1H NMR-based metabonomics for infertility diagnosis in men with varicocele

Biliary atresia and cholestasis plasma non-targeted metabolomics unravels perturbed metabolic pathways and unveils a diagnostic model for biliary atresia

The clinical diagnosis of biliary atresia (BA) poses challenges, particularly in distinguishing it from cholestasis (CS). Moreover, the prognosis for BA is unfavorable and there is a dearth of effective non-invasive diagnostic models for detection. Therefore, the aim of this study is to elucidate the metabolic disparities among children with BA, CS, and normal controls (NC) without any hepatic abnormalities through comprehensive metabolomics analysis. Additionally, our objective is to develop an advanced diagnostic model that enables identification of BA. The plasma samples from 90 children with BA, 48 children with CS, and 47 NC without any liver abnormalities children were subjected to metabolomics analysis, revealing significant differences in metabolite profiles among the 3 groups, particularly between BA and CS. A total of 238 differential metabolites were identified in the positive mode, while 89 differential metabolites were detected in the negative mode. Enrichment analysis revealed 10 distinct metabolic pathways that differed, such as lysine degradation, bile acid biosynthesis. A total of 18 biomarkers were identified through biomarker analysis, and in combination with the exploration of 3 additional biomarkers (LysoPC(18:2(9Z,12Z)), PC (22:5(7Z,10Z,13Z,16Z,19Z)/14:0), and Biliverdin-IX-α), a diagnostic model for BA was constructed using logistic regression analysis. The resulting ROC area under the curve was determined to be 0.968. This study presents an innovative and pioneering approach that utilizes metabolomics analysis to develop a diagnostic model for BA, thereby reducing the need for unnecessary invasive examinations and contributing to advancements in diagnosis and prognosis for patients with BA.

A metabolomic perspective on the mechanisms by which environmental pollutants and lifestyle lead to male infertility

The incidence of male infertility (MI) is rising annually. According to epidemiological studies, environmental pollution (e.g., organic, inorganic, and air pollutants), occupational exposure (e.g., high temperature, organic solvents, and pesticides), and poor lifestyle (e.g., diet, sleep, smoking, alcohol consumption, and exercise) are important non-genetic causative factors of MI. Due to multiple and complex causative factors, the dose-effect relationship, and the uncertainty of pathogenicity, the pathogenesis of MI is far from fully clarified. Recent data show that the pathogenesis of MI can be monitored by the metabolites in serum, seminal plasma, urine, testicular tissue, sperm, and other biological samples. It is considered that these metabolites are closely related to MI phenotypes and can directly reflect the individual pathological and physiological conditions. Therefore, qualitative and quantitative analysis of the metabolome, the related metabolic pathways, and the identification of biomarkers will help to explore the MI-related metabolic problems and provide valuable insights into its pathogenic mechanisms. Here, we summarized new findings in MI metabolomics biomarkers research and their abnormal metabolic pathways triggered by the presented non-genetic risk factors, providing a metabolic landscape of semen and seminal plasma in general MI patients. Then, we compared the similarities and differences in semen and seminal plasma biomarkers between MI patients exposed to environmental and poor lifestyle factors and MI patients in general, and summarized some common biomarkers. We provide a better understanding of the biological underpinnings of MI pathogenesis, which might offer novel diagnostic, prognostic, and precise treatment approaches to MI.

Metabolomics of male infertility

This review explores the use of metabolomics in male infertility. Metabolomics, an evolving omics technology that targets the products of cellular metabolism, is valuable for elucidating underlying pathophysiology of many disorders including male infertility. The identification of reliable biomarkers is essential for accurate diagnosis and for developing precision therapeutics for those afflicted by reproductive dysfunction. Unfortunately, despite significant progress to date, the intricate relationships between these metabolic pathways and male infertility remain elusive. It is clear, however, that additional research is required to more fully characterize the role of metabolomics in this disorder and in the potential development of targeted therapies for precision medicine.

Effect of varicocele and varicocelectomy on sperm deoxyribonucleic acid fragmentation rates in infertile men with clinical varicocele

Varicocele is the leading cause of male infertility. It can affect sperm quantity and quality through various non-mutually exclusive pathophysiological mechanisms, mainly oxidative stress. Excessive production of reactive oxygen species may overwhelm the sperm's defenses against oxidative stress and harm the sperm's DNA. Excessive sperm DNA breaks, so-called sperm DNA fragmentation, result from the oxidative stress cascade and are commonly found in the ejaculates of men with varicocele and fertility-related issues. Measuring sperm DNA fragmentation can provide valuable information on the extent of harm and might help select candidates for surgical treatment. Varicocelectomy is beneficial for alleviating oxidative stress-associated infertility and improving sperm DNA integrity. However, reproductive outcomes of infertile men with elevated sperm DNA fragmentation rates and surgically treated varicoceles remain poorly studied, and there is a need for well-designed trials to determine the impact of sperm DNA fragmentation reduction on natural and medically assisted reproduction.

