Acute altitude stress early warning detection kit

Abstract

The invention belongs to the technical field of biology, and particularly relates to an acute altitude stress early warning detection kit. According to the invention, the application of the microorganisms lively ruminococcus and pseudomicrochain bifidobacterium as the acute altitude stress early warning marker is found for the first time, the marker composition can be used for accurately predicting the risk of acute altitude stress, an optimal classification model is selected according to the AUC value of an ROC curve, and the AUC value can reach 0.85. Furthermore, an acute altitude stress early warning detection kit is constructed aiming at the microbial marker, and whether an individual has the acute altitude stress or not can be quickly judged through a real-time fluorescent quantitative PCR detection technology. The kit provides a scientific basis for prevention and intervention of acute altitude stress, and the successful application of the kit contributes to the accuracy and individuation of health management of healthy personnel in the altitude environment.

Description

Technical Field

[0001] The present invention belongs to the field of biotechnology, and particularly relates to a kit for early warning detection of acute altitude sickness. Background Art

[0002] Acute Mountain Sickness (AMS) is a pathological reaction caused by the hypoxic environment after people from plain areas rapidly enter the plateau. Typical symptoms include headache, palpitations, dyspnea, etc. In severe cases, it can develop into high altitude pulmonary edema or cerebral edema, threatening life. The hypoxic environment on the plateau leads to the imbalance of oxygen metabolism in the body, and then causes the dysfunction of multiple organs.

[0003] Acute altitude sickness is an important factor reducing the quality of life of healthy people after rapidly entering the plateau. It is very common among plateau tourists, and even endangers lives in severe cases. Early screening and reasonable intervention for people prone to acute altitude sickness are the keys to preventing and treating acute altitude sickness in plateau tourists.

[0004] Although there are already clinical diagnostic methods based on symptoms and blood biochemical indexes (such as hemoglobin concentration, blood oxygen saturation), these indexes lack the ability of early warning, and rely on the symptoms after plateau exposure, making it difficult to achieve preventive intervention. Although existing gene prediction models attempt to combine multiple gene mutation characteristics, their sensitivity and specificity are still insufficient.

[0005] As the human body flora, the "second genome" of humans, during the process of humans entering the plateau from the plain, due to factors such as low oxygen concentration in the plateau area, it will inevitably cause the remodeling of the flora composition and function in various parts of the body. CN114839369 identified a combination of microbial markers significantly related to AMS through fecal sample analysis, including 7 genera / species of bacteria such as g__gemmiger, s__gemmiger sp.an120, g__pseudoflavonifractor, etc. These flora may affect the AMS process through regulating host metabolism (such as short-chain fatty acid synthesis), inflammatory response, and oxygen free radical scavenging and other pathways. However, the characteristics of this model are complex, including 7 different marker characteristics, and its sensitivity and specificity still need to be improved.

[0006] Therefore, it is necessary to construct a simpler acute altitude sickness early warning model with higher specificity and sensitivity, and develop related detection products. Summary of the Invention

[0007] The purpose of the first aspect of the present invention is to provide the application of microbial markers in the preparation of products for detecting and preventing acute altitude sickness.

[0008] The purpose of the second aspect of the present invention is to provide the application of reagents in the preparation of products for detecting and preventing acute altitude sickness.

[0009] The purpose of the third aspect of the present invention is to provide a product.

[0010] The purpose of the fourth aspect of the present invention is to provide a method for constructing a model for evaluating the risk of acute altitude reaction.

[0011] The purpose of the fifth aspect of the present invention is to provide a system for evaluating the acute altitude reaction of an object.

[0012] In order to achieve the above purposes of the present invention, the technical solutions adopted by the present invention are as follows:

[0013] In the first aspect of the present invention, there is provided the use of microbial markers in the preparation of products for detecting and preventing acute altitude reaction;

[0014] The microbial markers include Mediterraneibacter gnavus (Mg) and Bifidobacterium pseudocatenulatum (Bp).

[0015] In the second aspect of the present invention, there is provided the use of a reagent in the preparation of products for detecting and preventing acute altitude reaction.

[0016] In some embodiments of the present invention, the reagent is a reagent for detecting the content or abundance of the microbial markers as described in the first aspect of the present invention in a subject sample.

