WO2022166485A1 - Kit pour le diagnostic de la maladie à coronavirus 2019 - Google Patents

Kit pour le diagnostic de la maladie à coronavirus 2019 Download PDF

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WO2022166485A1
WO2022166485A1 PCT/CN2021/142779 CN2021142779W WO2022166485A1 WO 2022166485 A1 WO2022166485 A1 WO 2022166485A1 CN 2021142779 W CN2021142779 W CN 2021142779W WO 2022166485 A1 WO2022166485 A1 WO 2022166485A1
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characteristic
polypeptide
mass
seq
sequence
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廖璞
孙巍
乔亮
吕倩
马庆伟
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北京毅新博创生物科技有限公司
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/62—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating the ionisation of gases, e.g. aerosols; by investigating electric discharges, e.g. emission of cathode
    • G01N27/626—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating the ionisation of gases, e.g. aerosols; by investigating electric discharges, e.g. emission of cathode using heat to ionise a gas
    • G01N27/628—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating the ionisation of gases, e.g. aerosols; by investigating electric discharges, e.g. emission of cathode using heat to ionise a gas and a beam of energy, e.g. laser enhanced ionisation
    • G—PHYSICS
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    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/62—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating the ionisation of gases, e.g. aerosols; by investigating electric discharges, e.g. emission of cathode
    • G01N27/626—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating the ionisation of gases, e.g. aerosols; by investigating electric discharges, e.g. emission of cathode using heat to ionise a gas
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/62—Detectors specially adapted therefor
    • G01N30/72—Mass spectrometers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/569—Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6848—Methods of protein analysis involving mass spectrometry
    • G01N33/6851—Methods of protein analysis involving laser desorption ionisation mass spectrometry
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
    • G01N2030/8809—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample
    • G01N2030/8813—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample biological materials
    • G01N2030/8822—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample biological materials involving blood
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
    • G01N2030/8809—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample
    • G01N2030/8813—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample biological materials
    • G01N2030/8831—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample biological materials involving peptides or proteins
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/005—Assays involving biological materials from specific organisms or of a specific nature from viruses
    • G01N2333/08—RNA viruses
    • G01N2333/165—Coronaviridae, e.g. avian infectious bronchitis virus

Definitions

  • the invention belongs to the field of detection, and relates to a technology for rapidly detecting novel coronavirus pneumonia by utilizing time-of-flight mass spectrometry technology.
  • Coronaviruses are a class of pathogens that mainly cause respiratory and intestinal diseases. There are many regularly arranged protrusions on the surface of such virus particles, and the whole virus particle is like an imperial crown, hence the name "coronavirus". In addition to humans, coronaviruses can also infect pigs, cattle, cats, dogs, minks, camels, bats, mice, hedgehogs and other mammals, as well as a variety of birds.
  • the new coronavirus COVID-19 is a new strain of the new coronavirus that has never been found in the human body before. Its transmission law, infection mechanism, and evolution and variation law are still unclear, which brings difficulties to prevention and treatment.
  • the rapid detection of new coronavirus pneumonia is particularly important.
  • the identification of coronaviruses has used traditional microbiological detection methods, namely morphological, physiological and biochemical characteristics and serological identification. Although this method has high accuracy, it takes too long to complete, and it takes more than ten hours to complete, which is difficult to adapt to the requirements of rapid detection.
  • the nucleic acid detection method based on multiplex PCR is of great significance for the early diagnosis of coronavirus and the discovery of the source of infection.
  • multiplex PCR detection targets multiple genes, and the false negative rate is lower than that of single-plex PCR.
  • the PCR detection method also has the characteristics of cumbersome detection process, high cost, and limited high-throughput detection, so it cannot face new types of novel coronaviruses similar to the Wuhan outbreak in time. Large-scale, high-throughput rapid detection of coronavirus pneumonia is required.
  • Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry is a mass spectrometry technique that came out in the late 1980s and developed rapidly.
  • the mass analyzer is an ion dirft tube.
  • the ions generated by the ion source are first collected. In the collector, all ions have a velocity of 0, and are accelerated by a pulsed electric field into the field-free drift tube. The constant velocity is flying towards the ion receiver.
  • the larger the ion mass the longer it takes to reach the receiver; the smaller the ion mass, the shorter the time it takes to reach the receiver.
  • ions of different masses can be separated according to their mass-to-charge ratios, and the molecular mass and purity of biological macromolecules such as polypeptides, proteins, nucleic acids, and polysaccharides can be accurately detected, with high accuracy, high flexibility, and high throughput. , the detection cycle is short, cost-effective advantages.
  • the components were concentrated by centrifugation and ultrafiltration to the required volume, and Paecilomyces variotii was used as the sensitive test indicator bacteria to track the antifungal active components, and the determined active components were used to judge the purity of the obtained protein; A single band was identified by MALDI-TOF. This method is only suitable for specific microorganisms, and requires multiple protein purification processes. Finally, MALDI-TOF is used to identify the characteristic polypeptide Pc-Arctin. The process is cumbersome and the scope of application is narrow, which cannot achieve the purpose of detecting viruses by mass spectrometry.
  • this method uses conventional treatment (treatment with absolute ethanol, formic acid and acetonitrile, supplemented by centrifugation, and finally aspirating the supernatant for detection), although it can characterize the bacterium to a certain extent, due to its
  • the analyte contains proteins, lipids, lipopolysaccharides and lipo-oligosaccharides, DNA, polypeptides and other ionized molecules, and the obtained map is essentially a set of maps of the above-mentioned molecules, so both processing and comparison are required.
  • the amount of information in the spectrum is too large, and the characteristics of the spectrum are low due to the large size of the molecule to be detected. It is only suitable for a specific bacteria and cannot be extended to other large-scale virus detection.
  • MALDI-TOF-MS matrix-assisted laser desorption ionization time-of-flight mass spectrometry
  • the first object of the present invention provides a set of compositions based on serum peptidome characteristic polypeptides, which can detect novel coronavirus (COVID-19) by MALDI-TOF mass spectrometry, wherein the characteristic polypeptide composition comprises the following 25 characteristic peptides of mass-to-charge ratio: 5158m/z, 5366m/z, 5893m/z, 6357m/z, 6654m/z, 6939m/z, 7364m/z, 7614m/z, 8034m/z, 8043m/z, 8226m /z, 8425m/z, 8560m/z, 8986m/z, 9626m/z, 13719m/z, 13765m/z, 13886m/z, 14049m/z, 14095m/z, 14102m/z, 15123m/z, 15867m/z , 28091m/z, 28232m/z, or 29 characteristic polypeptides with
  • the serum sample is If it is a positive sample, that is, it is determined that the patient is a patient with new coronary pneumonia, and the accuracy rate of ten-fold cross-validation is about 91%.
