Identification of Oxygen-Sensitive Transcriptional Programs in Human Embryonic Stem Cells
Bibliographic record
Abstract
To realize the full potential of human embryonic stem cells (hESCs), it is important to develop culture conditions that maintain hESCs in a pluripotent, undifferentiated state. A low O(2) atmosphere (approximately 4% O(2)), for example, prevents spontaneous differentiation and supports self-renewal of hESCs. To identify genes whose expression is sensitive to O(2) conditions, microarray analysis was performed on RNA from hESCs that had been maintained under either 4% or 20% O(2). Of 149 genes differentially expressed, 42 were up-regulated and 107 down-regulated under 20% O(2). Several of the down-regulated genes are most likely under the control of hypoxia-inducing factors and include genes encoding enzymes involved in carbohydrate catabolism and cellular redox state. Although genes associated with pluripotency, including OCT4, SOX2, and NANOG were generally unaffected, some genes controlled by these transcription factors, including LEFTY2, showed lowered expression under 20% O(2), while a few genes implicated in lineage specification were up-regulated. Although the differences between O(2) conditions were generally subtle, they were observed in two different hESC lines and at different passage numbers. The data are consistent with the hypothesis that 4% O(2) favors the molecular mechanisms required for the maintenance of pluripotency.
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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 | 0.000 |
| 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".