A method for analyzing a maize nitrogen response gene regulatory element

Abstract

The present application relates to a kind of corn nitrogen response gene regulatory element analysis method.By the fusion application of transcriptome sequencing, gene co-expression network construction, gene function annotation, element enrichment analysis and yeast one-hybrid technology, the complex gene regulatory network is simplified to excavate the key transcriptional regulatory factor therein.The regulatory element analysis and transcription factor mining using the method of the present application can reduce the analysis difficulty and improve the accuracy of prediction.

Description

Technical Field

[0001] This invention relates to a method for analyzing regulatory elements in complex gene networks and the use of this method for transcription factor mining and analysis. Technical Background

[0002] Gene regulatory element analysis is an important method for predicting gene function and can be used to predict transcription factors that regulate a gene. Current software for predicting and analyzing regulatory elements is highly efficient for single genes or groups of genes. However, for complex regulatory networks like the nitrogen response in maize, analyzing single or a few gene regulatory elements can lead to significant biases in predicting key transcription factors. Therefore, developing analytical methods for regulatory elements in complex gene networks is a pressing issue that needs to be addressed. Summary of the Invention

[0003] To address the accuracy challenges in analyzing complex network regulatory elements and transcription factors in crops such as maize, this invention utilizes methods such as transcriptome sequencing, co-expression network construction, gene function annotation, element enrichment analysis, and yeast one-hybrid analysis to perform complex network regulatory element analysis and transcription factor mining, thereby reducing the difficulty of analysis and improving its accuracy.

[0004] The specific technical solution of the present invention is as follows:

[0005] (1) Transcriptome sequencing was performed on samples treated with nitrogen for different durations, and differentially expressed genes (DEGs) were obtained by analyzing the transcriptome data;

[0006] (2) Construct a gene co-expression network using the differentially expressed gene data in step (1) and mine co-expression modules;

[0007] (3) Calculate the relationship between each module in step (2) and nitrogen treatment and nitrogen metabolism, and screen out the modules that are closely related to nitrogen treatment time and nitrogen response marker genes;

[0008] (4) Perform functional annotation on the modules obtained in step (3) to identify genes closely related to nitrogen biological processes; analyze the common regulatory elements in the promoter regions of these genes. Each module is analyzed independently in this step with no overlap.

[0009] (5) The common elements obtained in step (4) are subjected to enrichment analysis and nitrogen-related function prediction to screen out elements that are significantly enriched in the gene promoter regions of their own modules but not significantly enriched in the gene promoter regions of other modules, and elements that are closely related to nitrogen metabolism pathways. The enrichment analysis and function prediction analysis in this step are performed independently, the elements predicted by the two methods are combined, and duplicates are removed.

[0010] (6) The elements obtained in step (5) are compared with known databases, and elements with significant similarity to known transcription factor binding sites (q-value < 0.05) are retained. Yeast one-hybrid technology is used to verify and mine transcription factors involved in regulating nitrogen response-related genes. The correctly verified regulatory elements are the elements obtained in this invention.

[0011] Beneficial effects:

[0012] By integrating techniques such as transcriptome sequencing, gene co-expression network construction, gene function annotation, element enrichment analysis, and yeast one-hybrid assays, complex gene regulatory networks are simplified to uncover key transcriptional regulatory factors. Using this method for regulatory element analysis and transcription factor discovery reduces analytical complexity and improves predictive accuracy. Attached Figure Description

[0013] Figure 1 Flowchart of methods for analyzing gene regulatory elements and transcription factors

[0014] Figure 2 Results of yeast one-hybrid screening Detailed Implementation

[0015] The present invention is further illustrated below by way of examples, but the scope of protection of the present invention is not limited to the following examples, but is defined by the specification and claims of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0016] Example 1: Analysis of Regulatory Elements in the Maize Nitrogen Response Gene Expression Regulatory Network

[0017] When exposed to nitrogen stimulation, thousands of genes in maize exhibit a rapid response within a short period; these genes are known as initial nitrogen response genes. These genes form a complex network that mediates the plant's response to external nitrogen fluctuations. Identifying the regulatory elements of these genes and analyzing their functions and corresponding transcriptional regulators is crucial for elucidating the mechanisms of nitrogen response in maize.

