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Multiplatform metabolome and proteome profiling identifies serum metabolite and protein signatures as prospective biomarkers for schizophrenia

机译:多平台代谢组学和蛋白质组学分析将血清代谢物和蛋白质特征识别为精神分​​裂症的前瞻性生物标志物

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Schizophrenia is a severe mental illness with a biological basis. However, the search for reliable biomarkers suitable for clinical routine has been futile so far. Accordingly, there is a need for innovative approaches such as genomics and proteomics to achieve this goal. In the present study, we compared metabolomic and proteomic data from 26 schizophrenia patients as well as from unaffected controls carefully matched for age and gender in a multi-platform approach. The combined analysis identified many signatures with initially good biomarker characteristics. After statistical analysis and comparison of these identified serum metabolites (analysed by Gas Chromatography Mass Spectrometry) and hydrophobic serum proteins (analysed by matrix-assisted laser desorption ionisation mass spectrometry), several markers (e.g., 2-piperidinec carboxylic acid, 6-deoxy-mannofuranose, galactoseoxime and a serum peptide of m/z 3177) were determined as having the best discriminating value between the groups. Our findings represent a proof of principle indicating that metabolomic and proteomic approaches can be successfully used in psychiatric biomarker research, even though the results should be regarded as preliminary with a need for replication in larger samples.
机译:精神分裂症是一种具有生物学基础的严重精神疾病。然而,到目前为止,寻找适合于临床常规的可靠生物标志物是徒劳的。因此,需要创新的方法例如基因组学和蛋白质组学来实现该目标。在本研究中,我们以多平台方法比较了来自26位精神分裂症患者以及年龄和性别经过仔细匹配的未受影响对照的代谢组学和蛋白质组学数据。组合分析确定了许多最初具有良好生物标志物特征的特征。在对这些鉴定出的血清代谢物(通过气相色谱质谱分析)和疏水性血清蛋白(通过基质辅助激光解吸电离质谱分析)进行统计分析和比较之后,使用了几种标记物(例如2-哌啶子羧酸,6-脱氧-甘露聚糖,半乳糖肟和m / z 3177的血清肽被确定为在组之间具有最佳区分值。我们的发现代表了原理证明,表明代谢组学和蛋白质组学方法可以成功地用于精神病学生物标志物研究,即使该结果应视为初步的,且需要在较大的样本中进行复制。

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