  • the composition of the characteristic polypeptides comprises only characteristic polypeptides with mass-to-mass ratios of 8986 m/z, 28091 m/z, and 6939 m/z, 13886 m/z, 14049 m/z, and 14102 m/z, respectively.
  • the characteristic polypeptide 7614m/z, 8034m/z, 8226m/z, 8986m/z, 9626m/z, 11435m/z, 11495m/z, 11523m/z, 11680m/z, 15123m/z , 15867m/z, 28091m/z peaks are up-regulated, while the peaks of characteristic polypeptides 6939m/z, 13719m/z, 13765m/z, 13886m/z, 14049m/z, 14095m/z, 14102m/z are down-regulated, indicating that the The serum sample is a positive sample, that is, the patient is a patient with new coronary pneumonia, and the accuracy rate of ten-fold cross-validation is about 93.31%.
  • the composition of the characteristic polypeptides only comprises mass-to-mass ratios of 7614m/z, 8034m/z, 8226m/z, 8986m/z, 9626m/z, 11435m/z, 11495m/z, 11523m, respectively /z, 11680m/z, 15123m/z, 15867m/z, 28091m/z, and characteristic polypeptides of 6939m/z, 13719m/z, 13765m/z, 13886m/z, 14049m/z, 14095m/z, 14102m/z .
  • the second object of the present invention is to provide a mass spectrometry model for detecting novel coronavirus pneumonia, the mass spectrometry model is prepared from a characteristic polypeptide composition having a mass-to-charge ratio peak of any of the above schemes.
  • the mass spectrometry model consists of characteristic polypeptides 5158m/z, 5366m/z, 5893m/z, 6357m/z, 6654m/z, 6939m/z, 7364m/z, 7614m/z, 8034m/z, 8043m /z, 8226m/z, 8425m/z, 8560m/z, 8986m/z, 9626m/z, 13719m/z, 13765m/z, 13886m/z, 14049m/z, 14095m/z, 14102m/z, 15123m/z , 15867m/z, 28091m/z, 28232m/z, among which when the characteristic polypeptides 5158m/z, 5366m/z, 5893m/z, 7364m/z, 7614m/z, 8034m/z, 8043m/z, 8226m /z, 8425m/z, among which when
  • the mass spectrometry model is composed of characteristic polypeptides 5158m/z, 5366m/z, 5893m/z, 6357m/z, 6654m/z, 6939m/z, 7364m/z, 7614m/z, 8034m /z, 8043m/z, 8226m/z, 8425m/z, 8560m/z, 8986m/z, 9626m/z, 11435m/z, 11495m/z, 11523m/z, 11680m/z, 13719m/z, 13765m/z , 13886m/z, 14049m/z, 14095m/z, 14102m/z, 15123m/z, 15867m/z, 28091m/z, 28232m/z, among which when the characteristic polypeptides 5158m/z, 5366m/z, 5893m /z, 7364m/z, 76
  • the mass spectrometry model consists only of the following mass-to-combination ratios of 7614m/z, 8034m/z, 8226m/z, 8986m/z, 9626m/z, 11435m/z, 11495m/z, 11523m/z, respectively , 11680m/z, 15123m/z, 15867m/z, 28091m/z, and characteristic polypeptide compositions of 6939m/z, 13719m/z, 13765m/z, 13886m/z, 14049m/z, 14095m/z, 14102m/z Prepared, among which when characteristic polypeptides 7614m/z, 8034m/z, 8226m/z, 8986m/z, 9626m/z, 11435m/z, 11495m/z, 11523m/z, 11680m/z, 15123m/z, 15867m When the peaks of /z, 80
  • the mass spectrometry model is only prepared from the following characteristic polypeptide compositions with mass-to-mass ratios of 8986m/z, 28091m/z, 6939m/z, 13886m/z, 14049m/z, and 14102m/z, respectively , when the peaks of characteristic polypeptides 8986m/z and 28091m/z are up-regulated, while the peaks of characteristic polypeptides 6939m/z, 13886m/z, 14049m/z and 14102m/z are down-regulated, it means that the serum sample is a positive sample, that is, it is judged that The patient is a patient with new coronary pneumonia, and the accuracy rate of ten-fold cross-validation is about 91%.
  • the third object of the present invention is to provide a kit for detecting novel coronavirus pneumonia, which comprises the above-mentioned characteristic polypeptide composition, or the above-mentioned mass spectrometry model.
  • the polypeptide composition or mass spectrometry model consists of characteristic polypeptides 5158m/z, 5366m/z, 5893m/z, 6357m/z, 6654m/z, 6939m/z, 7364m/z, 7614m/z, 8034m /z, 8043m/z, 8226m/z, 8425m/z, 8560m/z, 8986m/z, 9626m/z, 13719m/z, 13765m/z, 13886m/z, 14049m/z, 14095m/z, 14102m/z , 15123m/z, 15867m/z, 28091m/z, 28232m/z, among which when the characteristic polypeptides 5158m/z, 5366m/z, 5893m/z, 7364m/z, 7614m/z, 8034m/z, 8043m /z, 8226m/z, 84
  • the polypeptide composition or mass spectrometry model consists of characteristic polypeptides 5158m/z, 5366m/z, 5893m/z, 6357m/z, 6654m/z, 6939m/z, 7364m/z, 7614m/z z, 8034m/z, 8043m/z, 8226m/z, 8425m/z, 8560m/z, 8986m/z, 9626m/z, 11435m/z, 11495m/z, 11523m/z, 11680m/z, 13719m/z, 13765m/z, 13886m/z, 14049m/z, 14095m/z, 14102m/z, 15123m/z, 15867m/z, 28091m/z, 28232m/z, among which when the characteristic polypeptides 5158m/z, 5366m/z z, 5893m/z, 7364m
  • the polypeptide composition or mass spectrometry model consists solely of the following mass-to-charge ratios of 7614m/z, 8034m/z, 8226m/z, 8986m/z, 9626m/z, 11435m/z, 11495m/z, respectively , 11523m/z, 11680m/z, 15123m/z, 15867m/z, 28091m/z, and 6939m/z, 13719m/z, 13765m/z, 13886m/z, 14049m/z, 14095m/z, 14102m/z Prepared from characteristic polypeptides, among which when characteristic polypeptides are 7614m/z, 8034m/z, 8226m/z, 8986m/z, 9626m/z, 11435m/z, 11495m/z, 11523m/z, 11680m/z, 15123m/z , 15867m/z
  • the polypeptide composition or mass spectrometry model is prepared only from the following characteristic polypeptides with mass-to-charge ratios of 8986m/z, 28091m/z, 6939m/z, 13886m/z, 14049m/z, 14102m/z, respectively.
