Method for evaluating toxicity of pollutants to nervous system based on intestinal-brain axis signals

Abstract

The invention provides a method for evaluating toxicity of pollutants to a nervous system based on intestinal-brain axis signals. The method comprises the following steps: collecting brain tissues and intestinal tissues of animals exposed to the pollutants; respectively screening target spots which have a key interference effect on metabolism of the 5-hydroxytryptamine and characteristic microorganisms in intestinal tracts under a pollutant exposure condition; further screening to obtain a core microorganism with high interference correlation with a 5-hydroxytryptamine metabolic pathway; the neurotoxicity of the pollutants is evaluated on the basis of intestinal-brain axis signals by taking the screened target spot which has a key interference effect on the metabolism of the 5-hydroxytryptamine under the exposure condition of the pollutants and the core microorganism which has high interference correlation with the metabolism of the 5-hydroxytryptamine as markers. A comprehensive analysis system which takes intestinal-brain axis signals as a core pathway and covers a micro-ecology-metabolism-neural network is constructed by combining structural changes of intestinal microflora and disturbance of a 5-hydroxytryptamine metabolic pathway, and the toxicity of pollutants to a nervous system can be systematically evaluated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pollutant toxicity evaluation, and particularly relates to a method for evaluating the toxicity of pollutants to the nervous system based on gut-brain axis signals. BACKGROUND

[0002] With the acceleration of industrialization and urbanization, there are more and more pollutants in the environment, which inevitably harm human health. Especially persistent organic pollutants (POPs) such as short-chain chlorinated paraffins (SCCP) and decabromodiphenyl ether (BDE-209) exist widely in the environment and can enter the body through the food chain, causing chronic toxicity hazards. Studies have shown that such pollutants can pass through the blood-brain barrier and cause potential damage to the central nervous system, manifesting as behavioral abnormalities, neurodevelopmental disorders, neurotransmitter disorders, etc. At present, the traditional methods for evaluating the neurotoxicity of POPs mainly focus on behavioral evaluation or single-point molecular index detection, and concentrate on the direct damage path of brain tissue, without considering the bidirectional regulation relationship between the intestinal flora and the central nervous system, ignoring the indirect path of intestinal flora regulating the nervous system, and being difficult to reveal the complex system biology response induced by environmental toxicants.

[0003] Therefore, it is urgent to develop a method for evaluating the toxicity of pollutants to the nervous system based on gut-brain axis signals, by constructing a comprehensive analysis system covering microecology-metabolism-neural network, so as to more accurately simulate the real exposure situation and reveal the damage mechanism of pollutants to the nervous system, and to provide a scientific basis for environmental toxicity evaluation and intervention. SUMMARY

[0004] To solve the problems in the background art, the present application provides a method for evaluating the toxicity of pollutants to the nervous system based on gut-brain axis signals, which constructs a comprehensive analysis system covering microecology-metabolism-neural network by combining the changes in intestinal flora structure and the disturbance of 5-hydroxytryptamine metabolic pathway, so as to systematically analyze and evaluate the toxicity of pollutants to the nervous system.

[0005] The technical solution of the present application to solve the above technical problems is as follows: A method for evaluating the toxicity of pollutants to the nervous system based on gut-brain axis signals, comprising: (a) collecting brain tissue and intestinal tissue of animals exposed to pollutants; (b) performing transcriptomic analysis on the brain tissue, and combining the determination of 5-hydroxytryptamine metabolic pathway in the brain tissue and intestinal tissue to screen and identify target points that have key interference effects on 5-hydroxytryptamine metabolism under the exposure condition of organic pollutants; (c) analyzing the effect of pollutant exposure on the intestinal microbial community structure based on metagenomic technology, and screening characteristic microorganisms in the intestine under pollutant exposure conditions; (d) constructing an association network of the characteristic microorganisms and the 5-hydroxytryptamine metabolic pathway, and further screening core microorganisms with high correlation with 5-hydroxytryptamine metabolic pathway interference under pollutant exposure conditions; (e) taking the target points that have a key interference effect on 5-hydroxytryptamine metabolism under pollutant exposure conditions and the core microorganisms with high correlation with 5-hydroxytryptamine metabolic interference as markers, and evaluating the nervous system toxicity of the pollutant based on the intestinal-brain axis signal.

[0006] According to the above scheme, the pollutant in step (a) is a single pollutant or a combination of at least two pollutants.

[0007] According to the above scheme, step (b) is specifically: determining the content of metabolites in the 5-hydroxytryptamine metabolic pathway, screening metabolites with significant changes, combining transcriptome data and the expression and activity of enzymes, and screening enzymes with significant changes in the 5-hydroxytryptamine metabolic pathway. The significantly changed metabolites and enzymes constitute key interference targets of 5-hydroxytryptamine metabolism under pollutant exposure conditions.

[0008] According to the above scheme, the metabolites in the 5-hydroxytryptamine metabolic pathway include 5-hydroxytryptamine, tryptophan and 5-hydroxyindoleacetic acid.

[0009] According to the above scheme, in step (c), characteristic microorganisms with significant differences between the intestinal tissues under pollutant exposure conditions and the conditions without pollution and exposure are obtained from the door, genus and species levels, respectively.

[0010] According to the above scheme, in step (d), the absolute value of the correlation coefficient between the abundance of the core microorganism and the expression level of the key interference target is greater than 0.8, and p<0.05.

[0011] According to the above scheme, in step (e), the metabolic effect level index MELI value is used to evaluate the nervous system toxicity of the pollutant.

[0012] According to the above scheme, the MELI value is calculated by the following formula, Formula 1 Formula 2 Wherein, MCi is the metabolic change of a biological component (i) in the pollutant exposure individual, Ai is the relative abundance ratio of a biological component (i) in the pollutant exposure individual relative to the average level of the control group, ln(1) is used to eliminate the influence of the metabolic level of the control group, and n is the total number of analyzed biological components.

[0013] The pollutant exposure is single pollutant exposure or combined pollutant exposure.

[0014] The biological components include target points that have a key interference effect on 5-hydroxytryptamine metabolism and core microorganisms that have a high correlation with 5-hydroxytryptamine metabolism interference. Further, the target points that have a key interference effect on 5-hydroxytryptamine metabolism include metabolites and enzymes that significantly change in the 5-hydroxytryptamine metabolic pathway under the condition of pollutant exposure, and the core microorganisms include microorganisms at the levels of phylum, genus and species.

