Construction method and application of micro-nano plastic combined toxicity AOP based on multi-omics integration

Abstract

The invention relates to the technical field of environmental toxicology, in particular to a construction method and application of micro-nano plastic combined toxicity (AOP) based on multi-omics integration, and the construction method comprises the following steps: respectively carrying out single exposure culture of multi-target risk substances by adopting human-derived hepatocyte HepaRG cells; determining a combined exposure dose; cell treatment and sample preparation; transcriptome analysis; analyzing metabolome; transcriptome and metabolome data are integrated, pathway cross analysis is carried out, a gene-metabolite interaction network is constructed, and a core regulation and control module is identified; and constructing an AOP framework: determining a molecular initial event, a key event and a harmful outcome of the AOP framework based on the integrated data, and importing an AOP-WIKI platform to generate a standardized AOP framework. According to the method, the limitation of a traditional single omics and fixed dose model is broken through, full-chain dynamic association from a molecular initial event to a harmful outcome is realized, and a scientific basis is provided for combined pollution risk assessment in the fields of environment and food.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental toxicology, and in particular to a method for constructing and applying a micro-nanoplastic combined toxicity AOP based on multi-omics integration. Background Art

[0002] As an emerging environmental pollutant, micro-nano plastics have a complex system that not only includes plastic bodies such as polyethylene and polypropylene and additives such as phthalate plasticizers and tetrabromobisphenol A flame retardants, but also adsorbs heavy metals (such as lead and cadmium), biotoxins (such as microcystins), antibiotics (such as tetracyclines) and other environmental pollutants due to their high specific surface area characteristics, forming a complex pollution carrier. However, existing toxicity assessment methods mostly focus on the independent effects of a single component and ignore the nonlinear toxic effects of joint exposure to multiple substances. For example, bisphenol A and heavy metals adsorbed on micro-nano plastics can aggravate oxidative stress through synergistic effects, while traditional fixed concentration ratio models cannot accurately characterize the "cocktail effect" of such cross-category, low-dose mixtures, resulting in significant deviations between risk assessment results and actual exposure scenarios, and potential health risks are systematically underestimated.

[0003] In terms of technical approaches, traditional single-omics analysis has the limitations of a single dimension and fragmented data. Transcriptomics is difficult to correlate with the dynamic responses of downstream metabolites, and metabolomics lacks the ability to trace upstream signal pathways, making it impossible to connect the entire chain mechanism of "adsorption-release-metabolism-toxic response". In addition, the construction of adverse outcome pathways (AOPs) mostly relies on animal experiments or isolated biomarkers, lacks a dynamic association model for in vitro multi-omics integration, and does not meet the OECD guidelines for the quantitative relationship between molecular initiating events (MIEs) and adverse outcomes (AOs), resulting in a weak chain of evidence and poor reproducibility in the AOP framework.

[0004] From the perspective of technical efficiency, traditional methods are limited by detection throughput and data integration capabilities, making it difficult to cope with the complexity of micro-nano plastic composite pollution systems. For example, targeted metabolomics only covers known metabolites, and low-throughput transcriptome sequencing easily misses low-abundance gene regulatory information, making it difficult to analyze the cross-mechanism synergistic toxicity of "low toxicity-multi-component" combined exposure (such as mitochondrial function interference). Therefore, the development of a high-precision, high-throughput multi-omics integration method to conduct standardized assessments of the combined toxicity of micro-nano plastic composite pollution is an urgent need in the field of environmental and food safety. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for constructing and applying a micro-nanoplastic combined toxicity AOP based on multi-omics integration, breaking through the limitations of traditional single-omics and fixed-dose models, realizing dynamic association of the entire chain from molecular initiation events to harmful outcomes, and providing a scientific basis for the risk assessment of complex pollution in the environment and food fields.

