No obvious unintended effects was found in gene editing rice through transcriptional and proteomic analysis

ABSTRACT

Unintended effects of gene edit crops may pose safety issues. Omics is a useful tool for researchers to evaluate these unexpected effects. Transcriptome and proteomics analyses were performed for two gene editors, CRISPR-Cas9 and adenine base editor (ABE) gene edit rice, as well as corresponding wild-type plants (Nipponbare). Transcriptome revealed 520 and 566 rice differentially expressed genes (DEGs) in the Cas9/Nip and ABE/Nip comparisons, respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that most DEGs participated in metabolism of terpenoids and polyketones, plant–pathogen interactions, and plant signal transduction. It mainly belongs to environmental adaptation. Proteomics revealed 298 and 54 rice differentially expressed proteins (DEPs) in the Cas9/Nip and ABE/Nip comparisons, respectively. KEGG pathway enrichment analysis showed that most DEPs participated in the biosynthesis of secondary metabolite and metabolic pathways.

According to integrated transcriptomes and proteomics analysis, the results showed that no newly generated genes were identified as new transcripts of these differentially expressed genes, and gene edit tools had little effect on rice transcription levels and no new proteins were generated in the gene-edited rice.

Keywords:

• Gene edit
• proteome
• transcriptome
• unintended effects

1. Introduction

Gene edit is one of the most rapidly developing biological technologies in recent years.¹ Type II CRISPR-Cas system has become the most widely used gene edit tool due to its high efficiency, precision, and simple operation, and has obtained achievements in gene breed and directed domestication of crop yield, quality improvement, disease resistance, and herbicide tolerance.^2–5 Although CRISPR-Cas system has significant advantages in crop breeding, foreign Cas9/nCas9 genes in gene-edited crops will be randomly inserted into other locations, potentially disrupting the genome structure, as well as potentially generating new transcripts or toxic proteins in the knocked out target genes. Its unexpected effects still pose challenges to the commercial application of gene-edited crops in the future.⁶ Thus, safety assessments of gene edit crops must be comprehensively carried out.

In recent years, evolving omics techniques had provided a favorable tool for a comprehensive safety assessment and detailed information on any unintended changes in the GM crops under study.^7–11 The Cas9 protein can bind to and cleave genomic sites homologous to the sgRNA, which can lead to unwanted off-target gene mutations, as well as the potential for unexpected unintended effects. At genomic level, one study showed that DSBs induced by only one Cas9 nuclease caused unexpected chromosomal truncations through a p53-dependent mechanism.¹² In another study, the two-cell blastomere of the mouse was edited by CRISPR-Cas9 and Base Editor,respectively. When mouse embryos developed into 14.5 days old, the whole embryos were digested into a single cell, the gene-edited and unedited cells were sorted out. Then the whole-genome sequencing was conducted to analyze the differences between the two groups. The results show that off-target single-nucleotide variants (SNVs) were rare in embryos edited by CRISPR-Cas9 or adenine base editor (ABE), with a frequency close to the spontaneous mutation rate. In contrast, cytosine base editing (CBE) induced SNVs at more than 20-fold higher frequencies.¹³ The results were also confirmed in plants by whole genome sequencing.¹⁴ At the transcriptome and proteome level, in animal knockout cell lines, it was reported that about 50% of cell lines produced exogenous mRNA and protein, and these aberrant protein products originated from base insertions or deletions. This result suggested that Cas9 edited target genes may trigger mRNA dysregulation at the target sites.¹⁵ In plants, the effects of CRISPR-Cas9 and base editors on plant transcription and protein levels have not been reported.

