CN116926187A - Application of intestinal microbial marker - Google Patents

Application of intestinal microbial marker Download PDF

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CN116926187A
CN116926187A CN202311104326.4A CN202311104326A CN116926187A CN 116926187 A CN116926187 A CN 116926187A CN 202311104326 A CN202311104326 A CN 202311104326A CN 116926187 A CN116926187 A CN 116926187A
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bacteroides
lipid metabolism
intestinal
sample
biomarker
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李爱静
郑书发
徐望
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Jinan Provincial Laboratory Of Microecology And Biomedicine
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Jinan Provincial Laboratory Of Microecology And Biomedicine
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Abstract

The invention discloses application of a biomarker of intestinal flora. Use of biomarkers comprising intestinal flora for the preparation of kits and products for diagnosis or assisted diagnosis of lipid metabolism disorders, for the preparation of products for prediction or assisted prediction of the risk of lipid metabolism disorders, as primers or probes for detecting the relative abundance of intestinal microorganisms, for the screening of drugs for the treatment of adult lipid metabolism disorders. The kit is simple and convenient to prepare, easy to popularize and provides thought for researching, preventing and treating pathogenesis of lipid metabolism disorder based on intestinal microorganisms with higher specificity, universality and reliability as biomarkers, and the key problem of early diagnosis of lipid metabolism disorder can be effectively solved.

Description

Application of intestinal microbial marker
Technical Field
The invention relates to application of a microbial marker, relates to the technical field of biological medicines, and in particular relates to application of an intestinal microbial marker.
Background
Worldwide, the incidence of metabolic diseases such as obesity is increasing. In general, lipid metabolism disorder generally refers to the occurrence of abnormality in various indexes such as cholesterol, triglyceride, low density lipoprotein and high density lipoprotein in blood or tissues, and it is clinically determined whether or not lipid metabolism disorder occurs based on the results and clinical manifestations of the tests performed mainly on blood and tissues. Lipid metabolism disorders are closely related to various factors such as high fat diet and obesity. Lipid metabolism disorders may be associated with a variety of complications, such as cardiovascular and cerebrovascular injury, including atherosclerosis and stenosis, myocardial infarction; insulin resistance may be caused, diabetes may be induced, and the like.
In recent years, it has been found that the intestinal flora plays an important role in maintaining normal physiological functions of the host. Disruption of the balance between host and gut microbiota interactions may have a close relationship with several metabolic disorders such as obesity, diabetes, non-alcoholic steatohepatitis, hypertension, etc. The structure and abundance of intestinal flora in obese people are different from those in normal weight. Individuals with low microbiota gene content exhibit more obesity, insulin resistance and dyslipidemia than those with high bacterial abundance. In humans, fecal microbiota transplantation showed some protective effect on patients with metabolic syndrome characteristics, demonstrating involvement of intestinal microbiota in obesity pathogenesis and its potential therapeutic effects.
Traditionally, diseases are mainly diagnosed by monitoring body weight or detecting blood fat, liver functions and the like, the risk of lipid metabolism disorder can not be predicted in early stage, and a noninvasive biomarker diagnosis method is lacked, so that a noninvasive biomarker diagnosis method which is easy to detect is found, and the existing clinical indexes are combined to predict and diagnose lipid metabolism disorder diseases, so that the method has great significance for early prevention and health risk assessment.
Disclosure of Invention
In order to solve the problems in the background art, the invention provides an application of an intestinal microbial marker for detecting lipid metabolism disorder.
The technical scheme adopted by the invention is as follows:
1. use of a biomarker for intestinal flora:
the use of said biomarker of intestinal flora in the preparation of a kit for diagnosis or assisted diagnosis of a lipid metabolism disorder in an adult.
The kit is provided with a detection reagent for detecting the relative abundance of intestinal microorganisms, and the detection reagent comprises: one or more species of listeria (dialister_sp.), bacteroides monoides (bacteroides_uniforms), streptococcus parahaemolyticus (streptococcus_pararasanguis), paraplectrum (paraapreviella_clara), bacteroides johnsonii (bacteroides_sartorii) or escherichia coli (bacteroides_sartorii).
