Identification of Two Novel Human Acyl-CoA Wax Alcohol Acyltransferases
Bibliographic record
Abstract
The esterification of alcohols such as sterols, diacylglycerols, and monoacylglycerols with fatty acids represents the formation of both storage and cytoprotective molecules. Conversely, the overproduction of these molecules is associated with several disease pathologies, including atherosclerosis and obesity. The human acyl-CoA:diacylglycerol acyltransferase (DGAT) 2 gene superfamily comprises seven members, four of which have been previously implicated in the synthesis of di- or triacylglycerol. The remaining 3 members comprise an X-linked locus and have not been characterized. We describe here the expression of DGAT2 and the three X-linked genes in Saccharomyces cerevisiae strains virtually devoid of neutral lipids. All four gene products mediate the synthesis of triacylglycerol; however, two of the X-linked genes act as acyl-CoA wax alcohol acyltransferases (AWAT 1 and 2) that predominantly esterify long chain (wax) alcohols with acyl-CoA-derived fatty acids to produce wax esters. AWAT1 and AWAT2 have very distinct substrate preferences in terms of alcohol chain length and fatty acyl saturation. The enzymes are expressed in many human tissues but predominate in skin. In situ hybridizations demonstrate a differentiation-specific expression pattern within the human sebaceous gland for the two AWAT genes, consistent with a significant role in the composition of sebum.
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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) | 0.002 | 0.001 |
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".