A method for detecting the multidimensional toxicity of the gut-metabolic axis in a subchronic exposure model of diamide insecticides.

Abstract

This invention discloses a multi-dimensional toxicity detection method for the gut-metabolic axis in a subchronic exposure model of diamide insecticides, belonging to the field of pesticide toxicology detection technology. Addressing the shortcomings of existing technologies, such as the lack of systematic assessment methods for the gut-metabolic axis, limited detection dimensions, and insufficient sensitivity, this method includes: subjecting test animals to 28 days of subchronic exposure; collecting blood samples 24 hours after the last exposure; and simultaneously collecting intestinal contents, intestinal tissue, and liver tissue samples within 30 minutes of sacrifice; after classification and processing, simultaneously conducting detections on four dimensions: gut microbiota, intestinal barrier, gut-metabolic axis association, and liver metabolism; and outputting all data in a unified structured format. This invention achieves early and accurate assessment of gut-metabolic axis toxicity through standardized simultaneous sampling, strict time control, and multi-dimensional joint detection, and is suitable for the toxicological evaluation and risk screening of diamide insecticides.

Description

Technical Field

[0001] This invention belongs to the field of pesticide toxicology detection technology, specifically relating to a method for detecting the multidimensional toxicity of diamide insecticides along the gut-metabolic axis. Background Technology

[0002] Diamide insecticides are a new class of insecticides that act on the ryanodine receptor (RyR) in insects. They mainly include chlorantraniliprole, cypermethrin, broflanilide, and tetrazolium. These insecticides are widely used in agricultural production due to their high efficacy and low acute mammalian toxicity. Oral exposure to pesticides is the most common route of exposure for humans and non-target organisms. The gastrointestinal tract is the first site of contact with exogenous chemicals and the primary target organ. It is not only the first line of defense against exogenous toxins, but also the core site for intestinal flora colonization, metabolic transformation, and immune regulation. The "gut-metabolism axis" refers to the regulatory network formed by the gut microbiota through metabolites (such as short-chain fatty acids, bile acids, and tryptophan metabolites) and the host's intestinal barrier and liver metabolic function. Disruption of this axis has become a cutting-edge area of ​​research in pesticide non-target toxicity. Current pesticide risk assessment systems primarily rely on conventional indicators such as acute toxicity tests, blood biochemistry, and histopathological examinations. These typically reflect end-organ damage or systemic stress responses to toxins, and their detection windows are relatively delayed. In contrast, the gut-metabolic axis, as the first interface of action for exogenous chemicals after oral exposure, responds earlier than traditional endpoint indicators: changes in gut microbiota structure can occur within days of exposure; downregulation of tight junction protein expression precedes visible damage in histopathology; and fluctuations in serum bile acids and tryptophan metabolites can indicate early disturbances in liver metabolic function. Therefore, gut-metabolic axis detection has early warning value and can capture subclinical toxic effects missed by traditional assessment systems. Although diamide insecticides are classified as compounds with low acute toxicity, they generally exhibit high lipid solubility (logP>2.5) and low water solubility, readily distributing and accumulating in the intestinal lipid environment, thus prolonging local intestinal exposure time. For example, chlorantraniliprole (logP 2.86) and brofenoxuron-methyl (logP 5.2) show significantly higher lipid solubility than their water-soluble counterparts. Previous studies have shown that chlorantraniliprole, under subchronic exposure conditions, can cause a decrease in gut microbiota diversity, increased intestinal barrier permeability, and altered liver metabolic function. However, these effects have not been considered in the traditional NOAEL / LOAEL assessment framework, potentially leading to an underestimation of the intestinal-metabolic toxicity risk to non-target organisms in risk assessments. In existing technologies, 16S rRNA sequencing, Western blot, gas chromatography-mass spectrometry (GC-MS), and liquid chromatography-tandem mass spectrometry (LC-MS) are all mature detection techniques. However, these techniques are applied independently to different research scenarios, and there is a lack of unified standards for test animals, exposure protocols, sampling time points, sample processing procedures, and data formats, making it impossible to directly correlate and compare detection results. In particular, in the 28-day subchronic exposure model of diamide insecticides, standardized technical solutions have not yet been established for the simultaneous collection and timeliness control of multiple sample types, classification and processing procedures, and structured data output. Therefore, there is an urgent need to establish a standardized detection method for the multidimensional toxicity of the gut-metabolic axis in a subchronic exposure model of diamide insecticides, in order to fill the gap in existing technologies. Summary of the Invention

