Mass spectrum method for rapidly identifying pantoea agglomerans
Patent Information
- Application Number
- CN202510933790.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-08
- Publication Date
- 2025-10-17
AI Technical Summary
Existing detection methods for Pantoea agglomerans are costly and time-consuming, and there is a lack of specific mass spectrometry databases for agricultural products, making it difficult to quickly and accurately identify them.
Matrix desorption ionization time-of-flight mass spectrometry was used to obtain the characteristic fingerprint of Pantoea agglomerans. The mass spectrometric data were fitted by the maximum likelihood method to establish a fruit and vegetable-specific database. Microbial homology analysis was performed using SARAMIS Premium software. Rapid identification was achieved by combining the maximum likelihood method with characteristic fingerprint comparison.
It has achieved accurate identification of Pantoea agglomerans in a short period of time, improved detection efficiency, ensured the microbial quality and safety of agricultural products, and enabled timely detection and treatment of contaminated agricultural products to protect consumer health.
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Figure CN120801475A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the field of agricultural product microorganism detection technology, and particularly relates to a mass spectrometry method for rapidly identifying Pantoea agglomerans. BACKGROUND
[0002] Pantoea agglomerans can be isolated from plants, seeds, vegetables, water and soil. For many years, the bacterium has been consistently considered as a plant pathogen, and it is particularly known to cause Chinese cabbage rot. However, it has been reported that the bacterium can infect a wound and cause septicemia. The DNA G+C mol% of the bacterium is 55.1-56.8 (Tm); the model strain is ATCC27155.
[0003] The existing detection and identification of Pantoea agglomerans are basically divided into two categories: biological identification and molecular identification. The biological identification is based on the biochemical characteristics for identification, and the advantages are accurate identification, and the disadvantages are time-consuming, laborious and high cost. The molecular biology identification method is based on the detection method of the conserved nucleic acid sequence, mainly using 16s rDNA sequencing for identification after comparison, and the cost is high and the time is long.
[0004] Matrix-Assisted Laser Desorption / Ionization Time of Flight Mass Spectrometry (MALDI-TOF MS) is a kind of soft ionization organic mass spectrometry. The analyte is released from the solid matrix containing strong ultraviolet absorbing substance by pulsed laser, so as to produce single-charged molecules or ions. The amplified current generated on the detector can be used as a function of time to measure the ion abundance and the mass-to-charge ratio (m / z). Due to the specificity of the protein of the microorganism, the time-of-flight mass spectrometry can also be used to identify the microorganism. The principle is to compare the characteristic mass spectrum peak database or the mass spectrum peak of the known microorganism through the characteristic spectrum peak of the microorganism, so as to achieve the purpose of rapid detection, identification and typing of the microorganism. At present, there are two kinds of databases that can be used for MALDI-TOF MS: MALDI Biotyper database (Bruker Daltonics, Germany) and Saramis (BioMerieux, France). Among them, the MALDI Biotyper and VITEK MS system can be used for the detection of clinical samples. However, the microorganism database is used for testing clinical samples, and there are differences in the identification results of the microorganism in different data systems. The essence of the difference is the difference in database capacity and microorganism source specificity. At present, there is no data system for specific microorganisms of agricultural products and ready-to-eat fruits and vegetables in the two kinds of mass spectrometry microorganism identification systems, and there is also a lack of specific identification spectrum related to Pantoea agglomerans of fruits and vegetables. SUMMARY
[0005] The purpose of the present invention is to provide a mass spectrometry method for rapid identification of Pantoea agglomerans to solve the technical problems of the existing technology that is high in cost and time-consuming.
[0006] To achieve the above objectives, the present invention provides the following technical solutions: The present invention provides a mass spectrometry method for rapidly identifying Pantoea agglomerans, comprising the following steps: S1. Obtaining the characteristic fingerprint of Pantoea agglomerans S11. Isolating Pantoea agglomerans from fruits and vegetables, and culturing the isolated Pantoea agglomerans to obtain a certain amount of bacteria for subsequent mass spectrometry analysis; S12. performing mass spectrometry analysis on the proteins of Pantoea agglomerans using matrix desorption ionization time-of-flight mass spectrometry and collecting mass spectrum data; S13, fitting the collected mass spectrum data by the maximum likelihood method to form a characteristic fingerprint spectrum; S2. Obtaining the mass-to-nuclear ratio of the characteristic fingerprint of Pantoea agglomerans and its corresponding weight based on the obtained characteristic fingerprint of Pantoea agglomerans; S3. Perform mass spectrometry detection on the unknown bacteria using the obtained characteristic fingerprint of Pantoea agglomerans, compare the mass spectrum of the unknown bacteria with the characteristic fingerprint, calculate a score, and determine whether the unknown bacteria is Pantoea agglomerans based on the score result.
