Multiomics analysis of the molecular and single-cell responses of rice after deep-space flight on Chang'e-5

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In this study, through multiomics analysis, changes were observed in rice at molecular and cellular levels after deep-space flight, including an increase in genomic variation frequency and mutations with preferences. While overall DNA methylation levels showed no significant changes, the increase in CHG methylation level was correlated with DNA methylation responses. RNA presented significantly elevated m6A modification levels, which positively regulated gene expression. The proportion of mesophyll cells decreased, and 188 genes were identified as affecting the differentiation of mesophyll cells. Integrated multiomics analysis revealed that the NAC family transcription factor SVT1 negatively regulated MAPK pathway genes to suppress differentiation in cells carrying mutations. Overall, this study comprehensively described the molecular map of rice after deep-space flight, and proposed a new mechanism for SVT1 to adapt to deep-space flight by inhibiting the differentiation of mutant cells. Biological sciences/Plant sciences/Plant stress responses/Abiotic Biological sciences/Plant sciences/Plant molecular biology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Over the past several decades, aiming to uncover the mechanisms by which plants adapt to space flight, scientists have carried plants on various platforms, such as recoverable satellites, spacecraft, and the International Space Station, to investigate changes in factors ranging from agronomic traits to the levels of different molecules 1-5 . The space flight environment encompasses extreme conditions such as ionizing radiation, microgravity, and variations in the magnetic field 6 . Research on the space flight response of plants can not only reveal the impact of extreme environmental factors on organisms in space, but also lay a data foundation for crop space mutation breeding, and provide interdisciplinary enlightenment for earth agriculture and medicine. Plants exposed to space flight experience multiple biological changes, including genomic mutations, changes in gene expression, and epigenetic modifications 4,7-10 ; the accumulation of antioxidants such as flavonoids; adjustments in energy and physiological metabolism through pathways such as oxidative stress and sugar signalling 8,11-13 ; and impacts on cellular structure and function, leading to altered cell proliferation and differentiation and abnormal growth and development 12,14-17 . The cascading systemic responses triggered by space flight not only directly influence plant growth and stress responses during missions but also may generate heritable molecular imprints that can affect plant responses postflight. These effects provide a unique resource for crop mutation breeding 18-20 . Furthermore, deep-space environments, such as those encountered in lunar or Martian missions, differ from that of low Earth orbit, with prolonged exposure to combined radiation and continuous microgravity, coupled with the absence of Earth's protective magnetic field, which may have more severe effects on organisms 21 . Rice, as one of the world's major food crops, was one of the earliest species to undergo space mutagenesis research and has been the subject of the most experimental launches, different lines of research, and significant achievements. However, the existing studies on space mutagenesis in rice have focused primarily on mutation frequencies, allelic variations in key agronomic traits, and the identification of mutant offspring. There is still a lack of systematic and cross omics in-depth analysis on how the complex environmental factors unique to space flight trigger rice molecular cascade reactions, including genomic instability, dynamic changes of epigenetic regulatory networks, transcription and metabolic pathway remodeling. Although the widespread application of omics technology in recent years has provided new opportunities for exploring the mechanism by which organisms adapt to space flight. While existing omics studies have achieved certain progress, most have focused on human astronauts, such as the NASA twin study 22-25 . Multiomics research on the molecular response mechanisms of rice to space flight is scarce, with only a few recent studies, mostly based on independent omics analyses and rarely involving the integration of two or more omics approaches. Additionally, space flight may trigger specific molecular responses in different cell types. Single-cell sequencing can overcome the averaging effects of traditional sequencing technologies, which obscure cellular heterogeneity, enabling the identification and tracking of gene expression dynamic regulatory networks and key molecular pathways in responsive cells. To date, there have been several studies published on the molecular mechanisms of space flight in humans, animals, and microorganisms at single-cell resolution, such as the discovery of “space flight signature” gene sets in astronauts and changes in mouse brain cell development 23,26,27 . Considering these findings, plant cells may also exhibit differential specific responses and mechanisms of differentiation regulation in space flight environments. Therefore, combining multiomics and single-cell sequencing will contribute to a more comprehensive and in-depth understanding of the mechanisms of rice adaptations to space flight. To clarify the response and adaptation mechanism of rice multiomics molecules and cells after deep-space flight, rice seeds were carried aboard the Chang'e-5 lunar probe, returned to Earth and planted. Then, 30 plants grown from deep-space flight samples and 10 wild-type samples as controls were randomly selected for analysis. The study began by assembling an new reference genome through de novo sequencing, followed by the application of multiomics technologies including whole-genome sequencing (WGS), RNA sequencing (RNA-seq), whole-genome bisulfite sequencing (WGBS), methylated RNA immunoprecipitation sequencing (MeRIP-seq), and single-cell RNA sequencing (scRNA-seq). Through variant screening, epigenetic modification detection, differential expression analysis, and cell differentiation analysis, a comprehensive molecular profile of rice after deep-space flight was produced. Through the integration of these multiomics data, a transcription factor SVT1 was identified. Combined with DNA affinity purification sequencing (DAP-seq) and mutant phenotype analysis, it showed that the number of genomic variations in its knockout mutants increased significantly, and inhibited the differentiation of cells carrying the variation by negatively regulating the gene expression of plant MAPK signalling pathway, affecting the transmission of genomic variation after space flight (Fig. 1a). Results HJXSM de novo sequencing and reference genome assembly To precisely identify mutation sites and analyse the differences in gene expression and epigenetic modifications between flown and unflown rice samples, we performed de novo sequencing and genome assembly of HJXSM using PacBio third-generation sequencing, Illumina next-generation sequencing, and Hi-C technology. PacBio sequencing yielded 18.5 Gb (47×) of data, Illumina sequencing provided 19.4 Gb (49×) of data, and Hi-C technology generated 61.4 Gb (155×) of data. The assembled genome size was 396.4 Mb, with a contig N50 value of 17.4 Mb (Supplementary Fig. 2a). Aligning the second-generation sequencing data back to the assembled genome resulted in a mapping rate of 98.52%, with 89.29% of the bases covered at a sequencing depth of greater than 30× (Supplementary Fig. 2c). The BUSCO genome completeness was 96.92% (Supplementary Fig. 2b). A total of 99.73% of the Hi-C data were accurately anchored to the 12 chromosomes, and the BUSCO genome completeness after chromosome anchoring was 96.88% (Supplementary Fig. 2d). Gene annotation predicted a total of 36,836 protein-coding genes, with a BUSCO completeness of 95.51% (Supplementary Fig. 2e). Compared with the IRGSP1.0 genome, HJXSM presented high similarity in structural features such as gene length, CDS length, exon length, exon count, and mRNA length (Supplementary Fig. 2f). From these results, we constructed the genome sequence map SK1 for HJXSM (Fig. 1 b), which served as the reference genome for subsequent studies. Genomic variation characteristics and preferences induced by deep-space flight To clarify the molecular characteristics of genomic variation caused by deep-space flight, WGS was performed on 30 TK and 10 CK samples. WGS obtained 770.6 Gb of clean data in total, the clean data were then filtered, retaining the variant sites that appeared in only a single sample. Variant sites that were present in two or more samples were classified as either background variants or false positives. This process resulted in the identification of true variant sites, and a total of 276,898 true variant sites were identified, which were uniformly distributed across the chromosomes without any obvious hotspots (Fig. 1 c). The mutation frequency in TK ranged from 1.78×10 − 7 to 6.08×10 − 4, with a significantly greater number of variant sites than in CK (Fig. 1 d). The number of mutation sites varied significantly among different samples, with TK15 having the highest number of mutation sites (265,320) and TK28 having the fewest (78) (Fig. 1 e). Both CK and TK variant sites were composed primarily of single nucleotide polymorphisms (SNPs); however, insertion-deletions (indels) accounted for a greater proportion of TK, reaching 42.37%. The heterozygous mutation ratio in TK was higher than that in CK, ranging from 90.15–100%, with heterozygous frequencies mostly between 0.1 and 0.2 (Supplementary Fig. 3a). The distribution of mutation sites across the genome with respect to gene bodies was similar for CK and TK, in the following order: intergenic > CDS > upstream and downstream (2 Kb) > UTR (Fig. 1 f). Among exon mutations, TK exhibited a greater proportion of nonframeshift and nonsense mutations (Fig. 1 f). Additionally, the proportions of A→G and T→C transitions were significantly greater in TK, whereas the G→A transition proportion was significantly lower (Fig. 1 f). These results suggest that deep-space flight is more likely to induce indel and heterozygous mutations than other types of mutations. In the TK samples, a total of 9,154 genes carried at least one variant site in the CDS, upstream and downstream sequence, or UTR. Enrichment analysis of the mutated genes revealed that these genes are related to biological processes such as DNA integration, recombination, packaging, and conformational changes (Supplementary Fig. 3c) and function in multiple DNA damage repair pathways (Supplementary Fig. 3d). To further explore the relationship between mutation susceptibility and gene structure, the genes were classified into three categories according to their mutation rates (number of mutations/gene length): high-frequency mutated genes (HMG), low-frequency mutated genes (LMG), and nonmutated genes (NMG) (Supplementary Data 1). Statistical analysis of 13 gene structural features revealed that the number of mutations was significantly positively correlated with 8 features, including gene length, and significantly negatively correlated with the GC content and exon GC content (Supplementary Fig. 3b). Compared with LMGs and NMGs, HMGs exhibited longer gene length, lower GC content, more and longer exons, lower exon GC content, more and longer introns, and significantly increased 5'UTR length (Supplementary Fig. 3e). These results indicate that DNA damage repair genes are more likely to mutate, resulting in the accumulation of variation, and the variation has the preference of gene structure. DNA methylation patterns after deep-space flight To investigate the impact of deep-space flight on DNA methylation patterns, WGBS was performed on CK and TK samples. Although there was no significant difference in genome-wide methylation levels between the CK and TK groups, the average methylation rate in the TK samples was slightly lower than that in the CK samples (Fig. 2 a). Among the three methylation types in CK and TK, CG methylation was the most common, followed by CHG, and then CHH (Fig. 2 b). The distribution of methylation levels across gene regions showed distinct trends; while all three types of methylation were higher in the upstream and downstream regions and lower at transcription start sites (TSSs) and transcription termination sites (TTSs), CG methylation was also relatively high in the gene body and exhibited a peak-like pattern. CHG methylation in the gene body was only slightly greater than that at the TSS and TTS, whereas CHH methylation in the gene body was nearly identical to that at the TSS and TTS (Fig. 2 c). The distribution of methylation in transposable element (TE) regions was opposite to that in gene regions, with all three types showing lower methylation levels in upstream and downstream regions and higher levels at the TSS and TTS. CG and CHG exhibited similar distribution patterns, with a significant increase in methylation levels in the gene body, whereas CHH methylation in the gene body increased only slightly (Fig. 2 d). A total of 148,914 differentially methylated cytosines (DMCs) were identified between the CK and TK groups. However, differentially methylated regions (DMRs) often have more significant biological implications than individual DMCs. In this study, 2,381 DMRs were detected, with CG accounting for 72.69%, CHG accounting for 19.54%, and CHH accounting for 7.77%. CG and CHH DMRs were distributed relatively uniformly across the genome, whereas CHG DMRs were primarily concentrated in the gene body. Additionally, CG DMRs were associated mainly with increased methylation rates, whereas CHG and CHH DMRs were associated primarily with decreased methylation rates (Fig. 2 e). GO enrichment analysis of the differentially methylated genes (DMGs) associated with the three types of DMRs revealed that CG DMGs were related to biological processes such as DNA recombination and nucleotide metabolism, CHH DMGs were associated with oxidative reduction and amino acid metabolism, and CHG DMGs were linked to nucleic acid metabolism and chromosome assembly (Fig. 2 f). KEGG enrichment analysis revealed that CG DMGs were involved in pathways related to the synthesis of various organic compounds, CHH DMGs were involved in the synthesis and metabolism of glutathione and other organic compounds, and CHG DMGs were involved in DNA damage repair pathways and plant MAPK signalling pathways (Fig. 2 g). Notably, 83.3% of the CHG DMRs associated with these pathways presented increased methylation rates. In conclusion, there was no significant change in the overall level of DNA methylation after deep-space flight, but the increase of CHG methylation level was related to the DNA methylation response after deep-space flight. Single-cell expression atlas after deep-space flight After showing the genomic variation and methylation changes at the DNA, we then turn to the research results at the RNA and single cell. To analyse gene expression patterns after deep-space flight at the single-cell level, we constructed a single-cell expression atlas of rice aerial tissues using scRNA-seq. The aerial tissue cells were classified into 13 clusters according to their distinct gene expression patterns (Fig. 3 a). These clusters were further organized into 5 cell types, namely, primordium, mesophyll, parenchymal, bulliform and epidermal (Fig. 3 b), according to their expression of cell marker genes (Supplementary Fig. 4a). The proportion of mesophyll cells in the scTK data was 54.28%, significantly lower than that in scCK (72.4%), whereas the proportions of the other 4 cell types increased slightly (Fig. 3 c, Supplementary Fig. 4b). PAGA analysis of these 5 cell types revealed that the primordium presented the earliest developmental stage, whereas the epidermis and mesophyll presented the highest differentiation levels (Fig. 3 d). The results of the pseudotime analysis were consistent with those of PAGA, indicating that differentiation started from the primordium, passed through 3 differentiation nodes, and ultimately produced 7 differentiation states (states 1–7) (Fig. 3 e, Supplementary Fig. 4c). The proportion of mesophyll (state 4) was significantly lower in scTK than in scCK (Supplementary Fig. 5a). In summary, the primordium first differentiates into parenchymal and bulliform primordia, followed by parenchymal differentiation into mesophyll and epidermal primordia. The differences in gene expression between scTK and scCK around differentiation node 3 were key drivers of the reduced mesophyll in the scTK samples. Among the 5 cell types, there were 4,424 intergroup differentially expressed