Transcriptome homeostasis-based quantitative evaluation method for safety of transgenic crops to non-target organisms

Abstract

The invention discloses a transcriptome homeostasis-based quantitative evaluation method for safety of transgenic crops to non-target organisms. The method comprises the following steps: selecting transgenic insect-resistant crops and contrast crops thereof; the non-target organisms and the target organisms are exposed to the transgenic insect-resistant crops and the control crops for feeding treatment; collecting the processed non-target organisms and target organisms, extracting total RNA (Ribonucleic Acid) to construct a library, and sequencing to obtain corresponding transcriptome data; bioinformatics analysis is carried out on the transcriptome data, transcriptome diversity parameters are calculated, and the transcriptome diversity parameters comprise information entropy, standardized information entropy and KL divergence; and carrying out single factor variance analysis on the diversity parameters of the insect transcriptomes feeding the transgenic insect-resistant crops and the control crops, and evaluating the influence of the transgenic insect-resistant crops on non-target organisms. According to the method, high-sensitivity, quantitative and efficient detection of the influence of the transgenic crops on non-target organisms is realized, and a new thought is provided for environmental safety evaluation of the transgenic crops.

Description

Technical Field

[0001] The present invention belongs to the technical field of environmental safety assessment of genetically modified crops, and particularly relates to a quantitative assessment method for the safety of genetically modified crops to non-target organisms based on transcriptome homeostasis. Background Art

[0002] Genetic modification breeding is the core content of modern biotechnology, that is, by directionally introducing genes with target traits such as disease and insect resistance, herbicide tolerance, etc. into the genome of the recipient organism. At present, China has successfully cultivated multiple insect-resistant genetically modified crop transformants with excellent target traits and agronomic traits. With the rapid development of biotechnology and the improvement of public acceptance, the industrialization process of global genetically modified crops has continued to accelerate, and China has also entered a crucial stage of large-scale popularization and application. It is worth noting that before the commercial application of genetically modified crops, a systematic assessment of their environmental safety is required, and the impact on non-target organisms is one of the important contents of environmental safety assessment.

[0003] Although the existing assessment technologies for the non-target safety of genetically modified crops are relatively perfect, there are still the following limitations: (1) Insufficient method adaptability: The existing technologies evaluate the impact of genetically modified crops on non-target insects through bioassays, such as fitness indicators such as survival rate, developmental duration, fecundity, and emergence rate. The core assumption is the virulence mechanism of Bt protein. However, various new types of genetically modified crops are emerging continuously, such as RNAi crops, new gene, and new trait genetically modified crops, whose action mechanisms are essentially different from those of Bt protein. Using traditional methods may lead to deviation in assessment results or omission of key risks; (2) Defects in detection efficiency and accuracy: Bioassays require long-term rearing of insects and observation of physiological indicators, with an experimental cycle of up to several months, and have high requirements for the stability of the insect rearing and bioassay systems; The existing methods comprehensively judge "whether there is a significant difference" based on multiple data such as survival rate, fecundity, and emergence rate, without a unified quantitative index; In bioassay experiments, when there are differences in the physiological indicators of non-target insects, it generally indicates that the non-target insects have been greatly affected, and there are limitations in the detection sensitivity of bioassays.

[0004] At present, the Shannon entropy of the transcriptome has been used for the environmental safety assessment of RNAi crops (Ma et al., 2022), but only using this single index of Shannon entropy is likely to result in a relatively one-sided assessment result and cannot comprehensively and completely reflect the changes in the transcriptome.

[0005] Based on the above deficiencies, there is an urgent need to propose a quantitative assessment method for the safety of genetically modified crops to non-target organisms based on transcriptome homeostasis. Summary of the Invention

[0006] To solve the above problems of insufficient technology, the present invention proposes a quantitative assessment method for the safety of genetically modified crops to non-target organisms based on transcriptome homeostasis.

