Deep Profiling of the CD8+ T-cell Compartment Identifies Activated Cell Subsets and Multifunctional Responses Associated With Control of Cytomegalovirus Viremia
Bibliographic record
Abstract
BACKGROUND: Human cytomegalovirus (HCMV) is a common opportunistic pathogen in transplant recipients. Patterns of viremia and reactivation are influenced by the host immune response, including CD8 T cells. However, the cellular deficits or phenotypic differences that account for differential outcomes during HCMV viremia are incompletely understood. METHODS: Peripheral blood mononuclear cells were collected from 20 transplant recipients (10 viremia controllers and 10 noncontrollers) at onset of HCMV viremia and 4 weeks postonset. We used mass cytometry to perform in-depth characterization of cell surface and intracellular CD8 T cell markers and to compare frequencies of these cells between groups. RESULTS: Deep profiling identified 2 central memory T cell subsets at onset and 5 terminally differentiated memory T (TEMRA) cell subsets at 4 weeks that were associated with control of HCMV viremia, in addition to 6 TEMRA subsets at onset and 4 weeks associated with relapsing or remitting HCMV viremia. In general, CD8 T-cell clusters associated with poorly controlled HCMV viremia lacked markers of activation or terminal differentiation including CD38, CD69, CD25, CD57, and HLA-DR. We also measured the production of 8 HCMV-specific effector molecules (TNFα, IFNγ, interleukin 2, granzyme B, perforin, macrophage inflammatory protein 1β, interleukin 10, and CD107a) in CD8 T cells. Viremia controllers had greater diversity of HCMV-specific multifunctional responses at both time points, including significantly higher frequencies of HCMV-specific TNFαIFNγ CD8 T cells at onset. These multifunctional cells had a phenotype consistent with activated TEM/TEMRA cells. CONCLUSIONS: Uncontrolled CMV viremia is associated with specific clusters of memory T-cell subsets and lower frequencies of HCMV-specific multifunctional CD8 T cells.
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 | 0.000 | 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".