[0003] The purpose of this invention is to provide a method for detecting the multidimensional toxicity of diamide insecticides along the gut-metabolic axis. By standardizing exposure protocols, synchronizing sample collection, classifying and processing procedures, and outputting structured data, the method enables the correlated acquisition of multidimensional toxicity detection data along the gut-metabolic axis. To achieve the above objectives, the present invention adopts the following technical solution: A method for detecting the multidimensional toxicity of the gut-metabolic axis in a subchronic exposure model of diamide insecticides includes the following steps: (1) Animal exposure treatment: The test animals were orally exposed to the test diamide insecticide for 28 consecutive days. A solvent control group and at least two dose groups were set up. (2) Sample collection: Blood samples were collected 24 hours after the last exposure; the animals were then euthanized, and intestinal contents, intestinal tissue and liver tissue samples were collected simultaneously within 30 minutes after euthanasia. (3) Sample classification and processing: Intestinal contents samples: collected aseptically and flash-frozen in liquid nitrogen, stored at -80°C, for microbial DNA extraction and short-chain fatty acid extraction; Blood samples: Centrifuge to separate serum / plasma, store at -80℃, for the detection of endotoxin, D-lactic acid and tryptophan metabolites; Intestinal tissue samples: divided into three parts. The first part was fixed in 4% paraformaldehyde for morphological examination. The second part was flash-frozen in liquid nitrogen and stored at -80°C for protein extraction. The third part was placed in pre-cooled Krebs-Ringer buffer for transepithelial electrical resistance measurement. Liver tissue samples were divided into two parts. One part was fixed in 4% paraformaldehyde for pathological sections, and the other part was flash-frozen in liquid nitrogen and stored at -80°C for metabolite extraction. (4) Simultaneous detection of multi-dimensional indicators: First dimension: Gut microbiota testing Total microbial DNA was extracted from intestinal contents samples, and 16S rRNA gene V3-V4 region sequencing was used to obtain data on microbial diversity, relative abundance of key genera, and Firmicutes / Bacteroidetes ratio. The content of short-chain fatty acids in intestinal contents was determined by gas chromatography-mass spectrometry to obtain the concentration data of acetic acid, propionic acid and butyric acid. Second dimension: Intestinal barrier detection Total protein was extracted from frozen intestinal tissue samples, and the expression of tight junction proteins was detected by Western blot to obtain the expression data of ZO-1, Occludin, and Claudin-1 proteins. Endotoxin concentrations were determined from plasma samples using the Limulus amebocyte lysate (LAL) assay to obtain endotoxin data. D-lactic acid concentration was determined from plasma samples using enzymatic or enzyme-linked immunosorbent assay (ELISA) methods to obtain intestinal permeability data. Fresh intestinal tissue samples were collected and, using the Using Chamber system, the transepithelial resistance (TEER) value was measured after equilibration at 37°C and 1-2 mV / cm voltage for 20-30 minutes to obtain intestinal epithelial ion permeability data. Third dimension: Gut-metabolic axis correlation detection Liquid chromatography-tandem mass spectrometry was used to determine the products of the tryptophan metabolic pathway in serum samples, and the concentration data of kynurenine, 5-hydroxytryptamine, and indole-3-propionic acid were obtained. Inflammatory factors were measured in serum samples using enzyme-linked immunosorbent assay (ELISA) to obtain data on the concentrations of interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α). Fourth dimension: Liver metabolic testing Metabolites were extracted from frozen liver tissue samples, and bile acid profiles were determined by liquid chromatography-tandem mass spectrometry to obtain total bile acid and subtype concentration data. Pathological sections were prepared from fixed liver tissue samples, and H&E staining and hepatic steatosis scoring were performed to obtain liver tissue morphology and pathological scoring data. (5) Structured data output: Output the multi-dimensional detection data obtained in step (4) in a unified format, which includes sample identification field, exposure condition field, detection dimension field, indicator name field and original detection value field. Preferably, the test animal in step (1) is a mouse. Preferably, the diamide insecticide in step (1) is chlorantraniliprole, bromonitrile cyanamide, bromonitrile diamide or tetrazolium amide, etc. Preferably, the dosage group in step (1) includes a low-dose group, a medium-dose group and a high-dose group, and the dosage setting is based on the acute toxicity data and subchronic toxicity reference values ​​of the test substance. Preferably, the intestinal contents sample in step (2) is cecal contents. Preferably, the intestinal tissue in step (2) is the duodenum, jejunum, ileum or colon. Preferably, the Krebs-Ringer buffer solution in step (3) consists of: NaCl 118 mM, KCl 4.7 mM, CaCl2 2.5 mM, MgSO4 1.2 mM, NaHCO3 25 mM, KH2PO4 1.2 mM, glucose 11 mM, and pH 7.4. Preferably, the key bacterial genera mentioned in the first dimension of step (4) include Akkermansia, Lactobacillus, and