Proteomics and metabolomics — Current and future perspectives in clinical andrology

Selvam, Manesh Kumar Panner; Finelli, Renata; Agarwal, Ashok; Henkel, Ralf

doi:10.1111/and.13711

Cited by 36 publications

(26 citation statements)

References 159 publications

(253 reference statements)

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“…Among them, proteomic approaches have been widely used to identify key proteins involved in infertility disorders, mainly by comparing sperm samples from different donors (e.g., normospermic samples from fertile vs. infertile men, asthenospermic vs. normospermic sperm, etc.). The results of these studies, focused on altered sperm parameters and some diseases, have already been compiled in excellent reviews (e.g., [ 15 , 16 , 17 , 18 , 19 , 20 ]).…”

Section: Introductionmentioning

confidence: 99%

Influence of Risk Factors for Male Infertility on Sperm Protein Composition

Bisconti

Simon

Grassi

et al. 2021

IJMS

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Male infertility is a common health problem that can be influenced by a host of lifestyle risk factors such as environment, nutrition, smoking, stress, and endocrine disruptors. These effects have been largely demonstrated on sperm parameters (e.g., motility, numeration, vitality, DNA integrity). In addition, several studies showed the deregulation of sperm proteins in relation to some of these factors. This review inventories the literature related to the identification of sperm proteins showing abundance variations in response to the four risk factors for male infertility that are the most investigated in this context: obesity, diabetes, tobacco smoking, and exposure to bisphenol-A (BPA). First, we provide an overview of the techniques used to identify deregulated proteins. Then, we summarise the main results obtained in the different studies and provide a compiled list of deregulated proteins in relation to each risk factor. Gene ontology analysis of these deregulated proteins shows that oxidative stress and immune and inflammatory responses are common mechanisms involved in sperm alterations encountered in relation to the risk factors.

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Section: Introductionmentioning

confidence: 99%

Influence of Risk Factors for Male Infertility on Sperm Protein Composition

Bisconti

Simon

Grassi

et al. 2021

IJMS

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“…In this sense, high-throughput technologies (including genomics, lipidomics, proteomics, metabolomics and transcriptomics) may hold the key for uncovering reliable fertility biomarkers in SP, since they provide a more in-depth understanding of reproductive processes [ 9 ]. In the last decade, many studies conducted in mammalian SP have employed these novel technologies to collect large amounts of data to discover novel fertility biomarkers [ 10 – 13 ].…”

Section: Introductionmentioning

confidence: 99%

Metabolomic fingerprinting of pig seminal plasma identifies in vivo fertility biomarkers

Mateo‐Otero

Fernández-López

Delgado-Bermúdez

et al. 2021

J Animal Sci Biotechnol

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Background Metabolomic approaches, which include the study of low molecular weight molecules, are an emerging -omics technology useful for identification of biomarkers. In this field, nuclear magnetic resonance (NMR) spectroscopy has already been used to uncover (in) fertility biomarkers in the seminal plasma (SP) of several mammalian species. However, NMR studies profiling the porcine SP metabolome to uncover in vivo fertility biomarkers are yet to be carried out. Thus, this study aimed to evaluate the putative relationship between SP-metabolites and in vivo fertility outcomes. To this end, 24 entire ejaculates (three ejaculates per boar) were collected from artificial insemination (AI)-boars throughout a year (one ejaculate every 4 months). Immediately after collection, ejaculates were centrifuged to obtain SP-samples, which were stored for subsequent metabolomic analysis by NMR spectroscopy. Fertility outcomes from 1525 inseminations were recorded over a year, including farrowing rate, litter size, stillbirths per litter and the duration of pregnancy. Results A total of 24 metabolites were identified and quantified in all SP-samples. Receiver operating characteristic (ROC) curve analysis showed that lactate levels in SP had discriminative capacity for farrowing rate (area under the curve [AUC] = 0.764) while carnitine (AUC = 0.847), hypotaurine (AUC = 0.819), sn-glycero-3-phosphocholine (AUC = 0.833), glutamate (AUC = 0.799) and glucose (AUC = 0.750) showed it for litter size. Similarly, citrate (AUC = 0.743), creatine (AUC = 0.812), phenylalanine (AUC = 0.750), tyrosine (AUC = 0.753) and malonate (AUC = 0.868) levels had discriminative capacity for stillbirths per litter; and malonate (AUC = 0.767) and fumarate (AUC = 0.868) levels for gestation length. Conclusions The assessment of selected SP-metabolites in ejaculates through NMR spectroscopy could be considered as a promising non-invasive tool to predict in vivo fertility outcomes in pigs. Moreover, supplementing AI-doses with specific metabolites should also be envisaged as a way to improve their fertility potential.

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“…Routine semen analysis performed in the workup of male infertility provides information regarding the quality of sperm, however it does not help elucidate the molecular mechanisms associated with infertility. The advent of proteomics has enabled to decipher the molecular mechanisms as well as the subcellular changes related to the fertilization potential of spermatozoa [19,20]. Hitherto, few proteomics studies have been conducted on semen samples of patients affected by specific types of cancer [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Dysregulation of Key Proteins Associated with Sperm Motility and Fertility Potential in Cancer Patients

Selvam

Finelli

Baskaran

et al. 2020

IJMS

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Cancer has adverse effects on male reproductive health. Conventional semen analysis does not explain the molecular changes in the spermatozoa of cancer patients. Currently, proteomics is being widely used to identify the fertility-associated molecular pathways affected in spermatozoa. The objective of this study was to evaluate the sperm proteome of patients with various types of cancer. Cryopreserved semen samples from patients (testicular cancer, n = 40; Hodgkin’s disease, n = 32; lymphoma, n = 20; leukemia, n = 17) before starting therapy were used for proteomic analysis, while samples from fertile donors (n = 19) were included as controls. The proteomic profiling of sperm was carried out by liquid chromatography-tandem mass spectrometry, and differentially expressed proteins involved in the reproductive processes were validated by Western blotting. Bioinformatic analysis revealed that proteins associated with mitochondrial dysfunction, oxidative phosphorylation, and Sirtuin signaling pathways were dysregulated in cancer patients, while oxidative phosphorylation and tricarboxylic acid cycle were predicted to be deactivated. Furthermore, the analysis revealed dysregulation of key proteins associated with sperm fertility potential and motility (NADH:Ubiquinone oxidoreductase core subunit S1, superoxide dismutase 1, SERPINA5, and cytochrome b-c1 complex subunit 2) in the cancer group, which were further validated by Western blot. Dysfunctional molecular mechanisms essential for fertility in cancer patients prior to therapy highlight the potential impact of cancer phenotype on male fertility.

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Proteomics and metabolomics — Current and future perspectives in clinical andrology

Cited by 36 publications

References 159 publications

Influence of Risk Factors for Male Infertility on Sperm Protein Composition

Influence of Risk Factors for Male Infertility on Sperm Protein Composition

Metabolomic fingerprinting of pig seminal plasma identifies in vivo fertility biomarkers

Dysregulation of Key Proteins Associated with Sperm Motility and Fertility Potential in Cancer Patients

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