The <i>Listeria</i> Protein Internalin B Mimics Hepatocyte Growth Factor‐Induced Receptor Trafficking
Bibliographic record
Abstract
Increased hepatocyte growth factor receptor (HGFR) signaling correlates closely with neoplastic invasion and metastatic potential of many human cancers. Hepatocyte growth factor receptor signaling is initiated by binding the physiological ligand HGF or the internalin B (InlB) protein of Listeria monocytogenes. Subsequent degradation of endocytosed HGFR terminates receptor signaling. Previously reported discrepancies in InlB and HGF-induced HGFR signaling could reflect differences in receptor internalization and degradation in response to these distinct ligands. We report that soluble InlB and HGF are mechanistically equivalent in triggering clathrin-dependent endocytosis and lysosomal degradation of HGFR. After internalization, InlB and HGF colocalize with Rab5, EEA1 and the transferrin receptor in classical early endosomes. Hepatocyte growth factor receptor internalization was prevented by overexpression of dominant negative mutants of dynamin 1 and epidermal growth factor phosphorylation substrate 15, but not caveolin 1, the GTPase Arf6 or the cholesterol-chelating drug Nystatin. Thus, HGFR internalization is principally clathrin-mediated and is not regulated by clathrin- independent pathways. Phosphatidylinositol 3-kinase signaling and HGF-regulated tyrosine kinase substrate were not required for ligand-triggered internalization of HGFR but were essential for subsequent lysosomal degradation. Thus, soluble InlB and HGF induce HGFR endocytosis and degradation by indistinguishable mechanisms, suggesting that InlB may be exploited to regulate pathogenic HGFR signaling.
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) | 0.001 | 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".