Microflora marker combination and kit for screening chronic mountain sickness

Abstract

The invention discloses a flora marker combination variety and application thereof in preparation of a kit for screening or diagnosing chronic mountain sickness (CMS), and particularly relates to the technical field of biological detection. A screening diagnosis model constructed based on human fecal flora finds that the provided fecal flora marker combination can specifically recognize CMS patients from plateau healthy people. When being used for screening and diagnosing CMS, the kit has the characteristics of high acceptability, noninvasiveness, high sensitivity and high specificity. The marker is suitable for carrying out large-scale CMS screening and early diagnosis on crowds living in plateau areas for a long time, a scientific basis is provided for early diagnosis and precise treatment of CMS, then health damage and life risks caused by chronic mountain sickness (CMS) of the crowds living in the plateau areas for a long time are relieved, and the living quality of residents in the high-altitude areas is improved.

Description

Technical Field

[0001] The present invention belongs to the field of molecular biotechnology and its applications, and relates to a combination of flora markers and a kit for screening chronic mountain sickness. Background Art

[0002] Chronic mountain sickness (CMS), also known as Monge's Disease, is a clinical syndrome commonly found in people who have lived at high altitudes (usually above 2,500 meters) for a long time. Its main characteristics include a series of clinical symptoms such as excessively elevated hematocrit, hypoxemia, etc., such as headache, fatigue, dizziness, palpitation, shortness of breath, nausea, vomiting, weakness, insomnia, blurred vision, dyspnea, numbness of hands and feet, cyanosis of lips and fingers, increased heart rate, sleep disorders, etc. The incidence of CMS is relatively high in the population at high altitudes, and its incidence is between 1.21% and 31.5% (Jiang C, Chen J, Liu F, Luo Y, Xu G, Shen HY, Gao Y, Gao W. Chronic mountain sickness in Chinese Han males who migrated to the Qinghai-Tibetan plateau: application and evaluation of diagnostic criteria for chronic mountain sickness. BMC PUBLIC HEALTH 2014, 14: 701.). The natural environment of the plateau is harsh, and chronic high-altitude hypoxia exposure can lead to the occurrence of CMS, seriously damaging physical health (Villafuerte FC, Corante N. Chronic Mountain Sickness: Clinical Aspects, Etiology, Management, and Treatment. HIGH ALT MED BIOL 2016, 17(2): 61-69.).

[0003] CMS is very harmful. Excessive proliferation of red blood cells can cause blood viscosity and microcirculatory dysfunction, resulting in hypoxia and damage to various organs, which can lead to decreased work ability and serious damage to the patient's quality of life (Gao Yuqi, Huang Jian. Inflammatory response and altitude sickness. Journal of the Third Military Medical University, 2016, 38(3):215-219.); in severe cases, thromboembolism may even occur in important organs such as the cardiovascular system, kidneys, spleen, and lungs, leading to sudden death (Leon-Velarde F, Maggiorini M, Reeves JT, Aldashev A, Asmus I, Bernardi L, Ge RL, Hackett P, Kobayashi T, Moore LG, Penaloza D, Richale JP, Roach R, Wu T, Vargas E, Zubieta-Castillo G, Zubieta-Calleja G. Consensus statement on chronic and subacute high altitude diseases. HIGH ALT MED BIOL 2005,6(2):147-157.). Therefore, CMS has become a major public health problem that seriously threatens permanent residents of the plateau.

[0004] CMS is characterized by its insidious onset and inherent difficulty in detecting itself. Therefore, early screening and diagnosis, as well as timely control of disease progression and targeted treatment, are crucial. Currently, CMS diagnosis is primarily based on clinical symptoms and laboratory tests, using the Qinghai Chronic Mountain Sickness Scoring System (Chinese Medical Association Plateau Medicine Branch, "Decision on the Unified Use of the 'Qinghai Criteria' for Chronic Mountain Sickness." Journal of Plateau Medicine 17.1 (2007):2). This diagnostic system requires high altitude medicine specialists and involves extensive labor-intensive procedures and invasive testing, making it difficult to screen and monitor large populations, severely limiting the effectiveness of CMS prevention and control. Therefore, there is an urgent need for a noninvasive, widely accepted, convenient, and effective screening method.

