Analysis method for mining candidate genes of altitude adaptability characters of plants

Abstract

The invention relates to the technical field of nucleic acid detection and identification methods including different detections, and discloses an analysis method for mining plant altitude adaptive character candidate genes. A specific quadrat establishment mode (the population is growth type or stable type, the populations at different altitudes have gene exchange, it is ensured that varieties are consistent, plants of all the quadrat are adaptive to the altitudes, and the varieties and secondary metabolism interference results are avoided) and a sampling mode (samples at different altitudes are sampled at the same time and frozen quickly, RNA in the samples is extracted for sequencing and database building, and the sampling mode is used for sampling the samples at different altitudes. A data comparison mode (only samples in upper and lower adjacent quadrats are compared, cross-region comparison is not needed, and the same result is prevented from being measured through cross-region comparison) and a data processing mode (intersection is taken through multiple methods, and the result is obtained through cross-region comparison and the same result is measured through cross-region comparison. Influences caused by different databases are removed), interference can be eliminated, and genes related to plant altitude adaptability characters are found out from differentially expressed genes.

Description

Technical Field

[0001] The present invention relates to the technical field of nucleic acid detection and identification methods including different detections, and in particular to an analysis method for mining candidate genes for plant altitude adaptation traits. Background Art

[0002] In the process of plant breeding, it is necessary to identify the genes related to a certain trait. Even if genetic modification is not used for breeding, if the genes related to a certain trait have been identified, it can also help judge the effect of breeding.

[0003] Ideal genes are like the tall and short stems of plants, with a small number and obvious effects. They are not only easy to locate, but also easy to regulate and detect. But many times, there is more than one gene associated with a certain trait. Taking altitude adaptation traits as an example, since the impact of altitude on plants is reflected in many aspects, including but not limited to temperature, soil, wind, light, insect populations, etc., there are a large number of genes related to altitude adaptation traits. Other similar ones include a series of traits with high economic value but complex associations, such as high yield and salt-alkali resistance.

[0004] For higher animals, finding differentially expressed genes is very helpful in determining genes related to a certain trait. This is because higher animals tend to create a stable internal microenvironment (such as constant temperature), so they do not have to make major adjustments to their biological pathways (such as various metabolic channels). For example, in the human discipline, in the genetic research of developmental disorders and intellectual disabilities, the screening of differentially expressed genes can be used to analyze the differences in gene expression between patients and normal people, revealing related genetic variations and their role in the occurrence of diseases. However, for plants, since they can only passively adapt to the environment, their internal biological pathways must be constantly adjusted to adapt to the environment. Even for the same plant, its internal biological pathways and even various biochemical indicators may change dramatically in different locations and weather (these changes are usually fatal to animals). At the same time, gene mutations and chimeras in plants occur more frequently (even the same plant can form chimeras due to local gene mutations, such as peach trees with red flowers on one side and white flowers on the other side that are spontaneously formed due to bud mutations without grafting).

[0005] Therefore, for plants, no matter how well the variables are controlled, the differentially expressed genes measured may not be associated with the traits used as variables, but may be something completely unrelated. In some cases, the genes associated are negative for breeding. For example, in a stressful environment, the genes measured may be related to secondary metabolism (such as the accumulation of toxic substances under drought conditions, but this toxic substance is not for improving drought adaptability). In this case, the results obtained are not only unhelpful for breeding, but often even harmful.

[0006] The inventors found that altitude adaptation is a very special trait. Unlike weather, salinity and other easily changeable external environmental factors, altitude does not change and can exert a stable influence on plants (for example, the temperature drops by 6°C for every 1 km increase in altitude). By using this feature, combined with specific sample establishment methods, sampling methods, data comparison methods, and data processing methods, interference can be eliminated and genes related to plant altitude adaptation traits can be found from differentially expressed genes. Summary of the invention

[0007] The invention provides an analysis method for mining candidate genes for plant altitude adaptation traits.

[0008] The technical problem to be solved is: because the internal biological pathways of plants must be constantly and significantly adjusted to adapt to the environment, and at the same time, gene mutations and chimeras in plants occur more frequently, it is difficult to screen genes related to a certain trait through differentially expressed genes.

