A single-strain metabolic characteristic map database of key oral microorganisms under high-sugar microenvironment and a construction method and application thereof

Abstract

The application provides a single-strain metabolic characteristic atlas database of oral cavity key microorganisms under a high-sugar microenvironment as well as a construction method and application thereof, and belongs to the technical field of oral microbiology. The application simulates the high-sugar microenvironment in the mouth in vitro, and carries out standardized single-strain culture, culture supernatant full-spectrum metabolic detection (non-target liquid chromatography-mass spectrometry LC-MS and non-target gas chromatography-mass spectrometry GC-MS), and identification analysis of the metabolism to construct the single-strain metabolic characteristic atlas database of oral cavity key microorganisms under the high-sugar microenvironment. The database can be used for source attribution and cross-validation of in-vivo metabolome results, and assists in screening high-sugar related key metabolites and key pathways. The application improves the credibility of metabolite source determination, reveals the directional influence of high sugar on the metabolic function of the flora, and standardizes the method and platformizes the resource.

Description

Technical Field

[0001] This invention belongs to the field of oral microbiology technology, and in particular relates to a single-strain metabolic characteristic map database of key oral microorganisms in a high-sugar microenvironment, its construction method and application. Background Technology

[0002] Oral microbiome research focuses on the impact of oral microbes on health and disease. By studying the oral microbiome, changes in the microbial community associated with oral diseases (such as dental caries, periodontal disease, and oral mucosal diseases) or systemic diseases (such as obesity, diabetes, and inflammatory bowel disease) can be identified, leading to the development of new disease prevention and treatment strategies. Metabolomics studies the metabolites and their dynamic changes within organisms. Metabolites are the final products of gene expression and components of regulatory systems. They not only serve as candidate biomarkers but also contain a wealth of information believed to explain and even predict biological functions and phenotypes. Research indicates that a key pathway by which the oral microbiome influences host physiology is through the production of small molecule chemicals that enter host tissues and circulate, participating in certain local and systemic metabolic processes. By analyzing metabolite profiles, biomarkers related to disease or environmental changes can be identified, revealing the production of abnormal metabolites in complex diseases and disease-related metabolic regulatory pathways, further promoting the development of precision medicine.

[0003] Oral microbes interact with the host, and their metabolic activities are influenced by changes in the oral microecological environment. The production and consumption of metabolites can also alter the oral microecology, potentially affecting the oral microbes' specific selection of their environment, enhancing bacterial pathogenicity, and ultimately leading to oral diseases. Studies have shown a complex network of interactions between microbes, metabolites, and the host in the oral microenvironment. Type 2 diabetes mellitus (T2DM)-related hyperglycemia can lead to elevated glucose levels in the oral microenvironment, altering the nutrient substrate supply and metabolic stress of oral microbes, thereby affecting microbial metabolic activities and the profile of metabolites produced or consumed. Currently, non-targeted metabolomics based on in vivo samples often exhibits the characteristic of "unknown origin": differential metabolites may originate from host cells, oral microbiota metabolism, or exogenous inputs such as diet / drugs. Without reference evidence regarding the microbial origin, it is difficult to accurately monitor the diversity of metabolites produced by oral microbes and their directional changes at different glucose concentrations, hindering the determination of the mechanisms linking microbial strains and host phenotypes.

[0004] Cultureomics, by obtaining the functional and metabolic phenotypes of single bacterial species / strains under diverse culture conditions, is an important supplement to elucidating microbe-host interactions. Currently, the application of cultureomics in oral microbiome research mainly focuses on innovating culture media and conditions to isolate and culture previously uncultured oral microorganisms. Cultureomics research on oral microorganisms is still in its early stages; no studies have yet used cultureomics to establish metabolic profiles of single oral microbiome strains. In particular, the impact of the high-glucose microenvironment in diabetic patients on microbial metabolites remains unclear, and the complete metabolic capacity of the oral microbiota requires further investigation based on cultureomics.

[0005] Existing oral microbiome and metabolomics studies based on in vivo samples (saliva / gingival crevicular fluid / plaster, etc.) can obtain macroscopic information about the oral microecology, but it is difficult to determine the exact source of specific metabolites (host, oral microbes, or exogenous environment), thus limiting the analysis of the interaction chain of "microbe-metabolite-host phenotype". In particular, it is difficult to identify key metabolites "produced / consumed by oral microbes" in the oral microenvironment of diabetes-associated high glucose. Summary of the Invention

[0006] In view of this, the purpose of this invention is to provide a single-strain metabolic characteristic profile database of key oral microorganisms in a high-sugar microenvironment, as well as its construction method and application, to solve the problems of in vivo omics' difficulty in distinguishing the source of metabolites and the lack of a standard reference system for "source attribution"; oral culture omics research mostly focuses on "whether new bacteria can be cultured / isolated", and there is insufficient systematic research on "metabolic phenotypic changes under disease-related environments (such as high sugar); the lack of a reusable and searchable oral single-strain metabolic reference database leads to the problem of difficulty in cross-validation between different studies.

