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      The effects of feeding ferric citrate on ruminal bacteria, methanogenic archaea and methane production in growing beef steers

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          Abstract

          Methane produced by cattle is one of the contributors of anthropogenic greenhouse gas. Methods to lessen methane emissions from cattle have been met with varying success; thus establishing consistent methods for decreasing methane production are imperative. Ferric iron may possibly act to decrease methane by acting as an alternative electron acceptor. The objective of this study was to assess the effect of ferric citrate on the rumen bacterial and archaeal communities and its impact on methane production. In this study, eight steers were used in a repeated Latin square design with 0, 250, 500 or 750 mg Fe/kg DM of ferric iron (as ferric citrate) in four different periods. Each period consisted of a 16 day adaptation period and 5 day sampling period. During each sampling period, methane production was measured, and rumen content was collected for bacterial and archaeal community analyses. Normally distributed data were analysed using a mixed model ANOVA using the GLIMMIX procedure of SAS, and non-normally distributed data were analysed in the same manner following ranking. Ferric citrate did not have any effect on bacterial community composition, methanogenic archaea nor methane production ( P>0.05). Ferric citrate may not be a viable option to observe a ruminal response for decreases in enteric methane production.

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          Most cited references31

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          QIIME allows analysis of high-throughput community sequencing data.

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            Search and clustering orders of magnitude faster than BLAST.

            Biological sequence data is accumulating rapidly, motivating the development of improved high-throughput methods for sequence classification. UBLAST and USEARCH are new algorithms enabling sensitive local and global search of large sequence databases at exceptionally high speeds. They are often orders of magnitude faster than BLAST in practical applications, though sensitivity to distant protein relationships is lower. UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters. UCLUST offers several advantages over the widely used program CD-HIT, including higher speed, lower memory use, improved sensitivity, clustering at lower identities and classification of much larger datasets. Binaries are available at no charge for non-commercial use at http://www.drive5.com/usearch.
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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

                Journal
                Access Microbiol
                Access Microbiol
                acmi
                acmi
                Access Microbiology
                Microbiology Society
                2516-8290
                2021
                23 November 2020
                23 November 2020
                : 3
                : 1
                : acmi000180
                Affiliations
                [ 1] departmentDepartment of Animal Science , University of Tennessee , Knoxville, Tennessee 37996, USA
                [ 2] USDA, ARS, U.S. Meat Animal Research Center, Clay Center , Nebraska, 68933, USA
                [ 3] departmentDepartment of Animal Science , Iowa State University , Ames, Iowa 50011, USA
                [ 4] departmentDepartment of Animal Science , University of Nebraska , Lincoln, Nebraska 68583, USA
                [ ]Present address: Department of Animal and Range Science, New Mexico State, Las Cruces, NM 88003, USA
                [ ]Present address: Department of Animal and Food Sciences, Texas Tech University, Lubbock, TX 79409, USA
                Author notes

                Sequence data is available from the NCBI Sequence Read Archive accession PRJNA635162.

                *Correspondence: Phillip R. Myer, pmyer@ 123456utk.edu
                Author information
                https://orcid.org/0000-0002-9344-6495
                https://orcid.org/0000-0003-4472-8047
                https://orcid.org/0000-0002-5555-118X
                https://orcid.org/0000-0003-3962-0605
                https://orcid.org/0000-0002-9039-5434
                https://orcid.org/0000-0002-1980-2105
                Article
                000180
                10.1099/acmi.0.000180
                8115977
                c31ade80-015f-4a3e-96a3-8415de601960
                Copyright @ 2020

                This is an open-access article distributed under the terms of the Creative Commons Attribution License. The Microbiology Society waived the open access fees for this article.

                History
                : 01 June 2020
                : 30 October 2020
                Categories
                Research Article
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                beef,ferric citrate,methane,microbiome
                beef, ferric citrate, methane, microbiome

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