Immunological Quantitation and Localization of ACAT-1 and ACAT-2 in Human Liver and Small Intestine
Bibliographic record
Abstract
By using specific anti-ACAT-1 antibodies in immunodepletion studies, we previously found that ACAT-1, a 50-kDa protein, plays a major catalytic role in the adult human liver, adrenal glands, macrophages, and kidneys but not in the intestine. Acyl-coenzyme A:cholesterol acyltransferase (ACAT) activity in the intestine may be largely derived from a different ACAT protein. To test this hypothesis, we produced specific polyclonal anti-ACAT-2 antibodies that quantitatively immunodepleted human ACAT-2, a 46-kDa protein expressed in Chinese hamster ovary cells. In hepatocyte-like HepG2 cells, ACAT-1 comprises 85-90% of the total ACAT activity, with the remainder attributed to ACAT-2. In adult intestines, most of the ACAT activity can be immunodepleted by anti-ACAT-2. ACAT-1 and ACAT-2 do not form hetero-oligomeric complexes. In differentiating intestinal enterocyte-like Caco-2 cells, ACAT-2 protein content increases by 5-10-fold in 6 days, whereas ACAT-1 protein content remains relatively constant. In the small intestine, ACAT-2 is concentrated at the apices of the villi, whereas ACAT-1 is uniformly distributed along the villus-crypt axis. In the human liver, ACAT-1 is present in both fetal and adult hepatocytes. In contrast, ACAT-2 is evident in fetal but not adult hepatocytes. Our results collectively suggest that in humans, ACAT-2 performs significant catalytic roles in the fetal liver and in intestinal enterocytes.
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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 | 0.001 | 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 | 0.001 |
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".