[0007] To achieve the above-mentioned objectives, the present invention provides the following technical solution: This invention provides a method for constructing a database of single-strain metabolic characteristic maps of key oral microorganisms in a high-sugar microenvironment, comprising the following steps: (1) The key oral microorganisms in the logarithmic growth phase were cultured in high sugar liquid culture medium, centrifuged, and the supernatant was obtained. The supernatant of the liquid culture medium was used as a blank control. (2) Metabolites were obtained by non-targeted LC-MS and non-targeted GC-MS detection of the supernatant and liquid culture medium; (3) Identify the metabolites, preprocess the identification results and perform principal component analysis and orthogonal partial least squares discriminant analysis. Determine the significantly different metabolites based on the variable weights and the P-value of the student's t test. Perform KEGG metabolic pathway analysis on the significantly different metabolites to obtain the pathways in which the different metabolites participate. (4) Using bacterial species / strain, liquid culture medium type, glucose concentration in high sugar liquid culture medium, culture stage, detection platform, metabolite name / ID, retention time / mass-to-charge ratio / retention index, peak area / relative abundance, direction of difference from blank control, variable weight value and P value as indicators, a single strain metabolic feature database of key oral microorganisms in high sugar microenvironment was constructed.

[0008] Preferably, the glucose concentration in the high-glucose liquid culture medium in step (1) is 5.5~50mM, and 6 biological replicates are set up for each glucose concentration of liquid culture medium.

[0009] Preferably, in step (2), a quality control sample is inserted during the non-targeted LC-MS detection and non-targeted GC-MS detection process, with one quality control sample inserted for every 5 to 15 analytical samples.

[0010] Preferably, the chromatographic conditions for the non-targeted LC-MS detection in step (2) are as follows: the chromatographic column is ACQUITY UPLC HSS T3, the ACQUITY UPLC HSS T3 has a size of 100 mm × 2.1 mm and a diameter of 1.8 μm; the mobile phase A is 95% water + 5% acetonitrile, the acetonitrile contains 0.1% formic acid; the mobile phase B is 47.5% acetonitrile + 47.5% isopropanol + 5% water, the water contains 0.1% formic acid; the injection volume is 3 μL; and the column temperature is 40 °C. The mass spectrometry conditions for the non-targeted LC-MS detection are as follows: scan range 70-1050 m / z, jet flow rate 60 arb, auxiliary gas flow rate 20 arb, heating temperature 350°C, capillary temperature 320°C, positive mode spray voltage 3400 V, negative mode spray voltage -3000 V, S-Lens voltage 70, collision energy 20%, 40%, 60%, resolution Full MS 60000, resolution MS² 15000.

[0011] Preferably, the chromatographic conditions for the non-targeted GC-MS detection in step (2) are as follows: injection volume 1µL, split ratio 10:1, chromatographic column is TG-5SILMS capillary column with specifications of 30m×0.25mm×0.25µm, injection port temperature 300℃, carrier gas is high-purity helium, carrier gas flow rate is 1.0 mL / min, septum purge flow rate is 3 mL / min; temperature program: initial temperature 80℃, equilibration 0 min, then increase to 310℃ at a rate of 20℃ / min and maintain for 8 min, total run time 20 min, solvent delay 2 min; The mass spectrometry conditions for the non-targeted GC-MS detection are as follows: Full Scan mode, scan range 35-500 m / z, resolution 30000, ion source temperature 250℃, ion source type EI, repulsion electrode 10V, ion source default voltage 5V, lens 1-50V, lens 2-0.5V, lens 3-35V, electron lens 15V, electron energy 70eV, and emission current 50μA.

[0012] Preferably, the identification in step (3) involves matching mass spectrometry information with a metabolic database and determining the types of metabolites based on the matching degree. The identification software includes the metabolomics processing software Progenesis QI v3.0 and Thermo CompoundDiscovery 3.3.SP3 software. The metabolic database includes NIST-2023 and GC-Orbitrap MetabolomicsLibrary_v2.

[0013] Preferably, the preprocessing tool in step (3) includes the Meiji Cloud Platform; the stability of the model is evaluated by 7 cycles of interactive validation after the principal component analysis and orthogonal partial least squares discriminant analysis; the variable weight value >1 and the p value of the student's test <0.05 are considered to be significantly different metabolites.

[0014] This invention also provides a database of single-strain metabolic characteristic maps of key oral microorganisms in a high-sugar microenvironment constructed using the aforementioned method.

[0015] This invention also provides the application of the single-strain metabolic characteristic map database of key oral microorganisms in a high-sugar microenvironment in screening metabolites in a diabetes-related high-sugar oral microenvironment.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention constructs a database of metabolic characteristic maps of key oral microorganisms under high-sugar microenvironment conditions by performing standardized single-strain culture, full-spectrum metabolic detection of culture supernatant (non-targeted liquid chromatography-mass spectrometry LC-MS, non-targeted gas chromatography-mass spectrometry GC-MS), and metabolic identification analysis on key oral microorganisms under standardized intraoral high-sugar microenvironment conditions.

[0017] This invention employs a system of "glucose gradient + standardized single-strain culture of multiple strains + blank control in culture medium," which enables the quantification and reproducibility of microbial metabolic production / consumption characteristics under high glucose conditions. Combined with LC-MS and GC-MS full-spectrum detection, it covers a broader metabolite spectrum including lipids, amino acids, organic acids, and sugar alcohols. It provides reusable data processing and differential screening rules (6 biological replicates, QC insertion frequency, 7 cross-validations, VIP and P-value thresholds, etc.) to improve data reliability and cross-study comparability. It outputs a searchable database of single-strain metabolic characteristics for cross-validation and source attribution of in vivo multi-omics results.

