A multi-omics-based approach to analyze the relationship between microbiota-gut-brain axis signaling and zebrafish neurobehavior

Abstract

The present invention provides a method for analyzing the relationship between microbiome-gut-brain axis signals and zebrafish neurobehavior based on multi-omics, and relates to the field of bioinformatics analysis technology. The method of the present invention constructs a zebrafish model of mental illness by inducing antibiotics, analyzes the changes in the microbial community structure, species composition, inter-group difference significance, and species differences of the intestinal tissue of the zebrafish model of mental illness to obtain intestinal microbial data, and screens out metabolomics and lipidomics biomarkers of zebrafish brain tissue of mental illness by performing non-targeted metabolomics and / or non-targeted lipidomics analysis on zebrafish brain tissue; and then analyzes the relationship between microbiome-gut-brain axis signals and zebrafish neurobehavior. This method can efficiently obtain the microbiome information, metabolomics and lipidomics information between the zebrafish model of mental illness and the normal control, providing an accurate and comprehensive analysis idea for the study of intestinal microbiome and neurobehavioral diseases.

Description

Technical Field

[0001] The present invention relates to the field of bioinformatics analysis technology, and in particular to a method for analyzing the relationship between microbiota-gut-brain axis signals and zebrafish neurobehavior based on multi-omics. Background Art

[0002] The Microbiome-Gut-Brain Axis (MGBA) is primarily composed of the gut microbiota located in the intestinal lumen, intestinal cells (including enterocytes, enteroendocrine cells (EECs), goblet cells), and neurons and glial cells in the central nervous system. The gut microbiota uses multiple communication channels to influence brain physiology, cognition, and behavior. The most prominent of these pathways include: (1) direct mediation between the brainstem and the gut through the vagus nerve; (2) mediation through metabolites and cytokines produced by members of the gut microbiota; and (3) mediation by metabolites and cytokines released by immune cells and their activated intestinal cells. Neurophysiological and behavioral processes affected by the state of the gut microbiota include neurogenesis, synaptic plasticity, neurotransmitter signaling, neuronal morphology, neuroinflammation, neurodevelopment, and neurochemistry in the hippocampus, amygdala, prefrontal cortex, and hypothalamus. Gut microbiota dysbiosis has been linked to neurobehavioral disorders such as neurodevelopmental disorders (autism spectrum disorder (ASD) and schizophrenia), neurodegenerative diseases (Alzheimer's disease (AD), Parkinson's disease (PD), and multiple sclerosis (MS), and mood disorders (depression and anxiety).

[0003] In recent years, an increasing number of studies have linked the microbiota to the structure and function of the nervous system, with a focus on discovering and elucidating the role of microbial-produced / induced metabolites in the brain in terms of cognitive abilities and psychological effects on the host. Currently, most of these studies have focused on mammalian models due to their close relationship with humans. However, studies of the microbiome-gut-brain axis using multi-omics techniques have also gradually developed in simpler neurogenetic model organisms (zebrafish, Drosophila melanogaster, and Caenorhabditis elegans).

[0004] Zebrafish have the characteristics of rapid in vitro development, high reproductive yield, good optical transparency, etc. At the same time, zebrafish have a series of behavioral characteristics similar to humans, including social, cognition, sleep, stress and anxiety, which make zebrafish a major advantageous model organism for exploring the interaction mechanism between the microbiota and the host nervous system.

[0005] In view of this, the present invention proposes a method for analyzing the relationship between microbiota-gut-brain axis signals and zebrafish neurobehavior based on multi-omics. Summary of the invention

[0006] The purpose of the present invention is to propose a method for analyzing the relationship between microbiota-gut-brain axis signals and zebrafish neurobehavior based on multi-omics. The present invention is based on multi-omics information analysis, especially combining the change data of the intestinal microbial spectrum of intestinal tissue with the non-targeted metabolomics and / or non-targeted lipidomics information of brain tissue, to establish a multi-level and multi-angle method for analyzing the effects of microbiota-gut-brain axis signal molecules on zebrafish behavior.

