Genetic Analysis of Genomic and Methylomic Variation and Construction of Multi-Trait Mutant Library in Rice Carried on Chang'e-5

Genetic Analysis of Genomic and Methylomic Variation and Construction of Multi-Trait Mutant Library in Rice Carried on Chang'e-5 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genetic Analysis of Genomic and Methylomic Variation and Construction of Multi-Trait Mutant Library in Rice Carried on Chang'e-5 Kai Sun, Jinrui Li, Zhe Zhao, Shiyi Chen, Yuanyuan Guo, Ying Ling, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8781482/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Stable yield and improved quality in rice are crucial for ensuring food security. Traditional breeding techniques are unable to meet the urgent demand for breakthrough germplasm resources, while space mutagenesis breeding provides a new approach for crop genetic improvement. Existing research lacks sufficient understanding of the genetic patterns of variation and has not fully explored the genetic diversity within mutant populations. In this study, rice seeds were carried by the Chang'e-5 lunar probe, upon their return to the ground, were propagated to establish lineages for genetic analysis of space-induced mutagenesis effects and for the construction of a multi-trait mutant library. The whole-genome sequencing results showed that the number of mutations was higher in the space mutation second generation than in the first generation. The SNPs, homozygous, and coding sequence mutations were more likely to be inherited compared to other types and positions of mutations. The whole-genome bisulfite sequencing results showed that the methylation level in the space mutation second generation was also higher than that in the first generation, with CG type showing the highest genetic stability. Differentially methylated cytosines in genebody were more heritable than those in gene upstream and downstream. Conversely, differentially methylated regions in gene upstream and downstream exhibited higher heritability compared to those in genebody. A total of 277 mutants covering multiple traits were screened. The mutant library constructed from these mutants showed a high gene coverage rate of 96.69% and a high functional mutation ratio of 59.39%. Based on RNA sequencing and the mutant library from representative mutants, 9 candidate genes related to nitrogen utilization, drought tolerance, cold tolerance, and grain type were identified. This study systematically reveals the genetic patterns of genomic and methylomic variation in rice induced by the space environment. The high-quality mutant library constructed here provides direct support for rice functional gene cloning and the development of new breeding materials. Rice Space mutagenesis Genomic variation DNA methylation Mutant library Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction With the continuous growth of global population, increasing scarcity of arable land resources, and the uncertainty brought by climate change to agricultural production, food security has become one of the major challenges facing humanity in the 21st century (Mehrabi et al. 2022 ; Yu et al. 2024 ). Rice, as the staple food for over half of the world's population, its stable yield and improved quality are crucial for ensuring food security (Luo et al. 2025). Traditional hybridization and mutagenesis breeding techniques have made significant contributions to the genetic improvement of rice, but their variation range is limited and their cycle is long, making it difficult to meet the urgent demand for breakthrough new germplasm. Space mutagenesis breeding, as an emerging technology that utilizes special space environments to induce genetic variation in organisms, has opened up a new path for crop genetic improvement. When organisms are exposed to the space environment, they encounter extreme conditions that are completely different from the ground. This environment mainly includes space radiation, microgravity, extreme temperature, and magnetic field changes, which together constitute a unique mutagenic source (Stolc et al. 2024 ; Mhatre et al. 2022 ). Compared with common ground induced mutations such as gamma rays and ion beams, space radiation has the characteristics of high linear energy transfer (LET), strong biological effects, and complex damage repair, and is believed to induce more diverse and rare genetic variations (Michaeli et al. 2024 ; Liang et al. 2023 ; Nishizawa-Yokoi et al. 2023 ). When organisms face the extreme space environment mentioned above, they will generate sustained stress responses, causing DNA damage and epigenetic modifications, ultimately leading to a series of unique mutation spectra at the genome sequence and epigenome levels that are different from ground mutagenesis. These mutations are also the genetic basis for the emergence of new traits (Xu et al. 2021 ; Takahashi et al. 2021 ). In recent decades, humans have screened batches of new germplasm with high yield, high quality, and disease tolerant in various crops such as rice, wheat, and vegetables through space mutagenesis technology, fully demonstrating the potential application of space mutagenesis breeding (Chen et al. 2024 ; Zhang et al. 2022 ; Fan et al. 2024 ). However, compared to the achievements made in practical applications, our understanding of the fundamental genetic laws behind space mutagenesis is still quite limited. Most existing research remains at the level of phenotype observation and preliminary molecular marker identification, lacking systematic and high-precision analysis of the types of genomic variations in organisms after space flight (Merikangas et al. 2022 ; Wheeler et al. 2022 ). At the same time, whether the mutations generated by space mutagenesis can be stably inherited is the key to breeding applications. However, existing research mostly focuses on observing a certain generation, and little is known about the rules of mutation transmission in generations (Yamazaki et al. 2023 ; Liu et al. 2023 ). The shortcomings of these studies greatly constrain the leap of space mutagenesis technology from "experience screening" to "precise design". In addition, previous studies have mostly focused on screening single or a few traits, failing to fully utilize the rich genetic diversity contained in the mutant population. Moreover, the gene coverage of existing mutant libraries is generally below 80%, which has not achieved genome-wide "saturation mutagenesis", making it impossible to systematically explore genetic associations between different traits and limiting the application of multi-trait aggregation breeding (Daware et al. 2023 ; Li et al. 2023 ). Therefore, building a mutant library that covers a large number of mutant materials, multiple important traits, and complete genomic information is of great value for large-scale mining of functional genes and promoting breeding innovation. This study conducted high-depth whole-genome sequencing (WGS) and whole-genome bisulfite sequencing (WGBS) on the space mutagenesis 1st generation (SP 1 ) and space mutagenesis 2nd generation (SP 2 ) of rice seeds carried on Chang'e-5 after returning to the ground, identifying space environment induced genomic and methylomic variations, and systematically elucidating their genetic and evolutionary patterns across different generations; Large scale and multi-trait mutant phenotype identification was conducted on SP 2 , and it was validated in the space mutagenesis 3rd generation (SP 3 ) to comprehensively explore the phenotypic diversity generated by space mutagenesis. By integrating the whole genome information of mutants, a multi-trait mutant library for rice space mutagenesis was constructed; Representative mutants were selected and combined with multi time point RNA sequencing (RNA-seq) to reveal the dynamic molecular response network of trait formation. Furthermore, candidate genes regulating key traits were identified through joint analysis of mutant library and transcriptome (Fig. 1 A), and their expression levels were validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR). The results of this study will systematically reveal the mutagenic effects of deep space environment on rice and its transgenerational inheritance patterns. The constructed mutant library provides direct support for the cloning of important trait genes and innovation of breeding materials. Results Analysis of Genetic Patterns of Genomic Variation To elucidate the genetic patterns of genomic variation caused by deep-space flight across different generations, 30 individual plants (TK1-TK30) were randomly selected from 2,500 mutants in the SP 1 generation, and 10 individual plants (CK1-CK10) were randomly selected from the control for WGS. 30 TK and 10 CK obtained a total of 392.6 Gb of clean data through sequencing, with an average sequencing depth of 25.3×. After aligning with the reference genome SK1, a total of 276,898 authentic mutation sites were screened. The mutation frequency of the samples ranged from 1.96×10 − 7 to 6.08×10 − 4 , and there were significant differences in the number of mutation sites between different samples. Among them, TK15 had the highest number of mutation sites, reaching 265,320, while TK28 had the lowest number of mutation sites, only 78 (Fig. 1 B). 30 TK were propagated per plant to produce 30 offspring strains (NTK1-NTK30). WGS was also conducted on 30 NTK. A total of 251.9 Gb of clean data was obtained from 30 NTK, with an average sequencing depth of 21.7×. A total of 565,698 authentic mutation sites were screened, with sample mutation frequencies ranging from 1.84×10 − 7 to 7.38×10 − 4 . Among them, NTK15 had the highest number of mutation sites (292,608), while NTK23 had the lowest number (73). These loci are evenly distributed on the chromosome and no obvious hotspot areas were found (Fig. S1 ). Compared with TK, the number of genomic variations in NTK significantly increased (Fig. 1 C). A comparison of the genomic variation characteristics between TK and NTK revealed that the proportion of SNPs, synonymous SNV, nonsynonymous SNV, and transitions in NTK significantly increased, while the proportion of InDel, 5'UTR, frameshift, nonframeshift, and transpositions significantly decreased (Fig. S2 A). Further, the genomic variations of TK and NTK were intersected, with the intersection being the genetic locus and the rest being nongenetic loci. 40.75% of the mutation sites in TK are inherited to NTK, with a total of 112,837. The number of genetic loci in the 30 samples ranged from 0 to 112,040, with the TK15-NTK15 group having the highest proportion of genetic loci, accounting for 42.25% of the total number of variant sites in TK15, while the TK25-NTK25 group had no genetic mutation sites (Fig. S2 B). A significant positive correlation was observed between the total variation of TK and the total variation and genetic loci of NTK, that is, the more total variation of TK, the more genetic loci and total variation of NTK (Fig. 1 D). The variation characteristics of genetic and nongenetic loci were compared, and it was found that the proportion of SNP, homozygous, CDS region, synonymous SNV, nonsynonymous SNV, and C/G → A/T increased in genetic loci, while the proportion of InDel, heterozygous, intergenic, frameshift, nonframeshift, and A/T → C/G decreased (Fig. 1 E). In addition, enrichment analysis was conducted on genes containing genetic loci and nongenetic loci. The results showed that genes containing genetic loci were mainly involved in various biological processes such as DNA activity, nucleotide metabolism, and protein modification, and were related to homologous recombination, sulfur transfer systems, pyrimidine metabolism, and the synthesis and metabolic pathways of various organic compounds; and genes containing nongenetic sites are mainly involved in various DNA dynamic changes, as well as RNA synthesis and metabolism processes. They are also related to homologous recombination, plant hormone signal transduction, and the synthesis and metabolism of various organic compounds (Fig. S3 A, B). In summary, the number of genomic variations in NTK was significantly increased compared to TK, with SNP, homozygous, CDS regions, synonymous SNV, nonsynonymous SNV, as well as mutation sites on genes related to nucleotide metabolism, protein modification, sulfur transfer system, pyrimidine metabolism, and other functions being more easily inherited. Analysis of Genetic Patterns of DNA Methylation To clarify the genetic patterns of DNA methylation induced by deep space flight across different generations, WGBS was conducted separately for 10 CK, 30 TK and 30 NTK. The conversion rate of Bisulfite treatment for all samples was above 99%. Compared with TK, the overall methylation rate of NTK was increased, especially the methylation rate of CHH type which was significantly increased (Fig. 2 A). The methylation rates of TK and NTK at different gene positions were compared, and it was found that the methylation rate of NTK was significantly increased in the upstream, intron, and 3'UTR of the gene. From the perspective of different methylation types, the methylation rate of CHH in the entire NTK genome was significantly increased, while the methylation rate of CHG was only significantly increased in introns and 3'UTR, whereas the methylation rate of CG was decreased in 3'UTR (Fig. S4). In addition, in contrast to genomic variations, no significant correlation was observed between TK, NTK, and genetic methylation sites (Fig. 2 B). Further screening was conducted to identify DMCs and DMRs of TK and NTK. A total of 26,399 DMCs were inherited from TK to NTK, accounting for 17.73% of the total DMCs in TK and 61.76% of the total DMCs in NTK, respectively (Fig. 2 C). The distribution of genetic and nongenetic DMCs on chromosomes is shown in Fig. S5A. At the same time, a total of 463 DMRs were inherited from TK to NTK, and their distribution on chromosomes is shown in Fig. S5B-D. Among them, the CG type had the highest number of DMRs, followed by CHG, while the CHH type had only one DMR (Fig. 2 D). The distribution of genetic and nongenetic DMCs and DMRs on the genome was compared, and the results showed that all three types of genetic DMCs had a higher proportion in the genebody, while nongenetic DMCs had a higher proportion upstream and downstream of the gene; conversely, DMRs had a higher proportion both upstream and downstream of the gene, while their proportion in the gene itself was lower (Fig. 2 E). Enrichment analysis was conducted on genes containing genetic DMRs and genes containing nongenetic DMRs. GO enrichment results showed that genetic DMRs genes were mainly involved in biological processes such as tobacco amine synthesis and metabolism, protein activity, oxidative phosphorylation, immune response, and ATP electron transfer; nongenetic DMRs genes were mainly involved in DNA and chromosome activity, response to stimuli, cell division, and metabolic pathways of various organic compounds (Fig. 2 F). The KEGG enrichment results showed that genetic DMRs genes were mainly involved in the synthesis and metabolism of various organic compounds such as anthocyanins and diterpenes, while nongenetic DMRs genes were mainly involved in plant hormone signal transduction, Calvin cycle, glycolysis, synthesis and metabolism of inorganic compounds such as sulfur, and synthesis and metabolism pathways of organic compounds such as phenylpropane (Fig. 2 G). In summary, similar to the genetic pattern of genomic variation, the genome-wide methylation rate of NTK is significantly higher than that of TK. CG had the highest proportion of genetic methylation sites, and DMCs on the genebody, DMRs on the upstream and downstream of the gene, as well as DMRs on genes related to grass amine synthesis and metabolism, protein activity, oxidative phosphorylation, immune response, ATP electron transfer, anthocyanins, diterpenes and other organic compounds were more easily inherited. Screening of Multi-trait Mutants Screening and identification of mutants were performed for 2,500 strains (25,000 individual plants) of SP 2 . Statistical analysis was conducted on the chlorate sensitivity of different materials, and among 2,500 strains, the chlorate sensitivity ranged from − 46.74% to 69.29% (Fig. 3 A). A total of 88 materials with the highest and lowest chlorate sensitivity were retained, and their chlorate inhibition rates were further identified in SP 3 (Fig. 3 B). Materials with consistent changes in representative types from two generations were retained. Finally, 52 mutants were screened, including 32 nitrogen efficient mutants and 20 chlorate insensitive mutants. The germination rate of SP 2 material after NaCl treatment ranged from 0-99.02% (Fig. 3 C), and 90 materials with the highest germination rate were retained. Based on the germination rate of SP 3 after NaCl treatment (Fig. 3 D), 17 salt-tolerant mutants were ultimately screened. After PEG-6000 drought stress treatment, the germination rate of SP 2 seeds ranged from 1.03% to 98% (Fig. 3 E). 