CN112442533A

CN112442533A - Flora marker for predicting risk of anaphylactoid purpura and kit thereof

Info

Publication number: CN112442533A
Application number: CN202011154839.2A
Authority: CN
Inventors: 聂晓晶; 张远真; 曾煜闺; 陈伊; 钱艺芳
Original assignee: 900th Hospital of the Joint Logistics Support Force of PLA
Current assignee: 900th Hospital of the Joint Logistics Support Force of PLA
Priority date: 2020-10-26
Filing date: 2020-10-26
Publication date: 2021-03-05

Abstract

The invention discloses a flora marker for predicting the risk of developing allergic purpura and a kit thereof, belonging to the technical field of molecular biology. The markers include at least the following genera: faecalibacterium, Prevotella _9, Escherichia-Shigella, Blauia and Ruminococcus _ gnavus _ group. The kit comprises a reagent for detecting the abundance of the bacterial marker. The invention can effectively predict the risk of the anaphylactoid purpura and provide auxiliary reference for prevention and treatment.

Description

Flora marker for predicting risk of anaphylactoid purpura and kit thereof

Technical Field

The invention belongs to the technical field of molecular biology, and particularly relates to a flora marker and a kit for predicting the risk of anaphylactoid purpura, and application of the flora marker and the kit in anaphylactoid purpura detection.

Background

Henoch-Scholein purpura (HSP) is an IgA-mediated vasculitis, the most common autoimmune disease in childhood. Clinical manifestations include palpable purpura or ecchymoses, joint pain, abdominal pain and kidney involvement, and pathological biopsy of the kidney suggests proliferative glomerulonephritis dominated by IgA deposition. Diagnosis of HSPs is primarily based on characteristic clinical manifestations, which may lead to delays in diagnosis and treatment. Many researchers have attempted to develop various biomarkers for the diagnosis of HSP, including platelet count, fecal calprotectin concentration, serum amyloid A, IL-6, WBC, IgA, and IgM, etc., but there are still no clear laboratory tests available for diagnosing HSP.

Intestinal bacteria have also been found to play an important role in the human body. Numerous studies have shown that altered gut flora composition may play an important role in the pathogenesis of immune-mediated diseases. The etiology and pathogenesis of HSP are not completely clarified, and at present, a lot of researches are limited to the aspects of susceptibility genes (HLA-B, HLA-DRB1, MEFV, C1GALT1 and the like), infection (bacteria, viruses or parasites), immunity (IgA, complement) and the like, and the researches on intestinal flora and HSP are less. Imbalance of intestinal flora plays an important role in the pathogenesis of HSP, but currently, research on HSP intestinal flora in different disease stages (initial and recurrent HSP) is not deep enough, and no diagnostic method for HSP based on intestinal flora exists.

Disclosure of Invention

In order to solve the problems of selection and detection of flora related to allergic purpura in the prior art, the inventor provides a flora marker for predicting the risk of developing allergic purpura and a detection kit thereof, and the technical scheme is as follows:

a flora marker for predicting the risk of developing allergic purpura comprises at least the following genera: faecalibacterium, Prevotella _9, Escherichia-Shigella, Blauia and Ruminococcus _ gnavus _ group.

The flora marker also comprises more than one of bacilloides, Subdoligranum, Parabacteroides, Fusobacterium, Diarister, Lachnocoridium, Bifidobacterium, Phascolatobacter, Roseburia and Veillonella.

A detection kit for a flora marker for predicting the risk of developing allergic purpura is characterized in that: the detection kit comprises Faecalibacterium, Prevotella _9, Escherichia-Shigella, Blautia and Ruminococcus _ gnavus _ group, and a reagent for quantifying the abundance of the 5 bacteria.

A detection kit for a flora marker for predicting the risk of developing allergic purpura is characterized in that: the detection kit also comprises a reagent for determining the abundance of more than one of Bacteroides, Subdoligranum, Parabacteroides, Fusobacterium, Diarister, Lachnocoridium, Bifidobacterium, Phascolatobacter, Roseburia and Veillonella.

Further, the flora abundance quantitative reagent is a PCR reagent, and the amplified OTU sequence is shown as SEQ ID NO: 5 to SEQ ID NO: shown at 9.

Further, the detection kit for the flora marker for predicting the risk of developing allergic purpura also comprises a quantitative abundance reagent of total bacteria.