[0007] To achieve the above object, the present invention provides a quantitative evaluation method for the safety of transgenic crops based on transcriptome homeostasis to non-target organisms, comprising the following steps:

[0008] Select a transgenic insect-resistant crop and its control crop;

[0009] Expose non-target organisms and target organisms to the transgenic insect-resistant crop and the control crop respectively for feeding treatment;

[0010] Collect the treated non-target insects and target insects, extract total RNA respectively, construct a library and perform sequencing to obtain the corresponding transcriptome data;

[0011] Perform bioinformatics analysis on the transcriptome data, calculate the transcriptome diversity parameters, and the transcriptome diversity parameters include information entropy, normalized information entropy and KL divergence;

[0012] Perform a one-way analysis of variance on the transcriptome diversity parameters of insects feeding on the transgenic insect-resistant crop and the control crop to evaluate the impact of the transgenic insect-resistant crop on non-target organisms.

[0013] Optionally, the transgenic insect-resistant crop is the RNAi insect-resistant rice transformant Csu260-16, and the control crop is the control parental rice Zhonghua 11;

[0014] The non-target insect is Cnaphalocrocis medinalis, and the target insect is Chilo suppressalis.

[0015] Optionally, the process of exposing non-target organisms and target organisms to the transgenic insect-resistant crop and the control crop respectively for feeding treatment includes:

[0016] Transfer newly hatched larvae of Cnaphalocrocis medinalis to the leaves of RNAi insect-resistant rice Csu260-16 and the control parental rice Zhonghua 11 respectively for 14 days of feeding; transfer newly hatched larvae of Chilo suppressalis to the main stems at the tillering stage of RNAi insect-resistant rice Csu260-16 and the control parental rice Zhonghua 11 respectively for 21 days and 28 days of feeding.

[0017] Optionally, the process of collecting the treated non-target insects and target insects, extracting total RNA respectively to construct a library and performing sequencing to obtain the corresponding transcriptome data includes:

[0018] Randomly collect 5 larvae of Cnaphalocrocis medinalis or Chilo suppressalis from different treatment groups after feeding treatment to form a biological replicate sample; set 4 biological replicates for each treatment for RNA extraction.

[0019] Optionally, the process of collecting the processed non-target insects and target insects, respectively extracting total RNA to construct a library and performing sequencing to obtain the corresponding transcriptome data further includes:

[0020] Use TRIzol reagent to extract total RNA from the processed non-target insect and target insect samples respectively, and add RNase-free DNase I to degrade genomic DNA to obtain pure RNA samples;

[0021] Purify mRNA from the pure RNA samples using magnetic beads conjugated with poly-T oligonucleotides;

[0022] Synthesize the first-strand cDNA using random hexamer primers and M-MuLV reverse transcriptase, and synthesize the second-strand cDNA using DNA polymerase I and RNase H to construct a double-stranded cDNA library;

[0023] Purify the synthesized double-stranded cDNA library fragments using AMPure XP reagent to screen out library fragments within the target length range;

[0024] Based on the library fragments within the target length range, perform PCR amplification using Phusion high-fidelity DNA polymerase, universal PCR primers, and Index primers;

[0025] Purify the PCR products using AMPure XP reagent;

[0026] On the Illumina Novaseq sequencing platform, perform high-throughput sequencing on the purified PCR products using the PE 150bp sequencing strategy to obtain transcriptome data.

[0027] Optionally, the process of performing bioinformatics analysis on the transcriptome data includes:

[0028] Preprocess the transcriptome data of Chilo suppressalis and Cnaphalocrocis medinalis to obtain the corresponding clean reads;

[0029] Obtain the reference genomes and gene model annotation files of Cnaphalocrocis medinalis and Chilo suppressalis, respectively construct indexes of the reference genomes, and align the corresponding clean reads to the reference genomes to obtain the TPM values of the expression levels of each gene.

[0030] Optionally, the calculation formula of information entropy is as follows:

[0031]

[0032] Among them, TPM ij is the TPM value of the i-th gene in the j-th transcriptome, and P ijis the relative frequency of the i-th gene in the j-th transcriptome, g is the total number of transcripts in the transcriptome of this species, and H j is the information entropy.

[0033] Optionally, the calculation formula for the normalized information entropy is as follows:

[0034]

[0035] where H s is the normalized information entropy.

