Biomarker for assessing male reproductive toxicity of bisphenol s and application thereof

Abstract

The application discloses a biomarker for evaluating bisphenol S male reproductive toxicity and application thereof, and belongs to the technical field of biological medicine. The biomarker for evaluating bisphenol S male reproductive toxicity is screened out based on multi-omics integration and functional phenotype, and the biomarker comprises one or more of a Cyp2e1 gene, a Tdo2 gene and a Ugt1a1 gene. The expression amount of the above biomarker is detected by a detection reagent to evaluate the male reproductive toxicity of bisphenol S, and the detection reagent has high accuracy and specificity. A corresponding detection kit and a use method are developed, and the male reproductive toxicity of bisphenol S can be quickly and accurately evaluated.

Description

Technical Field

[0001] This invention belongs to the field of biotechnology, and in particular relates to a biomarker for assessing the male reproductive toxicity of bisphenol S and its application. Background Technology

[0002] Bisphenol S (BPS), as a substitute for bisphenol A (BPA), is widely used in products such as thermal paper and food packaging, and its exposure levels are increasing year by year. Existing research mainly focuses on the reproductive toxicity of BPA, while research on BPS is relatively limited and uses single methods.

[0003] Currently, research on the reproductive toxicity of bisphenol S (BPS) as a substitute for bisphenol A (BPA) is insufficient, especially regarding its long-term, low-dose exposure mechanism affecting the male reproductive system. Existing technologies suffer from two major shortcomings: First, the assessment methods are limited and singular. Traditional techniques rely heavily on isolated indicators such as sperm count and testicular tissue staining, failing to comprehensively capture sperm motility (e.g., curve velocity, whiplash frequency, etc.) and dynamic changes in testicular cell apoptosis, let alone correlate with abnormalities in molecular metabolic pathways. Second, the depth of mechanistic research is insufficient. Existing molecular toxicology studies are mostly limited to single-mic analyses (transcriptomics or metabolomics only), lacking cross-integration and functional linkage analysis of transcriptomic and metabolomics data. A complete regulatory chain of gene-metabolite-pathway-phenotype has not yet been established, resulting in the core molecular mechanism of BPS-induced male reproductive toxicity remaining unclear. Furthermore, existing studies do not adequately focus on the reproductive effects of long-term, low-dose BPS exposure, failing to provide precise mechanistic and data-based evidence for setting its safety threshold. Currently, no effective biomarkers for assessing the male reproductive toxicity of BPS have been identified. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a biomarker for assessing the male reproductive toxicity of bisphenol S and its application. Based on multi-omics integration and functional phenotype screening, the Cyp2e1 gene, Tdo2 gene, and Ugt1a1 gene are identified as biomarkers for assessing the male reproductive toxicity of bisphenol S, which can rapidly and accurately assess the male reproductive toxicity of bisphenol S.

[0005] To achieve the above-mentioned objectives, the present invention provides the following technical solution: This invention provides the application of reagents for detecting the expression levels of biomarkers in the preparation of products for assessing the male reproductive toxicity of bisphenol S, wherein the biomarkers include one or more of the Cyp2e1 gene, Tdo2 gene, and Ugt1a1 gene.

[0006] Preferably, compared to the normal group, the Cyp2e1 gene is significantly upregulated, the Tdo2 gene is significantly upregulated, and the Ugt1a1 gene is significantly downregulated, indicating that bisphenol S produces male reproductive toxicity.

[0007] Preferably, the reagent includes one or more of primer pair 1, primer pair 2, and primer pair 3; primer pair 1 includes Cyp2e1-F and Cyp2e1-R for detecting the expression level of the Cyp2e1 gene, with nucleotide sequences as shown in SEQ ID NO. 1-2; primer pair 2 includes Tdo2-F and Tdo2-R for detecting the expression level of the Tdo2 gene, with nucleotide sequences as shown in SEQ ID NO. 3-4; primer pair 3 includes Ugt1a1-F and Ugt1a1-R for detecting the expression level of the Ugt1a1 gene, with nucleotide sequences as shown in SEQ ID NO. 5-6.

[0008] This invention provides a primer pair for detecting the expression level of a biomarker, wherein the primer pair includes one or more of primer pair 1, primer pair 2, and primer pair 3; wherein primer pair 1 for detecting the expression level of the Cyp2e1 gene is Cyp2e1-F and Cyp2e1-R, and the nucleotide sequences are as shown in SEQ ID NO. 1-2; primer pair 2 for detecting the expression level of the Tdo2 gene is Tdo2-F and Tdo2-R, and the nucleotide sequences are as shown in SEQ ID NO. 3-4; primer pair 3 for detecting the expression level of the Ugt1a1 gene is Ugt1a1-F and Ugt1a1-R, and the nucleotide sequences are as shown in SEQ ID NO. 5-6.

[0009] This invention provides a kit for detecting the expression level of biomarkers, containing the primer combination described above.

[0010] Preferably, the kit also contains 2×TB Green Premix Ex Taq II, template, and enzyme-free water.

[0011] This invention provides the application of the kit in the preparation of products for evaluating the male reproductive toxicity of bisphenol S.

[0012] This invention provides a system for evaluating the male reproductive toxicity of bisphenol S, comprising a detection module and a conclusion analysis module; the detection module is used to detect the expression levels of one or more of the Cyp2e1 gene, Tdo2 gene, and Ugt1a1 gene in the normal group and the test sample group; the conclusion analysis module is used to analyze the expression of one or more of the Cyp2e1 gene, Tdo2 gene, and Ugt1a1 gene in the test sample group relative to the normal group.

[0013] Preferably, in the conclusion analysis module, compared with the normal group, the Cyp2e1 gene is significantly upregulated, the Tdo2 gene is significantly upregulated, and the Ugt1a1 gene is significantly downregulated, indicating that bisphenol S produces male reproductive toxicity.

