A kit for diagnosing schistosomiasis

Abstract

The present invention discloses a kit for diagnosing schistosomiasis, which detects the hepatic granuloma and liver fibrosis induced by schistosome invasion by using the abundance changes of three bacteria, namely Bacteroides, Blautia, and Enterococcus, as intestinal flora biomarkers. Compared with traditional methods such as serological indicators, it has the advantages of high sensitivity, strong specificity, and non-invasiveness, thereby specifically detecting the degree of liver injury and achieving precise detection of schistosome-induced liver injury; compared with traditional blood detection methods such as AST and ALT, it is non-invasive and reduces contact with blood products; compared with imaging examinations such as B-ultrasound and CT, it has the advantages of portability and simplicity; the required equipment is simple, the test results are intuitive, the requirements for operators are low, it can effectively reduce the detection cost, and it is suitable for primary medical institutions and population screening sites.

Description

Technical Field

[0001] The present invention relates to the technical field of molecular detection in parasitology, and particularly to a kit for diagnosing schistosomiasis. Background Art

[0002] Schistosomiasis is a parasitic disease caused by infection with schistosomes, which has an adverse impact on human health and economic development. Schistosomiasis has been listed as a Class B legally reported infectious disease in the "Law of the People's Republic of China on the Prevention and Treatment of Infectious Diseases (Revised)". With the progress of modern medicine, schistosomiasis japonica, which was once widely spread and seriously threatened the health of the people in China, has been effectively controlled.

[0003] Schistosome infection can cause symptoms such as liver damage, splenomegaly, fever, diarrhea, etc. Among them, liver damage includes granuloma and liver fibrosis, which is the most serious health damage induced by schistosome infection. In the later stage, it can develop into advanced schistosomiasis, seriously affecting the patient's life satisfaction. Early detection, diagnosis, and treatment are important methods for controlling schistosomiasis at present. Therefore, developing a rapid and accurate detection method for the degree of liver damage induced by schistosome infection is one of the effective ways to contain schistosomiasis and its progression.

[0004] At present, the main methods for detecting schistosomiasis include three categories: epidemiological diagnosis, clinical diagnosis, and laboratory diagnosis. Laboratory diagnosis mainly includes immunological detection and molecular biological detection. Immunological diagnostic methods mainly include antigen detection such as immunochromatography and antibody detection such as enzyme-linked immunosorbent assay, chemiluminescence immunoassay, and immunochromatographic assay. Although these methods are also widely used in clinical diagnosis and epidemiological investigation, they also expose many problems. The etiological diagnosis method is prone to missed detection and often requires repeated tests. Immunological diagnosis does not directly detect pathogens, with low sensitivity and insufficient specificity. Molecular biological techniques are mainly based on nucleic acid amplification techniques, providing rapid, sensitive, specific, and stable detection methods for disease diagnosis. Commonly used for detecting schistosomiasis are invasive molecular biological detection methods such as serology and non-invasive methods such as B-ultrasound and CT. However, there is a lack of non-invasive detection methods that are convenient, accurate, sensitive, and highly specific.

[0005] The gut microbiota is known as the "second genome" of humans and plays an important role in the health of the host. In recent years, with the rapid development of high-throughput sequencing, our understanding of the gut microbiota has become deeper and deeper. There are articles reporting the effects of different degrees of Schistosoma japonicum infection on the gut microbiota of mice (Song Qiuyue, Zhang Yishu, Zhang Beibei, Liu Jiahua, Song Langui, Sun Xi, Wu Zhongdao. Effects of different degrees of Schistosoma japonicum infection on the gut microbiota of mice [J]. Journal of Tropical Medicine, 2020, 20(03): 303-308+428.), but the sensitivity and specificity of the gut microbiota proposed are relatively low, which is not conducive to accurately and sensitively judging liver injury caused by schistosomiasis. Therefore, a method for more accurate, sensitive and specific judgment of liver injury caused by schistosomiasis is still needed. Summary of the Invention

[0006] The object of the present invention is to overcome the above deficiencies of the prior art and provide a kit for diagnosing schistosomiasis.

[0007] The first object of the present invention is to provide the use of reagents for detecting Bacteroides, Blautia and / or Enterococcus flora in the preparation of a kit for diagnosing schistosomiasis.

[0008] The second object of the present invention is to provide a kit for diagnosing schistosomiasis.

[0009] In order to achieve the above object, the present invention is realized through the following solutions:

[0010] The use of reagents for detecting Bacteroides, Blautia and / or Enterococcus flora in the preparation of a kit for diagnosing schistosomiasis.

[0011] Preferably, the reagent is used to identify the species of Bacteroides, Blautia and / or Enterococcus flora.