Bacteroides. Preferably, the 16S rRNA gene sequencing in the first dimension of step (4) is performed by PCR amplification using the 341F / 806R primer pair. Preferably, the tight junction proteins in the second dimension of step (4) include ZO-1, Occludin and / or Claudin-1. Preferably, when the Using Chamber system in the second dimension of step (4) measures the TEER value, a chopstick electrode or an automatic electrode is used, and the resistance value is expressed in Ω·cm². Preferably, the tryptophan metabolic pathway products in the third dimension of step (4) include kynurenine, 5-hydroxytryptamine and indole-3-propionic acid. Preferably, the bile acids in the fourth dimension of step (4) include at least five subtypes of cholic acid, deoxycholic acid, chenodeoxycholic acid, taurocholic acid and glycocholic acid. Attached Figure Description Figure 1Flowchart of a standardized detection method for multidimensional toxicity of the gut-metabolical axis in a subchronic exposure model of diamide insecticides Figure 2 Diagram of field structure in structured data format Detailed Implementation The following embodiments are used to illustrate the technical solutions of the present invention and do not constitute a limitation on the scope of protection of the present invention. Example 1: Multidimensional detection method of gut-metabolic axis in patients with 28-day subchronic exposure to chlorantraniliprole (1) Animal exposure treatment: SPF grade C57BL / 6J male mice, 6-8 weeks old, were randomly divided into solvent control group, low-dose group, medium-dose group and high-dose group after 7 days of acclimatization, with 12 mice in each group. Chlorantraniliprole was used as the test substance, and the dose gradient was set according to its 28-day subchronic NOAEL and intestinal damage effect dose. The mice were continuously exposed by oral gavage for 28 days, once a day, with a gavage volume of 10 mL / kg. (2) Sample collection: Blood was collected 24 hours after the last exposure, and serum and plasma were separated; cecal contents, jejunal tissue and liver tissue were collected simultaneously within 30 minutes after sacrifice, and frozen, fixed or placed in buffer solution for later use. (3) Sample classification and processing: Process each type of sample according to the method described in step (3) of the invention. (4) Multi-dimensional indicator detection: The first dimension – gut microbiota detection: total microbial DNA was extracted from the contents of the cecum, and the V3-V4 region of the 16S rRNA gene was amplified and sequenced using 341F / 806R primers. The Shannon diversity index, relative abundance of key genera, and Firmicutes / Bacteroidetes ratio were analyzed. The contents of acetic acid, propionic acid, and butyric acid were detected by GC-MS. The second dimension – intestinal barrier detection: Western blot detection of ZO-1, Occludin, and Claudin-1 protein expression in jejunal tissue (β-actin as internal control); detection of plasma endotoxin using the Limulus amebocyte lysate (LAL) reagent method; detection of plasma D-lactic acid using enzymatic or ELISA methods; and determination of transepithelial electrical resistance (TEER) values ​​using the Ussing Chamber system. The third dimension – detection of gut-metabolism axis correlation: LC-MS / MS detection of serum tryptophan metabolites (kynurenine, 5-hydroxytryptamine, indole-3-propionic acid); ELISA detection of IL-6 and TNF-α concentrations. The fourth dimension – liver metabolism detection: LC-MS / MS detection of bile acid profiles in liver tissue; H&E staining followed by morphological observation and steatosis scoring of liver tissue. (5) Structured data output: Output in the unified format described in step (5) of the invention content. Example 2: Multidimensional detection method of gut-metabolic axis in patients with 28-day subchronic exposure to bromofenac. The test substance in Example 1 was replaced with bromfenacin, while the experimental animals, grouping, dosing regimen, sample collection, classification and processing, multi-dimensional index detection methods and structured data output remained the same as in Example 1. Beneficial effects The multidimensional toxicity detection method provided by this invention has the following beneficial effects: (1) By limiting the exposure period to 28 days and collecting blood samples 24 hours after the last exposure, and collecting multiple types of samples simultaneously within 30 minutes after sacrifice, a standardized sampling time control process was established, which reduced the interference of sample degradation on the test results and ensured the reliability of multi-dimensional test data. (2) By introducing TEER assay, D-lactic acid detection and tryptophan metabolism pathway product detection, the correlation detection dimension of gut-metabolism axis was strengthened, and the intestinal barrier assessment at the structural, functional and molecular levels was realized, providing a technical basis for revealing the linkage mechanism of microbiota-barrier-metabolism-inflammation. (3) Through the structured data output format, the systematic storage of four types of detection data, namely gut microbiota, gut barrier, gut-metabolism axis association and liver metabolism, was realized, providing a technical basis for subsequent data association analysis that can be directly called; (4) It is applicable to a variety of diamide insecticides and has good method versatility.

