Woo Hye-In, Chun Mi-Ryung, Yang Jeong-Soo, Lim Shinn-Won, Kim Min-Ji, Kim Seon-Woo, Myung Woo-Jae, Kim Doh-Kwan, Lee Soo-Youn
Department of Laboratory Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Korea.
CNS Neurosci Ther. 2015 May;21(5):417-24. doi: 10.1111/cns.12372. Epub 2015 Jan 22.
Amino acids are important body metabolites and seem to be helpful for understanding pathogenesis and predicting therapeutic response in major depressive disorder (MDD). We performed amino acid profiling to discover potential biomarkers in major depressive patients treated with selective serotonin reuptake inhibitors (SSRIs).
Amino acid profiling using aTRAQ™ kits for Amino Acid Analysis in Physiological Fluids on a liquid chromatography-tandem mass spectrometry (LC-MS/MS) system was performed on 158 specimens at baseline and at 6 weeks after the initiation of SSRI treatment for 68 patients with MDD and from 22 healthy controls.
Baseline alpha-aminobutyric acid (ABA) discriminated the patients according to the therapeutic response. Plasma glutamic acid concentration and glutamine/glutamic acid ratio were different between before and after SSRI treatment only in the response group. Comparing patients with MDD with healthy controls, alterations of ten amino acids, including alanine, beta-alanine, beta-aminoisobutyric acid, cystathionine, ethanolamine, glutamic acid, homocystine, methionine, O-phospho-L-serine, and sarcosine, were observed in MDD.
Metabolism of amino acids, including ABA and glutamic acid, has the potential to contribute to understandings of pathogenesis and predictions of therapeutic response in MDD.
氨基酸是重要的人体代谢产物,似乎有助于理解重度抑郁症(MDD)的发病机制并预测治疗反应。我们进行了氨基酸谱分析,以发现接受选择性5-羟色胺再摄取抑制剂(SSRI)治疗的重度抑郁症患者的潜在生物标志物。
使用用于生理体液中氨基酸分析的aTRAQ™试剂盒,在液相色谱-串联质谱(LC-MS/MS)系统上对68例MDD患者和22例健康对照者的158份标本在基线时以及SSRI治疗开始后6周进行氨基酸谱分析。
基线α-氨基丁酸(ABA)根据治疗反应区分患者。仅在反应组中,SSRI治疗前后血浆谷氨酸浓度和谷氨酰胺/谷氨酸比值有所不同。将MDD患者与健康对照者进行比较,在MDD中观察到十种氨基酸的变化,包括丙氨酸、β-丙氨酸、β-氨基异丁酸、胱硫醚、乙醇胺、谷氨酸、同型半胱氨酸、蛋氨酸、O-磷酸-L-丝氨酸和肌氨酸。
包括ABA和谷氨酸在内的氨基酸代谢可能有助于理解MDD的发病机制并预测治疗反应。