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A highly collateralized thalamic cell type with arousal-predicting activity serves as a key hub for graded state transitions in the forebrain.

ABSTRACT: Sleep cycles consist of rapid alterations between arousal states, including transient perturbation of sleep rhythms, microarousals, and full-blown awake states. Here we demonstrate that the calretinin (CR)-containing neurons in the dorsal medial thalamus (DMT) constitute a key diencephalic node that mediates distinct levels of forebrain arousal. Cell-type-specific activation of DMT/CR⁺ cells elicited active locomotion lasting for minutes, stereotyped microarousals, or transient disruption of sleep rhythms, depending on the parameters of the stimulation. State transitions could be induced in both slow-wave and rapid eye-movement sleep. The DMT/CR⁺ cells displayed elevated activity before arousal, received selective subcortical inputs, and innervated several forebrain sites via highly branched axons. Together, these features enable DMT/CR⁺ cells to summate subcortical arousal information and effectively transfer it as a rapid, synchronous signal to several forebrain regions to modulate the level of arousal.

SUBMITTER: Matyas F

PROVIDER: S-EPMC6441588 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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A highly collateralized thalamic cell type with arousal-predicting activity serves as a key hub for graded state transitions in the forebrain.

Mátyás Ferenc F Komlósi Gergely G Babiczky Ákos Á Kocsis Kinga K Barthó Péter P Barsy Boglárka B Dávid Csaba C Kanti Vivien V Porrero Cesar C Magyar Aletta A Szűcs Iván I Clasca Francisco F Acsády László L

Nature neuroscience 20181022 11

Sleep cycles consist of rapid alterations between arousal states, including transient perturbation of sleep rhythms, microarousals, and full-blown awake states. Here we demonstrate that the calretinin (CR)-containing neurons in the dorsal medial thalamus (DMT) constitute a key diencephalic node that mediates distinct levels of forebrain arousal. Cell-type-specific activation of DMT/CR<sup>+</sup> cells elicited active locomotion lasting for minutes, stereotyped microarousals, or transient disrup ...[more]

PMID: 30349105

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Project description:BackgroundProtein-protein interactions mediate a wide range of cellular functions and responses and have been studied rigorously through recent large-scale proteomics experiments and bioinformatics analyses. One of the most important findings of those endeavours was the observation that 'hub' proteins participate in significant numbers of protein interactions and play critical roles in the organization and function of cellular protein interaction networks (PINs) 12. It has also been demonstrated that such hub proteins may constitute an important pool of attractive drug targets.Thus, it is crucial to be able to identify hub proteins based not only on experimental data but also by means of bioinformatics predictions.ResultsA hub protein classifier has been developed based on the available interaction data and Gene Ontology (GO) annotations for proteins in the Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens genomes. In particular, by utilizing the machine learning method of boosting trees we were able to create a predictive bioinformatics tool for the identification of proteins that are likely to play the role of a hub in protein interaction networks. Testing the developed hub classifier on external sets of experimental protein interaction data in Methicillin-resistant Staphylococcus aureus (MRSA) 252 and Caenorhabditis elegans demonstrated that our approach can predict hub proteins with a high degree of accuracy.A practical application of the developed bioinformatics method has been illustrated by the effective protein bait selection for large-scale pull-down experiments that aim to map complete protein-protein interaction networks for several species.ConclusionThe successful development of an accurate hub classifier demonstrated that highly-connected proteins tend to share certain relevant functional properties reflected in their Gene Ontology annotations. It is anticipated that the developed bioinformatics hub classifier will represent a useful tool for the theoretical prediction of highly-interacting proteins, the study of cellular network organizations, and the identification of prospective drug targets - even in those organisms that currently lack large-scale protein interaction data.

Dataset Information

A highly collateralized thalamic cell type with arousal-predicting activity serves as a key hub for graded state transitions in the forebrain.

Publications

A highly collateralized thalamic cell type with arousal-predicting activity serves as a key hub for graded state transitions in the forebrain.

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