79 materials with the highest germination rate were preliminarily selected for subsequent validation. After drought tolerance testing of SP 3 (Fig. 3 F), a total of 32 drought-tolerant mutants were screened. The germination coefficient of SP 2 after cold treatment was between 9.09–18.08 (Fig. 3 G). The germination coefficient of cold-tolerant materials is high, usually starting from the 5th day, and the bud length is longer, with a final germination rate greater than 80%; in contrast, the germination coefficient of cold sensitive materials is low, with germination starting from the 7th day and shorter bud length, resulting in a final germination rate of less than 50%. Based on the germination coefficient, 41 cold-tolerant mutants were preliminarily screened in SP 2 , and after verification of the cold-tolerant phenotype in SP 3 (Fig. 3 H), a total of 27 cold-tolerant mutants were obtained. The grain length of SP 2 seeds was between 9.99–12.22 mm (Fig. 3 I), and the grain width was between 1.63–2.87 mm (Fig. 3 J). Based on the grain type data, a total of 59 grain-type mutants were screened. In SP 3 , the grain types of the 59 mutants were tested again (Fig. 3 K, L), resulting in 23 grain-type mutants, including 2 long-grain mutants, 10 short-grain mutants, 4 wide-grain mutants, and 7 narrow-grain mutants. After inoculation with rice blast fungus, 252 resistant materials were preliminarily screened from SP 2 materials (Fig. 3 M). Further rice blast tolerant testing was conducted in SP 3 , and 7 materials showed high tolerance (level 1 tolerant) in both generations. In addition to the screening of mutant phenotypes mentioned above, this study also conducted phenotype investigations on multiple agronomic traits throughout the entire growth period of all SP 2 materials, including plant height, tillering, plant type, growth period, fertility, and yield traits. After verifying the phenotype at SP 3 , a total of 119 mutants were screened (Fig. 3 N). Based on the above results, a total of 277 mutants were screened, numbered K1-K277. The specific information is shown in Table S2 . Construction of Multi-trait Mutant Library WGS was performed on the 277 mutants and 1 WT mentioned above to construct a multi-trait mutant library. A total of 2.49 Tb of clean data was obtained from the mutant library, with an average sequencing depth of 23.7×. After screening, a total of 2,558,421 mutation sites were identified, which were evenly distributed on 12 chromosomes and no obvious hotspot regions were found (Fig. 4 A). The number of mutation sites in a single sample ranged from 155 to 214,126, with mutation frequencies ranging between 3.91×10 − 7 -5.4×10 − 4 . The average number of mutations in a single sample was 9,137, and the majority of samples had relatively low levels of mutation sites. 84.12% of samples had fewer than 10,000 mutations, and 38.27% had fewer than 1,000 mutations (Fig. 4 B). There are at least one mutation site on 35,615 genes (from upstream 2 Kb to downstream 2 Kb), covering 96.69% of the total genes and causing changes in the transcripts of 26,737 genes, accounting for 72.58% of the total genes. The number of mutations on genes is inversely proportional to the number of genes (Fig. 4 C), with 95% of genes having mutant library are mainly SNPs, accounting for 69.83%, while InDel only accounts for 30.17%; in SNP mutations, the proportion of C/G → A/T is the highest (Fig. 4 D). From the perspective of genome distribution, there are mutation sites on all gene elements, with the highest number of mutations located in the intergenic region, followed by upstream and downstream and CDS regions; the functional mutations of mutation sites include all types, among which high impact mutations (frameshift, nonsynonymous SNV, stopgain, and stoploss) account for 59.39% (Fig. 4 E). Compared with WT, among the mutation sites in the mutant library, InDel, the proportion of exon regions, frameshift and nonframeshift, nonsynonymous SNV, and C/G → A/T type is higher (Fig. 4 F). The above results indicate that the rice space mutagenesis multi-trait mutant library constructed in this study has the characteristics of high mutation gene coverage, high mutation site coverage density, uniform coverage, and high proportion of functional mutations. RNA-seq of Representative Mutants One representative mutant with the most significant changes in nitrogen efficient, drought tolerant, cold tolerant, and grain phenotype was selected from the 277 mutants (Fig. S6), and samples were taken for RNA-seq at different treatments and time points. The nitrogen-efficient mutants K125 and WT showed clustering within sample groups treated differently, while exhibiting discrete distributions between groups (Fig. 5 A), indicating that different treatments significantly affected gene expression patterns. A total of 3,613 DEGs were screened, and the number of DEGs gradually decreased with the delay of sampling time (Fig. 5 B). Among them, 29 genes were differentially expressed in all three treatments (Fig. 5 C). GO and KEGG enrichment analyses were performed on them separately, and the results showed that on 8th day, the functions of DEGs were mainly related to DNA activity, cell cycle, and the synthesis and metabolism of organic compounds such as peptides and flavonoids. After KNO₃ treatment on the 12th day, the main functions of DEGs shifted to cell and component movement, stimulus response, hormone and signal transduction, etc. After treatment with KClO₃ on the 12th day, the main function of DEGs was mainly related to oxidative stress. Fatty acid and phenylpropane biosynthesis played a role throughout the entire sampling period (Fig. S7A, B). The functional center transformation of the above DEGs after different treatments also reflects the mutant's process of nitrogen utilization. After 3rd, 5th, and 7th days of drought treatment, RNA-seq was performed on the drought-tolerant mutant K12, and the drought treatment significantly affected the gene expression pattern (Fig. 5 D). After screening, a total of 6,361 DEGs were obtained, with the highest number of DEGs at 3rd day of drought treatment, while the number of DEGs at 5th and 7th day decreased significantly (Fig. 5 E), indicating that the seed germination response was more pronounced in the early stages of drought treatment, with 71 genes being differentially expressed in all three treatments (Fig. 5 F). The GO and KEGG enrichment results indicate that the function of DEGs during drought treatment for 3rd day is mostly related to oxidative stress and carbohydrate synthesis metabolism; the function of DEGs at 5th day of drought treatment is similar to that at 3rd day, and is related not only to oxidative stress but also to stimulus response and detoxification response; after 7th day of drought treatment, the function of DEGs shifted towards conventional life activities such as the synthesis and metabolism of various organic compounds (Fig. S8A, B). The above results indicate that the early stage of seed germination is the main period for responding to drought stress, and oxidative stress may be an important pathway for seeds to respond to drought stress. On the 7th, 14th, and 21st day after flowering of the grain-type mutant K44, seeds were harvested and RNA-seq was performed, and significant differences in gene expression patterns were observed at different sampling times (Fig. 5 G). After screening, a total of 9,851 DEGs were obtained, with the highest number of DEGs on 14th day and the lowest number on 7th day. Moreover, the number of upregulated DEGs was much higher than that of downregulated DEGs at all three sampling times (Fig. 5 H), indicating a more significant positive regulatory effect of genes during grain growth. A total of 799 genes were differentially expressed at all three sampling times (Fig. 5 I). The DEGs enrichment results at the three sampling times also showed high similarity, with significant enrichment in metabolic processes such as organic acids and carbon. In addition, the DEGs on the 7th day are also related to the TCA cycle and glycerol metabolism, the DEGs on the 14th day are related to coenzyme metabolism and the pentose phosphate pathway, and the DEGs on the 21st day are related to organic nitrogen compound metabolism and Calvin cycle carbon fixation processes (Fig. S9A, B). On the 6th day after germination under cold stress treatment in the cold-tolerant mutant K82, RNA-seq was performed (Fig. 5 J), and a total of 2,376 DEGs were screened, among which the number of upregulated DEGs was higher than that of downregulated DEGs (Fig. 5 K). Enrichment analysis was conducted on them, and DEGs were found to be related to the synthesis and metabolism of various organic compounds such as oxidation-reduction and sugars, as well as the synthesis of substances such as phenylpropane (Fig. S10A, B). To further explore the common molecular mechanisms of space mutagenesis in rice, GO and KEGG enrichment analyses were performed on the intersection genes of each mutant treated differently. The GO enrichment results showed that biological processes related to oxidative stress, ion transport, and glucose metabolism were significantly enriched multiple times (Fig. S11A). The KEGG enrichment results showed involvement in the synthesis and metabolism of various organic compounds such as sugars, as well as the biosynthesis of phenylpropane and other pathways (Fig. S11B). This indicates that the above pathways play an important role in the process of biological response to space mutagenic effects. Identification and Validation of Candidate Genes To further identify candidate genes that cause phenotypic changes in the mutant, the genomic variation results and RNA-seq results of the mutant library were jointly analyzed. According to the results of the mutant library, K125 had a total of 2,149 mutation sites and 65 high impact mutation sites, distributed across 61 genes, of which 92.31% were frameshift mutations (Fig. 6 A). An intersection of the 61 mutated genes with the 3,613 DEGs resulted in 2 genes (Fig. 6 B), namely SK1G00044581 and SK1G00052825 . In the K125 mutant, SK1G00044581 has a 7-bp frameshift deletion in the 11th exon region, resulting in a significant decrease in its expression level after KNO₃ treatment. SK1G00052825 has a 2-bp frameshift deletion in 1st exon, and its expression level increased significantly after KClO₃ treatment, reflecting the difference in response mechanisms between the two. K12 had a total of 3,707 mutation sites and 226 high impact mutation sites, distributed across 211 genes, with frameshift mutations accounting for 87.43% (Fig. 6 A). Taking the intersection of the 211 mutated genes and the 6,361 DEGs, a total of 20 genes were obtained (Fig. 6 B). Further analysis of the functions and pathways of these 20 genes, combined with existing reports, was focused on oxidative stress, ion transport, and other factors related to drought tolerant. Finally, three genes were selected, namely SK1G00042488 , SK1G0048986 , and SK1G00051356 . There is a 4-bp frameshift insertion in 3rd exon of SK1G00042488 , resulting in a significant decrease in its expression level on the 3rd day of drought treatment. There is a 1-bp frameshift insertion in the 1st exon of SK1G0048986 , which also leads to a significant decrease in its expression level on the third day of drought treatment. There is a 20-bp frameshift deletion in 1st exon of SK1G00051356 , resulting in a significant increase in its expression level on the 3rd day of drought treatment. Although the three candidate genes have different modes of action, their expression levels changed significantly on the 3rd day, indicating the importance of early response to seed germination after drought treatment. K82 had a total of 2,778 mutation sites and 183 high impact mutation sites, distributed across 171 genes, with frameshift mutations accounting for 93.69% (Fig. 6 A). Taking the intersection of the 171 mutated genes and the 2,376 DEGs, a total of 11 genes were obtained. Further screening based on gene function and previous reports resulted in two candidate genes (Fig. 6 B), SK1G00061753 and SK1G0048468 . SK1G00061753 has a 2-bp frameshift insertion in 2nd exon, and SK1G0048468 has an 88-bp frameshift insertion in 6th exon, both of which result in a significant decrease in gene expression. K44 had a total of 7,747 mutation sites and 175 high impact mutation sites, distributed across 110 genes, with nonsynonymous SNV accounting for 86.86% (Fig. 6 A). By taking the intersection of the 110 mutated genes and the 9,851 DEGs, a total of 25 genes were obtained. After further screening, 2 candidate genes were identified (Fig. 6 B), namely SK1G00047179 and SK1G00059434 . There is a nonsynonymous SNV in 1st exon of SK1G00047179 , where alanine is mutated to valine, resulting in a significant increase in its expression throughout the entire grain development period. There is a nonsynonymous SNV in 2nd exon of SK1G00059434 , where leucine is mutated to proline, resulting in a significant increase in its expression level on the 7th day of grain development. In summary, based on the variation information of the mutant and the DEGs of the transcriptome, 58 potential candidate genes were preliminarily screened, and their expression levels are shown in Fig. 6 C. Based on the functions and pathways of the genes, combined with existing reports, 9 candidate genes were ultimately screened (Table 1 ). To verify the authenticity of the 9 candidate genes mentioned above, their expression levels were detected by qRT-PCR. The results showed that the expression levels of the 9 candidate genes were consistent with the trend of transcriptome results (Fig. S12), and multiple genes had been reported by previous researchers, proving the authenticity and reliability of the candidate genes selected in this study. Table 1 Candidate gene information. SK1 id IRGSP-1.0 id Gene name Mutation location Functional mutation type Related traits SK1G00044581 Os03g0758100 OsPho1 Chr3_5569159 Frameshift deletion Nitrogen utilization SK1G00052825 Os04g0322100 CRK25 Chr4_16033435 Frameshift deletion Nitrogen utilization SK1G00042488 Os01g0207900 PER1 Chr1_30792148 Frameshift insertion Drought tolerant SK1G00048986 Os03g0281900 RCN1 Chr3_7476262 Frameshift insertion Drought tolerant SK1G00051356 Os03g0187800 OsPUP1 Chr3_34595972 Frameshift deletion Drought tolerant SK1G00061753 Os03g0215800 cys12 Chr3_7620043 Frameshift insertion Cold tolerant SK1G00048468 Os05g0365300 OsRP1L1 Chr5_3361761 Frameshift insertion Cold tolerant SK1G00047179 Os07g0561300 OsFBX257 Chr7_32899053 Nonsynonymous SNV Grain type SK1G00059434 Os02g0771100 OsCOP1 Chr2_18283681 Nonsynonymous SNV Grain type Discussion The Genomic Variation Induced by Space Mutagenesis Has a Clear Genetic Tendency Genomic variation is the foundation of biological genetic diversity and trait expression, and its ability to be stably inherited is influenced by various factors such as the type, location, function, and cell type of the variation (Chen et al. 2019; Zaid et al. 2017; Qin et al. 2021). In terms of mutation types, SNPs exhibit a stronger genetic tendency in intergenerational transmission compared to InDel (Xu et al. 2018). The results of this study also demonstrate this phenomenon, with a significantly higher proportion of SNPs than InDel in the genomic variation loci of SP 1 -SP 2 . The location of mutations in the genome determines their functional importance and genetic predisposition. In model organisms such as humans and rice, most of the mutations associated with complex traits or diseases are not located in protein coding regions, but are enriched in noncoding regulatory regions (such as promoters, enhancers) or intergenic regions (Wei et al. 2020; Vahedi et al. 2023). The variations in these regions contribute to the main part of trait heritability by regulating gene expression, showing a stronger genetic tendency. In contrast, although the number of nonsynonymous SNV that cause amino acid changes in the coding region is relatively small, they also have important genetic potential in certain traits due to their direct impact on protein function (Zaid et al. 2017; Sadowski et al. 2019). Although the genetic proportion of nonsynonymous SNV was also high in this study, synonymous SNV showed the same trend, and the proportion of variant inheritance in noncoding regulatory regions was lower than that in CDS regions. This result also reflects the specificity of space mutagenesis variation characteristics compared to natural variation. The genetic ability of variation is also related to the function of the gene it is located in. In the human genome, variations in functionally important and highly conserved positions are more likely to affect heritable diseases or phenotypes, and their variations are more likely to be inherited, while functional adaptive variations (such as environmental response) are not easily preserved (Sullivan et al. 2023). In this study, the heritability of genetic loci was significantly associated with gene function. Variations in relatively conserved functional genes, including those involved in protein modification and basal metabolism, were more likely to be inherited. The heritability of a plant is directly determined by whether it is a somatic or germ cell in the cell lineage where variation occurs due to the phenomenon of cell chimerism (Herrera et al. 2019; Martínez-Glez et al. 2020). The cell types in which variation exists affect the genetic efficiency of variation. In this study, the genetic rate of genomic variation in the samples ranged from 0 to 42.25%, which also reflected the influence of chimerism on the inheritance of variation. As the variation detection is based on somatic cells, the sex cells of low heritability samples may carry fewer variations, while high heritability samples carry more variations. In addition, there is a significant positive correlation between the heritability of sample variation and the number of sample variations. Therefore, increasing the number of variations in parents is also one of the methods to maintain a higher frequency of variation in offspring. Genetic Patterns of DNA Methylation Changes Environmental pressure or stress can induce DNA methylation changes, which can be inherited from offspring through sexual reproduction (Cao et al. 2024). DNA methylation is a relatively stable epigenetic marker in plant sexual reproduction, which can be inherited by offspring, but most methylation marks are actively erased in parental gametes (Greenberg et al. 2019). The results of this study confirmed this viewpoint, as the methylation sites of parents were not fully inherited by offspring, indicating that some methylation sites had been erased. Similar to genomic variation, the stability and tendency of DNA methylation inheritance are not uniform, mainly influenced by multiple factors such as methylation type, genomic location, and environmental factors. Methylation type is one of the core factors determining the heritability of DNA methylation. Among the three types of DNA methylation, CG methylation, with its symmetrical sequence, is stably inherited via maintenance methylation (Feng et al. 2021; Kikuchi et al. 2025), whereas CHG symmetry is less stable (Herle et al. 2025), and asymmetric CHH relies on dynamic de novo methylation, showing the weakest heritability (Wang et al. 2020). The results of this study were consistent with the above conclusions. Among the genetic methylation sites, CG type had the highest proportion, followed by CHG, and CHH had the smallest proportion. Secondly, the genomic location determines the genetic potential of methylation patterns. Usually, methylation in repeat sequence regions such as transposable elements is more stable and conserved, with a