Metabolomic Analysis Reveals a Unique Urinary Pattern in

Article pubs.acs.org/jpr

Metabolomic Analysis Reveals a Unique Urinary Pattern in Normozoospermic Infertile Men Jie Zhang,† Xiaoli Mu,† Yankai Xia,‡ Francis L Martin,§ Wei Hang,∥ Liangpo Liu,† Meiping Tian,† Qingyu Huang,† and Heqing Shen*,† †

Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road, Xiamen 361021, China ‡ Key Laboratory of Reproductive Medicine, Institute of Toxicology, Nanjing Medical University, Nanjing, China § Centre for Biophotonics, Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, U.K. ∥ Department of Chemistry, Key Laboratory of Analytical Sciences, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China S Supporting Information *

ABSTRACT: Normozoospermic infertility has become a common and important health problem worldwide. We designed this metabolomic case-control study to investigate the possible mechanism and urinary biomarkers of normozoospermic infertility. Normozoospermic infertile cases (n = 71) and fertile controls (n = 47) were recruited. A urinary metabolome pattern could discriminate normozoospermic infertile cases from fertile controls. A total of 37 potential biomarkers were identified; these have functionally important roles in energy production, antioxidation, and hormone regulation in spermatogenesis. This gave rise to a combined biomarker pattern of leukotriene E4, 3-hydroxypalmitoylcarnitine, aspartate, xanthosine, and methoxytryptophan pointing to a diagnostic capability (AUC = 0.901, sensitivity = 85.7%, and specificity = 86.8%) in a ROC model; these markers may highlight keynote events of normozoospermic infertility. Stalled medium- and long-chain fatty acid metabolism with improved ketone body metabolism, plus decreased levels of malate and aspartate could result in citrate cycle alterations via a malate-aspartate shuttle in ATP generation in spermatogenesis. Inhibitory alterations in the normal hormone-secreting activity in spermatogenesis were suggested in normozoospermic infertility. Folate deficiency and oxidative stress may jointly impact infertile patients. The disruption of eicosanoid metabolism and xanthine oxidase system, which were tightly associated with energy metabolism and oxidative stress, was also a potential underlying mechanism. In addition, depression might be associated with normozoospermic infertility via neural activity-related metabolites. This study suggests that the urinary metabolome can be used to differentiate normozoospermic infertile men from fertile individuals. Potential metabolic biomarkers derived from these analyses might be used to diagnose what remains a somewhat idiopathic condition and provide functional insights into its pathogenesis. KEYWORDS: biomarkers, male infertility, metabolomics, multivariate analysis, normozoospermic infertility, urine

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INTRODUCTION The male partner is responsible for approximately 60% of human infertility in reproductive-age couples, but the evaluation of this factor is often underestimated or postponed.1 This is because millions of men are infertile without exhibiting a clear clinical indicator. In addition to a medical history questionnaire, physical examination, karyotype analysis, Ychromosome microdeletion, and endocrine analysis, the most common diagnoses of male infertility are dependent on the results of semen quality. Despite these andrological diagnoses, men who have normal or inconclusive results in these tests, the more invasive surgical intervention (e.g., testicular biopsy) is usually applied to give a definite result. Therefore, a need to develop a noninvasive means of diagnosis is emerging, © 2014 American Chemical Society

especially for normozoospermic infertile patients without any other clinical implication; these are individuals whose total number of spermatozoa, the percentages of progressively motile and morphologically normal spermatozoa are equal to or above the lower reference limits.2 More importantly, very little is known about the molecular mechanisms underlying normozoospermic infertility. “Omics” analyses could lend insights at a molecular level into biological interactions from the genomic, proteomic, to metabolomic in a systems biology fashion; such approaches may generate signatures of male infertility and allow better Received: March 27, 2014 Published: May 6, 2014 3088

dx.doi.org/10.1021/pr5003142 | J. Proteome Res. 2014, 13, 3088−3099

Journal of Proteome Research

Article

function testing, hysteroscopy, β-ultrasonic examination, immunological tests, microbial inspection, karyotype analysis, and their reproductive function was evaluated as normal. The men had undergone complete historical and physical examination, hormone measurements, and evaluation of specific conditions associated with infertility. In order to exclude the influence of other confounders contributing to male infertility, we excluded the subject with any of the following conditions: (1) The subjects with small testis, abnormal sexual and ejaculatory functions, semen nonliquefaction were excluded. (2) The subjects with a history of cryptorchidism, vascular trauma, orchitis, vas deferens obstruction, vasectomy were excluded. (3) The subjects with medical history of infertile risk factors (varicocele, postvasectomy, or orchidopexy) and the subjects receiving hormonal treatments for infertility were excluded. (4) The subjects with other known causes (genetic disease, infection, and occupational exposure to the agents) related to male infertility were also excluded from the study. Controls were fertile men recruited and sampled during the same period. They were identified from the early pregnancy registry at the same hospitals; their partners were in the third month following a successful pregnancy. They were healthy men with normal reproductive function, prospectively confirmed by them fathering healthy babies within 6−8 months of attempting. The normozoospermic infertile cases and fertile controls had normal values on routine semen analyses. The criteria for sperm normality were as follows: semen volume ≥1.5 mL, sperm concentration ≥15 × 106/mL, total motility (%) ≥40, progressive motility (%) ≥32, and vitality (%) ≥58 (2). Finally, n = 71 normozoospermic infertile cases and n = 47 fertile controls were included in this study.

