Influenza-Specific T Cells from Older People Are Enriched in the Late Effector Subset and Their Presence Inversely Correlates with Vaccine Response
Bibliographic record
Abstract
T cells specific for persistent pathogens accumulate with age and express markers of immune senescence. In contrast, much less is known about the state of T cell memory for acutely infecting pathogens. Here we examined T cell responses to influenza in human peripheral blood mononuclear cells from older (>64) and younger (<40) donors using whole virus restimulation with influenza A (A/PR8/34) ex vivo. Although most donors had pre-existing influenza reactive T cells as measured by IFNγ production, older donors had smaller populations of influenza-responsive T cells than young controls and had lost a significant proportion of their CD45RA-negative functional memory population. Despite this apparent dysfunction in a proportion of the older T cells, both old and young donors' T cells from 2008 could respond to A/California/07/2009 ex vivo. For HLA-A2+ donors, MHC tetramer staining showed that a higher proportion of influenza-specific memory CD8 T cells from the 65+ group co-express the markers killer cell lectin-like receptor G1 (KLRG1) and CD57 compared to their younger counterparts. These markers have previously been associated with a late differentiation state or immune senescence. Thus, memory CD8 T cells to an acutely infecting pathogen show signs of advanced differentiation and functional deterioration with age. There was a significant negative correlation between the frequency of KLRG1(+)CD57(+) influenza M1-specific CD8 T cells pre-vaccination and the ability to make antibodies in response to vaccination with seasonal trivalent inactivated vaccine, whereas no such trend was observed when the total CD8(+)KLRG1(+)CD57(+) population was analyzed. These results suggest that the state of the influenza-specific memory CD8 T cells may be a predictive indicator of a vaccine responsive healthy immune system in old age.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.004 | 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".