higher genetic predisposition, while methylation in regions related to gene regulation is more dynamic and more susceptible to environmental factors, with a relatively lower genetic predisposition (Quadrana et al. 2016; Li et al. 2025; Cao et al. 2023). In this study, it was necessary to distinguish between DMC and DMR. The heritability of DMCs in gene regulation related regions was lower, while the heritability of coding regions was higher, while DMR was completely opposite. The coding region sequence had high conservation, and if the methylation of a single C site changes, it may affect codon function or mRNA stability. Therefore, evolution tends to stabilize inheritance through efficient maintenance mechanisms, resulting in a high heritability of DMC; However, the DMR in the coding region may lead to significant abnormalities in gene function, which are strictly limited by evolutionary selection and difficult to stably inherit, resulting in a low heritability. The methylation changes of a single C site in the regulatory region usually have limited and reversible effects on gene expression, and are susceptible to environmental signal interference and cannot be stably inherited, resulting in a low heritability of DMC; However, the DMR in the regulatory region is a key unit for gene expression regulation, and its pattern is closely related to cell fate and environmental adaptation. It needs to be partially stably transmitted through specific epigenetic mechanisms, so the heritability is actually higher. Finally, environmental stress is also an important driving force for inducing heritable methylation changes. Stress such as drought and pathogen infection can trigger genome-wide methylation changes, some of which can evade "reprogramming" erasure during gamete formation and fertilization, and stably pass on to offspring, forming so-called "epigenetic mutations" (Wang et al. 2022; Schönung et al. 2021). The space environment in this study, as an extreme stress, can also serve as a powerful driving force to induce changes in DNA methylation throughout the rice genome. Some of the methylation mutations occur in key stress responsive gene regulatory regions, which can be stably transmitted to the next generation and form heritable "epigenetic mutations". This mechanism provides an epigenetic basis for organisms to quickly adapt to environmental changes, which is of great significance in crop domestication and breeding. Breeding Value of Space Induced Multi-trait Mutant Library The mutant library is a breeding resource platform that covers target genome regions through systematic random mutations to create genetic variations on a large scale. Its core value lies in its ability to efficiently overcome the bottlenecks of long breeding cycles and limited genetic diversity in traditional breeding, providing a rich material foundation for crop genetic improvement (Ma et al. 2024). In terms of the construction methods of mutant libraries, they mainly include chemical mutagenesis, such as ethylmethanesulfonate (EMS) mutagenesis, and N-Nitroso-N-methylurea (MNU), insertion mutagenesis (T-DNA, transposon), and CRISPR/Cas9 targeted editing techniques. Researchers have directly selected a series of new germplasm with important agronomic traits from the mutant library, especially in nonmodel crops such as rapeseed and eggplant. The mutant library combined with molecular marker assisted selection significantly accelerates the process of trait improvement (Kubo et al. 2022; Chen et al. 2022; Wu et al. 2017). By combining deep sequencing with genotype phenotype association analysis, key genes can be systematically analyzed, providing theoretical basis and targets for precision breeding. However, this technology still faces some challenges, as there is functional redundancy in the genomes of crops such as rice, which make it difficult for some single gene mutations to present phenotypes; The inventory of T-DNA and transposon insertion suffers from uneven coverage and low labeling efficiency; CRISPR editing may cause lethal effects on mutations in essential genes; In addition, the high cost of large-scale phenotype identification and low genetic transformation efficiency of indica rice varieties are limiting factors that also affect the comprehensive application of mutant libraries (Hong et al. 2020; Pathak et al. 2022; Jiang et al. 2019). Compared with existing mutant library studies, the rice space mutagenesis mutant library systematically constructed in this study covered major agronomic traits and phenotypes under various stress treatments. 277 mutants with significant phenotypic variations were screened, maximizing the phenotypic diversity induced by space environment and ensuring the breadth and saturation of trait coverage in the mutant library. The multi-trait mutant library constructed in this study had a high gene coverage rate of 96.69% and a high functional mutation ratio of 59.39%, systematically solving the problems of transformation efficiency, coverage, functional redundancy, and lethality faced by existing technologies, providing important gene resources and breeding materials for rice genetic improvement. The Potential Application of Candidate Genes in Rice Breeding Space mutagenesis breeding can efficiently induce diverse mutations in the genome by exposing rice seeds to the space environment, providing abundant candidate gene resources for rice breeding. These candidate genes have shown significant potential in improving agronomic traits, enhancing stress tolerant, and accelerating breeding processes. Space mutagenesis can regulate key traits such as plant height and tillering, and the relevant candidate genes provide a molecular basis for cultivating dwarf or semi dwarf lodging resistant varieties. The dwarf mutant genes identified by radiation mutagenesis can be stably inherited and directly used for high-yield breeding (Cheng et al. 2022). The cloning of genes such as OsSAUR11 laid the foundation for improving root systems, enhancing drought tolerant, and maintaining yield (Xu et al. 2023). Space mutagenesis candidate genes can help cultivate cold resistant, heat resistant, and hypoxia tolerant varieties. The cold stress-related genes identified through genome-wide association analysis (GWAS) can be used to optimize germination and growth under low temperature conditions; High temperature responsive genes help maintain the source sink balance during the grain filling period and reduce yield losses (Khatab et al. 2022; Bheemanahalli et al. 2021). In addition, the ROS response pathway and MAPK cascade genes activated by space mutagenesis provide new strategies for rice to cope with extreme environments (Liu et al. 2023). Space mutagenesis breeding is closely integrated with modern biotechnology, significantly improving breeding efficiency. Technologies such as high-throughput sequencing, RNA-seq, and GWAS have accelerated the identification and functional verification of candidate genes. Molecular marker assisted selection (MAS) and CRISPR gene editing have achieved precise aggregation and targeted improvement of key genes, shortening the breeding cycle (Bai et al. 2022; Sao et al. 2022; Kato et al. 2020). This study utilized a space-induced multi-trait mutant library combined with RNA-seq screening to identify 9 candidate genes, demonstrating enormous potential for breeding applications in improving nutrient efficiency, enhancing environmental tolerant, and optimizing yield composition. Nitrogen fertilizer is one of the main production costs in rice production. The excellent allelic variations of OsPho1 and CRK25 genes can significantly improve the efficiency of nitrogen absorption and utilization in rice. CRK25 may also be related to nitrogen metabolism signal transduction, and its mutants show insensitivity to chlorates, which provides the possibility of cultivating rice varieties with more stable nitrogen absorption capacity in specific soil environments. The drought tolerant genes PER1 , RCN1 , and OsPUP1 , as well as the cold tolerant genes cys12 and OsRP1L1 , are closely related to known stress response pathways such as osmotic regulation, reactive oxygen species clearance, and amino acid metabolism, providing new gene resources for rice stress tolerant breeding. Grain length and width are important factors affecting rice yield and appearance quality. OsFBX257 and OsCOP1 have been reported in previous studies (Sharma et al. 2023; Hu et al. 2022), and have been identified again as key genes regulating grain type in this study. Together with known grain type genes, they form a regulatory network, extending our understanding of grain type regulatory molecular modules and providing new editing targets. In addition, developing functional molecular markers based on the mutation sites of candidate genes for precise screening in early generations of breeding can significantly improve selection efficiency and shorten breeding cycles. This study has several limitations, Removing shared mutation sites across multiple samples can effectively distinguish true mutations from background noise, significantly enhancing the specificity of mutation detection. However, if mutations induced by the space environment exhibit recurrence, this method may misidentify mutation hotspots as high-frequency noise, thereby leading to the loss of some genuine mutation information. The research was primarily focused on descriptive analyses of the inheritance patterns of genomic and methylation variations, without further investigation into the underlying mechanisms responsible for these genetic tendencies. In future studies, the dynamic genetic processes and regulatory networks of space-induced variations will be elucidated by detecting the activity, localization, and expression of regulatory factors of core proteins in DNA damage repair pathways, alongside analyses of the expression patterns, protein modifications, and chromosomal distribution changes of methyltransferases and demethylases. Furthermore, although 9 candidate genes related to nitrogen utilization, drought tolerance, cold tolerance, and grain type were screened and their expression was validated, their biological functions and practical utility in breeding remained speculative, lacking direct experimental verification. Subsequent research will involve the construction of knockout and overexpression transgenic plants, the identification of target phenotypes, and the further development of functional molecular markers. Conclusions This study systematically analyzed the mutagenic effects of the space environment on rice and its transgenerational inheritance patterns by carrying rice seeds on Chang'e-5. Space mutagenesis induced rich genomic variations and DNA methylation changes, among which SNPs, homozygous, and mutations located in the CDS region were more easily inherited to the next generation. The genetic stability of CG type loci in DNA methylation was the highest, and DMCs on the genebody and DMRs upstream and downstream of the gene were more easily inherited. A total of 277 mutants were screened in the SP 2 generation, covering nitrogen utilization, drought tolerant, salt tolerant, cold tolerant, grain type, disease tolerant, and major agronomic traits. The further constructed multi-trait mutant library had a high gene coverage rate of 96.69% and a high functional mutation ratio of 59.39%. By combining the mutant library and RNA-seq, 9 candidate genes closely related to key traits were screened. This study not only revealed the genetic characteristics of genomic variation and DNA methylation induced by space mutagenesis, but also provided genetic resources and theoretical support for functional gene mining and precision breeding of rice. Methods Chang'e-5 Carries Seeds and Materials for Planting The material carried on Chang'e-5 this time is pure line HJXSM of indica rice, which were selected by the National Plant Aerospace Breeding Engineering Technology Research Center of South China Agricultural University (SCAU). This strain aggregates multiple disease tolerant genes and aroma genes, and has stable agronomic traits. On November 24, 2020, dried HJXSM weighing approximately 32.8 g from the same individual plant were loaded onto Chang'e-5 lunar probe, with a total flight time of approximately 23 days. After carrying the seeds back to the ground, they were sown at the teaching and research base of SCAU in Guangzhou in 2021, resulting in 2,500 SP 1 plants. The seeds were harvested per plant and planted into 2,500 SP 2 strains, each containing 10 individual plants, for a total of 25,000 individual plants. At the same time, set up unloaded HJXSM as controls. Sampling of WGS and WGBS Materials 30 SP 1 plants (TK1-TK30) and 10 control plants (CK1-CK10) at the jointing stage were randomly selected, and one tender stem was collected from each tiller for subsequent research. 30 TK group materials were bagged and self pollinated, and seeds were harvested and planted into SP 2 lines (each line containing 10 individual plants). Samples were taken from 30 SP 2 lines, and one tender stem from a tiller was collected from each material. The tender stems of 10 individual plants from each line were mixed and renumbered as NTK1-NTK30. DNA and RNA Extraction and Quality Inspection Approximately 500 mg of each sample was used, extract DNA using the cetyltrimethylammonium bromide (CTAB) method, extract total RNA using the Omega Plant RNA kit (Omega Bio Tek, R6827), and detect DNA and RNA mass using Qubit (Thermo Fisher Scientific, USA) and Nanodrop (Thermo Fisher Scientific, USA). The integrity of RNA mass was determined using Agilent 2100 (Agilent Technologies, Germany). Samples that pass quality inspection were stored at -80 º C. WGS and Authentic Variant Site Screening DNA samples that passed quality inspection were sequenced on Illumina machines. Firstly, Quality control was performed using Fastp (v0.20.0) on Illumina sequencing data. Then, BWA (v0.7.15) was used to align the filtered reads to the reference genome SK1, which serves as the reference genome for the background of HJXSM constructed earlier. Use GATK (v3.4-46) for mutation detection and ANNOVAR (v2) for functional annotation to obtain the original mutation data. During the screening of raw mutation data, the 30 TK samples were first compared with the 10 CK samples to eliminate the influence of material background. Mutation sites present in two or more samples were considered as part of the genetic background or false-positive sites. Only mutation sites occurring in a single sample and supported by reads ≥ 5 were retained as authentic variant sites. The same method was applied to screen authentic variant sites by comparing NTK with CK. WGBS and Analysis A DNA library for bisulfite sequencing was prepared using DNA that had passed quality inspection. The specific steps were as follows: Genomic DNA was sonicated (Covaris, USA) and fragmented into 100–300 bp fragments, which were then purified using the MiniElute PCR Purification Kit (QIAGEN, USA). Subsequently, these fragments underwent terminal repair and an 'A' nucleotide was added at the 3' end. Afterwards, the genome fragments were connected to methylation sequencing adapters. The fragment with a linker was subjected to bisulfite conversion using the Methylation Gold Assay Kit (ZYMO, USA). During the sodium bisulfite treatment, unmethylated cytosine was converted to uracil. Finally, the transformed DNA fragments were amplified by PCR and subjected to Illumina sequencing. The raw sequencing data was converted into sequence data rawdata through base calling, and the filtered data was aligned to the genome sequence using BSMAP (v2.90) to obtain all genome base alignment information and all genome C-base methylation information. The TK group and the NTK group were separately compared with the CK group to identify differentially methylated cytosines (DMCs) using MethylKit (v1.4.1). The whole genome was scanned with 200-bp windows, and the average DNA methylation level within each window was calculated. Methylation levels across samples were compared for each window, ultimately yielding differentially methylated regions (DMRs). Screening of Nitrogen Utilization Mutants 100 clean and plump seeds were selected from each SP 2 material, washed, disinfected, soaked in sterile water, and sown into PCR plates with holes at the bottom after they had radicle emergence. First, cultivated them in sterile water for 2 days, and then replaced them with improved nitrogen deficient Hogland nutrient solution (formula standard dose: 1.678 g/L). Element content: NH 4 + 0 mmol, NO 3 − 0 mmol, P 1 mmol, K 6 mmol, Ca 4 mmol, Mg 2 mmol, S 4.5 mmol, Cl 8 mmol, and add 2 mmol/L KNO 3 as the sole nitrogen source. The cultivation temperature was 30°C, the relative humidity was 70%, and the light cycle was 14 hours of light and 10 hours of darkness. After 4 days, it was replaced with modified Hogland nutrient solution containing different concentration gradients of KClO 3 (1 mmol/L, 2 mmol/L, and 3 mmol/L) and cultured for another 4 days. Each processing setting is repeated 3 times. After cultivation, the entire seedling was scanned using a scanner and Canon MP Navigator (v4.0.9), and its phenotypic characteristics were recorded. Seedling length was measured and chlorate sensitivity was calculated using Image Pro Plus (v6.0), allowing for the preliminary screening of nitrogen utilization mutants. Using the same method described above, the mutant strains preliminarily screened in SP 2 were subjected to chlorate sensitivity testing again in SP 3 , resulting in the final identification of nitrogen utilization mutants. Chlorate sensitivity = (WT plant height-mutant plant height) / WT plant height×100% Screening of Drought-tolerant Mutants From each SP 2 material, 100 clean and plump seeds were selected, cleaned, disinfected, and subjected to drought treatment with a 22% PEG-6000 solution. Three replicates were set up per material and cultured under the aforementioned conditions. Germination was defined as an embryo bud length of ≥ 0.5 mm. The number of germinated seeds was recorded on 7th day, the germination rate was calculated, and a preliminary screening for drought-tolerant mutants was conducted. For the candidate drought-resistant materials preliminarily screened from SP 2 , drought treatment was repeated at the SP 3 stage, and the germination rate was assessed again. Germination rate = (number of germinated seeds in 7th day / total number of tested seeds)×100% Screening of Salt-tolerant Mutants For each SP 2 material, 100 clean and plump seeds were selected, cleaned, disinfected, and treated with a 340 mmol/L NaCl solution. Three replicates were established per material and cultured under the aforementioned conditions. The NaCl solution was changed every two days to maintain a constant concentration. The germination rate was calculated on the 7th day, enabling the preliminary screening of salt-tolerant mutants. To verify the phenotype, the mutants screened from SP 2 were subjected to NaCl solution treatment again in the SP 3 generation. Screening of Cold-tolerant Mutants For each SP 2 material, 100 clean and plump seeds were selected, cleaned, disinfected, and cultured in sterile water at 15°C. Three replicates were maintained per material under otherwise standard conditions. The germination rate was calculated every 24 hours over 10 consecutive days. Based on these data, the average germination days and germination coefficient were calculated to conduct a preliminary screening for cold-tolerant mutants. To verify the phenotype, the candidate cold-tolerant materials identified in SP 2 were subjected to cold stress treatment again in SP 3 , and their germination coefficient was determined. Average germination days = (∑ number of germinated grains on the day×number of days after soaking) / total number of grains in the experiment Germination coefficient = germination rate / average germination days Screening of Grain Type Mutants From each SP 2 material, 200–300 seeds of uniform size and plump appearance were taken. Grain type was scanned using a scanner and the SmartGrain software (Tanabata et al. 2012), and the batch analysis mode was selected according to the program instructions. Grain length and width were measured, compared with the WT, and used for the preliminary screening of grain type mutants. The candidate mutants preliminarily screened in SP 2 were subjected to grain type phenotype validation again at the SP 3 generation. Screening of Mutants Resistant to Rice Blast Disease and Major Agronomic Traits For each SP 2 material, seedlings at the three-leaf and one-heart stage were selected. From these, 5 cm long leaf segments were excised from the same leaf position. Epidermal wounds were gently made on the leaves using a needle gun. The leaves were then placed flat in a culture dish, allowing them to float completely on the surface of a 10 µg/mL 6-BA solution (pH = 7.0). A culture dish covered with fungal mycelium was selected, washed with ddH₂O containing 0.05% Tween-20, and the resulting liquid was filtered through gauze to obtain a spore suspension. The concentration of the spore suspension was adjusted using a hemocytometer. A 5 µL aliquot of the prepared suspension was dropped onto each puncture site. Following inoculation, the samples were placed in a 30°C illuminated incubator. On the inoculation day, the dishes were covered with black cloth for a 24-hour dark period, after which the light was restored and the samples were maintained continuously at 25°C for 5–7 days. Disease incidence was observed daily. The lesion length was measured based on the brown necrosis boundary, and the average value was calculated to serve as the rice susceptibility index, enabling the preliminary screening of blast-resistant mutants. The selected mutants were inoculated and cultivated again in the SP 3 generation to verify their resistance to rice blast. In addition, throughout the entire growth cycle of the SP 2 materials, multiple agronomic traits such as plant height, tillering, plant type, and growth period were observed and recorded. These traits were compared with those of WT to preliminarily screen for mutants. In the SP 3 generation, the phenotypes of these candidate mutants were investigated again to confirm the mutant traits. Construction of Multi-trait Mutant Library The multi-trait mutant materials selected via the above methods were obtained. From each material, 10 SP 3 seeds were germinated in sterile water for 7 days. The tender roots and shoots were then removed, genomic DNA was extracted, and WGS was conducted to identify authentic mutation sites and analyze mutation characteristics. Finally, a mutant library induced by rice space variation was constructed, in which mutant numbers, phenotypic data, and genomic variation information were recorded. RNA-seq Seeds representing mutant SP 3 were taken, each treatment was repeated three times, and corresponding WT was set. The nitrogen-efficient mutant was sampled on the 8th day (K125-1), 12th day KNO 3 control group (K125-2), and 12th day KClO 3 treatment group (K125-3) of the seedling stage. Samples of drought-tolerant mutants were taken on the 3rd day (K12-1), 5th day (K12-1), and 7th day (K12-1) of seed germination under 22% PEG-6000 solution treatment. Samples were taken on the 6th day of seed germination (K82) of the cold-tolerant mutant under 15°C cold stress treatment. Seed samples were collected for the grain type mutant on the 7th day (K44-1), 14th day (K44-2), and 21st day (K44-3) after flowering. After extracting high-quality total RNA from the processed samples, library construction and Illumina sequencing were performed. Comparative analysis was conducted based on reference genome using HISAT (v2. 1.0), including type statistics, gene coverage, sequencing randomness, and sequencing saturation analysis. Transcripts were reconstructed using Stringtie (v2.2.3) and RSEM(http://deweylab.Giyhub.Io/RESM/)was utilized to calculate the expression levels of all genes in each sample, display them as fragments per kilobase of exon model per million mapped fragments (FPKM), and use FPKM as a screening criterion. Differentially expressed genes (DEGs) were output with | log2FC | ≥ 1 and FDR < 0.05. The target gene was transferred to the Gene Ontology (GO) database༈http://www.geneontology.org/༉to map each term to obtain GO terms with significantly enriched genes. The target gene set was integrated with the Kyoto Encyclopedia of Genes and Genomes (KEGG) database༈https://www.kegg.jp/༉to combine enrichment analysis to screen for significantly enriched pathways. qRT-PCR Total RNA was extracted from samples of the WT and the aforementioned treatments. mRNA was reverse-transcribed using the Evo M-MLV Reverse Transcription Kit (AGbio, AG11728). qRT-PCR was performed on a CFX96 instrument (Bio-Rad, USA). The PCR reaction was conducted using target gene-specific primers, with UBQ ( Os03g13170 ) serving as the internal reference gene. The relative expression level of the target gene under each treatment was determined by calculating the 2 − ΔCt value, with three biological replicates included for each assay. The primer sequences are provided in Table S1. Statistical Analysis In the statistical analysis, data were presented as the mean ± standard deviation, with error bars included. A two-tailed t-test was used to determine significant differences between two groups. For datasets containing three or more experimental groups, one-way ANOVA was performed using IBM SPSS software (v21.0), followed by Duncan's multiple range test. Differences with a P-value less than 0.05 were considered statistically significant (* p < 0.05, ** p < 0.01, *** p < 0.001). Declarations Ethics Approval and Consent to Participate Not applicable. Consent for Publication Not applicable. Competing Interests The authors declare no competing interests. Funding The authors have no relevant financial or non-financial interests to disclose Author Contribution T.G., C.C. and K.S. designed the experiment. K.S., J.L., Z.Z., S.C., Y.G., Y.L., Y.W., Z.H., and Z.L. conducted experimental operation, data collection and analysis. W.X., J.F., G.Y., and Y.L. wrote the manuscript. All authors read and approved the final manuscript. Data Availability The datasets used and analysed during the current study are available from the corresponding author on reasonable request. 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Rice. 12:52 (N Y). https://doi.org/10.1186/s12284-019-0308-8 Cheng X, Huang Y, Tan Y, Tan L, Yin J, Zou G (2022) Potentially useful dwarfing or semi-dwarfing genes in rice breeding in addition to the sd1 gene. Rice (N Y) 15:66. https://doi.org/10.1186/s12284-022-00615-y Xu K, Lou Q, Wang D, Li T, Chen S, Li T, Luo L, Chen L (2023) Overexpression of a novel small auxin-up RNA gene, OsSAUR11, enhances rice deep rootedness. BMC Plant Biol 23:319. https://doi.org/10.1186/s12870-023-04320-w Khatab AA, Li J, Hu L, Yang J, Fan C, Wang L, Xie G (2022) Global identification of quantitative trait loci and candidate genes for cold stress and chilling acclimation in rice through GWAS and RNA-seq. Planta 256:82. https://doi.org/10.1007/s00425-022-03995-z Bheemanahalli R, Knight M, Quinones C, Doherty CJ, Jagadish SVK (2021) Genome-wide association study and gene network analyses reveal potential candidate genes for high night temperature tolerance in rice. Sci Rep 11:6747. https://doi.org/10.1038/s41598-021-85921-z Liu D, Lun Z, Liu N, Yuan G, Wang X, Li S, Peng YL, Lu X (2023) Identification and characterization of novel candidate effector proteins from Magnaporthe oryzae. J Fungi (Basel) 9:574. https://doi.org/10.3390/jof9050574 Bai S, Hong J, Su S, Li Z, Wang W, Shi J, Liang W, Zhang D (2022) Genetic basis underlying tiller angle in rice (Oryza sativa L.) by genome-wide association study. Plant Cell Rep 41:1707–1720. https://doi.org/10.1007/s00299-022-02873-y Sao R, Sahu PK, Patel RS, Das BK, Jankuloski L, Sharma D (2022) Genetic improvement in plant architecture, maturity duration and agronomic traits of three traditional rice landraces through gamma ray-based induced mutagenesis. Plants (Basel) 11:3448. https://doi.org/10.3390/plants11243448 Kato H, Li F, Shimizu A (2020) The Selection of gamma-ray irradiated higher yield rice mutants by directed evolution method. Plants (Basel) 9:1004. https://doi.org/10.3390/plants9081004 Sharma E, Bhatnagar A, Bhaskar A, Majee SM, Kieffer M, Kepinski S, Khurana P, Khurana JP (2023) Stress-induced F-Box protein-coding gene OsFBX257 modulates drought stress adaptations and ABA responses in rice. Plant Cell Environ 46:1207–1231. https://doi.org/10.1111/pce.14496 Hu S, Chen Y, Qian C, Ren H, Liang X, Tao W, Chen Y, Wang J, Dong Y, Han J, Ouyang X, Huang X (2024) Nuclear accumulation of rice UV-B photoreceptors is UV-B- and OsCOP1-independent for UV-B responses. Nat Commun 15:6396. https://doi.org/10.1038/s41467-024-50755-6 Tanabata T, Shibaya T, Hori K, Ebana K, Yano M (2012) SmartGrain: high-throughput phenotyping software for measuring seed shape through image analysis. Plant Physiol 160:1871–1880. https://doi.org/10.1104/pp.112.205120 Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials1.docx SupplementaryMaterials2.xlsx SupplementaryMaterials3.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8781482","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587937346,"identity":"7caa81ce-209a-4e6f-92c5-93d0e612a141","order_by":0,"name":"Kai Sun","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Sun","suffix":""},{"id":587937347,"identity":"e740381d-062e-4dac-ae19-2ff806f6ed83","order_by":1,"name":"Jinrui Li","email":"","orcid":"","institution":"South China Agricultural 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03:54:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8781482/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8781482/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102586579,"identity":"99c8e4fc-b561-4f1b-a33e-926f4ad87afa","added_by":"auto","created_at":"2026-02-13 10:22:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17652073,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical location of El Jadida Bay and rose diagram of significant wave height at SIMAR point 1,048,034 by Puertos del Estado during 2021 and 2024.\u003c/p\u003e","description":"","filename":"Figure.1.png","url":"https://assets-eu.researchsquare.com/files/rs-8781482/v1/b607c795c151f6bd54663d56.png"},{"id":102586582,"identity":"547aa201-9782-437e-a1cf-84c8d0286b3f","added_by":"auto","created_at":"2026-02-13 10:22:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":15904787,"visible":true,"origin":"","legend":"\u003cp\u003eTopographic data acquisition setup: (A) Field survey using the GPS-RTK rover in El Jadida Bay; (B) Fixed base station for differential correction.\u003c/p\u003e","description":"","filename":"Figure.2.png","url":"https://assets-eu.researchsquare.com/files/rs-8781482/v1/14fbfe0e81442568c4f2391d.png"},{"id":102747135,"identity":"be5bff2e-e56e-4c8c-8b3d-f836396804ac","added_by":"auto","created_at":"2026-02-16 09:03:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":20232952,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of the 77 cross-shore beach profiles along El Jadida Beach.\u003c/p\u003e","description":"","filename":"Figure.3.png","url":"https://assets-eu.researchsquare.com/files/rs-8781482/v1/58b008dcd62d8c6c666086aa.png"},{"id":102586583,"identity":"d7f73087-d169-40b5-a446-c2dcca44f736","added_by":"auto","created_at":"2026-02-13 10:22:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":15256264,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the methodology adopted to create DTMs\u003c/p\u003e","description":"","filename":"Figure.4.png","url":"https://assets-eu.researchsquare.com/files/rs-8781482/v1/58ad6d979d64ca59735c1b41.png"},{"id":102586580,"identity":"b3e3dabc-6def-43b5-842c-dce82bd38e76","added_by":"auto","created_at":"2026-02-13 10:22:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":11272373,"visible":true,"origin":"","legend":"\u003cp\u003eDigital Terrain Models (DTMs) of El Jadida Bay from the Four Topographic Survey Missions: (A) September 2021, (B) March 2022, (C) October 2022, and (D) March 2024.\u003c/p\u003e","description":"","filename":"Figure.5.png","url":"https://assets-eu.researchsquare.com/files/rs-8781482/v1/3f4320242cd4bf87d0947eaa.png"},{"id":102586581,"identity":"e3886dba-9250-411f-bfca-ea95691296bc","added_by":"auto","created_at":"2026-02-13 10:22:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":14999269,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the three profiles selected in different areas of El Jadida beach.\u003c/p\u003e","description":"","filename":"Figure.6.png","url":"https://assets-eu.researchsquare.com/files/rs-8781482/v1/5446b09cf7a5acb16e183d22.png"},{"id":106092962,"identity":"5682d638-3799-4c21-b430-f26423e55fd0","added_by":"auto","created_at":"2026-04-03 11:31:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":90005892,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8781482/v1/eb613f37-79ab-490b-a40d-f0710f5b3528.pdf"},{"id":102586585,"identity":"320d42da-344a-46ef-8dab-7fc84d0ad1ae","added_by":"auto","created_at":"2026-02-13 10:22:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":39477100,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8781482/v1/fe34cfdfc0e2a4d1d7a62682.docx"},{"id":102586577,"identity":"8ac6c97e-db17-4dbd-8d73-9070b45887c9","added_by":"auto","created_at":"2026-02-13 10:22:40","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10261,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8781482/v1/c9e5161925096e659a366473.xlsx"},{"id":102747597,"identity":"142503d1-8ba4-4923-bc00-28b149979884","added_by":"auto","created_at":"2026-02-16 09:05:02","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":14446,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8781482/v1/4b427bbb93cd26c9bbecc763.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic Analysis of Genomic and Methylomic Variation and Construction of Multi-Trait Mutant Library in Rice Carried on Chang'e-5","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith the continuous growth of global population, increasing scarcity of arable land resources, and the uncertainty brought by climate change to agricultural production, food security has become one of the major challenges facing humanity in the 21st century (Mehrabi et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yu et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Rice, as the staple food for over half of the world's population, its stable yield and improved quality are crucial for ensuring food security (Luo et al. 2025). Traditional hybridization and mutagenesis breeding techniques have made significant contributions to the genetic improvement of rice, but their variation range is limited and their cycle is long, making it difficult to meet the urgent demand for breakthrough new germplasm. Space mutagenesis breeding, as an emerging technology that utilizes special space environments to induce genetic variation in organisms, has opened up a new path for crop genetic improvement.\u003c/p\u003e\n\u003cp\u003eWhen organisms are exposed to the space environment, they encounter extreme conditions that are completely different from the ground. This environment mainly includes space radiation, microgravity, extreme temperature, and magnetic field changes, which together constitute a unique mutagenic source (Stolc et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mhatre et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Compared with common ground induced mutations such as gamma rays and ion beams, space radiation has the characteristics of high linear energy transfer (LET), strong biological effects, and complex damage repair, and is believed to induce more diverse and rare genetic variations (Michaeli et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Liang et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nishizawa-Yokoi et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). When organisms face the extreme space environment mentioned above, they will generate sustained stress responses, causing DNA damage and epigenetic modifications, ultimately leading to a series of unique mutation spectra at the genome sequence and epigenome levels that are different from ground mutagenesis. These mutations are also the genetic basis for the emergence of new traits (Xu et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Takahashi et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn recent decades, humans have screened batches of new germplasm with high yield, high quality, and disease tolerant in various crops such as rice, wheat, and vegetables through space mutagenesis technology, fully demonstrating the potential application of space mutagenesis breeding (Chen et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fan et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, compared to the achievements made in practical applications, our understanding of the fundamental genetic laws behind space mutagenesis is still quite limited. Most existing research remains at the level of phenotype observation and preliminary molecular marker identification, lacking systematic and high-precision analysis of the types of genomic variations in organisms after space flight (Merikangas et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wheeler et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). At the same time, whether the mutations generated by space mutagenesis can be stably inherited is the key to breeding applications. However, existing research mostly focuses on observing a certain generation, and little is known about the rules of mutation transmission in generations (Yamazaki et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liu et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The shortcomings of these studies greatly constrain the leap of space mutagenesis technology from \"experience screening\" to \"precise design\".