CN110734989A - medicinal plant symbiotic microorganism identification method and application thereof - Google Patents
medicinal plant symbiotic microorganism identification method and application thereof Download PDFInfo
- Publication number
- CN110734989A CN110734989A CN201911075058.1A CN201911075058A CN110734989A CN 110734989 A CN110734989 A CN 110734989A CN 201911075058 A CN201911075058 A CN 201911075058A CN 110734989 A CN110734989 A CN 110734989A
- Authority
- CN
- China
- Prior art keywords
- analysis
- microorganisms
- identifying
- software
- sample
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 67
- 244000005700 microbiome Species 0.000 title claims description 122
- 238000012165 high-throughput sequencing Methods 0.000 claims abstract description 16
- 108020004465 16S ribosomal RNA Proteins 0.000 claims abstract description 9
- 108090000623 proteins and genes Proteins 0.000 claims abstract description 7
- 238000004458 analytical method Methods 0.000 claims description 88
- 239000002689 soil Substances 0.000 claims description 45
- 235000006200 Glycyrrhiza glabra Nutrition 0.000 claims description 44
- 241000196324 Embryophyta Species 0.000 claims description 37
- 238000003908 quality control method Methods 0.000 claims description 31
- 235000001453 Glycyrrhiza echinata Nutrition 0.000 claims description 19
- 235000017382 Glycyrrhiza lepidota Nutrition 0.000 claims description 19
- 239000000090 biomarker Substances 0.000 claims description 15
- 238000012163 sequencing technique Methods 0.000 claims description 15
- 241000894007 species Species 0.000 claims description 14
- 238000012545 processing Methods 0.000 claims description 13
- 229940010454 licorice Drugs 0.000 claims description 12
- 230000008569 process Effects 0.000 claims description 11
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 10
- 241000566145 Otus Species 0.000 claims description 10
- 150000001875 compounds Chemical class 0.000 claims description 7
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims description 6
- 235000011089 carbon dioxide Nutrition 0.000 claims description 6
- 230000007246 mechanism Effects 0.000 claims description 6
- 230000000007 visual effect Effects 0.000 claims description 6
- 239000007788 liquid Substances 0.000 claims description 5
- 229910052757 nitrogen Inorganic materials 0.000 claims description 5
- 241000202807 Glycyrrhiza Species 0.000 claims description 4
- 230000009471 action Effects 0.000 claims description 4
- 201000010099 disease Diseases 0.000 claims description 4
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 4
- 238000012216 screening Methods 0.000 claims description 4
- 230000002194 synthesizing effect Effects 0.000 claims description 4
- 238000011160 research Methods 0.000 abstract description 9
- 230000000813 microbial effect Effects 0.000 abstract description 5
- 238000003766 bioinformatics method Methods 0.000 abstract 1
- 244000303040 Glycyrrhiza glabra Species 0.000 description 42
- 235000005911 diet Nutrition 0.000 description 33
- 230000037213 diet Effects 0.000 description 32
- 239000003814 drug Substances 0.000 description 22
- LPLVUJXQOOQHMX-QWBHMCJMSA-N glycyrrhizinic acid Chemical compound O([C@@H]1[C@@H](O)[C@H](O)[C@H](O[C@@H]1O[C@@H]1C([C@H]2[C@]([C@@H]3[C@@]([C@@]4(CC[C@@]5(C)CC[C@@](C)(C[C@H]5C4=CC3=O)C(O)=O)C)(C)CC2)(C)CC1)(C)C)C(O)=O)[C@@H]1O[C@H](C(O)=O)[C@@H](O)[C@H](O)[C@H]1O LPLVUJXQOOQHMX-QWBHMCJMSA-N 0.000 description 21
- 235000011477 liquorice Nutrition 0.000 description 19
- 241000699670 Mus sp. Species 0.000 description 18
- 239000001947 glycyrrhiza glabra rhizome/root Substances 0.000 description 17
- 235000009200 high fat diet Nutrition 0.000 description 14
- 241000209056 Secale Species 0.000 description 13
- 235000017443 Hedysarum boreale Nutrition 0.000 description 12
- 235000007858 Hedysarum occidentale Nutrition 0.000 description 12
- 235000020940 control diet Nutrition 0.000 description 12
- 238000012258 culturing Methods 0.000 description 12
- 108010050181 aleurone Proteins 0.000 description 10
- KWIUHFFTVRNATP-UHFFFAOYSA-O N,N,N-trimethylglycinium Chemical compound C[N+](C)(C)CC(O)=O KWIUHFFTVRNATP-UHFFFAOYSA-O 0.000 description 9
- 238000012360 testing method Methods 0.000 description 8
- 240000004670 Glycyrrhiza echinata Species 0.000 description 7
- 235000004294 Ononis spinosa Nutrition 0.000 description 7
- 241000209140 Triticum Species 0.000 description 7
- 235000021307 Triticum Nutrition 0.000 description 7
- 230000000694 effects Effects 0.000 description 7
- 230000000968 intestinal effect Effects 0.000 description 7
- KWIUHFFTVRNATP-UHFFFAOYSA-N Betaine Natural products C[N+](C)(C)CC([O-])=O KWIUHFFTVRNATP-UHFFFAOYSA-N 0.000 description 6
- 229960003237 betaine Drugs 0.000 description 6
- 235000004213 low-fat Nutrition 0.000 description 6
- 239000000463 material Substances 0.000 description 5
- 235000015099 wheat brans Nutrition 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 210000003608 fece Anatomy 0.000 description 4
- 230000008014 freezing Effects 0.000 description 4
- 238000007710 freezing Methods 0.000 description 4
- 238000000513 principal component analysis Methods 0.000 description 4
- 239000004480 active ingredient Substances 0.000 description 3
- 230000001580 bacterial effect Effects 0.000 description 3
- 210000001072 colon Anatomy 0.000 description 3
- 238000007405 data analysis Methods 0.000 description 3
- 230000004060 metabolic process Effects 0.000 description 3
- KSDSYIXRWHRPMN-UHFFFAOYSA-N 4'-O-beta-D-Galactopyranoside-6''-p-Coumaroylprunin-4',5,7-Trihydroxyflavanone Natural products OC1C(O)C(O)C(CO)OC1OC1=CC=C(C2OC3=CC(O)=CC(O)=C3C(=O)C2)C=C1 KSDSYIXRWHRPMN-UHFFFAOYSA-N 0.000 description 2
- JJMDCOVWQOJGCB-UHFFFAOYSA-N 5-aminopentanoic acid Chemical compound [NH3+]CCCCC([O-])=O JJMDCOVWQOJGCB-UHFFFAOYSA-N 0.000 description 2
- 244000261411 Astragalus glycyphyllos Species 0.000 description 2
- 235000010110 Astragalus glycyphyllos Nutrition 0.000 description 2
- 241000894006 Bacteria Species 0.000 description 2
- 241000186000 Bifidobacterium Species 0.000 description 2
- 240000008917 Glycyrrhiza uralensis Species 0.000 description 2
- 235000000554 Glycyrrhiza uralensis Nutrition 0.000 description 2
- XTFQIRIHLGODFV-NSHDSACASA-N L-phenylalanine betaine Chemical compound C[N+](C)(C)[C@H](C([O-])=O)CC1=CC=CC=C1 XTFQIRIHLGODFV-NSHDSACASA-N 0.000 description 2
- 241000186660 Lactobacillus Species 0.000 description 2
- VTAJIXDZFCRWBR-UHFFFAOYSA-N Licoricesaponin B2 Natural products C1C(C2C(C3(CCC4(C)CCC(C)(CC4C3=CC2)C(O)=O)C)(C)CC2)(C)C2C(C)(C)CC1OC1OC(C(O)=O)C(O)C(O)C1OC1OC(C(O)=O)C(O)C(O)C1O VTAJIXDZFCRWBR-UHFFFAOYSA-N 0.000 description 2
- DEMKZLAVQYISIA-ONJCETCRSA-N Liquiritin Natural products O([C@H]1[C@H](O)[C@@H](O)[C@H](O)[C@@H](CO)O1)c1ccc([C@@H]2Oc3c(C(=O)C2)ccc(O)c3)cc1 DEMKZLAVQYISIA-ONJCETCRSA-N 0.000 description 2