[0014] Preferably, the detection module includes a real-time fluorescence PCR detection method.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention is the first to propose the application of reagents for detecting the expression levels of one or more of the Cyp2e1, Tdo2, and Ugt1a1 genes in the preparation of products assessing the male reproductive toxicity of bisphenol S. Experimental verification showed that, compared to the normal group, the Cyp2e1 gene was significantly upregulated, the Tdo2 gene was significantly upregulated, and the Ugt1a1 gene was significantly downregulated in the test samples, indicating that the test samples exhibited male reproductive toxicity from bisphenol S. Furthermore, these biomarkers, Cyp2e1, Tdo2, and Ugt1a1, can rapidly, accurately, and specifically assess the male reproductive toxicity of bisphenol S. Attached Figure Description

[0016] Figure 1 The results show the effects of different concentrations of BPS exposure on sperm quality parameters after 35 days. A: Sperm motility, B: Straightness (STR), C: Linearity (LIN), D: Wobble (WOB), E: Curvilinear velocity (VCL), F: Mean path velocity (VAP), G: Alternating head sway (ALH), H: Whiplash frequency (BCF), and I: Mean angle of motion (MAD). ns indicates a significant difference, while ns indicates no significant difference.

[0017] Figure 2 The results show the effect of different concentrations of BPS on cell apoptosis after 35 days of treatment using the TUNEL assay. A: TUNEL image (scale bar: 20 μm); B: Statistical results of apoptosis index percentage; C: High-magnification TUNEL image after 35 days of treatment with 100 μg / kg BPS (scale bar: 50 μm). The difference was significant compared with the control group (P<0.05).

[0018] Figure 3 The results show the RNA sequencing analysis of mouse sperm after BPS exposure. A: PCA plot based on FPKM values; B: Volcano plot of differentially expressed genes between the BPS group and the control group (genes are divided into upregulated (red), downregulated (blue), and no significant difference (gray)); C: Bar chart showing the number of upregulated (orange) and downregulated (blue) differentially expressed genes between groups; D: Heatmap of normalized expression values ​​of top differentially expressed genes between samples (rows represent genes, columns represent samples, color scale represents Z-score normalized expression level); E: GO enrichment analysis plot of differentially expressed genes (bar length represents -log). 10 (P-value); F: KEGG pathway enrichment analysis diagram of differentially expressed genes; G: PPI network diagram of key molecules.

[0019] Figure 4 Metabolomics analysis of mouse sperm after BPS exposure. A: PCA plot based on metabolomics data; B: Volcano plot showing differentially regulated metabolites (red dots represent upregulated metabolites, blue dots represent downregulated metabolites); C: Venn diagram showing overlap between metabolism-related genes and transcriptome-related genes; D: Box plot comparing the abundance of key metabolites between the control and BPS-treated groups.

[0020] Figure 5 To verify the expression levels of Cyp2e1, Tdo2, and Ugt1a1 in the testicular tissue of mice exposed to BPS via RT-qPCR. Compared with the control group, P<0.001.

[0021] Figure 6 The results of ROC curve analysis of the diagnostic efficacy of Cyp2e1, Tdo2, and Ugt1a1 as single and combined biomarkers for male reproductive toxicity of BPS are shown. A: ROC curve and AUC value of Cyp2e1 as a single biomarker (0.8731); B: ROC curve and AUC value of Tdo2 as a single biomarker (0.8265); C: ROC curve and AUC value of Ugt1a1 as a single biomarker (0.8784); D: ROC curve and AUC value of the combined biomarker Cyp2e1+Tdo2+Ugt1a1 (0.9747). Detailed Implementation

[0022] This invention provides the application of reagents for detecting the expression levels of biomarkers in the preparation of products for evaluating the male reproductive toxicity of bisphenol S. The biomarkers include one or more of the Cyp2e1 gene, Tdo2 gene, and Ugt1a1 gene, preferably a combination of the Cyp2e1 gene, Tdo2 gene, and Ugt1a1 gene.

[0023] In this invention, compared to the normal group, the Cyp2e1 gene was significantly upregulated, the Tdo2 gene was significantly upregulated, and the Ugt1a1 gene was significantly downregulated, indicating that bisphenol S exhibits male reproductive toxicity. When the biomarkers are a combination of the Cyp2e1, Tdo2, and Ugt1a1 genes, compared to the normal group, the test sample showed significant upregulation of the Cyp2e1, Tdo2, and Ugt1a1 genes, indicating that the test sample exhibited male reproductive toxicity from bisphenol S.

[0024] In this invention, the reagent includes one or more of primer pair 1, primer pair 2, and primer pair 3; primer pair 1 includes Cyp2e1-F and Cyp2e1-R for detecting Cyp2e1 gene expression, with nucleotide sequences specifically Cyp2e1-F: TGGTCCTGCATGGCTACAAG (SEQ ID NO.1) and Cyp2e1-R: CGGGCCTCATTACCCTGTTT (SEQ ID NO.2); primer pair 2 includes Tdo2-F and Tdo2-R for detecting Tdo2 gene expression, with nucleotide sequences specifically Tdo2-F: ACGACGACTGTCATACCGTG (SEQ ID NO.3) and Tdo2-R: CCTTGTACCTGTCGCTCACA (SEQ ID NO.4); primer pair 3 includes Ugt1a1-F and Ugt1a1-R for detecting Ugt1a1 gene expression, with nucleotide sequences specifically Ugt1a1-F: TTGTGTGTGTTCGGTCCCTC (SEQ ID NO.4). NO.5), Ugt1a1-R: CACGCGCAGCAGAAAAGAAT (SEQ ID NO.6).

[0025] This invention provides a primer pair for detecting the expression level of a biomarker, wherein the primer pair includes one or more of primer pair 1, primer pair 2, and primer pair 3; wherein primer pair 1 for detecting the expression level of the Cyp2e1 gene is Cyp2e1-F and Cyp2e1-R, and the nucleotide sequences are as shown in SEQ ID NO. 1-2; primer pair 2 for detecting the expression level of the Tdo2 gene is Tdo2-F and Tdo2-R, and the nucleotide sequences are as shown in SEQ ID NO. 3-4; primer pair 3 for detecting the expression level of the Ugt1a1 gene is Ugt1a1-F and Ugt1a1-R, and the nucleotide sequences are as shown in SEQ ID NO. 5-6.