[0005] However, the diagnosis of CMS still faces the following difficulties, including the lack of specific biomarkers. The current diagnostic criteria mainly rely on hemoglobin and hematocrit, but these indicators lack specificity and are difficult to achieve early diagnosis. In addition, there are obvious individual differences, and different populations have different abilities to adapt to hypoxia, making it difficult to use diagnostic criteria for effective screening. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide a fecal microbiota biomarker combination for screening and diagnosing chronic mountain sickness (CMS). By non-invasive and non-invasive means to detect the fecal microbiota of the subject to be tested, it can quickly determine whether the subject has CMS, which has high application value in clinical practice.

[0007] To achieve the above purpose, the present invention provides the following technical solutions:

[0008] On the one hand, the present invention provides a microbiota biomarker combination for screening and diagnosing chronic mountain sickness (CMS), specifically including Klebsiella, Collinsella, Eubacterium_eligens_group, Alloprevotella, Paraprevotella, Prevotellaceae_NK3B31_group, Pseudomonas, Lachnospiraceae_UCG-003, Sphingomonas, and Comamonas.

[0009] Furthermore, the representative sequences of the microbiota biomarkers are as shown in SEQ ID No.1 to SEQ ID No.10.

[0010] The above-mentioned microbiota biomarker combination can be used as a diagnostic biomarker in the screening and diagnosis of chronic mountain sickness (CMS).

[0011] On the second hand, the present invention provides the application of reagents for detecting each microorganism in the microbiota biomarker combination in the preparation of reagents or kits for screening and diagnosing chronic mountain sickness (CMS), and the microbiota biomarker combination is composed of Klebsiella, Collinsella, Eubacterium_eligens_group, Alloprevotella, Paraprevotella, Prevotellaceae_NK3B31_group, Pseudomonas, Lachnospiraceae_UCG-003, Sphingomonas, and Comamonas.

[0012] Furthermore, the reagent is a reagent for detecting the abundance of each microorganism.

[0013] Furthermore, it includes primers, probes or sequencing reagents for measuring each microorganism in the microbiota biomarker combination.

[0014] The sample detected by the above reagents or kits is the feces of the subject to be tested.

[0015] Furthermore, in the application, it also includes software or algorithms for data analysis. Using the relative abundances of Klebsiella, Collinsella, Eubacterium_eligens_group, Alloprevotella, Paraprevotella, Prevotellaceae_NK3B31_group, Pseudomonas, Lachnospiraceae_UCG-003, Sphingomonas, and Comamonas, a combined diagnostic index is constructed to distinguish patients with chronic mountain sickness (CMS) from healthy high-altitude populations.

[0016] Furthermore, the representative sequences of Klebsiella, Collinsella, Eubacterium_eligens_group, Alloprevotella, Paraprevotella, Prevotellaceae_NK3B31_group, Pseudomonas, Lachnospiraceae_UCG-003, Sphingomonas, and Comamonas are shown as SEQ ID No.1 - SEQ ID No.10 in sequence.

[0017] Furthermore, the combined diagnostic index equation is logit(P) = 5.1×(relative abundance of Klebsiella) + 3.4×(relative abundance of Collinsella) + 250.1×(relative abundance of Eubacterium_eligens_group) + 105.7×(relative abundance of Alloprevotella) + 102.3×(relative abundance of Paraprevotella) - 50.9×(relative abundance of Paraprevotella) + 366.8×(relative abundance of Pseudomonas) - 44780.0×(relative abundance of Lachnospiraceae_UCG-003) - 3343.5×(relative abundance of Sphingomonas) - 13226.8×(relative abundance of Comamonas) - 0.2.

[0018] The combined diagnostic index of the above 10 characteristic bacteria has very accurate diagnostic value for CMS (AUC = 0.804), with a sensitivity of 84.62% and a specificity of 71.05%. It has strong diagnostic efficacy, high sensitivity and specificity. When logit(P) > 0.481, it can be determined as a patient with chronic mountain sickness (CMS). A specific biomarker and detection reagent are provided, which are not affected by individual differences.