understanding of the condition so to diagnose it more effectively.3 Recent genomic and proteomic studies have identified several potential male infertility biomarkers.3−10 Although genomics and proteomics can reveal altered components of functional pathways in confirmed infertility by analyzing organ- or tissue-specific samples, metabolomics that can be applied to the less-invasive analyses of urine or blood samples has several clear advantages in its potential application within a clinical setting. Metabolomics approaches can detect global changes in small molecular signatures. Because disease-altered metabolic patterns are the consequence of aberrant biological function, it may indicate infertility more directly than genomic or proteomic profiles. Nuclear magnetic resonance (NMR)based metabolic signatures of testicular tissue11 and seminal plasma12−15 have differentiated patients with azoospermia, oligoasthenozoospermia, spermatogenesis failure, or spinal neurotrauma-induced infertilities. Men with differing sperm concentrations have significantly different serum metabolic profiles, and the peptide C 3f was identified as a putative biomarker.16 The infertility pathogenesis may be deciphered further with the aid of metabolic signatures. For example, oxidative stress biomarkers (−CH, −NH, −OH, and −SH) may allow one to distinguish idiopathic patients from fertile men.17,18 These findings indicate the potential of metabolomic approaches in the study of male infertility. The urinary metabolome can indicate global bodily functional changes; therefore, urine can be employed as a surrogate sample in numerous disease diagnoses.19 Ours and other previous studies have reported that urinary metabolites (e.g., 5hydroxyindoleacetic acid, phytoestrogens) correlate with the development of male infertility;20,21 we also demonstrated that patients with oligozoospermia could be discriminated from fertile men by assessing their urinary metabolic pattern.22 However, with regards to normozoospermic infertility in particular, no comprehensive metabolomics study has been conducted to date. In the present study, a medium-sized casecontrol study was designed. The first objective of this preliminary study was to investigate whether the urinary metabolome can be used to differentiate normozoospermic infertile men from fertile individuals. Then, we further investigated whether potential metabolic biomarkers can be used to diagnose this condition and provide some functional insights into its pathogenesis.

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Semen Collection and Analysis

Semen samples were collected after at least a 3-day abstinence. Routine semen analyses were performed using a computeraided semen analyzer (CASA) (WLJY 9000, Weili New Century Science and Tech Dev., China), while referring to guidelines in the fourth edition World Health Organization (WHO) Laboratory Manual for the Examination of Human Semen.23 After collection, the semen was liquefied at 37 °C for 30 min and then analyzed in accordance with WHO guidelines; variables examined included semen volume, concentration, number per ejaculum, motility, vitality, progression, and motion parameters. Strict quality control measures were enforced throughout the entire study. Semen samples were assessed twice, and the counting procedure was automatic and performed by two technicians according to WHO recommendations. CASA results were compared directly with manual assessments using an improved Neubauer hemocytometer over a wide range of sperm concentrations. For sperm motility analysis, we used prepared videotapes to ensure that all motile spermatozoa were being identified using the current and consistent setup parameters.

EXPERIMENTAL SECTION

Participant Recruitment and Sample Collection

The study was approved by the institutional ethics committee and conducted in accordance with the Helsinki Declaration. The subjects were enrolled from the Nanjing Medical University (NJMU) Infertility Study between March 2006 and July 2009. Written consent was obtained from all participants, all being ethnically Han Chinese. A questionnaire was used to collect personal information including background, lifestyle factors, occupational and environmental exposures, genetic risk factors, sexual and reproductive status, medical history, and physical activity. The cases were the men who attended the affiliated hospitals of NJMU because of conception failure for at least 12 months; their wives had undergone complete historical and physical examinations, including conventional gynecological examination, serum hormone level measurements, tubal assessment, ovarian

Urine Preparation

Urine samples were collected before semen collection. Urine samples were centrifuged and filtered with 0.45 μm filters to remove cell sediment and then immediately stored at −80 °C. The urine samples were transported on dry ice to the analytical laboratory in Xiamen, where they were stored at −80 °C until metabolomic analysis. Samples were thawed at room temperature, mixed 1:1 with deionized water, and then centrifuged at 12000g at 4 °C for 10 min. Each sample was filtered through a 0.22 μm syringe filter prior to profile acquisition. An aliquot of 3089