The use of said biomarkers of intestinal flora for the preparation of a product for diagnosis or assisted diagnosis of lipid metabolism disorders in adults.
The use of said biomarker of the intestinal flora for the preparation of a product for predicting or aiding in predicting the risk of a lipid metabolism disorder in an adult human.
The use of said biomarker of the intestinal flora as a primer or probe for detecting the relative abundance of intestinal microorganisms.
The use of the biomarker of the intestinal flora in screening medicaments for treating adult lipid metabolism disorder.
Each biomarker can be used as a marker for diagnosing lipid metabolism disorder, and whether the tested person suffers from lipid metabolism disorder or the risk of suffering from lipid metabolism disorder or distinguishing lipid metabolism disorder from non-lipid metabolism disorder can be judged by detecting whether one or more of the biomarkers exist in the fecal sample of the tested person or detecting the relative abundance value of the biomarkers in the fecal sample of the tested person. Further, these biomarkers can also be used to screen for drugs that treat or monitor the therapeutic effect of a lipid metabolism disorder.
In practical applications, the biomarkers can be used as markers or targets for screening drugs for treating lipid metabolism disorders, for example, whether the candidate drugs can be used as drugs for treating or assisting in treating lipid metabolism disorders can be determined by detecting whether the relative abundance value of one or more of the biomarkers changes before and after the candidate drugs are contacted.
The biological markers of the intestinal flora are specifically listeria aniformis (Dialister sp.), bacteroides monoides (Bacteroides uniforms), streptococcus parahaemolyticus (Streptococcus paraprasugrel), parabacteroides parapleiones (paracresolvula clara), parabacteroides johnsonii (Bacteroides johnsonii) or escherichia coli (Bacteroides sartorii).
2. A method of diagnosing a disorder of lipid metabolism in an adult comprising:
obtaining a sample, detecting whether the relative abundance of intestinal microorganisms in the sample is significantly different from the relative abundance of intestinal microorganisms in a healthy state, and if so, indicating that the intestinal microorganisms belong to adult lipid metabolism disorder.
The intestinal microorganisms are specifically one or more of listeria species (dialiter_sp.), bacteroides monokinensis (bacteroides_uniforms), streptococcus parahaemolyticus (streptococcus_pararasanguis), parabacter paraplectania (paraprasuvorella_clara), bacteroides johnsonii (bacteroides_johnsonii) or escherichia coli (bacteroides_sartorii).
3. A diagnostic system for lipid metabolism disorder in adults:
the system comprises a sample collection device for collecting a fecal sample and separating extracted nucleic acids.
The system comprises a sequencing module for performing gene sequencing on the nucleic acid separated and extracted by the sample collection device by using a high-throughput sequencing technology and obtaining a sample sequencing result.
The system comprises a data calculation module for detecting the relative abundance of intestinal microorganisms of the sample according to the sample sequencing result obtained by the sequencing module.
The system includes a data storage module for storing the relative abundance values of intestinal microorganisms of the sample detected by the data calculation module.
The system comprises a data comparison module for comparing the relative abundance value of the intestinal microorganisms of the sample stored by the data storage module with the relative abundance value of the intestinal microorganisms in a healthy state, and obtaining the probability of diagnosing the lipid metabolism disorder of the adult through the fecal sample by the random forest model.
In the above applications or systems, the reagents for detecting the relative abundance values of the respective biomarkers in the fecal sample may comprise reagents for extracting nucleic acids in the fecal sample, reagents and/or instruments for sequencing nucleic acids in the fecal sample. Further, the reagent or instrument for extracting nucleic acid from the stool sample of the subject may be a reagent or instrument required for a nucleic acid extraction method commonly used in the prior art, or may be a commercially available nucleic acid extraction kit.
The reagents or instruments used to sequence nucleic acids in a fecal sample of a subject may be those required for nucleic acid sequencing methods commonly used in the art, including first generation sequencing methods and second generation sequencing methods.
