Introduction

Sperm quality has been declining in various countries, and while the exact causes remain unknown, environmental and lifestyle factors are suggested contributors with potential implications for offspring health (Skakkebæk et al. 2022; Cannarella et al. 2022). One particular focus is on environmental pollutants and substances with endocrine-disrupting properties, especially due to the rise of industrialization. Bisphenol A (BPA) is a widely recognized endocrine disruptor chemical (EDC) found in epoxy resins and polycarbonate plastics, exhibiting both estrogenic and anti-androgenic properties (Sharma et al. 2020; Wang et al. 2023). It can leach into food and beverages under conditions of high heat, physical manipulation, or repetitive use. Thus, ingestion is the main source of exposure (Sharma et al. 2020; Wang et al. 2023).

BPA has been detected in various body fluids, including urine, serum, seminal plasma, follicular fluid, umbilical cord plasma, and tissues (Cariati et al. 2020). Human biomonitoring surveys have revealed measurable amounts of BPA in urine in a significant percentage of the population, both in Europe (92.3% of adults) (Govarts et al. 2023) and the United States (93%) (Calafat et al. 2008). Mounting evidence indicates the negative impact of BPA on human health, including male fertility (Rochester 2013; Cariati et al. 2020; Sharma et al. 2020). Animal studies have shown that BPA disrupts the hypothalamic–pituitary–gonadal (HPG) axis, leading to decreased serum testosterone levels and impaired sperm quality and production (Santiago et al. 2021a; Cannarella et al. 2022; Yadav et al. 2022; Wang et al. 2023). Currently, the adverse effects on male fertility, specifically on seminal parameters, reproductive hormones, and infertility-related phenotypes, are mainly supported by studies based on urinary BPA concentration. While most studies have reported a negative association between urinary BPA levels and various seminal parameters, such as total sperm count, concentration, and motility (Meeker et al. 2011; Li et al. 2011; Lassen et al. 2014; Barbonetti et al. 2016; Eladak et al. 2018; Adoamnei et al. 2018; Grami et al. 2018), DNA damage (Meeker et al. 2010), and altered epigenetic patterns (Shi et al. 2019), some studies have found no significant association (Mendiola et al. 2010; Goldstone et al. 2015; Benson et al. 2021; Kiwitt-Cárdenas et al. 2021; Aghajani et al. 2023). Notably, a recent meta-analysis highlighted that the only statistically significant association was between urinary BPA levels and sperm motility, but this significance was lost after adjusting for publication bias and sensitivity analysis (Castellini et al. 2022). This result may reflect that urine may not be the best matrix to assess the impact of BPA on human sperm. In fact, we consider that for this evaluation, determining the concentration of BPA in seminal fluid is more appropriate, since this fluid comes into direct contact with spermatozoa potentially directly modulating their quality and molecular profile. The presence of BPA in seminal plasma and its correlation with semen quality was first reported in 2015, demonstrating a negative correlation with sperm concentration, total sperm count, and morphology (Vitku et al. 2016). However, the mechanisms by which BPA reaches seminal plasma are still unclear, with a recent study suggesting secretion from accessory glands rather than the testis as the main source (Ješeta et al. 2022).

Lifestyle factors, ageing, EDCs, and diseases can significantly impact the molecular profile of sperm, leading to infertility, fertilization failure, and poor reproductive and birth outcomes. BPA primarily acts by deregulating the HPG axis, but it also induces oxidative stress (Santiago et al. 2021a), modulates signaling pathways (Zhou et al. 2020), and causes epigenetic modifications, including DNA methylation, histone modification, and alterations in sperm non-coding RNAs (Cariati et al. 2020; Thorson et al. 2021). These changes, including abnormal microRNA (miRNA) expression, may contribute to altered sperm production, fertilization failure, and abnormal embryo development, leading to pregnancy loss (Santiago et al. 2021b). While the association between BPA exposure and altered miRNA expression has been reported in the seminal plasma of infertile men (Palak et al. 2021), it remains unclear whether similar changes occur directly in sperm.

Some studies have also indicated that BPA exposure can disrupt sperm proteins associated with male infertility risk factors (Bisconti et al. 2021), although the available proteomic studies on BPA-induced alterations in sperm proteome have been only conducted in mice (Rahman et al. 2016, 2017, 2018). In fact, considering that most molecular studies were performed in animal models, the translation of the findings to humans is often challenging.

Our main goals were to (1) investigate the correlation between seminal plasma BPA concentration and semen quality in a cohort of 102 Portuguese men and (2) identify altered sperm proteins and miRNAs that might be involved in fertilization failure, abnormal embryo development and/or epigenetics, using transcriptomics and proteomics. Despite the lack of significant correlation between BPA levels in seminal plasma and conventional semen parameters, we were able to establish a comprehensive molecular profile of sperm in individuals with varying concentrations of BPA in seminal plasma and identified several differentially expressed miRNAs and proteins that may serve as biological markers of BPA exposure.

Materials and Methods

Samples’ Collection and Preparation

This cross-sectional study was approved by the Ethics and Internal Review Board of the Hospital Infante D. Pedro E.P.E. (Aveiro, Portugal) (Process number: 36/AO; Approved on 14 April 2015) and by the Ethics Committee of Centro Hospitalar Vila Nova de Gaia/Espinho, E.P.E. (Vila Nova de Gaia, Portugal) (Process number: 12/2019-3; Approved on 18 July 2019) and was conducted following the ethical standards of the Declaration of Helsinki. All donors were consecutively recruited from the Hospital and signed an informed consent allowing the use of the samples for scientific purposes. Exclusion criteria were known diseases (e.g., varicocele, cryptorchidism, orchitis, epididymitis, endocrine hypogonadism, obstruction of the vas deferens), and medication.

Ejaculated semen samples were obtained from donors by masturbation into a sterile container after 2–7 days of sexual abstinence. Basic semen analyses were performed by qualified technicians according to the 5th Edition of the World Health Organization (WHO) laboratory manual for the examination and processing of human semen (World Health Organization. 2010) (Supplementary Table 1). To rule out the possibility of contamination by somatic cells and debris, we performed a density gradient sperm selection. After semen liquefaction, sperm cells were washed and viable spermatozoa were isolated by the density gradient method using SupraSperm® (Origio, Måløv, Denmark) according to the manufacturer’s instructions. Briefly, 90% and 45% gradients were prepared using SupraSperm®100 and Sperm Preparation Medium (Origio, Måløv, Denmark), and pre-equilibrate in a CO2 environment at 37 °C: 1 ml of each gradient was used per each 2 ml of the liquefied semen sample. The gradient was centrifuged at 300 × g for 20 min and the seminal plasma was collected and stored in glass tubes at −30 °C to further evaluate BPA levels. The pellet was washed twice with Sperm Preparation Medium (Origio, Måløv, Denmark) at 300 × g for 10 min and the motility and concentration of spermatozoa in the washed sample were determined. Optical phase contrast microscopic examination was also used to verify the elimination of the somatic cells. The viable fraction of spermatozoa was cryopreserved using CryoSperm™ (Origio, Måløv, Denmark) according to the manufacturer’s instruction, and stored at −80 °C until used for subsequent experiments. All steps in the sample collection protocol and subsequent processing were carried out using BPA-free equipment.

