CN117095748B - Method for constructing plant miRNA genetic regulation pathway - Google Patents
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- 230000009107 upstream regulation Effects 0.000 claims abstract description 9
- 230000009109 downstream regulation Effects 0.000 claims abstract description 6
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- 241000183024 Populus tremula Species 0.000 abstract description 9
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Abstract
The invention provides a method for constructing a plant miRNA genetic regulation pathway, and relates to the field of molecular genetics. The method for constructing the plant miRNA genetic regulation pathway can accurately, rapidly and efficiently identify the miRNA upstream regulation transcription factor and the miRNA downstream regulation target gene, is effective supplement for the research of only the miRNA downstream target gene, and provides important technical reference for analyzing the plant complex character genetic regulation pathway. By adopting the method provided by the invention, the upstream transcription factor and the downstream target gene of the aspen miR393a are obtained, and a genetic control path of influencing the wood tissue related property by the miR393a is constructed.
Description
Technical Field
The invention relates to the technical field of design molecular genetics, in particular to a method for constructing a plant miRNA genetic regulation pathway.
Technical Field
MicroRNA (micro RNA, miRNA) realizes negative regulation and control on a downstream target gene through shearing action or translational inhibition on the downstream target gene, and is widely involved in biological processes such as plant growth and development, important character formation and the like. Most previous researches only concern the genetic action mechanism of miRNA and a target gene at the downstream of the miRNA to influence important traits, but neglect the regulation and control process of an upstream regulation and control transcription factor on miRNA formation and greatly influence the analysis precision of the important traits. In particular, the current research only carries out deep analysis on the functional mechanism of miRNA at individual level, and fails to analyze the action mechanism of miRNA genetic control network from population level, and the main reasons comprise the following two factors: (1) The research of the miRNA genetic regulation pathway is carried out by molecular biology technology means, so that the time consumption is long, the flux is low, and the large-scale identification of the important miRNA genetic regulation pathway is difficult to realize; (2) Based on bioinformatics prediction means, the expression correlation between upstream and downstream genes is mostly considered only at individual level, and the expression mode at population level and the action mechanism analysis of genome allelic variation level are ignored, so that the analysis accuracy of miRNA genetic pathways is low and the false positive is high. Therefore, the prior art lacks a method for accurately, rapidly and efficiently constructing a plant miRNA genetic control path.
Disclosure of Invention
The invention aims to provide a method for identifying a plant miRNA genetic regulation pathway, which can accurately, quickly and efficiently identify a specific miRNA upstream regulation transcription factor and a specific miRNA downstream regulation target gene in a plant, and analyze the genetic action mechanism of the specific miRNA in the plant, wherein the genetic action mechanism affects important characters.
The invention provides a method for identifying a plant miRNA genetic control pathway, which comprises the following steps:
1) Obtaining a primary transcript sequence of a miRNA of a specific plant species, and dividing a miRNA precursor sequence contained in the primary transcript sequence from a mature miRNA sequence;
2) Predicting potential target genes of miRNA by utilizing PSRNATARGET (https:// www.zhaolab.org/PSRNATARGET) online websites, setting an prospect value to be 3, and setting other parameters to be default parameters;
3) Obtaining expression quantity data of miRNA to be detected and predicted potential target genes thereof in a specific plant germplasm resource group;
4) Calculating a pearson correlation coefficient r between the miRNA population expression level and the predicted potential target gene population expression level in the step 3), and identifying candidate target genes with high negative correlation with the miRNA expression level;
the conditions for identifying candidate target genes include: the correlation coefficient r < -0.6;
5) Based on the primary transcript sequence of miRNA, defining the first 2000bp as a miRNA promoter region, and predicting potential upstream transcription factors combined with cis-acting elements of the miRNA promoter;
6) Obtaining expression level data of potential upstream transcription factors in the step 5) in a specific plant germplasm resource group, calculating the expression correlation between the potential upstream transcription factors and miRNA in the group, and identifying upstream candidate transcription factors highly correlated with the miRNA expression level;
The conditions for the identification include: the correlation coefficient r < -0.6 or r >0.6;
7) Obtaining SNP genotype data of a miRNA precursor, a candidate transcription factor and a candidate target gene in a specific plant germplasm resource group;
8) Based on quantitative trait expression positioning strategy (expression Quantitative Trait Loci, eQTL), carrying out association analysis on the group SNP genotype data of the candidate transcription factors in the step S7 and the group expression quantity data of miRNA to be detected, determining SNP in the transcription factors obviously associated with the miRNA expression quantity, and recognizing the transcription factors as miRNA upstream regulation transcription factors;
The determined conditions include: any SNP in the candidate transcription factor is obviously related to the expression level of miRNA;
9) Carrying out association analysis on SNP data of the miRNA precursor sequence in the step S7 and the population expression level of the candidate target genes, determining SNP in the miRNA precursor sequence which is obviously associated with the population expression level of the candidate target genes, and recognizing the target genes as miRNA downstream regulation target genes;
the determined conditions include: any SNP in the miRNA precondition sequence to be detected is obviously related to the expression level of the candidate target gene;
10 And (3) combining the steps 8) and 9), determining an upstream transcription factor and a downstream target gene of the miRNA to be detected of the specific plant, and determining a genetic regulation path of the miRNA to be detected.