  • characteristic polypeptides 8986m/z and 28091m/z are up-regulated, while the peaks of characteristic polypeptides 6939m/z, 13886m/z, 14049m/z and 14102m/z are down-regulated, it means that the serum sample is a positive sample. That is to say, the patient is determined to be a patient with new coronary pneumonia, and the accuracy rate of ten-fold cross-validation is about 91%.
  • the kit includes a sample treatment solution developed by Beijing Yixin Bochuang Biotechnology Co., Ltd.
  • the kit further includes a standard mass spectrometry sample tube to ensure the accurate molecular weight measured by the mass spectrometer, and the sample tube can be either a plurality of sample tubes containing a single characteristic polypeptide, or a plurality of characteristic polypeptides.
  • the kit can contain the software or chip of the standard database of the characteristic polypeptides mentioned above, which can be used to provide standard data or curve comparison when the samples to be tested are subjected to mass spectrometry, so as to judge the expression of the characteristic polypeptides in the samples to be tested. situation.
  • the fourth object of the present invention is to provide the use of the characteristic polypeptide composition, or the mass spectrometry model, in the preparation of a product for diagnosing novel coronavirus pneumonia.
  • the polypeptide composition or mass spectrometry model consists of characteristic polypeptides 5158m/z, 5366m/z, 5893m/z, 6357m/z, 6654m/z, 6939m/z, 7364m/z, 7614m/z, 8034m /z, 8043m/z, 8226m/z, 8425m/z, 8560m/z, 8986m/z, 9626m/z, 13719m/z, 13765m/z, 13886m/z, 14049m/z, 14095m/z, 14102m/z , 15123m/z, 15867m/z, 28091m/z, 28232m/z, among which when the characteristic polypeptides 5158m/z, 5366m/z, 5893m/z, 7364m/z, 7614m/z, 8034m/z, 8043m /z, 8226m/z, 84
  • the polypeptide composition or mass spectrometry model consists of characteristic polypeptides 5158m/z, 5366m/z, 5893m/z, 6357m/z, 6654m/z, 6939m/z, 7364m/z, 7614m/z z, 8034m/z, 8043m/z, 8226m/z, 8425m/z, 8560m/z, 8986m/z, 9626m/z, 11435m/z, 11495m/z, 11523m/z, 11680m/z, 13719m/z, 13765m/z, 13886m/z, 14049m/z, 14095m/z, 14102m/z, 15123m/z, 15867m/z, 28091m/z, 28232m/z, among which when the characteristic polypeptides 5158m/z, 5366m/z z, 5893m/z, 7364m
  • the polypeptide composition or mass spectrometry model consists solely of the following mass-to-mass ratios of 7614m/z, 8034m/z, 8226m/z, 8986m/z, 9626m/z, 11435m/z, 11495m/z, respectively , 11523m/z, 11680m/z, 15123m/z, 15867m/z, 28091m/z, and 6939m/z, 13719m/z, 13765m/z, 13886m/z, 14049m/z, 14095m/z, 14102m/z Prepared from characteristic polypeptides, among which when characteristic polypeptides are 7614m/z, 8034m/z, 8226m/z, 8986m/z, 9626m/z, 11435m/z, 11495m/z, 11523m/z, 11680m/z, 15123m/z , 15867m/
  • the polypeptide composition or mass spectrometry model is prepared only from the following characteristic polypeptides with mass-to-mass ratios of 8986m/z, 28091m/z, 6939m/z, 13886m/z, 14049m/z, 14102m/z, respectively
  • the peaks of characteristic polypeptides 8986m/z and 28091m/z are up-regulated, while the peaks of characteristic polypeptides 6939m/z, 13886m/z, 14049m/z and 14102m/z are down-regulated, it means that the serum sample is a positive sample. That is to say, the patient is determined to be a patient with new coronary pneumonia, and the accuracy rate of ten-fold cross-validation is about 91%.
  • the product for diagnosing new coronary pneumonia refers to any conventional product for diagnosing new coronary pneumonia, including: detection reagents, detection chips, detection carriers, and detection kits.
  • the fifth object of the present invention is to provide a construction method for preparing the mass spectrometry model, including:
  • step 5 performs quality control processing on the obtained data, and selects characteristic polypeptides with the following mass-to-charge ratio peaks: 5158m/z, 5366m/z, 5893m/z, 6357m/z, 6654m/z, 6939m/z, 7364m/z, 7614m/z, 8034m/z, 8043m/z, 8226m/z, 8425m/z, 8560m/z, 8986m/z, 9626m/z, 11435m/z, 11495m/z, 11523m/ z, 11680m/z, 13719m/z, 13765m/z, 13886m/z, 14049m/z, 14095m/z, 14102m/z, 15123m/z, 15867m/z, 28091m/z, 28232m/z, for the feature
  • the peptides were identified by secondary mass spectrometry, and a mass spectrometry
  • the mass spectrometry model of step 5 is only composed of the following mass-to-combination ratios of 7614m/z, 8034m/z, 8226m/z, 8986m/z, 9626m/z, 11435m/z, 11495m/z, Characteristics of 11523m/z, 11680m/z, 15123m/z, 15867m/z, 28091m/z, and 6939m/z, 13719m/z, 13765m/z, 13886m/z, 14049m/z, 14095m/z, 14102m/z Prepared from polypeptides, among which when characteristic polypeptides 7614m/z, 8034m/z, 8226m/z, 8986m/z, 9626m/z, 11435m/z, 11495m/z, 11523m/z, 11680m/z, 15123m/z, When the peaks
  • the mass spectrometry model of step 5 is only prepared from the following characteristic polypeptides with mass-to-mass ratios of 8986m/z, 28091m/z, 6939m/z, 13886m/z, 14049m/z, 14102m/z, respectively.
  • the peaks of characteristic polypeptides 8986m/z and 28091m/z are up-regulated, while the peaks of characteristic polypeptides 6939m/z, 13886m/z, 14049m/z and 14102m/z are down-regulated, it means that the serum sample is a positive sample. That is to say, the patient is determined to be a patient with new coronary pneumonia, and the accuracy rate of ten-fold cross-validation is about 91%.