[0036] Optionally, the calculation formula for the KL divergence is as follows:

[0037]

[0038] D j = H Rj - H j ,

[0039] where t is the number of samples of all transcriptomes of the same species, H Rj is the information entropy of the probability distribution of all transcriptomes of a species, and D j is the distance between the probability distribution of a single sample and that of all samples of its species, that is, the KL divergence.

[0040] Optionally, the process of evaluating the impact of transgenic insect-resistant crops on non-target organisms by performing a one-way analysis of variance on the transcriptome diversity parameters of transgenic insect-resistant crops and control crops includes:

[0041] Use Student's t-test to compare the differences in information entropy between sample pairs in the same species, perform a one-way analysis of variance on the normalized information entropy and KL divergence of samples of two species, and then perform post hoc pairwise comparisons through statistical analysis functions to analyze the differences between samples of different species.

[0042] Compared with the prior art, the present invention has the following advantages and technical effects:

[0043] By introducing transcriptome diversity parameters such as normalized information entropy and KL divergence, the present invention can more sensitively detect the potential impact of transgenic insect-resistant crops on non-target insects. It should be noted that this method is not limited to RNAi insect-resistant crops, but is applicable to all transgenic insect-resistant crops, broadening the application scope. And compared with traditional fitness or physiological detection indicators based on survival rate, growth and development status, etc., it can keenly capture more subtle changes at the gene expression level, thereby discovering potential ecological safety risks in advance.

[0044] The present invention introduces a standardized information entropy, and a total transcriptome normalization factor is added in the calculation process, which effectively eliminates the systematic deviation caused by the difference in the total amount of transcripts between species, and compares the probability distributions of transcriptomes between different species. This improvement enables the present invention to be applicable not only to the evaluation of a single species, but also to the environmental safety assessment across species, broadening its application scope.

[0045] Traditional methods usually can only give comprehensive qualitative evaluation results, such as "there are significant differences" or "there are no significant differences", while the present invention provides quantitative evaluation indicators by calculating specific transcriptome diversity parameters, such as information entropy and KL divergence. This quantitative evaluation can more accurately reflect the impact degree of transgenic insect-resistant crops on non-target insects, providing a more scientific basis for environmental safety assessment. Brief Description of the Drawings

[0046] The drawings forming a part of this application are used to provide a further understanding of this application. The schematic embodiments and descriptions thereof of this application are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0047] Figure 1 It is a schematic diagram of the influence of RNAi insect-resistant rice in the embodiment of the present invention on the transcriptome diversity parameters of Chilo suppressalis and Cnaphalocrocis medinalis. Among them, (a) is a schematic diagram of the change of information entropy parameter; (b) is a schematic diagram of the change of standardized information entropy parameter; (c) is a schematic diagram of the change of KL divergence. Detailed Embodiments

[0048] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will refer to the drawings and combine the embodiments to detail this application.

[0049] Embodiment 1

[0050] In this embodiment, a quantitative evaluation method for the safety of transgenic crops to non-target organisms based on transcriptome homeostasis is provided, including the following steps:

[0051] Select a transgenic insect-resistant crop and its control crop;

[0052] Respectively expose non-target insects and target insects to the transgenic insect-resistant crop and the control crop for feeding treatment;

[0053] Collect the treated non-target insects and target insects, extract total RNA respectively, construct libraries and perform sequencing to obtain the corresponding transcriptome data;

[0054] Perform bioinformatics analysis on the transcriptome data and calculate transcriptome diversity parameters, where the transcriptome diversity parameters include information entropy, standardized information entropy and KL divergence;

[0055] One-way analysis of variance was performed on the transcriptome diversity parameters of transgenic insect-resistant crops and control crops to evaluate the impact of transgenic insect-resistant crops on non-target organisms.

[0056] As a specific implementation method, the test plants in this implementation were the RNAi insect-resistant rice transformant Csu260-16 and its control parent Zhonghua 11 (ZH11). This RNAi insect-resistant rice expresses the endogenous miRNA Csu-novel-260 of Chilo suppressalis, and Csu-novel-260 negatively regulates the ecdysteroid synthesis of Chilo suppressalis by inhibiting the expression of the Csdib gene encoding cytochrome P450 enzyme, and can be used for the control of Chilo suppressalis. The rice seeds were provided by the National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, its R & D unit.