genes (DEGs) (Fig. 3 f, Supplementary Data 2). Pseudotime analysis revealed 2,065 genes influencing cell differentiation fate around differentiation node 3 (Supplementary Data 3), with 1,438 of these genes showing significant differences in expression between scTK and scCK (Fig. 3 h). To supplement the scRNA-seq data, RNA-seq was also conducted. By combining scRNA-seq and RNA-seq, we can complement the advantages of the two, and give full play to the advantages of scRNA-seq high resolution and RNA-seq high sequencing depth, which helps us interpret the molecular mechanism of cells from two dimensions, and the results of the two methods can be mutually verified. RNA-seq identified 2,648 DEGs (Supplementary Data 4), with 1,390 upregulated and 1,258 downregulated in TK (Fig. 3 g). These DEGs were involved in biological processes such as protein phosphorylation (Supplementary Fig. 5b) and were associated with plant MAPK signalling pathways and phenylpropanoid and flavonoid biosynthesis (Supplementary Fig. 5c). Among the 1,438 intergroup DEGs affecting cell differentiation fate according to the scRNA-seq results, 188 were also found to be differentially expressed according to the RNA-seq results (Fig. 3 h). These results reveal that deep-space flight can significantly reduce the proportion of mesophyll cells by changing the gene expression of the key node of cell differentiation, and MAPK signaling pathway and other metabolic pathways may be involved in this process. Effects of deep-space flight on m6A modified regulatory gene expression RNA m6A methylation plays a significant role in gene expression, The MeRIP-seq results of CK and TK showed that deep-space flight significantly affects the m6A modification (Supplementary Fig. 6a). The number of peaks in TK was significantly greater than that in CK (Fig. 3 i), particularly with an increase in peaks at the start codon and CDS (Fig. 3 j). There were 429 differential peaks between CK and TK (Supplementary Data 5), with 134 RNAs showing increased methylation rates and 295 showing decreased rates (Supplementary Fig. 6b). These 429 differential peaks were located at 428 genes, and GO enrichment analysis indicated that these genes are involved in protein phosphorylation and hydrolytic processing (Supplementary Fig. 6d). KEGG enrichment analysis revealed their association with the synthesis and metabolism of various organic compounds, including phenylpropanoids (Supplementary Fig. 6e). These enrichment results were similar to those for the DEGs identified by RNA-seq. Correlation of the MeRIP-seq and RNA-seq data indicated that 60 out of the 428 genes with differential peaks also exhibited significant differences in expression levels (Supplementary Fig. 6c), and m6A-modified mRNAs presented overall higher expression levels than did those without m6A modification (Supplementary Fig. 6f, g). As the m6A modification positively regulates mRNA expression, we focused on genes whose expression changes were consistent with the m6A peak abundance trends. Specifically, m6A modification led to a significant increase in the expression of 17 genes and a significant decrease in that of 14 genes, as shown in Fig. 3 k (Supplementary Data 6). These results suggest that m6A modification influences the expression of genes involved in protein phosphorylation and the phenylpropanoid pathway. A total of 9 genes exhibited significant changes in expression due to m6A modification and were differentially expressed between the scRNA-seq cell groups, impacting mesophyll differentiation (Supplementary Fig. 6h). This indicates that deep-space flight significantly regulates the level of m6A of RNA through m6A methylation modification, affects the expression of genes related to protein phosphorylation and phenylpropane metabolic pathway, and is associated with the differentiation fate of mesophyll cells. Joint multiomics identification of the transcription factor SVT1 To explore the key regulatory factors affecting the differentiation and fate of mesophyll cells after deep-space flight, the 188 DEGs affecting cell differentiation fate were analysed for motifs, with the four most significant motifs all belonging to the NAC family (Fig. 4 a), indicating that these 188 genes are regulated primarily by NAC family transcription factors. KEGG enrichment analysis of these 188 genes revealed significant enrichment in pathways such as plant MAPK signalling and phenylpropanoid biosynthesis (Fig. 4 b). Among the 40,327 significant association signals detected by eQTL analysis, 99.57% were trans-associations. Similarly, among the 19,339 significant association signals detected by meQTL analysis, 99.32% were trans-associations, suggesting that trans-associations, namely, interactions with transcription factors, play an important regulatory role in adaptation to space flight. When the association signals from eQTLs and meQTLs were plotted on the chromosome in 1 Mb intervals, a hotspot region of colocalization was observed at the 24–30 Mb region on chromosome 4 (Fig. 4 c). The gene with the greatest number of association signals in this region was SK1G00053966 ( Os04g0515900 ), which is 2,387 bp in length and contains 3 exons and 2 introns, with the first and third exons encoding highly conserved NAC domains (Fig. 4 d). Based on these motif analysis results, we considered SK1G00053966 as a strong candidate regulatory factor. Single-cell expression analysis of SK1G00053966 revealed that its expression proportion and level were significantly greater in scTK than in scCK (Fig. 4 e), and it exhibited distinct spatiotemporal expression characteristics, with high expression only in cluster 5 (parenchymal-5) and cluster 2 (mesophyll-2), (Fig. 4 f). Mapping genomic variation sites to single cells revealed that the number and proportion of variation sites in scTK were significantly greater in the parenchymal-5 and mesophyll-2 subgroups than in the other subgroups, whereas no such phenomenon was observed in scCK (Fig. 4 g, h). Additionally, parenchymal-5 and mesophyll-2 have a direct differentiation relationship. We simplified the pseudotemporal analysis to model the differentiation trajectory from parenchymal-5 to mesophyll-2 (Fig. 4 i), identifying 1,754 genes influencing the cell differentiation fate (Supplementary Data 7). On the basis of these results, we hypothesize that the transcription factor SK1G00053966 inhibits the transmission of genomic variations by regulating the differentiation of parenchymal-5 and mesophyll-2 cells. Therefore, we named this gene SVT1, for suppressor of variation transmission 1. SVT1 inhibits the differentiation of cells carrying gene variants To clarify the function of SVT1, we constructed an SVT1 knockout mutant svt1 (deletion of the T base in exon 3, resulting in a frameshift mutation) in the ZH11 background (Fig. 5 a). Then, svt1 and wild-type (WT) seeds were subjected to γ-ray irradiation at 300 Gy, followed by seed germination assays. Seeds were germinated for 3 days, and seed embryos were collected for scRNA-seq and WGS (Fig. 5 b). scRNA-seq clustered the seed embryo cells into 24 clusters (Fig. 5 c), which were grouped into 10 cell types (coleoptile, coleorhiza, endosperm, plumule, epidermis, mesophyll, plumule, radicle, lateral scale, and scutellum) according to marker gene expression (Supplementary Fig. 7a). Additionally, some cells with similar expression patterns were difficult to distinguish, forming 5 cell regions: scutellum-lateral scale (SL), endosperm-plumule (EP), scutellum-radicle (SR), coleoptile-lateral scale (CL), and scutellum-lateral scale-coleorhiza (SLC) (Fig. 5 d). The proportions of SL, EP, plumule, and coleorhizal cells were significantly greater in svt1 than in WT, whereas the proportions of mesophyll and SR were lower in svt1 (Fig. 5 e, Supplementary Fig. 7b). SVT1 was specifically expressed in the SL cell region (Supplementary Fig. 7c), with particularly high expression in cluster 6 and cluster 16 (Fig. 5 f). Cluster 6 (SL-6) and cluster 16 (SL-16) exhibited a direct differentiation relationship, and pseudotime analysis revealed that 637 genes influenced this cell differentiation fate (Fig. 5 g, Supplementary Data 8). The WGS results revealed that, compared with the WT, svt1 presented a significantly greater number of genomic variants, with an average increase of 291 variant sites per sample (Fig. 5 h). Further mapping of genomic variant sites to single cells revealed that in the WT plants, variant sites were concentrated in the SL region, whereas in svt1, variant sites were uniformly distributed without obvious clustering (Fig. 5 i, j). Therefore, SVT1 knockout led to a significant increase in genomic variation, and the proportion of SL cells carrying the variation increased significantly. SVT1 mediates the inhibition of the transmission of genomic variation through the MAPK pathway To identify the target genes and regulatory mechanisms of SVT1, DAP-seq analysis was conducted, resulting in the identification of 27,011 peaks (Supplementary Data 9). Among these peaks, the top 10 most significant motifs were identified; the E-value of MEME1 was 8.3e-183, significantly stronger than that of the other motifs (Fig. 6 a). Motif similarity analysis indicated significant similarity between MEME1 and the motifs of multiple known NAC transcription factors (Fig. 6 a). Further screening revealed 828 candidate target genes with MEME-1 peaks located in promoter regions. These genes were intersected with 1,754 genes affecting the differentiation of parenchymal-5 to mesophyll-2 cells, and 65 overlapping genes were identified (Fig. 6 b). KEGG enrichment analysis of these 65 genes revealed significant enrichment in the plant MAPK signalling pathway (Fig. 6 c), which includes SK1G00063486 ( Os07g0126301 ), SK1G00070333 ( Os12g0586100 ), and SK1G00060048 ( Os05g0474800 ). The plant MAPK signalling pathway was observed as significantly enriched in multiple analyses in this study, with these three genes in the pathway showing potential interactions with SVT1 and possibly working together to influence cell differentiation. SK1G00063486 specifically impacted the differentiation fate of both parenchymal-5 to mesophyll-2 and SL-6 to SL-16 cells and was highly expressed in SL cell regions (Supplementary Fig. 7d). Moreover, its expression was significantly greater in svt1 than in WT (Fig. 6 d), indicating that SVT1 negatively regulates SK1G00063486. EMSA confirmed the interaction between SK1G00063486 and SVT1, validating this interaction (Supplementary Fig. 7e). On the basis of these results, we propose that SVT1 is highly expressed in cells carrying mutations and suppresses the differentiation of these cells by negatively regulating genes in the plant MAPK signalling pathway, thereby reducing the transmission of mutations in the cell population (Fig. 6 e). Discussion The space environment can cause genomic damage, changes in gene expression, and epigenetic modifications in plants, thereby interfering with genome stability and leading to the accumulation of mutations 28 . However, the multiomic molecular response mechanism and adaptive regulatory network of rice after space flight are still unclear. Therefore, in order to clarify the response and adaptation mechanism of rice multiomics molecules and cells after deep-space flight, Chang'e-5 was used to expose rice seeds to deep-space conditions, and multiomics technology was used to analyse the molecular response mechanisms of the seeds after they were returned to the ground. The results revealed that deep-space flight increased the frequency of rice genome variation and that HMG were characterized by long gene length and low GC content. The most commonly mutated genes were related to DNA damage repair and other processes. There was no significant difference in the overall level of DNA methylation, and the degree of CHG methylation was closely related to the response to deep-space flight. Single-cell transcriptome analysis revealed that the proportion of mesophyll cells was significantly reduced, and 188 genes related to the plant MAPK signalling pathway, phenylpropanoid synthesis pathway and other pathways affected the differentiation fate of mesophyll cells. The m6A methylation level increased significantly, affecting protein phosphorylation, phenylpropanoid synthesis and the expression of other related genes. The transcription factor SVT1, which inhibits the differentiation of variant cells by negatively regulating the plant MAPK signalling pathway, was identified with this multiomics strategy, revealing a new mechanism by which plants adapt to the deep-space environment. Space flight can lead to mutations in the genome of organisms, in which DNA repair genes are particularly fragile. The frequency of mutations caused by space flight is typically between 10 − 8 and 10–4 29−32 . In this study, the mutation frequency stimulated by deep-space flight in the rice genome ranged from 1.78×10 − 7 to 6.08×10 − 4, consistent with previous reports. However, there were significant differences among different samples, especially the TK15 sample, which has a high number of genomic variations, reaching 265,320. The space environment caused abnormal expression and function of DNA damage repair-related genes in TK15, leading to the inability to recognize and repair mutations, resulting in the accumulation of mutations in the TK15 sample (Supplementary Information). The research report of SJ-10 stated that spaceflight downregulates DNA repair genes and exacerbates the accumulation of genomic damage, which confirms the results of this study 33 . Additionally, some genomic variation characteristics in this study are consistent with previous findings, including the predominance of SNPs, a high proportion of heterozygous mutations, and low heterozygosity due to somatic mutation mosaicism 31 , 34 , 35 . Furthermore, while the mice from the International Space Station reported that space mutagenesis primarily involves CG→TA base transitions, we found a significant increase in A→G and T→C transition types, indicating a difference in base transition trends between deep-space flight and low Earth orbit 35 . The assumption that these mutations are random is increasingly being questioned 36 – 38 . Studies have shown that the mutagenic effects of space flight are often concentrated in hotspot regions of the genome 35 , 39 , 40 . Although we did not identify obvious mutation hotspot regions in our study, our characterization of the structural features of highly mutated genes suggested that genes related to DNA conformation and damage repair are more prone to mutations, exacerbating the tendency toward genomic instability and indicating a certain preference for genomic variations caused by deep-space flight. Therefore, this study has advanced our understanding by demonstrating that deep-space flight mutagenesis preferentially targets the key genes that maintain the stability of the genome itself, thus forming a vicious circle of damage accumulation. Multiple studies have shown that while the space environment may influence DNA methylation patterns, most of the significantly altered CpG sites return to baseline levels upon returning to Earth. For example, The Arabidopsis seeds carried by SJ-10 reported relatively low genome-wide CG, CHG, and CHH methylation levels, with methylated genes involved in hormone signalling, protein phosphorylation, and cell wall modification pathways 19 , 41 . The rice seeds flown in lunar orbit for 23 days presented reductions in average CG, CHG, and CHH methylation levels upon return to Earth and planting, although this change was not statistically significant. The differentially methylated genes were associated primarily with metabolic processes, stress responses, and transport processes 10 . In addition, the DNA methylation levels in human astronauts exhibited trend similar to those in plants. For example, based on the genome-wide methylation characteristics of six astronauts during long-term isolation in the Mars-500 project, the minimum changes in genome-wide DNA methylation were indicated; similar conclusions were presented in the NASA twin study 25 , 42 . The results of this study are consistent with these previous findings, with no significant differences in whole-genome methylation levels between deep-space flight samples and ground controls. The three types of methylation differed only slightly in their genomic distribution. The functions of differentially methylated genes, in addition to stress responses and metabolic processes, as previously mentioned, also include phenylpropane biosynthesis, plant MAPK signalling, and DNA damage repair. Furthermore, the functional relevance of CHG-type methylation is closely related to the findings of this study, potentially representing an important form through which deep-space flight impacts rice DNA methylation. By identifying