[0007] Furthermore, in step S13, the fitting method is specifically to import the mass-to-nuclear ratio information of the microbial mass spectrum obtained by separating fruits and vegetables and biochemically identified as Pantoea agglomerans into the MALDI-TOF / SARAMIS microbial identification system, use Taxonomy in the SARAMIS Premium software to perform identification peak sorting and microbial homology analysis, remove data islands with high homology and incomplete information, perform maximum quasi-multiplication analysis on the remaining data to ensure that the data source is reliable and non-duplicate, import the remaining valid data islands into the established folder, and create a new fruit and vegetable database.
[0008] Furthermore, the number of bacteria in the fitting process is greater than 50 strains, and the fitting deviation range is less than 0.75‰.
[0009] Furthermore, the specific steps in step S2 are: after fitting the mass spectrum data, screening the characteristic peaks with a mass-to-nuclear ratio of protein mass spectrum peaks greater than 80%, including a deviation of less than 0.75‰, to obtain a characteristic fingerprint spectrum with a mass-to-nuclear ratio of: 3122.1, 3991.6, 4159.3, 4418.4, 4443.3, 4635.9, 4752.0, 5396.7, 5631.5, 5885.4, 6199.8, 4221.7, 6253.2, 6401.0, 6819.3, 7133.5, 7245.6, 7264.5, 7336.4, 8330.1, 8352.9, 8897.9, 9320.8, 9510.0; According to the frequency of the characteristic peak, the weight is calculated and modified, and the more the frequency of the characteristic peak, the greater the weight, with a slope of 10%, and the peak group weight is formed as: 2.17, 2.17, 2.17, 2.17, 2.17, 2.17, 2.17, 10.87, 2.17, 6.52, 8.70, 2.17, 2.17, 4.35, 4.35, 2.17, 6.52, 8.70, 8.70, 2.17, 2.17, 2.17, 2.17, 8.70.
[0010] Further, the step S3 specifically comprises the following steps: S31, the unknown bacteria protein is subjected to time-of-flight mass spectrometry CHCA auxiliary analysis soft ionization, threshold offset 0.015Mv, threshold corresponding to 1.100, and the protein fingerprint spectrum is obtained; S32, the characteristic fingerprint spectrum of the group-forming panus is compared with the mass-to-charge ratio value correlation of the unknown bacteria protein fingerprint spectrum, and the deviation less than 0.75‰ is contained, and the mass spectrum peak mass-to-charge ratio with consistent response after fitting is screened out; S33, the corresponding weight integral of the consistent mass-to-charge ratio peak is added, and the score greater than 80 points is identified as the group-forming panus, and the score less than 80 points is identified as the non-group-forming panus.
[0011] Further, before obtaining the mass spectrum, it is also necessary to use Escherichia coli ATCC 8739 to calibrate and correct the mass spectrum peak.
[0012] Further, the mass spectrum conditions are as follows: fixed focusing 338nm, detector electron multiplier linear mode, positive ion mode collection, laser beam energy frequency 75-80Hz, and collection range 2000-18000m / z.
[0013] Based on the above technical scheme, the embodiment of the application can at least produce the following technical effects: (1) The mass spectrometry method for rapidly identifying Pantoea agglomerans provided by the application has a characteristic fingerprint containing a dispersion deviation of less than 0.75 ‰, which ensures the accommodation degree of sample identification. By comparison with the characteristic fingerprint, the interference of other similar bacteria can be excluded, and Pantoea agglomerans can be accurately identified; and by combining mass spectrometry technology with the characteristic fingerprint, unknown bacteria can be identified in a short time, greatly improving the detection efficiency.