- DEMKZLAVQYISIA-UHFFFAOYSA-N Liquirtin Natural products OC1C(O)C(O)C(CO)OC1OC1=CC=C(C2OC3=CC(O)=CC=C3C(=O)C2)C=C1 DEMKZLAVQYISIA-UHFFFAOYSA-N 0.000 description 2
- WWNNZCOKKKDOPX-UHFFFAOYSA-N N-methylnicotinate Chemical compound C[N+]1=CC=CC(C([O-])=O)=C1 WWNNZCOKKKDOPX-UHFFFAOYSA-N 0.000 description 2
- 235000007171 Ononis arvensis Nutrition 0.000 description 2
- 230000003213 activating effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 210000004534 cecum Anatomy 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 235000008242 dietary patterns Nutrition 0.000 description 2
- 230000000857 drug effect Effects 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 230000002550 fecal effect Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- BTCSSZJGUNDROE-UHFFFAOYSA-N gamma-aminobutyric acid Chemical compound NCCCC(O)=O BTCSSZJGUNDROE-UHFFFAOYSA-N 0.000 description 2
- LPLVUJXQOOQHMX-UHFFFAOYSA-N glycyrrhetinic acid glycoside Natural products C1CC(C2C(C3(CCC4(C)CCC(C)(CC4C3=CC2=O)C(O)=O)C)(C)CC2)(C)C2C(C)(C)C1OC1OC(C(O)=O)C(O)C(O)C1OC1OC(C(O)=O)C(O)C(O)C1O LPLVUJXQOOQHMX-UHFFFAOYSA-N 0.000 description 2
- 239000001685 glycyrrhizic acid Substances 0.000 description 2
- 229960004949 glycyrrhizic acid Drugs 0.000 description 2
- UYRUBYNTXSDKQT-UHFFFAOYSA-N glycyrrhizic acid Natural products CC1(C)C(CCC2(C)C1CCC3(C)C2C(=O)C=C4C5CC(C)(CCC5(C)CCC34C)C(=O)O)OC6OC(C(O)C(O)C6OC7OC(O)C(O)C(O)C7C(=O)O)C(=O)O UYRUBYNTXSDKQT-UHFFFAOYSA-N 0.000 description 2
- 235000019410 glycyrrhizin Nutrition 0.000 description 2
- AOHCBEAZXHZMOR-ZDUSSCGKSA-N hypaphorine Chemical compound C1=CC=C2C(C[C@H]([N+](C)(C)C)C([O-])=O)=CNC2=C1 AOHCBEAZXHZMOR-ZDUSSCGKSA-N 0.000 description 2
- LTINPJMVDKPJJI-UHFFFAOYSA-N iodinated glycerol Chemical compound CC(I)C1OCC(CO)O1 LTINPJMVDKPJJI-UHFFFAOYSA-N 0.000 description 2
- 229940039696 lactobacillus Drugs 0.000 description 2
- DEMKZLAVQYISIA-ZRWXNEIDSA-N liquiritin Chemical compound O[C@@H]1[C@@H](O)[C@H](O)[C@@H](CO)O[C@H]1OC1=CC=C([C@H]2OC3=CC(O)=CC=C3C(=O)C2)C=C1 DEMKZLAVQYISIA-ZRWXNEIDSA-N 0.000 description 2
- 238000001543 one-way ANOVA Methods 0.000 description 2
- 125000001997 phenyl group Chemical group [H]C1=C([H])C([H])=C(*)C([H])=C1[H] 0.000 description 2
- 238000002360 preparation method Methods 0.000 description 2
- 238000002791 soaking Methods 0.000 description 2
- GSZUGBAEBARHAW-UHFFFAOYSA-N sophoraflavone B Natural products OC1C(O)C(O)C(CO)OC1OC1=CC=C(C=2OC3=CC(O)=CC=C3C(=O)C=2)C=C1 GSZUGBAEBARHAW-UHFFFAOYSA-N 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- CJKONRHMUGBAQI-YFKPBYRVSA-N (2s)-2-(trimethylazaniumyl)propanoate Chemical compound [O-]C(=O)[C@H](C)[N+](C)(C)C CJKONRHMUGBAQI-YFKPBYRVSA-N 0.000 description 1
- 108091032973 (ribonucleotides)n+m Proteins 0.000 description 1
- 241000702460 Akkermansia Species 0.000 description 1
- 241000606125 Bacteroides Species 0.000 description 1
- 238000011746 C57BL/6J (JAX™ mouse strain) Methods 0.000 description 1
- 241000222290 Cladosporium Species 0.000 description 1
- 241000193403 Clostridium Species 0.000 description 1
- 241000222356 Coriolus Species 0.000 description 1
- 238000007400 DNA extraction Methods 0.000 description 1
- 241000588914 Enterobacter Species 0.000 description 1
- 235000018142 Hedysarum alpinum var americanum Nutrition 0.000 description 1
- 240000006461 Hedysarum alpinum var. americanum Species 0.000 description 1
- 238000001282 Kruskal–Wallis one-way analysis of variance Methods 0.000 description 1
- CMUNUTVVOOHQPW-LURJTMIESA-N L-proline betaine Chemical compound C[N+]1(C)CCC[C@H]1C([O-])=O CMUNUTVVOOHQPW-LURJTMIESA-N 0.000 description 1
- 241001112693 Lachnospiraceae Species 0.000 description 1
- 241000194036 Lactococcus Species 0.000 description 1
- 238000000585 Mann–Whitney U test Methods 0.000 description 1
- 241000699666 Mus <mouse, genus> Species 0.000 description 1
- 241000204031 Mycoplasma Species 0.000 description 1
- 241000785902 Odoribacter Species 0.000 description 1
- 241001112694 Peptococcaceae Species 0.000 description 1
- 241000605894 Porphyromonas Species 0.000 description 1
- 238000003559 RNA-seq method Methods 0.000 description 1
- 241000192031 Ruminococcus Species 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000003321 amplification Effects 0.000 description 1
- 238000012098 association analyses Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- -1 betaine compound Chemical class 0.000 description 1
- 238000007621 cluster analysis Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000378 dietary effect Effects 0.000 description 1
- 235000012041 food component Nutrition 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 210000000936 intestine Anatomy 0.000 description 1
- 235000015263 low fat diet Nutrition 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000002207 metabolite Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000013441 quality evaluation Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 108091092562 ribozyme Proteins 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- 229930000044 secondary metabolite Natural products 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000001225 therapeutic effect Effects 0.000 description 1
- 238000001757 thermogravimetry curve Methods 0.000 description 1
- REWYJJRDEOKNBQ-UHFFFAOYSA-N valine betaine Chemical compound CC(C)C(C([O-])=O)[N+](C)(C)C REWYJJRDEOKNBQ-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
- C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
- C12Q1/689—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for bacteria
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6869—Methods for sequencing
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
- C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
- C12Q1/6895—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for plants, fungi or algae
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B45/00—ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Engineering & Computer Science (AREA)
- Organic Chemistry (AREA)
- Health & Medical Sciences (AREA)
- Analytical Chemistry (AREA)
- Physics & Mathematics (AREA)
- Wood Science & Technology (AREA)
- Biotechnology (AREA)
- Zoology (AREA)
- General Health & Medical Sciences (AREA)
- Biophysics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Molecular Biology (AREA)
- General Engineering & Computer Science (AREA)
- Genetics & Genomics (AREA)
- Immunology (AREA)
- Microbiology (AREA)
- Biochemistry (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Medical Informatics (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Botany (AREA)
- Mycology (AREA)
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
Abstract
The invention belongs to the technical field of bioinformatics research of medicinal plants, and particularly relates to a bioinformatics analysis and identification method for microbial 16S rRNA genes in V3 and V4 regions based on high-throughput sequencing.