CHG methylation as a key mediator of space-induced epigenetic changes in rice, this study advances our understanding of how organisms adapt to deep-space stressors and underscores the need for targeted investigations into context-specific methylation mechanisms to mitigate long-term biological risks in space exploration. Spaceflight also leads to changes in RNA methylation, but few reports on this topic exist. Currently, only studies on m6A methylation have been conducted in astronauts. The first m6A methylation atlas of human astronauts showed that they experienced a significant increase in m6A methylation levels during their mission, impacting RNAs involved in pathways such as red blood cell regulation, stress induction, and immune changes 43 . Our study is the first to examine m6A methylation in rice after deep-space flight and similarly revealed that deep-space flight caused a significant increase in m6A methylation levels. However, unlike those in humans, the response genes affected in rice were mainly related to protein phosphorylation processes and phenylpropanoid synthesis. Humans have a more complex immune system than plants do, and plants lack an active immune system, relying primarily on enhanced phenylpropanoid metabolism to strengthen their cellular defences and antioxidant capabilities 44 , 45 . Additionally, astronauts' missions typically have longer durations, allowing cumulative systemic immune adaptation 8 , whereas the deep-space flight period for rice in this study was only 23 days, potentially triggering only acute stress responses and concentrating m6A modifications in rapid-response pathways. Despite these differences, both studies demonstrated the significant regulatory role of m6A methylation in response to environmental stress. Extraterrestrial environments reshape gene expression through multidimensional mechanisms, with effects characterized by species and tissue specificity, time dependence, and environmental interactivity. For example, space flight primarily suppresses the expression of genes related to mouse embryonic stem cells, inflammatory responses, and oxidative stress while significantly upregulating genes associated with the cell cycle and apoptosis. Similarly, DNA repair genes, such as BRCA1, are also upregulated in human astronauts 23 , 46 , 47 . Gene expression changes in plants under space flight conditions are a key focus of current space biology research, with reports on pathways involving cell wall remodelling, oxidative stress responses, metabolic adaptation, photosynthesis regulation, and DNA repair 4 , 13 , 48 – 52 . In this study, the transcriptomic DEGs also involved metabolic network reconstruction, including the sustained activation of genes related to lipid, flavonoid, and various sugar metabolism pathways. Additionally, space flight significantly impacts the expression of genes associated with protein phosphorylation, phenylpropanoid synthesis, plant hormone signalling, and the plant MAPK signalling pathway. Future research should prioritize elucidating the functional consequences and adaptive significance of these specific pathway alterations, particularly the sustained metabolic shifts and MAPK signalling dynamics in plants. The application of single-cell sequencing analysis to samples exposed to space environmental conditions provides a new perspective on the effects of space environments on cellular molecular mechanisms. scRNA-seq can reveal cellular heterogeneity in populations under space flight conditions, such as the significant differences in gene expression observed in breast cancer cell populations 53 . Using scRNA-seq, previous researchers identified a "space flight signature" gene set including genes related to oxidative phosphorylation, UV response, immune function and the TCF21 pathway. This gene set was verified in independent datasets (such as those from the NASA twin study), indicating its conservation across species and experimental conditions 23 . In this study, for the first time, scRNA-seq was conducted on rice subjected to deep-space flight, revealing cellular heterogeneity, specifically a significant reduction in the proportion of mesophyll cells in postflight samples. By combining pseudotime analysis and differential expression analysis, a specific gene set (188 genes) responsible for this phenomenon was identified, including genes related to oxidative stress, phenylpropanoid synthesis, sugar metabolism, and plant MAPK signalling. This highlights the impact of deep-space flight environments on gene expression and cellular adaptability. This study not only deepens our understanding of how the space environment reshapes plant cell composition and functional pathways, but more importantly, the conservative stress response mechanisms and specific cell type sensitivities it discovers provide key molecular targets for predicting and intervening in the effects of space environment on more complex biological systems, including future astronauts. The genomic instability induced by space flight is not only a critical pathological mechanism linking exposure to the space environment to various health risks for astronauts but also a core adaptive mechanism that allows plants to cope with the extreme conditions in extraterrestrial environments. In-depth research on this phenomenon is vital for ensuring astronaut safety, developing space crops, and elucidating space biology. Genomic instability caused by space flight typically manifests as DNA damage and genomic variations. Organisms respond primarily by enhancing their DNA damage repair capabilities 13 , 41 , 54 – 56 . In addition, human studies have reported cases in which P53, BRUCE, PUMA and other proteins influence genome stability by altering cell differentiation 57 – 60 . This study revealed a new mechanism by which rice adapts to deep-space flight, relying on the NAC family transcription factor SVT1 to inhibit the differentiation of cells carrying mutations. The NAC family of transcription factors is one of the largest plant-specific transcription factor families and plays a central role in response to abiotic stress 61 . For example, SOG1 in plants assumes a role analogous to that of P53 in animals, responding to DNA damage, activating downstream genes, and coordinating processes such as cell cycle arrest, DNA repair, and programmed cell death 62 , 63 . Although SVT1 belongs to the same NAC family as SOG1, its mechanism of action differs. SVT1 does not directly participate in the DDR but instead negatively regulates the MAPK signalling pathway, inhibiting the differentiation of cells carrying genomic variations. This reduces the proportion of variant cells and maintains overall genome stability. This newly identified mechanism of "differentiation inhibition" mechanism complements other strategies for coping with genomic instability induced by the space environment, such as direct repair, metabolic support, and epigenetic regulation, forming a multi-level regulatory network. The discovery of SVT1 not only reveals the unique strategies by which plants can adapt to space but also provides a new perspective for understanding the trade-off mechanism of “repair differentiation survival” in stress biology. This study has several limitations, we provide an initial exploration of SVT1 function, relying primarily on phenotypic analysis of knockout mutants and EMSA experiments, with insufficient support from overexpression mutant phenotypes and other protein‒DNA interaction experiments. While the plant MAPK signalling pathway has been proven to play a significant regulatory role in cell differentiation 64 , 65 , the molecular details of how SVT1 regulates the MAPK pathway to influence cell differentiation (such as protein phosphorylation and complex formation) remain unclear. Additionally, genomic variations exhibit substantial heterogeneity among cells, and whole-genome sequencing of tissue samples cannot provide comprehensive information about the full spectrum of variation in each individual cell, necessitating single-cell level analysis of the effects of SVT1 on cells carrying mutations. Future work will involve a deeper exploration of SVT1 function and regulatory mechanisms, including the construction of SVT1 overexpression lines, single-cell level detection of genomic variations, and cell culture experiments for the assessment of single-cell differentiation rates and mutation transmission efficiency, among other phenotypes, as well as gene expression-related experiments to validate SVT1 function. Through various protein‒DNA interaction experiments (such as yeast one-hybrid and dual-luciferase reporter assays), the binding patterns of SVT1 with MAPK pathway genes can be elucidated, and through immunoprecipitation experiments, upstream regulatory proteins of SVT1 can be screened and combined with protein phosphorylation analysis to clarify the interaction between MAPK proteins and SVT1. In summary, this study used rice seeds carried by Chang'e-5 spacecraft and combined multiple omics techniques to reveal the mechanisms by which deep-space environment affects plant genome variation, gene expression, epigenetic modifications, and cell differentiation. It identified a new mechanism by which transcription factor SVT1 regulates mutant cell differentiation by inhibiting the MAPK pathway. This study not only reveals the molecular strategies of plants to cope with deep-space environments, but also provides interdisciplinary insights for space breeding, extreme environmental biology, and human space exploration. Methods Plant materials The main materials includethe pure indica rice line HJXSM, which was developed by the National Engineering Research Center of Plant Space Breeding of South China Agricultural University (SCAU) through hybridization of the high-quality disease-resistant indica rice variety Huahang 31 and the restorer line Hanghui 1508 and pedigree selection. This line integrates multiple disease resistance and fragrance genes and exhibits stable agronomic traits. We also obtained SVT1 knockout transgenic lines with ZH11 background using CRSPR/Cas9 technology. All materials were grown under standardized conditions at the teaching and research base of SCAU in Guangzhou, Guangdong Province. Chang'e-5 carriage of HJXSM seeds and sequencing material sampling On November 24, 2020, approximately 32.8 g of dry HJXSM seeds derived from a single plant were carried aboard Chang'e-5. The total flight duration was approximately 23 days, during which the seeds were exposed to a radiation dose of 59.85 mGy. The seeds were sown at the teaching and research base of SCAU on February 26, 2021, with seeds from the same plant that had not been flown on Chang’e-5 serving as controls. On April 29, 2021, 30 plants (numbered TK1-TK30) grown from seeds exposed to deep-space were randomly selected (Supplementary Fig. 1), along with 10 plants (numbered CK1-CK10) from unflown seeds as controls. A sample of young tillers was collected from each plant for subsequent analyses. DNA and RNA extraction and quality inspection Take 500 mg of each sample, DNA was extracted using the cetyltrimethylammonium bromide (CTAB) method, and total RNA was extracted using the Omega Plant RNA Kit (Omega Bio-Tek, R6827). The quality of the DNA and RNA was evaluated using a Qubit instrument (Thermo Fisher Scientific, USA) and a NanoDrop instrument (Thermo Fisher Scientific, USA), and the integrity of the RNA was determined using an Agilent2100 instrument (Agilent Technologies, Germany). The samples that passed quality control were stored at -80°C. Reference genome assembly The study used both PacBio Sequel (Pacific Biosciences, USA) and Illumina (Illumina, USA) technologies, with Fastp (v0.20.0) used for quality control of Illumina sequencing data. The PacBio reads were spliced and assembled using MECAT (v1.0), and the Illumina reads were aligned to the assembled genome using Pilon (v1.23). The Illumina reads were realigned to the completed and error-corrected reference genome, and metrics such as the mapping rate, genome coverage, and depth distribution were calculated to evaluate the completeness of the genome assembly. Based on the three-dimensional spatial structure characteristics of chromatin and the interaction between sequences, scaffolds are constructed. The scaffolds are anchored to chromosomes through clustering models to determine the correct order and direction for mounting scaffolds. Hi-C data assembly is performed using LACHSIS (v2014-09-12.12), ALLHIC (v0.9.8), and 3D-DNA (v180114). Parametric full-length transcriptome analysis and reference genome annotation PacBio data were aligned to the reference genome using SMRTLink (v8.0.0) to obtain known and novel transcripts, which were combined with Illumina data for gene functional annotation, gene structure analysis, and gene expression analysis. The coding genes were predicted using Augustus (v3.2.1) and GeneMark (v4.72), and Maker (v2.29) to align known homologous coding protein sequences from other species with the new genome sequences. The results of sequence alignment were integrated using HISAT2 (v2.1.0), and sequence assembly was performed using StringTie (v1.3.1) to obtain a transcriptome-based gene set. Maker was used to integrate the data from transcriptomic analysis, homology analysis, and de novo analysis in proportion to obtain the final gene set, followed by BUSCO (v4) evaluation of the predicted coding genes. The predicted gene protein sequences were aligned with the database for functional annotation, and a threshold of < 1e-5 was used for filtering. WGS DNA samples that passed quality control were sequenced on an Illumina platform. The filtered reads were aligned to the reference genome using BWA (v0.7.15), and variant detection was performed using GATK ( https://software.broadinstitute.org/gatk/best-practices ). Raw variant data were obtained after functional annotation using ANNOVAR (v2). RNA-seq After obtaining high-quality total RNA, the library was constructed and sequenced. HISAT2 was used for reference genome-based alignment analysis, including type statistics, gene coverage, sequencing randomness, and sequencing saturation analysis. StringTie was used to reconstruct the transcripts, and RSEM ( http://deweylab.Giyhub.Io/RESM/ ) was used to calculate the expression levels of all genes in each sample, displayed as fragments per kilobase of exon model per million mapped fragments (FPKM). Using FPKM as the screening criterion, genes with |log2FC|≥1 and FDR < 0.05 were output as DEGs. The target genes were mapped to terms in the Gene Ontology (GO) database ( http://www.geneontology.org/ ), and significantly enriched GO terms were identified. The target gene set was combined with the Kyoto Encyclopedia of Genes and Genomes (KEGG) database ( https://www.kegg.jp/ ) for enrichment analysis to screen for significantly enriched pathways. WGBS Preparation of DNA library for bisulfite sequencing using DNA that has passed quality inspection. The specific steps are as follows: Genomic DNAs were fragmented into 100–300 bp by Sonication(Covaris, USA) and purified with MiniElute PCR Purification Kit(QIAGEN, USA). The fragmented DNAs were end repaired and a single “A” nucleotide was added to the 3’ end of the blunt fragments. Then the genomic fragments were ligated to methylated sequencing adapters. Fragments with adapters were bisulfite converted using Methylation-Gold kit(ZYMO, USA), unmethylated cytosine is converted to uracil during sodium bisulfite treatment. Finally, the transformed DNA fragments were amplified by PCR and subjected to Illumina sequencing. The sequencing data were converted into raw sequence data through base calling. Filtered data were aligned to the genome using BSMAP[39], generating genome-wide base alignment information and genome-wide C base methylation information. MethylKit[40] was used to identify DMCs. The average DNA methylation rate within each 200 bp window of the genome was calculated, and the methylation levels of each window across samples were compared to identify DMRs. MeRIP-seq Qualified RNA samples are used for library construction. The specific steps are as follows: use a reagent kit to break them into fragments of approximately 100 nt in length. Divide the RNA into two parts, one of which is used as an input control, and directly construct a transcriptome sequencing library to eliminate the background during the process of capturing methylated fragments. The other part of the RNA is enriched with m6A specific antibodies. After capturing m6A modified RNA, antibody elution was performed using magnetic beads to reduce background noise from non-specific binding. Construct strand specific libraries for two RNA samples separately. After the construction of the library is completed, perform quality testing on the library. Qualified libraries will undergo Illumina sequencing. Using exomePeak2 (v1.1.0), genome-wide peak scanning was performed with a p value < 1e-5 as the threshold, and peaks from replicate samples were filtered and merged. The genomic locations and peak sequences were analysed to identify peak-associated genes. MEMESuite ( http://meme-suite.org/ ) was used for TF motif analysis to identify significant motif sequences within the peaks. The relative methylation level of each peak was calculated, and exomePeak2 was then used for differential methylation analysis of RNA across all peaks in the comparison groups. Peaks with P 1 were selected as differential peaks. Preparation of protoplast suspension Cut the young stem tissues of 40 samples into thin slices using a blade and place them in 50 ml centrifuge tubes. Wash the tissue slices twice with 0.8 M mannitol and then perform enzymatic hydrolysis. Add 15 ml of enzymatic solution to each centrifuge tube, including Cellulose RS (1.5%), Pectinase Y23 (0.03%), mannitol (0.5 M), KCl(0.5 mM), MgCl (0.5 mM), MES (0.5 mM, pH 5.7), CaCl (10 mM), and BSA (0.1%). Subsequently, the centrifuge tube was placed in a shaker and incubated in the dark. The shaker was set at 30 ℃ and 75 rpm for 5 hours. During the incubation period, the protoplast status (including morphology, quantity, size, number of fragments, and cell viability) was observed under a microscope. After enzymatic hydrolysis, briefly shake the centrifuge tube to completely release the protoplasts. Filter the enzymatic hydrolysate using a 40 µm cell sieve into a new 15 ml centrifuge tube, add 2 ml of 0.8 M mannitol and shake well, centrifuge at 150×g for 5 min, repeat cleaning 3 times, and then use again. After discarding the supernatant, add 10 ml of 0.8 M mannitol and resuspend. Mix 5 µl of protoplast suspension with 5 µl of 0.4% trypan blue staining solution, and detect cell concentration and activity using a cell counting plate. Use 0.8 M mannitol to uniformly adjust the concentration of protoplast suspension for 40 samples to 1000 cells/µl. Then, take 1 ml of suspension from each of the 30 TK samples and mix them in equal amounts. Repeat this process three times and renumber them as scTK1-scTK3. The CK samples were mixed in the same way and renumbered as scCK1-scCK3. 