Quantitative Mass Spectrometry Identifies Novel Host Binding Partners for Pathogenic <i>Escherichia coli</i> Type III Secretion System Effectors
Bibliographic record
Abstract
Enteropathogenic and enterohemorrhagic Escherichia coli cause enteric diseases resulting in significant morbidity and mortality worldwide. These pathogens remain extracellular and translocate a set of type III secreted effector proteins into host cells to promote bacterial virulence. Effectors manipulate host cell pathways to facilitate infection by interacting with a variety of host targets, yet the binding partners and mechanism of action of many effectors remain elusive. We performed a mass spectrometry screen to identify host targets for a library of effectors. We found five known effector targets and discovered four novel interactions. Interestingly, we identified multiple effectors that interacted with the microtubule associated protein, ensconsin. Using co-immunoprecipitations, we confirmed that NleB1 and EspL interacted with ensconsin in a region that corresponded to its microtubule binding domain. Ensconsin is an essential cofactor of kinesin-1 that is required for intracellular trafficking, and we demonstrated that intracellular trafficking was severely disrupted during wild type EPEC infections but not during infections with ΔnleB1 or ΔespL mutants. Our findings demonstrate the efficacy of quantitative proteomics for identifying effector-host protein interactions and suggest that vesicular trafficking is a crucial cellular process that may be targeted by NleB1 and EspL through their interaction with ensconsin.
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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 | 0.000 |
| 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) | 0.001 | 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".