[0007] The technical solution provided by the present invention is as follows:

[0008] The present invention provides a method for analyzing the relationship between microbiota-gut-brain axis signals and zebrafish neurobehavior based on multi-omics, characterized in that it comprises:

[0009] (a) Construction of a zebrafish model of psychiatric disease by antibiotic induction;

[0010] (b) Collecting zebrafish brain tissue and intestinal tissue;

[0011] (c) evaluating the zebrafish model of mental illness by histopathological section analysis, transmission electron microscopy ultrastructural analysis, and neurological disease-related gene analysis;

[0012] (d) analyzing the changes in the microbial community structure, species composition, inter-group difference significance, and species difference of the intestinal tissue of the zebrafish model of mental illness to obtain intestinal microbial data;

[0013] (e) Screening metabolomics and / or lipidomics biomarkers of zebrafish brain tissues for psychiatric diseases by performing non-targeted metabolomics and / or non-targeted lipidomics analysis on zebrafish brain tissues;

[0014] (f) analyzing the relationship between biota-gut-brain axis signaling and zebrafish neurobehavior by combining the gut microbiome data with the metabolomics and / or lipidomics biomarkers.

[0015] In the present invention, antibiotics are induced in normal adult zebrafish so that the zebrafish exhibit behavioral characteristics of certain types of mental illness, thereby establishing a zebrafish model of mental illness.

[0016] In the present invention, 3-month-old adult wild-type AB zebrafish are usually selected for antibiotic induction and exposure.

[0017] In one embodiment, in step (a), the zebrafish model of mental illness is constructed by inducing ciprofloxacin on adult zebrafish, and the type of mental illness in the zebrafish is determined by animal behavior testing;

[0018] Preferably, constructing the zebrafish model of mental illness comprises exposing the adult zebrafish to water containing ciprofloxacin and acclimating the adult zebrafish for 28 days, wherein the concentration of ciprofloxacin is 100 μg / L-1000 μg / L.

[0019] In one embodiment, the type of mental illness of the zebrafish is determined by animal behavior testing. For example, the behaviors of zebrafish due to anxiety, such as tactility (preferring the edge / corner of the fish tank), immobility or unstable movement (sudden rapid swimming accompanied by high-speed continuous turning), reduced swimming distance, etc., can be used to measure the degree of anxiety in zebrafish and to evaluate the establishment of anxiety disease models.

[0020] Furthermore, compared with wild-type control animals, the animal model of mental illness may have one or more characteristics selected from the following group: increased anxiety-like behavior, increased anxiety level, increased depression-like behavior, increased depression level, etc. The mental illness model may include one or more mental illnesses, such as anxiety or depression or a combination of the two.

[0021] In one embodiment, the culture conditions of the adult zebrafish are a water temperature of 27.5±2°C and a light and dark cycle of 14h / 10h;

[0022] Furthermore, during the exposure period, more than 50% v / v of the exposure solution was replaced every 24 hours, and all of the exposure solution was replaced every 14 days.

[0023] In a specific embodiment, female zebrafish are first placed in clean water for 7 days, and then exposed to high and low concentrations of ciprofloxacin, and a control group is set up. Specifically, the low concentration can be selected as 100 μg / L, and the high concentration can be selected as 1000 μg / L. During the exposure period, 50% of the exposure solution is replaced every 24 hours and 200 μL of the exposure solution is collected to determine its actual exposure concentration. All the exposure solutions are replaced every 14 days, and the female fish in the exposure solution and the male fish raised in clean water are placed in clean water to spawn once. After 28 days of exposure, the adult fish are dissected, and the brain tissue and intestinal tissue are collected. After liquid nitrogen treatment, they are immediately frozen at -80°C.