The beneficial effects of the invention are as follows:
the invention provides a group of intestinal microbial markers and application thereof in diagnosis of lipid metabolism disorder, the high-throughput sequencing technology is used for carrying out gene sequencing on fecal samples of lipid metabolism disorder patients and normal group population, the difference of relative abundance values of the intestinal microbial markers of the lipid metabolism disorder patients and the normal group population is discussed, the key problem of early diagnosis of lipid metabolism disorder can be effectively solved based on intestinal microbes with higher specificity, universality and reliability as the biological markers, and the prepared kit is simple and convenient, is easy to popularize and popularize, and provides thought for research, prevention and treatment of pathogenesis of lipid metabolism disorder.
Drawings
FIG. 1 is a graph of analysis of the relative abundance of intestinal microorganisms and their correlation with BMI for the case group and the control group;
FIG. 2 is a schematic representation of the β diversity of the flora in the case group and the healthy control group;
FIG. 3 is a schematic diagram of intestinal microorganisms at the top ten relative abundance ranks at the genus level;
FIG. 4 is a graph of the difference in relative abundance of Listeria (dialister_sp.) between the two groups;
FIG. 5 is a graph showing the difference in relative abundance of Bacteroides simplex (Bacteroides_uniformis) between two groups;
FIG. 6 is a graph showing the difference in relative abundance of Streptococcus parahaemolyticus (Streptococcus_pamanaginis) between two groups;
FIG. 7 is a graph showing the difference in relative abundance of Parapreviella (Parapreviella_clara) between two groups;
FIG. 8 is a graph showing the difference in relative abundance of Paramycolatopsis johnsonii (Parabacteroides_johnsonii) between the two groups;
FIG. 9 is a graph showing the difference in relative abundance of Escherichia coli (Bactoides_sartorii) between two groups;
FIG. 10 is a graph of AUC and ROC curve results for a training set of random forest models;
fig. 11 is a graph of AUC and ROC curve results for a random forest model validation set.
Detailed Description
The invention will be described in further detail with reference to the accompanying drawings and specific examples.
Specific embodiments of the invention are as follows:
1. screening of differential markers:
subject information and sample collection:
1. subject information:
to verify the accuracy and specificity of using listeria species (dialister_sp.), bacteroides monoformis (bacteroides_uniforms), streptococcus parahaemolyticus (streptococcus_pararasanguis), parabacter paraplectania (paraparapreviella_clara), parabacter johnsonii, escherichia coli (bacteroides_sartorii) as a biomarker for lipid metabolism disorders, the present invention starts with the intestinal flora and verifies the use of intestinal microorganisms as a biomarker for lipid metabolism disorders. 67 overweight and obese subjects and 67 healthy volunteers were each signed with informed consent and approved by the local ethics committee. Subject qualification criteria: 1) All meet the diagnosis standard of lipid metabolism disorder; 2) No drug treatment, such as antibiotics or other weight loss products, has been taken recently. Healthy volunteers: age >18 years, no other disease. Fecal samples were collected from subjects and subject information is shown in table 1.
Table 1 sample clinical features
2. Sample collection:
taking 5-10g of fecal specimen from each subject in a prescribed time with sterile gloves before sampling, and sub-packaging in 3 1.5mL sterile centrifuge tubes. After the necessary information is marked, quick freezing is carried out by liquid nitrogen, and the obtained product is quickly transferred to a refrigerator at the temperature of minus 80 ℃ for preservation and waiting for concentrated detection.
3. Sample detection:
specific methods of manipulation, such as DNA extraction, amplification, sequencing, etc. of fecal samples are all well known in the art. After the sample is extracted, the integrity of the DNA of the sample is checked, and the unqualified sample is repeatedly detected or removed. The qualified sample judgment standard is that the target band of the DNA product is correct in size and is in the target area, the concentration and the total amount meet the library building requirement for 1 time or more than 1 time, and the DNA product can be used for subsequent library construction and sequencing.
Construction of a sequencing library:
1) The fecal sample adopts a genome DNA extraction kit to extract genome DNA, after extraction, the purity and concentration of the DNA are detected by 1% agarose gel electrophoresis, a proper amount of sample DNA is taken out in a centrifuge tube, and the sample DNA is diluted to 1 ng/. Mu.L by using sterile water.
2) PCR product acquisition, amplification region is 16SV3-V4,the primers used were 341F and 806R, and 15. Mu.L of the mixture of all PCRs was addedThe PCR products were obtained from High-Fidelity PCR Master Mix (New England Biolabs), 0.2. Mu.M primer and 10ng of genomic DNA template.
