Application of reagent for detecting abundance of intestinal flora markers in preparation of non-human cryptosporidium primate infection in-vitro diagnostic kit

Abstract

The invention discloses application of a reagent for detecting the abundance of intestinal flora markers in preparation of an in-vitro diagnostic kit for non-human primate cryptosporidium infection, which comprises the following steps: performing metagenome sequencing on bacterial components in faeces of a healthy non-human primate individual and a cryptosporidium infected person to obtain the abundance of bacteria; 23 specific species of florae are found through screening operation to serve as microbial markers, a cryptosporidium infection risk prediction model is constructed, model optimization and parameter adjustment are performed, and the accuracy and sensitivity of the model are improved. Experiments prove that the prediction model is high in prediction sensitivity and good in specificity, cryptosporidium infected patients and healthy individuals can be effectively distinguished in multiple samples, and the accuracy is 95% or above, so that the flora can be used as a cryptosporidium infection detection marker, and the detection accuracy is improved. A reagent for detecting the abundance of the intestinal flora marker can be used for preparing a non-human primate cryptosporidium infection in-vitro diagnostic kit, so that a foundation is laid for research on cryptosporidium infection diseases.

Description

Technical Field

[0001] The present invention belongs to the field of medical detection technology, and specifically relates to the application of intestinal flora markers in the preparation of in vitro diagnostic reagents for Cryptosporidium infection in non-human primates. Background Art

[0002] For individuals with normal immune function, Cryptosporidium infection has no clinical symptoms in the early stage of infection, and obvious symptoms usually appear after the oocysts mature, such as nausea, vomiting, abdominal pain, and weight loss. Currently, Cryptosporidium infection is usually diagnosed by pathogen detection, molecular detection, and immunological detection.

[0003] Among them, pathogen detection has low sensitivity; molecular detection has high sensitivity, but has disadvantages such as time-consuming, expensive reagents and instruments; immunological detection may cross-react with other parasitic infections, which may lead to a high false positive rate. Therefore, it is urgent to develop new methods that are sensitive, specific and have diagnostic value.

[0004] Molecular biological methods based on specific intestinal microbial markers have been proven to be sensitive, specific, and efficient detection methods in a variety of diseases. Therefore, it is of great significance to develop microbial markers with high specificity and good sensitivity for early diagnosis of Cryptosporidium infection. However, there are currently no reports on the use of microbial markers for early diagnosis of Cryptosporidium infection. Summary of the invention

[0005] The purpose of the present invention is to solve the technical problems of low detection sensitivity, false positives, high cost, etc. in the existing early diagnosis methods of Cryptosporidium infection, and to provide the use of a reagent for detecting the abundance of intestinal flora markers in the preparation of an in vitro diagnostic kit for Cryptosporidium infection in non-human primates, in order to provide a new direction for the development of Cryptosporidium infection diagnostic technology.

[0006] To achieve its purpose, the present invention adopts the following technical solution: The present invention provides a reagent for detecting the abundance of intestinal flora markers in the preparation of an in vitro diagnostic kit for non-human primate Cryptosporidium infection, wherein the intestinal flora markers include: s_Clostridiaceae_bacterium_OM08_6BH、 s_Clostridiaceae_unciassifed_SGB4769, s_Clostridium_fessum, s_Lacrimispora_amygdalina, s_GGB2983_SGB3965, s_GGB1244_SGB1664, s_Prevotella _copri_clade_A, s_Anaerobutyricum_hallii, s_Prevotella_SGB1680, s_GGB1243_SGB1663, s_GGB4780_SGB6615, s_Allisonella_histaminiformans, s_Ba cteroidaceae_bacterium, s_Butyricicoccus_sp_AM29_23AC, s_Blautia_wexlerae, s_GGB13181_SGB20397, s_Prevotella_copri_clade_C, s_Ruminococcu s_bicirculans, s_GGB3189_SGB4212, s_Blautia_obeum, s_Ruminococcaceae_bacterium_AM07_15, s_Prevotella_SGB1675, s_Clostridium_sp_AF20_17LB; As a further preferred embodiment of the technical solution of the present invention, the method for screening intestinal flora markers comprises the following steps: (1) Extracting microbial DNA from stool samples from healthy individuals and individuals infected with cryptosporidiosis; (2) Using the two microbial DNAs in step (1) as templates, amplify and construct libraries for metagenomic sequencing, and calculate the abundance values ​​of the intestinal flora at the species level; (3) constructing a random forest model using the abundance values ​​of the species described in step (2); (4) selecting the abundance value of a specific species that best predicts the number of microorganisms and inputting it into the random forest model in step (3) for training to obtain a trained random forest model; (5) Using the random forest model trained in step (4) for testing, the specific species with the best prediction of the number of microorganisms with an accuracy of not less than 85% are selected as microbial markers for Cryptosporidium infection.