[0006] To achieve the above objectives, the present invention provides a method for constructing a micro-nanoplastic combined toxicity AOP based on multi-omics integration, comprising the following steps:

[0007] S1. Cell model selection and exposure: Human hepatocytes HepaRG cells were used for single exposure culture of multiple target risk substances;

[0008] HepaRG cells have metabolic functions close to those of primary hepatocytes (such as stable expression of the CYP450 enzyme system) and high differentiation characteristics, making them suitable for simulating the metabolic processes of pollutants in the human body.

[0009] S2. Determination of combined exposure dose: The CCK-8 method is used to detect the cell activity after single exposure to multiple target risk substances for 24 hours. The half-maximal inhibitory concentration (IC20) of each target risk substance that maintains the cell activity at 80% is determined through the dose-effect curve. Based on the IC20 value, the equivalent toxicity ratio of the combined exposure of multiple target risk substances is determined using the isobologram method or the concentration addition model; the cell activity of the mixed group is ensured to be 80%, simulating the low-dose, dynamic ratio exposure scenario in the real environment.

[0010] S3. Cell treatment and sample preparation: HepaRG cells were seeded in 96-well plates and exposed to multiple risk substances for 24 hours. The culture medium was discarded and the cells were washed three times with pre-chilled PBS. The cell pellets were collected and transcriptome and metabolome samples were prepared respectively.

[0011] S4. Transcriptome analysis: RNA extraction, library construction and sequencing of transcriptome samples were performed to analyze differentially expressed genes and perform functional enrichment analysis;

[0012] S5. Metabolome analysis: Process and detect metabolome samples, identify metabolites, screen differential metabolites, and perform metabolic pathway enrichment analysis;

[0013] S6. Data integration: Integrate transcriptome and metabolome data to perform pathway cross-analysis, construct gene-metabolite interaction networks, and identify core regulatory modules.

[0014] S7. AOP framework construction: Based on the integrated data, the molecular initiation events, key events and harmful outcomes of the AOP framework are determined, and the standardized AOP framework is generated by importing the AOP-WIKI platform.

[0015] Preferably, the target risk substances in S1-S3 include polyethylene, polypropylene, polyvinyl chloride, polystyrene, dioctyl phthalate, tetrabromobisphenol A microplastic risk substances and one or more of their adsorbed heavy metals, biotoxins, antibiotics, and persistent organic pollutants.

[0016] Preferably, in S4, total RNA is extracted from the transcriptome sample using TRIzol reagent, the RNA integrity is tested, a strand-specific mRNA library is constructed (Illumina TruSeq Stranded mRNA Kit), and mRNA paired-end 150 bp sequencing is performed using the Illumina NovaSeq 6000 platform.

[0017] More preferably, the RNA integrity is RIN ≥ 7.0.

[0018] Preferably, in S4, DESeq2 software was used to analyze differentially expressed genes (DEGs), with the screening criteria being |log2FC| ≥ 1 and FDR < 0.05; ClusterProfiler was used to perform KEGG pathway and GO functional enrichment analysis on DEGs to identify toxicity-related signaling pathways (such as AMPK pathway and TNF signaling pathway).

[0019] Preferably, in S5, a pre-cooled methanol-acetonitrile mixture is added to the metabolomics sample to lyse the cells, and the supernatant is collected by centrifugation after ultrasonic disruption, concentrated to dryness by nitrogen blow-through, and redissolved in methanol solution, and non-targeted metabolomics detection is performed by high-resolution mass spectrometry.

[0020] More preferably, the ratio of methanol to acetonitrile in the methanol-acetonitrile mixture is 1:1, and the concentration of the methanol solution is 80%.

[0021] More preferably, the high-resolution mass spectrometer is in positive and negative ion mode with a resolution of 70,000.

[0022] Preferably, in S5, Compound Discoverer 3.3 software is used to match the HMDB and mzCloud databases, and the metabolite structures are confirmed and identified in combination with secondary mass spectrometry fragment ions; differential metabolites are screened based on the OPLS-DA model VIP>1, |log2FC|≥1 and p<0.05; KEGG pathway enrichment is performed using MetaboAnalyst 5.0 to analyze key metabolic pathways (such as pyrimidine metabolism and oxidized lipid metabolism).

