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Enregistrement W2324405370 · doi:10.1242/jeb.091207

LIFE AT HIGH pH: MANAGING AMMONIA

2013· article· en· W2324405370 sur OpenAlexaboutno aff
Nicola Stead

Notice bibliographique

RevueJournal of Experimental Biology · 2013
Typearticle
Langueen
DomaineEnvironmental Science
ThématiquePhysiological and biochemical adaptations
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésFish <Actinopterygii>AmmoniaGillUreaFisheryChemistryZoologyBiologyBiochemistry

Résumé

récupéré en direct d'OpenAlex

At the southernmost point of the Kenyan Rift Valley lies the inhospitable, alkaline Lake Magadi. With a pH of 10, the only fish that can survive here are Alcolapia grahami cichlids. For over two decades, Chris Wood, from McMaster University, Canada, has been intrigued by how these fish cope in their hostile habitat, and has made five expeditions to this lake to investigate. During his first expedition in 1987, Wood recalls the exciting discovery that these fish are 100% ureotelic – they only excrete their nitrogenous waste as urea. This was in stark contrast to other ammonotelic teleosts, who only excrete ammonia. But with such a high external pH, making it almost impossible to excrete ammonia, excreting urea seemed to be the only viable option for A. grahami. In more recent years, following the discovery that Rhesus (Rh) proteins facilitate ammonia transport across the gills in most fish, Wood wondered whether, in becoming ureotelic, these fish no longer had a need for Rh proteins. To find out, Wood assembled an international team of scientists and returned to Lake Magadi once more (p. 2998).From their makeshift laboratory in a nearby house, the team made early morning trips down to the lake, taking it in turns to wade into the caustic waters to capture the fish for their investigation. Upon return to the lab, Wood says: ‘We would let the fish settle down for a couple of hours and then we would put them into small chambers with an oxygen electrode so we could measure their oxygen consumption and also their urea excretion’. After a while, the team loaded the water with ammonia, and monitored the fish for 24 h. In parallel experiments, fish were regularly euthanized and the team collected blood, gill and other tissues samples for analysis back in Canada.While still in the field, the team saw that ammonia loading caused urea excretion levels to go up, but it was when they got back to Canada that the surprising results came to light. In the lab the team found that two Rh proteins, Rhbg and Rhcg2, were expressed in most tissues. Wood explains that this in itself was not entirely unexpected – the Rh proteins might be used to shuttle ammonia to other regions of the body to be converted to urea. However, he admits he was surprised to find Rh proteins in the gills. What's more, the team also found that the expression of these two Rh proteins and an ATPase essential for ammonia excretion increased after exposure to ammonia. Wood explains that increased expression of Rh proteins is a standard response in ammonotelic fish after ammonia loading; it is initiated by high cortisol levels, and sure enough, cortisol levels in the A. grahami exposed to ammonia were also raised.All the evidence suggests that even though these fish usually secrete urea, they've held onto the ammonia-sensitive ammonia excretion system. ‘Maybe when they're really challenged by high ammonia the urea mechanism is not sufficient and they might have to start pumping out ammonia as well’, speculates Wood. He adds, ‘This is quite relevant because there are areas of this lake where there are a lot of flamingos and they produce guano, and bacteria degrade the uric acid to produce a lot of ammonia in the water.’ However, quite how A. grahami does this against an ammonia gradient remains to be seen, although Wood suspects that the Rh proteins might pump ammonia into the gill cells to such an extent that it's at a higher concentration internally than externally. Wood is currently awaiting permission to return to the alkaline lake to find the answer.

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)0,002
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,028

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0020,001
Communication savante0,0030,004
Science ouverte0,0010,004
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,0080,003

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 Opus0,017
Tête enseignante GPT0,247
Écart entre enseignants0,231 · 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'étudeObservationnel
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
Publié2013
Routes d'admission1
Résumé présentoui

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Même revueJournal of Experimental Biology→Même sujetPhysiological and biochemical adaptations→Travaux en français237 207→