Transcriptome Analysis Reveals Docosahexaenoic Acid and α-Linolenic Acid Affect Cholesterol Metabolism and Migration of Monocytes via Common and Distinct Gene Pathways
Bibliographic record
Abstract
One of the earliest events in atherosclerotic plaque formation is the migration of monocytes to damaged blood vessels, and the accumulation of cholesterol in monocyte-derived macrophages. The omega-3 fatty acid docosahexaenoic acid (DHA) is known to inhibit this process. While there is limited evidence suggesting α-linolenic acid (ALA) has a similar effect, ALA has not been directly compared to DHA. The primary objective of this study was to compare the gene expression profiles of monocytes that have been exposed to either ALA or DHA and examine the effect of these fatty acids on monocyte cholesterol content and migration in a cell culture model. Transcriptome analysis was performed on total mRNA isolated from human THP-1 monocytes treated with ALA, DHA or vehicle for 48 h. Candidate genes identified via fold change and Ingenuity Pathway Analysis were validated by qPCR. Functional assays to measure total cholesterol content and migration were then performed on monocytes treated with ALA or DHA. Transcriptome analysis identified a series of genes associated with cholesterol metabolism and cell migration altered by ALA and DHA treatment. Changes in mRNA levels for candidate genes were validated by qPCR, with similar expression patterns as in the transcriptome analysis. Based on these data, both fatty acids were predicted to reduce cholesterol synthesis, ALA would increase migration and DHA would have no effect. Functional assays were then performed and revealed that ALA and DHA decreased cholesterol content to a similar extent. Additionally, contrary to our predictions, DHA significantly decreased migration, while ALA had no effect. The results suggest ALA and DHA may influence monocyte migration through distinct gene pathways, while cholesterol metabolism may be regulated by a common mechanism. Furthermore, only DHA treatment reduced monocyte migration in functional assays, while both fatty acids reduced cholesterol content. Due to the critical role of monocyte migration and cholesterol content in the pathophysiology of atherosclerosis, it may be concluded from this study that both DHA and ALA may exert protective effects involving different mechanisms as they relate to individuals at risk for cardiovascular disease. CIHR, NSERC
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".