[0018] The specific implementation process of the technical solution of the present invention is as follows: Figure 1 As shown

[0019] (1) Transcriptome data of nitrogen-treated maize inbred line B73 were downloaded from the NCBI SRA database (http: / / www.ncbi.nlm.nih.gov / sra / ) and JXB onine (https: / / academic.oup.com / jxb). Reads were aligned to the B73_v4 genome using TopHat, and the FPKM value of each gene was calculated as its expression level. Differentially expressed genes (DEGs) in the control and treatment groups were analyzed using the R package EdgeR. Differentially expressed genes from different sources were analyzed independently. Genes with FDR < 0.05 and |log2(Fold Change)| > 1.5 were identified as differentially expressed genes. GO enrichment analysis was performed on overlapping differentially expressed genes from different sources, and genes closely related to nitrogen biological processes (FDR < 0.05) were retained. Genes with differential expression patterns and similar expression characteristics in transcriptome data from different sources, and closely related to nitrogen biological processes, were defined as nitrogen-responsive marker genes (Table 1).

[0020] Table 1 Nitrogen-responsive marker genes

[0021]

[0022]

[0023] (2) Maize seedlings cultured under low nitrogen conditions for 2 weeks and exhibiting nitrogen deficiency symptoms were subjected to nitrogen stimulation treatment for 0 minutes, 15 minutes, 30 minutes, 1 hour, 2 hours, 4 hours, 8 hours, and 12 hours. Transcriptome sequencing (RNA-seq) was performed on maize roots from each treatment time point, with three biological replicates at each time point. The sequencing reads were aligned to the maize B73_V4 genome using TopHat software. FPKM values ​​were used to represent gene expression levels. The EdgeR package in R was used to calculate differentially expressed genes in the control and nitrogen-treated samples. The selection criteria for differentially expressed genes were: p-value < 0.05 at at least two treatment time points and |log2(Fold Change)| > 1.5. A total of 4499 genes were ultimately screened and used for further analysis.

[0024] (3) Gene co-expression networks were constructed from the 4499 differentially expressed genes obtained in step (2) using the Weighted Gene Co-Expression Network Analysis (WGCNA) R package. The network was constructed using an unsigned type of topological overlap matrix. Cut-off R 2 The threshold was set to 0.8, the soft-thresholding power β was set to 14, and the minimum module size was 100. Gene modules were detected using the topological overlap measure (TOM), and similar modules were merged (threshold 0.2), resulting in 16 co-expressed modules. These modules were named and classified using different colors.

[0025] (4) Calculate the correlation between the modules obtained in step (3) and nitrogen response marker genes and nitrogen treatment time. Rank all modules according to the correlation of module traits and p-value, and finally screen out 9 modules that are closely related to nitrogen treatment time and marker genes.

[0026] (5)Use agriGO( http: / / bioinfo.cau.edu.cn / agriGO The genes in the nine modules obtained in step (4) were annotated using Mapman software. Genes closely related to nitrogen metabolism in each module were used for motif enrichment analysis. This step can reduce false positives in motif analysis. A total of 237 differentially expressed genes were identified in the nine modules, including 65 genes from the "brown" module; 16 from the "cyan" module; 11 from the "darkgreen" module; 68 from the "darkolivegreen" module; 11 from the "lightyellow" module; 19 from the "pink" module; 25 from the "purple" module; 10 from the "steelblue" module; and 12 from the "tan" module. On a module-by-module basis, the MEME function in MEME-suit software was used to predict common regulatory elements of the screened genes. Each module was analyzed independently in this step, with no overlap. The prediction range was from 1000 bp upstream to 200 bp downstream of the gene transcription start region. The parameters were set as follows: minw 5-15, maxw 20-50, nmotifs 30-50. The final prediction yielded 420 common control elements (E-value < 0.05).

