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. 2026 Jan 27;19:39. doi: 10.1186/s12920-026-02318-5

Elucidating the mechanisms of bisphenols-induced male spermatogenesis disorder via network toxicology, molecular docking and bioinformatics

Minmin Tian 1,2,#, Yang Wu 3,#, Mi Tian 1, Lingqin Zhu 1,✉, Guanghua Li 1,4,✉
PMCID: PMC12918258  PMID: 41593631

Abstract

Bisphenols (BPs), are extensively utilized in various manufacturing industries, leading to increased human exposure and detection in diverse environments, posing potential risks to reproductive health. This study endeavors to investigate whether seven BP compounds result in male spermatogenesis disorder (SD) and explore the underlying molecular mechanisms involved. Network toxicology was integrated with molecular docking technology and bioinformatics data to systematically elucidate the roles, underlying targets and pathological mechanisms of each BP compound in the SD process. Network toxicology analysis revealed that all seven BPs induced SD through similar mechanisms. The protein-protein interaction (PPI) network identified key targets implicated in SD induced by each BP, while enrichment analysis highlighted that BPs primarily affected SD through hormone-dependent pathways, cell proliferation, apoptosis, and prostate cancer pathways. Molecular docking further confirmed the pivotal role of these targets in BPs-induced SD. Additionally, analysis of Gene Expression Omnibus (GEO) data demonstrated significant differential expression of these genes in the SD patients compared to normal controls. These results underscored the crucial effects of genes such as BCL2, PARP1, AKT1, EGFR, ESR1, ESR2, HIF1A, HSP90AA1 and GSK3B in the development of SD. Collectively, these findings indicated that BPs exposure could contribute to the onset of SD as an environmental trigger, while offering theoretical insights into the molecular mechanisms associated with BPs-elicited SD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-026-02318-5.

Keywords: Bisphenols, Spermatogenesis disorder, Network toxicology, Molecular docking

Introduction

Recent studies have shown that infertility affects 8–12% of couples worldwide, with male factors contributing to approximately 50% of these cases [1]. Among infertile men, 40–50% have spermatogenesis disorders (SD), which encompass abnormalities in sperm quality, quantity, morphology, and motility [2]. A systematic review revealed a 50–60% decline in sperm counts between 1973 and 2011 [3]. Furthermore, statistics indicated that 1–3% of men worldwide experience azoospermia, while oligospermia is more prevalent, affecting approximately 20–30% of men [4, 5]. Male SD can arise from various factors, including genetic predispositions, unhealthy lifestyle choices, reproductive system abnormalities, hormonal imbalances, and environmental pollutants. Among these, increased exposure to environmental contaminants is considered a significant contributing factor [6, 7].

Bisphenols (BPs) are a class of compounds characterized by the presence of two phenolic groups, including bisphenol A (BPA), BPS and BPF [8]. These compounds are widely used in the production of plastics, resins, and other synthetic materials [9]. Nevertheless, broad applications of BPs has increased its exposure in the human environment. Recent studies have highlighted their various toxic effects, including endocrine disruption, neurotoxicity, reproductive toxicity, and developmental toxicity [10, 11]. Long-term exposure to BPs has been closely linked to several health issues, such as reproductive system disorders, immune dysfunction, and neurodevelopmental impairments [12]. Within these, the reproductive toxicity of BP compounds, particularly their impact on male reproductive health, requires urgent investigation and intervention.

A study by Lassen et al. suggested that BPs disrupted normal hormone function through endocrine disruption mechanisms, thereby impacting reproductive health, particularly in males [13]. Extensive animal studies have consistently shown that exposure to BP compounds led to SD, encompassing a significant reduction in sperm count, impaired motility, and abnormal morphology [14–16]. Furthermore, BPs have been found to induce oxidative stress, alter the testicular microenvironment, and impair the function of spermatogenic epithelial cells, contributing to the disruption of spermatogenesis [17, 18]. These mechanisms cover the activation of reactive oxygen species (ROS) and subsequent cellular damage, which has been linked to mitochondrial dysfunction and apoptosis of spermatogenic cells [19]. Despite the growing body of evidence on the reproductive toxicity of BPs, the specific mechanisms underlying SD remain unknown. Therefore, further research is needed to clarify the molecular pathways and potential long-term impacts of BPs exposure on male fertility.

