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机器学习算法揭示了精神分裂症患者死后大脑中的谷氨酸能改变。

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

作者信息

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

机构信息

CEINGE Biotecnologie Avanzate, 80145, Naples, Italy.

Dipartimento di Scienze e Tecnologie Ambientali Biologiche e Farmaceutiche, Università degli Studi della Campania "Luigi Vanvitelli", 81100, Caserta, Italy.

出版信息

Schizophrenia (Heidelb). 2022 Feb 25;8(1):8. doi: 10.1038/s41537-022-00231-1.

Abstract

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.

摘要

精神分裂症是一种突触可塑性和连接异常的疾病,其中谷氨酸突触的主要功能障碍已被提出。然而,针对精神分裂症中谷氨酸信号传导相互作用分子的不同簇的多层次方法仍然缺乏。我们通过高效液相色谱法(HPLC)研究了精神分裂症患者和非精神科对照者的死后背外侧前额叶皮质(DLPFC)和海马中神经活性D-和L-氨基酸(L-谷氨酸、D-丝氨酸、甘氨酸、L-天冬氨酸、D-天冬氨酸)的水平。此外,通过定量逆转录聚合酶链反应(RT-PCR)和蛋白质印迹法,我们分别分析了参与谷氨酸能突触功能的突触前和突触后关键分子的mRNA和蛋白质水平,包括谷氨酸受体(NMDA、AMPA、代谢型)、它们相互作用的支架蛋白(PSD-95、Homer1b/c)、质膜和囊泡谷氨酸转运体(EAAT1、EAAT2、VGluT1、VGluT2)、参与谷氨酸依赖性GABA神经递质合成的酶(GAD65和67),或参与突触后NMDA受体介导信号传导的酶(钙/钙调蛋白依赖性蛋白激酶IIα,CAMKIIα)以及突触前标记物突触素-1。单变量分析显示,与对照组相比,在精神分裂症患者的死后DLPFC和海马中,所研究的分子均无差异表达。尽管如此,多变量假设驱动分析显示,精神分裂症的存在受到神经活性氨基酸水平和谷氨酸相关突触元件变化的显著影响。此外,无假设的机器学习揭示了其他有鉴别意义的分子簇,一个在DLPFC中,另一个在海马中。总体而言,在确认谷氨酸能突触在精神分裂症分子病理生理学中的关键作用的同时,我们报告了包含谷氨酸突触元件的分子特征,这些特征能够区分精神分裂症患者和正常个体。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7176/8881508/3da230974a88/41537_2022_231_Fig1_HTML.jpg

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