[0017] In some embodiments of the present invention, the reagent includes primers, probes, antisense oligonucleotides, aptamers or antibodies specific for the microbial markers.

[0018] In some embodiments of the present invention, the reagent detects the microbial markers by any one of the following methods: 16S sequencing, whole genome sequencing, quantitative polymerase chain reaction, PCR-pyrosequencing, fluorescence in situ hybridization, microarray, or PCR-ELISA.

[0019] In some embodiments of the present invention, the primers include primers with sequences as shown in SEQ ID NO: 1-4.

[0020] In the third aspect of the present invention, there is provided a product, including a reagent for detecting the microbial markers as described in the first aspect of the present invention.

[0021] In some embodiments of the present invention, the reagent includes primers, probes, antisense oligonucleotides, aptamers or antibodies specific for the microbial markers.

[0022] In some embodiments of the present invention, the reagent detects microbial markers by any of the following methods: 16S sequencing, whole-genome sequencing, quantitative polymerase chain reaction, PCR-pyrosequencing, fluorescence in situ hybridization, microarray, or PCR-ELISA.

[0023] In some embodiments of the present invention, the primers include primers with sequences shown in SEQ ID NO: 1-4.

[0024] The fourth aspect of the present invention provides a method for constructing a model for evaluating the risk of acute altitude reaction, the method comprising constructing a model using the microbial markers of the first aspect of the present invention.

[0025] In some embodiments of the present invention, the algorithms for constructing the model include at least one of logistic regression, linear discriminant analysis, linear discriminant analysis of characteristic genes, support vector machine, random forest, and recursive partitioning tree.

[0026] In some embodiments of the present invention, the algorithm includes logistic regression.

[0027] In some embodiments of the present invention, the early warning model obtained by logistic regression is: log(odds) = 8.0965550647483*10 -1 -1.99522397976559*10 -4 *Mg - 1.23094097616387*10 -4 *Bp. Wherein, Mg and Bp respectively represent the X values of Ruminococcus gnavus and Bifidobacterium pseudocatenulatum: the Cq value of the specific bacteria real-time fluorescence quantitative PCR detection minus the Cq value of the internal reference 16S.

[0028] The fifth aspect of the present invention provides

[0029] In some embodiments of the present invention, a system for evaluating the acute altitude reaction of an object, the system comprising:

[0030] i) An analysis unit, the analysis unit comprising: a detection substance for determining the relative abundance information of the microbial markers as described in the first aspect of the present invention in the test sample of the subject, and;

[0031] ii) An evaluation unit, the evaluation unit comprising: evaluating the risk of acute altitude reaction of the subject according to the relative abundance information of the microbial markers determined in i).

[0032] In some embodiments of the present invention, the reagent is a reagent for detecting the content or abundance of the microbial markers as described in the first aspect of the present invention in the sample of the subject.

[0033] In some embodiments of the present invention, the reagent includes primers, probes, antisense oligonucleotides, aptamers or antibodies specific for the microbial biomarker.

[0034] In some embodiments of the present invention, the reagent detects microbial biomarkers by any one of the following methods: 16S sequencing, whole genome sequencing, quantitative polymerase chain reaction, PCR-pyrosequencing, fluorescence in situ hybridization, microarray, or PCR-ELISA.

[0035] In some embodiments of the present invention, the primers include primers with sequences shown in SEQ ID NO: 1-4.

[0036] In some embodiments of the present invention, the evaluation unit includes a probability calculation formula.

[0037] In some embodiments of the present invention, with log(odds) = 8.0965550647483*10 -1 -1.99522397976559*10 -4 *Mg - 1.23094097616387*10 -4 *Bp, the probability calculation formula obtained by the model is: P = 1 / (1 + e^-log(odds)).

[0038] In some embodiments of the present invention, the prediction result (P value) of logistic regression represents the probability of an event occurring, and the value range is between 0 and 1 (P = 0.8, that is, the probability of the event occurring is 80%). If P ≥ 0.691, it is predicted as a low risk of acute altitude reaction; if P < 0.691, it is predicted as a high risk of acute altitude reaction.