[0007] Preferably, in step (2), the abundance value calculation method is: counting the number of sequencing reads corresponding to each species-level genome sequence obtained by sequencing to obtain the species abundance value, and using the BLAST method to search and match to the bacterial database for classification annotation.

[0008] Preferably, in step (4), the method for determining the optimal predicted number of microorganisms is a ten-fold cross validation method, that is, the average error rate of gradually increasing numbers of bacterial variables is calculated, and the number of species with the minimum error rate is the optimal predicted number of microorganisms.

[0009] Preferably, in step (4), the specific species abundance values ​​that best predict the number of microorganisms are selected by sorting the variable features from high to low importance scores, and selecting the abundance values ​​of 23 specific species with higher importance scores.

[0010] Preferably, in step (4), the training also includes inputting the optimal mtry parameter.

[0011] Preferably, the screening method for the optimal mtry parameter is to perform a grid search using a ten-fold cross validation method.

[0012] The above-mentioned cryptosporidiosis microbial markers are used to construct a prediction model for detecting cryptosporidiosis.

[0013] Compared with the prior art, the present invention has the following beneficial effects: The present invention obtains the abundance of bacteria by performing metagenomic sequencing on the bacterial components in the feces of healthy non-human primates and Cryptosporidium-infected individuals, finds 23 specific species of flora as microbial markers through screening operations, constructs a Cryptosporidium infection risk prediction model, and uses the data with class labels to optimize the model parameters to improve the accuracy and sensitivity of the model. Experiments have shown that the Cryptosporidium infection risk prediction model of the present invention has high prediction sensitivity and good specificity, and can effectively distinguish Cryptosporidium-infected patients and healthy individuals in multiple samples with an accuracy of more than 95%. Therefore, the flora can be used as a Cryptosporidium infection detection marker, and the reagent for detecting the abundance of the intestinal flora marker can be used to prepare an in vitro diagnostic kit for Cryptosporidium infection in non-human primates, thereby laying a foundation for the study of Cryptosporidium infection diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The results of ANCOM analysis of intestinal flora abundance; Figure 2 A species-level intestinal microbial diagnostic model for Cryptosporidium built using random forests; Figure 3 MDS diagrams of the control group and the infection group; Figure 4 AUC curves obtained for the training and test sets of the Random Forest model. DETAILED DESCRIPTION

[0015] The present invention is described in detail below with reference to the accompanying drawings.

[0016] From 2021 to 2022, the applicant collected a total of 348 fecal samples from crab-eating macaques in cages in Yuanjiang County, Yunnan Province. The samples were obtained by carefully extracting the middle layer of the feces using a disposable sterile spoon, taking about 5 to 10 grams of fresh feces, and placing it in a sterile sampling tube containing DNA preservation solution. For each sample, detailed information such as monkey identification number, sex, age, species, sampling location, and health status were recorded in detail. After collection, the samples were transported to the laboratory on dry ice and stored at -80°C until further analysis. Fecal DNA was extracted using the EZNA ® Fecal DNA Kit (D4015-00, OMEGA, USA). Cryptosporidium DNA was amplified from all fecal DNA samples using a nested PCR method according to the primer sequence and amplification scheme of SSU rRNA. In addition, to ensure that the differences in microbiome results were caused by Cryptosporidium, the applicant implemented a PCR amplification-based method to exclude infection with other common parasites. A total of five parasites (Trichomonas, Enterosporidae, Cyclospora, Giardia, and Nematodes) were screened using qPCR or PCR methods. Samples from both groups containing these listed pathogens were excluded for further analysis.

