Kinetic analysis of the selectivity of acylcarnitine synthesis in rat mitochondria
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».