Method for characterizing rice grain-filling process based on dynamic expression of transcriptome and application thereof
By screening gene sets with monotonically increasing and decreasing expression patterns in the rice grain transcriptome, a functional model of grain filling index versus time was constructed, solving the problem of accurate characterization and prediction of dynamic changes in the rice grain filling process, and improving the stability and accuracy of the analysis.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SANYA NATIONAL INSTITUTE OF SOUTHERN BREEDING CHINESE ACADEMY OF AGRICULTURAL SCIENCES
- Filing Date
- 2026-05-21
- Publication Date
- 2026-07-24
AI Technical Summary
Existing research is insufficient to fully reflect the dynamic changes in the grain-filling process of rice, and lacks systematic mining of continuous time series data, making it difficult to establish accurate quantitative indicators.
By analyzing the dynamic expression characteristics of the transcriptome, gene sets with monotonically increasing and monotonically decreasing expression patterns were screened out, and a grain-filling index and functional relationship model were constructed to achieve quantitative characterization of the grain-filling process of rice.
It improves the accuracy and stability of characterization during the grouting stage, enables accurate prediction of grouting time for unknown samples, and provides a reliable molecular analysis method for grouting process research and breeding applications.
Smart Images
Figure CN122245417B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of rice genomics technology, specifically to a method and application for characterizing the grain-filling process of rice based on dynamic gene expression in the transcriptome. Background Technology
[0002] Rice (Oryza sativa L.) is an important food crop, and its grain-filling process directly determines the final yield and quality, representing a crucial physiological stage in rice growth and development. The grain-filling process is essentially the transport and accumulation of assimilates from "source" to "sink," involving a series of complex biological processes such as the synthesis and transport of photosynthetic products, as well as starch synthesis and deposition within the grain. Existing research indicates that rice grain filling is synergistically regulated by multiple factors, including photosynthetic capacity, assimilate transport efficiency, starch biosynthesis capacity, and plant hormone regulation.
[0003] Currently, some genes related to rice grain filling have been cloned and their functions analyzed, but these studies are mostly focused on single or a few genes, making it difficult to fully reflect the dynamic changes in the rice grain filling process.
[0004] Furthermore, rice grain filling is a continuously changing dynamic process, and its molecular regulatory network exhibits significant time dependence and stage specificity. Existing research methods typically analyze data based on a single or limited time point, lacking systematic mining of continuous time series data, making it difficult to establish quantitative indicators that can accurately characterize the grain filling process. Summary of the Invention
[0005] To address at least one of the above technical problems, this invention provides a method and application for characterizing the rice grain-filling process based on dynamic transcriptome gene expression. Based on the analysis of dynamic transcriptome expression characteristics, it systematically mines gene sets with monotonically increasing and monotonically decreasing expression patterns throughout the rice grain-filling process, and constructs quantitative indicators and models based on these gene sets to achieve accurate characterization and prediction of the rice grain-filling process.
[0006] The first aspect of the present invention provides a method for characterizing the grain-filling process of rice based on dynamic gene expression from the transcriptome, comprising:
[0007] Step 1: Obtain transcriptome expression data of rice grains during the grain-filling process based on the chronologically arranged time sequence;
[0008] Step 2: Based on transcriptome expression data, screen gene sets that exhibit monotonically increasing and monotonically decreasing expression patterns over time during the grain filling process;
[0009] Step 3: Construct the granulation index based on the selected gene set;
[0010] Step 4: Based on the grain filling index, achieve quantitative characterization of the grain filling process of rice.
[0011] Preferably, in step 1, the transcriptome expression data are expressed as a gene expression matrix, and the gene expression level is expressed as a TPM value.
[0012] In any of the above schemes, step 2 preferably includes:
[0013] Step 211: Screen genes whose gene expression levels show a monotonically increasing trend according to the time sequence to form the first increasing gene set;
[0014] Step 212: Within the first incremental gene set, select genes whose gene expression level at the endpoint is not less than T1 and whose gene expression level at intermediate time points is not less than T2 to form the second incremental gene set.
[0015] Step 213: In the second incremental gene set, select genes whose expression level at the endpoint is less than the expression level at the starting point (T3), thus forming an incremental gene expression set.