6 mixed samples of single-cell suspension were placed on ice for single-cell library construction. Take 50 seeds each from WT and svt1, and irradiate them with 300 Gy of gamma rays at a dose rate of 1 Gy/min for 5 h. After irradiation, disinfect the seeds with sodium hypochlorite, then peel off the seed shells, germinate at 30 ℃ in the dark, and cut off the seed embryo tissue after 3 days. Prepare protoplast suspension using the same method as above. scRNA-seq Combine gel beads containing barcode information with the mixture of cells and enzymes, and then wrap them with oil surfactant droplets in the microfluidic “double cross” system to form Gel Beads In Emotions (GEMs). GEMs flow into the reservoir and are collected. The gel beads dissolve and release Barcode sequences, reverse transcribe cDNA fragments, and label the samples. The gel beads were broken and the oil drops were broken, and the cDNA was used as the template for PCR amplification. Mix all GEMs products and construct a standard sequencing library. Perform high-throughput sequencing on the constructed library using Illumina NovaSeq X Plus dual end sequencing mode.The reads were aligned to the reference genome using CellRanger (v2.0.0) and annotated to specific genes. After UMIs were corrected and counted, an unfiltered feature-barcode matrix was obtained. The cells and noncells in the data were identified and distinguished. Gene quantification was performed using UMI counts to obtain cell-gene expression profiles. The expression matrix was imported into Seurat (v4.2.0), and multicell filtering was conducted using DoubletFinder (v3). Data integration and batch effect correction were performed using Harmony ( https://github.com/immunogenomics/harmony ). The cell subcluster classification results were subjected to uniform manifold approximation and projection (UMAP) for nonlinear clustering to visualize the different single-cell subclusters. PAGA first inputs the expression matrix into Scanpy (v1.6.0), then performs dimensionality reduction transformation and maps it to a low dimensional scatter plot. The cells are then divided into several subgroups and represented in the form of nodes based on topological structure, with the strength of connectivity represented by the thickness of lines. For pseudotime analysis, the gene expression matrix was input into Monocle2 (v2.26.0), and cells were arranged into a differentiation trajectory containing branches and nodes. The cell cluster with the most primitive differentiation state was defined as the cell cluster with the lowest pseudotime value in the trajectory, and pseudotime values for all cells were calculated. Negative binomial generalized linear models were fitted for the two branches, and differential genes dependent on different branches were identified and tested by comparing the two models. The screening criterion was set as an FDR < 1e-7. Single-cell variant site mapping Vartrix ( https://github.com/10×Genomics/vartrix ) with default parameters was used with VCF variant files, BAM files, genomic variant data, and single-cell matrix files as input and genomic variant site information, the row number of barcodes in barcodes.tsv, and the corresponding barcode (cell) variant detection results as output. A UMAP plot was generated for visualization of the aforementioned results. eQTL and meQTL Genomic mutation sites were jointly analysed with transcriptome gene expression levels for expression quantitative trait loci (eQTL) analysis, and genomic mutation sites are jointly analysed with DMCs for methylation quantitative trait loci (meQTL) analysis. The qqnorm() function in R software ( https://www.R-project.org/ ) was used for normalization, and the normalized values were used as the final values. The peer ( https://github.com/PMBio/peer/wiki/Tutorial ) tool was employed to check for hidden effects and compute the matrix. The GLM from MatrixEQTL (v2.3) was utilized for the eQTL and meQTL analyses, with a significance threshold of 0.01; pairs with p values below this threshold were considered significant. In the genome-wide analysis of the annotated genes, the majority of the eQTL and meQTL distances were within 10,000 Kb, with this distance used to distinguish between cis- and trans-association signals. DAP-seq Mix the DNA of 10 CK samples after quality inspection in equal amounts, then crush them into short fragments using ultrasonic technology. After end repair and 3’ end addition, connect them to Illumina sequencing adapters and select 100–300 bp DNA fragments for PCR amplification. Finally, a qualified library for sequencing was obtained. The coding sequence (CDS) of SVT1 was subsequently cloned and inserted into a plasmid containing the Halo tag, followed by in vitro expression. The purified SVT1 protein was incubated with the Hangjuxiangsimiao DNA library, and the DNA fragments bound to SVT1 were extracted. Sequencing was performed using the Illumina platform. Reads were statistically analysed using deepTools (v3.2.0), with a window size of 50 bp, and the average read depth within each window was calculated. MACS2 (v2.1.2) was used for peak calling across the genome with a threshold of a q value < 0.05, and the genomic positions and sequences of the peaks were analysed to identify peak-associated genes. All peaks within the group were then merged, and unified peak data were output. The ChIPseeker (v1.16.1) R package was used to annotate the peak-associated genes. Electrophoretic mobility shift assay (EMSA) The SVT1 was cloned and inserted into the pET-28a-SUMO prokaryotic expression vector. Positive plasmids were transformed into E. coli DE3 competent cells. Positive strains were identified by PCR. Bacterial lysates were sonicated, and the supernatant was collected as the protein sample. After SDS-PAGE, the protein was purified using magnetic beads to obtain the purified SVT1 protein. Three DNA binding probes targeting the promoter regions of the target genes were synthesized (Supplementary Table 1), along with mutant probes. After biotin labelling and annealing to form double-stranded DNA, unlabelled binding probes were used as competitive probes. The SUMO protein was incubated with binding probes as a negative control. The SVT1 protein was separately incubated with biotin-labelled binding probes and mutant probes, and differences in migration rates were evaluated for the competition experiments. Statistical analyses In statistical analysis, data is presented in the form of mean ± standard deviation with error bars attached. We used a two tailed t-test to determine significant differences between two groups. When analyzing datasets containing three or more experimental groups, we performed one-way ANOVA using IBM SPSS software (v21.0) and combined it with Duncan's multiple range test. When the p-value is less than 0.05, it is considered statistically significant (* p < 0.05, ** p < 0.01, *** p < 0.001). Data availability Data supporting the findings of this work are available within this paper and its Supplementary Information files. The de novo sequencing (CRA027830), WGS (CRA027612), RNA-seq (CRA027698), WGBS (CRA027613), MeRIP-seq (CRA027615), scRNA-seq (CRA027871), DAP-seq (CRA027869), scRNA-seq (CRA027764) and WGS (CRA027868) of mutants have been deposited in the Genome Sequence Archive (GSA) at the National Genomics Data Center. Source data are provided with this paper. Declarations Acknowledgments This work was supported by the National Key Research and Development Program of China (2022YFD1200703 to T.G.), the Key-Area Research and Development Program of Guangdong Province (2022B0202060006 to H.W.). The work of carrying rice seeds on Chang'e-5 was supported by the third phase project of the lunar exploration of National Space Administration Lunar Exploration and Aerospace Engineering Center. Author contributions T.G. and K.S. designed the experiment. K.S., J.Z., H.L., W.S., C.Z., Z.H., and J.Z. conducted experimental operation, data collection and analysis. T.G., Z.C., K.S., Q.Z., L.M., J.W., W.X., G.Y., M.H., C.H., D.Z., R.S., C.C., M.Z., P.W., Y.L., J.Z., and H.W. jointly wrote the paper. All the authors read and approved the final manuscript. Competing interests The authors declare no competing interests. References Cho, Y. et al. Cellular and physiological functions of SGR family in gravitropic response in higher plants. J. Adv. 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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-7211908","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":503570658,"identity":"f5b0c429-1d9f-49f1-96b5-2a6e2458df60","order_by":0,"name":"Tao 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University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Zeng","suffix":""},{"id":503570679,"identity":"c5b9294f-7848-4b69-b582-213540b041ce","order_by":21,"name":"Yongzhu Liu","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yongzhu","middleName":"","lastName":"Liu","suffix":""},{"id":503570680,"identity":"bfe942d5-fc84-4beb-8eaa-04ae5f1dcc02","order_by":22,"name":"Hui Wang","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Wang","suffix":""},{"id":503570681,"identity":"3fcd32b8-1596-40e2-86e2-97648ecf26b6","order_by":23,"name":"Zhiqiang Chen","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Zhiqiang","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-07-25 08:11:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7211908/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7211908/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89632439,"identity":"0468a91c-4185-4f22-ba0e-dfc8ad6f0040","added_by":"auto","created_at":"2025-08-22 06:54:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":6857198,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the study design, the SK1 reference genome, and genomic variation characteristics.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e Chang'e-5 carried HJXSM seeds, which were planted after return, and we randomly selected 30 TK and 10 CK plants for six types of omics sequencing, including de novo sequencing, whole-genome sequencing (WGS), RNA sequencing (RNA-seq), whole-genome bisulfite sequencing (WGBS), methylated RNA immunoprecipitation sequencing (MeRIP-seq), and single-cell RNA sequencing (scRNA-seq). After multiomics integration identified the transcription factor SVT1, DNA affinity purification sequencing (DAP-seq) for SVT1 and WGS and scRNA-seq of SVT1 knockout lines and wildtype (WT) plants were performed. \u003cstrong\u003eb\u003c/strong\u003e Assembly of the SK1 reference genome, with circular diagrams from outside to inside representing chromosomes, gene density, GC content, repeats, Long terminal repeats (LTRs), long interspersed repeated sequences (LINEs), gene expression levels, and homologous gene analysis. The gene density, repeats, LTRs, and LINEs are represented as counts within 300 Kb windows. \u003cstrong\u003ec\u003c/strong\u003e Distribution of mutation sites across the genome, with circular diagrams from outside to inside showing chromosome lengths, histograms of gene density in different chromosomal regions, scatter plots of SNP density in different chromosomal regions, orange lines indicating duplication locations, purple lines indicating deletions, green lines indicating insertions, blue lines indicating inversions, and inner circle lines indicating breakends. \u003cstrong\u003ed\u003c/strong\u003e Comparison of the number of variants between CK and TK samples. Data represent means ± SD (CK n = 10 biological replicates, TK n = 30 biological replicates). \u003cstrong\u003ee\u003c/strong\u003e Number of variants in individual CK and TK samples. \u003cstrong\u003ef\u003c/strong\u003e Comparison of variant site characteristics between CK and TK (CK n = 10 biological replicates, TK n = 30 biological replicates).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7211908/v1/de066eb615fe5198413d9325.png"},{"id":89630000,"identity":"31449ee6-6fc2-4565-8ca0-9a8899cd4dc3","added_by":"auto","created_at":"2025-08-22 06:38:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3493430,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDNA methylation patterns after deep-space flight. a\u003c/strong\u003eDifferences in global methylation levels between CK and TK (CK n = 10 biological replicates, TK n = 30 biological replicates). \u003cstrong\u003eb\u003c/strong\u003e Proportions of the three methylation types in CK and TK. \u003cstrong\u003ec\u003c/strong\u003e Distribution of the three types of methylation in different gene regions. \u003cstrong\u003ed\u003c/strong\u003e Distribution of the three methylation types in TE regions. \u003cstrong\u003ee\u003c/strong\u003e Proportions of CG, CHG, and CHH in methylated regions (DMRs), their genomic distribution, and the proportions of methylation rate upregulation and downregulation. \u003cstrong\u003ef\u003c/strong\u003e GO enrichment of differentially methylated genes (DMGs) and the top 20 biological processes according to -log10(Q value), where the circle size represents the ratio of target genes enriched in the pathway to background genes enriched in the pathway, and the colour represents the significance level. \u003cstrong\u003eg\u003c/strong\u003e KEGG enrichment of DMGs and the top 20 pathways according to -log10(Q value), where the circle size represents the ratio of target genes enriched in the pathway to background genes enriched in the pathway, and the colour represents the significance level.