Further, the application of the detection kit of the flora marker for predicting the risk of developing anaphylactoid purpura in predicting the initial risk of developing anaphylactoid purpura is provided.

Further, the application of the detection kit of the flora marker for predicting the risk of developing anaphylactoid purpura in the prediction of the risk of relapse of anaphylactoid purpura is provided.

Different from the prior art, the technical scheme has the advantages that:

by detecting intestinal flora markers related to the initial onset and recurrence of the allergic purpura and analyzing the flora structure, the risk of the allergic purpura can be prompted, auxiliary reference is provided for the diagnosis of the allergic purpura, and a direction is provided for the treatment of the allergic purpura.

Drawings

FIG. 1 is a phylum level species composition according to embodiments.

FIG. 2 is a genus level species composition according to embodiments.

Fig. 3 shows the results of the NMDS analysis based on OTU levels according to the embodiments.

Fig. 4 is a histogram of LDA value distribution according to an embodiment.

FIG. 5 is a diagram of a multi-group difference analysis according to an embodiment.

FIG. 6 is a ROC plot of an initial disease model according to embodiments.

FIG. 7 is a ROC plot of disease recurrence model A, according to embodiments.

FIG. 8 is a ROC plot of disease recurrence model B according to embodiments.

Detailed Description

To explain technical contents, structural features, and objects and effects of the technical solutions in detail, the following detailed description is given with reference to the accompanying drawings in conjunction with the embodiments.

Example 1

1. Experimental methods

Stool samples of 23 healthy children and 34 allergic purpura patients (18 of them were the initial allergic purpura patients and 16 of them were the recurrent allergic purpura patients) were collected. The healthy group was labeled CH, the disease onset group was labeled AT, and the disease relapse group was labeled BT.

1.1 extraction of fecal DNA

DNA from stool samples was extracted using a stool DNA extraction kit (purchased from Meiji, Guangzhou).

1.2 library construction

(1) Primer design

Specific primers are designed aiming at a V3-V4 region of a 16S rRNA gene, and an overlap sequence is added, wherein the specific sequences are as follows:

an upstream primer F:

TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG(SEQ ID NO:1)

a downstream primer R:

GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC(SEQ ID NO:2)

(2) PCR amplification and labeling of targets

a) The reaction system was prepared in 0.2mL PCR tube:

wherein, the PCR Amplification Mix is purchased from Nanjing Novozam, and the Primer Mix1 is TE Buffer containing the above Primer.

b) Mixing the prepared Mix uniformly, placing the mixture into a PCR instrument, and executing the following programs:

c) after the reaction, 2. mu.L of the PCR product was subjected to agarose gel electrophoresis to obtain about 500bp PCR product. Electrophoresis conditions: 1% Agarose, D2000 ladder, 120V, 20 min.

(3) PCR product purification

And purifying the PCR product by using AMPure XP magnetic beads.

(4) Establishing index labels for target points

a) PCR Amplification Mix (2X), 10. mu.M/. mu.L of introduced PCR primer i5 and introduced PCR primer i7 were removed from a-20 ℃ refrigerator and thawed on ice;

only one set of Index sequences is listed below, the remaining sequences are seen in the Nextera Index Kit-PCR Primers Kit.

i5：AATGATACGGCGACCACCGAGATCTACACCTCTCTATTCGTCGGCAGCGTC(SEQ ID NO:3)

i7：CAAGCAGAAGACGGCATACGAGATTCGCCTTAGTCTCGTGGGCTCGG(SEQ ID NO:4)

b) The reaction system was prepared in a 0.2mL centrifuge tube as follows:

c) the prepared Mix is gently blown and beaten for 10 times and mixed evenly, and then placed in a PCR instrument, and the reaction procedure is as follows:

(5) library purification

And purifying the PCR product in the last step by using magnetic beads.

(6) Library quality inspection

a) 1 μ L was assayed for concentration using a Qubit3.0 fluorometer (QubitdsDNA HS Assay kit) and the concentration was recorded.

b) The library was tested for band distribution using the Agilent 2200High Sensitivity D1000 Kit.

1.3Miseq sequencing

The constructed library and chip were placed together in a Miseq sequencer and sequenced using Miseq Reagent Kit v3 Kit (purchased from Illumina).

