Liang Qun, Liu Han, Li Xiuli, Hairong Panguo, Sun Peiyang, Yang Yang, Du Chunpeng
ICU Center, First Affiliated Hospital, School of Pharmacy, Heilongjiang University of Chinese Medicine Heping Road 24, Xiangfang District Harbin 150040 China
Simon Fraser University (SFU) Burnaby British Columbia Canada.
RSC Adv. 2019 Jan 24;9(6):3351-3358. doi: 10.1039/c8ra07572g. eCollection 2019 Jan 22.
High-throughput metabolic profiling technology has been used for biomarker discovery and to reveal underlying metabolic mechanisms. Sepsis-induced myocardial dysfunction (SMD) is a common complication in sepsis patients, and severely affects their quality of life. However, the pathogenesis of SMD is currently unclear, and there has been inadequate basic research. In this study, metabolic profiling was explored by liquid chromatography/mass spectrometry (LC/MS) combined with chemometrics and bioinformatic analysis. The global metabolome data were analyzed using chemometrics analysis including principal component analysis and partial least squares discriminant analysis for significant metabolites. Variable importance for projection values obtained utilizing a pattern recognition method were used to identify potential biomarkers. The differential metabolites were putatively identified using the metabolome database and bioinformatics analysis was conducted Ingenuity Pathway Analysis (IPA) to predict the likely functional alterations. In total, 21 differential metabolites were found in SMD and these were involved in phenylalanine, tyrosine and tryptophan biosynthesis, arachidonic acid metabolism, glycine, serine and threonine metabolism, and so on. The analysis revealed that the metabolites were strongly related to molecular transport, and small molecule biochemistry metabolic pathways. The present study indicates that high-throughput metabolic profiling, combined with chemometrics and a bioinformatic platform, can reveal the likely functional alterations in disease and could provide more precise and credible information in the basic research of disease pathogenesis.
高通量代谢谱技术已被用于生物标志物的发现和揭示潜在的代谢机制。脓毒症诱导的心肌功能障碍（SMD）是脓毒症患者常见的并发症，严重影响其生活质量。然而，SMD的发病机制目前尚不清楚，基础研究也不足。在本研究中，通过液相色谱/质谱联用（LC/MS）结合化学计量学和生物信息学分析来探索代谢谱。利用化学计量学分析，包括主成分分析和偏最小二乘判别分析，对显著代谢物的全局代谢组数据进行分析。利用模式识别方法获得的投影变量重要性值用于识别潜在的生物标志物。使用代谢组数据库对差异代谢物进行初步鉴定，并进行 Ingenuity 通路分析（IPA）以预测可能的功能改变。在SMD中总共发现了21种差异代谢物，它们参与苯丙氨酸、酪氨酸和色氨酸的生物合成、花生四烯酸代谢、甘氨酸、丝氨酸和苏氨酸代谢等。分析表明，这些代谢物与分子转运和小分子生物化学代谢途径密切相关。本研究表明，高通量代谢谱结合化学计量学和生物信息学平台，可以揭示疾病中可能的功能改变，并能在疾病发病机制的基础研究中提供更精确和可靠的信息。
