A quantitative evaluation method for the safety of transgenic crops to non-target organisms based on transcriptome homeostasis

Abstract

The present invention discloses a method for quantitatively evaluating the safety of transgenic crops against non-target organisms based on transcriptome steady-state. The method comprises the following steps: selecting a transgenic insect-resistant crop and a control crop; exposing non-target organisms and target organisms to the transgenic insect-resistant crop and control crop for feeding; collecting the treated non-target organisms and target organisms, extracting total RNA to construct libraries, and sequencing the libraries to obtain corresponding transcriptome data; performing bioinformatics analysis on the transcriptome data to calculate transcriptome diversity parameters, which include information entropy, normalized information entropy, and KL divergence; and performing 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 assess the impact of the transgenic insect-resistant crop on non-target organisms. The present invention achieves highly sensitive, quantitative, and efficient detection of the impact of transgenic crops on non-target organisms, providing a new approach for environmental safety assessment of transgenic crops.

Description

Technical Field

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

[0002] Transgenic breeding is a core component of modern biotechnology, involving the targeted introduction of genes for target traits, such as pest and disease resistance and herbicide tolerance, into the genome of recipient organisms. my country has successfully cultivated several insect-resistant transgenic crop transformants with excellent target and agronomic traits. With the rapid development of biotechnology and increasing public acceptance, the global industrialization of transgenic crops continues to accelerate, and my country has entered a critical stage of large-scale promotion and application. It is important to note that prior to the commercial application of transgenic crops, a systematic environmental safety assessment must be conducted, with impacts on non-target organisms being a key component of this assessment.

[0003] Although the existing technology for evaluating the safety of transgenic crops on non-target insects is relatively complete, it still has the following limitations: (1) Insufficient adaptability of the method: Existing technology uses bioassays, such as fitness indicators such as survival rate, developmental period, egg production, and emergence rate, to evaluate the impact of transgenic crops on non-target insects. The core assumption is the toxic mechanism of Bt protein. However, various new transgenic crops are constantly emerging, such as RNAi crops, new genes, and new traits. Their mechanisms of action are essentially different from those of Bt protein. The use of traditional methods may lead to biased evaluation results or omission of key risks; (2) Detection efficiency and accuracy defects: Bioassays require a long time to raise insects and observe physiological indicators. The experimental cycle can last for several months, and the stability of the insect breeding and bioassay systems is high. Existing methods use multiple data such as survival rate, egg production, and emergence rate to comprehensively judge whether there is a significant difference, and there is no unified quantitative indicator. In bioassay experiments, when there are differences in physiological indicators of non-target insects, it generally indicates that the non-target insects have been greatly affected. The detection sensitivity of bioassays is limited.

[0004] Currently, the Shannon entropy of the transcriptome has been used to evaluate the environmental safety of RNAi crops (Ma et al., 2022). However, using only the Shannon entropy as a single indicator can easily lead to one-sided evaluation results and fail to fully reflect the changes in the transcriptome.

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

[0006] To address the above technical deficiencies, the present invention proposes a quantitative evaluation method for the safety of transgenic crops to non-target organisms based on transcriptome homeostasis.

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

[0008] Select transgenic insect-resistant crops and their control crops;

[0009] Non-target organisms and target organisms were exposed to transgenic insect-resistant crops and control crops, respectively, and subjected to feeding treatments;

[0010] Collect the treated non-target insects and target insects, extract total RNA, construct libraries and sequence them to obtain corresponding transcriptome data;

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

[0012] One-way ANOVA was performed on the transcriptome diversity parameters of insects feeding on transgenic insect-resistant crops and control crops to evaluate the effects of transgenic insect-resistant crops 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 parent rice Zhonghua No. 11;

[0014] The non-target insect is the rice leaf roller, and the target insect is the rice stem borer.

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

[0016] The newly hatched larvae of the rice leaf roller were transferred to the leaves of the RNAi insect-resistant rice plant Csu260-16 and the control parent rice Zhonghua 11, and fed for 14 days; the newly hatched larvae of the rice stem borer were transferred to the tillering main stems of the RNAi insect-resistant rice plant Csu260-16 and the control parent rice Zhonghua 11, and fed for 21 days and 28 days, respectively.