[0027] (6) The AME and CentriMo functions in MEME-suit software were used to analyze the 420 common regulatory elements predicted in step (5). Seventeen common regulatory elements (E-value < 0.05) were selected that were significantly enriched in the promoter regions of all genes in the module in which the common regulatory element was located, but not significantly enriched in other modules.

[0028] The GOMo function in MEME-suit software was used to analyze the 420 common regulatory elements predicted in step (5), and 51 common regulatory elements (E-value < 0.05) closely related to the nitrogen metabolism pathway were screened out. The FIMO function was used to analyze the 51 common regulatory elements, and common regulatory elements with occurrences < 60% were removed. Finally, 30 common regulatory elements were obtained. The common regulatory elements obtained by the two methods were combined, and one duplicate element was removed, resulting in 46 common regulatory elements.

[0029] (7) Using the Tomtom function in MEME-suit, the 46 common regulatory elements obtained in step (6) were compared with known common regulatory element databases (DAP motifs database of Arabidopsis thaliana, CIS-BP2.00 Single Specise DNA of Zea mays, CIS-BP 2.00 Single Specise DNA of Sorghumbiclor). 28 common regulatory elements were screened and found to have significant similarity to known transcription factor binding sites (q-value < 0.05). Among these, 20 common regulatory elements showed significant similarity to the binding sites of transcription factors from the LBD, MYB, NAC, NLP, MADS, and TGA families, which have been shown to play important roles in nitrogen metabolism; 8 transcription factor binding sites included AP2 / EREBP family transcription factor binding sites, which have not yet been experimentally confirmed to play an important role in maize nitrogen metabolism.

[0030] (8) The screening results of step (7) were verified using yeast one-hybrid technology. Motif 1 (q-value = 2.47906e-07), which showed the highest matching degree with the binding sites of AP2 / EREBP family transcription factors, was selected for yeast one-hybrid screening. Finally, four AP2 / EREBP family transcription factors were obtained: ZmEREB97 (Zm00001d002364), ZmEREB205 (Zm00001d026271), ZmEREB92 (Zm00001d000339), and ZmEREB196 (Zm00001d038585). Figure 2 ).

Claims

1. A method for analyzing regulatory elements of nitrogen-responsive genes in maize, characterized in that, Includes the following steps: (1) Transcriptome sequencing was performed on samples treated with nitrogen for different durations, and differentially expressed genes (DEGs) were obtained by analyzing the transcriptome data; (2) Construct a gene co-expression network using the differentially expressed gene data in step (1) and mine co-expression modules; (3) Calculate the relationship between each module in step (2) and nitrogen treatment and nitrogen metabolism, and screen out the modules that are closely related to nitrogen treatment time and nitrogen response marker genes; (4) Perform functional annotation on the modules obtained in step (3) to identify genes closely related to nitrogen biological processes; analyze the common regulatory elements in the promoter regions of these genes. Each module is analyzed independently in this step, with no overlap. (5) The AME and CentriMo functions in MEME-suit software were used to analyze the common regulatory elements predicted in step (4) and screen out a portion of common regulatory elements that are significantly enriched in the promoter regions of all genes in the module in which the common regulatory element is located and not significantly enriched in other modules. The GOMo function in MEME-suit software was used to analyze the common regulatory elements predicted in step (4) and screen out common regulatory elements closely related to nitrogen metabolism pathways. The FIMO function was used to analyze the selected common regulatory elements closely related to nitrogen metabolism pathways and remove common regulatory elements with occurrences <60%. Finally, a portion of common regulatory elements were obtained. The common regulatory elements obtained by the two methods were merged and duplicate common regulatory elements were removed. Finally, common regulatory elements of gene promoter regions closely related to nitrogen biological processes were obtained. (6) Compare the elements obtained in step (5) with known databases and retain the elements that have significant similarity to known transcription factor binding sites; (7) Use yeast one-hybrid technology to verify the screening results of step (6) and discover transcription factors involved in regulating nitrogen response-related genes. Verify that the correct regulatory element is the obtained element.

