Method for constructing a microbial proteome database and method for identifying a microorganism

Abstract

The present invention provides a method for constructing a bacterial database and a method for identifying mixed bacteria by using the same.

Description

Technical Field

[0001] The present invention relates to the field of bacterial identification. More specifically, it relates to a method for constructing a bacterial proteome database and a method for identifying mixed bacteria. Background Art

[0002] In clinical practice, traditional bacterial identification methods include co-culturing with multiple auxiliary selective media, observing the appearance morphology, Gram staining to observe the morphology under a microscope, and some biochemical identification methods. In addition, there are currently identification methods such as gene sequencing and molecular biology. These methods are respectively characterized by time-consuming, complex technology, and high cost. They cannot meet the requirements of rapid and high-throughput identification of clinical specimens.

[0003] Matrix-assisted laser desorption (MALDI) was first proposed by Hillenkamp and karas in 1987. Its advantage lies in that the sample is mixed with a chemical matrix to generate ions, greatly reducing the sample pretreatment work before analysis. Matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF MS) combines two technologies, including matrix-assisted laser desorption (MALDI) and time-of-flight mass spectrometry (TOF). By combining the MALDI "soft ionization" technology with time-of-flight mass spectrometry, it realizes the rapid and accurate measurement of the molecular weight of biological macromolecules. This technology has been quickly applied to microbial identification and is increasingly favored by the clinical detection field. However, in clinical applications, most are for single colony identification, which increases the time for subculture and purification, and the establishment of an accurate strain spectrum information database will improve the role and application potential of MALDI-TOF MS in microbial identification. Summary of the Invention

[0004] The present invention provides a method for constructing a microbial proteome database and a method for identifying mixed microorganisms using the microbial proteome database. The microbial proteome database constructed by the present invention can accurately identify mixed microorganisms, saving the time for subculture and purification during single colony identification.

[0005] In a first aspect, the present invention provides a method for constructing a microbial proteome database, the method comprising the following steps:

[0006] (a) Establishing a set of n protein databases based on microbial species or genera, each protein database corresponding to a species or a genus of microorganisms, where n≥2; and

[0007] (b) Comparing the proteins in every two of the n protein databases, and removing the proteins that interfere with each other from the respective databases to form a set of n×(n - 1) new databases.

[0008] In some embodiments, a set of n protein databases are re-established based on an existing comprehensive database, with each database corresponding to a microorganism of a particular species or genus. The n protein databases correspond to microorganisms of n species or n genera.

[0009] In some embodiments, the comprehensive database includes, but is not limited to, the NCBInr database, the UniProt database, the BioGRID database, the Database of Interacting Proteins database, the MINT database, etc.

[0010] In some embodiments, databases are established at the genus level for microorganisms with small inter-species protein differences and at the species level for microorganisms with large inter-species protein differences.

[0011] In some embodiments, the microorganism is a bacterium or a fungus.

[0012] In some embodiments, the microorganism proteome database is a bacterium proteome database.

[0013] In some embodiments, the microorganism proteome database is a fungus proteome database.

[0014] The bacteria include, but are not limited to, Escherichia coli, Klebsiella pneumoniae, Citrobacter freundii, Enterobacter asburiae, Hafnia alvei, Serratia marcescens, Proteus vulgaris, Staphylococcus aureus, Staphylococcus epidermidis, Enterococcus faecium, Streptococcus pyogenes, Stenotrophomonas maltophilia, Acinetobacter baumannii, Burkholderia cepacia, Pseudomonas aeruginosa, Neisseria sicca, Salmonella enterica, Corynebacterium striatum, Bacillus subtilis, Group A Streptococcus, Mycobacterium tuberculosis, Legionella pneumophila, Neisseria gonorrhoeae, Listeria monocytogenes, Campylobacter, Bordetella pertussis, Campylobacter jejuni, Clostridium perfringens, Salmonella typhi, Shigella sonnei, Staphylococcus haemolyticus, Enterococcus faecalis, Enterococcus durans, Coagulase-negative Staphylococcus, Streptococcus viridans, Corynebacterium minutissimum, etc.

[0015] The fungi include, but are not limited to, Candida albicans, Aspergillus niger, Cryptococcus, Pneumocystis jirovecii, Saccharomyces cerevisiae, Alternaria alternata, Trichoderma, Aureobasidium pullulans, Cladosporium cladosporioides, Gliocladium, Drechslera australiensis, Gliomastix murorum, Candida guilliermondii, Verticillium chlamydosporium, Penicillium, Phoma exigua, Pulvinaria papyracea, and Human Filobasidiella.

[0016] In some embodiments, each of the n×(n - 1) new databases contains 15 - 25 proteins.

[0017] In some embodiments, removing the proteins that interfere with each other from every two databases from their respective databases includes removing the proteins that interfere with each other with a Mass Tolerance in the range of ≤ 1000 - 2000 PPM, preferably in the range of ≤ 1200 - 1800 PPM, more preferably in the range of ≤ 1300 - 1700 PPM, more preferably in the range of ≤ 1300 - 1500 PPM, and even more preferably ≤ 1300 PPM.

[0018] In some embodiments, the protein is a ribosomal protein. In the case of a bacterial database, the protein is a 30S ribosomal protein and / or a 50S ribosomal protein. In the case of a fungal database, the protein is a 40S ribosomal protein and / or a 60S ribosomal protein.

[0019] In some embodiments, the method further includes the step of combining the specific proteins of every two protein databases in step (a) into a set of n×(n - 1) / 2 specific protein combination databases.

[0020] In some embodiments, the method further includes obtaining the specific proteins in each protein database of step (a), and the step of combining the specific proteins of every two protein databases into a set of n×(n - 1) / 2 specific protein combination databases.

[0021] In some embodiments, each of the specific protein combination databases contains 5 to 15 specific proteins for two species or two genera of bacteria. Preferably, the specific protein is a ribosomal protein. In the case of a bacterial database, the protein is a 30S ribosomal protein and / or a 50S ribosomal protein. In the case of a fungal database, the protein is a 40S ribosomal protein and / or a 60S ribosomal protein.

[0022] In a second aspect, the present invention provides an apparatus for constructing the microbial proteome database of the first aspect, the apparatus comprising:

[0023] (a) A first module for establishing a set of n protein databases in units of microbial species or genera, each protein database corresponding to a species or a genus of microorganisms, where n≥2; and

[0024] (b) A second module for comparing the proteins in every two of the n protein databases, removing the proteins that interfere with each other from their respective databases, and forming a set of n×(n - 1) new databases.

[0025] In some embodiments, a set of n protein databases is re-established based on the existing comprehensive data, with each database corresponding to a microorganism of a particular species or genus. The n protein databases correspond to microorganisms of n species or n genera.

[0026] In some embodiments, the comprehensive database includes, but is not limited to, the NCBInr database, the UniProt database, the BioGRID database, the Database of Interacting Proteins database, the MINT database, etc.

[0027] In some embodiments, databases are established at the genus level for microorganisms with small inter-species protein differences, and at the species level for microorganisms with large inter-species protein differences.

[0028] In some embodiments, the microorganisms are bacteria or fungi.

[0029] In some embodiments, the microorganism proteome database is a bacterial proteome database.

[0030] In some embodiments, the microorganism proteome database is a fungal proteome database.

[0031] The bacteria include, but are not limited to, Escherichia coli, Klebsiella pneumoniae, Citrobacter freundii, Enterobacter asburiae, Hafnia alvei, Serratia marcescens, Proteus vulgaris, Staphylococcus aureus, Staphylococcus epidermidis, Enterococcus faecium, Streptococcus pyogenes, Stenotrophomonas maltophilia, Acinetobacter baumannii, Burkholderia cepacia, Pseudomonas aeruginosa, Neisseria sicca, Salmonella enterica, Corynebacterium striatum, Bacillus subtilis, Group A Streptococcus, Mycobacterium tuberculosis, Legionella pneumophila, Neisseria gonorrhoeae, Listeria monocytogenes, Campylobacter spp., Bordetella pertussis, Campylobacter jejuni, Clostridium perfringens, Salmonella typhi, Shigella sonnei, Staphylococcus haemolyticus, Enterococcus faecalis, Enterococcus durans, Coagulase-negative staphylococci, Streptococcus viridans, Corynebacterium minutissimum, etc.

[0032] The fungi include, but are not limited to, Candida albicans, Aspergillus niger, Cryptococcus spp., Pneumocystis jirovecii, Saccharomyces cerevisiae, Alternaria alternata, Trichoderma spp., Aureobasidium pullulans, Cladosporium cladosporioides, Gliocladium spp., Drechslera australiensis, Graphium graminicola, Candida guilliermondii, Verticillium chlamydosporium, Penicillium spp., Phoma exigua, Pulvinaria papyracea, and Human lineate basidiomycetes.