[0016] Preferably, in any of the above schemes, step 2 further includes:
[0017] Step 221: Screen genes whose gene expression levels show a monotonically decreasing trend according to the time sequence to form the first set of decreasing genes;
[0018] Step 222: Within the first decreasing gene set, select genes whose gene expression level at the starting point is not less than T1 and whose gene expression level at intermediate time points is not less than T2 to form the second decreasing gene set.
[0019] Step 223: In the second decreasing gene set, select genes with T3 whose expression level at the starting point minus the expression level at the ending point is greater than the expression level at the ending point, thus forming a decreasing gene expression set.
[0020] In any of the above schemes, the preferred embodiment is that, in step 2, the combination of the incremental expression gene set and the decremental expression gene set constitutes a gene set with monotonically increasing and monotonically decreasing expression patterns during the grouting process.
[0021] In any of the above schemes, the starting point refers to the first time node in the time node sequence, the ending point refers to the last time node in the time node sequence, and the intermediate time nodes refer to the time nodes in the time node sequence other than the starting point and the ending point.
[0022] In any of the above schemes, it is preferable that T1 is 10, T2 is 1, and T3 is 10%.
[0023] In any of the above schemes, step 3 preferably includes:
[0024] Step 31: For each gene in the incrementally expressed gene set, at each time point in the time node sequence, calculate... The upward adjustment index at the current time point;
[0025] Step 32: For each gene in the decreasing gene expression set, at each time point in the time node sequence, calculate... As a downward adjustment index at the current point in time;
[0026] Step 33: For each time node in the time node sequence, calculate the average of the upregulation index of all genes in the increasing expression gene set and the downregulation index of all genes in the decreasing expression gene set, and use it as the filling index of the current time node to form the filling index time series.
[0027] In any of the above schemes, step 4 includes: establishing a functional relationship model between the grain filling index and the grain filling time based on the grain filling index time series and the time node series, so as to quantitatively characterize the grain filling process of rice grains through the grain filling index.
[0028] In any of the above schemes, the preferred embodiment is that, in step 4, the functional relationship model is an S-shaped functional relationship model.
[0029] A second aspect of the present invention provides a method for predicting the grain-filling stage of rice, comprising:
[0030] Step S1: Obtain transcriptome expression data of rice grain samples to be predicted;
[0031] Step S2: Extract the gene expression level of each gene in the gene set with monotonically increasing and monotonically decreasing expression patterns from Step 2;
[0032] Step S3: Calculate the grain filling index based on the gene expression level of each gene, following the method in Step 3;
[0033] Step S4: Substitute the grouting index into the functional relationship model established in step 4 to obtain the corresponding grouting time.
[0034] A third aspect of the present invention provides an application of a method for characterizing the grain-filling process of rice based on the dynamic expression of genes from the transcriptome, including its application in the identification of the grain-filling process of rice, and / or its application in the analysis of the same organ development stage of crops.
[0035] The method and application of the present invention for characterizing the rice grain-filling process based on dynamic gene expression from the transcriptome have the following beneficial effects:
[0036] 1. This invention screens a gene set with stable dynamic expression characteristics based on transcriptome data, constructs a grain filling index based on the gene expression levels of the gene set, and establishes a functional relationship model between the grain filling index and grain filling time, thereby realizing continuous quantitative characterization of the grain filling process. Compared with analysis methods based on single / few genes or single / limited time points, this invention comprehensively utilizes the dynamic expression information of multiple genes, improving the accuracy and stability of the characterization of the grain filling stage.
[0037] 2. By establishing a functional relationship model between the grain filling index and the grain filling time, for samples with unknown grain filling stages, it is only necessary to obtain their transcriptome data and calculate the grain filling index to infer their grain filling time, thereby achieving accurate prediction of the sample's developmental stage.
[0038] 3. It can provide reliable molecular-level analytical methods for grain filling process research and breeding applications; and the method can be adapted and transferred to the analysis of other crop organ development stages. Attached Figure Description
[0039] Figure 1 This is a flowchart illustrating a preferred embodiment of the method for characterizing the grain-filling process of rice based on dynamic gene expression of the transcriptome according to the present invention.