Description
Technical Field
The invention belongs to the technical field of bioinformatics research, and particularly relates to an identification method of medicinal plant symbiotic microorganisms based on high-throughput sequencing and application thereof.
Background
The high-throughput sequencing technology is also called a second generation sequencing technology, and commonly adopts sequencing platforms such as Roche/454FLX, Illumna/GenomeAnalyzer/Hiseq/Miseq, Applied Biosystems SOLID and the like, the method for researching the 16S rRNA of the microorganism based on the high-throughput sequencing technology is which is the most effective and important method for currently knowing and analyzing the structure and the function of a microorganism mixed system, along with the development of the theory of omics such as modern genomics, proteomics, metabonomics and the like, the introduction of the system biology visual angle and the application of bioinformatics, the related information such as the metabolism path, the metabolism target point and the inter-species difference can be obtained by analyzing the sequencing data of the related RNA and the 16S rRNA of the species, and the acquisition of the information brings great revolution to the research of the plants, and particularly brings new opportunities to the field of the medicine research in the plants.
The content of the main components of the traditional Chinese medicine has non-negligible influence on the health of a host with the medicine, and the influence of rhizosphere microorganisms of the medicine on the content of the main components of the medicine does not vary greatly. The species components of the microorganism can be relatively comprehensively detected by using a high-throughput sequencing method, the content of the microorganism on the components contained in the plant and the effect of the microorganism are identified, and the plant is utilized to the maximum extent.
However, the existing analysis methods have the disadvantages of slow analysis speed, poor accuracy, long whole analysis and identification period, etc., and the analysis methods have significance of statistical analysis, so that the results are not scientific and practical, etc. therefore, analysis methods based on theoretical researches of system biology, genomics, bioinformatics, etc. are needed to be researched, and the active ingredients of plants, especially medicinal plants, are utilized to the maximum extent.
Disclosure of Invention
In order to solve the problems, the invention provides an identification method of symbiotic microorganisms of medicinal plants, which can be used for rapidly identifying sample microorganisms in the application field of Chinese medicinal materials, so that the identification of the planting mode of the Chinese medicinal materials and the exploration of the specific action mechanism of the Chinese medicinal materials can be carried out, the possibility of utilizing the drug effect of the Chinese medicinal materials to the maximum extent is further provided, and the identification method has the characteristics of short identification period, high result accuracy and the like.
The invention is realized by adopting the following technical scheme:
method for identifying symbiotic microorganisms of medicinal plants, which comprises the following steps:
s1, collecting a sample and carrying out pretreatment operation;
s2, obtaining sequence double-chain sequencing data of V3 and V4 regions of the microorganism 16S rRNA gene in the sample by adopting a high-throughput sequencing method;
s3, screening qualified sequencing data by means of FastQC software, synthesizing a forward chain and a reverse chain of the qualified sequencing data to obtain a total chain file, and performing quality control processing on the total chain file by Mothur software to obtain a quality control file;
and S4, analyzing the quality control file by means of Qiime software to obtain a microorganism species abundance table, drawing a visual result analysis chart of the microorganisms in the sample by means of R software and LEfSe software, and identifying the microorganisms in the sample by comparing the analysis results.
Preferably, in step S1, the preprocessing operation includes the specific steps of: collecting samples to be detected, carrying out liquid nitrogen treatment on all samples according to the mutability and the distance of the RNA enzyme, and storing the samples in a dry ice box.
Preferably, in step S3, the quality control process includes operations of finding chimeras and removing chimeras.
Preferably, in step S3, before performing quality control on the global chain file, a script is required to remove a linker sequence added in the high-throughput sequencing, where the linker sequence is:
-a AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC
-A AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT。
preferably, in step S4, the Qiime software is used to analyze the quality control file using an analysis flow of "pick _ de _ novo _ atus. py" or "pick _ closed _ reference _ atus. py", and as a more preferred embodiment of step , the Qiime software is used to analyze the quality control file using an analysis flow of "pick _ closed _ reference _ atus. py"
Preferably, in step S4, the step of analyzing the quality control file by means of Qiime software to obtain the abundance table of species includes obtaining the OTU table of the microorganism sample by performing cluster analysis of OTU, obtaining the α -diversity data table and the β -diversity data table by performing analysis of microorganism species diversity, and obtaining the relative abundance table by performing analysis of relative abundance.
In a preferred scheme, the OTU table and the relative abundance table of the microorganism sample need to be matched with the name data of the biological taxonomy, so that R mapping can be conveniently used for analyzing the difference between rhizosphere and root soil microorganisms on the genus level, and LEfSe software can be conveniently used for analyzing a Biomarker.
Preferably, in step S4, the analysis chart of the visualized result includes a PCoA differential analysis chart, a relative abundance composition analysis chart and a classification Biomarker analysis chart.
Preferably, the step of comparing the analysis results includes comparing the abundance composition of the microorganism and the Biomarker in step S4.
The method for identifying the symbiotic microorganisms of the medicinal plants is applied to identifying the rhizosphere microorganisms of the liquorice and the soil microorganisms of the root system. The liquorice is a plant medicinal material and is a main component of a liquorice Chinese medicinal preparation, and the liquorice is recorded in pharmacopoeia of the people's republic of China; the rhizosphere microorganism of licorice can produce secondary metabolite, and has recorded regulation and promotion effect on the growth and development of host plants; the identification method of the medicinal plant symbiotic microorganism of the glycyrrhiza rhizosphere microorganism is not recorded in the current research literature.
The medicinal plant symbiotic microorganism identification method is applied to identification of the liquorice planting mode. The identification method of the symbiotic microorganisms of the medicinal plants in the liquorice planting mode is not recorded in the current research literature.