[0019] The third aspect of the present invention is to provide a screening and diagnostic kit for chronic mountain sickness (CMS), comprising a reagent for detecting the combination of the flora markers described in claim 1.

[0020] Furthermore, the reagent is a reagent for detecting the abundance of each microorganism.

[0021] Furthermore, the reagent is a primer, probe or sequencing reagent for measuring each microorganism in the combination of flora markers.

[0022] The present invention has the following advantages:

[0023] The present invention combines genomics, microbiomics and other technologies with clinical data analysis to study the differences in the fecal flora composition and structure between patients with chronic mountain sickness (CMS) and plateau healthy populations, and further discovers important significantly changed fecal flora in CMS through Wilcoxon and LEfSe statistical algorithms. The present invention finds that the fecal flora of CMS patients has changed significantly compared with that of plateau healthy populations, and develops new biomarkers and detection and diagnosis strategies. Furthermore, a screening and diagnostic model for CMS is constructed, new methods for diagnosing CMS are found and early large-scale population screening is carried out to improve the diagnostic accuracy and reduce misdiagnosis and missed diagnosis.

[0024] Based on the screening and diagnostic model constructed from the human fecal microbiota, the present invention discovers that a human fecal microbiota biomarker combination consisting of Klebsiella, Collinsella, Eubacterium_eligens_group, Alloprevotella, Paraprevotella, Prevotellaceae_NK3B31_group, Pseudomonas, Lachnospiraceae_UCG-003, Sphingomonas, and Comamonas can specifically identify patients with chronic mountain sickness (CMS). This fecal microbiota biomarker has great potential for screening and diagnosing CMS patients. This non-invasive operation method can be used for early diagnosis and large-scale population screening of CMS patients. By detecting the abundances of Klebsiella, Collinsella, Eubacterium_eligens_group, Alloprevotella, Paraprevotella, Prevotellaceae_NK3B31_group, Pseudomonas, Lachnospiraceae_UCG-003, Sphingomonas, and Comamonas in feces, it is expected to provide a scientific basis for the early diagnosis and precise treatment of CMS, thereby reducing the health damage and life risks brought about by the occurrence of CMS in people living on the plateau for a long time and improving the quality of life of residents in high-altitude areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] To make the objectives, technical solutions, and beneficial effects of the present invention clearer, the following drawings are provided for illustration of the present invention.

[0026] The structures, ratios, sizes, etc. depicted in this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the limiting conditions for the implementation of the present invention. Therefore, they do not have technical substantial significance. Any modification of the structure, change in the proportional relationship, or adjustment of the size, without affecting the efficacy that the present invention can produce and the objectives that can be achieved, should still fall within the scope covered by the technical content disclosed in the present invention.

[0027] Figure 1 It is a Venn diagram of the fecal microbiota of CMS patients and healthy plateau population provided in an embodiment of the present invention;

[0028] Figure 2 This is a comparison chart of the alpha diversity of fecal microbiota in CMS patients and plateau healthy populations provided by the embodiments of the present invention. Among them, A is a comparison chart of the chao1 index, an alpha diversity index of the fecal microbiota in the two groups, and B is a comparison chart of the ace index, an alpha diversity index of the fecal microbiota in the two groups;

[0029] Figure 3 This is a comparison chart of the beta diversity of fecal microbiota in CMS patients and plateau healthy populations provided by the embodiments of the present invention. Among them, A is a two-dimensional analysis chart of NMDS_unweighted_unifrac of the beta diversity of the fecal microbiota in the two groups, and B is a three-dimensional analysis chart of NMDS_unweighted_unifrac of the beta diversity of the fecal microbiota in the two groups;

[0030] Figure 4 This is an analysis chart of the composition of the fecal microbiota at the phylum level in CMS patients and plateau healthy populations provided by the embodiments of the present invention;

[0031] Figure 5 This is an analysis chart of the composition of the fecal microbiota at the genus level in CMS patients and plateau healthy populations provided by the embodiments of the present invention;