Exposure to PM2.5, seminal plasma metabolome, and semen quality among Chinese adult men: Association and potential mediation analyses

Exposure to ambient fine particulate matter (PM2.5) has been linked to a decline in semen quality, but the underlying mechanisms for this association remain unclear. We aimed to examine whether specific metabolites act as mediators in the association between PM2.5 exposure and changes in semen quality. We conducted untargeted metabolomics analysis using LC-MS/MS platforms to identified seminal plasma metabolites associated with various semen quality parameters among 200 Chinese adult men. Additionally, we performed mediation analyses to examine the effects of the seminal plasma metabolites on the association between PM2.5 exposure and semen quality. We identified 140 differential metabolites between the normal and abnormal semen groups, involving two metabolic pathways: Alanine, aspartate and glutamate metabolism, and Aminoacyl-tRNA biosynthesis. We additionally identified 7 specific seminal plasma metabolites that were associated with discrepant metabolic networks related to semen quality. The mediation analysis revealed that D-Aspartate might play a mediating role in the adverse effects of ambient PM2.5 exposure on both total and progressive motility during spermatogenesis period (70-90 days before ejaculation), with a proportion of mediation up to 16% and 17%, respectively. Exposure to PM2.5 was associated with alterations in D-Aspartate levels, which might partially mediate the association between PM2.5 and reduced sperm motility.

Performance evaluate of different chemometrics formalisms used for prostate cancer diagnosis by NMR-based metabolomics

INTRODUCTION

In general, two characteristics are ever present in NMR-based metabolomics studies: (1) they are assays aiming to classify the samples in different groups, and (2) the number of samples is smaller than the feature (chemical shift) number. It is also common to observe imbalanced datasets due to the sampling method and/or inclusion criteria. These situations can cause overfitting. However, appropriate feature selection and classification methods can be useful to solve this issue.

OBJECTIVES

Investigate the performance of metabolomics models built from the association between feature selectors, the absence of feature selection, and classification algorithms, as well as use the best performance model as an NMR-based metabolomic method for prostate cancer diagnosis.

METHODS

We evaluated the performance of NMR-based metabolomics models for prostate cancer diagnosis using seven feature selectors and five classification formalisms. We also obtained metabolomics models without feature selection. In this study, thirty-eight volunteers with a positive diagnosis of prostate cancer and twenty-three healthy volunteers were enrolled.

RESULTS

Thirty-eight models obtained were evaluated using AUROC, accuracy, sensitivity, specificity, and kappa's index values. The best result was obtained when Genetic Algorithm was used with Linear Discriminant Analysis with 0.92 sensitivity, 0.83 specificity, and 0.88 accuracy.

CONCLUSION

The results show that the pick of a proper feature selection method and classification model, and a resampling method can avoid overfitting in a small metabolomic dataset. Furthermore, this approach would decrease the number of biopsies and optimize patient follow-up. 1H NMR-based metabolomics promises to be a non-invasive tool in prostate cancer diagnosis.

Comprehensive metabolomics profiling of seminal plasma in asthenozoospermia caused by different etiologies

BACKGROUND

Asthenozoospermia (AZS) is a disease characterized by decreased sperm motility induced by multiple etiologies, and the pathological mechanisms of various AZS are unclear. We simultaneously analyzed the metabolic profiling of four representative AZS to provide new insights into the etiologies of AZS.

METHOD

Seminal plasma samples were collected from healthy control (HC; n = 30) and four AZS induced by varicocele (VA, n = 30), obesity (OA, n = 22), reproductive system infections (RA; n = 17) and idiopathic (IA, n = 30), respectively, and were analyzed using gas chromatography-mass spectrometry. Disturbed metabolites and metabolic pathways were compared between AZS and HC, as well as IA and the other three AZS.

RESULTS

A total of 40 different metabolites were identified in the seminal plasma of AZS and HC, of which lactic acid, fructose, citric acid, glutamine and pyruvic acid metabolic abnormalities associated with all the AZS groups, while each AZS group had unique metabolic changes. RA was significantly separated from the other three AZS, and metabolites such as cholesterol, octadecanoic acid and serine mainly contributed to the separation.