\u003c/p\u003e\n\u003cp\u003eIn addition, previous studies have mostly focused on screening single or a few traits, failing to fully utilize the rich genetic diversity contained in the mutant population. Moreover, the gene coverage of existing mutant libraries is generally below 80%, which has not achieved genome-wide \"saturation mutagenesis\", making it impossible to systematically explore genetic associations between different traits and limiting the application of multi-trait aggregation breeding (Daware et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Li et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, building a mutant library that covers a large number of mutant materials, multiple important traits, and complete genomic information is of great value for large-scale mining of functional genes and promoting breeding innovation.\u003c/p\u003e\n\u003cp\u003eThis study conducted high-depth whole-genome sequencing (WGS) and whole-genome bisulfite sequencing (WGBS) on the space mutagenesis 1st generation (SP\u003csub\u003e1\u003c/sub\u003e) and space mutagenesis 2nd generation (SP\u003csub\u003e2\u003c/sub\u003e) of rice seeds carried on Chang'e-5 after returning to the ground, identifying space environment induced genomic and methylomic variations, and systematically elucidating their genetic and evolutionary patterns across different generations; Large scale and multi-trait mutant phenotype identification was conducted on SP\u003csub\u003e2\u003c/sub\u003e, and it was validated in the space mutagenesis 3rd generation (SP\u003csub\u003e3\u003c/sub\u003e) to comprehensively explore the phenotypic diversity generated by space mutagenesis. By integrating the whole genome information of mutants, a multi-trait mutant library for rice space mutagenesis was constructed; Representative mutants were selected and combined with multi time point RNA sequencing (RNA-seq) to reveal the dynamic molecular response network of trait formation. Furthermore, candidate genes regulating key traits were identified through joint analysis of mutant library and transcriptome (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA), and their expression levels were validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR). The results of this study will systematically reveal the mutagenic effects of deep space environment on rice and its transgenerational inheritance patterns. The constructed mutant library provides direct support for the cloning of important trait genes and innovation of breeding materials.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eAnalysis of Genetic Patterns of Genomic Variation\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp;To elucidate the genetic patterns of genomic variation caused by deep-space flight across different generations, 30 individual plants (TK1-TK30) were randomly selected from 2,500 mutants in the SP\u003csub\u003e1\u003c/sub\u003e generation, and 10 individual plants (CK1-CK10) were randomly selected from the control for WGS. 30 TK and 10 CK obtained a total of 392.6 Gb of clean data through sequencing, with an average sequencing depth of 25.3\u0026times;. After aligning with the reference genome SK1, a total of 276,898 authentic mutation sites were screened. The mutation frequency of the samples ranged from 1.96\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e to 6.08\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, and there were significant differences in the number of mutation sites between different samples. Among them, TK15 had the highest number of mutation sites, reaching 265,320, while TK28 had the lowest number of mutation sites, only 78 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). 30 TK were propagated per plant to produce 30 offspring strains (NTK1-NTK30). WGS was also conducted on 30 NTK. A total of 251.9 Gb of clean data was obtained from 30 NTK, with an average sequencing depth of 21.7\u0026times;. A total of 565,698 authentic mutation sites were screened, with sample mutation frequencies ranging from 1.84\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e to 7.38\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e. Among them, NTK15 had the highest number of mutation sites (292,608), while NTK23 had the lowest number (73). These loci are evenly distributed on the chromosome and no obvious hotspot areas were found (Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Compared with TK, the number of genomic variations in NTK significantly increased (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). A comparison of the genomic variation characteristics between TK and NTK revealed that the proportion of SNPs, synonymous SNV, nonsynonymous SNV, and transitions in NTK significantly increased, while the proportion of InDel, 5'UTR, frameshift, nonframeshift, and transpositions significantly decreased (Fig. \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003eA).\u003c/p\u003e\n\u003cp\u003eFurther, the genomic variations of TK and NTK were intersected, with the intersection being the genetic locus and the rest being nongenetic loci. 40.75% of the mutation sites in TK are inherited to NTK, with a total of 112,837. The number of genetic loci in the 30 samples ranged from 0 to 112,040, with the TK15-NTK15 group having the highest proportion of genetic loci, accounting for 42.25% of the total number of variant sites in TK15, while the TK25-NTK25 group had no genetic mutation sites (Fig. \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003eB). A significant positive correlation was observed between the total variation of TK and the total variation and genetic loci of NTK, that is, the more total variation of TK, the more genetic loci and total variation of NTK (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). The variation characteristics of genetic and nongenetic loci were compared, and it was found that the proportion of SNP, homozygous, CDS region, synonymous SNV, nonsynonymous SNV, and C/G \u0026rarr; A/T increased in genetic loci, while the proportion of InDel, heterozygous, intergenic, frameshift, nonframeshift, and A/T \u0026rarr; C/G decreased (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE). In addition, enrichment analysis was conducted on genes containing genetic loci and nongenetic loci. The results showed that genes containing genetic loci were mainly involved in various biological processes such as DNA activity, nucleotide metabolism, and protein modification, and were related to homologous recombination, sulfur transfer systems, pyrimidine metabolism, and the synthesis and metabolic pathways of various organic compounds; and genes containing nongenetic sites are mainly involved in various DNA dynamic changes, as well as RNA synthesis and metabolism processes. They are also related to homologous recombination, plant hormone signal transduction, and the synthesis and metabolism of various organic compounds (Fig. \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003eA, B).\u003c/p\u003e\n\u003cp\u003eIn summary, the number of genomic variations in NTK was significantly increased compared to TK, with SNP, homozygous, CDS regions, synonymous SNV, nonsynonymous SNV, as well as mutation sites on genes related to nucleotide metabolism, protein modification, sulfur transfer system, pyrimidine metabolism, and other functions being more easily inherited.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eAnalysis of Genetic Patterns of DNA Methylation\u003c/h3\u003e\n\u003cp\u003eTo clarify the genetic patterns of DNA methylation induced by deep space flight across different generations, WGBS was conducted separately for 10 CK, 30 TK and 30 NTK. The conversion rate of Bisulfite treatment for all samples was above 99%. Compared with TK, the overall methylation rate of NTK was increased, especially the methylation rate of CHH type which was significantly increased (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). The methylation rates of TK and NTK at different gene positions were compared, and it was found that the methylation rate of NTK was significantly increased in the upstream, intron, and 3'UTR of the gene. From the perspective of different methylation types, the methylation rate of CHH in the entire NTK genome was significantly increased, while the methylation rate of CHG was only significantly increased in introns and 3'UTR, whereas the methylation rate of CG was decreased in 3'UTR (Fig. S4). In addition, in contrast to genomic variations, no significant correlation was observed between TK, NTK, and genetic methylation sites (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\n\u003cp\u003eFurther screening was conducted to identify DMCs and DMRs of TK and NTK. A total of 26,399 DMCs were inherited from TK to NTK, accounting for 17.73% of the total DMCs in TK and 61.76% of the total DMCs in NTK, respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). The distribution of genetic and nongenetic DMCs on chromosomes is shown in Fig. S5A. At the same time, a total of 463 DMRs were inherited from TK to NTK, and their distribution on chromosomes is shown in Fig. S5B-D. Among them, the CG type had the highest number of DMRs, followed by CHG, while the CHH type had only one DMR (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). The distribution of genetic and nongenetic DMCs and DMRs on the genome was compared, and the results showed that all three types of genetic DMCs had a higher proportion in the genebody, while nongenetic DMCs had a higher proportion upstream and downstream of the gene; conversely, DMRs had a higher proportion both upstream and downstream of the gene, while their proportion in the gene itself was lower (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE). Enrichment analysis was conducted on genes containing genetic DMRs and genes containing nongenetic DMRs. GO enrichment results showed that genetic DMRs genes were mainly involved in biological processes such as tobacco amine synthesis and metabolism, protein activity, oxidative phosphorylation, immune response, and ATP electron transfer; nongenetic DMRs genes were mainly involved in DNA and chromosome activity, response to stimuli, cell division, and metabolic pathways of various organic compounds (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF). The KEGG enrichment results showed that genetic DMRs genes were mainly involved in the synthesis and metabolism of various organic compounds such as anthocyanins and diterpenes, while nongenetic DMRs genes were mainly involved in plant hormone signal transduction, Calvin cycle, glycolysis, synthesis and metabolism of inorganic compounds such as sulfur, and synthesis and metabolism pathways of organic compounds such as phenylpropane (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eG).\u003c/p\u003e\n\u003cp\u003eIn summary, similar to the genetic pattern of genomic variation, the genome-wide methylation rate of NTK is significantly higher than that of TK. CG had the highest proportion of genetic methylation sites, and DMCs on the genebody, DMRs on the upstream and downstream of the gene, as well as DMRs on genes related to grass amine synthesis and metabolism, protein activity, oxidative phosphorylation, immune response, ATP electron transfer, anthocyanins, diterpenes and other organic compounds were more easily inherited.\u003c/p\u003e\n\u003ch3\u003eScreening of Multi-trait Mutants\u003c/h3\u003e\n\u003cp\u003eScreening and identification of mutants were performed for 2,500 strains (25,000 individual plants) of SP\u003csub\u003e2\u003c/sub\u003e. Statistical analysis was conducted on the chlorate sensitivity of different materials, and among 2,500 strains, the chlorate sensitivity ranged from \u0026minus;\u0026thinsp;46.74% to 69.29% (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). A total of 88 materials with the highest and lowest chlorate sensitivity were retained, and their chlorate inhibition rates were further identified in SP\u003csub\u003e3\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). Materials with consistent changes in representative types from two generations were retained. Finally, 52 mutants were screened, including 32 nitrogen efficient mutants and 20 chlorate insensitive mutants. The germination rate of SP\u003csub\u003e2\u003c/sub\u003e material after NaCl treatment ranged from 0-99.02% (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC), and 90 materials with the highest germination rate were retained. Based on the germination rate of SP\u003csub\u003e3\u003c/sub\u003e after NaCl treatment (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD), 17 salt-tolerant mutants were ultimately screened. After PEG-6000 drought stress treatment, the germination rate of SP\u003csub\u003e2\u003c/sub\u003e seeds ranged from 1.03% to 98% (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE). 79 materials with the highest germination rate were preliminarily selected for subsequent validation. After drought tolerance testing of SP\u003csub\u003e3\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eF), a total of 32 drought-tolerant mutants were screened. The germination coefficient of SP\u003csub\u003e2\u003c/sub\u003e after cold treatment was between 9.09\u0026ndash;18.08 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eG). The germination coefficient of cold-tolerant materials is high, usually starting from the 5th day, and the bud length is longer, with a final germination rate greater than 80%; in contrast, the germination coefficient of cold sensitive materials is low, with germination starting from the 7th day and shorter bud length, resulting in a final germination rate of less than 50%. Based on the germination coefficient, 41 cold-tolerant mutants were preliminarily screened in SP\u003csub\u003e2\u003c/sub\u003e, and after verification of the cold-tolerant phenotype in SP\u003csub\u003e3\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eH), a total of 27 cold-tolerant mutants were obtained. The grain length of SP\u003csub\u003e2\u003c/sub\u003e seeds was between 9.99\u0026ndash;12.22 mm (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eI), and the grain width was between 1.63\u0026ndash;2.87 mm (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eJ). Based on the grain type data, a total of 59 grain-type mutants were screened. In SP\u003csub\u003e3\u003c/sub\u003e, the grain types of the 59 mutants were tested again (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eK, L), resulting in 23 grain-type mutants, including 2 long-grain mutants, 10 short-grain mutants, 4 wide-grain mutants, and 7 narrow-grain mutants. After inoculation with rice blast fungus, 252 resistant materials were preliminarily screened from SP\u003csub\u003e2\u003c/sub\u003e materials (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eM). Further rice blast tolerant testing was conducted in SP\u003csub\u003e3\u003c/sub\u003e, and 7 materials showed high tolerance (level 1 tolerant) in both generations.\u003c/p\u003e\n\u003cp\u003eIn addition to the screening of mutant phenotypes mentioned above, this study also conducted phenotype investigations on multiple agronomic traits throughout the entire growth period of all SP\u003csub\u003e2\u003c/sub\u003e materials, including plant height, tillering, plant type, growth period, fertility, and yield traits. After verifying the phenotype at SP\u003csub\u003e3\u003c/sub\u003e, a total of 119 mutants were screened (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eN). Based on the above results, a total of 277 mutants were screened, numbered K1-K277. The specific information is shown in Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eConstruction of Multi-trait Mutant Library\u003c/h3\u003e\n\u003cp\u003eWGS was performed on the 277 mutants and 1 WT mentioned above to construct a multi-trait mutant library. A total of 2.49 Tb of clean data was obtained from the mutant library, with an average sequencing depth of 23.7\u0026times;. After screening, a total of 2,558,421 mutation sites were identified, which were evenly distributed on 12 chromosomes and no obvious hotspot regions were found (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). The number of mutation sites in a single sample ranged from 155 to 214,126, with mutation frequencies ranging between 3.91\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e-5.4\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e. The average number of mutations in a single sample was 9,137, and the majority of samples had relatively low levels of mutation sites. 