CN114113569B - Method for establishing BmNPV resistance strain silkworm screening standard based on metabonomics technology - Google Patents

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

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CN114113569B
CN114113569B CN202111418130.3A CN202111418130A CN114113569B CN 114113569 B CN114113569 B CN 114113569B CN 202111418130 A CN202111418130 A CN 202111418130A CN 114113569 B CN114113569 B CN 114113569B
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bmnpv
silkworm
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CN114113569A (en
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王学杨
苏志浩
赵紫芹
吴阳春
李木旺
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Jiangsu University of Science and Technology
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6806Determination of free amino acids
    • G01N33/6812Assays for specific amino acids
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/50Mutagenesis
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B35/00ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
    • G16B35/20Screening of libraries
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B50/00ICT programming tools or database systems specially adapted for bioinformatics

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

Method for establishing BmNPV resistance 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 BmNPV resistance strain silkworm screening standards 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 pathway enrichment analysis and a metabolite database.
Background
Silkworm is an important economic insect, the agriculture-assisted income-increasing of the silkworm industry is greatly promoted, and the silkworm is a local important economic benefit source. In the pathological research of silkworms, virus diseases are one of the most harmful silkworm diseases in silkworm breeding, wherein the damage of silkworm blood type sepsis caused by silkworm nuclear polyhedrosis virus (BmNPV) is great. The sepsis is an acute infectious disease, and when a silkworm infects the disease, abnormal behaviors such as slow development, insomnia, inedibility and the like can occur; obvious symptoms such as swelling of body joints, milky white body color, milky white turbid blood flow and the like can appear during the onset of diseases. Therefore, screening and breeding the silkworm strain resisting BmNPV has important significance for improving the economic production of the silkworm industry.
Metabonomics is a new field developed in recent years, and unlike the complexity of gene and protein modification processing, metabolites reflect the environment of cells more, which is closely related to the nutritional status of cells and the influence of other external factors, and directly reflects the stress state of cells after external stimulus. In addition, analysis of metabolites of biological fluids can reflect physiological and pathological states of the body. Through metabonomics detection of spectrograms of a series of samples and combination of a chemical pattern recognition method, the pathophysiological state of an organism, the function of genes, the toxicity and the efficacy of medicines and the like can be judged, and metabolites related to the biological substances can be possibly found out, and the diagnostic value of the metabolites can be analyzed and used as a pathology assessment index. Metabonomics is therefore of great advantage in studying pathological analysis of silkworms in response to viral infection.
The oil silkworm mutant op50 is a uric acid metabolism defective mutant, and the uric acid salt content in the body is lower than that of normal silkworms, so that transparent or semitransparent epidermis is presented. When BmNPV infects four-age p50 and op50, the oil silkworm mutant is found to have higher virus titer, which shows that the resistance of the oil silkworm mutant is reduced compared with normal silkworms due to the abnormal op50 metabolism, so that molecular targets with diagnostic value can be screened out from the metabolic pathway of the oil silkworm mutant, and the oil silkworm mutant can be used as a standard for screening BmNPV resistant strain silkworms.
Disclosure of Invention
The invention aims to: the invention provides a method for establishing BmNPV resistance strain silkworm screening standard based on metabonomics technology.
The technical scheme is as follows: the method for establishing BmNPV resistance strain silkworm screening standard based on metabonomics technology detects haemolymph metabonomics data before and after p50 and its oil silkworm mutant op50 infect BmNPV virus based on targeting metabonomics technology, and researches the change condition of metabolite after virus infection; the differences of metabolic modes among different treatment silkworm groups are displayed through OPLS-DA, the differences are accurately distinguished through Fisher classification, and then the biological markers with diagnostic value are analyzed through ROC; and (3) identifying the biomarker according to the pathway enrichment analysis and the metabolite database, and selecting a molecular target for detection so as to establish a screening standard of the BmNPV resistant strain silkworm. The method comprises the following specific steps:
(1) Collecting normal silkworm p50 (p 50-), oil silkworm op50 (op 50-), and blood lymph samples of p50 (p 50+) and op50 (op 50+) after puncture injection of BmNPV respectively;
(2) Collecting metabolism map information of p50 and op50 haemolymph samples of normal silkworms p50 and oil silkworms op50 after puncture injection of BmNPV;
(3) Preprocessing metabonomics data;
(4) Analyzing the difference significance of the data, screening out metabolites with significant changes, and recording as biological markers;
(5) And (3) carrying out path enrichment analysis on the metabolites, identifying the biomarkers according to a metabolite database, taking the selected molecular targets as the basis for identifying the resistance level of silkworms, and establishing the screening standard of BmNPV resistance strain silkworms.
