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Identification and characterization of a novel population of hypothalamic neurons sensitive to leptin and GLP-1

2023· article· en· W4378649640 on OpenAlexaboutno aff
Olivier Lavoie, William James Desrosiers, Julie Plamondon, Natalie J. Michael, Alexandre Caron

Bibliographic record

VenuePhysiology · 2023
Typearticle
Languageen
FieldNeuroscience
TopicRegulation of Appetite and Obesity
Canadian institutionsnot available
Fundersnot available
KeywordsArcuate nucleusLeptinArc (geometry)BiologyLeptin receptorPopulationEnergy homeostasisHypothalamusGABAergicGlucose homeostasisInternal medicineEndocrinologyNeuroscienceInhibitory postsynaptic potentialInsulinInsulin resistanceMedicine

Abstract

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The arcuate nucleus of the hypothalamus (ARC) is considered a major site for the integration of metabolic signals and the regulation of energy balance. In the ARC, ‘catabolic’ pro-opiomelanocortin (POMC) and ‘anabolic’ agouti-related peptide (AgRP)-expressing neurons are widely recognized for their role in the regulation of energy homeostasis. Leptin and glucagon-like peptide 1 (GLP-1) are two important hormonal signals of the energy state that can mediate some of their effects through POMC and AgRP neurons. However, recent work suggests that unidentified GABAergic neurons of the ARC may also be crucial for the integration of metabolic signals and the regulation of energy balance. Based on RNAseq studies, we recently identified an uncharacterized neuronal population of the ARC that robustly express the New arcuate transcript ( Nat). According to their localization and GABAergic phenotype, we hypothesize that Nat-expressing neurons are sensitive to various metabolic signals and that they play an important role in the control of energy homeostasis. Therefore, we aimed to characterize the molecular signature of Nat-expressing neurons in the ARC. We also aimed to determine whether leptin and GLP-1 can modulate the activity of these neurons.We first used the RNAscope ® multiplex in situ hybridization technology on coronal brain slices from male mice. This allowed us to validate the GABAergic nature of Nat-expressing neurons, to confirm that they are distinct from POMC and AgRP neurons, and to determine whether they express the leptin and GLP-1 receptors ( Lepr and Glp1r respectively). We then administered leptin and the GLP1R agonist liraglutide to mice and evaluated C-FOS immunoreactivity in Nat-expressing neurons of the ARC.We found that Nat-expressing neurons represent a GABAergic population highly enriched in the ARC, and distinct from POMC and AgRP neurons. Subsets of Nat-expressing neurons co-express Lepr (18.3%), while others co-express Glp1r (35.3%). Finally, C-FOS immunoreactivity suggests that the activity of Nat-expressing neurons can be modulated in response to some metabolic signals. Together, our results suggest a potential role for Nat-expressing neurons in the integration of various metabolic cues. The recent development of a novel Nat-IRES-Cre mouse model will allow us to better characterize the metabolic functions of this new GABAergic neuronal population of the ARC. This work was supported by funding from the Canada Research Chairs Program (to A.C.), the Montreal Diabetes Research Center (to A.C.), the foundation of the Quebec Heart and Lung Institute (FIUCPQ, to A.C), and the Fonds d'enseignement et de recherche (FER) of the Faculty of Pharmacy of Université Laval (to N.J.M. and A.C.). N.J.M. was supported by a Sentinel North Partnered Research Chair in Sleep Pharmacometabolism (Canada First Research Excellence Fund) and a Fonds de Recherche du Québec – Santé (FRQS) Research Scholar J1 award. O.L. was supported by a Canada graduate scholarship (CIHR) and a Fonds de Recherche du Québec – Santé (FRQS) graduate scholarship. This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.184

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.270
Teacher spread0.241 · 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 teacher head, 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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