Claims

1. A method for detecting the multidimensional toxicity of the gut-metabolic axis in a subchronic exposure model of diamide insecticides. Its features are, include: (1) The test animals were orally exposed to the test diamide insecticide for 28 consecutive days; (2) Collect blood samples 24 hours after the last exposure; The animal was then euthanized, and intestinal contents, intestinal tissue, and liver tissue samples were collected simultaneously within 30 minutes of euthanasia. (3) Classify and process intestinal contents samples, blood samples, intestinal tissue samples and liver tissue samples; (4) Conduct multi-dimensional indicator testing simultaneously: First dimension: Microbial DNA was extracted from intestinal contents samples for 16S rRNA sequencing and short-chain fatty acid determination; The second dimension: Proteins were extracted from intestinal tissue samples for tight junction protein detection, endotoxin and D-lactic acid concentrations were measured from blood samples, and transepithelial resistance values ​​were measured from fresh intestinal tissue samples. The third dimension: measuring tryptophan metabolism pathway products and inflammatory factors from blood samples; The fourth dimension: extracting metabolites from liver tissue samples to determine bile acid profiles, and obtaining morphological data from fixed liver tissue samples to score hepatic steatosis. (5) Output the multi-dimensional detection data in a unified format.

2. The method according to claim 1, characterized in that, The test animal mentioned in step (1) is a mouse.

3. The method according to claim 1, characterized in that, The diamide insecticide mentioned in step (1) is chlorantraniliprole, bromonitrile cyanidin, bromonitrile diamide or tetrazolium amide.

4. The method according to claim 1, characterized in that, The intestinal contents sample mentioned in step (2) is cecal contents.

5. The method according to claim 1, characterized in that, The intestinal tissue sample in step (3) was divided into three parts. The first part was fixed in 4% paraformaldehyde, the second part was flash-frozen in liquid nitrogen and stored at -80°C, and the third part was placed in pre-cooled Krebs-Ringer buffer for transepithelial resistance measurement. The liver tissue sample was divided into two parts. One part was fixed in 4% paraformaldehyde, and the other part was flash-frozen in liquid nitrogen and stored at -80°C.

6. The method according to claim 1, characterized in that, Step (4) The 16S rRNA sequencing in the first dimension targets the V3-V4 variable region, and the key bacterial genera include Akkermania, Lactobacillus and Bacteroides; and obtain Firmicutes / Bacteroides ratio data.

7. The method according to claim 1, characterized in that, The tight junction proteins mentioned in the second dimension of step (4) include ZO-1, Occludin and / or Claudin-1; the transepithelial resistance measurement is performed using the Ussing Chamber system, and is measured after equilibration for 20-30 minutes at 37°C and 1-2 mV / cm voltage.

8. The method according to claim 1, characterized in that, The tryptophan metabolism pathway products mentioned in step (4) third dimension include kynurenine, 5-hydroxytryptamine and indole-3-propionic acid; the inflammatory factors include interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α).

9. The method according to claim 1, characterized in that, The bile acids mentioned in step (4) in the fourth dimension include at least five subtypes of cholic acid, deoxycholic acid, chenodeoxycholic acid, taurocholic acid and glycocholic acid.

10. The method according to claim 1, characterized in that, The unified format mentioned in step (5) includes the sample identifier field, exposure condition field, detection dimension field, indicator name field, and original detection value field.

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