[0015] According to the above scheme, when there are at least two pollutants, the independent action model is used to evaluate the joint toxicity effect of the at least two pollutants.

[0016] According to the above scheme, the independent action model is as follows: Formula 3 cmix represents the total concentration of mixed pollutants, E(cmix) represents the predicted MELI value of the joint effect, E(ci) represents the MELI value of the i-th pollutant alone, and the MELI value of the predicted joint effect is compared with the actual MELI value of the joint effect. If they are the same, the actual value is higher or lower than the predicted value, it is determined as additive effect, synergistic effect or antagonistic effect, respectively.

[0017] The beneficial effects of the present application are: The present application integrates transcriptome, metagenome, metabolome and other multi-omics data, comprehensively considers the regulation of intestinal microorganisms on the gut-brain axis signal, and constructs a correlation network and comprehensive analysis and evaluation system with the gut-brain axis signal as the core pathway, covering the microecological-metabolic-neural network, breaks through the limitations of traditional single index evaluation, provides a precise and efficient new analysis tool for toxicology research and risk prediction of pollutant exposure, and provides a theoretical basis and technical support for the risk evaluation and single or multiple pollutant joint toxicity prevention and control caused by environmental exposure. Further, other metabolites and enzymes related to metabolism except 5-hydroxytryptamine are introduced in the analysis of the 5-hydroxytryptamine metabolic pathway, and the nervous system is evaluated more comprehensively through multiple biochemical indicators. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1Representative H&E staining images of brain tissues in Example 1 of the present application. A-G correspond to the control group (CK), the SCCP low-dose group (SCCP-LOW), the SCCP high-dose group (SCCP-HIGH), the BDE-209 low-dose group (BDE-LOW), the BDE-209 high-dose group (BDE-HIGH), the joint low-dose group (MIX-LOW), and the joint high-dose group (MIX-HIGH), respectively. Scale bar = 100 μm.

[0019] Figure 2 Characteristics of neurotoxicity injury in zebrafish brain tissues in Example 1 of the present application. (A) Representative immunofluorescence images, scale bar = 40 μm; (B) Quantitative analysis of fluorescence intensity; (C) Acetylcholinesterase (AChE) activity assay; (D-F) Oxidative stress level evaluation.

[0020] Figure 3 Characteristics of brain partial metabolic changes provided in Example 1 of the present application. (A) Principal component analysis (PCA) results; (B) Sample clustering heat map based on Pearson correlation coefficient; (C) Number of differentially expressed genes (DEGs) identified between each exposure group and the control group; (D-E) Comparison of differentially expressed genes between low-dose and high-dose exposure groups.

[0021] Figure 4 Disorder of 5-HT signaling pathway in brain tissues in Example 1 of the present application. (A) Changes in the expression of 5-HT metabolism-related genes under single and joint exposure conditions; (B-D) 5-HT metabolism gene co-expression network constructed based on Pearson correlation analysis (p < 0.05) in the SCCP, BDE-209, and joint exposure groups; (E) Heat map analysis of 5-HT-related key metabolic enzymes and metabolites; (F-G) Enzyme activity detection of acetaldehyde dehydrogenase (ALDH) and monoamine oxidase (MAO). Data with statistical differences (p < 0.05) are marked with different letters.

[0022] Figure 5Characteristics of neurotoxicity in the intestinal system in Example 1 of the present application. (A-E) Representative images of H&E staining of intestinal tissue in the control group, 20 μg / L SCCP exposure group, 20 μg / L BDE-209 exposure group, 10 μg / L combined exposure group and 20 μg / L combined exposure group; (F) Heatmap analysis of villus width, villus height, crypt depth, villus height / crypt depth ratio, and villus height / width ratio under different exposure conditions (n = 3); (G) Acetylcholinesterase (AChE) activity detection; (H-J) Oxidative stress-related indicators, including malondialdehyde (MDA) content, superoxide dismutase (SOD) and catalase (CAT) activity; (K) Heatmap analysis of 5-HT-related metabolic enzymes and metabolites; (L-M) Aldehyde dehydrogenase (ALDH) and monoamine oxidase (MAO) enzyme activity detection; (N) Correlation analysis of 5-HT metabolism between brain tissue and intestinal tissue. Black arrows indicate apoptosis and necrosis, and red arrows indicate villus arrangement disorder. Data with statistically significant differences (p < 0.05) are marked with different letters.

[0023] Figure 6 Disorder characteristics of intestinal flora structure in Example 1 of the present application. (A) Classification composition of zebrafish intestinal flora at the phylum level; (B) Changes in intestinal microbial community structure at the phylum level under different single and combined exposure conditions; (C-D) Analysis of flora richness and evenness based on Chao index and Simpson index; (E-F) System clustering analysis and distance heatmap analysis between each treatment group; (G) Non-metric multidimensional scaling analysis (NMDS); (H) Wilcoxon rank sum test to evaluate the difference in β diversity between groups.

[0024] Figure 7 Disorder characteristics of intestinal microbial community at the genus and species levels in Example 1 of the present application. (A-B) Classification composition of zebrafish intestinal microorganisms at the genus and species levels; (C-D) Changes in intestinal microbial community structure at the genus and species levels under different single and combined exposure conditions.

[0025] Figure 8Key roles of gut microbes at the phylum level in the joint toxicity in Example 1 of the present application. (A) Venn diagram analysis of gut microbial composition at the phylum level under single and joint exposure conditions; (B-C) ROC curve analysis based on gut microbial abundance at the phylum level to distinguish the low / high dose joint exposure group from the control group; (D) LEfSe analysis of different gut microbes at the phylum level; (E-G) Abundance changes of Pseudomonadota phylum in different treatment groups and its ROC analysis for distinguishing the low / high dose joint exposure group from the control group; (H-J) Abundance changes of Bacillota phylum and related ROC analysis; (K-M) Abundance changes of Actinomycetota phylum and related ROC analysis; (N) Correlation network analysis between the top 10 dominant gut bacterial phyla (p<0.05).