[0026] This invention also provides a kit for detecting the expression level of biomarkers, containing the primer combination described above. The kit of this invention also contains 2×TB Green Premix Ex Taq II, a template, and enzyme-free water.

[0027] This invention provides the application of the kit in the preparation of products for evaluating the male reproductive toxicity of bisphenol S.

[0028] This invention also provides a system for evaluating the male reproductive toxicity of bisphenol S, comprising a detection module and a conclusion analysis module. The detection module is used to detect the expression levels of one or more of the Cyp2e1, Tdo2, and Ugt1a1 genes in a normal group and a test sample group. The conclusion analysis module is used to analyze the expression of one or more of the Cyp2e1, Tdo2, and Ugt1a1 genes in the test sample group relative to the normal group. In the conclusion analysis module of this invention, compared with the normal group, the Cyp2e1 gene is significantly upregulated, the Tdo2 gene is significantly upregulated, and the Ugt1a1 gene is significantly downregulated in the test sample, indicating that the test sample exhibits male reproductive toxicity from bisphenol S. The detection module of this invention includes a quantitative real-time PCR detection method.

[0029] This invention also provides a method for assessing the male reproductive toxicity mechanism of bisphenol S based on multi-omics integration and functional phenotype, comprising the following steps: Sample collection: setting up a normal group and a bisphenol S-treated group, and separating the epididymis and testis of each group after treatment; Functional phenotype assessment: using a computer-aided sperm analysis system to assess sperm motility parameters of each group, using the TUNEL method to detect testicular tissue cell apoptosis, and determining the dosage of bisphenol S for male reproductive toxicity; Transcriptomics analysis: extracting RNA from testicular tissue of each group, constructing RNA sequencing libraries, performing quality control on the raw sequencing data, sequence alignment, and transcript assembly. This invention employs quantitative analysis, differentially expressed gene screening, and functional enrichment analysis to visualize the association between differentially expressed genes and functional items. Metabolomics analysis involves processing sperm samples from each group, obtaining supernatant, and performing chromatographic separation and mass spectrometry detection using an ultra-high performance liquid chromatography-mass spectrometry system. Data preprocessing and multivariate statistical analysis yield differential metabolite data. Multi-omics integration analysis focuses on the steroid hormone biosynthesis pathway, linking differentially expressed genes and metabolites to construct a three-level network model encompassing gene expression regulation, metabolite response, and pathway perturbation, clarifying the upstream and downstream regulatory relationships between key nodes. This invention first uses functional phenotype to determine toxicity dosage, then uses transcriptomics analysis to identify key genes regulating apoptosis and spermatogenesis, and uses metabolomics to find key metabolites affecting sperm motility. Finally, through multi-omics cross-integration, genes, metabolism, and phenotype are integrated to form a complete regulatory chain, thereby identifying biomarkers for assessing the male reproductive toxicity of bisphenol S. The method described in this invention not only achieves high-throughput assessment of multidimensional functional phenotypes such as sperm motility and testicular cell apoptosis, but its core lies in revealing the gene expression-metabolic flux-functional phenotype cascade perturbation network caused by BPS exposure through an integrated analysis process of transcriptomics and metabolomics data. In particular, it elucidates the molecular mechanism by which it disrupts steroid hormone synthesis homeostasis by interfering with key toxic metabolism genes Cyp2e1, Tdo2, and Ugt1a1, ultimately inducing sperm motility disorders and testicular cell apoptosis. In this invention, the dosage of bisphenol S treatment is 100-1000 μg / kg / day, preferably 100-500 μg / kg / day or 500-1000 μg / kg / day. The bisphenol S treatment described in this invention results in decreased sperm motility and an increased apoptosis index in testicular tissue cells.

[0030] In this invention, a gene-metabolite-pathway regulatory network is constructed by analyzing differentially expressed genes (DEGs) from transcriptomics analysis and differentially expressed metabolites from metabolomics analysis. This invention uses |log2Fold Change|≥1 and P≤0.05 to screen transcriptome DEGs, and uses VIP≥1.0, P≤0.05, FC≥1.2 or FC≤1 / 1.2 to screen differential metabolites. Gene function annotations (GO / KEGG) corresponding to the DEGs and pathway attribution information of the differential metabolites are extracted to establish a gene-metabolite basic association dataset. Using Cytoscape 3.9.1 software, with the steroid hormone biosynthesis pathway as the core hub, pathway-related DEGs (such as Cyp2e1, Tdo2, Ugt1a1, Akap4, Prm1, etc.) and differential metabolites (such as Xestoaminol C, PGF2α dimethylamine, 11β-prostaglandin E2, etc.) are associated to construct a three-level network model including gene expression regulation, metabolite response, and pathway perturbation, clarifying the upstream and downstream regulatory relationships between key nodes.

[0031] This invention focuses on the steroid hormone biosynthesis pathway, verifying the consistency between gene expression and metabolite changes, as well as the consistency between pathway and phenotype, forming a closed loop of toxicity mechanisms, and obtaining potential biomarkers for assessing the male reproductive toxicity of bisphenol S. The potential biomarkers for assessing the male reproductive toxicity of bisphenol S are experimentally verified, and the verification results are obtained. This invention is based on multi-omics joint analysis and verification: in the steroid hormone biosynthesis and prostaglandin metabolism pathway, upregulation of Cyp2e1 can drive disordered arachidonic acid metabolism and steroid synthesis, leading to a significant upregulation and accumulation of PGF2α dimethylamine (a prostaglandin derivative); downregulation of Ugt1a1 weakens the detoxification and clearance capacity of exogenous / endogenous metabolites, further exacerbating the retention of toxic metabolites; the expression trends of both are completely consistent with the direction of metabolite changes, confirming that abnormalities in key genes directly drive sperm metabolic disorders. This invention links pathway perturbations with functional phenotypes: abnormalities in steroid hormone synthesis and prostaglandin signaling pathways directly correspond to a phenotype of significantly decreased total sperm motility detected by CASA; Tdo2 upregulation is consistent with an elevated testicular cell apoptosis index phenotype; thus forming a complete logical loop where pathway perturbations affect gene and metabolic abnormalities, thereby influencing sperm motility disorders and testicular cell apoptosis. Based on transcriptome PPI network analysis, multi-omics intersection gene and pathway enrichment analysis, this invention screened and identified Cyp2e1-Tdo2-Ugt1a1 as the core regulatory gene cluster. This invention identified three key genes connecting transcriptome and metabolome changes: Cyp2e1, Tdo2, and Ugt1a1. Upregulation of Cyp2e1 leads to upregulation of steroid metabolic intermediates, downregulation of Ugt1a1 leads to accumulation of toxic metabolites, and upregulation of Tdo2 leads to apoptosis.