Preferably, the step 1) does not limit the primary transcript of the miRNA to be detected in the specific plant and the internal structure dividing method thereof.
Preferably, the expression amounts of the miRNA to be detected, the potential target gene and the potential transcription factor obtained in the steps 3) and 6) are required to be in the same plant tissue or organ, and the expression amount obtaining mode is not limited.
Preferably, the software for calculating the pearson correlation coefficient r in steps 4) and 6) includes SPSS v19.0.
Preferably, the method for predicting the upstream potential transcription factor in the step 5) includes, but is not limited to PLANTREGMAP (http:// plantregmap. Gao-lab. Org /) online prediction tools, parameters and screening criteria are software dependent and are required to meet the requirement of biometrics.
Preferably, the population SNP data of step 7) is obtained based on a plant whole genome resequencing technique.
Preferably, the SNP genotype frequency in said manipulation step is greater than 10%.
Preferably, the number of individuals in the specific plant germplasm resource group is greater than 200 plants.
Preferably, the method for performing the correlation analysis in the steps 8) to 9) is a hybrid linear model in TASSEL v 5.0.0; obtaining the significance level of the correlation between each SNP locus to be detected and the specific expression quantity by using software to obtain a P value; FDR multiplex detection is carried out on the P value to obtain a Q value, and SNP loci with P less than or equal to 0.01 and Q less than or equal to 0.1 are screened as SNP loci obviously associated with specific expression level.
Preferably, the gene expression level data in the steps 3) to 6) is the expression level of a plant tissue or organ functionally related to the miRNA genetic control pathway.
The invention provides a method for constructing a plant miRNA genetic regulation pathway. The miRNA genetic regulation path is verified based on the molecular biology experimental means in the prior art, so that the time consumption is long and the precision is low; and based on traditional bioinformatics means, the false positive of the miRNA genetic regulation pathway is predicted to be higher, the accuracy is lower, and the accurate and rapid identification of the plant miRNA genetic regulation pathway is difficult to realize. Therefore, the method for constructing the plant miRNA genetic regulation pathway can accurately, rapidly and efficiently identify the miRNA upstream regulation transcription factor and the miRNA downstream regulation target gene, is effective supplement for the research of only the miRNA downstream target gene, and provides important technical reference for analyzing the plant complex character genetic regulation pathway.
The results of the embodiment of the invention show that: by adopting the method provided by the invention, the upstream transcription factor and the downstream target gene of the aspen miR393a are obtained, and a genetic control path of influencing the wood tissue related property by the miR393a is constructed.
Drawings
FIG. 1 is a genetic control network of aspen miR393 a;
FIG. 2 is an analytical flow chart of the identification method of the present invention.