  • the characteristic polypeptide compositions, mass spectrometry models, detection products, uses, and construction methods may involve only 19 species with the following mass-to-charge ratios and polypeptide sequences.
  • a characteristic polypeptide with a mass-to-charge ratio of 6939m/z whose polypeptide sequence is selected from the sequence shown in SEQ ID No.1;
  • a characteristic polypeptide with a mass-to-charge ratio of 7614m/z whose polypeptide sequence is selected from the sequence shown in SEQ ID No.2;
  • a characteristic polypeptide with a mass-to-charge ratio of 8034m/z whose polypeptide sequence is selected from the sequence shown in SEQ ID No.3;
  • a characteristic polypeptide with a mass-to-charge ratio of 8226m/z whose polypeptide sequence is selected from the sequence shown in SEQ ID No.4;
  • a characteristic polypeptide with a mass-to-charge ratio of 8986m/z whose polypeptide sequence is selected from the sequence shown in SEQ ID No.5;
  • a characteristic polypeptide with a mass-to-charge ratio of 9626m/z whose polypeptide sequence is selected from the sequence shown in SEQ ID No.6;
  • a characteristic polypeptide with a mass-to-charge ratio of 13719m/z whose polypeptide sequence is selected from the sequence shown in SEQ ID No.7;
  • a characteristic polypeptide with a mass-to-charge ratio of 13765m/z whose polypeptide sequence is selected from the sequence shown in SEQ ID No.8;
  • a characteristic polypeptide with a mass-to-charge ratio of 13886m/z whose polypeptide sequence is selected from the sequence shown in SEQ ID No.9;
  • a characteristic polypeptide with a mass-to-charge ratio of 14049m/z whose polypeptide sequence is selected from the sequence shown in SEQ ID No.10;
  • a characteristic polypeptide with a mass-to-charge ratio of 14095m/z whose polypeptide sequence is selected from the sequence shown in SEQ ID No.11;
  • a characteristic polypeptide with a mass-to-charge ratio of 14102 m/z whose polypeptide sequence is selected from the sequence shown in SEQ ID No.12;
  • a characteristic polypeptide with a mass-to-charge ratio of 15123m/z whose polypeptide sequence is selected from the sequence shown in SEQ ID No.13;
  • a characteristic polypeptide with a mass-to-charge ratio of 15867m/z whose polypeptide sequence is selected from the sequence shown in SEQ ID No.14;
  • a characteristic polypeptide with a mass-to-charge ratio of 28091m/z whose polypeptide sequence is selected from the sequence shown in SEQ ID No.15;
  • a characteristic polypeptide with a mass-to-charge ratio of 11435m/z whose polypeptide sequence is selected from the sequence shown in SEQ ID No.16;
  • a characteristic polypeptide with a mass-to-charge ratio of 11495m/z whose polypeptide sequence is selected from the sequence shown in SEQ ID No.17;
  • a characteristic polypeptide with a mass-to-charge ratio of 11523m/z whose polypeptide sequence is selected from the sequence shown in SEQ ID No.18;
  • a characteristic polypeptide with a mass-to-charge ratio of 11680 m/z whose polypeptide sequence is selected from the sequence shown in SEQ ID No. 19.
  • pretreatment comprises diluting serum proteins or polypeptides in the stabilized sample with a sample treatment solution.
  • the two groups of serum proteins are diluted and read by using a peptide mass spectrometry universal pretreatment kit to obtain the fingerprints of the two groups of serum polypeptides.
  • the same mass spectrometry parameters are used to detect the crystallization point of the blank matrix.
  • the following 8 characteristic peaks are selected as quality control peaks: 6426m/z, 6623m/z, 8753m/z, 8785m/z, 8904m/z z, 9118m/z, 9409m/z, 9700m/z.
  • the quality of mass spectrometry is affected by multiple conditions such as individual differences, sample quality, changes in ambient temperature and humidity, and the crystalline state of the sample and matrix.
  • the above-mentioned 8 characteristic peaks common in human serum were introduced as quality control peaks, and the appearance of quality control peaks has nothing to do with whether the patient suffers from novel coronavirus pneumonia.
  • 683 spectra can detect all 8 QC peaks (accounting for 81.0% of the total spectra), and 156 spectra can detect 7 QC peaks. (18.5% of total spectra).
  • the following spectrum quality control conditions are set: in the spectrum of a single sample, the number of quality control peaks appears 6 to 8 and the deviation of the molecular weight of the internal standard peak is less than 0.002 (or the offset range does not exceed 2 ⁇ ). Qualified for quality control. Unacceptable spectra need to be retested.
  • the present invention combines bioinformatics methods to screen out corresponding novel coronavirus pneumonia markers and establish a detection model for analysis and detection. Serum signature polypeptides and building mass spectrometry models, and optionally including the use of LR algorithms to build and validate mass spectrometry models, etc. Wherein, in the experimental quality control treatment, the mass spectrum data with the number of internal standard peaks not less than 6 are retained, and the internal standard peak is used for secondary calibration of the spectrum.
  • Ten-fold cross-validation the English name is 10-fold cross-validation, is used to test the accuracy of the algorithm. is a commonly used test method. The dataset is divided into ten parts, and 9 of them are used as training data and 1 is used as test data in turn for experimentation. Each trial results in a corresponding correct rate (or error rate). The average value of the correct rate (or error rate) of the 10 results is used as an estimate of the accuracy of the algorithm. Generally, multiple 10-fold cross-validation (for example, 10 times of 10-fold cross-validation) is required, and then the average value is calculated as the algorithm. Estimates of accuracy. It should be noted that the ten-fold cross-validation accuracy is correlated but not equivalent to the actual detection accuracy (or sensitivity).
  • the effect conforms to the ten-fold cross-validation accuracy rate of the confidence interval. If there is a correlation change with the number of characteristic polypeptides and reaches a value that is feasible for clinical diagnosis, it indicates that these polypeptides
  • the constructed mass spectrometry model meets the requirements of clinical diagnosis.
  • SAA protein (Serum amyloid A protein) belongs to the serum amyloid A family, is an acute phase response protein, and belongs to the heterogeneous class of proteins in the apolipoprotein family. There are four serum amyloid A genes in the human body, namely SAA1-SAA4, of which SAA1 and SAA2 are two proteins in the acute phase called A-SAA.
