Method for analyzing and mining key genes for regulating and controlling non-structural carbohydrate distribution of emmenopterys henryi under shading condition based on weighted gene co-expression network

Abstract

The invention relates to a method for analyzing and excavating key genes for regulating and controlling non-structural carbohydrate distribution of emmenopterys henryi under a shading condition based on a weighted gene co-expression network. The method comprises the following steps: S1, obtaining root, stem and leaf samples of emmenopterys henryi under different illumination conditions; s2, non-structural carbon index determination and transcriptome sequencing; s3, establishing a weighted gene co-expression network model, and dividing the weighted gene co-expression network model into a plurality of gene modules; calculating the correlation between the feature gene of the gene module and a non-structural carbon index, and screening a key gene module; and performing gene connectivity calculation and gene function annotation on the key gene module, and screening key genes. The 19 key candidate genes participating in regulation and control of the non-structural carbon distribution of the emmenopterys henryi under the shading condition are successfully excavated and identified, a foundation is laid for further clarification of a molecular mechanism of highlight stress response of the emmenopterys henryi, theoretical support is provided for development of an effective emmenopterys henryi conservation technology and promotion of emmenopterys henryi population recovery, and the important significance is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of plant molecular biology, and in particular to a method for mining key genes regulating the distribution of non-structural carbohydrates in fragrant fruit trees under shading conditions based on weighted gene co-expression network analysis. Background Art

[0002] The genus of the genus of the genus of the Rubiaceae family is a rare tree species of a single genus. It has high ornamental, medicinal, timber and economic value. It is also an important scientific material for the study of the systematic evolution, morphological succession and plant geographical flora of the Rubiaceae family. Due to the continuous decline of the wild genus of the genus, the geographical distribution area has gradually shrunk, and it has gradually been listed as an endangered plant. It is worth taking measures such as protection and research on breeding conditions. Studies have found that under natural conditions, the germination of seeds, seedling growth and sexual reproduction of the genus of the genus are closely related to the population size of the genus. The intensity of environmental light has a significant effect on the germination, growth and development, flowering process and survival rate of the genus of the genus. Therefore, the study of the light adaptation mechanism of the genus of the genus is an important scientific basis for revealing its endangered mechanism, and is of great significance for formulating reasonable conservation measures and promoting the recovery of the genus of the genus. Non-structural carbohydrates are the main energy supply for plant metabolism and growth and development. The content, composition and distribution of non-structural carbohydrates in plants among various organs are regulated by the light environment, and their dynamic changes reflect the adaptability of plants to the light environment. However, the molecular mechanism of how environmental light intensity regulates the content, composition and distribution of non-structural carbon in C. cyrtonema remains unclear. Summary of the invention

[0003] The purpose of the present invention is to reveal how the ambient light intensity regulates the non-structural carbon content, composition and distribution of the fragrant fruit tree, so as to provide a method for mining the key genes that regulate the distribution of non-structural carbohydrates in the fragrant fruit tree under shade conditions based on weighted gene co-expression network analysis.

[0004] The purpose of the present invention can be achieved by the following technical solutions:

[0005] One of the technical solutions of the present invention is to provide a method for mining key genes regulating the distribution of non-structural carbohydrates in fragrant fruit trees under shading conditions based on weighted gene co-expression network analysis, comprising the following steps:

[0006] S1. Obtaining samples: cultivating seedlings of Cibotium obesum under different light conditions to obtain root, stem and leaf samples of Cibotium obesum;

[0007] S2. Obtaining initial data: performing non-structural carbon index determination and transcriptome sequencing on the samples obtained in step S1 to obtain non-structural carbon index data and transcriptome data;

[0008] S3, data processing: establishing a weighted gene co-expression network model based on the transcriptome data obtained in step S2, and dividing the transcriptome data into multiple gene modules;

[0009] Calculate the correlation between characteristic genes of gene modules and non-structural carbon indicators to screen key gene modules;

[0010] Gene connectivity calculation and gene function annotation were performed on key gene modules to screen key genes.

[0011] In some specific embodiments, in step S1, the culturing of the citrus aurantium seedlings under different lighting conditions is specifically as follows:

[0012] A full light treatment group was used as the control group, and a 10% to 90% full light treatment group was used as the shade treatment group. After the seedlings of the fragrant fruit tree were cultivated for one year, the roots, stems and leaves were collected.