[0018] This invention improves the reliability of metabolite source determination: It focuses on the difference between "single-strain culture supernatant vs. culture medium blank control," providing direct evidence for metabolites that "microorganisms can produce / consume"; it reveals the directional effect of high glucose on microbial community metabolic function: the same strain can exhibit differences in metabolite types and abundance at different glucose concentrations, forming a "high glucose response profile" that can be used for mechanism studies; it standardizes the process: glucose gradient, logarithmic phase sampling, QC strategy, and difference screening threshold are clearly defined, facilitating database expansion and multi-center reuse; and it platformizes resources: the database supports rapid retrieval and cross-validation, reducing the trial-and-error costs of subsequent research.

[0019] This invention establishes a standardized method for single-strain culture and culture supernatant collection to simulate a high-glucose microenvironment in the oral cavity in vitro; it establishes a full-spectrum metabolic detection and data processing workflow based on non-targeted LC-MS and GC-MS to extract differential metabolic sets under different glucose concentrations; it constructs a database of metabolic characteristic maps of key oral microorganisms by single strains and provides a method for joint analysis / cross-validation with in vivo research results.

[0020] This invention's database simulates oral microenvironment conditions at different glucose concentrations (high glucose) in vitro, systematically characterizing the metabolic production / consumption features of key oral microorganisms; it provides "microbial origin evidence" for in vivo multi-omics studies, supporting the attribution and cross-validation of differential metabolites; and it provides reusable resources for subsequent mechanism studies, targeted validation, and biomarker screening. It can be used to support the attribution of differential metabolites in vivo (determining whether they can be produced / consumed by a specific bacterial species); compare the directional changes in metabolism of the same bacterial species under different glucose concentrations (forming "high glucose response characteristics"); and provide a set of candidate metabolites and pathways for mechanism studies and targeted validation. Attached Figure Description

[0021] Figure 1 This is a flowchart of the construction process for a database of metabolic characteristic maps of key oral microorganisms in a high-sugar microenvironment. Figure 2 This is a schematic diagram of the glucose concentration gradient added to the culture medium; Figure 3This is a QC sample correlation analysis (where a is non-targeted LC-MS detection and b is non-targeted GC-MS detection). Figure 4 yes P.g PLS-DA analysis of six biological replicates obtained by culturing in media with five different glucose concentrations (where 1-5 correspond to glucose concentrations of 0, 5.5, 10, 20, and 50 mM, respectively). Figure 5 yes WY Scatter plot of differential metabolites between culture supernatant and culture medium control. (Red: upregulated metabolite count; blue: downregulated metabolite count; A, E, and E correspond to glucose concentrations of 0, 5.5, 10, 20, and 50 mM in the culture medium, respectively). Figure 6 yes WY Venn diagram showing the differences in metabolites between the culture supernatant and the culture medium control (where 1-5 correspond to glucose concentrations of 0, 5.5, 10, 20, and 50 mM in the culture medium, respectively). Figure 7 This study is an enrichment analysis of galactose, galactitol, and sorbitol in the galactose metabolic pathway by in vitro cultured microorganisms. Detailed Implementation

[0022] This invention provides a method for constructing a database of single-strain metabolic characteristic maps of key oral microorganisms in a high-sugar microenvironment, comprising the following steps: (1) The key oral microorganisms in the logarithmic growth phase were cultured in high sugar liquid culture medium, centrifuged, and the supernatant was obtained. The supernatant of the liquid culture medium was used as a blank control. (2) Metabolites were obtained by non-targeted LC-MS and non-targeted GC-MS detection of the supernatant and liquid culture medium; (3) Identify the metabolites, preprocess the identification results and perform principal component analysis and orthogonal partial least squares discriminant analysis. Determine the significantly different metabolites based on the variable weights and the P-value of the student's t test. Perform KEGG metabolic pathway analysis on the significantly different metabolites to obtain the pathways in which the different metabolites participate. (4) Using bacterial species / strain, liquid culture medium type, glucose concentration in high sugar liquid culture medium, culture stage, detection platform, metabolite name / ID, retention time / mass-to-charge ratio / retention index, peak area / relative abundance, direction of difference from blank control, variable weight value and P value as indicators, a single strain metabolic feature database of key oral microorganisms in high sugar microenvironment was constructed.

[0023] In this invention, key oral microorganisms in the logarithmic growth phase are cultured in high-glucose liquid culture medium, centrifuged, and the supernatant is obtained, with the supernatant of the liquid culture medium serving as a blank control. The key oral microorganisms include *Porphyromonas gingivalis*, *Actinomyces actinomycetes*, *Veillonella spp.*, *Fusobacterium nucleatum* subsp. *nucleatum*, *Prevotella intermedius*, *Prevotella melanogaster*, *Streptococcus Gordonii*, *Streptococcus mutans*, and *Streptococcus salivans*. The culture medium for *Porphyromonas gingivalis*, *Actinomyces actinomycete*, *Veillonella spp.*, and *Fusobacterium nucleatum* subsp. *nucleatum* is a modified BHI medium, which includes BHI broth powder and heme-vitamin K chloride. The culture medium for *Prevotella intermedius* and *Prevotella melanogaster* is GAM medium, and the culture medium for *Streptococcus Gordonii*, *Streptococcus mutans*, and *Streptococcus salivarius* is BHI medium. The culture methods for *Porphyromonas gingivalis*, *Veillonella spp.*, *Fusobacterium nucleatum* subsp. *nucleatum*, *Prevotella intermedius*, and *Prevotella melanogaster* are anaerobic, the culture method for *Actinomyces actinomycete* is facultative anaerobic, and the culture method for *Streptococcus Gordonii*, *Streptococcus mutans*, and *Streptococcus salivarius* is microaerophilic. The glucose concentration in the high-glucose liquid culture medium is 5.5–50 mM, and six biological replicates are set up for each glucose concentration of liquid culture medium.

