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Machine Learning algorithm unveils glutamatergic alterations in the post-mortem schizophrenia brain

Schizophrenia is a disorder of synaptic plasticity and aberrant connectivity in which a major dysfunction in glutamate synapse has been suggested. However, a multi-level approach tackling diverse clusters of interacting molecules of the glutamate signaling in schizophrenia is still lacking. We inves...

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Autores principales: De Rosa, Arianna, Fontana, Andrea, Nuzzo, Tommaso, Garofalo, Martina, Di Maio, Anna, Punzo, Daniela, Copetti, Massimiliano, Bertolino, Alessandro, Errico, Francesco, Rampino, Antonio, de Bartolomeis, Andrea, Usiello, Alessandro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8881508/
https://www.ncbi.nlm.nih.gov/pubmed/35217646
http://dx.doi.org/10.1038/s41537-022-00231-1
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author De Rosa, Arianna
Fontana, Andrea
Nuzzo, Tommaso
Garofalo, Martina
Di Maio, Anna
Punzo, Daniela
Copetti, Massimiliano
Bertolino, Alessandro
Errico, Francesco
Rampino, Antonio
de Bartolomeis, Andrea
Usiello, Alessandro
author_facet De Rosa, Arianna
Fontana, Andrea
Nuzzo, Tommaso
Garofalo, Martina
Di Maio, Anna
Punzo, Daniela
Copetti, Massimiliano
Bertolino, Alessandro
Errico, Francesco
Rampino, Antonio
de Bartolomeis, Andrea
Usiello, Alessandro
author_sort De Rosa, Arianna
collection PubMed
description Schizophrenia is a disorder of synaptic plasticity and aberrant connectivity in which a major dysfunction in glutamate synapse has been suggested. However, a multi-level approach tackling diverse clusters of interacting molecules of the glutamate signaling in schizophrenia is still lacking. We investigated in the post-mortem dorsolateral prefrontal cortex (DLPFC) and hippocampus of schizophrenia patients and non-psychiatric controls, the levels of neuroactive d- and l-amino acids (l-glutamate, d-serine, glycine, l-aspartate, d-aspartate) by HPLC. Moreover, by quantitative RT-PCR and western blotting we analyzed, respectively, the mRNA and protein levels of pre- and post-synaptic key molecules involved in the glutamatergic synapse functioning, including glutamate receptors (NMDA, AMPA, metabotropic), their interacting scaffolding proteins (PSD-95, Homer1b/c), plasma membrane and vesicular glutamate transporters (EAAT1, EAAT2, VGluT1, VGluT2), enzymes involved either in glutamate-dependent GABA neurotransmitter synthesis (GAD65 and 67), or in post-synaptic NMDA receptor-mediated signaling (CAMKIIα) and the pre-synaptic marker Synapsin-1. Univariable analyses revealed that none of the investigated molecules was differently represented in the post-mortem DLPFC and hippocampus of schizophrenia patients, compared with controls. Nonetheless, multivariable hypothesis-driven analyses revealed that the presence of schizophrenia was significantly affected by variations in neuroactive amino acid levels and glutamate-related synaptic elements. Furthermore, a Machine Learning hypothesis-free unveiled other discriminative clusters of molecules, one in the DLPFC and another in the hippocampus. Overall, while confirming a key role of glutamatergic synapse in the molecular pathophysiology of schizophrenia, we reported molecular signatures encompassing elements of the glutamate synapse able to discriminate patients with schizophrenia and normal individuals.
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spelling pubmed-88815082022-03-17 Machine Learning algorithm unveils glutamatergic alterations in the post-mortem schizophrenia brain De Rosa, Arianna Fontana, Andrea Nuzzo, Tommaso Garofalo, Martina Di Maio, Anna Punzo, Daniela Copetti, Massimiliano Bertolino, Alessandro Errico, Francesco Rampino, Antonio de Bartolomeis, Andrea Usiello, Alessandro Schizophrenia (Heidelb) Article Schizophrenia is a disorder of synaptic plasticity and aberrant connectivity in which a major dysfunction in glutamate synapse has been suggested. However, a multi-level approach tackling diverse clusters of interacting molecules of the glutamate signaling in schizophrenia is still lacking. We investigated in the post-mortem dorsolateral prefrontal cortex (DLPFC) and hippocampus of schizophrenia patients and non-psychiatric controls, the levels of neuroactive d- and l-amino acids (l-glutamate, d-serine, glycine, l-aspartate, d-aspartate) by HPLC. Moreover, by quantitative RT-PCR and western blotting we analyzed, respectively, the mRNA and protein levels of pre- and post-synaptic key molecules involved in the glutamatergic synapse functioning, including glutamate receptors (NMDA, AMPA, metabotropic), their interacting scaffolding proteins (PSD-95, Homer1b/c), plasma membrane and vesicular glutamate transporters (EAAT1, EAAT2, VGluT1, VGluT2), enzymes involved either in glutamate-dependent GABA neurotransmitter synthesis (GAD65 and 67), or in post-synaptic NMDA receptor-mediated signaling (CAMKIIα) and the pre-synaptic marker Synapsin-1. Univariable analyses revealed that none of the investigated molecules was differently represented in the post-mortem DLPFC and hippocampus of schizophrenia patients, compared with controls. Nonetheless, multivariable hypothesis-driven analyses revealed that the presence of schizophrenia was significantly affected by variations in neuroactive amino acid levels and glutamate-related synaptic elements. Furthermore, a Machine Learning hypothesis-free unveiled other discriminative clusters of molecules, one in the DLPFC and another in the hippocampus. Overall, while confirming a key role of glutamatergic synapse in the molecular pathophysiology of schizophrenia, we reported molecular signatures encompassing elements of the glutamate synapse able to discriminate patients with schizophrenia and normal individuals. Nature Publishing Group UK 2022-02-25 /pmc/articles/PMC8881508/ /pubmed/35217646 http://dx.doi.org/10.1038/s41537-022-00231-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
De Rosa, Arianna
Fontana, Andrea
Nuzzo, Tommaso
Garofalo, Martina
Di Maio, Anna
Punzo, Daniela
Copetti, Massimiliano
Bertolino, Alessandro
Errico, Francesco
Rampino, Antonio
de Bartolomeis, Andrea
Usiello, Alessandro
Machine Learning algorithm unveils glutamatergic alterations in the post-mortem schizophrenia brain
title Machine Learning algorithm unveils glutamatergic alterations in the post-mortem schizophrenia brain
title_full Machine Learning algorithm unveils glutamatergic alterations in the post-mortem schizophrenia brain
title_fullStr Machine Learning algorithm unveils glutamatergic alterations in the post-mortem schizophrenia brain
title_full_unstemmed Machine Learning algorithm unveils glutamatergic alterations in the post-mortem schizophrenia brain
title_short Machine Learning algorithm unveils glutamatergic alterations in the post-mortem schizophrenia brain
title_sort machine learning algorithm unveils glutamatergic alterations in the post-mortem schizophrenia brain
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8881508/
https://www.ncbi.nlm.nih.gov/pubmed/35217646
http://dx.doi.org/10.1038/s41537-022-00231-1
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