[0017] In addition, the applicant conducted a statistical analysis of the gender, age, and BMI of the Cryptosporidium infected and uninfected groups of cynomolgus monkeys in the same region. The results are shown in the following table.

[0018] The results showed that there were no significant differences in gender, age, and BMI between the Cryptosporidium-infected and uninfected groups of crab-eating macaques in the same region, indicating that the subsequent differences in the microbiota between the two groups would not be the result of the influence of these factors.

[0019] The metagenomic sequencing in the present invention is performed by using the high-throughput sequencing platform Illumina NovaSeq X high-throughput sequencing.

[0020] The present invention provides a reagent for detecting the abundance of intestinal flora markers in the preparation of an in vitro diagnostic kit for non-human primate Cryptosporidium infection, wherein the intestinal flora markers include: s_Clostridiaceae_bacterium_OM08_6BH、 s_Clostridiaceae_unciassifed_SGB4769, s_Clostridium_fessum, s_Lacrimispora_amygdalina, s_GGB2983_SGB3965, s_GGB1244_SGB1664, s_Prevotella _copri_clade_A, s_Anaerobutyricum_hallii, s_Prevotella_SGB1680, s_GGB1243_SGB1663, s_GGB4780_SGB6615, s_Allisonella_histaminiformans, s_Ba cteroidaceae_bacterium, s_Butyricicoccus_sp_AM29_23AC, s_Blautia_wexlerae, s_GGB13181_SGB20397, s_Prevotella_copri_clade_C, s_Ruminococcu s_bicirculans, s_GGB3189_SGB4212, s_Blautia_obeum, s_Ruminococcaceae_bacterium_AM07_15, s_Prevotella_SGB1675, s_Clostridium_sp_AF20_17LB.

[0021] The method for screening upper intestinal flora markers includes the following steps: (1) Extract microbial DNA from stool samples from healthy individuals and individuals infected with cryptosporidiosis.

[0022] (2) Using the two microbial DNAs in step (1) as templates, amplify and construct libraries for metagenomic sequencing. Count the number of sequencing reads corresponding to each species-level genome sequence obtained by sequencing to obtain the abundance value of the intestinal flora at the species level. Use the BLAST method to search and match the bacterial database for classification annotation.

[0023] Figure 1 The results of the analysis using ANCOM showed that at the species level, 18 bacteria showed significant differences between the infected and control groups, of which 16 increased in abundance after infection and 2 decreased in abundance after infection. Most of the bacteria with increased abundance at the annotation level were Clostridia.

[0024] (3) Construct a random forest model using the abundance values ​​of the species described in step (2).

[0025] In the present invention, it is preferred to use machine learning to select the model algorithm. The random forest model uses a classifier of multiple decision trees, and the category of its output is determined by the mode of the category output by the individual trees.

[0026] Figure 2 A species-level Cryptosporidium intestinal microbial diagnostic model was constructed using random forests, which included 23 bacteria whose relative abundance differed between the control group and the infected group, and also showed that the increase of Clostridium was indeed an important feature after Cryptosporidium infection.

[0027] (4) According to the ten-fold cross-validation method, the average error rate of gradually increasing the number of bacterial variables is calculated, and the number of species with the minimum error rate is 23, which is the best predicted number of microorganisms; according to the importance scores of the variable features, the abundance values ​​of 23 specific species of bacteria of a specific genus with a Mean Decrease Accuracy value greater than 4 are input into the random forest model in step (3), and then a grid search is performed using the ten-fold cross-validation method to obtain the optimal mtry parameter. In the present invention, the optimal mtry parameter is preferably 2, and then the optimal mtry parameter is input into the model for training to obtain a trained random forest model.

[0028] (5) Using the random forest model trained in step (4) for testing, the specific species with the best prediction of the number of microorganisms with an accuracy of not less than 85% are selected as microbial markers for Cryptosporidium infection.

[0029] Figure 3 The MDS diagram of the control group and the infected group shows that the random forest model is able to distinguish the control group from the infected group on the test set, which indicates that the random forest model has shown certain effectiveness in distinguishing the two groups of samples.

