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Record W4379388868 · doi:10.1242/jeb.245011

Jellyfish are sated thanks to GLWamide

2023· article· en· W4379388868 on OpenAlexaff
Andrea Murillo

Bibliographic record

VenueJournal of Experimental Biology · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Invertebrate Physiology and Ecology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsJellyfishBiologyPredationOrganismBrine shrimpZoologyEcologyGenetics

Abstract

fetched live from OpenAlex

At some point, we have all sat down to an excellent meal and after devouring as much as we could, no longer had the hankering to eat again for some time. So, what tells our bodies to stop eating? Hormones and chemicals in our brains called neurotransmitters guide the process of feeling full, but how far back in evolution did those processes become established? Vladimiros Thoma and colleagues from Tohoku University, Japan, with collaborators from other universities in Japan, set out to answer this very question by investigating the origins of these chemicals that control appetite in the jellyfish Cladonema pacificum.To identify the genes that regulate feeding in jellyfish, the team investigated which genes are activated or switched off during feeding by comparing famished with recently fed jellyfish. The researchers found that recently fed jellyfish increased the expression of several genes involved in nerve cell activity, leading the scientists to focus on proteins involved in communication between nerve cells. After testing 43 different proteins – a truly Herculean effort – the researchers narrowed the molecules that regulate satiety down to just a few, including one called GLWamide.When the Cladonema jellyfish feed, they ensnare prey such as brine shrimp with their tentacles, pull them back into their bell and ingest the shrimp. To see what part of this feeding ritual GLWamide impacted, the researchers measured how effectively the jellyfish captured their prey and how swiftly their tentacles retracted if the jellyfish were given GLWamide while still hungry. Thoma and colleagues found that hungry jellyfish were not as good at capturing prey when given GLWamide, but they were still better than jellyfish that had just eaten. The hungry animals dosed with GLWamide delayed tentacle contraction when they were fed just like the ones that had already had their fill. This suggests that GLWamide is an appetite suppressor that stops the tentacles from contracting. Next, the team wanted to see where the GLWamide was produced in the jellyfish's body and whether the levels of GLWamide changed based on when the jellyfish were fed. The researchers found that levels of GLWamide in the base of the tentacle increased 3–6 h after feeding, which may be how it suppressed the appetite of the jellyfish.Finally, the team wanted to explore the possibility that GLWamide represents an ancestral signalling system for appetite suppression in all animals. To do this, the researchers looked at a related chemical, called myoinhibitory peptide, in fruit flies. When the hungry jellyfish were given fruit fly myoinhibitory peptide, it reduced the jellyfish's appetite. Additionally, when the researchers gave fruit flies GLWamide, it reduced their appetites as well, showing that GLWamide represents an ancestral signal for appetite suppression. Thoma and colleagues also point out the importance of investigating the function of these chemicals in even more ancient animals than jellyfish, such as single-celled organisms, to discover the true origin of these appetite-suppressing systems. So, the next time you are feeling full after a huge meal, know that your body is working just like it has been programmed to for millions of years.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0090.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.266
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
Has abstractyes

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