This study aimed to comprehensively investigate the effects of seven BP compounds on SD and expound underlying mechanisms by combining network toxicology, molecular docking, and bioinformatics. These compounds contain BPA, BPS, BPF, BPE, BPB, BPAF, and tetrabromobisphenol A (TBBPA). Network toxicology helps predict potential mechanisms underlying the effects of BPs, while molecular docking simulations assess the binding stability between molecules, providing valuable insights into their interactions and potential efficacy. Bioinformatics, particularly through the analysis of gene expression profiles from publicly available database like GEO, allows for the validation and comparison of these mechanistic studies. The integration of these methods offers a more holistic understanding of the interactions between BPs and SD, facilitating the identification of potential therapeutic targets. The detailed workflow was depicted in Fig. 1.

Fig. 1.

Fig. 1

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The description of current study

Methods

Screening of targets for BPs

First, we identified six analogues of BPA through a literature review and the ProTox-3.0 database [20]. The canonical SMILES representations of these compounds were retrieved from the PubChem database [21], and the details of the seven BPs were listed in Table S1. Then, the TargetNet database were used to obtain the target proteins for each BP, selecting targets with a probability greater than 0.5 [22]. Meanwhile, we explored the ChEMBL database for BP targets, designating “Homo sapiens” as the species [23]. In addition, for the validation these targets, we consulted the SwissTargetPrediction database [24]. Ultimately, to guarantee both accuracy and comprehensiveness, we merged the protein targets from all three databases, cross-referenced them, and applied normalization based on the UniProt database [25].

Collection of SD-related targets

Based on the concept of SD, we identified targets linked to SD from the GeneCards database (https://www.genecards.org/) using search terms including “spermatogenic failure”, “sperm toxicity”, “spermatogenesis disorder”, “spermatotoxicity”, “oligospermia”, “asthenozoospermia”, “azoospermia” and “teratospermia”. The obtained targets were then deduplicated and ranked according to the GeneCards Inference Function Score (GIFtS), with only those scoring equal to or greater than the median retained for further analysis. This approach allowed us to focus on targets most strongly associated with SD. Finally, these targets were standardized using the UniProt database [25].

Construction of PPI network

The common targets for each BP compound and SD were identified and visualized using Venn diagrams. These targets were subsequently uploaded to the STRING database (http://string-db.org), with species limited to “Homo sapiens” and a minimum confidence score of 0.4, so that could generate a highly reliable PPI network.

Screening of hub targets

To enhance the network and identify the most influential proteins, we employed the CytoHubba plugin for advanced network analysis within Cytoscape [26]. The Maximum Clique Centrality (MCC) algorithm was utilized to rank nodes (proteins) according to their involvement in densely connected regions of the network, enabling the identification of central genes. A network for the top 10 central genes was subsequently constructed using Cytoscape 3.9.1. In addition, the MCODE algorithm in Metascape was used to identify densely connected components or modules within the PPI network [27]. These modules represented protein complexes that could uncover potential targets and molecular mechanisms underlying SD induced by BPs.

Enrichment analysis

To explicit the functions and signaling pathways of these common targets, we submitted them to the Metascape platform (http://metascape. org) for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Stringent parameters were applied, including a minimum overlap of three genes between enriched terms and the input gene lists, along with a p-value threshold of 0.05 to ensure the identification of only statistically significant pathways. The results were subsequently visualized using the Microbial Informatics website [28].

Molecular Docking for BPs and key targets

Molecular docking simulations were performed using AutoDock Vina 1.5.7 to predict the binding affinity and conformation of each BP with core protein targets. The active ingredients were retrieved in sdf format from PubChem and converted to pdb format via Open Babel GUI. The crystal structures of the core proteins were obtained from the RCSB Protein Data Bank (PDB), and water molecules, along with any unique ligands, were removed using PyMOL 1.8.6. The protein structures were further prepared in AutoDock Tools 1.5.7, where dehydration and hydrogenation were carried out to ensure the optimal structures for docking. A grid box was defined for each target protein to specify the active site, with its size adjusted according to the molecular sizes of the ligands. The docking results were assessed based on binding energy, with values below − 4.5 kJ/mol indicating strong binding affinity [29]. The lowest binding energy model was selected for further analysis, and the interactions were visualized in 3D using PyMOL and in 2D using LigPlot+, providing detailed insights into the binding modes and key interactions, including hydrogen bonds and hydrophobic contacts, between BPs and the core targets.

Analysis of the gene expression of core targets in SD

Gene expression data related to SD were retrieved from the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/). After a thorough comparison of Gene Series Expression datasets associated with SD, the GSE6872, which has the largest sample size (including 13 normal samples and 8 SD samples), was selected to ensure the robustness and reliability of the analysis. The data were then downloaded and analyzed using RStudio. To prepare the data for analysis, gene and probe information was merged. Differential gene expression was assessed using the limma package, a widely used tool for analyzing gene expression data across different conditions.

