Journal Highlight: Untargeted metabolomic profiling of seminal plasma in nonobstructive azoospermia men: A noninvasive detection of spermatogenesis

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  • Published: Aug 8, 2017
  • Author: separationsNOW
  • Channels: Laboratory Informatics
thumbnail image: Journal Highlight: Untargeted metabolomic profiling of seminal plasma in nonobstructive azoospermia men: A noninvasive detection of spermatogenesis

Untargeted metabolomic profiling of the seminal plasma in men with nonobstructive azoospermia was studied by GC/MS and advance chemometrics, leading to 36 potential biomarkers of the condition.

Untargeted metabolomic profiling of seminal plasma in nonobstructive azoospermia men: A noninvasive detection of spermatogenesis

Biomedical Chromatography, 2017, 31, online
Kambiz Gilany, Ahmad Mani-Varnosfaderani, Arash Minai-Tehrani, Fateme Mirzajani, Alireza Ghassempour, Mohammed Reza Sadeghi, Mehdi Amini and Hassan Rezadoost

Abstract: 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.

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