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Enregistrement W2594929550 · doi:10.1242/jeb.147280

Haemoglobin's low affinity for predictability

2017· article· en· W2594929550 sur OpenAlexaff
Matthew D. Regan

Notice bibliographique

RevueJournal of Experimental Biology · 2017
Typearticle
Langueen
DomaineEnvironmental Science
ThématiquePhysiological and biochemical adaptations
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésAffinitiesBiologyAmino acidEvolutionary biologyFunction (biology)GeneticsZoologyBiochemistry

Résumé

récupéré en direct d'OpenAlex

There's more than one way to crack an egg, shear a sheep or do pretty well anything else, but what about the evolution of physiological traits? If the trait in question is a protein's function, then shouldn't biophysical constraints on protein structure demand that the independent evolution of the same function arise from the same amino acid substitution? Chandrasekhar Natarajan, from the University of Nebraska, USA, and his team were sufficiently intrigued by this question to devise a truly impressive comparative study to get to the bottom of it, and used haemoglobin, and its affinity for oxygen, as their representative protein.The team started by clustering 56 avian species into 28 closely related species pairs, each pair comprising a low- and high-altitude native relative. After isolating each bird's haemoglobin, the team measured their affinities for oxygen and found that the high-altitude inhabitants consistently had higher affinity haemoglobins than their low-altitude counterparts, the evolutionary result of adaptation to a consistently more hypoxic environment. The question then became whether the evolved increases in haemoglobin–oxygen affinities were caused by amino acid substitutions at the same sites.They set about answering this by sequencing each species’ haemoglobins and searching for common amino acid substitutions that might be responsible for the higher affinities that evolved independently in multiple high-altitude species. When assessing their results across the entire family tree of birds, they found no consistency in the amino acid substitutions that they identified in the high-affinity haemoglobins, suggesting that there are multiple ways to make a high-affinity haemoglobin. However, when they zoomed in on closely related branches within the broad family tree – focusing in particular on the hummingbirds – they noticed a glycine-to-serine substitution that consistently appeared in the high-altitude hummingbirds at a site on the protein (amino acid 83 in the β chain) that could potentially enhance its affinity for oxygen. Intriguingly, a substitution at the same site also appeared in the high-altitude flowerpiercers.This parallelism hinted at a common mechanism for enhancement of oxygen affinity within closely related species, but one that was not effective in more distantly related species. The team tested this hypothesis by reconstructing the ancestral haemoglobin sequences for hummingbirds, flowerpiercers, Neoaves (which are the common ancestor of all extant birds excluding the ducks, chickens and kin) and Neornithes (which are the common ancestor of all currently surviving birds). They used site-directed mutagenesis to introduce the key amino acid substitution in each haemoglobin sequence, and then tasked Escherichia coli with physically manufacturing these ‘new’ haemoglobins. After isolating the proteins, the team measured their oxygen affinities and found that the mutations significantly increased the affinities of ancestral hummingbird and flowerpiercer haemoglobins, but had no effect on the more distantly related haemoglobins. These results plainly showed that despite environmental consistency – all of the species were adapted to high-altitude hypoxia and so the proteins were adapted to increased oxygen affinity – the causal genetic mechanism was only similar among closely related species. When the team zoomed out to look at more distantly related species, they found that other differences dispersed throughout the haemoglobin chains precluded the mutation at β83 from having the same effect on oxygen affinity as it did within the closely related hummingbirds and flowerpiercers. Enhanced affinities therefore evolved in these more distantly related species through a different set of mutations.So, yes, there's more than one way to shine a penny, shoe a horse and catch a rabbit. Now we know that there's also more than one way to evolve a high-affinity haemoglobin. As idioms go, it's a good one, but it should probably be reserved for use among physiologists.

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)0,001
score de la tête « metaresearch » (Gemma)0,003
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: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,021

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0020,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0060,002

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 Opus0,034
Tête enseignante GPT0,311
Écart entre enseignants0,277 · 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
Publié2017
Routes d'admission1
Résumé présentoui

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