Multi-omic characterization of the sow colostrum and milk microbiome and proteome
Bibliographic record
Abstract
Abstract Sow colostrum and milk provide essential nutrients, immune protection, and one of the earliest microbial exposures for piglets. However, the microbial composition, functional potential, and host interactions of these mammary secretions remain poorly characterized. Here, we combined culturomics, metagenomics, and proteomics to comprehensively characterize the microbiome and proteome of sow colostrum and milk collected at farrowing and at 7 and 21 days postpartum. We recovered 132 bacterial isolates representing at least 42 species, including 15 putative novel taxa. These isolates included both potentially pathogenic species such as Sarcina perfringens and Streptococcus suis and potentially beneficial bacterial species like Lactobacillus amylovorus and Lactiplantibacillus plantarum . The microbial composition and functional potential shifted significantly as the milk matured, with L. amylovorus , Limosilactobacillus reuteri , and Rothia spp. among the most relatively abundant taxa. Several antimicrobial resistance genes, including erm (C), tet(K) , tet (M), lnu(A) , poxtA , and fexB , were identified on contigs encoding plasmid replicons in the isolates, indicating potential for horizontal gene transfer. Functional annotation of isolate genomes indicated broad carbohydrate-active enzyme (CAZyme) repertoires, including those conferring β-galactosidase activity and the capacity to metabolize milk oligosaccharides. The colostrum and milk proteome also shifted during lactation, reflecting declining immune-related proteins and increasing metabolic and structural proteins. Correlations between specific microbial taxa and host proteins, including Rothia spp. and immune proteins or glycoproteins, suggested potential host–microbe interactions during lactation. Together, these findings provide a multi-omic perspective on how mammary microbiome dynamics and host responses during lactation may influence neonatal microbial colonization and health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".