Oral flora marker for early warning plateau polycythemia and kit thereof

Abstract

The invention discloses oral flora marker types, an oral flora marker combination and application of the oral flora marker combination in preparation of a kit for early diagnosis of high altitude polycythemia (HAPC), and particularly relates to the technical field of biological detection. An early diagnosis model constructed based on the oral flora finds that the provided oral flora marker combination can specifically recognize HAPC patients from plateau healthy controls. The kit is used for disease early warning and early diagnosis of HAPC, and has the advantages of high acceptability, non-invasive property, convenience, high sensitivity and high specificity. The marker is suitable for carrying out large-scale HAPC early warning and early diagnosis on crowds which are chronic exposed to plateau regions, and guiding prevention and control treatment of HAPC, so that health damage and life risks caused by HAPC are reduced.

Description

Technical Field

[0001] The invention belongs to the field of molecular biotechnology and its application, and relates to an oral flora marker and a kit for early warning of high-altitude erythrocytosis. Background Art

[0002] High altitude polycythemia (HAPC) is a common chronic high altitude disease characterized by excessive proliferation of red blood cells. The incidence rate in people living at altitudes of 3000-4500 m for a long time is between 1.21% and 24.0% (C Jiang, Cui J, Liu F, et al. Mitochondrial DNA 10609T promotes hypoxia-induced increase of intracellular ROS and is a risk factor of high altitude polycythemia[J]. PLoS One, 2014, 9(1): e87775.). HAPC is very harmful to the human body. Excessive proliferation of red blood cells can cause blood viscosity and microcirculatory dysfunction, resulting in hypoxia and damage to various organs (Gao Yuqi, Huang Jian. Inflammatory response and altitude sickness [J]. Journal of the Third Military Medical University, 2016, 38(3): 215-219.); in severe cases, thrombosis and embolism of various organs may even occur, leading to sudden death of the patient (Gao Yuqi. Plateau Military Medicine [M]. Chongqing Publishing House, 2006.). At present, there are more than 60 million residents living in plateau areas with an average altitude of more than 3,000 meters all year round. Now, more than 20 million people enter the plateau from the plains every year to engage in tourism, trade and construction (Liu Hao, Hou Yunpeng, Ran Zhuang, et al. Monitoring of common air indicators in the Qinghai-Tibet Plateau oxygen supply train compartments and their impact on human comfort [J]. Journal of Preventive Medicine of the People's Liberation Army, 2020.). Therefore, HAPC has become a major public health issue that seriously threatens residents living in the plateau.

[0003] HAPC is insidious and difficult to detect, so early warning diagnosis, timely control of disease progression and targeted treatment are particularly important. Early warning diagnosis of HAPC has always been a hot research topic in plateau medicine. Previous studies have shown that it is related to biological processes and gene site variations, such as worsening hypoxemia caused by respiratory dysfunction, continuous activation of the HIF-EPO pathway, inflammatory immune disorders in the bone marrow hematopoietic microenvironment, excessive iron supply, increased androgen synthesis, and excessive oxidative stress damage. (P Li, Zheng SJ, Jiang CH, et al. Th2 lymphocytes migrating to the bone marrow under high-altitude hypoxia promote erythropoiesis via activin Aand interleukin-9[J]. Exp Hematol, 2014, 42(9): 804-815.; YS Liu, Huang H, Zhou SM, et al. Excessive Iron Availability Caused by Disorders of Interleukin-10andInterleukin-22Contributes to High Altitude Polycythemia[J].Front Physiol,2018,9548.;L Gesang,Gusang L,Dawa C,et al.Whole-Genome Sequencing Identifies the Egl Nine Homologue 3(egln3 / phd3)and Protein Phosphatase 1RegulatoryInhibitor Subunit 2(PPP1R2P1)Associated with High-Altitude Polycythemia in Tibetans at High Altitude[J]. Dis Markers, 2019, 20195946461.). However, the above process indicators have poor accuracy and sensitivity in early warning diagnosis of HAPC, which cannot meet the needs of HAPC early warning diagnosis. Therefore, a HAPC early warning diagnostic marker with strong sensitivity and high specificity is urgently needed to meet the actual needs of major disease prevention and control.

