CN114113569B - A method to establish screening standards for BmNPV-resistant lines of silkworms based on metabolomics technology - Google Patents

Abstract

The invention relates to a method for establishing BmNPV resistance strain silkworm screening standard based on metabonomics technology, collecting silkworm strain p50, oil silkworm mutant strain and blood lymph sample of the two strains by puncture injection of silkworm nuclear polyhedrosis virus (BmNPV); collecting metabolic map information of a haemolymph sample; preprocessing metabonomics data; and (3) performing difference significance analysis, screening out metabolites which change significantly between normal silkworms and oil silkworm mutant silkworms, and marking as biological markers. And analyzing the metabolic pathways enriched by the differential metabolites, identifying the biomarkers through a metabolite database, selecting molecular targets, and establishing a screening standard of the BmNPV resistant strain silkworm. The invention researches the difference of the body metabolic pathway change of normal line silkworm p50 and its oil silkworm mutant op50 after being infected by BmNPV, and screens molecular targets as indexes for screening BmNPV-resistant silkworms, and establishes screening standards of antiviral line silkworms.

Description

A method to establish screening standards for BmNPV-resistant silkworm lines based on metabolomics technology method

Technical field

The invention belongs to the field of bioinformatics and mainly involves screening anti-viral metabolites from op50 oil silkworms based on targeted metabolomics technology, and selecting molecular targets among them through pathway enrichment analysis and metabolite database identification, thereby Method to establish screening standards for BmNPV-resistant lines of silkworms.

Background technique

Bombyx mori is an important economic insect. The sericulture industry has been vigorously promoted to help farmers increase income and is an important source of local economic income. In the pathological research of silkworms, viral diseases are one of the most harmful silkworm diseases in silkworm production, among which the blood-type pus disease of silkworms caused by Bombyx mori nuclear polyhedrovirus (BmNPV) is extremely harmful. This purulent disease is an acute infectious disease. When silkworms are infected with this disease, they will develop abnormal behaviors such as slow growth, sleeplessness, and not eating mulberry. When the disease occurs, obvious symptoms such as swollen body segments, milky white body color, and milky white turbid blood will appear. Therefore, screening and cultivating BmNPV-resistant silkworm strains is of great significance for improving the economic production of sericulture.

Metabolomics is a new field that has developed in recent years. Different from the complexity of gene and protein modification and processing, metabolites more reflect the environment of cells, which is closely related to the nutritional status of cells and the influence of other external factors. It directly reflects the stress state of cells after being stimulated by external factors. In addition, analysis of metabolites of biological fluids can reflect the physiological and pathological status of the body. By detecting the spectra of a series of samples through metabolomics, combined with chemical pattern recognition methods, it is possible to determine the pathophysiological state of the organism, the function of genes, the toxicity and efficacy of drugs, etc., and it is possible to find out the related metabolism objects, analyze their diagnostic value, and serve as pathological evaluation indicators. Therefore, metabolomics has more advantages in studying the pathological analysis of silkworms in response to virus infection.

The op50 mutant of oil silkworm is a mutant with defective uric acid metabolism. Its urate content in the body is lower than that of normal silkworms, resulting in a transparent or translucent epidermis. When BmNPV infected the fourth instar p50 and op50, it was found that the oil silkworm mutant had a higher virus titer, indicating that the abnormal metabolism of op50 led to lower resistance than normal silkworms. Therefore, molecular targets with diagnostic value can be screened in its metabolic pathway. , can be used as a standard for screening BmNPV-resistant silkworm lines.

Contents of the invention

Purpose of the invention: The present invention provides a method for establishing screening standards for BmNPV-resistant strains of silkworms based on metabolomics technology.

Technical solution: The method of the present invention to establish screening standards for BmNPV-resistant silkworm strains based on metabolomics technology is based on targeted metabolomics technology to detect the hemolymph metabolomics of p50 and its oily silkworm mutant op50 before and after infection with BmNPV virus. Data were used to study the changes in metabolites after virus infection; OPLS-DA was used to show the differences in metabolic patterns between different treatment groups of silkworms, and the Fisher classification method was used to accurately distinguish these differences, and then ROC was used to analyze biomarkers with diagnostic value. ; Identify biomarkers based on pathway enrichment analysis and metabolite database, and select molecular targets for detection to establish screening criteria for BmNPV-resistant silkworm lines. Specific steps are as follows:

(1) Collect hemolymph samples of normal silkworm p50 (p50-), oil silkworm op50 (op50-), and p50 (p50+) and op50 (op50+) after puncture injection of BmNPV;

(2) Collect metabolic profile information of normal silkworm p50, oil silkworm op50, and p50 and op50 hemolymph samples after puncture injection of BmNPV;

(3) Preprocessing of metabolomics data;

(4) Analyze the difference significance of the data, screen out significantly changed metabolites, and record them as biomarkers;

(5) Pathway enrichment analysis of metabolites, and identification of these biomarkers based on the metabolite database, using the selected molecular targets as a basis for identifying the resistance level of silkworms, and establishing screening criteria for BmNPV-resistant strains of silkworms.