[0033] In some embodiments, each of the n×(n - 1) new databases contains 15 - 25 proteins.

[0034] In some embodiments, the protein is a ribosomal protein. In the case of a bacterial database, the protein is a 30S ribosomal protein and / or a 50S ribosomal protein. In the case of a fungal database, the protein is a 40S ribosomal protein and / or a 60S ribosomal protein.

[0035] In some embodiments, removing the proteins that interfere with each other in every two databases from their respective databases includes removing the proteins that interfere with each other with a Mass Tolerance in the range of ≤1000 - 2000 PPM, preferably in the range of ≤1200 - 1800 PPM, more preferably in the range of ≤1300 - 1700 PPM, more preferably in the range of ≤1300 - 1500 PPM, and even more preferably ≤1300 PPM.

[0036] In some embodiments, the device further includes a third module, which is used to combine the specific proteins of every two protein databases in step (a) into a set of n×(n - 1) / 2 specific protein combination databases.

[0037] In some embodiments, the third module is used to obtain the specific proteins in each protein database in step (a), and to combine the specific proteins of every two protein databases into a set of n×(n - 1) / 2 specific protein combination databases.

[0038] In some embodiments, each of the specific protein combination databases contains 5 to 15 specific proteins targeting two species or two genera of bacteria. Preferably, the specific protein is a ribosomal protein. In the case of a bacterial database, the protein is a 30S ribosomal protein and / or a 50S ribosomal protein. In the case of a fungal database, the protein is a 40S ribosomal protein and / or a 60S ribosomal protein.

[0039] In a third aspect, the present invention provides a method for identifying mixed microorganisms using the microbial proteome database constructed by the construction method of the first aspect or the device of the second aspect, including the following steps:

[0040] (a) Collect the fingerprint of the mixed microorganisms to be identified;

[0041] (b) Compare the collected fingerprint of the mixed microorganisms to be identified with the set of specific protein combination databases to preliminarily determine the species or genera of two microorganisms that may be contained in the mixed microorganisms;

[0042] (c) Identify the mixed microorganisms to be identified respectively using two new databases corresponding to the two species or genera of the microorganisms that may be contained, and obtain the identification result.

[0043] When reliable results are obtained respectively by two new databases in identifying the mixed microorganism to be identified, it indicates that the mixed microorganism is a mixed microorganism of the microorganisms corresponding to these two new databases. The reliable result determination criterion is that the matching degree of the measured proteins with the proteins in the new database reaches more than 85%, preferably more than 90%. For example, if the new database consists of 15 - 25 proteins, and 14 - 23 proteins are successfully matched as reliable results.

[0044] When two new databases are respectively identifying the mixed microorganism to be identified, and one of the new databases does not obtain a reliable result, it indicates that the mixed microorganism is not a mixed microorganism of the microorganisms corresponding to these two new databases, and other subsequent steps need to be carried out to identify the mixed microorganism to be identified. The subsequent steps can be steps or methods conventionally selected by those skilled in the art for identifying microorganisms.

[0045] In some embodiments, the reliable result is that the new database contains the mixed microorganism to be detected.

[0046] In some embodiments, a mass spectrometer is used to collect the fingerprint of the mixed microorganism to be identified. Preferably, a MALDI - TOF MS mass spectrometer is used to collect the fingerprint of the mixed microorganism to be identified.

[0047] In some embodiments, the mixed microorganism is a mixed microorganism of two microorganisms.

[0048] In some embodiments, the microorganism is a bacterium or a fungus.

[0049] In some embodiments, the microorganism proteome database is a bacterium proteome database.

[0050] In some embodiments, the microorganism proteome database is a fungus proteome database.

[0051] The bacteria include but are not limited to Escherichia coli, Klebsiella pneumoniae, Citrobacter freundii, Enterobacter asburiae, Hafnia alvei, Serratia marcescens, Proteus vulgaris, Staphylococcus aureus, Staphylococcus epidermidis, Enterococcus faecium, Streptococcus pyogenes, Stenotrophomonas maltophilia, Acinetobacter baumannii, Burkholderia cepacia, Pseudomonas aeruginosa, Neisseria sicca, Salmonella enterica, Corynebacterium striatum, Bacillus subtilis, Group A Streptococcus, Mycobacterium tuberculosis, Legionella pneumophila, Neisseria gonorrhoeae, Listeria monocytogenes, Campylobacter, Bordetella pertussis, Campylobacter jejuni, Clostridium perfringens, Salmonella typhi, Shigella sonnei, Staphylococcus haemolyticus, Enterococcus faecalis, Enterococcus durans, Coagulase - negative staphylococcus, Streptococcus viridans, Corynebacterium minutissimum, etc.

[0052] The fungi include, but are not limited to, Candida albicans, Aspergillus niger, Cryptococcus, Pneumocystis jirovecii, Saccharomyces cerevisiae, Alternaria alternata, Trichoderma, Aureobasidium pullulans, Cladosporium cladosporioides, Gliocladium, Drechslera australiensis, Graphium graminicola, Monilia cinerea, Verticillium chlamydosporium, Penicillium, Phoma exigua, Pulvinaria acericola, and Filobasidiella neoformans.

[0053] In some embodiments, the protein is a ribosomal protein. In the case of a bacterial database, the protein is a 30S ribosomal protein and / or a 50S ribosomal protein. In the case of a fungal database, the protein is a 40S ribosomal protein and / or a 60S ribosomal protein.

[0054] In a fourth aspect, the present invention provides an apparatus for identifying a mixed microorganism, the apparatus comprising:

[0055] (a) A first module for collecting a fingerprint of the mixed microorganism to be identified;

[0056] (b) A second module for comparing the collected fingerprint of the mixed microorganism to be identified with a set of the specific protein combination databases to preliminarily determine the species or genera of two microorganisms that may be contained in the mixed microorganism;

[0057] (c) A third module for respectively identifying the mixed microorganism to be identified by using two new databases corresponding to two species or genera of the microorganisms that may be contained, and obtaining an identification result.

[0058] When reliable results are obtained by respectively identifying the mixed microorganism to be identified with the two new databases, it indicates that the mixed microorganism is a mixed microorganism of the microorganisms corresponding to the two new databases. The reliable result determination criterion is that the measured protein has a match degree of 85% or more, preferably 90% or more, with the protein in the new database. For example, the new database consists of 15-25 proteins, and 14-23 proteins are successfully matched as reliable results.

[0059] When the mixed microorganism to be identified is respectively identified with the two new databases, if a reliable result is not obtained with one of the new databases, it indicates that the mixed microorganism is not a mixed microorganism of the microorganisms corresponding to the two new databases, and other subsequent steps need to be performed to identify the mixed microorganism to be identified. The subsequent steps may be steps or methods conventionally selected by those skilled in the art for identifying microorganisms.

[0060] In some embodiments, the reliable result is that the new database contains the mixed microorganism to be detected.

[0061] In some embodiments, a mass spectrometer is used to collect the fingerprint of the mixed microorganisms to be identified. Preferably, a MALDI-TOF MS mass spectrometer is used to collect the fingerprint of the mixed microorganisms to be identified.

[0062] In some embodiments, the mixed microorganisms are a mixture of two microorganisms.

[0063] In some embodiments, the microorganisms are bacteria or fungi.

[0064] In some embodiments, the microorganism proteome database is a bacterial proteome database.

[0065] In some embodiments, the microorganism proteome database is a fungal proteome database.

[0066] The bacteria include, but are not limited to, Escherichia coli, Klebsiella pneumoniae, Citrobacter freundii, Enterobacter asburiae, Hafnia alvei, Serratia marcescens, Proteus vulgaris, Staphylococcus aureus, Staphylococcus epidermidis, Enterococcus faecium, Streptococcus pyogenes, Stenotrophomonas maltophilia, Acinetobacter baumannii, Burkholderia cepacia, Pseudomonas aeruginosa, Neisseria sicca, Salmonella enterica, Corynebacterium striatum, Bacillus subtilis, Group A Streptococcus, Mycobacterium tuberculosis, Legionella pneumophila, Neisseria gonorrhoeae, Listeria monocytogenes, Campylobacter spp., Bordetella pertussis, Campylobacter jejuni, Clostridium perfringens, Salmonella typhi, Shigella sonnei, Staphylococcus haemolyticus, Enterococcus faecalis, Enterococcus durans, Coagulase-negative staphylococci, Streptococcus viridans, Corynebacterium minutissimum, etc.

[0067] The fungi include, but are not limited to, Candida albicans, Aspergillus niger, Cryptococcus, Pneumocystis jirovecii, Saccharomyces cerevisiae, Alternaria alternata, Trichoderma spp., Aureobasidium pullulans, Cladosporium cladosporioides, Gliocladium spp., Drechslera australiensis, Graphium graminicola, Monilia cinerea, Verticillium chlamydosporium, Penicillium spp., Phoma herbarum, Pulvinaria papyracea, and Human lineate basidiomycetes.