[0004] The oral microbiome is a collection of microorganisms that are planted in the human oral cavity, and they mostly perform microbial physiological functions in the form of biofilms. Recent studies have shown that the oral microbiome is closely related to systemic diseases such as oral tumors, diabetes, rheumatoid arthritis, cardiovascular disease, and premature birth, and the structural characteristics of the oral microbiome can also serve as an important marker for oral and systemic health warning (Xin X, Junzhi H, Xuedong Z. Oral microbiota: a promising predictor of human oral and systemic diseases. West China Journal of Stomatology. 2015 Dec 1; 33(6): 555-60.). However, there are no reports on the relationship between changes in the oral flora and HPAC, and there is no oral flora marker for early warning and diagnosis of HAPC. Summary of the invention

[0005] In view of this, the object of the present invention is to provide an oral flora marker for early warning of high altitude polycythemia, and also to provide a kit containing the oral flora marker for detecting the oral flora marker.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] 1. An oral flora marker combination for early warning of high-altitude polycythemia, wherein the oral microbial marker combination is composed of Muribaculaceae, Abiotrophia and Granulicatella.

[0008] 2. Use of a reagent for detecting a combination of oral flora markers in the preparation of a warning reagent or kit for high-altitude polycythemia vera, wherein the combination of oral microbial markers is: Muribaculaceae, Abiotrophia and Granulicatella.

[0009] Furthermore, the application is specifically to detect the relative abundance of oral flora markers of the subject.

[0010] Furthermore, the relative abundance of the oral flora markers was upregulated in patients with high-altitude polycythemia, while the genera Abiotrophia and Granulicatella were downregulated.

[0011] Furthermore, the reagent or kit is used for testing saliva, oral swab or throat swab samples of the test subject.

[0012] Furthermore, the relative abundance of Muribaculaceae, Abiotrophia and Granulicatella was used to construct a combined diagnostic index to distinguish patients with high-altitude polycythemia from healthy people.

[0013] Furthermore, the equation for the combined diagnostic index was logit(P)=1078.3×(relative abundance of Muribaculaceae)-172.6×(relative abundance of Abiotrophia)-61.4×(relative abundance of Granulicatella)-0.5.

[0014] 3. An early warning diagnostic kit for high-altitude polycythemia, comprising a reagent for detecting the relative abundance of the oral flora marker combination described in claim 1.

[0015] Further, in the early warning diagnostic kit, the relative abundance of the oral flora marker combination is obtained by the following method: isolating and extracting the receiver operating characteristic curve nucleic acid in the biological sample, constructing a library, sequencing, performing bioinformatics analysis on the sequencing data, obtaining the sequence number of the oral microbial marker, and standardizing the sequence number to obtain the relative abundance of the bacterial species.

[0016] Compared with the existing technology, the present invention discovered the difference in the composition structure of oral flora between HAPC patients and plateau people, and conducted robot learning analysis (random forest analysis) to explore the important characteristic oral flora of HAPC. The present invention found that the oral flora of HAPC patients changed significantly compared with the healthy people in the plateau. Further, based on the characteristic oral flora, an early warning diagnosis model for HAPC was constructed, new methods for early warning of HAPC were found, and early large-scale population screening was carried out to promote the prevention, control and treatment of HAPC.

[0017] The present invention finds that the early warning diagnosis model constructed based on oral flora includes oral flora markers with Muribaculaceae (Muribacteroides), Abiotrophia (oligotrophy) and Granulicatella (granular streptomyces) as a combination can specifically identify HAPC patients, and oral flora markers have great early warning diagnosis potential for HAPC patients. It can be seen from the receiver operating characteristic curve that the relative abundance of oral flora markers with Muribaculaceae (Muribacteroides), Abiotrophia (oligotrophy) and Granulicatella (granular streptomyces) as a combination provided by the present invention, and the construction of a joint diagnostic index based on relative abundance can accurately diagnose HAPC patients. Moreover, the easy accessibility and non-invasive operation method of oral saliva samples are simple and convenient, and early warning diagnosis and large-scale population screening of HAPC patients can be performed. By detecting the abundance of Muribaculaceae, Abiotrophia and Granulicatella in oral saliva, HAPC can be screened and diagnosed with early warning, disease prevention and control and treatment can be guided, thereby reducing the health damage and life risks caused by the occurrence of HAPC in people living in the plateau for a long time. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to make the purpose, technical solution and beneficial effects of the present invention clearer, the present invention provides the following drawings for explanation.