[0039] The beneficial effects of the present invention are:

[0040] The present invention discovers for the first time the application of the microbial Ruminococcus gnavus (Mg) and Bifidobacterium pseudocatenulatum (Bp) as early warning biomarkers for acute altitude reaction. The biomarker composition can be used to accurately predict the risk of acute altitude reaction, and the best classification model is selected according to the AUC value of the ROC curve, and the AUC value can reach 0.85.

[0041] Furthermore, the present invention constructs an early warning detection kit for acute altitude reaction for microbial markers. Through real-time fluorescence quantitative PCR detection technology, it can quickly determine whether an individual will have an acute altitude reaction. This kit provides a scientific basis for the prevention and intervention of acute altitude reaction. The successful application of the kit will contribute to the precision and personalization of the health management of healthy people in high-altitude environments. Description of the Drawings

[0042] The present invention will be further described below in conjunction with the drawings and embodiments, where:

[0043] Figure 1 Results of the analysis of the microbial community diversity of high-risk and low-risk populations for acute reactions, where A is the PCoA diversity analysis, B is the NMDS1 analysis, C is the Shannon index diversity analysis, and D is the Simpson index diversity analysis.

[0044] Figure 2 Results of the LEfse analysis of high-risk and low-risk populations for acute reactions.

[0045] Figure 3 Results of the specificity test of the Mg (left) and Bp (right) primers, where SA is Staphylococcus aureus, AB is Acinetobacter baumannii, EC is Escherichia coli, KP is Klebsiella pneumoniae, PA is Pseudomonas aeruginosa, CA is Propionibacterium acnes, and SE is Staphylococcus epidermidis.

[0046] Figure 4 Results of the sensitivity test of the Mg (left) and Bp (right) primers.

[0047] Figure 5 Results of the ROC curve of the prediction model. Detailed Embodiments

[0048] The following will clearly and completely describe the concept and technical effects of the present invention in conjunction with the embodiments to fully understand the purpose, features, and effects of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative efforts shall fall within the scope of protection of the present invention.

[0049] Example 1 Screening of Markers

[0050] To identify the key gut microbiota associated with the risk of acute altitude reaction, in this example, 546 healthy individuals who rapidly ascended to high altitude were recruited with the approval of the Ethics Committee of Sun Yat-sen University and informed consent was obtained. The changes in gut microbiota before and after rapid ascent to high altitude were explored. Combining with the international diagnostic criteria for acute altitude reaction - the Lake Louise Scoring System (AMS-LLSS), individuals with a score of ≥3 were diagnosed with acute altitude reaction. Accordingly, the volunteers were divided into high-risk and low-risk groups for acute altitude reaction, and finally, an association analysis was performed between the risk of acute altitude reaction and gut microbiota characteristics.

[0051] First, a microbiota diversity analysis of the gut microbiota was conducted, and it was found that there were significant differences in microbiota diversity between the high-risk and low-risk groups after rapid ascent, indicating that the rapid change in altitude had different impacts on the gut microbiota of these two groups (the results are shown as follows). Figure 1 The results of LEfse analysis showed that Figure 2 there were obvious differential species between these two groups. Compared with the low-risk group, before rapid ascent, the gut microbiota of the high-risk group was mainly enriched with species such as Bifidobacterium pseudocatenulatum, Ruminococcus gnavus, Anaerostipes hadrus, Dialister hominis, Blautia hansenii, Lachnospira eligens, Ruminococcus torques, Bacteroides sp. HF-162, Anaerobutyricum hallii, and Dorea longicatena, etc., while the low-risk group was mainly enriched with species such as Campylobacter, Succinivibrio, and Clostridium perfringens, etc.

[0052] Furthermore, in this example, the above differential species were used as potential markers for the acute altitude reaction early warning detection kit, and specific primers were designed for each bacterium. Finally, through primer specificity verification, the only specific primers that could accurately identify the bacteria were those for Ruminococcus gnavus (Mediterraneibacter gnavus, Mg) and Bifidobacterium pseudocatenulatum (Bp). Figure 3 Based on this, the specific primers for Mg and Bp were applied to the kit detection.