[0024] In one embodiment, in step (c), the tissue pathology section analysis includes performing HE staining and Nissl staining on the zebrafish brain tissue to analyze the number, morphology and health status of neurons and glial cells in the brain tissue, and performing HE staining on the intestinal tissue to analyze the integrity of the intestinal villi and the number of intestinal epithelial cells in the intestinal tissue;

[0025] The transmission electron microscope ultrastructure analysis includes observing the subcellular structure of neurons and analyzing the health status of the double nuclear membrane, synapses, mitochondria and myelin sheath of neurons; preferably, the observation is performed under an electron microscope with a magnification of 10,000, 20,000 and 30,000;

[0026] The neurological disease-related gene analysis includes analyzing the expression levels of the following genes: ache, a gene related to acetylcholinesterase activity; Th1, dat, Nr4a2b, and bdnf, genes related to dopamine levels; Th2, Htr1aa, Htr1ab, Htr5a, Htr1b, and Htr2a, genes related to serotonin receptors; Syn2a, mbp, shha, α1-tubulin, gfap, Elavl3, Gap43, nestin, and manf, genes related to the development of the zebrafish central nervous system; and chrnα7, a gene related to nicotinic choline receptor α7.

[0027] In one embodiment, the expression level of the gene is analyzed by real-time fluorescence quantitative PCR technology.

[0028] In one embodiment, in step (d), the changes in the structure of the intestinal microbial community are analyzed by performing 16s amplicon sequencing on the intestinal tissue of zebrafish; the species composition information is obtained by single-sample Alpha diversity analysis and different sample Beta diversity analysis; the Weighted_unifrac or Unweighted_unifrac distance in the Adonis or Anosim analysis method is used to analyze whether the differences in community structure between groups are significant; and the biomarker species with significant differences between groups at the phylum or genus classification level are obtained by LEfSe and T-test.

[0029] In one embodiment, more than 5 zebrafish intestinal tissue samples are analyzed as one sample, and the total weight of the samples is about 100-120 mg.

[0030] In a specific embodiment, genomic DNA is extracted from the sample and 16s amplicon is amplified by PCR, and then the purified PCR product is used for library construction and sequencing analysis.

[0031] In one embodiment, the obtained microbial information is subjected to species composition analysis, mainly including single sample Alpha diversity analysis and different sample Beta diversity analysis. The Alpha diversity index reflects the richness and diversity of species in the sample, and the Beta diversity index combines multivariate statistical methods such as principal component analysis (PCA, Principal Component Analysis), principal coordinate analysis (PCoA, Principal Co-ordinates Analysis) or non-metric multidimensional calibration method (NMDS, Non-Metric Multi-Dimensional Scaling) and other dimensionality reduction diagrams to reflect the degree of difference in microbial community structure between samples.

[0032] In one embodiment, whether the differences in community structure between groups are significant is analyzed by using the Weighted_unifrac or Unweighted_unifrac distance in the Adonis or Anosim analysis method.

[0033] In one embodiment, the obtained microbial information is subjected to species difference analysis, mainly including finding the Biomarker species with significant differences between groups at the phylum or genus classification level (p value<0.05) through LEfSe (LDA Effect Size) and T-test.

[0034] In addition, in some embodiments, the present invention also includes performing functional prediction on the obtained 16S sequencing data of the microorganism based on the KEGG and KO databases.

[0035] In one embodiment, the step (e) comprises pre-treating the zebrafish brain tissue and then performing chromatography-mass spectrometry analysis.

[0036] In one embodiment, more than 5 zebrafish brain tissue samples are analyzed as one sample, and the total weight of the samples is about 40-50 mg.

[0037] In one embodiment, a mixture of methanol and water is added to the zebrafish brain tissue, ultrasonically disrupted and centrifuged, the supernatant and the solids at the bottom are collected, the supernatant is dried with nitrogen, re-dissolved with methanol and water, and centrifuged again, and the supernatant is collected for non-targeted metabolomics chromatography-mass spectrometry analysis, and data are collected using a Q-Exactive Orbitrap mass spectrometer.

[0038] In one embodiment, a mixture of dichloromethane and methanol is added to the bottom solid, ultrasonically disrupted and centrifuged, the supernatant is collected, the supernatant is dried with nitrogen, re-dissolved with methanol and water, and centrifuged again, and the supernatant is collected for non-targeted lipidomics chromatography mass spectrometry analysis, and a SYNAPT XS high-resolution mass spectrometer is used to collect data.

