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Innovative Approach for Human Semen Quality Assessment Based on Volatilomics.

ABSTRACT: The volatilome profile of some biofluids (blood, urine, and human semen) identified by Solid-Phase Microextraction-Gas Chromatography/Mass Spectrometry (SPME-GC/MS) and collected from young men living in two high-pollution areas in Italy, i.e., Land of Fires and Valley of Sacco River, have been coupled to sperm parameters obtained by spermiogram analysis to build general multiple regression models. Panels of volatile organic compounds (VOCs) have been selected to optimize the models and used as predictive variables to estimate the different sperm quality parameters (sperm cell concentration, total and progressive motility/immotile cells, total/head/neck/tail morphology anomalies, semen round cell concentration). The results of the multiple linear regression models based on the different subgroups of data joining VOCs from one/two or three biofluids have been compared. Surprisingly, the models based on blood and urine VOCs have allowed an excellent estimate of spermiogram values, paving the way towards a new method of indirect evaluation of semen quality and preventive screening. The significance of VOCs in terms of toxicity and dangerousness was discussed with the support of chemical databases available online.

SUBMITTER: Capone S

PROVIDER: S-EPMC11360181 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Innovative Approach for Human Semen Quality Assessment Based on Volatilomics.

Capone Simonetta S Forleo Angiola A Radogna Antonio Vincenzo AV Longo Valentina V My Giulia G Genga Alessandra A Ferramosca Alessandra A Grassi Giuseppe G Casino Flavio F Siciliano Pietro P Notari Tiziana T Pappalardo Sebastiana S Piscopo Marina M Montano Luigi L

Toxics 20240727 8

The volatilome profile of some biofluids (blood, urine, and human semen) identified by Solid-Phase Microextraction-Gas Chromatography/Mass Spectrometry (SPME-GC/MS) and collected from young men living in two high-pollution areas in Italy, i.e., Land of Fires and Valley of Sacco River, have been coupled to sperm parameters obtained by spermiogram analysis to build general multiple regression models. Panels of volatile organic compounds (VOCs) have been selected to optimize the models and used as ...[more]

PMID: 39195645

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Project description:This study aimed to develop a comprehensive approach for assessing fresh ejaculate from Muscovy duck (Cairina moschata) drakes to fulfil the requirements of artificial insemination in farm practices. The approach combines sperm kinetics (CASA) with non-kinetic parameters, such as vitality, enzyme activities (alkaline phosphatase (AP), creatine kinase (CK), lactate dehydrogenase (LDH), and γ-glutamyl-transferase (GGT)), and total DNA methylation as training features for a set of machine learning (ML) models designed to enhance the predictive capacity of sperm parameters. Samples were classified based on their progressive motility and DNA methylation features, exhibiting significant differences in total and progressive motility, curvilinear velocity (VCL), velocity of the average path (VAP), linear velocity (VSL), amplitude of lateral head displacement (ALH), beat-cross frequency (BCF), and live normal sperm cells in favour of fast motility ones. Additionally, there were significant differences in enzyme activities for AP and CK, with correlations to LDH and GGT levels. Although motility showed no correlation with total DNA methylation, ALH, wobble of the curvilinear trajectory (WOB), and VCL were significantly different in the newly introduced classification for "suggested good quality", where both motility and methylation were high. The performance differences observed while training various ML classifiers using different feature subsets highlight the importance of DNA methylation for achieving more accurate sample quality classification, even though there is no correlation between motility and DNA methylation. The parameters ALH, VCL, triton extracted LDH, and VAP were top-ranking for "suggested good quality" predictions by the neural network and gradient boosting models. In conclusion, integrating non-kinetic parameters into machine-learning-based sample classification offers a promising approach for selecting kinetically and morphologically superior duck sperm samples that might otherwise be hindered by a predominance of lowly methylated cells.

Dataset Information

Innovative Approach for Human Semen Quality Assessment Based on Volatilomics.

Publications

Innovative Approach for Human Semen Quality Assessment Based on Volatilomics.

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