[0057] Two rice strains were planted in the greenhouse. The rice seeds were first sown in seedling trays, and after the seedlings grew to the four-leaf stage, they were transplanted into 50 buckets filled with soil. The buckets were 35 cm in diameter and 32 cm in height. During the rice cultivation process, local agricultural management measures were strictly followed, but no pesticides were applied. After the experiment, all plant residues of each strain were burned.

[0058] As a specific implementation method, the test insects in this implementation were Cnaphalocrocis medinalis and Chilo suppressalis. The Cnaphalocrocis medinalis and Chilo suppressalis used in the experiment were collected from the field. The Cnaphalocrocis medinalis population was from the field in Wuhan, Hubei Province. The Chilo suppressalis population was from larvae collected from the field in Fuzhou, Fujian Province and reared indoors to the test instar according to the established method, and they were not exposed to double-stranded RNA (dsRNA) during the rearing process. Newly emerged adult Cnaphalocrocis medinalis and Chilo suppressalis were placed in pairs in mating cages, and tillering-stage rice plants and 10% honey solution were provided in the cages for oviposition. The collected Chilo suppressalis egg masses and Cnaphalocrocis medinalis eggs were transferred to glass petri dishes (9 cm in diameter) lined with moist filter paper and incubated at 27 ± 1 °C, 70–80% relative humidity, and a 16 h:8 h (light:dark) photoperiod for 4–5 days until the eggs hatched. Newly hatched larvae (8–10 hours old) were used for the experiment, and they were also not exposed to dsRNA during the rearing process.

[0059] As a specific implementation method, non-target insects and target insects were respectively exposed to transgenic insect-resistant crops and control crops, and the process of feeding treatment included:

[0060] Newly hatched larvae of Cnaphalocrocis medinalis were transferred to the leaves of RNAi insect-resistant rice Csu260-16 and the control rice line, and fed for 14 days. Newly hatched larvae of Chilo suppressalis were transferred to the fresh main stems at the tillering stage of RNAi insect-resistant rice Csu260-16 and the control rice line, and fed for 21 d and 28 d, respectively. All larvae were reared under the following conditions: temperature 27±1 °C, relative humidity 70 - 80%, light-dark cycle L:D = 16 h:8 h. After the RNAi feeding treatment, 5 larvae of Cnaphalocrocis medinalis or Chilo suppressalis were randomly collected from each treatment group as a biological replicate for RNA extraction, with 4 biological replicates for each treatment.

[0061] As a specific implementation manner, the process of collecting the treated non-target insects and target insects, respectively extracting total RNA to construct libraries and performing sequencing to obtain the corresponding transcriptome data includes:

[0062] Total RNA of Chilo suppressalis or Cnaphalocrocis medinalis larvae was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA), and RNase-free DNase I was added during the process to degrade genomic DNA. Subsequently, mRNA was purified from the total RNA using magnetic beads bound with poly-T oligonucleotides. The first-strand cDNA was synthesized using random hexamer primers and M-MuLV reverse transcriptase (RNase H-), and the second-strand cDNA synthesis was completed using DNA polymerase I and RNase H. The library fragments of 370 - 420 bp were purified using AMPure XP reagent (Beckman Coulter, Beverly, USA). PCR amplification was performed using Phusion high-fidelity DNA polymerase, universal PCR primers, and Index (X) primers. Finally, the PCR products were purified using AMPure XP, and the library quality was evaluated using the Agilent Bioanalyzer 2100 system.

[0063] After adding adapters, the library was sequenced using the PE 150 bp strategy on the Illumina Novaseq platform.

[0064] As a specific implementation manner, the process of performing bioinformatics analysis on the transcriptome data includes:

[0065] The raw data (fastq format) was preprocessed to remove adapter sequences, reads containing nearly N, and low-quality reads to obtain clean reads. At the same time, Q20, Q30, and GC content were calculated. The Q20 and Q30 values were used to evaluate the quality of the sequencing data, and the GC content was used to understand the base composition characteristics of the samples, providing basic data for subsequent analysis. All subsequent analyses were based on these high-quality clean reads.