[0068] In some embodiments, the protein is a ribosomal protein. In the case of the bacterial database, the protein is a 30S ribosomal protein and / or a 50S ribosomal protein. In the case of the fungal database, the protein is a 40S ribosomal protein and / or a 60S ribosomal protein.

[0069] The microbial proteome database of the present invention excludes the range that interferes with the interpretation of two microorganisms from a single database, that is, the range of Mass Tolerance ≤ 1000 - 2000 PPM, preferably ≤ 1200 - 1800 PPM, more preferably ≤ 1300 - 1700 PPM, more preferably ≤ 1300 - 1500 PPM, and more preferably proteins within 1300 PPM with a value of ≤ 1300 PPM. A new database is established using the remaining protein peaks; the present invention also uses highly conserved, species- or genus-specific ribosomal proteins as specific proteins to enable the specific proteins to function stably. The specific proteins guide the use of the new database for identification. When identifying mixed bacteria with the new database, the time for pure culture during the identification of a single colony can be saved. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The following further elaborates on the specific embodiments of the present invention in conjunction with the accompanying drawings.

[0071] Figure 1 Schematic diagram showing the database classification of the embodiments of the present invention.

[0072] Figure 2(a) shows a schematic diagram of the superimposed 1:1 group of 6-well fingerprint patterns of the embodiments of the present invention.

[0073] Figure 2(b) shows a schematic diagram of the superimposed 4:1 group of 6-well fingerprint patterns of the embodiments of the present invention.

[0074] Figure 2(c) shows a schematic diagram of the superimposed 1:4 group of 6-well fingerprint patterns of the embodiments of the present invention.

[0075] Figure 2(d) shows a schematic diagram of the superimposed randomly proportioned mixed group of 6-well fingerprint patterns of the embodiments of the present invention.

[0076] Figure 3 Schematic diagram showing the randomly proportioned mixed fingerprint pattern of Escherichia coli + Klebsiella pneumoniae of the embodiments of the present invention.

[0077] Figure 4 Schematic diagram showing the randomly proportioned mixed fingerprint pattern of Escherichia coli + Citrobacter freundii of the embodiments of the present invention.

[0078] Figure 5 Schematic diagram showing the randomly proportioned mixed fingerprint pattern of Escherichia coli + Enterobacter asburiae of the embodiments of the present invention.

[0079] Figure 6 Schematic diagram showing the randomly proportioned mixed fingerprint pattern of Escherichia coli + Hafnia alvei of the embodiments of the present invention.

[0080] Figure 7Schematic diagram of the random proportion mixture fingerprint of Escherichia coli + Serratia marcescens showing an embodiment of the present invention.

[0081] Figure 8 Schematic diagram of the random proportion mixture fingerprint of Escherichia coli + Proteus vulgaris showing an embodiment of the present invention.

[0082] Figure 9 Schematic diagram of the random proportion mixture fingerprint of Klebsiella pneumoniae + Citrobacter freundii showing an embodiment of the present invention.

[0083] Figure 10 Schematic diagram of the random proportion mixture fingerprint of Klebsiella pneumoniae + Enterobacter asburiae showing an embodiment of the present invention.

[0084] Figure 11 Schematic diagram of the random proportion mixture fingerprint of Klebsiella pneumoniae + Hafnia alvei showing an embodiment of the present invention.

[0085] Figure 12 Schematic diagram of the random proportion mixture fingerprint of Klebsiella pneumoniae + Serratia marcescens showing an embodiment of the present invention.

[0086] Figure 13 Schematic diagram of the random proportion mixture fingerprint of Klebsiella pneumoniae + Proteus vulgaris showing an embodiment of the present invention.

[0087] Figure 14 Schematic diagram of the random proportion mixture fingerprint of Citrobacter + Enterobacter asburiae showing an embodiment of the present invention.

[0088] Figure 15 Schematic diagram of the random proportion mixture fingerprint of Citrobacter freundii + Hafnia alvei showing an embodiment of the present invention.

[0089] Figure 16 Schematic diagram of the random proportion mixture fingerprint of Citrobacter + Serratia marcescens showing an embodiment of the present invention.

[0090] Figure 17 Schematic diagram of the random proportion mixture fingerprint of Citrobacter freundii + Proteus vulgaris showing an embodiment of the present invention.

[0091] Figure 18 Schematic diagram of the random proportion mixture fingerprint of Enterobacter asburiae + Hafnia alvei showing an embodiment of the present invention.

[0092] Figure 19 Schematic diagram of the random proportion mixture fingerprint of Enterobacter asburiae + Serratia marcescens showing an embodiment of the present invention.

[0093] Figure 20Schematic diagram of the random proportion mixed fingerprint of Enterobacter asburiae + Proteus vulgaris showing an embodiment of the present invention.

[0094] Figure 21 Schematic diagram of the random proportion mixed fingerprint of Hafnia alvei + Serratia marcescens showing an embodiment of the present invention.

[0095] Figure 22 Schematic diagram of the random proportion mixed fingerprint of Hafnia alvei + Proteus vulgaris showing an embodiment of the present invention.

[0096] Figure 23 Schematic diagram of the random proportion mixed fingerprint of Serratia marcescens + Proteus vulgaris showing an embodiment of the present invention.

[0097] Figure 24 Schematic diagram of the fingerprint of Escherichia coli + Staphylococcus aureus showing an embodiment of the present invention.

[0098] Figure 25 Schematic diagram of the fingerprint of Escherichia coli + Staphylococcus epidermidis showing an embodiment of the present invention.

[0099] Figure 26 Schematic diagram of the fingerprint of Escherichia coli + Enterococcus faecium showing an embodiment of the present invention.

[0100] Figure 27 Schematic diagram of the fingerprint of Escherichia coli + Streptococcus pyogenes showing an embodiment of the present invention.

[0101] Figure 28 Schematic diagram of the fingerprint of Escherichia coli + Stenotrophomonas maltophilia showing an embodiment of the present invention.

[0102] Figure 29 Schematic diagram of the fingerprint of Escherichia coli + Acinetobacter baumannii showing an embodiment of the present invention.

[0103] Figure 30 Schematic diagram of the fingerprint of Escherichia coli + Burkholderia cepacia showing an embodiment of the present invention.

[0104] Figure 31 Schematic diagram of the fingerprint of Escherichia coli + Pseudomonas aeruginosa showing an embodiment of the present invention.

[0105] Figure 32 Schematic diagram of the fingerprint of Escherichia coli + Neisseria sicca showing an embodiment of the present invention.

[0106] Figure 33 Schematic diagram of the fingerprint of Escherichia coli + Salmonella enterica showing an embodiment of the present invention.

[0107] Figure 34Schematic diagram of the fingerprint of Escherichia coli + Corynebacterium striatum showing an embodiment of the present invention.

[0108] Figure 35 Schematic diagram of the fingerprint of Escherichia coli + Bacillus subtilis showing an embodiment of the present invention.

[0109] Figure 36 Schematic diagram of the fingerprint of Staphylococcus aureus + Staphylococcus epidermidis showing an embodiment of the present invention.

[0110] Figure 37 Schematic diagram of the fingerprint of Staphylococcus aureus + Enterococcus faecalis showing an embodiment of the present invention.

[0111] Figure 38 Schematic diagram of the fingerprint of Staphylococcus aureus + Streptococcus pyogenes showing an embodiment of the present invention.

[0112] Figure 39 Schematic diagram of the fingerprint of Staphylococcus aureus + Stenotrophomonas maltophilia showing an embodiment of the present invention.

[0113] Figure 40 Schematic diagram of the fingerprint of Staphylococcus + Acinetobacter baumannii showing an embodiment of the present invention.

[0114] Figure 41 Schematic diagram of the fingerprint of Staphylococcus aureus + Burkholderia cepacia showing an embodiment of the present invention.

[0115] Figure 42 Schematic diagram of the fingerprint of Staphylococcus aureus + Pseudomonas aeruginosa showing an embodiment of the present invention.

[0116] Figure 43 Schematic diagram of the fingerprint of Staphylococcus aureus + Neisseria sicca showing an embodiment of the present invention.

[0117] Figure 44 Schematic diagram of the fingerprint of Staphylococcus aureus + Salmonella enterica showing an embodiment of the present invention.

[0118] Figure 45 Schematic diagram of the fingerprint of Staphylococcus aureus + Corynebacterium striatum showing an embodiment of the present invention.

[0119] Figure 46 Schematic diagram of the fingerprint of Staphylococcus aureus + Bacillus subtilis showing an embodiment of the present invention.

[0120] Figure 47 Schematic diagram of the fingerprint of Staphylococcus epidermidis + Enterococcus faecalis showing an embodiment of the present invention.

