Investigating the Cellular Responses of Cancer Cells to Physiological and Hypoxic Oxygen Conditions
Bibliographic record
Abstract
Most incubators used in cell culture do not regulate O2 levels, making the headspace O2 concentration ~18%. In contrast, most human tissues are exposed to 2–9% O2 in vivo (physioxia). The main goal of this thesis project was to gain a better understanding of how supraphysiological O2 levels affect cell behavior in vitro, with a focus on cancer cell biology. Using RNA-seq, I studied how culture in either 5% O2 or 18% O2 affects gene expression in four human cancer cells. I found that O2 level in culture affected hundreds of genes, however, in a largely cell-type specific manner. Further, gene targets of the hypoxia-inducible factors (HIFs) were upregulated at 5% O2 compared to 18% O2 in all cell lines. This led me to investigate how culturing cancer cells at a baseline level of 5% O2 or 18% O2 affects their response to hypoxia (here, 1.1% O2). My results indicate that baseline O2 level substantially affects the transcriptional response of prostate cancer cells (PC-3) to hypoxia. Notably, cells grown in 18% O2 and then exposed to hypoxia showed an enhanced induction of HIF-regulated genes, particularly genes involved in glucose metabolism. This in turn resulted in an enhanced glucose uptake rate of cells taken from 18% O2 to hypoxia, compared to cells preadapted to 5% O2 and then exposed to hypoxia. Finally, while acute hypoxia did not affect proliferation or migration of PC-3 cells regardless of their baseline O2 level, cells preadapted to 5% O2 did show higher proliferation and migration rates compared to cells in 18% O2. We conclude that O2 levels in culture affect gene expression, glucose consumption, and growth in cancer cells, which, in turn, might result in differential sensitivity towards anticancer drugs, highlighting the importance of maintaining physiological O2 conditions 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 | 0.000 |
| 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".