Oral microorganisms for identifying taste sensitivity, their identification method and application

Abstract

This invention belongs to the field of microbial technology, specifically relating to an oral microorganism for identifying taste sensitivity, its identification method, and its application. The microorganisms include... Haemophilus genus, Fusobacterium genus, Aggregatibacter genus, Lachnoanaerobaculum genus and Oribacterium One or more of the above-mentioned microorganisms are identified, and the abundance of one or more of these microorganisms is significantly positively correlated with taste sensitivity. The application method is as follows: 1) Collect oral biological samples; 2) Extract DNA from saliva samples and perform sequencing analysis; 3) Detect the abundance of microbial assemblage; 4) Verify the correlation between key characteristic oral microorganisms and individual taste sensitivity. The advantage is that it is a non-invasive and non-surgical method to determine individual differences in taste sensitivity, aiming to achieve efficient, low-cost, and accurate identification of individual taste sensitivity.

Description

Technical Field

[0001] This invention belongs to the field of microbial technology, specifically relating to an oral microorganism for identifying taste sensitivity, its identification method, and its application. Background Technology

[0002] Food perception is a person's response and feedback to food stimuli, which in turn influences food selection, acceptance, and intake. Differences in food perception are one of the driving factors influencing food consumption and also a moderating factor affecting dietary needs and personal preferences. The oral cavity, as the beginning of the digestive system, provides an important ecological site for the interaction and accumulation of food particles and microorganisms, directly affecting an individual's ability to perceive taste. Among these, oral microorganisms are the second largest microbial community in the human body, possessing powerful metabolic capabilities. Oral microorganisms can influence the host's sensitivity to taste perception, regulate sensory decisions, and affect food choices.

[0003] Currently, there are relatively few published patents both domestically and internationally that use oral microorganisms as microbial markers, and almost none of them involve the early diagnosis of taste sensitivity.

[0004] (1) The application of oral microorganisms as microbial markers is mainly concentrated in the medical field. Published patents focus on diabetes, obesity, esophageal cancer, lung cancer, and neuropsychiatric symptoms. These patents involve constructing population cohorts (experimental group and healthy control group), collecting saliva samples, extracting DNA, sequencing, and analyzing the data. Conventional statistical analysis and other theoretical methods are used to identify key oral microorganisms. Among these related patents, some use machine learning algorithms such as random forests and logistic regression to replace conventional statistical analysis, screening key oral microorganisms at a single taxonomic level (genus or species).

[0005] (2) Regarding the method for judging taste sensitivity, a published patent for a method and composition for diagnosing and treating loss and / or distortion of taste or smell is similar: the degree of loss of taste or smell of an individual is diagnosed by measuring the level of Sonic Hedgehog in nasal mucus, saliva or a combination thereof.

[0006] Conventional statistical methods can identify oral microorganisms with significant differences between groups. However, the oral cavity is a complex ecosystem with diverse microorganisms and a large amount of data, making it difficult to accurately identify key microorganisms using conventional statistical methods alone. Machine learning algorithms can handle complex data well, but are limited to the single species level. This invention combines differential analysis methods and machine learning algorithms to select the best classification model from four candidate classification models: logistic regression, linear support vector machine, Bayesian ridge regression, and Gaussian process classifier, in order to accurately identify key oral microorganisms used to determine the taste sensitivity of different individuals.

[0007] Using the level of the sound hedgehog protein to determine individual sensory loss, i.e., using protein as a marker for differentiation, has good results. However, antibody-based ELISA detection methods are prone to false positives due to cross-reactivity and dimers, and their accuracy in assessing taste degeneration in large populations needs improvement. This invention selects oral microorganisms based on DNA information as markers, and combines key oral microorganisms identified by machine learning algorithms, resulting in high accuracy in assessing taste sensitivity. Summary of the Invention

[0008] Currently, there is a lack of methods for identifying oral microbial combinations specifically associated with taste sensitivity. To address this gap, this patent application provides an oral microbial combination for identifying taste sensitivity and a screening method thereof. Since taste sensitivity exhibits significant heterogeneity among different age groups (taste sensitivity generally decreases with age), selecting individuals from both young adults and the elderly as research subjects can cover a wide range of characteristics of taste differences, thereby enhancing the accuracy and universality of screening oral microbial combinations significantly related to taste sensitivity. By comparing the differences in taste sensitivity among individuals of different age groups, and based on difference analysis combined with machine learning algorithms, characteristic oral microbial combinations are accurately identified. This non-invasive and non-surgical approach to assess individual differences in taste sensitivity aims to achieve efficient, low-cost, and accurate identification of individual taste sensitivity, providing a reliable basis for personalized nutritional interventions.