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Figure 1. Urinary metabolome chromatograms and score plots following PCA and OPLS-DA. (A) Representative base peak intensity (BPI) chromatograms; (B) PCA scores plot; (C) OPLS-DA scores plot; and (D) is t-predicted scatter plot of the test set, in which the Hotelling T2 ellipse = 95% confidence interval. Training set of the patients (red ■); training set of the controls (●); test set of the patients (red ▲); test set of the controls (black □); and QC samples (blue □). Toward a training set, 80% samples (57 cases and 37 controls) were used to construct the OPLS-DA model. The remaining 20% samples (14 cases and 10 controls) were used as a test set to evaluate the model. The tight clustering of in-run QC samples in PCA scores plot demonstrated the good overall repeatability during the whole sequence, indicating the acquired data are worth further study.

fashion to remove possible uncertainties from artifact-related injection order and gradual changes of instrument sensitivity in batch runs. MS/MS mode was used to identify potential biomarkers with argon as collision gas. Collision energy was adjusted from 10 to 40 eV for the biomarkers. Raw chromatograms (Figure 1A) were processed using Profile Analysis 2.0 (Bruker). The parameters were set as follows: retention time range, 1.5−9.5 min; mass window, 0.5 Da; and retention time window, 1 min. The intensity of extracted variables was normalized to the total areas to reduce the variations from sample injection and enrichment factor. After peak deconvolution, alignment, integration, and normalization, a table containing retention time, exact mass pairs, and normalized intensities of each variable were obtained for multivariate statistical analysis. In order to assess the stability and reproducibility of the analytical instrument and sample carryover, one QC and one blank were injected three times at the beginning and the end of the sequence and injected at regular intervals (every 15 samples) during the sequence. A tight QC clustering was observed in the scores plot following principal component analysis (PCA) (Figure 1B). Of the variables, 54.4% exhibited a CV < 15% (median RSD = 8.09%) and 77.3% exhibited a CV < 30% (median RSD = 10.71%) across the QC samples, which

each sample was taken, and all were mixed to give a quality control (QC) sample representative of the entire sample set. Urinary Metabolome Analysis

Urine metabolic profiling was conducted using an Ultimate3000 LC (Dionex) coupled to a MicrOTOF-Q II mass spectrometer (Bruker Daltonics). Chromatographic separation was performed on a 2.1 × 150 mm 2.6 μm Kinetex core−shell C18 column (Phenomenex). For each sample, the run time was 15 min at a flow rate of 200 μL/min. The mobile phases were (A) H2O with 0.1% formic acid and (B) acetonitrile with 0.1% formic acid. The programmed gradient was 0 min, 5% B; 1.5 min, 20% B; 10 min, 60% B; 10.1 min, 95% B; 12 min, 95% B; 12.1 min, 5% B; and 15 min, 5% B. The column was maintained at 30 °C and the injection volume was 5 μL. A subset of participants (10 normozoospermic infertile cases and 10 fertile controls) was selected for method development. The two groups could not be discriminated under negative ionization mode, so only the positive ionization mode was chosen for the metabolome analysis of all samples. The mass spectrometer was operated with a scan range of 50 to 1000 m/z. Capillary voltage and end-plate offset potential were set at 4500 and −500 V, respectively. Nebulizer gas pressure was set at 0.6 bar and dry gas flow rate at 6 L/min at 200 °C. Data was collected in the centroid mode. All the samples were run in a randomized 3090



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Table 1. Clinical Data of Participants training set case (n = 57) age semen volume (mL) sperm concentration (106 per mL) total motility (PR + NR, %) progressive motility (PR, %) vitality (live spermatozoa, %)

28.63 3.38 92.43 76.34 62.56 73.80

± ± ± ± ± ±

3.58 1.39 56.85 20.38 17.62 11.24

test set

control (n = 37)

P value

± ± ± ± ± ±

0.360 0.759 0.134 0.140 0.264 0.312

28.56 3.39 75.47 81.48 66.43 75.49

5.27 1.20 41.52 21.93 18.99 9.40

case (n = 14) 29.30 2.79 99.93 73.32 57.45 77.33

± ± ± ± ± ±

2.81 1.12 57.43 13.31 11.81 8.68

control (n = 10)

P value

± ± ± ± ± ±

0.014 0.292 0.380 0.815 0.598 0.356

25.93 2.93 77.74 73.76 61.02 73.76

4.69 0.68 35.91 11.56 12.20 11.56

regressions, in which the defined outcomes of fertility and infertility, respectively, were counted based on the quartile cutoffs of the biomarker abundances. Statistical significance was set at the P value < 0.05 level with 95% confidence intervals. Data were expressed as mean ± SD.

