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Record W4412702961 · doi:10.1371/journal.pbio.3003283

Enhancing thymic function improves T-cell reconstitution and immune responses in aged mice

2025· article· en· W4412702961 on OpenAlexaff
Abigail Morales‐Sánchez, Marieke Lavaert, Melanie S. Vacchio, Gustavo Ulises Martinez‐Ruíz, Daniel Egbase, Yongge Zhao, Ross Lake, Masaki Ishikawa, Fatima Zohra Braikia, Dragana Janković, Ranjan Sen, Rémy Bosselut, Avinash Bhandoola, Jennifer E. Cowan

Bibliographic record

VenuePLoS Biology · 2025
Typearticle
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsInstitute of Aging
FundersNational Institutes of HealthWellcome Trust
KeywordsThymic involutionBiologyInvolution (esoterism)CD8Immune systemT cellImmunologyImmunityCell biologyNeuroscience

Abstract

fetched live from OpenAlex

Age-related thymic involution leads to diminished output of naïve T-cells. While this process is suggested to increase the risk of disease severity in the elderly following infection, direct evidence is lacking. We developed two mouse models that allow us to experimentally prevent or reverse thymic involution. Constitutive Myc expression in thymic epithelial cells (TEC) of middle-aged mice enhanced thymic function, and increased numbers of peripheral naïve CD4 and CD8 T-cells. Inducible Myc expression reversed age-related thymic involution and partially recovered peripheral naïve T-cell numbers. Importantly, improving thymic function in these settings preserved T-cell-dependent antibody responses and significantly reduced T-cell-associated mortality after infection with Toxoplasma gondii. Improved thymic function also rebalanced age-associated alterations in the Treg pool, and mitigated loss of the transcriptional Th1 signature in aged conventional T-cells. Our findings support the value of TEC-focused thymic regeneration strategies for enhancement of T-cell-mediated immunity in the elderly.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.303
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2025
Admission routes1
Has abstractyes

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