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Enregistrement W2480093512 · doi:10.1113/jp272457

PET imaging of glucose movement into tissues <i>in vivo</i> sheds new light on an old problem

2016· letter· en· W2480093512 sur OpenAlexaff
Chris I. Cheeseman

Notice bibliographique

RevueThe Journal of Physiology · 2016
Typeletter
Langueen
DomaineNursing
ThématiqueBiochemical Analysis and Sensing Techniques
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésGlucose transporterTransporterIn vivoMembraneGalactoseFructoseBiochemistryBiologyCell biologyChemistryEndocrinologyGeneInsulinGenetics

Résumé

récupéré en direct d'OpenAlex

Since the first demonstrations that glucose movement across cell membranes is mediated by transport proteins enormous effort has been expended over the intervening 60 years to understand these processes at both the molecular and the tissue level (Widdas, 1951). While most of the major transporters responsible for these key metabolic processes have now been cloned, molecular techniques have not been able to provide a full understanding of how glucose enters the body and is subsequently distributed between tissues. Two types of transporter have been implicated in these processes; the first are members of the SLC2A protein family (GLUTs), which allow hexoses such as glucose, galactose and fructose to move across cell membranes down their concentration gradients (Mueckler & Thorens, 2013). The second belong to a separate gene family, SGLTs, which couple the movement of hexoses to the electrochemical gradient of sodium across cell membranes to enable the transport of glucose against a concentration gradient (Wright et al. 2011). When epithelial cells express these different transporters on opposite poles it is possible for the intestine or kidney to achieve a vectorial flux essential for the body to acquire carbohydrate from the diet and ensure glucose is not lost into the urine after glomerular filtration. However, while these functional models are supported by extensive indirect evidence using in vitro and in vivo techniques, there are some significant gaps in our understanding of the role of these transporters in a number of key tissues. For instance, the established model of intestinal and renal glucose handling is that SGLT1 and/or -2 provides uphill entry across the apical membrane with the subsequent exit across the basolateral pole down the concentration gradient mediated by GLUT2. However, in GLUT2 knock-out mice absorption of glucose from the diet appears to be normal, challenging this long accepted concept (Stumpel et al. 2001). Similarly, while GLUTs have long been considered to be the primary route by which glucose crosses the blood–brain barrier it has not been rigorously demonstrated in vivo. Finally, there have been indications that SGLTs might also play a role in the uptake of hexoses into cardiomyocytes. Molecular techniques are able to help with ascertaining in which tissues certain transporter proteins are found, but do not provide measures of their relative functional capacities. Also, locating proteins alone does not allow for determining physiological processes which modulate activity within the course of minutes or hours. Consequently, any approach which can follow the flux of hexoses between the blood and various tissues over relatively short time periods in vivo has enormous potential to advance our understanding of the role of these transporters. In this issue of The Journal of Physiology, Sala-Rabanal et al. (2016) report on a series of studies in live mice in which the distribution of hexose analogues specific for different transporters has been followed using PET imaging. They then determined with compartmental analysis which tissues employed SGLTs and/or GLUTs to handle these substrates. The authors were able to reach a number of conclusions. First, although SGLTs appear to be expressed in the blood–brain barrier, the primary route of entry for glucose into the brain is mediated by GLUT1 or -3, as this uptake was not affected in GLUT2−⁄− mice and the SGLT-specific analogue, 4-methyl-fluoro-deoxy-D-glucose, did not enter the brain. Second, they were able to confirm the model for glucose reabsorption across the proximal convoluted tubule in which uptake is mediated by SGLT1 and -2 and exit into the blood requires GLUT2. Finally, they also confirmed that GLUT2 mediates glucose fluxes into and out of hepatocytes in the liver and thus plays a major role in glucose homeostasis. However, there are still a number of significant unanswered questions with regard to glucose fluxes across and between tissues. There is strong evidence that in the intestine during the course of a single meal the carbohydrate capacity is up-regulated within minutes, matching the load so that there is no overspill from the small intestine into the colon. Part of this increase in capacity can be accounted for by rapid insertion of SGLT1 into the membrane, but there is also contested evidence that GLUT2 can also be inserted into the apical membrane to help with the nutrient load at the start of a meal (Kellett & Brot-Laroche, 2005; Röder et al. 2014). The signalling pathways for these responses appear to involve taste receptors and a neuroendocrine mechanism (Nguyen et al. 2012). Another puzzle regarding glucose absorption in the small intestine is the role of GLUT2 in mediating glucose efflux into the blood. Some studies have shown normal transport of glucose across the epithelium in GLUT2−⁄− mice (Stumpel et al. 2001) while more recent work indicate that it is significantly reduced in the absence of GLUT2 (Röder et al. 2014). This has raised the possibility that there might still be another route of exit for hexoses. If there is another transporter present that has previously been ignored, could other tissues also make use of the same system? New imaging techniques should help us to answer these important questions. None.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,275
Score d'incertitude au seuil0,638

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,008
Tête enseignante GPT0,259
Écart entre enseignants0,250 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

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