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      Escherichia coli and Pseudomonas aeruginosa Isolated From Urine of Healthy Bovine Have Potential as Emerging Human and Bovine Pathogens

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          Abstract

          The study of livestock microbiota has immediate benefits for animal health as well as mitigating food contamination and emerging pathogens. While prior research has indicated the gastrointestinal tract of cattle as the source for many zoonoses, including Shiga-toxin producing Escherichia coli and antibiotic resistant bacteria, the bovine urinary tract microbiota has yet to be thoroughly investigated. Here, we describe 5 E. coli and 4 Pseudomonas aeruginosa strains isolated from urine of dairy Gyr cattle. While both species are typically associated with urinary tract infections and mastitis, all of the animals sampled were healthy. The bovine urinary strains were compared to E. coli and P. aeruginosa isolates from other bovine samples as well as human urinary samples. While the bovine urinary E. coli isolates had genomic similarity to isolates from the gastrointestinal tract of cattle and other agricultural animals, the bovine urinary P. aeruginosa strains were most similar to human isolates suggesting niche adaptation rather than host adaptation. Examination of prophages harbored by these bovine isolates revealed similarity with prophages within distantly related E. coli and P. aeruginosa isolates from the human urinary tract. This suggests that related urinary phages may persist and/or be shared between mammals. Future studies of the bovine urinary microbiota are needed to ascertain if E. coli and P. aeruginosa are resident members of this niche and/or possible sources for emerging pathogens in humans.

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing.

            The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.
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              FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments

              Background We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. Methodology/Principal Findings Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the “CAT” approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100–1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. Conclusions/Significance FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.
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                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                07 March 2022
                2022
                : 13
                : 764760
                Affiliations
                [1] 1Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais , Belo Horizonte, Brazil
                [2] 2Bioinformatics Program, Loyola University Chicago , Chicago, IL, United States
                [3] 3Empresa de Pesquisa Agropecuária de Minas Gerais – EPAMIG , Uberaba, Brazil
                [4] 4Department of Biology, Loyola University Chicago , Chicago, IL, United States
                [5] 5Department of Microbiology and Immunology, Stritch School of Medicine, Loyola University Chicago , Maywood, IL, United States
                Author notes

                Edited by: Santiago Castillo Ramírez, National Autonomous University of Mexico, Mexico

                Reviewed by: Pallavi Singh, Northern Illinois University, United States; Gamaliel López-Leal, National Council of Science and Technology (CONACYT), Mexico

                *Correspondence: Edel F. Barbosa-Stancioli, edelfb@ 123456icb.ufmg.br

                This article was submitted to Evolutionary and Genomic Microbiology, a section of the journal Frontiers in Microbiology

                Article
                10.3389/fmicb.2022.764760
                8940275
                ae97e96b-af91-423c-9302-e285a75626ef
                Copyright © 2022 Giannattasio-Ferraz, Ene, Gomes, Queiroz, Maskeri, Oliveira, Putonti and Barbosa-Stancioli.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 25 August 2021
                : 31 January 2022
                Page count
                Figures: 3, Tables: 1, Equations: 0, References: 72, Pages: 11, Words: 8371
                Funding
                Funded by: National Science Foundation, doi 10.13039/100000001;
                Award ID: 1661357
                Funded by: Conselho Nacional de Desenvolvimento Científico e Tecnológico, doi 10.13039/501100003593;
                Funded by: Fundação de Amparo à Pesquisa do Estado de Minas Gerais, doi 10.13039/501100004901;
                Funded by: Loyola University Chicago, doi 10.13039/100007656;
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                microbiota,prophage,escherichia coli,pseudomonas aeruginosa,bovine,urine,emerging pathogens

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