CN114113569A - Method for establishing BmNPV resistant strain silkworm screening standard based on metabonomics technology - Google Patents

Method for establishing BmNPV resistant strain silkworm screening standard based on metabonomics technology Download PDF

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CN114113569A
CN114113569A CN202111418130.3A CN202111418130A CN114113569A CN 114113569 A CN114113569 A CN 114113569A CN 202111418130 A CN202111418130 A CN 202111418130A CN 114113569 A CN114113569 A CN 114113569A
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王学杨
苏志浩
赵紫芹
吴阳春
李木旺
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Abstract

The invention relates to a method for establishing a BmNPV resistant strain silkworm screening standard based on a metabonomics technology, which comprises the steps of collecting a silkworm strain p50, an oil silkworm mutant strain thereof and hemolymph samples of silkworm nuclear polyhedrosis virus (BmNPV) which are punctured and injected by the two strains; collecting metabolic map information of a hemolymph sample; preprocessing metabonomics data; and (4) analyzing the significance of the difference, screening out metabolites which change significantly between the normal silkworms and the oil silkworm mutation silkworms, and recording the metabolites as biomarkers. Analyzing the metabolic pathway enriched by the differential metabolite, identifying the biomarker through a metabolite database, selecting a molecular target, and establishing a screening standard of the BmNPV resistant strain silkworm. According to the invention, the difference of organism metabolic pathway changes of the normal strain silkworm p50 and the oil silkworm mutant op50 after being infected by BmNPV is researched, the screening molecular target is used as an index for screening the BmNPV-resistant silkworm, and the screening standard of the antiviral strain silkworm is established.

Description

Method for establishing BmNPV resistant strain silkworm screening standard based on metabonomics technology
Technical Field
The invention belongs to the field of bioinformatics, and mainly relates to a method for establishing a BmNPV resistant strain silkworm screening standard by screening metabolites related to virus resistance from op50 oil silkworms based on a targeted metabonomics technology, and identifying and selecting molecular targets in the metabolites through path enrichment analysis and a metabolite database.
Background
Silkworm is an important economic insect, and the silkworm industry is greatly promoted for helping agriculture and increasing income, and is an important local economic income source. In the pathological research of silkworms, the virus disease is one of the silkworm diseases with the greatest harm in the silkworm breeding production, wherein the silkworm nuclear polyhedrosis virus (BmNPV) causes the silkworm nuclear polyhedrosis virus with the greatest harm. The pyosis is an acute infectious disease, and abnormal behaviors such as slow development, insomnia, no mulberry feeding and the like can occur when the silkworm is infected with the pyosis; when the disease occurs, obvious symptoms such as swelling of body nodes, milky white body, milky turbid blood flow and the like can appear. Therefore, the method for screening and breeding BmNPV-resistant silkworm strains has important significance for improving the economic production of the sericulture industry.
Metabonomics is a new field developed in recent years, and different from the complexity of modification processing of genes and proteins, metabolites reflect the environment of cells more, are closely related to the nutritional state of the cells and the influence of other external factors, and directly reflect the stress state of the cells after being stimulated by the external factors. In addition, the analysis of the metabolites of biological fluids can reflect the physiological and pathological states of the body. The method can judge the pathophysiology state of the born object, the function of genes, the toxicity and the efficacy of medicaments and the like by detecting spectrograms of a series of samples through metabonomics and combining a chemical pattern recognition method, and can possibly find out metabolites related to the spectrograms, analyze the diagnostic value of the metabolites and serve as a pathological evaluation index. Therefore, metabonomics has more advantages in the pathological analysis for researching the silkworm response virus infection.
The oil silkworm mutant op50 is a uric acid metabolism defective mutant, and the content of urate in the body of the oil silkworm mutant op50 is lower than that of normal silkworms, so that the oil silkworm mutant appears to be transparent or semitransparent epidermis. When the BmNPV infects four-instar p50 and op50, the oily silkworm mutant is found to have higher virus titer, which indicates that the op50 metabolic abnormality causes the resistance of the silkworm to be reduced compared with that of a normal silkworm, so that a molecular target with diagnostic value can be screened in the metabolic pathway, and the molecular target can be used as the standard for screening BmNPV resistant strain silkworms.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides a method for establishing a BmNPV resistant strain silkworm screening standard based on a metabonomics technology.