[0032] The technical solutions provided by the present invention will be described in detail below with reference to the embodiments, but they should not be construed as limiting the scope of protection of the present invention.

[0033] Unless otherwise specified, the following embodiments are all conventional methods.

[0034] Unless otherwise specified, all materials and reagents used in the following examples are commercially available.

[0035] Example 1 I. Sample Preparation and Exposure Treatment Experimental animals: Male BALB / c mice (18-22g) aged 4-6 weeks, acclimatized for 7 days. All experimental procedures were approved by the Laboratory Animal Ethics Review Committee (IACUC) of Southern Medical University, approval number SMUL2021029. After acclimatization, mice were randomly divided into four groups (n≥6 per group): control group (0.1% DMSO sterile BPS), low-dose group (10μg / kg / day BPS), medium-dose group (100μg / kg / day BPS), and high-dose group (1000μg / kg / day BPS).

[0036] Exposure method: BPS (CAS No.: 80-09-1, purity ≥98%, Sigma-Aldrich) was dissolved in DMSO and administered daily by gavage for 35 days. For oral gavage administration, continuous exposure for 35 days was performed, with weekly monitoring of mouse body weight and clinical signs to ensure no systemic toxicity interfered with reproductive toxicity evaluation.

[0037] Sample collection: After exposure, mice were euthanized by cervical dislocation, and the testes and epididymis were removed for sperm analysis, transcriptomics and metabolomics analysis, and apoptosis detection.

[0038] II. Functional Phenotypic Assessment 1. Sperm motility function analysis Sample pretreatment: (1) Take the epididymal tissue (bilateral epididymal head + epididymal body, remove epididymal tail connective tissue) of mice separated after 35 days of exposure, rinse 3 times with sterile physiological saline to remove surface blood and impurities. (2) Put the epididymal tissue into a centrifuge tube containing 1 mL of sperm separation buffer preheated to 37℃, and incubate in a 37℃, 5% CO2 constant temperature incubator for 10 min to allow the sperm to swim naturally into the buffer. (3) After incubation, gently pipette the buffer 5 times (avoid violent shaking to avoid damaging the sperm), let stand for 5 min, and transfer the upper 800 μL of sperm suspension to a new centrifuge tube. (4) Centrifuge at 37℃, 500 rpm for 5 min, discard the precipitate (remove tissue fragments), and retain the supernatant as a pure sperm suspension for subsequent detection.

[0039] The sperm motility parameters of mice exposed to different concentrations of BPS (0, 10, 100, and 1000 μg / kg) for 35 days were evaluated using a computer-aided sperm analysis (CASA) system (Nanning Songjing Tianlun Biotechnology Co., Ltd., China). Brief procedure: 5 μL of sperm suspension was placed in a 20 μm deep counting chamber and analyzed at 37°C. At least 500 sperm were evaluated from each sample. Parameters measured included total sperm motility, linearity, linearity, wobble, curvilinear velocity, average path velocity, head lateral swing amplitude, whiplash frequency, and average motility angle.

[0040] The results are as follows Figure 1 . Figure 1 As shown in Figure A, sperm motility in the BPS-100 and BPS-1000 groups was significantly lower than that in the control group. However, there was no significant difference in sperm motility between the BPS-100 and BPS-1000 groups, suggesting that the effect of BPS on sperm motility may plateau at higher concentrations. Other parameters, such as linearity (…), also showed… Figure 1 B) Linearity ( Figure 1 C) Swinging ( Figure 1 D) Curve speed ( Figure 1 E), Average path speed ( Figure 1 F), Head lateral tilt amplitude ( Figure 1 G), Whipping frequency ( Figure 1 H) and average angle of motion ( Figure 1 I) There were no significant differences among all treatment groups. These results indicate that exposure to BPS at concentrations of 100 μg / kg and above can affect sperm motility, but no further decline was observed at the highest tested concentration. Based on these results, BPS-100 concentration was selected for subsequent experiments to further investigate the reproductive effects of BPS.

[0041] 2. Testicular cell apoptosis detection: The TUNEL FITC apoptosis detection kit (Nanjing Novizan Biotechnology Co., Ltd., China) was used. Testicular tissues isolated from each group after 35 days of exposure were collected, and the degree of testicular cell apoptosis was detected according to the instructions. Observation and imaging were performed using a Leica DMi8 fluorescence microscope (Wetzlar, Germany), with the excitation filter set to 490nm and the emission filter set to 520nm. The proportion of apoptotic cells (green fluorescence) to total cells (blue fluorescence) was calculated using ImageJ software. Results are shown below. Figure 2 .

[0042] like Figure 2 As shown in A and 2C, representative fluorescence images show a dose-dependent increase in TUNEL-positive cells (green FITC signal) in seminiferous tubules, while apoptotic cells are extremely rare in the control group tissue. Figure 2 Quantitative analysis of B showed that the apoptosis index of the BPS-100 group was significantly higher than that of the control group. However, there was no significant difference in the apoptosis index between the BPS-1000 group and the control group, indicating that the apoptosis response may tend to be stable or variable at higher concentrations, which is corroborated by the analysis of high sperm motility.

[0043] Example 2: Multi-omics detection and integrated analysis I. Transcriptomics Analysis 1. Sample pretreatment and RNA extraction (1) Sample processing: 30 mg of testicular tissue from the control group (Ctrl group) and the BPS-100 group (BPS group) after 35 days of exposure was taken and quickly placed into a pre-cooled RNAase-Free grinding tube. 500 μL of RNAiso Plus reagent (Takara) was added and the tissue was quickly ground into a homogenate under liquid nitrogen to ensure that the tissue cells were fully broken.