[0009] In view of this, the present invention provides an oral microbial assemblages for identifying taste sensitivity, a method for identifying them, and their application. This method compares the differences in taste sensitivity among individuals of different age groups, accurately identifies characteristic oral microbial assemblages using differential analysis and machine learning algorithms, and determines individual differences in taste sensitivity through non-invasive and non-surgical means. To achieve the above-mentioned objectives, the present invention adopts the following technical solution: The first aspect of the present invention is to provide oral microorganisms for identifying taste sensitivity, said microorganisms comprising Haemophilus genus, Fusobacterium genus, Aggregatibacter genus, Lachnoanaerobaculum genus and Oribacterium One or more of the above-mentioned microorganisms are included in the genus, and the abundance of one or more of the above-mentioned microorganisms is significantly positively correlated with taste sensitivity.

[0010] Furthermore, the taste described includes one or more of the following: sour, sweet, bitter, salty, and umami.

[0011] Furthermore, the abundance of the aforementioned microorganisms is: Haemophilus The percentage ranged from 0.05% to 20.42%. Fusobacterium The percentage ranged from 0 to 11.4%. AggregatibacterThe percentage ranged from 0% to 3.76%. Lachnoanaerobaculum The percentage was 0–1.22%. Oribacterium The percentage ranged from 0% to 3.88%.

[0012] A second aspect of the present invention is to provide a method for identifying oral microorganisms with sensitive taste, specifically comprising the following steps: (1) Data collection: Based on age differences, elderly and young subjects were recruited, and their taste sensitivity was tested and oral biological samples were collected. (2) Data transformation: DNA is extracted from oral biological samples and the DNA information is transformed into oral microbial community information using bioinformatics methods based on sequencing analysis; (3) Screening of key oral microorganisms: Initial screening of key oral microorganisms based on differential analysis; (4) Identify key oral microorganisms: Based on the individual's taste sensitivity, select the model with better classification effect from multiple machine learning algorithm candidate classification models and optimize the hyperparameters. Use the optimized model to identify characteristic oral microorganisms, and further combine the key oral microorganisms obtained in (3) to obtain key characteristic oral microorganisms. (5) Validation of key features of oral microbiology: Validation analysis was conducted to verify the correlation between key features of oral microbiology and individual taste sensitivity.

[0013] Furthermore, the oral biological samples mentioned in step (1) include one or more of the following: saliva samples, tongue dorsal samples, and supragingival plaque samples.

[0014] Furthermore, the taste described in step (1) includes one or more of the following: sour, sweet, bitter, salty, and umami.

[0015] Furthermore, the taste sensitivity mentioned in step (1) includes one or more of the following: the detection threshold, the recognition threshold, and the difference threshold.

[0016] Furthermore, the bioinformatics methods described in step (2) include one or more of 16S rRNA sequencing and metagenomic sequencing.

[0017] Furthermore, the difference analysis described in step (3) involves using abundance difference analysis to identify microbial groups with significant abundance differences between the two groups, and using LEfSe analysis to further identify key differentially expressed microorganisms.

[0018] Furthermore, the candidate classification model of the machine learning algorithm described in step (4) includes one or more of logistic regression, linear support vector machine, Bayesian ridge regression and Gaussian process classifier.

[0019] A third aspect of the present invention is to provide an application for identifying oral microorganisms for taste sensitivity, wherein the application is not for the purpose of disease diagnosis, but specifically for a method of identifying the taste sensitivity of a subject, the steps of which are as follows: 1) Collect oral biological samples; 2) Microbial sample testing: DNA is extracted from saliva samples and sequenced for analysis; 3) Detect the abundance of the microbial assemblage as described in the first aspect; 4) Verify the correlation between key oral microbiome features and individual taste sensitivity.