CONCLUSION

The comprehensive metabolomic analysis and comparison of four various AZS provided evidence and clues for the mechanism mining, which will benefit future etiology, diagnosis and treatment of AZS.

The '-ics' of male reproduction: genomics, epigenetics, proteomics, metabolomics, and microbiomics

PURPOSE OF REVIEW

To review the most current findings, from the past 2 years, in various '-ics' fields in male infertility, with a specific focus on nonobstructive azoospermia, the most severe form, and varicocele, the most common correctable cause of male infertility.

RECENT FINDINGS

Recent studies confirm previously identified causes and identify previously unknown genetic mutations as causes for nonobstructive azoospermia and varicocele.

SUMMARY

Infertility is a common problem for couples with approximately half of cases attributable to male factor infertility. Although advances in assisted reproductive technology have permitted many more men with infertility to father biological children, the majority of infertile men continue to have unknown causes. The recent explosion of the '-ics' fields, including genomics, epigenetics, proteomics, metabolomics, and microbiomics, has shed light on previously unknown causes for various diseases. New information in these fields will not only shed light on the pathogenesis of these conditions but also may shift the paradigm in clinical testing that may allow clinicians to provide more precise counseling and prognostic information for men with infertility.

Metabolomics of Human Semen: A Review of Different Analytical Methods to Unravel Biomarkers for Male Fertility Disorders

Background: Human life without sperm is not possible. Therefore, it is alarming that the fertilizing ability of human spermatozoa is continuously decreasing. The reasons for that are widely unknown, but there is hope that metabolomics-based investigations may be able to contribute to overcoming this problem. This review summarizes the attempts made so far. Methods: We will discuss liquid chromatography–mass spectrometry (LC-MS), gas chromatography (GC), infrared (IR) and Raman as well as nuclear magnetic resonance (NMR) spectroscopy. Almost all available studies apply one of these methods. Results: Depending on the methodology used, different compounds can be detected, which is (in combination with sophisticated methods of bioinformatics) helpful to estimate the state of the sperm. Often, but not in all cases, there is a correlation with clinical parameters such as the sperm mobility. Conclusions: LC-MS detects the highest number of metabolites and can be considered as the method of choice. Unfortunately, the reproducibility of some studies is poor, and, thus, further improvements of the study designs are needed to overcome this problem. Additionally, a stronger focus on the biochemical consequences of the altered metabolite concentrations is also required.

Effects of varicocele and microsurgical varicocelectomy on the metabolites in semen

The influence of varicocele and microsurgical varicocelectomy on semen quality remains unclear. Few studies have investigated the relationship between semen metabolism and the abnormalities in reproductive function caused by varicocele, however, there is no study on the changes of semen metabolism after microsurgical varicocelectomy. Here, we used the non-targeted and targeted metabolic analysis to investigate the different metabolites in seminal plasma within normal, varicocele, and varicocelectomy groups. We clearly showed that varicocele significantly affects semen metabolism, and microsurgical varicocelectomy can reverse this metabolic abnormality. Moreover, we characterized the landscape of three dipeptides in the seminal plasma of patients with varicocele that have not been identified previously in human tissues or biofluids. Interestingly, the levels of these three dipeptides decreased after microsurgical varicocelectomy coincident with an improvement in semen quality. Western blotting confirmed the downregulation of DPEP3 (dipeptidase 3) in the varicocele group and the upregulation of DPEP3 in the varicocelectomy group. Furthermore, we found that eight metabolites may be helpful to distinguish varicocele patients from normal subjects. Our results may be applied to earlier diagnosis or to predict the outcome of microsurgery for varicocele.

Metabolomics-based screening analysis of PPCPs in water pretreated with five different SPE columns