Further, the preparation method of the haemolymph 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.
Further, the process of step (2) includes: the 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 are chromatographed, and the 28 analytes detected are endogenous metabolites present in the silkworm hemolymph, the purity of the chemicals used is greater than 98%, and all the analytes are dissolved in water.
Further, the step (3) of data preprocessing includes: preprocessing the original data by Progenesis QI software; then according to the related parameter R in the orthogonal partial least squares discriminant analysis result obtained by the SIMCA-P13.0 software processing 2 X、R 2 Y and Q 2 Differences in metabolic profiles between the different groups and the relative intensities of metabolites were evaluated.
Further, the difference significance analysis of step (4) includes: analysis of statistical differences using SPSS 23 software, analysis of significant differences between different haemolymph samples by one-way analysis of variance 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 step-by-step discriminant method; evaluating classification accuracy by using a cross-validation method; the selected metabolites will be further analyzed by ROC and, in combination with the OPLS-DA analysis, the metabolites that differ significantly, noted as biological markers.
Further, the process of step (5) includes: the metabolic mechanism of p50 and op50 against BmNPV infection was obtained by analyzing the metabolic pathway enriched with differential metabolites through Metaboanalysis.ca website, these biomarkers were then identified by KEGG and HMDB metabolite databases, and molecular targets for detection were selected therein, thereby establishing screening criteria for BmNPV resistant strain silkworms.
The beneficial effects are that: the invention researches the differences of the changes of the body metabolic pathways of normal line silkworm p50 and its oil silkworm mutant op50 after being infected by BmNPV based on metabonomics technology, screens related molecular targets, is used as indexes for screening BmNPV-resistant silkworms, and provides theoretical basis and method for establishing screening standards of antiviral line silkworms.
Drawings
FIG. 1 is a flow chart for establishing disease resistance screening criteria;
FIG. 2 is a UPLC-MS/MS chromatogram of a standard sample tested.
Detailed Description
The invention detects haemolymph metabonomics data before and after p50 and the oil silkworm mutant op50 thereof infect BmNPV virus based on a targeting metabonomics technology, researches the change condition of silkworm metabolites after virus infection, searches related biomarkers, selects molecular targets according to silkworm metabolic mechanisms, and finally establishes the screening standard of BmNPV resistant strain silkworms.
Embodiments of the invention are as follows: during rearing of p50 in silkworm rearing room, a mutant op50 of oil silkworm, which is phenotypically translucent, was found. Compared with normal silkworms, the silkworm feed has slower development and poorer immunity. The metabolic process of silkworm has important physiological function, and some metabolic pathways have immune regulation function, and are related to disease resistance of silkworm. The oil silkworm is a mutant strain of silkworm with abnormal uric acid metabolism, but the abnormal metabolic pathway can lead to the disorder of other metabolic pathways in the body, but the oil silkworm is just an ideal material for researching the metabolic regulation mechanism of the silkworm. The decrease of the op50 disease resistance of the oil silkworms indicates that the regulation level of the metabolic pathways of the silkworms can be used as the basis for screening the resistant strain, and molecular targets with diagnostic value are selected based on the pathway enrichment analysis of the metabolites and the identification of biomarkers by a metabolite database, so as to establish the screening standard of the BmNPV resistant strain silkworms.