  • the present invention has the following advantages:
  • the present invention uses multiple characteristic protein combinations that are different between patients with new coronary pneumonia and normal people, pulmonary tuberculosis patients and control patients with symptoms of new coronary pneumonia to detect serum samples, and adopts traditional statistics and modern bioinformatics methods.
  • the combined method is used for data processing, so as to obtain the peptide fingerprint detection model of pneumonia patients, healthy people and other control patients, and the found series of protein mass-to-charge ratio peaks provide the basis for finding new and more ideal markers. resource.
  • the design of the construction method of the model of the present invention is reasonable and feasible, provides a new screening method for providing the clinical cure rate of new coronary pneumonia, and also provides a new idea for exploring the mechanism of the occurrence and development of new coronary pneumonia.
  • the present invention proposes to search for a combination of multiple characteristic proteins with differences based on 146 confirmed patients with new coronary pneumonia, 46 normal people, 33 pulmonary tuberculosis patients, and 73 controls with symptoms of new coronary pneumonia, breaking through the traditional only It is limited to the research idea of finding characteristic peptides in normal people and patients with new coronary pneumonia, which can effectively avoid the infection of false positive results similar to the symptoms of new coronary pneumonia.
  • the detection accuracy rate reaches 99%, the sensitivity is 98%, and the specificity is 100%.
  • the results show that the serum peptidomics characteristic polypeptide model of the present invention can be quickly used for screening new crowns in the population pneumonia patient.
  • the newly introduced 4 characteristic characteristic peptides (ie SEQ ID NOs: 16-19) belong to the SAA protein marker family, which can be used as biomarkers in clinical practice Bacterial and viral infections are diagnosed by ELASA, immunoturbidimetric method, colloidal gold method, and immunofluorescence chromatography.
  • the present invention proposes for the first time to use SAA protein markers for laser flight mass spectrometry to detect viruses, and for the first time accurately identify specific SAA protein sequences (ie SEQ ID NO: 16- 19), which can effectively avoid the clinical misdiagnosis of normal samples.
  • Figure 1 Comparison of serum polypeptide fingerprints of different groups (healthy group, pulmonary tuberculosis group, similar symptom group, and new crown patient group).
  • Figure 2-1 The 20 most repetitive peaks in LASSO.
  • Figure 2-2 The 20 most important peaks of VIP change in PLS-DA.
  • Figure 2-3 Top 10 peaks with the highest cross-validation accuracy in RFECV.
  • Figure 3 The intensity of each characteristic peak, in which the left column is the negative control group, and the right column is the positive control group.
  • Figure 4-1 Various machine learning methods, comparison of training set ROC curves.
  • Figure 4-2 Test set ROC curve comparison.
  • Figure 5 Predicted results of the test set confusion matrix for the true grouping.
  • Figure 6 Process flow for establishing a mass spectrometry model of characteristic peptides for rapid screening of patients with new coronary pneumonia (COVID-19).
  • Figure 7 The mass spectrum peak of the characteristic peptide m/z 5157.6, the upper image is the mass spectrum of the non-COVID-19 control, and the lower image is the COVID-19 mass spectrum.
  • Figure 8 The mass spectrum peak of the characteristic peptide m/z 5366.2, the upper image is the mass spectrum of the non-COVID-19 control, and the lower image is the COVID-19 mass spectrum.
  • Figure 9 The mass spectrum of the characteristic peptide m/z 5892.9, the top is the mass spectrum of the non-COVID-19 control, and the bottom is the COVID-19 mass spectrum.
  • Figure 10 The mass spectrum peak of the characteristic peptide m/z 6357.4, the top is the mass spectrum of the non-COVID-19 control, and the bottom is the COVID-19 mass spectrum.
  • Figure 11 The mass spectrum peaks of the characteristic peptide m/z 6654.0, the top is the mass spectrum of the non-COVID-19 control, and the bottom is the COVID-19 mass spectrum.
  • Figure 12 The mass spectrum peaks of the characteristic polypeptide m/z 6939.1, the top is the mass spectrum of the non-COVID-19 control, and the bottom is the COVID-19 mass spectrum.
  • Figure 13 The mass spectrum peak of the characteristic polypeptide m/z 7364.2, the upper image is the mass spectrum of the non-COVID-19 control, and the lower image is the COVID-19 mass spectrum.
  • Figure 14 The mass spectrum peak of the characteristic polypeptide m/z 7614.2, the upper image is the mass spectrum of the non-COVID-19 control, and the lower image is the COVID-19 mass spectrum.
  • Figure 15 The mass spectrum peak of the characteristic polypeptide m/z 8034.3, the upper image is the mass spectrum of the non-COVID-19 control, and the lower image is the COVID-19 mass spectrum.
  • Figure 16 The mass spectrum peak of the characteristic polypeptide m/z 8042.7, the top is the mass spectrum of the non-COVID-19 control, and the bottom is the COVID-19 mass spectrum.
  • Figure 17 The mass spectrum of the characteristic peptide m/z 8226.4, the top is the mass spectrum of the non-COVID-19 control, and the bottom is the COVID-19 mass spectrum.
  • Figure 18 The mass spectrum peak of the characteristic polypeptide m/z 8424.9, the upper image is the mass spectrum of the non-COVID-19 control, and the lower image is the COVID-19 mass spectrum.
  • Figure 19 The mass spectrum of the characteristic peptide m/z 8559.8, the top is the mass spectrum of the non-COVID-19 control, and the bottom is the COVID-19 mass spectrum.
  • Figure 20 The mass spectrum peak of the characteristic peptide m/z 8986.1, the upper image is the mass spectrum of the non-COVID-19 control, and the lower image is the COVID-19 mass spectrum.
  • Figure 21 The mass spectrum peak of the characteristic polypeptide m/z 9626.4, the upper image is the mass spectrum of the non-COVID-19 control, and the lower image is the COVID-19 mass spectrum.
  • Figure 22 The mass spectrum peak of the characteristic polypeptide m/z 13719.2, the top is the mass spectrum of the non-COVID-19 control, and the bottom is the COVID-19 mass spectrum.
  • Figure 23 The mass spectrum of the characteristic peptide m/z 13765.2, the top is the mass spectrum of the non-COVID-19 control, and the bottom is the COVID-19 mass spectrum.
  • Figure 24 The mass spectrum peak of the characteristic polypeptide m/z 13886.1, the upper image is the mass spectrum of the non-COVID-19 control, and the lower image is the COVID-19 mass spectrum.
  • Figure 25 The mass spectrum peak spectrum of the characteristic polypeptide m/z 14049.4, the upper image is the mass spectrum of the non-COVID-19 control, and the lower image is the COVID-19 mass spectrum.