[0020] Furthermore, the abundance of the detected microorganisms is: Haemophilus The percentage ranged from 0.05% to 20.42%. Fusobacterium The percentage ranged from 0 to 11.4%. Aggregatibacter The percentage ranged from 0% to 3.76%. Lachnoanaerobaculum The percentage was 0–1.22%. Oribacterium The percentage ranged from 0% to 3.88%.

[0021] Furthermore, the taste described includes one or more of the following: sour, sweet, bitter, salty, and umami.

[0022] Furthermore, the sequencing described in step 2) is second-generation or third-generation high-throughput sequencing.

[0023] Compared with the prior art, the advantages of the present invention are as follows: 1. Based on a comparative study of young and elderly populations, the selected oral microbial combinations cover a wide range of characteristics of differences in taste sensitivity, and have good accuracy and universality.

[0024] 2. It adopts a non-invasive sample collection method, which is easy to operate and meets the needs of large-scale population screening.

[0025] 3. By combining differential analysis with machine learning algorithms, five oral microorganisms were accurately identified to assess an individual's taste sensitivity, and all of them showed a significant positive correlation with taste sensitivity. Attached Figure Description

[0026] Figure 1 This outlines the overall methods and steps for implementing the specific plan; Figure 2 Results of the perception and recognition thresholds of sweetness for different age groups (***) p <0.001, N = 60); Figure 3 A diagram showing the genus-level composition of oral microbiota in different age groups; Figure 4 A comparative graph of oral microbial alpha diversity in different age groups; Figure 5 UMAP map of oral microbial abundance (left) and UMAP map of sweet taste perception threshold (right); Figure 6 The results of the abundance difference analysis of oral microorganisms (left) and the results of the LEfSe analysis (right); Figure 7 The initial classification performance of the candidate classification model (left) and the classification performance of the LR and SVC models after hyperparameter optimization (right). Figure 8 Key features of oral microorganisms identified based on LR and SVC models; Figure 9 The results validate the correlation between key features such as oral microbiota and sweetness threshold. Figure 10 The results validate the correlation between key features such as oral microbiota and saltiness threshold. Figure 11 The results validate the correlation between key features of oral microbiome and acidity threshold; Figure 12 The correlation between the taste threshold of an independent population and the oral microbiome was validated (*p<0.05, **p<0.01). Detailed Implementation

[0027] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments, so as to fully understand the purpose, features and effects of the present invention. Unless otherwise specified, the methods described are conventional methods. Unless otherwise specified, the materials described are all available from publicly available commercial sources. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0028] Example 1: Microbial Screening and Identification according to Figure 1 Design and implement the experiment of the process: 1. Data Collection (1) Subject recruitment Based on age differences, 30 participants were recruited from each of the following groups: young adults (20-30 years old) and older adults (60-70 years old). Recruitment requirements: Participants were in good health, had no smoking or heavy drinking habits, had not received specific periodontal treatment, and had not taken antibiotics in the past 3 months. Furthermore, participants were required to have no oral diseases such as bleeding gums, oral ulcers, or toothache on the day of participation. All participants signed informed consent forms before proceeding with the study. Information on all participants is shown in Table 1.

[0029] Table 1. Statistics on basic information of the subjects

[0030] (2) Collect information on taste sensitivity This study investigated differences in individual taste sensitivity among different groups using a three-point ascending forced selection method. A 15 mL sample of taste stimulus (food-grade sucrose) of a specific concentration was prepared using purified water and provided to the participants along with two 15 mL reference samples (purified water). Participants were asked to choose one sample from three progressively increasing concentrations and evaluate its taste attributes (choosing one from seven: sour, sweet, bitter, salty, umami, watery, and indescribable). The test cups were identical and labeled with three random numbers. The presentation order was sequential, increasing in concentration. All samples were tasted at room temperature, with purified water provided for oral cleaning. In this embodiment, all participants meeting the inclusion criteria were required to have refrained from eating (water was permitted) for at least one hour prior to sampling and to rinse their mouths with purified drinking water before testing. The perception threshold and recognition threshold for sweetness were calculated using the following formulas:

[0031] in, C For the threshold, C n This is the last concentration that was not correctly identified. C n+1 For the last correctly identified concentration QUOTE QUOTE QUOTE The result is as follows Figure 2 As shown, the detection and recognition thresholds for sweetness in the youth group were 2.05±1.80 g / L and 2.98±2.09 g / L, respectively, while those in the elderly group were 11.76±11.50 g / L and 13.53±11.15 g / L, respectively. This indicates that the sweetness detection and recognition thresholds in the elderly group were significantly higher than those in the youth group. Furthermore, the threshold results for both groups showed a wide distribution, effectively covering the broad characteristics of differences in taste sensitivity among different population groups.