[0040] Figure 2 For the method of characterizing rice grain-filling process based on dynamic gene expression of transcriptome according to the present invention, as follows: Figure 1 The flowchart of step 2 in the illustrated embodiment is shown.
[0041] Figure 3 For the method of characterizing rice grain-filling process based on dynamic gene expression of transcriptome according to the present invention, as follows: Figure 1 The flowchart of step 3 in the illustrated embodiment is shown.
[0042] Figure 4 In another embodiment of the method for characterizing rice grain-filling process based on transcriptome dynamic gene expression according to the present invention, the gene expression levels of the gene set with monotonically increasing and monotonically decreasing expression patterns obtained by screening are normalized by Z-score.
[0043] Figure 5 For the method of characterizing rice grain-filling process based on dynamic gene expression of transcriptome according to the present invention, as follows: Figure 4 The TPM value distribution of the gene set with monotonically increasing and monotonically decreasing expression patterns obtained in the illustrated embodiment.
[0044] Figure 6 For the method of characterizing rice grain-filling process based on dynamic gene expression of transcriptome according to the present invention, as follows: Figure 4In the illustrated embodiment, the upregulation / downregulation index distribution of the gene set with monotonically increasing and monotonically decreasing expression patterns obtained by screening at each time point is shown.
[0045] Figure 7 For the method of characterizing rice grain-filling process based on dynamic gene expression of transcriptome according to the present invention, as follows: Figure 4 In the illustrated embodiment, the granulation index of the gene set with monotonically increasing and monotonically decreasing expression patterns obtained by screening is shown at each time point.
[0046] Figure 8 For the method of characterizing rice grain-filling process based on dynamic gene expression of transcriptome according to the present invention, as follows: Figure 4 In the illustrated embodiment, an S-shaped function relationship model is shown between the granulation index time series and the time node series of the gene set with monotonically increasing and monotonically decreasing expression patterns obtained through screening. Detailed Implementation
[0047] To better understand the present invention, the present invention will be described in detail below with reference to specific embodiments.
[0048] Example 1
[0049] like Figure 1 As shown, a method for characterizing the grain-filling process of rice based on dynamic gene expression from the transcriptome includes:
[0050] Step 1: Obtain transcriptome expression data of rice grains during the grain-filling process based on the chronologically arranged time sequence;
[0051] Step 2: Based on transcriptome expression data, screen gene sets that exhibit monotonically increasing and monotonically decreasing expression patterns over time during the grain filling process;
[0052] Step 3: Construct the granulation index based on the selected gene set;
[0053] Step 4: Based on the grain filling index, achieve quantitative characterization of the grain filling process of rice.
[0054] In step 1, transcriptome expression data are presented as a gene expression matrix, with gene expression levels expressed as TPM values.
[0055] like Figure 2 As shown, step 2 includes:
[0056] Step 211: Screen genes whose gene expression levels show a monotonically increasing trend according to the time sequence to form the first increasing gene set;
[0057] Step 212: Within the first incremental gene set, select genes whose gene expression level at the endpoint is not less than T1 and whose gene expression level at intermediate time points is not less than T2 to form the second incremental gene set.
[0058] Step 213: In the second incremental gene set, select genes whose expression level at the endpoint is less than or equal to the expression level at the starting point (T3), thus forming an incremental expression gene set; and:
[0059] Step 221: Screen genes whose gene expression levels show a monotonically decreasing trend according to the time sequence to form the first set of decreasing genes;
[0060] Step 222: Within the first decreasing gene set, select genes whose gene expression level at the starting point is not less than T1 and whose gene expression level at intermediate time points is not less than T2 to form the second decreasing gene set.
[0061] Step 223: In the second decreasing gene set, select genes with T3 expression levels where the difference between the starting gene expression level and the ending gene expression level is greater than the ending gene expression level, thus forming a decreasing gene expression set; and:
[0062] Step 23: The set of increasing and decreasing gene expression constitutes the gene set with monotonically increasing and monotonically decreasing expression patterns during the grouting process.
[0063] Step 2 is used to screen out a set of characteristic genes that can characterize the grouting process. The set of genes with increasing expression and the set of genes with decreasing expression reflect different dynamic expression characteristics during the grouting process.