  • Figure 26 The mass spectrum peak of the characteristic polypeptide m/z 14094.7, the upper image is the mass spectrum of the non-COVID-19 control, and the lower image is the COVID-19 mass spectrum.
  • Figure 27 The mass spectrum of the characteristic polypeptide m/z 14101.8, the top is the mass spectrum of the non-COVID-19 control, and the bottom is the COVID-19 mass spectrum.
  • Figure 28 The mass spectrum peak of the characteristic polypeptide m/z 15123.4, the upper image is the mass spectrum of the non-COVID-19 control, and the lower image is the COVID-19 mass spectrum.
  • Figure 29 The mass spectrum peak spectrum of the characteristic polypeptide m/z 15866.5, the upper image is the mass spectrum of the non-COVID-19 control, and the lower image is the COVID-19 mass spectrum.
  • Figure 30 The mass spectrum of the characteristic peptide m/z 28091.4, the top is the mass spectrum of the non-COVID-19 control, and the bottom is the COVID-19 mass spectrum.
  • Figure 31 The mass spectrum peak of the characteristic polypeptide m/z 28231.5, the upper image is the mass spectrum of the non-COVID-19 control, and the lower image is the COVID-19 mass spectrum.
  • Figure 32 The mass spectrum peak of the characteristic polypeptide m/z 11435.1, the upper image is the mass spectrum of the non-COVID-19 control, and the lower image is the COVID-19 mass spectrum.
  • Figure 33 The mass spectrum peak of the characteristic polypeptide m/z 11495.3, the upper image is the mass spectrum of the non-COVID-19 control, and the lower image is the COVID-19 mass spectrum.
  • Figure 34 The mass spectrum peak spectrum of the characteristic polypeptide m/z 11522.8, the upper image is the mass spectrum of the non-COVID-19 control, and the lower image is the COVID-19 mass spectrum.
  • Figure 35 The peak spectrum of the characteristic peptide m/z 11680.3, the upper image is the mass spectrum of the non-COVID-19 control, and the lower image is the COVID-19 mass spectrum.
  • the serum samples of 146 confirmed patients came from a hospital in Chongqing in February 2020. All patients were positive for nucleic acid tests and were strictly classified according to the guidelines.
  • the 152 non-coronary pneumonia serum samples used as controls were from a hospital in Chongqing in March 2020, including 46 normal people, 33 pulmonary tuberculosis patient controls, and 73 controls with new coronary pneumonia-type symptoms.
  • Mass spectrometry pretreatment of serum samples Before performing mass spectrometry detection experiments, extract 1 tube of subpackaged serum samples from the low-temperature refrigerator and place them on wet ice. Thaw for 60-90 minutes. Aspirate 5uL of serum sample, add 45uL of sample treatment solution, vortex at 1200rpm for 30s; aspirate 10uL of the treated sample solution, add 10uL of prepared matrix solution, vortex at 1200rpm for 30s; Spot 1uL of the mixture on the target plate, each sample needs Point three experiments to repeat, air dry naturally, and then perform mass spectrometry detection.
  • the matrix-assisted laser desorption time-of-flight mass spectrometry Clin-TOF and the general pretreatment kit for peptide mass spectrometry were developed by China Bioyong Company.
  • the MALDIquant program was used for data preprocessing, the square root transformation was performed on the processed data, and the filter fitting method was used for smoothing and baseline correction.
  • the mass spectrometer was calibrated with a mixture of peptides and proteins of known molecular weight. The mass drift of the calibrator should be within 500ppm. 500 spectra were collected for each sample point.
  • the molecular weight collection range is m/z 3000 ⁇ 30000.
  • Figure 1 Comparison of serum polypeptide fingerprints of different groups, which from top to bottom are negative healthy people, negative pulmonary tuberculosis, negative similar symptoms, and positive new crown patients).
  • negative healthy people 5158m/z, 5366m/z, 5893m/z, 7364m/z, 7614m/z, 8034m/z, 8043m/z, 8425m/z, 8560m/z, 8986m/z, 9626m/ z, 11435m/z, 11523m/z, 15123m/z, 15867m/z, 28091m/z have lower peak intensities, while 6357m/z, 6654m/z, 6939m/z, 13719m/z, 13765m/z, 13886m/ The peak intensities of z, 14049m/z, 14095m/z, 14102m/z, and 28232m/z were higher
  • a Clin-TOF mass spectrometer was used. Set the appropriate laser energy to acquire a certain point of the crystallization point of the sample. 50 laser bombardment positions were selected for each sample point, and each position was bombarded 10 times, that is, 500 laser bombardments were performed on each sample crystallization point, and the spectra were collected. Laser frequency: 30Hz. Data collection range: 3 ⁇ 30KDa. Before each sample crystallization point was collected, an external standard calibration was performed with a standard, and the average molecular weight deviation was less than 500 ppm.
  • the original MALDI-TOF data were calibrated with the internal standard calibration software for secondary calibration, and saved as a txt file.
  • the m/z of the internal standard peaks used are: 6426 m/z, 6623 m/z, 8753 m/z, 8785 m/z, 8904 m/z, 9118 m/z, 9409 m/z, 9700 m/z.
  • the spectra were then processed with the MALDIquant program. Spectral processing includes smoothing, baseline correction, and molecular weight calibration. Peak detection is performed with a signal-to-noise ratio of 3. Peaks are binned using the binPeaks command with a tolerance of 0.002. Retain peaks with a peak frequency of not less than 25% in the group. Finally, the resulting matrix is used for the following analysis.
  • the peak intensity matrix was quantile normalized with the R package limma. In all samples, missing values are filled with the minimum value.
  • the COVID-19 patient data and control sample data were randomly divided into training group and test group with a distribution ratio of 2:1.
  • LASSO Least absolute shrinkage and selection operator, which is a compression estimation. It obtains a more refined model by constructing a penalty function, so that it compresses some regression coefficients, that is, forcing the sum of the absolute values of the coefficients to be less than a certain fixed value; at the same time, it sets some regression coefficients to zero. Therefore, the advantage of subset shrinkage is preserved, which is a biased estimation for dealing with complex collinear data.
  • Figure 2-1 shows the 20 most frequently repeated peaks in LASSO.
  • the vertical axis is the mass-to-nucleus ratio of each preferred characteristic peak.
  • Partial least squares discriminant analysis (PLS-DA) is a multivariate statistical analysis method used for discriminant analysis.