[0024] In this invention, metabolites are obtained from the supernatant and the supernatant of the liquid culture medium by non-targeted LC-MS and non-targeted GC-MS detection. Quality control samples are inserted during the non-targeted LC-MS and non-targeted GC-MS detection processes, with one quality control sample inserted for every 5-15 analytical samples. The chromatographic conditions for the non-targeted LC-MS detection are as follows: the chromatographic column is an ACQUITY UPLC HSS T3, and the ACQUITY UPLC HSS T3 column has dimensions of 100 mm × 2.1 mm. The column diameter was 1.8 μm, and the mobile phase A was 95% water + 5% acetonitrile, containing 0.1% formic acid. The mobile phase B was 47.5% acetonitrile + 47.5% isopropanol + 5% water, containing 0.1% formic acid. The injection volume was 3 μL, and the column temperature was 40 °C. The mass spectrometry conditions for the non-targeted LC-MS detection were: scan range 70-1050 m / z, jet flow rate 60 arb, auxiliary gas flow rate 20 arb, heating temperature 350 °C, capillary temperature 320 °C, positive mode spray voltage 3400 V, negative mode spray voltage -3000 V, S-Lens voltage 70, collision energy 20%, 40%, 60%, and resolution Full MS. The chromatographic conditions for the non-targeted GC-MS detection were: injection volume 1µL, split ratio 10:1, TG-5SILMS capillary column (30m × 0.25mm × 0.25µm), injection port temperature 300℃, carrier gas high-purity helium at a flow rate of 1.0 mL / min, septum purge flow rate of 3 mL / min; temperature program: initial temperature 80℃, equilibration for 0 min, then increased to 310℃ at a rate of 20℃ / min and held for 8 min, total run time 20 min, solvent delay 2 min; the mass spectrometry conditions for the non-targeted GC-MS detection were: scan mode Full. Scan, Scan range 35-500 m / z, Resolution 30000, Ion source temperature 250℃, Ion source type EI, Repulsion electrode 10V, Default ion source voltage 5V, Lens 1-50V, Lens 2-0.5V, Lens 3-35V, Electron lens 15V, Electron energy 70eV, Emission current 50μA.

[0025] In this invention, metabolites are identified, and the identification results are preprocessed before principal component analysis and orthogonal partial least squares discriminant analysis (P-squared analysis). Significantly differentially expressed metabolites are identified based on variable weights and the p-value of Student's t-test. KEGG metabolic pathway analysis is then performed on these significantly differentially expressed metabolites to obtain the pathways involved. The identification process involves matching mass spectrometry information with a metabolic database, and the type of metabolite is determined based on the matching degree. The identification software includes the metabolomics processing software Progenesis QI v3.0 and Thermo Compound Discovery 3.3.SP3. The metabolic database includes NIST-2023 and GC-Orbitrap Metabolomics Library_v2. The preprocessing tool includes the Meiji Cloud platform. The stability of the model is evaluated using seven-cycle interactive validation after principal component analysis and orthogonal partial least squares discriminant analysis. Metabolites with variable weights >1 and a p-value <0.05 in Student's t-test are considered significantly differentially expressed.

[0026] In this invention, a single-strain metabolic characteristic map database of key oral microorganisms in a high-glucose microenvironment is constructed using indicators such as bacterial species / strain, type of liquid culture medium, glucose concentration in high-glucose liquid culture medium, culture stage, detection platform, metabolite name / ID, retention time / mass-to-charge ratio / retention index, peak area / relative abundance, direction of difference from blank control, variable weight value, and P-value. The culture stage is the logarithmic phase, and the detection platform is LC-MS or GC-MS.

[0027] This invention also provides a database of single-strain metabolic characteristic maps of key oral microorganisms in a high-sugar microenvironment constructed using the aforementioned method.

[0028] This invention also provides the application of the single-strain metabolic characteristic map database of key oral microorganisms in a high-sugar microenvironment in screening metabolites in a diabetes-related high-sugar oral microenvironment.

[0029] The technical solutions provided by the present invention will be described in detail below with reference to the embodiments, but they should not be construed as limiting the scope of protection of the present invention.

[0030] Example 1

[0031] 1. Microbial strains and culture conditions

[0032] 1.1 BHI culture medium

[0033] ①BHI broth medium: Dissolve 3.7g of BHI broth powder in 100ml of distilled water. Autoclave at 121°C for 15 minutes. Cool to room temperature and store in a 4°C refrigerator, protected from light, for later use.

[0034] ②BHI blood agar plate medium: Mix BHI powder, agar powder, and distilled water in a ratio of 3.7g:2g:100ml. Autoclave at 121°C for 15 minutes, then reduce the temperature to 50°C. Pour onto sterile plates, allow to cool and solidify, then store in a 4°C refrigerator, protected from light, for later use.

[0035] 1.2 Modified BHI medium

[0036] ① Modified BHI broth medium: Dissolve 3.7g of BHI broth powder in 100ml of distilled water. Autoclave at 121°C for 15 minutes, then cool to 50°C. Add heme chloride-vitamin K at a concentration of 1:100, and mix thoroughly by shaking. Cool to room temperature, then store in a 4°C refrigerator, protected from light, for later use.

