Naive T cells are intrinsically biased in helper T cell functions
Bibliographic record
Abstract
Abstract CD5, a negative regulator of T cell receptor (TCR) signals, is a cell surface protein whose expression reflects the self-reactivity of the TCR for self-peptide. Recent studies have shown that CD5lo versus CD5hi T cells differentially respond to foreign antigen in primary and recall responses and may also have biased differentiation potential. Within the CD4+ T cell lineage, in particular, evidence points to the preferential recruitment of CD5hi CD4+ cells into the memory compartment and biased regulatory T cell development. However, the extent to which CD5 levels influence T cell differentiation and effector cytokine production is unknown. We have expanded upon these findings to show that ex vivo activated murine CD5lo CD4+ T cells produce relatively greater amounts of the Th1 cytokine IFNγ compared to their CD5hi counterparts. Interestingly, this difference is specific to IFNγ production and is not attributable to differential expression of the Th1 cell-specific transcription factor T-bet. Given the distinct metabolic programs associated with these T cell subsets and recent evidence that glycolysis regulates IFNγ production through an epigenetic mechanism, we propose that CD5 levels shape T cell function by direct or indirect regulation of cellular metabolism. Consistent with this, our preliminary data suggests that modulation of cellular acetate levels can restore IFNγ production in CD5hi cells similar to that of CD5lo cells. Our ongoing studies are focused on epigenetic and metabolic profiling of CD5lo and CD5hi cells in order to better understand the observed lineage bias.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".