The identification method of the symbiotic microorganisms of the medicinal plants is applied to analyzing the action mechanism of the traditional Chinese medicine compound for treating diseases. The components of the traditional Chinese medicine compound medicine are recorded in the pharmacopoeia of the people's republic of China; the components and active ingredients of the traditional Chinese medicine compound have been recorded to have therapeutic effect on corresponding diseases; the change mechanism of the traditional Chinese medicine compound on the intestinal flora of the host and the mechanism for achieving the treatment effect by changing the intestinal flora are not recorded.
The Lefse software analysis, namely LDA Effect Size software analysis, is analysis tools for finding and explaining high-dimensional data biological markers (genes, channels, classification units and the like), can carry out comparison of two or more groups, emphasizes statistical significance and biological relevance, can search biological markers (biomerkers) with statistical difference between groups, and the biomerker analysis which can be carried out based on the Lefse software is the software which is required for identifying the rhizosphere microorganisms and the root soil microorganisms of the liquorice root system, and has the advantages that A, a nonparametric factor Krkal-Wallis rank and test which is firstly adopted in a plurality of groups of samples are used for detecting the species with remarkable abundance difference between different groups are used for obtaining a statistically reliable difference conclusion, B, Wilcon rank and test are used for checking whether all the comparison of the subspecies in the species with remarkable difference is similar to classification level, the accuracy of the difference conclusion is ensured, and C, Linear Discriminant Analysis (LDA) is used for reducing the dimension of the data and evaluating the influence of the species with remarkable difference, namely the Biomarker and the readability of the Biomarker is improved.
The invention takes plants, especially medicinal plants as research objects, and researches by using the theory and technology of bioinformatics based on the thinking of system biology, thereby achieving the purpose of utilizing the active ingredients of the plants to the maximum extent.
The invention has the beneficial effects that:
1. the identification method of the symbiotic microorganisms of the medicinal plants utilizes computer software to perform data processing and analysis, has the characteristics of high speed, high accuracy and the like, and is short in whole identification period, wherein in embodiments, 151 rhizosphere microorganisms and root system soil microorganisms of liquorice to be identified are obtained, the data size is 21.73GB, all data processing and subsequent analysis processes can be completed in 23 days, and compared with the identification time of relevant companies in the market, the method is short in time and high in analysis depth.
2. The method for identifying the symbiotic microorganisms of the medicinal plants can obtain analysis data with statistical significance by a high-throughput sequencing method, and has higher accuracy and practicability compared with the traditional method.
3. The method for identifying the symbiotic microorganisms of the medicinal plants, disclosed by the invention, explores the influence of rhizosphere microorganisms on the content of the liquiritin and the glycyrrhizic acid, so that the possibility is provided for exploring the influence of the rhizosphere microorganisms on the content of the liquiritin and the glycyrrhizic acid and further utilizing the drug effect of the liquorice to the maximum extent.
4. In the method for identifying symbiotic microorganisms of medicinal plants, computer software such as Fastq, Mothur, Qiime and the like is approved by researchers in the field, and journals such as Nature, Science, Cell and the like which are high-influence factors all acknowledge the accuracy and effectiveness of the computer software and have authority and universality.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for identifying symbiotic microorganisms of medicinal plants.
FIG. 2 is a partial schematic diagram of an example of a FastQC software positive strand quality report for a licorice sample.
FIG. 3 is a schematic flow chart of FastQC software and Mothur software in the identification method of symbiotic microorganisms of medicinal plants.
FIG. 4 is a schematic flow chart of Qiime software, R software and LefSe software in the identification process.
FIG. 5 is a graph of the relative abundance of microorganisms PcoA in licorice samples grown in different ways.
FIG. 6 is the analysis of the diversity of the relative abundance of microorganisms at the genus level in the licorice sample under different cultivation modes.
In fig. 5 and 6, CMTN is for culturing three-year-old licorice root microorganisms, CMTP is for culturing -year-old licorice root microorganisms, CSTN is for culturing three-year-old licorice root soil microorganisms, CSTP is for culturing -year-old licorice root soil microorganisms, WMWT is for wild licorice root microorganisms, WSWT is for wild licorice root soil microorganisms, CM is for culturing licorice root soil microorganisms, CS is for culturing licorice root soil microorganisms, and WS is for wild licorice root soil microorganisms.
FIG. 7 is a biomarker analysis, i.e., biomarker analysis, of Glycyrrhiza glabra rhizosphere microorganisms and root system soil microorganisms.
Fig. 8 is the betaine levels in the colon of whole-wheat diet mice, fig. 8a, HF represents a high-fat control diet, R1 represents native rye bran, R2 represents bioprocessed rye bran, a1 represents native wheat aleurone, a4 represents milled and bioprocessed wheat aleurone, asterisk, i.e., value of one-way anova, i.e., HF compared to all bran-rich diets, indicates p <0.05, in terms of the feeding composition of the mice; denotes p < 0.01; p < 0.001; in fig. 8b, HF represents a high-fat control diet, HFLF represents an alternating high-fat low-fat control diet, LF represents a Principal Component Analysis (PCA) of the microbial composition in the cecal contents of mice on a low-fat control diet, a1 and a4 represent diets rich in aleurone, and R1 and R2 represent diets rich in rye bran, according to the feeding composition of the mice.
Fig. 9 is an RDA analysis among betaine compounds, diet pattern, genus level microorganisms, triangular nominal environmental variables representing nominal environmental variables, round dot sanmple representing samples, thick arrowed bacterial phenyl representing directed bacterial names, thin arrowed meta phenyl directing metabolite names, arrowed bran-enriched diets set, arrowed girder diets representing diet bran enriched control group, arrowed girder diets representing control group of diet including HF, HFLF and LF, where HF represents high fat control diet group, HFLF represents alternating high fat low fat control diet group, and LF represents low fat control diet group.
FIG. 10 is a thermogram analysis of 48 microorganisms and their correlation with betaine compounds, dietary pattern, wherein the left graph shows the correlation between the relative abundance of the taxonomic units of microorganisms in the caecal contents of mice in the HF and bran diet group and betaine compounds in the caecal tissue, Pearson correlation, with p <0.05 with circles; the middle panel shows, for comparison of all diet groups, Kruskal-Wallis one-way anova, p <0.05 with a circle mark, and between the bran-enriched treatment diet and the control diet, fold change in relative abundance of microbial taxa between rye bran and aleurone-enriched diet, and between diets containing raw and bioprocessed rye bran or aleurone, Mann-Whitney U test, p <0.05 with a circle; the right panel shows the normalized mean bacterial abundance in each diet group.
Figure 11 is an analysis of ceca flora α diversity from C57BL/6J mice fed different diets, wherein the left panel shows α diversity as measured by Shannon's index and the right panel shows α diversity as measured by Shannon's index.
Detailed Description
The technical solutions in the embodiments will be described clearly and completely with reference to the drawings in the embodiments of the present invention, it is obvious that the described embodiments are only partial embodiments of the present invention, rather than all embodiments.
