Host Immune Selection of Rumen Bacteria through Salivary Secretory IgA
Bibliographic record
Abstract
The rumen microbiome is integral to efficient production in cattle and shows strong host specificity, yet little is known about what host factors shape rumen microbial composition. Secretory immunoglobulin A (SIgA) is produced in large amounts in the saliva, can coat both commensal and pathogenic microbes within the gut, and presents a plausible mechanism of host specificity. However, the role salivary SIgA plays in commensal bacteria selection in ruminants remains elusive. The main objectives of this study were to develop an immuno-affinity benchtop method to isolate SIgA-tagged microbiota and to determine if salivary SIgA preferentially binds selected bacteria. We hypothesized that SIgA-tagged bacteria would differ from total bacteria, thus supporting a potential host-derived mechanism in commensal bacterial selection. Whole rumen (n = 9) and oral secretion samples (n = 10) were incubated with magnetic beads conjugated with anti-secretory IgA antibodies to enrich SIgA-tagged microbiota. Microbial DNA from the oral secretion, whole rumen, SIgA-tagged oral secretion, and SIgA-tagged rumen was isolated for amplicon sequencing of V1-V3 region of 16S rDNA genes. Whole rumen and oral secretion had distinctive (P < 0.05) bacterial compositions indicated by the NMDS plot using Euclidean distance metrics. The SIgA-tagged microbiota from rumen and oral secretion had similar abundance of Bacteroidetes, Actinobacteria, Fibrobacter, candidate phyla TM7, and Tenericutes and are clustered tightly. Composition of SIgA-tagged oral secretion microbiota was more similar to whole rumen microbiota than whole oral secretion due to enrichment of rumen bacteria (Lachnospiraceae) and depletion of oral taxa (Streptococcus, Rothia, Neisseriaceae, and Lactobacillales). In conclusion, SIgA-tagged oral secretion microbiota had an increased resemblance to whole rumen microbiota, suggesting salivary SIgA-coating may be one host-derived mechanism impacting commensal colonization. Further studies, to explore the variations in antibody affinity between different animals as a driver of microbial composition are warranted.
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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 | 0.000 | 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".