Quantification of BPA Levels in Seminal Plasma

Seminal plasma samples were stored in BPA-free glass tubes and sent in dry ice to the Institute of Endocrinology of Prague, Czech Republic, for BPA quantification. After the separation of spermatozoa and seminal fluid by centrifugation, BPA levels were determined in seminal plasma using liquid chromatography–tandem mass spectrometry (LC–MS/MS) according to a previously validated method (Vitku et al. 2015a). Briefly, a volume of 1000 μl of seminal plasma was spiked with an internal standard mixture, diluted with 500 μl 0.9% saline and extracted using 2 ml of 99.9% methyl tert-butyl ether (Sigma-Aldrich, St. Louis, MO, USA) for 1 min. The organic phase was evaporated until dryness and the derivatization step proceeded: a volume of 50 µl of bicarbonate buffer (100 mM, pH 10.5) (Sigma-Aldrich, St. Louis, MO, USA) and 50 µl of dansyl chloride in acetone (1 mg/ml) (Sigma-Aldrich, St. Louis, MO, USA) were added to the dry residues and the mixture was incubated at 60 °C for 5 min and thereafter evaporated until dryness. Dry residues were reconstituted with 300 μl of methanol (Merck, Darmstadt, Germany) and 50 μl of this solution was transferred to the vial with a glass insert, where 50 μl of the 10 mM ammonium formate in ultrapure water (Merck, Darmstadt, Germany) was pre-pipetted. The volume of 50 µl of the sample was injected into LC–MS/MS [(Eksigent ultraLC 110 system (Redwood City, California, USA) connected to API 3200 mass spectrometer (Sciex, Concord, Canada)] for analysis. Multiple reaction monitoring (MRM) transitions and optimized conditions for the mass spectrometer are listed in Supplementary Table 2. Every step in the protocol was performed using glass-based equipment, such as Pasteur pipettes, glass syringes, and glass tubes to avoid BPA contamination.

The limit of quantification (LOQ) for seminal BPA was 28.9 pg/ml. Precision, evaluated for both inter-day and intra-day variations, as well as accuracy, was assessed across three concentration levels: low (pooled seminal plasma), medium (pooled seminal plasma with an additional 0.16 ng/ml of BPA), and high (pooled seminal plasma with an added 0.8 ng/ml of BPA). The relative standard deviations (RSDs) for intra-day precision were 9.25%, 6.89%, and 5.36% at the low, medium, and high levels, respectively. For inter-day precision, RSDs were determined as 6.6%, 6.5%, and 1.7% at the low, medium, and high levels, respectively. Accuracy was calculated using the formula [(concentration of BPA in spiked sample − concentration of BPA in non-spiked sample)/concentration of added BPA] × 100 (%), resulting in values of 99.7% and 103.8%. Each batch included four quality control (QC) samples: a doublet of low QC samples and a doublet of high QC samples. The low QC level consisted of pooled seminal plasma, while the high QC level comprised seminal plasma with an addition of 0.25 ng/ml of BPA. Furthermore, procedural and solvent blanks were included in each batch. Additional information can be found in Vitku et al. (2015a).

Total RNA Isolation

Immediately before RNA extraction, sperm cells were thawed by warming cryotubes at room temperature (RT) for 5 min and washed twice in phosphate-buffered saline (PBS) (500 × g for 5 min at RT) to remove traces of CryoSperm™ (Origio, Copenhagen, Denmark). Total RNA (>18 nt) was extracted from spermatozoa using miRNeasy® Mini Kit (Qiagen, Hilden, Germany), according to the manufacturer’s recommendations. Briefly, 10–30 × 106 cells were disrupted and homogenized in 700 µl of QIAzol lysis reagent and, after the addition of 140 µl of chloroform, the homogenate was separated into aqueous and organic phases by centrifugation at 12,000 × g for 15 min, 4 °C. The upper aqueous phase was collected for a new tube, 1.5 volumes of ethanol were added creating conditions that promote selective binding of RNA to the RNeasy membrane, and the sample was applied to the spin column where total RNA binds to the membrane and phenol and other contaminants were washed away. Buffer RWT (700 μl) was added to the RNeasy spin column and centrifuged for 15 s at ≥8000 × g to wash the spin column membrane. RPE wash buffer (500 μl) was added to the RNeasy spin column and centrifuged twice, for 15 s and 2 min at 8000 × g, respectively. Finally, the RNA was eluted in 30 µl of RNase-free water and stored at −80 °C. RNA concentration was determined using Qubit 2.0 (Invitrogen, Massachusetts, USA) and the samples were subjected to several quality controls. To determine the purity of the RNA, the samples were checked spectrophotometrically at 230, 260 and 280 nm using the Nanodrop ND-2000 (Thermo Fisher Scientific, Massachusetts, USA). To determine the RNA integrity, the samples were analyzed using a TapeStation (Agilent Technologies, California, USA).

Small RNA Sequencing

Fifteen sperm RNA samples from normozoospermic (NZ) men were selected according to RNA quality and BPA levels measured in seminal plasma (Supplementary Table 3). An average amount of 100 ng of total RNA was used for library preparation using NEXTflex Small RNA Seq v3 (PerkinElmer, Massachusetts, EUA) according to the manufacturer’s instructions and using the gel-free size selection option. The size distribution of the final library was evaluated by Agilent High Sensitivity D1000 assay (Agilent Technologies, California, USA) and the concentration by Qubit dsDNA HS assay (Invitrogen, Massachusetts, USA). Sequencing was done at the iBiMED’s Genome Medicine Platform (Aveiro, Portugal) using Illumina NextSeq 550 Sequencing System (Illumina, California, USA) and a 75 cycle, single end run.