Example 1
Referring to fig. 1, an application of methods for identifying symbiotic microorganisms of medicinal plants in identifying rhizosphere microorganisms and root soil microorganisms of liquorice is realized by adopting a method for identifying symbiotic microorganisms of medicinal plants, and the method comprises the following steps:
s1, collecting rhizosphere samples of wild liquorice and cultivated liquorice and corresponding root system soil samples according to different planting modes, soaking all samples in a liquid nitrogen tank for freezing, and finally storing in a dry ice box, wherein the method specifically comprises the following steps:
the method comprises the steps of collecting rhizosphere samples of wild liquorice and cultivated liquorice in Shijicun of Gaoshaohuozhen province in Yanchi county, wherein the rhizosphere samples are respectively referred to as wild samples and cultivation samples, 25 wild samples and 25 cultivation samples are obtained, 25 cultivation year-old samples and 25 cultivation three year-old samples are obtained, corresponding root system soil samples are referred to as soil samples, and the number of the soil samples is 25.
During collection, the temperature and the pH value of the soil sample can be measured, and the physical and chemical characteristics of local soil can be conveniently analyzed.
Cutting rhizosphere samples into 5-10cm in length, filling the soil samples into a marked sealing bag, soaking all samples in a liquid nitrogen tank for five minutes, and finally storing the samples in a dry ice box;
in this embodiment, the number of the rhizosphere sample and the test data of the soil sample are shown in tables 1, 2 and 3, respectively, and each table includes number information of the rhizosphere sample and test data information of the soil sample;
TABLE 1 data information of wild licorice and corresponding root soil samples
TABLE 2 data information of year old licorice roots cultivated and corresponding root soil samples
TABLE 3 data information for three-year-old licorice root cultivation and corresponding root soil samples
S2, extracting and amplifying DNA of rhizosphere sample microorganisms of wild samples and cultivation samples and corresponding soil sample microorganisms respectively, and analyzing V3 and V4 regions of 16S rRNA genes under high-throughput sequencing based on an Illuminal Miseq 300 instrument, so as to obtain microorganism components of rhizosphere samples and root system soil samples in different planting modes; the specific operation is as follows:
taking 150 soil samples corresponding to wild samples and cultivation samples out of a dry ice box, respectively carrying out DNA extraction and amplification on microorganisms of the soil samples and carrying out analysis on V3 and V4 regions of 16S rRNA genes under high-throughput sequencing of an Illuminal Miseq 300 instrument, and further obtaining microorganism components of rhizosphere samples and root system soil samples under two planting modes, for example, ten rows of data before cultivation of a -year-old licorice soil sample CM18TN1 are as follows:
@M04670:191:000000000-BVBHP:1:1101:13958:1454 1:N:0:GTTAGCGC+AGTCACGA
TCCTACGGGAGGCAGCAGTGGGGAATATTGGACAATGGGCGCAAGCCTGATCCAGC AATGCCGCGTGAGTGATGAAGGCCTTAGGGTTGTAAAGCTCTTTCGCCCGCGACGATGA TGACGGTAGCGGGAGAAGGAGCCCCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACG GAGGGGGCTAGCGTTGTTCGGAATTACTGGGCGTAAAGGGCGCGCAGGCGGCTGCTCA AGTCAGGCGTGAAAGGCCT
+
AAAABFAABAD2EFGGFGEGGGGGGHFHHGHHHFHHHHHHGGGGGHGHGHHHHGF HFHHGHHGGGGGGGHGFFFFF3GEHHHFHEHCGCGHEGHHHHGGHFA/EFGGGGFGGCC GGHDFDGFFGGGFF@？G.CF.:C.CD？？D@DFGGFBAEGFEG0AFFEEFGFFFFFFBFFDDD；BB FFFF.BDF.DFAB.-99EBF9BFF.AB9-..:/.:9-；---9AAF-＝BAF/BBBBB/BB/@D-../:AEEE
@M04670:191:000000000-BVBHP:1:1101:15916:1458 1:N:0:GTTAGCGC+AGTCACGA
ATACGGTAGGCAGCAGTGAGGAATCTTGCGCAATGGGCGAAAGCCTGACGCAGCC ATGCCGCGTGAATGATGAAGGTCTTAGGATTGTAAAATTCTTTCAGCAGGGACGATAATG ACGGTACCTGCAGAAGAAGCCCCGGCTAACTTCGTGCCAGCAGCCGCGGTAATACGAA GGGGGCTAGCGTTGCTCGGAATTACTGGGCGTAAAGGGCGCGTAGGCGGGTCGTCAAG TTGGGGGTGAAAGCCCAGG
+
AAAAA？1>>AFFEFGGGGFGGGHHHHHHHGGGGAGHHHGGGGGGHHHHHGGGGGG GHHHHGGGGGGEHHGFHHHHHHHHHHFFFHGHHHHHHHHGFHHBG2GGHGGGGGGGG GHHHHCGGHGGFHFHHHHHHHHHHHGGGCGGHHHHHGGGGGHHGHHGHGCGGCFEF GGGGGGCGG@@@FFF@FB@FFFF@BFFFFFFFFFF@@@FFFFFF<@@？？@FF？？＝>＝->A-B FFFBF<@@-@EFFFFFFEFF
@M04670:191:000000000-BVBHP:1:1101:13834:1460 1:N:0:GTTAGCGC+AGTCACGA
TTACGGTAGGCGGCAGTCGGGAATTTTGGGCAATGGGGGAAACCCTGACCCAGCA ACGCCGCGTGAAGGATGAAGTTTTTCGGAATGTAAACTTCGAAAGAATAGGAAGAATCA ATGACGGTACTATTTTTAAGGTCCGGCTAACTACGTGCCAGCAGCCGCGGTGATACGTAG GGACCAAGCGTTGTTCGGTTTTACTGGGCGTAAAGGGCGCGTAGGCTGCGTGGTAAGTC ACTTGCGAAATCTCTGA
+
the operation of step S2 may be performed by a jingzhi company, such as the yunzhi biotechnology limited, wherein the high-throughput sequencing method is performed by an Illuminal Miseq 300 instrument, and comprises the following steps: 1. sample segmentation; 2. library construction (library preparation); 3. sequencing reaction (sequencing reaction); 4. data an1 analysis and the like.
S3, performing quality analysis on the data by means of FastQC software, aiming at screening qualified data meeting analysis requirements, generating a webpage version data quality analysis report by the operation of the step, referring to FIG. 2, wherein the webpage version data quality analysis report is hereinafter referred to as report 1, all qualified microorganism double-end sequencing data can be selected according to the content of the report 1, and is hereinafter referred to as data 1, and then performing the quality control analysis of the next step;
in this embodiment, the data amount of "data 1" is 150 in total;
s4, referring to fig. 3, synthesizing the forward and reverse strands of all samples "data 1" into a total file of the two strands, respectively;
for example, the forward chain file may be named "a.fasta", and the reverse chain file may be named "b.fasta";
then combining the "a.fasta" and the "a.fasta" into a total chain file (group file), for example, the total chain file may be named as "ab.fasta", and performing the quality control processing with the Mothur software, the quality control processing includes finding chimera and removing chimera operation;
before quality control processing, a script is required to remove a linker sequence added in high-throughput sequencing, and in this embodiment, the linker sequence is:
-a AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC
-A AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT。
removing chimeras to generate a total fasta format file, namely a quality control file, and adding a sample number line to the quality control file so as to analyze data in the next steps;
s5, referring to FIG. 4, activating Qiime environment of Linux, and carrying out steps of analysis on the data with chimera removed;
the method comprises the following steps of 1, carrying out OTU clustering analysis on quality control files to obtain an OTU table of a microorganism sample, 2, carrying out α -diversity analysis and β -diversity analysis on the quality control files to obtain a α -diversity data table and a β -diversity data table, and 3, carrying out analysis on relative abundance composition of the quality control files at a genus level to obtain a relative abundance table;
in this process, the analysis flow of pick _ de _ node _ OTUs. py or pick _ closed _ reference _ OTUs. py can be used, the difference between the two methods is that Qiime software differs for the method of clustering OTUs: 1. the analysis process of the 'pick _ de _ novo _ otus. py' is to cluster all reads, does not support data parallel, has low speed, and needs to adopt the method when no corresponding reference sequence exists; 2. the analysis process of the "pick _ closed _ reference _ otus. py" is that reads are clustered into a reference sequence, the reads which are not compared are removed, do not participate in subsequent analysis, have high speed and are more accurate in tree building and species annotation;
in the method for identifying symbiotic microorganisms of medicinal plants, the planting mode of liquorice can be identified by adopting two methods of' pick _ de _ novo _ OTUs.