[0032] (3) Collect oral biological samples To avoid the influence of circadian rhythms on saliva secretion, saliva collection was conducted consistently between 9:00 AM and 11:00 AM daily. Subjects sat in a chair, tilting their heads 45 degrees forward, their tongues lightly touching their front teeth, allowing saliva to flow and accumulate naturally in their mouths. They then gently spat the saliva into the oval funnel of a sterile sampler, collecting saliva secreted during their natural resting state (non-stimulated state). In this embodiment, all participants meeting the inclusion criteria were required to refrain from eating for at least one hour prior to sampling (drinking water was permitted), and to rinse their mouths with pure drinking water before sampling to ensure no food residue remained in their oral cavity.

[0033] 2. Data Transformation DNA was extracted from saliva samples using the FastPure Soil DNA Isolation Kit. The integrity of the extracted genomic DNA was detected by 1% agarose gel electrophoresis, and the DNA concentration and purity were determined using NanoDrop2000.

[0034] Using the extracted DNA as a template, PCR amplification of the V3-V4 variable region of the 16S rRNA gene was performed using upstream primer 338F (5'-ACTCCTACGGGAGGCAGCAG-3', SEQ ID NO.1) and downstream primer 806R (5'-GGACTACHVGGGTWTCTAAT-3', SEQ ID NO.2). The PCR reaction system was as follows: 4 μL of 5×TransStart FastPfu buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of 5 μM primers, 0.4 μL of TransStart FastPfu DNA polymerase, 10 ng of template DNA, and the volume was brought to 20 μL.

[0035] The amplification program was as follows: 95℃ pre-denaturation for 3 min; 95℃ denaturation for 30 s, 55℃ annealing for 30 s, 72℃ extension for 30 s, 7 cycles; 72℃ extension for 10 min. PCR products were subjected to 2% agarose gel electrophoresis to determine the amplification efficiency.

[0036] The purified PCR products were used to construct libraries using the NEXTFLEX Rapid DNA-Seq Kit, and sequencing was performed using the Illumina PE300 platform. Sequencing data were assembled and filtered to obtain high-quality sequences, which were then clustered into operational taxonomic units (OUT) for species classification at a 97% similarity level. Sequences annotated to chloroplasts and mitochondria were removed from all samples. OTU taxonomic annotation was performed against the Silva 16S rRNA gene database to obtain oral microbial community information. Alpha diversity analysis was used to assess the richness and diversity of the oral microbiota. Results are shown below. Figure 3As shown: A total of 421 genera of oral microorganisms were detected in the two groups of people, with the 10 genera with the highest abundance being Streptococcus ( ). Streptococcus ), Prevotella spp. Prevotella ), Neisseria ( Neisseria Haemophilus spp. Haemophilus ), genus Roselle ( Rothia ), Veillonella spp. Veillonella ), Porphyromonas ( Porphyromonas ), Ciliophytes ( Leptotrichia ), Fusobacterium genus ( Fusobacterium ), *Streptococcus* genus ( Granulicatella species Further analysis revealed significant differences between the two groups in α-diversity indices, including the observed species index, ace index, and Chao 1 index. Figure 4 This indicates a significant difference in the species richness of oral microbiota between young and elderly populations.