Initial quality checking of the raw reads and possible contaminations was performed with FASTQC v0.11.7 and miRTrace software (Kang et al. 2018). From the raw data reports, the library size varied between 27.7 and 9.0 million reads per sample, well above the 5 million reads per sample described in the literature for miRNA sequencing in semen samples. Parameters were adjusted to improve the quality of the reads using the Cutadapt program (Martin 2011), followed by the removal of rRNA sequences with Bowtie (Langmead et al. 2009). The mapping and quantification of the known miRNAs and isomiRs were performed with QuickMIRSeq (Zhao et al. 2017) which uses the Bowtie program to perform the alignment and the mapping step against miRNA, hairpin, small RNA and mRNA sequences (with strand information). To reduce background reads and improve miRNA detection, the program filters miRNA reads that have average counts below 2 per sample and are absent in more than 60% of the samples. The reference used for the alignment and annotation was Homo sapiens UCSC: hg38; Ensembl: GRCh38; miRbase: release22. The raw counts were submitted to an in-house pipeline using the DESeq2 R package (R v4.1.3; RStudio v2022.02.1 build 461). miRNA with raw counts in all samples and in all samples but one below 5 counts were filtered out before further analysis. A normalization step was performed using the number of total spermatozoa for the adjustment, and an extra filter (rowSums(counts(dds) ≥ 10) ≥ 4) was included to keep only the miRNAs that are representative. After the normalization step (Supplementary Fig. 1), exploratory and differential gene expression analysis was performed. Principal component analysis (PCA), and unsupervised hierarchical clustering were performed with vst transformation using prcomp and hclust R functions, respectively (Supplementary Fig. 1). Significant differentially expressed miRNAs (DEMs) were identified by comparing samples based on BPA levels in the seminal plasma (p-value < 0.05; log2 fold change (LFC) = 0.59). Considering that the variable of interest is continuous, the reported LFC is per unit of change of the variable (Love et al. 2014). miRNA counts follow the formula: counts proportional to \({2}^{a*x}\), where a is the LFC and x is the continuous covariate BPA. The results were presented in volcano plots and heatmaps using ggplot2 (v3.3.5) and ggheatmap (v2.1) R functions, respectively.

Protein Extraction and LC–MS/MS Analysis

A total of 20 NZ sperm samples were selected and divided into four groups according to BPA levels measured in seminal plasma: Non-detectable BPA levels group (N-BPA; BPA below the LOQ; LOQ = 0.0289 ng/ml); Low BPA levels group (L-BPA; LOQ < BPA ≤ 0.066 ng/ml); Moderate BPA levels group (M-BPA; 0.066 ng/ml < BPA ≤ 0.132 ng/ml); and High BPA levels group (H-BPA; BPA > 0.132 ng/ml); and prepared for LC–MS/MS analysis (Supplementary Table 4). The concentrations used to group samples were selected based on results previously obtained in this fluid by Vitku et al. in men with different degrees of fertility (Vitku et al. 2015b, 2016). Fifteen million sperm cells per sample were homogenized in 100 µl lysis buffer containing 10% sodium dodecyl sulfate (SDS, Sigma-Aldrich, Missouri, USA) and 100 mM triethylammonium bicarbonate (TEAB, Sigma-Aldrich, Missouri, USA), pH 8.5. Next, samples were heated for 10 min at 95 °C to denature all proteins and sonicated with 3 pulses of 10 s at an amplitude of 20% using a 3 mm probe, with incubation on ice for 30 s between pulses. After centrifugation for 15 min at 20,000 × g at RT (to remove insoluble components), the protein concentration was measured by bicinchoninic acid (BCA) assay (Thermo Fisher Scientific, Massachusetts, USA) according to the manufacturer’s instructions and using bovine serum albumin (VWR, Pennsylvania, USA) standard curve ranging from 0.125 to 2 µg/µl. Complete samples (15–100 µg) were used to continue the protocol. Proteins were reduced by the addition of 15 mM dithiothreitol (Merck-Millipore, Massachusetts, USA) and incubation for 30 min at 55 °C and then alkylated by the addition of 30 mM iodoacetamide (Fluka/Thermo Fisher Scientific, Massachusetts, USA) and incubation for 15 min at RT in the dark. Phosphoric acid (Sigma-Aldrich, Missouri, USA) was added to a final concentration of 2.75% and subsequently, samples were diluted sevenfold with binding buffer containing 90% methanol (Acros Organics/Thermo Fisher Scientific, Massachusetts, USA) in 100 mM TEAB, pH 7.55. After loading the samples to S-trap micro columns (Protifi, New York, USA) using centrifugation for 30 s at 4000 × g, the columns were washed three times with 150 µl binding buffer and 20 µl 50 mM TEAB containing 1 µg trypsin (1/100, w/w, Promega, Wisconsin, USA) was added for digestion overnight at 37 °C. Peptides were eluted three times, first with 40 µl 50 mM TEAB, then with 40 µl 0.2% formic acid (FA, Biosolve, Valkenswaard, The Netherlands) in water and finally with 40 µl 0.2% FA in water/acetonitrile (ACN, Acros Organics/Thermo Fisher Scientific, Massachusetts, USA) (50/50, v/v). Eluted peptides were transferred to HPLC inserts and dried completely by vacuum centrifugation. Each sample was solubilized in 20 µl loading solvent A [0.1% trifluoroacetic acid (Biosolve, Valkenswaard, The Netherlands) in water:ACN (98:2, v:v)] moments before analysis. 2 µg of the sample measured on Dropsense16 (Unchained Labs) was injected for LC–MS/MS analysis in an Ultimate 3000 RSLCnano system in-line connected to an Orbitrap Fusion Lumos mass spectrometer (Thermo Fisher Scientific, Massachusetts, USA). Trapping was performed at 10 μl/min for 4 min in loading solvent A on a 20 mm trapping column (made in-house, 100 μm internal diameter (I.D.), 5 μm beads, C18 Reprosil-HD, Dr. Maisch, Germany). The peptides were separated on a 50 cm µPAC™ column with C18-endcapped functionality (prototype, Thermo Scientific). It was kept at a constant temperature of 50 °C. Peptides were eluted by a linear gradient reaching 22.5% MS solvent B [0.1% FA in water/acetonitrile (2:8, v/v)] after 109 min, 30.5% MS solvent B at 135 min, 55% MS solvent B at 153 min, 70% MS solvent B at 155 min followed by a 5-min wash at 70% MS solvent B and re-equilibration with MS solvent A (0.1% FA in water). The flow rate was set to 250 nl/min. The mass spectrometer was operated in a data-dependent mode. Full-scan MS spectra (300–1500 m/z) were acquired at a resolution of 120,000 in the Orbitrap analyzer after accumulation to a target AGC value of 200,000 with a maximum injection time of 50 ms. The precursor ions were filtered for charge states (2–7 required), dynamic exclusion (60 s; ±10 ppm window) and intensity (minimal intensity of 5E4). The precursor ions were selected in the quadrupole with an isolation window of 1.2 Da and accumulated to an AGC target of 1.2E4 or a maximum injection time of 40 ms and activated using CID fragmentation (34% NCE). The fragments were analyzed in the Ion Trap Analyzer at a turbo scan rate. QCloud was used to control instrument longitudinal performance during the project (Chiva et al. 2018; Olivella et al. 2021), amongst others by daily assessment of the numbers of peptides and proteins identified after injecting 25 ng of a tryptic hela digest (Promega, Wisconsin, USA).