[0121] Figure 48Schematic diagram of the fingerprint of Staphylococcus epidermidis + Streptococcus pyogenes according to an embodiment of the present invention.

[0122] Figure 49 Schematic diagram of the fingerprint of Staphylococcus epidermidis + Stenotrophomonas maltophilia according to an embodiment of the present invention.

[0123] Figure 50 Schematic diagram of the fingerprint of Staphylococcus epidermidis + Acinetobacter baumannii according to an embodiment of the present invention.

[0124] Figure 51 Schematic diagram of the fingerprint of Staphylococcus epidermidis + Burkholderia cepacia according to an embodiment of the present invention.

[0125] Figure 52 Schematic diagram of the fingerprint of Staphylococcus epidermidis + Pseudomonas aeruginosa according to an embodiment of the present invention.

[0126] Figure 53 Schematic diagram of the fingerprint of Staphylococcus epidermidis + Neisseria sicca according to an embodiment of the present invention.

[0127] Figure 54 Schematic diagram of the fingerprint of Staphylococcus epidermidis + Salmonella enterica according to an embodiment of the present invention.

[0128] Figure 55 Schematic diagram of the fingerprint of Staphylococcus epidermidis + Corynebacterium striatum according to an embodiment of the present invention.

[0129] Figure 56 Schematic diagram of the fingerprint of Staphylococcus epidermidis + Bacillus subtilis according to an embodiment of the present invention.

[0130] Figure 57 Schematic diagram of the fingerprint of Enterococcus faecalis + Streptococcus pyogenes according to an embodiment of the present invention.

[0131] Figure 58 Schematic diagram of the fingerprint of Enterococcus faecalis + Stenotrophomonas maltophilia according to an embodiment of the present invention.

[0132] Figure 59 Schematic diagram of the fingerprint of Enterococcus faecalis + Acinetobacter baumannii according to an embodiment of the present invention.

[0133] Figure 60 Schematic diagram of the fingerprint of Enterococcus faecalis + Burkholderia cepacia according to an embodiment of the present invention.

[0134] Figure 61 Schematic diagram of the fingerprint of Enterococcus faecalis + Pseudomonas aeruginosa according to an embodiment of the present invention.

[0135] Figure 62 Schematic diagram of the fingerprint of Enterococcus faecalis + Neisseria sicca according to an embodiment of the present invention.

[0136] Figure 63 Schematic diagram of the fingerprint of Enterococcus faecium + Salmonella enterica showing an embodiment of the present invention.

[0137] Figure 64 Schematic diagram of the fingerprint of Enterococcus faecium + Corynebacterium striatum showing an embodiment of the present invention.

[0138] Figure 65 Schematic diagram of the fingerprint of Enterococcus faecium + Bacillus subtilis showing an embodiment of the present invention.

[0139] Figure 66 Schematic diagram of the fingerprint of Streptococcus pyogenes + Stenotrophomonas maltophilia showing an embodiment of the present invention.

[0140] Figure 67 Schematic diagram of the fingerprint of Streptococcus pyogenes + Acinetobacter baumannii showing an embodiment of the present invention.

[0141] Figure 68 Schematic diagram of the fingerprint of Streptococcus pyogenes + Burkholderia cepacia showing an embodiment of the present invention.

[0142] Figure 69 Schematic diagram of the fingerprint of Streptococcus pyogenes + Pseudomonas aeruginosa showing an embodiment of the present invention.

[0143] Figure 70 Schematic diagram of the fingerprint of Streptococcus pyogenes + Neisseria sicca showing an embodiment of the present invention.

[0144] Figure 71 Schematic diagram of the fingerprint of Streptococcus pyogenes + Salmonella enterica showing an embodiment of the present invention.

[0145] Figure 72 Schematic diagram of the fingerprint of Streptococcus pyogenes + Corynebacterium striatum showing an embodiment of the present invention.

[0146] Figure 73 Schematic diagram of the fingerprint of Streptococcus pyogenes + Bacillus subtilis showing an embodiment of the present invention.

[0147] Figure 74 Schematic diagram of the fingerprint of Stenotrophomonas maltophilia + Acinetobacter baumannii showing an embodiment of the present invention.

[0148] Figure 75 Schematic diagram of the fingerprint of Stenotrophomonas maltophilia + Burkholderia cepacia showing an embodiment of the present invention.

[0149] Figure 76 Schematic diagram of the fingerprint of Stenotrophomonas maltophilia + Pseudomonas aeruginosa showing an embodiment of the present invention.

[0150] Figure 77 Schematic diagram of the fingerprint of Stenotrophomonas maltophilia + Neisseria sicca showing an embodiment of the present invention.

[0151] Figure 78 Schematic diagram of the fingerprint of Stenotrophomonas maltophilia + Salmonella enterica showing an embodiment of the present invention.

[0152] Figure 79 Schematic diagram of the fingerprint of Stenotrophomonas maltophilia + Corynebacterium striatum showing an embodiment of the present invention.

[0153] Figure 80 Schematic diagram of the fingerprint of Stenotrophomonas maltophilia + Bacillus subtilis showing an embodiment of the present invention.

[0154] Figure 81 Schematic diagram of the fingerprint of Acinetobacter baumannii + Burkholderia cepacia showing an embodiment of the present invention.

[0155] Figure 82 Schematic diagram of the fingerprint of Acinetobacter baumannii + Pseudomonas aeruginosa showing an embodiment of the present invention.

[0156] Figure 83 Schematic diagram of the fingerprint of Acinetobacter baumannii + Neisseria sicca showing an embodiment of the present invention.

[0157] Figure 84 Schematic diagram of the fingerprint of Acinetobacter baumannii + Salmonella enterica showing an embodiment of the present invention.

[0158] Figure 85 Schematic diagram of the fingerprint of Acinetobacter baumannii + Corynebacterium striatum showing an embodiment of the present invention.

[0159] Figure 86 Schematic diagram of the fingerprint of Acinetobacter baumannii + Bacillus subtilis showing an embodiment of the present invention.

[0160] Figure 87 Schematic diagram of the fingerprint of Burkholderia cepacia + Pseudomonas aeruginosa showing an embodiment of the present invention.

[0161] Figure 88 Schematic diagram of the fingerprint of Burkholderia cepacia + Neisseria sicca showing an embodiment of the present invention.

[0162] Figure 89 Schematic diagram of the fingerprint of Burkholderia cepacia + Salmonella enterica showing an embodiment of the present invention.

[0163] Figure 90 Schematic diagram of the fingerprint of Burkholderia cepacia + Corynebacterium striatum showing an embodiment of the present invention.

[0164] Figure 91Schematic diagram of the fingerprint of Burkholderia cepacia + Bacillus subtilis showing an embodiment of the present invention.

[0165] Figure 92 Schematic diagram of the fingerprint of Pseudomonas aeruginosa + Neisseria sicca showing an embodiment of the present invention.

[0166] Figure 93 Schematic diagram of the fingerprint of Pseudomonas aeruginosa + Salmonella enterica showing an embodiment of the present invention.

[0167] Figure 94 Schematic diagram of the fingerprint of Pseudomonas aeruginosa + Corynebacterium striatum showing an embodiment of the present invention.

[0168] Figure 95 Schematic diagram of the fingerprint of Pseudomonas aeruginosa + Bacillus subtilis showing an embodiment of the present invention.

[0169] Figure 96 Schematic diagram of the fingerprint of Neisseria sicca + Salmonella enterica showing an embodiment of the present invention.

[0170] Figure 97 Schematic diagram of the fingerprint of Neisseria sicca + Corynebacterium striatum showing an embodiment of the present invention.

[0171] Figure 98 Schematic diagram of the fingerprint of Neisseria sicca + Bacillus subtilis showing an embodiment of the present invention.

[0172] Figure 99 Schematic diagram of the fingerprint of Salmonella enterica + Corynebacterium striatum showing an embodiment of the present invention.

[0173] Figure 100 Schematic diagram of the fingerprint of Salmonella enterica + Bacillus subtilis showing an embodiment of the present invention.

[0174] Figure 101 Schematic diagram of the fingerprint of Corynebacterium striatum + Bacillus subtilis showing an embodiment of the present invention. Detailed implementation manners

[0175] To more clearly illustrate the present invention, the present invention will be further described below in conjunction with preferred embodiments and drawings. Those skilled in the art should understand that the specific content described below is illustrative rather than restrictive, and should not be used to limit the protection scope of the present invention.

[0176] An embodiment of the present invention provides a method for constructing a bacterial proteome database, including:

[0177] Establishing n databases with bacteria in the comprehensive database as units of genus or species, and forming a set of n databases;

[0178] Protein alignment is performed between every two databases, and proteins that interfere with each other within the range of Mass Tolerance ≤ 1300 PPM in the two databases are removed from their respective databases, forming a set of n×(n - 1) new databases.