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7211908/v1/4fa6049c52fd993cbbecd883.png"},{"id":89630899,"identity":"bcc8cdf8-5aa6-43aa-9757-8c6c59d25f85","added_by":"auto","created_at":"2025-08-22 06:46:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2948012,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle cell expression atlas and RNA methylation pattern after deep-space flight. a \u003c/strong\u003eUMAP plots of scCK and scTK, divided into 13 clusters. \u003cstrong\u003eb\u003c/strong\u003e The scCK and scTK cells were classified into 5 cell types. \u003cstrong\u003ec\u003c/strong\u003e Differences in the proportions of the 5 cell types between scCK and scTK. \u003cstrong\u003ed\u003c/strong\u003e PAGA analysis of the differentiation levels of the 5 cell types, from low to high: primordium, bulliform, parenchymal, epidermal and mesophyll. \u003cstrong\u003ee\u003c/strong\u003ePseudotime analysis of cell differentiation trajectories, starting from the primordium, with 3 differentiation nodes and 7 differentiation states, arrows indicate the direction of differentiation. \u003cstrong\u003ef\u003c/strong\u003e Number of differentially expressed genes (DEGs) between the scCK and scTK groups for the 5 cell types. \u003cstrong\u003eg\u003c/strong\u003eDEGs identified by RNA sequencing (RNA-seq). \u003cstrong\u003eh\u003c/strong\u003e Intersection of RNA-seq DEGs, single-cell RNA sequencing (scRNA-seq) intergroup DEGs, and genes affecting cell differentiation. \u003cstrong\u003ei\u003c/strong\u003e Comparison of m6A peak numbers between CK and TK (CK n = 10 biological replicates, TK n = 30 biological replicates). \u003cstrong\u003ej\u003c/strong\u003eDistribution of m6A peaks in the genome between CK and TK. \u003cstrong\u003ek\u003c/strong\u003e A 9-quadrant plot from joint methylated RNA immunoprecipitation sequencing (MeRIP-seq) and RNA-seq analysis, with the x-axis representing the log2-fold change in m6A peak abundance and the y-axis representing the log2-fold change in transcriptome gene expression levels. The dashed lines on the x- and y-axes represent the default thresholds for differential genes/peaks between the two omics analyses, |log2FC|\u0026gt;1. Each point represents a gene/peak, grey points indicate nonDEGs, green and yellow points indicate genes with consistent changes in abundance with m6A peaks, and blue and red points indicate genes with opposite changes in abundance compared with m6A peaks.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7211908/v1/1264d8631b93b1edb43cb5e3.png"},{"id":89630008,"identity":"e12a28c6-9934-4616-ae42-ef90848422b6","added_by":"auto","created_at":"2025-08-22 06:38:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3725552,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultiomics identification of the transcription factor SVT1. a\u003c/strong\u003e Motif analysis of 188 DEGs affecting cell differentiation. \u003cstrong\u003eb\u003c/strong\u003e KEGG enrichment of 188 DEGs affecting cell differentiation fate, the top 10 pathways based on the -log10 (Q value) are shown. The size of the circle represents the number of genes, and the colour represents the significance level. \u003cstrong\u003ec\u003c/strong\u003e Distribution of significant expression quantitative trait loci (eQTL) and methylation quantitative trait loci (meQTL) association signals on chromosomes, with an interval size of 1 Mb, the dashed box represents the hotspot interval. \u003cstrong\u003ed\u003c/strong\u003eGene structure of SVT1. \u003cstrong\u003ee\u003c/strong\u003e Expression level and proportion differences of SVT1 in scCK and scTK. \u003cstrong\u003ef\u003c/strong\u003e Expression level and proportion differences in SVT1 across 13 clusters, the circle size indicates the absolute scale value, colour represents the sign of the scale value, and the line height represents the average gene expression level. \u003cstrong\u003eg\u003c/strong\u003e Distribution of genomic variation sites in scTK and scCK, the colour represents the number of mutation sites in cells. \u003cstrong\u003eh\u003c/strong\u003e Comparison of the average number of variants per cell in cluster 2 and cluster 5. Data represent means ± SD (n = 3 biological replicates). \u003cstrong\u003ei\u003c/strong\u003e Pseudotemporal differentiation trajectories of parenchymal-5 and mesophyll-2, with arrows indicating the direction of differentiation.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7211908/v1/a78b36f80c6502254b0adf37.png"},{"id":89630010,"identity":"aa3477b0-e386-4557-95bd-28a5bbe52c16","added_by":"auto","created_at":"2025-08-22 06:38:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3333810,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSVT1 inhibits the differentiation of cells carrying gene variants.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003eThe right side shows the SVT1 knockout mutant svt1 in the ZH11 background, and the left side shows the ZH11 wild type (WT). \u003cstrong\u003eb\u003c/strong\u003e Experimental design for SVT1 functional validation: svt1 and WT seeds were subjected to 300 Gy γ-ray irradiation. After 3 d of seed germination, the seed embryos were removed, dissociated into protoplasts, and then mixed. The mixed samples were divided into 3 replicates, and each replicate was subjected to WGS and scRNA-seq. \u003cstrong\u003ec\u003c/strong\u003eUMAP plot of WT and svt1 cells grouped into 24 clusters. \u003cstrong\u003ed\u003c/strong\u003e WT and svt1 cells were classified into 10 cell types and 5 cell regions. \u003cstrong\u003ee\u003c/strong\u003eDifferences in the proportions of cell types and regions between WT and svt1 (n = 3 biological replicates). \u003cstrong\u003ef\u003c/strong\u003e Expression levels and proportions of SVT1 cells across 24 clusters. The circle size represents the absolute scale value, the colour indicates the sign of the scale value, and the line height indicates the average gene expression level. \u003cstrong\u003eg\u003c/strong\u003e Pseudotime-based differentiation trajectories of scutellum-lateral scale 6 (SL-6) and SL-16, with arrows indicating the direction of differentiation. \u003cstrong\u003eh\u003c/strong\u003e Differences in the number of genomic variant sites between WT and svt1 (n = 3 biological replicates). \u003cstrong\u003ei\u003c/strong\u003eDistribution of genomic variant sites in single WT and svt1 cells, the colours indicate the number of mutation sites in the cells,the dashed box represents the distribution location of the variations. \u003cstrong\u003ej\u003c/strong\u003e Comparison of average mutation numbers per cell in cluster 6 and cluster 16 with those in other clusters. Significant differences were observed in the WT samples but not in the svt1 samples. Data represent means ± SD (n = 3 biological replicates).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7211908/v1/0550a9775ef13f3dfbc26631.png"},{"id":89630021,"identity":"0d4b4953-cbcb-410f-9a7b-d401ccb6d538","added_by":"auto","created_at":"2025-08-22 06:38:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4184959,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSVT1 mediates the inhibition of the transmission of genomic variation through the MAPK pathway.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e The 10 most significantly enriched motifs from the DAP-seq peaks, comparison with known motifs, and the proportion of peaks in each motif relative to the total number of peaks. \u003cstrong\u003eb\u003c/strong\u003e Target genes with MEME-1 peaks in promoter regions and their overlap with genes affecting cell differentiation. \u003cstrong\u003ec\u003c/strong\u003e KEGG enrichment of 65 overlapping genes was performed, and the top 10 pathways based on -log10(Q value) are shown, with circle size indicating the number of genes and colour indicating the significance level. \u003cstrong\u003ed\u003c/strong\u003e Expression levels and proportions of SK1G00063486 in WT and svt1. \u003cstrong\u003ee\u003c/strong\u003e SVT1 regulatory mechanism model, where blue cells represent unmutated cells and red cells represent cells carrying mutations. SVT1 is highly expressed in mutated cells and inhibits the expression of MAPK pathway genes, thereby suppressing the differentiation of mutated cells and ultimately reducing their representation in the plant tissues.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7211908/v1/da579a5248940975fe23ab39.png"},{"id":89634144,"identity":"70903277-a249-4a80-9e32-ffedc4e122c0","added_by":"auto","created_at":"2025-08-22 07:02:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":23203415,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7211908/v1/728d620f-074c-4aa4-b1d7-d4e68e626f95.pdf"},{"id":89632438,"identity":"6bffaa98-9e43-4d8e-803e-90e43d100e97","added_by":"auto","created_at":"2025-08-22 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06:46:03","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":159871,"visible":true,"origin":"","legend":"Supplementary Data 10","description":"","filename":"SupplementaryData10.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7211908/v1/fc3ec9602ca80ad8eb95990e.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Multiomics analysis of the molecular and single-cell responses of rice after deep-space flight on Chang'e-5","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOver the past several decades, aiming to uncover the mechanisms by which plants adapt to space flight, scientists have carried plants on various platforms, such as recoverable satellites, spacecraft, and the International Space Station, to investigate changes in factors ranging from agronomic traits to the levels of different molecules\u003csup\u003e1-5\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe space flight environment encompasses extreme conditions such as ionizing radiation, microgravity, and variations in the magnetic field\u003csup\u003e6\u003c/sup\u003e. Research on the space flight response of plants can not only reveal the impact of extreme environmental factors on organisms in space, but also lay a data foundation for crop space mutation breeding, and provide interdisciplinary enlightenment for earth agriculture and medicine. Plants exposed to space flight experience multiple biological changes, including genomic mutations, changes in gene expression, and epigenetic modifications\u003csup\u003e4,7-10\u003c/sup\u003e; the accumulation of antioxidants such as flavonoids; adjustments in energy and physiological metabolism through pathways such as oxidative stress and sugar signalling\u003csup\u003e8,11-13\u003c/sup\u003e; and impacts on cellular structure and function, leading to altered cell proliferation and differentiation and abnormal growth and development\u003csup\u003e12,14-17\u003c/sup\u003e. The cascading systemic responses triggered by space flight not only directly influence plant growth and stress responses during missions but also may generate heritable molecular imprints that can affect plant responses postflight. These effects provide a unique resource for crop mutation breeding\u003csup\u003e18-20\u003c/sup\u003e. Furthermore, deep-space environments, such as those encountered in lunar or Martian missions, differ from that of low Earth orbit, with prolonged exposure to combined radiation and continuous microgravity, coupled with the absence of Earth\u0026apos;s protective magnetic field, which may have more severe effects on organisms\u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eRice, as one of the world\u0026apos;s major food crops, was one of the earliest species to undergo space mutagenesis research and has been the subject of the most experimental launches, different lines of research, and significant achievements. However, the existing studies on space mutagenesis in rice have focused primarily on mutation frequencies, allelic variations in key agronomic traits, and the identification of mutant offspring. There is still a lack of systematic and cross omics in-depth analysis on how the complex environmental factors unique to space flight trigger rice molecular cascade reactions, including genomic instability, dynamic changes of epigenetic regulatory networks, transcription and metabolic pathway remodeling.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough the widespread application of omics technology in recent years has provided new opportunities for exploring the mechanism by which organisms adapt to space flight. While existing omics studies have achieved certain progress, most have focused on human astronauts, such as the NASA twin study\u003csup\u003e22-25\u003c/sup\u003e. Multiomics research on the molecular response mechanisms of rice to space flight is scarce, with only a few recent studies, mostly based on independent omics analyses and rarely involving the integration of two or more omics approaches. Additionally, space flight may trigger specific molecular responses in different cell types. Single-cell sequencing can overcome the averaging effects of traditional sequencing technologies, which obscure cellular heterogeneity, enabling the identification and tracking of gene expression dynamic regulatory networks and key molecular pathways in responsive cells. To date, there have been several studies published on the molecular mechanisms of space flight in humans, animals, and microorganisms at single-cell resolution, such as the discovery of \u0026ldquo;space flight signature\u0026rdquo; gene sets in astronauts and changes in mouse brain cell development\u003csup\u003e23,26,27\u003c/sup\u003e. Considering these findings, plant cells may also exhibit differential specific responses and mechanisms of differentiation regulation in space flight environments. Therefore, combining multiomics and single-cell sequencing will contribute to a more comprehensive and in-depth understanding of the mechanisms of rice adaptations to space flight.\u003c/p\u003e\n\u003cp\u003eTo clarify the response and adaptation mechanism of rice multiomics molecules and cells after deep-space flight, rice seeds were carried aboard the Chang\u0026apos;e-5 lunar probe, returned to Earth and planted. Then, 30 plants grown from deep-space flight samples and 10 wild-type samples as controls were randomly selected for analysis. The study began by assembling an new reference genome through de novo sequencing, followed by the application of multiomics technologies including whole-genome sequencing (WGS), RNA sequencing (RNA-seq), whole-genome bisulfite sequencing (WGBS), methylated RNA immunoprecipitation sequencing (MeRIP-seq), and single-cell RNA sequencing (scRNA-seq). Through variant screening, epigenetic modification detection, differential expression analysis, and cell differentiation analysis, a comprehensive molecular profile of rice after deep-space flight was produced. Through the integration of these multiomics data, a transcription factor SVT1 was identified. Combined with DNA affinity purification sequencing (DAP-seq) and mutant phenotype analysis, it showed that the number of genomic variations in its knockout mutants increased significantly, and inhibited the differentiation of cells carrying the variation by negatively regulating the gene expression of plant MAPK signalling pathway, affecting the transmission of genomic variation after space flight (Fig. 1a).\u003c/p\u003e"},{"header":"Results","content":"\n\u003ch3\u003eHJXSM de novo sequencing and reference genome assembly\u003c/h3\u003e\n\u003cp\u003eTo precisely identify mutation sites and analyse the differences in gene expression and epigenetic modifications between flown and unflown rice samples, we performed de novo sequencing and genome assembly of HJXSM using PacBio third-generation sequencing, Illumina next-generation sequencing, and Hi-C technology. PacBio sequencing yielded 18.5 Gb (47\u0026times;) of data, Illumina sequencing provided 19.4 Gb (49\u0026times;) of data, and Hi-C technology generated 61.4 Gb (155\u0026times;) of data. The assembled genome size was 396.4 Mb, with a contig N50 value of 17.4 Mb (Supplementary Fig.\u0026nbsp;2a). Aligning the second-generation sequencing data back to the assembled genome resulted in a mapping rate of 98.52%, with 89.29% of the bases covered at a sequencing depth of greater than 30\u0026times; (Supplementary Fig.\u0026nbsp;2c). The BUSCO genome completeness was 96.92% (Supplementary Fig.\u0026nbsp;2b). A total of 99.73% of the Hi-C data were accurately anchored to the 12 chromosomes, and the BUSCO genome completeness after chromosome anchoring was 96.88% (Supplementary Fig.\u0026nbsp;2d). Gene annotation predicted a total of 36,836 protein-coding genes, with a BUSCO completeness of 95.51% (Supplementary Fig.\u0026nbsp;2e). Compared with the IRGSP1.0 genome, HJXSM presented high similarity in structural features such as gene length, CDS length, exon length, exon count, and mRNA length (Supplementary Fig.\u0026nbsp;2f). From these results, we constructed the genome sequence map SK1 for HJXSM (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), which served as the reference genome for subsequent studies.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eGenomic variation characteristics and preferences induced by deep-space flight\u003c/h3\u003e\n\u003cp\u003eTo clarify the molecular characteristics of genomic variation caused by deep-space flight, WGS was performed on 30 TK and 10 CK samples. WGS obtained 770.6 Gb of clean data in total, the clean data were then filtered, retaining the variant sites that appeared in only a single sample. Variant sites that were present in two or more samples were classified as either background variants or false positives. This process resulted in the identification of true variant sites, and a total of 276,898 true variant sites were identified, which were uniformly distributed across the chromosomes without any obvious hotspots (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). The mutation frequency in TK ranged from 1.78\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;7 to 6.08\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;4, with a significantly greater number of variant sites than in CK (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). The number of mutation sites varied significantly among different samples, with TK15 having the highest number of mutation sites (265,320) and TK28 having the fewest (78) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). Both CK and TK variant sites were composed primarily of single nucleotide polymorphisms (SNPs); however, insertion-deletions (indels) accounted for a greater proportion of TK, reaching 42.37%. The heterozygous mutation ratio in TK was higher than that in CK, ranging from 90.15\u0026ndash;100%, with heterozygous frequencies mostly between 0.1 and 0.2 (Supplementary Fig.