The lipid acyltransferase LYCAT controls phosphatidylinositol‐3,4,5‐trisphosphate (PIP3) signaling
Bibliographic record
Abstract
Phosphatidylinositol‐3,4,5‐trisphospate (PIP3) is a potent signaling lipid that exerts key control over cell growth, survival, adhesion and migration. PIP3 signaling is often disrupted in various cancers, making understanding the regulation of PIP3 signaling an important priority. Much has been learned from the study of phosphorylation of the inositol headgroup of phosphoinositides, such as the phosphorylation of phosphatidylinositol‐4,5‐bisphospate (PIP2) to produce PIP3, as well as negative regulation of PIP3 by phosphoinositide phosphatases such as PTEN. However, much less is known about another key dimension of control of phosphoinositide action, the regulation of the fatty acyl profile of these lipids. Indeed, phosphatidylinositol and phosphoinositides exhibit remarkable selectivity of acyl chains (>50% harboring 1‐stearoyl, 2‐arachidonoyl), an acyl profile quite distinct from other phospholipids. This suggests that control of fatty acyl profile of phosphoinositides may be an important determinant of the function of these lipids that has to date remained largely unexplored. We recently uncovered that the acyltransferase LYCAT is a key regulator of the acyl profile of specific phosphoinositides such as PIP2, thus exerting control over membrane traffic phenomena dependent on these phosphoinositides (Bone LN et al 2017 Mol Biol Cell . 28:161–172). We now examine how LYCAT controls PIP3 signaling and PIP3‐dependent control of cell physiology, including the activation of Akt and control of actin dynamics. We find that LYCAT perturbation impairs the activation of Akt and the activation of a number of Akt substrates, which in turn impacts cell growth and survival. Furthermore, LYCAT perturbation elicits dramatic changes in actin filament morphology, cell migration and cancer cell invasion. These results indicate that control of phosphoinositide acyl chain profile is an important novel dimension of regulation of PIP3 signaling, impacting proliferative, growth and migration signaling, and as such may be a novel dimension to control of cancer cell growth and progression. Support or Funding Information This work was supported by an Ontario Early Researcher Award, a Canada Research Chair Award, and a Natural Sciences and Engineering Research Council Grant to R.J.B, and a Project Grant and New Investigator Salary Award from the Canadian Institutes of Health Research to C.N.A. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".