[0009] In order to solve the above technical problems, the present invention adopts the following technical solution: an analysis method for mining candidate genes for plant altitude adaptation traits, which is used to mine candidate genes for altitude adaptation traits of specific plant species, and the plant species to be analyzed is recorded as the sampled species. The analysis method comprises the following steps: Step 1: Select samples at different altitudes. The sampled species in each sample are of the same variety and the plants in each sample are adapted to the altitude. Step 2: On the same day, select mature leaves of healthy adult sampling plants in each plot for sampling, and freeze the samples immediately to prevent the RNA in the samples from changing; Step 3: Extract RNA from the sample for sequencing and library construction; Step 4: Analyze differentially expressed genes. Every two adjacent plots are recorded as a comparison group, and the analysis is performed based on the comparison group; Step 5: Perform enrichment analysis and obtain altitude-specific gene modules.

[0010] Furthermore, in step 1, if the samples in different quadratologies meet the following conditions: Condition 1.1: Under natural conditions, the sampled species mainly reproduces sexually; Condition 1.2: The population of the sampled species in each plot is of the growth type or stable type; Condition 1.3: All samples come from the same sunny slope or the same shady slope; Condition 1.4: There are smooth pollination channels between the populations of plants of the sampled species in different plots; They are considered to be of the same variety and the plants in each plot are adapted to the altitude.

[0011] Furthermore, the sampled species is Rosa canina.

[0012] Furthermore, in step 2, liquid nitrogen is used to freeze the sample so that the RNA in the sample does not change, and then the sample is stored at a temperature not higher than -80°C until the sample extraction stage.

[0013] Furthermore, in step 3, the total RNA of all samples was extracted using the RNApre Pure kit, and then the RNA quality was tested. After passing the test, the library was sequenced and constructed. Further, step 4 includes the following sub-steps: Step 4.1: Filter raw data to get clean data; Step 4.2: Use Trinity software to assemble the transcriptome and perform functional annotation of genes; Step 4.3: Use DESeq2 to analyze differentially expressed genes, with the criteria of |log2fold change|≥1 and padj<0.05; Step 4.4: Perform volcano plot analysis on the screened differentially expressed genes to determine the overall distribution of the differentially expressed genes.

[0014] Furthermore, step five: at least two different methods are used for enrichment analysis, sets are established for biological pathways enriched with different differentially expressed genes, and the intersection of the conclusions of all methods is selected for analysis to obtain altitude-specific gene modules.

[0015] Furthermore, in step five, KEGG enrichment and GO enrichment were used for enrichment analysis, KOBAS 2.0 was used for KEGG enrichment, and Goseq was used for GO enrichment, with the P value threshold set to 0.05; finally, WGCNA analysis was used to divide gene modules and screen altitude-specific gene modules.

[0016] Compared with the prior art, the analysis method for mining candidate genes for plant altitude adaptation traits of the present invention has the following beneficial effects: In the present invention, the characteristic that the altitude does not change and can exert a stable influence on plants is utilized, and a specific sample establishment method, sampling method, data comparison method, and data processing method are combined to eliminate interference and find genes related to plant altitude adaptation traits from differentially expressed genes. Among them: Sample plot establishment method: the population is growing or stable, and there is gene exchange between populations at different altitudes to ensure that the varieties are consistent (it is very difficult to judge plant varieties in nature) and the plants in each sample plot are adapted to the altitude to avoid the interference of varieties and secondary metabolism; Sampling method: Samples from different altitudes are collected simultaneously and frozen, and then RNA is extracted from the samples for sequencing and library construction to ensure that the actual gene expression under a given condition is measured; Data comparison method: only compare samples in adjacent plots, not across regions, to avoid getting the same results across regions. Because biological pathways do not change monotonically with altitude, the same expression level may correspond to multiple different altitudes; Data processing method: multiple methods are used to take the intersection and remove the impact caused by different databases. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Volcano plots of sample genes from different samples; Figure 2 This is a functional enrichment analysis diagram of differentially expressed genes at different altitudes; Figure 3 This is the KEGG enrichment analysis diagram of differentially expressed genes at different altitudes; Figure 4 The differentially expressed gene map for redox balance; Figure 5 This is the characteristic gene expression map of the altitude-specific module; Figure 6 This is the association analysis diagram between gene modules and leaf functional traits; DETAILED DESCRIPTION

[0018] An analysis method for mining candidate genes for plant altitude adaptation traits, used for mining candidate genes for altitude adaptation traits of specific plant species, the plant species to be analyzed is recorded as a sampling species, and the analysis method comprises the following steps: Step 1: Select samples at different altitudes. The sampled species in each sample belong to the same variety and the plants in each sample are adapted to the altitude to avoid the variety and secondary metabolism interfering with the results. In this example, five R. lutea communities at different altitudes (1050 m, 1190 m, 1250 m, 1350 m, and 1550 m) were selected, each 30 m × 30 m, named A to E. Each plot was divided into three 10 m × 10 m plots, for a total of 15 plots.

