Transient inhibition of type I interferon enhances CD8 <sup>+</sup> T cell stemness and vaccine protection
Bibliographic record
Abstract
SUMMARY Developing vaccines that promote CD8 + T cell memory is a challenge for infectious disease and cancer immunotherapy. TCF-1 + stem cell-like memory T (T SCM ) cells are important determinants of long-lived memory. Yet, the developmental requirements for T SCM formation are unclear. Here, we identify the temporal window for type I interferon (IFN-I) receptor (IFNAR) blockade to drive T SCM cell generation. T SCM cells were transcriptionally distinct and emerged from a transitional precursor of exhausted (T PEX ) cellular state concomitant with viral clearance. T SCM differentiation correlated with T cell retention within the lymph node paracortex, due to increased CXCR3 chemokine abundance which disrupted gradient formation. These affects were due a counterintuitive increase in IFNψ, which controlled cell location. Combining IFNAR inhibition with mRNA-LNP vaccination promoted specific T SCM differentiation and enhanced protection against chronic infection. These finding propose a new approach to vaccine design whereby modulation of inflammation promotes memory formation and function. HIGHLIGHTS Early, transient inhibition of the type I interferon (IFN) receptor (IFNAR) during acute viral infection promotes stem cell-like memory T (T SCM ) cell differentiation without establishing chronic infection. T SCM and precursor of exhausted (T PEX ) cellular states are distinguished transcriptionally and by cell surface markers. Developmentally, T SCM cell differentiation occurs via a transition from a T PEX state coinciding with viral clearance. Transient IFNAR blockade increases IFNψ production to modulate the ligands of CXCR3 and couple T SCM differentiation to cell retention within the T cell paracortex of the lymph node. Specific promotion of T SCM cell differentiation with nucleoside-modified mRNA-LNP vaccination elicits enhanced protection against chronic viral challenge.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".