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Enregistrement W4318619587 · doi:10.1242/jeb.244991

Did microRNAs make octopuses smart?

2023· article· en· W4318619587 sur OpenAlexaff
Brittney G. Borowiec

Notice bibliographique

RevueJournal of Experimental Biology · 2023
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueCephalopods and Marine Biology
Établissements canadiensUniversity of Waterloo
Organismes subventionnairesnon disponible
Mots-clésoctopus (software)CephalopodBiologyMessenger RNAGeneNeuroscienceGeneticsEcologyChemistry

Résumé

récupéré en direct d'OpenAlex

Octopuses are geniuses: they can unscrew jars from the inside, solve mazes and pull off Houdini-like feats of disguise and escape. Bizarrely, there aren't many compelling explanations for how octopuses are so smart; although their brains are large, the genes and proteins used to build them are similar to those of less intelligent invertebrates such as oysters. Fascinated by the mysterious ‘how’ of the cephalopod nervous system, an international team of researchers led by Grygoriy Zolotarov, affiliated with the Max Delbrück Center for Molecular Medicine, Germany, used bioinformatics to delve into the mind of the octopus. Their work suggests that the brilliance of the octopus lies not in what they have but in how they use it.All of the information required to build cells is stored within the nucleus in the DNA, and this is translated into messenger RNA (mRNA) for protein synthesis. Perhaps, reasoned the researchers, octopuses were modifying their mRNA in some way to use their genes in unorthodox ways, allowing them to construct their bizarre nervous system. But when they tested their hypothesis on the common octopus, Octopus vulgaris, it fell apart. After characterizing the amount and types of mRNA in 18 different tissues, including areas of the brain and a cluster of nerve cells in the gut, the researchers realized that the octopus's mRNA didn't have many unusual features that made the molecules stand out from the mRNA of other invertebrates – except for one thing. Many of the octopus mRNAs had unusually long tails compared with those of other invertebrates. Tails are an important part of mRNA, as proteins and other molecules use them as handles to grab onto the molecules. A longer tail could change how that process works and add an extra layer of control to the mRNA.The researchers turned their attention to one well-known type of tail-grabber: microRNA (miRNA). These are tiny, hairpin-shaped molecules that can mark mRNA for disposal or prevent the message from being translated in protein by cellular machines. By halting protein production, miRNAs control the kinds and amounts of proteins that cells make. Octopuses have an extraordinary repertoire of miRNAs with at least 164 miRNA genes belonging to 138 families at their disposal, compared with oysters, which have only 20, putting octopuses on a par with zebrafish and chickens. miRNA expansions of this degree are exceedingly rare, having only been observed in the intellectual rivals of octopuses: vertebrates.How much of this expanded miRNA repertoire is concerned with brain power? A lot. Thirty-four of the 43 miRNA families only found in octopuses (and not in close relatives such as squids) are concentrated in neural tissue. Many of these miRNAs were super active in embryos and hatchlings: exactly as expected if they were crucial for the development of a complex nervous system. And, when the team compared the common octopus's miRNAs with those of a far-flung relative, the California two-spot octopus (Octopus bimaculoides), they were quite similar, despite 50 million years of evolution between them, suggesting that they were useful enough to keep (or at least not worth throwing out). Overall, the new miRNAs showed up in the right places and at the right times to imply that they played key roles in nervous system development and evolution.In short, it seems that great minds think alike. While we can't know what chickens, zebrafish or octopuses are really thinking, it seems that they all owe some of their brainpower to vast repertories of miRNAs. The alien brain of the octopus is perhaps not so alien after all.

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)0,002
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 enseignants0,004
Score d'incertitude au seuil0,013

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

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 Opus0,028
Tête enseignante GPT0,283
Écart entre enseignants0,256 · 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
Publié2023
Routes d'admission1
Résumé présentoui

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