To identify the core targets associated with SD, the top five targets from each BP were selected, and duplicates were removed, resulting in 13 distinct key targets. These genes were then categorized into five functional groups: proliferation and apoptosis-related genes (BCL2, PARP1, AKT1, EGFR), hormone receptor-related genes (ESR1, ESR2, HIF1A), stress and heat shock protein-related genes (HSP90AA1, HSP90AB1), targets involved in gene expression regulation and transcription factors (GSK3B, MMP9), inflammation and immune response-related genes (PTGS2, NFKB1). To assess the expression differences of these genes between the control and SD groups, t-tests were conducted using SPSS 26.0 statistical software. A significance threshold of p-value < 0.05 was applied to identify genes with statistically expression differences between the normal and diseased samples.

Results

Effects and mechanisms of BPA on SD

A total of 76 common targets of BPA and SD were identified (Fig. 2A), which were subsequently used to construct a PPI network (Fig. 2B). Using the MCC algorithm, we identified core targets, including BCL2, EGFR, MMP9, ESR1, HSP90AA1, HSP90AB1, GSK3B, KRAS, AKT1, and PARP1 (Fig. 2C). A densely connected network component was identified by the MCODE algorithm, which included signal transduction, metabolic processes, apoptosis, and immune responses (Fig. 2D).

Fig. 2.

Fig. 2

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Potential target genes and pathways for BPA induced SD. A The venn diagram for BPA targets in SD. B The PPI network for BPA targets in SD. C The top ten hub targets with the highest scores identified. D Enriched processes identified by MCODE analysis. E GO enrichment of BPA targets in SD. F KEGG pathway analysis of BPA targets in SD. G-H 3D molecular docking analysis of BPA with BCL2. I 2D molecular docking patterns of BPA with BCL2

To determined the biological functions and signaling pathways of the 76 overlapping genes, we conducted GO and KEGG enrichment analysis. These targets were primarily localized in intracellular and membrane-bound structures, characterized by kinase activity and ATP-dependent functions, and predominantly involved in phosphorylation-related signaling pathways (Fig. 2E). The targets were mainly enriched in pathways related to cancer, prostate cancer, chemical carcinogenesis, and endocrine resistance (Fig. 2F).

Molecular docking was performed to assess the interactions between BPA and the top five core targets (BCL2, EGFR, MMP9, ESR1, and HSP90AA1). The binding energies, as shown in Table S2, indicated that the binding energies for all five ligand-receptor pairs were exhibited binding energies well below zero, suggesting that each protein could spontaneously bind to BPA. The docking results for the interaction between BPA and BCL2 was visualized in Fig. 2G-I.

Effects and mechanisms of BPS on SD

A total of 61 overlapping targets of BPS and SD were identified (Fig. 3A), and a PPI network were constructed (Fig. 3B). The top ten targets were determined using the MCC algorithm, which included BCL2, HSP90AA1, ESR1, MMP9, PARP1, CCNB1, SIRT1, GSK3B, AR and PTGS2 (Fig. 3C). Using the MCODE algorithm, a more densely connected network component was identified, which likely represents pathways and mechanisms related to cancer, hormone signaling, and cellular stress response (Fig. 3D).

Fig. 3.

Fig. 3

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Potential target genes and pathways for BPS induced SD. A The venn diagram for BPS targets in SD. B The PPI network for BPS targets in SD. C The top ten hub targets with the highest scores identified. D Enriched processes identified by MCODE analysis. E GO enrichment of BPS targets in SD. F KEGG pathway analysis of BPS targets in SD. G-H 3D molecular docking analysis of BPS with BCL2. I 2D molecular docking patterns of BPS with BCL2

GO enrichment analysis revealed that the common targets were primarily localized to key intracellular structures, such as the cytosol, mitochondria, and membrane-bound organelles. These targets were significantly involved in processes like phosphorylation, apoptosis regulation, and gene expression control (Fig. 3E). Pathway enrichment analysis showed that the targets were predominantly associated with the PI3K-Akt signaling pathway, cell cycle regulation, and prostate cancer (Fig. 3F).

Molecular docking analysis was conducted to assess the binding interactions between BPS and the five key core targets (BCL2, HSP90AA1, ESR1, MMP9, PARP1). As shown in Table S2, the binding energies for all ligand-receptor pairs were substantially lower than zero. The docking results for the interaction between BPS and BCL2 were illustrated in Fig. 3G-I.