3) The PCR products are detected by electrophoresis through 2% agarose gel, the detected PCR products are purified and quantified, the equal amount of mixing is carried out according to the concentration of the PCR products, and the PCR products are detected by electrophoresis through 2% agarose gel after fully mixing.
4) Use of NEBUltra TM II FSDNA PCR-free Library Prep Kit library construction kit is used for library construction, the constructed library is quantified by a fluorescence quantitative instrument Qubit and PCR, and after the library is qualified, a high-throughput sequencing platform NovaSeq6000 is used for on-machine sequencing of PE250 (paired-end 250).
Bioinformation analysis:
1. and (3) data quality control:
according to the Barcode sequence (a base sequence used for distinguishing different samples) and the PCR amplification primer sequence, splitting each sample data from the next machine data, cutting the Barcode and the primer sequence, and then splicing the sequencing data reads of each sample, wherein the obtained spliced sequence is the original tag data. And then, carrying out strict filtering treatment on the spliced original tag data to obtain high-quality tag data, wherein the tag data obtained after the processing needs to be subjected to processing of removing the chimeric sequences, detecting the chimeric sequences by comparing the tag sequences with a species annotation database (Silva database), and finally removing the chimeric sequences in the tag sequences to obtain final effective data.
2. Amplicon sequence variant ASVs noise reduction and species annotation:
and (3) carrying out noise reduction on the obtained effective data by using a DADA2 module or deblur method in the amplicon analysis tool, so as to obtain the final amplicon sequence variation ASVs and the characteristic table. Species annotation was performed using amplicon analysis tool methods, and rapid multiple sequence alignment, yielding phylogenetic relationships for all ASVs sequences. And finally, carrying out homogenization treatment on the data of each sample, and carrying out homogenization treatment by taking the minimum data quantity in the sample as a standard.
3. Species abundance statistics:
and according to ASVs annotation results and a sample characteristic table, obtaining a species abundance table at the level of kingdom, phylum, class, order, family, genus and species, and accumulating the gene abundance annotated by the same species to obtain the relative abundance of the species in the sample.
4. And calculating beta diversity of the species by using the abundance information of the sample sequence, and judging whether the groups have obvious microbial community differences by adopting a Bray-Curti distance algorithm in non-metric multidimensional analysis NMDS. As shown in fig. 2, the beta diversity for the obese case group and the healthy control group was demonstrated. The graph shows that the intestinal flora composition of the patients with lipid metabolism disorder is significantly different from that of the healthy control group.
Screening potential biomarkers using relative abundance:
the composition of the intestinal flora of both groups of samples is mainly at the phylum of the Firmicutes, bacteroides (bacterioidota), proteus (proteus), verrucomicrobios (verrucomicrobios), actinomycetes (actionobacteria), fusobacterium (fusobacterium), desulphurized (desulphurized) phylum. Among them, bacteroides (bacteroides) and desulphus (desulphus) have significant differences in abundance between the two groups.
As shown in fig. 3, the top ten intestinal microorganisms are shown at the genus level, including Bacteroides (bacterides), escherichia coli-Shigella (Escherichia-Shigella), prevotella (Bifidobacterium), bifidobacterium (bifidum), small bacillus (Dialister), megamonas (Megamonas), ackermannia (Akkermansia), clostridium praecox (faechanium), clostridium (Fusobacterium), klebsiella (Klebsiella), with significant differences in the relative abundance of small bacillus (Dialister) and clostridium praecox (faechanium) between the two groups. At the species level, the top ten species level microorganisms are, depending on relative abundance, bacteroides dorsalis (Bacteroides dorei), bacteroides vulgare (Bacteroides vulgatus), prevotella (copril), listeria monocytogenes (dialist sp.), parabacteroides dirachta (Parabacteroides distasonis), listeria species (dialist_inventus), bifidobacterium breve (bifidobacteria breve), megamonas simplex (Megamonas Funiformis), and Bacteroides vulgaris (Bacteroides pleuroius), respectively, wherein there is a significant difference in the relative abundance of listeria monocytogenes (dialist sp.) and Bifidobacterium breve (bifidobacteria). This demonstrates that healthy and lipid metabolism disorder groups can be distinguished by exploiting the relative abundance of gut microorganisms.