[0044] (2) Extraction procedure: Total RNA was extracted using the RNAiso Plus kit (Takara Bio Inc., Japan) strictly following the kit instructions. RNA quality and concentration were assessed using a nano-spectrophotometer (Thermo Fisher Scientific, USA), and only samples with RNA integrity number (RIN) ≥ 8.0 and 260 / 280 ratio ≥ 1.8 were selected for library construction.

[0045] 2. Subsequent experiments after RNA extraction Total RNA extracted was used to construct a transcriptome sequencing library, which was then sequenced using the Illumina NovaSeq 6000 platform. Finally, gene expression data were obtained for differential analysis and functional enrichment studies.

[0046] (1) RNA sequencing libraries were constructed using the NEBNext Ultra II RNA Library Preparation Kit (New England Biolabs, USA).

[0047] (2) Library quality control: The constructed library was initially quantified by Qubit 3.0 fluorescence quantitative instrument (concentration ≥1ng / μL), the size of the inserted fragment was detected by Qsep400 high-throughput analysis system (in line with the expected 180~280bp), and then accurately quantified by Q-PCR (effective concentration ≥2nM) to ensure that the library quality is qualified.

[0048] (3) Sequencing process: The qualified libraries are mixed at equimolar concentration and loaded into the flow cell of the Illumina NovaSeq 6000 sequencing platform; the paired-end (PE) sequencing mode is adopted, the sequencing read length is set to 150bp, and the library fragments are clustered by bridge PCR; during the sequencing process, when the fluorescently labeled dNTPs bind to the extended DNA strand, they emit specific fluorescence. The sequencer captures the fluorescence signal through the optical detection system, converts it into base sequence information, and finally generates raw sequencing data.

[0049] (4) Data processing Processing environment: All data processing was performed on the BMKCloud platform (www.biocloud.net) and a local Linux server (Ubuntu 20.04 system). Scripts were written in Perl, Python and R (version 4.2.0) and analyzed in conjunction with bioinformatics tools.

[0050] Specific processing steps: (1) Raw data quality control: The FastQC quality control (v0.11.9) software is used to assess the quality of Raw Data. Reads containing adapter sequences, N ratio >10%, and low quality (Q≤10 bases >50%) are removed using Trimmonatic (v0.39) software to obtain high-quality clean data. (2) Sequence alignment: The Clean Reads are aligned with the mouse reference genome (GRCm39) using HISAT2 (v2.2.1) software. The parameter "-dta-cufflinks" is set to obtain the location information of the Reads on the genome. The alignment efficiency must be ≥95%. (3) Transcript assembly and quantification: The Transcripts are reconstructed using StringTie (v2.2.1) software. The gene structure is optimized by combining the reference genome annotation information. The expression level of each gene is quantified using FPKM (Fragments Per Kilobase of transcript per Millionfragments) as an indicator. (4) Screening of differentially expressed genes (DEGs): The DESeq2 (v1.34.0) R package was used to perform intergroup differential analysis. The screening criteria were set as |log2Fold Change|≥1 and P≤0.05 to obtain a list of upregulated and downregulated DEGs.

[0051] (5) Functional enrichment analysis Enrichment targets: Functional enrichment analysis was performed on the differentially expressed genes (DEGs) selected from the screening, focusing on the gene population whose expression changed significantly after BPS exposure.

[0052] The clusterProfiler (v4.2.2) R package was used in conjunction with the following databases for analysis: (1) GO (GeneOntology) database: containing three major categories of annotations: biological process, molecular function, and cellular component; (2) KEGG (Kyoto Encyclopedia of Genes and Genomes) database: used for pathway enrichment analysis, focusing on steroid hormone biosynthesis and neuroactive ligand-receptor interaction pathways.

[0053] Enrichment analysis of DEGs using Gene Ontology (GO) biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was performed using clusterProfiler (v4.2.2), with adjusted P < 0.05 considered statistically significant. Protein-protein interaction (PPI) networks of key DEGs were constructed using the STRING database (v11.5) (minimum interaction score 0.4) and visualized using Cytoscape (v3.9.1).

[0054] Enrichment results: (1) Significant entries in the GO / KEGG enrichment analysis were obtained (P≤0.05), clarifying the concentrated distribution of DEGs in biological functions and metabolic pathways; (2) Enrichment bubble charts, bar charts, and enrichment chords were generated to visually demonstrate the association between differentially expressed genes and functional entries. Results are as follows: Figure 3 .

[0055] Principal component analysis (PCA) based on FPKM values ​​from RNA sequencing data showed the global transcriptome profiles of the control group and the BPS-treated group, with clear separation between the two groups. Figure 3 A). Based on the criteria of false discovery rate (FDR) < 0.05 and |log2 fold change) | ≥ 1, a total of 327 differentially expressed genes were identified, of which 178 were upregulated genes and 149 were downregulated genes. Figure 3 B, C). The heatmap of differentially expressed genes shows that the expression patterns within the group are consistent. Figure 3 D). GO enrichment analysis showed significant enrichment in top biological processes, indicating that differentially expressed genes are significantly involved in biological processes such as water homeostasis and bacterial responses at the multicellular level. Figure 3 E). KEGG pathway analysis revealed the most significantly enriched pathways, indicating that differentially expressed genes were enriched in pathways such as steroid hormone biosynthesis and neuroactive ligand-receptor interactions. Figure 3F). PPI network analysis identified several core genes, including SOX2 (germ cell stemness), GFAP (supporting cell function), and CYP2E1 (exogenous substance metabolism), etc. Figure 3 (G). Among them, CYP2E1 and GFAP are particularly noteworthy, as they have potential roles in testicular oxidative stress and Sertoli cell damage responses, respectively, and may be potential mechanisms underlying the observed apoptosis and antioxidant-related phenotypes.

