Proving Lipid Rafts Exist: Membrane Domains in the Prokaryote Borrelia burgdorferi Have the Same Properties as Eukaryotic Lipid Rafts
Bibliographic record
Abstract
Lipid rafts in eukaryotic cells are sphingolipid and cholesterol-rich, ordered membrane regions that have been postulated to play roles in many membrane functions, including infection. We previously demonstrated the existence of cholesterol-lipid-rich domains in membranes of the prokaryote, B. burgdorferi, the causative agent of Lyme disease [LaRocca et al. (2010) Cell Host & Microbe 8, 331-342]. Here, we show that these prokaryote membrane domains have the hallmarks of eukaryotic lipid rafts, despite lacking sphingolipids. Substitution experiments replacing cholesterol lipids with a set of sterols, ranging from strongly raft-promoting to raft-inhibiting when mixed with eukaryotic sphingolipids, showed that sterols that can support ordered domain formation are both necessary and sufficient for formation of B. burgdorferi membrane domains that can be detected by transmission electron microscopy or in living organisms by Förster resonance energy transfer (FRET). Raft-supporting sterols were also necessary and sufficient for formation of high amounts of detergent resistant membranes from B. burgdorferi. Furthermore, having saturated acyl chains was required for a biotinylated lipid to associate with the cholesterol-lipid-rich domains in B. burgdorferi, another characteristic identical to that of eukaryotic lipid rafts. Sterols supporting ordered domain formation were also necessary and sufficient to maintain B. burgdorferi membrane integrity, and thus critical to the life of the organism. These findings provide compelling evidence for the existence of lipid rafts and show that the same principles of lipid raft formation apply to prokaryotes and eukaryotes despite marked differences in their lipid compositions.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".