[0013] In some specific embodiments, in step S2, the samples for the determination of non-structural carbon index are first treated as follows: the root, stem and leaf samples of the fragrant fruit tree are sterilized at 105°C for 30 minutes and then dried at 80°C for 48 hours.

[0014] In some specific embodiments, in step S2, the non-structural carbon index includes soluble sugar concentration, starch concentration, non-structural carbon concentration, sugar pool, starch pool, non-structural carbon pool, sugar pool / starch pool, sugar pool / non-structural carbon pool, sugar pool / total non-structural carbon pool, starch pool / non-structural carbon pool, starch pool / total non-structural carbon pool.

[0015] Among them, non-structural carbon concentration = soluble sugar concentration + starch concentration;

[0016] Sugar pool: the product of the biomass of a certain organ (leaves, stems or roots) and the concentration of soluble sugars;

[0017] Starch pool: the product of the biomass of a certain organ (leaf, stem or root) and the starch concentration;

[0018] Non-structural carbon pool: the product of the biomass of a certain organ (leaves, stems or roots) and the non-structural carbon concentration;

[0019] Total nonstructural carbon pool: the sum of the nonstructural pools in leaves, stems and roots.

[0020] The soluble sugar concentration can be determined by the anthrone-sulfuric acid method, and the starch concentration can be determined by the anthrone-sulfuric acid method.

[0021] In some specific embodiments, in step S2, the samples for transcriptome sequencing are first processed as follows: the root, stem, and leaf samples of the fragrant fruit tree are quickly frozen with liquid nitrogen.

[0022] In some specific embodiments, in step S2, the transcriptome sequencing includes: performing transcriptome sequencing using the Illumina HiSeq platform, removing reads containing ploy-N and low-quality reads to obtain clean reads, and performing de novo assembly on the clean reads to obtain unigenes, and finally filtering genes with an expression standard deviation of ≤0.8 to obtain transcriptome data.

[0023] In some specific embodiments, in step S3, the establishment of a weighted gene co-expression network model divides the transcriptome data into multiple gene modules, including: based on the transcriptome data, establishing a weighted gene co-expression network model through WGCNA, selecting a power value range of 1 to 30, and obtaining multiple gene modules.

[0024] In some specific embodiments, a power value of 22 is selected to obtain 20 gene modules.

[0025] In some specific embodiments, in step S3, the screening gene module includes: using the Pearson correlation algorithm to calculate the correlation between the characteristic genes of the gene module and the non-structural carbon index, the correlation includes a correlation coefficient and a p-value, and screening is performed based on the absolute value of the correlation coefficient ≥ 0.3 and the p-value is less than 0.05 as the threshold to obtain the key gene module.

[0026] In some specific embodiments, in step S3, the screening criteria for screening key genes are: gene connectivity is greater than 15, and the genes are related to non-structural metabolism.

[0027] Based on the weighted gene co-expression network analysis method and combined with the non-structural carbon index, this paper successfully excavated and identified 19 key candidate genes involved in regulating the non-structural carbon allocation of the fruit tree under shading conditions, laying the foundation for further clarifying the molecular mechanism of the response of the fruit tree to high light stress, and also providing theoretical support for the development of effective fruit tree conservation technology and promoting the recovery of the fruit tree population, which is of great significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 Parameter selection for weighted gene co-expression network model construction.

[0029] Figure 2 Gene clustering tree and gene module division in the construction of weighted gene co-expression network model.

[0030] Figure 3 is the correlation between the characteristic genes of each gene module and the non-structural carbon indicators. DETAILED DESCRIPTION

[0031] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0032] In the following examples, unless otherwise specified, raw materials or processing techniques are conventional commercially available raw materials or conventional processing techniques in the art.

[0033] Embodiment 1:

[0034] This embodiment provides a method for mining key genes regulating non-structural carbon allocation of fragrant fruit trees under shading conditions based on weighted gene co-expression network analysis, which specifically includes the following steps:

[0035] (1) Material treatment and sample collection: Selected seedlings of the fragrant fruit tree with good growth and consistent development, and placed them under full light (as the control group), 40% full light, and 10% full light (as the shade treatment group) respectively. After culturing for 365 days, roots, stems, and leaves were collected. One part was quick-frozen with liquid nitrogen and stored at -80°C for transcriptome sequencing; the other part was sterilized at 105°C for 30 minutes and then dried at 80°C for 48 hours for the determination of non-structural carbon indicators. Three parallel experiments were set up.