The technical scheme is as follows: the method for establishing the BmNPV resistant strain silkworm screening standard based on the metabonomics technology detects hemolymph metabonomics data before and after the p50 and the oil silkworm mutant op50 thereof infect BmNPV viruses based on the targeted metabonomics technology, and researches the change condition of metabolites after the viruses infect; the difference of metabolic patterns among different treated silkworm groups is displayed through OPLS-DA, the difference is accurately distinguished by a Fisher classification method, and then a biomarker with diagnostic value is analyzed by ROC; and identifying the biomarkers according to the pathway enrichment analysis and the metabolite database, and selecting the molecular targets for detection so as to establish the screening standard of the BmNPV resistant strain silkworm. The method comprises the following specific steps:
(1) collecting hemolymph samples of normal silkworm p50(p50-), oily silkworm op50(op50-), and p50(p50+) and op50(op50+) after the puncture injection of BmNPV;
(2) collecting metabolic map information of normal silkworm p50, oil silkworm op50, p50 and op50 hemolymph samples after BmNPV puncture injection;
(3) preprocessing metabonomics data;
(4) analyzing the difference significance of the data, screening out metabolites with significant changes, and recording as biomarkers;
(5) and (3) enriching and analyzing the passage of the metabolite, identifying the biomarkers according to a metabolite database, and establishing a screening standard of the BmNPV resistant strain silkworm by taking the selected molecular target as a basis for identifying the resistance level of the silkworm.
Further, the hemolymph sample for detection in step (2) is prepared as follows: thawing silkworm hemolymph at room temperature, adding acetonitrile for protein precipitation, stirring and mixing, centrifuging, and collecting supernatant.
Further, the process of step (2) includes: performing chromatographic separation on 28 metabolites of L-phenylalanine, L-asparagine, L-ornithine, L-lysine, L-methionine, L-histidine, L-tryptophan, hydroxyproline, L-citrulline, L-proline, L-cystine, L-aspartic acid, L-glutamine, L-arginine, citric acid, alpha-ketoglutaric acid, malic acid, succinic acid, L-cysteine, L-phenylalanine, L-serine, L-threonine, L-tyrosine, L-valine, pyruvic acid, sarcosine, fumaric acid and lactic acid, the 28 analytes tested were all endogenous metabolites present in silkworm hemolymph, used chemicals with a purity of greater than 98%, and were all dissolved in water.
Further, the step (3) of data preprocessing comprises: preprocessing original data through Progenetics QI software; then, according to the related parameter R in the result of the orthogonal partial least square discriminant analysis obtained by the software processing of SIMCA-P13.02X、R2Y and Q2Differences in metabolic profiles between different groups and relative intensities of metabolites were evaluated.
Further, the analysis of the significance of the difference in step (4) includes: analyzing statistical differences by using SPSS 23 software, and analyzing significant differences among different hemolymph samples through one-way variance analysis and independent sample T test; evaluating the diagnostic value of the measured data by Fisher classification, and distinguishing p50-, op50-, p50+ and op50+ according to a stepwise discrimination method; evaluating the classification accuracy by using a cross-validation method; selected metabolites will be further analyzed by ROC and combined with OPLS-DA analysis results to distinguish among the significantly different metabolites, which are scored as biomarkers.
Further, the process of step (5) includes: ca website analysis of metabolic pathway of differential metabolite enrichment, obtaining metabolic mechanism of p50 and op50 in response to BmNPV infection, then identifying the biomarkers through KEGG and HMDB metabolite database, and then selecting molecular targets for detection, thereby establishing the screening standard of BmNPV resistant strain silkworms.