Effects and mechanisms of BPF on SD

We identified 54 shared targets of BPF and SD (Fig. 4A), and constructed a PPI network (Fig. 4B). The top ten targets were screened via the MCC algorithm, including BCL2, HSP90AA1, HSP90AB1, ESR1, PTGS2, MMP9, PARP1, CDK4, IGF1R, CASP9 (Fig. 4C). Applying the MCODE algorithm, a tightly connected network module was detected. The clusters indicated these targets participated in hormone signaling and cell cycle regulation (Fig. 4D).

Fig. 4.

Fig. 4

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Potential target genes and pathways for BPF induced SD. A The venn diagram for BPF targets in SD. B The PPI network for BPF targets in SD. C The top ten hub targets with the highest scores identified. D Enriched processes identified by MCODE analysis. E GO enrichment of BPF targets in SD. F KEGG pathway analysis of BPF targets in SD. G-H 3D molecular docking analysis of BPF with BCL2. I 2D molecular docking patterns of BPF with BCL2

GO analysis suggested that these targets were primarily located in mitochondria, and biological functions mainly focused on protein phosphorylation and male gonad development (Fig. 4E). KEGG analysis found these targets were chiefly associated with pathways in cancer, PI3K-Akt signaling pathway, and prostate cancer pathway (Fig. 4F).

To explore the interactions between BPF and the top five core targets (BCL2, HSP90AA1, HSP90AB1, ESR1 and PTGS2), molecular docking was conducted and the results were shown in Table S2, the binding energies of all ligand receptor pairs were much lower than zero. As shown in Fig. 4G-I, the docking interaction between BPF and BCL2 was depicted.

Effects and mechanisms of BPE on SD

46 common targets to both BPE and SD were identified (Fig. 5A), leading to the construction of a corresponding PPI network (Fig. 5B). The top ten targets were selected using the MCC algorithm, with key proteins including BCL2, HSP90AA1, HSP90AB1, ESR1, MMP9, MDM2, PARP1, CASP9, MAP2K1, and PTGS2. (Fig. 5C). A more densely connected network component was detected by the MCODE algorithm. These pathways highlighted the clusters’ relevance in cancer progression, endocrine regulation, and cellular signaling processes (Fig. 5D).

Fig. 5.

Fig. 5

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Potential target genes and pathways for BPE induced SD. A The venn diagram for BPE targets in SD. B The PPI network for BPE targets in SD. C The top ten hub targets with the highest scores identified. D Enriched processes identified by MCODE analysis. E GO enrichment of BPE targets in SD. F KEGG pathway analysis of BPE targets in SD. G-H 3D molecular docking analysis of BPE with BCL2. I 2D molecular docking patterns of BPE with BCL2

GO analysis hinted that common targets were significantly involved in protein phosphorylation and male gonad development processes, localized to intracellular components such as the cytoplasm and nucleus, as well participated in molecular functions like kinase activity and ATP binding (Fig. 5E). KEGG analysis indicated that many genes were involved in cancer-related signaling pathways, hormone regulation, and endocrine resistance (Fig. 5F).

To explore the interactions between BPE and the top five core targets (BCL2, HSP90AA1, HSP90AB1, ESR1, MMP9), molecular docking simulation was conducted and the results were shown in Table S2. The binding energies of all ligand receptor pairs were much lower than zero. The docking interaction between BPE and BCL2 was illustrated in Fig. 5G-I.

Effects and mechanisms of BPB on SD

We identified 51 common targets of BPB and SD (Fig. 6A), further constructed a PPI network (Fig. 6B). The top ten targets (BCL2, ESR1, MMP9, AKT1, PARP1, CCNB1, CDK2, CCNA2, HSP90AA1, HSP90AB1) were screened via the MCC algorithm (Fig. 6C). Moreover, these modules provided insights into potential therapeutic targets for disorders involving cell cycle dysregulation, hormone signaling, and steroid metabolism through MCODE analysis (Fig. 6D).

Fig. 6.

Fig. 6

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Potential target genes and pathways for BPB induced SD. A The venn diagram for BPB targets in SD. B The PPI network for BPB targets in SD. C The top ten hub targets with the highest scores identified. D Enriched processes identified by MCODE analysis. E GO enrichment of BPB targets in SD. F KEGG pathway analysis of BPB targets in SD. G-H 3D molecular docking analysis of BPB with BCL2. I 2D molecular docking patterns of BPB with BCL2

The overlapping targets in GO enrichment were crucial for hormonal biosynthesis, signal transduction, cell structure, and molecular interactions vital for maintaining homeostasis (Fig. 6E). KEGG emphasized that these genes played important roles in regulating cancer, prostate cancer, and endocrine function (Fig. 6F).