[0037] ② Modified BHI blood agar plate medium: Mix BHI powder, agar powder, and distilled water at a ratio of 3.7g:2g:100ml. Autoclave at 121℃ for 15 minutes, then cool to 50℃. Add sterile defibrinated sheep blood and heme-vitamin K chloride at concentrations of 1:10 and 1:100 respectively, and mix thoroughly by shaking. Pour onto sterile plates, allow to cool and solidify, then store in a 4℃ refrigerator, protected from light, for later use.

[0038] 1.3 GAM medium

[0039] ①GAM plate medium: Weigh 4.9g of GAM medium powder, add 2g of agar, dissolve in 100ml of distilled water, and autoclave at 121℃ for 15 minutes. When cooled to about 50℃, add 1ml of sterile 0.02% vitamin K1 solution and 1ml of heme chloride (1mg / ml), and 14ml of defibrinated sheep blood to every 200ml of medium, and shake to mix well. Pour onto sterile plates, allow to cool and solidify, and store in a 4℃ refrigerator, protected from light, for later use.

[0040] ②GAM liquid culture medium: Weigh 4.9g of GAM culture medium powder, dissolve it in 90ml of distilled water by heating, and autoclave at 121℃ for 15 minutes. When cooled to about 50℃, add 1ml of sterile 0.02% vitamin K1 solution and 1ml of heme chloride (1mg / ml), and 20ml of fetal bovine serum to every 180ml of culture medium, and shake to mix well. Cool to room temperature, store in a 4℃ refrigerator, protected from light, for later use.

[0041] Table 1 Information on cultured microbial strains

[0042] Note: Of the above bacterial strains, Porphyromonas gingivalis W83 was provided by the Central Laboratory of Peking University School of Stomatology, and the rest were purchased from Beina Biotechnology Co., Ltd.

[0043] Table 2. Culture conditions for each bacterial strain

[0044] Anaerobic culture conditions: After inoculation, the culture medium and the anaerobic gas generator are placed together in an anaerobic culture bag, and the bag is placed in a 5% carbon dioxide incubator and cultured at 37°C and ≥95% RH.

[0045] Facultative anaerobic culture conditions: After inoculation, the culture medium and the facultative anaerobic gas generator are placed together in an anaerobic culture bag, and the bag is placed in a 5% carbon dioxide incubator and cultured at 37°C and ≥95% RH.

[0046] Microaerophilic culture conditions: After inoculation, the culture medium was placed directly in a 5% carbon dioxide incubator and cultured at 37°C and ≥95% RH.

[0047] 2. Single-strain culture and supernatant acquisition in an in vitro simulated high-glucose microenvironment

[0048] 2.1 Resuscitation of strains

[0049] Add 0.5 ml of the corresponding liquid culture medium to the lyophilized bacterial powder, gently pipette to fully dissolve and form a bacterial suspension. Take 200 μL of the suspension from each of the two agar plates, spread evenly, and incubate under the specified conditions.

[0050] Single clones of 10 bacterial strains were obtained by passage 1-2 times using plate culture medium, and then used for subsequent experiments.

[0051] 2.2 Plotting growth curves

[0052] (1) Pick a single colony from the plate culture medium and inoculate it into the corresponding liquid culture medium and incubate for 48 hours.

[0053] (2) Mix the bacterial solution evenly, add 200 μL of bacterial solution to a 96-well plate, and incubate under specified conditions.

[0054] (3) Co-culture anaerobic or facultative anaerobic bacteria for 48 hours. Take out the 96-well plate from the anaerobic bag every 2 hours, shake and mix well, and then measure the OD600 value with an enzyme-linked immunosorbent assay (ELISA) reader. Co-culture microaerophilic bacteria for 12 hours. Shake and mix well every 1 hour, and then measure the OD600 value with an ELISA reader.

[0055] (4) Plot the growth curves of the 10 strains respectively to obtain their logarithmic growth phase.

[0056] 2.3 In vitro culture of bacteria in a simulated high-sugar environment

[0057] (1) Prepare liquid culture media with different glucose concentration gradients: 0 mM, 5.5 mM, 10 mM, 20 mM, and 50 mM. A schematic diagram of the glucose concentration gradients in the culture media is shown below.Figure 2 As shown.

[0058] (2) Using liquid culture medium without added glucose, expand the single clone of the strain under specified conditions until the logarithmic growth phase.

[0059] (3) Mix the bacterial solution thoroughly and adjust the concentration to 1.0 × 10⁻⁶. 8 CFU / mL was inoculated into five liquid culture media with different glucose concentrations, with six biological replicates for each glucose concentration. The media were cultured under specified conditions until the logarithmic growth phase.

[0060] (4) Centrifuge at 10000 rpm and 4℃ for 10 min, take 1 mL of the supernatant and dispense it into two 1.5 mL ep tubes, freeze at -80℃, and use them for subsequent non-targeted LC-MS and GC-MS detection.

[0061] The various culture media used in the preservation culture were used as blank controls. 1 mL of the supernatant was aliquoted into two 1.5 mL ep tubes. Three biological replicates were set up for each culture medium. The tubes were frozen at -80 °C for subsequent non-targeted LC-MS and GC-MS detection.

