Distinct effects of α-linolenic acid and docosahexaenoic acid on the expression of genes related to cholesterol metabolism and the response to infection in THP-1 monocytes and immune cells of obese humans
Bibliographic record
Abstract
BACKGROUND: Monocytes play a large role in chronic inflammatory conditions such as obesity, atherosclerosis and infection. Marine-derived omega-3 fatty acids such as docosahexaenoic acid (DHA) beneficially alter immune function and attenuate chronic inflammation in part by modifying gene expression. Comparisons with plant-derived omega-3 α-linolenic acid (ALA) on immune cell gene expression and function are limited. METHODS: Transcriptome analysis was performed on THP-1 human monocytes treated with ALA, DHA or vehicle for 48 hr using fold change analysis, principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), variable importance analysis (VIP), and ingenuity pathway analysis (IPA). Candidate genes were validated by qPCR. Functional assays evaluated the transcriptomic predictions. Expression of candidate transcripts identified in THP-1 cells were examined in PBMC from clinical trial (OXBIO; NCT03583281) participants consuming ALA- or DHA-rich oil supplements. FINDINGS: ALA and DHA-treated monocytes presented distinct transcriptomic profiles as per VIP and PLS-DA. Both fatty acids were predicted to reduce cellular cholesterol content, while ALA would uniquely increase response to infection and chemotactic signals. Functional assays revealed ALA and DHA decreased cholesterol content. DHA significantly decreased the response to infection and chemotaxis, but ALA had no effect. Candidate transcripts responded similarly in PBMC from n-3 PUFA supplemented women with obesity. CONCLUSION: ALA and DHA differentially alter the transcription profiles and functions associated with the response to infection, chemotaxis, and cholesterol metabolism in mononuclear immune cells. Thus, they may uniquely affect related disease processes contributing to obesity, atherosclerosis, and the response to infection.
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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) | 0.001 | 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".