The molecular docking analysis was performed on the top five genes—BCL2, ESR1, MMP9, AKT1, and PARP1. The binding energies for all these genes were significantly below zero, indicating strong binding affinity. The docking interaction between BPB and BCL2 was depicted in Fig. 6G-I.

Effects and mechanisms of BPAF on SD

We constructed a PPI network for the selected 36 mutual targets of BPAF and SD (Fig. 7A-B). The top ten targets were screened via the MCC algorithm, including ESR1, ESR2, PTGS2, BCL2, NFKB1, JUN, PRKACA, IGF1R, AR and NOS3 (Fig. 7C). Furthermore, analysis using the MCODE algorithm demonstrated that these clusters were closely tied to pathways, such as cancer development, apoptosis regulation, and steroid hormone synthesis (Fig. 7D).

Fig. 7.

Fig. 7

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Potential target genes and pathways for BPAF induced SD. A The venn diagram for BPAF targets in SD. B The PPI network for BPAF targets in SD. C The top ten hub targets with the highest scores identified. D Enriched processes identified by MCODE analysis. E GO enrichment of BPAF targets in SD. F KEGG pathway analysis of BPAF targets in SD. G-H 3D molecular docking analysis of BPAF with ESR1. I 2D molecular docking patterns of BPAF with ESR1

GO analysis revealed that most targets primarily performed molecular and biological functions in the endoplasmic reticulum membrane and cytoplasm. Their biological roles were chiefly connected to the steroid biosynthetic process, testosterone biosynthetic process, male gonad development, and androgen biosynthetic process (Fig. 7E). KEGG analysis revealed that numerous genes were involved in metabolic pathways, cancer, and the PI3K-AKT signaling pathway (Fig. 7F).

To investigate the interactions between BPAF and the top five core targets (ESR1, ESR2, PTGS2, BCL2, and NFKB1), molecular docking simulations were performed, and the results were presented in Table S2. The docking model of BPAF with ESR1 was illustrated in Fig. 7G-I. The results exposed that BPAF exhibited strong docking interactions with these core targets.

Effects and mechanisms of TBBPA on SD

63 common targets of TBBPA and SD were screened (Fig. 8A), and a PPI network were constructed (Fig. 8B). The top ten targets were identified via the MCC algorithm, including TP53 (non-coding RNA), BCL2, EGFR, PTGS2, HIF1A, GSK3B, MAPK14, CASP9, CXCL8 and NFE2L2 (Fig. 8C). The MCODE clusters further revealed functional protein groupings involved in signal transduction, immune and inflammatory responses, hormone metabolism, and tumor suppression (Fig. 8D).

Fig. 8.

Fig. 8

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Potential target genes and pathways for TBBPA induced SD. A The venn diagram for TBBPA targets in SD. B The PPI network for TBBPA targets in SD. C The top ten hub targets with the highest scores identified. D Enriched processes identified by MCODE analysis. E GO enrichment of TBBPA targets in SD. F KEGG pathway analysis of TBBPA targets in SD. G-H 3D molecular docking analysis of TBBPA with BCL2. I 2D molecular docking patterns of TBBPA with BCL2

The GO analysis revealed that the genes were predominantly involved in hormonal regulation, stress responses, DNA repair, and transcriptional regulation. They were localized to key organelles such as the mitochondria and the endoplasmic reticulum. At the molecular level, the analysis highlighted functions related to steroid metabolism, protein interactions, and hormonal signaling pathways (Fig. 8E). The KEGG analysis indicated that these genes were enriched in critical oncogenic pathways, hormonal and metabolic regulation, drug resistance, and immune interactions (Fig. 8F).

Molecular docking simulation was performed to expound the interactions between TBBPA and the top five core targets (BCL2, EGFR, PTGS2, HIF1 and GSK3B), and the results were represented in Table S2. The binding energies of all ligand-receptor pairs were far below zero, demonstrating that TBBPA robust connections to these core targets. The docking diagram of TBBPA and BCL2 was illustrated in Fig. 8G-I.

