Rac guanine nucleotide exchange factors promoting Lgl1 phosphorylation in glioblastoma
Bibliographic record
Abstract
ABSTRACT The protein Lgl1 has key roles in the regulation of cell polarity. We have shown that Lgl1 is inactivated by hyperphosphorylation in glioblastoma as a consequence of PTEN loss and aberrant activation of the PI 3-kinase pathway; this contributes to glioblastoma pathogenesis both by promoting invasion and repressing glioblastoma cell differentiation. Lgl1 is phosphorylated by atypical protein kinase C in a complex with Par6 and activated Rac. Here we have investigated the role of specific Rac guanine nucleotide exchange factors in Lgl1 hyperphosphorylation in glioblastoma. We used CRISPR/Cas9 to knockout PREX1, a PI 3-kinase pathway-responsive Rac guanine nucleotide exchange factor, in patient-derived glioblastoma cells. Knockout cells had reduced Lgl1 phosphorylation which could be reversed by re-expressing PREX1. PREX1 knockout cells showed reduced motility and an altered phenotype suggestive of partial neuronal differentiation; consistent with this, RNA-seq analyses identified sets of PREX1-regulated genes associated with changes in cell motility and neuronal differentiation. PREX1 knockout in glioblastoma cells from a second patient did not affect Lgl1 phosphorylation. These cells overexpressed a short isoform of the Rac guanine nucleotide exchange factor TIAM1; knockdown of TIAM1 in PREX1-knockout cells from this patient reduced Lgl1 phosphorylation. These data show that PREX1 links aberrant PI 3-kinase signaling to Lgl1 phosphorylation in glioblastoma, but that TIAM1 can also promote Lgl phosphorylation in a subset of patients. While this shows redundant mechanisms for Lgl1 phosphorylation, PREX1 appears to have a non-redundant role in glioblastoma cell motility, as this was impaired in PREX1 knockout cells from both patients.
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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".