LC–MS/MS Data Analysis

Data analysis was performed with MaxQuant algorithm (version 2.0.1.0) using the Andromeda search engine with default search settings including a false discovery rate (FDR) set at 1% on both the peptide-to-spectrum matches (PSMs), peptide and protein levels. Spectra were searched against the human reference proteome (version of 2021_01, UP000005640). The mass tolerance for precursor and fragment ions was set to 4.5 and 20 ppm, respectively, during the main search. Enzyme specificity was set as C-terminal to arginine and lysine, also allowing cleavage at proline bonds with a maximum of two missed cleavages. Variable modifications were set to oxidation of methionine residues and acetylation of protein N-termini and fixed modifications to carbamidomethylation on the cysteine residues. Matching between all runs was enabled with a matching time window of 0.7 min and an alignment time window of 20 min. Only proteins with at least one unique or razor peptide were retained. Proteins were quantified by the MaxLFQ algorithm integrated into the MaxQuant software. A minimum ratio count of two unique or razor peptides was required for quantification. A total of 759,167 PSMs were identified, resulting in 32,629 identified unique peptides, corresponding to 3955 protein groups. Further data analysis of the shotgun results was performed with an in-house R script, using the proteinGroups output table from MaxQuant. Reverse database hits were removed, LFQ intensities were log2 transformed and replicate samples were grouped. Proteins with less than three valid values in at least one group were removed and missing values were imputed from a normal distribution centered around the detection limit (package DEP) (Zhang et al. 2018), leading to a list of 1839 quantified proteins in the experiment, used for further data analysis. To compare protein abundance between pairs of sample groups, statistical testing for differences between two group means was performed, using the package limma (Ritchie et al. 2015). Statistical significance for differential regulation was set to a p-value < 0.05 and fold change of >4.5- or <0.58-fold (|log2FC| = 1.5). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository (Perez-Riverol et al. 2022) with the dataset identifier PXD047280 and https://doi.org/10.6019/PXD047280.

Bioinformatic Analysis

For differentially expressed proteins (DEPs), the UniProt database was used to retrieve gene ontology (GO) information (data was downloaded on 14/12/2022). To gain molecular insight into the DEPs identified, a GO enrichment analysis was performed for biological processes using DAVID Bioinformatics Resources (v22q4) (Huang et al. 2009; Sherman et al. 2022). Only terms with a p-value < 0.05 were retrieved. All the identified proteins were searched in the PubMed database (Bethesda, MD, USA) to identify studies that report their role in mammalian spermatozoa and fertilization (search performed until January 2023). The HIPPIE and STRING databases were used for retrieving human protein–protein interaction (PPI) data (downloaded 24/01/2023). HIPPIE database is regularly updated by incorporating interaction data from major expert-curated experimental PPI databases (such as Bell09, BioGRID, HPRD, IntAct, and MINT). The STRING database integrates all known and predicted associations between proteins, including both physical interactions as well as functional associations. To predict the target genes of DEM, we used TargetScan Release 8.0 (McGeary et al. 2019) (downloaded 18/01/2023) and miRDB (downloaded 18/01/2023) (Liu and Wang 2019; Chen and Wang 2020). TargetScan predicts possible biological targets of miRNA by searching for the presence of conserved 8mer, 7mer, and 6mer sites that match the seed region of each miRNA (Lewis et al. 2005). miRDB is an online database for miRNA target prediction and functional annotations, using the bioinformatics tool MirTarget, which was developed by analyzing thousands of miRNA-target interactions from high-throughput sequencing experiments. The 15 miRNAs’ target genes were compiled, and the duplicates were removed. The target genes were analyzed using DAVID to determine the significantly enriched biological processes possibly being regulated in samples with high BPA levels. Network analyses were performed using Cytoscape (version 3.9.1; Bethesda, MD, USA).

Protein Extraction and Slot Blot

For protein extraction, an adequate volume of SDS 1% (5 µl of buffer per million of spermatozoa) was added to the sperm pellet and incubated for 5 min with agitation. The lysates were centrifuged for 15 min at 4 °C at 16,000 × g and the supernatant (soluble fraction) was collected and stored at −30 °C. The Thermo Scientific™ Pierce™ BCA Protein Assay Kit (Fisher Scientific, Loures, Portugal) was used to determine the protein concentration of the samples according to the manufacturer’s instructions. Protein samples were diluted to 0.05 μg/μl and blotted under vacuum into a nitrocellulose membrane, 0.45 μm pore size (GE Healthcare, Chicago, Illinois, USA) inside the slot blot device (BioRad Portugal, Sintra, Portugal). The membranes were blocked with 5% (w/v) nonfat dry milk in tris-buffered saline (TBS) with 0.1% Tween™ 20 (TBS-T) for 1 h at RT. Incubation with the primary antibodies was carried out for 1 h at RT using the following dilutions: polyclonal rabbit anti-Human DEFB126 Antibody (aa21‑111) (1:400, LS-C373839-100, LSBio, Seattle, USA), mouse monoclonal anti-HDAC11 (C-5) (1:500, sc-390737, Santa Cruz Biotechnology, Inc., Dallas, Texas, USA). After being washed in TBS-T, membranes were incubated with appropriate secondary antibodies—IRDye® 800CW goat anti-mouse (1:10,000) or IRDye 680RD® goat anti-rabbit (1:10,000)—for 1 h at RT. Membranes were scanned using the Odyssey Infrared Imaging System (LI-COR® Biosciences, Lincoln, Nebraska, USA). Results were normalized to Ponceau staining performed before antibody probing.

Statistical Analyses

Descriptive statistics of all data were calculated using RStudio Version 1.2.5033. BPA values outside the interval between the first quartile minus 1.5 times the interquartile range (IQR) and the third quartile plus 1.5 times the IQR (Q1 − 1.5*IQR to Q3 + 1.5*IQR) were considered outliers, following the IQR rule, and replaced by the median value of the distribution of BPA values. For the determination of correlations between BPA levels, age, seminal parameters, and protein levels, and after the evaluation of normality using the Shapiro–Wilk test, the Spearman correlation test was applied. The Kruskal–Wallis test was used to detect differences between independent groups. The significance level was set at 0.05. All analyses were conducted using RStudio Version 1.2.5033.

Results

BPA Levels in the Seminal Plasma of Portuguese Men Do Not Correlate with Semen Parameters

We have investigated the correlation between BPA levels in the seminal plasma of Portuguese men and semen parameters. The cohort comprised 102 Portuguese men from the Aveiro region, with ages ranging from 19 to 56 years (mean age 35.07 ± 6.51 years) (Table 1 and Supplementary Table 1). Among the samples, ten exhibited translucid appearance, 6 had incomplete liquefaction, and 28 showed increased viscosity. Based on the semen analysis, 62 men were classified as normozoospermic (NZ), while 40 men exhibited anomalies in at least one sperm quality parameter (non-normozoospermic samples).