The expression of core genes in SD

Based on the GSE6872 dataset, as shown in Fig. 9A, compared to the normal group, the SD group exhibited significantly higher expression of 1852 genes and significantly lower expression of 5517 genes (p < 0.05). The SD group demonstrated a decrease in the expression of PARP1 and AKT1, along with an increase in EGFR expression compared to the normal group (p < 0.05), and a slight decrease in BCL2 expression, though these differences did not reach statistical significance (p > 0.05). In addition, further analysis of apoptosis-related genes, BCL2A1, revealed a significant decrease in the SD group (p < 0.05). Moreover, ESR1 and ESR2 were upregulated in the SD group, while genes such as HIF1A, HSP90AA1 and GSK3B were obviously downregulated (p < 0.05; Fig. 9C-E). These findings suggested that these genes could contribute to the pathogenesis of SD. The expression alterations of these genes in SD patients were listed in Table S3.

Fig. 9.

Fig. 9

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Expression of BPs core targets associated with SD. A Volcano plot of SD-related genes. B Relative expression levels of genes associated with proliferation and apoptosis. C Relative expression levels of genes related to hormone receptors. D Relative expression levels of genes associated with stress response and heat shock proteins. E Relative expression levels of genes involved in gene expression regulation and transcription factors. F Relative expression levels of genes related to inflammation and immune responses. The SD group versus the normal group, *p < 0.05, ** p < 0.01, ***p < 0.001

Dicussion

In recent years, global male fertility has experienced a significant decline, and male reproductive health is facing a serious crisis [30]. A recent study revealed a sharp downward trend in both sperm quantity and quality. In the 1970 s, the standard for male sperm count was 100 million sperm per milliliter, while the global average sperm concentration has now decreased to 49 million sperm per milliliter. Furthermore, the threshold for qualified semen concentration has been lowered to 20 million sperm per milliliter [31]. Sperm motility has also notably diminished, leaving the current sperm count and quality well below the established standards. As sperm quality and quantity continue to decline worldwide, the incidence of male reproductive system diseases is rising [32]. Statistics indicated that approximately 300 million men globally suffer from reproductive health issues, with about 10% affected by infertility [33]. The causes of this decline are multifactorial, with environmental BP compounds playing a crucial role.

BPA, a well-known environmental endocrine disruptor, has been extensively studied due to its endocrine-disrupting, immunotoxic, neurotoxic, and reproductive and developmental toxic effects [34]. Consequently, numerous countries and regions have imposed restrictions or outright bans on its use. BPA is particularly harmful to the male reproductive system, where it significantly impacts spermatogenesis, sperm quality, and testicular function [35]. BPA interferes with male hormone synthesis and secretion by binding to estrogen receptors, inhibiting testosterone production in the testes, which in turn impacts sperm production and quality [15]. Research has demonstrated that BPA exposure elicits oxidative stress and triggers apoptosis in the rat testes, accompanied by dysregulation of the PI3K/AKT signaling pathway [36]. Moreover, concurrent exposure to BPA and nonylphenol has been shown to significantly upregulate the mRNA expression of AKT and the protein levels of phosphorylated AKT, while downregulating the expression of BCL2. This molecular cascade promotes excessive apoptosis and autophagy in the testes of prepubertal rats [37]. In this study, we obtained similar results by identifying 76 common targets between BPA and SD using multiple databases. These targets were primarily localized in the cytoplasm and involved in the estrogen signaling pathway and cancer-related proteoglycans, both of which were directly linked to SD. Additionally, the ErbB signaling pathway and resistance to EGFR tyrosine kinase inhibitors could influence spermatogenesis by regulating cell proliferation and differentiation. Molecular docking revealed a strong interaction between the hub targets and BPA.

BPS, structurally similar to BPA, is increasingly used as a substitute for BPA in various consumer products. BPS has been detected in a wide range of environments, including food, indoor dust, personal care products, and paper products [15]. Exposure to BPS occurs primarily through ingestion, inhalation, and skin contact. In a study conducted across the United States and seven Asian countries, BPS was detected in 81% of human urine samples, with an average concentration of 0.654 ng/mL, comparable to BPA levels [38]. Rosenmai et al. demonstrated that BPS binds to estrogen receptors and exhibits estrogenic and anti-androgenic effects similar to those of BPA [39]. Neither the research on the reproductive toxicity of BPS remains limited. Given the current gaps, the present study employed network toxicology, molecular docking, and bioinformatics approaches to screen 61 potential targets through which BPS induced SD. Further analysis revealed that, like BPA, these targets were predominantly distributed in the cytoplasm. BPS impaired sperm production by interfering with the estrogen signaling pathway, disrupting the cell cycle and impairing programmed ovarian maturation. Our finding is consistent with previous studies, which have reported similar effects of BPS on sperm production. Jeseta M et al. reported a significant negative correlation between BPS concentration and sperm ejection volume, as well as total sperm count [40].