  • Discriminant analysis is a commonly used statistical analysis method to judge how the research object is classified according to the observed or measured values of several variables. The principle is to train the characteristics of different processing samples (such as observation samples and control samples) respectively to generate a training set and test the reliability of the training set.
  • Figure 2-2 shows the 20 most important peaks of VIP change in PLS-DA.
  • the vertical axis is the mass-to-nucleus ratio of each preferred characteristic peak.
  • RFECV refers to finding the optimal number of features through cross-validation. Among them, RFE (Recursive feature elimination) refers to recursive feature elimination, which is used to rate the importance of features.
  • CV Cross Validation refers to cross-validation, that is, after feature rating, through cross-validation, select the best number of features.
  • Figure 2-3 shows the 10 peaks with the highest cross-validation accuracy in RFECV.
  • the vertical axis is the mass-to-nucleus ratio of each preferred characteristic peak.
  • each characteristic peak is shown in Figure 3.
  • Each row in the figure represents a characteristic peak
  • each column represents a spectral data
  • the shade of color in the figure represents the intensity of the peak.
  • the left column is the negative control group
  • the right column is the positive group.
  • the peaks of characteristic polypeptides 6357m/z, 6654m/z, 6939m/z, 13719m/z, 13765m/z, 13886m/z, 14049m/z, 14095m/z, 14102m/z, 28232m/z are in the negative group
  • the expression level is generally higher than that of the positive group, and the characteristic polypeptides are 5158m/z, 5366m/z, 5893m/z, 7364m/z, 7614m/z, 8034m/z, 8043m/z, 8226m/z, 8425m/z, 8560m/z , 8986m/z, 9626m/z, 11435m/z, 11495m/z, 11523m/z, 11680m/z, 15123m/z, 15867m/z, 28091m/z peaks in the positive group were generally higher than the negative group. The intensity of these peaks differed
  • LR Logistic Regression
  • SVM Support Vector Machine
  • RF Random Forest
  • NB Naive Bayes
  • GBDT Gradient Descent Tree
  • KNN K-Nearest Algorithm
  • DT Decision Tree
  • Adaboost Adaptive Boosting Algorithm
  • Figure 4-1 and Figure 4-2 show the model results of the training group and the test group in the form of ROC curves, respectively.
  • the ROC curve is a curve drawn according to a series of different binary classification methods (cutoff value or decision threshold), with the true positive rate (sensitivity) as the ordinate and the false positive rate (1-specificity) as the abscissa.
  • AUC area under the ROC curve
  • the confusion matrix of the LR model in the test set is shown in Figure 5.
  • the vertical axis in the figure represents the actual grouping of the samples, the upper row represents the number of negative samples, and the lower row represents the number of positive samples; the horizontal axis represents the model prediction results, and the left column represents the judgment by the model.
  • the number of samples that are negative, and the right column represents the number of samples that are judged to be positive by the model.
  • the judgment accuracy (ie model specificity) of the negative samples was 100%; among the 49 positive samples, 1 was misjudged as negative, and 48 were judged as positive,
  • the positive sample judgment accuracy (ie model sensitivity) was 98.0%.
  • the process includes: (1) collecting new coronary pneumonia patients and negative control populations and collecting serum samples; (2) using a kit to preprocess the serum samples by mass spectrometry; (3) MALDI-TOF MS mass spectrometry detection to obtain spectral information; (4) spectral processing and obtaining peak list; (5) bioinformatics analysis; (6) determination of mass spectrometry model.
  • Example 3 Establishment of a screening model for patients with new coronary pneumonia
  • the remaining samples (49 patients with new coronary pneumonia, 12 normal people, 14 cases of tuberculosis, and 25 cases of new coronary pneumonia-like symptoms) were used as validation samples for blind selection tests.
  • the processing method is the same as above.
  • a mass spectrometry model of the novel coronavirus pneumonia polypeptide was established by using the serum characteristic polypeptide peaks of the novel coronavirus pneumonia patients screened in Example 1-2.
  • the model is determined to use 29 characteristic peaks, namely: 5158m/z, 5366m/z, 5893m/z, 6357m/z, 6654m/z, 6939m/z, 7364m/z, 7614m/z, 8034m/z, 8043m /z, 8226m/z, 8425m/z, 8560m/z, 8986m/z, 9626m/z, 11435m/z, 11495m/z, 11523m/z, 11680m/z, 13719m/z, 13765m/z, 13886m/z , 14049m/z, 14095m/z, 14102m/z, 15123m/z, 15867m/z, 28091m/z, 28232m
  • the training set and validation set AUC of the LR model are both greater than 0.99.
  • the accuracy of the test set is 99%, the sensitivity is 98%, and the specificity is 100%.
  • the model has good predictive power.
  • the results of the training group samples are: 34 of the 34 normal patients were judged correctly, with a specificity of 100.00%; 97 of the 97 patients were judged correctly, with a sensitivity of 100.00%; 19 patients with pulmonary tuberculosis Of the 19 cases, the judgment was correct, and the sensitivity was 100.00%; 48 of the 48 patients with similar symptoms were correct, and the sensitivity was 100.00%.
  • peaks to be identified are determined according to Examples 2 and 3, 7 serum samples with different intensities of the peaks to be identified in the pre-processed samples are searched. After the sample was reduced by DTT, ultrafiltration and centrifugation were performed to remove proteins with a molecular weight greater than 50 kDa. The filtered small protein/polypeptide was separated by tricine-SDS-PAGE. After in-gel digestion, each band was identified by secondary mass spectrometry.
  • Peptide sequence identification was performed using nano-LC-MS/MS platforms, including nanoflow HPLC (Thermo Fisher Scientific, USA) and Q-Exactive mass spectrometer (Thermo Fisher Scientific, USA).
  • the ion mode is positive ion mode, and the scanning range is 300-1400 m/z.
  • the resolution of the primary mass spectrum was 70,000, and the resolution of the secondary mass spectrum was 17,500.
  • Liquid analysis column Model: Exsil Pure 120 C18 (Dr. Maisch GmbH, USA); Specification: 360 ⁇ m ⁇ 12cm; Inner diameter: 150 ⁇ m; Particle: 1.9um.
  • Elution mode The mobile phase was linearly eluted from 7% B solution (80% acetonitrile, 0.1% formic acid) to 45% B solution. Flow rate: 600 nl/min; total time 38 minutes. The identification results are shown in Tables 3 and 4.