Has the advantages that: the invention researches the difference of organism metabolic pathway changes of normal strain silkworm p50 and its oil silkworm mutant op50 after being infected by BmNPV based on metabonomics technology, screens related molecular targets, is used as an index for screening BmNPV-resistant silkworms, and provides theoretical basis and method for establishing screening standard of antiviral strain silkworms.
Drawings
FIG. 1 is a flow chart for establishing criteria for screening for disease resistance;
FIG. 2 is a UPLC-MS/MS chromatogram of a standard sample tested;
Detailed Description
The method is based on the targeted metabolome technology to detect hemolymph metabonomic data before and after p50 and the oil silkworm mutant op50 infect the BmNPV virus, study the change condition of the silkworm metabolite after the virus infection, search for related biomarkers, select molecular targets according to the silkworm metabolic mechanism, and finally establish the screening standard of the BmNPV resistant strain silkworms.
The examples of the invention are as follows: during breeding p50 in a silkworm room, an oil silkworm mutant op50 with semitransparent phenotype is found. Compared with normal silkworms, the silkworms have slower development and poorer immunocompetence. The metabolism process of the silkworm has important physiological functions, and some metabolic pathways have the function of immune regulation and are related to the disease resistance of the silkworm. The oil silkworm is a mutant strain of the silkworm with abnormal uric acid metabolism, but the abnormality of the metabolic pathway can cause the disorder of other metabolic pathways in the body of the silkworm, and the fact just indicates that the oil silkworm is an ideal material for researching the metabolic regulation and control mechanism of the silkworm. The reduction of the disease resistance of the oil silkworm op50 indicates that the regulation level of the metabolic pathway of the silkworm can be used as the basis for screening the resistant strain, the metabolic pathway enrichment analysis and the metabolite database are used for identifying the biomarker, the molecular target with diagnostic value is selected, and the screening standard of the BmNPV resistant strain silkworm is established.
The p50 and op50 silkworm and BmNPV viruses of the example are provided by the national academy of agricultural sciences silkworm research institute, and op50 is a natural mutationThe result is obtained; BmNPV is an engineered budding virus (BV-EGFP) containing enhanced green fluorescent protein. The silkworm used in the experiment is larva of five-instar dormancy, and a micro blood sampling straw is used for carrying out subcutaneous puncture injection of BV-EGFP on the silkworm larva, the injection amount is 2.5 mu L/head, the virus concentration is 1 multiplied by 108Pfu/mL; the control group silkworm was injected with TC-100 cell culture medium by puncture, and the injection amount was 2.5. mu.L/head. The silkworms with large bleeding volumes need to be discarded and reinjected during the operation. The silkworm after injection is normally fed with mulberry leaves, hemolymph is collected to 1.5mL EP tube after 24h, 48h and 72h, 1 tube is used for each silkworm, 100 mu L of each tube is used, 10 biological replicates are taken for each group, and thiourea is added into the tubes to prevent the hemolymph from being oxidized. And storing in a-80 ℃ ultra-low temperature refrigerator for later use immediately after collection.
The hemolymph sample of this example was prepared as follows: thawing silkworm hemolymph at room temperature, adding 300 μ L methanol into each tube, stirring, mixing, and vortexing for 1 min. After centrifugation at 12,000rpm for 10min at 4 ℃ the supernatant was collected into a 1.5mL EP tube. The supernatant was collected into a sample bottle after ice-cooling for 30min, and centrifugation at 12,000rpm for 8min at 4 ℃.
The hemolymph sample measurement conditions of this example are as follows: the 27 metabolites were chromatographed on a Waters ACQUITY UPLC amide C18 column (2.1 mm. times.100 mm, 1.7m) using a Xevo TQ-S miniature triple quadrupole mass spectrometer.
