Dataset Information

Exploring the Occurrence of Organic Contaminants in Human Semen through an Innovative LC-HRMS-Based Methodology Suitable for Target and Nontarget Analysis.

ABSTRACT: Understanding the potential impact of organic contaminants on male fertility is crucial, yet limited studies have examined these chemicals in semen, with most focusing on urine and blood. To address this gap, we developed and validated a robust LC-HRMS methodology for semen analysis, with a focus on polar and semipolar chemicals. Our methodology enables the quantitative (or semiquantitative) analysis of >2000 chemicals being compatible with suspect and nontarget strategies and providing unprecedented insights into the occurrence and potential bioaccumulation of diverse contaminants in this matrix. We comprehensively analyzed exogenous organic chemicals and associated metabolites in ten semen samples from Spanish participants collected in an area with a large presence of the chemical industry included in the LED-FERTYL Spanish study cohort. This investigation revealed the presence of various contaminants in semen, including plastic additives, PFAS, flame retardants, surfactants, and insecticides. Notably, prevalent plastic additives such as phthalic acid esters and bisphenols were identified, indicating potential health risks. Additionally, we uncovered previously understudied chemicals like the tire additive 2-mercaptobenzothiazole and specific organophosphate flame retardants. This study showcases the potential of our methodology as a valuable tool for large-scale cohort studies, providing insights into the association between contaminant exposure and the risk of male fertility impairments.

SUBMITTER: Sanchez-Resino E

PROVIDER: S-EPMC10722465 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Exploring the Occurrence of Organic Contaminants in Human Semen through an Innovative LC-HRMS-Based Methodology Suitable for Target and Nontarget Analysis.

Sánchez-Resino Elena E Marquès Montse M Gutiérrez-Martín Daniel D Restrepo-Montes Esteban E Martínez María Ángeles MÁ Salas-Huetos Albert A Babio Nancy N Salas-Salvadó Jordi J Gil-Solsona Rubén R Gago-Ferrero Pablo P

Environmental science & technology 20231107 48

Understanding the potential impact of organic contaminants on male fertility is crucial, yet limited studies have examined these chemicals in semen, with most focusing on urine and blood. To address this gap, we developed and validated a robust LC-HRMS methodology for semen analysis, with a focus on polar and semipolar chemicals. Our methodology enables the quantitative (or semiquantitative) analysis of >2000 chemicals being compatible with suspect and nontarget strategies and providing unpreced ...[more]

PMID: 37934628

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Project description:Nontarget analysis by liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is now widely used to detect pollutants in the environment. Shifting away from targeted methods has led to detection of previously unseen chemicals, and assessing the risk posed by these newly detected chemicals is an important challenge. Assessing exposure and toxicity of chemicals detected with nontarget HRMS is highly dependent on the knowledge of the structure of the chemical. However, the majority of features detected in nontarget screening remain unidentified and therefore the risk assessment with conventional tools is hampered. Here, we developed MS2Quant, a machine learning model that enables prediction of concentration from fragmentation (MS2) spectra of detected, but unidentified chemicals. MS2Quant is an xgbTree algorithm-based regression model developed using ionization efficiency data for 1191 unique chemicals that spans 8 orders of magnitude. The ionization efficiency values are predicted from structural fingerprints that can be computed from the SMILES notation of the identified chemicals or from MS2 spectra of unidentified chemicals using SIRIUS+CSI:FingerID software. The root mean square errors of the training and test sets were 0.55 (3.5×) and 0.80 (6.3×) log-units, respectively. In comparison, ionization efficiency prediction approaches that depend on assigning an unequivocal structure typically yield errors from 2× to 6×. The MS2Quant quantification model was validated on a set of 39 environmental pollutants and resulted in a mean prediction error of 7.4×, a geometric mean of 4.5×, and a median of 4.0×. For comparison, a model based on PaDEL descriptors that depends on unequivocal structural assignment was developed using the same dataset. The latter approach yielded a comparable mean prediction error of 9.5×, a geometric mean of 5.6×, and a median of 5.2× on the validation set chemicals when the top structural assignment was used as input. This confirms that MS2Quant enables to extract exposure information for unidentified chemicals which, although detected, have thus far been disregarded due to lack of accurate tools for quantification. The MS2Quant model is available as an R-package in GitHub for improving discovery and monitoring of potentially hazardous environmental pollutants with nontarget screening.

Dataset Information

Exploring the Occurrence of Organic Contaminants in Human Semen through an Innovative LC-HRMS-Based Methodology Suitable for Target and Nontarget Analysis.

Publications

Exploring the Occurrence of Organic Contaminants in Human Semen through an Innovative LC-HRMS-Based Methodology Suitable for Target and Nontarget Analysis.

Similar Datasets

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