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Enregistrement W2083790759 · doi:10.1242/jeb.112128

Seasonal swings match latitudinal shifts in shut down

2015· article· en· W2083790759 sur OpenAlexaff
Katie E. Marshall

Notice bibliographique

RevueJournal of Experimental Biology · 2015
Typearticle
Langueen
DomaineEnvironmental Science
ThématiquePhysiological and biochemical adaptations
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésShut downEnvironmental scienceClimatologyAtmospheric sciencesMeteorologyGeographyGeologyComputer science

Résumé

récupéré en direct d'OpenAlex

Winter is generally not the best time of year for organisms. Temperatures are low and food is scarce, and for those species that can't fly south for the winter, surviving is a matter of hunkering down and making the best of a bad lot. But some individuals are lucky enough to carry different versions of the same gene (alleles), which make them better at surviving the demands of winter than others; particularly those animals endowed with genes that are able to reduce their metabolic demands, allowing them to survive winter with less food. Not surprisingly, these extra-tough creatures are often those that live at higher latitudes where winters tend to be harsher and longer. Now, a team of researchers from Stony Brook University and the University of Pennsylvania, USA, led by Rodrigo Cogni, have shown that the genetic changes associated with survival at higher latitudes in the fruit fly Drosophila melanogaster are also associated with seasonal shifts in metabolism.Drosophila melanogaster is a short-lived fly that can produce a new generation every few weeks in warm weather, so the researchers were curious whether there could be adaptive changes in the frequency at which these tougher alleles are found over the span of seasons, as well as in geographic space. Previously, they had shown that the version of a gene known as couch potato, which is associated with the seasonal shutdown of metabolism, was found more commonly at higher latitudes in spring than at lower latitudes in summer. In their most recent study, they broadened their scope to genes responsible for the major branching points in metabolism to see whether the way that versions of couch potato varied over time and space could be generalized across the major metabolic pathways.The team began by focusing on single nucleotide polymorphisms (SNPs) in 46 genes that code for central metabolic enzymes identified from the 37 different genome sequences for Drosophila melanogaster published in the genetic database Flybase. To study populations along a latitudinal gradient, they collected flies from 20 different populations in eastern North America, from the city of Sudbury in Northern Ontario through to southern Florida. They also collected flies from a single orchard in the southern part of the range several times to study the population over the course of a year.From these collections, Cogni and his team sequenced copies of each of the genes that they were interested in, then estimated how often different base pairs were found at each SNP in each population, then correlated the frequency of each allele to latitude and time of year of collection. They found that 31 out of 46 genes had allele frequencies that changed significantly with latitude, but only two had frequencies that changed with time of year. But those genes whose allele frequencies tended to be highly correlated with latitude also tended to have allele frequencies that were highly correlated with the time of year. And this pattern became stronger when the team looked only at those SNPs that were correlated with latitude, which the authors suggest means that these areas are under particular selection for adaptation to cold weather whether due to seasonal shifts or latitudinal.For a fly out in the cold the challenges are best handled by a well-adapted set of genes. And it turns out that the exact same pathways that adapt flies to a northern climate also help with seasonal shifts too.

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: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,022
Score d'incertitude au seuil0,075

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0220,004

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 Opus0,036
Tête enseignante GPT0,292
Écart entre enseignants0,255 · 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
Publié2015
Routes d'admission1
Résumé présentoui

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