2. Specific microorganisms differentiate healthy groups from groups of lipid metabolism disorders:
to further accurately distinguish healthy from patients with lipid metabolism disorders, candidate biomarkers are evaluated using non-parametric tests and random forest models, in combination with clinical features of the subject.
By correlation analysis in combination with the subject's body mass index BMI, the following species were found to have a significant negative correlation with Listeria animalis (dialiter_sp.), bacteroides simplex (Bacillus parapraanaginis), streptococcus parapratensis (Parapreverella clara), paramycolatopsis johnsonii (Parabacterial_johnsonii), escherichia coli (Bactoides sartorii) BMI, and FIG. 1 is a graph showing correlation analysis of potential microbial markers with subject BMI. Differences in the relative abundance of potential microbial markers in two groups of subjects were analyzed using a rank sum test. Fig. 4 to 9 show the relative abundance values of listeria species (dialiter_sp.), bacteroides simplex (bacteroides_uniforms), streptococcus parahaemolyticus (streptococcus_pararasanguis), parabacter paraplectania (paraprasuvorella_clara), bacteroides johnsonii (bacteroides_johnsonii), escherichia coli (bacteroides_sartorii) between the two groups, respectively.
As shown in fig. 10, the efficacy of the identification of the microbial markers of the random forest model on the training set samples was auc=94.3% and the 95% confidence interval ci= 91.27-97.33% by the ROC curve and the area under the ROC curve, which are the working characteristics of the subjects for the identification of patients with lipid metabolism disorder and healthy controls of the training set. The intestinal flora biomarker combination obtained by the model can be used as a potential biomarker for distinguishing a case group from a healthy group.
The model was validated using a validation set, as shown in fig. 11, which is an AUC and ROC curve for the discrimination of the lipid metabolism disorder patients and healthy controls in the validation set by the microbial markers of the random forest model, the discrimination efficiency for the validation set samples was auc=92.73%, and the 95% confidence interval ci=85.33-100%. The intestinal flora biomarker combination obtained by the model can be used as a potential biomarker for distinguishing a case group from a healthy group.
Accordingly, the present invention discloses a kit for assisting in diagnosing lipid metabolism disorders based on the abundance of listeria (dialister_sp.), bacteroides monokinensis (bacteroides_uniforms), streptococcus parahaemolyticus (streptococcus_paragonis), parabacter paraplectania (paraapreverella_clara), bacteroides johnsonii (bacteroides_johnsonii), escherichia coli (bacteroides_sartorii). The kit components are as follows, primers for Listeria (Dialister_sp.), bacteroides simplex (Bacteroides_uniforms), streptococcus parahaemolyticus (Streptococcus pararasanguins), parapreviella paraplectania (Parapreviella clara), paramycolatopsis johnsonii (Parabacterium johnsonii), escherichia coli (Bacillus sartorii), and some conventional reagents such as buffers, nucleic acid extraction reagents, reactives, and the like.
Based on the correlation of listeria (Dialister sp.), bacteroides monoformis (Bacteroides sp.), streptococcus parahaemolyticus (Streptococcus pararasanguis), parabacteroides (paracresolvula clara), parabacteroides johnsonii, escherichia coli (Bacteroides sartorii) with obesity, diagnosis of metabolic disorders of obesity or evaluation of the efficacy of an adjuvant therapy can be carried out by detecting the correlation of listeria (Dialister sp.), bacteroides monoformis (Bacteroides monoformis), streptococcus parahaemolyticus (paracocci pararasguiis), parabacteroides (paracresolvula clara), parabacteroides johnsonii (Bacteroides johnsonii), escherichia coli (Bacteroides sartorii) in a fecal sample or evaluating the abundance of a lipid metabolism disorder.
According to the invention, through sequencing the gene of the 16SrDNA amplicon for the patients with lipid metabolism disorder, the relative abundance value of the sequencing intestinal flora is obtained, the intestinal flora with abundance difference from normal people is found, the difference flora is further screened and analyzed, the differential flora is found to have higher sensitivity and specificity for diseases and diagnosis, and the differential flora can be used as early detection and auxiliary diagnosis of lipid metabolism disorder.