Further, the preparation method of the hemolymph sample for detection in step (2) is as follows: thaw the silkworm hemolymph at room temperature, add acetonitrile for protein precipitation, stir and mix, and collect the supernatant after centrifugation.

Further, the process of step (2) includes: L-phenylalanine, L-asparagine, L-ornithine, L-lysine, L-methionine, L-histidine, L-chromate Acid, hydroxyproline, L-citrulline, L-proline, L-cystine, L-aspartic acid, L-glutamine, L-arginine, citric acid, α- Ketoglutarate, malic acid, succinic acid, L-cysteine, L-phenylalanine, L-serine, L-threonine, L-tyrosine, L-valine, pyruvate, 28 metabolites of sarcosine, fumaric acid, and lactic acid were separated by chromatography. The 28 analytes detected were all endogenous metabolites present in the hemolymph of silkworms. The purity of the chemicals used was greater than 98%, and all were dissolved. in water.

Further, step (3) data preprocessing includes: preprocessing the original data through Progenesis QI software; and then processing the relevant parameters R ² , R ² Y and Q ² , to evaluate the differences in metabolic profiles between different groups and the relative intensity of metabolites.

Further, the significance analysis of the differences described in step (4) includes: using SPSS 23 software to analyze statistical differences, analyzing significant differences between different hemolymph samples through one-way analysis of variance and independent samples T-test; testing through Fisher classification The diagnostic value of the obtained data will be evaluated, and p50-, op50-, p50+ and op50+ will be distinguished according to the stepwise discriminant method; the cross-validation method will be used to evaluate the classification accuracy; the selected metabolites will be further analyzed by ROC and combined with OPLS-DA analysis As a result, significantly different metabolites were distinguished and recorded as biomarkers.

Further, the process of step (5) includes: analyzing the metabolic pathways of differential metabolite enrichment through the Metaboanalyst.ca website to obtain the metabolic mechanisms of p50 and op50 in response to BmNPV infection, and then identifying these biomarkers through KEGG and HMDB metabolite databases objects, and then select molecular targets for detection among them to establish screening criteria for BmNPV-resistant silkworm lines.

Beneficial effects: The present invention is based on metabolomics technology to study the differences in metabolic pathway changes in the normal strain of silkworm p50 and its oily silkworm mutant op50 after being infected with BmNPV, and screens related molecular targets, which are used as indicators for screening anti-BmNPV silkworms. , providing theoretical basis and methods for establishing screening standards for antiviral strains of silkworms.

Description of the drawings

Figure 1 is a flow chart for establishing disease resistance screening criteria;

Figure 2 is the UPLC-MS/MS chromatogram of the tested standard sample.

Detailed ways

The present invention detects hemolymph metabolomics data of p50 and its oily silkworm mutant op50 before and after infection with BmNPV virus based on targeted metabolome technology, studies changes in silkworm metabolites after virus infection, and searches for relevant biomarkers. The molecular targets were selected through the metabolic mechanism of silkworm, and the screening criteria for BmNPV-resistant silkworm lines were finally established.

Examples of the present invention are as follows: During the process of rearing p50 in silkworm chambers, a translucent oil silkworm mutant op50 was discovered. Compared with normal silkworms, their development is slower and their immunity is poorer. The metabolic process of silkworms has important physiological functions, and some metabolic pathways have immune regulatory effects and are related to the disease resistance of silkworms. Oil silkworm is a mutant strain of silkworm with abnormal uric acid metabolism. However, the abnormality of this metabolic pathway may lead to the disorder of other metabolic pathways in the body. This just shows that oil silkworm is an ideal material for studying the metabolic regulation mechanism of silkworm. The decline in disease resistance of silkworm op50 shows that the regulatory level of metabolic pathways in silkworms can be used as a basis for screening resistant strains. Based on metabolite pathway enrichment analysis and metabolite databases, biomarkers are identified and molecular targets with diagnostic value are selected. , to establish screening criteria for BmNPV-resistant lines of silkworms.