[0053] Example 2 Construction of the risk model

[0054] 1) Kit construction

[0055] In this example, a detection kit was constructed for Mediterraneibacter gnavus (Mg) and Bifidobacterium pseudocatenulatum (Bp), including fluorescence quantitative PCR reaction reagents: 2x SYBR Green Pro Taq HS Premix (qPCR premix reagent), Mg-specific primers F / R, Bp-specific primers F / R, internal reference gene 16S rRNA primers F / R, and RNase-free water.

[0056] The primer nucleotide sequences of this kit are as follows:

[0057] Mg-specific primer F: 5’-GGTTTGTCTGCGCAAATCGT-3’ (SEQ ID NO: 1);

[0058] Mg-specific primer R: 5’-CTGCGATTCGTTTCGCACTT-3’ (SEQ ID NO: 2);

[0059] Bp-specific primer F: 5’-TTTCCACGCGTGCTTCTTTC-3’ (SEQ ID NO: 3);

[0060] Bp-specific primer R: 5’-GCTTCCCTCGTATGATGGGTT-3’ (SEQ ID NO: 4);

[0061] Internal reference gene 16S rRNA primer F: 5’-GTGCCAGCMGCCGCGGTAA-3’ (SEQ ID NO: 5);

[0062] Internal reference gene 16S rRNA primer R: 5’-GGACTACHVGGGTWTCTAAT-3’ (SEQ ID NO: 6).

[0063] 2) Specificity verification of the kit

[0064] Select common bacteria such as Escherichia coli, Acinetobacter baumannii, Propionibacterium acnes, Klebsiella pneumoniae, Staphylococcus epidermidis, Staphylococcus aureus, Pseudomonas aeruginosa, etc. Use a commercial bacterial genomic DNA extraction kit to obtain DNA templates of different bacteria. Use the Mg and Bp specific primers in the kit for PCR detection to determine the specificity of the primers. During the detection, add 10 μL of qPCR premixed reagent (2x SYBR Green Pro Taq HS Premix, Aikerui Biotech), 8.8 μL of enzyme-free water, 0.4 μL of sample DNA (concentration about 20 ng / μL), and 0.4 μL of each upstream and downstream specific primer into the same system. Set the program according to 95 °C pre-denaturation for 30 s, denaturation at 95 °C for 10 s, annealing and extension at 60 °C for 60 s (40 cycles of denaturation and annealing). Calculate the Cq values of the internal reference, Mg, and Bp, and the relative abundances of Mg and Bp can be obtained.

[0065] The results are as Figure 3 shown. No non-specific amplification occurred for the Mg and Bp specific primers, and the primer specificity was good.

[0066] 3) Verification of the sensitivity of the kit

[0067] Use PCR high-fidelity Taq enzyme to amplify the full-length specific gene fragments of Mg and Bp bacteria, and recover the purified DNA by gel extraction, which is the positive DNA template. Dilute the DNA templates of the two genes by 10-fold gradient and then perform kit qPCR detection respectively to finally obtain the lowest template copy number detected by the kit. During the detection, add 10 μL of qPCR premixed reagent (2x SYBR Green Pro Taq HS Premix, Aikerui Biotech), 8.8 μL of enzyme-free water, 0.4 μL of sample DNA (concentration about 20 ng / μL), and 0.4 μL of each upstream and downstream specific primer into the same system. Set the program according to 95 °C pre-denaturation for 30 s, denaturation at 95 °C for 10 s, annealing and extension at 60 °C for 60 s (40 cycles of denaturation and annealing). Calculate the Cq values of the internal reference, Mg, and Bp, and the relative abundances of Mg and Bp can be obtained.

[0068] The results are as Figure 4 shown. The DNA detection limit value of the kit is not higher than 10 2 copies / μL, and the detection sensitivity is high.

[0069] 4) Fecal sample treatment and qPCR detection

[0070] Collect 0.5 - 2 g of feces from the subjects to be tested, and obtain fecal DNA samples using a bacterial DNA extraction kit; perform qPCR detection using Mg and Bp specific primers. When detecting, add 10 μL of qPCR premixed reagent (2x SYBR Green Pro Taq HS Premix, Aikerui Biotech), 8.8 μL of enzyme-free water, 0.4 μL of sample DNA (concentration about 20 ng / μL), and 0.4 μL of each upstream and downstream specific primer into the same system. Set the program according to 95°C pre-denaturation for 30 s, denaturation at 95°C for 10 s, annealing and extension at 60°C for 60 s (40 cycles of denaturation and annealing). Calculate the Cq values of the internal reference, Mg, and Bp, and the relative abundances of Mg and Bp can be obtained.