[0019] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0020] Figure 1 A Venn diagram of oral flora of HAPC patients and plateau healthy people provided in an embodiment of the present invention;

[0021] Figure 2 A comparison chart of the alpha diversity of oral flora of HAPC patients and plateau healthy people provided in an embodiment of the present invention, A is a comparison chart of the chao1 index of the alpha diversity index of the oral flora of the two groups, and B is a comparison chart of the ace index of the alpha diversity index of the oral flora of the two groups;

[0022] Figure 3A comparison diagram of oral flora beta diversity between HAPC patients and plateau healthy people provided in an embodiment of the present invention, A is a PCoA_binary_jaccard analysis diagram of oral flora beta diversity of the two groups, and B is a PCoA unweighted unifrac analysis diagram of oral flora beta diversity of the two groups;

[0023] Figure 4 The composition analysis diagram of the oral flora at the phylum level of HAPC patients and plateau healthy people provided in the embodiment of the present invention;

[0024] Figure 5 The composition analysis diagram of the genus level of the oral flora of HAPC patients and plateau healthy people provided in the embodiment of the present invention;

[0025] Figure 6 Oral flora marker flora analysis diagram of HAPC patients and plateau healthy people provided in the embodiment of the present invention;

[0026] Figure 7 An analysis diagram of HAPC early warning diagnostic characteristic bacteria (genus level) mined based on random forest analysis provided in an embodiment of the present invention;

[0027] Figure 8 This is a diagram showing the diagnostic performance of the early warning diagnosis model for HAPC based on the top three HAPC characteristic bacteria in positive order of importance measurement standard scores provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The preferred embodiment of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention. The experimental methods for which specific conditions are not specified in the embodiments are usually carried out under conventional conditions or under conditions recommended by the manufacturer.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0030] Example 1

[0031] Differences in oral microbial flora between patients with high-altitude polycythemia and healthy subjects

[0032] The present invention collects saliva samples from 30 patients with high altitude polycythemia (HAPC) and 30 healthy people at the same altitude (Non-HAPC), extracts DNA of oral flora in saliva samples, performs 16S rRNA gene amplification and library construction, and completes 16S rRNA gene sequencing comparative analysis on Illumina Miseq sequencing platform to study the differences in oral flora composition between HAPC patients and healthy people at the plateau, and finds oral microbial markers that can be used to diagnose HAPC. The clinical data of the subjects of oral flora specimens are shown in Table 1.

[0033] Table 1 Clinical data of subjects with oral flora specimens

[0034]

[0035]

[0036] Note: HAPC: patients with high-altitude polycythemia; Non-HAPC: healthy people at high altitude. Normal distribution data are expressed as mean ± standard deviation, and non-normal distribution data are expressed as median (interquartile range).

[0037] 1. Materials

[0038] The above subjects were all male volunteers who moved from the plains to work in the plateau area at an altitude of 4,300 meters for about one year. Their whole oral saliva was collected and physical examinations were performed.

[0039] High altitude disease specialists systematically diagnose and differentiate HAPC from non-HAPC according to the criteria recommended by the VI World Congress on Mountain Medicine and High Altitude Physiology (F Leon-Velarde, Maggiorini M, Reeves JT, et al. Consensus statement on chronic and subacute high altitude diseases [J]. High Alt Med Biol, 2005, 6(2): 147-157.). The specific criteria are as follows: (1) living continuously for more than 3 months from the plains to the plateau (altitude: >2500 m); (2) normal lung function and no other chronic lung diseases that aggravate hypoxemia; (3) venous hemoglobin (HGB) content of male ≥210 g / L and female HGB ≥190 g / L; (4) polycythemia vera and other secondary polycythemia diseases are excluded. According to the above criteria, 30 HAPC patients and 30 healthy subjects at the same altitude were included in this study.

[0040] 2. Methods:

[0041] (1) Saliva specimen collection steps:

[0042] In the morning, 2 hours before saliva collection, fasting, drinking water, and gargling were prohibited. 10 ml of saliva was collected from the above personnel in a centrifuge tube, which was numbered and stored at -80°C for later use.

[0043] (2) DNA extraction and 16S rRNA gene PCR amplification

[0044] The genomic DNA of the samples was extracted using the MagPure Soil DNA LQ Kit (Magan, D6356-02), and the detailed extraction method was carried out according to the instructions of the kit. The concentration and purity of the DNA were detected by NanoDrop 2000 (Thermo Fisher Scientific, USA) and agarose gel electrophoresis, and the extracted DNA was stored at -20°C. The extracted genomic DNA was used as a template, and the bacterial 16S rRNA gene was PCR amplified using specific primers with barcodes and Tks Gflex DNA Polymerase high-fidelity enzyme kit (Takara, R060B). The universal primers 343F (5'-TACGGRAGGCAGCAG-3', SEQ ID No.1) and 798R (5'-AGGGTATCTAATCCT-3', SEQ ID No.2) were used to amplify the V3-V4 variable region of the 16S rRNA gene for bacterial diversity analysis.