The p50 and op50 silkworm breeds and BmNPV virus in this example were provided by the Sericulture Research Institute of the Chinese Academy of Agricultural Sciences. op50 is produced by natural mutation; BmNPV is a modified budding virus containing enhanced green fluorescent protein (BV-EGFP). The silkworms used in the experiment were all larvae from the fifth instar. The silkworm larvae were subcutaneously injected with BV-EGFP using a micro-blood collection pipette. The injection volume was 2.5 μL/head and the virus concentration was 1×10 ⁸ Pfu/mL; control group silkworms Puncture and inject TC-100 cell culture medium, and the injection volume is also 2.5 μL/head. During the operation, silkworms that bleed heavily need to be discarded and re-injected. After injection, the silkworms were fed mulberry leaves normally, and the hemolymph was collected into 1.5mL EP tubes after 24h, 48h, and 72h. Each silkworm had one tube, each tube was 100 μL. There were 10 biological replicates in each group, and thiourea was added to the tubes. Prevents hemolymph oxidation. After collection, immediately store in -80°C ultra-low temperature refrigerator for later use.

The hemolymph sample preparation method in this example is as follows: Thaw the silkworm hemolymph at room temperature, add 300 μL methanol to each tube, stir and mix, and then vortex for 1 minute to mix thoroughly. Centrifuge at 12,000 rpm for 10 min at 4°C and collect the supernatant into a 1.5 mL EP tube. Incubate on ice for 30 minutes, centrifuge at 12,000 rpm for 8 minutes at 4°C, and collect the supernatant into a sample bottle.

The hemolymph sample determination conditions in this example are as follows: 28 metabolites were chromatographically separated using a Xevo TQ-S miniature triple quadrupole mass spectrometer on a Waters ACQUITY UPLC amide C18 column (2.1 mm × 100 mm, 1.7 m).

Positive ion mode measurement conditions: The mobile phase is composed of 0.1% ammonia (A) and acetonitrile (B). The method used in gradient elution is as follows: 0.0~0.3 min, 70% B; 0.3~1.0 min, 70~30% B; 1.0~2.0 min, 30% B; 2.0~2.3min, 30%~70% B; 2.3~3.0 min, 70% B. The flow rate is 0.4 mL/min (0~3.0 min); the injection volume is 1 μL. To detect amino acid metabolites in multiple reaction monitoring (MRM) mode, the mass spectrometry conditions required for the determination are: column temperature is 30°C, capillary voltage is 2.5 kV; ionization source temperature is 150°C; cone gas and desolvation gas are nitrogen. The desolvation temperature is 600°C, the cone gas flow rate is 150 L/h, and the desolvation gas flow rate is 1000 L/h.

Negative ion mode measurement conditions: the column temperature is 30°C; the mobile phase is composed of acetonitrile (A) and 0.3% formic acid (B). The method used in the mobile phase gradient elution is as follows: 0~0.2 min, 5% A; 0.2 ~1.0 min, 5%~90% A; 1.0~1.5 min, 90% A; 1.5~1.8 min, 90%~5% A; 1.8~4.0 min, 90% A. The flow rate is 0.3 mL/min (0~4.0min); the equilibrium time after the gradient is 1 min; the injection volume is 2 μL. To detect amino acid metabolites in multiple reaction monitoring (MRM) mode, the required mass spectrometry conditions for determination are: capillary voltage 2.5 kV; ionization source temperature 150 °C; desolvation gas temperature 400 °C, cone gas flow 150 L/h, desolvation Air flow rate 1000 L/h.

The 28 analytes tested are all endogenous metabolites present in silkworm hemolymph. The purity of the chemicals used are all greater than 98%, and all are dissolved in a solution of water:methanol=1:1, with a concentration of 100 μg/ mL. Dilute the stock solution with water/methanol (v/v, 1:1) to 0.075~20 μg/mL before use. Quality control (QC) samples used to test precision and accuracy were prepared following the same procedure. Calibration curves were established by plotting the peak area ratios of all analytes against the concentrations of calibration standards. UPLC-MS/MS metabolomics data were obtained by Masslynx4.1 software.

The data processing process of this example is as follows: the original data is processed by Progenesis QI software for peak extraction, peak alignment, peak matching and peak intensity correction; then the relevant parameters in the OPLS-DA results obtained are processed according to SIMCA-P 13.0 software ( ^{R 2 _}

Statistical differences of metabolites were analyzed using SPSS 23 software, and significant differences between different hemolymph samples were analyzed by one-way analysis of variance and independent samples T-test. Fisher classification was used to distinguish differences between groups in OPLS-DA results; classification accuracy was evaluated by cross-validation method; metabolites with significant differences were distinguished as biomarkers through further analysis by ROC. Then upload it to the Metaboanalyst.ca website to analyze the enriched metabolic pathways and functionally identify biomarkers through metabolite databases such as KEGG and HMDB.