Table 1 Characterization of study sample (n = 102) and correlation between BPA concentration, age, and seminal parameters
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In our study, we successfully detected BPA in 88% of the seminal plasma samples. In 12 samples, the BPA levels were below the lower LOQ (LOQ = 28.9 pg/ml), indicating lower concentrations. Considering that Portugal had 10,347,892 habitants, in 2021, and the results of Human biomonitoring surveys that revealed measurable amounts of BPA in almost 93% of adult Europeans (Govarts et al. 2023), our sample size (n = 102) is suitable to have a confidence level of 95% that the real value of BPA prevalence is within ± 5% of the measured value in our population. Among the samples with detectable BPA levels, the mean BPA concentration in seminal plasma was 0.175 ng/ml ± 0.133 ng/ml (Table 1 and Supplementary Table 5). Regarding the characteristics of the samples, 8% exhibited a translucid appearance, while 5% showed incomplete liquefaction, and 25% displayed increased viscosity. From these samples, 53 men were classified as normozoospermic (NZ group), representing individuals with normal sperm quality, while 37 men were categorized as non-normozoospermic (nNZ group). Interestingly, the seminal BPA concentrations in the nNZ group were not significantly different compared to the NZ group (p-value = 0.292). However, the nNZ patients had a slightly higher concentration of BPA in the seminal fluid (mean BPA 0.1968 ± 0.1482 ng/ml) compared to NZ men (mean BPA 0.1606 ± 0.1211 ng/ml) (Fig. 1A and Supplementary Table 5). Furthermore, we found no correlation between the BPA levels in seminal plasma and the evaluated seminal parameters. Also, there was no correlation observed between BPA concentration and age (Table 1).

Fig. 1
figure 1

The concentration of BPA in the seminal plasma of Portuguese men does not correlate with semen quality but affects sperm molecular profile. A Comparison between BPA levels in the seminal plasma of normozoospermic (NZ, n = 53) and non-normozoospermic men (nNZ, n = 37). The horizontal line displays the median, the box edges show the 25th and 75th percentiles and the whiskers show the smallest and highest value within 1.5 box lengths from the box. B Heatmap representing the z-score of the level of expression for the 15 miRNAs significantly correlated with BPA levels. Normalized expression values were transformed using log2. Red means high expression (above average), whereas green means low expression (below average). C Integrative network of relevant biological processes associated with the 35 DEPs identified in the high BPA levels (H-BPA) group. Square nodes represent the significant biological processes, and the round nodes represent the DEPs associated (represented by gene name). Box and whisker plots show the effect of BPA on the protein levels of HDAC11 (D) and DEFB126 (E) in NZ sperm samples. Ponceau S was used as the protein-loading control. The horizontal line displays the median, the box edges show the 25th and 75th percentiles and the whiskers show the smallest and highest value within 1.5 box lengths from the box (n = 7 for N-BPA; n = 4 for L-BPA and M-BPA; n = 8 for H-BPA)

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Sperm miRNA Profile is Different in Men with Higher BPA Concentration in Seminal Plasma

In order to investigate the relationship between BPA concentration in seminal plasma and sperm miRNA content, we isolated total RNA from 15 NZ human sperm samples with varying BPA concentrations ranging from below LOQ to 0.290 ng/ml. Subsequently, small RNAs were sequenced. The majority of reads aligned with small RNA, and the average number of identified miRNAs was consistent with findings from recent studies (Donkin et al. 2016; Hua et al. 2019; Lorente et al. 2021). Our dataset exhibited a high percentage of overall mapped reads and a substantial number of reads corresponding to small RNA (Supplementary Table 6).

Through this analysis, we identified a total of 2172 miRNAs in at least one sample, with 310 miRNAs detected in all samples or all but one sample (Supplementary Table 7). As BPA concentration is a continuous variable, we assessed the correlation between BPA concentration and the Log2 Fold Change (LFC) of miRNA expression. Our analysis revealed 15 miRNAs that exhibited significant correlation with BPA concentration, including 8 miRNAs with positive correlation and 7 miRNAs with negative correlation (Table 2 and Fig. 1B).

Table 2 The significative (p-value < 0.05; |LFC| > 0.59) miRNAs correlated with BPA concentration in seminal plasma
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Using TargetScan and miRDB, two miRNA target prediction databases, we identified 14,520 target genes for the 15 miRNAs that correlated with BPA levels after excluding duplicates. To gain insights into the biological processes associated with these target genes, we performed a GO-enrichment analysis using DAVID, which revealed several significantly enriched biological processes (Supplementary Table 8). Among the most significantly enriched biological processes were the positive and negative regulation of transcription from RNA polymerase II promoter (GO:0045944, GO:0000122, GO:0006357), positive and negative regulation of transcription, DNA-templated (GO:0045893, GO:0045892, GO:0006355), nervous system development (GO:0007399), signal transduction (GO:0007165, GO:0035556), and protein phosphorylation (GO:0006468). Additionally, although not the most significant terms, the targets of DEMs in sperm from men with the highest levels of BPA in seminal plasma were significantly associated with “in utero embryonic development” (GO:0001701), post-embryonic development (GO:0009791), and “response to estradiol” (GO:0032355). Furthermore, other significantly enriched biological processes included “cellular response to DNA damage stimulus” (GO:0006974), “response to hypoxia” (GO:0001666), “cellular response to hypoxia” (GO:0071456), and “response to endoplasmic reticulum stress” (GO:0034976).

Sperm of Men with Higher Levels of BPA in Seminal Plasma Present Distinct Protein Expression Profile

To investigate the sperm protein profile of men with different levels of BPA in seminal plasma and identify DEPs based on these levels, we performed LC–MS/MS analysis on 20 NZ human sperm samples. There were no significant differences observed in terms of age and seminal parameters among the groups (Supplementary Table 4). In total, we identified 759,167 peptide-spectrum matches (PSMs), 32,629 peptides, and 3955 protein groups (Supplementary Table 9). Among these, 1839 protein groups were reliably quantified, meaning they had at least three valid label-free quantification (LFQ) intensity values in one of the experimental conditions (listed in Supplementary Table 10).

The proteomic analysis revealed 62 DEPs between the groups (Supplementary Tables 11 and 12). Specifically, we found 14 DEPs (3 upregulated and 11 downregulated), 18 DEPs (8 upregulated and 10 downregulated), and 8 DEPs (5 upregulated and 3 downregulated) that were differentially expressed between the N-BPA group and the groups with low (L), moderate (M), and high (H) BPA levels, respectively. Furthermore, we identified 14 DEPs (11 upregulated and 3 downregulated) between the L-BPA and M-BPA groups, and 20 DEPs (17 upregulated and 3 downregulated) between the L-BPA and H-BPA groups. Finally, we found 9 DEPs (1 upregulated and 8 downregulated) between the M-BPA and H-BPA groups (Table 3).