[0014] (2) The mass spectrometry method for rapidly identifying Pantoea agglomerans provided by the application is suitable for detecting Pantoea agglomerans in agricultural products, and has important significance for ensuring the microbial quality safety of agricultural products. Through rapid identification, contaminated agricultural products can be discovered and treated in time to ensure consumer health; and support can also be provided for risk assessment of the microbial quality safety of agricultural products. Through detection of Pantoea agglomerans, the degree of microbial contamination of agricultural products can be evaluated to provide a basis for formulating prevention and control measures. BRIEF DESCRIPTION OF DRAWINGS
[0015] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the structures shown in the drawings.
[0016] Figure 1 is the characteristic fingerprint of Pantoea agglomerans of the application; Figure 2 is the mass spectrum of Pantoea agglomerans 1 separated from fruits and vegetables; Figure 3 is the mass spectrum of Pantoea agglomerans 2 separated from fruits and vegetables; Figure 4 is the mass spectrum of dispersed Pantoea agglomerans 3 separated from fruits and vegetables; Figure 5 is the mass spectrum of Bacillus cereus 4 separated from fruits and vegetables; Figure 6 is the mass spectrum of Salmonella enteritidis 5 separated from fruits and vegetables. DETAILED DESCRIPTION
[0017] The technical solutions in the embodiments of the present application will be clearly and completely described below. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work are within the protection scope of the present application. In addition, the technical solutions of various embodiments can be combined with each other, but it should be considered that the combination of technical solutions does not exist and is not within the protection scope of the present application when the combination of technical solutions appears contradictory or unachievable on the basis that it can be achieved by those of ordinary skill in the art.
[0018] Example 1 Obtaining Pandoravirus characteristic fingerprint The Pandoravirus in the Pandoravirus protein characteristic fingerprint library is separated from fruits and vegetables, the characteristic fingerprint fitting process is not less than 50 strains of bacteria, the fitting deviation range is less than 0.75‰, the collection method of the protein spectrum is matrix desorption ionization time-of-flight mass spectrometry method, and before obtaining the mass spectrum, Escherichia coli ATCC 8739 needs to be used for mass spectrum peak calibration correction.
[0019] The fitting method is to import the mass spectrum information (m / z) of the microorganism separated from fruits and vegetables and biochemically identified as Pandoravirus into the MALDI-TOF / SARAMIS microorganism identification system, and use Taxonomy in SARAMIS Premium software to arrange and analyze the homology of the microorganism. The data islands with high homology and incomplete information are removed, the remaining data is analyzed by maximum fitting method, the data source is ensured to be reliable and not repeated, the remaining effective data islands are imported into the established folder and a new fruit and vegetable database is created.
[0020] As shown in Figure 1 After fitting the mass spectrum data, the characteristic peaks with protein mass spectrum peak mass ratio frequency > 80% are screened, including a deviation of less than 0.75‰, to obtain the characteristic fingerprint mass ratio as follows: 3122.1, 3991.6, 4159.3, 4418.4, 4443.3, 4635.9, 4752.0, 5396.7, 5631.5, 5885.4, 6199.8, 4221.7, 6253.2, 6401.0, 6819.3, 7133.5, 7245.6, 7264.5, 7336.4, 8330.1, 8352.9, 8897.9, 9320.8, 9510.0; According to the weight calculation modification of the characteristic peak frequency, the more the characteristic peak frequency, the greater the weight, and the peak group weight formed is 10% as the slope. 2.17, 2.17, 2.17, 2.17, 2.17, 2.17, 2.17, 10.87, 2.17, 6.52, 8.70, 2.17, 2.17, 4.35, 4.35, 2.17, 6.52, 8.70, 8.70, 2.17, 2.17, 2.17, 2.17, 8.70.
[0021] Example 2 Identification of Pantoea agglomerans in fruits and vegetables Microbial source: Isolated from sprouts and strawberries, and identified as Pantoea agglomerans by biochemical identification. Comparative tests were performed with Pantoea dispersa isolated from strawberries and sprouts, Escherichia coli, Salmonella enteritidis. The biochemical profiles of the Pantoea agglomerans isolated from fruits and vegetables and the comparative bacteria are shown in Table 1 below.