[0045] 1) The first round of PCR reaction system and parameters are shown in Table 2 and Table 3

[0046] Table 2 First round PCR reaction system

[0047]

[0048]

[0049] Table 3 First round PCR reaction parameters

[0050]

[0051] 2) The second round of PCR reaction system and parameters are shown in Table 4 and Table 5

[0052] Table 4 Second round PCR reaction system

[0053]

[0054]

[0055] (3) Library construction and sequencing

[0056] The PCR amplification product was detected by agarose gel electrophoresis. Then it was purified using AMPure XP beads and used as a second-round PCR template for second-round PCR amplification. It was purified again using magnetic beads, and the purified second-round product was quantified by Qubit, and then the concentration was adjusted for sequencing. Sequencing was performed using the Illumina NovaSeq 6000 sequencing platform, and 250bp double-end reads were generated.

[0057] (4) Bioinformatics and statistical analysis

[0058] The raw data is in FASTQ format. After the data is downloaded, the raw data sequence is first cut off by using Cutadapt software. Then, the qualified double-end raw data in the previous step is subjected to quality control analysis such as quality filtering, noise reduction, splicing and chimera removal according to the default parameters of QIIME 2 using DADA2 software and its default parameters (Callahan BJ, Mcmurdie PJ, Rosen MJ, et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 2016) to obtain the representative sequence (Amplicon Sequence Variant, ASV). After selecting each ASV using the QIIME 2 software (Bolyen E, Rideout JR, Dillon MR, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology, 2019 37: 852–857.) package, all ASV sequences were compared and annotated with the Silva (version 138) database (https: / / www.arb-silva.de / documentation / release-138 / ). Species alignment annotation was analyzed using the q2-feature-classifier software (https: / / github.com / qiime2 / q2-feature-classifier.git) with default parameters. QIIME 2 software was used for α and β diversity analysis. The α diversity of the samples was evaluated using α diversity including the Simpson index and the Shannon index. The unweighted Unifrac distance matrix calculated using R was used to perform unweighted Unifrac principal coordinate analysis (PCoA) to evaluate the β diversity of the samples. Based on the R package, the Kruskal Wallis statistical algorithm was used for differential analysis, and LEfSe was used to perform differential analysis of species abundance profiles.

[0059] The results are as follows Figure 1As shown in the Venn diagram, the results showed that 2948 bacterial community characteristics were detected in the HAPC group, 2499 bacterial community characteristics were detected in the Non-HAPC group, and there were 1519 common bacterial community characteristics in the two groups.

[0060] result:

[0061] (1) Comparison of clinical data between HAPC and Non-HAPC groups

[0062] As shown in Table 1, there were significant differences in age, heart rate, blood oxygen saturation and hemoglobin content between the HAPC and Non-HAPC groups (all P < 0.05), but no significant difference in body mass index.

[0063] (2) Analysis of oral flora diversity in HAPC and Non-HAPC groups

[0064] The Chao1 and Ace indices were used to evaluate the α diversity of the oral flora in the two groups. The results were compared as follows: Figure 2 As shown in the figure, there is a significant difference in the α diversity of the oral flora between the two groups, indicating that the diversity of the oral flora between the two groups has changed significantly. The β diversity of the oral flora in the two groups was evaluated by PCoA_binary_jaccard and PCoA unweighted unifrac methods, and the comparison results are shown in the figure. Figure 3 As shown, the results showed that there were significant differences in β diversity between the two groups, indicating that there were significant changes in the oral flora of the two groups as a whole.

[0065] (3) Analysis of species composition of oral flora at the phylum level in HAPC and Non-HAPC groups

[0066] Figure 4 The present invention provides two groups of oral flora phylum level composition analysis diagram; wherein Firmicutes, Proteobacteria, Bacteroidota, Fusobacteriota, Actinobacteriota, Campilobacterota, Spirochaetota, Patescibacteria, Desulfobacterota, Deferribacterota, Elusimicrobiota, Myxococcota, Acidobacteriota, Gemmatimonadota and Sva0485 are the main constituent phyla of the two groups of oral flora, accounting for an average of more than 99%.