[0179] The construction method further includes extracting specific proteins from each database, and combining the specific proteins of every two databases to form a set of n×(n - 1) / 2 corresponding specific combined protein databases for two mixed bacteria.

[0180] In a specific example, as Figure 1 shown, ribosomal proteins of the target bacteria are separated from the comprehensive database constructed from ribosomal proteins by genus or species to establish several databases, such as database A, database B, and database C. In the statistical list, all proteins in database A and database B are arranged together to select proteins that interfere with each other within the range of Mass Tolerance ≤ 1300 PPM (Mass Tolerance is the range identified by the interference system, and changes such as increasing or decreasing 1300 PPM can be made). And the interfering proteins are removed from database A and database B respectively. The proteins remaining in database A that are not interfered with by database B during identification are named database A'; the proteins remaining in database B that are not interfered with by database A during identification are named database B'. Similarly, the interfering proteins are removed from database A and database C respectively. The proteins remaining in database A that are not interfered with by database C during identification are named database A"; the proteins remaining in database C that are not interfered with by database A during identification are named database C'; the interfering proteins are removed from database B and database C respectively. The proteins remaining in database B that are not interfered with by database C during identification are named database B"; the proteins remaining in database C that are not interfered with by database B during identification are named database C".

[0181] By counting the proteins in database A, the genus or species specific proteins of bacteria A can be obtained. Similarly, the genus or species specific proteins of other single bacteria libraries such as database B and database C can be obtained. The specific proteins of every two databases are combined to form a specific combined protein database for mixed bacteria. For example, for the mixed bacteria of bacteria A and bacteria B, this specific combined protein for the mixed bacteria is the combination of the specific proteins of bacteria A and bacteria B; similarly, for the mixed bacteria of bacteria A and bacteria C, this specific combined protein for the mixed bacteria is the combination of the specific proteins of bacteria A and bacteria C; for the mixed bacteria of bacteria B and bacteria C, this specific combined protein for the mixed bacteria is the combination of the specific proteins of bacteria B and bacteria C.

[0182] In a specific example, databases are established by genus for bacteria with small interspecies protein differences, such as each genus of Enterobacteriaceae. Databases are established by species for bacteria with large interspecies protein differences, such as each species in the genus Staphylococcus.

[0183] In a specific example, each mixed-bacteria specific protein database contains 5 to 15 proteins as specific proteins.

[0184] In a specific example, each strain of bacteria in the new databases (such as library A' and library B') established by removing interfering proteins contains 15 to 25 proteins.

[0185] Another embodiment of the present invention provides a method for identifying two types of mixed bacteria using the database constructed by the method provided in the previous embodiment of the present invention, including:

[0186] Collect the fingerprint map of the mixed bacteria to be identified;

[0187] Match the collected fingerprint map of the mixed bacteria to be identified with the mixed-bacteria specific combined protein database. The matching result will indicate the single-bacteria or single-bacteria genus protein database corresponding to the specific combined protein database, and further identification will be carried out;

[0188] Identify the mixed bacteria to be identified using two new databases corresponding to the possible specific combined proteins respectively, and obtain the identification result.

[0189] In a specific example, the collection system collects the fingerprint map of the mixed bacteria to be identified. After comparison with the mixed-bacteria specific protein database, it is concluded that the mixed bacteria to be identified may be a combination of bacteria A and bacteria B. Then, the new databases A' and B' established by removing interfering proteins are used to identify the mixed bacteria to be identified respectively once.

[0190] In a specific example, the identification result includes:

[0191] When reliable results are obtained for identifying the mixed bacteria to be identified using the two new databases respectively, it indicates that the mixed bacteria is a mixture of the bacteria corresponding to these two new databases;

[0192] When reliable results are not obtained for identifying the mixed bacteria to be identified using one of the two new databases respectively, it indicates that the mixed bacteria is not a mixture of the bacteria corresponding to these two new databases, and subsequent processing is required to identify the mixed bacteria to be identified.

[0193] In a specific example, a mass spectrometry microbial identification system is used to identify the mixed bacteria to be identified, and the identification result is as follows:

[0194] When the identification results of both Library A' and Library B' are reliable, it indicates that the result is a reliable value; if one or both of Library A' and Library B' are identified as reference (unreliable), it means the result is an unreliable value, and the signal-to-noise ratio of the fingerprint spectrum should be examined to see if parameters such as the minimum signal-to-noise ratio and the Intensed Peaks Flattened value need to be adjusted. The minimum signal-to-noise ratio is generally set between 1 and 7, which determines the number of peaks. The Intensed Peaks Flattened value affects the peak height. For example, if it is set to 95, then the heights of the first 95 peaks in the fingerprint spectrum will be the same as the height of the 95th peak. Generally, the value is set to be less than the number of peaks.

[0195] In a specific example, the reliable result is that Library A' and Library B' contain the mixed bacteria to be detected.

[0196] MS is used to detect the mass-to-charge ratio (m / z ratio). MALDI-TOF MS provides a rapid, accurate, and sensitive spectrum of the bioanalytes in the sample. MALDI is an ionization technique in which the matrix absorbs the energy of the ultraviolet laser to generate ions from large molecules with the smallest fragments. The m / z ratio can be determined by the flight time of the ions, and the detector measures the flight time of the ions to calculate the ion mass.

[0197] The 70S ribosome of bacteria consists of a 30S small subunit and a 50S large subunit. The 30S small subunit contains 16S RNA and 21 ribosomal proteins; the 50S large subunit contains 5S RNA, 23S RNA, and 31 ribosomal proteins. The 80S ribosome of fungi consists of a 40S small subunit and a 60S large subunit. The 40S small subunit contains 18S RNA and 33 ribosomal proteins; the 60S large subunit contains 5S RNA, 28S RNA, 5.8S RNA, and 46 ribosomal proteins.

[0198] The following describes the preferred embodiments of the present invention. The present invention is not limited to the following preferred embodiments. It should be noted that for those skilled in the art, based on the inventive concept of this invention, several deformations and improvements made all fall within the protection scope of the present invention. Reagents not indicating the manufacturer can be obtained as conventional products through commercial purchase.

[0199] Example 1 Identification of Escherichia coli and Staphylococcus aureus

[0200] Data collection uses the QuanToF I mass spectrometer QuanTOF acquisition system. The acquisition mode is linear ion mode, with a mass range of 2K - 20KDa, a central mass of 10KDa, a spectrum size of 0.3995mm, a detector voltage of -0.75 to -0.55kv, an extraction voltage of -3.32kv, a source voltage of -20kv, a laser pulse energy of 5.5 - 7.0, 800 shots per spectrum, a data analysis system of QuanID microbial identification system, and a reference comprehensive database of y-sherry 17.0.sqlites.

[0201] In this embodiment, the detector voltage used for collection is -0.63kv, the laser pulse energy is 6.3uJ, the identification range is 3K - 15KDa, the minimum signal-to-noise ratio is 4 or 5, the number of Peaks is maintained at 100 - 120, and the intensity flattening value is 80 - 100.

[0202] The bacteria used in this embodiment are standard strains such as ATCC25922 Escherichia coli and CICC10789 Staphylococcus aureus.

[0203] Six groups are used, namely single Escherichia coli, single Staphylococcus aureus, a 1:1 mixed bacteria of Escherichia coli and Staphylococcus aureus, a 4:1 mixed bacteria of Escherichia coli and Staphylococcus aureus, a 1:4 mixed bacteria of Escherichia coli and Staphylococcus aureus, and any mixed bacteria of Escherichia coli and Staphylococcus aureus. Six samples are spotted in each group, with a total of 36 samples for identification.

[0204] After statistically analyzing the Escherichia coli and Staphylococcus aureus libraries, a genus-specific protein combination database of ribosomal proteins as shown in Table 1(a) for the Escherichia coli genus library and Staphylococcus aureus genus library can be obtained.

[0205] Table 1(a)

[0206]

[0207] By matching the above specific proteins, the mixed bacteria were identified separately using an Escherichia library with interfering proteins removed and a Staphylococcus aureus library with interfering proteins removed. The results of identifying the 1:1 mixed bacteria of Escherichia coli and Staphylococcus aureus, the 4:1 mixed bacteria of Escherichia coli and Staphylococcus aureus, the 1:4 mixed bacteria of Escherichia coli and Staphylococcus aureus, and any mixed bacteria of Escherichia coli and Staphylococcus aureus using the Escherichia library are shown in Table 2(a) - Table 2(d). The credibility column shows "credible", indicating that Escherichia coli was detected in the 1:1 mixed bacteria of Escherichia coli and Staphylococcus aureus, the 4:1 mixed bacteria of Escherichia coli and Staphylococcus aureus, the 1:4 mixed bacteria of Escherichia coli and Staphylococcus aureus, and any mixed bacteria of Escherichia coli and Staphylococcus aureus.