[0066] Obtain the reference genome and gene model annotation file of Cnaphalocrocis medinalis or Chilo suppressalis. Use HISAT2 software (v2.0.5) to construct the index of the reference genome, and align the paired-end clean reads to the reference genome to obtain the TPM value of the expression level of each gene, that is, the number of times expressed per million transcripts.

[0067] As a specific implementation, information entropy is an index that can be used to quantify transcriptome diversity. For the transcriptome under each treatment condition, the relative frequency of each gene is p ij , representing the relative frequency of the i-th gene in the j-th transcriptome, which is calculated by the TPM value of the gene. The formula for calculating the relative frequency of transcripts is as follows:

[0068]

[0069] Calculate the information entropy accordingly:

[0070]

[0071] Among them, TPM ij is the TPM value of the i-th gene in the j-th transcriptome, Pij is the relative frequency of the i-th gene in the j-th transcriptome, g is the total number of transcripts in the transcriptome of this species, and H j is the information entropy.

[0072] In order to make horizontal comparisons among different species, the information entropy is standardized:

[0073]

[0074] H s = 1 indicates that all transcripts have the same expression level.

[0075] In order to further analyze and evaluate the differences in the expression distribution of transcripts, the concept of KL divergence (D j ) is introduced:

[0076]

[0077] D j = H Rj - H j [6]

[0078] Among them, t is the number of samples of all transcriptomes of the same species, H Rj is the information entropy of the probability distribution of all transcriptomes of a species, and D j is the distance between a single sample and the probability distribution of all samples of its species, that is, the KL divergence.

[0079] As a specific implementation, the statistical analysis of transcriptome diversity parameters is completed using R software. The process of evaluating the impact of RNAi transgenic insect-resistant crops on non-target organisms by performing a one-way analysis of variance on the transcriptome diversity parameters of RNAi transgenic insect-resistant crops and control crops includes:

[0080] Use Student's t-test to compare the differences in information entropy between sample pairs within the same species, perform a one-way analysis of variance on the normalized information entropy and KL divergence of samples from two species, and then conduct post hoc pairwise comparisons using statistical analysis functions to analyze the differences between samples from different species.

[0081] As a specific implementation, this example uses transcriptome diversity parameters to efficiently evaluate the environmental safety of RNAi insect-resistant crops against non-target insects. Compared with traditional methods, this experiment only uses insect samples treated for 14 - 28 days in two types of insects, and only about 20 insects are required for each treatment group, significantly improving the detection efficiency. Traditional bioassay methods usually take at least 45 days from hatching rate, emergence rate to the egg-laying amount of the next generation, and 60 - 200 insects are required for each treatment group. According to existing scientific research experimental results, the action time of RNAi in insects is generally 6 - 48 hours. Therefore, the detection time of this example is expected to be further shortened in the future.

[0082] Secondly, traditional detection methods usually give qualitative results by synthesizing multiple indicators, while this example uses transcriptome diversity parameters to quantitatively represent the impact of RNAi transgenic crops on non-target insects. This shift from qualitative to quantitative reflects the significant advantage of this example.

[0083] Furthermore, when transgenic crops have an impact on individual fitness or physiological indicators such as the survival rate and growth and development status of non-target insects, it usually means that the phenotypes of non-target insects have been significantly affected, and the sensitivity of such indicators to early subtle changes is limited. In contrast, the transcriptome analysis method used in this example can detect changes in a small number of genes and low-level transcriptomes at the gene expression level. Therefore, the sensitivity of this example is significantly better than traditional methods, and it can significantly improve the early warning ability, providing more accurate technical support for the environmental safety evaluation of transgenic crops.

