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Enregistrement W2233626750 · doi:10.1242/jeb.135442

Gas movement through aquaporins is significant

2015· article· en· W2233626750 sur OpenAlexaboutno aff
Kathryn Knight

Notice bibliographique

RevueJournal of Experimental Biology · 2015
Typearticle
Langueen
DomaineEnvironmental Science
ThématiquePhysiological and biochemical adaptations
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMembraneAquaporinZebrafishBespokeMembrane proteinChemistryNanotechnologyBiologyBiophysicsCell biologyBiochemistryMaterials scienceBusinessGene

Résumé

récupéré en direct d'OpenAlex

The movement of gases across biological structures is essential for life from the instant of conception to the last gasp. However, Katie Gilmour from the University of Ottawa, Canada, explains that there is much debate about the mechanisms by which gas molecules pass across membranes to move into and out of cells. Although gases can simply diffuse across membranes, certain membrane-embedded pore proteins – such as aquaporin water channels and Rhesus proteins – also allow gas molecules to pass through membranes. She says, ‘Arguing that membrane proteins are physiologically important for gas movement when membrane proteins are relatively limited (in comparison to overall membrane area) becomes challenging.’ So, in a bid to resolve the mystery, Gilmour and her colleagues turned to 4-day-old zebrafish larvae to find out just how significant aquaporins are in the transfer of gases across cell membranes.Gilmour explains that zebrafish larvae were great animals for her team to work with because it is possible to directly switch off the production of specific proteins and measure the impact on the amount of gas passing through cell membranes. However, she admits that working with the minute animals was extremely fiddly. ‘Measuring CO2 excretion in tiny aquatic animals requires that very small increases be measured in the CO2 concentration of the water in which the animals are held’, says Gilmour. But Mike Murphy, a talented engineer in the University of Ottawa's electrical workshop, eventually designed and built a bespoke CO2 analyser to allow Gilmour and her colleagues to make the sensitive measurements.Then, Krystle Talbot painstakingly injected a molecule specially designed to switch off production of aquaporin protein into newly fertilized zebrafish eggs and allowed them to develop for 4 days before measuring the amount of CO2 produced by the larvae. Amazingly, the larvae's CO2 excretion rate fell by 35%, despite consuming the same amount of oxygen as larvae with aquaporin proteins embedded in their cell membranes. The aquaporin proteins were contributing significantly to the movement of gas molecules across cell membranes.However, it was not clear whether the aquaporin proteins were involved in CO2 moving across red blood cell membranes or the membrane surrounding the larvae's yolk sac. So Talbot bathed the tiny animals in phenylhydrazine-water, to remove their red blood cells, and measured how much CO2 they produced. The larvae were unaffected, excreting as much CO2 as fish with red blood cells. However, when Talbot tested the CO2 production of larvae that lacked both red blood cells and aquaporin proteins, she found that it fell, so the aquaporin molecules embedded in the yolk sac membrane were responsible for CO2 excretion.Aquaporins have also been suggested to excrete toxic ammonia gas through cell membranes, so Talbot then measured ammonia excretion in larvae that did not produce aquaporin and in a second group of larvae that did not produce the Rhesus ammonia channel. Not surprisingly, the larvae lacking the Rhesus protein channel had significantly reduced ammonia excretion rates, but so too did the fish lacking aquaporin. And, when Talbot and Raymond Kwong investigated aquaporin gene expression and protein production in larvae that lacked the Rhesus protein when there were high levels of ammonia in the environment, they found that the larvae were mobilising more aquaporin. So, aquaporins could be working together with Rhesus proteins to excrete nitrogenous waste, in addition to helping the animals remove CO2 from their bodies.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut 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: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,022

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,002
Communication savante0,0020,004
Science ouverte0,0010,002
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0070,002

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 Opus0,056
Tête enseignante GPT0,302
Écart entre enseignants0,246 · 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 source (Gemma direct ou Codex distillé), 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
GenreEmpirique

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
Publié2015
Routes d'admission1
Résumé présentoui

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