[0039] In one embodiment, the volume ratio of methanol to water in the mixture of methanol and water is 1:1.

[0040] In one embodiment, the ultrasonic disruption conditions are ultrasonic treatment for 5 seconds, interval for 10 seconds, and 120 cycles.

[0041] In one embodiment, the centrifugation after ultrasonic disruption is performed at 15,300 rpm and 4° C. for 10 min.

[0042] In one embodiment, the centrifugation after reconstitution is performed at 10,000 rpm for 10 min at room temperature.

[0043] In one embodiment, the chromatographic conditions for non-targeted metabolomics chromatography mass spectrometry analysis are to use WatersXbridge C18 2.1x100mm, 3.5μm as a chromatographic column, mobile phase A is acetonitrile-water (5:5, V / V) + 10mM ammonium acetate and mobile phase B is acetonitrile-water (95:5, V / V) + 10mM ammonium acetate for gradient elution. The flow rate is 200μL / min; the column temperature is 40°C, the injection plate temperature is 4°C, and the injection volume is 5μL.

[0044] In one embodiment, the chromatographic conditions for non-targeted lipidomics chromatography mass spectrometry analysis are ACQUITY UPLCCSH C18 2.1x100mm, 1.7μm as a chromatographic column, mobile phase A is acetonitrile-water (6:4, V / V) + 10mM ammonium acetate, and mobile phase B is isopropanol-acetonitrile (9:1, V / V) + 10mM ammonium acetate for gradient elution. The flow rate is 400μL / min; the column temperature is 55°C, and the injection volume is 2μL.

[0045] In one embodiment, the mass spectrometry conditions for non-targeted metabolomics chromatography-mass spectrometry analysis are to use ESI as the ion source, perform full scan in positive and negative ion scanning modes, the spray voltage is 4.00 KV, the capillary temperature is 320°C, the scanning mode is Full MS (70000 Resolution)-dd / MS2 (17500 Resolution; collision energy 30 eV), and the scanning range is 50-1200 m / z.

[0046] In one embodiment, the mass spectrometry conditions for non-targeted lipidomics chromatography mass spectrometry analysis are ion mobility TOF, resolution, MS E The system was in acquisition mode and ESI as the ion source. Full scan was performed in positive and negative ion scanning modes. The spray voltages in positive and negative ion scanning modes were 2.0 KV and 1.0 KV respectively, the cone voltage was 30 V, the degassing voltage was 550 °C, the degassing gas flow rate was 900 L / Hr, the ion source temperature was 120 °C, and the scanning range was 50-1200 m / z.

[0047] The present invention uses an analytical method based on ion mobility technology to analyze non-targeted lipidomics. Compared with the traditional high-resolution mass spectrometer analysis of non-targeted lipidomics workflow, SYNAPT XS high-resolution mass spectrometry supports ion mobility technology. In the ion mobility experiment, in addition to the precise ion mass, the ion mobility experiment also allows the measurement of the ion collision cross-section (CCS) related to the chemical structure and three-dimensional conformation of the ion, which increases the credibility of the lipid identification results of non-targeted lipidomics, helps to determine the ion name or study its structure, thereby reducing interference and improving the signal-to-noise ratio (S:N).

[0048] The present invention also includes mixing equal amounts of the analysis sample supernatants of non-targeted metabolomics and non-targeted lipids to prepare quality control samples.

[0049] In one embodiment, the method further comprises using Compound Discover and Progenesis QI software to perform peak alignment, peak extraction, noise reduction, and normalization on the collected data, and then screen the differential metabolites after obtaining the raw data matrix. The specific method is to import the obtained non-targeted metabolomics and non-targeted lipidomics raw data files into Compound Discover 3.3 software and Progenesis QI software respectively to perform data preprocessing such as peak alignment, peak extraction, noise reduction, and normalization, compound identification, multivariate data statistical analysis, and screening of differential metabolites.

[0050] In one embodiment, the method further comprises performing metabolic pathway enrichment analysis on the obtained differential metabolites. The metabolic pathway enrichment analysis is mainly based on the Metabolika database built into Compound Discoverer 3.0 and the KEGG metabolic pathway database MetaboAnalyst online software to analyze and determine potential biomarkers and related metabolic pathways.