CRISPR-Cas9 and base editors (ABE and CBE) are the two most widely used gene edit systems in plants. In general, the sgRNA-Cas9 system generates base knockout, and the esgRNA-base editors generates single base replacement. Off-target phenomena have been reported in both systems at the plant genome level,^14,16 although genomics provide off-target information on gene sequences, these data are only available at the genomic level. Transcriptomics and proteomics can further explain whether new transcripts and new viral proteins will be formed after the target gene is knocked out.¹⁷ The cytochrome P450 monooxygenase encoded by the rice CYP81A6 gene (Bel) is resistant to bendazone and sulfonylurea herbicides, and the recessive homozygous mutant of Bel is sensitive to bendazone, which can be used as a chemical lethal marker, rice is an important food crop and a well-researched model plant.^18,19 Here, using CYP81A6 as the target gene, we generated CRISPR-Cas9 and adenine base editor edited rice plants to evaluate the unexpected effects of the two edit systems at the transcriptome and proteome levels.

2. Materials and Methods

2.1. Vectors and Experimental Materials

CRISPR/Cas9-pP1C.7 vectors were purchased from Nanjing Jirui Biotechnology Co., Ltd. pH-PABE-7-esgRNA is a gift from Caixia Gao (Addgene plasmid # 115620; http://n2t.net/addgene:115620; RRID:Addgene_115620). Phanta Max Super ̄Fidelity DNA Polymerase, Taq DNA polymerase, dNTPs, DNA gel purification kit, and quality particle test kit were purchased from Vazyme Biotech Co., Ltd. Restriction enzymes (EcoRI, XbaI, BsaI) were purchased from New England Biolabs (NEB). In-Fusion HD Cloning kits were purchased from Bazaar Medical Technology Co., Ltd. (TaKaRa). The vector transformation strain Ecoli TOP10 was purchased from Biomed Biotechnology Co., Ltd.

2.2. SgRna Sequence Design

Design the sgRNA at CRISPR-P (http://crispr.hzau.edu.cn/CRISPR2/).

CRISPR-Cas9 gene edit rice, sgRNA1:GTGCGCGGAGAGCAGCTGCACGG(guide3);

sgRNA2: CCTCGCCGCTGTAAGCCGGAGGG(guide4).

Adenine Base Editor gene edit rice, esgRNA: GATCATCCCGCACATCGGCGCGG(guide1).

2.3. SgRna Vector Construction

2.3.1. Steps of CRISPR-Cas9 Vector

According to the designed sgRNA sequence, we used pP1C.7 vector as a template, the sgRNA cloning frame was amplified by Oligo1-U3-4/Oligo2-U6-3 primers, then the sgRNA cloning frame was purified. After agarose electrophoresis, the target fragment of approximately 1000 bp was recovered, and the pP1C.7 vector was digested with EcoRI and XbaI. The sgRNA cloning frame sequence was ligated into the restriction enzyme vector, and the recombinant vector was transformed into E. coli TOP10 competent cells. The positive clones were identified by colony PCR and sequenced. The correct strains were subjected to plasmid extraction, the plasmid was used to transform Agrobacterium EHA105, and Agrobacterium infection was used to obtain transgenic rice.

2.3.2. Steps of Adenine Base Editor vector

We obtained the esgRNA cloning frame by phosphorylating the specific primer ABE-1-F/R, and the pH-PABE-7-esgRNA vector was digested with BsaI. The remaining steps are the same as those for CRISPR-Cas9 construction.

2.4. Sanger Sequencing of Gene Edit Mutations

The transgenic plants were detected by Cas9 primer pair (RTCas9-F/RTCas9-R)，the edit sites of Cas9 and adenine base editor in transgenic plants were detected by Guide3-4F/Guide3-4 R and ABEJC-F/ABEJC-R, respectively (all primers are in Supplementary Table S1).

2.5. Plant Materials

Plants of the gene edit rice lines CYP8 (Cas9-CYP81A6) and ABE (adenine base editor-CYP81A6) and corresponding recipient plant NIP (Oryza sativa L. ssp. Japonica cv. Nipponbare) were utilized in this study. The type of edited in the CYP8 lines is a 40-bp base deletion at the target site, and the type of edited in the ABE lines is a single base A to G substitution at the target site. Homozygous edited plants were selected from the T0 generation as experimental material. All plants were planted in the Langfang experimental field of the Chinese Academy of Agricultural Sciences.