indicated the data set quality in the following analysis (Figure S1 of the Supporting Information).24 No carryover was observed during the batch. All data in this table were Pareto-scaled and then introduced to SIMCA-P v12 (Umetrics, Sweden) for multivariate analysis. PCA was performed to reduce the dimensionality of the data and to reveal any clustering in an unsupervised manner. Orthogonal projection to latent structures discriminant analysis (OPLS-DA), a supervised pattern recognition approach, was then used to improve the classification of the different groups and for biomarker screening. All the samples were randomly divided into two independent data sets (i.e., training set and test set). A portion (80%) of the samples (57 cases and 37 controls) was used as a training set to construct the OPLS-DA model. The remaining 20% samples (14 cases and 10 controls) were used as a test set to evaluate the model. Potential biomarkers were screened based on the significance of their contribution to group variation. In this study, the significance of variable was quantified by the variable importance in the project (VIP) plot of the established OPLS-DA model. Potential biomarkers were further validated using the nonparametric Wilcoxon−Mann−Whitney test. The detailed method for biomarker identification was described in our previous report.25 Briefly, a pooled urine sample was subjected to MS and MS/MS analysis to acquire accurate mass, isotopic pattern, and fragment ions for potential biomarkers. Possible formulas of potential biomarkers were calculated using Data Analysis 4.0 (Bruker Daltonics). Element number restriction, LEWIS check, and the isotopic pattern, hydrogen/carbon element ratio check were used to confirm candidate formulas. Information on potential biomarkers was searched on the HMDB urine (http://www.urinemetabolome. ca/) and METLIN (http://metlin.scripps.edu/) databases. An accepted mass difference of 20 mDa was set during the search. Candidate biomarkers were further validated by searching against reported structural information in the literature and/or by comparing characteristics of their product ions with Mass Frontier (Thermo). Biomarker identities were finally confirmed by comparison with commercial standards. When standards were unavailable, identities were tentatively assigned based on online databases and literature.

Receiver Operating Characteristic (ROC) Analysis

The ROC curve analysis was performed using SPSS 18 (SPSS Inc.). The training set was used for ROC model development, and AUC was used as a metric of sensitivity and specificity of the biomarkers. By searching sensitivity and specificity, the best cutoff point was determined for each biomarker. The test set was then subjected to the ROC model to evaluate its diagnostic ability.

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RESULTS

Participant Demographics

Demographic data of the participants is summarized in Table 1. As expected, there were no significant differences in age, BMI, smoking and drinking status, semen volume, sperm concentration, total motility, progressive motility, or vitality between the infertile versus fertile group. Multivariate Statistical Analysis of Metabolic Profiles

Metabolic differences could be observed by comparing the representative base peak ion chromatograms of infertile men versus fertile controls (Figure 1A). PCA was initially performed on the entire data set to explore clustering in the samples. Although there did not appear to be a clear segregation of the metabolomic profiles of normozoospermic infertile men from the controls, a trend of intergroup separation was significant (Figure 1B). A further supervised analysis, OPLS-DA, was conducted using the training set. The performance characteristics of this multivariate model from a descriptive and predictive point of view were: R2(X) = 0.24; R2(Y) = 0.73; and Q2(Y) = 0.31. This model was statistically validated (P value of CV ANOVA = 1.51e−7). The permutation test (200 random permutations) also validated the model, and no overfitting of the data was observed (Figure S2 of the Supporting Information). The scores plot (Figure 1C) shows a distinct segregation of the two group samples, indicating the urinary metabolic disturbance was significant in the infertile men. The test-set samples were subjected to the model. Nine of ten (90%) fertile samples and 11 (9 in case and 2 in gray area) of 14 (79%) infertile samples were correctly predicted (Figure 1D).

Statistical Analysis

Statistical analysis was performed using SPSS 18 (SPSS Inc.). The Mann−Whitney test was used to compare the participants’ characteristics and evaluate if significant differences of the biomarker levels exist between case and control groups. Spearman correlations were investigated between the individual sets of biomarkers and between biomarkers versus semen parameters. The effects of the potential markers on the disease outcome were expressed by odds ratios (ORs), and the related dose-related trends were analyzed using binary logistic

Biomarker Screening

Extracted variables that contributed the most in the casecontrol group distinction were chosen as the biomarkers of normozoospermic infertility. Strict criteria were adopted in the screening: (1) the variables (n = 84) were brought into the superset of biomarkers using VIP scores > 4; (2) then 3091



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Table 2. Potential Urinary Biomarkers of Normozoospermic Infertility no.

HMDB ID

formula

P valueb 10−03(1.15 −03 f

fold changec −02

10.24

8.00 × × 10 8.81 × 10 ) 2.65 × 10−02 (2.21 × 10−02)g 2.40 × 10−02

9.3 8.02

1.27 × 10−07 2.97 × 10−05

0.50 0.65

C13H23NO6 C10H12N4O6

leukotriene E4 (LTE4)e 6,15-diketo,13,14-dihydroPGF1αe C6-DC carnitinee xanthosinee

7.63 7.52

8.81 × 10−03 4.56 × 10−06

0.74 0.65

HMDB00812

C6H9NO5

N-acetyl-aspartic acid (NAA)e

6.88

9.48 × 10−05

0.49

HMDB00033

C9H14N4O3

carnosined

6.88

4.92 × 10−03

0.73

HMDB01173

C24H25Nh C11H15N5O3S

6.82 6.63

1.54 × 10−03 3.12 × 10−07

0.78 2.21

1

HMDB00714

C9H9NO3

hippurate

2

HMDB06344

C13H16N2O4

3

HMDB00267

C5H7NO3

phenylacetylglutamine (PAGN)e pyroglutamatee

4 5

HMDB02200 HMDB01979

C23H37NO5S C20H32O6

6 7

HMDB00552 HMDB00299

8 9 10 11

VIPa

name d

methylthioadenosine (MTA)

