Dynamics of the Type III Secretion System Activity of Enteropathogenic Escherichia coli
Bibliographic record
Abstract
UNLABELLED: Type III secretion systems (TTSSs) are employed by pathogens to translocate host cells with effector proteins, which are crucial for virulence. The dynamics of effector translocation, behavior of the translocating bacteria, translocation temporal order, and relative amounts of each of the translocated effectors are all poorly characterized. To address these issues, we developed a microscopy-based assay that tracks effector translocation. We used this assay alongside a previously described real-time population-based translocation assay, focusing mainly on enteropathogenic Escherichia coli (EPEC) and partly comparing it to Salmonella. We found that the two pathogens exhibit different translocation behaviors: in EPEC, a subpopulation that formed microcolonies carried out most of the translocation activity, while Salmonella executed protein translocation as planktonic bacteria. We also noted variability in host cell susceptibility, with some cells highly resistant to translocation. We next extended the study to determine the translocation dynamics of twenty EPEC effectors and found that all exhibited distinct levels of translocation efficiency. Further, we mapped the global effects of key TTSS-related components on TTSS activity. Our results provide a comprehensive description of the dynamics of the TTSS activity of EPEC and new insights into the mechanisms that control the dynamics. IMPORTANCE: EPEC and the closely related enterohemorrhagic Escherichia coli (EHEC) represent a global public health problem. New strategies to combat EPEC and EHEC infections are needed, and development of such strategies requires better understanding of their virulence machinery. The TTSS is a critical virulence mechanism employed by these pathogens, and by others, including Salmonella. In this study, we aimed at elucidating new aspects of TTSS function. The results obtained provide a comprehensive description of the dynamics of TTSS activity of EPEC and new insights into the mechanisms that control these changes. This knowledge sets the stage for further analysis of the system and may accelerate the development of new ways to treat EPEC and EHEC infections. Further, the newly described microscopy-based assay can be readily adapted to study the dynamics of TTSS activity in other pathogens.
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) | 0.000 | 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 | 0.000 |
| 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 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".