SBDS-deficiency results in deregulation of reactive oxygen species leading to increased cell death and decreased cell growth
Bibliographic record
Abstract
BACKGROUND: Shwachman-Diamond syndrome (SDS) is characterized by reduced hematopoietic and exocrine pancreatic cell numbers and a marked propensity for leukemia. Most patients have mutations in the SBDS gene. We previously reported that SBDS-deficient cells overexpress Fas, undergo accelerated spontaneous and Fas-mediated apoptosis and grow slowly. However the mechanism of how SBDS regulates apoptosis remains unknown. Several studies have shown that reactive oxygen species (ROS) regulate cell growth and spontaneous and Fas-mediated cell death. Therefore, we hypothesized that SBDS-deficiency disrupts ROS regulation and subsequently increases sensitivity to Fas stimulation and reduced cell growth. PROCEDURE: SBDS was knocked down in HeLa cervical cancer cells and TF-1 myeloid cells using short hairpin RNA. ROS levels were evaluated by oxidation of 2',7'-dichlorodihydrofluorescein diacetate. Apoptosis and cell growth were evaluated with and without antioxidants by annexin V/propidium iodide and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assays, respectively. RESULTS: We found that shRNA mediated SBDS-knockdown resulted in a significant increase in ROS levels compared to control cells. Fas stimulation further increased ROS levels in the SBDS-knockdown HeLa cells more than in the controls. Importantly, balancing ROS levels by antioxidants rescued SBDS-deficient cells from spontaneous and Fas-mediated apoptosis and reduced cell growth. CONCLUSIONS: ROS levels are increased in SBDS-deficient cells, which leads to increased apoptosis and decreased cell growth. Increased baseline and Fas-mediated ROS levels in SBDS-deficient cells can enhance the sensitivity to Fas stimulation. By balancing ROS levels, antioxidants can improve cell growth and survival in SBDS-deficient cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".