Journal: Animal Microbiome

Article Title: Integrating uterine microbiome and metabolome to advance the understanding of the uterine environment in dairy cows with metritis

doi: 10.1186/s42523-024-00314-7

Figure Lengend Snippet: Comparison of uterine microbiome at a genus level at calving ( A , B , C ) and on the day of metritis diagnosis (3, 7, or 10 days after calving; D , E , F ) between dairy cows that developed metritis (MET; orange; n = 52) and dairy cows that did not develop metritis (NoMET; blue; n = 52). The uterine microbiome was identified by amplification of the V4 hypervariable region of the bacterial/archaeal 16 S rRNA. Panel A and D: results from principal coordinate analyses with Bray-Curtis distances at calving and at metritis diagnosis, respectively. Percentages within each principal component (PCo) correspond to the percentage of variation explained by the component. The ellipses correspond to 95% confidence intervals. P -values correspond to permutational analysis of variance (PERMANOVA) based on Bray-Curtis distances with 9,999 permutations including the effect of metritis (MET vs. NoMet), parity (multiparous vs. primiparous) and their interaction. Full models also included the effects of calving related disorders (CRD; calving P = 0.89; diagnosis P = 0.66), interaction between metritis and CRD (calving P = 0.90; diagnosis P = 0.97), interaction between parity and CRD (calving P = 0.22; diagnosis P = 0.33), and interaction between metritis, parity, and CRD (calving P = 0.32; diagnosis P = 0.53). The model on the day of metritis diagnosis also contained the effect of day of metritis diagnosis (d3, d7, d10; P = 0.20); Panel B, C, E, F: individual bacteria genera comparison as relative abundance ( B , E ) and as estimated counts ( C , F ) at calving and at metritis diagnosis, respectively, between cows that developed metritis and cows that did not develop metritis. Bacteria genera with less than 1% relative abundance were grouped together as “Other”. Significance was tested using Wilcoxon tests with Bonferroni corrections for multiple testing. Effect size was tested using linear discriminant analysis effect size (LEfSe). Circles represent median and lines crossing circles horizontally represent the interquartile range. Asterisks correspond to adjusted P < 0.05. Estimated bacterial counts were calculated multiplying the total bacterial 16 S rRNA by the relative abundance of each bacteria genus. Figures were created using the ggplot2 package of Rstudio Version 2023.06.1 + 524 (RStudio, PBC, Boston, MA)

Article Snippet: Figures were created using the ggplot2 package of Rstudio Version 2023.06.1 + 524 (RStudio, PBC, Boston, MA) Cows that developed metritis had greater Shannon ( P < 0.01; Supplemental Figure C) and Simpson ( P < 0.01; Supplemental Figure D) indexes at the day of metritis diagnosis compared with cows that did not develop metritis.

Techniques: Comparison, Amplification, Bacteria

Journal: Animal Microbiome

Article Title: Integrating uterine microbiome and metabolome to advance the understanding of the uterine environment in dairy cows with metritis

doi: 10.1186/s42523-024-00314-7

Figure Lengend Snippet: Comparison of uterine metabolome at calving ( A , B ) and on the day of metritis diagnosis (3, 7, or 10 days after calving; C , D ) between dairy cows that developed metritis (MET; orange; n = 52) and dairy cows that did not develop metritis (NoMET; blue; n = 52). The uterine metabolome was identified by gas chromatography time-of-flight mass spectrometry. Panel A and C: results from partial least squares - discriminant analysis (PLS-DA) at calving and at metritis diagnosis, respectively. Percentages within each principal component (PCo) correspond to the percentage of variation explained by the component. The ellipses correspond to 95% confidence intervals. P -values correspond to permutational analysis of variance (PERMANOVA) based on Euclidean distances with 9,999 permutations including the effect of metritis (MET vs. NoMet), parity (multiparous vs. primiparous) and their interaction. Full models also included the effects of calving related disorders (CRD; calving P = 0.31; diagnosis P = 0.73), interaction between metritis and CRD (calving P = 0.30; diagnosis P = 0.40), interaction between parity and CRD (calving P = 0.12; diagnosis P = 0.37), and interaction between metritis, parity, and CRD (calving P = 0.31; diagnosis P = 0.49). The model on the day of metritis diagnosis also contained the effect of day of metritis diagnosis (d3, d7, d10; P < 0.01); Panel B and D: top 10 metabolites sorted by effect size resulting from individual metabolite comparisons at calving and at metritis diagnosis, respectively, between cows that developed metritis and cows that did not develop metritis. Significance was tested using Wilcoxon tests with Bonferroni corrections for multiple testing. Effect size was tested using linear discriminant analysis effect size (LEfSe). Circles represent median and lines crossing circles horizontally represent the interquartile range. Asterisks correspond to adjusted P < 0.05. 3–3.glutaric acid, 3-hydroxy-3-methylglutaric acid; 3–4.propionic acid, 3-(4-hydroxyphenyl) propionic acid; 4 h.phenylacetic acid, 4-hydroxyphenylacetic acid. Figures were created using the ggplot2 package of Rstudio Version 2023.06.1 + 524 (RStudio, PBC, Boston, MA)

Article Snippet: Figures were created using the ggplot2 package of Rstudio Version 2023.06.1 + 524 (RStudio, PBC, Boston, MA) Cows that developed metritis had greater Shannon ( P < 0.01; Supplemental Figure C) and Simpson ( P < 0.01; Supplemental Figure D) indexes at the day of metritis diagnosis compared with cows that did not develop metritis.

Techniques: Comparison, Gas Chromatography, Mass Spectrometry