Understanding and exploiting the response of EHEC O157:H7 to human gastrointestinal chemical signals
Bibliographic record
Abstract
Enterohemorrhagic Escherichia coli (EHEC) is a clinically relevant foodborne pathogen, resulting in over 95,000 cases of EHEC-associated illness and 60 deaths each year in the US alone. Since EHEC is a continuous global issue with new outbreaks constantly occurring, the development of new therapeutic strategies is vital to minimizing the cases of infection seen each year. A key aspect in new drug development is the identification of vulnerabilities in EHEC’s pathogenicity, in particular, during its transit through the human gastrointestinal (GI) tract. As EHEC passes through the human GI tract to its site of colonization in the large intestine, it faces a multitude of host assaults including acute acid stress in the stomach, bile salt stress and cationic antimicrobial peptide exposure in the small intestine, and short chain fatty acid (SCFA) stress in the large intestine. The research carried out in this doctoral dissertation focuses on understanding how EHEC senses chemical cues from the host’s innate immune responses and how this knowledge can be exploited to develop effective antimicrobial strategies. Our findings successfully demonstrate that a novel antimicrobial peptide ameliorates infection in a mouse model of infection by enhancing acid-induced pathogen killing during gastric passage, and that the DNAbinding protein, Dps, plays a significant role in protecting EHEC against peptide killing. Moreover, this research successfully shows that varying concentrations of SCFAs result in differential modulation of EHEC virulence – a finding that contributes to our understanding of the role of diet and commensal flora in host susceptibility to infection. Together the findings of this research demonstrate how the selected innate host defences throughout the human GI tract can be exploited and/or manipulated to effectively prevent infection by the human pathogen EHEC.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".