[0023] Preferably, in S6, the signal pathways enriched in the transcriptome are cross-mapped with the metabolic pathways enriched in the metabolome to screen key nodes; the gene-metabolite interaction network is constructed using Cytoscape 3.9, and the core regulatory modules are identified based on Spearman correlation analysis with |r|>0.7 and p<0.01.

[0024] More preferably, the key node is a key node regulated by both genes and metabolites, such as the CYP1A1 gene is associated with uridine metabolism.

[0025] Preferably, in S7, the molecular initiating events (MIEs), key events (KEs), and adverse outcomes (AOs) of the AOP framework are determined based on the upstream and downstream relationships and consistency of the transcriptomics and metabolomics data results. The correlation between the above MIEs, KEs, and AOs is imported into the AOP-WIKI platform (https: / / aopwiki.org / ), and the OECD AOP development guidelines are followed to define weighted evidence (such as in vitro data, quantitative associations) and generate a standardized AOP framework.

[0026] The present invention also provides an application of an AOP framework constructed based on a multi-omics integrated micro-nanoplastic combined toxicity AOP construction method, which is characterized by being applied to environmental monitoring, food safety assessment, new resource food approval, and combined toxicity research and risk prevention and control of micro-nanoplastic risk substances in the medical field.

[0027] Beneficial effects of the present invention:

[0028] (1) The present invention uses a human HepaRG hepatocyte model with high metabolic activity and combines it with the isobologram method to dynamically design the combined exposure dose of multiple pollutants, significantly improving the physiological relevance and dosage scientificity of in vitro toxicity experiments, and overcoming the problems of insufficient metabolic function and rigid concentration ratio of traditional models.

[0029] (2) The present invention adopts a cross-dimensional data fusion method of transcriptome and metabolome, and realizes the analysis of the whole chain mechanism from gene expression to substrate metabolism through the gene-metabolite interaction network and pathway cross-mapping algorithm, breaking through the limitations of single-omics information fragmentation.

[0030] (3) This invention is developed based on the AOP-WIKI platform, which dynamically associates multiple groups of biomarkers with toxic events, generates standardized adverse outcome pathways (AOPs) that comply with OECD guidelines, and visualizes the data results, filling the gap in the lack of a unified output format in traditional methods.

[0031] (4) The present invention uses high-throughput sequencing and mass spectrometry technology to simultaneously detect more than 10,000 genes and more than 2,000 metabolites, combines the transcription-metabolism regulatory relationship to screen key marker combinations, and analyzes the toxicity time sequence (such as ROS → DNA damage → apoptosis). The efficiency is 20 times higher than that of traditional methods, and the intervention window is accurately located.

[0032] (5) The present invention accurately characterizes the synergistic / antagonistic combined toxic effects of multiple, low-toxic, and cross-class risk substances, and provides a highly reliable "alternative animal experiment" solution that can be expanded to risk assessments in multiple fields such as environment, food, and medicine.

[0033] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 Schematic diagram of the effects of different concentrations of micro-nano plastics and their additives on HepaRG cell viability;

[0035] in, Figure 1 A in the figure is a schematic diagram showing the effect of PS on HepaRG cell viability. Figure 1 B in the figure is a schematic diagram of the effect of DEHP on HepaRG cell viability. Figure 1 C in the figure is a schematic diagram showing the effect of TCPP on HepaRG cell viability. Figure 1 D in the figure is a schematic diagram showing the effect of Mix (PS-DEHP-TCPP) on HepaRG cell viability;

[0036] Figure 2 It is the volcano plot of differentially expressed genes in the transcriptome of the present invention;

[0037] Figure 3 is a cluster heat map of differentially expressed genes in the transcriptome of the present invention;

[0038] Figure 4 is a schematic diagram of the GO enrichment analysis results of the Con vs Mix group of the present invention;

[0039] Figure 5 It is a bubble chart of the KEGG enrichment analysis results of the Con vs Mix group of the present invention;

[0040] Figure 6 It is a volcano plot of differential metabolites screened by the metabolome of the present invention;

[0041] Figure 7 It is a bubble chart of the pathway enrichment analysis results of the Con vs Mix group of the present invention;

[0042] Figure 8 It is a schematic diagram of the AOP framework constructed by the present invention. DETAILED DESCRIPTION

[0043] The present invention is further described below with reference to the accompanying drawings and examples. Unless otherwise defined, technical or scientific terms used herein shall have the same meanings as those commonly understood by persons of ordinary skill in the art to which the present invention pertains. The above-mentioned features or features described in the specific examples of the present invention may be combined in any manner. These specific examples are intended only to illustrate the present invention and are not intended to limit the scope of the present invention.