[0012] More preferably, the reagent is further used to detect the abundance of Bacteroides, Blautia and / or Enterococcus flora.

[0013] More preferably, the reagent is for detecting Bacteroides, Blautia and / or Enterococcus flora in the intestine.

[0014] More preferably, the schistosomiasis is organ damage induced by Schistosoma invasion.

[0015] More preferably, the organ damage is liver injury.

[0016] More preferably, the liver injury is liver granuloma and / or liver fibrosis.

[0017] Preferably, the schistosome is Schistosoma japonicum.

[0018] Preferably, the reagent for diagnosing human schistosomiasis japonica is a reagent for detecting the abundances of Blautia and Enterococcus in the intestinal flora, and the reagent for diagnosing murine schistosomiasis japonica is a reagent for detecting the abundances of Bacteroides and Blautia in the intestinal flora.

[0019] A kit for diagnosing schistosomiasis, which contains the reagent.

[0020] Preferably, the schistosomiasis is organ damage induced by schistosome invasion.

[0021] More preferably, the organ damage is liver injury.

[0022] More preferably, the liver injury is liver granuloma and / or liver fibrosis.

[0023] Preferably, the schistosome is Schistosoma japonicum, Schistosoma mansoni, Schistosoma haematobium, Schistosoma intercalatum and / or Schistosoma mekongi.

[0024] More preferably, the schistosome is Schistosoma japonicum.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] The present invention discloses a kit for diagnosing schistosomiasis, which detects liver granuloma and liver fibrosis induced by schistosome invasion by using the abundance changes of the intestinal flora biomarkers Bacteroides, Blautia and Enterococcus. Compared with traditional methods such as serological indexes, it has the advantages of high sensitivity, strong specificity and non-invasiveness, thus specifically detecting the degree of liver injury and realizing the accurate detection of schistosome-induced liver injury; compared with traditional blood detection methods such as AST and ALT, it is non-invasive and reduces the contact with blood products; compared with imaging examinations such as B-ultrasound and CT, it has the advantages of portability and simplicity; the required equipment is simple, the detection result is intuitive, the requirements for operators are low, it can effectively reduce the detection cost, is suitable for primary medical institutions and population screening sites, and has good application value and popularization prospect. Description of the Drawings

[0027] Figure 1 Samples were collected at the same time point (the 8th week) after mice were infected with Schistosoma japonicum, and the differences in intestinal flora α-diversity (based on OTUs), where Control is the negative control group and SJ is the Schistosoma japonicum infection group.

[0028] Figure 2Samples were taken at different time points (the 7th week and the 8th week) after mice were infected with Schistosoma japonicum, and there were differences in the α-diversity of the intestinal flora (based on Shannon).

[0029] Figure 3 Samples were taken at the same time point (the 8th week) after mice were infected with Schistosoma japonicum, and there were differences in the β-diversity of the intestinal flora (based on PLS-DA).

[0030] Figure 4 Samples were taken at different time points (the 7th week and the 8th week) after mice were infected with Schistosoma japonicum, and the biomarkers (based on STAMP) that could distinguish the negative control group and the Schistosoma japonicum infection group in the intestinal flora were obtained. Here, Control is the negative control group, and SJ is the Schistosoma japonicum infection group, and it has statistical significance.

[0031] Figure 5 Samples were taken at the same time point (the 8th week) after mice were infected with Schistosoma japonicum, and the markers that could distinguish the negative control group and the Schistosoma japonicum infection group in the intestinal flora were obtained. Here, Control is the negative control group, and SJ is the Schistosoma japonicum infection group, and the LDA value ≥ 3.

[0032] Figure 6 Samples were taken at different time points (the 7th week and the 8th week) after mice were infected with Schistosoma japonicum, and there were differences in the β-diversity of the intestinal flora (based on PLS-DA).

[0033] Figure 7 Samples were taken at different time points (the 7th week and the 8th week) after mice were infected with Schistosoma japonicum, and the biomarkers (based on random forest analysis) that could distinguish the negative control group and the Schistosoma japonicum infection group in the intestinal flora were obtained.

[0034] Figure 8 Based on Spearman analysis, the interactions of the intestinal flora in mice after being infected with Schistosoma japonicum and the correlations with serological detection indicators were analyzed. It can be seen that the granuloma area (Granuloma), liver fibrosis (Fibrosis), hydroxyproline level (Hydroxyproline), as well as ALT, AST, etc. were significantly correlated with a variety of bacteria. The size of the circle represents the size of the correlation, and it indicates statistical significance; different colors represent whether the correlation is positive or negative.

[0035] Figure 9 There were differences in the β-diversity of the intestinal flora (based on PLS-DA) between the group of humans infected with Schistosoma japonicum and the group of humans not infected with Schistosoma japonicum.