2.6. RNA Extraction and Library Construction for Transcriptome Sequencing

RNA was extracted from young leaves of the 15 WT rice plants, 15 Cas9-edited lines, and 15 adenine base editor-edited lines using RNAprep Pure kit (TIANGEN, Cat. #DP441). Three biological replicates of the three different rice lines were used for the transcriptome analysis performed in this study. Final libraries were sequenced on an Illumina novaseq6000 at the Biomarker Technologies company (Beijing, China). The Clean Data of each sample are above 6.04Gb, and the percentage of Q30 base are above 92.42%.

2.7. Protein Extraction and Library Construction for Proteome Sequencing

Fifteen rice young leaves per line were rapidly ground in liquid nitrogen, and the proteins were extracted with protein lysate buffer (1 mmol/L PMSF, 0.4 g/kg SDS, 2 mol/L thiourea, 7 mol/L urea, 25 mmol/L Tris-HCl (pH 8.5), 2 mmol/L EDTA and protease inhibitor cocktail). Three biological replicates of the three different rice lines were used for the proteome analysis performed in this study.

Proteomics utilizes a peptide in vitro labeling technique TMT™ (Tandem Mass Tag™) to specifically label amino groups of peptides and then perform tandem mass spectrometry to simultaneously compare the relative protein content in 16 different samples. The TMT™ label consists of three parts: the report ion, the balance group, and the reaction group, and the total molecular weight of each label is consistent. TMT™ reagents can efficiently label peptides after enzymatic hydrolysis by means of reaction groups. The QExactive HF-X mass spectrometer was used for on-board experiments. In primary mass spectrometry, the same peptide segment in different samples labeled with TMT™ reagent shows the same mass–charge ratio. In secondary mass spectrometry, the shearable arm can be preferentially broken in order to release the reporter ion. The peak strength of the reported ion can reflect the sample abundance of the labeled polypeptide, and then the quantitative information of the protein can be obtained by software processing. The experiment was done at Biomarker Technologies company (Beijing, China).

2.8. Bioinformatics Analysis of Transcriptome

In this study, the genome of rice nipponbare (Oryza sativa Japonica) was used as a reference for sequence comparison, annotation, and follow-up analysis (https://plants.ensembl.org/Oryza_sativa/Info/Index). The sequence alignment software is HISAT2,²⁰ and after the comparative analysis was completed, the corresponding reads were assembled and quantified with StringTie.²¹ Based on the comparison results of HISAT2 between each sample reads and the reference genome sequence, GATK software was used to identify the single base mismatch and insertion deletion between the sequenced sample and the reference genome, identify potential SNPs and InDel.Annotate variation and predict variation effects according to SnpEff.²² DESeq2 was used for differential expression analysis between samples to obtain a set of differentially expressed genes between two biological samples.²³ In the process of differential expression gene detection, Fold Change ≥ 1.5 and P-value <0.01 were used as screening criteria. Functional annotation and enrichment analysis of differentially expressed genes were performed in NR,²⁴ Swiss-Prot, GO,²⁵ COG,²⁶ KOG, Pfam,²⁷ KEGG,²⁸ STRING,²⁹ Ensembl and Cosmic databases.

2.9. Bioinformatics Analysis of Proteome

In this experiment, the Uniprot_Pseudomonas syringae pv.tomato (2020.04.22 download) database was used to compare, annotate, and analyze the subsequent data,³⁰ the database was searched by PD2.4 software. All identified proteins matched at least one unique peptide with a confidence of ≥95%, where the protein fold change was ≥2 or ≤0.5, and P ≤ .05 were considered different-rich proteins (DAPs). Functional classification of DAPs was performed by Gene Ontology (GO) enrichment analysis using the gene ontology database (http://www.geneontology). Pathway enrichment analysis of DAPs was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database(http://www.genome.jp/kegg/). Map the protein–protein interaction (PPI) network using the online STRING 10.5 tool (http://string-db.org) and visualize it using Cytoscape (3.2.0).³¹