84.12% of samples had fewer than 10,000 mutations, and 38.27% had fewer than 1,000 mutations (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\n\u003cp\u003eThere are at least one mutation site on 35,615 genes (from upstream 2 Kb to downstream 2 Kb), covering 96.69% of the total genes and causing changes in the transcripts of 26,737 genes, accounting for 72.58% of the total genes. The number of mutations on genes is inversely proportional to the number of genes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC), with 95% of genes having mutant library are mainly SNPs, accounting for 69.83%, while InDel only accounts for 30.17%; in SNP mutations, the proportion of C/G \u0026rarr; A/T is the highest (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD). From the perspective of genome distribution, there are mutation sites on all gene elements, with the highest number of mutations located in the intergenic region, followed by upstream and downstream and CDS regions; the functional mutations of mutation sites include all types, among which high impact mutations (frameshift, nonsynonymous SNV, stopgain, and stoploss) account for 59.39% (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE). Compared with WT, among the mutation sites in the mutant library, InDel, the proportion of exon regions, frameshift and nonframeshift, nonsynonymous SNV, and C/G \u0026rarr; A/T type is higher (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e\n\u003cp\u003eThe above results indicate that the rice space mutagenesis multi-trait mutant library constructed in this study has the characteristics of high mutation gene coverage, high mutation site coverage density, uniform coverage, and high proportion of functional mutations.\u003c/p\u003e\n\u003ch3\u003eRNA-seq of Representative Mutants\u003c/h3\u003e\n\u003cp\u003eOne representative mutant with the most significant changes in nitrogen efficient, drought tolerant, cold tolerant, and grain phenotype was selected from the 277 mutants (Fig. S6), and samples were taken for RNA-seq at different treatments and time points. The nitrogen-efficient mutants K125 and WT showed clustering within sample groups treated differently, while exhibiting discrete distributions between groups (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA), indicating that different treatments significantly affected gene expression patterns. A total of 3,613 DEGs were screened, and the number of DEGs gradually decreased with the delay of sampling time (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB). Among them, 29 genes were differentially expressed in all three treatments (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). GO and KEGG enrichment analyses were performed on them separately, and the results showed that on 8th day, the functions of DEGs were mainly related to DNA activity, cell cycle, and the synthesis and metabolism of organic compounds such as peptides and flavonoids. After KNO₃ treatment on the 12th day, the main functions of DEGs shifted to cell and component movement, stimulus response, hormone and signal transduction, etc. After treatment with KClO₃ on the 12th day, the main function of DEGs was mainly related to oxidative stress. Fatty acid and phenylpropane biosynthesis played a role throughout the entire sampling period (Fig. S7A, B). The functional center transformation of the above DEGs after different treatments also reflects the mutant's process of nitrogen utilization.\u003c/p\u003e\n\u003cp\u003eAfter 3rd, 5th, and 7th days of drought treatment, RNA-seq was performed on the drought-tolerant mutant K12, and the drought treatment significantly affected the gene expression pattern (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD). After screening, a total of 6,361 DEGs were obtained, with the highest number of DEGs at 3rd day of drought treatment, while the number of DEGs at 5th and 7th day decreased significantly (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eE), indicating that the seed germination response was more pronounced in the early stages of drought treatment, with 71 genes being differentially expressed in all three treatments (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eF). The GO and KEGG enrichment results indicate that the function of DEGs during drought treatment for 3rd day is mostly related to oxidative stress and carbohydrate synthesis metabolism; the function of DEGs at 5th day of drought treatment is similar to that at 3rd day, and is related not only to oxidative stress but also to stimulus response and detoxification response; after 7th day of drought treatment, the function of DEGs shifted towards conventional life activities such as the synthesis and metabolism of various organic compounds (Fig. S8A, B). The above results indicate that the early stage of seed germination is the main period for responding to drought stress, and oxidative stress may be an important pathway for seeds to respond to drought stress.\u003c/p\u003e\n\u003cp\u003eOn the 7th, 14th, and 21st day after flowering of the grain-type mutant K44, seeds were harvested and RNA-seq was performed, and significant differences in gene expression patterns were observed at different sampling times (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eG). After screening, a total of 9,851 DEGs were obtained, with the highest number of DEGs on 14th day and the lowest number on 7th day. Moreover, the number of upregulated DEGs was much higher than that of downregulated DEGs at all three sampling times (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eH), indicating a more significant positive regulatory effect of genes during grain growth. A total of 799 genes were differentially expressed at all three sampling times (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eI). The DEGs enrichment results at the three sampling times also showed high similarity, with significant enrichment in metabolic processes such as organic acids and carbon. In addition, the DEGs on the 7th day are also related to the TCA cycle and glycerol metabolism, the DEGs on the 14th day are related to coenzyme metabolism and the pentose phosphate pathway, and the DEGs on the 21st day are related to organic nitrogen compound metabolism and Calvin cycle carbon fixation processes (Fig. S9A, B).\u003c/p\u003e\n\u003cp\u003eOn the 6th day after germination under cold stress treatment in the cold-tolerant mutant K82, RNA-seq was performed (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eJ), and a total of 2,376 DEGs were screened, among which the number of upregulated DEGs was higher than that of downregulated DEGs (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eK). Enrichment analysis was conducted on them, and DEGs were found to be related to the synthesis and metabolism of various organic compounds such as oxidation-reduction and sugars, as well as the synthesis of substances such as phenylpropane (Fig. S10A, B).\u003c/p\u003e\n\u003cp\u003eTo further explore the common molecular mechanisms of space mutagenesis in rice, GO and KEGG enrichment analyses were performed on the intersection genes of each mutant treated differently. The GO enrichment results showed that biological processes related to oxidative stress, ion transport, and glucose metabolism were significantly enriched multiple times (Fig. S11A). The KEGG enrichment results showed involvement in the synthesis and metabolism of various organic compounds such as sugars, as well as the biosynthesis of phenylpropane and other pathways (Fig. S11B). This indicates that the above pathways play an important role in the process of biological response to space mutagenic effects.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eIdentification and Validation of Candidate Genes\u003c/h2\u003e\nTo further identify candidate genes that cause phenotypic changes in the mutant, the genomic variation results and RNA-seq results of the mutant library were jointly analyzed. According to the results of the mutant library, K125 had a total of 2,149 mutation sites and 65 high impact mutation sites, distributed across 61 genes, of which 92.31% were frameshift mutations (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). An intersection of the 61 mutated genes with the 3,613 DEGs resulted in 2 genes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB), namely \u003cem\u003eSK1G00044581\u003c/em\u003e and \u003cem\u003eSK1G00052825\u003c/em\u003e. In the K125 mutant, \u003cem\u003eSK1G00044581\u003c/em\u003e has a 7-bp frameshift deletion in the 11th exon region, resulting in a significant decrease in its expression level after KNO₃ treatment. \u003cem\u003eSK1G00052825\u003c/em\u003e has a 2-bp frameshift deletion in 1st exon, and its expression level increased significantly after KClO₃ treatment, reflecting the difference in response mechanisms between the two.\n\u003cp\u003eK12 had a total of 3,707 mutation sites and 226 high impact mutation sites, distributed across 211 genes, with frameshift mutations accounting for 87.43% (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). Taking the intersection of the 211 mutated genes and the 6,361 DEGs, a total of 20 genes were obtained (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB). Further analysis of the functions and pathways of these 20 genes, combined with existing reports, was focused on oxidative stress, ion transport, and other factors related to drought tolerant. Finally, three genes were selected, namely \u003cem\u003eSK1G00042488\u003c/em\u003e, \u003cem\u003eSK1G0048986\u003c/em\u003e, and \u003cem\u003eSK1G00051356\u003c/em\u003e. There is a 4-bp frameshift insertion in 3rd exon of \u003cem\u003eSK1G00042488\u003c/em\u003e, resulting in a significant decrease in its expression level on the 3rd day of drought treatment. There is a 1-bp frameshift insertion in the 1st exon of \u003cem\u003eSK1G0048986\u003c/em\u003e, which also leads to a significant decrease in its expression level on the third day of drought treatment. There is a 20-bp frameshift deletion in 1st exon of \u003cem\u003eSK1G00051356\u003c/em\u003e, resulting in a significant increase in its expression level on the 3rd day of drought treatment. Although the three candidate genes have different modes of action, their expression levels changed significantly on the 3rd day, indicating the importance of early response to seed germination after drought treatment.\u003c/p\u003e\n\u003cp\u003eK82 had a total of 2,778 mutation sites and 183 high impact mutation sites, distributed across 171 genes, with frameshift mutations accounting for 93.69% (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). Taking the intersection of the 171 mutated genes and the 2,376 DEGs, a total of 11 genes were obtained. Further screening based on gene function and previous reports resulted in two candidate genes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB), \u003cem\u003eSK1G00061753\u003c/em\u003e and \u003cem\u003eSK1G0048468\u003c/em\u003e. \u003cem\u003eSK1G00061753\u003c/em\u003e has a 2-bp frameshift insertion in 2nd exon, and \u003cem\u003eSK1G0048468\u003c/em\u003e has an 88-bp frameshift insertion in 6th exon, both of which result in a significant decrease in gene expression.\u003c/p\u003e\n\u003cp\u003eK44 had a total of 7,747 mutation sites and 175 high impact mutation sites, distributed across 110 genes, with nonsynonymous SNV accounting for 86.86% (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). By taking the intersection of the 110 mutated genes and the 9,851 DEGs, a total of 25 genes were obtained. After further screening, 2 candidate genes were identified (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB), namely \u003cem\u003eSK1G00047179\u003c/em\u003e and \u003cem\u003eSK1G00059434\u003c/em\u003e. There is a nonsynonymous SNV in 1st exon of \u003cem\u003eSK1G00047179\u003c/em\u003e, where alanine is mutated to valine, resulting in a significant increase in its expression throughout the entire grain development period. There is a nonsynonymous SNV in 2nd exon of \u003cem\u003eSK1G00059434\u003c/em\u003e, where leucine is mutated to proline, resulting in a significant increase in its expression level on the 7th day of grain development.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn summary, based on the variation information of the mutant and the DEGs of the transcriptome, 58 potential candidate genes were preliminarily screened, and their expression levels are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC. Based on the functions and pathways of the genes, combined with existing reports, 9 candidate genes were ultimately screened (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). To verify the authenticity of the 9 candidate genes mentioned above, their expression levels were detected by qRT-PCR. The results showed that the expression levels of the 9 candidate genes were consistent with the trend of transcriptome results (Fig. S12), and multiple genes had been reported by previous researchers, proving the authenticity and reliability of the candidate genes selected in this study.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCandidate gene information.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSK1 id\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIRGSP-1.0 id\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGene name\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMutation location\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFunctional mutation type\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRelated traits\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSK1G00044581\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOs03g0758100\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOsPho1\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChr3_5569159\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrameshift deletion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNitrogen utilization\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSK1G00052825\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOs04g0322100\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eCRK25\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChr4_16033435\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrameshift deletion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNitrogen utilization\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSK1G00042488\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOs01g0207900\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePER1\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChr1_30792148\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrameshift insertion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDrought tolerant\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSK1G00048986\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOs03g0281900\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eRCN1\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChr3_7476262\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrameshift insertion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDrought tolerant\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSK1G00051356\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOs03g0187800\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOsPUP1\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChr3_34595972\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrameshift deletion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDrought tolerant\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSK1G00061753\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOs03g0215800\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ecys12\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChr3_7620043\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrameshift insertion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCold tolerant\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSK1G00048468\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOs05g0365300\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOsRP1L1\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChr5_3361761\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrameshift insertion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCold tolerant\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSK1G00047179\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOs07g0561300\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOsFBX257\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChr7_32899053\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNonsynonymous SNV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGrain type\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSK1G00059434\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOs02g0771100\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOsCOP1\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChr2_18283681\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNonsynonymous SNV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGrain type\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv\u003e\n\u003ch2\u003eThe Genomic Variation Induced by Space Mutagenesis Has a Clear Genetic Tendency\u003c/h2\u003e\n\u003cp\u003eGenomic variation is the foundation of biological genetic diversity and trait expression, and its ability to be stably inherited is influenced by various factors such as the type, location, function, and cell type of the variation (Chen et al. 2019; Zaid et al. 2017; Qin et al. 2021). In terms of mutation types, SNPs exhibit a stronger genetic tendency in intergenerational transmission compared to InDel (Xu et al. 2018). The results of this study also demonstrate this phenomenon, with a significantly higher proportion of SNPs than InDel in the genomic variation loci of SP\u003csub\u003e1\u003c/sub\u003e-SP\u003csub\u003e2\u003c/sub\u003e. The location of mutations in the genome determines their functional importance and genetic predisposition. In model organisms such as humans and rice, most of the mutations associated with complex traits or diseases are not located in protein coding regions, but are enriched in noncoding regulatory regions (such as promoters, enhancers) or intergenic regions (Wei et al. 2020; Vahedi et al. 2023). The variations in these regions contribute to the main part of trait heritability by regulating gene expression, showing a stronger genetic tendency. In contrast, although the number of nonsynonymous SNV that cause amino acid changes in the coding region is relatively small, they also have important genetic potential in certain traits due to their direct impact on protein function (Zaid et al. 2017; Sadowski et al. 2019). Although the genetic proportion of nonsynonymous SNV was also high in this study, synonymous SNV showed the same trend, and the proportion of variant inheritance in noncoding regulatory regions was lower than that in CDS regions. This result also reflects the specificity of space mutagenesis variation characteristics compared to natural variation. The genetic ability of variation is also related to the function of the gene it is located in. In the human genome, variations in functionally important and highly conserved positions are more likely to affect heritable diseases or phenotypes, and their variations are more likely to be inherited, while functional adaptive variations (such as environmental response) are not easily preserved (Sullivan et al. 2023). In this study, the heritability of genetic loci was significantly associated with gene function. Variations in relatively conserved functional genes, including those involved in protein modification and basal metabolism, were more likely to be inherited. The heritability of a plant is directly determined by whether it is a somatic or germ cell in the cell lineage where variation occurs due to the phenomenon of cell chimerism (Herrera et al. 2019; Mart\u0026iacute;nez-Glez et al. 2020). The cell types in which variation exists affect the genetic efficiency of variation. In this study, the genetic rate of genomic variation in the samples ranged from 0 to 42.25%, which also reflected the influence of chimerism on the inheritance of variation. As the variation detection is based on somatic cells, the sex cells of low heritability samples may carry fewer variations, while high heritability samples carry more variations. In addition, there is a significant positive correlation between the heritability of sample variation and the number of sample variations. Therefore, increasing the number of variations in parents is also one of the methods to maintain a higher frequency of variation in offspring.