[0036] Figure 10 Based on the differences in the abundance of the intestinal flora between the group of humans infected with Schistosoma japonicum and the group of humans not infected with Schistosoma japonicum, the biomarkers of different groups were obtained. For example, g_Blautia (at the genus level) was the biomarker for those infected with schistosomiasis, and the LDA value ≥ 3.

[0037] Figure 11 For the group infected with Schistosoma japonicum in humans and the non-infected group, the biomarkers (based on STAMP) of the intestinal flora that distinguish the group infected with Schistosoma japonicum from the non-infected group are statistically significant. Among them, Control is the negative control group (i.e., the group not infected with Schistosoma japonicum), and SJ is the Schistosoma japonicum infection group.

[0038] Figure 12 For the group infected with Schistosoma japonicum in humans and the non-infected group, the biomarkers of the intestinal flora that distinguish the group infected with Schistosoma japonicum from the non-infected group (based on random forest analysis).

[0039] Figure 13 It is about the diagnostic effect of intestinal flora biomarkers on Schistosoma japonicum-induced liver injury in humans and mice. The red line represents humans, and the blue line represents mice. AUC (Area Under Curve) represents the area enclosed by the ROC (Receiver operating characteristic curve) curve and the coordinate axes. The closer the value is to 1, the higher the authenticity of the detection method. A P-value less than 0.05 indicates statistical significance.

[0040] Figure 14 It is a comparison of the intestinal flora biomarkers in mice for Schistosoma japonicum infection and non-parasitic infection factors (in this experiment, Bacillus subtilis infection was selected), which further verifies and supplements the previous experimental results. Among them, Control is the negative control group (i.e., the group not infected with Schistosoma japonicum); SJ is the Schistosoma japonicum infection group; BS is the Bacillus subtilis infection group, by oral gavage.

[0041] Figure 15 It shows the comparison of the β-diversity differences of the intestinal flora among the three groups by PLS-DA analysis, that is, the differences in the intestinal flora structure, and it is found that the three groups are separated from each other; among them, Control is the negative control group (i.e., the group not infected with Schistosoma japonicum); SJ is the Schistosoma japonicum infection group; BS is the Bacillus subtilis infection group, by oral gavage.

[0042] Figure 16 It is to obtain the biomarkers of the specific types of intestinal flora that can distinguish the three groups (i.e., the control group, the Schistosoma japonicum infection group, and the Bacillus subtilis infection group) through LEfSe analysis; among them, Control is the negative control group (i.e., the group not infected with Schistosoma japonicum); SJ is the Schistosoma japonicum infection group; BS is the Bacillus subtilis infection group, by oral gavage.

[0043] Figure 17For random forest analysis, biomarkers of specific types of intestinal flora that can distinguish between two groups (i.e., the Schistosoma japonicum-infected group and the Bacillus subtilis-infected group) were obtained, with a screening value of LDA Score ≥ 3. Among them, Control is the negative control group (i.e., the group not infected with Schistosoma japonicum); SJ is the Schistosoma japonicum-infected group; BS is the Bacillus subtilis-infected group, which was orally gavaged.

[0044] Figure 18 For LEfSe analysis, biomarkers of specific types of intestinal flora that can distinguish between two groups (i.e., the Schistosoma japonicum-infected group and the Bacillus subtilis-infected group) were obtained, with a screening value of LDA Score ≥ 3. Among them, Control is the negative control group (i.e., the group not infected with Schistosoma japonicum); SJ is the Schistosoma japonicum-infected group; BS is the Bacillus subtilis-infected group, which was orally gavaged. Specific implementation manners

[0045] The present invention will be further elaborated in detail below in conjunction with the accompanying drawings of the specification and specific embodiments. The embodiments are only used to explain the present invention and are not intended to limit the scope of the present invention. The test methods used in the following embodiments are all conventional methods unless otherwise specified; the materials, reagents, etc. used are, unless otherwise specified, reagents and materials that can be obtained from commercial channels.

[0046] Example 1 Analysis of differences in intestinal flora by high-throughput sequencing

[0047] Total DNA obtained from intestinal flora was subjected to high-throughput 16S rRNA gene sequencing.

[0048] 1. Extraction of DNA from samples to be tested

[0049] (1) Mice were purchased from the Guangdong Provincial Animal Center, and Oncomelania hupensis releasing Schistosoma japonicum cercariae were purchased from the Shanghai Institute of Parasitic Diseases.

