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      The Core Proteome of Biofilm-Grown Clinical Pseudomonas aeruginosa Isolates

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          Abstract

          Comparative genomics has greatly facilitated the identification of shared as well as unique features among individual cells or tissues, and thus offers the potential to find disease markers. While proteomics is recognized for its potential to generate quantitative maps of protein expression, comparative proteomics in bacteria has been largely restricted to the comparison of single cell lines or mutant strains. In this study, we used a data independent acquisition (DIA) technique, which enables global protein quantification of large sample cohorts, to record the proteome profiles of overall 27 whole genome sequenced and transcriptionally profiled clinical isolates of the opportunistic pathogen Pseudomonas aeruginosa. Analysis of the proteome profiles across the 27 clinical isolates grown under planktonic and biofilm growth conditions led to the identification of a core biofilm-associated protein profile. Furthermore, we found that protein-to-mRNA ratios between different P. aeruginosa strains are well correlated, indicating conserved patterns of post-transcriptional regulation. Uncovering core regulatory pathways, which drive biofilm formation and associated antibiotic tolerance in bacterial pathogens, promise to give clues to interactions between bacterial species and their environment and could provide useful targets for new clinical interventions to combat biofilm-associated infections.

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          A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

          Heng Li (2011)
          Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. http://samtools.sourceforge.net. hengli@broadinstitute.org.
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            Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads.

            High-volume sequencing of DNA and RNA is now within reach of any research laboratory and is quickly becoming established as a key research tool. In many workflows, each of the short sequences ("reads") resulting from a sequencing run are first "mapped" (aligned) to a reference sequence to infer the read from which the genomic location derived, a challenging task because of the high data volumes and often large genomes. Existing read mapping software excel in either speed (e.g., BWA, Bowtie, ELAND) or sensitivity (e.g., Novoalign), but not in both. In addition, performance often deteriorates in the presence of sequence variation, particularly so for short insertions and deletions (indels). Here, we present a read mapper, Stampy, which uses a hybrid mapping algorithm and a detailed statistical model to achieve both speed and sensitivity, particularly when reads include sequence variation. This results in a higher useable sequence yield and improved accuracy compared to that of existing software.
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              Mapping differential interactomes by affinity purification coupled with data independent mass spectrometry acquisition

              Characterizing changes in protein-protein interactions associated with sequence variants (e.g. disease-associated mutations or splice forms) or following exposure to drugs, growth factors or hormones is critical to understanding how protein complexes are built, localized and regulated. Affinity purification (AP) coupled with mass spectrometry permits the analysis of protein interactions under near-physiological conditions, yet monitoring interaction changes requires the development of a robust and sensitive quantitative approach, especially for large-scale studies where cost and time are major considerations. To this end, we have coupled AP to data-independent mass spectrometric acquisition (SWATH), and implemented an automated data extraction and statistical analysis pipeline to score modulated interactions. Here, we use AP-SWATH to characterize changes in protein-protein interactions imparted by the HSP90 inhibitor NVP-AUY922 or melanoma-associated mutations in the human kinase CDK4. We show that AP-SWATH is a robust label-free approach to characterize such changes, and propose a scalable pipeline for systems biology studies.
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                Author and article information

                Journal
                Cells
                Cells
                cells
                Cells
                MDPI
                2073-4409
                23 September 2019
                October 2019
                : 8
                : 10
                : 1129
                Affiliations
                [1 ]Institute for Molecular Bacteriology, TWINCORE GmbH, Centre for Experimental and Clinical Infection Research, a joint venture of the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover 30625, Germany
                [2 ]Research Core Unit Proteomics and Institute of Toxicology, Hannover Medical School, Hannover 30625, Germany
                [3 ]Department of Molecular Bacteriology, Helmholtz Center for Infection Research, Braunschweig 38124, Germany
                [4 ]Institute of Clinical Chemistry, Bioanalytics, University Medical Center Göttingen, Göttingen 37075, Germany; christof.lenz@ 123456med.uni-goettingen.de
                [5 ]Max Planck Institute for Biophysical Chemistry, Bioanalytical Mass Spectrometry, Göttingen 37077, Germany
                Author notes
                [* ]Correspondence: susanne.haeussler@ 123456helmholtz-hzi.de ; Tel.: +45-3545-7784
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0001-6928-3326
                https://orcid.org/0000-0002-0946-8166
                Article
                cells-08-01129
                10.3390/cells8101129
                6829490
                31547513
                ae9e7829-f2ec-47fc-a25e-896e8956d3a9
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 26 August 2019
                : 19 September 2019
                Categories
                Article

                bacteria,dia,mass spectrometry,microbiology,swath
                bacteria, dia, mass spectrometry, microbiology, swath

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