In the embodiment, the planting mode of the liquorice is identified by adopting an analysis process of 'pick _ closed _ reference _ otus. py';
referring to fig. 4, the quality control file is analyzed by Qiime software to obtain a microorganism species abundance table, a visualized result analysis chart of the microorganisms in the licorice sample is drawn by R software and LEfSe software, and the microorganisms in the sample are identified by comparing the analysis results.
Drawing a PCoA graph, a relative abundance graph, a Biomarker analysis graph and other visual result analysis graphs of the liquorice rhizosphere microorganisms and the root soil microorganisms by means of R software and LEfSe software;
partial results are shown in fig. 5 and fig. 6, and the difference between the glycyrrhiza uralensis rhizosphere microorganisms and the root system soil microorganisms is obtained by comparing the abundance composition and the Biomarker, so that the glycyrrhiza uralensis planting mode is identified.
In the figure, CMTN is for culturing three-year-old licorice rhizosphere microorganisms, CMTP is for culturing -year-old licorice rhizosphere microorganisms, CSTN is for culturing three-year-old licorice root system soil microorganisms, CSTP is for culturing -year-old licorice root system soil microorganisms, WMWT is wild licorice root rhizosphere microorganisms, WSWT is wild licorice root system soil microorganisms, CM is for culturing licorice root system microorganisms, CS is for culturing licorice root system soil microorganisms, and WS is wild licorice root system soil microorganisms.
As can be seen from FIG. 5, the differences between the rhizosphere microorganisms and the root soil microorganisms of licorice under different planting modes are very large.
As can be seen from FIG. 6, the relative abundance composition of microorganisms in different groups of samples has a significant difference at the genus level, and the name of a specific species is shown in the English part of the figure, wherein the total relative abundance value is 0.7, and the remaining 0.3 is the Other part.
As can be seen from FIG. 7, the difference between the licorice rhizosphere microorganisms and the root soil microorganisms (the root soil microorganisms are shown as soil microorganisms in FIG. 7) biomar is significant, and the LDA value (Linear diagnostics Analysis) is greater than 3.6.
In the embodiment, the number of rhizosphere microorganisms and root soil microorganism samples of liquorice to be identified is 151, the data size is 21.73GB, all data processing and subsequent analysis processes can be completed within 23 days, and compared with the identification time of relevant companies in the market, the method is short in time and high in analysis depth.
Example 2
The application of the medicinal plant symbiotic microorganism identification method in identification of the liquorice planting mode is completed by the method described in the embodiment 1, as can be seen from the graphs in fig. 5-7, rhizosphere microorganisms and root system soil microorganisms of liquorice in different planting modes are very different, and identification of the liquorice planting mode to be determined is completed according to the result of comparison of abundance composition of the microorganisms.
Example 3
The application of symbiotic microbe identification method for medicinal plants in analyzing the action mechanism of Chinese medicine compound for treating diseases is realized by the following method, which comprises the following steps:
s1, collecting a traditional Chinese medicine sample, a subject genome sample, a feces sample before the subject takes the traditional Chinese medicine sample and a feces sample after the subject takes the traditional Chinese medicine sample, and carrying out pretreatment operation;
the pretreatment operation comprises the following specific steps: collecting traditional Chinese medicine samples, subject genome samples, feces samples before the subjects take the traditional Chinese medicine samples and feces samples after the subjects take the traditional Chinese medicine samples, freezing all the samples at the temperature below 60 ℃ and storing the samples in a dry ice box, wherein the freezing process is specifically carried out by immersing all the samples in a liquid nitrogen tank for freezing.
S2, carrying out chemical analysis on the traditional Chinese medicine sample, and inquiring related data to obtain the main activity and the main treatment function of the traditional Chinese medicine sample;
obtaining a genome sample of a subject, a fecal sample before the subject takes the traditional Chinese medicine sample and double-chain sequencing data of 16S rRNA genes of intestinal flora in the fecal sample after the subject takes the traditional Chinese medicine sample by adopting a high-throughput sequencing method;
s3, performing quality analysis on the data by means of FastQC software, aiming at screening qualified data meeting analysis requirements, generating a webpage version data quality analysis report by the operation of the step, referring to FIG. 2, wherein the webpage version data quality analysis report is hereinafter referred to as report 1, all qualified microorganism double-end sequencing data can be selected according to the content of the report 1, and is hereinafter referred to as data 1, and then performing the quality control analysis of the next step;
s4, referring to fig. 3, synthesizing the forward and reverse strands of all samples "data 1" into a total file of the two strands, respectively;
for example, the forward chain file may be named "a.fasta", and the reverse chain file may be named "b.fasta";
then combining the "a.fasta" and the "a.fasta" into a total chain file (group file), for example, the total chain file may be named as "ab.fasta", and performing the quality control processing with the Mothur software, the quality control processing includes finding chimera and removing chimera operation;
before quality control processing, a script is required to remove a linker sequence added in high-throughput sequencing, and in this embodiment, the linker sequence is:
-a AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC
-A AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTA。
removing chimeras to generate a total fasta format file, namely a quality control file, and adding a sample number line to the quality control file so as to analyze data in the next steps;
s5, referring to FIG. 4, activating Qiime environment of Linux, and carrying out steps of analysis on the data with chimera removed;
the method comprises the following steps of 1, clustering OTUs of microorganism sample data to obtain an OTU table of the microorganism sample, 2, carrying out α -diversity analysis and β -diversity analysis on the sample to obtain a α -diversity data table and a β -diversity data table, and 3, analyzing relative abundance composition of the microorganism sample on a genus level to obtain a relative abundance table;
in the embodiment, the planting mode of the liquorice is identified by adopting an analysis process of 'pick _ closed _ reference _ otus. py';
referring to fig. 4, a Qiime software is used for analyzing a quality control file to obtain a data analysis table, the obtained data analysis table is matched with a biological taxonomy name table automatically generated by the software, and then a PCoA chart, a relative abundance chart, a Biomarker analysis chart and other visual result analysis charts of the glycyrrhiza rhizosphere microorganisms and the root system soil microorganisms are drawn by an R software;
example 4
In this example, the method of example 3 is used to test the effect of intestinal flora on the dietary metabolism of mice, except that different dietary components are used to replace the traditional Chinese medicine sample in example 3, and the content of the natural betaine compound serving as the reference object is determined, and the test result shows that:
figure 8a is the betaine levels in the colon of whole wheat diet mice, wherein, according to the mouse feeding composition, HF represents a high fat control diet, R1 represents native rye bran, R2 represents bioprocessed rye bran, a1 represents native wheat aleurone, a4 represents milled and bioprocessed wheat aleurone, the asterisk (i.e.. a one-way analysis of variance) indicates the p-value (i.e. the value of HF compared to all bran-rich diets), and a denotes p < 0.05; denotes p < 0.01; p < 0.001.