[0044] Example

[0045] This example examines micro- and nanoplastics, plasticizers, and flame retardants detected in emerging microalgae-based foods. Micro- and nanoplastics are ubiquitous in various environmental media and foods, and the combined toxicity of multiple pollutants they carry has significant health consequences.

[0046] As shown in the figure, the present invention provides a method for constructing a micro-nanoplastic combined toxicity AOP based on multi-omics integration, comprising the following steps:

[0047] S1. Cell model selection and exposure

[0048] Human hepatocytes HepaRG cells were used and the density was 1×10 6 HepaRG cells at 100 cells / mL were seeded in a 96-well plate in RPMI 1640 medium supplemented with 10% fetal bovine serum and cultured overnight in a 37° C., 5% CO 2 incubator.

[0049] Then, some experimental wells were selected from the well plate, and gradient concentrations (μg / mL) of PS (polystyrene), DEHP (dioctyl phthalate), and TCPP (trichloropropyl phosphate) were added to the experimental wells respectively. The cells were cultured at 37°C and 5% CO2 for 24 hours to perform single exposure culture of multiple target risk substances.

[0050] S2. Determination of combined exposure dose

[0051] The CCK-8 method was used to detect the cell viability after single exposure to multiple target risk substances for 24 hours. 10 μL of CCK-8 solution was added to each single exposure experimental well and incubated in the dark for 1 hour. The OD value of each well was measured at a wavelength of 450 nm using a microplate reader to calculate the survival rate of HepaRG cells.

[0052] like Figure 1 As shown in Figures AC, based on the dose-effect curves, the IC20s for PS, DEHP, and TCPP, which maintain approximately 80% cell viability, are 50 μg / mL, 100 μg / mL, and 50 μg / mL, respectively. Using the concentration additive model, the equivalent toxicity ratio for combined exposure to multiple risk substances is calculated to be 1:2:1.

[0053] S3. Cell processing and sample preparation

[0054] like Figure 1 As shown in D in Figure 2, the multi-target risk substances for joint exposure were configured according to the equivalent toxicity ratio obtained in S2, and HepaRG cells were seeded in 96-well plates (density of 1×10 6cells / mL), and gradient concentrations of multi-target risk substances were added to the experimental wells. After 24 hours of combined exposure to multi-target risk substances, the culture medium was discarded and the cells were washed three times with pre-cooled PBS. The cell pellets were collected and transcriptome samples and metabolome samples were prepared respectively.

[0055] Multi-target risk substances were mixed in a ratio of PS:DEHP:TCPP = 1:2:1, with gradient concentrations of L (10 μg / mL PS, 20 μg / mL DEHP, and 10 μg / mL TCPP), M (50 μg / mL MPs, 100 μg / mL DEHP, and 50 μg / mL TCPP), and H (100 μg / mL MPs, 200 μg / mL DEHP, and 100 μg / mL TCPP).

[0056] S4. Transcriptome Analysis

[0057] S4.1. Transcriptome sample pre-processing

[0058] For transcriptome samples, 5×10 cells were sampled. 6 Total RNA was isolated from cell samples using an RNA Trizol kit at a ratio of 1 mL to 1 mL of TRIzol reagent. RNA integrity (RIN ≥ 7.0) and total amount were accurately detected using an Agilent 2100 bioanalyzer. mRNA paired-end 150 bp sequencing was performed on the cell samples using the Illumina NovaSeq 6000 platform.

