Utilization of Stable Isotope Tail-Vein Infusion to Assess Glutamine Utilization by Physiologically Activated CD8+ T cells in vivo
Bibliographic record
Abstract
Abstract Metabolic reprogramming is an essential part of the T cell activation program. Upon activation, T cells undergo dramatic rewiring of their metabolic pathways to promote ATP production and biosynthesis sufficient to support the rapid exponential growth of antigen-specific T cells. The metabolic profile of T cells is shaped by both cell-intrinsic factors, such as genetics and receptor-mediated signaling, and environmental conditions, such as nutrient availability. However, our understanding of T cell metabolism has largely been shaped by studies in vitro, where oxygen and nutrients are in excess. Here, we combined bioenergetic profiling and 13C-glutamine infusion techniques to investigate the metabolism of CD8+ T cells responding to Listeria infection. Similar to in vitro-activated T cells, glutamine is a prominent source of fuel for the TCA cycle during early expansion (3 dpi). However, at later timepoints of infection (6 dpi), the use of glutamine is substantially decreased by Teff cells. Similar to the observation of decreased glucose usage by T cells over the course of infection (Ma et al Immunity 2019). Our data suggests a dynamic flexibly in nutrient usage by T cells in vivo to fuel the different phases of T cell functions, from the early T cell response (3 dpi) to the late (6 dpi). Our work highlights the differences in T cell metabolism in vivo compared to in vitro cultures and a new method to study T cell nutrient utilization in vivo.
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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".