[0062] 3. Non-targeted LC-MS and GC-MS detection

[0063] 3.1 Non-targeted LC-MS detection

[0064] 3.1.1 Sample preparation

[0065] (1) Accurately transfer 200 μL of sample into a 1.5 mL centrifuge tube; (2) Add 800 μL of extraction buffer (methanol:acetonitrile = 1:1 (v:v)) containing four internal standards (L-2-chlorophenylalanine (0.02 mg / mL), etc.); (3) After vortexing for 30 seconds, extract by low-temperature ultrasonication for 30 minutes (5℃, 40KHz); (4) Place the sample at -20℃ for 30 min; (5) Centrifuge for 15 min (13000 g, 4℃), transfer the supernatant and dry it with nitrogen gas; (6) Add 120 μL of reconstitution solution (acetonitrile:water = 1:1) to reconstitute; (7) Vortex mix for 30 s, then perform low-temperature ultrasonic extraction for 5 min (5℃, 40KHz); (8) Centrifuge for 10 min (13000g, 4℃), transfer the supernatant to a vial with an inner tube for analysis; (9) In addition, 20 μL of supernatant was transferred from each sample and mixed to serve as a quality control sample.

[0066] 3.1.2 Non-targeted LC-MS detection conditions

[0067] The instrument platform used for this LC-MS analysis was the Thermo Fisher Scientific UHPLC-Exploris240 system.

[0068] Chromatographic conditions: The column was an ACQUITY UPLC HSS T3 (100 mm × 2.1 mm id, 1.8 μm; Waters, Milford, USA); mobile phase A was 95% water + 5% acetonitrile (containing 0.1% formic acid), mobile phase B was 47.5% acetonitrile + 47.5% isopropanol + 5% water (containing 0.1% formic acid), the injection volume was 3 μL, and the column temperature was 40℃.

[0069] Mass spectrometry conditions: The sample was electrospray ionized, and mass spectrometry signals were acquired using both positive and negative ion scanning modes. Specific parameters are shown in Table 3 below.

[0070] Table 3 Mass spectrometry parameters for non-targeted LC-MS detection

[0071] 3.1.3 Quality Control

[0072] QC samples are prepared by mixing equal volumes of extracts from all samples. Each QC sample has the same volume as the original sample and is processed and tested using the same methods as the analytical samples. During instrumental analysis, a QC sample is inserted every 5 to 15 analytical samples to examine the stability of the entire testing process.

[0073] Experimental results: such as Figure 3 As shown in a, correlation analysis was performed on 15 QC samples detected by non-targeted LC-MS. The correlations were between 0.99 and 1, indicating that the detection results had good repeatability and the experimental process was stable.

[0074] 3.1.4 Identification of Metabolites

[0075] Raw data were imported into the metabolomics processing software Progenesis QI v3.0 (Waters Corporation, Milford, USA) for baseline filtering, peak identification, integration, retention time correction, and peak alignment, ultimately yielding a data matrix containing information such as retention time, mass-to-charge ratio, and peak intensity. Subsequently, the software was used for characteristic peak search and identification, matching MS and MS / MS mass spectrometry information with metabolic databases. The MS mass error was set to less than 10 ppm, and metabolites were identified based on the secondary mass spectrometry matching scores. The main databases used included mainstream public databases such as http: / / www.hmdb.ca / and https: / / metlin.scripps.edu / .

[0076] 3.2 Non-targeted GC-MS detection

[0077] 3.2.1 Preparation of n-alkane standard solutions

[0078] Take 770 μL of n-hexane and transfer it to a 1.5 mL centrifuge tube. Add appropriate amounts of commercially available mixed standards for C10-C25, and then add appropriate amounts of n-alkane standards for C26, C27, C28, C29, C30, C31, C32, and C33. Vortex to mix thoroughly, obtaining a stock solution of C10-C33 n-alkane standards (50 μg / mL). Dilute the stock solution to 10 μg / mL and analyze it in the same batch as the sample.

[0079] 3.2.2 Sample Pretreatment

[0080] (1) Take 100 µL of sample and add 300 µL of extraction solution (methanol:acetonitrile = 2:1 (containing 0.05 mg / mL internal standard ribitol)); (2) After vortexing for 30 s to mix, ultrasonically extract in an ice-water bath for 30 min; (3) Let the sample stand at -20℃ for 30 min, and then centrifuge at high speed for 15 min (4℃, 13000 rcf); (4) Take the supernatant from the centrifuged liquid, put it into a glass derivatization vial, and dry it with nitrogen gas; (5) Add 80 µL of methoxyamine hydrochloride pyridine solution (15 mg / mL) to a glass derivatization vial, vortex for 2 min and then carry out the oxime reaction for 90 min in a shaking incubator at 37 ℃; (6) Add 80 µL of BSTFA (containing 1% TMCS) derivatizing reagent, vortex for 2 min, and react at 70 °C for 60 min; (7) After taking out the sample, place it at room temperature for 30 min and perform GC-MS metabolomics analysis.

[0081] 3.2.3 Non-targeted GC-MS detection conditions

[0082] The analytical instrument used in this experiment was an Orbitrap Exploris GC gas chromatograph-mass spectrometer from Thermo Fisher Scientific (Thermo, Germany).

[0083] Chromatographic conditions: After derivatization, the sample was injected into the GC-MS system in split mode with an injection volume of 1 µL and a split ratio of 10:1. The sample was separated by a TG-5 SILMS capillary column (30 m × 0.25 mm × 0.25 µm, Thermo 26096-1420) before being detected by mass spectrometry. The injection port temperature was 300 °C, the carrier gas was high-purity helium at a flow rate of 1.0 mL / min, and the septum purge flow rate was 3 mL / min. Temperature program: initial temperature 80 °C, equilibration for 0 min, then increased to 310 °C at a rate of 20 °C / min and held for 8 min, for a total run time of 20 min, with a solvent delay of 2 min.