[0019] Step 2: On the same day, select mature leaves of healthy adult sampling plants in each plot for sampling, and freeze the samples immediately to prevent the RNA in the samples from changing, ensuring that the actual gene expression under a given condition is measured; RNA can reflect actual gene expression, but it is unstable and will change during sample storage, so it needs to be frozen. The reason for quick freezing is to ensure that all samples are sampled synchronously to avoid factors such as weather and light that change over time and interfere with the results.

[0020] In this embodiment, 5 healthy adult yellow thorn roses were selected from each plot, for a total of 75 plants. Sampling was carried out from 9:00 to 11:00 on August 17, 2023, and mature leaves were collected.

[0021] Step 3: Extract RNA from the sample for sequencing and library construction; Step 4: Analyze differentially expressed genes. Every two adjacent plots are recorded as a comparison group, and the analysis is performed based on the comparison group; Only compare samples in adjacent plots, not across regions, to avoid getting the same results across regions. Because biological pathways do not change monotonically with altitude, the same expression level may correspond to multiple different altitudes; for example, a specific biological pathway has an expression level of 3x at altitude A, 4x at altitude B, and 3x at altitude C, and cross-region comparisons will miss this biological pathway.

[0022] Step 5: Perform enrichment analysis and obtain altitude-specific gene modules.

[0023] In step 1, if the samples in different quadrat meet the following conditions: Condition 1.1: Under natural conditions, the sampled species mainly reproduces sexually; Condition 1.2: The population of the sampled species in each plot is of the growth type or stable type; Condition 1.3: All samples come from the same sunny slope or the same shady slope; Condition 1.4: There are smooth pollination channels between the populations of plants of the sampled species in different plots; They are considered to be of the same variety and the plants in each plot are adapted to the altitude.

[0024] "Variety" itself is a concept for artificially cultivated animals, plants and microorganisms. It is difficult to determine the varieties of plants in nature, and even plants in the same population may have very different traits. However, if there is sufficient gene exchange within the population, the individuals in the population can be regarded as one variety. But even the same variety may have completely different forms on the sunny slope and the shady slope, such as shepherd's purse, so it is necessary to limit sampling to the same sunny slope or the same shady slope. If the population is a growing or stable type (field investigation is required), it means that its growth is not under stress, it adapts to the altitude, and the influence of secondary metabolism caused by stress can be ruled out. "There are unobstructed pollination channels", that is, there are no obstacles to block pollination media (wind, water, insects, etc.).

[0025] In the present embodiment, the sampled species is yellow thorn rose. The Loess Plateau is located in the middle reaches of the Yellow River, with the world's largest loess accumulation area, and the terrain is very different. It is one of the areas with the most serious soil erosion in the world. Yellow thorn rose is distributed in various provinces in northern my country. It is a dominant shrub in the Loess Plateau in western Shanxi. It has a relatively developed root system, wide ecological adaptability, tolerance to barrenness, salt and alkali, drought resistance, cold resistance, and shade tolerance. It has ecosystem service functions such as hydrological regulation, windbreak and sand fixation, ditch consolidation and slope protection, carbon fixation, and sink enhancement; the fruit yield is high, rich in high-activity superoxide dismutase, and has good nutritional and health value; the flowers are golden yellow, with a large amount of flowers and a long flowering period, and have extremely high ornamental value. Therefore, the transcriptome sequencing and analysis of the altitude environment adaptation of yellow thorn rose are of great significance to the improvement of the natural environment of the Loess Plateau, the improvement of ecosystem service functions, and the development of forest economy.

[0026] In step 2, liquid nitrogen is used to freeze the sample so that the RNA in the sample will not change, and then the sample is stored at a temperature not higher than -80°C until the sample is extracted.

[0027] In step 3, the total RNA of all samples was extracted using the RNApre Pure kit, and then the RNA quality was tested. Each sample was repeated 3 times, and the qualified samples were sequenced and library constructed. Step 4 includes the following sub-steps: Step 4.1: Filter raw data to get clean data; Step 4.2: Use Trinity software to assemble the transcriptome and perform functional annotation of genes; Step 4.3: Use DESeq2 to analyze differentially expressed genes, with the criteria of |log2fold change|≥1 and padj<0.05; Step 4.4: Perform volcano plot analysis on the screened differentially expressed genes to determine the overall distribution of the differentially expressed genes.

