Localization of alpha‐actinin‐4 during infections by actin remodeling bacteria
Bibliographic record
Abstract
Bacterial pathogens cause disease by subverting the structure and function of their target host cells. Several foodborne agents such as Listeria monocytogenes (L. monocytogenes), Shigella flexneri (S. flexneri), Salmonella enterica serovar Typhimurium (S. Typhimurium) and enteropathogenic Escherichia coli (EPEC) manipulate the host actin cytoskeleton to cause diarrheal (and systemic) infections. During infections, these invasive and adherent pathogens hijack the actin filaments of their host cells and rearrange them into discrete actin-rich structures that promote bacterial adhesion (via pedestals), invasion (via membrane ruffles and endocytic cups), intracellular motility (via comet/rocket tails) and/or intercellular dissemination (via membrane protrusions and invaginations). We have previously shown that actin-rich structures generated by L. monocytogenes contain the host actin cross-linker α-actinin-4. Here we set out to examine α-actinin-4 during other key steps of the L. monocytogenes infectious cycle as well as characterize the subcellular distribution of α-actinin-4 during infections with other model actin-hijacking bacterial pathogens (S. flexneri, S. Typhimurium and EPEC). Although α-actinin-4 is absent at sites of initial L. monocytogenes invasion, we show that it is a new component of the membrane invaginations formed during secondary infections of neighboring host cells. Importantly, we reveal that α-actinin-4 also localizes to the major actin-rich structures generated during cell culture infections with S. flexneri (comet/rocket tails and membrane protrusions), S. Typhimurium (membrane ruffles) and EPEC (pedestals). Taken together, these findings suggest that α-actinin-4 is a host factor that is exploited by an assortment of actin-hijacking bacterial pathogens.
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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".