Detailed Description
The invention provides a method for identifying a plant miRNA genetic control pathway, which comprises the following steps:
1) Obtaining a primary transcript sequence of a miRNA of a specific plant species, and dividing a miRNA precursor sequence contained in the primary transcript sequence from a mature miRNA sequence;
2) Predicting potential target genes of miRNA by utilizing PSRNATARGET (https:// www.zhaolab.org/PSRNATARGET) online websites, setting an prospect value to be 3, and setting other parameters to be default parameters;
3) Obtaining expression quantity data of miRNA to be detected and predicted potential target genes thereof in a specific plant germplasm resource group;
4) Calculating a pearson correlation coefficient r between the miRNA population expression level and the predicted potential target gene population expression level in the step 3), and identifying candidate target genes with high negative correlation with the miRNA expression level;
the conditions for identifying candidate target genes include: the correlation coefficient r < -0.6;
5) Based on the primary transcript sequence of miRNA, defining the first 2000bp as a miRNA promoter region, and predicting potential upstream transcription factors combined with cis-acting elements of the miRNA promoter;
6) Obtaining expression level data of potential upstream transcription factors in the step 5) in a specific plant germplasm resource group, calculating the expression correlation between the potential upstream transcription factors and miRNA in the group, and identifying upstream candidate transcription factors highly correlated with the miRNA expression level;
The conditions for the identification include: the correlation coefficient r < -0.6 or r >0.6;
7) Obtaining SNP genotype data of a miRNA precursor, a candidate transcription factor and a candidate target gene in a specific plant germplasm resource group;
8) Based on quantitative trait expression positioning strategy (expression Quantitative Trait Loci, eQTL), carrying out association analysis on the group SNP genotype data of the candidate transcription factors in the step S7 and the group expression quantity data of miRNA to be detected, determining SNP in the transcription factors obviously associated with the miRNA expression quantity, and recognizing the transcription factors as miRNA upstream regulation transcription factors;
The determined conditions include: any SNP in the candidate transcription factor is obviously related to the expression level of miRNA;
9) Carrying out association analysis on SNP data of the miRNA precursor sequence in the step S7 and the population expression level of the candidate target genes, determining SNP in the miRNA precursor sequence which is obviously associated with the population expression level of the candidate target genes, and recognizing the target genes as miRNA downstream regulation target genes;
the determined conditions include: any SNP in the miRNA precondition sequence to be detected is obviously related to the expression level of the candidate target gene;
10 And (3) combining the steps 8) and 9), determining an upstream transcription factor and a downstream target gene of the miRNA to be detected of the specific plant, and determining a genetic regulation path of the miRNA to be detected.
The kind of the plant is not particularly limited in the present invention, and in the embodiment of the present invention, the plant is preferably populus tomentosa.
In the invention, the step 1) does not limit the primary transcription of the miRNA to be detected of the specific plant and the internal structure dividing method thereof.
In the invention, the expression amounts of the miRNA to be detected, the potential target genes and the potential transcription factors obtained in the steps 3) and 6) are required to be in the same plant tissue or organ, and the expression amount obtaining mode is not limited.
In the present invention, the software for calculating the pearson correlation coefficient r in the steps 4) and 6) includes SPSS v19.0.
In the present invention, the method for predicting the upstream potential transcription factor in the step 5) includes, but is not limited to PLANTREGMAP (http:// plantatregmap. Gao-lab. Org /) online prediction tools, parameters and screening criteria are determined by software, and are required to meet the requirement of biometrics.
In the invention, the SNP data of the group in the step 7) are obtained based on the whole genome resequencing technology of plants.
In the present invention, the SNP genotype frequency in the operation step is required to be more than 10%.
In the invention, the number of individuals in the specific plant germplasm resource group is required to be more than 200.
In the invention, the method for carrying out the association analysis in the steps 8) to 9) is a mixed linear model in TASSEL v 5.0.0; obtaining the significance level of the correlation between each SNP locus to be detected and the specific expression quantity by using software to obtain a P value; FDR multiplex detection (Q) is carried out on the P value, and SNP loci with P less than or equal to 0.01 and Q less than or equal to 0.1 are screened as SNP loci obviously associated with specific expression levels.
In the present invention, the gene expression level data in the steps 3) to 6) is the expression level of a plant tissue or organ functionally related to the miRNA genetic control pathway.
The following describes in further detail a method for identifying a plant miRNA genetic control pathway according to the present invention with reference to specific examples, and the technical scheme of the present invention includes but is not limited to the following examples.
Example 1
By using the method for constructing the plant miRNA genetic regulation pathway shown in the figure 2, the genetic regulation pathway of the aspen miR393a affecting the wood quality character is constructed, and the genetic action mechanism of the genetic regulation pathway affecting the aspen wood quality character is analyzed.
Step S1, obtaining a primary transcript (Pri-miR 393 a) sequence of aspen miR393a, and dividing a miR393 precursor sequence (Pre-miR 393 a) and a mature sequence (miR 393a, UCCAAAGGGAUCGCAUUGAUC) contained in the aspen miR393a according to a miRNA sequencing result.
MiR393a potential target gene information predicted in Table 1
S2, predicting miR393 target genes by utilizing PSRNATARGET online websites (https:// www.zhaolab.org/PSRNATARGET), taking a populus tomentosa gene database as a target gene library, setting an prospect value to be 3 (the smaller the numerical value is, the greater the probability that the gene is a miRNA target gene is), and predicting potential target genes of populus tomentosa miR393a by taking the value range of 1-5 as a target gene library and other settings as default parameters; the predicted potential target genes for 9 miR393a were identified altogether, see table 1 in detail.
