Dolutegravir induces endoplasmic reticulum stress at the blood–brain barrier
Bibliographic record
Abstract
Abstract Dolutegravir (DTG)‐based antiretroviral therapy is the contemporary first‐line therapy to treat HIV infection. Despite its efficacy, mounting evidence has suggested a higher risk of neuropsychiatric adverse effect (NPAE) associated with DTG use, with a limited understanding of the underlying mechanisms. Our laboratory has previously reported a toxic effect of DTG but not bictegravir (BTG) in disrupting the blood–brain barrier (BBB) integrity. The current study aimed to investigate the underlying mechanism of DTG toxicity. Primary cultures of mouse brain microvascular endothelial cells were treated with DTG and BTG at therapeutically relevant concentrations. RNA sequencing, qPCR, western blot analysis, and cell stress assays (Ca 2+ flux, H2DCFDA, TMRE, MTT) were applied to assess the results. The gene ontology (GO) analysis revealed an enriched transcriptome signature of endoplasmic reticulum (ER) stress following DTG treatment. We demonstrated that therapeutic concentrations of DTG but not BTG activated the ER stress sensor proteins (PERK, IRE1, p‐IRE1) and downstream ER stress markers (eIF2α, p‐eIF2α, Hspa5 , Atf4 , Ddit3 , Ppp1r15a , Xbp1 , spliced‐Xbp1 ). In addition, DTG treatment resulted in a transient Ca 2+ flux, an aberrant mitochondrial membrane potential, and a significant increase in reactive oxygen species in treated cells. Furthermore, we found that prior treatment with ER sensor or ER stress inhibitors significantly mitigated the DTG‐induced downregulation of tight junction proteins (Zo‐1, Ocln, Cldn5) and elevation of pro‐inflammatory cytokines and chemokines ( Il6 , Il23a , Il12b , Cxcl1 , Cxcl2 ). The current study provides valuable insights into DTG‐mediated cellular toxicity mechanisms, which may serve as a potential explanation for DTG‐associated NPAEs in the clinic.
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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 | 0.001 |
| 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".