[0071] 5) Prediction of the risk of acute altitude reaction

[0072] Substitute the relative abundance data of the above Mg and Bp into the logistic regression warning model to determine the risk of acute altitude reaction in the subjects.

[0073] Construct a prediction equation: Use metagenomic sequencing data, calculate the relative abundances of each microbial community, and label classification tags (0 or 1) according to the physical constitution type of the subjects (such as susceptible vs. tolerant). Divide the dataset in a ratio of 7:3, with 70% as the training set for model construction and 30% as the test set to evaluate the model performance. Use the training set data to fit the logistic regression equation and calculate the regression coefficients of each microbial community. Evaluate the model performance on the test set and calculate indicators such as accuracy, ROC curve, and AUC value. Substitute the relative abundances of the microbial communities of the new subjects, calculate the probability of their physical constitution type, and make a judgment based on this.

[0074] Substitute into the prediction equation:

[0075] log(odds) = 8.0965550647483 * 10 -1 -1.99522397976559 * 10 -4 *Mg - 1.23094097616387

[0076] *10 -4 *Bp.

[0077] Mg and Bp respectively represent the X values of Ruminococcus gnavus and Bifidobacterium pseudocatenulatum: the Cq value of the specific bacteria real-time fluorescence quantitative PCR detection minus the Cq value of the internal reference 16S.

[0078] Probability calculation formula: P = 1 / (1 + e^(-log(odds))).

[0079] Judgment result: The prediction result (P value) of logistic regression represents the probability of the event occurring, with a value range between 0 and 1 (P = 0.8, that is, the probability of the event occurring is 80%). If P ≥ 0.691, it is predicted as low risk of acute altitude reaction; if P < 0.691, it is predicted as high risk of acute altitude reaction.

[0080] The ROC curve result of this classification model is as Figure 5 shown, and its AUC value can reach 0.85.

[0081] Verification of the biomarker in Example 3

[0082] To verify the accuracy of this model in predicting acute altitude reaction, the present invention included 140 subjects for verification. Among them, 37 were randomly selected from the previous 546-person sample, and 113 were newly recruited volunteers. The questionnaire survey results showed that according to the Lake Louise International Diagnostic Scoring System (AMS-LLSS), it was determined that 21 people had acute altitude reaction after rapidly ascending to the plateau, belonging to the high-risk group; 119 people did not show acute altitude reaction, being the low-risk group. Fecal DNA samples of 140 subjects were obtained through a bacterial DNA extraction kit; qPCR detection was performed using Mg and Bp specific primers, and the relative abundances of the two bacteria were substituted into the prediction model obtained in Example 2 for calculation. The prediction results showed that 27 people belonged to the high-risk group of acute altitude reaction, and 113 people belonged to the low-risk group of acute altitude reaction. Combining with the statistical results of the questionnaire survey, 118 people had accurate prediction results in the laboratory kit detection, with an accuracy rate of 84.29% (Table 1).

[0083] Table 1 Accuracy test results of the early warning detection kit for acute altitude reaction

[0084]

[0085] Screening of staff suitable for plateau work in Example 4

[0086] To screen for staff who can adapt to high-altitude work, kit detection was performed one week before entering the plateau to evaluate the risk of acute altitude reaction in three subjects. The specific operation steps are as follows:

[0087] (1) Collection of fecal samples from subjects

[0088] Use a fecal sampler to intercept the middle part of the fecal sample (the fecal surface contains exfoliated intestinal mucosal cells), put 0.5 - 2 g of feces into a fecal preservation tube, and label each subject sample.

