Cholesterol, Eukaryotic Lipid Domains, and an Evolutionary Perspective of Transmembrane Signaling
Bibliographic record
Abstract
Transmembrane signaling is essential for complex life forms.Communication across a bilayer lipid barrier is elaborately organized to convey precision and to fine-tune strength.Looking back, the steps that it has taken to enable this seemingly mundane errand are breathtaking, and with our survivorship bias, Darwinian.While this review is to discuss eukaryotic membranes in biological functions for coherence and theoretical footing, we are obliged to follow the evolution of the biological membrane through time.Such a visit is necessary for our hypothesis that constraints posited on cellular functions are mainly via the biomembrane, and relaxation thereof in favor of a coordinating membrane environment is the molecular basis for the development of highly specialized cellular activities, among them transmembrane signaling.We discuss the obligatory paths that have led to eukaryotic membrane formation, its intrinsic ability to signal, and how it set up the platform for later integration of protein-based receptor activation. THE EVOLUTIONARY ORIGIN OF THE EUKARYOTIC MEMBRANELipids Coming of Age P hospholipids are the basic ingredient of bio- logical membranes, usually with a glycerol core, two acyl tails at sn-1 and sn-2 positions, and an sn-3 phosphate.Archaeal lipids use snglycerol-1-phosphate, in contrast to eubacterial and eukaryotic lipids (Koga 2011).In addition, archaeal lipids contain isoprenoid tails attached to glycerol via an ether rather than an ester bond, and some longer phytanyl chains are capped with glycerol at both ends to create a monolayer in place of the bilayer (De Rosa et al. 1983).The stable ether bond and highly branched isoprenoid chains are densely packed, permitting adaptations to extreme environments, and are hence preferred by extremophile archaea (Albers et al. 2000).Ether lipids are mostly absent in eubacteria and eukaryotes.This is known as the lipid divide (Fig. 1; Koga 2014; Mencía 2020).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".