[0017] Optionally, the process of collecting treated non-target insects and target insects, extracting total RNA from each of the target insects to construct libraries and sequencing the libraries to obtain corresponding transcriptome data includes:

[0018] Five larvae of rice leaf roller or rice stem borer were randomly collected from different feeding treatment groups to form a biological replicate sample; four biological replicates were set for each treatment for RNA extraction.

[0019] Optionally, the process of collecting the treated non-target insects and target insects, extracting total RNA from each of the target insects to construct libraries and sequencing the libraries to obtain corresponding transcriptome data further includes:

[0020] Total RNA was extracted from the treated non-target insect and target insect samples using TRIzol reagent, and genomic DNA was degraded by adding RNase-free DNase I to obtain pure RNA samples.

[0021] Purify mRNA from pure RNA samples using poly-T oligonucleotide-bound magnetic beads;

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

[0023] The synthesized double-stranded cDNA library fragments were purified using AMPure XP reagent to screen for library fragments within the target length range;

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

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

[0026] The purified PCR products were sequenced on the Illumina Novaseq sequencing platform using the PE 150bp sequencing strategy to obtain transcriptome data.

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

[0028] The transcriptome data of Chilo suppressalis and Rice leaf folder were preprocessed to obtain the corresponding clean reads;

[0029] The reference genomes and gene model annotation files of rice leaf folder and rice stem borer were obtained, and the reference genome indexes were constructed respectively. The corresponding clean reads were aligned to the reference genomes to obtain the TPM value of each gene expression.

[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, 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 the species, H j is the information entropy.

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

[0034]

[0035] Among them, H s is the normalized information entropy.

[0036] Alternatively, the KL divergence is calculated 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, D j is the distance between a single sample and the probability distribution of all samples of its species, that is, the KL divergence.

[0040] Optionally, a one-way ANOVA analysis is performed on the transcriptome diversity parameters of the transgenic insect-resistant crop and the control crop. The process of evaluating the effects of the transgenic insect-resistant crop on non-target organisms includes:

[0041] The Student's t-test was used to compare the information entropy differences between pairs of samples from the same species. The standardized information entropy and KL divergence of samples from two species were subjected to one-way analysis of variance, followed by post hoc pairwise comparisons using the statistical analysis function to analyze the differences between samples from different species.

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

[0043] By incorporating transcriptome diversity parameters such as normalized information entropy and KL divergence, this method can more sensitively detect the potential impacts of transgenic insect-resistant crops on non-target insects. It is worth noting that this method is not limited to RNAi-infected insect-resistant crops but is applicable to all transgenic insect-resistant crops, broadening its scope of application. Furthermore, compared with traditional fitness or physiological indicators based on survival rate, growth and development status, this method can keenly capture more subtle changes in gene expression, thereby identifying potential ecological safety risks in advance.

[0044] This paper introduces standardized information entropy and incorporates a normalization factor for the total transcriptome during the calculation process, effectively eliminating systematic biases caused by differences in transcriptome totals between species and enabling comparison of transcriptome probability distributions across species. This improvement makes the present invention applicable not only to single-species assessments but also to cross-species environmental safety assessments, broadening its scope of application.

[0045] Traditional methods typically only provide comprehensive, qualitative assessments, such as "significant difference" or "no significant difference." This invention, however, provides quantitative assessment metrics by calculating specific transcriptome diversity parameters, such as information entropy and KL divergence. This quantitative assessment more accurately reflects the impact of transgenic insect-resistant crops on non-target insects, providing a more scientific basis for environmental safety assessments. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0047] Figure 1 Schematic diagram of the effect of RNAi insect-resistant rice on transcriptome diversity parameters of Chilo suppressalis and Rice leaf folder according to an embodiment of the present invention, wherein (a) is a schematic diagram of information entropy parameter changes; (b) is a schematic diagram of normalized information entropy parameter changes; and (c) is a schematic diagram of KL divergence changes. DETAILED DESCRIPTION

[0048] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0049] Example 1

[0050] This embodiment provides a method for quantitatively evaluating the safety of transgenic crops to non-target organisms based on transcriptome homeostasis, comprising the following steps:

[0051] Select transgenic insect-resistant crops and their control crops;

[0052] Non-target insects and target insects were exposed to transgenic insect-resistant crops and control crops, respectively, and subjected to feeding treatments;

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

[0054] Perform bioinformatics analysis on the transcriptome data to calculate transcriptome diversity parameters, including information entropy, normalized information entropy, and KL divergence;

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

[0056] As a specific embodiment, the test plants used in this study were the RNAi-infected insect-resistant rice transformant Csu260-16 and its control parent, Zhonghua 11 (ZH11). This RNAi-infected rice expresses the endogenous miRNA Csu-novel-260, which negatively regulates ecdysone synthesis in the stem borer by inhibiting the expression of the Csdib gene, which encodes a cytochrome P450 enzyme. This negatively regulates ecdysone synthesis in the stem borer and can be used for stem borer control. The rice seeds were provided by the National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, the research and development unit of the rice.