Table 3 The suggestive (p-value < 0.05 and |log2FC| = 1.5) differentially expressed proteins (DEPs) between the groups according to BPA concentration
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In the sperm samples with BPA levels below the lower LOQ (N-BPA), we observed lower levels of UBE2I, RPL13A, RPL13AP3, and PGMA1/PGMA4 compared to samples from men with L- and M-BPA levels. Additionally, the N-BPA group exhibited higher levels of APOD, DEFB126, and KLK2 compared to samples from men with BPA levels higher than 0.066 ng/ml (M and H-BPA groups). Furthermore, among the DEPs identified, HDAC11 and SSR4 consistently showed downregulation, while PSD3 showed upregulation in samples from the L-BPA group compared to samples with BPA levels higher than 0.066 ng/ml (M and H-BPA groups). In samples with the highest levels of BPA in seminal plasma, DEFB126 was downregulated, and HDAC11 was upregulated compared to the L-BPA group (BPA ≤ 0.066 ng/ml), but not compared to the M-BPA group. To further support these findings, the levels of HDAC11 and DEFB126 were evaluated by slot blot in a new cohort of 23 NZ sperm samples divided into four groups based on BPA levels (Supplementary Table 13). Although no significant differences were observed in the levels of these proteins (Fig. 1D, E and Supplementary Table 13), DEFB126 was slightly decreased in samples from the H-BPA group, consistent with the results from the LC–MS/MS analysis.

Furthermore, an enrichment analysis was performed to identify significant biological processes associated with the 35 deregulated sperm proteins in the H-BPA group (listed in Supplementary Table 12). The most significant terms were “protein sumoylation” (GO:0016925) and “cytoplasmic translation” (GO:0002181) (Fig. 1C and Supplementary Table 14). Except for PGAM4, GNAL, RPL32, and SUMO2, all the DEPs had been previously identified in other proteomic studies using ejaculated human spermatozoa (Santiago et al. 2019) (Supplementary Table 12). However, PGAM4 (Okuda et al. 2012) and SUMO2 (Vigodner et al. 2013) were previously detected by immunocytochemistry in human spermatozoa.

Integrative Network of Altered miRNAs and Proteins in the Sperm of Men with the Highest Levels of Seminal Plasma BPA

A PPI network was constructed using the DEPs identified in the H-BPA group compared to the other three groups (Fig. 2). The PPI network consisted of a total of 35 proteins represented as round nodes, with 19 interactions between them. Among the proteins in the dataset, 18 did not have available PPI data. Thirteen proteins were upregulated in the H-BPA group (indicated by a green outline in Fig. 2), while 22 proteins were downregulated (indicated by a red outline in Fig. 2) compared to the N-BPA (yellow nodes), L-BPA (lilac nodes), and/or M-BPA (blue nodes) groups. The network also included the 15 differentially expressed miRNAs (DEMs), represented as diamond nodes. Analysis of miRNA targets revealed that most of the DEMs targeted genes encoding the identified DEPs (indicated by arrows in Fig. 2). Only five miRNAs did not regulate the gene expression of the identified DEPs.

Fig. 2
figure 2

Network representing the relationship between the 15 DEMs (diamond nodes) and the 35 DEPs (round nodes) found in sperm samples of H-BPA group. The green and red outlines represent upregulated (n = 13) and downregulated (n = 22) proteins, respectively. The yellow, lilac, and blue round nodes represent DEPs in H-BPA group compared with the groups N-BPA (n = 8), L-BPA (n = 20) and M-BPA (n = 9), respectively. Green and red diamond nodes correspond to miRNAs positively (n = 8) or negatively (n = 7) correlated with BPA levels, respectively. The arrows indicate that the miRNA targets the gene coding the DEP identified. The bigger nodes correspond to the DEPs and DEMs with a higher degree. All proteins are represented by gene names

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Discussion

Exposure to BPA has been linked to detrimental effects on male reproductive health, although many uncertainties still exist. While previous studies mainly focused on urinary BPA, seminal plasma has emerged as the most suitable biological fluid for assessing the impact of BPA on seminal parameters and sperm molecular profile. This is because it not only represents the testicular environment but also includes the contribution of accessory glands. Furthermore, this fluid comes into direct contact with spermatozoa after their production in the testis; thus, BPA that reaches seminal fluid acts directly on sperm during the maturation process possibly affecting their quality.

In our cohort study, we did not find any significant correlation between levels of BPA in seminal plasma and the evaluated semen parameters, which contradicts previous studies conducted in different populations (Vitku et al. 2015b, 2016; Palak et al. 2021). Discrepancies in this association may be attributed to geographic distribution, methodological variations (e.g., BPA exposure mode and duration), differences in population selection and enrollment settings, demographic characteristics, available clinical data, and confounding factors (e.g., diet, smoking).

Furthermore, we observed no significant differences in BPA levels between the NZ and nNZ groups, consistent with the findings of Palak and colleagues, who compared BPA levels in seminal plasma between NZ and oligoasthenoteratozoospermic men (Palak et al. 2021). The small increase in BPA concentration observed in our study in the nNZ group compared to the NZ group may not be enough to exclusively explain the abnormal concentration, motility and morphology observed in these samples, but can constitute an important risk factor that acts in combination with other variables (e.g., other chemicals, lifestyle, diet). In fact, it is crucial to evaluate the effects of EDC mixtures to clarify potential additive or synergistic effects that may not be observed in studies focusing on isolated EDCs. Finally, and considering that urinary samples are the gold standard matrix for estimating human exposure to BPA, it would be interesting to compare the BPA levels in urine and seminal plasma to determine to what extent the BPA levels in seminal plasma reflect the real exposure to this EDC.

The lack of effect on conventional semen parameters does not rule out the possibility of molecular alterations induced by BPA in spermatozoa. In fact, previous studies have described genetic and epigenetic changes in sperm, including different patterns of miRNA expression in the seminal plasma of infertile men exposed to BPA (Palak et al. 2021), and in vivo investigations have revealed an association between BPA levels and altered epigenetic patterns in the male reproductive system (Cariati et al. 2020). These alterations could potentially contribute to failure in assisted reproductive technology (ART) procedures, poor reproductive outcomes, and the transgenerational inheritance of fertility-related traits (Cariati et al. 2020). However, the precise mechanisms through which BPA modulates miRNA content in sperm remain unclear. Therefore, we examined the correlation between sperm miRNA expression and BPA levels in seminal plasma using 15 normozoospermic samples. Our analysis identified 15 miRNAs, all previously detected in ejaculated human spermatozoa (Pantano et al. 2015; Schuster et al. 2016; Ingerslev et al. 2018; Hua et al. 2019; Xu et al. 2020), except for miR-6124. Among the identified altered miRNAs, miR-451a and miR-4661 were the most significantly affected by BPA. Although the exact role of miR-451a is uncertain, it has been found to be differentially expressed in the sperm of patients undergoing in vitro fertilization (IVF) with low rates of high-quality embryos, suggesting its importance for normal early embryogenesis (Xu et al. 2020). Conversely, miR-29b-3p and miR-34b-3p, which were differentially expressed, have been previously associated with sperm quality and fertility potential. For instance, miR-29b expression was negatively correlated with fertility scores and showed higher levels in low-fertile bulls (Menezes et al. 2020). Similarly, our findings indicate that miR-29b-3p was increased in men with higher BPA levels, indicating that although no visible effects of BPA on semen parameters were observed, it does influence the miRNA content and quality of sperm. In the case of miR-34b, reduced levels have previously been observed in the sperm of subfertile men compared to fertile men (Abu-Halima et al. 2013, 2014), and its combination with miR-93-3p has shown potential for diagnosing cases of unexplained male infertility (Corral-Vazquez et al. 2019). Furthermore, other studies have demonstrated a significant negative correlation between miR-34b in sperm and men’s age, which is another factor associated with poor sperm quality (Salas-Huetos et al. 2015). Our findings align with these studies, as men with higher BPA levels exhibited decreased levels of this miRNA. Taken together, these data highlight miR-34b-3p and miR-29b-3p as potential markers for poor sperm quality.

