Gilany Kambiz, Mani-Varnosfaderani Ahmad, Minai-Tehrani Arash, Mirzajani Fateme, Ghassempour Alireza, Sadeghi Mohammed Reza, Amini Mehdi, Rezadoost Hassan
Reproductive Biotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran.
Department of Chemistry, Faculty of Sciences, Chemometrics Laboratory, Tarbiat Modares University, Tehran, Iran.
Biomed Chromatogr. 2017 Aug;31(8). doi: 10.1002/bmc.3931. Epub 2017 Feb 5.
Male factor infertility is involved in almost half of all infertile couples. Lack of the ejaculated sperm owing to testicular malfunction has been reported in 6-10% of infertile men, a condition named nonobstructive azoospermia (NOA). In this study, we investigated untargeted metabolomic profiling of the seminal plasma in NOA men using gas chromatography-mass spectrometry and advance chemometrics. In this regard, the seminal plasma fluids of 11 NOA men with TESE-negative, nine NOA men with TESE-positive and 10 fertile healthy men (as a control group) were collected. Quadratic discriminate analysis (QDA) technique was implemented on total ion chromatograms (TICs) for identification of discriminatory retention times. We developed multivariate classification models using the QDA technique. Our results revealed that the developed QDA models could predict the classes of samples using their TIC data. The receiver operating characteristic curves for these models were >0.88. After recognition of discriminatory retention time's asymmetric penalized least square, evolving factor analysis, correlation optimized warping and alternating least squares strategies were applied for preprocessing and deconvolution of the overlapped chromatographic peaks. We could identify 36 discriminatory metabolites. These metabolites may be considered discriminatory biomarkers for different groups in NOA.
男性因素导致的不孕不育几乎占所有不孕夫妇的一半。据报道,6%至10%的不育男性存在因睾丸功能障碍导致射精精子缺乏的情况,这种病症被称为非梗阻性无精子症(NOA)。在本研究中,我们使用气相色谱 - 质谱联用技术和先进的化学计量学方法,对NOA男性的精浆进行了非靶向代谢组学分析。在这方面,收集了11例睾丸精子提取术(TESE)阴性的NOA男性、9例TESE阳性的NOA男性以及10例生育能力正常的健康男性(作为对照组)的精浆样本。对总离子色谱图(TICs)实施二次判别分析(QDA)技术,以识别具有鉴别性的保留时间。我们使用QDA技术开发了多变量分类模型。我们的结果表明,所开发的QDA模型能够利用其TIC数据预测样本类别。这些模型的受试者工作特征曲线大于0.88。在识别出具有鉴别性的保留时间后,应用非对称惩罚最小二乘法、渐进因子分析、相关优化曲线拟合法和交替最小二乘法策略对重叠色谱峰进行预处理和解卷积。我们能够识别出36种具有鉴别性的代谢物。这些代谢物可被视为NOA中不同组别的鉴别性生物标志物。