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      Metabolomic networks and pathways associated with feed efficiency and related-traits in Duroc and Landrace pigs

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          Abstract

          Improving feed efficiency (FE) is a major goal of pig breeding, reducing production costs and providing sustainability to the pig industry. Reliable predictors for FE could assist pig producers. We carried out untargeted blood metabolite profiling in uncastrated males from Danbred Duroc (n = 59) and Danbred Landrace (n = 50) pigs at the beginning and end of a FE testing phase to identify biomarkers and biological processes underlying FE and related traits. By applying linear modeling and clustering analyses coupled with WGCNA framework, we identified 102 and 73 relevant metabolites in Duroc and Landrace based on two sampling time points. Among them, choline and pyridoxamine were hub metabolites in Duroc in early testing phase, while, acetoacetate, cholesterol sulfate, xanthine, and deoxyuridine were identified in the end of testing. In Landrace, cholesterol sulfate, thiamine, L-methionine, chenodeoxycholate were identified at early testing phase, while, D-glutamate, pyridoxamine, deoxycytidine, and L-2-aminoadipate were found at the end of testing. Validation of these results in larger populations could establish FE prediction using metabolomics biomarkers. We conclude that it is possible to identify a link between blood metabolite profiles and FE. These results could lead to improved nutrient utilization, reduced production costs, and increased FE.

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          Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data.

          Metabolomics is a rapidly evolving field that holds promise to provide insights into genotype-phenotype relationships in cancers, diabetes and other complex diseases. One of the major informatics challenges is providing tools that link metabolite data with other types of high-throughput molecular data (e.g. transcriptomics, proteomics), and incorporate prior knowledge of pathways and molecular interactions. We describe a new, substantially redesigned version of our tool Metscape that allows users to enter experimental data for metabolites, genes and pathways and display them in the context of relevant metabolic networks. Metscape 2 uses an internal relational database that integrates data from KEGG and EHMN databases. The new version of the tool allows users to identify enriched pathways from expression profiling data, build and analyze the networks of genes and metabolites, and visualize changes in the gene/metabolite data. We demonstrate the applications of Metscape to annotate molecular pathways for human and mouse metabolites implicated in the pathogenesis of sepsis-induced acute lung injury, for the analysis of gene expression and metabolite data from pancreatic ductal adenocarcinoma, and for identification of the candidate metabolites involved in cancer and inflammation. Metscape is part of the National Institutes of Health-supported National Center for Integrative Biomedical Informatics (NCIBI) suite of tools, freely available at http://metscape.ncibi.org. It can be downloaded from http://cytoscape.org or installed via Cytoscape plugin manager. metscape-help@umich.edu; akarnovs@umich.edu Supplementary data are available at Bioinformatics online.
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            Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA.

            Pathway-level analysis is a powerful approach enabling interpretation of post-genomic data at a higher level than that of individual biomolecules. Yet, it is currently hard to integrate more than one type of omics data in such an approach. Here, we present a web tool 'IMPaLA' for the joint pathway analysis of transcriptomics or proteomics and metabolomics data. It performs over-representation or enrichment analysis with user-specified lists of metabolites and genes using over 3000 pre-annotated pathways from 11 databases. As a result, pathways can be identified that may be disregulated on the transcriptional level, the metabolic level or both. Evidence of pathway disregulation is combined, allowing for the identification of additional pathways with changed activity that would not be highlighted when analysis is applied to any of the functional levels alone. The tool has been implemented both as an interactive website and as a web service to allow a programming interface. The web interface of IMPaLA is available at http://impala.molgen.mpg.de. A web services programming interface is provided at http://impala.molgen.mpg.de/wsdoc. kamburov@molgen.mpg.de; r.cavill@imperial.ac.uk; h.keun@imperial.ac.uk Supplementary data are available at Bioinformatics online.
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              Metatranscriptomic Profiling Reveals Linkages between the Active Rumen Microbiome and Feed Efficiency in Beef Cattle.

              Exploring compositional and functional characteristics of the rumen microbiome can improve the understanding of its role in rumen function and cattle feed efficiency. In this study, we applied metatranscriptomics to characterize the active rumen microbiomes of beef cattle with different feed efficiencies (efficient, n = 10; inefficient, n = 10) using total RNA sequencing. Active bacterial and archaeal compositions were estimated based on 16S rRNAs, and active microbial metabolic functions including carbohydrate-active enzymes (CAZymes) were assessed based on mRNAs from the same metatranscriptomic data sets. In total, six bacterial phyla (Proteobacteria, Firmicutes, Bacteroidetes, Spirochaetes, Cyanobacteria, and Synergistetes), eight bacterial families (Succinivibrionaceae, Prevotellaceae, Ruminococcaceae, Lachnospiraceae, Veillonellaceae, Spirochaetaceae, Dethiosulfovibrionaceae, and Mogibacteriaceae), four archaeal clades (Methanomassiliicoccales, Methanobrevibacter ruminantium, Methanobrevibacter gottschalkii, and Methanosphaera), 112 metabolic pathways, and 126 CAZymes were identified as core components of the active rumen microbiome. As determined by comparative analysis, three bacterial families (Lachnospiraceae, Lactobacillaceae, and Veillonellaceae) tended to be more abundant in low-feed-efficiency (inefficient) animals (P 2 with a P value of <0.05) between two groups. Among them, 30 metabolic pathways and 11 CAZymes were more abundant in the rumen of inefficient cattle, while 2 metabolic pathways and 1 CAZyme were more abundant in efficient animals. These findings suggest that the rumen microbiomes of inefficient cattle have more diverse activities than those of efficient cattle, which may be related to the host feed efficiency variation.IMPORTANCE This study applied total RNA-based metatranscriptomics and showed the linkage between the active rumen microbiome and feed efficiency (residual feed intake) in beef cattle. The data generated from the current study provide fundamental information on active rumen microbiome at both compositional and functional levels, which serve as a foundation to study rumen function and its role in cattle feed efficiency. The findings that the active rumen microbiome may contribute to variations in feed efficiency of beef cattle highlight the possibility of enhancing nutrient utilization and improve cattle feed efficiency through modification of rumen microbial functions.
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                Author and article information

                Contributors
                hajak@dtu.dk
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                14 January 2020
                14 January 2020
                2020
                : 10
                : 255
                Affiliations
                [1 ]ISNI 0000 0001 2181 8870, GRID grid.5170.3, Quantitative Genomics, Bioinformatics and Computational Biology, Department of Applied Mathematics and Computer Science, , Technical University of Denmark, ; Kongens Lyngby, Denmark
                [2 ]ISNI 0000 0001 2163 588X, GRID grid.411247.5, Department of Genetics and Evolution, , Federal University of São Carlos, ; São Carlos, Brazil
                Author information
                http://orcid.org/0000-0003-1082-5535
                http://orcid.org/0000-0001-6294-382X
                Article
                57182
                10.1038/s41598-019-57182-4
                6959238
                31937890
                9c46561d-37e4-4cb7-8b79-1b04b9255f21
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 21 May 2019
                : 23 December 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100004836, Det Frie Forskningsråd (Danish Council for Independent Research);
                Award ID: 4184-00268B
                Award ID: 4184-00268B
                Award ID: 4184-00268B
                Award ID: 4184-00268B
                Award Recipient :
                Categories
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                © The Author(s) 2020

                Uncategorized
                genetics,animal breeding
                Uncategorized
                genetics, animal breeding

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