Differential Branched-Chain Amino Acid Metabolism in Tissues of Tumor-Bearing Mice
Bibliographic record
Abstract
Abstract Cancer cachexia is a multifactorial syndrome characterized by the involuntary loss of skeletal muscle and adipose tissue, often resistant to nutritional support. The branched-chain amino acids (BCAA: leucine, isoleucine, and valine) stimulate protein synthesis, yet BCAA-targeted therapies have yielded limited clinical benefit, and inconsistent results. In this study, a C26 colon cancer mouse model was used to examine how tumor burden alters BCAA metabolism across skeletal muscle, liver, kidney, and adipose tissue. Tumors accumulated BCAA and showed increased oxidation of these amino acids, whereas peripheral sites displayed widespread BCAA depletion, reduced expression of the amino acid (AA) transporter LAT1, and suppression of mechanistic target of rapamycin complex 1 (mTORC1) signaling. Notably, the soleus muscle maintained mTORC1 activity despite reduced BCAA availability, suggesting fiber-type–specific adaptations. These findings indicate that tumors act as metabolic sinks, diverting systemic AA away from host tissues. Such reprogramming may underlie the limited success of BCAA-based interventions in cachexia and highlight the need for therapies that address both tumor and host metabolism. New and Noteworthy This is the first study to profile branched-chain α-keto acid (BCKA) levels together with branched-chain amino acid (BCAA) metabolism across multiple tissues in a cancer cachexia model. Tumors accumulated BCAA while some peripheral sites showed depletion, and all peripheral tissues exhibited reduced expression of the transporter LAT1. These tissue-specific adaptations reveal systemic metabolic reprogramming and may explain the limited efficacy of BCAA-based therapies.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".