[0037] 3. Screening for key oral microorganisms The distribution of oral microbial abundance and sweetness sensitivity information between the two groups was observed using the UMAP dimensionality reduction algorithm. The results are as follows: Figure 5 As shown, the UMAP-based classification method can clearly distinguish the oral microbial information of the two groups, and the distribution is consistent with the threshold results. At the genus level, abundance differential abundance analysis was used to initially screen for differentially expressed microorganisms between the elderly and young groups. LEfSe analysis was used to further identify key differentially expressed microorganisms at multiple levels from phylum to genus. The results are as follows: Figure 6 As shown, the selected key oral microorganisms include: Key oral microorganisms with significantly increased relative abundance in the elderly group: Actinomyces , Clostridia_ UCG- and ​ Key oral microorganisms with significantly reduced relative abundance in the elderly group: ​ , ​ , ​ , ​ , ​ , ​ , ​ , ​ .

[0038] 4. Identify key characteristics of oral microbes Four machine learning algorithms—Logistic Regression (LR), Linear Support Vector Machine (SVC), Bayesian Ridge Regression (BRR), and Gaussian Process Classifier (GPC)—were selected as candidate classification models. Classification accuracy was evaluated based on the Area Under the Receiver Operating Characteristic (AUC) curve. Based on the AUC ROC scores from 5-fold cross-validation, classification models with higher accuracy were initially selected, namely LR and SVC. ​ A).

[0039] Based on this, the hyperparameters of these two models were further optimized using a random search method: in the LR model, the parameters param_l1_ratio and param_C were optimized, and in the SVC model, the parameters paradm_fit_intercept and param_C were optimized.

[0040] Both optimized models achieved high AUC ROC scores of around 0.9. ​ B). Feature importance analysis was performed using SHAP feature selection to identify the top 10 most important features of oral microorganisms in both models. ​ Combining the SHAP feature selection and the key oral microbiome results screened in step 3, key characteristic oral microorganisms were selected for... ​ genus, ​ genus, ​ genus, ​ genus and ​ Attribute. Result as follows ​ As shown.

[0041] 5. Key features: Oral microbiome validation All data were first subjected to the Shapiro-Wilk (SW) test to determine their distribution. Since the test results did not conform to a normal distribution, nonparametric methods were used for analysis. Spearman correlation analysis was used to validate the correlation between key oral microbiota characteristics and individual taste sensitivity. The five key oral microbiota characteristics were analyzed... ​ genus, ​ genus, ​ genus and ​ All genera showed a significant negative correlation with the sweetness perception threshold. p <0.05), meaning it showed a significant positive correlation with sweetness sensitivity, as shown in the results. ​ As shown. This indicates that the key features of oral microbiota identified based on the method can be used to determine an individual's sweetness sensitivity.

[0042] Example 2: Verification of saltiness sensitivity.

[0043] The only difference from Example 1 is that food-grade sodium chloride was used as the taste stimulus sample and the saltiness threshold was calculated in the collection of taste sensitivity information. The correlation between key oral microbiota characteristics and individual saltiness sensitivity was verified, and the five key oral microbiota characteristics were used to verify this. ​ genus, ​ genus, ​ genus and ​ genus and ​ All of them showed a significant negative correlation with the salty taste perception threshold. p <0.05), meaning it showed a significant positive correlation with saltiness sensitivity (results are shown in Figure 1). ​ (As shown). This indicates that the key oral microbial features identified based on the method can be used to determine an individual's sensitivity to saltiness.

[0044] Example 3: Verification of sour taste sensitivity.

[0045] The only difference from Example 1 is that food-grade sodium citrate monohydrate was used as the taste stimulus sample in the collection of taste sensitivity information, and the sour taste threshold was calculated. The correlation between key oral microbiota characteristics and individual sour taste sensitivity was verified, and the five key oral microbiota characteristics were analyzed. ​ genus, ​ genus, ​ genus and ​ All genera showed a significant negative correlation with the sour taste detection threshold. p <0.05), meaning it showed a significant positive correlation with sourness sensitivity (results are shown in Figure 1). ​ (As shown). This indicates that the key features of oral microbiota identified based on the method can be used to determine an individual's sensitivity to acidity.

[0046] Example 4: Independent Validation of Different Populations 1. Data collection: A new batch of subjects (independent population, basic information as shown in Table 2) were recruited according to the method described in Example 1. Their sweet, sour and salty taste thresholds were tested and their saliva samples were collected.

[0047] Table 2. Statistics on basic information of independent participants

[0048] 2. Data transformation: 16S rRNA sequencing was performed on saliva samples from independent populations according to the method described in Example 1, and the abundance of the five oral microorganisms was calculated.