The p50 and op50 silkworm species and BmNPV viruses of this example are provided by the national academy of agricultural sciences of silkworm industry, and the op50 is generated by natural mutation; bmNPV is a modified budding virus (BV-EGFP) containing enhanced green fluorescent protein. The silkworms used in the experiment are all five-year-old sleeping larvae, and the silkworms are subjected to subcutaneous puncture injection of BV-EGFP (BV-EGFP) by using a micro blood collection straw, wherein the injection amount is 2.5 mu L/head, and the virus concentration is 1 multiplied by 10 8 Pfu/mL; the control group silkworms are injected with TC-100 cell culture medium in a penetrating way, and the injection quantity is 2.5 mu L/head. During the operation, the silkworms with large bleeding amount need to be discarded and re-injected. The injected silkworms are normally fed with mulberry leaves, blood lymph is collected into 1.5mL EP tubes after 24h, 48h and 72h, 1 tube is used for each silkworm, 100 mu L is used for each tube, 10 biological repetitions are taken for each group, and thiourea is added into the tubes to prevent the oxidation of the blood lymph. And after the collection, the materials are immediately stored in an ultralow temperature refrigerator at-80 ℃ for refrigeration for standby.
The preparation method of the haemolymph sample of the example is as follows: thawing silkworm hemolymph at room temperature, adding 300 μl of methanol into each tube, stirring, mixing, and swirling for 1min for thoroughly mixing. 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 centrifugation at 12,000rpm for 8min at 4℃with an ice bath for 30 min.
The conditions for the measurement of the haemolymph sample of this example are as follows: 28 metabolites were chromatographed on a Waters ACQUITY UPLC amide C18 column (2.1 mm X100 mm,1.7 m) using a Xex 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 procedure 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% of B; 2.3-3.0 min,70% B. The flow rate is 0.4-mL/min (0-3.0 min); the sample volume was 1. Mu.L. Amino acid metabolites were detected in a Multiple Reaction Monitoring (MRM) mode, and the mass spectrometry conditions required for the assay were: the chromatographic column temperature is 30 ℃, and the capillary voltage is 2.5 kV; the temperature of the ionization source is 150 ℃; the taper hole gas and the desolventizing gas are nitrogen, the desolventizing temperature is 600 ℃, the taper gas flow 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 procedure used in the mobile phase gradient elution was as follows: 0-0.2 min,5% A; 0.2-1.0 min,5% -90% of A; 1.0-1.5 min,90% A; 1.5-1.8 min,90% -5% of A; 1.8-4.0 min,90% A. The flow rate is 0.3-mL/min (0-4.0 min); the equilibration time after gradient was 1 min; the sample volume was 2. Mu.L. Amino acid metabolites were detected in a Multiple Reaction Monitoring (MRM) mode, and the mass spectrometry conditions required for the assay were: capillary voltage 2.5 kV; the temperature of the ionization source is 150 ℃; the desolventizing gas temperature is 400 ℃, the cone gas flow rate is 150L/h, and the desolventizing gas flow rate is 1000L/h.
All of the 28 analytes detected were endogenous metabolites present in silkworm hemolymph, all used chemicals were greater than 98% pure and all dissolved in water: methanol=1:1, at a concentration of 100 μg/mL. The stock solution is diluted to 0.075-20 μg/mL with water/methanol (v/v, 1:1) prior to use. Quality Control (QC) samples for testing precision and accuracy were prepared following the same procedure. Calibration curves were established by plotting the peak area ratio of all analytes against the concentration of the calibration standard. UPLC-MS/MS metabonomics data was obtained from Masslynx4.1 software.
The data processing procedure of this example is as follows: carrying out processing such as peak extraction, peak alignment, peak matching, peak intensity correction and the like on the original data through Progenesis QI software; relevant parameters (R) in the OPLS-DA results obtained by processing according to SIMCA-P13.0 software 2 X、R 2 Y and Q 2 ) Differences in metabolic profiles between the different groups and the relative intensities of metabolites were evaluated.
Statistical differences in metabolites were analyzed using SPSS 23 software, and significant differences between different haemolymph samples were analyzed by one-way analysis of variance and independent sample T-test. Distinguishing the group difference in the OPLS-DA result through Fisher classification; the classification accuracy is evaluated by a cross-validation method; metabolites with significant differences were distinguished as biomarkers by ROC further analysis. And then uploading to a Metaboanalysis.ca website to analyze the enriched metabolic pathways and perform functional identification on the biomarker through a KEGG, HMDB and other metabolite databases.