The functions and mechanisms of most identified miRNAs remain unclear, both in the context of BPA exposure and in terms of reproductive impacts. To elucidate the biological processes regulated by these altered miRNAs, we obtained their predicted target genes using the TargetScan and miRDB databases. Interestingly, we identified 14,520 target mRNAs involved in various biological processes, including embryonic development. Notably, bovine sperm-borne miR-499b, which showed a negative correlation with BPA levels in our study, has been associated with early embryonic development (Wang et al. 2017). Its expression in embryos has been shown to improve the first cleavage and H3K9 acetylation levels at the 2-cell to 8-cell stage while reducing apoptosis in blastocysts (Wang et al. 2017). Additionally, miR-34b, downregulated in samples with high BPA levels, and miR-34c have been associated with clinical outcomes of intracytoplasmic sperm injection (ICSI) (Cui et al. 2015; Yeh et al. 2022) and IVF (Shi et al. 2020), suggesting their involvement in fertilization and embryonic development. Given that BPA exhibits a weaker affinity for nuclear estrogen receptors (ER) than estradiol but a similar affinity for membrane ER (Gould et al. 1998; Alonso-Magdalena et al. 2012), it is not surprising that the identified miRNAs are significantly associated with response to estradiol. Other enriched biological processes include cellular response to DNA damage, response to hypoxia, and response to endoplasmic reticulum stress, which are pathways expected to be activated by BPA. BPA is known to induce oxidative stress, leading to DNA damage, lipid peroxidation, and protein oxidation in spermatozoa (Santiago et al. 2021a) which, in turn, triggers the activation of endogenous antioxidant systems and stress response pathways like endoplasmic reticulum (UPRER) or mitochondrial (UPRmt) unfolded protein responses (Santiago et al. 2019, 2020, 2021a). Nevertheless, the true roles of the miRNAs identified remain unexplored in human sperm, and future in vivo studies are worth to clarify their mechanisms of action and impact on the reproductive process.

Considering that miRNAs regulate gene expression post-transcriptionally and BPA binds to various receptors (estrogen and estrogen-related, androgen, thyroid, and glucocorticoid receptors), it is likely that BPA affects sperm protein content beyond endocrine disruption (Santiago et al. 2021a). However, to date, studies on sperm proteome alterations following BPA exposure have only been conducted in mice (Rahman et al. 2016, 2017, 2018). In our study, we characterized the protein profiles of 20 normozoospermic sperm samples divided into four groups based on BPA levels in seminal plasma. The proteomic analysis revealed 62 DEPs between the groups, all previously identified in human spermatozoa, except for GNAL and RPL32 (Okuda et al. 2012; Vigodner et al. 2013; Santiago et al. 2019). The expression of several DEPs in the H-BPA group is regulated by the identified dysregulated miRNAs. Since BPA is present in the secretions of accessory glands, which contribute to most of the ejaculate volume in humans (Ješeta et al. 2022), some of the identified proteins in sperm may reflect not only the direct impact of BPA on human sperm but also its impact on other organs of the male reproductive tract. For example, some DEPs identified were highly expressed in the testis (UBE2I, SPACA4, PRM2, SPANXD, GFER, HDAC11, TEX101, PPP3CC, GGPS1), epididymis (DEFB126, PSD3, ELSPBP1), or prostate (KLK2, OLFM4). The levels of ELSPBP1, a protein involved in binding dead spermatozoa during epididymal transit in bulls and stallions (D’Amours et al. 2010; Kasimanickam et al. 2019; Griffin et al. 2022; Saraf et al. 2022), and linked to high sperm DNA fragmentation in humans (Belardin et al. 2023), were found to be increased in the H-BPA group, suggesting a higher level of DNA fragmentation. Similarly, the levels of FLG1, a fibrinogen-like protein produced by the caudal epididymis that specifically binds to nonviable spermatozoa (Nagdas et al. 2016), were found to increase in the sperm of men with high BPA levels. Considering that in the present study, all samples were processed as they usually are in ART procedures, selecting the best sperm fraction, these markers indicate that BPA may induce molecular alterations such as DNA fragmentation, despite not affecting conventional semen parameters. Incorporating these markers and others into traditional sperm preparation methods could be valuable for optimizing sample preparation in ART procedures.