[0036] (2) The tissue samples obtained in step (1) are respectively subjected to determination of soluble sugar, starch, non-structural carbon (NSC) concentration and pool, and the specific method is as follows:

[0037] Determination of soluble sugar concentration: Take 0.1g of dry powder sample, add 8mL of 80% (v / v) ethanol, and store in 80℃ water bath for 30min. Repeat the extraction three times. After centrifugation, collect the supernatant and combine. Determine the soluble sugar concentration using the anthrone-sulfuric acid method;

[0038] Determination of starch concentration: Dry the residue after ethanol extraction, add 5 mL of distilled water, and incubate in a boiling water bath for 15 min. Add 2 mL of 9.2 mol / L perchloric acid to the test tube, and then incubate in a boiling water bath for 15 min. After cooling, dilute to 10 mL with distilled water, filter, and determine the starch concentration using the anthrone-sulfuric acid method;

[0039] NSC concentration = soluble sugar concentration + starch concentration;

[0040] The sugar or starch or NSC pool is the product of the biomass of the organ and the soluble sugar or starch or NSC concentration;

[0041] The total NSC pool is the sum of the leaf, stem, and root NSC pools.

[0042] The results are shown in Table 1:

[0043] Table 1 Effects of different light intensities on the distribution of non-structural carbohydrates in Cibotium obesum

[0044]

[0045] (3) Transcriptome sequencing was performed on the tissue samples obtained in step (1): RNA from leaves, roots, and stems of different groups was extracted using Trizol (Invitrogen), and qualified samples were used for cDNA library construction according to conventional requirements. The constructed cDNA library was sequenced using the Illumina HiSeq platform (Illumina, USA). The raw data obtained by sequencing were processed, and reads containing ploy-N and low-quality reads were removed using quality control tools (such as NGSQC Toolkit) to obtain high-quality effective sequencing data (clean reads); clean reads were de novo assembled using Trinity software (https: / / trinityrnaseq.sourceforge.net) to obtain unigenes. As can be seen from Table 2, the amount of clean data obtained for each sample reached more than 99.56% of the original sequence amount, and the number of bases with a sequencing error rate of <1‰ accounted for more than 93.90% of the total number of bases (Q30 value), and the ratio of guanine and cytosine in the four bases of the DNA sequence (GC content) was between 43.50% and 44.44%, indicating that the sample preparation and sequencing quality were good. Genes with low expression fluctuations (standard deviation ≤ 0.8) were filtered, and 18,289 genes were finally obtained.

[0046] Table 2 Read quality statistical evaluation table

[0047]

[0048] (4) Construction of weighted gene co-expression network model through WGCNA

[0049] The data of the transcriptomes of the above 27 samples were processed by WGCNA to construct a weighted gene co-expression network model, and the power value was set from 1 to 30, and the correlation coefficient and average connectivity of the network were calculated. The higher the correlation coefficient (maximum 1), the closer the network is to the scale-free network distribution, but at the same time, a certain gene connectivity must be guaranteed. The final power value selected was 22, as shown in Figure 1 shown.

[0050] Based on the correlation coefficients between genes, a clustering tree Cluster Dendrogram is constructed. The dynamic tree cut algorithm is used to divide the gene modules. Genes with similar expression patterns will be classified into the same module. Each color represents a module, and the preliminary module division result Dynamic Tree Cut is obtained. Further, the modules with similar expression patterns are merged to obtain the final divided module Merged dynamic ( Figure 2 ), and finally the 18289 genes were divided into 20 gene modules, among which the grey module (Grey) is a set of genes that cannot be assigned to any gene module. Each gene module and the corresponding number of genes are shown in Table 3.

[0051] Table 3 Number of genes in gene modules

[0052]

[0053] (5) Figure 3 As shown, the correlation coefficient between the module characteristic gene (referring to the first principal component of the expression matrix of the corresponding module) and the non-structural carbon index is calculated using the Pearson correlation algorithm ( Figure 3 Numbers outside the brackets) and p-values ​​( Figure 3 The * in the square brackets indicate p<0.05, ** indicates p<0.01, and *** indicates p<0.001). The correlation coefficients between the modules and samples from different tissue sites are different, indicating that different tissue sites are associated with gene groups in different modules. The correlation coefficients between the modules and non-structural carbon indicators are both negatively correlated and positively correlated. Red represents positive correlation, and blue represents negative correlation. The darker the color, the more significant the correlation between the module and the indicator.