[0056] II. Metabolomics Detection 1. Sample pretreatment and metabolite extraction Sperm samples (sperm suspension prepared in step two of Example 2) were collected from the control group and the BPS-100 group after 35 days of exposure, with a yield of 50 mg (approximately 5 × 10⁻⁶ mg). 6 Add 500 μL of ice-bath methanol-water (80:20, v / v, containing 0.1% formic acid) to a tissue homogenizer (TissueLyser II, Qiagen) at 30 Hz for 2 minutes; centrifuge at 14,000 rpm for 15 minutes at 4°C, and dry the supernatant under nitrogen; reconstitute the residue with 100 μL of acetonitrile-water (50:50, v / v), vortex, and centrifuge at 14,000 rpm for 10 minutes at 4°C, and use the supernatant for metabolomics analysis.

[0057] 2. UHPLC-Q Exactive HF-X system (with clearly defined parameter settings) Chromatographic separation and mass spectrometry detection were performed using an ultra-high performance liquid chromatography (UHPLC) system (UltiMate 3000, Thermo Fisher Scientific) coupled with a Q Exactive HF-X mass spectrometer (equipped with a HESI source). Raw LC-MS data were analyzed using Compound Discoverer 3.3 software (Thermo Fisher Scientific, USA) for peak detection, alignment, and integration.

[0058] Chromatographic separation parameters: Column: Thermo Scientific Hypersil GOLD C18 column (2.1 mm x 100 mm, 1.9 μm); Column temperature: 40℃; Flow rate: 0.3 mL / min; Injection volume: 5 μL; Mobile phase A: ultrapure water (containing 0.1% formic acid), Mobile phase B: acetonitrile (containing 0.1% formic acid); Gradient elution program: 0~2 min, 5% B; 2~10 min, 5%~95% B; 10~12 min, 95% B; 12~12.1 min, 95%~5% B; 12.1~15 min, 5% B (equilibration column).

[0059] Mass spectrometry detection parameters: Ionization source: Electrospray ionization source (ESI), simultaneous detection in positive ion mode (ESI+) and negative ion mode (ESI-); Spray voltage: 3.5kV for positive ion mode and 3.0kV for negative ion mode; Capillary temperature: 320℃; Sheath gas pressure: 40Arb; Auxiliary gas pressure: 10Arb; Scan mode: Full Scan combined with data-dependent secondary scan (dd-MS2); Full scan range: m / z 80~1000; Resolution: 70,000 for full scan and 17,500 for secondary scan; Collision energy: 20, 40, and 60 eV (gradient collision, used for metabolite structure identification).

[0060] 3. The data processing flow (clearly defining the processing objects and tool connections) is as follows: Raw data conversion: Import the raw mass spectrometry data (.raw format) acquired by the UHPLC-Q Exactive HF-X system into Compound Discoverer 3.3 software for data format conversion and preprocessing.

[0061] Peak identification and quantification: The processing object is the converted mass spectrometry data (including retention time, mass-to-charge ratio, peak intensity, etc. of metabolites). The specific operation is as follows: Set peak identification parameters (signal-to-noise ratio ≥ 3, peak area threshold ≥ 1000). The software automatically identifies chromatographic peaks and performs peak matching, peak alignment, and deduplication. The external standard method (using a standard metabolite of known concentration as a reference) is used to quantify the matched metabolites, and a metabolite quantification data table is output (including the peak area value of each metabolite in each sample).

[0062] Data preprocessing: The quantitative data were normalized (using the total peak area normalization method), missing values ​​were filled (using the K-nearest neighbor algorithm), and logarithmic transformation was performed to obtain a standardized metabolomics data matrix for subsequent multivariate statistical analysis.

[0063] 4. Multivariate statistical analysis (clearly define the analysis object and screening logic) Principal Component Analysis (PCA): Analysis object: standardized metabolomics data matrix; Operation tool: SIMCA 14.1 software; Purpose: to analyze the overall distribution characteristics of the sample in an unsupervised manner, determine the repeatability of samples within the group and the separation trend between groups, and screen potential discrete points.

[0064] Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA): Analysis object: Standardized metabolomics data matrix after removing discrete points; Operation tool: SIMCA 14.1 software; Model construction: PLS-DA model is constructed with sample group (BPS group / control group) as dependent variable (Y variable) and metabolite peak area as independent variable (X variable); OPLS-DA model is further constructed by removing noise irrelevant to grouping through orthogonal filtering. Model validation: Seven rounds of interactive validation and 200 rounds of permutation test were used to ensure that the model was not overfitted (Q2 intercept < 0). The overall distribution characteristics were evaluated to determine the repeatability of samples within groups and the separation trend between groups, and to screen potential discrete points.

[0065] Differential metabolite screening: Screening targets: all metabolites in the OPLS-DA model; Screening criteria: VIP (projected importance value) ≥ 1.0, and P ≤ 0.05 (independent samples t-test), FC (fold change) ≥ 1.2 or FC ≤ 1 / 1.2; Output: List of differential metabolites (including metabolite name, retention time, m / z, VIP value, FC value, upregulation / downregulation trend, etc.). Results are as follows... Figure 4 .

[0066] PCA plots based on metabolomics data showed significant clustering between the control group and the BPS-treated group, separating along principal component 1 (PC1, 31.7%), indicating a significant difference in the global metabolomics profiles between the two groups. Figure 4 A). The volcano plot shows the Log2 (fold change) and -log of differential metabolites. 10 The relationship between (P-values) highlights the metabolic changes induced by BPS. Figure 4 B). Venn diagram ( Figure 4 C) shows the overlap between metabolism-related genes and transcriptome-related genes, identifying three common genes (Cyp2e1, Tdo2, Ugt1a1) as key cross-regulatory nodes connecting transcriptome and metabolome changes. Box plot ( Figure 4 D) A detailed comparison of the abundance of key metabolites (involved in arachidonic acid metabolism and the prostaglandin signaling pathway, closely related to steroid hormone regulation) was conducted between the control and BPS-treated groups. Most metabolites showed no significant differences (labeled as ns), including 11β-prostaglandin E2, 13,14-dihydro-15-keto-tetranorprostaglandin F1β, 15-deoxy-12,14-prostaglandin E2, and 15(R)-prostaglandin E2. Only prostaglandin F2α dimethylamine was significantly downregulated in the BPS-treated group. (P<0.001). These metabolomics results collectively demonstrate that BPS exposure specifically alters the abundance of metabolites involved in lipid signaling and steroid-related pathways in sperm (especially prostaglandin F2α-dimethylamine), which may lead to the observed impairment of sperm motility and testicular function. These metabolic disturbances provide a direct molecular link between BPS exposure and reproductive toxicity, particularly through disruption of prostaglandin and steroid metabolism.