2.10. Qrt-PCR Detection of Gene-Edited Rice CYP8 and ABE

Rice total RNA was extracted using an RNAprep Pure plant extraction kit (TIANGEN, China). The extracted RNA was reverse transcribed to cDNA through reverse transcription kit (TaKaRa, Dalian, China). Using the first cDNA strand as a template, qRT-PCR was performed on an ABI7500 Real-Time System (Applied Biosystems, USA), using a SYBR Premix ExTaq kit (TaKaRa, Dalian, China). For qRT-PCR analysis, the actin gene was used as an internal control, and the relative quantification method was used to assess the fold changes of the target genes. Three biological and three technical replicates were performed for each sample. The primers ACTINF/ACTINR were used for actin gene amplification. The primer pairs qCYP81A6F/qCYP81A6R were used for CYP81A6 gene amplification, PCR was performed for 15 sec at 95°C, followed by 45 cycles of 95°C for 5 sec and 58°C for 40 sec. Statistical analysis was carried out using SPSS software. The fold change means obtained for different samples were compared using T-tests at P < .05 (SPSS software).

3. Results

3.1. Confirmation of Gene Edit Rice Lines

Plants were used to study the transcriptome and proteomic differences between two gene edit rice lines (CYP8 and ABE) and their non-GM parent NIP. PCR sequencing was carried out for the identification of edit plant. A 40 bp deletion at the target site was induced by Cas9 in the CYP8 line; The ABE line was induced by the adenine base editor to generate single base A-G substitution at the target site (Supplementary Figure S1).

3.2. Transcriptome Studies in Gene-Edited Plants

In this study, we selected the plant CYP8 with 40 bp base deletion of the target CYP81A6 gene as the editing type, the plant ABE with single base A-G substitution at the target site of the target CYP81A6 gene, and the corresponding receptor rice Nipponbare (NIP) for research, in order to explore the possible unexpected effects of the two groups edit plants at the transcriptome level. Sample correlation – Pearson correlation coefficient r is in the Supplementary Figure S2.

3.2.1. Identification of Differentially Expressed Genes and Common Differentially Expressed Genes

In this study, 58349 expression genes were identified, and 520 differentially expressed genes were identified in CYP8/NIP, including 110 up-regulated differentially expressed genes and 410 down-regulated differentially expressed genes; A total of 566 differentially expressed genes were identified in ABE/NIP, including 170 up-regulated differentially expressed genes and 396 down-regulated differentially expressed genes (Table 1). The sample ABE and CYP8 are the materials expressing lines with CYP81A6 gene knocked out. The results of differential gene screening show that there are few differential genes between ABE/CYP8, and the differential genes obtained from ABE/NIP and CYP8/NIP are at the same level, indicating that the gene editors has little effect on the expression of plant differential genes, mainly due to the influence on the expression of CYP81A6 gene and related pathways leading to the expression of differential genes.

Table 1. Differential genes obtained from three groups of materials.

Parameters	group	DEGs_ total	DEGs _up	DEGs_ down
DESeq2_EBSeq FDR=0.01 FC=2	ABE-1&ABE-2_vs_CYP8–1&CYP8–2&CYP8–3	67	11	56
	NIP-1&NIP-2&NIP-3_vs_ABE-1&ABE-2	566	396	170
	NIP-1&NIP-2&NIP-3_vs_CYP8–1&CYP8–2&CYP8–3	520	410	110