[0064] It should be noted that the starting point refers to the first time node in the time node sequence, the ending point refers to the last time node in the time node sequence, and the intermediate time nodes refer to the time nodes in the time node sequence other than the starting point and the ending point. It should also be noted that the values of T1, T2, and T3 can be adaptively adjusted according to actual circumstances.
[0065] like Figure 3 As shown, step 3 includes:
[0066] Step 31: For each gene in the incrementally expressed gene set, at each time point in the time node sequence, calculate... The upward adjustment index at the current time point;
[0067] Step 32: For each gene in the decreasing gene expression set, at each time point in the time node sequence, calculate... As a downward adjustment index at the current point in time;
[0068] Step 33: For each time node in the time node sequence, calculate the average of the upregulation index of all genes in the increasing expression gene set and the downregulation index of all genes in the decreasing expression gene set, and use it as the filling index of the current time node to form the filling index time series.
[0069] In step 4, a functional relationship model between the grain-filling index and the grain-filling time is established based on the time series and time node series of the grain-filling index, so as to quantitatively characterize the grain-filling process of rice grains through the grain-filling index. In this embodiment, it is preferred that the functional relationship model is an S-shaped functional relationship model.
[0070] In this embodiment, a gene set with stable dynamic expression characteristics was obtained by screening based on transcriptome data, and a granulation index was constructed based on the gene expression levels of the gene set. A functional relationship model between the granulation index and granulation time was established, realizing continuous quantitative characterization of the granulation process. Compared with analysis methods based on single / few genes or single / limited time points, the comprehensive use of multi-gene dynamic expression information improves the accuracy and stability of the characterization of the granulation stage.
[0071] Example 2
[0072] This embodiment provides a method for predicting the grain-filling stage of rice, including:
[0073] Step S1: Obtain transcriptome expression data of rice grain samples to be predicted;
[0074] Step S2: Extract the gene expression level of each gene in the gene set with monotonically increasing and monotonically decreasing expression patterns from Step 2;
[0075] Step S3: Calculate the grain filling index based on the gene expression level of each gene, following the method in Step 3;
[0076] Step S4: Substitute the grouting index into the functional relationship model established in step 4 to obtain the corresponding grouting time.
[0077] It should be noted that different rice varieties may have slight differences in the gene sets with monotonically increasing and monotonically decreasing expression patterns, and the established functional relationship models may also have subtle differences. However, based on the gene sets with monotonically increasing and monotonically decreasing expression patterns and the functional relationship model determined in step 2 for a certain rice variety, the grain-filling stage of other rice varieties can still be roughly determined. For more precise determination of the grain-filling time of different rice varieties, the method described in Example 1 can be used to determine the corresponding gene sets with monotonically increasing and monotonically decreasing expression patterns and the functional relationship model for each rice variety. When predicting the grain-filling stage, the gene sets with monotonically increasing and monotonically decreasing expression patterns and the functional relationship model corresponding to the rice variety can be used to achieve accurate identification of the grain-filling stage of a specific rice variety.
[0078] It should be further noted that when calculating the grain filling index of the rice to be predicted in step S3, the endpoint gene expression level and the starting gene expression level used to calculate the up-regulation index and the down-regulation index are all data determined when establishing the functional relationship model of the rice variety to be predicted in step 3.
[0079] In this embodiment, by establishing a functional relationship model between the granulation index and the granulation time, for samples with unknown granulation stages, it is only necessary to obtain their transcriptome data and calculate the granulation index to infer their granulation time, thereby achieving accurate prediction of the sample's developmental stage.
[0080] Example 3
[0081] This embodiment provides an application of a method for characterizing the grain-filling process of rice based on the dynamic expression of genes from the transcriptome, including its application in the identification of the grain-filling process of rice, and / or its application in the analysis of the same organ development stage of crops.
[0082] In this embodiment, the method described above can be adapted and applied to analyze other crop organ development stages.
[0083] Example 4
[0084] This embodiment is similar to Embodiment 1, except that a specific rice variety is used in this embodiment to illustrate the method for characterizing the rice grain-filling process based on the dynamic expression of genes from the transcriptome.
