CARMIL2 deficiency disrupts activation-induced metabolic reprogramming in T cells and is partially rescued by glutamine supplementation
Bibliographic record
Abstract
BACKGROUND: T-cell activation requires signaling through the T-cell receptor and costimulatory molecules, including CD28, triggering metabolic reprogramming to support growth and proliferation of the activating T cell. CARMIL2, a scaffold protein, facilitates CD28-mediated signaling. Individuals with CARMIL2 mutations experience inborn errors of immunity, leading to T-cell dysfunction and severe infectious and inflammatory comorbidities. However, how CARMIL2 deficiency impacts T-cell metabolic reprogramming remains unknown. OBJECTIVE: We sought to investigate how CARMIL2 deficiency affects activation-induced metabolic reprogramming in T cells. METHODS: T cells were isolated from patients with CARMIL2 deficiency and matched healthy controls. A transcriptomic profile was analyzed by bulk RNA sequencing and whole-cell metabolomics by LC-MS/MS. Activation markers and signaling pathways were measured by flow cytometry. These approaches informed identification of specific amino acids for rescue experiments. RESULTS: T cells. CONCLUSION: CARMIL2 deficiency disrupts T-cell metabolic reprogramming and was partially rescued ex vivo with glutamine supplementation. These findings highlight a potential therapeutic approach targeting metabolism to improve immune function in individuals with CARMIL2 deficiency.
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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) | 0.001 | 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 | 0.001 |
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".