[0050] A mouse model infected with Schistosoma japonicum was constructed by the skin-patch method. The main process was to first shave the belly of the mouse, then moisten the shaved area with water, attach Schistosoma japonicum cercariae (5 cercariae / mouse, 10 cercariae / mouse, 15 cercariae / mouse, 20 cercariae / mouse or 60 cercariae / mouse respectively) on a glass slide, stick it on the belly of the mouse, and conduct a Schistosoma japonicum infection experiment on the mouse for 15 minutes. After infection, fecal samples were collected at the 7-week and 8-week time points. Mice not infected with Schistosoma japonicum were used as the control group. There were 6 replicates in each group.

[0051] Samples at different time points (7-week and 8-week time points) were obtained, including the total DNA of the intestine and its contents of animals, or the extraction and purification of intestinal flora DNA, to obtain a solution containing the total DNA to be detected in the samples. Subsequently, high-throughput sequencing was performed to find the intestinal flora biomarkers of mice infected with Schistosoma japonicum.

[0052] (2) Population data were from NCBI (accession number PRJNA625383). Those infected with schistosomiasis were the infected group (SJ); those not infected were the control group (Control). By comparing the fecal intestinal flora of patients infected with Schistosoma japonicum and healthy humans not infected, the intestinal flora biomarkers of humans infected with Schistosoma japonicum were found.

[0053] 2. Total DNA extraction and purification

[0054] The Hipure Stool DNA Kit (purchased from Magen Company, Guangzhou) was used to extract and purify the flora DNA from the sampled samples by DNA polymerase amplification and agarose gel electrophoresis.

[0055] 3. High-throughput sequencing

[0056] Amplicon sequencing of the 16S rRNA gene was used.

[0057] The primers were:

[0058] 338F 5'-ACTCCTACGGGAGGCAGCA-3';

[0059] 806R 5'-GGACTACHVGGGTWTCTAAT-3'.

[0060] The amplification program was as follows: The prepared amplification reaction system was placed in an EP tube and denatured at 95°C for 5 min; 95°C for 30 s, 50°C for 30 s, 72°C for 40 s, for 25 cycles; and extended at 72°C for 5 min.

[0061] According to the PCR amplification primer sequence and Barcode sequence, the relevant data were obtained. In the downloaded data, the data related to the Barcode and primer sequences were split and truncated from the original data Raw data of each sample. FLASH (V1.2.7) was used to splice the reads of each sample to obtain the spliced sequence as the original Tags data (Raw tags), and high-quality Tags data (Clean tags) were obtained after filtration. Specifically, referring to the tags quality control process of Qiime (V1.7.0), the following operations were performed:

[0062] a. Tags truncation: The Raw tags are truncated at the first low-quality base site where the number of consecutive low-quality values (default quality threshold <= 19) reaches the set length value of 3 bases.

[0063] b. Tags length filtering: The tags dataset obtained after truncating the tags is further filtered to remove tags with a consecutive high-quality base length less than 75% of the tags length.

[0064] The tags obtained after the above processing are subjected to chimera sequence removal. The tags sequences are aligned with the database (Gold database) through the (UCHIME Algorithm) to detect chimera sequences, and finally the chimera sequences are removed to obtain the final effective data (Effective tags).

[0065] Use the Uparse software (Uparse v7.0.1001) to cluster all the Effective Tags of all samples. By default, the sequences are clustered into OTUs (Operational Taxonomic Units) with 97% identity, and at the same time, the sequence with the highest occurrence frequency is selected as the representative sequence of the OTUs. Perform species annotation on the representative sequences of the OTUs, and use the Mothur method to analyze species annotation with the SSUrRNA database of SILVA (set the threshold to 0.8 - 1) to obtain taxonomic information and count the community composition of each sample at each taxonomic level: kingdom, phylum, class, order, family, genus, and species. Use the MUSCLE (Version 3.8.31) software for rapid multiple sequence alignment to obtain the phylogenetic relationships of all the representative sequences of the OTUs. Finally, perform normalization processing on the data of each sample, taking the sample with the least amount of data as the standard for normalization processing. Subsequent alpha diversity analysis and beta diversity analysis are both based on the normalized data.

[0066] The Qiime software (Version 1.7.0) was used to calculate the Observed-species, Chao1, Shannon, Simpson, ACE, Goods coverage, and PD whole tree indices based on OTUs. The R software (Version 2.25.3) was used to plot the rarefaction curve, Rank abundance curve, and species accumulation curve, and the R software was used for the analysis of the differences in α-diversity indices between groups; for the analysis of the differences in α-diversity indices between groups, parametric tests and non-parametric tests were performed separately. If there are only two groups, T-test and wilcox test are selected. If there are more than two groups, Tukey test and wilcox test of the agricolae package are selected.