[0085] In this embodiment, the rice material used is Zhonghui 210 (R210), which was planted in a certain region from June to October 2023. During the planting process, grain samples were collected according to a predetermined time sequence, and N replicates of transcriptome (RNA-seq) sequencing were performed.
[0086] The time node sequence and the value of N can be adaptively adjusted as needed. In this embodiment, it is preferred that the time node sequence is preset to the 5th, 10th, 15th, 20th, 25th and 30th days after pollination; and the value of N is set to 3.
[0087] For transcriptome (RNA-seq) sequencing, fastp (Chen, S., Zhou, Y., Chen, Y., and Gu, J. (2018). Fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34:i884-i890. 10.1093 / bioinformatics / bty560.) was used for data quality control. Then, hisat2 was used for sequence alignment to the Nipponbare T2T genome (Shang, L., He, W., Wang, T., Yang, Y., Xu, Q., Zhao, X., Yang, L., Zhang, H., Li, X., Lv, Y., et al. (2023). A complete assembly of the rice Nipponbare reference genome. Molecular On Plant16:1232-1236.10.1016 / j.molp.2023.08.003., the counts matrix was calculated using featureCounts (Liao, Y., Smyth, GK, and Shi, W. (2014). FeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30:923-930. 10.1093 / bioinformatics / btt656.), and finally the TPM value of gene expression was calculated using the counts matrix.
[0088] It should be understood that for each time point and each replicate in the time-node sequence, the gene expression level of the corresponding sample is calculated, and the gene expression levels of all samples are merged to construct an overall gene expression matrix, where behavioral genes are listed as samples corresponding to different time points and replicates. In this embodiment, the overall gene expression matrix contains gene expression data of 18 samples.
[0089] Based on the overall gene expression matrix mentioned above, the gene expression levels of three replicate samples at the same time point are averaged to obtain the average gene expression level corresponding to each time point, thereby constructing a gene expression matrix containing 6 time points.
[0090] Based on the gene expression matrix containing six time points, gene sets exhibiting monotonically increasing and monotonically decreasing expression patterns with time point sequences are screened. Specifically, following steps 211-213 and 221-223, increasing and decreasing expression gene sets are selected. The combined sets of increasing and decreasing expression gene sets constitute the gene sets exhibiting monotonically increasing and monotonically decreasing expression patterns. The TPM values of the increasing and decreasing expression gene sets are standardized as follows: Figure 4 As shown, its TPM value distribution is as follows: Figure 5 As shown. It should be noted that, in this embodiment, it is preferred that T1 is 10, T2 is 1, and T3 is 10%. That is, when screening for a gene set with increasing expression, the conditions to be met include: gene expression levels showing an increasing trend in chronological order; the endpoint gene expression level not less than 10 and the gene expression levels at the four intermediate time points not less than 1; and the endpoint gene expression level minus the starting gene expression level > 10% of the starting gene expression level. The value of the starting gene expression level is not required. Similarly, when screening for a gene set with decreasing expression, the conditions to be met include: gene expression levels showing a decreasing trend in chronological order; the starting gene expression level not less than 10 and the gene expression levels at the four intermediate time points not less than 1; and the starting gene expression level minus the endpoint gene expression level > 10% of the endpoint gene expression level. The value of the starting gene expression level is not required.
[0091] Based on the selected gene set, the upregulation / downregulation index for each time point is calculated according to steps 31 and 32. Specifically, for each gene in the increasing expression gene set, the TPM value at each time point is compared with the TPM value on day 30 to obtain the upregulation index for that gene at each time point; for each gene in the decreasing expression gene set, the TPM value at each time point is compared with the TPM value on day 5, and then the difference between 1 and this ratio is obtained to obtain the downregulation index for that gene at each time point. The distribution of the upregulation / downregulation index of the selected gene set at each time point is as follows: Figure 6 As shown.
[0092] Then, following step 33, the filling index for each time point is calculated to obtain the filling index time series. Specifically, for each time point, the average of the upregulation index of each gene in the increasing expression gene set and the downregulation index of each gene in the decreasing expression gene set is taken as the filling index for the current time point, thus obtaining the filling indices for 6 time points, forming the filling index time series. The filling indices of the selected gene set at each time point are as follows: Figure 7 As shown.