17.37 (16.17, 5.38)f 16.3 (6.2)g

e

,

1.43 (1.52, 1.49)f 1.22 (1.24)g 1.17

12 13

HMDB00296

C9H12N2O6

uridined

6.59 6.31

8.77 × 10−06 7.99 × 10−05

0.40 0.73

14

HMDB00089

C9H13N3O5

cytidined

6.28

2.87 × 10−02

1.22

15

HMDB02339

C12H14N2O3

methoxytryptophand

6.07

6.10 × 10−06

0.46

hydroxylauratee

5.88 5.74 5.43 5.43 5.37 5.25 5.2

3.50 4.92 3.11 7.51 2.08 9.21 5.97

10−04 10−03 10−04 10−03 10−02 10−03 10−05

1.24 0.78 0.58 0.70 1.45 0.82 0.71

serined

5.09

4.06 × 10−03

1.45

2.52 × 10−03 2.92 × 10−02 7.13 × 10−05

0.53 0.84 0.49

16 17 18 19 20 21 22 23 24

C16H10N2O7h

× × × × × × ×

HMDB00187

C6H7N5O C17H31NO5 C26H27Nh C6H8O4 C4H6O5 C16H14N2Oh C12H24O3 C10H25N3O6h C3H7NO3

25 26 27

HMDB13 288 HMDB04195

C25H29NO5h C16H31NO4 C7H14N2O6S

C9 carnitine glutamyl-taurinee

5.09 5.05 5.05

28

HMDB00684

C10H12N2O3

kynurenined

5.01

6.38 × 10−03

0.60

29

HMDB00472

C11H12N2O3

hydroxytryptophan (5-HTP)d

4.91

5.11 × 10−04

0.60

31

HMDB13 336

C23H45NO5

C16−OH carnitine (3hydroxypalmitoylcarnitine)e

4.76

1.90 × 10−07

0.54

4.75 4.73

4.26 × 10−03 3.48 × 10−02

0.79 1.24

4.71 4.68

1.73 × 10−02

0.68

32 33 34 35

HMDB06040 HMDB13 202

e

HMDB00522 HMDB00156 HMDB00387

methylguanine ketodecanoylcarnitinee methylglutaconatee malated

e

h

HMDB00707

C24H25N C9H8O4

HMDB00884

C11H18N2O4h C10H14N2O6

hydroxyphenylpyruvate (HPPA)e methyluridinee

4.23 × 10

glutaconic acide indoleacetic acid (IAA)e hydroxy-leukotriene E4 (HKP)e threonined

4.55 4.49 4.45 4.45

9.21 1.99 1.03 3.17

10−03 10−07 10−03 10−03

1.69 1.82 0.61 0.70

C5H5N5 C17H19NO4h C3H7NO2

adenined

4.42 4.41 4.36

9.01 × 10−03

1.25

1.57 × 10−04

2.16

C19H21N7O6 C25H18N6O5h C6H8O3h

dihydrofolate (DHF)e

4.32 4.26 4.23

7.86 × 10−03 5.54 × 10−03 5.04 × 10−03

0.79 0.63 0.84

HMDB00191

C6H14N4O2

aspartate

37 38 39 40

HMDB00620 HMDB00197 HMDB12 639 HMDB00167

C5H6O4 C8H11NO2 C23H37NO6S C4H9NO3

41 42 43

HMDB00034

44 45 46

HMDB00271 HMDB01056

−06

4.61

36

d

sarcosinee

3092

× × × ×

0.40

class amino acids and derivatives amino acids and derivatives amino acids and derivatives leukotrienes prostaglandins acylcarnitines nucleosides, nucleotides, and analogues amino acids and derivatives amino acids and derivatives nucleosides, nucleotides, and analogues nucleosides, nucleotides, and analogues nucleosides, nucleotides, and analogues amino acids and derivatives hypoxanthines acylcarnitines fatty acids and conjugates fatty acids and conjugates fatty acids and conjugates amino acids and derivatives acylcarnitines amino acids and derivatives amino acids and derivatives amino acids and derivatives acylcarnitines

benzyl alcohols and derivatives nucleosides, nucleotides, and analogues amino acids and derivatives fatty acids and conjugates indoles leukotrienes amino acids and derivatives purines amino acids and derivatives pteridines and derivatives



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Table 2. continued no.