[0026] Figure 9 Mechanism analysis of gut microbes regulating 5-HT metabolism in Example 1 of the present application. (A) Functional impact of gut microbes on 5-HT metabolic pathways under joint exposure conditions; (B) Abundance of ALDH and MAO functional annotations related to the three dominant bacterial phyla (Actinomycetota, Bacillota and Pseudomonadota); (C) Comparison analysis of ALDH and MAO functional annotation abundance of different microbial groups in each treatment group; (D) Correlation analysis between microbial abundance and its corresponding functional annotation abundance (|correlation coefficient|>0.5, p<0.05).

[0027] Figure 10 Key roles of gut microbes at the genus and species levels in the joint toxicity in Example 1 of the present application. (A) LEfSe analysis of different gut microbes at the genus level in Actinomycetota phylum; (B-D) Abundance changes of Mycolicibacterium genus in different treatment groups and its ROC analysis for distinguishing the low / high dose joint exposure group from the control group; (E) LEfSe analysis of different gut microbes at the species level in Mycolicibacterium genus; (F-H) Abundance of Mycolicibacterium vinylchloridicum and related ROC analysis; (I-K) Abundance of Mycolicibacterium rhodesiae and related ROC analysis.

[0028] Figure 11To analyze the mechanism of 5-HT metabolism regulated by microorganisms in the Actinomycetota phylum in Example 1 of the present application. (A) Relative contribution of Mycolicibacterium to the functional annotation abundance of ALDH and MAO under different treatment conditions; (B) Relative contribution of Mycolicibacterium vinylchloridicum and Mycolicibacterium rhodesiae to the functional annotation abundance of ALDH and MAO under different treatment conditions.

[0029] Figure 12 To analyze the 5-HT metabolism related to the gut-brain axis in Example 1 of the present application. DETAILED DESCRIPTION

[0030] The principles and characteristics of the present application are described below in combination with the accompanying drawings and specific examples, which are used to explain the present application and are not intended to limit the scope of the present application.

[0031] The method for evaluating the toxicity of pollutants to the nervous system based on the gut-brain axis signal of the present application can be applied to evaluate the toxicity of various pollutants to the nervous system. In the following examples, persistent organic pollutants (POPs) short-chain chlorinated paraffins (SCCP) and decabromodiphenyl ether (BDE-209) are taken as examples, and other pollutants are also suitable for the method of the present application. Short-chain chlorinated paraffins (SCCP), decabromodiphenyl ether (BDE-209), and the combination of short-chain chlorinated paraffins (SCCP) and decabromodiphenyl ether (BDE-209) are selected, zebrafish are exposed to the above pollutants, and single-exposure zebrafish models and joint-exposure zebrafish models of the pollutants are constructed, and the toxicity of the pollutants to the nervous system of zebrafish is analyzed based on the gut-brain axis signal.

[0032] 1. Construction of pollutant-exposed zebrafish model The 4-month-old wild-type AB strain zebrafish was selected as a model organism and acclimated for 2 weeks in the laboratory conditions. The zebrafish was maintained at a water temperature of about 28°C and continuously oxygenated, and the light condition was set as a 14-hour light / 10-hour dark cycle, and the water pH value was controlled between 6.8-7.5. During the feeding period, newly hatched brine shrimp larvae were fed twice a day. SCCP and BDE-209 were prepared into 100 mg / L and 2500 mg / L stock solutions using dimethyl sulfoxide (DMSO), respectively, and diluted with water to the target exposure concentration of 10 μg / L and 20 μg / L, respectively, according to the experimental design. To avoid the toxic effects caused by the solvent, the final concentration of DMSO in the zebrafish exposure water was always controlled below 0.1%. Seven groups were set up, including a blank control group (CK), a low-dose SCCP exposure group (10 μg / L, SCCP-LOW), a high-dose SCCP exposure group (20 μg / L, SCCP-HIGH), a low-dose BDE-209 exposure group (10 μg / L, BDE-LOW), a high-dose BDE-209 exposure group (20 μg / L, BDE-HIGH), a low-dose mixed exposure group (SCCP and BDE each 10 μg / L, MIX-LOW), and a high-dose mixed exposure group (SCCP and BDE each 20 μg / L, MIX-HIGH). The exposure concentration of each pollutant was determined by referring to the environmental residue level and related toxicological effect reported in the existing literature. The ratio of the mixed group was based on the no-effect predicted concentration (PNEC) of the two compounds, the PNEC value of SCCP was 0.425 μg / L, and the PNEC value of BDE-209 was 0.34 μg / L, with a ratio of about 1:1, so the mixed exposure group was treated with equal concentration mixing. The zebrafish exposure experiment lasted for 21 days, during which the exposure solution was completely replaced every 2 days, and the zebrafish was observed throughout the experiment to ensure that there was no obvious damage or death. After the exposure ended, the zebrafish was sacrificed using ice bath method, and the brain and intestinal tissues were collected for subsequent analysis, with every 6 zebrafish (balanced male and female) mixed as one sample for brain and intestinal damage index detection, and part of the brain and intestinal tissues were fixed in 4% paraformaldehyde for histological analysis. The experiment strictly followed the "3R" principle of animal experiments.

[0033] 2. Brain tissue neurotoxicity evaluation The brain tissue was subjected to histological analysis, immunofluorescence staining, and physiological and biochemical index detection to comprehensively evaluate the damage effect of single or combined exposure of pollutants on the nervous system of zebrafish brain.

[0034] Histopathological evaluation: After the brain tissues were fixed with 4% paraformaldehyde, histopathological evaluation was performed using Hematoxylin and Eosin (H&E) staining. After paraffin embedding, the tissue samples were sectioned at a thickness of 7 μm, and after deparaffinization, staining was performed. The overall structural changes of the tissues were observed by H&E staining. Quantitative analysis of histopathology was processed using ImageJ software. Representative images were taken using an optical microscope. At least 3 zebrafish were selected for analysis in each treatment group. The results are shown in Figure 1 H&E staining showed no obvious structural abnormalities.