[0051] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0052] The present invention proposes a method for analyzing the relationship between intestinal microbiota and neurobehavioral diseases based on multi-omics integration, which is a new analytical research method combining histopathology, electron microscopy scanning, real-time fluorescence quantitative PCR technology, 16s amplicon sequencing, non-targeted metabolomics and non-targeted lipidomics;

[0053] Most of the research experiments in the prior art are not designed reasonably, and the results produced are of low credibility. The present invention provides an accurate and comprehensive analysis approach for the study of intestinal microbiota and neurobehavioral diseases. Most of the prior art studies the behavioral abnormalities caused by changes in a single strain of zebrafish intestinal microbes. The present invention can analyze the relationship between the overall signal changes of the intestinal microbial spectrum (the composition and changes of the entire microbial community) and neurobehavior.

[0054] The present invention is a more comprehensive analytical method for studying the effects of bacterial gut microbiota on microbe-gut-brain axis signaling and central nervous system (CNS) diseases, which can provide a theoretical basis for microbial treatment strategies for neurobehavioral diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 The results of the novel water tank experiment provided in Example 1 of the present invention;

[0057] Figure 2 HE staining pathological sections of brain tissues of the control group and the high-concentration model group in Example 1 of the present invention;

[0058] Figure 3 This is a Nissl-stained pathological section of brain tissue of the control group and the high-concentration model group in Example 1 of the present invention;

[0059] Figure 4 HE staining pathological sections of intestinal tissues of the control group and the high-concentration model group in Example 1 of the present invention;

[0060] Figure 5 The transmission electron microscopic ultrastructure of the brain tissue of the control group and the high-concentration model group in Example 1 of the present invention;

[0061] Figure 6 This is a heat map of the expression of genes related to mental illness in the control group, low-concentration model group and high-concentration model group in Example 1 of the present invention;

[0062] Figure 7 The relative abundance diagram of the microbial community at the phylum classification level of the control group, the low-concentration model group and the high-concentration model group in Example 1 of the present invention;

[0063] Figure 8 This is the total ion current graph of non-targeted lipidomics of brain tissue of the control group in Example 1 of the present invention;

[0064] Fig. 9 This is a principal component analysis (PCA) score diagram of non-targeted metabolomics of brain tissue in Example 1 of the present invention.

[0065] Fig.10 This is a diagram of the non-targeted lipidomics metabolic pathway analysis of brain tissue in Example 1 of the present invention. DETAILED DESCRIPTION

[0066] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] Example

[0068] 1. Methods for constructing zebrafish models of mental illness

[0069] Adult wild-type AB zebrafish aged 3 months were selected for antibiotic exposure in an environment with a culture water temperature of 27.5±2°C and a light-dark cycle of 14h / 10h.

[0070] Before the formal exposure experiment, the female zebrafish to be exposed were placed in clean water for 7 days of acclimatization. A control group, low-concentration (100 μg / L), and high-concentration (1000 μg / L) ciprofloxacin exposure groups were set up. During the exposure period, 50% of the exposure solution was replaced every 24 hours and 200 μL of the exposure solution was collected to determine its actual exposure concentration. All the exposure solution was replaced every 7 days, and the female fish in the exposure solution and the male fish raised in clean water were placed in clean water to spawn once. After 28 days of exposure, the adult fish were dissected, and the brain and intestinal tissues were collected. After being treated with liquid nitrogen for 5 minutes, they were immediately frozen at -80°C.

[0071] A new water tank experiment was used to evaluate the effects of external factors on the behavior of zebrafish. The experimental device was a circular glass container with a diameter of 12.5 cm. The glass container was divided into a central area and an edge area by concentric circle segmentation. Generally speaking, the preference of zebrafish for the outer edge of space is related to anxiety behavior, while the reduction of exploratory behavior is related to the lack of interest in the depression model. The swimming behavior of zebrafish was recorded within 5 minutes, and the zebrafish trajectory map and heat map obtained were shown in Figure 1The results showed that compared with the control group, zebrafish in the exposed group showed depressive and anxious behaviors, specifically manifested by reduced movement trajectories and a preference for the outer edge of the space.

