Disruption of Lipid Rafts Stimulates Phospholipase D Activity in Human Lymphocytes: Implication in the Regulation of Immune Function
Bibliographic record
Abstract
Recent evidence suggests that phospholipase D (PLD) can be regulated through its association/dissociation to lipid rafts. We show here that modifying lipid rafts either by cholesterol depletion using methyl-beta-cyclodextrin and filipin or by conversion of sphingomyelin to ceramide with exogenous bacterial sphingomyelinase (bSMase) markedly activated the PLD of human PBMC. bSMase was the most potent PLD activator, giving maximal 6- to 7-fold increase in PLD activity. Triton X-100-treated lysates prepared from control PBMC and from bSMase-treated cells were fractionated by centrifugation on sucrose density gradient. We observed that bSMase treatment of the cells induced a larger ceramide increase in raft than in nonraft membranes and displaced both the Src kinase Lck and PLD1 out of the raft fractions. In addition, the three raft-modifying agents markedly inhibited the lymphoproliferative response to mitogenic lectin. To examine further the potential role of PLD activation in the control of lymphocyte responses, we transiently overexpressed either of the PLD1 and PLD2 isoforms in Jurkat cells and analyzed the phorbol ester plus ionomycin-induced expression of IL-2 mRNA, which is one of the early responses of lymphocyte to activation. We observed a 43% decrease of IL-2 mRNA level in Jurkat cells overexpressing PLD1 as compared with mock- or PLD2-transfected cells, which indicates that elevated PLD1, but not PLD2, activity impairs lymphocyte activation. Altogether, the present results support the hypothesis that PLD1 is activated by exclusion from lipid rafts and that this activation conveys antiproliferative signals in lymphoid cells.
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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".