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      Interrelationship of myo-inositol pathways with systemic metabolic conditions in two strains of high-performance laying hens during their productive life span

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          Abstract

          Adaptation to metabolic challenges is an individual process in animals and human, most likely based on genetic background. To identify novel pathways of importance for individual adaptation to a metabolic challenge such as egg production in laying hens, myo-inositol (MI) metabolism and plasma metabolite profiles during the productive lifespan were examined in two genetically different strains, Lohmann Brown-Classic (LB) and LSL-Classic (LSL) hens. They were housed during the productive lifespan and sampled at 10, 16, 24, 30 and 60 weeks of age. The targeted AbsoluteIDQ p180 Kit was used for metabolite profiling in plasma whereas a MI enzymatic kit and ELISAs were used to quantify tissue MI concentrations and MI key enzymes (IMPase 1 and MIOX), respectively. As major finding, kidney MIOX was differently expressed in LB and LSL hens with higher amounts in LB. The onset of egg laying between week 16 and 24 of life span was associated with a clear change in the metabolite profiles, however LSL hens and LB hens adapt differently. Pearson’s correlation analyses over all hens at all time points indicated that higher expression of MI degrading enzyme MIOX was related to markers indicating metabolic stress.

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          The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research

          Reproducible science requires transparent reporting. The ARRIVE guidelines (Animal Research: Reporting of In Vivo Experiments) were originally developed in 2010 to improve the reporting of animal research. They consist of a checklist of information to include in publications describing in vivo experiments to enable others to scrutinise the work adequately, evaluate its methodological rigour, and reproduce the methods and results. Despite considerable levels of endorsement by funders and journals over the years, adherence to the guidelines has been inconsistent, and the anticipated improvements in the quality of reporting in animal research publications have not been achieved. Here, we introduce ARRIVE 2.0. The guidelines have been updated and information reorganised to facilitate their use in practice. We used a Delphi exercise to prioritise and divide the items of the guidelines into 2 sets, the “ARRIVE Essential 10,” which constitutes the minimum requirement, and the “Recommended Set,” which describes the research context. This division facilitates improved reporting of animal research by supporting a stepwise approach to implementation. This helps journal editors and reviewers verify that the most important items are being reported in manuscripts. We have also developed the accompanying Explanation and Elaboration (E&E) document, which serves (1) to explain the rationale behind each item in the guidelines, (2) to clarify key concepts, and (3) to provide illustrative examples. We aim, through these changes, to help ensure that researchers, reviewers, and journal editors are better equipped to improve the rigour and transparency of the scientific process and thus reproducibility.
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            Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis

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              BioVenn – a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams

              Background In many genomics projects, numerous lists containing biological identifiers are produced. Often it is useful to see the overlap between different lists, enabling researchers to quickly observe similarities and differences between the data sets they are analyzing. One of the most popular methods to visualize the overlap and differences between data sets is the Venn diagram: a diagram consisting of two or more circles in which each circle corresponds to a data set, and the overlap between the circles corresponds to the overlap between the data sets. Venn diagrams are especially useful when they are 'area-proportional' i.e. the sizes of the circles and the overlaps correspond to the sizes of the data sets. Currently there are no programs available that can create area-proportional Venn diagrams connected to a wide range of biological databases. Results We designed a web application named BioVenn to summarize the overlap between two or three lists of identifiers, using area-proportional Venn diagrams. The user only needs to input these lists of identifiers in the textboxes and push the submit button. Parameters like colors and text size can be adjusted easily through the web interface. The position of the text can be adjusted by 'drag-and-drop' principle. The output Venn diagram can be shown as an SVG or PNG image embedded in the web application, or as a standalone SVG or PNG image. The latter option is useful for batch queries. Besides the Venn diagram, BioVenn outputs lists of identifiers for each of the resulting subsets. If an identifier is recognized as belonging to one of the supported biological databases, the output is linked to that database. Finally, BioVenn can map Affymetrix and EntrezGene identifiers to Ensembl genes. Conclusion BioVenn is an easy-to-use web application to generate area-proportional Venn diagrams from lists of biological identifiers. It supports a wide range of identifiers from the most used biological databases currently available. Its implementation on the World Wide Web makes it available for use on any computer with internet connection, independent of operating system and without the need to install programs locally. BioVenn is freely accessible at .
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                Author and article information

                Journal
                Scientific Reports
                Sci Rep
                Springer Science and Business Media LLC
                2045-2322
                December 2021
                February 25 2021
                December 2021
                : 11
                : 1
                Article
                10.1038/s41598-021-84169-x
                3abfa08d-db17-4eb8-889d-60cce7ac07c6
                © 2021

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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