[0057] Two rice varieties were grown in a greenhouse. Rice seeds were sown in seedling trays. Once the seedlings reached the four-leaf stage, they were transplanted into 50 soil-filled buckets, each 35 cm in diameter and 32 cm high. Local agricultural practices were strictly adhered to during cultivation, but no pesticides were used. After the experiment, all plant residues from each variety were incinerated.

[0058] As a specific embodiment, the test insects used in this experiment are the rice leaf folder and the striped stem borer. The rice leaf folder and striped stem borer used in the experiment were collected from the field. The rice leaf folder population was obtained from fields in Wuhan City, Hubei Province. The striped stem borer population was obtained from larvae collected from fields in Fuzhou City, Fujian Province. These larvae were reared indoors according to established methods to the test instars without exposure to double-stranded RNA (dsRNA) during rearing. Newly emerged rice leaf folder and striped stem borer adults were placed in pairs in mating cages containing tillering rice plants and a 10% honey solution for egg laying. The collected striped stem borer egg masses and eggs were transferred to glass Petri dishes (9 cm diameter) lined with moistened filter paper and incubated at 27±1°C, 70–80% relative humidity, and a 16h:8h (light:dark) photoperiod for 4–5 days until the eggs hatched. Newly hatched larvae (8–10 hours old) were used for the experiments and were also not exposed to dsRNA during rearing.

[0059] As a specific embodiment, the process of exposing non-target insects and target insects to transgenic insect-resistant crops and control crops, respectively, and performing feeding treatment includes:

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

[0061] As a specific embodiment, the process of collecting treated non-target insects and target insects, extracting total RNA from each of them to construct libraries and sequence them, and obtaining corresponding transcriptome data includes:

[0062] Total RNA from Chilo suppressalis or Cnaphalocrocis medinalis larvae was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA), with the addition of RNase-free DNase I to degrade the genome. Subsequently, mRNA was purified from the total RNA using poly-T oligonucleotide-conjugated magnetic beads. First-strand cDNA was synthesized using random hexamer primers and M-MuLV reverse transcriptase (RNase H-), and second-strand cDNA synthesis was completed using DNA polymerase I and RNase H. A 370-420 bp library fragment was 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, PCR products were purified using AMPure XP, and library quality was assessed using an Agilent Bioanalyzer 2100 system.

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

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

[0065] Raw data (Fastq format) were preprocessed to remove adapter sequences, reads near N, and low-quality reads to obtain clean reads. Q20, Q30, and GC content were also calculated. Q20 and Q30 values ​​were used to assess sequencing data quality, while GC content was used to understand the base composition characteristics of the sample, providing basic data for subsequent analysis. All subsequent analyses were performed based on these high-quality clean reads.

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

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

[0068]

[0069] Calculate the information entropy based on this:

[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 the species, H j is the information entropy.

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

[0073]

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

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

[0076]

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

[0078] 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, 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 embodiment, the statistical analysis of transcriptome diversity parameters is performed using R software. A one-way ANOVA is performed on the transcriptome diversity parameters of RNAi transgenic insect-resistant crops and control crops to evaluate the effects of RNAi insect-resistant crops on non-target organisms. The process includes:

[0080] The Student's t-test was used to compare the information entropy differences between pairs of samples from the same species. The standardized information entropy and KL divergence of samples from two species were subjected to one-way analysis of variance, followed by post hoc pairwise comparisons using the statistical analysis function to analyze the differences between samples from different species.

[0081] As a specific implementation method, 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 used insect samples treated for 14 to 28 days in two types of insects, and only about 20 insects were required for each treatment group, which significantly improved the detection efficiency. Traditional bioassay methods usually require at least 45 days from hatching rate, emergence rate to the next generation of egg production, and each treatment group requires 60-200 insects. According to existing scientific research experimental results, the effect time of RNAi in insects is generally 6 to 48 hours. Therefore, the detection time of this example is expected to be further shortened in the future.