Positive ion mode measurement conditions: the mobile phase consisted of 0.1% ammonia (a) and acetonitrile (B), and the method used in the gradient elution was as follows: 0.0-0.3 min, 70% B; 0.3-1.0 min, 70-30% B; 1.0-2.0 min, 30% B; 2.0-2.3 min, 30-70% B; 2.3-3.0 min, 70% B. The flow rate is 0.4mL/min (0-3.0 min); the injection volume was 1. mu.L. Amino acid metabolites were detected in a Multiple Reaction Monitoring (MRM) mode, the mass spectrometric conditions required for the determination were: the temperature of the chromatographic column is 30 ℃, and the capillary voltage is 2.5 kV; the temperature of an ionization source is 150 ℃; the taper hole gas and the desolventizing gas are nitrogen, the desolventizing temperature is 600 ℃, the taper gas flow rate is 150L/h, and the desolventizing gas flow rate is 1000L/h.
Negative ion mode measurement conditions: the temperature of the chromatographic column is 30 ℃; the mobile phase consisted of acetonitrile (a) and 0.3% formic acid (B), and the method used in mobile phase gradient elution was 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 0.3mL/min (0-4.0 min); the equilibration time after gradient is 1 min; the injection volume was 2. mu.L. Amino acid metabolites were detected in a Multiple Reaction Monitoring (MRM) mode, the mass spectrometric conditions required for the determination were: capillary voltage 2.5 kV; the temperature of an ionization source is 150 ℃; the temperature of the desolventizing gas is 400 ℃, the cone gas flow rate is 150L/h, and the desolventizing gas flow rate is 1000L/h.
The 27 analytes tested were all endogenous metabolites present in silkworm hemolymph, with the chemicals used having a purity of greater than 98% and all dissolved in a 1: 1 solution of water to methanol at a concentration of 100. mu.g/mL. Before use, stock solutions were diluted to 0.075-20. mu.g/mL with water/methanol (v/v, 1: 1). Quality Control (QC) samples for testing accuracy and precision were prepared following the same procedure. The calibration curve is established by plotting the peak area ratio of all analytes against the concentration of a calibration standard. UPLC-MS/MS metabonomics data were obtained by Masslynx4.1 software.
The data processing procedure of this example is as follows: processing the original data by Progenetics QI software such as peak extraction, peak alignment, peak matching, peak intensity correction and the like; the related parameters (R) in the obtained OPLS-DA results were then processed according to SIMCA-P13.0 software2X、R2Y and Q2) Differences in metabolic profiles between different groups and relative intensities of metabolites were evaluated.
Statistical differences in metabolites were analyzed using SPSS 23 software, and significant differences between different hemolymph samples were analyzed by one-way analysis of variance and independent sample T-test. Differentiating differences among groups in the OPLS-DA result through Fisher classification; the classification accuracy is evaluated by a cross-validation method; by further analysis by ROC, significantly different metabolites were distinguished as biomarkers. Uploading the obtained product to Metabionalyst. ca website to analyze the enriched metabolic pathway and performing functional identification on the biomarker by using a metabolite database such as KEGG, HMDB and the like.
Based on the pathway enrichment analysis and the information comparison of the metabolite database, the metabolites of p50-, op50-, p50+ and op50+ are mainly regulated and changed in the pathways of sulfur metabolism, TCA cycle, urea cycle, glycolysis and amino acid metabolism. Three molecular targets of alpha-ketoglutaric acid, succinic acid and serine are screened out from the biomarkers with obvious fluctuation, obvious metabolic changes occur between p50 and op50 and after BmNPV infection, and the three molecular targets can be used as screening indexes of BmNPV resistant strain silkworms.
In conclusion, the hemolymph metabonomics data of p50-, op50-, p50+ and op50+ are analyzed on the basis of the UPLC-MS/MS technology; obtaining a biomarker with diagnostic value through OPLS-DA, Fisher classification and ROC analysis; the method is characterized in that biomarkers are identified according to the path enrichment analysis and the metabolite database, and the selected molecular targets are used as indexes for identifying the resistance of the bombyx mori BmNPV, so that the screening standard of the resistant strain bombyx mori is established.