Based on pathway enrichment analysis and comparison of information in the metabolite database, p50-, op50-, p50+ and op50+ metabolites are regulated and changed mainly in sulfur metabolism, TCA cycle, urea cycle, glycolysis and amino acid metabolic pathways. Three molecular targets of alpha-ketoglutarate, succinic acid and L-serine are screened out from the biomarkers with remarkable fluctuation, and remarkable metabolic changes occur between p50 and op50 and after BmNPV infection, so that the three molecular targets can be used as screening indexes of BmNPV resistant strain silkworms.
In conclusion, the invention analyzes the haemolymph metabonomics data of p50-, op50-, p50+, op50+ based on UPLC-MS/MS technology; biomarkers of diagnostic value are obtained by OPLS-DA, fisher classification and ROC analysis; the molecular targets selected are used as indexes for identifying the BmNPV resistance of the silkworms according to the pathway enrichment analysis and the metabolite database, so that the screening standard of the resistant strain silkworms is established, and the invention meets the requirements of actual agricultural production.

Claims (6)

1. A method for establishing BmNPV resistance strain silkworm screening standard based on metabonomics technology is characterized in that: the method comprises the following steps:
(1) Collecting p50-, op 50-and p50+ haemolymph samples of normal silkworms after puncture injection of BmNPV;
(2) Collecting metabolism map information of p50+ and op50+ hemolymph samples of normal silkworms, oil silkworms op 50-and BmNPV after puncture injection;
(3) Preprocessing metabonomics data;
(4) Analysis of the significance of the difference in the data, a metabolite that has significantly changed from p 50-and op 50-and from p50+ and op50+ following BmNPV infection, was noted as a biomarker;
(5) And (3) carrying out path enrichment analysis on the metabolites, identifying the biomarkers according to a metabolite database, screening molecular targets from the biomarkers with remarkable fluctuation, taking the selected molecular targets as the basis for identifying the resistance level of silkworms, and establishing a screening standard of BmNPV resistance strain silkworms.
2. The method for establishing BmNPV-resistant line silkworm screening criteria based on metabonomics technology according to claim 1, wherein: the preparation method of the haemolymph sample used for detection in the step (2) comprises the following steps: thawing silkworm hemolymph at room temperature, adding acetonitrile for protein precipitation, stirring and mixing, centrifuging, and collecting supernatant.
3. The method for establishing BmNPV-resistant line silkworm screening criteria based on metabonomics technology according to claim 1, wherein: the process of step (2) comprises: the 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 are chromatographed, and the 28 analytes detected are endogenous metabolites present in the silkworm hemolymph, the purity of the chemicals used is greater than 98%, and all the analytes are dissolved in water.
4. The method for establishing BmNPV-resistant line silkworm screening criteria based on metabonomics technology according to claim 1, wherein: the step (3) of data preprocessing comprises the following steps: preprocessing the original data by Progenesis QI software; then the result of the orthogonal partial least squares discriminant analysis is processed according to SIMCA-P13.0 softwareRelated parameter R 2 X、R 2 Y and Q 2 Differences in metabolic profiles between the different groups and the relative intensities of metabolites were evaluated.
5. The method for establishing BmNPV-resistant line silkworm screening criteria based on metabonomics technology according to claim 1, wherein: the difference significance analysis of step (4) comprises: analysis of statistical differences using SPSS 23 software, analysis of significant differences between different haemolymph samples by one-way analysis of variance 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 step-by-step discriminant method; evaluating classification accuracy by using a cross-validation method; the selected metabolites will be further analyzed by ROC and, in combination with the OPLS-DA analysis, the metabolites that differ significantly, noted as biological markers.
6. The method for establishing BmNPV-resistant line silkworm screening criteria based on metabonomics technology according to claim 1, wherein: the process of step (5) comprises: the metabolic mechanism of p50 and op50 against BmNPV infection was obtained by analyzing the metabolic pathway enriched with differential metabolites through Metaboanalysis.ca website, these biomarkers were then identified by KEGG and HMDB metabolite databases, and molecular targets for detection were selected therein, thereby establishing screening criteria for BmNPV resistant strain silkworms.
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