[0059] Use tools such as FastQC to assess the quality of raw sequencing data. Based on quality indicators, use data cleaning tools such as Trimmomatic or Cutadapt to remove low-quality sequences and adapter sequences and eliminate possible technical contamination.

[0060] S4.2. Transcriptome differentially expressed gene analysis

[0061] The differentially expressed genes (DEGs) were analyzed using DESeq2 software, with the screening criteria of |log2FC|>1.2 and FDR<0.05. 519 differentially expressed genes were screened out, such as Figure 2 As shown, the downregulated genes in the mixed exposure group far exceeded the upregulated genes, of which 115 were upregulated and 404 were downregulated.

[0062] The differentially expressed gene data were imported into the BioLadder online bioinformatics analysis platform. After logarithmic transformation and Pareto normalization preprocessing, statistical tests were performed using analysis of variance (ANOVA) with an adjusted p-value threshold of 0.05, supplemented by post-hoc analysis to determine differences between groups. Based on p-value significance screening, the hierarchical Pearson clustering method was used to visualize the significantly differentially expressed genes. The results are shown in Figure 2. Figure 3 As shown, the results showed that the six samples within each group expressed similarly, with high consistency. Of the 15 significantly differentially expressed genes, the first three in the exposure group showed no significant changes compared to the control group, while the last 10 genes were significantly downregulated. Further analysis revealed that these significantly altered genes primarily involved three categories: stress response and protein quality control, metabolism and detoxification, and cellular structure and function.

[0063] S4.3. Transcriptome differentially expressed gene enrichment analysis

[0064] S4.3.1. GO enrichment analysis

[0065] Functional enrichment analysis was performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genomes (KEGG) databases. The ClusterProfiler software package was used for analysis. Under the condition of significance threshold P < 0.05, the enrichment features of the Con vs Mix group (control group vs combined exposure group, the control group was a blank group with the same volume of basal culture medium added) were concentrated in three major functional modules: DNA binding and transcriptional regulation (involving GO: 1990837, GO: 0000977, etc.), RNA polymerase II related functions (covering GO: 0000981, GO: 0000978), and metabolic process regulation (including GO: 0080090, GO: 0031323). Non-specific regulatory functions such as ion binding (GO: 0043167) and nucleic acid binding (GO: 0001067) also showed significant enrichment features. GO enrichment analysis of the Con vs Mix group is shown in Figure 2. Figure 4 As shown, the results indicate that micro-nanoplastics and their additives interfere with cell homeostasis through multiple mechanisms: reducing DNA repair capacity by inhibiting homologous recombination repair (mediated by JAG1), weakening the telomere maintenance system (downregulation of ZSCAN31) and damage signal transduction (DGKK inhibition).

[0066] S4.3.2 KEGG enrichment analysis

[0067] The results of functional annotation of differentially expressed genes based on the KEGG database are as follows: Figure 5In the Con vs Mix group, the differentially expressed genes showed enrichment characteristics in multiple signal transduction pathway modules, including TNF signaling pathway, AMPK signaling pathway, PI3K-AKT signaling network, PPAR signaling system, glycerolipid metabolism pathway and TGF-β signaling cascade.

[0068] S5. Metabolome Analysis

[0069] S5.1 Metabolomics Sample Processing and Detection

[0070] 1 mL of methanol-acetonitrile mixture was added to the metabolomics sample to lyse the cells. After ultrasonic disruption, the supernatant was centrifuged and concentrated to dryness with nitrogen blow-through. The supernatant was redissolved in 80% methanol solution, vortexed and mixed, and centrifuged. Non-targeted metabolomics detection was performed using an ultra-high performance liquid chromatography (UHPLC) system with an electrospray ionization source (ESI) in positive and negative ion acquisition modes.

[0071] S5.2. Identification of metabolites

[0072] Compound Discoverer 3.3 software was used to match the HMDB and mzCloud databases, and the metabolite structures were confirmed and identified in combination with secondary mass spectrometry fragment ions.