[0022] Table 1 Biochemical profiles of Pantoea agglomerans isolated from fruits and vegetables and comparative bacteria Note: LIP lipase, ßNAG ß-nitroacetylglucosamine, MAL d-maltose, GLU d-glucose, SAC sucrose, GAT d-galacturonate assimilation, URE urease, AHD arginine dihydrolase, INO inositol, aMAL a-maltose, CEL d-cellulobiose, ßGAL ß-galactosidase, AspA L-asparagine arylamidase, TRE d-trehalose, aGLU a-glucosidase, LARAL-L-arabinose, ODC ornithine decarboxylase, IND indole, MAN d-mannitol, RP phenol red, 5KG 5-keto•gluconate sodium, LARL L-arabinol, LDC lysine decarboxylase, ßGLU ß-glucosidase, MNT malonate, SOR d-sorbitol, ßGUR ß-glucuronidase, ADO adonitol, RHA L-rhamnose, aGAL a-galactosidase, DARL D-arabinol, PLE proline.
[0023] Atlas microbial mass spectrum identification: fixed focus 338nm, detector multiple dynode linear mode, positive ion mode collection, laser beam energy frequency 75~80Hz, collection range 2000~18000(m / z), collection method protein time-of-flight mass spectrum CHCA auxiliary resolution soft ionization, threshold offset 0.015Mv, threshold corresponding 1.100, obtain protein fingerprint spectrum, 100 times collection peak superposition per sample (including 0.75% deviation rate), calibrant ATCC 8739 Escherichia coli, the characteristic fingerprint spectrum of Pantoea agglomerans in the present application is used to compare the mass number ratio values of the fingerprint spectra of 5 microorganisms (Pantoea agglomerans 1, Pantoea agglomerans 2, Pantoea dispersa 3, Bacillus cereus 4, Salmonella enteritidis 5) isolated from fruits and vegetables (including a deviation of less than 0.75‰), and the mass spectrum peaks with consistent response after fitting are screened out. The corresponding weight integrals of the consistent mass number ratio peaks are added, and the score greater than 80 is identified as Pantoea agglomerans, and the score less than 80 is identified as non-Pantoea agglomerans.
[0024] Microbial identification peak fitting: the mass spectrum peak spectrum of Pantoea agglomerans in fruits and vegetables is obtained Figures 2-6 , Table 2 is the fitting and deviation of the microbial identification peak and the characteristic fingerprint spectrum of Pantoea agglomerans, and the score results of Pantoea agglomerans 1 and Pantoea agglomerans 2 isolated from fruits and vegetables are 87.0 and 95.7, respectively, and effective identification results are obtained. The score calculation of Pantoea dispersa 3, Escherichia coli 4 and Salmonella enteritidis 5 is 67.4, 28.3 and 28.3 respectively, and the microorganisms 3-5 are determined as non-Pantoea agglomerans.
[0025] Table 2 Fitting and deviation of microbial identification peak and characteristic fingerprint spectrum of Pantoea agglomerans The fitting results of the Pantoea agglomerans fingerprint spectrum identified by biochemistry are more than 80 points, and although the Pantoea dispersa is similar to the Pantoea agglomerans in biochemical type, the score is between 60-70, and the characteristic fingerprint spectrum of the microorganism similar to the Pantoea agglomerans can also be obviously distinguished, the Salmonella and the Escherichia coli are common soil pollution pathogenic bacteria in fruits and vegetables, and they are also Enterobacteriaceae microorganisms with the Pantoea agglomerans, but after scoring by the fingerprint spectrum, the score is lower, and they can be obviously distinguished, which shows that the differences of proteins of different microorganisms are obvious.
[0026] The above shows and describes the basic principles and main features of the present application and the advantages of the present application. Those skilled in the art should understand that the present application is not limited by the above examples, and the above examples and descriptions in the specification are only to illustrate the principles of the present application, and various changes and improvements can be made without departing from the spirit and scope of the present application, and these changes and improvements all fall within the scope of the present application. The scope of protection of the present application is defined by the appended claims and their equivalents.
