Ethanol-dependent expression of the NKG2D ligands MICA/B in human cell lines and leukocytes
Bibliographic record
Abstract
Alcohol consumption affects the human immune system, causing a variety of disorders. However, the mechanisms of development of these changes are not fully understood. We hypothesized that ethanol may influence the expression of MICA and MICB, stress-induced molecules capable of regulating the activity of cytotoxic lymphocytes through the interaction with receptor NKG2D, which substantially affects the functionality of cellular immunity. We analyzed the effects of ethanol on MICA/B expression in tumor cell lines and human leukocytes. In the cell line models, ethanol caused different changes in the surface expression of MICA/B; in particular, it induced the translocation of intracellular proteins MICA/B to the cell surface and shedding of MICA (in soluble and microparticle-associated forms) from the plasma membrane. The observed results are not linked with cell death in cultures, taking place only under higher doses of ethanol. Ethanol at physiologically relevant concentrations (and higher) stimulated expression of MICA/B genes in different cell types. The effect of ethanol was more pronounced in hepatocyte line HepG2 compared with hematopoietic cell lines K562, Jurkat, and THP-1. Among the tested leukocytes, the most sensitive to ethanol action were T cells activated ex vivo with IL-2, in which the increase of MICA/B mRNA expression was registered with the smallest dose of ethanol (0.125%). In human monocytes, ethanol may lead to elevations in surface MICA/B levels. Presumably, changes in MICA/B expression caused by ethanol can affect the functions of NKG2D-positive cytotoxic lymphocytes, modulating immune reactions at excessive alcohol consumption.
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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".