[0067] III. Multi-omics integrated analysis: By linking the DEGs obtained in step 1 of this embodiment with the differential metabolites obtained in step 2 of this embodiment, a gene-metabolite-pathway regulatory network is constructed; with the steroid hormone biosynthesis pathway as the core, the consistency between gene expression and metabolite changes is verified, forming a closed loop of toxicity mechanism.

[0068] 1. Data association preprocessing: DEGs (|log2Fold Change|≥1 and P≤0.05) selected from the transcriptome data and differential metabolites (VIP≥1.0, P≤0.05, FC≥1.2 or FC≤1 / 1.2) from the metabolome data were used to extract gene function annotations (GO / KEGG) corresponding to the DEGs and pathway attribution information of differential metabolites, and a gene-metabolite basic association dataset was established.

[0069] 2. Regulatory network association analysis: Using Cytoscape 3.9.1 software, multi-omics data were integrated. Taking the steroid hormone biosynthesis pathway as the core hub, differentially expressed genes (Cyp2e1, Tdo2, Ugt1a1, Akap4, Prm1) and differentially expressed metabolites (PGF2α dimethylamine, 11β-prostaglandin E2, etc.) were associated. Combined with the phenotypic characteristics of male reproductive toxicity, the upstream and downstream regulatory relationships of key molecules were clarified, and the molecular mechanism of BPS-induced male reproductive damage was revealed.

[0070] 3. Consistency Verification Analysis: based on Figure 3 (transcriptomics) Figure 4 Multi-omics joint analysis (metabolomics) validated the role of steroid hormone biosynthesis and prostaglandin metabolism pathways. Figure 3 The identified upregulated differentially expressed gene Cyp2e1 can drive disorders in arachidonic acid metabolism and steroid synthesis, leading to... Figure 4 The differentially identified metabolite PGF2α dimethylamine (a prostaglandin derivative) showed significant abnormal accumulation; Figure 3 The downregulated differentially expressed gene Ugt1a1 weakens the detoxification and clearance capacity of exogenous / endogenous metabolites, further exacerbating the retention of toxic metabolites; the expression trends of both genes are completely consistent with the direction of metabolite changes, confirming that abnormalities in key genes directly drive sperm metabolic disorders.

[0071] Pathway-phenotype consistency: Figure 3 , Figure 4 Pathway perturbations identified and Figure 1 , Figure 2 Functional phenotype is associated with abnormal steroid hormone synthesis and prostaglandin signaling pathway. Figure 1 The significantly decreased total sperm motility detected by CASA in group A directly corresponds to the phenotype. Figure 3 , Figure 4 The upregulated gene Tdo2 was identified and Figure 2 The elevated testicular cell apoptosis index in both A and B showed a consistent phenotype; thus, a complete logical loop was formed in which pathway perturbations affect gene and metabolic abnormalities, thereby influencing sperm motility disorders and testicular cell apoptosis.

[0072] Summary of key regulatory nodes and mechanisms: Based on Figure 3 G's transcriptome PPI network, Figure 4 C's multi-omics intersection genes and Figure 3 Pathway enrichment analysis of F identified Cyp2e1-Tdo2-Ugt1a1 as the core regulatory gene cluster; its synergistic abnormalities can interfere with steroid hormone biosynthesis and prostaglandin metabolic homeostasis, ultimately leading to... Figure 1 A's sperm motility disorder and Figure 2 Testicular cell apoptosis in A / B completes the entire research chain from multi-omics integration to mechanism analysis to phenotypic validation.

[0073] Example 3 To verify the correlation between Cyp2e1, Tdo2, and Ugt1a1 expression and BPS male reproductive toxicity, RT-qPCR was used to detect the expression levels of Cyp2e1, Tdo2, and Ugt1a1 in testicular tissue of mice exposed to BPS. The experiment included a control group and a 100 μg / kg / day BPS exposure group, with 10 male BALB / c mice in each group. Testicular tissue was collected after 35 days of continuous gavage, and the processing of the testicular tissue followed the same procedures as described in the transcriptomics analysis above. Total RNA was extracted using Takara Bio Engineering (Dalian) Co., Ltd. (TaKaRa) RNAisoPlus (catalog number: 9109), and the RNA was reverse transcribed into cDNA using TaKaRa PrimeScript™ RT Master Mix (catalog number: RR036A).

[0074] cDNA was detected by real-time quantitative PCR, with β-actin as an internal reference gene. The primers were synthesized by Sangon Biotech (Shanghai) Co., Ltd. Specificity was verified by NCBI Primer-BLAST, and the primer Tm values ​​were confirmed to be matched. The primer sequences are as follows: Cyp2e1-F: TGGTCCTGCATGGCTACAAG (SEQ ID NO.1), Cyp2e1-R: CGGGCCTCATTACCCTGTTT (SEQ ID NO.2); Tdo2-F: ACGACGACTGTCATACCGTG (SEQ ID NO.3), Tdo2-R: CCTTGTACCTGTCGCTCACA (SEQ ID NO.4); Ugt1a1-F: TTGTGTGTGTTCGGTCCCTC (SEQ ID NO.5), Ugt1a1-R: CACGCGCAGCAGAAAAGAAT (SEQ ID NO.6); β-actin-F: AGCCATGTACGTAGCCATCCA (SEQ ID NO.7), β-actin-R: TCTCCGGAGTCCATCACAATG (SEQ ID NO.8). The total volume of the RT-qPCR system was 20 μL, containing 10 μL of 2×TB Green Premix Ex Taq II, 0.4 μL each of 10 μM forward and reverse primers, 2 μL of cDNA template, and enzyme-free water to the total volume. The RT-qPCR reaction conditions were 95℃ pre-denaturation for 30 s, 40 cycles (95℃ for 5 s, 60℃ for 30 s), followed by melting curve analysis. Each group had 3 replicates. -ΔΔCt The relative expression level was calculated using the method described above. Statistical analysis was performed using Student's t-test, with P < 0.05 considered statistically significant.

