Accelerated hepatic glycerol synthesis in rainbow smelt (Osmerus mordax) is fuelled directly by glucose and alanine: a1H and13C nuclear magnetic resonance study
Bibliographic record
Abstract
At seawater temperatures below 1 degrees C, rainbow smelt (Osmerus mordax) accumulate plasma levels of glycerol up to 400 mM. Aspects of the synthesis of glycerol in liver and its regulation were previously investigated, but the pathways leading to glycerol synthesis remained unconfirmed. Here, we report nuclear magnetic resonance (NMR) studies which elucidate, in more detail, the fuel sources for rapid glycerol synthesis in rainbow smelt. Initial NMR analysis of liver homogenates from fish held at cold (-1 degrees C) temperatures and from fish transferred from 8 degrees C to -1 degrees C showed elevated glycerol, whereas those from fish held at 8 degrees C had far lower glycerol levels. These results confirm a temperature-responsive glycerol synthesis and show that NMR is a suitable approach to investigate the phenomenon. Further studies with fish held at low temperature and injected with labelled L-[2,3-(13)C(2)] alanine or D-[U-(13)C(6)]glucose revealed conversion of both alanine and glucose to glycerol. (13)C spectra showed satellites ((1)J(CC)=41.1 Hz) about the glycerol resonances indicating intact incorporation of a (13)C-(13)C unit in liver glycerol of fish injected with L-[2,3-(13)C(2)]alanine and a (13)C-(13)C-(13)C unit in liver glycerol of fish injected with D[U-(13)C(6)]glucose. Thus, glycerol can be efficiently produced directly from amino acid precursors by glyceroneogenesis, which is an abbreviated gluconeogenesis process leading to glycerol through dihydroxyacetone phosphate (DHAP). Glucose can also be metabolised to glycerol via an abbreviated form of glycolysis that similarly leads to glycerol through DHAP.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".