[0035] Immunofluorescence analysis: The brain tissue sections prepared according to the above method were subjected to TUNEL staining and glial fibrillary acidic protein (GFAP) immunofluorescence staining, respectively. TUNEL staining used a commercial apoptosis detection kit, and the operation was performed according to the instructions: after incubating the sections with TUNEL reaction solution at 37°C for 1 hour, DAPI was used for nuclear restaining to mark the apoptotic cells. In GFAP immunofluorescence staining, the sections were incubated with rabbit anti-GFAP primary antibody at 4°C overnight, then incubated with fluorescently labeled secondary antibody, and the nuclei were restained with DAPI. Quantitative analysis of immunofluorescence images was processed using ImageJ software. Images were taken using a fluorescence microscope. At least 3 zebrafish were analyzed in each group. The results are shown in Figure 2 A and 2B, TUNEL immunofluorescence results showed that significant neuronal apoptosis was induced under single and combined exposure conditions. In particular, in the combined exposure group, the apoptosis rate increased by 126% compared with the control group, indicating a synergistic toxic effect; to further investigate the neuroinflammatory response, the expression of glial fibrillary acidic protein (GFAP), a classic marker of astrocyte activation, was detected. The results showed that the fluorescence intensity of GFAP in the combined exposure group was significantly higher than that in the control group by 146%, indicating that the astrocyte response was significantly enhanced. Although astrocytes play a key role in synaptic regulation and neural development, their excessive activation can lead to the release of pro-inflammatory and neurotoxic factors, ultimately exacerbating neuronal damage.

[0036] Oxidative stress and neural injury assessment: To explore the potential molecular mechanisms of neurotoxicity, further detection of biomarkers related to oxidative stress was performed to assess the level of oxidative stress by detecting the activities of superoxide dismutase (SOD) and catalase (CAT) and the content of malondialdehyde (MDA) in brain tissue. The related kits were provided by Beijing Solaybao Technology Co., Ltd. The determination of acetylcholinesterase (AChE) activity in brain tissue was used to assess the degree of neural injury. All experimental operations were performed according to the kit instructions. Protein concentration was determined using the BCA protein quantification kit provided by Shanghai Blue Sky Biotechnology Co., Ltd., and protease and phosphatase inhibitors were added during protein extraction to prevent degradation. Each experiment was performed at least three independent repeats, and the data were presented as a percentage of the control group. The results are shown in Figures 6A-6C. Figure 2 C-2F, single or combined exposure of pollutants significantly increased acetylcholinesterase (AChE) activity and malondialdehyde (MDA) content, while significantly inhibited the activities of superoxide dismutase (SOD) and catalase (CAT). The above results collectively indicate that single or combined exposure of pollutants disrupts the redox homeostasis and disturbs the body's endogenous antioxidant defense system.

[0037] 3. Brain tissue transcriptome and 5-hydroxytryptamine (5-HT) metabolic pathway analysis High-throughput transcriptome analysis was performed on brain tissue, and the metabolic pathway of the neurotransmitter 5-hydroxytryptamine (5-HT) in brain and intestinal tissue was determined to screen and identify target points that have a key interfering effect on 5-hydroxytryptamine (5-HT) metabolism under single or combined exposure conditions.

[0038] High-throughput transcriptome analysis: RNA extraction and transcriptome sequencing (n=3) were performed on zebrafish brain tissue samples by Shanghai Meiji Biotechnology Co., Ltd. A brief workflow is as follows: Total RNA was extracted using Trizol reagent, and mRNA was enriched using oligo(dT) magnetic beads for library construction. Purified mRNA was reverse transcribed to generate cDNA, which was then amplified by PCR to obtain the library product. Finally, the cDNA was sequenced using an Illumina high-throughput sequencing platform. The raw sequencing data underwent quality control and screening, aligned to the zebrafish reference genome, and underwent bioinformatics analysis including expression quantification, differential expression analysis, and functional annotation. Transcriptome data were analyzed using the Majorbio cloud platform (cloud.majorbio.com). Other statistical analyses were performed using SPSS software. Differences between groups were assessed using one-way ANOVA, followed by pairwise comparisons using Fisher's least significant difference (LSD) method (p<0.05). Before performing ANOVA, the Shapiro–Wilk and Levene tests were used to verify the normality and homogeneity of variance of the data, respectively. Graphical visualization was performed using GraphPad Prism 8.0. The results are as follows: Figure 3 As shown, unsupervised principal component analysis (PCA) results revealed that the low-dose and high-dose combined exposure groups were significantly different from the control group and each individual exposure group in terms of metabolic levels. Figure 3 A) indicates that combined exposure significantly disrupted endogenous metabolic processes. Further correlation analysis also confirmed the disruption of metabolic homeostasis, with the most significant dysregulation observed in the combined exposure group. Figure 3 B). Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to identify key differentially expressed genes (DEGs). DEGs were identified in multiple treatment groups based on adjusted p-values ​​<0.05 and fold change (FC) values ​​>2 or <0.5. The results showed that compared to the control group, 204, 283, 144, 291, 2,830, and 1,758 DEGs were identified in the SCCP-LOW, SCCP-HIGH, BDE-LOW, BDE-HIGH, MIX-LOW, and MIX-HIGH treatment groups, respectively. The number of DEGs induced by the combined exposure group was significantly higher than that in each single exposure group, indicating that combined exposure induced a broader transcriptional response. Figure 3 C). Further analysis revealed that exposure to SCCP and BDE-209 alone caused only limited gene perturbations (95 and 12 DEGs at low and high doses, respectively); Figure 3D-E), while compared with the joint exposure group, the number of DEG increased significantly, especially in the low dose condition. Among them, the difference between the low dose BDE-209 group and the joint exposure group was the largest, further supporting the enhanced effect of joint exposure on gene expression levels.

[0039] The key interference target points of 5-hydroxytryptamine (5-HT) metabolism under single or joint exposure conditions related to 5-hydroxytryptamine metabolism were screened by transcriptome analysis, and the results are shown in Figure 4 Figure 4 The results of 5-HT signal pathway disorder in brain tissue are shown. The 5-HT signal pathway covers three key stages: synthesis, transport and degradation. Tryptophan (TRP) as the metabolic precursor of 5-HT is converted into 5-hydroxytryptamine (5-hydroxytryptophan) by tryptophan hydroxylase (TPH) and aromatic L-amino acid decarboxylase (AADC) in turn, and then into 5-HT. At the same time, TRP can also enter other metabolic branches, such as conversion into tryptamine, kynurenine and indole compounds, etc. 5-HT released into the synaptic cleft is mainly reuptaken by presynaptic neurons through 5-HT transporter (SERT), and is re-encapsulated into vesicles via vesicular monoamine transporter (VMAT) or is degraded into 5-hydroxyindoleacetic acid (5-HIAA) by monoamine oxidase (MAO) and aldehyde dehydrogenase (ALDH) for discharge. As shown in Figure 4 A, single or joint exposure has a significant impact on the transcription level of 5-HT metabolism related genes. The expression of genes related to the synthesis pathway is down-regulated, suggesting that the TRP metabolism may be diverted to other branches such as the kynurenine pathway. At the same time, the expression of SERT is significantly up-regulated, and the expression level of MAO and ALDH is also increased, indicating that the degradation process of 5-HT is enhanced. These changes indicate that the homeostasis of 5-HT metabolism is systematically disturbed, and the neural function may be impaired. To further analyze the regulation mechanism of the above changes, a co-expression network of 5-HT related genes was constructed Figure 4 B-D). 136 pairs of significant intergenic interactions were identified in the joint exposure group, much higher than the SCCP group (85 pairs) and the BDE-209 group (97 pairs), reflecting that the 5-HT regulatory network is subjected to stronger interference under joint exposure conditions.

