Obesity Reduces mTORC1 Activity in Mucosal-Associated Invariant T Cells, Driving Defective Metabolic and Functional Responses
Bibliographic record
Abstract
Obesity underpins the development of numerous chronic diseases, such as type II diabetes mellitus. It is well established that obesity negatively alters immune cell frequencies and functions. Mucosal-associated invariant T (MAIT) cells are a population of innate T cells, which we have previously reported are dysregulated in obesity, with altered circulating and adipose tissue frequencies and a reduction in their IFN-γ production, which is a critical effector function of MAIT cells in host defense. Hence, there is increased urgency to characterize the key molecular mechanisms that drive MAIT cell effector functions and to identify those which are impaired in the obesity setting. In this study, we found that MAIT cells significantly upregulate their rates of glycolysis upon activation in an mTORC1-dependent manner, and this is essential for MAIT cell IFN-γ production. Furthermore, we show that mTORC1 activation is dependent on amino acid transport via SLC7A5. In obese patients, using RNA sequencing, Seahorse analysis, and a series of in vitro experiments, we demonstrate that MAIT cells isolated from obese adults display defective glycolytic metabolism, mTORC1 signaling, and SLC7A5 aa transport. Collectively, our data detail the intrinsic metabolic pathways controlling MAIT cell cytokine production and highlight mTORC1 as an important metabolic regulator that is impaired in obesity, leading to altered MAIT cell responses.
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) | 0.000 | 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".