191 Evaluating the Efficacy of Lactobacilli-Based Direct-Fed Microbials (DFM) in reducing Shiga-toxigenic Escherichia coli (STEC) O157:H7 colonization using cell and tissue culture models.
Bibliographic record
Abstract
Abstract Background: STEC O157:H7 is a major global food safety and public health concern, with cattle serving as the primary reservoir. Colonization of STEC O157 in the cattle gastrointestinal tract, particularly the terminal rectum, leads to beef contamination and causing frequent outbreaks. Effective on-farm interventions are essential to reduce STEC transmission. Direct-Fed Microbials (DFMs) are live microorganisms, including beneficial bacteria or yeast, that are directly administered to animals to enhance gut health, reduce pathogenic load, and improve overall performance. Among 14 DFM candidates screened in our previous study, Ligilactobacillus agilis strains L3 and L6 showed the highest in vitro antimicrobial activity, making them promising candidates for reducing STEC O157 colonization. However, there is limited research on their efficacy in reducing STEC O157 attachment to intestinal epithelial cells and tissues. Objective: This study evaluates the potential of L. agilis DFM strains L3 and L6 to reduce STEC O157 colonization using in vitro cell (cattle ileum epithelial cells) and cattle tissue (terminal rectum) culture models. Methodology: STEC O157 strains R508N and R318N were used. Cattle ileum epithelial cells were isolated via enzymatic digestion and mechanical scraping, followed by purification and culture. Terminal rectum tissue cultures were prepared from fresh cattle intestinal tissues. For the attachment assay, epithelial cells were seeded in 24-well plates and exposed to STEC O157 suspensions. Tissue pieces (2.5 cm²) were inoculated with STEC O157 (10⁶ CFU) and incubated for 4 hours. Bacterial adherence was quantified by lysing cells or tissues, followed by plating and colony counting. L. agilis strains L3 and L6 will be applied to these models to assess their ability to reduce STEC colonization. Results: Preliminary results showed STEC O157 effectively colonized both cell and tissue cultures, with attachment levels ranging from 10⁶ to 10⁷ CFU. The next phase involves applying DFM (L3 and L6) to cell and tissue culture models to evaluate their ability to inhibit STEC O157 colonization. The attachment assay will assess DFM impact on STEC attachment to epithelial cells, while tissue models will evaluate colonization reduction in the terminal rectum. The goal is to identify DFM formulations that effectively inhibit STEC colonization, providing a potential intervention strategy for reducing pathogen load in cattle. Conclusions and Expected Outcomes: The expected outcomes of this study include demonstrating the ability of L3 and L6 to reduce STEC O157 attachment to epithelial cells and terminal rectum tissues. By investigating the interactions between STEC O157, DFMs, and the host, this research will provide mechanistic insights that contribute to the development of effective strategies for controlling this pathogen in cattle and mitigating its transmission to humans. The application of L. agilis DFMs shows promising potential in reducing STEC colonization, thereby enhancing food safety and improving public health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".