Abstract 2119: Transcriptomic Profiling Reveals Therapeutic Potential Of Docosahexaenoic Acid For SARS Coronaviruses Infection Management
Bibliographic record
Abstract
Prior work from our laboratory not only suggested that docosahexaenoic acid (DHA) may upregulate immune response pathways in human monocytes, it also revealed that DHA reduces angiotensin converting enzyme 2 (ACE2), the cellular receptor for SARS coronaviruses (SARS-CoV), in various rat tissues and human cultured cells. Also, DHA was found to inhibit cellular entry of SARS-CoV-2 pseudovirus. Thus, it was hypothesized that DHA has the potential to help manage SARS-CoV infection. RNA-seq was performed on DHA-treated (20 or 125 μM for 8 h) and control human EA.hy926 cells in both the growing and quiescent states. The data were processed by the RSEM-STAR-DESeq2 pipeline, and then subjected to gene set enrichment analysis (GSEA) with clusterProfiler. GSEA revealed that only in quiescent cells, 20 μM DHA downregulated pathways related to SARS-CoV-1/2-host interactions, specifically the processes by which the virus disrupts host protein translation and global mRNA splicing to suppress host defenses. The Reactome term “potential therapeutics for SARS” was positively enriched by both 20 and 125 μM DHA in quiescent cells only, including genes related to nuclear factor erythroid 2-related factor 2 (Nrf2) pathway, interleukin-6 pathway, heat shock proteins, and TBK1 . However, in growing cells, terms related to SARS-CoV-1/2-host interactions were upregulated by DHA. Beyond derailing SARS-CoV via translation machinery and ACE2, DHA was also previously found to concomitantly reduce ACE1 protein levels, thus preserving the ACE1/ACE2 balance. This is significant because the balance of ACE1/ACE2 is important for maintaining renin-angiotensin system homeostasis, a factor critical in the pathogenesis of long COVID. Overall, our findings advance a novel perspective on the therapeutic potential of DHA for managing SARS-CoV infection, via its ability to hinder virus-host interactions. Moreover, the beneficial effects of DHA only occurred in quiescent (healthy) endothelial cells but not growing (dysfunctional) endothelial cells, implying that COVID-19 patients without CVD may be more responsive to DHA treatment compared to patients with underlying CVD. Further in vitro , in vivo , and even clinical studies are required to validate these effects of DHA on SARS-CoV.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".