[0040] ​Further assessment of the 5-HT metabolic pathway was conducted by combining metabolite levels, protein expression, and enzyme activity in brain tissue. Specifically, the concentrations of serotonin (5-HT), its precursor tryptophan (TRP), and the major metabolite 5-hydroxyindoleacetic acid (5-HIAA) were detected using an enzyme-linked immunosorbent assay (ELISA) kit. Simultaneously, the levels of enzymes related to 5-HT metabolism, including tryptophan hydroxylase (TPH), monoamine oxidase (MAO), aldehyde dehydrogenase (ALDH), and aromatic L-amino acid decarboxylase (AADC), were measured using kits provided by Shanghai Enzyme-Link Biotechnology Co., Ltd. Furthermore, the enzyme activities of MAO and ALDH were detected using kits provided by Nanjing Jiancheng Bioengineering Research Institute. Each experiment was performed at least three times independently, and results are expressed as a percentage relative to the control group. The results showed that the 5-HT level in the exposed group's brain tissue decreased by 34% compared to the control group. Figure 4 The presence of EG indicates that its synthesis is inhibited and degradation is enhanced, leading to a decrease in neurotransmitter availability. The levels of 5-hydroxytryptamine (5-HT), 5-hydroxyindoleacetic acid (5-HIAA), and enzymes related to 5-HT metabolism, such as monoamine oxidase (MAO) and aldehyde dehydrogenase (ALDH), in the metabolites showed significant changes in the control and combined exposure groups. Combined exposure induced systemic dysregulation of the 5-HT synthesis-transport-degradation pathway, which exacerbated the neurotoxic effects through a multi-target interference mechanism. The levels of 5-hydroxytryptamine (5-HT), 5-hydroxyindoleacetic acid (5-HIAA), and enzymes related to 5-HT metabolism, such as monoamine oxidase (MAO) and aldehyde dehydrogenase (ALDH), in the metabolites can be used as biomarkers to evaluate the neurotoxicity of combined exposure to SCCP and BDE-209 in zebrafish.

[0041] 6. Assessment of intestinal neurotoxicity Histological evaluation and physiological and biochemical index determination of intestinal tissue were performed to systematically analyze the damage to the intestinal nervous system of zebrafish caused by combined exposure to pollutants.

[0042] Given that the gut is one of the main sites of 5-HT synthesis, the toxicological effects of combined SCCP and BDE-209 exposure on intestinal tissue and its potential role in systemic 5-HT metabolism were further evaluated. Results are as follows: Figure 5 As shown, histological analysis revealed that low-dose single exposure did not cause significant morphological changes, while high-dose treatment led to focal apoptosis and necrosis of intestinal epithelial cells. Figure 5 In contrast, combined exposure induced epithelial cell degeneration in the intestinal crypt region even at low concentrations, and at high concentrations, it showed more pronounced villus disorder. Figure 5 DE). Quantitative morphological analysis further showed that the height and width of villi were significantly reduced in all exposed groups, and the villi height / crypt depth ratio and villi height / width ratio were also significantly decreased. Figure 5F). These structural changes suggest that the integrity of the intestinal structure is impaired, which can lead to decreased absorption function and barrier dysfunction. Considering that the intestinal epithelium is the first line of defense against exogenous toxins in the lumen, its damage can promote the systemic transport of toxins. The degree of damage in the combined exposure group was much higher than in the single exposure group, suggesting a synergistic intestinal toxicity effect. To explore the underlying mechanisms, further detection of neurotoxicity and oxidative stress-related biomarkers was performed. All exposure groups showed increased acetylcholinesterase (AChE) activity ( Figure 5 G), increased malondialdehyde (MDA) content ( Figure 5 H), and decreased activities of key antioxidant enzymes such as superoxide dismutase (SOD) and catalase (CAT) ( Figure 5 I-J). These changes were most pronounced in the combined exposure group, indicating that the intestinal system experienced more intense oxidative stress and neurotoxicity damage. Given the central role of the intestine in 5-HT synthesis, its structural and functional disorders can trigger 5-HT metabolic imbalance. Further evaluation of the 5-HT metabolic pathway in combination with metabolite levels, protein expression, and enzyme activity in intestinal tissue is consistent with the fact that 5-HT degradation in intestinal tissue was significantly enhanced, and MAO enzyme activity increased by up to 12-fold in the combined exposure group ( Figure 5 K-M), which is consistent with the trend in brain tissue. Correlation analysis further showed a significant positive correlation between 5-HT degradation activity in brain and intestinal tissue ( Figure 5 N), suggesting that combined exposure can cause intestinal disturbance through the gut-brain axis pathway, thereby regulating central neurotransmitter dynamics and forming a systemic neurotoxicity mechanism.

[0043] 7. Analysis of intestinal microbial colony structure Intestinal microbes, as key regulators of host metabolism and neural function, play a central role in the 5-HT signaling pathway. To evaluate the effects of single or combined exposure on the composition of the microbiome, zebrafish intestinal samples were subjected to metagenomic sequencing analysis.

