Adaptation of Human CD4+ T Cells to Pathophysiological Hypoxia: A Transcriptome Analysis
Bibliographic record
Abstract
OBJECTIVE: Inflamed tissues are usually characterized by low oxygen levels. We investigated whether pathophysiological hypoxia (pO(2) < 1%) as found in the rheumatoid synovium modulates the transcriptome of human CD4+ T cells. METHODS: We analyzed the extent to which hypoxia influences the transcriptome in the rheumatoid synovium according to a gene cluster reflecting adaptation to low oxygen levels. Hypoxia-inducible factor-1alpha (HIF-1alpha) was detected in the rheumatoid synovium using immunohistochemistry. Isolated human CD4+ T cells were exposed to hypoxia and analyzed using microarray analysis, quantitative polymerase chain reaction, and immunoblot detection. RESULTS: In rheumatoid arthritis (RA) synovial tissue samples, hypoxia modulates the transcription profile. This profile is similar, but not identical, to that found in isolated CD4+ T cells incubated under hypoxic conditions. We show that HIF-1alpha is expressed in synovial tissue samples and in hypoxic CD4+ cells; and that hypoxia directly affects differential gene expression in human T cells with up to 4.8% modulation of the transcriptome. Functional genome analysis revealed substantial effects of hypoxia on immune response, transcriptional regulation, protein modification, cell growth and proliferation, and cell metabolism. CONCLUSION: Severe hypoxia, a feature of joint inflammation, considerably modulates the transcriptome of cells found in the rheumatoid synovium. Human CD4+ T cells adapt to hypoxic conditions mainly by HIF-1-driven effects on the transcriptome reflecting a profound influence on immune functions. Thus, hypoxia must be taken into account when therapeutically targeting inflammation.
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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".