Claims (6)

1. A method for establishing BmNPV resistant strain silkworm screening standard based on metabonomics technology is characterized in that: the method comprises the following steps:
(1) collecting hemolymph samples of normal silkworm p50(p50-), oily silkworm op50(op50-), and p50(p50+) and op50(op50+) after the puncture injection of BmNPV;
(2) collecting metabolic map information of normal silkworm p50, oil silkworm op50, p50 and op50 hemolymph samples after BmNPV puncture injection;
(3) preprocessing metabonomics data;
(4) analyzing the difference significance of the data, screening out metabolites with significant changes, and recording as biomarkers;
(5) and (3) enriching and analyzing the passage of the metabolite, identifying the biomarkers according to a metabolite database, and establishing a screening standard of the BmNPV resistant strain silkworm by taking the selected molecular target as a basis for identifying the resistance level of the silkworm.
2. The method for establishing the selection standard of the bombyx mori, which is a BmNPV resistant strain, based on the metabonomic technology according to claim 1, wherein: the preparation method of the hemolymph sample for detection in the step (2) is as follows: thawing silkworm hemolymph at room temperature, adding acetonitrile for protein precipitation, stirring and mixing, centrifuging, and collecting supernatant.
3. The method for establishing the selection standard of the bombyx mori, which is a BmNPV resistant strain, based on the metabonomic technology according to claim 1, wherein: the process of the step (2) comprises the following steps: performing chromatographic separation on 28 metabolites of L-phenylalanine, L-asparagine, L-ornithine, L-lysine, L-methionine, L-histidine, L-tryptophan, hydroxyproline, L-citrulline, L-proline, L-cystine, L-aspartic acid, L-glutamine, L-arginine, citric acid, alpha-ketoglutaric acid, malic acid, succinic acid, L-cysteine, L-phenylalanine, L-serine, L-threonine, L-tyrosine, L-valine, pyruvic acid, sarcosine, fumaric acid and lactic acid, the 28 analytes tested were all endogenous metabolites present in silkworm hemolymph, used chemicals with a purity of greater than 98%, and were all dissolved in water.
4. The method for establishing the selection standard of the bombyx mori, which is a BmNPV resistant strain, based on the metabonomic technology according to claim 1, wherein: the data preprocessing in the step (3) comprises the following steps: preprocessing original data through Progenetics QI software; and then evaluating the difference of metabolic spectra and the relative strength of metabolites among different groups according to related parameters R2X, R2Y and Q2 in an orthogonal partial least squares discriminant analysis result obtained by the software processing of SIMCA-P13.0.
5. The method for establishing the selection standard of the bombyx mori, which is a BmNPV resistant strain, based on the metabonomic technology according to claim 1, wherein: the difference significance analysis in the step (4) comprises the following steps: analyzing statistical differences by using SPSS 23 software, and analyzing significant differences among different hemolymph samples through one-way variance analysis and independent sample T test; evaluating the diagnostic value of the measured data by Fisher classification, and distinguishing p50-, op50-, p50+ and op50+ according to a stepwise discrimination method; evaluating the classification accuracy by using a cross-validation method; selected metabolites will be further analyzed by ROC and combined with OPLS-DA analysis results to distinguish among the significantly different metabolites, which are scored as biomarkers.
6. The method for establishing the selection standard of the bombyx mori, which is a BmNPV resistant strain, based on the metabonomic technology according to claim 1, wherein: the process of the step (5) comprises the following steps: ca website analysis of metabolic pathway of differential metabolite enrichment, obtaining metabolic mechanism of p50 and op50 in response to BmNPV infection, then identifying the biomarkers through KEGG and HMDB metabolite database, and then selecting molecular targets for detection, thereby establishing the screening standard of BmNPV resistant strain silkworms.
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CN117347643A (en) * 2023-12-05 2024-01-05 成都泰莱生物科技有限公司 Metabolic marker combination for judging benign and malignant pulmonary nodule, screening method and application thereof
CN117347643B (en) * 2023-12-05 2024-02-06 成都泰莱生物科技有限公司 Metabolic marker combination for judging benign and malignant pulmonary nodule, screening method and application thereof

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