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003ch2\u003eGenetic Patterns of DNA Methylation Changes\u003c/h2\u003e\n\u003cp\u003eEnvironmental pressure or stress can induce DNA methylation changes, which can be inherited from offspring through sexual reproduction (Cao et al. 2024). DNA methylation is a relatively stable epigenetic marker in plant sexual reproduction, which can be inherited by offspring, but most methylation marks are actively erased in parental gametes (Greenberg et al. 2019). The results of this study confirmed this viewpoint, as the methylation sites of parents were not fully inherited by offspring, indicating that some methylation sites had been erased. Similar to genomic variation, the stability and tendency of DNA methylation inheritance are not uniform, mainly influenced by multiple factors such as methylation type, genomic location, and environmental factors. Methylation type is one of the core factors determining the heritability of DNA methylation. Among the three types of DNA methylation, CG methylation, with its symmetrical sequence, is stably inherited via maintenance methylation (Feng et al. 2021; Kikuchi et al. 2025), whereas CHG symmetry is less stable (Herle et al. 2025), and asymmetric CHH relies on dynamic de novo methylation, showing the weakest heritability (Wang et al. 2020). The results of this study were consistent with the above conclusions. Among the genetic methylation sites, CG type had the highest proportion, followed by CHG, and CHH had the smallest proportion. Secondly, the genomic location determines the genetic potential of methylation patterns. Usually, methylation in repeat sequence regions such as transposable elements is more stable and conserved, with a higher genetic predisposition, while methylation in regions related to gene regulation is more dynamic and more susceptible to environmental factors, with a relatively lower genetic predisposition (Quadrana et al. 2016; Li et al. 2025; Cao et al. 2023). In this study, it was necessary to distinguish between DMC and DMR. The heritability of DMCs in gene regulation related regions was lower, while the heritability of coding regions was higher, while DMR was completely opposite. The coding region sequence had high conservation, and if the methylation of a single C site changes, it may affect codon function or mRNA stability. Therefore, evolution tends to stabilize inheritance through efficient maintenance mechanisms, resulting in a high heritability of DMC; However, the DMR in the coding region may lead to significant abnormalities in gene function, which are strictly limited by evolutionary selection and difficult to stably inherit, resulting in a low heritability. The methylation changes of a single C site in the regulatory region usually have limited and reversible effects on gene expression, and are susceptible to environmental signal interference and cannot be stably inherited, resulting in a low heritability of DMC; However, the DMR in the regulatory region is a key unit for gene expression regulation, and its pattern is closely related to cell fate and environmental adaptation. It needs to be partially stably transmitted through specific epigenetic mechanisms, so the heritability is actually higher. Finally, environmental stress is also an important driving force for inducing heritable methylation changes. Stress such as drought and pathogen infection can trigger genome-wide methylation changes, some of which can evade \"reprogramming\" erasure during gamete formation and fertilization, and stably pass on to offspring, forming so-called \"epigenetic mutations\" (Wang et al. 2022; Sch\u0026ouml;nung et al. 2021). The space environment in this study, as an extreme stress, can also serve as a powerful driving force to induce changes in DNA methylation throughout the rice genome. Some of the methylation mutations occur in key stress responsive gene regulatory regions, which can be stably transmitted to the next generation and form heritable \"epigenetic mutations\". This mechanism provides an epigenetic basis for organisms to quickly adapt to environmental changes, which is of great significance in crop domestication and breeding.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003ch2\u003eBreeding Value of Space Induced Multi-trait Mutant Library\u003c/h2\u003e\n\u003cp\u003eThe mutant library is a breeding resource platform that covers target genome regions through systematic random mutations to create genetic variations on a large scale. Its core value lies in its ability to efficiently overcome the bottlenecks of long breeding cycles and limited genetic diversity in traditional breeding, providing a rich material foundation for crop genetic improvement (Ma et al. 2024). In terms of the construction methods of mutant libraries, they mainly include chemical mutagenesis, such as ethylmethanesulfonate (EMS) mutagenesis, and N-Nitroso-N-methylurea (MNU), insertion mutagenesis (T-DNA, transposon), and CRISPR/Cas9 targeted editing techniques. Researchers have directly selected a series of new germplasm with important agronomic traits from the mutant library, especially in nonmodel crops such as rapeseed and eggplant. The mutant library combined with molecular marker assisted selection significantly accelerates the process of trait improvement (Kubo et al. 2022; Chen et al. 2022; Wu et al. 2017). By combining deep sequencing with genotype phenotype association analysis, key genes can be systematically analyzed, providing theoretical basis and targets for precision breeding. However, this technology still faces some challenges, as there is functional redundancy in the genomes of crops such as rice, which make it difficult for some single gene mutations to present phenotypes; The inventory of T-DNA and transposon insertion suffers from uneven coverage and low labeling efficiency; CRISPR editing may cause lethal effects on mutations in essential genes; In addition, the high cost of large-scale phenotype identification and low genetic transformation efficiency of indica rice varieties are limiting factors that also affect the comprehensive application of mutant libraries (Hong et al. 2020; Pathak et al. 2022; Jiang et al. 2019). Compared with existing mutant library studies, the rice space mutagenesis mutant library systematically constructed in this study covered major agronomic traits and phenotypes under various stress treatments. 277 mutants with significant phenotypic variations were screened, maximizing the phenotypic diversity induced by space environment and ensuring the breadth and saturation of trait coverage in the mutant library. The multi-trait mutant library constructed in this study had a high gene coverage rate of 96.69% and a high functional mutation ratio of 59.39%, systematically solving the problems of transformation efficiency, coverage, functional redundancy, and lethality faced by existing technologies, providing important gene resources and breeding materials for rice genetic improvement.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003ch2\u003eThe Potential Application of Candidate Genes in Rice Breeding\u003c/h2\u003e\n\u003cp\u003eSpace mutagenesis breeding can efficiently induce diverse mutations in the genome by exposing rice seeds to the space environment, providing abundant candidate gene resources for rice breeding. These candidate genes have shown significant potential in improving agronomic traits, enhancing stress tolerant, and accelerating breeding processes. Space mutagenesis can regulate key traits such as plant height and tillering, and the relevant candidate genes provide a molecular basis for cultivating dwarf or semi dwarf lodging resistant varieties. The dwarf mutant genes identified by radiation mutagenesis can be stably inherited and directly used for high-yield breeding (Cheng et al. 2022). The cloning of genes such as \u003cem\u003eOsSAUR11\u003c/em\u003e laid the foundation for improving root systems, enhancing drought tolerant, and maintaining yield (Xu et al. 2023). Space mutagenesis candidate genes can help cultivate cold resistant, heat resistant, and hypoxia tolerant varieties. The cold stress-related genes identified through genome-wide association analysis (GWAS) can be used to optimize germination and growth under low temperature conditions; High temperature responsive genes help maintain the source sink balance during the grain filling period and reduce yield losses (Khatab et al. 2022; Bheemanahalli et al. 2021). In addition, the ROS response pathway and MAPK cascade genes activated by space mutagenesis provide new strategies for rice to cope with extreme environments (Liu et al. 2023). Space mutagenesis breeding is closely integrated with modern biotechnology, significantly improving breeding efficiency. Technologies such as high-throughput sequencing, RNA-seq, and GWAS have accelerated the identification and functional verification of candidate genes. Molecular marker assisted selection (MAS) and CRISPR gene editing have achieved precise aggregation and targeted improvement of key genes, shortening the breeding cycle (Bai et al. 2022; Sao et al. 2022; Kato et al. 2020). This study utilized a space-induced multi-trait mutant library combined with RNA-seq screening to identify 9 candidate genes, demonstrating enormous potential for breeding applications in improving nutrient efficiency, enhancing environmental tolerant, and optimizing yield composition. Nitrogen fertilizer is one of the main production costs in rice production. The excellent allelic variations of \u003cem\u003eOsPho1\u003c/em\u003e and \u003cem\u003eCRK25\u003c/em\u003e genes can significantly improve the efficiency of nitrogen absorption and utilization in rice. \u003cem\u003eCRK25\u003c/em\u003e may also be related to nitrogen metabolism signal transduction, and its mutants show insensitivity to chlorates, which provides the possibility of cultivating rice varieties with more stable nitrogen absorption capacity in specific soil environments. The drought tolerant genes \u003cem\u003ePER1\u003c/em\u003e, \u003cem\u003eRCN1\u003c/em\u003e, and \u003cem\u003eOsPUP1\u003c/em\u003e, as well as the cold tolerant genes \u003cem\u003ecys12\u003c/em\u003e and \u003cem\u003eOsRP1L1\u003c/em\u003e, are closely related to known stress response pathways such as osmotic regulation, reactive oxygen species clearance, and amino acid metabolism, providing new gene resources for rice stress tolerant breeding. Grain length and width are important factors affecting rice yield and appearance quality. \u003cem\u003eOsFBX257\u003c/em\u003e and \u003cem\u003eOsCOP1\u003c/em\u003e have been reported in previous studies (Sharma et al. 2023; Hu et al. 2022), and have been identified again as key genes regulating grain type in this study. Together with known grain type genes, they form a regulatory network, extending our understanding of grain type regulatory molecular modules and providing new editing targets. In addition, developing functional molecular markers based on the mutation sites of candidate genes for precise screening in early generations of breeding can significantly improve selection efficiency and shorten breeding cycles.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations, Removing shared mutation sites across multiple samples can effectively distinguish true mutations from background noise, significantly enhancing the specificity of mutation detection. However, if mutations induced by the space environment exhibit recurrence, this method may misidentify mutation hotspots as high-frequency noise, thereby leading to the loss of some genuine mutation information. The research was primarily focused on descriptive analyses of the inheritance patterns of genomic and methylation variations, without further investigation into the underlying mechanisms responsible for these genetic tendencies. In future studies, the dynamic genetic processes and regulatory networks of space-induced variations will be elucidated by detecting the activity, localization, and expression of regulatory factors of core proteins in DNA damage repair pathways, alongside analyses of the expression patterns, protein modifications, and chromosomal distribution changes of methyltransferases and demethylases. Furthermore, although 9 candidate genes related to nitrogen utilization, drought tolerance, cold tolerance, and grain type were screened and their expression was validated, their biological functions and practical utility in breeding remained speculative, lacking direct experimental verification. Subsequent research will involve the construction of knockout and overexpression transgenic plants, the identification of target phenotypes, and the further development of functional molecular markers.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study systematically analyzed the mutagenic effects of the space environment on rice and its transgenerational inheritance patterns by carrying rice seeds on Chang'e-5. Space mutagenesis induced rich genomic variations and DNA methylation changes, among which SNPs, homozygous, and mutations located in the CDS region were more easily inherited to the next generation. The genetic stability of CG type loci in DNA methylation was the highest, and DMCs on the genebody and DMRs upstream and downstream of the gene were more easily inherited. A total of 277 mutants were screened in the SP\u003csub\u003e2\u003c/sub\u003e generation, covering nitrogen utilization, drought tolerant, salt tolerant, cold tolerant, grain type, disease tolerant, and major agronomic traits. The further constructed multi-trait mutant library had a high gene coverage rate of 96.69% and a high functional mutation ratio of 59.39%. By combining the mutant library and RNA-seq, 9 candidate genes closely related to key traits were screened. This study not only revealed the genetic characteristics of genomic variation and DNA methylation induced by space mutagenesis, but also provided genetic resources and theoretical support for functional gene mining and precision breeding of rice.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eChang'e-5 Carries Seeds and Materials for Planting\u003c/h2\u003e\n\u003cp\u003eThe material carried on Chang'e-5 this time is pure line HJXSM of indica rice, which were selected by the National Plant Aerospace Breeding Engineering Technology Research Center of South China Agricultural University (SCAU). This strain aggregates multiple disease tolerant genes and aroma genes, and has stable agronomic traits. On November 24, 2020, dried HJXSM weighing approximately 32.8 g from the same individual plant were loaded onto Chang'e-5 lunar probe, with a total flight time of approximately 23 days.\u003c/p\u003e\n\u003cp\u003eAfter carrying the seeds back to the ground, they were sown at the teaching and research base of SCAU in Guangzhou in 2021, resulting in 2,500 SP\u003csub\u003e1\u003c/sub\u003e plants. The seeds were harvested per plant and planted into 2,500 SP\u003csub\u003e2\u003c/sub\u003e strains, each containing 10 individual plants, for a total of 25,000 individual plants. At the same time, set up unloaded HJXSM as controls.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eSampling of WGS and WGBS Materials\u003c/h2\u003e\n\u003cp\u003e30 SP\u003csub\u003e1\u003c/sub\u003e plants (TK1-TK30) and 10 control plants (CK1-CK10) at the jointing stage were randomly selected, and one tender stem was collected from each tiller for subsequent research. 30 TK group materials were bagged and self pollinated, and seeds were harvested and planted into SP\u003csub\u003e2\u003c/sub\u003e lines (each line containing 10 individual plants). Samples were taken from 30 SP\u003csub\u003e2\u003c/sub\u003e lines, and one tender stem from a tiller was collected from each material. The tender stems of 10 individual plants from each line were mixed and renumbered as NTK1-NTK30.\u003c/p\u003e\n\u003ch2\u003eDNA and RNA Extraction and Quality Inspection\u003c/h2\u003e\n\u003cp\u003eApproximately 500 mg of each sample was used, extract DNA using the cetyltrimethylammonium bromide (CTAB) method, extract total RNA using the Omega Plant RNA kit (Omega Bio Tek, R6827), and detect DNA and RNA mass using Qubit (Thermo Fisher Scientific, USA) and Nanodrop (Thermo Fisher Scientific, USA). The integrity of RNA mass was determined using Agilent 2100 (Agilent Technologies, Germany). Samples that pass quality inspection were stored at -80 \u0026ordm; C.