  • a model involving input variables of 25 characteristic polypeptide fragments of SEQ ID NO: 1-15 and a model of input variables involving 29 characteristic polypeptide fragments of SEQ ID NO: 1-19 are established,
  • a model of the input variables of the sequenced 19 characteristic polypeptide fragments ie, sequences SEQ 1-19 was established.
  • Example 3 According to the method of Example 3, the above three models were used to blindly select and predict the samples of 49 patients with new coronary pneumonia, 12 normal people, 14 cases of pulmonary tuberculosis, and 21 cases of type symptoms, and determine the type of the sample.
  • the method is the same as the above implementation. example.
  • the results are shown in Table 5-1, Table 5-2, and Table 5-3, respectively.
  • the blind selection detection accuracy of the novel coronavirus pneumonia group using the complete variables of 29 characteristic polypeptides in the present invention is basically the same as that of the model training, but the prediction result for the non-new coronavirus group reaches 100%.
  • the experimenter can completely eliminate false positive results through subtle optimization, which shows that the diagnosis results of positive results are true and reliable, and the missed diagnosis and/or misdiagnosis are avoided to the greatest extent, so it has positive significance.

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Abstract

L'invention concerne une composition polypeptidique caractéristique pour détecter le COVID-19, comprenant 29 polypeptides caractéristiques ayant un rapport masse-charge spécifique. L'appartenance de l'échantillon à un patient atteint de COVID-19 peut être déterminée au moyen de l'analyse de l'expression des polypeptides caractéristiques. L'invention concerne également des utilisations d'un modèle de spectrométrie de masse produit selon la composition polypeptidique caractéristique, un produit pour le diagnostic de COVID-19, etc. Il a été proposé pour la première fois de rechercher de multiples combinaisons de protéines caractéristiques présentant des différences en fonction de patients atteints de COVID-19/personnes normales, de patients souffrant de symptômes témoins de type tuberculose et COVID-19, permettant de rompre avec les réflexions de recherches classiques, lesquelles sont limitées à la recherche de polypeptides caractéristiques chez des personnes normales et des patients atteints de COVID-19, et permettant d'éviter efficacement, en raison de faux résultats positifs, une infection similaire aux symptômes du COVID-19, et présentant un fonctionnement simple, de faibles coûts de détection, une grande précision, et étant prévues pour être utilisées pour un criblage à grande échelle pour le COVID-19.
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WO2023125749A1 (fr) * 2021-12-30 2023-07-06 北京毅新博创生物科技有限公司 Procédé pour évaluer si un individu achève une vaccination ou un changement immunitaire individuel

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Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005043111A2 (fr) * 2003-07-14 2005-05-12 Ciphergen Biosystems, Inc. Marqueurs biologiques seriques pour le sras
US20050266397A1 (en) * 2003-04-26 2005-12-01 Ecker David J Methods for identification of coronaviruses
CN101196526A (zh) * 2006-12-06 2008-06-11 许洋 一种结核快速诊断的质谱分析试剂盒和方法
CN111220686A (zh) * 2018-11-23 2020-06-02 中国科学院大连化学物理研究所 一种基于病毒鉴定的质谱数据库建立方法
WO2020128538A1 (fr) * 2018-12-19 2020-06-25 Rudjer Boskovic Institute Nouveau procédé d'identification de virus et kit de diagnostic l'utilisant
CN111455062A (zh) * 2020-04-01 2020-07-28 中国人民解放军总医院 一种用于新型冠状病毒易感基因检测的试剂盒及平台
CN111830120A (zh) * 2020-06-10 2020-10-27 北京东西分析仪器有限公司 应用质谱系统鉴定新冠病毒的试剂盒及其使用方法
CN112798679A (zh) * 2020-10-16 2021-05-14 北京毅新博创生物科技有限公司 用于诊断新冠肺炎的试剂盒

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
ATE497506T1 (de) * 2003-04-15 2011-02-15 Ca Minister Health & Welfare Sars-verwandte proteine
CN110632326A (zh) * 2019-10-01 2019-12-31 北京毅新博创生物科技有限公司 用于质谱诊断地中海贫血症的特征蛋白标记组合物及其诊断产品
CN111499692B (zh) * 2020-06-16 2020-12-04 国家纳米科学中心 靶向新型冠状病毒covid-19的多肽及其应用

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050266397A1 (en) * 2003-04-26 2005-12-01 Ecker David J Methods for identification of coronaviruses
WO2005043111A2 (fr) * 2003-07-14 2005-05-12 Ciphergen Biosystems, Inc. Marqueurs biologiques seriques pour le sras
CN101196526A (zh) * 2006-12-06 2008-06-11 许洋 一种结核快速诊断的质谱分析试剂盒和方法
CN111220686A (zh) * 2018-11-23 2020-06-02 中国科学院大连化学物理研究所 一种基于病毒鉴定的质谱数据库建立方法
WO2020128538A1 (fr) * 2018-12-19 2020-06-25 Rudjer Boskovic Institute Nouveau procédé d'identification de virus et kit de diagnostic l'utilisant
CN111455062A (zh) * 2020-04-01 2020-07-28 中国人民解放军总医院 一种用于新型冠状病毒易感基因检测的试剂盒及平台
CN111830120A (zh) * 2020-06-10 2020-10-27 北京东西分析仪器有限公司 应用质谱系统鉴定新冠病毒的试剂盒及其使用方法
CN112798679A (zh) * 2020-10-16 2021-05-14 北京毅新博创生物科技有限公司 用于诊断新冠肺炎的试剂盒

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
SUN ZEYU, ET AL: "Mass Spectrometry Analysis of Newly Emerging Coronavirus HCoV-19 Spike Protein and Human ACE2 Reveals Camouflaging Glycans and Unique Post-Translational Modifications", ENGINEERING, vol. 7, no. 10, 1 October 2021 (2021-10-01), pages 1441 - 1451, XP055957278, ISSN: 2095-8099, DOI: 10.1016/j.eng.2020.07.014 *
WANG, JUN: "Establishment and Application of PCR-Time-of-flight Mass Spectrometer for SARS-CoV-2 Nucleic Acid Detection", CHINESE GENERAL PRACTICE, ZHONGGUO QUANKE YIXUE, CN, vol. 23, no. 35, 1 December 2020 (2020-12-01), CN , pages 4430 - 4442, XP055956678, ISSN: 1007-9572, DOI: 10.12114/j.issn.1007-9572.2020.00.588 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023125749A1 (fr) * 2021-12-30 2023-07-06 北京毅新博创生物科技有限公司 Procédé pour évaluer si un individu achève une vaccination ou un changement immunitaire individuel

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