Additional file 1 of Ruminal-buccal microbiota transmission and their diagnostic roles in subacute rumen acidosis in dairy goats
Bibliographic record
Abstract
Additional file 1: Fig. S1 Analysis of gene expression in the rumen epithelium. (A) Venn diagram of the identified genes in the CON, LRDSS and HRDSS groups. (B) The numbers of differentially expressed genes (DEGs) in CON vs. LRDSS and LRDST vs. LRDSS. Red and blue indicate upregulated and downregulated genes, respectively. Fig. S2 Ruminal differential genera identified in the comparison among groups of dairy goats varying between SARA susceptibility. (A) The number of ASVs in each rumen samples. (B) The number of filtered sequences in each rumen samples. (C and D) Comparison of ruminal microbial alpha diversity with the Chao1 index (C) and PD index (D) among the CON, LRDSS, LRDST, HRDSS, and HRDST groups. (E) Differential genera selected from the comparison between the LRDST group and the LRDSS group. (F) Differential genera selected from the comparison between the HRDST group and the HRDSS group. * indicates that the difference is significant at P < 0.05, ** indicates that the difference is significant at P < 0.01, *** indicates that the difference is significant at P < 0.001. Fig. S3 The numbers of ASVs and sequences in buccal cavity and tooth. (A-B) The number of ASVs (A) and filtered sequences (B) in each buccal sample. (C and D) The number of ASVs (C) and filtered sequences (D) in each tooth sample. Fig. S4 Buccal differential genera identified in the comparison among groups of dairy goats varying in SARA susceptibility. (A) Differential genera selected from the comparison between the LRDST group and the LRDSS group. (B) Differential genera selected from the comparison between the HRDST group and the HRDSS group. * indicates that the difference is significant at P < 0.05, ** indicates that the difference is significant at P < 0.01, *** indicates that the difference is significant at P< 0.001. (C) The 15 most predictive buccal genera to classify samples of the S group versus the H group were selected by random forest classification analysis. (D) The accuracy of distinguishing the S group from the H group based on buccal Prevotellaceae_UCG-003. Fig. S5 Comparison of tooth microbiota of dairy goats exhibiting SARA occurrence (SARA susceptible) or healthy (control and SARA tolerance) status. (A) The differential genera identified when the comparison between the CON group and SARA (LRDSS and HRDSS) groups was performed. (The genera that gradually increased along the CON, LRDSS and HRDSS groups are highlighted in brown, and the genera that gradually decreased along the CON group, LRDSS group and HRDSS group are highlighted in purple). (B) Differential genera selected from the comparison between the LRDST group and the LRDSS group. (C) Differential genera selected from the comparison between the HRDST group and the HRDSS group. * indicates that the difference is significant with FDR < 0.05, ** indicates that the difference is significant with FDR < 0.01, *** indicates that the difference is significant with FDR < 0.001. (D) Spearman correlation between the common genus-level differences in the bacteria in dairy goats from the CON group and SARA (LRDSS and HRDSS) groups and their rumen fermentation parameters. (E) The accuracy of distinguishing SARA dairy goats from healthy dairy goats based on tooth Bacteroides and Romboutsia. The figures presented from left to right were based on the comparison groups of the CON vs. LRDSS, CON vs. HRDSS, and health vs. SARA. Fig. S6 The identification of the association between oral microbiota and rumen microbiota and the connection between rumen microbiota and genes that affected the occurrence of epithelial inflammation. (A) The correlation among buccal Prevotellaceae_UCG-003, ruminal differential genera and the genes that were differentially expressed in ruminal epithelium based on Spearman's rank correlation coefficient analysis (correlation coefficient > 0.6 and P < 0.05). (B) The affiliation relationship between identified rumen epithelial immune-related differentially expressed genes and their involved GO enrichment terms, the genes showed in this graph were all significantly associated with the identified differential ruminal bacteria. Fig. S7 Comparison of ruminal and oral microbiota. (A) Chao1 index of ruminal and oral (buccal mucosa and tooth) microbiota. * indicates that the difference is significant at P < 0.05, ** indicates that the difference is significant at P < 0.01, *** indicates that the difference is significant at P < 0.001. (B) Principal coordinate analysis (PCoA) of ruminal and oral (buccal mucosa and tooth) microbiota. (C) Average relative abundance of microbiota at the genus level of ruminal and oral (buccal mucosa and tooth) microbiota; those bacteria whose relative abundance was less than 1% were classified as others. (D) Two different microbial types were identified based on the genera of ruminal and oral (buccal mucosa and tooth) sample types. (E) The microbial type distribution was compared between ruminal, buccal and tooth sample types. (F) Average relative abundance of microbiota at the genus level of the two different microbial types; those bacteria whose relative abundance was less than 1% were classified as others. Fig. S8 The oral and ruminal microbiota diversity comparison between the Healthy-R and SARA-R groups. (A-B) The oral Chao1 index (A) and beta diversity (B) were compared between goats from the Healthy-R and SARA-R groups. (C-D) The ruminal Chao1 index (C) and beta diversity (D) were compared between goats from the Healthy-R and SARA-R groups. The Mann-Whitney U test was employed to test microbial alpha diversity differences between the two groups. ANOSIM analysis based on Bray-Curtis distance matrices was used to identify beta diversity differences. Table S1. The ingredients and nutrient composition of the three diets on a dry matter (DM) basis. Table S2. Comparison of rumen LPS and lactate concentrations among the CON, LRDST, LRDSS, HRDST and HRDSS groups. Table S3. Identification of key genera based on calculated degree centrality, closeness centrality, and betweenness centrality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.847 | 0.109 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".