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. 2024 Feb 5;12:20. doi: 10.1186/s40168-023-01734-4

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© The Author(s) 2024

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

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Fig. 6 — Untargeted metabolomic analysis of M-CON and M-PEDV piglet gut contents. A Partial least squares discrimination analysis (PLS-DA) (n=8/group). B Correlation of differentially abundant metabolites (top 30) with a significance of P<0.05. C Volcano plot analysis of differentially abundant metabolites with a threshold of VIP > 1.0, FC > 1.5, or FC < 0.667, and P-value < 0.05. D Metastats analysis counted the significantly up and downregulated differential metabolites (top 8). E Functional comparative analysis of metabolic components in the KEGG database (top 20). F Hierarchical clustering analysis of metabolic expression patterns. G Correlation analysis between macrogenomics and metabolomics based on Pearson correlation coefficient. The results are presented as the means ± SD, and statistical significance was calculated by one-way ANOVA. ****P < 0.0001