[0208] Table 2(a) Identification results of the 1:1 group of mixed bacteria identified by the Escherichia library

[0209]

[0210] Table 2(b) Identification results of the 4:1 group of mixed bacteria identified by the Escherichia library

[0211]

[0212] Table 2(c) Identification results of the 1:4 group of mixed bacteria identified by the Escherichia library

[0213]

[0214] Table 2(d) Identification results of the randomly proportioned mixed group of mixed bacteria identified by the Escherichia library

[0215]

[0216] The results of identifying the 1:1 mixed bacteria of Escherichia coli and Staphylococcus aureus, the 4:1 mixed bacteria of Escherichia coli and Staphylococcus aureus, the 1:4 mixed bacteria of Escherichia coli and Staphylococcus aureus, and any mixed bacteria of Escherichia coli and Staphylococcus aureus using the Staphylococcus aureus library are shown in Table 3(a) - Table 3(d). The credibility column shows "credible", indicating that Staphylococcus aureus was detected in the 1:1 mixed bacteria of Escherichia coli and Staphylococcus aureus, the 4:1 mixed bacteria of Escherichia coli and Staphylococcus aureus, the 1:4 mixed bacteria of Escherichia coli and Staphylococcus aureus, and any mixed bacteria of Escherichia coli and Staphylococcus aureus. Reliable results were obtained using both libraries, indicating that regardless of the proportion, these four groups of mixed bacteria contain both Escherichia coli and Staphylococcus aureus.

[0217] Table 3(a) Identification results of the 1:1 group of mixed bacteria identified by the Staphylococcus aureus library

[0218]

[0219] Schematic diagram of the identification results of the 4:1 group of mixed bacteria in the Staphylococcus aureus library identification

[0220]

[0221] Table 3(c) Identification results of the 1:4 group of mixed bacteria in the Staphylococcus aureus library identification

[0222]

[0223] Table 3(d) Identification results of the randomly proportioned mixed group of mixed bacteria in the Staphylococcus aureus library identification

[0224]

[0225] Since the single Escherichia coli group and the single Staphylococcus aureus group do not have specific proteins from each other and cannot match the identification of the Escherichia coli library after removing interfering proteins and the Staphylococcus aureus library after removing interfering proteins, the identification results will be unreliable, as shown in Tables 4(a)-4(d), and the credibility is for reference, that is, the identification results are unreliable.

[0226] Table 4(a) Identification results of the single Escherichia coli group (direct smear) using the Escherichia coli library

[0227]

[0228] Table 4(b) Identification results of the single Staphylococcus aureus group (direct smear) using the Escherichia coli library

[0229]

[0230] Table 4(c) Identification results of the single Escherichia coli group (direct smear) using the Staphylococcus aureus library

[0231]

[0232] Table 4(d) Identification results of the single Staphylococcus aureus group (direct smear) using the Staphylococcus aureus library

[0233]

[0234] This experiment has repeatability. The 6-well fingerprint patterns of each group are superimposed as Figure 2(a)-Figure 2(d) shown.

[0235] Example 2 Identification of mixed bacteria of each genus in Enterobacteriaceae

[0236] Data acquisition was performed using the QuanToF I mass spectrometer QuanTOF acquisition system. The acquisition mode was linear ion mode, with a mass range of 2K - 20KDa, a central mass of 10KDa, a spectrum size of 0.3995 mm, a detector voltage of -0.75 to -0.55 kV, an extraction voltage of -3.32 kV, a source voltage of -20 kV, a laser pulse energy of 5.5 - 7.0, 800 shots per spectrum, a data analysis system of the QuanID microbial identification system, and a reference comprehensive database of y-sherry 17.0.sqlites.

[0237] In this example, the detector voltage was -0.63 kV, the laser pulse energy was 6.3 uJ, the identification range was 3K - 15KDa, the minimum signal-to-noise ratio was 4 or 5, the number of Peaks was maintained at 100 - 120, and the intensity flattening value was 80 - 100.

[0238] The bacteria used in this example were all ATCC and CICC standard strains, including Escherichia coli (E. coli), Klebsiella pneumoniae (K. pneumoniae), Citrobacter freundii, Enterobacter asburiae, Proteus vulgaris, Serratia marcescens, Hafnia alvei, etc.

[0239] The purpose of this example was to verify the accuracy of the method of the present invention. Taking the random proportion mixing between every two genera of Enterobacteriaceae as an example, a mixed bacteria experiment was conducted for strains with similar biological characteristics.

[0240] A ribosomal protein-based genus-specific protein combination database for Escherichia and Klebsiella was established as shown in Table 1(b).

[0241] Table 1(b)

[0242]

[0243]

[0244] A ribosomal protein-based genus-specific protein combination database for Escherichia and Citrobacter was established as shown in Table 1(c).

[0245] Table 1(c)

[0246]

[0247]

[0248] A ribosomal protein-based genus-specific protein combination database for Escherichia and Enterobacter was established as shown in Table 1(d).

[0249] Table 1(d)

[0250]

[0251]

[0252] Establish a database of ribosomal proteins as a genus- and species-specific protein combination of Escherichia and Hafnia as shown in Table 1(e).

[0253] Table 1(e)

[0254]

[0255] Establish a database of ribosomal proteins as a genus- and species-specific protein combination of Escherichia and Serratia as shown in Table 1(f).

[0256] Table 1(f)

[0257]

[0258]

[0259] Establish a database of ribosomal proteins as a genus- and species-specific protein combination of Escherichia and Proteus as shown in Table 1(g).

[0260] Table 1(g)

[0261]

[0262] Establish a database of ribosomal proteins as a genus- and species-specific protein combination of Klebsiella and Citrobacter as shown in Table 1(h).

[0263] Table 1(h)

[0264]

[0265] Establish a database of ribosomal proteins as a genus- and species-specific protein combination of Klebsiella and Enterobacter as shown in Table 1(i).

[0266] Table 1(i)

[0267]

[0268]

[0269] Establish a database of ribosomal proteins as a genus- and species-specific protein combination of Klebsiella and Hafnia as shown in Table 1(j).

[0270] Table 1(j)

[0271]

[0272]

[0273] Establish a database of ribosomal proteins as a genus-specific protein combination of Klebsiella and Serratia as shown in Table 1(k).

[0274] Table 1(k)

[0275]

[0276] Establish a database of ribosomal proteins as a genus-specific protein combination of Klebsiella and Proteus as shown in Table 1(l).

[0277] Table 1(l)

[0278]

[0279]

[0280] Establish a database of ribosomal proteins as a genus-specific protein combination of Citrobacter and Enterobacter as shown in Table 1(m).

[0281] Table 1(m)

[0282]

[0283]

[0284] Establish a database of ribosomal proteins as a genus-specific protein combination of Citrobacter and Hafnia as shown in Table 1(n).

[0285] Table 1(n)

[0286]

[0287] Establish a database of ribosomal proteins as a genus-specific protein combination of Citrobacter and Serratia as shown in Table 1(o).

[0288] Table 1(o)

[0289]

[0290] Establish a database of ribosomal proteins as a genus-specific protein combination of Citrobacter and Proteus as shown in Table 1(p).

[0291] Table 1(p)

[0292]

[0293]

[0294] Establish a database of ribosomal proteins as a genus-specific protein combination of Enterobacter and Hafnia as shown in Table 1(q).

[0295] Table 1(q)

[0296]

[0297]

[0298] Establish a database of ribosomal proteins as a species-specific protein combination of Enterobacter and Serratia as shown in Table 1(r).

[0299] Table 1(r)

[0300]

[0301] Establish a database of ribosomal proteins as a species-specific protein combination of Enterobacter and Proteus as shown in Table 1(s).

[0302] Table 1(s)

[0303]

[0304] Establish a database of ribosomal proteins as a species-specific protein combination of Hafnia and Serratia as shown in Table 1(t).

[0305] Table 1(t)

[0306]

[0307]

[0308] Establish a database of ribosomal proteins as a species-specific protein combination of Hafnia and Proteus as shown in Table 1(u).

[0309] Table 1(u)

[0310]

[0311]

[0312] Establish a database of ribosomal proteins as a species-specific protein combination of Serratia and Proteus as shown in Table 1(v).

