Defining the extra-thymic role of HEB in the development of CD8+ T stem cell-like immunological memory
Bibliographic record
Abstract
Abstract The transcription factor HEB is involved in multiple stages of intrathymic T cell development, complexing with stage-specific partners to regulate different genes in a context-specific manner. Remarkably, little is known about how HEB influences the differentiation of peripheral T cells, and even less so of how the transcriptional circuitry in early T cell development may be re-invoked to generate long-lasting immunity. While T cells are detected in the periphery of HEB-deficient mice (HEB cKO), the contribution of HEB to the function of T cells upon pathogen encounter is obscure. Thus, HEB cKO mice were challenged with LCMV to determine the impact of HEB in the activation and differentiation of CD8+ T cells into effector and memory lineages. Throughout the course of infection, HEB cKO mice exhibited critical differences in their antigen-specific CD8+ response and had enhanced levels of stem cell-like memory T cells (Tscm). This implicated a role for HEB downstream of T cell activation, which we further confirmed using well-established in vitro cultures supplemented with or without Tscm-inducing cytokines. Consistently, HEB transcripts were detected in publicly available scRNA datasets from P14 transgenic mice infected with LCMV. To greater define the transcriptional pattern of HEB, and of others that orchestrate thymic T cell development in the context of peripheral T cell immunity, we will conduct a scRNA-seq comparative bioinformatic analysis of developing thymocytes to splenic LCMV-specific CD8+ T cells. This will advance our understanding of how early transcriptional cascades, like those initiated by HEB, are propagated in downstream immunological reactions, which may lead to therapies that can revitalize T cell immunity. Supported by a grant from NIH (1P01AI102853-06) and the The AAI Intersect Fellowship Program for Computational Scientists and Immunologists
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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 | 0.001 | 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 | 0.001 |
| 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".