BPF is a component of epoxy resin commonly used in industrial flooring, coatings, dental sealants, and food packaging materials. It has been detected in indoor dust and various food items [41]. A study indicated that, compared to BPA, BPF exhibited an average estrogenic potency of 1.07 ± 1.20 [42]. Consequently, the endocrine-disrupting effects of BPF may be comparable to, or potentially stronger than, those of BPA. Although BPF exposure has been shown to alter steroidogenesis both in vivo and in vitro, its effects on spermatogenesis remains unclear [43]. This study identified SD-related genes potentially associated with BPF, showing similar effects to BPA and BPS. These genes were primarily cytoplasmic and enriched in key pathways—including estrogen/androgen receptor signaling and endocrine resistance—all critical in spermatogenesis impairment.

BPE is an organic compound frequently utilized in the synthesis of high-performance resins, plastics, and materials that are resistant to high temperatures and corrosion [44]. As a result, BPE has been detected in various environmental matrices, including sludge, wastewater, aquatic organisms, drinking water, and paper products [45]. Due to its structural similarity to BPA, BPE has also been shown to possess endocrine-disrupting properties [46]. However, data on BPE exposure and its effects on spermatogenesis remain limited. This study detected 46 genes that could mediate the impact of BPE on SD. These targets were primarily involved in the development of male gonads, steroid biosynthesis, and androgen synthesis. By modulating endocrine resistance and steroid hormone biosynthesis pathways, BPE disrupted spermatogenesis.

BPB, a bisphenol A analog used in the plastics industry, has been found to leach from plastic containers, contaminating canned food. Meanwhile, BPB has been detected in human biological samples, including serum and urine [47]. Due to its estrogenic and anti-androgenic properties, BPB is considered a potential endocrine-disrupting chemical. It was reported that acute exposure to BPB resulted in a significant reduction in sperm count, impaired sperm morphology, and a marked decline in sperm quality and vitality in adolescent male mice [48]. Similarly, our study demonstrated that BPB-induced SD likely disrupts reproductive function through concurrent interference with endocrine signaling and cellular proliferation pathways.

BPAF, a fluorine-substituted derivative of BPA, is widely utilized in the manufacturing of gaskets, hoses, polycarbonate copolymers, and high-temperature composites [49]. In recent years, BPAF has been detected in various environmental media, including drinking water, air, food, soil, sewage, and sludge [50]. Studies showed exposure to BPAF (at doses of 5, 20, and 50 mg/kg/day) over 28 consecutive days in sexually mature mice generated damage to the blood-testis barrier, a reduction in sperm count, and decreased sperm quality, exhibiting a dose-dependent effect [51]. In vitro studies on TM3 cells demonstrated that exposure to 10 µM BPAF for 24 and 48 h disrupts cell cycle progression, thereby impairing spermatogenesis [52]. This study pinpointed 36 common targets shared between BPAF and SD, the majority of which were enriched in the cytoplasm. These targets modulated key pathways, including steroid hormone synthesis, cell apoptosis, and hormone signaling, all of which contributed to sperm dysfunction.

TBBPA is primarily used as a reactive flame retardant in plastics, paper, textiles, and circuit boards [53]. The general population is exposed to TBBPA through inhalation, dermal contact, and consumption of fish and shellfish [54]. Structurally, TBBPA resembles thyroid hormones and steroid estrogens, classifying it as an endocrine disruptor [55]. Research showed that TBBPA stimulated cytotoxicity and interfered with the endocrine system in animals [56]. Experimental studies on male rodents displayed that TBBPA exposure significantly reduced sperm count [57]. However, due to species-specific differences, variations in exposure duration, and differences in experimental design, the effects of TBBPA on spermatogenesis remain contentious. To address the limitations of small sample sizes and inconsistencies in study methodologies, this study integrated data from multiple authoritative databases, network toxicology, and bioinformatics approaches. Our findings also suggested that TBBPA disrupts spermatogenesis through modulation of steroid hormone biosynthesis, endocrine resistance pathways, and apoptosis induction.

Using the GEO database, the top five genes of each BP were selected based on their scores, resulting in a total of 13 unique genes after deduplication. Statistical analysis of these genes disclosed a decrease in the expression of apoptosis- and proliferation-related genes (BCL2A1, PARP1, and AKT1), while EGFR expression was elevated. BCL2A1 and BCL2 have similar functions and both are members of the BCL protein family, which encodes anti-apoptotic proteins [58]. These proteins protect cells from death by inhibiting intracellular apoptotic pathways. In a rat model of SD, the expression of p-AKT1 and BCL2 was diminished, suggesting that these disorders could result from impaired cell survival, proliferation, and excessive apoptosis [59]. A population case-control study exhibited that abnormal expression or polymorphisms in PARP1 caused male infertility with oligospermia (Chen et al., 2014). EGFR, a cell surface receptor, plays a crucial role in spermatogenesis, particularly in the early stages, where it regulates the proliferation and differentiation of sperm cells. Abnormal expression or function of EGFR has led to testicular developmental defects and impaired spermatogenesis in goats [60]. These findings corroborate our research, demonstrating that BPs promote SD by modulating cell proliferation and apoptosis pathways in an abnormal manner.