\u0026nbsp;3a). The distribution of mutation sites across the genome with respect to gene bodies was similar for CK and TK, in the following order: intergenic\u0026thinsp;\u0026gt;\u0026thinsp;CDS\u0026thinsp;\u0026gt;\u0026thinsp;upstream and downstream (2 Kb)\u0026thinsp;\u0026gt;\u0026thinsp;UTR (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). Among exon mutations, TK exhibited a greater proportion of nonframeshift and nonsense mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). Additionally, the proportions of A\u0026rarr;G and T\u0026rarr;C transitions were significantly greater in TK, whereas the G\u0026rarr;A transition proportion was significantly lower (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). These results suggest that deep-space flight is more likely to induce indel and heterozygous mutations than other types of mutations.\u003c/p\u003e\u003cp\u003eIn the TK samples, a total of 9,154 genes carried at least one variant site in the CDS, upstream and downstream sequence, or UTR. Enrichment analysis of the mutated genes revealed that these genes are related to biological processes such as DNA integration, recombination, packaging, and conformational changes (Supplementary Fig.\u0026nbsp;3c) and function in multiple DNA damage repair pathways (Supplementary Fig.\u0026nbsp;3d).\u003c/p\u003e\u003cp\u003eTo further explore the relationship between mutation susceptibility and gene structure, the genes were classified into three categories according to their mutation rates (number of mutations/gene length): high-frequency mutated genes (HMG), low-frequency mutated genes (LMG), and nonmutated genes (NMG) (Supplementary Data 1). Statistical analysis of 13 gene structural features revealed that the number of mutations was significantly positively correlated with 8 features, including gene length, and significantly negatively correlated with the GC content and exon GC content (Supplementary Fig.\u0026nbsp;3b). Compared with LMGs and NMGs, HMGs exhibited longer gene length, lower GC content, more and longer exons, lower exon GC content, more and longer introns, and significantly increased 5'UTR length (Supplementary Fig.\u0026nbsp;3e). These results indicate that DNA damage repair genes are more likely to mutate, resulting in the accumulation of variation, and the variation has the preference of gene structure.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eDNA methylation patterns after deep-space flight\u003c/h2\u003e\u003cp\u003eTo investigate the impact of deep-space flight on DNA methylation patterns, WGBS was performed on CK and TK samples. Although there was no significant difference in genome-wide methylation levels between the CK and TK groups, the average methylation rate in the TK samples was slightly lower than that in the CK samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Among the three methylation types in CK and TK, CG methylation was the most common, followed by CHG, and then CHH (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The distribution of methylation levels across gene regions showed distinct trends; while all three types of methylation were higher in the upstream and downstream regions and lower at transcription start sites (TSSs) and transcription termination sites (TTSs), CG methylation was also relatively high in the gene body and exhibited a peak-like pattern. CHG methylation in the gene body was only slightly greater than that at the TSS and TTS, whereas CHH methylation in the gene body was nearly identical to that at the TSS and TTS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The distribution of methylation in transposable element (TE) regions was opposite to that in gene regions, with all three types showing lower methylation levels in upstream and downstream regions and higher levels at the TSS and TTS. CG and CHG exhibited similar distribution patterns, with a significant increase in methylation levels in the gene body, whereas CHH methylation in the gene body increased only slightly (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA total of 148,914 differentially methylated cytosines (DMCs) were identified between the CK and TK groups. However, differentially methylated regions (DMRs) often have more significant biological implications than individual DMCs. In this study, 2,381 DMRs were detected, with CG accounting for 72.69%, CHG accounting for 19.54%, and CHH accounting for 7.77%. CG and CHH DMRs were distributed relatively uniformly across the genome, whereas CHG DMRs were primarily concentrated in the gene body. Additionally, CG DMRs were associated mainly with increased methylation rates, whereas CHG and CHH DMRs were associated primarily with decreased methylation rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). GO enrichment analysis of the differentially methylated genes (DMGs) associated with the three types of DMRs revealed that CG DMGs were related to biological processes such as DNA recombination and nucleotide metabolism, CHH DMGs were associated with oxidative reduction and amino acid metabolism, and CHG DMGs were linked to nucleic acid metabolism and chromosome assembly (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef). KEGG enrichment analysis revealed that CG DMGs were involved in pathways related to the synthesis of various organic compounds, CHH DMGs were involved in the synthesis and metabolism of glutathione and other organic compounds, and CHG DMGs were involved in DNA damage repair pathways and plant MAPK signalling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg). Notably, 83.3% of the CHG DMRs associated with these pathways presented increased methylation rates. In conclusion, there was no significant change in the overall level of DNA methylation after deep-space flight, but the increase of CHG methylation level was related to the DNA methylation response after deep-space flight.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSingle-cell expression atlas after deep-space flight\u003c/h3\u003e\n\u003cp\u003eAfter showing the genomic variation and methylation changes at the DNA, we then turn to the research results at the RNA and single cell. To analyse gene expression patterns after deep-space flight at the single-cell level, we constructed a single-cell expression atlas of rice aerial tissues using scRNA-seq.\u0026nbsp;The aerial tissue cells were classified into 13 clusters according to their distinct gene expression patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). These clusters were further organized into 5 cell types, namely, primordium, mesophyll, parenchymal, bulliform and epidermal (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), according to their expression of cell marker genes (Supplementary Fig.\u0026nbsp;4a). The proportion of mesophyll cells in the scTK data was 54.28%, significantly lower than that in scCK (72.4%), whereas the proportions of the other 4 cell types increased slightly (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, Supplementary Fig.\u0026nbsp;4b). PAGA analysis of these 5 cell types revealed that the primordium presented the earliest developmental stage, whereas the epidermis and mesophyll presented the highest differentiation levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). The results of the pseudotime analysis were consistent with those of PAGA, indicating that differentiation started from the primordium, passed through 3 differentiation nodes, and ultimately produced 7 differentiation states (states 1\u0026ndash;7) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee, Supplementary Fig.\u0026nbsp;4c). The proportion of mesophyll (state 4) was significantly lower in scTK than in scCK (Supplementary Fig.\u0026nbsp;5a). In summary, the primordium first differentiates into parenchymal and bulliform primordia, followed by parenchymal differentiation into mesophyll and epidermal primordia. The differences in gene expression between scTK and scCK around differentiation node 3 were key drivers of the reduced mesophyll in the scTK samples. Among the 5 cell types, there were 4,424 intergroup differentially expressed genes (DEGs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef, Supplementary Data 2). Pseudotime analysis revealed 2,065 genes influencing cell differentiation fate around differentiation node 3 (Supplementary Data 3), with 1,438 of these genes showing significant differences in expression between scTK and scCK (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo supplement the scRNA-seq data, RNA-seq was also conducted. By combining scRNA-seq and RNA-seq, we can complement the advantages of the two, and give full play to the advantages of scRNA-seq high resolution and RNA-seq high sequencing depth, which helps us interpret the molecular mechanism of cells from two dimensions, and the results of the two methods can be mutually verified. RNA-seq identified 2,648 DEGs (Supplementary Data 4), with 1,390 upregulated and 1,258 downregulated in TK (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg). These DEGs were involved in biological processes such as protein phosphorylation (Supplementary Fig.\u0026nbsp;5b) and were associated with plant MAPK signalling pathways and phenylpropanoid and flavonoid biosynthesis (Supplementary Fig.\u0026nbsp;5c). Among the 1,438 intergroup DEGs affecting cell differentiation fate according to the scRNA-seq results, 188 were also found to be differentially expressed according to the RNA-seq results (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh). These results reveal that deep-space flight can significantly reduce the proportion of mesophyll cells by changing the gene expression of the key node of cell differentiation, and MAPK signaling pathway and other metabolic pathways may be involved in this process.\u003c/p\u003e\n\u003ch3\u003eEffects of deep-space flight on m6A modified regulatory gene expression\u003c/h3\u003e\n\u003cp\u003eRNA m6A methylation plays a significant role in gene expression, The MeRIP-seq results of CK and TK showed that deep-space flight significantly affects the m6A modification (Supplementary Fig.\u0026nbsp;6a). The number of peaks in TK was significantly greater than that in CK (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ei), particularly with an increase in peaks at the start codon and CDS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ej). There were 429 differential peaks between CK and TK (Supplementary Data 5), with 134 RNAs showing increased methylation rates and 295 showing decreased rates (Supplementary Fig.\u0026nbsp;6b). These 429 differential peaks were located at 428 genes, and GO enrichment analysis indicated that these genes are involved in protein phosphorylation and hydrolytic processing (Supplementary Fig.\u0026nbsp;6d). KEGG enrichment analysis revealed their association with the synthesis and metabolism of various organic compounds, including phenylpropanoids (Supplementary Fig.\u0026nbsp;6e). These enrichment results were similar to those for the DEGs identified by RNA-seq.\u0026nbsp;Correlation of the MeRIP-seq and RNA-seq data indicated that 60 out of the 428 genes with differential peaks also exhibited significant differences in expression levels (Supplementary Fig.\u0026nbsp;6c), and m6A-modified mRNAs presented overall higher expression levels than did those without m6A modification (Supplementary Fig.\u0026nbsp;6f, g). As the m6A modification positively regulates mRNA expression, we focused on genes whose expression changes were consistent with the m6A peak abundance trends. Specifically, m6A modification led to a significant increase in the expression of 17 genes and a significant decrease in that of 14 genes, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ek (Supplementary Data 6). These results suggest that m6A modification influences the expression of genes involved in protein phosphorylation and the phenylpropanoid pathway. A total of 9 genes exhibited significant changes in expression due to m6A modification and were differentially expressed between the scRNA-seq cell groups, impacting mesophyll differentiation (Supplementary Fig.\u0026nbsp;6h). This indicates that deep-space flight significantly regulates the level of m6A of RNA through m6A methylation modification, affects the expression of genes related to protein phosphorylation and phenylpropane metabolic pathway, and is associated with the differentiation fate of mesophyll cells.\u003c/p\u003e\n\u003ch3\u003eJoint multiomics identification of the transcription factor SVT1\u003c/h3\u003e\n\u003cp\u003eTo explore the key regulatory factors affecting the differentiation and fate of mesophyll cells after deep-space flight, the 188 DEGs affecting cell differentiation fate were analysed for motifs, with the four most significant motifs all belonging to the NAC family (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), indicating that these 188 genes are regulated primarily by NAC family transcription factors. KEGG enrichment analysis of these 188 genes revealed significant enrichment in pathways such as plant MAPK signalling and phenylpropanoid biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Among the 40,327 significant association signals detected by eQTL analysis, 99.57% were trans-associations. Similarly, among the 19,339 significant association signals detected by meQTL analysis, 99.32% were trans-associations, suggesting that trans-associations, namely, interactions with transcription factors, play an important regulatory role in adaptation to space flight. When the association signals from eQTLs and meQTLs were plotted on the chromosome in 1 Mb intervals, a hotspot region of colocalization was observed at the 24\u0026ndash;30 Mb region on chromosome 4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). The gene with the greatest number of association signals in this region was \u003cem\u003eSK1G00053966\u003c/em\u003e (\u003cem\u003eOs04g0515900\u003c/em\u003e), which is 2,387 bp in length and contains 3 exons and 2 introns, with the first and third exons encoding highly conserved NAC domains (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). Based on these motif analysis results, we considered SK1G00053966 as a strong candidate regulatory factor.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSingle-cell expression analysis of \u003cem\u003eSK1G00053966\u003c/em\u003e revealed that its expression proportion and level were significantly greater in scTK than in scCK (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee), and it exhibited distinct spatiotemporal expression characteristics, with high expression only in cluster 5 (parenchymal-5) and cluster 2 (mesophyll-2), (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef). Mapping genomic variation sites to single cells revealed that the number and proportion of variation sites in scTK were significantly greater in the parenchymal-5 and mesophyll-2 subgroups than in the other subgroups, whereas no such phenomenon was observed in scCK (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg, h). Additionally, parenchymal-5 and mesophyll-2 have a direct differentiation relationship. We simplified the pseudotemporal analysis to model the differentiation trajectory from parenchymal-5 to mesophyll-2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ei), identifying 1,754 genes influencing the cell differentiation fate (Supplementary Data 7). On the basis of these results, we hypothesize that the transcription factor SK1G00053966 inhibits the transmission of genomic variations by regulating the differentiation of parenchymal-5 and mesophyll-2 cells. Therefore, we named this gene SVT1, for suppressor of variation transmission 1.