[0073] S5.3. Screening of differential metabolites

[0074] Based on multiple screening criteria, including OPLS-DA model VIP>1, log2FC>1 or <-1 and T test p value <0.05, significant differential metabolites were screened. The results are as follows Figure 6 As shown, a total of 755 potential differential metabolites were screened in the Con vs Mix groups, of which 701 metabolites showed an up-regulated trend and 54 metabolites showed a down-regulated trend.

[0075] S5.4 Metabolite pathway enrichment analysis

[0076] The identified differential metabolites were annotated with the KEGG database and subjected to metabolic pathway enrichment analysis using the MetaboAnalyst online analysis platform. The Mix treatment group induced 145 key significantly differential metabolites, which were closely related to lipid metabolism, oxidative stress response, and immune regulation.

[0077] Significant metabolic pathways were screened based on the dual criteria of statistical significance (P<0.05) and pathway impact value (Pathway Impact>0.1). Figure 7As shown in the figure, the metabolic pathways enriched with significant differences in the Con vs Mix group were pyrimidine metabolism, nicotinate and nicotinamide metabolism, and purine metabolism, followed by galactose metabolism, and alanine, aspartate, and glutamate metabolism.

[0078] S6. Data integration: Cross-map the signal pathways enriched in the transcriptome with the metabolic pathways enriched in the metabolome to screen key nodes; construct a gene-metabolite interaction network using Cytoscape 3.9, and identify core regulatory modules based on Spearman correlation analysis with |r|>0.7 and p<0.01.

[0079] A total of 65 significantly differential metabolic pathways were enriched in the Con vs Mix groups, among which 10 metabolic pathways, including PI3K-AKT signaling pathway, mTOR signaling pathway, AMPK signaling pathway, apoptosis signaling pathway, DNA replication signaling pathway, cytochrome P450 metabolic signaling pathway of exogenous substances, glycerophospholipid metabolism signaling pathway, and P53 signaling pathway, were closely related to pyrimidine metabolism. The activation of these pathways can further affect pyrimidine metabolism by regulating protein synthesis, intracellular amino acid levels and nucleotide metabolism.

[0080] Among them, seven pathways: oxidative phosphorylation pathway, glycolysis and TCA cycle pathway, NAD+ metabolism pathway, cell stress response pathway, cell apoptosis pathway, lipid metabolism pathway, and DNA repair pathway are closely related to nicotinate and nicotinamide metabolism, and regulate lipid levels in the blood by activating related receptors in hepatocytes.

[0081] Thirteen pathways are closely related to purine metabolism, including AMPK signaling pathway, glycerophospholipid metabolism signaling pathway, cytokine-cytokine receptor interaction signaling pathway, JAK-STAT signaling pathway, apoptosis signaling pathway, mTOR signaling pathway, base excision repair signaling pathway, DNA replication signaling pathway, PI3K-AKT signaling pathway, oxidative phosphorylation signaling pathway, pyrimidine metabolism signaling pathway, TCA cycle signaling pathway, and tryptophan metabolism signaling pathway, which affect the level of intracellular purine nucleotides by participating in cell proliferation and energy metabolism.

[0082] The six pathways are interrelated with galactose metabolism, glycolysis signaling pathway, glycogen synthesis and decomposition signaling pathway, glycerophospholipid synthesis signaling pathway, oxidative phosphorylation signaling pathway, TCA cycle signaling pathway, and apoptosis signaling pathway.

[0083] S7. Based on the upstream and downstream relationships and consistency of transcriptomics and metabolomics data results, determine the molecular initiating events (MIEs), key events (KEs), and adverse outcomes (AOs) of the AOP framework. Import the above-mentioned MIE, KEs, and AOs into the AOP-WIKI platform (https: / / aopwiki.org / ), follow the OECD AOP development guidelines, define weighted evidence (such as in vitro data, quantitative association), and generate a standardized AOP framework. The AOP framework is as follows: Figure 8 As shown, the pathway network within the AOP framework involves the metabolic transformation of exogenous substances, the pathological progression of tissue fibrosis, the inflammatory response cascade, the oxidative stress defense system, and the programmed cell death regulatory module. The network model includes five linearized adverse outcome pathways (AOPs), among which the molecular initiating event (MIE) clearly points to the peroxisome proliferator-activated receptor (PPAR) ligand activation process. The network nodes cover 10 different levels of biological events, including molecular interactions, cellular responses, tissue structural changes, and organ function damage, ultimately linking to three clinically significant terminal pathological phenotypes.

