Sialic acid exacerbates gut dysbiosis-associated mastitis through the microbiota-gut-mammary axis by fueling gut microbiota disruption
Bibliographic record
Abstract
Abstract Background Mastitis is one of the most severe diseases in humans and animals, especially on dairy farms. Mounting evidence indicates that gastrointestinal dysbiosis caused by induction of subacute ruminal acidosis (SARA) by high-grain diet consumption and low in dietary fiber is associated with mastitis initiation and development, however, the underlying mechanism remains unknown. Results In the present study, we found that cows with SARA-associated mastitis have altered metabolic profiles in the rumen, with increased sialic acids level in particular. Consumption of sialic acid (SA) in antibiotic-treated mice, but not healthy mice, induced marked mastitis. SA treatment of antibiotic-treated mice also induced mucosal and systemic inflammatory responses, as evidenced by increased colon and liver injuries and several inflammatory markers. In addition, gut dysbiosis caused by antibiotic impaired gut barrier integrity, which was aggravated by SA treatment. SA potentiated serum LPS level caused by antibiotic treatment, leading to increased activation of the TLR4-NF-κB/NLRP3 pathways in the mammary gland and colon. Moreover, SA facilitated gut dysbiosis caused by antibiotic, and especially enhanced Enterobacteriaceae and Akkermansiaceae, which correlated with mastitis parameters. Fecal microbiota transplantation from SA-antibiotic-treated mice mimicked mastitis in recipient mice. In vitro experiments showed that SA prompted Escherichia coli growth and virulence gene expression, leading to higher proinflammatory cytokine production in macrophages. Targeting the inhibition of Enterobacteriaceae by sodium tungstate or treating with the commensal Lactobacillus reuteri alleviated SA-facilitated mastitis. In addition, SARA cows had distinct ruminal microbial structure by the enrichment of SA-utilizing opportunistic pathogenic Moraxellaceae and the depletion of SA-utilizing commensal Prevotellaceae. Treating mice with the specific sialidase inhibitor zanamivir reduced SA production and Moraxellaceae abundance, and improved mastitis in mice caused by ruminal microbiota transplantation from cows with SARA-associated mastitis. Conclusions This study, for the first time, indicates that SA aggravates gut dysbiosis-induced mastitis by promoting gut microbiota disturbance and is regulated by commensal bacteria, indicating the important role of the microbiota-gut-mammary axis in mastitis pathogenesis and suggesting a potential strategy for mastitis intervention based on gut metabolism regulation. Video Abstract
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.111 | 0.005 |
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; both teacher heads agree on what is shown here.
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".