\u003c/p\u003e\n\u003ch3\u003eSVT1 inhibits the differentiation of cells carrying gene variants\u003c/h3\u003e\n\u003cp\u003eTo clarify the function of SVT1, we constructed an SVT1 knockout mutant svt1 (deletion of the T base in exon 3, resulting in a frameshift mutation) in the ZH11 background (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Then, svt1 and wild-type (WT) seeds were subjected to γ-ray irradiation at 300 Gy, followed by seed germination assays. Seeds were germinated for 3 days, and seed embryos were collected for scRNA-seq and WGS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). scRNA-seq clustered the seed embryo cells into 24 clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec), which were grouped into 10 cell types (coleoptile, coleorhiza, endosperm, plumule, epidermis, mesophyll, plumule, radicle, lateral scale, and scutellum) according to marker gene expression (Supplementary Fig.\u0026nbsp;7a). Additionally, some cells with similar expression patterns were difficult to distinguish, forming 5 cell regions: scutellum-lateral scale (SL), endosperm-plumule (EP), scutellum-radicle (SR), coleoptile-lateral scale (CL), and scutellum-lateral scale-coleorhiza (SLC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). The proportions of SL, EP, plumule, and coleorhizal cells were significantly greater in svt1 than in WT, whereas the proportions of mesophyll and SR were lower in svt1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee, Supplementary Fig.\u0026nbsp;7b).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSVT1 was specifically expressed in the SL cell region (Supplementary Fig.\u0026nbsp;7c), with particularly high expression in cluster 6 and cluster 16 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef). Cluster 6 (SL-6) and cluster 16 (SL-16) exhibited a direct differentiation relationship, and pseudotime analysis revealed that 637 genes influenced this cell differentiation fate (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eg, Supplementary Data 8). The WGS results revealed that, compared with the WT, svt1 presented a significantly greater number of genomic variants, with an average increase of 291 variant sites per sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eh). Further mapping of genomic variant sites to single cells revealed that in the WT plants, variant sites were concentrated in the SL region, whereas in svt1, variant sites were uniformly distributed without obvious clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ei, j). Therefore, SVT1 knockout led to a significant increase in genomic variation, and the proportion of SL cells carrying the variation increased significantly.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSVT1 mediates the inhibition of the transmission of genomic variation through the MAPK pathway\u003c/h2\u003e\u003cp\u003eTo identify the target genes and regulatory mechanisms of SVT1, DAP-seq analysis was conducted, resulting in the identification of 27,011 peaks (Supplementary Data 9). Among these peaks, the top 10 most significant motifs were identified; the E-value of MEME1 was 8.3e-183, significantly stronger than that of the other motifs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Motif similarity analysis indicated significant similarity between MEME1 and the motifs of multiple known NAC transcription factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Further screening revealed 828 candidate target genes with MEME-1 peaks located in promoter regions. These genes were intersected with 1,754 genes affecting the differentiation of parenchymal-5 to mesophyll-2 cells, and 65 overlapping genes were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). KEGG enrichment analysis of these 65 genes revealed significant enrichment in the plant MAPK signalling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec), which includes \u003cem\u003eSK1G00063486\u003c/em\u003e (\u003cem\u003eOs07g0126301\u003c/em\u003e), \u003cem\u003eSK1G00070333\u003c/em\u003e (\u003cem\u003eOs12g0586100\u003c/em\u003e), and \u003cem\u003eSK1G00060048\u003c/em\u003e (\u003cem\u003eOs05g0474800\u003c/em\u003e). The plant MAPK signalling pathway was observed as significantly enriched in multiple analyses in this study, with these three genes in the pathway showing potential interactions with SVT1 and possibly working together to influence cell differentiation. \u003cem\u003eSK1G00063486\u003c/em\u003e specifically impacted the differentiation fate of both parenchymal-5 to mesophyll-2 and SL-6 to SL-16 cells and was highly expressed in SL cell regions (Supplementary Fig.\u0026nbsp;7d). Moreover, its expression was significantly greater in svt1 than in WT (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed), indicating that SVT1 negatively regulates SK1G00063486. EMSA confirmed the interaction between \u003cem\u003eSK1G00063486\u003c/em\u003e and SVT1, validating this interaction (Supplementary Fig.\u0026nbsp;7e). On the basis of these results, we propose that SVT1 is highly expressed in cells carrying mutations and suppresses the differentiation of these cells by negatively regulating genes in the plant MAPK signalling pathway, thereby reducing the transmission of mutations in the cell population (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe space environment can cause genomic damage, changes in gene expression, and epigenetic modifications in plants, thereby interfering with genome stability and leading to the accumulation of mutations\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. However, the multiomic molecular response mechanism and adaptive regulatory network of rice after space flight are still unclear. Therefore, in order to clarify the response and adaptation mechanism of rice multiomics molecules and cells after deep-space flight, Chang'e-5 was used to expose rice seeds to deep-space conditions, and multiomics technology was used to analyse the molecular response mechanisms of the seeds after they were returned to the ground. The results revealed that deep-space flight increased the frequency of rice genome variation and that HMG were characterized by long gene length and low GC content. The most commonly mutated genes were related to DNA damage repair and other processes. There was no significant difference in the overall level of DNA methylation, and the degree of CHG methylation was closely related to the response to deep-space flight. Single-cell transcriptome analysis revealed that the proportion of mesophyll cells was significantly reduced, and 188 genes related to the plant MAPK signalling pathway, phenylpropanoid synthesis pathway and other pathways affected the differentiation fate of mesophyll cells. The m6A methylation level increased significantly, affecting protein phosphorylation, phenylpropanoid synthesis and the expression of other related genes. The transcription factor SVT1, which inhibits the differentiation of variant cells by negatively regulating the plant MAPK signalling pathway, was identified with this multiomics strategy, revealing a new mechanism by which plants adapt to the deep-space environment.\u003c/p\u003e\u003cp\u003eSpace flight can lead to mutations in the genome of organisms, in which DNA repair genes are particularly fragile. The frequency of mutations caused by space flight is typically between 10\u0026thinsp;\u0026minus;\u0026thinsp;8 and 10\u0026ndash;4\u003csup\u003e29\u0026minus;32\u003c/sup\u003e. In this study, the mutation frequency stimulated by deep-space flight in the rice genome ranged from 1.78\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;7 to 6.08\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;4, consistent with previous reports. However, there were significant differences among different samples, especially the TK15 sample, which has a high number of genomic variations, reaching 265,320. The space environment caused abnormal expression and function of DNA damage repair-related genes in TK15, leading to the inability to recognize and repair mutations, resulting in the accumulation of mutations in the TK15 sample (Supplementary Information). The research report of SJ-10 stated that spaceflight downregulates DNA repair genes and exacerbates the accumulation of genomic damage, which confirms the results of this study\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Additionally, some genomic variation characteristics in this study are consistent with previous findings, including the predominance of SNPs, a high proportion of heterozygous mutations, and low heterozygosity due to somatic mutation mosaicism\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Furthermore, while the mice from the International Space Station reported that space mutagenesis primarily involves CG\u0026rarr;TA base transitions, we found a significant increase in A\u0026rarr;G and T\u0026rarr;C transition types, indicating a difference in base transition trends between deep-space flight and low Earth orbit\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The assumption that these mutations are random is increasingly being questioned\u003csup\u003e\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Studies have shown that the mutagenic effects of space flight are often concentrated in hotspot regions of the genome\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Although we did not identify obvious mutation hotspot regions in our study, our characterization of the structural features of highly mutated genes suggested that genes related to DNA conformation and damage repair are more prone to mutations, exacerbating the tendency toward genomic instability and indicating a certain preference for genomic variations caused by deep-space flight. Therefore, this study has advanced our understanding by demonstrating that deep-space flight mutagenesis preferentially targets the key genes that maintain the stability of the genome itself, thus forming a vicious circle of damage accumulation.\u003c/p\u003e\u003cp\u003eMultiple studies have shown that while the space environment may influence DNA methylation patterns, most of the significantly altered CpG sites return to baseline levels upon returning to Earth. For example, The Arabidopsis seeds carried by SJ-10 reported relatively low genome-wide CG, CHG, and CHH methylation levels, with methylated genes involved in hormone signalling, protein phosphorylation, and cell wall modification pathways\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. The rice seeds flown in lunar orbit for 23 days presented reductions in average CG, CHG, and CHH methylation levels upon return to Earth and planting, although this change was not statistically significant. The differentially methylated genes were associated primarily with metabolic processes, stress responses, and transport processes\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In addition, the DNA methylation levels in human astronauts exhibited trend similar to those in plants. For example, based on the genome-wide methylation characteristics of six astronauts during long-term isolation in the Mars-500 project, the minimum changes in genome-wide DNA methylation were indicated; similar conclusions were presented in the NASA twin study\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The results of this study are consistent with these previous findings, with no significant differences in whole-genome methylation levels between deep-space flight samples and ground controls. The three types of methylation differed only slightly in their genomic distribution. The functions of differentially methylated genes, in addition to stress responses and metabolic processes, as previously mentioned, also include phenylpropane biosynthesis, plant MAPK signalling, and DNA damage repair. Furthermore, the functional relevance of CHG-type methylation is closely related to the findings of this study, potentially representing an important form through which deep-space flight impacts rice DNA methylation. By identifying CHG methylation as a key mediator of space-induced epigenetic changes in rice, this study advances our understanding of how organisms adapt to deep-space stressors and underscores the need for targeted investigations into context-specific methylation mechanisms to mitigate long-term biological risks in space exploration.\u003c/p\u003e\u003cp\u003eSpaceflight also leads to changes in RNA methylation, but few reports on this topic exist. Currently, only studies on m6A methylation have been conducted in astronauts. The first m6A methylation atlas of human astronauts showed that they experienced a significant increase in m6A methylation levels during their mission, impacting RNAs involved in pathways such as red blood cell regulation, stress induction, and immune changes\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Our study is the first to examine m6A methylation in rice after deep-space flight and similarly revealed that deep-space flight caused a significant increase in m6A methylation levels. However, unlike those in humans, the response genes affected in rice were mainly related to protein phosphorylation processes and phenylpropanoid synthesis. Humans have a more complex immune system than plants do, and plants lack an active immune system, relying primarily on enhanced phenylpropanoid metabolism to strengthen their cellular defences and antioxidant capabilities\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Additionally, astronauts' missions typically have longer durations, allowing cumulative systemic immune adaptation\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, whereas the deep-space flight period for rice in this study was only 23 days, potentially triggering only acute stress responses and concentrating m6A modifications in rapid-response pathways. Despite these differences, both studies demonstrated the significant regulatory role of m6A methylation in response to environmental stress.\u003c/p\u003e\u003cp\u003eExtraterrestrial environments reshape gene expression through multidimensional mechanisms, with effects characterized by species and tissue specificity, time dependence, and environmental interactivity. For example, space flight primarily suppresses the expression of genes related to mouse embryonic stem cells, inflammatory responses, and oxidative stress while significantly upregulating genes associated with the cell cycle and apoptosis. Similarly, DNA repair genes, such as BRCA1, are also upregulated in human astronauts\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Gene expression changes in plants under space flight conditions are a key focus of current space biology research, with reports on pathways involving cell wall remodelling, oxidative stress responses, metabolic adaptation, photosynthesis regulation, and DNA repair\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan additionalcitationids=\"CR49 CR50 CR51\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. In this study, the transcriptomic DEGs also involved metabolic network reconstruction, including the sustained activation of genes related to lipid, flavonoid, and various sugar metabolism pathways. Additionally, space flight significantly impacts the expression of genes associated with protein phosphorylation, phenylpropanoid synthesis, plant hormone signalling, and the plant MAPK signalling pathway. Future research should prioritize elucidating the functional consequences and adaptive significance of these specific pathway alterations, particularly the sustained metabolic shifts and MAPK signalling dynamics in plants.\u003c/p\u003e\u003cp\u003eThe application of single-cell sequencing analysis to samples exposed to space environmental conditions provides a new perspective on the effects of space environments on cellular molecular mechanisms. scRNA-seq can reveal cellular heterogeneity in populations under space flight conditions, such as the significant differences in gene expression observed in breast cancer cell populations\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Using scRNA-seq, previous researchers identified a \"space flight signature\" gene set including genes related to oxidative phosphorylation, UV response, immune function and the TCF21 pathway. This gene set was verified in independent datasets (such as those from the NASA twin study), indicating its conservation across species and experimental conditions\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In this study, for the first time, scRNA-seq was conducted on rice subjected to deep-space flight, revealing cellular heterogeneity, specifically a significant reduction in the proportion of mesophyll cells in postflight samples. By combining pseudotime analysis and differential expression analysis, a specific gene set (188 genes) responsible for this phenomenon was identified, including genes related to oxidative stress, phenylpropanoid synthesis, sugar metabolism, and plant MAPK signalling. This highlights the impact of deep-space flight environments on gene expression and cellular adaptability. This study not only deepens our understanding of how the space environment reshapes plant cell composition and functional pathways, but more importantly, the conservative stress response mechanisms and specific cell type sensitivities it discovers provide key molecular targets for predicting and intervening in the effects of space environment on more complex biological systems, including future astronauts.