[0067] The Unifrac distance was calculated and the UPGMA sample clustering tree was constructed using the Qiime software (Version 1.7.0). The PCA, PCoA, and NMDS plots were drawn using the R software (Version 2.25.3). The ade4 package and ggplot2 package of the R software were used for PCA analysis, the WGCNA, stats, and ggplot2 packages of the R software were used for PCoA analysis, and the vegan package of the R software was used for NMDS analysis. The R software was used for the analysis of the differences in β-diversity indices between groups, and parametric tests and non-parametric tests were performed separately. If there are only two groups, T-test and wilcox test are selected. If there are more than two groups, Tukey test and wilcox test of the agricolae package are selected.

[0068] The LEfSe software was used for LEfSe analysis, and the default screening value of LDAScore was set to 4. For Metastats analysis, the R software was used to perform the permutation test between groups at each taxonomic level (Phylum, Class, Order, Family, Genus, Species) to obtain the p-value, and then the Benjamini and Hochberg False Discovery Rate method was used to correct the p-value to obtain the q-value. Spearman correlation, principal co-ordinates analysis (PCoA), Statistical analysis of metagenomic profiles (STAMP), Random forest, and Receiver Operating Characteristic (ROC) were analyzed using the R language. For the analysis of species with significant differences between groups, the R software was used to perform the T-test between groups and draw the graph.

[0069] Differences in the Intestinal Microbiota of Mice Infected with Schistosoma japonicum and Uninfected Mice

[0070] I. Experimental Methods

[0071] Using R language, the differences in the intestinal microbiota of the mice in Example 1 infected with Schistosoma japonicum and uninfected mice were analyzed by the between-group difference analysis of α-diversity index and the between-group difference analysis method of β-diversity index.

[0072] II. Experimental Results

[0073] α-diversity is used to analyze the microbial community diversity within a sample (Within-community), which can reflect the richness and diversity of the microbial community within the sample. α-diversity indices include richness, diversity, evenness, etc. When α-diversity is applied to the analysis of intestinal microbiota, it is used to measure the diversity of the microbiota within an individual, note that it is a single individual and does not involve comparisons between individuals.

[0074] The calculation of β-diversity is an index to characterize the similarity of microbial composition between individuals. The presence or absence and inconsistency of species between individuals usually affect the β-diversity index. Of course, the α-diversity index also affects the β-diversity index.

[0075] There were significant differences in α-diversity and β-diversity in the intestinal microbiota of mice infected and uninfected with Schistosoma japonicum. From Figure 1 and Figure 2 it can be seen that there were significant differences in the OUT values of the intestinal microbiota of mice infected and uninfected with Schistosoma japonicum (P < 0.05), indicating that there were significant differences in the diversity of the intestinal microbiota of mice infected and uninfected with Schistosoma japonicum. From Figure 3 and Figure 4 it can be seen that the intestinal microbiota structures of mice infected and uninfected with Schistosoma japonicum were significantly different (PERMANOVA < 0.05). Among them, PLS-DA refers to the partial least squares regression analysis method.

[0076] Example 3 Intestinal Microbiota Biomarkers (biomarker) of Mice Infected with Schistosoma japonicum and Uninfected Mice

[0077] I. Experimental Methods

[0078] Using R language, the changes in the intestinal microbiota of the mice in Example 1 infected with Schistosoma japonicum and uninfected mice at the genus level were analyzed by Linear discriminant analysis Effect Size (LEfSe), Statistical analysis of metagenomic profiles (STAMP) and Random forest.

[0079] II. Experimental Results

[0080] Figure 5 Specific species of intestinal flora of mice infected with Schistosoma japonicum and uninfected with Schistosoma japonicum obtained by LEfSe analysis; Figure 6 Specific species of intestinal flora of mice infected with Schistosoma japonicum and uninfected with Schistosoma japonicum obtained by STAMP analysis. The P value is on the right. P<0.05 indicates statistical significance; Figure 7 Specific species of intestinal flora of mice infected with Schistosoma japonicum and uninfected with Schistosoma japonicum obtained by random forest analysis. Among them, "mean decrease accuracy" represents the degree of reduction in the prediction accuracy of the random forest. The larger this value, the greater the importance of the variable; different colors of the circles represent different bacteria, and different positions of the circles represent the accuracy values. The above results show that at the genus level, intestinal flora such as Bacteroides and Blautia can be used as biomarkers for mice infected and uninfected with Schistosoma japonicum.

[0081] Example 4 There is a significant correlation between intestinal flora biomarkers of mice and liver injuries such as liver fibrosis and granuloma induced by Schistosoma japonicum infection

[0082] I. Experimental Methods

[0083] Using R language and Spearman correlation analysis method, analyze the interaction of intestinal flora of mice infected with Schistosoma japonicum in Example 1 and its correlation with serological detection indicators (including aspartate aminotransferase AST and alanine aminotransferase ALT), and then analyze that intestinal flora biomarkers Bacteroides, Blautia and Enterococcus are significantly correlated with the degree of liver injuries such as liver fibrosis and granuloma induced by Schistosoma japonicum infection.

