The Role Of Senolytics In Reducing Senescence Induced By Cannabinoids And Nicotine In Prenatal Human Lung
Bibliographic record
Abstract
Tandem exposure to cannabinoids and nicotine during pregnancy poses significant risks to fetal development and health including low birth weight, preterm birth, and perinatal death. However, the effect of co-exposure with the most prominent cannabinoids found in Cannabis sativa (Trans-Δ-9- tetrahydrocannabinol (THC) and cannabidiol (CBD)) alongside nicotine on fetal lung development is not well understood. This study aims to investigate the combinatorial impact of CBD, THC, and nicotine, on human lung development through investigating cellular senescence and the potential role of senolytics in alleviating these effects. Human fetal lung explants from 10 to 16 weeks gestation were cultured on air-liquid interface and treated with various concentrations of nicotine (1µM), CBD, and THC (5µM, or 2.5µM each when used in combination), for 72 hours. To assess the effect of senolytics, the explants were also treated for 48 hours with a combination of CBD, THC, and nicotine. Subsequently, senolytics, Dasatinib (D, 250nM) and Quercetin (Q, 375nM), were added or not for an additional 24 hours. After the 72-hour treatment, explants were collected and either fixed for immunofluorescence staining (IF) analyses or used for RNA extraction and quantitative reverse transcription PCR (qRT-PCR). Additionally, the supernatants were used to evaluate the expression of the senescence-associated secretory phenotype (SASP), conducted by Eve Technologies Corporation-Canada. This study used Luminex xMAP technology for multiplexed quantification of 96 Human cytokines, chemokines, and growth factors. Our results demonstrated that co-exposure to CBD, THC, and nicotine increases the expression of the senescence markers CDKN1A (0.1163±0.02384, p=0.0094, n=8) and CDKN2A (0.005793±0.002157, p=0.0516, n=8) encoding P21 and P16 respectively. Moreover, the IF staining showed increased expression of the DNA damage marker γH2A.X following treatment with CBD+THC+nicotine (7.375±2.154% vs 2.245±0.8329%, p=0.0604, n=6). Furthermore, the lung explants treated with cannabinoids and nicotine released cytokines and chemokines involved in immune responses and inflammation among others IL-6, IL-8, M-CSF, MCP-3, MIP-1β, TNFα, MIP-3β, and TSLP (p<0.05). The addition of D+Q reduced SASP secretion and showed a decreasing trend in expression of the senescence genes CDKN1A and CDKN2A compared to the triple treatment. Taken together, our results showed that the combinatorial exposure to CBD, THC, and nicotine increases senescence, DNA damage, and the senescence-associated secretory phenotypes (SASP) secretion. The use of senolytics (D+Q) may prove to be a promising therapeutic option, alleviating several of the negative effects induced by the different substances. RO1HL171915-01A1 RO!HL155104 RO1HL158532-03 ROIHL160570 This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".