Defining how oncogenic and developmental mutations of <i>PIK3R1</i> alter the regulation of class IA phosphoinositide 3-kinases
Bibliographic record
Abstract
Summary The class IA PI3Ks are key signalling enzymes composed of a heterodimer of a p110 catalytic subunit and a p85 regulatory subunit, with PI3K mutations being causative of multiple human diseases including cancer, primary immunodeficiencies, and developmental disorders. Mutations in the p85α regulatory subunit encoded by PIK3R1 can both activate PI3K through oncogenic truncations in the iSH2 domain, or inhibit PI3K through developmental disorder mutations in the cSH2 domain. Using a combined biochemical and hydrogen deuterium exchange mass spectrometry approach we have defined the molecular basis for how these mutations activate both the p110α/p110δ catalytic subunits. We find that the oncogenic Q572* truncation of PIK3R1 disrupts all inhibitory inputs, with p110α being hyper-activated compared to p110δ. In addition, we find that the R649W mutation in the cSH2 of PIK3R1 decreases sensitivity to activation by receptor tyrosine kinases. This work reveals novel insight into isoform specific regulation of p110s by p85α. Highlights - An oncogenic variant of p85α, Q572*, leads to hyper-activation of p110α compared to p110δ - HDX-MS revealed that Q572* leads to disruption of all inhibitory interfaces in p110α - A SHORT syndrome mutation in p85α leads to decreased sensitivity to RTKs for p110α/δ
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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".