A potential tool for diagnosis of male infertility: Plasma metabolomics based on GC–MS
Graphical abstract
Introduction
Infertility, defined as the failure of a couple to conceive after one year of regular sexual activity without a contraceptive method, is regarded to be a major public health problem [1]. It is conservatively estimated that the male partner contributes approximately 30–55% of human infertility in reproductive-age couples [2]. Male infertility (MI) patients are classified into two phenotypes according to the presence or absence of normal sexual function [3]. The MI with normal sexual function (e.g., asthenospermia, oligozoospermia, azoospermia) and MI with sexual dysfunction (e.g., erectile dysfunction) have different pathogenesis. Erectile dysfunction (ED), a kind of sexual dysfunction, was defined as the inability to produce or maintain an erection for the sexual intercourse. It has a significant impact on quality of life of millions of men [4]. Furthermore, erectile dysfunction is less responsive to normal sperm examination. The incidence of the patients with normal sexual function (the so called semen abnormalities, SA) is usually accompanied by qualitative (e.g., asthenospermia) and quantitative (e.g., azoospermia) abnormalities of sperm [5].
In addition to a medical history questionnaire, physical examination, endocrine evaluation and so on, the most common diagnosis of male infertility involves the evaluation of basic semen analysis (sperm concentration, motility, and morphology) [6]. Moreover, some diagnostic techniques (e.g., testicular biopsy) that are invasive and may cause testicular damage are usually applied for the men who have normal or inconclusive results in routine tests to give a definite result [7]. These tests were labor intensive, time consuming and uncomfortable or unacceptable for patients. Furthermore, the molecular mechanisms underlying MI are still obscure. Specifically, 60–75% of MI patients are given descriptive diagnosis that do not provide a clear cause for their idiopathic infertility [8]. Therefore, it is necessary to find a non-invasive platform and screen robust biomarkers in clinical diagnosis and molecular mechanism study of ED and SA.
Metabolomics is a newly developed approach for systems biology. It encompasses the comprehensive and systematic profiling of metabolites and cellular and systemic changes in response to environmental influences, genetic modulations, and disease or drug perturbations [9]. Equipped with advanced high-throughput analytical technologies and bioinformatics tools, metabolomics has become an irreplaceable technology to provide for assessment of global changes in small molecular signatures [10]. These assets make metabolomics suitable for the system analysis in MI.
In metabolic profiling studies of MI, seminal fluid is usually used, but the production of a semen sample is an embarrassing, difficult, and stressful experience [11], [12], [13]. Instead, plasma or serum may be better potential biologic matrix as they are easy and convenient to acquire [14]. Recently, serum metabolic profiles from an uncontrolled population presenting different sperm concentrations were found to be significantly different, and the peptides related to the protein complement C3f were identified as putative biomarkers. The plasma metabolome can indicate global bodily functional changes [15]. Many previous studies have highlighted the intimate correlation between plasma and several diseases, including isolated post-challenge diabetes [16], metabolic syndrome [17], and sepsis-induced acute lung injury [18]. However, with regards to MI in particular, no comprehensive plasma metabolomics study based on GC–MS has been conducted to date.
Gas chromatography–mass spectrometry (GC–MS) was used as a robust analytical tool in metabolomics, due to high separation efficiency, sensitive detection and good reproducibility to resolve the complex biological mixtures [19], [20]. Besides, the identification of metabolites based on GC–MS is convenient by comparing their mass spectrum and retention index with authentic reference standards or commercial libraries [21].
A cornerstone of metabolomics study is the analysis of large amount and multi-dimensional data obtained from these technologies [17]. As a solution, many chemometrics methods were employed to project the multivariate data into lower dimensions and explore differences between groups of samples [22]. Partial least squares discriminant analysis (PLS-DA) is the most attractive method in metabolomics research among all known methods [23], [24], [25]. PLS-DA can provide good insight into the causes of discrimination via weights and loadings, which gives it a unique role in exploratory data analysis, for example in metabolomics via visualization of significant variables such as metabolites [26]. There are several PLS-DA based variable selection methods used for screening accurate and robust biomarkers, including the variable importance on projection (VIP), original coefficients of PLS-DA (β) and so on.
In this study, to characterize plasma profiles in male infertility, a metabolomics method was proposed, based on GC–MS and PLS-DA followed by two variable selection methods. The objective of this preliminary study was to comprehensively investigate whether the plasma metabolome can be used to differentiate fertile individuals from MI patients including ED and SA, subsequently to explore the potential biomarkers for the complementary prognosis of ED and SA, respectively.
Section snippets
Chemicals and reagents
BSTFA+1% TMCS (N,O-bis (trimethylsilyl) trifluoroacetamide with 1% trimethylchlorosilane, for GC) (>99.0% purity), methoxyamine hydrochloride (>98.0% purity) and pyridine (>99.8% purity), internal standard 2-isopropylmalic acid (>98.0% purity) and the other 18 chemical standards of metabolites (shown in Table 1) were commercially obtained from Sigma-Aldrich (St. Louis, MO, USA). High performance liquid chromatography (HPLC) grade methanol was purchased from the Tedia Company (Inc., Fairfield,
GC–MS profiles of plasma samples
The typical total ion chromatograms (TICs) of plasma metabolic profiling for a healthy control, a SA patient and an ED patient are shown in Fig. 1. As can be seen in Fig. 1, the plasma metabolites in all groups were similar, while the concentrations of some metabolites were different. The result illustrated that these GC–MS profiles could represent the differences among three groups.
Thirty-eight metabolites were qualitatively and quantitatively analyzed in detail, as shown in Table 1. Among the
Conclusions
In summary, this study demonstrated that using GC–MS method followed by multivariate statistical analysis, plasma profile could discriminate the controls, SA patients and ED patients. Moreover, the identified potential biomarkers could be used to distinguish SA patients from healthy controls and ED patients from healthy controls by two variable selection methods based on PLS-DA. Two metabolites, 1,5-anhydro-sorbitol and α-hydroxyisovaleric acid were selected as common biomarker candidates for
Acknowledgments
The authors gratefully thank the National Natural Science Foundation of China for support of the projects (Grant nos. 21175157 and 21375151).
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