[0032] Figure 6 This is an analysis chart of the bacteria (at the genus level) significantly correlated with erythrocyte proliferation parameters in CMS patients and plateau healthy populations provided by the embodiments of the present invention;

[0033] Figure 7 This is an analysis chart of the signature microbiota (at the genus level) of the significantly different fecal microbiota in CMS patients and plateau healthy populations provided by the embodiments of the present invention;

[0034] Figure 8 This is a diagnostic efficacy chart of CMS by a screening and diagnostic model constructed based on the significantly different fecal signature microbiota. Detailed implementation manners

[0035] Next, the technical solutions of the preferred embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention. For the experimental methods without specific conditions noted in the embodiments, they are usually carried out according to conventional conditions or according to the conditions recommended by the manufacturers.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this invention belongs. The terms used in the description of the present invention herein are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the related listed items.

[0037] Example 1 Analysis of differences in fecal microbiota between patients with chronic mountain sickness and healthy high-altitude populations

[0038] First, 16S rRNA sequencing was used to compare and analyze the fecal microbiota of 37 patients with chronic mountain sickness (CMS) and 42 healthy high-altitude populations (Non-CMS) at the same altitude to study the differences in the composition of fecal microbiota between the two groups. (The clinical data of the subjects of the fecal microbiota specimens are shown in Table 1).

[0039] Table 1 Clinical data of CMS patients and healthy high-altitude populations

[0040]

[0041]

[0042] Note: CMS: Patients with chronic mountain sickness; Non-CMS: Healthy high-altitude populations; HGB: Hemoglobin content.

[0043] 1. Materials

[0044] The above-mentioned populations were all male volunteers who had migrated from the plain to work in the high-altitude area at an altitude of 4300 meters for about 1 year. Their fecal specimens were collected and physical examinations were performed after working at high altitude for 1 year. Specialist doctors in high-altitude diseases followed the "Qinghai Criteria for Chronic Mountain Sickness" formulated by the Sixth World Congress on High-Altitude Medicine (Chinese Medical Association, High-Altitude Medicine Branch. "Decision on the unified use of the 'Qinghai Criteria' for chronic high-altitude (mountain) sickness." Journal of High-Altitude Medicine 17.1 (2007): 2.).

[0045] The specific criteria are as follows:

[0046] (1) Migrated from the plain to the high-altitude area (altitude: >2500m) and lived continuously for more than 3 months;

[0047] (2) Normal lung function, no other chronic lung diseases that exacerbate hypoxemia, excluding polycythemia vera and other secondary polycythemia diseases;

[0048] (3) "Qinghai Criteria for Chronic Mountain Sickness" Symptom Scoring and Judgment Criteria: 1) Dyspnea and / or palpitations: None is scored 0 points, mild is scored 1 point, moderate is scored 2 points, and severe is scored 3 points; 2) Sleep disorder: Normal sleep is scored 0 points, inability to fall asleep normally is scored 1 point, insufficient sleep and intermittent wakefulness are scored 2 points, and inability to fall asleep is scored 3 points; 3) Cyanosis: No cyanosis is scored 0 points, mild cyanosis is scored 1 point, moderate cyanosis is scored 2 points, and severe cyanosis is scored 3 points; 3) Vasodilation: No vasodilation is scored 0 points, mild vasodilation is scored 1 point, moderate vasodilation is scored 2 points, and severe vasodilation is scored 3 points; 4) Paresthesia: No paresthesia is scored 0 points, mild paresthesia is scored 1 point, moderate paresthesia is scored 2 points, and severe paresthesia is scored 3 points; 5) Headache: No headache is scored 0 points, mild headache is scored 1 point, moderate headache is scored 2 points, and severe headache is scored 3 points; 6) Tinnitus: No tinnitus is scored 0 points, mild tinnitus is scored 1 point, moderate tinnitus is scored 2 points, and severe tinnitus is scored 3 points; 7) Hemoglobin: For males, hemoglobin content (Hemoglobin, HGB) < 210 g / L is scored 0 points, ≥ 210 g / L is scored 3 points, for females, HGB < 190 g / L is scored 0 points, ≥ 190 g / L is scored 3 points; 8) CMS diagnosis and determination of its severity: Add up the above scores, where no CMS = 0 - 5 points, mild CMS = 6 - 10 points, moderate CMS = 11 - 14 points, and severe CMS ≥ 15 points. After judging according to the above criteria, there were 37 CMS patients and 42 healthy plateau people at the same altitude in this study.