1.4 sequencing data analysis

(1) Sequencing data processing

Splitting each sample data from the off-line data according to the Barcode sequence, splicing reads of each sample by using FLASH after the Barcode is cut off, and obtaining a splicing sequence which is original Tags data (Raw Tags); the Raw Tags obtained by splicing need to be strictly filtered to obtain high-quality tag data (Clean Tags). Referring to the Tags quality control flow of QIIME, the following operations are carried out: a) and (5) intercepting Tags: truncating Raw Tags from the first low-quality base site of consecutive low-quality values (default quality threshold of 19) base numbers to a set length (default length value of 3); b) tags length filtration: and (3) further filtering the Tags data set obtained by intercepting the Tags, wherein the length of the continuous high-quality base is less than 75% of the length of the Tags. Primers and chimeras were removed from the Tags obtained by the above treatment.

And (4) obtaining high-quality reads by using Mothur filtering treatment, and then further removing the chimera sequence to complete the treatment of the sequencing data.

(2) OTU clustering and species annotation

Clustering samples subjected to data processing by using Mothur software, clustering sequences into OTUs (operational taxomic units) by using 97% consistency (Identity) as a default, selecting representative sequences of the OTUs, and screening the sequences with the highest occurrence frequency in the OTUs as the representative sequences of the OTUs according to the algorithm principle. Species annotation was performed on OTUs representative sequences, species annotation analysis was performed using the mortur method and the SSUrRNA database of SILVA (set threshold 0.8-1), and taxonomic information was obtained and separately at each classification level: kingdom, phylum, class, order, family, genus, species, and statistics of community composition for each sample.

And according to the species annotation result, selecting the species with the maximum abundance ranking 10 above the maximum abundance ranking of each sample or group on each classification level, and generating a species relative abundance column accumulation chart so as to visually check the species with higher relative abundance and the proportion thereof of each sample on different classification levels.

(3) Species diversity and inter-group differential analysis

According to the species annotation results, Alpha, Beta diversity and inter-group difference analyses were performed, and whether the differences in species diversity among the analyses were significant was examined by Kruskal-Wallis H Test.

Biomarker with statistical differences, i.e. species with significant differences between groups, was searched for between groups using the LEfSe (LDA Effect size) method.

2. Results of the experiment

(1) Composition of species

AT group, BT group and CH group, wherein species composition phylum level of the three groups is shown in figure 1, and genus level is shown in figure 2.

At the phylum level, the phylum Firmicutes, Bacteroidetes, Proteobacteria and actinomycetes are the dominant phyla of the three groups.

At the genus level, Bacteroides (Bacteroides) are the highest in proportion, and Faecalibacterium (Faecalibacterium) is the next. In the three groups of the AT group, the BT group and the CH group, the Prevotella (Prevotella _9) and the Parasaxate (Parastutterella) show a decreasing trend; escherichia coli (Escherichia-Shigella), Subdoligurum, Lachnocrossdium increased.

(2) Variety of species

Non-metric multidimensional scaling (NMDS) was used for species diversity, see fig. 3, with Stress less than 0.2, indicating that NMDS can accurately reflect the degree of variation between samples.

(3) Analysis of differences between groups

The differences in the flora composition were compared using the LefSe assay and the results are shown in fig. 4. Multiple sets of sample analyses were performed using the Kruskal-Wallis rank sum test, and the results are shown in FIG. 5. Experimental results show that the differences between Faecalibacterium, Prevotella _9, Escherichia-Shigella, Blautia and Ruminococcus _ gnavus _ group among three groups have statistical significance.

Further analysis shows that 15 genera, namely Faecalibacterium, Prevotella _9, Escherichia-Shigella, Blautia, Ruminococcus _ gnavus _ group, Bacteroides, subdoligranum, Parabacteroides, Fusobacterium, Dialister, lachnocoridium, Bifidobacterium, phascolarcotobacterium, Roseburia and veilliella are key genera and can be used as biomarkers for the risk prediction of the onset of anaphylactoid purpura.

Example 2

5 types of genera with remarkable differences, namely Faecalibacterium, Prevotella _9, Escherichia-Shigella, Blautia and Ruminococcus _ gnavus _ group, are selected to establish a risk prediction model.