The identified differential genes were clustered and grouped according to the expression level. The results of cluster analysis showed that the clustering of rice lines ABE and CYP8 was closer than that of NIP, while the clustering of NIP and CYP8 was closer than that of ABE (Fig. 1a); Among them, 24 differentially expressed genes were highly expressed in ABE strain but low in CYP8 and NIP strain; The 73 differentially expressed genes were highly expressed in ABE and CYP8 strains, while the expression level in NIP was very low. These 73 differentially expressed genes were common differentially expressed genes in gene-editing plants; the GO classification notes of 24 and 73 differentially expressed genes are as shown in the figure (Fig. 1b, c). Among the GO classification notes of 24 differentially expressed genes, they mainly participate in the response to drought in the biological process; Participate in the formation of non-membranous organelles in the cell components; Participate in RNA-dependent DNA polymerase activity in molecular function; Among the GO classification notes of 73 differential genes, they mainly participated in riboflavin biosynthesis and photosynthetic electron transport chain in biological process. In molecular function, it participates in serine-type carboxypeptidase activity and heme binding. It is involved in the formation of plant cell wall in cell components.

Figure 1. Cluster pattern for identification of differentially expressed genes. (a) Comparison of the clusters of ABE, CYP8 and NIP differential expression gene; (b) GO functional annotation of 24 differential expression gene; (c) GO functional annotation of 73 differential expression gene.

Figure 1. (Continued)

3.2.2. GO Enrichment of Differentially Expressed Genes

Gene Ontology database was used for GO functional enrichment analysis. The results showed that the differentially expressed genes identified in the CYP8/NIP comparison were annotated into 53 functional groups. In cellular components, differential genes were involved in membrane formation; Molecular function analysis showed that the differentially expressed genes were involved in protein biosynthesis and catalytic activity. They are involved in metabolic processes and cellular activities in the category of biological processes, as shown in Fig. 2a. The differentially expressed genes obtained by the ABE/NIP comparison were labeled as 53 functional groups. Cellular component classification, molecular function, and biological processes are involved in the same major activities as CYP8/NIP (Fig. 2b). This result indicated that the GO functional annotation and enrichment of differentially expressed genes obtained by ABE and CYP8 were the same, and there was no difference between the two groups editing materials, and the gene editor had no obvious effect on the plants.

Figure 2. GO enrichment of the identified differentially expressed gene (a) To determine the function of the differentially expressed genes identified in CYP8/NIP; (b) to determine the function of differentially expressed genes identified in ABE/NIP.

3.2.3. KEGG Pathway Enrichment of Differential Genes

KEGG pathway enrichment analysis was performed on the identified differentially expressed genes using KEGG pathway database. The results showed that the differentially expressed genes of CYP8/NIP were mainly involved in plant–pathogen interactions, metabolism of terpenoids and polyketones, and plant signal transduction, mainly belonging to environmental adaptation. Similarly, most of the differential genes of ABE/NIP were involved in plant–pathogen interaction, phosphatidylinositol signaling system, starch, and sucrose metabolism, and mainly belonged to environmental adaptation. As shown in Fig. 3, this result indicated that the differential genes obtained from the two groups of gene-edited plants did not differ in KEGG pathway, and the gene editor had no obvious effect on the plants.

Figure 3. KEGG pathway enrichment of differential genes (a) KEGG pathway enrichment of CYP8/NIP differentially expressed genes; (b) KEGG pathway enrichment of ABE/NIP differentially expressed genes.

3.2.4. CYP81A6 Gene Related Pathways

The expression of CYP81A6 gene was disrupted in ABE and CYP8 lines. The CYP81A6 gene-related pathways in the two groups of gene editing materials were analyzed to explore whether the disruption of the gene coding region would produce new transcripts and other unexpected effects. Using qRT-PCR technology, the expression of CYP81A6 gene in the three groups of materials was counted, as shown in Fig. 4. The results showed that the expression level of CYP81A6 gene was the highest in recipient Nipponbare, followed by ABE lines, and the lowest in CYP8 lines (Edit type of ABE lines: single base A-G substitution; CYP8 lines edit type: 40 bp deletion at the target site).

Figure 4. Expression levels of CYP81A6 gene in the three groups of samples.

