 29 October 2021
                Page count
                supplementary-material: 8, Figures: 6, Tables: 0, Equations: 0, References: 70, Pages: 19, Words: 13164
                Funding
                Funded by: HHS | NIH | National Institute of Allergy and Infectious Diseases (NIAID), FundRef https://doi.org/10.13039/100000060;
                Award ID: AI129940
                Award Recipient :
                Funded by: HHS | NIH | National Institute of Allergy and Infectious Diseases (NIAID), FundRef https://doi.org/10.13039/100000060;
                Award ID: AI138576
                Award Recipient :
                Funded by: HHS | NIH | National Institute of Allergy and Infectious Diseases (NIAID), FundRef https://doi.org/10.13039/100000060;
                Award ID: AI150098
                Award Recipient :
                Funded by: HHS | NIH | National Institute of General Medical Sciences (NIGMS), FundRef https://doi.org/10.13039/100000057;
                Award ID: GM137554
                Award Recipient :
                Funded by: The Swedish Research Council;
                Award Recipient :
                Funded by: The Knut and Alice Wallenberg Foundation;
                Award Recipient :
                Funded by: The Kempe Foundation;
                Award Recipient :
                Funded by: The Laboratory of Molecular Infection Medicine Sweden;
                Award Recipient :
                Funded by: Swedish Society of Medical Investigation;
                Award Recipient :
                Categories
                Research Article
                bacteriology, Bacteriology
                Custom metadata
                November/December 2021

                Life sciences
                elsl,pbp1a,carboxypeptidase,cell envelope,lipopolysaccharide,outer membrane,peptidoglycan
                Life sciences
                elsl, pbp1a, carboxypeptidase, cell envelope, lipopolysaccharide, outer membrane, peptidoglycan

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