Step S3, detecting the expression level of miR393a and 9 potential target genes in xylem of populus tomentosa germplasm resource group (303 strains), which specifically comprises the following steps: collecting mature xylem of individual 303 strains of populus tomentosa germplasm resources, immediately placing the mature xylem into a liquid nitrogen environment (-196 ℃) for storage after collection, extracting RNA of the mature xylem by using PLANT QIAGEN RNAEASY KIT (QIAGEN CHINA, shanghai, china) kit, and carrying out miRNA and mRNA sequencing by a biological company after quality inspection is qualified to obtain the expression level of miR393a and 9 potential target genes in the populus tomentosa germplasm resources.
And S4, calculating a pearson correlation coefficient r between the population expression quantity of the miR393a and the population expression quantity of the 9 candidate genes by using SPSS v19.0 software. As a result, the expression level of 4 candidate genes and the expression level of miR393a are strongly and inversely correlated (r < -0.60), and the 4 candidate genes are determined to be candidate target genes of miR393a, wherein the 4 candidate genes are Pto-AFB2.1, pto-AFB2.2, pto-TIR1.1 and Pto-TIR1.2 respectively, and specific information is shown in Table 1.
Step S5, based on the genome information of the populus tomentosa, a 2000bp sequence before the Pri-miR393a is obtained based on the Pri-miR393a sequence and is defined as a promoter region of the miR393 a; on-line prediction of potential transcription factors upstream of miR393a by PLANTREGMAP (http:// plantaregmap. Gao-lab. Org /), 5 potential upstream transcription factors were identified by taking P <1E-06 and Q <1E-02 as screening conditions, and specific information is shown in Table 2.
TABLE 2 predicted potential upstream transcription factor information
Step S6, detecting the group expression level of the 5 potential upstream transcription factors in step S5 based on the whole genome gene expression level data obtained in step S3, and calculating the expression levels of the 5 potential upstream transcription factors and Pri-miR393 a; as a result, it was found that there were 2 transcription factor expression levels showing a strong correlation (r < -0.60 or r > 0.60) with the expression level of Pri-miR393a in the population, and these two genes were: pto-AGL20.2 and Pto-DREB26, define these two genes as candidate transcription factors.
Step S7, SNP genotype data of Pri-miR393a, 2 candidate transcription factors and 4 candidate target genes in a population is obtained, and the specific flow is as follows:
Taking 435 individuals in a natural populus tomentosa population as materials, extracting DNA of all individuals for resequencing, and obtaining whole genome SNP data and the position of the SNP data in the genome based on a populus tomentosa reference genome. And carrying out sequence alignment on the genes and a reference genome by utilizing bioedit software to obtain the position information of the genes. And combining whole genome SNP data to obtain group SNP genotype data of the genes. SNP with genotype frequency greater than 10% was screened, and finally 134 SNPs in 7 genes were detected, and detailed information is shown in Table 3.
TABLE 3 SNP genotyping data related to this patent
Step S8, based on quantitative trait expression localization strategy (expression Quantitative Trait Loci, eQTL), using the mixed linear model in TASSEL v5.0, performing association analysis on population SNP genotype data within the 2 candidate transcription factors in step S7 and population expression levels of miR393a, determining SNPs significantly correlated with miR393a expression levels, the determined conditions comprising: the association result that any SNP in 2 candidate transcription factors is obviously associated with the expression level of miR393a, namely P is less than or equal to 0.01, and Q is less than or equal to 0.1 shows that the candidate transcription factors have genetic regulation effect on miR393 a. The result shows that 3 SNPs in Pto-DREB26 are obviously related to miR393a expression level, so that Pto-DREB26 is used as a transcription factor for upstream regulation to regulate miR393a expression, and detailed information is shown in Table 4.
TABLE 4 analysis result of correlation between SNP to be measured and candidate Gene expression level (P.ltoreq.0.01, Q.ltoreq.0.1)
Step S9, carrying out association analysis on the genotype data of the Pri-miR393a and the group expression levels of 3 candidate target genes in step S7 by using a mixed linear model in TASSEL v 5.0.0, and determining the significance relation between the SNP in the Pri-miR393a and the expression levels of the 3 candidate target genes, wherein the determined conditions comprise: the correlation result that any SNP in the Pri-miR393a is obviously correlated with the expression level of the candidate target gene, namely P is less than or equal to 0.01 and Q is less than or equal to 0.1 shows that the miR393a has genetic control effect on the downstream target gene. As a result, it was found that 6 SNPs in Pri-miR393a were significantly associated with the expression levels of two candidate target genes Pto-AFB2.1 and Pto-TIR1.2, and that Pto-AFB2.1 and Pto-TIR1.2 were determined to function as downstream target genes of miR393a, and detailed information is shown in Table 3.