[0313] Table 1(v)

[0314]

[0315] See the experimental results in: Table 5(a) - Table 5(b), Table 6(a) - Table 6(b), Table 7(a) - Table 7(b), Table 8(a) - Table 8(b), Table 9(a) - Table 9(b), Table 10(a) - Table 10(b), Table 11(a) - Table 11(b), Table 12(a) - Table 12(b), Table 13(a) - Table 13(b), Table 14(a) - Table 14(b), Table 15(a) - Table 15(b), Table 16(a) - Table 16(b), Table 17(a) - Table 17(b), Table 18(a) - Table 18(b), Table 19(a) - Table 19(b), Table 20(a) - Table 20(b), Table 21(a) - Table 21(b), Table 22(a) - Table 22(b), Table 23(a) - Table 23(b), Table 24(a) - Table 24(b), Table 25(a) - Table 25(b). Figure 3-Figure 23 。

[0316] Table 5(a) Identification of Escherichia coli + Klebsiella pneumoniae Random Proportion Mixed Bacteria by the Newly Established Escherichia Genus Library

[0317]

[0318] Table 5(b) Identification of Escherichia coli + Klebsiella pneumoniae Random Proportion Mixed Bacteria by the Newly Established Klebsiella Genus Library

[0319]

[0320] Table 6(a) Identification of Escherichia coli + Citrobacter freundii Random Proportion Mixed Bacteria by the Newly Established Escherichia Genus Library

[0321]

[0322] Table 6(b) Identification of Escherichia coli + Citrobacter freundii Random Proportion Mixed Bacteria by the Newly Established Citrobacter Genus Library

[0323]

[0324] Table 7(a) Identification of Escherichia coli + Enterobacter asburiae Random Proportion Mixed Bacteria by the Newly Established Escherichia Genus Library

[0325]

[0326] Table 7(b) Identification of Escherichia coli + Enterobacter asburiae Random Proportion Mixed Bacteria by the Newly Established Enterobacter Genus Library

[0327]

[0328] Table 8(a) Identification of Escherichia coli + Hafnia alvei Random Proportion Mixed Bacteria by the Newly Established Escherichia Genus Library

[0329]

[0330] Table 8(b) Identification of Escherichia coli + Hafnia alvei Random Proportion Mixed Bacteria by the Newly Established Hafnia Library

[0331]

[0332] Table 9(a) Identification of Escherichia coli + Serratia marcescens Random Proportion Mixed Bacteria by the Newly Established Escherichia Library

[0333]

[0334] Table 9(b) Identification of Escherichia coli + Serratia marcescens Random Proportion Mixed Bacteria by the Newly Established Serratia Library

[0335]

[0336] Table 10(a) Identification of Escherichia coli + Proteus vulgaris Random Proportion Mixed Bacteria by the Newly Established Escherichia Library

[0337]

[0338] Table 10(b) Identification of Escherichia coli + Proteus vulgaris Random Proportion Mixed Bacteria by the Newly Established Proteus Library

[0339]

[0340] Table 11(a) Identification of Klebsiella pneumoniae + Citrobacter freundii Random Proportion Mixed Bacteria by the Newly Established Klebsiella Library

[0341]

[0342] Table 11(b) Identification of Klebsiella pneumoniae + Citrobacter freundii Random Proportion Mixed Bacteria by the Newly Established Citrobacter Library

[0343]

[0344] Table 12(a) Identification of Klebsiella pneumoniae + Enterobacter asburiae Random Proportion Mixed Bacteria by the Newly Established Klebsiella Library

[0345]

[0346] Table 12(b) Identification of Klebsiella pneumoniae + Enterobacter asburiae Random Proportion Mixed Bacteria by the Newly Established Enterobacter Library

[0347]

[0348] Table 13(a) Identification of randomly proportioned mixed bacteria of Klebsiella pneumoniae + Hafnia alvei using the newly established Klebsiella genus library

[0349]

[0350] Table 13(b) Identification of randomly proportioned mixed bacteria of Klebsiella pneumoniae + Hafnia alvei using the newly established Hafnia genus library

[0351]

[0352] Table 14(a) Identification of randomly proportioned mixed bacteria of Klebsiella pneumoniae + Serratia marcescens using the newly established Klebsiella genus library

[0353]

[0354] Table 14(b) Identification of randomly proportioned mixed bacteria of Klebsiella pneumoniae + Serratia marcescens using the newly established Serratia genus library

[0355]

[0356] Table 15(a) Identification of randomly proportioned mixed bacteria of Klebsiella pneumoniae + Proteus vulgaris using the newly established Klebsiella genus library

[0357]

[0358] Table 15(b) Identification of randomly proportioned mixed bacteria of Klebsiella pneumoniae + Proteus vulgaris using the newly established Proteus genus library

[0359]

[0360] Table 16(a) Identification of randomly proportioned mixed bacteria of Citrobacter + Enterobacter asburiae using the newly established Enterobacter genus library

[0361]

[0362] Table 16(b) Identification of randomly proportioned mixed bacteria of Citrobacter + Enterobacter asburiae using the newly established Citrobacter genus library

[0363]

[0364] Table 17(a) Identification of randomly proportioned mixed bacteria of Citrobacter + Hafnia alvei using the newly established Citrobacter genus library

[0365]

[0366] Table 17(b) Identification of randomly proportioned mixed bacteria of Citrobacter + Hafnia alvei using the newly established Hafnia genus library

[0367]

[0368] Table 18(a) Identification of randomly proportioned mixed bacteria of Citrobacter freundii + Serratia marcescens using the newly established Citrobacter genus library

[0369]

[0370] Table 18(b) Identification of randomly proportioned mixed bacteria of Citrobacter freundii + Serratia marcescens using the newly established Serratia genus library

[0371]

[0372] Table 19(a) Identification of randomly proportioned mixed bacteria of Citrobacter freundii + Proteus vulgaris using the newly established Citrobacter genus library

[0373]

[0374] Table 19(b) Identification of randomly proportioned mixed bacteria of Citrobacter freundii + Proteus vulgaris using the newly established Proteus genus library

[0375]

[0376] Table 20(a) Identification of randomly proportioned mixed bacteria of Enterobacter asburiae + Hafnia alvei using the newly established Enterobacter genus library

[0377]

[0378] Table 20(b) Identification of randomly proportioned mixed bacteria of Enterobacter asburiae + Hafnia alvei using the newly established Hafnia genus library

[0379]

[0380] Table 21(a) Identification of randomly proportioned mixed bacteria of Enterobacter asburiae + Serratia marcescens using the newly established Enterobacter genus library

[0381]

[0382] Table 21(b) Identification of randomly proportioned mixed bacteria of Enterobacter asburiae + Serratia marcescens using the newly established Serratia genus library

[0383]

[0384] Table 22(a) Identification of randomly proportioned mixed bacteria of Enterobacter asburiae + Proteus vulgaris using the newly established Enterobacter genus library

[0385]

[0386] Table 22(b) Identification of randomly proportioned mixed bacteria of Enterobacter asburiae + Proteus vulgaris using the newly established Proteus genus library

[0387]

[0388] Table 23(a) Identification of randomly proportioned mixed bacteria of Hafnia alvei + Serratia marcescens using the newly established Hafnia genus library

[0389]

[0390] Table 23(b) Identification of randomly proportioned mixed bacteria of Hafnia alvei + Serratia marcescens using the newly established Serratia genus library

[0391]

[0392] Table 24(a) Identification of randomly proportioned mixed bacteria of Hafnia alvei + Proteus vulgaris using the newly established Hafnia genus library

[0393]

[0394] Table 24(b) Identification of randomly proportioned mixed bacteria of Hafnia alvei + Proteus vulgaris using the newly established Proteus genus library

[0395]

[0396] Table 25(a) Identification of randomly proportioned mixed bacteria of Serratia marcescens + Proteus vulgaris using the newly established Serratia genus library

[0397]

[0398] Table 25(b) Identification of randomly proportioned mixed bacteria of Serratia marcescens + Proteus vulgaris using the newly established Proteus genus library

[0399]

[0400] Example 3 Identification of common bacterial species or genera

[0401] Data collection was performed using the QuanToF I mass spectrometer QuanTOF acquisition system. The acquisition mode was linear ion mode, with a mass range of 2K - 20KDa, a central mass of 10KDa, a spectrum size of 0.3995mm, a detector voltage of -0.75 to -0.55kv, an extraction voltage of -3.32kv, a source voltage of -20kv, a laser pulse energy of 5.5 - 7.0, 800 shots per spectrum, and the data analysis system was the QuanID microbial identification system. The reference comprehensive database was y-sherry 17.0.sqlites.

[0402] In this example, the detector voltage used for collection was -0.63kv, the laser pulse energy was 6.3uJ, the identification range was 3K - 15KDa, the minimum signal-to-noise ratio was 4 or 5, the number of Peaks was maintained at 100 - 120, and the intensity flattening value was 80 - 100.

[0403] All the bacteria used in this example are standard strains of ATCC and CICC, including Escherichia coli, Staphylococcus aureus, Staphylococcus epidermidis, Enterococcus faecium, Streptococcus pyogenes, Stenotrophomonas maltophilia, Acinetobacter baumannii, Burkholderia cepacia, Pseudomonas aeruginosa, Neisseria sicca, Salmonella enterica, Corynebacterium striatum, Bacillus subtilis, etc.