[0093] Based on the time series and time node series of the grain filling index, function fitting is performed to obtain a functional relationship model between the grain filling index and the grain filling time, so as to quantitatively characterize the grain filling process of rice through the grain filling index. In this embodiment, preferably, the functional relationship model is an S-shaped functional relationship model, such as... Figure 8 As shown.
[0094] It should be noted that, as another implementation method, when calculating the grouting index time series, the grouting index can also be calculated for each time node based on the screened gene set, and then the average value of the key indices of the three replicates can be taken at each time node.
[0095] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the foregoing embodiments have described the present invention in detail, those skilled in the art should understand that modifications can be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein, and these substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the present invention.
Claims
1. A method for characterizing the grain-filling process of rice based on dynamic gene expression from the transcriptome, characterized in that: include: Step 1: Obtain transcriptome expression data of rice grains during the grain-filling process based on the chronologically arranged time sequence; Step 2: Based on transcriptome expression data, screen gene sets that exhibit monotonically increasing and monotonically decreasing expression patterns over time during the grain filling process; Step 3: Construct the granulation index based on the selected gene set; Step 4: Based on the grain-filling index, achieve quantitative characterization of the rice grain-filling process; Step 2 includes: Step 211: Screen genes whose gene expression levels show a monotonically increasing trend according to the time sequence to form the first increasing gene set; Step 212: Within the first incremental gene set, select genes whose gene expression level at the endpoint is not less than T1 and whose gene expression level at intermediate time points is not less than T2 to form the second incremental gene set. Step 213: In the second incremental gene set, select genes whose expression level at the endpoint is less than the expression level at the starting point (T3), thus forming an incremental expression gene set. Step 2 also includes: Step 221: Screen genes whose gene expression levels show a monotonically decreasing trend according to the time sequence to form the first set of decreasing genes; Step 222: Within the first decreasing gene set, select genes whose gene expression level at the starting point is not less than T1 and whose gene expression level at intermediate time points is not less than T2 to form the second decreasing gene set. Step 223: In the second decreasing gene set, select genes with T3 whose gene expression level at the starting point minus the gene expression level at the ending point is greater than the gene expression level at the ending point, to form a decreasing gene expression set; Step 2 also includes: Step 23: The set of increasing and decreasing gene expression constitutes the gene set with monotonically increasing and monotonically decreasing expression patterns during the grouting process.
2. The method for characterizing the rice grain-filling process based on dynamic gene expression from the transcriptome as described in claim 1, characterized in that: In step 1, transcriptome expression data are presented as a gene expression matrix, with gene expression levels expressed as TPM values.
3. The method for characterizing the rice grain-filling process based on dynamic gene expression from the transcriptome as described in claim 1, characterized in that: The value of T1 is 10, the value of T2 is 1, and the value of T3 is 10%.
4. The method for characterizing the rice grain-filling process based on dynamic gene expression from the transcriptome as described in claim 1, characterized in that: Step 3 includes: Step 31: For each gene in the incrementally expressed gene set, at each time point in the time node sequence, calculate... The upward adjustment index at the current time point; Step 32: For each gene in the decreasing gene expression set, at each time point in the time node sequence, calculate... As a downward adjustment index at the current point in time; Step 33: For each time node in the time node sequence, calculate the average of the upregulation index of all genes in the increasing expression gene set and the downregulation index of all genes in the decreasing expression gene set, and use it as the filling index of the current time node to form the filling index time series.
5. The method for characterizing the rice grain-filling process based on dynamic gene expression from the transcriptome as described in claim 4, characterized in that: Step 4 includes: establishing a functional relationship model between the grain filling index and the grain filling time based on the grain filling index time series and the time node series, so as to quantitatively characterize the grain filling process of rice through the grain filling index.
6. The application of a method for characterizing the grain-filling process of rice based on dynamic gene expression from the transcriptome as described in any one of claims 1-5, characterized in that: This includes its application in identifying the grain-filling process of rice, and / or its application in analyzing the developmental stage of the same organ in crops.