[0084] II. Experimental Results

[0085] The results of Spearman correlation analysis are shown in Figure 8, the size of the circle represents the size of the correlation, and it indicates statistical significance; different colors represent whether the correlation is positive or negative. Through Spearman correlation analysis, the types of intestinal flora in mice that have significant correlations with granuloma area, liver fibrosis, hydroxyproline level, ALT, and AST were found. The above results indicate that granuloma area, liver fibrosis, hydroxyproline level, as well as alanine aminotransferase (ALT) and aspartate aminotransferase (AST) can be used as indicators to measure liver injury, and these indicators are significantly correlated with the intestinal flora of mice infected with Schistosoma japonicum respectively.

[0086] Example 5: There are differences in the human intestinal flora between those infected and not infected with Schistosoma japonicum

[0087] I. Experimental method

[0088] Using R language, by principal co-ordinates analysis (PCoA) and Analysis of Variance (ANOVA) analysis, the changes in the intestinal flora of humans infected and not infected with Schistosoma japonicum in Example 1 at the genus level were analyzed.

[0089] II. Experimental results

[0090] ANOVA analysis found that there are significant differences in the structure of the human intestinal flora between those infected and not infected with Schistosoma japonicum. Figure 9 It shows that there are differences in the structure of the intestinal flora between those infected and not infected with Schistosoma japonicum, and the results show that there is statistical significance in the structure of the intestinal flora between the two (ANOSIM: P<0.05). Figure 10 It shows that there are differences in the abundances of bacteria such as Bacteroides, Blautia, and Enterococcus in the human intestinal flora between those infected and not infected with Schistosoma japonicum, and different bar charts represent the relative abundances of bacteria.

[0091] Example 6: The biomarkers of the human intestinal flora between those infected and not infected with Schistosoma japonicum are similar to those of mice

[0092] I. Experimental method

[0093] Using R language, the changes in the gut microbiota of humans and mice infected and not infected with Schistosoma japonicum in Example 1 at the genus level were analyzed by Statistical analysis of metagenomic profiles (STAMP) and Random forest analysis methods.

[0094] II. Experimental Results

[0095] The gut microbiota biomarkers of humans and mice before and after infection with Schistosoma japonicum are similar, mainly including Bacteroides, Blautia, and Enterococcus. Figure 11 It shows that the differences in the gut microbiota of humans infected and not infected with Schistosoma japonicum at the phylum, class, order, family, and genus levels obtained by STAMP analysis demonstrate the main bacterial species, such as Blautia bacteria being a biomarker after schistosome infection. Figure 12 It shows the specific species of the gut microbiota of humans and mice infected and not infected with Schistosoma japonicum obtained by random forest analysis.

[0096] Efficacy of Biomarkers Bacteroides, Blautia, and Enterococcus in Example 7

[0097] I. Experimental Methods

[0098] 1. Efficacy of Bacteroides, Blautia, or Enterococcus Alone

[0099] Using R language, by Receiver Operating Characteristic (ROC) analysis method, the changes in the gut microbiota of humans and mice infected and not infected with Schistosoma japonicum in Example 1 at the genus level were analyzed to obtain the sensitivity and specificity of predicting Schistosoma japonicum-induced liver injury in humans and mice using Bacteroides, Blautia, or Enterococcus alone.

[0100] 2. Combined Efficacy of Bacteroides, Blautia, or Enterococcus

[0101] Group Bacteroides, Blautia or Enterococcus into: Bacteroides and Blautia group, Bacteroides and Enterococcus group, Blautia and Enterococcus group, Bacteroides, Blautia and Enterococcus group. Analyze the changes in the gut microbiota of humans and mice infected and uninfected with Schistosoma japonicum at the genus level by receiver operating characteristic (ROC) analysis in R language, and calculate the AUC value and statistical significance using methods with extreme value exclusion rates of 10%, 0% and 5% respectively.

[0102] II. Experimental methods

[0103] 1. Efficacy of Bacteroides, Blautia or Enterococcus alone

[0104] The results showed that the biomarkers Bacteroides, Blautia or Enterococcus had relatively high sensitivity and specificity in predicting Schistosoma japonicum-induced liver injury in humans and mice. The AUC values were: humans: 0.8385, 0.8182, 0.8438; mice: 0.9639, 0.8478, 0.5354. Except for the predictive efficacy of Enterococcus in mice, other values were statistically significant (P<0.05). See specifically Figure 13 , where the red line is for humans and the blue line is for mice. AUC (Area Under Curve) represents the area enclosed by the ROC (Receiver operating characteristic curve) curve and the coordinate axes, and the value closer to 1 indicates the higher authenticity of the detection method.