[0075] The results are as follows Figure 5 The results showed that, compared with the control group, Cyp2e1 expression was significantly upregulated (P < 0.001) and Tdo2 expression was significantly upregulated in the testicular tissue of mice treated with 100 μg / kg / day BPS, consistent with the trend of testicular cell apoptosis (P < 0.001), while Ugt1a1 expression was significantly downregulated (P < 0.001). The expression trends of the three genes were completely consistent with the in vivo functional phenotype, transcriptomics, and metabolomics results. In summary, compared with the normal group, the upregulation of Cyp2e1, Tdo2, and Ugt1a1 can serve as marker molecular events of BPS-induced male reproductive toxicity, and the combination of the three can serve as a specific biomarker for assessing reproductive damage caused by BPS exposure.

[0076] Example 4 To determine the evaluation effectiveness of different biomarkers, the area under the curve (AUC) was used as the evaluation criterion. An AUC between 0.5 and 1 indicates that the biomarker has evaluation value; the higher the value, the better the identification and prediction effect of bisphenol S male reproductive toxicity.

[0077] Eighty independent validation cohort samples were selected (from different batches of the transcriptome / metabolome screening samples in Example 2, ensuring sufficient sample size and meeting statistical requirements), and divided into a 30-case normal control group and a 50-case BPS treatment group (100 μg / kg / day). Tissue samples were collected from both groups, total RNA was extracted and reverse transcribed into cDNA. The relative expression levels of Cyp2e1, Tdo2, and Ugt1a1 were detected using RT-qPCR. The RT-qPCR detection method was the same as in Example 3.

[0078] Gene expression level was used as the test variable, and sample group status (normal control / BPS treatment) was used as the state variable. Receiver operating characteristic (ROC) curves were plotted using statistical software such as SPSS, and the area under the curve was calculated to evaluate the assessment efficacy of each biomarker and combined biomarkers on the male reproductive toxicity of bisphenol S.

[0079] like Figure 6 The results showed that the AUC was 0.8731 when Cyp2e1 was used as a biomarker, 0.8265 when Tdo2 was used, and 0.8784 when Ugt1a1 was used. The AUCs of all three biomarkers, when used individually, were significantly higher than 0.5, indicating that they all possess good value in assessing the male reproductive toxicity of bisphenol S. When the combined biomarker of Cyp2e1, Tdo2, and Ugt1a1 was used, the AUC reached as high as 0.9747, demonstrating significantly better efficacy in assessing the male reproductive toxicity effects of bisphenol S exposure than any single biomarker. Therefore, compared to single biomarkers of Cyp2e1, Tdo2, and Ugt1a1, the combination of these three is a superior biomarker combination for assessing the male reproductive toxicity of bisphenol S.

[0080] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. The application of a reagent for detecting the expression level of a biomarker in the preparation of products for assessing the male reproductive toxicity of bisphenol S, characterized in that, The biomarkers include one or more of the Cyp2e1 gene, Tdo2 gene, and Ugt1a1 gene.

2. The application as described in claim 2, characterized in that, Compared to the normal group, the Cyp2e1 gene was significantly upregulated, the Tdo2 gene was significantly upregulated, and the Ugt1a1 gene was significantly downregulated, indicating that bisphenol S produces male reproductive toxicity.

3. The application as described in claim 2, characterized in that, The reagent includes one or more of primer pair 1, primer pair 2, and primer pair 3; primer pair 1 includes Cyp2e1-F and Cyp2e1-R for detecting Cyp2e1 gene expression, with nucleotide sequences as shown in SEQ ID NO. 1-2; primer pair 2 includes Tdo2-F and Tdo2-R for detecting Tdo2 gene expression, with nucleotide sequences as shown in SEQ ID NO. 3-4; primer pair 3 includes Ugt1a1-F and Ugt1a1-R for detecting Ugt1a1 gene expression, with nucleotide sequences as shown in SEQ ID NO. 5-6.

4. A primer combination for detecting the expression level of a biomarker, characterized in that, The primer pair includes one or more of primer pair 1, primer pair 2, and primer pair 3; Among them, primer pair 1 for detecting Cyp2e1 gene expression level is Cyp2e1-F and Cyp2e1-R, and the nucleotide sequences are as shown in SEQ ID NO.1-2; primer pair 2 for detecting Tdo2 gene expression level is Tdo2-F and Tdo2-R, and the nucleotide sequences are as shown in SEQ ID NO.3-4; primer pair 3 for detecting Ugt1a1 gene expression level is Ugt1a1-F and Ugt1a1-R, and the nucleotide sequences are as shown in SEQ ID NO.5-6.

5. A kit for detecting the expression level of a biomarker, characterized in that, It contains the primer combination described in claim 4.

6. The reagent kit as described in claim 6, characterized in that, The kit also contains 2×TB Green Premix ExTaq II, template, and enzyme-free water.

7. The use of the kit as described in claim 5 or 6 in the preparation of products for evaluating the male reproductive toxicity of bisphenol S.

8. A system for evaluating the male reproductive toxicity of bisphenol S, characterized in that, It includes a detection module and a conclusion analysis module; the detection module is used to detect the expression levels of one or more of the Cyp2e1 gene, Tdo2 gene, and Ugt1a1 gene as described in claim 1 in the normal group and the test sample group; the conclusion analysis module is used to analyze the expression status of one or more of the Cyp2e1 gene, Tdo2 gene, and Ugt1a1 gene in the test sample group relative to the normal group.

9. The system for evaluating the male reproductive toxicity of bisphenol S as described in claim 8, characterized in that, In the conclusion analysis module, compared with the normal group, the Cyp2e1 gene was significantly upregulated, the Tdo2 gene was significantly upregulated, and the Ugt1a1 gene was significantly downregulated, indicating that bisphenol S produces male reproductive toxicity.

10. The system for evaluating the male reproductive toxicity of bisphenol S as described in claim 8, characterized in that, The detection module includes a real-time PCR detection method.

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