[0089] (2) Fecal sample processing and qPCR detection

[0090] Fecal DNA samples were obtained using a bacterial DNA extraction kit; qPCR detection was performed using Mg and Bp specific primers. During the detection, 10 μL of qPCR premixed reagent, 8.8 μL of enzyme-free water, 0.4 μL of sample DNA, and 0.4 μL of each upstream and downstream specific primer were added to the same system. The program was set as pre-denaturation at 95 °C for 30 s, denaturation at 95 °C for 10 s, and annealing and extension at 60 °C for 60 s (40 cycles of denaturation and annealing). By calculating the Cq values of the internal reference and Mg and Bp, the relative abundances of Mg and Bp can be obtained. The relative abundances of Mg and Bp for the three subjects were ① 6.67 and 3.57, ② 5.11 and 7.24, and ③ 22.82 and 24.09, respectively.

[0091] (3) Acute altitude reaction risk prediction

[0092] The relative abundance data of Mg and Bp above were substituted into the logistic regression warning model to determine the risk of acute altitude reaction in the subjects.

[0093] Substitute into the prediction equation:

[0094] ① log(odds) = 8.0965550647483×10 -1 -1.99522397976559×10 -4 *6.67 - 1.23094097616387×10 -4 *3.57.

[0095] Probability calculation formula: P = 1 / (1 + e^-log(odds)) = 0.69165.

[0096] ② log(odds) = 8.0965550647483×10 -1 -1.99522397976559×10 -4 *5.11 - 1.23094097616387×10 -4 *7.24.

[0097] Probability calculation formula: P = 1 / (1 + e^-log(odds)) = 0.69162.

[0098] ③ log(odds) = 8.0965550647483×10 -1 -1.99522397976559×10 -4 *22.82 - 1.23094097616387×10 -4 *24.09.

[0099] Probability calculation formula: P = 1 / (1 + e^-log(odds)) = 0.69043.

[0100] Result determination: For subjects ① and ②, the calculated P ≥ 0.691, predicting a low risk of acute altitude reaction; for subject ③, the calculated P < 0.691, predicting a high risk of acute altitude reaction. It shows that subjects ① and ② are more suitable candidates for plateau work.

Claims

1. Application of microbial markers in the preparation, detection and prevention of acute altitude sickness products; The microbial markers include Mediterraneibacter gnavus and Bifidobacteriumpseudocatenulatum.

2. Application of reagents in the preparation of products for detecting and preventing acute altitude sickness; The reagent is a reagent for detecting the content or abundance of the microbial marker as claimed in claim 1 in a sample of a subject.

3. The use according to claim 2, characterized in that: The reagents include primers, probes, antisense oligonucleotides, aptamers or antibodies specific to the microbial markers.

4. The use according to claim 3, characterized in that: The reagent detects microbial markers by any of the following methods: 16S sequencing, whole genome sequencing, quantitative polymerase chain reaction, PCR-pyrosequencing, fluorescence in situ hybridization, microarray, or PCR-ELISA.

5. The use according to claim 3, characterized in that: The primers include primers with sequences shown in SEQ ID NOs: 1-4.

6. A product comprising a reagent for detecting the microbial marker of claim 1.

7. The product according to claim 6, characterized in that: The product includes a test kit, a test paper, or a chip.

8. The product according to claim 7, characterized in that: The reagents include primers, probes, antisense oligonucleotides, aptamers or antibodies specific to the microbial markers; Preferably, the reagent detects microbial markers by any of the following methods: 16S sequencing, whole genome sequencing, quantitative polymerase chain reaction, PCR-pyrosequencing, fluorescence in situ hybridization, microarray, or PCR-ELISA; Preferably, the primers include primers whose sequences are shown in SEQ ID NOs: 1-4.

9. A method for constructing a model for assessing the risk of acute mountain sickness, the method comprising constructing a model using the microbial markers of claim 1; Preferably, the algorithm for constructing the model includes at least one of logistic regression, linear discriminant analysis, characteristic gene linear discriminant analysis, support vector machine, random forest, and recursive partitioning tree.

10. A system for evaluating a subject for acute mountain sickness, the system comprising: i) an analysis unit, the analysis unit comprising: a detection substance for determining the relative abundance information of the microbial marker according to claim 1 in a test sample of a test subject, and; ii) an evaluation unit, comprising: evaluating the risk of acute mountain sickness for the subject according to the relative abundance information of the microbial markers determined in i).

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