Culture of Cancer Cells at Physiological Oxygen Levels Affects Gene Expression in a Cell-Type Specific Manner
Bibliographic record
Abstract
Standard cell culture is routinely performed at supraphysiological oxygen levels (~18% O2). Conversely, O2 levels in most mammalian tissues range from 1–6% (physioxia). Such hyperoxic conditions in cell culture can alter reactive oxygen species (ROS) production, metabolism, mitochondrial networks, and response to drugs and hormones. The aim of this study was to investigate the transcriptional response to different O2 levels and determine whether it is similar across cell lines, or cell line-specific. Using RNA-seq, we performed differential gene expression and functional enrichment analyses in four human cancer cell lines, LNCaP, Huh-7, PC-3, and SH-SY5Y cultured at either 5% or 18% O2 for 14 days. We found that O2 levels affected transcript abundance of thousands of genes, with the affected genes having little overlap between cell lines. Functional enrichment analysis also revealed different processes and pathways being affected by O2 in each cell line. Interestingly, most of the top differentially expressed genes are involved in cancer biology, which highlights the importance of O2 levels in cancer cell research. Further, we observed several hypoxia-inducible factor (HIF) targets, HIF-2α targets particularly, upregulated at 5% O2, consistent with a role for HIFs in physioxia. O2 levels also differentially induced the transcription of mitochondria-encoded genes in most cell lines. Finally, by comparing our transcriptomic data from LNCaP and PC-3 with datasets from the Prostate Cancer Transcriptome Atlas, a correlation between genes upregulated at 5% O2 in LNCaP cells and the in vivo prostate cancer transcriptome was found. We conclude that the transcriptional response to O2 over the range from 5–18% is robust and highly cell-type specific. This latter finding indicates that the effects of O2 levels are difficult to predict and thus highlights the importance of regulating O2 in cell culture.
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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 | 0.001 |
| 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) | 0.002 | 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".