[0049] 2. Experimental methods:

[0050] (1) Steps for fecal specimen collection:

[0051] After all subjects got up in the morning, 5 g of feces were collected using the MGIEasy Fecal Collection Kit (MGItech, GSC02), numbered separately, and stored at -80 °C for later use.

[0052] (2) DNA extraction and 16S rRNA gene PCR amplification

[0053] The genomic DNA of feces was extracted using the MagPure Soil DNA LQ Kit (Magan, D6356-02), and the detailed extraction method was carried out according to the kit instructions. The concentration and purity of the DNA were detected using NanoDrop 2000 (Thermo Fisher Scientific, USA) and agarose gel electrophoresis, and the extracted DNA was stored at -20 °C. Using the extracted genomic DNA as a template, PCR amplification of the bacterial 16S rRNA gene was performed using specific primers with Barcode and the Tks Gflex DNA Polymerase high-fidelity enzyme kit (Takara, R060B). The universal primers 343F (5’-TACGGRAGGCAGCAG-3’) and 798R (5’-AGGGTATCTAATCCT-3’) were used to amplify the V3-V4 variable region of the 16S rRNA gene for microbiota diversity analysis.

[0054] 1) The reaction system and parameters of the first-round PCR are shown in Tables 2 and 3 in detail.

[0055] Table 2 First-round PCR reaction system

[0056]

[0057] Table 3 First-round PCR reaction parameters

[0058]

[0059] 2) The reaction system and parameters of the second-round PCR are shown in Tables 4 and 5 in detail.

[0060] Table 4 Second-round PCR reaction system

[0061]

[0062] Table 5 Second-round PCR reaction parameters

[0063]

[0064] (3) Library construction and sequencing

[0065] The PCR amplification products were detected using agarose gel electrophoresis. Then, they were purified using AMPure XP beads magnetic beads, and after purification, they were used as the template for the second-round PCR and subjected to second-round PCR amplification. The magnetic beads were used for purification again, and the purified second-round products were taken for Qubit quantification, and then the concentration was adjusted for sequencing. Sequencing was performed using an Illumina MiSeq sequencer, and 250 bp paired-end reads were generated.

[0066] (4) Bioinformatics and statistical analysis

[0067] The original data is in FASTQ format. After the data is downloaded from the sequencer, first use the Cutadapt software to cut off the primer sequences from the raw data sequences. Then use the DADA2 software and its default parameters (Callahan B J, Mcmurdie P J, Rosen M J, et al. DADA2: High-resolution sample inference from Illumina amplicon data. 《Nature Methods》, 2016) to perform quality filtering, denoising, splicing, and chimera removal and other quality control analyses on the paired-end raw data qualified in the previous step according to the default parameters of QIIME 2 to obtain the representative sequences (Amplicon Sequence Variant, ASV). After using the QIIME 2 software (Bolyen E, Rideout J R, Dillon M R, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. 《Nature Biotechnology》, 2019 37:852–857.) package to select each ASV, align and annotate all ASV sequences with the Silva (version 138) database (https: / / www.arb-silva.de / documentation / release-138 / ) and the Greengenes database (ftp: / / greengenes.microbio.me / greengenes_release). Species alignment and annotation are analyzed using the default parameters of the q2-feature-classifier software (https: / / github.com / qiime2 / q2-feature-classifier.git). Perform α and β diversity analyses using the QIIME 2 software. Use α diversity including Simpson index and Shannon index to evaluate the α diversity of samples. Use the unweighted Unifrac distance matrix calculated by R to perform unweighted UnifracN MDS analysis to evaluate the β diversity of samples. Based on the R package, use the Wilcoxon statistical algorithm for differential analysis, use LEfSe for differential analysis of species abundance spectra, and use Spearman correlation for correlation analysis. As Figure 1As shown in the Venn diagram, the results showed that 2,803 microbial features were detected in the CMS group, 3,033 microbial features were detected in the Non-CMS group, and 1,166 microbial features were common to both groups.