This study found that in patients with SD, the expression of ESR1 and ESR2 was upregulated, while HIF1A expression was downregulated. The reduced expression of HIF1A compromised the testes’ capacity to respond to low-oxygen environments. Concurrently, the upregulation of ESR1 and ESR2 disrupted hormone balance, inhibited androgen synthesis, and impaired testicular cell function. The combined effects of these changes obstructed spermatogenesis, ultimately leading to male infertility or decreased sperm quality. These results suggested that BP compounds influenced spermatogenesis by modulating hormone receptor pathways.

HSP90AA1, as a crucial molecular chaperone protein, involves in various physiological processes. During male reproduction, HSP90AA1 regulates testicular cell stress and modulates hormone signaling pathways to control spermatogenesis. Abnormal expression or dysfunction of HSP90AA1 triggered testicular cell damage, hormonal imbalances, and impaired sperm production [61]. In this study, the SD group exhibited altered expression of HSP90AA1 compared to the normal group. GSK3B plays a significant role in regulating spermatogonial proliferation, differentiation, testosterone synthesis, as well as influencing apoptosis and autophagy processes. Abnormal GSK3B activity disrupted sperm production, potentially resulting in male infertility [62]. In our study, GSK3B expression in patients with spermatogenic disorders was significantly lower than that in the normal group. These findings suggested that BPs might contribute to SD through the abnormal expression of HSP90AA1 and GSK3B.

While this computational study systematically explored BPs-induced SD, several key limitations must be acknowledged: First, although network toxicology and molecular docking provided mechanistic hypotheses, these predictions require rigorous experimental validation through both in vitro cellular models and in vivo animal studies. Second, our GEO data analysis was constrained by (1) limited sample sizes across datasets, (2) heterogeneity in patient populations, and (3) a focus on specific infertility phenotypes, potentially limiting generalizability. Third, the study did not account for real-world exposure scenarios involving bisphenol mixtures or dose-response relationships. Future research should integrate transcriptomic/proteomic analyses, prospective clinical studies with larger sample sizes, and well-controlled exposure models to substantiate these in silico findings.

Conclusions

Collectively, this study suggested that seven BP compounds might contribute to SD through common molecular pathways. The findings indicated potential involvement of hormone signaling, cell proliferation, and apoptosis mechanisms, with several candidate targets identified (e.g., BCL2, AKT1, ESR1/2). These preliminary results highlighted the need for further investigation into the role of environmental BPs in SD development.

Supplementary Information

Supplementary Material 1. (22.3KB, docx)

Acknowledgements

Not applicable.

Abbreviations

BPs

Bisphenols

SD

Spermatogenesis disorder

PPI

Protein-protein interaction

GEO

Gene Expression Omnibus

ROS

Reactive oxygen species

TBBPA

Tetrabromobisphenol A

MCC

Maximum Clique Centrality

GO

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

PDB

Protein Data Bank

Authors’ contributions

M.M.T: Writing – original draft, Data curation, Methodology, Visualization, Formal analysis, Funding acquisition. Y.W.: Investigation, Visualization, Conceptualization, Funding acquisition. M.T.: Visualization, Investigation, Formal analysis. L.Z.: Writing – review & editing, Project administration, Funding acquisition, Conceptualization. G.L.: Writing – review & editing, Project administration, Conceptualization, Resources, Supervision.

Funding

This study was supported by the Natural Science Foundation of Ningxia (2024AAC03288, 2024AAC03737), the Scientific Research Plan Projects of Shaanxi Education Department (24JK0409) and the Science and Technology Department of Shaanxi Province (2025JC-YBQN-1275).

Data availability

Data will be made available on request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Minmin Tian and Yang Wu contributed equally to this work.

Contributor Information

Lingqin Zhu, Email: zhulingqin1217@163.com.

Guanghua Li, Email: ghlee0404@163.com.

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Supplementary Materials

Supplementary Material 1. (22.3KB, docx)

Data Availability Statement

Data will be made available on request.

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