[0072] 2. Evaluation of the zebrafish model of psychiatric disorders by histopathological sections, transmission electron microscopy ultrastructure, and neurological disease-related gene analysis

[0073] The histopathological section analysis method mainly involves HE staining and Nissl staining of brain tissue to analyze the number, morphology and health status of neurons and glial cells in brain tissue. Figure 2 and Figure 3 The results showed that the number of neurons and glial cells in the control group decreased and their morphology changed. At the same time, the integrity of the intestinal villi and the number of intestinal epithelial cells in the intestinal tissue were analyzed by HE staining. Figure 4 The analysis showed that the integrity of the intestinal villi was lost and the number of goblet cells in the intestine was reduced.

[0074] The content of transmission electron microscopy ultrastructural analysis mainly involves observing the subcellular structure of neurons under the electron microscope field of view with magnifications of 10,000, 20,000 and 30,000, and analyzing the health status of the double nuclear membrane, myelinated nerve fibers, mitochondria and myelin sheath of neurons. Figure 5 .

[0075] The 22 genes used to evaluate the zebrafish model of mental illness specifically include: ache, a gene related to acetylcholinesterase activity; Th1, dat, Nr4a2b, and bdnf, genes related to dopamine levels; Th2, Htr1aa, Htr1ab, Htr5a, Htr1b, and Htr2a, genes related to serotonin receptors; Syn2a, mbp, shha, α1-tubulin, gfap, Elavl3, Gap43, nestin, and manf, genes related to the development of the zebrafish central nervous system (CNS); and chrnα7, a gene related to nicotinic cholinergic receptor α7. Figure 6 Compared with the control group, the expression of dopamine neurotransmitter and its receptor were significantly downregulated (P<0.01), the expression of acetylcholine, serotonin and γ-aminobutyric acid neurotransmitters and their receptors were significantly upregulated (P<0.05), and the expression of genes related to neural development were significantly downregulated.

[0076] 3.16S amplicon sequencing method for analyzing the intestinal microbial community structure of the model group

[0077] First, genomic DNA was extracted and PCR amplified from zebrafish intestinal samples (5 intestinal tissue samples as one sample, about 100-120 mg), and then the library of the mixed and purified PCR products was constructed and sequenced and analyzed.

[0078] The main contents and methods of intestinal flora data analysis are as follows: First, species composition analysis mainly includes single sample Alpha diversity analysis and different sample Beta diversity analysis. The Alpha diversity index reflects the richness and diversity of species in the sample, and the Beta diversity index combines the multivariate statistical method principal coordinate analysis (PCoA, Principal Co-ordinates Analysis) to reflect the degree of difference in microbial community structure between samples. Figure 7 Compared with the control group, the richness and diversity of the microbiota in the exposed group were significantly reduced, indicating that the stability of the body's resistance was reduced.

[0079] In addition, the Unweighted_unifrac distance in the Anosim analysis method was used to analyze whether the differences in community structure between groups were significant. At the same time, the species difference analysis mainly used LEfSe (LDA Effect Size) and T-test to find the Biomarker species with significant differences between groups at the phylum or genus classification level (p value < 0.05). The Biomarker species with significant differences between groups at the genus classification level were mainly cetobacterium, Plesiomonas, Lactococcus, etc.

[0080] 4. Sample pretreatment method for zebrafish brain tissue

[0081] After thawing brain tissue samples on ice (5 brain tissue samples as one sample, about 40-50 mg), 1.5 mL of pre-cooled methanol / water (1:1, V / V) mixed extract was added, and the sample mixture was ultrasonically broken (working conditions were ultrasonic 5s, interval 10s, and 120 cycles), and centrifuged at 15300 rpm and 4°C for 10 min, and the supernatant and bottom solids after centrifugation were collected. The supernatant after centrifugation was nitrogen blown to dryness on a 45°C nitrogen blower, and redissolved in 120 μL of methanol / water (1:1, V / V) solution, vortexed until completely redissolved, and then centrifuged at 10000 rpm at room temperature for 10 min. The supernatant was aspirated and placed on a Q-Exactive Orbitrap mass spectrometer for non-targeted metabolomics data acquisition.