[0084] Mass spectrometry conditions: See Table 4 below for specific parameters.

[0085] Table 4 Mass spectrometry parameters for non-targeted GC-MS detection

[0086] 3.2.4 Quality Control

[0087] To evaluate the stability of the analytical system during the instrumentation process, QC samples are prepared. QC samples are prepared by mixing equal volumes of extracts from all samples, with each QC sample having the same volume as the original sample. They are processed and tested using the same methods as the analytical samples. During instrumental analysis, one QC sample is inserted every 5–15 analytical samples to examine the stability of the entire detection process.

[0088] Experimental results: such as Figure 3 As shown in b in the figure. Correlation analysis was performed on 15 QC samples detected by non-targeted GC-MS. The correlations were between 0.98 and 1, indicating that the detection results had good repeatability and the experimental process was stable.

[0089] 3.2.5 Identification of Metabolites

[0090] The raw GC / MS OE files were searched, identified, and preprocessed using Thermo Compound Discovery 3.3.SP3 software. This software first matches the mass spectrometry information with metabolic databases, identifying metabolites based on the mass spectrometry matching degree. The search software used was Compound Discoverer 3.3.SP3, with the main databases being NIST-2023, GC-Orbitrap Metabolomics Library_v2, and a self-built database. A three-dimensional data matrix in XLSX format was exported, containing information such as sample name, metabolite name, and peak area. Known false positive peaks (including noise, column bleed, and derivative reagent peaks) were removed from the data matrix, and redundancy was eliminated and peaks were merged. Derivatized group information in metabolite names was removed through reduction.

[0091] 4. Data Processing and Statistical Analysis

[0092] The data matrix after database searching was uploaded to the Meiji Cloud Platform (cloud.majorbio.com) for analysis. First, the data matrix underwent preprocessing as follows: Missing values ​​were removed using the 80% rule, meaning variables with at least 80% or more non-zero values ​​in at least one sample group were retained. Then, missing values ​​were filled (the minimum value in the original matrix was used to fill the missing values). To reduce errors caused by sample preparation and instrument instability, the summation normalization method was used to normalize the response intensity of the sample mass spectrometry peaks, resulting in a normalized data matrix. Simultaneously, variables with a relative standard deviation (RSD) > 30% for QC samples were removed, and logarithmic transformation (log10) was performed to obtain the final data matrix used for subsequent analysis.

[0093] Secondly, principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed on the preprocessed data matrix using the ropls package in R (Version 1.6.2), and the stability of the model was evaluated using seven-cycle cross-validation. The selection of significantly different metabolites was based on the variable weights (VIPs) obtained from the OPLS-DA model and the student's test. P The value determines whether VIP > 1. P Metabolites with a positivity rate <0.05 were considered significantly differentially expressed. Differentially expressed metabolites were annotated using the KEGG database (https: / / www.kegg.jp / kegg / pathway.html) to identify the pathways involved. Pathway enrichment analysis was performed using the Python package scipy.stats, and Fisher's exact test was used to identify the biological pathways most relevant to the experimental treatment.

[0094] Experimental results: such asFigure 4 As shown. P.g For example, PLS-DA at five glucose concentrations showed good separation between groups and aggregation within groups, suggesting that glucose concentration significantly affects metabolic composition and abundance.

[0095] like Figure 5 As shown. Taking WY as an example, under the conditions of 0 / 5.5 / 10 / 20 / 50 mM, the number of upregulated (downregulated) metabolites relative to the blank control of culture medium were 309 (604), 305 (568), 307 (593), 316 (592), and 316 (599), respectively, suggesting that high glucose conditions can induce stable and quantifiable differential metabolic responses.

[0096] Taking WY as an example, the supernatants obtained from its culture at five different glucose concentrations were compared with the corresponding glucose concentration in modified BHI medium blank control to obtain five differential metabolic sets. Venn diagrams of these five differential metabolic sets were plotted, as shown below. Figure 6 As shown, after culturing in medium with different glucose concentrations, most of the differential metabolites in the supernatant of each culture medium were the same, but each also had its own specific metabolites. The mediums with glucose concentrations from low to high corresponded to 52, 14, 36, 9, and 39 differential metabolites that were not found in the mediums with other glucose concentrations.

[0097] 5. Database Construction and Application

[0098] Construct a "Database of Metabolic Characteristics of Key Oral Microorganisms by Single Strains". Each record should include at least: species / strain, culture medium type, glucose concentration, culture stage (log phase), detection platform (LC-MS or GC-MS), metabolite name / ID, retention time / mass-to-charge ratio or retention index, peak area / relative abundance, direction of difference from blank control and significance parameters (VIP, P value), etc.

[0099] 6. Database used for in vivo conjoint analysis

[0100] By matching differentially expressed metabolites screened in vivo with this database, candidate metabolites that can be produced / consumed by specific bacterial species can be identified, and related metabolic pathways can be further located, providing support for mechanism research.

[0101] Using the NCBI database, we obtained the genetic information of 10 bacterial species cultured in vitro in this study. Combined with the results of differential metabolite association analysis screened from another study based on in vivo samples, we screened out key microorganisms and key metabolites. We then used KEGG MAPPER to perform KEGG pathway annotation analysis on key microorganisms and key metabolites, which can annotate more than 100 metabolic pathways. Finally, we combined the literature to screen metabolic pathways.

