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Enregistrement W4389051287 · doi:10.1242/jeb.245037

Cell self-cleaning helps fruit flies handle the heat

2023· article· en· W4389051287 sur OpenAlexaff
Andrea Murillo

Notice bibliographique

RevueJournal of Experimental Biology · 2023
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueInsect behavior and control techniques
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésAutophagyEctothermCell biologyBiologyCellProgrammed cell deathEcologyToxicologyBiochemistryApoptosis

Résumé

récupéré en direct d'OpenAlex

As climate change threatens various species, researchers are particularly interested in how animals can deal with heat, particularly coldblooded (ectothermic) critters whose temperatures fluctuate with the environment. One of the many damaging effects of toasty temperatures starts in the cell. As conditions get hotter, proteins and other molecules in the cell can unfurl, become sticky and form hazardous protein bundles that can damage the rest of the cell. Thankfully, animals have a built-in protective mechanism that can overcome these perils. As damage occurs, animals can activate autophagy – where cell components consume these menacing protein clumps, broken small structures and old proteins to clean up – along with other protective mechanisms. To figure out whether autophagy can also help insects handle the heat, Quentin Willot and colleagues from Stellenbosch University in South Africa were interested in seeing whether an increase in autophagy led to an increase in the fruit flies’ heat tolerance.The team's first step was chemically turning on the cell's self-cleaning system in the flies. To do this, Willot and colleagues fed the fruit flies rapamycin, a chemical that triggers autophagy. They also knew that rapamycin limits growth, so they could tell whether they had successfully activated autophagy because the flies whose self-cleaning process had been triggered would grow more slowly. Additionally, they predicted that there would be an increase in the number of lysosomes – structures within the cell that break down damaged or old cellular components. Sure enough, the researchers found that flies fed rapamycin took 4–5 days longer to develop than flies that were not fed the drug. They also found that flies fed rapamycin had more lysosomes containing damaged cell parts in midgut cells. The researchers had successfully chemically activated the cell's autophagic self-cleaning process.To determine the relationship between the cell's self-cleaning system and heat stress, Willot and the team then measured the ability of flies that had been fed rapamycin for 2 days to withstand heat in two different scenarios. First, they put the flies into glass vials and immersed these vials in a hot water bath at 37°C. Every 15 minutes, the team gently shook the vials, looked for flies that had fallen and could not get up or move, and recorded the time it took them to lose this balance. In a second set of experiments, the team measured how long the rapamycin-fed flies took to recover after they lost balance as a result of the stressful effects of the heat. In this case, the team immersed the flies in a hot water bath at 41°C until they fell and could not get up after a gentle shake. Then, they were immediately put at room temperature and the team monitored how long it took the flies to stand on their legs without toppling or falling over after gentle shaking.Impressively, the team found that the fruit flies that had been fed rapamycin took longer to fall and lose control as a result of heat stress compared with those not fed rapamycin. This suggests that activating autophagy protects the flies, allowing them to handle higher temperatures without losing coordination. Moreover, the researchers found that when the flies were fed rapamycin, they recovered from the ill effects of heat faster, showing that the cells’ self-cleaning defended them from the heat. This is extremely important as it highlights autophagy as a vital process for surviving future environmental stresses, as insects have little control over their internal temperatures.

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)0,000
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: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,037
Score d'incertitude au seuil0,215

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
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 savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,023
Tête enseignante GPT0,268
Écart entre enseignants0,245 · 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
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
Publié2023
Routes d'admission1
Résumé présentoui

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