HMDB ID

formula

VIPa

name

P valueb −02

fold changec

47 48

HMDB13 127 HMDB00854

C11H21NO5 C6H10N2O4

C4−OH carnitine formiminoglutamate (FIGLU)e

4.18 4.09

3.75 × 10 7.34 × 10−03

1.19 0.76

49

HMDB02014

C21H39NO4

C14:1 carnitinee

4.09

2.17 × 10−03

0.68

e

a

class acylcarnitines amino acids and derivatives acylcarnitines

b

Obtained from OPLS-DA model with a threshold of 4. Calculated with nonparametric Mann−Whitney test. cFold change >1 indicates a relatively higher concentration of the marker present in infertile patients, whereas 0 (Figure S3 of the Supporting Information); and (3) the difference of candidate levels between the case-control groups were tested with nonparametric Mann−Whitney test (P < 0.05). Finally, 51 variables were incorporated into the biomarker set and 37 of the variables were identified and listed in Table 2. Correlations between Biomarkers and Semen Parameters

When setting the criteria of P < 0.01, no biomarkers correlated with sperm concentration, total motility, progressive motility, or vitality (Table S1 of the Supporting Information). When setting P < 0.05, methylglutaconate (r = −0.213), glutamyltaurine (r = −0.214), and aspartate (r = −0.253) negatively correlated with sperm concentrations; N-acetyl-aspartic acid (NAA) (r = 0.221 and 0.229, respectively) and hydroxytryptophan (r = 0.242 and 0.218, respectively) positively correlated with total motility and vitality, and methoxytryptophan (r = −0.227) negatively correlated with progressive motility (Table S1 of the Supporting Information). Diagnosis Evaluation of the Biomarkers

The biomarkers identified were subjected to ROC analysis to assess diagnostic ability. The AUC ranged from 0.5 to 1.0 indicated the diagnostic accuracy from no discrimination to perfect discrimination. For most of the biomarkers, their AUCs were < 0.7 (Table S2 of the Supporting Information), indicating their poor prediction. The AUCs of eight biomarkers, namely leukotriene E4 (LTE4), C16−OH carnitine, indoleacetic acid (IAA), methylthioadenosine (MTA), aspartate, xanthosine, methoxytryptophan, and diketo-dihydroprostaglandin F1α (PGF1α) were > 0.75 (Table S2 of the Supporting Information) and suggest a potential in diagnosis. The effects of the 16 biomarkers with AUC > 0.7 on the normozoospermic infertility were further evaluated by ORs (Figure 2). Twelve of sixteen biomarkers negatively correlate with ORs (i.e., negative biomarker), whereas MTA, IAA, sarcosine, and methylguanine positively correlated with ORs (i.e., positive biomarker). Comparing the fourth quartile (set OR = 1) of the negative biomarkers, the OR for LTE4 and glutamyl-taurine significantly increased from the third to first quartile. ORs for aspartate, uridine, xanthosine, methoxytryptophan, hydroxytryptophan, NAA, and hydroxyl-LTE4 significantly increase from the second to first quartile. However, ORs for PGF1a, hydroxylaurate only significantly increase at the first quartile. Comparing the first quartile (set OR = 1) of the positive biomarkers, ORs for sarcosine and IAA were significantly increased from the third to fourth quartile; ORs for methylguanine only significantly increased at the fourth quartile. All the samples falling in the fourth quartile of MTA or first quartile of C16−OH carnitine were correctly identified as cases. More details are in Table S3 of the Supporting Information.

Figure 2. Concentration-dependent associations of urinary metabolic biomarkers (AUC > 0.7) with odds ratio (OR) of normozoospermic infertility. The curves of aspartate, xanthosine, and methoxytryptophan overlapped due to they have identical OR values. For more detailed information, including 95% CI and P values for ORs, see Table S3 of the Supporting Information.

Our previous report demonstrated that a combination of biomarkers could provide higher predictive power than single ones.25 The ROC curves of the combinations of the biomarkers are shown in Figure 3. The combination of the top five negative biomarkers (LTE4, C16−OH carnitine, aspartate, xanthosine, and methoxytryptophan) has the highest AUC = 0.901, with sensitivity and specificity equal to 85.7% and 86.8%, respectively. The diagnostic ability of this combination was tested by using the remaining 20% samples in the test set. All samples, except 2 cases and 2 controls, could be correctly predicted (Figure S4 of the Supporting Information). 3093



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Figure 3. ROC curves (top) and table (bottom) of the combined marker patterns. (A) leukotriene E4; (B) C16−OH carnitine; (C) aspartate; (D) xanthosine; (E) methoxytryptophan; and (F) 6,15-diketo,13,14-dihydro-PGF1α.

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DISCUSSION There is a lack of published literature regarding the application of urinary metabolomics in characterizing male infertility. For the first time, we observe that a urinary metabolome pattern can discriminate normozoospermic infertile cases from fertile controls. A total of 37 potential biomarkers were identified, with functionally important roles in energy production, antioxidation, and hormone regulation in spermatogenesis. From a metabolic viewpoint (http://www.genome.jp/kegg/; http://www.hmdb.ca/metabolites/), these biomarkers include: amino acids and their derivatives; fatty acids and products of carnitine acylation or arachidonic metabolism; and nucleotides and their analogues (Figure 4).