[0028] Step 5: Use at least two different methods to perform enrichment analysis, establish sets for biological pathways enriched with different differentially expressed genes, select the intersection of the conclusions of all methods for analysis, and obtain altitude-specific gene modules.

[0029] In step 5, KEGG enrichment and GO enrichment were used for enrichment analysis, KOBAS 2.0 was used for KEGG enrichment, and Goseq was used for GO enrichment, with the P value threshold set at 0.05; finally, WGCNA analysis was used to divide gene modules and screen altitude-specific gene modules.

[0030] GO enrichment analysis: Based on the GO (Gene Ontology) database. The GO database aims to provide a semantic vocabulary standard that is applicable to various species to define and describe the functions of genes and proteins. It provides a standardized description of gene products from three aspects: molecular function (MF), cellular component (CC), and biological process (BP). KEGG (Kyoto Encyclopedia of Genes and Genomes) is a database that integrates a variety of biological information, covering the metabolic pathways of gene products in cells and the functions of these gene products. KEGG enrichment analysis is based on this database to perform functional annotation and pathway enrichment analysis on genes in the gene set. These two enrichment analyses have their own emphasis, the former focuses on results, and the latter focuses on processes. The two are consistent and can effectively ensure the closure of the causal chain.

[0031] The following are the analysis results of the embodiment: (1) A total of 208 differentially expressed genes (DEGs) were screened in the B vs A comparison group, of which 102 were upregulated and 106 were downregulated; a total of 224 differentially expressed genes were screened between the C vs B comparison group, of which 75 were upregulated and 149 were downregulated; a total of 2075 differentially expressed genes were screened between the D vs C comparison group, of which 1165 were upregulated and 910 were downregulated; a total of 1466 differentially expressed genes were screened between the E vs D comparison group, of which 643 were upregulated and 823 were downregulated ( Figure 1 ).

[0032] (2) The results of GO enrichment analysis of differentially expressed genes in leaves of Rosa rugosa at different altitudes are as follows Figure 2As shown, in the B Vs A comparison group, the differentially expressed genes were mainly enriched in the transmembrane transport (biological process) and transporter activity (molecular function) items. In the CVs B comparison group, the differentially expressed genes were mainly enriched in the oxidoreductase activity (molecular function) item. In the D Vs C comparison group, the differentially expressed genes were mainly enriched in the ribosome, cell wall and external packaging structure (cellular component); ribosome biogenesis, cell wall organization or biosynthesis and lipid metabolism process (biological process); structural molecule activity, antioxidant activity, oxidoreductase activity (molecular function) and other items. In the E Vs D comparison group, the differentially expressed genes were mainly enriched in the ribosome, cell wall and external packaging structure (cellular component); ribosome biogenesis, cell wall and external packaging structure (cellular component); structural molecule activity, antioxidant activity, phenylglutamic acid alanine aminotransferase activity, oxidoreductase activity and transport activity (molecular function) items.

[0033] (3) KEGG enrichment results of differentially expressed genes in leaves of Rosa rugosa at different altitudes are as follows Figure 3 As shown, in the B vs. A comparison group, the differentially expressed genes were mainly enriched in pathways such as ribosome, tryptophan metabolism, MAPK signaling pathway, peroxisome, and glyoxylate and dicarboxylate metabolism. In the C vs. B comparison group, the differentially expressed genes were mainly enriched in pathways such as plant hormone signal transduction, phenylpropanoid biosynthesis, MAPK signaling pathway, cyanamine metabolism, starch and sucrose metabolism, and peroxisome. In the D vs. C comparison group, the differentially expressed genes were mainly enriched in pathways such as ribosome, ABC transporter, glycolysis / gluconeogenesis, tryptophan metabolism, glutathione metabolism, and phenylpropanoid biosynthesis. In the E vs. D comparison group, the differentially expressed genes were mainly enriched in pathways such as ribosome, unsaturated fatty acid biosynthesis, glycolysis / gluconeogenesis, and phenylpropanoid biosynthesis.

[0034] (4) Redox balance: The most differentially expressed genes were those involved in the removal of reactive oxygen species by enzymes. Most of the differentially expressed genes were upregulated in plot B and downregulated in plots D and E. Figure 4 ). The differentially expressed genes in the redox regulation of glutathione were glutathione S-transferase genes, most of which were down-regulated in sample C, and one gene was up-regulated in sample E. In the redox regulation based on thiol, most of the peroxide reductase genes were up-regulated in samples D and E; two thioredoxin genes were up-regulated in samples B and C; the thioredoxin reductase gene was up-regulated in sample D; two methylsulfoxide reductase genes were down-regulated in sample DE, and one gene was up-regulated in sample D. In the redox regulation based on ascorbate, the biosynthesis genes were up-regulated in samples D and E; the reductase activity genes were down-regulated in sample D.

