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      Optimum ratio of dietary protein and carbohydrate that maximises lifespan is shared among related insect species

      brief-report
      1 , 2 ,
      Aging Cell
      John Wiley and Sons Inc.
      ageing, ecological specialisation, precision nutrition

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          Abstract

          Animals often regulate the intake and quantity of nutrients to maximise fitness through life‐history traits such as lifespan, but we still lack a proper understanding of how specific nutrients influence these traits. Here, I developed an algorithm which allowed me to create a nutrient‐specific database from literature data, and investigated how the requirements of protein (P) and carbohydrate (C) needed to maximise lifespan evolved across nine insect species. I found moderate evidence of a phylogenetic signal on the optimal ratio of protein to carbohydrate ratio (PC ratio) that maximised lifespan, suggesting that optimal PC ratio for lifespan could have evolved non‐independently among related species. I also found evidence for weak‐to‐strong sex‐specific optimal PC ratios for lifespan, suggesting that sex‐specific nutritional needs to maximise lifespan can emerge and persist in some species. Although limited in the number of species, the approach adopted here is portable to experiments with n number of nutrients and, thus, can be used in complex comparative precision nutrition studies for insights into the evolution of animal nutrition.

          Abstract

          Does one diet feed them all? Morimoto developed an algorithm to study whether the protein and carbohydrate ratios (PC ratios) that maximise lifespan are shared among related insect species. Related species likely share optimum PC ratios for lifespan. There is evidence of sex‐specific optimal PC ratios, indicating nutritional trade‐offs between males and females in the same species. Such sex‐specific trade‐off may also be shared among related species.

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          Most cited references73

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          phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data

          Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.
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                Author and article information

                Contributors
                juliano.morimoto@abdn.ac.uk
                Journal
                Aging Cell
                Aging Cell
                10.1111/(ISSN)1474-9726
                ACEL
                Aging Cell
                John Wiley and Sons Inc. (Hoboken )
                1474-9718
                1474-9726
                13 December 2023
                March 2024
                : 23
                : 3 ( doiID: 10.1111/acel.v23.3 )
                : e14067
                Affiliations
                [ 1 ] Institute of Mathematics, University of Aberdeen, King's College Aberdeen UK
                [ 2 ] Programa de Pós‐graduação em Ecologia e Conservação Universidade Federal do Paraná Curitiba Brazil
                Author notes
                [*] [* ] Correspondence

                Juliano Morimoto, Institute of Mathematics, University of Aberdeen, King's College, Aberdeen AB24 3FX, UK.

                Email: juliano.morimoto@ 123456abdn.ac.uk

                Author information
                https://orcid.org/0000-0003-3561-1920
                Article
                ACEL14067 ACE-23-0794-SC.R1
                10.1111/acel.14067
                10928577
                38093527
                c416f8f5-109b-4ed0-a8b9-0d0f260c5d7b
                © 2023 The Authors. Aging Cell published by Anatomical Society and John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 21 November 2023
                : 12 October 2023
                : 27 November 2023
                Page count
                Figures: 2, Tables: 1, Pages: 9, Words: 6636
                Funding
                Funded by: Biotechnology and Biological Sciences Research Council , doi 10.13039/501100000268;
                Award ID: BB/V015249/1
                Categories
                Short Communication
                Short Communication
                Custom metadata
                2.0
                March 2024
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.9 mode:remove_FC converted:12.03.2024

                Cell biology
                ageing,ecological specialisation,precision nutrition
                Cell biology
                ageing, ecological specialisation, precision nutrition

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