2. The method for analyzing maize nitrogen-responsive gene regulatory elements according to claim 1, characterized in that, In step (1), maize seedlings cultured under low nitrogen conditions for 2 weeks and exhibiting nitrogen deficiency symptoms were subjected to nitrogen stimulation treatment for 0 minutes, 15 minutes, 30 minutes, 1 hour, 2 hours, 4 hours, 8 hours, and 12 hours. Transcriptome sequencing was performed on maize root samples from each treatment time. Three biological replicates were set up for each time point. The sequencing reads were aligned to the maize B73_V4 genome using TopHat software. The FPKM value was used to represent gene expression levels. The R language package... EdgeR The differentially expressed genes were used to calculate the differentially expressed genes in the control and nitrogen-treated samples. The selection criteria for differentially expressed genes were: p-value < 0.05 and |log2| > 1.5 at at least two treatment times.

3. The method for analyzing nitrogen-responsive gene regulatory elements in maize according to claim 1, characterized in that, In step (2), the weighted gene co-expression network is constructed using the R package Weighted Gene Co-Expression Network Analysis to analyze the differentially expressed genes obtained in step (1). The network is constructed using a dimensionless topological overlap matrix. Cut-off R 2 The threshold is set to 0.8, the soft threshold β is set to 14, the minimum module size is 100, the gene modules are detected by topological overlap measurement and similar modules are merged, the threshold is 0.2, and these modules are named and classified with different colors.

4. The method for analyzing nitrogen-responsive gene regulatory elements in maize according to claim 1, characterized in that, The method for obtaining nitrogen response marker genes in step (3) is as follows: download nitrogen response-related transcriptome data from public databases, independently analyze differentially expressed genes in each group of data, analyze overlapping differentially expressed genes in data from different sources, and perform functional annotation on them. Genes that are differentially expressed in data from different sources and have similar expression patterns and are closely related to nitrogen biological processes are defined as nitrogen response marker genes.

5. The method according to claim 4, characterized in that, Step (3) Calculate the correlation between the modules obtained in step (2) and the nitrogen response marker gene and nitrogen treatment time. Rank all modules according to the correlation of module traits and p-value, and finally screen out the modules that are closely related to nitrogen treatment time and marker genes.

6. The method according to claim 1, characterized in that, Step (4) The genes in the nine modules obtained in step (3) were annotated using agriGO (http: / / bioinfo.cau.edu.cn / agriGO) and Mapman software. Genes closely related to nitrogen metabolism in each module were used for common regulatory element enrichment analysis. This step can reduce the false positives of common regulatory element analysis. Finally, differentially expressed genes were identified in all modules. Using modules as units, the MEME function in the MEME-suit software was used to predict common regulatory elements in the screened genes. Each module was analyzed independently with no overlap. The prediction range was from 1000 bp upstream to 200 bp downstream of the gene transcription initiation region. The parameters were set as follows: minw 5-15, maxw 20-50, nmotifs 30-50. The common regulatory elements were finally predicted, and the E-value was <0.

05.

7. The method according to claim 1, characterized in that... Step (6) uses the TomTom function in MEME-suit to connect the common control element obtained in step (5) with the known motifs database DAP motifs database. Arabidopsis thaliana , CIS-BP 2.00 Single Specise DNA of Zea mays , CIS-BP 2.00 SingleSpecise DNA of Sorghum biclor By comparison and screening, common regulatory elements were found to have significant similarities with known transcription factor binding sites.

8. The method according to claim 1, characterized in that... Step (7) uses yeast one-hybrid technology to verify the screening results of step (6), selects the common regulatory element with the highest matching degree with the binding site of AP2 / EREBP family transcription factors, performs yeast one-hybrid screening, and finally obtains AP2 / EREBP family transcription factors.

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