[0067] (4) Analysis of species composition of oral flora at the genus level in HAPC and Non-HAPC groups

[0068] Figure 5The genus-level composition analysis diagram of two groups of oral flora provided in an embodiment of the present invention; wherein Streptococcus, Haemophilus, Prevotella, Neisseria, Leptotrichia, Fusobacterium, Bergeyella, Porphyromonas, Alloprevotella, Granulicatella, Aggregatibacter, Capnocytophaga, Rothia, Campylobacter and Gemella are the main genera constituting the two groups of oral flora, accounting for an average of more than 85%.

[0069] (5) Analysis of oral flora markers in the HAPC and Non-HAPC groups

[0070] Figure 6 The results of oral flora markers of the two groups were analyzed by LEfSe. LEfSe analysis showed that (log10LDA>2 and p<0.05 as the standard) the oral flora markers of the HAPC group were: ① p__Desulfobacterota at the phylum level; ② c__Desulfovibrionia at the class level; ③ o__Desulfovibrionales at the order level; ④ f__Paludibacteraceae, f__Rikenellaceae, f__Bacteroidaceae, f__Muribaculaceae, f__Oscillospiraceae, f__Desulfovibrionales at the family level. sulfovibrionaceae and f__Helicobacteraceae; ⑤The bacterial genus level includes g__Romboutsia, g__Rikenellaceae_RC9_gut_group, g__Blautia, g__Muribaculaceae, g__Bacte roides, g__Helicobacter, g__Lachnospiraceae_NK4A136_group, g__F0332, g__F0058, g__Johnsonella, g__Prevotellaceae_UCG_001 and g__Alistipes.

[0071] The characteristic oral flora of the Non-HAPC group were as follows: ① at the order level, there was o__Staphylococcales; ② at the family level, there were f__Gemellaceae, f__Ruminococcaceae, f__Carnobacteriaceae and f__Aerococcaceae; ③ at the genus level, there were g__Gemella, g__Granulicatella, g__Moraxella, g__Faecalibacterium and g__Abiotrophia.

[0072] Example 2

[0073] Study on the early warning diagnostic efficacy of oral flora markers in patients with high altitude polycythemia (HAPC)

[0074] Based on the above research, this part intends to use robot learning method (random forest analysis) on the oral flora data obtained in the previous part to find the characteristic bacteria (genus level) markers of patients with high altitude polycythemia (HAPC) for early warning diagnosis, and construct a joint diagnostic index through binary logistic regression analysis. Finally, the ROC curve is used to evaluate the diagnostic efficacy of the characteristic bacteria joint diagnostic index for HAPC.

[0075] 1. Statistical analysis methods

[0076] Robot learning method (random forest analysis) was performed based on the RandomForest_R language package. Binary Logestic regression analysis was performed using the statistical software SPSS 19.0 to construct a joint diagnostic index. ROC curve analysis was performed using MedCalc 19.20. The Kruskal Wallis statistical algorithm was used for intergroup comparison and difference analysis. When P < 0.05, it was considered to be statistically significant. Among them, AUC reflects the diagnostic efficacy (AUC = 0.5, no diagnostic efficacy; 0.5 < AUC < 0.7, very small diagnostic value; 0.7 < AUC < 0.9, quite accurate diagnostic value; 0.9 < AUC < 1, very accurate diagnostic value).

[0077] 2. Results Analysis

[0078] (1) Random forest analysis method to find characteristic bacteria for early warning diagnosis of HAPC

[0079] Figure 7The random forest analysis results of HAPC oral marker bacteria show that a total of 30 characteristic bacteria were found, which were sorted according to the importance measurement standard score (MeanDecreaseGini) and selected with MeanDecreaseGini>1.1 as the selection standard. The three characteristic bacteria obtained were: Muribaculaceae (MeanDecreaseGini=7.727054476), Abiotrophia (MeanDecreaseGini=2.68485643) and Granulicatella (MeanDecreaseGini=1.36395525). The sequences of the characteristic bacteria are shown in Table 6.

[0080] Table 6 Representative sequences of three characteristic oral bacteria

[0081]

[0082]

[0083] Note: Muribaculaceae: Bacteroides murreatus; Abiotrophia: Abiotrophia; Granulicatella: Granulicatella.

[0084] (2) Construction of joint diagnostic index based on three characteristic bacteria

[0085] The relative abundance of the three characteristic bacteria in the two groups was input into SPSS19.0 software, and the joint diagnostic index was constructed by binary logistic regression analysis. The equation of the joint diagnostic index was: logit(P) = 1078.3 × (relative abundance of Muribaculaceae) - 172.6 × (relative abundance of Abiotrophia) - 61.4 × (relative abundance of Granulicatella) - 0.5. The relative abundance values ​​of the three characteristic bacteria in the HAPC and plateau healthy control groups are shown in Table 7.