[0068] 3. Experimental results:

[0069] (1) Comparison of clinical data between the CMS and Non-CMS groups

[0070] As shown in the results of Table 1, there were significant differences between the CMS and Non-CMS groups in terms of blood oxygen saturation and the Qinghai standard score for chronic mountain sickness (all P < 0.05), while there were no significant differences in the other indicators.

[0071] (2) Analysis of fecal microbiota diversity in the CMS and Non-CMS groups

[0072] The Chao1 and Ace indices were used to evaluate the α-diversity of the fecal microbiota in both groups. The comparison results are as Figure 2 shown, indicating that there was no significant difference in the α-diversity of the fecal microbiota between the two groups. The β-diversity of the fecal microbiota in both groups was evaluated by the unweighted UnifracN MDS method. The comparison results are as Figure 3 shown, and the results showed that there was a significant difference in the β-diversity between the two groups (Stress = 0.156 < 0.2), indicating that there were significant overall changes in the fecal microbiota of the two groups.

[0073] (3) Analysis of the species composition of fecal microbiota at the phylum level in the CMS and Non-CMS groups

[0074] Figure 4 This is the analysis diagram of the phylum-level composition of the fecal microbiota provided in the embodiments of the present invention; among them, Firmicutes, Proteobacteria, Bacteroidota, Actinobacteriota, Fusobacteriota, Desulfobacterota, Campilobacterota, Acidobacteriota, Gemmatimonadota, Spirochaetota, Deferribacterota, Chloroflexi, Verrucomicrobiota, Patescibacteria, and Myxococcota are the main phyla of the fecal microbiota in both groups, with an average proportion exceeding 99%.

[0075] (4) Analysis of the species composition of fecal microbiota at the genus level in the CMS and Non-CMS groups

[0076] Figure 5Analysis chart of the genus-level composition of two groups of fecal microbiota provided by the embodiments of the present invention; among them, Faecalibacterium, Escherichia-Shigella, Prevotella, Blautia, [Eubacterium]_coprostanoligenes_group, Bacteroides, Bifidobacterium, Subdoligranulum, Roseburia, Romboutsia, Dialister, Klebsiella, Collinsella, [Ruminococcus]_torques_group, and Agathobacter are the main constituent genera of the two groups of fecal microbiota, with an average proportion exceeding 70%.

[0077] (5) Analysis of fecal bacteria (genus level) related to hypoxic erythrocytosis parameters between the CMS and Non-CMS groups

[0078] Figure 6 Results of correlation analysis, Spearman correlation analysis showed that: ① Collinsella was significantly positively correlated with HGB (hemoglobin content), HCT (hematocrit), and RBC (red blood cell count); ② Klebsiella was significantly positively correlated with HCT (hematocrit); ③ Escherichia-Shigella was significantly negatively correlated with HCT (hematocrit).

[0079] Study on the screening and diagnostic efficacy of fecal microbiota markers for patients with chronic mountain sickness (CMS) in Example 2

[0080] Based on the above research, this example intends to perform Wilcoxon statistical algorithm analysis on the fecal microbiota data obtained above to find fecal microbiota (genus level) markers with significant differences between the two groups, construct a combined diagnostic index through binary Logestic regression analysis, and finally evaluate the diagnostic efficacy of the combined diagnostic index of fecal microbiota markers for CMS through the ROC curve (establish a new validation population according to the criteria in Example 1, and the specific clinical data are shown in Table 6).

[0081] Table 6 Clinical data of the validation population

[0082]

[0083]

[0084] Note: CMS: Patients with chronic mountain sickness; Non-CMS: Healthy plateau population; HGB: Hemoglobin content.

