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      Integrated Metagenomic Assessment of Multiple Pre-harvest Control Points on Lettuce Resistomes at Field-Scale

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          Abstract

          An integrated understanding of factors influencing the occurrence, distribution, and fate of antibiotic resistance genes (ARGs) in vegetable production systems is needed to inform the design and development of strategies for mitigating the potential for antibiotic resistance propagation in the food chain. The goal of the present study was to holistically track antibiotic resistance and associated microbiomes at three distinct pre-harvest control points in an agroecosystem in order to identify the potential impacts of key agricultural management strategies. Samples were collected over the course of a single growing season (67 days) from field-scale plots amended with various organic and inorganic amendments at agronomic rates. Dairy-derived manure and compost amendment samples ( n = 14), soil samples ( n = 27), and lettuce samples ( n = 12) were analyzed via shotgun metagenomics to assess multiple pre-harvest factors as hypothetical control points that shape lettuce resistomes. Pre-harvest factors of interest included manure collection during/post antibiotic use, manure composting, and soil amended with organic (stockpiled manure/compost) versus chemical fertilizer. Microbial community resistome and taxonomic compositions were unique from amendment to soil to lettuce surface according to dissimilarity analysis. The highest resistome alpha diversity (i.e., unique ARGs, n = 642) was detected in amendment samples prior to soil application, while the composted manure had the lowest total ARG relative abundance (i.e., 16S rRNA gene-normalized). Regardless of amendment type, soils acted as an apparent ecological buffer, i.e., soil resistome and taxonomic profiles returned to background conditions 67 d-post amendment application. Effects of amendment conditions surprisingly re-emerged in lettuce phyllosphere resistomes, with the highest total ARG relative abundances recovered on the surface of lettuce plants grown in organically-fertilized soils (i.e., compost- and manure-amended soils). Co-occurrence analysis identified 55 unique ARGs found both in the soil amendments and on lettuce surfaces. Among these, arnA and pmrF were the most abundant ARGs co-occurring with mobile genetic elements (MGE). Other prominent ARG-MGE co-occurrences throughout this pre-harvest lettuce production chain included: TetM to transposon ( Clostridiodies difficile) in the manure amendment and TriC to plasmid ( Ralstonia solanacearum) on the lettuce surfaces. This suggests that, even with imposing manure management and post-amendment wait periods in agricultural systems, ARGs originating from manure can still be found on crop surfaces. This study demonstrates a comprehensive approach to identifying key control points for the propagation of ARGs in vegetable production systems, identifying potential ARG-MGE combinations that could inform future surveillance. The findings suggest that additional pre-harvest and potentially post-harvest interventions may be warranted to minimize risk of propagating antibiotic resistance in the food chain.

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            Prodigal: prokaryotic gene recognition and translation initiation site identification

            Background The quality of automated gene prediction in microbial organisms has improved steadily over the past decade, but there is still room for improvement. Increasing the number of correct identifications, both of genes and of the translation initiation sites for each gene, and reducing the overall number of false positives, are all desirable goals. Results With our years of experience in manually curating genomes for the Joint Genome Institute, we developed a new gene prediction algorithm called Prodigal (PROkaryotic DYnamic programming Gene-finding ALgorithm). With Prodigal, we focused specifically on the three goals of improved gene structure prediction, improved translation initiation site recognition, and reduced false positives. We compared the results of Prodigal to existing gene-finding methods to demonstrate that it met each of these objectives. Conclusion We built a fast, lightweight, open source gene prediction program called Prodigal http://compbio.ornl.gov/prodigal/. Prodigal achieved good results compared to existing methods, and we believe it will be a valuable asset to automated microbial annotation pipelines.
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              Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB

              A 16S rRNA gene database ( http://greengenes.lbl.gov ) addresses limitations of public repositories by providing chimera screening, standard alignment, and taxonomic classification using multiple published taxonomies. It was found that there is incongruent taxonomic nomenclature among curators even at the phylum level. Putative chimeras were identified in 3% of environmental sequences and in 0.2% of records derived from isolates. Environmental sequences were classified into 100 phylum-level lineages in the Archaea and Bacteria .
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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
                09 July 2021
                2021
                : 12
                : 683410
                Affiliations
                [1] 1Department of Biological Systems Engineering, Virginia Tech , Blacksburg, VA, United States
                [2] 2Department of Civil and Environmental Engineering, Virginia Tech , Blacksburg, VA, United States
                [3] 3The Interdisciplinary PhD Program in Genetics, Bioinformatics, and Computational Biology, Virginia Tech , Blacksburg, VA, United States
                [4] 4Department of Dairy Science, Virginia Tech , Blacksburg, VA, United States
                [5] 5Department of Animal Sciences, University of Reading , Reading, United Kingdom
                [6] 6Department of Food Science and Technology, Virginia Tech , Blacksburg, VA, United States
                Author notes

                Edited by: John P. Brooks, Agricultural Research Service, United States Department of Agriculture, United States

                Reviewed by: Aria Dolatabadian, University of Manitoba, Canada; Jinxin Liu, Nanjing Agricultural University, China

                *Correspondence: Lauren Wind, wlauren@ 123456vt.edu

                This article was submitted to Antimicrobials, Resistance and Chemotherapy, a section of the journal Frontiers in Microbiology

                Article
                10.3389/fmicb.2021.683410
                8299786
                34305845
                d4014b37-fc73-4dc2-99a5-5879cd591cfa
                Copyright © 2021 Wind, Keenum, Gupta, Ray, Knowlton, Ponder, Hession, Pruden and Krometis.

                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
                : 20 March 2021
                : 07 June 2021
                Page count
                Figures: 4, Tables: 0, Equations: 0, References: 79, Pages: 14, Words: 0
                Funding
                Funded by: National Institute of Food and Agriculture 10.13039/100005825
                Award ID: 2014-05280
                Award ID: 2015-68003-23050
                Award ID: 2017-68003-26498
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                antibiotic resistance genes,antibiotic resistome,antimicrobial resistance,agriculture,manure,lettuce,metagenomics,next-generation sequence

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