[0054] Among them, the non-structural carbon indicators include sugar pool, soluble sugar concentration, starch pool, starch concentration, NSC pool, NSC concentration, sugar pool / starch pool, sugar pool / NSC pool, sugar pool / total NSC pool, starch pool / NSC pool, starch pool / total NSC pool.

[0055] According to the threshold value of correlation coefficient absolute value ≥ 0.3 and p value < 0.05, four key gene modules were screened out: blue module, skyblue2 module, magenta module, and pink4 module.

[0056] (6) Based on the connectivity calculation results of the four key gene modules in the model and the gene function annotation during transcriptome sequencing, genes with high connectivity (>15) and involved in non-structural carbon metabolism were selected as key genes. A total of 19 key genes were screened (Table 4).

[0057] Table 4 Key genes involved in nonstructural carbon allocation

[0058]

[0059] The above description of the embodiments is to facilitate the understanding and use of the invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to these embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments, and improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the present invention should be within the scope of protection of the present invention.

Claims

1. A method for mining key genes regulating the distribution of non-structural carbohydrates in Cibotium wilfordii under shading conditions based on weighted gene co-expression network analysis, characterized in that: The steps include: S1. Obtaining samples: cultivating seedlings of Cibotium obesum under different light conditions to obtain root, stem and leaf samples of Cibotium obesum; S2. Obtaining initial data: performing non-structural carbon index determination and transcriptome sequencing on the samples obtained in step S1 to obtain non-structural carbon index data and transcriptome data; S3, data processing: establishing a weighted gene co-expression network model based on the transcriptome data obtained in step S2, and dividing the transcriptome data into multiple gene modules; Calculate the correlation between characteristic genes of gene modules and non-structural carbon indicators to screen key gene modules; Gene connectivity calculation and gene function annotation were performed on key gene modules to screen key genes.

2. The method according to claim 1, characterized in that In step S1, the cultivation of the citrus aurantium seedlings under different light conditions is specifically as follows: A full light treatment group was used as the control group, and a 10% to 90% full light treatment group was used as the shade treatment group. After the seedlings of the fragrant fruit tree were cultivated for one year, the roots, stems and leaves were collected.

3. The method according to claim 1, characterized in that In step S2, the samples for the determination of non-structural carbon index are first treated as follows: the root, stem and leaf samples of the fragrant fruit tree are sterilized at 105°C for 30 minutes and then dried at 80°C for 48 hours.

4. The method according to claim 1, characterized in that: In step S2, the non-structural carbon indicators include soluble sugar concentration, starch concentration, non-structural carbon concentration, sugar pool, starch pool, non-structural carbon pool, sugar pool / starch pool, sugar pool / non-structural carbon pool, sugar pool / total non-structural carbon pool, starch pool / non-structural carbon pool, starch pool / total non-structural carbon pool.

5. The method according to claim 1, characterized in that In step S2, the samples for transcriptome sequencing are first processed as follows: the root, stem and leaf samples of the fragrant fruit tree are quickly frozen with liquid nitrogen.

6. The method according to claim 1, characterized in that In step S2, the transcriptome sequencing includes: using the Illumina HiSeq platform to perform transcriptome sequencing, removing reads containing ploy-N and low-quality reads to obtain clean reads, and performing de novo assembly on the clean reads to obtain unigenes, and finally filtering genes with an expression standard deviation of ≤0.8 to obtain transcriptome data.

7. The method according to claim 1, characterized in that In step S3, the weighted gene co-expression network model is established to divide the transcriptome data into multiple gene modules, including: based on the transcriptome data, a weighted gene co-expression network model is established by WGCNA, and a power value range of 1 to 30 is selected to obtain multiple gene modules.

8. The method according to claim 7, characterized in that The power value was selected as 22, and 20 gene modules were obtained.

9. The method according to claim 1, characterized in that: In step S3, the screening gene module includes: using the Pearson correlation algorithm to calculate the correlation between the characteristic genes of the gene module and the non-structural carbon index, the correlation includes a correlation coefficient and a p-value, and screening is performed based on the absolute value of the correlation coefficient ≥ 0.3 and the p-value is less than 0.05 as the threshold to obtain the key gene module.

10. The method according to claim 1, characterized in that In step S3, the screening criteria for screening key genes are: gene connectivity greater than 15, and genes related to non-structural carbon metabolism.

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