In fig. 8b, HF represents a high-fat control diet, HFLF represents an alternating high-fat low-fat control diet, LF represents a Principal Component Analysis (PCA) of the microbial composition in the cecal contents of mice on a low-fat control diet, a1 and a4 represent diets rich in aleurone, and R1 and R2 represent diets rich in rye bran, according to the feeding composition of the mice.
As can be seen from fig. 8a, the content of 8 betaine compounds, which are 5-aminopentanoic acid betaine, alanine betaine, phenylalanine betaine, piperidinic acid betaine, proline betaine, trigonelline, tryptophan betaine, valine betaine, in the colon of mice fed whole wheat bran diet was significantly increased compared to mice fed high fat diet (HF);
and piperidinic acid betaine, phenylalanine betaine, and tryptophan betaine were detected only in the intestines of mice fed the whole wheat bran diet, and the latter two compounds were limited to the rye bran-enriched diet group.
By 16S RNA sequencing analysis, referring to fig. 8b, it was found that the composition of the intestinal flora of mice in the whole wheat bran diet group (R1, R2, a1, a4) was not significantly different from that of the control group (HF, LF, HFLF), in which the intestinal flora of mice in the low fat diet group (LF) constituted mice closer to that of the whole wheat bran diet group.
Referring to fig. 9, 10, the analysis results of RDA showed that the relative abundance of microbial groups including akkermansia, bacteroides, bifidobacterium, coriolus, enterobacter, lactobacillus, mucospirillum, ruminococcus was higher in the whole wheat bran diet group mice, while the relative abundance of other genera, clostridium, descurvulum, mucirillum, Odoribacter and rius was higher in the control group.
The rye bran-enriched diet resulted in a significant increase in the relative abundance of bacteria of the genera cladosporium, devulcanium, lactococcus, lachnospiraceae, peptococcaceae, compared to the bran paste-enriched diet group. Feeding processed wheat aleurone promotes the relative abundance of two bacteria of Mycoplasma and Porphyromonas to be increased, and the relative abundance of Bifidobacterium, Muospirillum and Lactobacillus to be decreased. In mice of experimental groups fed with processed or unprocessed rye bran diets, there was no significant difference in the relative abundance of intestinal flora.
Referring to fig. 11, α results of diversity analysis showed that mice in the a1 and a4 diet group based on wheat aleurone had significantly reduced diversity in ceca α, and that the control group had no significant difference in α diversity compared to the experimental group on the rye bran-enriched diet compared to the R1 and R2 diet group on rye bran and the high fat diet group (HF).
In the above examples 1-4, the FastQC software (v0.11.8) is Java-based software, which can quickly perform quality evaluation on sequencing data;
the Mothur software (v1.43.0) is software which can be matched with Qiime to carry out OTU clustering and realize chimera finding and chimera removing;
the Qiime software (v1.9.1) is the most authoritative and commonly used data analysis software in the field of microorganism bioinformatics data analysis at present, and has the functional characteristics of: 1. processing the sequence file in the fastq format to generate an OTU table; 2. processing an OTU table; 3. alpha diversity analysis; 4. beta diversity analysis and taxonomic composition and abundance analysis, such as generation of a TaxaSummary graph, abundance difference analysis, association analysis, core species analysis, and the like;
the R software (v3.5.3) is software which belongs to the GNU system and is free and has open source codes, wherein the operating environment for performing statistical analysis and drawing on data by using the R language is ;
the LEfSe software (LDA Effect Size, v1.0.8.post1) can realize comparison among a plurality of groups, and can also carry out subgroup comparison analysis inside the group comparison, thereby finding out species with significant difference in abundance among the groups, namely biomaker analysis which can be carried out based on the LEfSe software, and has the advantages that A. firstly, a nonparametric factor Kruskal-Wallis rank and test are adopted in a plurality of groups of samples to detect the species with significant difference in abundance among different groups, a statistically reliable difference conclusion can be obtained, B. then, Wilcoxon rank and test are utilized to check whether all the comparison of the subspecies in the species with significant difference is similar to the classification level of , the accuracy of the difference conclusion is ensured, and C. finally, linear discriminant analysis, namely LDA, is utilized to carry out dimension reduction on data and evaluate the influence of the species with significant difference, namely LDA scoro, the discrimination of the biomakers is evaluated, and the readability of the Biomarkers is improved.
The invention is not limited to the above-described examples, and various modifications or alterations without inventive work may be made by those skilled in the art within the scope of the invention defined by the claims appended hereto.
Claims (10)
1, method for identifying symbiotic microorganisms of medicinal plants, which is characterized by comprising the following steps:
s1, collecting a sample and carrying out pretreatment operation;
s2, obtaining sequence double-chain sequencing data of V3 and V4 regions of the microorganism 16S rRNA gene in the sample by adopting a high-throughput sequencing method;
s3, screening qualified sequencing data by means of FastQC software, synthesizing a forward chain and a reverse chain of the qualified sequencing data to obtain a total chain file, and performing quality control processing on the total chain file by Mothur software to obtain a quality control file;
and S4, analyzing the quality control file by means of Qiime software to obtain a microorganism species abundance table, drawing a visual result analysis chart of the microorganisms in the sample by means of R software and LEfSe software, and identifying the microorganisms in the sample by comparing the analysis results.
2. The method for identifying symbiotic microorganisms of medicinal plants according to claim 1, wherein in step S1, the pretreatment comprises the following specific steps: and collecting samples to be detected, carrying out liquid nitrogen treatment on all samples and storing the samples in a dry ice box.
3. The method for identifying symbiotic microorganisms of medicinal plants according to claim 1, wherein in step S3, before the quality control treatment of the global chain file, a script is used to remove the linker sequence added in the high-throughput sequencing, wherein the linker sequence is:
-a AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC
-A AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT。
4. the method for identifying symbiotic microorganisms belonging to medicinal plants as claimed in claim 1, wherein in step S4, the Qiime software is used to analyze the quality control file by using the analysis process of "pick _ de _ novo _ otus.
5. The method for identifying symbiotic microorganisms of medicinal plants according to claim 1, wherein in step S4, the step of analyzing the quality control file by means of Qiime software to obtain the abundance table of species comprises obtaining the OTU table of the microorganism sample by performing OTU clustering analysis, obtaining the α -diversity data table and the β -diversity data table by performing microorganism species diversity analysis, and obtaining the relative abundance table by performing relative abundance analysis.
6. The method for identifying symbiotic microorganisms of claim 5, wherein in step S4, the visual result analysis chart comprises a PCoA difference analysis chart, a relative abundance composition analysis chart and a classification Biomarker analysis chart.