(1) Model for predicting initial risk of anaphylactoid purpura

Establishing an allergic purpura initial risk prediction model by using the data of an initial group (AT) and a healthy group (CH), and comprising the following steps:

Y＝a+b₁×x₁+b₂×x₂+…+b₅×x₅ (1)

wherein a is a constant and b_iIs a regression coefficient, x_iY is the predicted value for the relative abundance of key genera.

For children who have not developed allergic purpura, the model can be used for predicting the initial onset risk.

By drawing the ROC curve and calculating the AUC area, the method is taken as a typical measure for evaluating the classification effect of the two classes. AUC was found to be 0.786 as shown in fig. 6. The model has good effect of predicting the initial incidence risk of the anaphylactoid purpura, and can predict the incidence risk of the anaphylactoid purpura according to the model. The optimal critical point is 0.7, namely when the predicted value is more than 0.7 and classified as healthy people, and the predicted value is less than 0.7 and classified as patients, the prediction effect is best.

(2) Model for predicting recurrence risk of anaphylactoid purpura

Establishing a recurrence risk prediction model A of the anaphylactoid purpura according to data of a recurrence group (BT) and a health group (CH), and the following steps:

Z＝c+d₁×x₁+d₂×x₂+…+d₅×x₅ (2)

wherein c is a constant and d_iIs a regression coefficient, x_iZ is the predicted value, being the relative abundance of the key genus.

For the population cured by the allergic purpura, the model can be used for predicting the recurrence risk of the disease.

By drawing the ROC curve and calculating the AUC area, the method is taken as a typical measure for evaluating the classification effect of the two classes. AUC was found to be 0.792 after calculation, as shown in FIG. 7. The model has good effect of predicting the recurrence risk of the anaphylactoid purpura, and can predict the morbidity risk of the anaphylactoid purpura according to the model. The optimal critical point is 0.546, i.e. when the predicted value is greater than 0.546 and classified as a healthy person, and the predicted value is less than 0.546 and classified as a patient, the prediction effect is the best.

Establishing a anaphylactoid purpura recurrence risk prediction model B according to data of an initial group (AT) and a recurrence group (BT), wherein the model B comprises the following steps:

W＝g+h₁×x₁+h₂×x₂+…+h₅×x₅ (3)

wherein g is a constant, h_iIs a regression coefficient, x_iW is the predicted value for the relative abundance of key genera.

For patients with incipient allergic purpura, the model can be used to predict the risk of disease recurrence.

By drawing the ROC curve and calculating the AUC area, the method is taken as a typical measure for evaluating the classification effect of the two classes. AUC was obtained after calculation and was 0.771, as shown in FIG. 8. The model has good effect of predicting the recurrence risk of the anaphylactoid purpura, and the recurrence risk of the anaphylactoid purpura can be predicted according to the model. The optimal critical point is 0.325, i.e. when the predicted value is less than 0.325, there is a risk of recurrence.

Example 3

A anaphylactoid purpura flora marker detection kit comprises a reagent for quantifying the abundance of the microbiota. The microbial population is Faecalibacterium, Prevotella _9, Escherichia-Shigella, Blautia, Ruminococcus _ gnavus _ group. Detecting the abundance of 5 genera in the excrement of the examinee, and substituting the abundance into the initial risk prediction model in the embodiment 2 to obtain the risk degree of the initial attack of the allergic purpura of the examinee; the obtained product is substituted into the recurrence risk prediction model A described in example 2 to obtain the recurrence risk degree of allergic purpura of the examinee. If the examinee is an initial patient with allergic purpura, the patient is substituted into the recurrence risk prediction model B described in example 2, and the degree of risk of the recurrence of allergic purpura of the examinee can be obtained.

As a further improvement of the kit, the kit is a PCR kit, and the amplified OTU sequence is shown as SEQ ID NO: 5 to SEQ ID NO: shown at 9.

Further, the reagent for quantifying the abundance of the microbial flora is applied to the preparation of a kit for predicting the risk of anaphylactoid purpura diseases.

A detection kit for a purpura Henoch flora marker comprises reagents for quantifying Faecalibacterium, Prevotella _9, Escherichia-Shigella, Blautia, Ruminococcus _ gnavus _ group and total bacterial DNA content.