[0035] (5) Gene modules were divided by WGCNA analysis. The red module is the altitude-specific high-expression module B and C, the blue module is the altitude-specific high-expression module A, and the magenta module is the altitude-specific high-expression module E. Figure 5 ).

[0036] The gene expression of the specific high-expression magenta module in plots B and C was significantly positively correlated with LDMC; it was extremely significantly negatively correlated with LNC, with the highest negative correlation coefficient of 0.78; and it was significantly negatively correlated with LWC. The gene expression of the specific high-expression blue module in plot A was extremely significantly positively correlated with LCC, with the highest positive correlation coefficient of 0.75; it was significantly positively correlated with LCP and LNP, and significantly negatively correlated with LPC. The gene expression of the specific high-expression magenta module in plot E was extremely significantly positively correlated with LNC, and significantly negatively correlated with LTD and LCN ( Figure 6 ).

[0037] The embodiments described above are merely descriptions of preferred implementation modes of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. An analysis method for mining candidate genes for plant altitude adaptation traits, used for mining candidate genes for altitude adaptation traits of specific plant species, the plant species to be analyzed is recorded as a sample species, characterized in that: The analytical method comprises the following steps: Step 1: Select samples at different altitudes. The sampled species in each sample are of the same variety and the plants in each sample are adapted to the altitude. Step 2: On the same day, select mature leaves of healthy adult sampling plants in each plot for sampling, and freeze the samples immediately to prevent the RNA in the samples from changing; Step 3: Extract RNA from the sample for sequencing and library construction; Step 4: Analyze differentially expressed genes. Every two adjacent plots are recorded as a comparison group, and the analysis is performed based on the comparison group; Step 5: Perform enrichment analysis and obtain altitude-specific gene modules.

2. The method for analyzing plant altitude adaptation traits according to claim 1, characterized in that: In step 1, if the samples in different quadrat meet the following conditions: Condition 1.1: Under natural conditions, the sampled species mainly reproduces sexually; Condition 1.2: The population of the sampled species in each plot is of the growth type or stable type; Condition 1.3: All samples come from the same sunny slope or the same shady slope; Condition 1.4: There are smooth pollination channels between the populations of plants of the sampled species in different plots; They are considered to be of the same variety and the plants in each plot are adapted to the altitude.

3. The method for analyzing plant altitude adaptation traits according to claim 1, characterized in that: The sampled species is Rosa canina.

4. The method for analyzing plant altitude adaptation traits according to claim 1, characterized in that: In step 2, liquid nitrogen is used to freeze the sample so that the RNA in the sample will not change, and then the sample is stored at a temperature not higher than -80°C until the sample is extracted.

5. The method for analyzing plant altitude adaptation traits according to claim 1, characterized in that: In step three, the total RNA of all samples was extracted using the RNApre Pure kit, and then the RNA quality was tested. After passing the test, the sequencing library was constructed.

6. The method for analyzing plant altitude adaptation traits according to claim 1, characterized in that: Step 4 includes the following sub-steps: Step 4.1: Filter raw data to get clean data; Step 4.2: Use Trinity software to assemble the transcriptome and perform functional annotation of genes; Step 4.3: Use DESeq2 to analyze differentially expressed genes, with the criteria of |log2fold change|≥1 and padj <0.05; Step 4.4: Perform volcano plot analysis on the screened differentially expressed genes to determine the overall distribution of the differentially expressed genes.

7. The analysis method for mining candidate genes for plant altitude adaptation traits according to claim 1, characterized in that: Step 5: Use at least two different methods to perform enrichment analysis, establish sets for biological pathways enriched with different differentially expressed genes, select the intersection of the conclusions of all methods for analysis, and obtain altitude-specific gene modules.

8. The analysis method for mining candidate genes for plant altitude adaptation traits according to claim 7, characterized in that: In step 5, KEGG enrichment and GO enrichment were used for enrichment analysis, KOBAS 2.0 was used for KEGG enrichment, and Goseq was used for GO enrichment, with the P value threshold set at 0.05; finally, WGCNA analysis was used to divide gene modules and screen altitude-specific gene modules.

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