[0084] Furthermore, in the prior art, information entropy has been used to compare the transcriptome changes of non-target species before and after treatment with RNAi insect-resistant crops for environmental safety assessment (Ma et al., 2022), but there are obvious defects: firstly, it is impossible to horizontally compare the transcriptome changes between different species; secondly, relying on a single indicator can only reflect a certain dimension of the changes, and the combination of multiple parameters can measure the transcript frequency distribution characteristics in the transcriptome from multiple different angles. A significant change in any parameter means that the transcriptome has changed significantly. In view of the deficiencies of the prior art, in this embodiment, standardized information entropy is first introduced, and on the basis of considering the differences in the sizes of the transcriptomes of different species, the information entropy is transformed, so as to realize the comparison of the transcriptome changes between different species. As Figure 1 shown, the information entropy H j failed to clearly detect the differences between different species ( Figure 1 a), while the standardized information entropy H s showed significant differences in the transcriptomes of two species, Cnaphalocrocis medinalis and Chilo suppressalis ( Figure 1 b). In addition, in this embodiment, KL divergence is also introduced to detect the changes in the gene expression frequency of the transcriptome from different angles. As Figure 1 shown, the information entropy H j failed to clearly detect the differences between different treatments ( Figure 1 a), and the KL divergence data showed that there were significant differences in the gene expression frequency of the transcriptome of Chilo suppressalis after 21 days of treatment with RNAi insect-resistant rice ( Figure 1 c). Figure 1 In Figure 1 , the CK group is the control group that fed on the control parent Zhonghua 11, and the RNAi group is the treatment group that fed on the RNAi insect-resistant rice Csu260-16 transformant. In the sample labels: Csu-21 and Csu-28 are the sample groups of Chilo suppressalis feeding on rice for 21 days and 28 days respectively, and Cme-14 is the sample group of Cnaphalocrocis medinalis feeding on rice for 14 days. In figure (a), student's t-test is used, ns represents P>0.05, and in figures (b) and (c), one-way ANOVA and paired t-test are used. Different lowercase letters indicate significant differences between groups (P<0.05).

[0085] Furthermore, in this embodiment, the effects of RNAi insect-resistant rice on target and non-target insects are evaluated through information entropy analysis. As Figure 1 shown in a, the information entropy range of the target pest Chilo suppressalis after feeding on the RNAi insect-resistant rice Csu260-16 is 9.79-10.49, while the information entropy of the non-target insect Cnaphalocrocis medinalis is 8.55-9.73. Statistical analysis shows that there is no significant difference in the information entropy parameters between the RNAi treatment group and the control group.

[0086] To deeply evaluate the transcriptome diversity among samples, the normalized information entropy of two insects was further compared and analyzed. After feeding on Csu260-16 rice, the H s value of Chilo suppressalis was between 0.69 and 0.74, and the H s value of Cnaphalocrocis medinalis was 0.60 - 0.68, and there was no obvious change before and after feeding on RNAi insect-resistant rice ( Figure 1 b). It is worth noting that the H s value of Chilo suppressalis was significantly higher than that of Cnaphalocrocis medinalis, indicating that its gene expression distribution was more uniform, that is, the stability of the transcriptome of Chilo suppressalis in the RNAi insect-resistant rice treatment group was significantly better than that of Cnaphalocrocis medinalis. The results of one-way ANOVA showed that there was no significant difference in the KL divergence between sample groups (F(5,16) = 2.249, P = 0.0994); paired t-tests showed that there were significant differences in the transcriptome gene expression frequencies of Chilo suppressalis 21 days after treatment with RNAi crops ( Figure 1 c), indicating that after the target pest Chilo suppressalis fed, there were significant changes in the gene distribution frequencies of its transcriptome.

[0087] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A quantitative evaluation method for the safety of transgenic crops to non-target organisms based on transcriptome homeostasis, characterized in that, It includes the following steps: Select transgenic insect-resistant crops and their control crops; Expose non-target organisms and target organisms to transgenic insect-resistant crops and control crops for feeding treatment; Collect the treated non-target organisms and target organisms, extract total RNA to construct a library and perform sequencing to obtain corresponding transcriptome data; Perform bioinformatics analysis on the transcriptome data, calculate transcriptome diversity parameters, and the transcriptome diversity parameters include information entropy, normalized information entropy, and KL divergence; Perform a one-way analysis of variance on the transcriptome diversity parameters of insects feeding on transgenic insect-resistant crops and control crops to evaluate the impact of transgenic insect-resistant crops on non-target organisms.