[0044] Metagenomic sequencing and analysis: Metagenomic analysis was completed by Shanghai Meiji Biotechnology Co., Ltd. (n=3). Briefly, genomic DNA was extracted using PF Mag-Bind Soil DNA Kit, and DNA purity was evaluated using Nanodrop 2000 and Quantus fluorometer, and DNA integrity was detected by agarose gel electrophoresis. The cDNA library was constructed using the NEXTFLEX Rapid DNA-Seq kit, and sequencing was performed on the NovaSeq 6000 platform (S4 kit, 300 cycles, Illumina). Metagenomic data were analyzed and processed by the Majorbio cloud platform (cloud.majorbio.com). Other statistical analyses were performed using SPSS software.

[0045] The results showed that the intestinal flora structure was disordered after single or combined exposure, and its disorder characteristics were shown in Figure 6 and Figure 7 Classification results at the phylum level showed that the main flora included Pseudomonadota, Actinomycetota, Planctomycetota, and Bacillota ( Figure 6 A). At the genus and species levels, the microbial population showed a highly dispersed composition characteristic, and the main dominant genera included Tabrizicola, Aestuariivirga, and Rhabdothermincola, among which the most enriched species were Tabrizicola rongguiensis, Aestuariivirga litoralis, and Rhabdothermincola salaria ( Figure 7 A-B). SCCP, BDE-209, and their combined exposure all caused significant changes in the microbial community structure. Although the relative abundance of the main phyla fluctuated, the overall dominant position remained stable ( Figure 6 B), suggesting a certain ecological robustness at the high classification level. However, there were obvious changes in the composition at the genus and species levels ( Figure 7 C-D), indicating that pollutant exposure might selectively reshape the intestinal microecology, thereby affecting the interaction between microorganisms and the host and related physiological functions. Alpha diversity analysis showed that the Chao index of all exposure groups increased while the Simpson index decreased ( Figure 6C-D), indicating an increase in species richness but a decrease in community evenness, which is a typical manifestation of gut microbiota dysbiosis. This imbalance might be attributed to the loss of beneficial bacteria and the invasion of environmental bacteria, which was particularly evident in the low-dose combined exposure group. Given the critical role of gut microbes in maintaining the integrity of the mucosal barrier, their compositional dysbiosis might directly lead to intestinal dysfunction. Hierarchical clustering analysis further revealed a dose-dependent trend in the compositional changes of gut microbiota ( Figure 6 E-F). The low-dose single exposure groups clustered closer to the control group, while the high-dose treatments were significantly separated. Notably, the degree of gut microbiota dysbiosis induced by the low-dose combined exposure group even exceeded that of the high-dose group, suggesting a nonlinear response feature of the intestinal microecosystem to exposure. Non-metric multidimensional scaling (NMDS) analysis further confirmed the significant changes in the structure of microbial communities (stress = 0.062; Figure 6 G), Wilcoxon rank-sum test showed that the microbial composition was significantly different among multiple exposure groups, especially in the low-dose BDE-209 group and the combined exposure group, which also exhibited higher intra-group variability (p < 0.05) Figure 6 H), indicating a significant decrease in their ecological stability. In summary, the combined exposure of SCCP and BDE-209 significantly disturbed the gut microbiota of zebrafish, particularly at the genus and species levels. These changes might disrupt microbial homeostasis and functional stability, interfere with the 5-HT metabolic pathway, and amplify neurotoxic effects through the gut-brain axis signaling. Most importantly, the significant effects under low-dose exposure suggest that the potential health risks of low-level, long-term combined pollution in the environment should be highly concerned.

[0046] Figure 8 demonstrated the key role of intestinal microbes at the phylum level in combined toxicity. A total of 138 bacterial phyla were identified by metagenomic sequencing, and the overall phylum-level structure remained relatively stable among the treatment groups ( Figure 8 A). Although the composition at the phylum level seemed conservative, ROC curve analysis showed that the microbial changes induced by low-dose combined exposure could significantly distinguish the treatment groups from the control group (AUC = 1), while the discriminative ability of high-dose exposure was weaker (AUC = 0.67) ( Figure 8 B-C), suggesting that the intestinal microecosystem might produce a more selective response at low concentrations. Linear discriminant analysis effect size (LEfSe) was further used to screen the key differentially abundant taxa (LDA > 1.5, p < 0.05) Figure 8D). Among them, Pseudomonadota was significantly enriched in the control group, which might play an important role in maintaining the basic microbial homeostasis; while Bacillota and Actinomycetota were significantly enriched in the low-dose and high-dose combined exposure groups, respectively, which might be involved in environmental adaptation and exogenous substance metabolism process. The changes of Pseudomonadota showed a clear dose-dependent bidirectional trend: its abundance increased by 123% under low-dose combined exposure, while decreased by 81% under high-dose exposure Figure 8 E). This phylum has strong metabolic capacity and is one of the sensitive indicators of ecological disturbance. Its abundance changes are associated with intestinal inflammation and neurodegenerative diseases (such as Parkinson's disease), and may mediate chronic inflammation through the Toll-like receptor 5 (TLR5) pathway. The increase in the abundance of Bacillota under low-dose combined exposure was particularly significant, reaching 444% Figure 8 H). This phylum contains a variety of anaerobic probiotics, which are involved in amino acid fermentation, immune regulation, and neural regulation. The imbalance of Bacillota is closely related to the occurrence of major depressive disorder and Parkinson's disease, and may play a role through immune activation and gut-brain axis disturbance. Actinomycetota also significantly increased in all exposure groups, with the largest increase (227%) in the high-dose combined exposure group Figure 8 K). This change trend is consistent with the results of microecological disturbance caused by nano-plastic, triclosan, and benzalkonium chloride and other pollutants. Further ROC analysis verified the classification discrimination ability of the above three phyla in the combined exposure group Figure 8 E-M). Pseudomonadota and Actinomycetota showed good discrimination performance under different dose conditions, while Bacillota had the most prominent discriminant effect under low-dose exposure Figure 8 H-J). Correlation network analysis showed that Bacillota had a positive regulatory effect on multiple bacterial groups (p<0.05), and the correlation coefficient between Bacillota and Pseudomonadota was as high as 0.71 Figure 8 N), suggesting that the interaction between bacteria may play a synergistic role in the adaptation of the intestinal flora to environmental stress. In summary, the combined exposure of SCCP and BDE-209 induced the reconstruction of the composition and function of intestinal flora, enhancing its ability to recognize, metabolize, and respond to environmental pollutants. This microecological remodeling may be an adaptive response of the host to complex environmental stress, and may also be involved in the gut-brain axis mechanism that amplifies neurotoxicity.