[0082] Secondly, while traditional detection methods typically combine multiple indicators to produce qualitative results, 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 analysis demonstrates the significant advantages of this example.

[0083] Furthermore, when transgenic crops affect non-target insects' individual fitness or physiological indicators, such as survival rate and growth and development, this typically indicates that the non-target insect's phenotype has already been significantly affected, and such indicators have limited sensitivity to subtle early changes. In contrast, the transcriptome analysis method used in this example can detect a small number of genes and low-level transcriptome changes at the gene expression level. Therefore, this example significantly outperforms traditional methods in sensitivity and can significantly enhance early warning capabilities, providing more accurate technical support for the environmental safety assessment of transgenic crops.

[0084] Furthermore, the existing technology uses information entropy to compare transcriptome changes in non-target species before and after RNAi insect-resistant crop treatment to conduct environmental safety assessments (Ma et al., 2022), but there are obvious defects: First, it is impossible to compare transcriptome changes between different species horizontally; second, relying on a single indicator can only reflect a certain dimension of change. The combination of multiple parameters can measure the transcript distribution frequency characteristics in the transcriptome from multiple different angles. A significant change in any parameter means that the transcriptome has undergone a more significant change. In response to the shortcomings of the existing technology, this embodiment first introduces standardized information entropy, and on the basis of considering the differences in transcriptome size of different species, the information entropy is transformed, thereby realizing the comparison of transcriptome changes between different species. As Figure 1 As shown, the information entropy H j No clear differences between species could be detected ( Figure 1 a), and the standardized information entropy H s The transcriptomes of Cnaphalocrocis medinalis and Chilo suppressalis were significantly different ( Figure 1 b) In addition, this embodiment also introduces KL divergence to detect the changes in transcriptome gene expression frequency from different angles. Figure 1 As shown, the information entropy H j No clear differences between treatments could be detected ( Figure 1 a), KL divergence data showed that the expression frequencies of genes in the transcriptome of Chilo suppressalis were significantly different 21 days after RNAi insect-resistant rice treatment ( Figure 1 c). Figure 1 The CK group (shown in the figure) was fed with the control parent Zhonghua 11, and the RNAi group was fed with the RNAi-resistant rice transformant Csu260-16. Sample labels: Csu-21 and Csu-28 represent samples fed with rice by Chilo suppressalis for 21 and 28 days, respectively, and Cme-14 represents a sample fed with rice by Cnaphalocrocis medinalis for 14 days. (a) Figure analyzed using the Student's t-test; ns indicates P > 0.05. Figures (b) and (c) were analyzed using one-way ANOVA and paired t-tests. Different lowercase letters indicate significant differences between groups (P < 0.05).

[0085] Furthermore, this example evaluated the effects of RNAi insect-resistant rice on target and non-target insects through information entropy analysis. Figure 1 As shown in Figure a, the information entropy of the target insect pest Chilo suppressalis after feeding on the RNAi-resistant rice strain Csu260-16 ranged from 9.79 to 10.49, while the information entropy of the non-target insect Cnaphalocrocis medinalis ranged from 8.55 to 9.73. Statistical analysis showed that there was no significant difference in the information entropy parameters between the RNAi-treated and control groups.

[0086] To further evaluate the transcriptome diversity between samples, the normalized information entropy of the two insects was further compared and analyzed. s The values ​​ranged from 0.69 to 0.74, and the H s The value was 0.60-0.68, and no significant changes were observed before and after feeding RNAi insect-resistant rice ( Figure 1 b) It is worth noting that the H s The value was significantly higher than that of the rice leaf roller, indicating that its gene expression distribution was more uniform. In other words, the stability of the transcriptome of the rice stem borer fed on the RNAi insect-resistant rice was significantly better than that of the rice leaf roller. The results of the one-way analysis of variance showed that there was no significant difference in the KL divergence between the sample groups (F(5,16)=2.249, P=0.0994); the paired t-test showed that there was a significant difference in the frequency of gene expression in the transcriptome of the rice stem borer 21 days after the RNAi crop treatment ( Figure 1 c) shows that the target pest Chilo suppressalis feeds on the rice stem borer, and the distribution frequency of its transcriptome genes changes significantly.