[0030] Figure 4 The AUC curves obtained for the training set and test set of the random forest model. The AUC curve analysis results show that the constructed model has an AUC of 1 in the training set (n = 20) and an AUC of 0.95 in the test set (n = 20), indicating that the model has good test accuracy, which indicates that the intestinal flora marker provided by the present invention provides an effective non-invasive method for monitoring Cryptosporidium infection. The reagent for detecting the abundance of the intestinal flora marker can be used to prepare an in vitro diagnostic kit for Cryptosporidium infection in non-human primates, thereby laying a foundation for the study of Cryptosporidium infection diseases.

Claims

1. Use of a reagent for detecting the abundance of intestinal flora markers in the preparation of an in vitro diagnostic kit for Cryptosporidium infection in non-human primates, characterized in that: The intestinal flora markers include: s_Clostridiaceae_bacterium_OM08_6BH、 s_Clostridiaceae_unciassifed_SGB4769, s_Clostridium_fessum, s_Lacrimispora_amygdalina, s_GGB2983_SGB3965, s_GGB1244_SGB1664, s_Prevotella _copri_clade_A, s_Anaerobutyricum_hallii, s_Prevotella_SGB1680, s_GGB1243_SGB1663, s_GGB4780_SGB6615, s_Allisonella_histaminiformans, s_Ba cteroidaceae_bacterium, s_Butyricicoccus_sp_AM29_23AC, s_Blautia_wexlerae, s_GGB13181_SGB20397, s_Prevotella_copri_clade_C, s_Ruminococcu s_bicirculans, s_GGB3189_SGB4212, s_Blautia_obeum, s_Ruminococcaceae_bacterium_AM07_15, s_Prevotella_SGB1675, s_Clostridium_sp_AF20_17LB.

2. Use of the reagent for detecting the abundance of intestinal flora markers according to claim 1 in the preparation of an in vitro diagnostic kit for non-human primate Cryptosporidium infection, characterized in that: The method for screening intestinal flora markers comprises the following steps: (1) Extracting microbial DNA from stool samples from healthy individuals and individuals infected with cryptosporidiosis; (2) Using the two microbial DNAs in step (1) as templates, amplify and construct libraries for metagenomic sequencing, and calculate the abundance values ​​of the intestinal flora at the species level; (3) constructing a random forest model using the abundance values ​​of the species described in step (2); (4) selecting the abundance value of a specific species that best predicts the number of microorganisms and inputting it into the random forest model in step (3) for training to obtain a trained random forest model; (5) Using the random forest model trained in step (4) for testing, the specific species with the best prediction of the number of microorganisms with an accuracy of not less than 85% are selected as microbial markers for Cryptosporidium infection.

3. Use of the reagent for detecting the abundance of intestinal flora markers according to claim 2 in the preparation of an in vitro diagnostic kit for non-human primate Cryptosporidium infection, characterized in that: In step (2), the abundance value calculation method is as follows: the number of sequencing reads corresponding to each species-level genome sequence obtained by sequencing is counted to obtain the species abundance value, and the BLAST method is used to search and match the bacterial database for classification annotation.

4. Use of the reagent for detecting the abundance of intestinal flora markers as claimed in claim 3 in the preparation of an in vitro diagnostic kit for Cryptosporidium infection in non-human primates, characterized in that: In step (4), the method for determining the optimal predicted number of microorganisms is a ten-fold cross validation method, that is, calculating the average error rate of gradually increasing numbers of bacterial variables, and the number of species with the minimum error rate is the optimal predicted number of microorganisms.

5. Use of the reagent for detecting the abundance of intestinal flora markers according to claim 4 in the preparation of an in vitro diagnostic kit for Cryptosporidium infection in non-human primates, characterized in that: In step (4), the specific species abundance values ​​that best predict the number of microorganisms are selected by sorting the variable features from high to low importance scores, and selecting the abundance values ​​of 23 specific species with higher importance scores.

6. Use of the reagent for detecting the abundance of intestinal flora markers according to claim 5 in the preparation of an in vitro diagnostic kit for non-human primate Cryptosporidium infection, characterized in that: In step (4), the training also includes the input of the optimal mtry parameter.

7. Use of the reagent for detecting the abundance of intestinal flora markers according to claim 6 in the preparation of an in vitro diagnostic kit for Cryptosporidium infection in non-human primates, characterized in that: The optimal mtry parameter is screened by grid search using a ten-fold cross-validation method.

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