2. The method according to claim 1, wherein the transgenic insect-resistant crop is the RNAi insect-resistant rice transformant Csu260-16, and the control crop is the control parental rice Zhonghua 11; the non-target organism is Cnaphalocrocis medinalis, and the target organism is Chilo suppressalis.

3. The method according to claim 2, wherein the process of exposing non-target organisms and target organisms to transgenic insect-resistant crops and control crops respectively for feeding treatment includes: Transfer newly hatched larvae of Cnaphalocrocis medinalis to the leaves of the RNAi insect-resistant rice transformant Csu260-16 and the control parental rice Zhonghua 11 respectively for 14 days of feeding; transfer newly hatched larvae of Chilo suppressalis to the main stems at the tillering stage of the RNAi insect-resistant rice transformant Csu260-16 and the control parental rice Zhonghua 11 respectively for 21 days and 28 days of feeding.

4. The method according to claim 1, wherein the process of collecting the treated non-target organisms and target organisms, extracting total RNA to construct a library and performing sequencing to obtain corresponding transcriptome data includes: Randomly collect 5 larvae of Cnaphalocrocis medinalis or Chilo suppressalis from different insect treatment groups after feeding treatment to form a biological replicate sample; set 4 biological replicates for each treatment for RNA extraction.

5. The method according to claim 4, wherein the process of collecting the treated non-target insects and target insects, extracting total RNA, constructing a library and performing sequencing to obtain corresponding transcriptome data further includes: Use TRIzol reagent to extract total RNA from the treated non-target insect and target insect samples respectively, and add RNase-free DNase I to degrade genomic DNA to obtain pure RNA samples; Purify mRNA from the pure RNA samples using magnetic beads conjugated with poly-T oligonucleotides; Synthesize the first-strand cDNA using random hexamer primers and M-MuLV reverse transcriptase, and synthesize the second-strand cDNA using DNA polymerase I and RNase H to construct a double-stranded cDNA library; Use AMPure XP reagent to purify the synthesized double-stranded cDNA library fragments and screen out library fragments within the target length range; Based on the library fragments within the target length range, perform PCR amplification using Phusion high-fidelity DNA polymerase, universal PCR primers, and Index primers; Purify the PCR products using AMPure XP reagent; On the Illumina Novaseq sequencing platform, perform high-throughput sequencing on the purified PCR products using the PE 150bp sequencing strategy to obtain transcriptome data.

6. The method according to claim 1, wherein The process of bioinformatics analysis of the transcriptome data includes: Preprocess the transcriptome data of Cnaphalocrocis medinalis and Chilo suppressalis to obtain corresponding clean reads; Obtain the reference genomes and gene model annotation files of Cnaphalocrocis medinalis and Chilo suppressalis, respectively construct the indexes of the reference genomes, and align the corresponding clean reads to the reference genomes to obtain the transcript per million reads values corresponding to the expression levels of each gene.

7. The method according to claim 6, wherein The calculation formula of information entropy is as follows: Among them, TPM ij is the TPM value of the i-th gene in the j-th transcriptome, P ij is the relative frequency of the i-th gene in the j-th transcriptome, g is the total number of transcripts in the species transcriptome, H j is the information entropy.

8. The method according to claim 7, wherein The calculation formula of normalized information entropy is as follows: Among them, H s is the normalized information entropy.

9. The method according to claim 7, wherein The calculation formula of KL divergence is as follows: D j = H Rj - H j , where t is the number of samples of all transcriptomes of the same species, and H Rj is the information entropy of the probability distribution of all transcriptomes of a species, and D j is the distance between the probability distribution of a single sample and that of all samples of its species, i.e., the KL divergence.

10. The method according to claim 1, wherein The process of evaluating the impact of transgenic crops on non-target insects by performing one-way ANOVA on the transcriptome diversity parameters of insects feeding on transgenic insect-resistant crops and control crops includes: Use Student's t-test to compare the information entropy differences between sample pairs within the same species, perform one-way ANOVA on the normalized information entropy and KL divergence of the two species samples, and then perform post hoc pairwise comparisons through statistical analysis functions to analyze the differences between different species samples.

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