[0404] For the experimental results, see Table 26, Figure 24-Figure 101 。

[0405] Table 26

[0406]

[0407]

[0408] The examples are only illustrations for clearly explaining the present invention, rather than limitations on the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is impossible to enumerate all the implementation manners here. Any obvious changes or modifications derived from the technical solutions of the present invention still fall within the protection scope of the present invention.

Claims

1. A method for constructing a microbial proteome database, the method comprising the following steps: (a) Establishing a set of n protein databases based on microbial species or genera, each protein database corresponding to a species or a genus of microorganisms, where n≥2; and (b) Comparing the proteins in every two of the n protein databases, and removing the proteins that interfere with each other from each of the two databases, to form a set of n×(n - 1) new databases, wherein, A microbial proteome database with small interspecies protein differences is established based on genera, and a microbial proteome database with large interspecies protein differences is established based on species, wherein each of the n×(n - 1) new databases contains 15 - 25 proteins, wherein the removing of the proteins that interfere with each other from each of the two databases includes removing the proteins that interfere with each other within the range of MassTolerance ≤ 1300PPM, wherein the method further includes the steps of obtaining the specific proteins in each protein database of step (a), and combining the specific proteins of every two protein databases into a set of n×(n - 1) / 2 specific protein combination databases.

2. The construction method according to claim 1, characterized in that, Based on an existing comprehensive database, a set of n protein databases is established based on microbial species or genera, and the n protein databases correspond to n species of microorganisms or n genera of microorganisms.

3. The construction method according to claim 2, characterized in that, The comprehensive database is selected from: NCBInr database, UniProt database, BioGRID database, Database of Interacting Proteins database or MINT database.

4. The construction method according to claim 1, characterized in that, The microorganism is a bacterium or a fungus, and the microbial proteome database is a bacterial proteome database or a fungal proteome database.

5. The construction method according to claim 4, characterized in that, The bacteria are selected from Escherichia coli, Klebsiella pneumoniae, Citrobacter freundii, Enterobacter asburiae, Hafnia alvei, Serratia marcescens, Proteus vulgaris, Staphylococcus aureus, Staphylococcus epidermidis, Enterococcus faecium, Streptococcus pyogenes, Stenotrophomonas maltophilia, Acinetobacter baumannii, Burkholderia cepacia, Pseudomonas aeruginosa, Neisseria sicca, Salmonella enterica, Corynebacterium striatum, Bacillus subtilis, Group A Streptococcus, Mycobacterium tuberculosis, Legionella pneumophila, Neisseria gonorrhoeae, Listeria monocytogenes, Campylobacter genus, Bordetella pertussis, Campylobacter jejuni, Clostridium perfringens, Salmonella typhi, Shigella sonnei, Staphylococcus haemolyticus, Enterococcus faecalis, Enterococcus durans, Coagulase-negative staphylococci, Streptococcus viridans or Corynebacterium minutissimum.

6. The construction method according to claim 4, characterized in that, The fungi are selected from Candida albicans, Aspergillus niger, Cryptococcus, Pneumocystis jirovecii, Saccharomyces cerevisiae, Alternaria alternata, Trichoderma, Aureobasidium pullulans, Cladosporium cladosporioides, Gliocladium, Drechslera australiensis, Gliomastix cerealis, Monilia cinerea, Verticillium chlamydosporium, Penicillium, Phoma exigua, Pulvinaria papyracea or Filobasidiella humanii.

7. The construction method according to claim 1, wherein, the protein is a ribosomal protein.

8. The construction method according to claim 4, wherein, the microbial proteome database is a bacterial proteome database, and the protein is a 30S ribosomal protein and / or a 50S ribosomal protein.

9. The construction method according to claim 4, wherein, the microbial proteome database is a fungal proteome database, and the protein is a 40S ribosomal protein and / or a 60S ribosomal protein.

10. The construction method according to claim 1, wherein, each of the specific protein combination databases contains 5 to 15 specific proteins targeting two species or two genera of bacteria.

11. The construction method according to claim 1, wherein, the specific protein is a ribosomal protein.

12. The construction method according to claim 4, wherein, the specific protein combination database is a bacterial specific protein combination database, and the protein is a 30S ribosomal protein and / or a 50S ribosomal protein.

13. The construction method according to claim 4, wherein, the specific protein combination database is a fungal specific protein combination database, and the protein is a 40S ribosomal protein and / or a 60S ribosomal protein.

14. An apparatus for the construction method of the microbial proteome database according to any one of claims 1 - 13, the apparatus comprises: (a) A first module for establishing a set of n protein databases in units of microbial species or genera, each protein database corresponding to a species or a genus of microorganisms, where n≥2; and (b) A second module for comparing the proteins in every two of the n protein databases, removing the proteins that interfere with each other from their respective databases, and forming a set of n×(n - 1) new databases, The apparatus further includes a third module for combining the specific proteins of every two protein databases in step (a) into a set of n×(n - 1) / 2 specific protein combination databases.

15. A microbial proteome database constructed by the construction method according to any one of claims 1 - 13.

16. A method for identifying mixed microorganisms using the apparatus according to claim 14, comprising the following steps: (a) Collecting the fingerprint of the mixed microorganisms to be identified; (b) Comparing the collected fingerprint of the mixed microorganisms to be identified with the set of specific protein combination databases to preliminarily determine the species or genera of two microorganisms that may be contained in the mixed microorganisms; (c) Identify the mixed microorganisms to be identified using two corresponding new databases of two species or genera of microorganisms that may be included, and obtain the identification results.

17. The method according to claim 16, wherein: When reliable results are obtained for identifying the mixed microorganisms to be identified using the two new databases respectively, it indicates that the mixed microorganisms are a mixture of the microorganisms corresponding to the two new databases, and the criterion for determining a reliable result is that the measured protein has a match degree of 85% or more with the protein in the new database; When identifying the mixed microorganisms to be identified using the two new databases respectively, if a reliable result is not obtained for one of the new databases, it indicates that the mixed microorganisms are not a mixture of the microorganisms corresponding to the two new databases.

18. The method according to claim 16, wherein, A fingerprint map of the mixed microorganisms to be identified is collected using a mass spectrometer.

19. The method according to claim 16, wherein, A fingerprint map of the mixed microorganisms to be identified is collected using a MALDI-TOF MS mass spectrometer.

20. The method according to claim 16, wherein, The mixed microorganisms are a mixture of two microorganisms.

21. The method according to claim 16, wherein, The microorganisms are bacteria or fungi.

22. The method according to claim 21, wherein, The bacteria are selected from Escherichia coli, Klebsiella pneumoniae, Citrobacter freundii, Enterobacter asburiae, Hafnia alvei, Serratia marcescens, Proteus vulgaris, Staphylococcus aureus, Staphylococcus epidermidis, Enterococcus faecium, Streptococcus pyogenes, Stenotrophomonas maltophilia, Acinetobacter baumannii, Burkholderia cepacia, Pseudomonas aeruginosa, Neisseria sicca, Salmonella enterica, Corynebacterium striatum, Bacillus subtilis, Group A Streptococcus, Mycobacterium tuberculosis, Legionella pneumophila, Neisseria gonorrhoeae, Listeria monocytogenes, Campylobacter, Bordetella pertussis, Campylobacter jejuni, Clostridium perfringens, Salmonella typhi, Shigella sonnei, Staphylococcus haemolyticus, Enterococcus faecalis, Enterococcus durans, Coagulase-negative Staphylococcus, Streptococcus viridans, Corynebacterium minutissimum, Haemophilus ducreyi or Helicobacter pylori; The fungi are selected from Candida albicans, Aspergillus niger, Cryptococcus, Pneumocystis jirovecii, Saccharomyces cerevisiae, Alternaria alternata, Trichoderma, Aureobasidium pullulans, Cladosporium cladosporioides, Gliocladium, Drechslera australiensis, Gliomastix murorum, Candida guilliermondii, Verticillium chlamydosporium, Penicillium, Phoma exigua, Pulvinaria papyracea or Human Filobasidiella.

23. The method according to claim 17, wherein, The criterion for determining a reliable result is that the measured protein has a match degree of 90% or more with the protein in the new database.

24. An apparatus for the method of identifying mixed microorganisms according to any one of claims 16-23, the apparatus comprising: (a) A first module for collecting a fingerprint map of the mixed microorganisms to be identified; (b) The second module, which is used to compare the fingerprint map of the collected mixed microorganisms to be identified with the set of the specific protein combination databases, so as to preliminarily determine the species or genera of two microorganisms that may be contained in the mixed microorganisms; (c) The third module, which is used to respectively identify the mixed microorganisms to be identified by using two new databases corresponding to two species or genera of the microorganisms that may be contained, and obtain the identification result.

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