Kinetic analysis of the selectivity of acylcarnitine synthesis in rat mitochondria
Bibliographic record
Abstract
Mitochondrial acylcarnitine synthesis is an obligatory step in the transport of cytosolic long-chain FA into the mitochondria. It is an important control point in the partitioning of cytosolic fatty acids to synthetic pathways or to mitochondrial beta-oxidation. Mitochondrial carnitine palmitoyltransferase I (CPT I; EC 2.3.1.21) is the enzyme that catalyzes the transformation of long-chain fatty acylCoA esters to acylcarnitine. Additionally, the isoform of acylCoA synthetase (EC 6.2.1.3) found in mitochondria, which is in close proximity to CPT I on the outer membrane, may act in concert with CPT I to form acylcarnitines from cytosolic nonesterified FA (NEFA). The mitochondrial acylcarnitine synthesis pathway is exposed to multiple fatty acid substrates present simultaneously in the cell milieu, with each fatty acid present at varying pool sizes. The selectivity of this pathway for any particular fatty acid substrate under conditions of multisubstrate availability has not yet been tested experimentally. Our objective was to develop mathematical equations that make use of kinetic constants derived from single-substrate experiments to predict the selectivity of the acylcarnitine synthesis pathway under conditions in which two or more substrates are present simultaneously. In addition, the derived equations must be verifiable by experiment. Our approach was to begin with a Michaelis-Menten model that describes the initial rates of an enzyme system acting on multiple and mutually competitive substrates. From this, we derived equations expressing ratios of reaction rates and fractional turnover rates for pairs of substrates. The derived equations do not require assumptions concerning the degree of enzyme saturation. Using rat mitochondrial preparations and the NEFA substrate pairs, linolenic-oleic acids and palmitic-linoleic acids, we showed that the shape of the experimentally derived data on acylcarnitine synthesis fits the predictions of the derived model equations. We further validated the derived equations by showing that their predictions calculated from previously published kinetic constants were consistent with data from actual experiments. Thus, we are able to conclude that with respect to acylcarnitine synthesis, the fractional turnover rate of the linolenic acid pool would always be 2.9-fold faster than that of the oleate pool regardless of the pool size of either fatty acid. Similarly, the fractional turnover rate of the palmitate pool would always be 1.8-fold faster than that of the linoleate pool regardless of pool size. We extended our kinetic model to more than two mutually competitive substrates. Using previously published rate constants for eight physiologically relevant fatty acids, the derived model predicts that regardless of pool size of any of the fatty acids, the linolenate pool, whether as NEFA or as a CoA ester, would always have the highest fractional turnover rate with respect to acylcarnitine synthesis. Conversely, the stearate pool whether as NEFA or as CoA ester will have the lowest fractional turnover rate relative to all the other fatty acids.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".