The selection of solid phase extraction (SPE) columns in the pretreatment process plays a decisive role in the screening and quantification of pharmaceutical and personal care products (PPCPs). As growing PPCPs have frequently been detected in the aquatic environment, it is a burdensome task through one-by-one recovery comparison to judge which column presents relatively ideal pretreatment results for PPCPs. In view of this, we developed a novel metabolomics-based screening method based on ultrahigh-performance liquid chromatography-tandem mass spectrometer (UHPLC-MS/MS) results to accurately, rapidly and comprehensively choose a suitable column from 5 different kinds to handle 64 PPCPs in two water environments (50 μg L^-1/pH ≅ 7.0/pure water and 1 μg L^-1/pH ≅ 7.0/reservoir water) through seeking 'biomarkers', for which multivariate and univariate analyses were adopted. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) play a crucial role in multivariate analysis, and the pairwise t-test and fold change judgement in univariate analysis. Each column group was fully separated from the other 4 groups in PCA and OPLS-DA plots, laying a foundation to distinguish 'biomarkers' between groups. The S-Plot, permutation and variable importance in projection (VIP) in OPLS-DA were employed to screen and identify 'biomarkers', which were further verified by a pairwise t-test and fold change judgement. Eventually, the 64 PPCPs as 'biomarkers' were divided into 5 groups, which correspond to 5 column groups, consistent with the findings of traditional PPCP recovery comparison, proving the validity of the metabolomics-based screening method. This novel method will exhibit greater superiority in choosing suitable SPE columns to handle a growing and larger number of PPCPs in water environments and beyond.

Insight Into the Metabolomic Characteristics of Post-Transplant Diabetes Mellitus by the Integrated LC-MS and GC-MS Approach- Preliminary Study

Post-transplantation diabetes mellitus (PTDM) is a common metabolic complication after solid organ transplantation, which not only results in elevated microvascular morbidity, but also seriously impacts graft function and recipient survival. However, its underlying mechanism is not yet fully understood. In this study, an integrated liquid chromatography- mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) based-metabolomics approach was adopted to dissect the metabolic fluctuations and deduce potential mechanism associated with PTDM. 68 adult liver transplant recipients were recruited and classified as 32 PTDM and 36 non-PTDM subjects. PTDM group and non-PTDM group were well matched in gender, age, BMI, family history of diabetes, alcohol drinking history, ICU length of stay and hepatitis B infection. Peripheral blood samples from these recipients were collected and prepared for instrument analysis. Data acquired from LC-MS and GC-MS demonstrated significant metabolome alterations between PTDM and non-PTDM subjects. A total of 30 differential metabolites (15 from LC-MS, 15 from GC-MS) were screened out. PTDM patients, compared with non-PTDM subjects, were characterized with increased levels of L-leucine, L-phenylalanine, LysoPE (16:0), LysoPE (18:0), LysoPC (18:0), taurocholic acid, glycocholic acid, taurochenodeoxycholic acid, tauroursodeoxycholic acid, glycochenodeoxycholic acid, glycoursodeoxycholic acid, etc, and with decreased levels of LysoPC (16:1), LysoPC (18:2), LysoPE (22:6), LysoPC (20:4), etc. Taken collectively, this study demonstrated altered metabolites in patients with PTDM, which would provide support for enhancing mechanism exploration, prediction and treatment of PTDM.

Association of the serum metabolomic profile by nuclear magnetic resonance spectroscopy with sperm parameters: a cross-sectional study of 325 men

OBJECTIVE

To determine whether 155 circulating metabolic measures relevant to lifestyle and metabolic health are associated with sperm parameters, as measured by concentration, motility, and total motile sperm count (TMSC).

STUDY DESIGN

Cross sectional.

SETTING

University hospital.

PATIENT(S)

Three hundred twenty-five men prospectively recruited between April 1, 2017 and March 31, 2019.

INTERVENTION(S)

Detailed demographic, lifestyle, fertility, medical history, and semen analysis with quantification of nonfasting serum lipids, lipoprotein subclasses, and low-molecular weight metabolites (including amino acids, glycolysis, and inflammatory markers) by nuclear magnetic resonance (NMR) spectroscopy.

MAIN OUTCOME MEASURE(S)

Association of serum metabolic profiles with sperm parameters.

RESULT(S)

The age of the participants was mean 37.2 years, with a median sperm concentration of 35 million/mL and median motility of 53%. Of these men, 76% had a TMSC >15 million, 10% had 5-15 million, and 14% had <5 million. In both univariate and confounder adjusted analyses, an extensive range of lipids and lipoproteins, glycolysis-related metabolites, amino acids, ketone bodies, creatinine, or albumin showed no strong statistically significant association with sperm concentration, motility, or the odds of having a reduced or low TMSC. Higher levels of glycolysis metabolites and ketone bodies were associated with an increased odds of TMSC <15 million compared with ≥15 million (odds ratios of ∼1.2-1.3), and several lipids/lipoprotein concentrations appeared to protect against very low TMSC (<5 million compared with ≥5 million) with odds ratios of ∼0.8 or greater.

CONCLUSION(S)

Several metabolites exhibited potentially clinically relevant strength of association with the odds of a low TMSC and warrant replication.