The gene sequence related to the invention is as follows:
SEQ ID No.1: nucleotide sequence of 341F primer
The source is as follows: artificial sequence (Artificial Sequence)
CCTAYGGGRBGCASCAG
SEQ ID No.2: nucleotide sequence of 806R primer
The source is as follows: artificial sequence (Artificial Sequence)
GGACTACNNGGGTATCTAAT

Claims (10)

1. Use of a biomarker for the intestinal flora, characterized in that: the use of said biomarker of intestinal flora in the preparation of a kit for diagnosis or assisted diagnosis of a lipid metabolism disorder in an adult.
2. Use of a biomarker for the intestinal flora according to claim 1, characterized in that: the kit is provided with a detection reagent for detecting the relative abundance of intestinal microorganisms, and the detection reagent comprises: one or more species of listeria (dialister_sp.), bacteroides monoides (bacteroides_uniforms), streptococcus parahaemolyticus (streptococcus_pararasanguis), paraplectrum (paraapreviella_clara), bacteroides johnsonii (bacteroides_sartorii) or escherichia coli (bacteroides_sartorii).
3. Use of a biomarker for the intestinal flora, characterized in that: the use of said biomarkers of intestinal flora for the preparation of a product for diagnosis or assisted diagnosis of lipid metabolism disorders in adults.
4. Use of a biomarker for the intestinal flora, characterized in that: the use of said biomarker of the intestinal flora for the preparation of a product for predicting or aiding in predicting the risk of a lipid metabolism disorder in an adult human.
5. Use of a biomarker for the intestinal flora, characterized in that: the use of said biomarker of the intestinal flora as a primer or probe for detecting the relative abundance of intestinal microorganisms.
6. Use of a biomarker for the intestinal flora, characterized in that: the use of the biomarker of the intestinal flora in screening medicaments for treating adult lipid metabolism disorder.
7. Use of a biomarker for the intestinal flora according to any of claims 1 to 6, characterized in that: the biological markers of the intestinal flora are specifically listeria aniformis (Dialister sp.), bacteroides monoides (Bacteroides uniforms), streptococcus parahaemolyticus (Streptococcus paraprasugrel), parabacteroides parapleiones (paracresolvula clara), parabacteroides johnsonii (Bacteroides johnsonii) or escherichia coli (Bacteroides sartorii).
8. A method of diagnosing a disorder of lipid metabolism in an adult comprising: obtaining a sample, detecting whether the relative abundance of intestinal microorganisms in the sample is significantly different from the relative abundance of intestinal microorganisms in a healthy state, and if so, indicating that the intestinal microorganisms belong to adult lipid metabolism disorder.
9. The method of diagnosing an adult lipid metabolism disorder of claim 8, wherein: the intestinal microorganisms are specifically one or more of listeria species (dialiter_sp.), bacteroides monokinensis (bacteroides_uniforms), streptococcus parahaemolyticus (streptococcus_pararasanguis), parabacter paraplectania (paraprasuvorella_clara), bacteroides johnsonii (bacteroides_johnsonii) or escherichia coli (bacteroides_sartorii).
10. A diagnostic system for lipid metabolism disorders in adults, characterized by: comprises a sample collection device for collecting a fecal sample and separating and extracting nucleic acid;
the sequencing module is used for carrying out gene sequencing on the nucleic acid separated and extracted by the sample collecting device by using a high-throughput sequencing technology and obtaining a sample sequencing result;
the data calculation module is used for detecting the relative abundance of intestinal microorganisms of the sample according to the sample sequencing result obtained by the sequencing module;
a data storage module including a data storage module for storing the relative abundance value of the intestinal microorganisms of the sample detected by the data calculation module;
the data comparison module is used for comparing the relative abundance value of the intestinal microorganisms of the sample stored by the data storage module with the relative abundance value of the intestinal microorganisms in a healthy state and obtaining the probability of diagnosing the adult lipid metabolism disorder of the fecal sample through a random forest model.
CN202311104326.4A 2023-08-30 2023-08-30 Application of intestinal microbial marker Pending CN116926187A (en)

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