Human influenza-specific effector and memory CD8 T cells from older adults show signs of terminal differentiation and senescence (104.3)
Bibliographic record
Abstract
Abstract Little is known about normal human aging and its effects on the function of T cells specific for acutely infecting pathogens. To address this issue, we conducted a multicolour flow cytometry study of older persons’ responses to influenza virus challenge ex vivo. Young and elderly donors were recruited in the fall of 2008. PBMCs were stained directly ex vivo or stimulated with A/Puerto Rico/8/34 (PR8) for 18 hours and assessed for functional markers. The data show that most donors have pre-existing influenza-specific T cells (measured by IFNγ) that are reactivated by PR8, although older donors had smaller pre-vaccination populations of flu-responsive T cells. Effector cells identified upon restimulation with influenza displayed a more terminally differentiated phenotype in older compared to younger adults. This phenotype did not change post-vaccination with trivalent inactivated vaccine, which lacks significant internal viral proteins and is a poor inducer of CD8 T cell responses. For HLA-A2+ donors, it was determined by tetramer staining that elderly influenza-specific CD8 T cells express higher levels of senescence markers KLRG1 and CD57 compared to young controls. Our study shows that influenza specific effector and memory T cells from older individuals show signs of terminal differentiation and senescence. Thus, as has been shown for persistent life-long infections, memory CD8 T cells to an acutely infecting pathogen show signs of deterioration with age.
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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) | 0.003 | 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".