[0085] 1. Statistical analysis method

[0086] The Wilcoxon analysis was performed based on the RandomForest_R language package. Binary Logistic regression analysis was performed using the statistical software SPSS 19.0 to construct a combined diagnostic index. ROC curve analysis was performed using MedCalc 19.20. Statistical significance was considered when P < 0.05. Among them, the AUC reflects the diagnostic efficacy (AUC = 0.5, no diagnostic efficacy; 0.5 < AUC < 0.7, very little diagnostic value; 0.7 < AUC < 0.9, fairly accurate diagnostic value; 0.9 < AUC < 1, very accurate diagnostic value).

[0087] 2. Result analysis

[0088] (1) Wilcoxon analysis to find significantly different fecal microbiota markers between two groups

[0089] Figure 7 The results of the Wilcoxon analysis showed that 10 bacteria, namely Klebsiella, Collinsella, Eubacterium_eligens_group, Alloprevotella, Paraprevotella, Prevotellaceae_NK3B31_group, Pseudomonas, Lachnospiraceae_UCG-003, Sphingomonas, and Comamonas, were significantly different between the two groups (all P < 0.05). The representative sequences of the marker bacteria are shown in Table 7.

[0090] Table 7 Representative sequences of fecal microbiota markers

[0091]

[0092]

[0093]

[0094]

[0095] Note: Klebsiella: Klebsiella; Collinsella: Collinsella; Eubacterium_eligens_group: Eubacterium eligens group; Alloprevotella: Alloprevotella; Paraprevotella: Paraprevotella; Prevotellaceae_NK3B31_group: Prevotella NK3B31 group; Pseudomonas: Pseudomonas; Lachnospiraceae_UCG-003: Lachnospiraceae UCG-003; Sphingomonas: Sphingomonas; Comamonas: Comamonas.

[0096] (2) Construction of combined diagnostic index based on 10 marker bacteria

[0097] The relative abundances of 10 marker bacteria in the two groups were input into SPSS 19.0 software, and a combined diagnostic index was constructed through binary Logestic regression analysis. The combined diagnostic index equation was obtained as follows: logit(P) = 5.1 × (relative abundance of Klebsiella) + 3.4 × (relative abundance of Collinsella) + 250.1 × (relative abundance of Eubacterium_eligens_group) + 105.7 × (relative abundance of Alloprevotella) + 102.3 × (relative abundance of Paraprevotella) - 50.9 × (relative abundance of Paraprevotella) + 366.8 × (relative abundance of Pseudomonas) - 44780.0 × (relative abundance of Lachnospiraceae_UCG-003) - 3343.5 × (relative abundance of Sphingomonas) - 13226.8 × (relative abundance of Comamonas) - 0.2 (when logit(P) > 0.481, it can be determined as a patient with chronic mountain sickness (CMS)). The distribution of the relative abundance values of these 10 characteristic bacteria in the two groups of CMS patients and plateau healthy populations is shown in Table 8.

[0098] Table 8 Abundance expression data of fecal flora markers in CMS patients and plateau healthy populations

[0099]

[0100]

[0101] Note: CMS: Patients with chronic mountain sickness; Non-CMS: Healthy high-altitude population; Klebsiella: Klebsiella; Collinsella: Collinsella; Eubacterium_eligens_group: Eubacterium eligens group; Alloprevotella: Alloprevotella; Paraprevotella: Paraprevotella; Prevotellaceae_NK3B31_group: Prevotella NK3B31 group; Pseudomonas: Pseudomonas; Lachnospiraceae_UCG-003: Lachnospiraceae UCG-003; Sphingomonas: Sphingomonas; Comamonas: Comamonas.

[0102] (3) Analysis of the diagnostic efficacy of the combined diagnostic index of fecal marker bacteria for CMS

[0103] The working characteristic curve of the combined index is as Figure 8 shown. The specific data of the ROC curve are shown in Table 9. The results show that the combined diagnostic index of 10 characteristic bacteria has very accurate diagnostic value for CMS (AUC = 0.804), with a sensitivity of 84.62% and a specificity of 71.05%, indicating that the combined index has strong diagnostic efficacy and good sensitivity and specificity. At the same time, the diagnostic discrimination value is also given, that is, when logit(P)>0.481, it can be discriminated as a patient with chronic mountain sickness (CMS).