[0084] The AOP framework constructed by the present invention is applied to the detection of typical pollutants, micro-nanoplastics and their additives in microalgae foods, and the microalgae foods are Haematococcus pluvialis, Chlorella, and Spirulina foods.

[0085] The algae powder samples were processed and then analyzed using pyrolysis gas chromatography-mass spectrometry (Py-GC / MS) to determine the presence of microplastics in the algae. PE was detected in all three microalgae foods. Quantification of 1-tetradecene (m / z 97), a typical degradation product of PE, showed that the content of microplastics in the Haematococcus pluvialis samples ranged from 27.95 to 35.60 μg / g, in the Spirulina samples from 20.67 to 871.45 μg / g, and in the Chlorella samples from 35.83 to 66.21 μg / g.

[0086] High-performance liquid chromatography-tandem mass spectrometry (HPLC-MS / MS) using electrospray chemical ionization (ESI) in positive ionization (ESI+) and multiple reaction monitoring (MRM) mode was used to determine the levels of DEHP and TCPP in Chlorella from Wuhan, Hubei, Zhengzhou, Henan, Liwan, Guangdong, Ordos, Inner Mongolia, and Pu'er, Yunnan, as well as in Spirulina from Zhengzhou, Henan, Shanghai, Ruijin, Jiangxi, Nanjing, Jiangsu, and Lijiang, Yunnan. The average DEHP content in Spirulina was 281.5 μg / kg, and the average TCPP content was 117.4 μg / kg. The average DEHP content in Chlorella was 301.7 μg / kg, and the average TCPP content was 129.8 μg / kg.

[0087] The detected occurrence level data of microplastics (such as PE) and additives (DEHP, TCPP) in microalgae foods, including the content range of microplastics in Haematococcus pluvialis, Spirulina and Chlorella, and the average content of DEHP and TCPP in Spirulina and Chlorella, were input into the AOP model constructed by the present invention. Key events were identified according to the actual exposure levels of micro-nanoplastics and their additives in microalgae, and matched and analyzed. The lowest exposure level of microplastics in this study (20.67 μg / g) can bind to peroxisome proliferator receptors, activate fibrosis-related pathological signal transduction networks (TGF-β signaling axis), interleukin 6 (IL-6)-led inflammatory regulatory cascades, phosphatidylinositol 3-kinase-protein kinase B signal transduction (PI3K-AKT signaling axis) and other signaling pathways, inducing liver toxicity in terms of lipid metabolism changes, ROS generation, oxidative stress, ATP production and inflammatory response.

[0088] The AOP framework constructed based on this invention can systematically assess the health risks of micro- and nanoplastic contamination in microalgae-based foods, providing comprehensive toxicological data for the approval of new resource foods. This is of great significance for ensuring food safety and promoting the healthy development of the food industry. Furthermore, this method can be extended to risk assessment of micro- and nanoplastic contamination in other foods and environmental samples, providing strong support for environmental protection and human health.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for constructing a micro-nanoplastic combined toxicity AOP based on multi-omics integration, characterized by: The following steps are involved: S1. Cell model selection and exposure: Human hepatocytes HepaRG cells were used for single exposure culture of multiple target risk substances; S2. Determination of combined exposure dose: The CCK-8 assay was used to detect cell viability after 24 hours of single exposure to multiple target risk substances. The IC20 of each target risk substance that maintains 80% cell viability was determined using a dose-effect curve. Based on the IC20 value, the equivalent toxicity ratio of the combined exposure to multiple target risk substances was determined using the isobologram method or the concentration addition model. S3. Cell treatment and sample preparation: HepaRG cells were seeded in 96-well plates and exposed to multiple risk substances for 24 hours. The culture medium was discarded and the cells were washed three times with pre-chilled PBS. The cell pellets were collected and transcriptome and metabolome samples were prepared respectively. S4. Transcriptome analysis: RNA extraction, library construction and sequencing of transcriptome samples were performed to analyze differentially expressed genes and perform functional enrichment analysis; S5. Metabolome analysis: Process and detect metabolome samples, identify metabolites, screen differential metabolites, and perform metabolic pathway enrichment analysis; S6. Data integration: Integrate transcriptome and metabolome data to perform pathway cross-analysis, construct gene-metabolite interaction networks, and identify core regulatory modules. S7. AOP framework construction: Based on the integrated data, the molecular initiation events, key events and harmful outcomes of the AOP framework are determined, and the standardized AOP framework is generated by importing the AOP-WIKI platform.