[0047] 8. Regulatory mechanisms of 5-HT metabolic metabolism by microbial colony structure and related analysis of 5-HT metabolism mediated by gut-brain axis Combined with functional annotation analysis, the regulatory mechanism of characteristic bacteria on 5-HT metabolic pathway under combined exposure was explored, and the correlation network between characteristic intestinal flora and host 5-HT metabolism was constructed to further analyze the mechanism of intestinal microbiota regulating 5-HT metabolism mediated by combined toxicity of pollutants. Figures 9-11 The mechanism of intestinal microorganisms regulating 5-HT metabolism was demonstrated. The results of metagenomic functional annotation showed that intestinal flora played a key role in regulating multiple tryptophan metabolic pathways, thereby participating in the synthesis, transport and degradation of 5-HT. Figure 9 A). Notably, combined exposure significantly activated the kynurenine metabolic pathway, while inhibiting other metabolic branches, thereby reducing the availability of 5-HT precursors. At the same time, the expression of 5-HT degradation-related genes (such as ALDH and MAO) was significantly up-regulated, and the expression of transporters (VMAT, SERT) and receptors (HTR1, HTR2, HTR7) was also significantly enhanced, suggesting that combined exposure disrupts 5-HT homeostasis through multiple pathways and synergistic mechanisms, which may affect host neural function. To identify key microbial contributors, the roles of the three dominant bacterial phyla, Actinomycetota, Bacillota and Pseudomonadota, in ALDH and MAO functional annotation were further analyzed. Figure 9 B). In the control group, these three types of bacterial phyla collectively contributed more than 80% of the relevant functional annotation capacity, among which the metabolic capacity of Actinomycetota and Pseudomonadota was the most significant. After combined exposure, the functional contribution of Actinomycetota increased significantly, while that of Pseudomonadota decreased significantly, which was consistent with the trend of abundance change at the phylum level. Figure 9 C). Although Bacillota had a low abundance, it was extremely sensitive to combined exposure, with a functional change fold (FC) of up to 30. Correlation analysis further confirmed the significant positive correlation between microbial abundance and its corresponding functional annotation level, especially the correlation coefficient between Actinomycetota and ALDH, MAO activity was the highest (|correlation coefficient|>0.5, p<0.05; Figure 9 D), indicating that it played a core regulatory role in 5-HT degradation. At the genus level, Mycolicibacterium (belonging to the phylum Actinomycetota) was identified as a key biomarker under combined exposure, with an abundance increase of up to 46 times. Figure 10 B), and showed strong classification discrimination ability in ROC analysis. Figure 10CD). Two species—M. vinylchloridicum and M. rhodesiae—were identified as major responders, showing increases of 30,433-fold and 4,052-fold, respectively. Figure 10 F and I), and has an extremely high AUC value ( Figure 10 GH and JK) indicate that they play an important role in the combined toxicity response. Functional annotation results show that these two species are significantly involved in the upregulation of 5-HT degrading enzymes (MAO and ALDH) expression after combined exposure. Figure 11 It is noteworthy that *M. vinylchloridicum* has previously been shown to degrade vinyl chloride. Considering its similarity to SCCP and BDE-209 in terms of halogenated structure and chemical stability, it is speculated that this species may also be involved in the biodegradation of these two pollutants. Furthermore, its enrichment under combined exposure conditions not only suggests its important role in exogenous substance metabolism but also indicates that it may participate in pollutant-induced neurotoxic responses by mediating gut-brain axis dysfunction through activation of the 5-HT degradation pathway.

[0048] To elucidate the systemic role of gut microbiota in the regulation of serotonin (5-HT) metabolism, this study integrated multi-omics data from brain tissue, gut samples, and the microbiome to construct a network linking microbial composition and host 5-HT metabolic activity. The results are as follows: Figure 12 As shown in the figure. Analysis results showed that under combined SCCP and BDE-209 exposure conditions, the key microbial groups phylum Actinomycetota, genus Mycolicibacterium, species M. vinylchloridicum, and species M. rhodesiae Pseudomonadota were significantly positively correlated with the expression levels of 5-HT and its degrading enzymes (ALDH, MAO) in intestinal and brain tissues (|correlation coefficient|>0.8, p<0.05). Figure 12). The above results show that the specific gut microbiota Actinomycetota phylum, Mycolicibacterium genus, M. vinylchloridicum and M. rhodesiae Pseudomonadota species as the core microorganisms regulating the degradation pathway of 5-HT, mediate neurotransmitter signal transduction in the gut-brain axis, further highlighting the core role of microbial flora in maintaining 5-HT metabolic homeostasis, and suggesting that microbial-driven 5-HT imbalance may be one of the key mechanisms of neurotoxicity induced by combined exposure. The above Actinomycetota phylum, Mycolicibacterium genus, M. vinylchloridicum and M. rhodesiae Pseudomonadota and 5-hydroxytryptamine (5-HT), 5-hydroxyindoleacetic acid (5-HIAA) in the metabolites in the 5-hydroxytryptamine metabolic pathway, and the abundance, content or enzyme activity of enzymes related to 5-HT metabolism, such as monoamine oxidase (MAO) and acetaldehyde dehydrogenase (ALDH), changed significantly under the combined exposure of persistent organic pollutants SCCP and BDE-209, and can be used as markers for evaluating the neurotoxicity of zebrafish under the combined exposure of SCCP and BDE-209.

[0049] 9. Neurotoxicity evaluation based on gut-brain axis-mediated neurotoxicity The metabolic effect level index (MELI) was used to evaluate the neurotoxicity of pollutant exposure.

[0050] The MELI value was used to evaluate the overall metabolic impact under single or combined exposure conditions. First, the metabolic change value (MCi) of each biological component was calculated based on formula 1, and then the change values of each biological component were summed and divided by the total number of metabolites to obtain the MELI value of the sample (formula 2).

[0051] Formula 1 Formula 2 Where MCi is the metabolic change of a biological component (i) in the pollutant exposure individual, Ai is the relative abundance ratio of a biological component (i) in the pollutant exposure individual relative to the average level of the control group, ln(1) is used to eliminate the influence of the control group metabolic level, and n is the total number of metabolites analyzed.

[0052] The pollutant exposure is single pollutant exposure or combined pollutant exposure.