cycle, the availability of glycine and the production of glutathione (GSH) can be connected to elevated pyroglutamate, which is an important urinary marker of glycine change. Increased GSH synthesis may occur to counter elevated levels of ROS in male infertility. Many factors can induce oxygen stress (OS) in humans. Pollutant detoxification-induced OS may be indicated by hippurate, which is a product of glycine acylation with benzoic acid (the metabolite of toxic compounds like toluene, N-ethyl-benzamide, and gasoline).27 Reduced levels of minor acylcarnitines and the antioxidant carnosine28 are altered in response to elevated ROS. Not unexpectedly, the dipeptide glutamyltaurine, which is formed from taurine29,30 via the cell antioxidant defense mechanism, is also decreased. Oxidative stress can generate DNA damage in infertile men,31,32 especially in normozoospermic infertility.33,34 The combined action of GSH and these biomarkers may maintain the oxidant/ antioxidant equilibrium in normozoospermic infertile men. Spermatogenesis is highly energy-dependent, but such supplementation may be interrupted in normozoospermic infertile men. Similar to what we have observed in oligozoospermic infertility,22 the levels of five medium- and long-chain acylcarnitines decreased in the urine samples of normozoospermic infertile men. These biomarkers are tightly associated with energy production via the citrate cycle by the

Biological Significance

Folate deficiency and oxidative stress may jointly impact infertile patients (Figure 5). Such folate deficiency is primarily indicated by reduced DHF and FIGLU along with elevated sarcosine and MTA. MTA is an important intermediate in the methionine salvage pathway; methionine is essential in the folate-mediated one-carbon cycle.26 Although not significantly altered, glycine may play a key role linking folate deficiency indicators from serine to sarcosine. Also, when it is formed from serine, THF will be consumed; in humans, THF is biosynthesized from DHF directly. Through the γ-glutamyl 3094



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Figure 4. Overview of the metabolic connections among the identified urinary biomarkers in the normozoospermic infertility. Green font = decreased biomarker; red font = increased biomarker.

fatty acid β-oxidation in mitochondria. As in other types of male infertility,35,36 the citrate cycle in normozoospermic infertility appears to be altered. In addition to a fatty acid βoxidation intermediate, specific acylcarnitine can be generated by a ketosis-induced metabolite, 3-hydroxybutyrate.37 Stalled medium- and long-chain fatty acid metabolism with improved ketone body metabolism, plus decreased levels of malate and aspartate, could result in the citrate cycle being altered via a malate-aspartate shuttle in ATP generation. Although not incorporated into the biomarkers, a significant alteration of arachidonic acid (AA) (VIP = 2.34, P = 0.02; foldchange = 0.77) was observed. Many important AA metabolites (i.e., LTE4, hydroxyl-LTE4, and PGF1α) have been included in the biomarker set, indicating eicosanoid metabolism disturbance (Figure 6) may be an underlying mechanism in normozoospermic infertility. LTE4 is considered a reliable marker for endogenous cysteinyl-leukotriene (CysLT),38 which plays an important role in the fertilizing capacity of spermatozoa through receptor binding.39 LTs were reported to be able to modulate hormone secretion in vivo and in vitro,40,41 but little is known regarding the role of CysLT in male infertility-related hormone regulation. CysLTs are also believed to indicate oxidative stress and inflammation.38 PGF1α is an important urinary metabolite of prostacyclin (PGI2), which is produced via cyclooxygenases COX-1 and COX-2.42 An early study has correlated PGI2 with the impaired sperm motility.43 Recent studies have highlighted that alterations in

COXs can interrupt testicular function and induce male infertility.44,45 Aspartate is present in brain, testis, seminal plasma, and spermatozoa and has an important role in controlling the release of hormones such as luteinizing hormone, folliclestimulating hormone, and growth hormone46,47 via the hypothalamus-pituitary-gonad pathway;48 NAA may mediate its function via the neuronal system.49,50 Decreased aspartate and NAA may also suggest inhibitory alterations in normal hormone-secreting activity during spermatogenesis in normozoospermic infertile men. The observed trend of decreased aspartate levels was also observed in oligozoospermia.22 Altered levels of uridine, methyluridine, cytidine, xanthosine, and adenine suggested disruption of pyrimidine and purine metabolism in normozoospermic infertility. Uridine is essential for phospholipase activity and AA formation during acrosomal exocytosis and the binding of spermatozoa to the zonapellucida of the oocyte.13 It is also important to promote sperm motility.13,51 The increased adenine may cross-talk with elevated MTA via the methionine salvage pathway. Adenine is also the precursor of the chemical energy transporter adenosine triphosphate (ATP) in cellular metabolism. Xanthosine is a nucleoside derived from xanthine, an elevated chemical in the seminal plasma of vasectomized men.51 The disruption of the xanthine oxidase system is tightly associated with energy metabolism and oxidative stress in male infertility.52,53 3095



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Figure 5. Folate deficiency biomarkers cross-talked to the oxidative biomarkers via glycine metabolism and γ-glutamyl cycle. Solid line suggested one step reaction; line of dashes shown more than one step reaction; green font = decreased biomarker; red font = increased biomarker.