[0105] 2. Combined efficacy of Bacteroides, Blautia or Enterococcus (extreme value exclusion rate is 10% for all)

[0106] The results showed that the combinations between the biomarkers Bacteroides, Blautia, and Enterococcus: Bacteroides and Blautia, Bacteroides and Enterococcus, Blautia and Enterococcus, Bacteroides, Blautia, and Enterococcus, had relatively high sensitivity and specificity for Schistosoma japonicum-induced liver injury in humans and mice. The AUC values were as follows: for humans: 0.8308, 0.8385, 0.8701, 0.8308; for mice: 0.965, 0.9446, 0.8596, 0.9538. All were statistically significant (P<0.05), and the specific values are shown in Table 1.

[0107] Table 1 Efficiency of diagnosing Schistosoma japonicum-induced liver injury by combinations of gut microbiota using ROC curve analysis (extreme value exclusion rate was 10%)

[0108]

[0109] Note: a The extreme value exclusion rate was 10% for all. b P<0.05 indicates significant difference.

[0110] 3. Efficiency of combinations of Bacteroides, Blautia, or Enterococcus (extreme value exclusion rate was 0% for all)

[0111] The results showed that there were differences in the sensitivity and specificity of the combinations between the biomarkers Bacteroides, Blautia, and Enterococcus: Bacteroides and Blautia, Bacteroides and Enterococcus, Blautia and Enterococcus, Bacteroides, Blautia, and Enterococcus, for Schistosoma japonicum-induced liver injury in humans, and relatively high sensitivity and specificity for Schistosoma japonicum-induced liver injury in mice. The AUC values were as follows: for humans: 0.6727, 0.6788, 0.8182, 0.6667, and the combination of Blautia and Enterococcus was statistically significant (P<0.05); for mice: 0.939, 0.902, 0.719, 0.09107, all were statistically significant (P<0.05), and the specific data are shown in Table 2.

[0112] Table 2 Efficiency of diagnosing Schistosoma japonicum-induced liver injury by combinations of gut microbiota using ROC curve analysis (extreme value exclusion rate was 0%)

[0113]

[0114]

[0115] c The extreme value exclusion rates are all 0%, d P < 0.05 indicates a significant difference.

[0116] 4. The combined efficacy of Bacteroides, Blautia or Enterococcus (the extreme value exclusion rates are all 5%)

[0117] The results showed that the combinations between the biomarkers Bacteroides, Blautia and Enterococcus: Bacteroides and Blautia, Bacteroides and Enterococcus, Blautia and Enterococcus, Bacteroides, Blautia and Enterococcus, had relatively high sensitivity and specificity for liver injury induced by Schistosoma japonicum in humans and mice. The AUC values were: for humans: 0.7552, 0.8385, 0.8701, 0.7552; for mice: 0.965, 0.9371, 0.8287, 0.9457. All were statistically significant (P < 0.05), and the specific data are shown in Table 3.

[0118] Table 3 The efficacy of ROC curve analysis of the combination of gut microbiota in diagnosing liver injury induced by Schistosoma japonicum

[0119]

[0120] Note: e The extreme value exclusion rates are all 5%, f P < 0.05 indicates a significant difference.

[0121] Example 8 Differences in gut microbiota between mice infected with Bacillus subtilis and mice infected with Schistosoma japonicum

[0122] I. Experimental method

[0123] The method for infecting mice with Schistosoma japonicum is shown in Example 1.

[0124] Bacillus subtilis (CMCC(B)63501) was purchased from Solarbio Life Sciences Co., Ltd. Mice were infected with Bacillus subtilis on the same day as the mice infected with Schistosoma japonicum. It was expanded and cultured through LB medium (add 10 g of tryptone, 5 g of yeast extract, and 10 g of NaCl to 1 L of water), and the route was by oral gavage. The initial concentration was 3×10 9, each mouse was gavaged with 0.3 mL each time. After the first infection, gavage was performed every other day until 1 week before sacrificing the mice (at the 7th and 8th weeks after Schistosoma japonicum infection), and then the gavage of Bacillus subtilis was stopped. Mice infected with Schistosoma japonicum were the infected group (SJ); mice infected with Bacillus subtilis were the other control group (BS); non-infected mice were the negative control group (Control).

[0125] For the total DNA of the mouse intestine and its contents, or the intestinal flora DNA, at different time points (7-week and 8-week time points) obtained, extraction and purification were carried out to obtain a solution containing the total DNA to be detected in the sample. Each group had at least 6 replicates.