[0053] Under the condition of SCCP combined with BDE-209 exposure, the relevant metabolites (including 5-hydroxytryptamine (5-HT), 5-hydroxyindoleacetic acid (5-HIAA)), relevant enzymes (monoamine oxidase (MAO), acetaldehyde dehydrogenase (ALDH)), and relevant intestinal microorganisms (Actinomycetota phylum, Mycolicibacterium genus, M.vinylchloridicum and M.rhodesiae species) are taken as input variables to calculate the MELI value, and based on the regulation of intestinal flora on the 5-HT degradation pathway, the toxic effects of SCCP, BDE-209 and their combined exposure on the nervous system of zebrafish are evaluated from the perspective of gut-brain axis mechanism. Then, the independent action model (IA model) is used to evaluate the joint toxicity effect of SCCP and BDE-209 (formula 3): Formula 3 Wherein, cmix represents the total concentration of mixed pollutants, E(cmix) represents the joint effect thereof, E(ci) represents the MELI value of the i th pollutant component alone, and E(ci) is limited to the range of 0-1. In order to make the MELI value fall within the appropriate range, extreme value normalization processing is required.

[0054] Finally, the predicted MELI value E(cmix) of the joint effect is compared with the actually measured MELI value of the mixed pollutants: if they are the same, the actual value is higher or lower than the predicted value, it is judged as additive effect, synergistic effect or antagonistic effect respectively. The results are shown in Table 1.

[0055]

[0056] Table 1 shows the evaluation results of the joint toxicity effect of the gut-brain axis 5-HT metabolism under the combined exposure of SCCP and BDE-209 based on the independent action model and MELI index. The MELI summary results show that the joint exposure has the strongest response to the neurotoxicity effect mediated by the intestinal flora regulating the 5-HT degradation pathway, followed by the SCCP alone exposure, and the effect of BDE-209 alone exposure is the weakest. The independent action model (IA model) evaluation results show that under low and high doses, the joint exposure shows synergistic joint toxicity effect in the gut-brain axis mechanism, revealing the joint toxicity mechanism of SCCP and BDE-209 based on the gut-brain axis.

[0057] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for assessing the neurotoxicity of pollutants based on gut-brain axis signals, characterized in that, include: (a) Collect brain and intestinal tissues from animals exposed to contaminants; (b) Transcriptomic analysis of brain tissue, combined with the determination of 5-hydroxytryptamine metabolic pathway in brain and intestinal tissues, to screen for targets that play a key role in interfering with 5-hydroxytryptamine metabolism under pollutant exposure conditions. (c) Based on metagenomics technology, analyze the impact of pollutant exposure on gut microbiota colony structure and screen characteristic microorganisms in the gut under pollutant exposure conditions; (d) Construct a network linking the characteristic microorganisms with the 5-hydroxytryptamine metabolic pathway, and further screen for core microorganisms that are highly correlated with interference of the 5-hydroxytryptamine metabolic pathway under pollutant exposure conditions; (e) The neurotoxicity of pollutants was evaluated based on gut-brain axis signals by using the targets that have key interference effects on 5-hydroxytryptamine metabolism under the screened pollutant exposure conditions and the core microorganisms that are highly correlated with 5-hydroxytryptamine metabolism interference as biomarkers.

2. The method for assessing the neurotoxicity of pollutants based on gut-brain axis signals according to claim 1, characterized in that, The pollutant in step (a) is a single pollutant or a combination of at least two pollutants.

3. The method for assessing the neurotoxicity of pollutants based on gut-brain axis signals according to claim 1, characterized in that, Step (b) specifically involves: determining the content of metabolites in the 5-hydroxytryptamine metabolic pathway, screening out metabolites with significant changes, and combining transcriptomic data with enzyme expression levels and activities to screen out enzymes with significant changes in the 5-hydroxytryptamine metabolic pathway. The significantly changed metabolites and enzymes constitute key interference targets for 5-hydroxytryptamine metabolism under pollutant exposure conditions.

4. The method for assessing the neurotoxicity of pollutants based on gut-brain axis signals according to claim 3, characterized in that, Metabolites in the serotonin metabolic pathway include serotonin, tryptophan, and 5-hydroxyindoleacetic acid.

5. The method for assessing the neurotoxicity of pollutants based on gut-brain axis signals according to claim 1, characterized in that, In step (c), characteristic microorganisms that show significant differences in intestinal tissue under pollutant exposure conditions and without pollutant exposure conditions are obtained at the phylum, genus and species levels, respectively.

6. The method for assessing the neurotoxicity of pollutants based on gut-brain axis signals according to claim 1, characterized in that, The absolute value of the correlation coefficient between the abundance of the core microorganisms and the expression level of the key interference targets in step (d) is greater than 0.8, and p < 0.

05.

7. The method for assessing the neurotoxicity of pollutants based on gut-brain axis signals according to any one of claims 1 to 6, characterized in that, In step (e), the Metabolic Effect Level Index (MELI) is used to evaluate the neurotoxicity of the pollutants.

8. The method for assessing the neurotoxicity of pollutants based on gut-brain axis signals according to claim 7, characterized in that, The MELI value is calculated using the following formula. Formula 1 Formula 2 Where Ai is the relative abundance ratio of a certain biological component in the pollutant-exposed individual to the average level of the control group, ln(1) is used to eliminate the influence of the metabolic level of the control group, and n is the total number of biological components analyzed, including targets that have a key interfering effect on 5-hydroxytryptamine metabolism and core microorganisms that are highly correlated with 5-hydroxytryptamine metabolic interference.

9. The method for assessing the neurotoxicity of pollutants based on gut-brain axis signals according to claim 8, characterized in that, When at least two pollutants are present, the combined toxic effects of the at least two pollutants are assessed using an independent action model.

10. The method for assessing the neurotoxicity of pollutants based on gut-brain axis signals according to claim 9, characterized in that, The independent action model is as follows: Formula 3 cmix represents the total concentration of the mixed pollutants, E(cmix) represents the predicted MELI value of the combined effect, and E(ci) represents the MELI value of the i-th pollutant alone. Extreme value normalization is used to limit the range of E(ci) to 0~1. The predicted MELI value of the combined effect is compared with the actual MELI value of the combined effect. If the two are the same, or the actual value is higher or lower than the predicted value, it is determined to be an additive effect, a synergistic effect, or an antagonistic effect, respectively.

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