Plasma membranes as heat stress sensors: From lipid-controlled molecular switches to therapeutic applications
Bibliographic record
Abstract
The classic heat shock (stress) response (HSR) was originally attributed to protein denaturation. However, heat shock protein (Hsp) induction occurs in many circumstances where no protein denaturation is observed. Recently considerable evidence has been accumulated to the favor of the "Membrane Sensor Hypothesis" which predicts that the level of Hsps can be changed as a result of alterations to the plasma membrane. This is especially pertinent to mild heat shock, such as occurs in fever. In this condition the sensitivity of many transient receptor potential (TRP) channels is particularly notable. Small temperature stresses can modulate TRP gating significantly and this is influenced by lipids. In addition, stress hormones often modify plasma membrane structure and function and thus initiate a cascade of events, which may affect HSR. The major transactivator heat shock factor-1 integrates the signals originating from the plasma membrane and orchestrates the expression of individual heat shock genes. We describe how these observations can be tested at the molecular level, for example, with the use of membrane perturbers and through computational calculations. An important fact which now starts to be addressed is that membranes are not homogeneous nor do all cells react identically. Lipidomics and cell profiling are beginning to address the above two points. Finally, we observe that a deregulated HSR is found in a large number of important diseases where more detailed knowledge of the molecular mechanisms involved may offer timely opportunities for clinical interventions and new, innovative drug treatments. This article is part of a Special Issue entitled: Membrane Structure and Function: Relevance in the Cell's Physiology, Pathology and Therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".