The KEGG pathway analysis of CYP81A6 gene in CYP8/NIP and ABE/NIP groups showed that the KO number of CYP81A6 gene was KO00943. Figure 5 shows that genes on the related metabolic pathways of CYP81A6 genes were all down-regulated in the CYP8/NIP group. The down-regulated genes CYP81E (LOC_Os03g55240), CYP93A, CYP81E9, CYP93C, and CYP71D9 were all members of the cytochrome P450 family in rice. KEGG pathway analysis showed that they were involved in the biosynthesis of secondary metabolites and isoflavone biosynthesis, which was the same as the KEGG pathway of differential genes in CYP8/NIP. These results suggest that these down-regulated genes may have some relationship with the target gene CYP81A6, and no new transcripts were generated after the coding region of the target gene was changed. It can be concluded that the gene editor has little effect on life activities outside the target site of the gene edited plants, and no new transcripts are detected, only the abundance of differential genes is changed.

Figure 5. KEGG pathway involved by CYP81A6 gene in CYP8/NIP.

3.3. Proteome Studies of Gene-Edited Plants

In this study, CYP8, a plant with 40 bp base deletion in CYP81A6 gene and ABE, a plant with single base A-G substitution at the target site of CYP81A6 gene, and its corresponding receptor material, Nipponbare (NIP), were selected for research to explore the possible unexpected effects of two groups edit plants at the proteome level.

3.3.1. Identification of Differentially Expressed Proteins and Common Differentially Expressed Proteins

This study is based on iTRAQ technology for proteome mass spectrometry detection, and T-test is used to analyze the difference of protein expression among three groups of samples. The screening conditions are significance test value P = .05 and quantitative ratio of two groups of samples FC = 1.2. Results: 4336 proteins were identified. In CYP8/NIP comparison, 298 differentially expressed proteins (DAPs) were identified, of which 243 were up-regulated and 55 were down-regulated. In ABE/NIP, a total of 54 differentially expressed proteins were identified, of which 40 differentially expressed proteins were up-regulated, and 14 differentially expressed proteins were down-regulated (Table 2). The sample ABE edit type is single base substitution, and the CYP8 edit type is 40 bp deletion. There are few differential proteins between ABE/CYP8, and there are many differential proteins between CYP8 and NIP, indicating that the gene editor has little effect on the expression of plant differential proteins, mainly due to the influence on the expression of CYP81A6 gene and the related pathways leading to the expression of differential proteins.

Table 2. Differential proteins obtained from the three groups of materials.

Parameters	group	DEPs total	DEPs up	DEPs down
参数 P value=0.05 FC=1.2	NIP-1&NIP-2&NIP-3_vs_CYP8–1&CYP8–2&CYP8–3	298	243	55
	ABE-1&ABE-2&ABE-3_vs_CYP8–1&CYP8–2&CYP8–3	39	8	31
	NIP-1&NIP-2&NIP-3_vs_ABE-1&ABE-2&ABE-3	54	40	14

The identified differential proteins were clustered and grouped according to the expression level. The results of cluster analysis showed that the clustering of rice lines ABE and CYP8 is closer than that of NIP, while the clustering of NIP and ABE is closer than that of CYP8 (Fig. 6a); GO cluster analysis was carried out on 21 identified common differential proteins, which mainly participated in metabolic, cellular, and other biological processes in biological process; Cell components are mainly involved in the synthesis of cell membranes and organelles; The molecular function is mainly involved in the catalytic activity and transport activity of proteins (Fig. 6b).

Figure 6. Expression patterns of identified differential proteins and common differential proteins (a)Comparison of ABE, CYP8 and NIP protein expression cluster; (b)Cluster analysis and GO annotation enrichment of 21 common differential proteins.