[0104] Table 9 Working characteristic curve data of fecal microbiota marker combinations

[0105]

[0106] Note: CMS: Patients with chronic mountain sickness; Non-CMS: Healthy high-altitude population; Fecal microbiota marker combination: Composed of Klebsiella (Klebsiella), Collinsella (Collinsella), Eubacterium_eligens_group (Eubacterium eligens group), Alloprevotella (Alloprevotella), Paraprevotella (Paraprevotella), Prevotellaceae_NK3B31_group (Prevotella NK3B31 group), Pseudomonas (Pseudomonas), Lachnospiraceae_UCG-003 (Lachnospiraceae UCG-003), Sphingomonas (Sphingomonas) and Comamonas (Comamonas).

[0107] The inventors found significant differences in fecal microbiota between the CMS and Non-CMS groups through 16S rRNA detection and analysis of fecal microbiota and bioinformatics statistics. Further, the Wilcoxon algorithm was used to screen out the marker bacteria for diagnosing CMS, and a combined diagnostic index of 10 marker bacteria and its calculation equation were constructed. The results showed that the combined index of 10 marker bacteria had a very accurate value for the diagnosis of chronic mountain sickness (CMS). This invention helps with the early diagnosis of CMS and large-scale population screening, thereby reducing the health damage and life risks brought to the population living on the plateau for a long time due to the high incidence of CMS.

[0108] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and detail without departing from the scope defined by the claims of the present invention.

Claims

1. Use of a combination of flora markers as a diagnostic marker in screening and diagnosing chronic mountain sickness, characterized in that, The combination of the microbial markers consists of Klebsiella, Collinsella, Eubacterium_eligens_group, Alloprevotella, Paraprevotella, Prevotellaceae_NK3B31_group, Pseudomonas, Lachnospiraceae_UCG-003, Sphingomonas, and Comamonas.

2. Use of reagents for detecting each microorganism in the combination of the microbial markers described in claim 1 in the preparation of reagents or kits for screening and diagnosing chronic mountain sickness.

3. The application according to claim 2, characterized in that, The reagent is a reagent for detecting the abundance of each microorganism.

4. The application according to claim 2, characterized in that, It includes primers, probes, or sequencing reagents for measuring each microorganism in the combination of the microbial markers.

5. The application according to claim 2, wherein The sample of the reagent or kit is the feces of the subject to be tested.

6. The application according to claim 3, characterized in that It further includes software or algorithms for data analysis, which uses the relative abundances of Klebsiella, Collinsella, Eubacterium_eligens_group, Alloprevotella, Paraprevotella, Prevotellaceae_NK3B31_group, Pseudomonas, Lachnospiraceae_UCG-003, Sphingomonas, and Comamonas to construct a combined diagnostic index to distinguish between patients with chronic mountain sickness and healthy people at high altitudes.

7. The application according to claim 6, characterized in that, The equation of the combined diagnostic index is logit(P) = 5.1×(relative abundance of Klebsiella) + 3.4×(relative abundance of Collinsella) + 250.1×(relative abundance of Eubacterium_eligens_group) + 105.7×(relative abundance of Alloprevotella) + 102.3×(relative abundance of Paraprevotella) - 50.9×(relative abundance of Paraprevotella) + 366.8×(relative abundance of Pseudomonas) - 44780.0×(relative abundance of Lachnospiraceae_UCG-003) - 3343.5×(relative abundance of Sphingomonas) - 13226.8×(relative abundance of Comamonas) - 0.

2.

8. A screening and diagnostic kit for chronic mountain sickness, characterized in that, A reagent for detecting the combination of microbial markers described in claim 1.

9. The screening and diagnostic kit according to claim 8, wherein The reagent is a reagent for detecting the abundance of each microorganism.

10. The screening and diagnostic kit according to claim 8, wherein The reagent is a primer, probe or sequencing reagent for measuring each microorganism in the combination of microbial markers.

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