IDH1 deficiency attenuates gluconeogenesis in mouse liver by impairing amino acid utilization
Bibliographic record
Abstract
Although the enzymatic activity of isocitrate dehydrogenase 1 (IDH1) was defined decades ago, its functions in vivo are not yet fully understood. Cytosolic IDH1 converts isocitrate to α-ketoglutarate (α-KG), a key metabolite regulating nitrogen homeostasis in catabolic pathways. It was thought that IDH1 might enhance lipid biosynthesis in liver or adipose tissue by generating NADPH, but we show here that lipid contents are relatively unchanged in both IDH1-null mouse liver and IDH1-deficient HepG2 cells generated using the CRISPR-Cas9 system. Instead, we found that IDH1 is critical for liver amino acid (AA) utilization. Body weights of IDH1-null mice fed a high-protein diet (HPD) were abnormally low. After prolonged fasting, IDH1-null mice exhibited decreased blood glucose but elevated blood alanine and glycine compared with wild-type (WT) controls. Similarly, in IDH1-deficient HepG2 cells, glucose consumption was increased, but alanine utilization and levels of intracellular α-KG and glutamate were reduced. In IDH1-deficient primary hepatocytes, gluconeogenesis as well as production of ammonia and urea were decreased. In IDH1-deficient whole livers, expression levels of genes involved in AA metabolism were reduced, whereas those involved in gluconeogenesis were up-regulated. Thus, IDH1 is critical for AA utilization in vivo and its deficiency attenuates gluconeogenesis primarily by impairing α-KG-dependent transamination of glucogenic AAs such as alanine.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".