Host miRNAs regulate Escherichia coli O157 mucosal colonization through host-mucosa-attached microbiota interactions in calves
Bibliographic record
Abstract
INTRODUCTION: Host responses to pathogen colonization are central to understanding host homeostasis dynamics. Here, we used Shiga toxin (Stx)-producing Escherichia coli (STEC) O157 as an example to illustrate how pathogen colonization alters host-microbiome interactions and stimulates host responses. The STEC O157 is a critical foodborne pathogen, and cattle are the major asymptotic carrier with rectal anal junction (RAJ) being the major colonization site, leading to the transmission of this organism through the production chain. Therefore, this study leverages the multi-omics to evaluate host mechanisms to STEC O157 and to illustrate how mucosa-attached microbiome together with host miRNAs respond to the colonization of STEC O157. RESULTS: The calf model was orally challenged with E. coli O157 with and without Stx2a during the 30-day trial. Mucosa-attached microbiome analysis revealed that mucosal E. coli O157 colonization limited niche occupancy of mucosa-attached microbiota regardless of the presence or absence of Stx2a. The production of Stx2a did not induce proper local host mRNA responses but miRNA profiles were more responsive to this virulent factor during high fecal shedding. The shift of toll-like receptor (TLR) expressions together with Stx2a production possibly underlined varied miRNAome-mucosa-attached microbiota interactions. For instance, during the high fecal shedding, the increased expression of TLR2 promoted bta-miR-181b mediated host functionality, a response that was possibly blocked by Stx2a. Decreased fecal O157 shedding promoted activation of TLR4-stimulated host responses, which were coregulated by multiple miRNAs (i.e. bta-miR-146a and-184) and mucosa-attached microbes. CONCLUSION: Host mechanisms regulating STEC O157 colonization are complex interplay among mucosa-attached microbiota and host miRNAs where virulence factors could modulate such crosstalk and cause differential host responses, highlighting the importance of host-microbiome-pathogen virulence factor interactions for pathogen colonization process. 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".