The using method of the kit comprises the following steps:

(1) extracting bacterial DNA from the subject's stool;

(2) the DNA content of 5 genera and total bacteria is detected by the kit;

(3) respectively comparing the DNA content of the 5 bacteria with the DNA content of the total bacteria, and calculating to obtain the relative content of the 5 bacteria;

(4) substituting the relative contents of the 5 kinds of bacteria into the initial risk prediction model described in the embodiment 2 to obtain the risk prediction value of the initial occurrence of allergic purpura of the examinee;

(5) for the subject with allergic purpura at the beginning, the relative content of 5 bacteria can be substituted into the recurrence risk prediction model A or B described in example 2, and the risk prediction value of the allergic purpura recurrence of the subject can be obtained.

Example 4

15 genera, namely Faecalibacterium, Prevotella _9, Escherichia-Shigella, Blautia, Ruminococcus _ gnavus _ group, Bacteroides, Subdoligurum, Parabacteroides, Fusobacterium, Diarister, Lachnocoridium, Bifidobacterium, Phascolatobacter, Roseburia and Vellonella, are selected to establish a prediction model of the risk of the allergic purpura.

R＝e+f₁×x₁+f₂×x₂+…+f₁₅×x₁₅ (4)

Wherein e is a constant, f_iIs a regression coefficient, x_iR is the predicted value for the relative abundance of key genera.

This model can be used for the prediction of the risk of the onset of allergic purpura. By plotting the ROC curve, the AUC area was calculated to be 0.812.

The anaphylactoid purpura flora marker detection kit comprises a reagent for quantifying the abundance of the 15 genera. And detecting the abundance of 15 bacterial genera in the excrement of the examinee, and substituting the abundance into the risk prediction model to obtain the risk degree of the examinee suffering from the allergic purpura.

In conclusion, the abundance of pathogenic bacteria related to the anaphylactoid purpura can be used as an index for auxiliary diagnosis of the anaphylactoid purpura; the risk prediction model can be combined to carry out prediction analysis on the morbidity risk of the children suffering from the anaphylactoid purpura and further diagnose the anaphylactoid purpura.

The risk prediction model can further comprehensively judge the risk of the anaphylactoid purpura by combining blood routine (WBC, PLT and MPV), immunological indexes (IgA, IgG, IgE, IgM and complement C3), blood coagulation function indexes (D-dimer and IgA/C3) and the like so as to improve the accuracy of prediction.

It should be noted that, although the above embodiments have been described herein, the invention is not limited thereto. Therefore, based on the innovative concepts of the present invention, the technical solutions of the present invention can be directly or indirectly applied to other related technical fields by making changes and modifications to the embodiments described herein, or by using equivalent structures or equivalent processes performed in the content of the present specification and the attached drawings, which are included in the scope of the present invention.

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Claims

1. A flora marker for predicting the risk of developing allergic purpura is characterized in that: the markers include at least the following genera: faecalibacterium, Prevotella _9, Escherichia-Shigella, Blauia and Ruminococcus _ gnavus _ group.

2. A detection kit for a bacterial population marker for the risk prediction of the onset of allergic purpura according to claim 1, which comprises: the detection kit comprises a reagent for quantifying abundance of the genus of claim 1.

3. The bacterial population marker detection kit for the risk prediction of allergic purpura according to claim 2, wherein: the flora abundance quantitative reagent is a PCR reagent, and the amplified OTU sequence is shown as SEQ ID NO: 5 to SEQ ID NO: shown at 9.

4. The bacterial population marker for the risk prediction of allergic purpura according to claim 1, wherein: the marker also comprises more than one of bacilloides, Subdoligranum, Parabacteroides, Fusobacterium, Diarister, Lachnocoridium, Bifidobacterium, Phascolatobacterium, Roseburia and Veillonella.

5. A bacterial colony marker detection kit for the risk prediction of the onset of allergic purpura according to claim 4, wherein the kit comprises: the detection kit comprises a reagent for quantifying abundance of the genus of claims 1 and 4.

6. Use of a detection kit for a bacterial population marker as defined in claim 2 or 3 for the prediction of the risk of the initial onset of allergic purpura.

7. Use of the flora marker assay kit according to claim 2 or 3 for the prediction of risk of recurrence of allergic purpura.

8. Use of a detection kit for a bacterial population marker according to claim 5 for the prediction of risk of allergic purpura.