indicating they have moderate diagnostic power for normozoospermic infertility. Furthermore, dose-dependent correlations between infertility outcome and these biomarkers were observed. Combining biomarkers is intuitively the next step to establish a diagnostic model, and it has been adopted in metabolomics research. We previously showed that a combination of biomarkers provides a better diagnosis for bladder cancer than a single biomarker.25 Here, a combination of the top five negative biomarkers gave an AUC of 0.901, a 10% to 50% improvement in prediction using combined vs single biomarkers. Possible reasons for this improvement may be that a combined biomarker pattern could characterize the global perturbation of the metabolic network in normozoospermic infertility [e.g., LTE4 (eicosanoid metabolism), C16− OH carnitine (fatty acid β-oxidation), aspartate (aspartate metabolism), xanthosine (purine metabolism), and methoxytryptophan (tryptophan metabolism)]. At its best cutoff point, the sensitivity and specificity of combined biomarker pattern for normozoospermic infertility detection were 85.7% and 86.8%, respectively. The test using the remaining 20% samples further proved its diagnostic ability. These data suggest a great potential for the use of a combined urinary biomarker pattern in the accurate detection of normozoospermic infertility.

Interestingly, the identified biomarkers suggested a potential association between normozoospermic infertility and a psychological problem. NAA and glutamyltaurine is most concentrated in the brain neurons, and their variety of effects in both neural and non-neural systems may start from neural activity. Decreased NAA has been associated with numerous neuropathological conditions,54 and in this study it implied that altered brain function is the adverse effector in normozoospermic infertility. Decreased tryptophan-related biomarkers (i.e., 5HTP and methoxytryptophan, Figure 6) implied a decreased serotonin in the brain. 5-HTP has been hypothesized to give rise to depression.55 This evidence suggests that depression may play a role in normozoospermic infertile patients. Urinary Biomarkers Correlated with Semen Parameters

The urinary biomarkers were screened using an OPLS-DA model with disease classification (control vs normozoospermic infertility); infertile men and fertile controls enrolled in this study exhibited no significant differences in semen parameters, so it is to be expected that no correlation was found between the majority of biomarkers and semen parameters. Certain biomarkers showed correlations with semen parameters at a cutoff point of P = 0.05 (e.g., NAA and HTP). However, the correlation coefficients were very low (0.7, 3096



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Figure 6. Biomarkers involved tryptophan, lysine, and leucine metabolism (left) and arachidonic acid metabolism (right). Solid line suggested one step reaction; line of dashes shown more than one step reaction; green font = decreased biomarker; red font = increased biomarker.

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infertility may also suffer from folate deficiency and neural disorder. The combined biomarker pattern of LTE4, C16−OH carnitine, aspartate, xanthosine, and methoxytryptophan suggest a satisfactory diagnostic ability in the ROC model; these markers may imply the keynote events of normozoosperimic infertility. Although this is interesting metabolic insight, the results are preliminary and need to be further tested; also, the involved pathways need to be demonstrated using an in vivo model. In addition, the characterized normozoospermic infertility metabolome needs to be tested in other sample matrices such as seminal plasma and blood, especially seminal plasma because it may offer extra tissuespecific information instead of global information that is more likely to be derived from urine and blood.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel/Fax: 86-592-6190771. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank Dr. Chensong Pan and Hai Pu from Bruker Daltonics for technical support. This work was financially supported by the Chinese Academy of Sciences (CAS) Knowledge Innovation Programs (Grant KZCX2-EWQN408) and NSFC foundation (Grants 21177123 and 21307126) and the related NSFC-RC International Exchange Programme 2013.

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ASSOCIATED CONTENT

* Supporting Information

ABBREVIATIONS AA, arachidonic acid; AUC, area under the curve; COX, cyclooxygenase; CysLT, cysteinyl leukotriene; GSH, glutathione; HKP, hydroxy-leukotriene E4; HPPA, hydroxyphenylpyruvate; LC, liquid chromatography; LO, lipoxygenase; LT, leukotriene; LTE4, leukotriene E4; MTA, methylthioadenosine; NAA, N-acetyl-aspartic acid; OPLS-DA, orthogonal projection to latent structures discriminant analysis; OR, odds ratio; OS, Oxidative stress; PAGN, phenylacetylglutamine; PCA, principal component analysis; PG, prostaglandin; PGF1α, diketodihydro-prostaglandin F1α; PGI2, prostacyclin; QC, quality control; QTOF-MS, quadrupole time-of-flight mass spectrometry; ROC, receiver operating characteristic; ROS, reactive

S

CV values of the variables extracted from urine metabolic profile. Random permutation-test results (n = 200) of the supervised OPLS-DA model. VIP plot of the supervised OPLSDA model. AUC model diagnostic results of test set using the combined biomarker pattern of five biomarkers. Correlations between urine concentrations of potential normozoospermic infertility biomarkers and CASA parameters. Results of ROC analysis for the potential normozoospermic infertility biomarkers. Results of binary logistic regressions of biomarkers (AUC > 0.7) with normozoospermic infertility outcomes. This material is available free of charge via the Internet at http:// pubs.acs.org. 3097



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oxygen species; TCA, tricarboxylic acid; VIP, variable importance in the project

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Metabolomic Analysis Reveals a Unique Urinary Pattern in

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