Salmonella exploits LRRK2-dependent plasma membrane dynamics to invade host cells
Bibliographic record
Abstract
Salmonella utilizes type 3 secreted effector proteins to induce plasma membrane (PM) perturbations during invasion of host cells1. The effectors drive mobilization of host membranes to generate cell surface ruffles, followed by invagination and scission of the PM to generate Salmonella-containing vacuoles (SCVs)2. Here, we show that LRRK2 kinase generates membrane reservoirs exploited by Salmonella during invasion. The reservoirs are tubular compartments associated with the PM under basal conditions and are formed through the phosphorylation of RAB10 GTPase by LRRK2. Mobilization of membrane reservoirs to generate invasion ruffles mediates delivery of phosphorylated RAB10 to invasion sites. Subsequently, RAB10 dephosphorylation is required for its inactivation by a bacterial GTPase activating protein and subsequent scission of the PM. RAB10 dephosphorylation is mediated by a TLR4/PIEZO1/TMEM16F-dependent pathway and is inhibited by hyperactive variants of LRRK2. Our findings reveal how Salmonella exploits LRRK2-dependent PM dynamics during invasion and provide new insight into how LRRK2 variants can protect against bacterial infection3,4. Zhu et al show that LRRK2 kinase promotes invagination of the plasma membrane to create ‘reservoirs’, which dynamically store or release excess membrane. Salmonella can exploit these membrane reservoirs during invasion of host cells by controlling the activity of LRRK2.
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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".