[0086] Table 7 Abundance expression data of oral flora markers in HAPC patients and healthy controls

[0087]

[0088] Note: HAPC: patients with high-altitude polycythemia; Non-HAPC: healthy people in high altitude; Muribaculaceae: Bacteroides; Abiotrophia: Abiotrophia; Granulicatella: Granulicatella.

[0089] (3) Analysis of the diagnostic efficacy of HAPC based on the combined diagnostic index of three characteristic bacteria

[0090] Saliva samples from 25 patients with high altitude polycythemia (HAPC) and 21 healthy people at the same altitude (Non-HAPC) were collected for verification, and the detection method was the same as in Example 1. The oral flora detection results of the verification population were used to draw a working characteristic curve. Figure 8 The specific data of the ROC curve are shown in Table 9. The results show that the combined diagnostic index of the three characteristic bacteria has a very accurate diagnostic value for HAPC (AUC = 0.948), with a sensitivity of 83.33% and a specificity of 93.33%, indicating that the combined index has strong diagnostic efficacy, good sensitivity and specificity. The diagnostic discrimination value is also given, that is, when logit (P)>0.494, the relative abundance of the corresponding bacteria of each person is brought into the joint equation to calculate logit (P). If logit (P)>0.494, it can be identified as a patient with high altitude polycythemia (HAPC).

[0091] Table 8 Clinical data of validation population

[0092]

[0093] Note: HAPC: patients with high altitude polycythemia; Non-HAPC: healthy people in plateau.

[0094] Table 9 Operating characteristic curve data of oral flora markers

[0095]

[0096] Note: HAPC: patients with high-altitude polycythemia; Non-HAPC: healthy people in the plateau; oral flora marker combination: composed of Muribaculaceae (Bacteroides musseri), Abiotrophia (Abiotrophia) and Granulicatella (Granulocyte Streptomyces).

[0097] The inventors found significant differences in oral flora between HAPC and Non-HAPC groups through 16S sRNA detection and analysis of oral flora and bioinformatics statistics. The robot learning method was further used to screen out characteristic bacteria for diagnosing HAPC, and a combined diagnostic index of three characteristic bacteria and its calculation equation were constructed. The results showed that the combined index of three characteristic bacteria has a very accurate value for the diagnosis of HAPC. This invention is helpful for early warning diagnosis and large-scale population screening of HAPC, thereby reducing the health damage and life risks brought to people living in the plateau for a long time due to the high incidence of HAPC.

[0098] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. 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 details without departing from the scope defined by the claims of the present invention.

Claims

1. A combination of oral flora markers for early warning of high altitude polycythemia, characterized in that: The oral microbial marker combination is composed of the genus Muribaculaceae, the genus Abiotrophia and the genus Granulicatella.

2. Use of a reagent for detecting the oral flora marker combination according to claim 1 in the preparation of a reagent or kit for early warning of high-altitude polycythemia.

3. The use according to claim 2, characterized in that: The application is specifically to detect the relative abundance of oral flora markers of a subject.

4. The use according to claim 3, characterized in that: The relative abundance of the oral flora markers was upregulated in patients with high-altitude polycythemia, while the genera Muribaculaceae and Abiotrophia were downregulated.

5. The use according to claim 3, characterized in that: The reagent or kit is used for testing saliva, oral swab or throat swab samples of a test subject.

6. The use according to claim 3, characterized in that: The relative abundance of Bacteroides murrenii, Abiotrophia and Granulicatella of Muribaculaceae was used to construct a joint diagnostic index to distinguish patients with high-altitude polycythemia from healthy people.

7. The use according to claim 6, characterized in that: The equation of the combined diagnostic index was logit(P)=1078.3×(relative abundance of Muribaculaceae)-172.6×(relative abundance of Abiotrophia)-61.4×(relative abundance of Granulicatella)-0.

5.

8. An early warning diagnostic kit for high altitude polycythemia, characterized in that: Comprising a reagent for detecting the relative abundance of the oral flora marker combination as claimed in claim 1.

9. The early warning diagnostic kit according to claim 8, characterized in that: The relative abundance of the oral flora marker combination is obtained by the following method: separating and extracting nucleic acids from biological samples, constructing libraries, sequencing, performing bioinformatics analysis on sequencing data, obtaining the sequence number of the oral microbial marker, and standardizing the sequence number to obtain the relative abundance of bacterial species.

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