3.3.2. GO Enrichment of Differential Proteins

The Gene Ontology database was used to conduct the functional enrichment analysis of GO to reveal the functions of the identified differential proteins. The results showed that in the CYP8/NIP comparison, the identified differential proteins were labeled as 46 functional groups, and the differential proteins in the cell components mainly involved in the formation of organelles and cell membranes. Molecular function analysis showed that the differential proteins were mainly involved in the catalytic activity and transport activity of proteins; In the category of biological processes, differential proteins mainly involve metabolic, cellular, and other biological processes, as shown in Fig. 7a and b. The differential proteins obtained from the ABE/NIP comparison are labeled as 46 functional groups. GO enrichment analysis is mainly involved in biological processes in cell composition, molecular function, and biological processes, which are the same as CYP8/NIP. The results showed that there was no difference between the functional annotation and enrichment of the differential protein GO obtained by the two groups edit materials ABE and CYP8.

Figure 7. GO enrichment of the identified DAPs. (a) To determine the function of the differentially expressed proteins identified in CYP8/NIP; (b) to determine the function of differentially expressed proteins identified in ABE/NIP.

3.3.3. KEGG Pathway Enrichment of Differential Proteins

The KEGG pathway enrichment analysis of the identified differential proteins was carried out using the KEGG pathway database. The results showed that the differential proteins identified by CYP8/NIP are mainly involved in phenylpropanoid biosynthesis, phenylpropane metabolism, and photosynthesis. The differential proteins identified by ABE/NIP are mainly involved in metabolic pathways and biosynthesis of secondary metabolites. As shown in Fig. 8a,b the results showed that the differential proteins obtained by the two groups of gene-edit plants did not differ in the KEGG pathway, and the gene editor had no obvious effect on the plants.

Figure 8. KEGG pathway enrichment of DAPs. (a) KEGG pathway enrichment of CYP8/NIP differentially expressed protein; (b) ABE/NIP differential expression protein KEGG pathway enrichment.

3.3.4. PPI of Common Differential Proteins

Venn diagram of differential protein (Fig. 9) shows 21 differentially expressed proteins (G0: differentially expressed proteins produced by CYP8/NIP; G2: differential proteins generated by ABE/NIP) and constructed a common differential protein interaction network (Fig. 10), in which four co-DAPs (Os01g0198200, Os04g0630400, Os05g0449600, and Os06g0604300) participate in metabolic pathways, including ether and lipid metabolism Carbohydrate metabolism and secondary metabolism process; three co-DAPs (Os10g0469900, Os10g0447900 and Os07g0291100) participate in the activity of transporters, including the activity of oligopeptide transmembrane transporters; five co-DAPs (Os05g0382600, LOC_Os03g16960, Os01g0563000, Os09g0471900, and Os07g0240600) participate in the stress response, belonging to environmental adaptation; two co-DAPs (Os07g0150100, Os09g0425900) participate in the formation of cell membrane; Os04g0630400 participates in biosynthesis; Os08g0117300 participates in ribosome formation; Os01g0712700 participates in the formation of RNA polymerase. It can be concluded that the gene editor has little impact on the life activities beyond the target of the gene editing plant. It mainly belongs to the stress response, and the response to biological stimulation. No new protein is detected, only the protein abundance changes.

Figure 9. Venn diagram of differential protein.

Figure 10. PPI diagram of common differential proteins.

4. Discussion

Early whole genome sequencing (WGS) studies of human cells found that the incidence of off-target mutations in Cas9 was low.^32,33 An article “Unexpected mutations after CRISPR-Cas9 editing in vivo” was published in Nature Methods in 2017.⁶ The paper showed that the two mice were able to regain their sight by using CRISPR-Cas9 to edit the gene responsible for their blindness. Whole genomes of the restored mice were sequenced and found to contain more than 1,500 single nucleotide mutations, as well as large insertions and deletions at more than 100 sites. The article sparked widespread public concern about the safety of gene editing technology. However, the paper was retracted a year later due to improper design (only three mice were involved and the mice were from different genetic backgrounds). Then, the researchers analyzed and counted 1,423 predicted off-target sites from 81 groups of gene-edited rats and mice, whole genome sequencing results showed that there were 32 real off-target sites in these mice and rats. At the same time, a total of 43 predicted off-target sites were identified in 10 single sgRNA-edited mouse embryos by whole genome sequencing.³⁴ Although these results contradict the previous conclusions, unexpected effects caused by the CRISPR-Cas system should not be ignored. Given the widespread use of CRISPR-Cas systems in agriculture, whole-genome (WGS), transcriptome, and proteome analyses of Cas9 and ABE systems, which are widely used in crops, are needed. These studies will help assess the safety of Cas9 and ABE in precision crop breeding and provide valuable information to scientists, breeders, regulators, and consumers.