Step S10, combining the steps S8 and S9, determining a genetic regulation path of the aspen miR393a, namely Pto-DREB26 is an upstream regulation transcription factor, pto-AFB2.1 and Pto-TIR1.2 are downstream target genes of the miR393a (figure 1), and participating in the genetic regulation process of aspen wood tissues.
Claims (6)
1. A method for constructing a plant miRNA genetic control pathway, comprising the steps of:
1) Obtaining a primary transcript sequence of a miRNA of a specific plant species, and dividing a miRNA precursor sequence contained in the primary transcript sequence from a mature miRNA sequence;
2) Predicting the potential target gene of the miRNA, wherein PSRNATARGET online websites are utilized for predicting the potential target gene of the miRNA, an prospect value is set to be 3, and other parameters are set as default parameters
3) Obtaining expression quantity data of miRNA to be detected and predicted potential target genes thereof in a specific plant germplasm resource group;
4) Calculating a pearson correlation coefficient r between the miRNA population expression level and the predicted potential target gene population expression level in the step 3), and identifying candidate target genes with high negative correlation with the miRNA expression level;
Conditions for identifying candidate target genes include: the correlation coefficient r is < -0.6;
5) Based on the primary transcript sequence of miRNA, defining the first 2000bp as a miRNA promoter region, and predicting potential upstream transcription factors combined with cis-acting elements of the miRNA promoter;
6) Obtaining expression level data of potential upstream transcription factors in the step 5) in a specific plant germplasm resource group, calculating the expression correlation between the potential upstream transcription factors and miRNA in the group, and identifying upstream candidate transcription factors highly correlated with the miRNA expression level;
the conditions for the identification include: the correlation coefficient r < -0.6 or r > 0.6;
The miRNA to be detected, the potential target genes and the potential transcription factor expression quantity are obtained from the steps 3) and 6) in the same plant tissue or organ;
7) Obtaining SNP genotype data of a miRNA precursor, a candidate transcription factor and a candidate target gene in a specific plant germplasm resource group;
8) Based on quantitative trait expression positioning strategy, carrying out association analysis on the population SNP genotype data of the candidate transcription factors in the step 7) and the population expression quantity data of miRNA to be detected, determining SNP in the transcription factors obviously associated with the miRNA expression quantity, and recognizing the transcription factors as miRNA upstream regulation transcription factors;
The determined conditions include: any SNP in the candidate transcription factor is obviously related to the expression level of miRNA;
9) Carrying out association analysis on SNP data of the miRNA precursor sequence in the step S7 and the population expression level of the candidate target genes, determining SNP in the miRNA precursor sequence which is obviously associated with the population expression level of the candidate target genes, and recognizing the target genes as miRNA downstream regulation target genes;
the determined conditions include: any SNP in the miRNA precondition sequence to be detected is obviously related to the expression level of the candidate target gene;
10 Combining the steps 8) and 9), determining an upstream transcription factor and a downstream target gene of the miRNA to be detected of the specific plant, and determining a genetic regulation path of the miRNA to be detected;
the method for predicting the upstream potential transcription factor in the step 5) comprises PLANTREGMAP on-line prediction tools, wherein parameters and screening standards depend on software;
The gene expression level data in the steps 3) to 6) are the expression level of plant tissues or organs which are related to the functions of the miRNA genetic control pathway.
2. The method according to claim 1, wherein the software for calculating the pearson correlation coefficient r in steps 4) and 6) comprises SPSS v19.0.
3. The method of claim 1, wherein the step 7) population SNP data is obtained based on a plant whole genome resequencing technique.
4. The method of claim 1, wherein the SNP genotype frequency is greater than 10%.
5. The method of claim 1, wherein the number of individuals in the population of specific plant germplasm resources is greater than 200 plants.
6. The method of claim 1, wherein the method of performing the correlation analysis in steps 8) to 9) is a hybrid linear model in TASSEL v 5.0.0; obtaining the significance level of the correlation between each SNP locus to be detected and the specific expression quantity by using software to obtain a P value; FDR multiplex detection (Q) is carried out on the P value, and SNP loci with P less than or equal to 0.01 and Q less than or equal to 0.1 are screened as SNP loci obviously associated with specific expression levels.
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