2. The method for constructing a micro-nanoplastic combined toxicity AOP based on multi-omics integration according to claim 1, characterized in that: The target risk substances in S1-S3 include polyethylene, polypropylene, polyvinyl chloride, polystyrene, dioctyl phthalate, tetrabromobisphenol A microplastic risk substances and one or more of the heavy metals, biotoxins, antibiotics, and persistent organic pollutants adsorbed by them.

3. The method for constructing a micro-nanoplastic combined toxicity AOP based on multi-omics integration according to claim 1, characterized in that: In S4, total RNA was extracted from transcriptome samples using TRIzol reagent, RNA integrity was tested, strand-specific mRNA libraries were constructed, and mRNA paired-end 150bp sequencing was performed using the Illumina NovaSeq 6000 platform.

4. The method for constructing a micro-nanoplastic combined toxicity AOP based on multi-omics integration according to claim 1, characterized in that: In S4, DESeq2 software was used to analyze differentially expressed genes (DEGs), with the screening criteria of |log2FC| ≥ 1 and FDR < 0.05; KEGG pathway and GO function enrichment analysis of DEGs was performed using ClusterProfiler.

5. The method for constructing a micro-nanoplastic combined toxicity AOP based on multi-omics integration according to claim 1, characterized in that: In S5, pre-cooled methanol-acetonitrile mixture was added to the metabolomics sample to lyse the cells. After ultrasonic disruption, the supernatant was collected by centrifugation, concentrated to dryness with nitrogen blown, and redissolved in methanol solution for non-targeted metabolomics detection using a high-resolution mass spectrometer.

6. The method for constructing a micro-nanoplastic combined toxicity AOP based on multi-omics integration according to claim 1, characterized in that: In S5, Compound Discoverer 3.3 software was used to match the HMDB and mzCloud databases, and the metabolite structures were confirmed and identified in combination with secondary mass spectrometry fragment ions. Differential metabolites were screened based on the OPLS-DA model with VIP>1, |log2FC|≥1, and p<0.

05. KEGG pathway enrichment was performed using MetaboAnalyst5.

0.

7. The method for constructing a micro-nanoplastic combined toxicity AOP based on multi-omics integration according to claim 1, characterized in that: In S6, the signal pathways enriched in the transcriptome were cross-mapped with the metabolic pathways enriched in the metabolome to screen key nodes; Cytoscape 3.9 was used to construct a gene-metabolite interaction network, and the core regulatory modules were identified based on Spearman correlation analysis with |r|>0.7 and p<0.

01.

8. The method for constructing a micro-nanoplastic combined toxicity AOP based on multi-omics integration according to claim 1, characterized in that: In S7, after determining the molecular initiating events, key events, and adverse outcomes of the AOP framework, the relevant relationships were imported into the AOP-WIKI platform, and the OECD's AOP development guidelines were followed to define the weight of evidence to generate a standardized AOP framework.

9. An application of the AOP framework constructed by the multi-omics integrated micro-nanoplastics combined toxicity AOP construction method according to any one of claims 1 to 3, characterized in that: It is used in environmental monitoring, food safety assessment, new resource food approval, and joint toxicity research and risk prevention and control of micro-nano plastic risk substances in the medical field.

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