In RNA-seq study, the sequencing method of Illumina high-throughput sequencing platform was used to analyze the expression levels of differential genes in gene edited plants (CYP8, ABE) and receptor plants (Nipponbare). A total of 58,349 differentially expressed genes were identified. In CYP8/NIP plants, 520 differentially expressed genes were identified.

Among them, 110 differentially expressed genes were up-regulated and 410 were down-regulated. Most of these differential genes are involved in plant–pathogen interactions, metabolism of terpenoids and polyketones, and signal transduction in plants, which are mainly related to environmental adaptation. In ABE/NIP plants, a total of 566 differentially expressed genes were identified. Among them, 170 differentially expressed genes were up-regulated and 396 differentially expressed genes were down-regulated. These differentially expressed genes are also mainly involved in plant–pathogen interaction, phosphatidylinositol signaling system, starch, and sucrose metabolism, which mainly belong to environmental adaptation. These results indicated that no newly produced genes in these differentially expressed genes were identified as new transcripts, and gene editing tools had little effect in rice transcriptome, and these differential genes are not only detected in gene-edited crops but also can be observed in conventional plant breeding, hybridization, or different growing environments.

In proteome sequencing studies, proteome mass spectrometry was performed based on iTRAQ technology, T test was used to analyze the difference of protein expression among the three groups, the screening conditions were significance test value P = .05 and quantitative ratio FC = 1.2, a total of 4336 proteins were identified. In CYP8/NIP plant, a total of 298 differentially expressed proteins were identified, among them, 243 differentially expressed proteins were up-regulated and 55 differentially expressed proteins were down-regulated, these differential proteins are mainly involved in phenylpropane biosynthesis, phenylpropane metabolism, and photosynthesis. In ABE/NIP plant, a total of 54 differentially expressed proteins were identified, among them, 40 differential proteins were up-regulated and 14 differential proteins were down-regulated, these differential proteins are mainly involved in metabolic pathways and biosynthesis of secondary metabolites. A total of 21 common differential proteins were identified in two gene-edited rice line, and protein–protein interaction analysis of these shared differential proteins, the results show that most of these interacting proteins are involved in stress responses, mainly in response to biological stimuli and protein processing in endoplasmic reticulum and metabolic pathways, while others are identified as ribosome components. According to the results of proteomic analysis, no new proteins were produced in the gene-edited rice, and only changes in the abundance of protein expression were detected.

By analyzing the transcriptome and proteome of gene edited rice CYP8, ABE and receptor material rice Nipponbare, to elucidate the changes of gene and protein expression in gene-edited rice compared with receptor material, it provides a scientific basis for the safety evaluation of gene-edited rice from the perspective of omics sequencing, and can be used as a new method for the safety evaluation of gene-edited organisms.

Author Contribution

Wang Zhi-Xing and Wang Xu-Jing designed the experiments. Liu Xiao-Jing and Xing Bao performed the experiments and wrote the manuscript. Wang Meng-Yu and Li Xiao-Man assisted with data analysis. All authors read, revised, and approved the final manuscript.

Acknowledgments

This work was supported by the Major Project of Agricultural Biological Breeding (2022ZD0402003).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/21645698.2023.2229927

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

The work was supported by the Major Project of Agricultural Biological Breeding [2022ZD0402003].