PSIII-18 Identification of microbial interactions and markers associated with Shiga toxin-producing bacteria colonization in the rectum of beef steers
Bibliographic record
Abstract
Abstract This study aimed to identify whether microbial interactions in the rectum contribute to Shiga toxin producing bacteria colonization. In total, 12 rectal digesta samples based on the previously identified Shiga toxin 2 gene (stx2) abundance (DNA) and expression (RNA) in Shiga toxin-producing bacteria (Stx2- group: detectable DNA, n=6; Stx2+ group: detectable DNA and RNA, n = 6) were subjected to microbial profiling using amplicon sequencing. Firmicutes (72.7 ± 2.0 %) and Bacteroidetes (24.6 ± 1.9 %) are the most predominant phyla of rectal microbiota, and no compositional differences were identified between two groups at the phylum level. The Shannon and Chao1 indices weren’t different in rectal digesta microbial communities between two groups. Twenty-four and thirteen taxa were identified to be group-specific genera in microbial communities from Stx2- and Stx2+ group, respectively (2 out of 6, average relative abundance >0.1%). The network analysis indicated 12 and 14 keystone taxa (Generalists, densely connected with other taxa) in microbial communities between Stx2- and Stx2+ groups, respectively. Eight out of 12 and six out of 14 generalists in the Stx2- and Stx2+ group are belonging to group-specific genera, respectively. Generalists belonging to group-specific genera were broadly distributed in Stx2- network while centralized distributed in the Stx2+ network, suggesting the higher stability of the Stx2- network structure in comparison of Stx2+ network computed by the natural connectivity measurement. However, 66 core genera shared by microbial communities between two groups were not classified into network generalists. Overall, our results indicate microbial crosstalks and keystone taxa in microbial communities between two groups differed, suggesting that the microbial interactions rather than the shifts in taxa abundance may be more important affecting host. Moreover, group-specific genera play a vital ecological role in the microbial interactions, indicating the potential for being microbial markers to differentiate Shiga toxin-producing bacteria colonization in beef cattle.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".