[0049] 3. Correlation Analysis: The correlation between the abundance of five oral microorganisms and taste thresholds was analyzed. All data were first subjected to the Shapiro-Wilk (SW) test to determine their distribution. Since the test results did not conform to a normal distribution, a non-parametric method was used for analysis. Spearman correlation analysis was used to analyze the correlation between the abundance of five oral microorganisms used to identify taste sensitivity and their taste thresholds.

[0050] The results are as follows ​ As shown, the abundance of the five oral microorganisms in the independent population was significantly negatively correlated with the salty, sour, and sweet taste thresholds (p<0.05), which is significantly positively correlated with taste sensitivity, thus demonstrating the relationship between oral microbial ensemble and taste sensitivity.

[0051] The embodiments described above are only some embodiments of the present invention, not all embodiments. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the scope of protection of the present invention.

Claims

1. An oral microorganism for discriminating taste sensitivity, characterized by, The microorganisms include Haemophilus one or more of the genera Fusobacterium one or more of the genera Aggregatibacter one or more of the genera Lachnoanaerobaculum one or more of the genera and Oribacterium one or more of the genera, wherein the abundance of the one or more microorganisms is significantly positively correlated with taste sensitivity.

2. The microorganism of claim 1, wherein, The taste includes one or more of sour, sweet, bitter, salty, and umami.

3. The microorganism of claim 1, wherein, The abundance of the microorganism is: Haemophilus the genus is 0.05-20.42%, Fusobacterium the genus is 0-11.4%, Aggregatibacter the genus is 0-3.76%, Lachnoanaerobaculum the genus is 0-1.22%, Oribacterium the genus is 0-3.88%.

4. A method for identifying an oral microorganism for taste sensitivity discrimination, characterized by, Specifically includes the following steps: (1) Data collection: recruit elderly and young subjects according to age difference as the grouping basis, test the taste sensitivity of individuals, and collect oral biological samples; (2) Data transformation: extract DNA from the oral biological samples, and convert the DNA information into oral microbial community information by using bioinformatics methods based on sequencing analysis; (3) Screening of key oral microorganisms: preliminary screening of key oral microorganisms based on difference analysis; (4) Identification of key feature oral microorganisms: based on the individual taste sensitivity characterization, select a better model from a variety of machine learning algorithm candidate classification models and optimize the hyperparameters, identify the feature oral microorganisms using the optimized model, and further combine the key oral microorganisms obtained in (3) to obtain the key feature oral microorganisms; (5) Verification of key feature oral microorganisms: analyze the correlation between the key feature oral microorganisms and the individual taste sensitivity for verification analysis.

5. The method of claim 4, wherein, The taste in step (1) includes one or more of sour, sweet, bitter, salty, and umami, and the taste sensitivity includes one or more of detection threshold, recognition threshold, and difference threshold.

6. The method of claim 4, wherein, The bioinformatics method in step (2) includes one or more of 16s rRNA sequencing and metagenomic sequencing.

7. The method of claim 4, wherein, The difference analysis in step (3) is to identify microorganisms with significant abundance difference between the two groups by using abundance difference analysis, and further identify key difference microorganisms by using LEfSe analysis.

8. The method of claim 4, wherein, The machine learning algorithm candidate classification model in step (4) includes one or more of logistic regression, linear support vector machine, Bayesian ridge regression, and Gaussian process classifier.

9. Use of an oral microorganism for identifying taste sensitivity, which is not for the purpose of disease diagnosis, characterized by, The specific application is a method for identifying the taste sensitivity of a subject, and the steps of the method are as follows: (1) Collecting oral biological samples; (2) Microbial sample detection: extracting DNA from the saliva sample and performing sequencing analysis; (3) Detecting the abundance of the microbial combination according to claim 1 or 2; (4) Verifying the correlation between the key feature oral microorganisms and the individual taste sensitivity.

10. Use according to claim 9, characterized in that, The abundance of the microorganism is detected as: Haemophilus the genus is 0.05-20.42%, Fusobacterium the genus is 0-11.4%, Aggregatibacter the genus is 0-3.76%, Lachnoanaerobaculum the genus is 0-1.22%, Oribacterium the genus is 0-3.88%.

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