Based on pathway enrichment analysis and information comparison of metabolite databases, p50-, op50-, p50+ and op50+ metabolites mainly undergo regulatory changes in sulfur metabolism, TCA cycle, urea cycle, glycolysis and amino acid metabolism pathways. Among the significantly fluctuating biomarkers, three molecular targets, α-ketoglutarate, succinic acid, and L-serine, were selected. Significant metabolic changes occurred between p50 and op50 and after BmNPV infection, and they can be used as anti-BmNPV antibodies. Screening indicators for sexual strain silkworms.

In summary, the present invention analyzes the hemolymph metabolomics data of p50-, op50-, p50+, and op50+ based on UPLC-MS/MS technology; biomarkers with diagnostic value are obtained through OPLS-DA, Fisher classification and ROC analysis. Biomarkers are identified according to pathway enrichment analysis and metabolite database, and the selected molecular targets are used as indicators for identifying BmNPV resistance in silkworms, thereby establishing screening standards for resistant strains of silkworms. The present invention meets the needs of actual agricultural production. .

Claims (6)

1. A method for establishing screening standards for BmNPV-resistant lines of silkworms based on metabolomics technology, which is characterized by: including the following steps: (1) Collect hemolymph samples of normal silkworm p50-, oil silkworm op50-, and p50+ and op50+ after puncture injection of BmNPV; (2) Collect metabolic profile information of normal silkworm p50-, oil silkworm op50-, and p50+ and op50+ hemolymph samples after puncture injection of BmNPV; (3) Preprocessing of metabolomics data; (4) Difference significance analysis of data, metabolites that have significantly changed between p50- and op50-, and in p50+ and op50+ after BmNPV infection, are recorded as biomarkers; (5) Pathway enrichment analysis of metabolites, and identification of these biomarkers based on the metabolite database, screening out molecular targets among significantly fluctuating biomarkers, and using the selected molecular targets as a basis for identifying the resistance level of silkworms , to establish screening criteria for BmNPV-resistant lines of silkworms. 2. The method for establishing screening standards for BmNPV-resistant strains of silkworms based on metabolomics technology according to claim 1, characterized in that: the preparation method of the hemolymph sample for detection in step (2) is as follows: thawing the silkworm hemolymph at room temperature , add acetonitrile for protein precipitation, stir and mix, and collect the supernatant after centrifugation. 3. The method for establishing BmNPV-resistant silkworm screening standards based on metabolomics technology according to claim 1, characterized in that: the process of step (2) includes: L-phenylalanine, L-asparagine , L-ornithine, L-lysine, L-methionine, L-histidine, L-tryptophan, hydroxyproline, L-citrulline, L-proline, L-cystine Acid, L-aspartic acid, L-glutamine, L-arginine, citric acid, alpha-ketoglutarate, malic acid, succinic acid, L-cysteine, L-phenylalanine 28 metabolites, L-serine, L-threonine, L-tyrosine, L-valine, pyruvate, sarcosine, fumaric acid, and lactic acid were separated by chromatography, and all 28 analytes detected were It is an endogenous metabolite that exists in the hemolymph of silkworms. The purity of the chemicals used is greater than 98%, and all of them are dissolved in water. 4. The method for establishing screening standards for BmNPV-resistant strains of silkworms based on metabolomics technology according to claim 1, characterized in that: step (3) data preprocessing includes: preprocessing the original data through Progenesis QI software; and then According to the ^relevant parameters ^{R 2} . 5. The method for establishing screening standards for BmNPV-resistant strains of silkworms based on metabolomics technology according to claim 1, characterized in that: the difference significance analysis described in step (4) includes: using SPSS 23 software to analyze statistical differences. , analyze the significant differences between different hemolymph samples through one-way analysis of variance and independent samples T test; evaluate the diagnostic value of the measured data through Fisher classification, and distinguish p50-, op50-, p50+ and op50+ according to the stepwise discriminant method; use The cross-validation method evaluates the classification accuracy; the selected metabolites will be further analyzed by ROC, and combined with the OPLS-DA analysis results to distinguish significantly different metabolites, recorded as biomarkers. 6. The method of establishing screening standards for BmNPV-resistant strains of silkworms based on metabolomics technology according to claim 1, characterized in that: the process of step (5) includes: analyzing the metabolism of differential metabolite enrichment through the Metaboanalyst.ca website pathway, obtain the metabolic mechanism of p50 and op50 in response to BmNPV infection, and then identify these biomarkers through KEGG and HMDB metabolite databases, and then select molecular targets for detection among them to establish screening of BmNPV-resistant silkworm lines. standard.