7. The method of claim 6, wherein the step of comparing the analysis results comprises comparing the abundance composition of the microorganisms with a Biomarker in step S4.
8. Use of the method of any one of claims 1 to 7 to for identifying the rhizosphere microorganisms and root soil microorganisms of licorice.
9. Use of the method of any one of claims 1 to 7 to for identifying a licorice planting pattern.
10. The use of the method of identifying symbiotic microorganisms of medicinal plants of any one of claims 1 to 7 to in the analysis of the action mechanism of Chinese herbal compound for treating diseases.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911075058.1A CN110734989A (en) | 2019-11-06 | 2019-11-06 | medicinal plant symbiotic microorganism identification method and application thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911075058.1A CN110734989A (en) | 2019-11-06 | 2019-11-06 | medicinal plant symbiotic microorganism identification method and application thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110734989A true CN110734989A (en) | 2020-01-31 |
Family
ID=69272311
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911075058.1A Pending CN110734989A (en) | 2019-11-06 | 2019-11-06 | medicinal plant symbiotic microorganism identification method and application thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110734989A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111524555A (en) * | 2020-04-20 | 2020-08-11 | 上海欧易生物医学科技有限公司 | Automatic typing method based on human intestinal flora |
| CN114767884A (en) * | 2022-05-17 | 2022-07-22 | 国家纳米科学中心 | Visual prodrug activating compound, prodrug system, preparation method and application thereof |
| CN116612820A (en) * | 2023-07-20 | 2023-08-18 | 山东省滨州畜牧兽医研究院 | Dairy product production intelligent management platform based on data analysis |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140162274A1 (en) * | 2012-06-28 | 2014-06-12 | Taxon Biosciences, Inc. | Compositions and methods for identifying and comparing members of microbial communities using amplicon sequences |
| CN105525025A (en) * | 2016-02-17 | 2016-04-27 | 南京大学 | Method for detecting rhizosphere soil prokaryotic microorganisms of various soybeans based on 16SrDNA deep sequencing |
-
2019
- 2019-11-06 CN CN201911075058.1A patent/CN110734989A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140162274A1 (en) * | 2012-06-28 | 2014-06-12 | Taxon Biosciences, Inc. | Compositions and methods for identifying and comparing members of microbial communities using amplicon sequences |
| CN105525025A (en) * | 2016-02-17 | 2016-04-27 | 南京大学 | Method for detecting rhizosphere soil prokaryotic microorganisms of various soybeans based on 16SrDNA deep sequencing |
Non-Patent Citations (8)
| Title |
|---|
| 朱菲莹等: "西瓜根际土壤细菌群落多样性对枯萎病发生的影响", 《中国农学通报》 * |
| 朱菲莹等: "西瓜根际土壤细菌群落多样性对枯萎病发生的影响", 《中国农学通报》, no. 17, 31 December 2018 (2018-12-31), pages 70 * |
| 李岩等: "不同生境黑果枸杞根际与非根际土壤微生物群落多样性", 《生态学报》 * |
| 李岩等: "不同生境黑果枸杞根际与非根际土壤微生物群落多样性", 《生态学报》, no. 17, 30 September 2018 (2018-09-30), pages 5985 * |
| 潘惠霞等: "甘草、麻黄根际土壤微生物的生态分布特性", 《西北植物学报》 * |
| 潘惠霞等: "甘草、麻黄根际土壤微生物的生态分布特性", 《西北植物学报》, vol. 23, no. 10, 31 December 2003 (2003-12-31), pages 2 - 6 * |
| 马晓梅等: "荒漠河岸林植物根际和非根际微生物的季节性变化", 《干旱区资源与环境》, no. 05, 31 May 2009 (2009-05-31), pages 169 * |
| 黄璐琦等: "贺兰山植物资源图志", 福州：福建科学技术出版社, pages: 436 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111524555A (en) * | 2020-04-20 | 2020-08-11 | 上海欧易生物医学科技有限公司 | Automatic typing method based on human intestinal flora |
| CN114767884A (en) * | 2022-05-17 | 2022-07-22 | 国家纳米科学中心 | Visual prodrug activating compound, prodrug system, preparation method and application thereof |
| CN114767884B (en) * | 2022-05-17 | 2024-01-26 | 国家纳米科学中心 | Visualized prodrug activating compound, prodrug system, preparation method and application thereof |
| CN116612820A (en) * | 2023-07-20 | 2023-08-18 | 山东省滨州畜牧兽医研究院 | Dairy product production intelligent management platform based on data analysis |
| CN116612820B (en) * | 2023-07-20 | 2023-09-19 | 山东省滨州畜牧兽医研究院 | Dairy product production intelligent management platform based on data analysis |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108804875B (en) | Method for analyzing microbial population function by using metagenome data | |
| Xia et al. | Statistical analysis of microbiome data with R | |
| Knight et al. | Best practices for analysing microbiomes | |
| Yu et al. | Microfluidic-based mini-metagenomics enables discovery of novel microbial lineages from complex environmental samples | |
| Sanders et al. | Baleen whales host a unique gut microbiome with similarities to both carnivores and herbivores | |
| Godoy-Vitorino et al. | Comparative analyses of foregut and hindgut bacterial communities in hoatzins and cows | |
| EP2694669B1 (en) | Methods for making or creating a synthetic microbial consortium identified by computational analysis of amplicon sequences | |
| De Queiroz | For consensus (sometimes) | |
| Warnecke et al. | Metagenomic and functional analysis of hindgut microbiota of a wood-feeding higher termite | |
| CN110734989A (en) | medicinal plant symbiotic microorganism identification method and application thereof | |
| Taipale et al. | Vertical diversity of bacteria in an oxygen-stratified humic lake, evaluated using DNA and phospholipid analyses | |
| Prakash et al. | Technicalities and glitches of terminal restriction fragment length polymorphism (T-RFLP) | |
| CN110827917A (en) | Method for identifying individual intestinal flora type based on SNP | |
| Xia et al. | Bioinformatic analysis of microbiome data | |
| Ntushelo | Identifying bacteria and studying bacterial diversity using the 16S ribosomal RNA gene-based sequencing techniques: A review | |
| Gryta et al. | Methodological aspects of multiplex terminal restriction fragment length polymorphism-technique to describe the genetic diversity of soil bacteria, archaea and fungi | |
| CN109154019A (en) | The recognition methods of microorganism | |
| Lee et al. | DNA sequencing diagnosis of off-season spirochetemia with low bacterial density in Borrelia burgdorferi and Borrelia miyamotoi infections | |
| Cohen et al. | The microbiome’s fiber degradation profile and its relationship with the host diet | |
| Lau et al. | Cross-comparison of microbiota in the oropharynx, hypopharyngeal squamous cell carcinoma and their adjacent tissues through quantitative microbiome profiling | |
| Yang et al. | Beyond samples: A metric revealing more connections of gut microbiota between individuals | |
| CN103667460B (en) | The method of composition of transgenic plants whether is contained in a kind of auxiliary detection food | |
| CN110656153B (en) | Intestinal microbial markers highly correlated with anorexia behavior in small-weight chickens | |
| Hiltemann et al. | 16S Microbial Analysis with mothur (extended) | |
| Sugitha et al. | Soil metagenomics |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200131 |