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      Uncovering sperm metabolome to discover biomarkers for bull fertility

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          Abstract

          Background

          Subfertility decreases the efficiency of the cattle industry because artificial insemination employs spermatozoa from a single bull to inseminate thousands of cows. Variation in bull fertility has been demonstrated even among those animals exhibiting normal sperm numbers, motility, and morphology. Despite advances in research, molecular and cellular mechanisms underlying the causes of low fertility in some bulls have not been fully elucidated. In this study, we investigated the metabolic profile of bull spermatozoa using non-targeted metabolomics. Statistical analysis and bioinformatic tools were employed to evaluate the metabolic profiles high and low fertility groups. Metabolic pathways associated with the sperm metabolome were also reported.

          Results

          A total of 22 distinct metabolites were detected in spermatozoa from bulls with high fertility (HF) or low fertility (LF) phenotype. The major metabolite classes of bovine sperm were organic acids/derivatives and fatty acids/conjugates. We demonstrated that the abundance ratios of five sperm metabolites were statistically different between HF and LF groups including gamma-aminobutyric acid (GABA), carbamate, benzoic acid, lactic acid, and palmitic acid. Metabolites with different abundances in HF and LF bulls had also VIP scores of greater than 1.5 and AUC- ROC curves of more than 80%. In addition, four metabolic pathways associated with differential metabolites namely alanine, aspartate and glutamate metabolism, β-alanine metabolism, glycolysis or gluconeogenesis, and pyruvate metabolism were also explored.

          Conclusions

          This is the first study aimed at ascertaining the metabolome of spermatozoa from bulls with different fertility phenotype using gas chromatography-mass spectrometry. We identified five metabolites in the two groups of sires and such molecules can be used, in the future, as key indicators of bull fertility.

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          Most cited references106

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          MetPA: a web-based metabolomics tool for pathway analysis and visualization.

          MetPA (Metabolomics Pathway Analysis) is a user-friendly, web-based tool dedicated to the analysis and visualization of metabolomic data within the biological context of metabolic pathways. MetPA combines several advanced pathway enrichment analysis procedures along with the analysis of pathway topological characteristics to help identify the most relevant metabolic pathways involved in a given metabolomic study. The results are presented in a Google-map style network visualization system that supports intuitive and interactive data exploration through point-and-click, dragging and lossless zooming. Additional features include a comprehensive compound library for metabolite name conversion, automatic generation of analysis report, as well as the implementation of various univariate statistical procedures that can be accessed when users click on any metabolite node on a pathway map. MetPA currently enables analysis and visualization of 874 metabolic pathways, covering 11 common model organisms. Freely available at http://metpa.metabolomics.ca.
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            A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding.

            The predominance of partial least squares-discriminant analysis (PLS-DA) used to analyze metabolomics datasets (indeed, it is the most well-known tool to perform classification and regression in metabolomics), can be said to have led to the point that not all researchers are fully aware of alternative multivariate classification algorithms. This may in part be due to the widespread availability of PLS-DA in most of the well-known statistical software packages, where its implementation is very easy if the default settings are used. In addition, one of the perceived advantages of PLS-DA is that it has the ability to analyze highly collinear and noisy data. Furthermore, the calibration model is known to provide a variety of useful statistics, such as prediction accuracy as well as scores and loadings plots. However, this method may provide misleading results, largely due to a lack of suitable statistical validation, when used by non-experts who are not aware of its potential limitations when used in conjunction with metabolomics. This tutorial review aims to provide an introductory overview to several straightforward statistical methods such as principal component-discriminant function analysis (PC-DFA), support vector machines (SVM) and random forests (RF), which could very easily be used either to augment PLS or as alternative supervised learning methods to PLS-DA. These methods can be said to be particularly appropriate for the analysis of large, highly-complex data sets which are common output(s) in metabolomics studies where the numbers of variables often far exceed the number of samples. In addition, these alternative techniques may be useful tools for generating parsimonious models through feature selection and data reduction, as well as providing more propitious results. We sincerely hope that the general reader is left with little doubt that there are several promising and readily available alternatives to PLS-DA, to analyze large and highly complex data sets.
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              Metabolomics activity screening for identifying metabolites that modulate phenotype

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                Author and article information

                Contributors
                em149@ads.msstate.edu
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                18 September 2019
                18 September 2019
                2019
                : 20
                : 714
                Affiliations
                [1 ]ISNI 0000 0001 0816 8287, GRID grid.260120.7, Department of Animal and Dairy Sciences, , Mississippi State University, ; 4025 Wise Center, Mississippi State, MS 39762 USA
                [2 ]ISNI 0000 0001 2160 0329, GRID grid.8395.7, Department of Animal Sciences, , Federal University of Ceara, ; Fortaleza, Brazil
                [3 ]ISNI 0000 0001 2308 7215, GRID grid.17242.32, Department of Reproduction and Artificial Insemination, , Selcuk University, ; Konya, Turkey
                [4 ]Alta Genetic Inc., Watertown, WI USA
                Author information
                http://orcid.org/0000-0002-8335-5645
                Article
                6074
                10.1186/s12864-019-6074-6
                6749656
                31533629
                29d1e87d-2138-4197-94f3-4ce2fc189927
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 17 October 2018
                : 30 August 2019
                Funding
                Funded by: USDA National Institute of Food and Agriculture
                Award ID: 2017-67016-26507
                Award ID: 2017-67016-26507
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2019

                Genetics
                metabolomics,bovine,spermatozoa,gas chromatography,mass spectrometry
                Genetics
                metabolomics, bovine, spermatozoa, gas chromatography, mass spectrometry

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