[0082] 1.6 mL of pre-cooled dichloromethane / methanol (3:1, V / V) mixed extract was added to the collected solids at the bottom after centrifugation. The sample mixture was ultrasonically broken (working conditions were ultrasonic 5 s, interval 10 s, and 120 cycles), and centrifuged at 15300 rpm and 4 °C for 10 min. The supernatant was collected and blown to dryness on a 45 °C nitrogen blower, and redissolved in 120 μL of methanol / water (1:1, V / V) solution. After vortexing until completely redissolved, it was centrifuged at 10000 rpm at room temperature for 10 min. The supernatant was aspirated and placed on a SYNAPT XS high-resolution mass spectrometer for non-targeted lipidomics data acquisition. Figure 7 .

[0083] The supernatants of the non-targeted metabolomics and non-targeted lipid analysis samples were mixed in equal amounts for the preparation of quality control samples.

[0084] 5. Zebrafish Brain Tissue Sample Collection Method

[0085] When the Q-Exactive Orbitrap mass spectrometer was used to collect non-targeted metabolomics data, the mass spectrometry conditions used ESI as the ion source, full scan in positive and negative ion scan modes, and the scan range was 50-1200 m / z. The liquid phase conditions used acetonitrile-water (5:5, V / V) + 10 mM ammonium acetate as mobile phase A and acetonitrile-water (95:5, V / V) + 10 mM ammonium acetate as mobile phase B for gradient elution, and the injection volume was 5 μL;

[0086] When non-targeted lipidomics data were collected on a SYNAPT XS high-resolution mass spectrometer, the mass spectrometry conditions used ion mobility TOF and resolution as the acquisition mode, ESI as the ion source, full scan in positive and negative ion scanning modes, and the scanning range was 50-1200 m / z. The liquid phase conditions used mobile phase A as acetonitrile-water (6:4, V / V) + 10 mM ammonium acetate and mobile phase B as isopropanol-acetonitrile (9:1, V / V) + 10 mM ammonium acetate for gradient elution, and the injection volume was 2 μL.

[0087] 6. Non-targeted metabolomics and lipidomics data analysis methods

[0088] The collected non-targeted metabolomics and lipidomics data were analyzed and processed. The specific method was to import the obtained non-targeted metabolomics and non-targeted lipidomics raw data files into Compound Discover 3.3 software and Progenesis QI software for data preprocessing such as peak alignment, peak extraction, noise reduction, and normalization. Metabolites were identified using the mz cloud database and HMDB database provided by Compound Discover 3.3 software. Lipid compounds were identified based on the lipid library provided by Waters QI and Lipidmaps matching. The identified data matrix was then imported into MetaboAnalyst software for PCA principal component analysis (see Figure 2). Fig. 9 The differential metabolites were screened with (P < 0.05 and VIP) 1 as the screening condition, and the metabolic pathway enrichment analysis was mainly based on the MetaboAnalyst online software KEGG metabolic pathway database analysis. Fig.10 The results showed that the main enrichment pathways of differential metabolites were amino acid metabolism and fatty acid metabolism. The main enrichment pathways of differential lipid compounds were sphingomyelin metabolism and glycerophospholipid metabolism.

