Evidence map›Paper›PMID 25992917›Full record

ArticleJournal of visualized experiments : JoVE2015

Immunohistochemical visualization of hippocampal neuron activity after spatial learning in a mouse model of neurodevelopmental disorders.

Giovanni Provenzano, Luca Pangrazzi, Andrea Poli, Nicoletta Berardi, Yuri Bozzi

Abstract readVideo-Audio Media
In one paragraph

Article in Journal of visualized experiments : JoVE, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Giovanni ProvenzanoCentre for Integrative Biology (CIBIO), University of Trento.
Luca PangrazziCentre for Integrative Biology (CIBIO), University of Trento.
Andrea PoliCNR Neuroscience Institute, Pisa, Italy.
Nicoletta BerardiCNR Neuroscience Institute, Pisa, Italy.
Yuri BozziCentre for Integrative Biology (CIBIO), University of Trento; CNR Neuroscience Institute, Pisa, Italy; yuri.bozzi@unitn.it.

Funding

Telethon GGP13034
6 · The paper itself

Abstract

Induction of phosphorylated extracellular-regulated kinase (pERK) is a reliable molecular readout of learning-dependent neuronal activation. Here, we describe a pERK immunohistochemistry protocol to study the profile of hippocampal neuron activation following exposure to a spatial learning task in a mouse model characterized by cognitive deficits of neurodevelopmental origin. Specifically, we used pERK immunostaining to study neuronal activation following Morris water maze (MWM, a classical hippocampal-dependent learning task) in Engrailed-2 knockout (En2(-/-)) mice, a model of autism spectrum disorders (ASD). As compared to wild-type (WT) controls, En2(-/-) mice showed significant spatial learning deficits in the MWM. After MWM, significant differences in the number of pERK-positive neurons were detected in specific hippocampal subfields of En2(-/-) mice, as compared to WT animals. Thus, our protocol can robustly detect differences in pERK-positive neurons associated to hippocampal-dependent learning impairment in a mouse model of ASD. More generally, our protocol can be applied to investigate the profile of hippocampal neuron activation in both genetic or pharmacological mouse models characterized by cognitive deficits.

Indexed as

Disease Models, AnimalAnimalsCognition DisordersExtracellular Signal-Regulated MAP KinasesFemaleHippocampusImmunohistochemistryMaleMiceMice, KnockoutNeurodevelopmental DisordersNeuronsSpatial LearningExtracellular Signal-Regulated MAP Kinases

Identifiers

PMID25992917
PMCPMC4542650

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.