[0126] Using R language, Shannon index, PLS-DA analysis, Linear discriminant analysis Effect Size (LEfSe), and Random forest were used to analyze the differences in the intestinal flora of mice infected with Schistosoma japonicum, infected with Bacillus subtilis, and non-infected mice.

[0127] II. Experimental Results

[0128] There were differences in the α-diversity of the intestinal flora of mice infected with Schistosoma japonicum, infected with Bacillus subtilis, and non-infected mice. Among them, there were significant differences in the α-diversity of the intestinal flora of mice infected with Schistosoma japonicum and infected with Bacillus subtilis. From Figure 14 it can be seen that there were significant differences in the Shannon values of the intestinal flora of mice infected with Schistosoma japonicum, infected with Bacillus subtilis, and non-infected mice; and there were significant differences in the Shannon values of the intestinal flora of mice infected with Schistosoma japonicum and infected with Bacillus subtilis (P<0.05), indicating that there were significant differences in the diversity of the intestinal flora of mice infected with Schistosoma japonicum and infected with Bacillus subtilis. There were differences in the β-diversity of the intestinal flora of mice infected with Schistosoma japonicum, infected with Bacillus subtilis, and non-infected mice. From Figure 15 it can be seen that there were differences in the intestinal flora structures of mice infected with Schistosoma japonicum, infected with Bacillus subtilis, and non-infected mice, and they were separated from each other. Among them, PLS-DA refers to partial least squares regression analysis method.

[0129] Figure 16 The specific species of the intestinal flora of mice infected with Schistosoma japonicum, infected with Bacillus subtilis, and non-infected mice obtained by LEfSe analysis, with the default setting of the screening value of LDA Score ≥ 3; Figure 17The specific types of intestinal flora in mice infected with Schistosoma japonicum and Bacillus subtilis obtained by random forest analysis. "mean decrease accuracy" represents the degree of reduction in the prediction accuracy of the random forest. The larger this value, the greater the importance of the variable; different colors of the circles represent different bacteria, and different positions of the circles represent the accuracy values. Figure 18 The specific types of intestinal flora in mice infected with Schistosoma japonicum and Bacillus subtilis obtained by LEfSe analysis, with the default screening value of LDA Score ≥ 3. The above results indicate that at the genus level, intestinal flora such as Bacteroides and Blautia are biomarkers for mice infected with Schistosoma japonicum, infected with Bacillus subtilis, and the uninfected group.

[0130] Determination of the sensitivity and specificity of other biomarkers such as Lachnospiraceae_NK4A136_group, Desulfovibrio, Alistipes, and Lactobacillus in Comparative Example 1

[0131] I. Experimental method

[0132] Using R language, the receiver operating characteristic (ROC) analysis method was used to analyze the changes in intestinal flora at the genus level in humans and mice infected and uninfected with Schistosoma japonicum.

[0133] II. Experimental results

[0134] The results showed that the biomarkers Lachnospiraceae_NK4A136_group, Desulfovibrio, Alistipes, and Lactobacillus had relatively high sensitivity and specificity in predicting Schistosoma japonicum-induced liver injury in mice, with AUC values of 0.7560, 0.7756, 0.7102, and 0.6710 for mice, respectively. For humans, only Alistipes with a relatively high relative abundance was used as a biomarker, with an AUC value of 0.6303, which was not significant. Generally speaking, based on the comparison of AUC values and significance, although these intestinal microorganisms had certain sensitivity and specificity as biomarkers for predicting Schistosoma japonicum infection, their effects were not as good as those of Bacteroides, Blautia, and Enterococcus as biomarkers. The specific values are shown in Table 4. Because the relative abundances of some bacteria in the human population were relatively small, they were excluded from the analysis to reduce errors.

[0135] Table 4 Other biomarkers: Sensitivity and specificity of Lachnospiraceae_NK4A136_group, Desulfovibrio, Alistipes, and Lactobacillus

[0136]

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the protection scope of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description and ideas. It is not necessary and impossible to list all implementation manners here. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. Detection Blautia Use of a reagent for detecting a flora in the preparation of a kit for diagnosing liver injury caused by Schistosoma japonicum characterized in that The reagent is used for identifying Blautia the types of bacterial flora; The reagent is also used for detecting Blautia the abundance of the flora; The reagent is for detecting Blautia intestinal flora.

2. The application according to claim 1, characterized in that The reagent for diagnosing human schistosomiasis is a reagent for detecting the abundances of Blautia and Enterococcus in the intestinal flora.

3. The application according to claim 1, characterized in that The reagent for diagnosing murine schistosomiasis is a reagent for detecting the abundances of Blautia and Bacteroides in the intestinal flora.

4. The application according to claim 1, characterized in that the liver injury is liver granuloma and / or liver fibrosis.

Images