In this study, we found that men in the N-BPA group exhibited reduced levels of UBE2I, RPL13A/RPL13AP3, and PGAM1/4 compared to the L- and M-BPA groups. Conversely, they showed increased levels of APOD, KLK2, and DEFB126 compared to the M- and H-BPA groups. Additionally, DEFB126 was downregulated, and HDAC11 was upregulated in the H-BPA group compared to the N- and L-BPA groups. These proteins play crucial roles in fertilization and embryo development (Yudin et al. 2005; Tollner et al. 2008a, b, 2011; Duan et al. 2015; Aram et al. 2019; Boroujeni et al. 2019). DEFB126, a glycosylated polypeptide, is adsorbed onto the entire sperm surface during epididymal maturation, protecting sperm from immunorecognition in the female reproductive tract, facilitating penetration through cervical mucus, and promoting attachment to oviductal epithelial cells (Yudin et al. 2005; Tollner et al. 2008a, b, 2011). Mutations in the DEFB126 gene, such as rs140685149, have been associated with reduced sperm-mucus penetration, lower clinical pregnancy rates following intrauterine insemination (IUI), and decreased live birth outcomes in couples where the male partner has the del/del genotype (Tollner et al. 2011; Duan et al. 2015; Boroujeni et al. 2019). Other mutations in DEFB126 have been linked to male infertility, including asthenozoospermia (Duan et al. 2015; He et al. 2022). Recent findings showed a higher proportion of DEFB126-positive sperm in fertile men compared to those with varicocele or altered semen parameters (Aram et al. 2019). Based on these data, we hypothesize that high BPA levels lead to a reduction in DEFB126 on the sperm surface, resulting in poor fertility and potentially hindering fertilization in men. The exact mechanisms through which BPA causes this reduction are still unclear. However, two possible mechanisms can be proposed: (1) BPA may act on epididymal cells, particularly principal cells, attenuating DEFB126 synthesis and release through epididymosomes; (2) BPA may induce molecular alterations at the sperm surface that prevent DEFB126 from being adsorbed during epididymal maturation. Couples with DEFB126 mutations or reduced levels may benefit from alternative assisted reproductive techniques such as IVF and ICSI. Promising therapeutic approaches include the addition of recombinant DEFB126 to human sperm during ART treatments (e.g., preparation media) or sexual intercourse (e.g., vaginal gels) (Tollner et al. 2011; Duan et al. 2015; Boroujeni et al. 2019). On the other hand, HDAC11, a histone deacetylase, plays an essential role in maintaining constant acetylation levels of lysine residues on core histones (Seto and Yoshida 2014). HDAC11 mRNA has been identified as a paternal contribution to zygotes, indicating its involvement in early zygotic activities (Ntostis et al. 2017). It is now accepted that histone acetylation represents an epigenetic mark transmitted to the oocyte, regulating early gene expression in the developing embryo. Hypoacetylation leads to gene silencing by increasing the histone affinity to DNA (Steger et al. 2011; Seto and Yoshida 2014). The upregulation of HDAC11 in the H-BPA group may indicate an epigenetic marker of BPA exposure, potentially affecting gene expression patterns during early embryonic development. Further studies are needed to confirm the value of DEFB126 and HDAC11 as molecular markers for BPA exposure and to elucidate the specific mechanisms by which BPA influences their expression and their implications for embryogenesis.

Proteomic analysis revealed that the most significant biological processes associated with the deregulated sperm proteins in the H-BPA group were “Protein SUMOylation” and “cytoplasmic translation.” SUMOylation is a post-translational modification involving the covalent binding of small ubiquitin-related modifiers (SUMOs) to lysine residues on target proteins (Yang et al. 2017). There are four SUMO paralogues (SUMO1 to SUMO4), all downregulated in the H-BPA group except SUMO1. In human sperm, SUMO2/3 are enriched in the neck, and with less extent in sperm heads and tail (Vigodner et al. 2013). UBC9 (UBE2I), the SUMO-conjugating enzyme responsible for transferring SUMO to target proteins (Yang et al. 2017), was downregulated in the H-BPA group compared with L-BPA group. Excessive SUMOylation has been associated with abnormal spermatozoa, especially those with two tails, curled tails, and abnormal heads (microcephalic or acephalic) (Vigodner et al. 2013). Aberrant SUMOylation patterns have also been implicated in male infertility and embryo development disorders (Wang et al. 2014). The second most associated biological process, cytoplasmic translation, is connected to the SUMOylation process through the interaction between SUMO2 and RPS8. Interestingly, 22% of SUMOylated proteins in human sperm were previously associated with transcription, RNA-binding, translation, and histones, including the ribosomal proteins RPL27A and RPS8 (Vigodner et al. 2013). Several ribosomal proteins, including RPL27, RPL13, and RPL32, associated with cytoplasmic translation, were found to be upregulated in the H-BPA group compared with L-BPA and M-BPA groups. RPL13 and RPL27 were previously associated with idiopathic male infertility (Lukkani et al. 2022) and asthenozoospermia (Bansal et al. 2015). This suggests an increase in cytoplasmic ribosome number and possibly translation in response to BPA exposure, which is quite surprising since for many years spermatozoa were considered cells virtually absent of transcription and translation. In the past years, new evidence suggested the occurrence of de novo translation of human and bovine cytoplasmic sperm mRNAs during in vitro capacitation, apparently by mitochondrial ribosomes (Gur and Breitbart 2006; Zhao et al. 2009; Bisconti et al. 2022). However, how mitochondrial ribosomes could translate nuclear-encoded mRNAs and how these transcripts are transported from the nucleus to the mitochondria remains to be elucidated. Additionally, the presence of functional cytoplasmic ribosomes in mature sperm has been debated due to the absence of intact 28S and 18S ribosomal RNAs (Cappallo-Obermann et al. 2011). Further investigation is needed to determine if BPA affects protein synthesis during spermatogenesis and if ribosomal proteins remain in sperm after previous maturation stages, or if ribosomes are functional in mature sperm and de novo translation occurs in response to BPA.

Overall, there is a need for further research to understand how the identified miRNAs, proteins and pathways can explain BPA-induced male infertility and be used as efficient markers of exposure to improve clinical outcomes. Despite the significant progress in biological research, the role of many biological markers here identified in reproduction remains unknown. Nevertheless, we believe that some may be useful to clarify situations of unexplained infertility (e.g., miR-29b-3p, miR-34b-3p, ELSPBP1, FLG1, DEFB126), ART failure (e.g., miR-451a, miR-34b-3p, DEFB126, HDAC11), or repeated abortion (e.g., ELSPBP1). Also, their potential application for assessing the efficacy of interventions and evaluating sperm quality and/or fertility, overcoming the subjectivity of conventional semen analysis, must be considered.

Considering the reduced number of samples used in the present study, a validation study using a larger cohort, well characterized in terms of confounding factors such as lifestyle, environmental and occupational exposure to toxicants, ethnicity, and geographical distribution should be designed. This validation will be extremely important to identify the most promising markers of exposure to BPA and assess the efficacy of interventions such as reduction of exposure.

Conclusions

BPA is recognized as a potent EDC, but its impact on male fertility remains an unexplored field of investigation. Understanding the molecular mechanisms underlying BPA-induced male infertility and identifying effective exposure markers capable of determining the extent of health and reproductive impact represent the major challenges in this field. Our findings revealed that although BPA levels in seminal plasma did not correlate with conventional semen parameters, the molecular profile of sperm is affected. Specific miRNAs (miR-34b-3p, miR-29b-3p) and proteins (ELSPBP1, FLG1) previously associated with DNA fragmentation, sperm viability, and overall poor sperm quality were altered in viable spermatozoa used in assisted reproductive techniques (ART). This raises concerns about the safety and efficacy of using such sperm samples in ART procedures and how these molecular alterations may influence the output of the technique used. Furthermore, the presence of miRNAs and proteins crucial for fertilization (DEFB126) and embryo development (HDAC11, miR-451a) in this fraction of ejaculate highlights the need to develop screening methods for exposure markers, replacing subjective conventional semen analysis. Additionally, several differentially expressed miRNAs and proteins identified in the samples from men with high levels of BPA in seminal plasma have been linked to sperm defects, semen quality, and fertility-related conditions, underscoring their potential as markers of sperm quality, fertility, and explaining fertilization failures and abnormal embryo development.