Probing the structure, dynamics and regulation of lipid signalling enzymes and their role in human disease
Bibliographic record
Abstract
Lipid phosphoinositides play fundamental roles in virtually all pathways that control a cell's decision to grow, move, divide, and die. Because of this, kinases that phosphorylate phosphoinositide lipids are critically involved in myriad essential functions including growth, development, and membrane trafficking. The mis‐regulation of the activity of phosphoinositide kinases is critical in human diseases, including cancer, primary immunodeficiencies, developmental disorders, and inflammation. Phosphoinositide kinases also play critical roles in mediating bacterial and viral infection, including many potent human pathogens. Inhibitors of parasite phosphoinositide kinases are in development as therapies for both malaria and cryptosporidiosis. Therefore, understanding how phosphoinositide kinases are regulated has implications for the treatment of many devastating human diseases. My research is focused on understanding the molecular basis for regulation of phosphoinositide kinases, and how they are involved in human disease. We have used a synergy of biophysical approaches involving X‐ray crystallography, hydrogen deuterium exchange mass spectrometry, Cryo‐EM, and biochemical assays to study the structure, dynamics, and molecular interactions of these critical signalling enzymes. I will discuss our work on studying the molecular mechanism of how mutations in phosphoinositide 3‐kinases mediate primary immunodeficiencies and cancer, and how viruses manipulate phosphatidylinositol 4‐kinase protein interaction networks to mediate infection. Overall this work is important in understanding how mis‐regulation of phosphoinositide lipid signals mediate disease. We will also discuss how structural information on these enzymes is being leveraged to generate novel small molecule inhibitors of these enzymes as potential therapeutics for cancer, viral infection, and malaria. Support or Funding Information J.E.B. is supported by the Canadian Institute of Health Research (new investigator grant and CIHR open operating grant FRN 142393), a discovery research grant from the Natural Sciences and Engineering Research Council of Canada (NSERC‐2014‐05218), and a Cancer Research Society operating grant (CRS‐22641). This abstract is from the Experimental Biology 2018 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 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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".