[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 it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for analyzing the relationship between microbiota-gut-brain axis signals and zebrafish neurobehavior based on multi-omics, characterized in that: include: (a) Construction of a zebrafish model of psychiatric disease by antibiotic induction; (b) Collecting zebrafish brain tissue and intestinal tissue; (c) evaluating the mental illness zebrafish model by histopathological section analysis, transmission electron microscopy ultrastructural analysis, and neurological disease-related gene analysis; (d) analyzing the changes in the microbial community structure, species composition, inter-group difference significance, and species difference of the intestinal tissue of the zebrafish model of mental illness to obtain intestinal microbial data; (e) Screening metabolomics and / or lipidomics biomarkers of zebrafish brain tissues for psychiatric diseases by performing non-targeted metabolomics and / or non-targeted lipidomics analysis on zebrafish brain tissues; (f) analyzing the relationship between biota-gut-brain axis signaling and zebrafish neurobehavior by combining the gut microbiome data with the metabolomics and / or lipidomics biomarkers; In the step (c), the tissue pathology section analysis includes performing HE staining and Nissl staining on the zebrafish brain tissue to analyze the number, morphology and health status of neurons and glial cells in the brain tissue, and performing HE staining on the intestinal tissue to analyze the integrity of the intestinal villi and the number of intestinal epithelial cells in the intestinal tissue; The transmission electron microscope ultrastructure analysis includes observing the subcellular structure of neurons and analyzing the health status of the double nuclear membrane, synapses, mitochondria and myelin sheath of neurons; The neurological disease-related gene analysis includes analyzing the expression levels of the following genes: genes related to acetylcholinesterase activity ache ; Genes associated with dopamine levels Th1 , dat , Nr4a2 , bdnf ; Genes associated with serotonin receptors Th2 , Htr1aa , Htr1ab , Htr5a , Htr1b , Htr2a ; Genes related to the development of the zebrafish central nervous system Syn2a , mbp , shha , α1-tubulin ,gfap, Elavl3 , Gap43 , nestin , manf ; The gene chrn α7 related to nicotinic cholinergic receptor α7.

2. The method according to claim 1, characterized in that In the step (a), the zebrafish model of mental illness is constructed by inducing ciprofloxacin on adult zebrafish, and the type of mental illness in the zebrafish is determined by animal behavior testing; The method for constructing the zebrafish model of mental illness comprises exposing the adult zebrafish to water containing ciprofloxacin for more than 28 days, wherein the concentration of ciprofloxacin is 100 μg / L-1000 μg / L.

3. The method according to claim 2, characterized in that The culture conditions of the adult zebrafish are water temperature of 27.5±2°C and a light and dark cycle of 14h / 10h; During the exposure period, more than 50% of the exposure solution was replaced every 24 hours, and all of the exposure solution was replaced every 7 days.

4. The method according to claim 1, characterized in that: In the step (d), the changes in the structure of the intestinal microbial community are analyzed by performing 16s amplicon sequencing on the intestinal tissue of zebrafish; the species composition information is obtained by single-sample Alpha diversity analysis and different sample Beta diversity analysis; whether the differences in community structure between groups are significant is analyzed by the Weighted_unifrac or Unweighted_unifrac distance in the Adonis or Anosim analysis method; and the biomarker species with significant differences between groups at the phylum or genus classification level are obtained by LEfSe and T-test.

5. The method according to claim 1, characterized in that: The step (e) comprises pre-treating the zebrafish brain tissue and then performing chromatography-mass spectrometry analysis.

6. The method according to claim 5, characterized in that A mixture of methanol and water was added to the zebrafish brain tissue, ultrasonically disrupted and centrifuged, the supernatant and the bottom solid were collected, the supernatant was dried with nitrogen, re-dissolved with methanol and water, and centrifuged again, the supernatant was collected for non-targeted metabolomics chromatography-mass spectrometry analysis, and data were collected using a Q-Exactive Orbitrap mass spectrometer.

7. The method according to claim 6, characterized in that A mixture of dichloromethane and methanol was added to the bottom solid, ultrasonically disrupted and centrifuged, and the supernatant was collected. The supernatant was dried with nitrogen, re-dissolved with methanol and water, and centrifuged again. The supernatant was collected for non-targeted lipidomics chromatography mass spectrometry analysis, and data were collected using a SYNAPT XS high-resolution mass spectrometer.

8. The method according to claim 6 or 7, characterized in that: The method also includes using CompoundDiscover and Progenesis QI software to perform peak alignment, peak extraction, noise reduction, and normalization on the collected data, and then screening differential metabolites after obtaining the original data matrix.

9. The method according to claim 8, characterized in that The method also includes performing metabolic pathway enrichment analysis on the obtained differential metabolites.

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