Claims
1. A mass spectrometry method for rapid identification of Pantoea agglomerans, characterized in that: The following steps are involved: S1. Obtaining the characteristic fingerprint of Pantoea agglomerans S11. Isolating Pantoea agglomerans from fruits and vegetables, and culturing the isolated Pantoea agglomerans to obtain a certain amount of bacteria for subsequent mass spectrometry analysis; S12, performing mass spectrometry analysis on the proteins of Pantoea agglomerans using matrix desorption ionization time-of-flight mass spectrometry, and collecting mass spectrum data; S13, fitting the collected mass spectrum data by the maximum likelihood method to form a characteristic fingerprint spectrum; S2. Obtaining the mass-to-nuclear ratio of the characteristic fingerprint of Pantoea agglomerans and its corresponding weight based on the obtained characteristic fingerprint of Pantoea agglomerans; S3. Perform mass spectrometry detection on the unknown bacteria using the obtained characteristic fingerprint of Pantoea agglomerans, compare the mass spectrum of the unknown bacteria with the characteristic fingerprint, calculate a score, and determine whether the unknown bacteria is Pantoea agglomerans based on the score result.
2. The mass spectrometry method for rapid identification of Pantoea agglomerans according to claim 1, characterized in that: In step S13, the fitting method specifically includes importing the mass-to-nuclear ratio information of the microbial mass spectrum obtained from fruits and vegetables and biochemically identified as Pantoea agglomerans into the MALDI-TOF / SARAMIS microbial identification system, using Taxonomy in SARAMIS Premium software to perform peak sorting and microbial homology analysis, removing data islands with high homology and incomplete information, performing maximum pseudo-multiplication analysis on the remaining data to ensure that the data source is reliable and non-duplicate, and importing the remaining valid data islands into a created folder to create a new fruit and vegetable database.
3. The mass spectrometry method for rapid identification of Pantoea agglomerans according to claim 2, characterized in that: The number of bacteria involved in the fitting process is greater than 50 strains, and the fitting deviation range is less than 0.75‰.
4. The mass spectrometry method for rapid identification of Pantoea agglomerans according to claim 1, characterized in that: The specific steps in step S2 are: after fitting the mass spectrum data, screening the characteristic peaks with a mass-to-nuclear ratio of protein mass spectrum peaks greater than 80%, including a deviation less than 0.75‰, and obtaining the characteristic fingerprint spectrum mass-to-nuclear ratio as follows: 3122.1、3991.6、4159.3、4418.4、4443.3、4635.9、4752.0、5396.7、5631.5、5885.4、6199.8、4221.7、6253.2、6401.0、6819.3、7133.5、7245.6、7264.5、7336.4、8330.1、8352.9、8897.9、9320.8、9510.0； The weight calculation and modification are performed based on the frequency of occurrence of the characteristic peaks. The more the frequency of the characteristic peaks, the greater the weight. With a slope of 10%, the peak group weight is: 2.17、2.17、2.17、2.17、2.17、2.17、2.17、10.87、2.17、6.52、8.70、2.17、2.17、4.35、4.35、2.17、6.52、8.70、8.70、2.17、2.17、2.17、2.17、8.70。 5. The mass spectrometry method for rapid identification of Pantoea agglomerans according to claim 1, characterized in that: The S3 specifically includes the following steps: S31. Perform CHCA-assisted soft ionization of the unknown bacterial protein using time-of-flight mass spectrometry with a threshold offset of 0.015 Mv and a corresponding threshold of 1.100 to obtain a protein fingerprint peak spectrum. S32, comparing the mass-to-nuclear ratio numerical correlation of the characteristic fingerprint of Pantoea agglomerans with the protein fingerprint peak spectrum of the unknown bacteria, including a deviation of less than 0.75‰, and screening the mass-to-nuclear ratio of the mass spectrum peaks with consistent responses after fitting; S33. The corresponding weight integrals of the mass-to-nuclear ratio peaks with consistent fitting are added together, and the peaks with a score greater than 80 are identified as agglomerated Pantoea, and the peaks with a score less than 80 are identified as non-agglomerated Pantoea.
6. The mass spectrometry method for rapid identification of Pantoea agglomerans according to claim 1, characterized in that: Before obtaining the mass spectrum, it is necessary to use Escherichia coli ATCC 8739 to calibrate the mass spectrum peak scale.
7. The mass spectrometry method for rapid identification of Pantoea agglomerans according to claim 1, characterized in that: The mass spectrometry conditions were as follows: fixed focus at 338 nm, detector electron multiplier tube in linear mode, positive ion mode collection, laser beam frequency of 75–80 Hz, and collection range of 2000–18000 m / z.