[0102] Experimental results: such as Figure 7 As shown, the galactose metabolic pathway can be screened. The key metabolites galactose, galactitol, and sorbitol, along with genes from several key microorganisms—*Porphyromonas gingivalis* Pg (ATCC 33277), *Fusobacterium nucleatum* subsp. *nucleatum* Fn, *Prevotella melanogaster* Pn, *Streptococcus Gordonii* Sg, *Streptococcus salivarius* Ss, and *Streptococcus mutans* Sm—can be co-annotated to this pathway.

[0103] As can be seen from the above examples, glucose concentration is an important environmental factor affecting the metabolic function of oral microorganisms, which can significantly change the types and abundance distribution of metabolites. The standardized construction method formed by the present invention can stably obtain reproducible single-strain metabolic profiles and output a searchable metabolic feature database. This database can be used for the source attribution and cross-validation of in vivo metabolomics results, and assist in the screening of key metabolites and key pathways related to high glucose.

[0104] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for constructing a database of single-strain metabolic characteristic maps of key oral microorganisms in a high-sugar microenvironment, characterized in that, Includes the following steps: (1) The key oral microorganisms in the logarithmic growth phase were cultured in high sugar liquid culture medium, centrifuged, and the supernatant was obtained. The supernatant of the liquid culture medium was used as a blank control. (2) Metabolites were obtained by non-targeted LC-MS and non-targeted GC-MS detection of the supernatant and liquid culture medium; The chromatographic conditions for the non-targeted LC-MS detection were as follows: the column was an ACQUITY UPLC HSS T3 with dimensions of 100 mm × 2.1 mm and a diameter of 1.8 μm; mobile phase A was 95% water + 5% acetonitrile, containing 0.1% formic acid; mobile phase B was 47.5% acetonitrile + 47.5% isopropanol + 5% water, containing 0.1% formic acid; the injection volume was 3 μL; and the column temperature was 40 °C. The mass spectrometry conditions for the non-targeted LC-MS detection are as follows: scan range 70-1050 m / z, jet flow rate 60 arb, auxiliary gas flow rate 20 arb, heating temperature 350°C, capillary temperature 320°C, positive mode spray voltage 3400V, negative mode spray voltage -3000V, S-Lens voltage 70V, collision energy 20%, 40%, 60%, resolution Full MS 60000, resolution MS² 15000; The chromatographic conditions for the non-targeted GC-MS detection were as follows: injection volume 1 µL, split ratio 10:1, TG-5SILMS capillary column (30 m × 0.25 mm × 0.25 µm), injection port temperature 300 °C, carrier gas high-purity helium at a flow rate of 1.0 mL / min, septum purge flow rate 3 mL / min; temperature program: initial temperature 80 °C, equilibration for 0 min, then increased to 310 °C at a rate of 20 °C / min and held for 8 min, total run time 19.5 min, solvent delay 2 min. The mass spectrometry conditions for the non-targeted GC-MS detection are as follows: Full Scan mode, scan range 35-500 m / z, resolution 30000, ion source temperature 250℃, ion source type EI, repulsion electrode 10V, ion source default voltage 5V, lens 1-50V, lens 2-0.5V, lens 3-35V, electron lens 15V, electron energy 70eV, and emission current 50μA. (3) Identify the metabolites, preprocess the identification results and perform principal component analysis and orthogonal partial least squares discriminant analysis. Determine the significantly different metabolites based on the variable weights and the P-value of the student's t test. Perform KEGG metabolic pathway analysis on the significantly different metabolites to obtain the pathways in which the different metabolites participate. (4) Using bacterial species / strain, liquid culture medium type, glucose concentration in high sugar liquid culture medium, culture stage, detection platform, metabolite name / ID, retention time / mass-to-charge ratio / retention index, peak area / relative abundance, direction of difference from blank control, variable weight value and P value as indicators, a single strain metabolic feature database of key oral microorganisms in high sugar microenvironment was constructed.

2. The construction method according to claim 1, characterized in that, In step (1), the glucose concentration in the high-glucose liquid culture medium is 5.5~50mM, and 6 biological replicates are set up for each glucose concentration of liquid culture medium.

3. The construction method according to claim 1, characterized in that, In step (2), quality control samples are inserted during the non-targeted LC-MS detection and non-targeted GC-MS detection processes, with one quality control sample inserted for every 5 to 15 analysis samples.

4. The construction method according to claim 1, characterized in that, The identification process in step (3) involves matching mass spectrometry information with a metabolic database and determining the types of metabolites based on the matching degree. The identification software includes the metabolomics processing software Progenesis QI v3.0 and Thermo Compound Discovery 3.3.SP3 software. The metabolic database includes NIST-2023 and GC-Orbitrap Metabolomics Library_v2.

5. The construction method according to claim 1, characterized in that, The preprocessing tools in step (3) include the Meiji Cloud Platform; the stability of the model is evaluated by 7 cycles of interactive validation after principal component analysis and orthogonal partial least squares discriminant analysis; metabolites with variable weight values ​​>1 and student's t test p-value <0.05 are considered to have significant differences.

6. A database of single-strain metabolic characteristic maps of key oral microorganisms in a high-sugar microenvironment, constructed by the construction method according to any one of claims 1 to 5.

7. The application of the single-strain metabolic characteristic map database of key oral microorganisms in a high-sugar microenvironment as described in claim 6 in screening metabolites in a diabetes-related high-sugar oral microenvironment.

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