[0087] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for quantitatively evaluating the safety of transgenic crops to non-target organisms based on transcriptome homeostasis, characterized in that: The following steps are involved: Select transgenic insect-resistant crops and their control crops; Non-target organisms and target organisms were exposed to GM 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 sequence it to obtain the corresponding transcriptome data; Perform bioinformatics analysis on the transcriptome data to calculate transcriptome diversity parameters, including information entropy, normalized information entropy, and KL divergence; One-way ANOVA was performed on the transcriptome diversity parameters of insects feeding on transgenic insect-resistant crops and control crops to evaluate the effects of transgenic insect-resistant crops on non-target organisms.

2. The method according to claim 1, characterized in that The transgenic insect-resistant crop is the RNAi insect-resistant rice transformant Csu260-16, and the control crop is the control parent rice Zhonghua 11; The non-target organism is the rice leaf roller, and the target organism is the rice stem borer.

3. The method according to claim 2, characterized in that The process of exposing non-target organisms and target organisms to transgenic insect-resistant crops and control crops, respectively, and conducting feeding treatments includes: The newly hatched larvae of the rice leaf roller were transferred to the leaves of the RNAi insect-resistant rice transformant Csu260-16 and the control parent rice Zhonghua 11, and fed for 14 days; the newly hatched larvae of the rice stem borer were transferred to the tillering main stems of the RNAi insect-resistant rice transformant Csu260-16 and the control parent rice Zhonghua 11, and fed for 21 days and 28 days, respectively.

4. The method according to claim 1, wherein The process of collecting treated non-target organisms and target organisms, extracting total RNA from each to construct libraries and sequence them to obtain corresponding transcriptome data includes: Five larvae of Cnaphalocrocis medinalis or Chilo suppressalis were randomly collected from each feeding insect treatment group to form a biological replicate sample; four biological replicates were set for each treatment for RNA extraction.

5. The method according to claim 4, characterized in that The process of collecting treated non-target insects and target insects, extracting total RNA, constructing libraries and sequencing them, and obtaining corresponding transcriptome data also includes: Total RNA was extracted from the treated non-target insect and target insect samples using TRIzol reagent, and genomic DNA was degraded by adding RNase-free DNase I to obtain pure RNA samples. Purify mRNA from pure RNA samples using poly-T oligonucleotide-bound magnetic beads; The first-strand cDNA was synthesized using random hexamer primers and M-MuLV reverse transcriptase, and the second-strand cDNA was synthesized using DNA polymerase I and RNase H to construct a double-stranded cDNA library; The synthesized double-stranded cDNA library fragments were purified using AMPure XP reagent to screen for library fragments within the target length range; Based on the library fragments within the target length range, PCR amplification was performed using Phusion high-fidelity DNA polymerase, universal PCR primers, and index primers; Purify the PCR product using AMPure XP reagent; The purified PCR products were sequenced on the Illumina Novaseq sequencing platform using the PE 150bp sequencing strategy to obtain transcriptome data.

6. The method according to claim 1, wherein The process of bioinformatics analysis of transcriptome data includes: The transcriptome data of rice leaf roller and rice stem borer were preprocessed to obtain the corresponding clean reads; The reference genomes and gene model annotation files of rice leaf roller and rice stem borer were obtained, and the reference genome indexes were constructed respectively. The corresponding clean reads were aligned to the reference genomes to obtain the transcript per million reads value corresponding to the expression level of each gene.

7. The method according to claim 6, characterized in that 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, characterized in that The calculation formula for normalized information entropy is as follows: Among them, H s is the normalized information entropy.

9. The method according to claim 7, characterized in that 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, H Rj is the information entropy of the probability distribution of all transcriptomes of a species, D j is the distance between a single sample and the probability distribution of all samples of its species, that is, the KL divergence.

10. The method according to claim 1, characterized in that One-way ANOVA was performed on the transcriptome diversity parameters of insects feeding on transgenic insect-resistant crops and control crops. The process of evaluating the impact of transgenic crops on non-target insects includes: The Student's t-test was used to compare the information entropy differences between pairs of samples from the same species. The standardized information entropy and KL divergence of samples from two species were subjected to one-way analysis of variance, followed by post hoc pairwise comparisons using the statistical analysis function to analyze the differences between samples from different species.

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