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eWGS and Authentic Variant Site Screening\u003c/h2\u003e\n\u003cp\u003eDNA samples that passed quality inspection were sequenced on Illumina machines. Firstly, Quality control was performed using Fastp (v0.20.0) on Illumina sequencing data. Then, BWA (v0.7.15) was used to align the filtered reads to the reference genome SK1, which serves as the reference genome for the background of HJXSM constructed earlier. Use GATK (v3.4-46) for mutation detection and ANNOVAR (v2) for functional annotation to obtain the original mutation data. During the screening of raw mutation data, the 30 TK samples were first compared with the 10 CK samples to eliminate the influence of material background. Mutation sites present in two or more samples were considered as part of the genetic background or false-positive sites. Only mutation sites occurring in a single sample and supported by reads\u0026thinsp;\u0026ge;\u0026thinsp;5 were retained as authentic variant sites. The same method was applied to screen authentic variant sites by comparing NTK with CK.\u003c/p\u003e\n\u003ch2\u003eWGBS and Analysis\u003c/h2\u003e\n\u003cp\u003eA DNA library for bisulfite sequencing was prepared using DNA that had passed quality inspection. The specific steps were as follows: Genomic DNA was sonicated (Covaris, USA) and fragmented into 100\u0026ndash;300 bp fragments, which were then purified using the MiniElute PCR Purification Kit (QIAGEN, USA). Subsequently, these fragments underwent terminal repair and an 'A' nucleotide was added at the 3' end. Afterwards, the genome fragments were connected to methylation sequencing adapters. The fragment with a linker was subjected to bisulfite conversion using the Methylation Gold Assay Kit (ZYMO, USA). During the sodium bisulfite treatment, unmethylated cytosine was converted to uracil. Finally, the transformed DNA fragments were amplified by PCR and subjected to Illumina sequencing. The raw sequencing data was converted into sequence data rawdata through base calling, and the filtered data was aligned to the genome sequence using BSMAP (v2.90) to obtain all genome base alignment information and all genome C-base methylation information. The TK group and the NTK group were separately compared with the CK group to identify differentially methylated cytosines (DMCs) using MethylKit (v1.4.1). The whole genome was scanned with 200-bp windows, and the average DNA methylation level within each window was calculated. Methylation levels across samples were compared for each window, ultimately yielding differentially methylated regions (DMRs).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eScreening of Nitrogen Utilization Mutants\u003c/h2\u003e\n\u003cp\u003e100 clean and plump seeds were selected from each SP\u003csub\u003e2\u003c/sub\u003e material, washed, disinfected, soaked in sterile water, and sown into PCR plates with holes at the bottom after they had radicle emergence. First, cultivated them in sterile water for 2 days, and then replaced them with improved nitrogen deficient Hogland nutrient solution (formula standard dose: 1.678 g/L). Element content: NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e 0 mmol, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e 0 mmol, P 1 mmol, K 6 mmol, Ca 4 mmol, Mg 2 mmol, S 4.5 mmol, Cl 8 mmol, and add 2 mmol/L KNO\u003csub\u003e3\u003c/sub\u003e as the sole nitrogen source. The cultivation temperature was 30\u0026deg;C, the relative humidity was 70%, and the light cycle was 14 hours of light and 10 hours of darkness. After 4 days, it was replaced with modified Hogland nutrient solution containing different concentration gradients of KClO\u003csub\u003e3\u003c/sub\u003e (1 mmol/L, 2 mmol/L, and 3 mmol/L) and cultured for another 4 days. Each processing setting is repeated 3 times. After cultivation, the entire seedling was scanned using a scanner and Canon MP Navigator (v4.0.9), and its phenotypic characteristics were recorded. Seedling length was measured and chlorate sensitivity was calculated using Image Pro Plus (v6.0), allowing for the preliminary screening of nitrogen utilization mutants. Using the same method described above, the mutant strains preliminarily screened in SP\u003csub\u003e2\u003c/sub\u003e were subjected to chlorate sensitivity testing again in SP\u003csub\u003e3\u003c/sub\u003e, resulting in the final identification of nitrogen utilization mutants.\u003c/p\u003e\n\u003cp\u003eChlorate sensitivity = (WT plant height-mutant plant height) / WT plant height\u0026times;100%\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eScreening of Drought-tolerant Mutants\u003c/h2\u003e\n\u003cp\u003eFrom each SP\u003csub\u003e2\u003c/sub\u003e material, 100 clean and plump seeds were selected, cleaned, disinfected, and subjected to drought treatment with a 22% PEG-6000 solution. Three replicates were set up per material and cultured under the aforementioned conditions. Germination was defined as an embryo bud length of \u0026ge;\u0026thinsp;0.5 mm. The number of germinated seeds was recorded on 7th day, the germination rate was calculated, and a preliminary screening for drought-tolerant mutants was conducted. For the candidate drought-resistant materials preliminarily screened from SP\u003csub\u003e2\u003c/sub\u003e, drought treatment was repeated at the SP\u003csub\u003e3\u003c/sub\u003e stage, and the germination rate was assessed again.\u003c/p\u003e\n\u003cp\u003eGermination rate = (number of germinated seeds in 7th day / total number of tested seeds)\u0026times;100%\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eScreening of Salt-tolerant Mutants\u003c/h2\u003e\n\u003cp\u003eFor each SP\u003csub\u003e2\u003c/sub\u003e material, 100 clean and plump seeds were selected, cleaned, disinfected, and treated with a 340 mmol/L NaCl solution. Three replicates were established per material and cultured under the aforementioned conditions. The NaCl solution was changed every two days to maintain a constant concentration. The germination rate was calculated on the 7th day, enabling the preliminary screening of salt-tolerant mutants. To verify the phenotype, the mutants screened from SP\u003csub\u003e2\u003c/sub\u003e were subjected to NaCl solution treatment again in the SP\u003csub\u003e3\u003c/sub\u003e generation.\u003c/p\u003e\n\u003ch2\u003eScreening of Cold-tolerant Mutants\u003c/h2\u003e\n\u003cp\u003eFor each SP\u003csub\u003e2\u003c/sub\u003e material, 100 clean and plump seeds were selected, cleaned, disinfected, and cultured in sterile water at 15\u0026deg;C. Three replicates were maintained per material under otherwise standard conditions. The germination rate was calculated every 24 hours over 10 consecutive days. Based on these data, the average germination days and germination coefficient were calculated to conduct a preliminary screening for cold-tolerant mutants. To verify the phenotype, the candidate cold-tolerant materials identified in SP\u003csub\u003e2\u003c/sub\u003e were subjected to cold stress treatment again in SP\u003csub\u003e3\u003c/sub\u003e, and their germination coefficient was determined.\u003c/p\u003e\n\u003cp\u003eAverage germination days = (\u0026sum; number of germinated grains on the day\u0026times;number of days after soaking) / total number of grains in the experiment\u003c/p\u003e\n\u003cp\u003eGermination coefficient\u0026thinsp;=\u0026thinsp;germination rate / average germination days\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eScreening of Grain Type Mutants\u003c/h2\u003e\n\u003cp\u003eFrom each SP\u003csub\u003e2\u003c/sub\u003e material, 200\u0026ndash;300 seeds of uniform size and plump appearance were taken. Grain type was scanned using a scanner and the SmartGrain software (Tanabata et al. 2012), and the batch analysis mode was selected according to the program instructions. Grain length and width were measured, compared with the WT, and used for the preliminary screening of grain type mutants. The candidate mutants preliminarily screened in SP\u003csub\u003e2\u003c/sub\u003e were subjected to grain type phenotype validation again at the SP\u003csub\u003e3\u003c/sub\u003e generation.\u003c/p\u003e\n\u003ch2\u003eScreening of Mutants Resistant to Rice Blast Disease and Major Agronomic Traits\u003c/h2\u003e\n\u003cp\u003eFor each SP\u003csub\u003e2\u003c/sub\u003e material, seedlings at the three-leaf and one-heart stage were selected. From these, 5 cm long leaf segments were excised from the same leaf position. Epidermal wounds were gently made on the leaves using a needle gun. The leaves were then placed flat in a culture dish, allowing them to float completely on the surface of a 10 \u0026micro;g/mL 6-BA solution (pH\u0026thinsp;=\u0026thinsp;7.0). A culture dish covered with fungal mycelium was selected, washed with ddH₂O containing 0.05% Tween-20, and the resulting liquid was filtered through gauze to obtain a spore suspension. The concentration of the spore suspension was adjusted using a hemocytometer. A 5 \u0026micro;L aliquot of the prepared suspension was dropped onto each puncture site. Following inoculation, the samples were placed in a 30\u0026deg;C illuminated incubator. On the inoculation day, the dishes were covered with black cloth for a 24-hour dark period, after which the light was restored and the samples were maintained continuously at 25\u0026deg;C for 5\u0026ndash;7 days. Disease incidence was observed daily. The lesion length was measured based on the brown necrosis boundary, and the average value was calculated to serve as the rice susceptibility index, enabling the preliminary screening of blast-resistant mutants. The selected mutants were inoculated and cultivated again in the SP\u003csub\u003e3\u003c/sub\u003e generation to verify their resistance to rice blast.\u003c/p\u003e\n\u003cp\u003eIn addition, throughout the entire growth cycle of the SP\u003csub\u003e2\u003c/sub\u003e materials, multiple agronomic traits such as plant height, tillering, plant type, and growth period were observed and recorded. These traits were compared with those of WT to preliminarily screen for mutants. In the SP\u003csub\u003e3\u003c/sub\u003e generation, the phenotypes of these candidate mutants were investigated again to confirm the mutant traits.\u003c/p\u003e\n\u003ch2\u003eConstruction of Multi-trait Mutant Library\u003c/h2\u003e\n\u003cp\u003eThe multi-trait mutant materials selected via the above methods were obtained. From each material, 10 SP\u003csub\u003e3\u003c/sub\u003e seeds were germinated in sterile water for 7 days. The tender roots and shoots were then removed, genomic DNA was extracted, and WGS was conducted to identify authentic mutation sites and analyze mutation characteristics. Finally, a mutant library induced by rice space variation was constructed, in which mutant numbers, phenotypic data, and genomic variation information were recorded.\u003c/p\u003e\n\u003ch2\u003eRNA-seq\u003c/h2\u003e\n\u003cp\u003eSeeds representing mutant SP\u003csub\u003e3\u003c/sub\u003e were taken, each treatment was repeated three times, and corresponding WT was set. The nitrogen-efficient mutant was sampled on the 8th day (K125-1), 12th day KNO\u003csub\u003e3\u003c/sub\u003e control group (K125-2), and 12th day KClO\u003csub\u003e3\u003c/sub\u003e treatment group (K125-3) of the seedling stage. Samples of drought-tolerant mutants were taken on the 3rd day (K12-1), 5th day (K12-1), and 7th day (K12-1) of seed germination under 22% PEG-6000 solution treatment. Samples were taken on the 6th day of seed germination (K82) of the cold-tolerant mutant under 15\u0026deg;C cold stress treatment. Seed samples were collected for the grain type mutant on the 7th day (K44-1), 14th day (K44-2), and 21st day (K44-3) after flowering.\u003c/p\u003e\n\u003cp\u003eAfter extracting high-quality total RNA from the processed samples, library construction and Illumina sequencing were performed. Comparative analysis was conducted based on reference genome using HISAT (v2. 1.0), including type statistics, gene coverage, sequencing randomness, and sequencing saturation analysis. Transcripts were reconstructed using Stringtie (v2.2.3) and RSEM(http://deweylab.Giyhub.Io/RESM/)was utilized to calculate the expression levels of all genes in each sample, display them as fragments per kilobase of exon model per million mapped fragments (FPKM), and use FPKM as a screening criterion. Differentially expressed genes (DEGs) were output with | log2FC | \u0026ge; 1 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The target gene was transferred to the Gene Ontology (GO) database༈http://www.geneontology.org/༉to map each term to obtain GO terms with significantly enriched genes. The target gene set was integrated with the Kyoto Encyclopedia of Genes and Genomes (KEGG) database༈https://www.kegg.jp/༉to combine enrichment analysis to screen for significantly enriched pathways.\u003c/p\u003e\n\u003ch2\u003eqRT-PCR\u003c/h2\u003e\n\u003cp\u003eTotal RNA was extracted from samples of the WT and the aforementioned treatments. mRNA was reverse-transcribed using the Evo M-MLV Reverse Transcription Kit (AGbio, AG11728). qRT-PCR was performed on a CFX96 instrument (Bio-Rad, USA). The PCR reaction was conducted using target gene-specific primers, with \u003cem\u003eUBQ\u003c/em\u003e (\u003cem\u003eOs03g13170\u003c/em\u003e) serving as the internal reference gene. The relative expression level of the target gene under each treatment was determined by calculating the 2\u0026thinsp;\u0026minus;\u0026thinsp;\u0026Delta;Ct value, with three biological replicates included for each assay. The primer sequences are provided in Table S1.\u003c/p\u003e\n\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n\u003cp\u003eIn the statistical analysis, data were presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, with error bars included. A two-tailed t-test was used to determine significant differences between two groups. For datasets containing three or more experimental groups, one-way ANOVA was performed using IBM SPSS software (v21.0), followed by Duncan's multiple range test. Differences with a P-value less than 0.05 were considered statistically significant (* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for Publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting Interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eT.G., C.C. and K.S. designed the experiment. K.S., J.L., Z.Z., S.C., Y.G., Y.L., Y.W., Z.H., and Z.L. conducted experimental operation, data collection and analysis. W.X., J.F., G.Y., and Y.L. wrote the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eThe datasets used and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMehrabi Z, Delzeit R, Ignaciuk A, Levers C, Braich G, Bajaj K, Amo-Aidoo A, Anderson W, Balgah RA, Benton TG, Chari MM, Ellis EC, Gahi NZ, Gaupp F, Garibaldi LA, Gerber JS, Godde CM, Grass I, Heimann T, Hirons M, Hoogenboom G, Jain M, James D, Makowski D, Masamha B, Meng S, Monprapussorn S, M\u0026uuml;ller D, Nelson A, Newlands NK, Noack F, Oronje M, Raymond C, Reichstein M, Rieseberg LH, Rodriguez-Llanes JM, Rosenstock T, Rowhani P, Sarhadi A, Seppelt R, Sidhu BS, Snapp S, Soma T, Sparks AH, Teh L, Tigchelaar M, Vogel MM, West PC, Wittman H, You L (2022) Research priorities for global food security under extreme events. 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Plant Physiol 160:1871\u0026ndash;1880. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1104/pp.112.205120\u003c/span\u003e\u003cspan address=\"10.1104/pp.112.205120\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rice, Space mutagenesis, Genomic variation, DNA methylation, Mutant library","lastPublishedDoi":"10.21203/rs.3.rs-8781482/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8781482/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStable yield and improved quality in rice are crucial for ensuring food security. Traditional breeding techniques are unable to meet the urgent demand for breakthrough germplasm resources, while space mutagenesis breeding provides a new approach for crop genetic improvement. Existing research lacks sufficient understanding of the genetic patterns of variation and has not fully explored the genetic diversity within mutant populations. In this study, rice seeds were carried by the Chang'e-5 lunar probe, upon their return to the ground, were propagated to establish lineages for genetic analysis of space-induced mutagenesis effects and for the construction of a multi-trait mutant library. The whole-genome sequencing results showed that the number of mutations was higher in the space mutation second generation than in the first generation. The SNPs, homozygous, and coding sequence mutations were more likely to be inherited compared to other types and positions of mutations. The whole-genome bisulfite sequencing results showed that the methylation level in the space mutation second generation was also higher than that in the first generation, with CG type showing the highest genetic stability. Differentially methylated cytosines in genebody were more heritable than those in gene upstream and downstream. Conversely, differentially methylated regions in gene upstream and downstream exhibited higher heritability compared to those in genebody. A total of 277 mutants covering multiple traits were screened. The mutant library constructed from these mutants showed a high gene coverage rate of 96.69% and a high functional mutation ratio of 59.39%. Based on RNA sequencing and the mutant library from representative mutants, 9 candidate genes related to nitrogen utilization, drought tolerance, cold tolerance, and grain type were identified. This study systematically reveals the genetic patterns of genomic and methylomic variation in rice induced by the space environment. The high-quality mutant library constructed here provides direct support for rice functional gene cloning and the development of new breeding materials.\u003c/p\u003e","manuscriptTitle":"Genetic Analysis of Genomic and Methylomic Variation and Construction of Multi-Trait Mutant Library in Rice Carried on Chang'e-5","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-13 10:22:35","doi":"10.21203/rs.3.rs-8781482/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b7f91c27-25e5-4570-9bcb-3e5cce319ba7","owner":[],"postedDate":"February 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-13T10:11:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-13 10:22:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8781482","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8781482","identity":"rs-8781482","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}