\u003c/p\u003e\u003cp\u003eThe genomic instability induced by space flight is not only a critical pathological mechanism linking exposure to the space environment to various health risks for astronauts but also a core adaptive mechanism that allows plants to cope with the extreme conditions in extraterrestrial environments. In-depth research on this phenomenon is vital for ensuring astronaut safety, developing space crops, and elucidating space biology. Genomic instability caused by space flight typically manifests as DNA damage and genomic variations. Organisms respond primarily by enhancing their DNA damage repair capabilities\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan additionalcitationids=\"CR55\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. In addition, human studies have reported cases in which P53, BRUCE, PUMA and other proteins influence genome stability by altering cell differentiation\u003csup\u003e\u003cspan additionalcitationids=\"CR58 CR59\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis study revealed a new mechanism by which rice adapts to deep-space flight, relying on the NAC family transcription factor SVT1 to inhibit the differentiation of cells carrying mutations. The NAC family of transcription factors is one of the largest plant-specific transcription factor families and plays a central role in response to abiotic stress\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. For example, SOG1 in plants assumes a role analogous to that of P53 in animals, responding to DNA damage, activating downstream genes, and coordinating processes such as cell cycle arrest, DNA repair, and programmed cell death\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Although SVT1 belongs to the same NAC family as SOG1, its mechanism of action differs. SVT1 does not directly participate in the DDR but instead negatively regulates the MAPK signalling pathway, inhibiting the differentiation of cells carrying genomic variations. This reduces the proportion of variant cells and maintains overall genome stability. This newly identified mechanism of \"differentiation inhibition\" mechanism complements other strategies for coping with genomic instability induced by the space environment, such as direct repair, metabolic support, and epigenetic regulation, forming a multi-level regulatory network. The discovery of SVT1 not only reveals the unique strategies by which plants can adapt to space but also provides a new perspective for understanding the trade-off mechanism of \u0026ldquo;repair differentiation survival\u0026rdquo; in stress biology.\u003c/p\u003e\u003cp\u003eThis study has several limitations, we provide an initial exploration of SVT1 function, relying primarily on phenotypic analysis of knockout mutants and EMSA experiments, with insufficient support from overexpression mutant phenotypes and other protein‒DNA interaction experiments. While the plant MAPK signalling pathway has been proven to play a significant regulatory role in cell differentiation\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e, the molecular details of how SVT1 regulates the MAPK pathway to influence cell differentiation (such as protein phosphorylation and complex formation) remain unclear. Additionally, genomic variations exhibit substantial heterogeneity among cells, and whole-genome sequencing of tissue samples cannot provide comprehensive information about the full spectrum of variation in each individual cell, necessitating single-cell level analysis of the effects of SVT1 on cells carrying mutations. Future work will involve a deeper exploration of SVT1 function and regulatory mechanisms, including the construction of SVT1 overexpression lines, single-cell level detection of genomic variations, and cell culture experiments for the assessment of single-cell differentiation rates and mutation transmission efficiency, among other phenotypes, as well as gene expression-related experiments to validate SVT1 function. Through various protein‒DNA interaction experiments (such as yeast one-hybrid and dual-luciferase reporter assays), the binding patterns of SVT1 with MAPK pathway genes can be elucidated, and through immunoprecipitation experiments, upstream regulatory proteins of SVT1 can be screened and combined with protein phosphorylation analysis to clarify the interaction between MAPK proteins and SVT1.\u003c/p\u003e\u003cp\u003eIn summary, this study used rice seeds carried by Chang'e-5 spacecraft and combined multiple omics techniques to reveal the mechanisms by which deep-space environment affects plant genome variation, gene expression, epigenetic modifications, and cell differentiation. It identified a new mechanism by which transcription factor SVT1 regulates mutant cell differentiation by inhibiting the MAPK pathway. This study not only reveals the molecular strategies of plants to cope with deep-space environments, but also provides interdisciplinary insights for space breeding, extreme environmental biology, and human space exploration.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ePlant materials\u003c/h2\u003e\u003cp\u003eThe main materials includethe pure indica rice line HJXSM, which was developed by the National Engineering Research Center of Plant Space Breeding of South China Agricultural University (SCAU) through hybridization of the high-quality disease-resistant indica rice variety Huahang 31 and the restorer line Hanghui 1508 and pedigree selection. This line integrates multiple disease resistance and fragrance genes and exhibits stable agronomic traits.\u003c/p\u003e\u003cp\u003eWe also obtained \u003cem\u003eSVT1\u003c/em\u003e knockout transgenic lines with ZH11 background using CRSPR/Cas9 technology. All materials were grown under standardized conditions at the teaching and research base of SCAU in Guangzhou, Guangdong Province.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eChang'e-5 carriage of HJXSM seeds and sequencing material sampling\u003c/h2\u003e\u003cp\u003eOn November 24, 2020, approximately 32.8 g of dry HJXSM seeds derived from a single plant were carried aboard Chang'e-5. The total flight duration was approximately 23 days, during which the seeds were exposed to a radiation dose of 59.85 mGy. The seeds were sown at the teaching and research base of SCAU on February 26, 2021, with seeds from the same plant that had not been flown on Chang\u0026rsquo;e-5 serving as controls. On April 29, 2021, 30 plants (numbered TK1-TK30) grown from seeds exposed to deep-space were randomly selected (Supplementary Fig.\u0026nbsp;1), along with 10 plants (numbered CK1-CK10) from unflown seeds as controls. A sample of young tillers was collected from each plant for subsequent analyses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eDNA and RNA extraction and quality inspection\u003c/h2\u003e\u003cp\u003eTake 500 mg of each sample, DNA was extracted using the cetyltrimethylammonium bromide (CTAB) method, and total RNA was extracted using the Omega Plant RNA Kit (Omega Bio-Tek, R6827). The quality of the DNA and RNA was evaluated using a Qubit instrument (Thermo Fisher Scientific, USA) and a NanoDrop instrument (Thermo Fisher Scientific, USA), and the integrity of the RNA was determined using an Agilent2100 instrument (Agilent Technologies, Germany). The samples that passed quality control were stored at -80\u0026deg;C.\u003c/p\u003e\u003cp\u003e\u003cb\u003eReference genome assembly\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study used both PacBio Sequel (Pacific Biosciences, USA) and Illumina (Illumina, USA) technologies, with Fastp (v0.20.0) used for quality control of Illumina sequencing data. The PacBio reads were spliced and assembled using MECAT (v1.0), and the Illumina reads were aligned to the assembled genome using Pilon (v1.23). The Illumina reads were realigned to the completed and error-corrected reference genome, and metrics such as the mapping rate, genome coverage, and depth distribution were calculated to evaluate the completeness of the genome assembly. Based on the three-dimensional spatial structure characteristics of chromatin and the interaction between sequences, scaffolds are constructed. The scaffolds are anchored to chromosomes through clustering models to determine the correct order and direction for mounting scaffolds. Hi-C data assembly is performed using LACHSIS (v2014-09-12.12), ALLHIC (v0.9.8), and 3D-DNA (v180114).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eParametric full-length transcriptome analysis and reference genome annotation\u003c/h2\u003e\u003cp\u003ePacBio data were aligned to the reference genome using SMRTLink (v8.0.0) to obtain known and novel transcripts, which were combined with Illumina data for gene functional annotation, gene structure analysis, and gene expression analysis. The coding genes were predicted using Augustus (v3.2.1) and GeneMark (v4.72), and Maker (v2.29) to align known homologous coding protein sequences from other species with the new genome sequences. The results of sequence alignment were integrated using HISAT2 (v2.1.0), and sequence assembly was performed using StringTie (v1.3.1) to obtain a transcriptome-based gene set. Maker was used to integrate the data from transcriptomic analysis, homology analysis, and de novo analysis in proportion to obtain the final gene set, followed by BUSCO (v4) evaluation of the predicted coding genes. The predicted gene protein sequences were aligned with the database for functional annotation, and a threshold of \u0026lt;\u0026thinsp;1e-5 was used for filtering.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eWGS\u003c/h2\u003e\u003cp\u003eDNA samples that passed quality control were sequenced on an Illumina platform. The filtered reads were aligned to the reference genome using BWA (v0.7.15), and variant detection was performed using GATK (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://software.broadinstitute.org/gatk/best-practices\u003c/span\u003e\u003cspan address=\"https://software.broadinstitute.org/gatk/best-practices\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Raw variant data were obtained after functional annotation using ANNOVAR (v2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eRNA-seq\u003c/h2\u003e\u003cp\u003eAfter obtaining high-quality total RNA, the library was constructed and sequenced. HISAT2 was used for reference genome-based alignment analysis, including type statistics, gene coverage, sequencing randomness, and sequencing saturation analysis. StringTie was used to reconstruct the transcripts, and RSEM (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://deweylab.Giyhub.Io/RESM/\u003c/span\u003e\u003cspan address=\"http://deweylab.Giyhub.Io/RESM/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to calculate the expression levels of all genes in each sample, displayed as fragments per kilobase of exon model per million mapped fragments (FPKM). Using FPKM as the screening criterion, genes with |log2FC|\u0026ge;1 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were output as DEGs. The target genes were mapped to terms in the Gene Ontology (GO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.geneontology.org/\u003c/span\u003e\u003cspan address=\"http://www.geneontology.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and significantly enriched GO terms were identified. The target gene set was combined with the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kegg.jp/\u003c/span\u003e\u003cspan address=\"https://www.kegg.jp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for enrichment analysis to screen for significantly enriched pathways.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eWGBS\u003c/h2\u003e\u003cp\u003ePreparation of DNA library for bisulfite sequencing using DNA that has passed quality inspection. The specific steps are as follows: Genomic DNAs were fragmented into 100\u0026ndash;300 bp by Sonication(Covaris, USA) and purified with MiniElute PCR Purification Kit(QIAGEN, USA). The fragmented DNAs were end repaired and a single \u0026ldquo;A\u0026rdquo; nucleotide was added to the 3\u0026rsquo; end of the blunt fragments. Then the genomic fragments were ligated to methylated sequencing adapters. Fragments with adapters were bisulfite converted using Methylation-Gold kit(ZYMO, USA), unmethylated cytosine is converted to uracil during sodium bisulfite treatment. Finally, the transformed DNA fragments were amplified by PCR and subjected to Illumina sequencing. The sequencing data were converted into raw sequence data through base calling. Filtered data were aligned to the genome using BSMAP[39], generating genome-wide base alignment information and genome-wide C base methylation information. MethylKit[40] was used to identify DMCs. The average DNA methylation rate within each 200 bp window of the genome was calculated, and the methylation levels of each window across samples were compared to identify DMRs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eMeRIP-seq\u003c/h2\u003e\u003cp\u003eQualified RNA samples are used for library construction. The specific steps are as follows: use a reagent kit to break them into fragments of approximately 100 nt in length. Divide the RNA into two parts, one of which is used as an input control, and directly construct a transcriptome sequencing library to eliminate the background during the process of capturing methylated fragments. The other part of the RNA is enriched with m6A specific antibodies. After capturing m6A modified RNA, antibody elution was performed using magnetic beads to reduce background noise from non-specific binding. Construct strand specific libraries for two RNA samples separately. After the construction of the library is completed, perform quality testing on the library. Qualified libraries will undergo Illumina sequencing. Using exomePeak2 (v1.1.0), genome-wide peak scanning was performed with a p value\u0026thinsp;\u0026lt;\u0026thinsp;1e-5 as the threshold, and peaks from replicate samples were filtered and merged. The genomic locations and peak sequences were analysed to identify peak-associated genes. MEMESuite (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://meme-suite.org/\u003c/span\u003e\u003cspan address=\"http://meme-suite.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for TF motif analysis to identify significant motif sequences within the peaks. The relative methylation level of each peak was calculated, and exomePeak2 was then used for differential methylation analysis of RNA across all peaks in the comparison groups. Peaks with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2FC|\u0026gt;1 were selected as differential peaks.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003ePreparation of protoplast suspension\u003c/h2\u003e\u003cp\u003eCut the young stem tissues of 40 samples into thin slices using a blade and place them in 50 ml centrifuge tubes. Wash the tissue slices twice with 0.8 M mannitol and then perform enzymatic hydrolysis. Add 15 ml of enzymatic solution to each centrifuge tube, including Cellulose RS (1.5%), Pectinase Y23 (0.03%), mannitol (0.5 M), KCl(0.5 mM), MgCl (0.5 mM), MES (0.5 mM, pH 5.7), CaCl (10 mM), and BSA (0.1%). Subsequently, the centrifuge tube was placed in a shaker and incubated in the dark. The shaker was set at 30 ℃ and 75 rpm for 5 hours. During the incubation period, the protoplast status (including morphology, quantity, size, number of fragments, and cell viability) was observed under a microscope. After enzymatic hydrolysis, briefly shake the centrifuge tube to completely release the protoplasts. Filter the enzymatic hydrolysate using a 40 \u0026micro;m cell sieve into a new 15 ml centrifuge tube, add 2 ml of 0.8 M mannitol and shake well, centrifuge at 150\u0026times;g for 5 min, repeat cleaning 3 times, and then use again. After discarding the supernatant, add 10 ml of 0.8 M mannitol and resuspend. Mix 5 \u0026micro;l of protoplast suspension with 5 \u0026micro;l of 0.4% trypan blue staining solution, and detect cell concentration and activity using a cell counting plate. Use 0.8 M mannitol to uniformly adjust the concentration of protoplast suspension for 40 samples to 1000 cells/\u0026micro;l. Then, take 1 ml of suspension from each of the 30 TK samples and mix them in equal amounts. Repeat this process three times and renumber them as scTK1-scTK3. The CK samples were mixed in the same way and renumbered as scCK1-scCK3. 6 mixed samples of single-cell suspension were placed on ice for single-cell library construction.\u003c/p\u003e\u003cp\u003eTake 50 seeds each from WT and svt1, and irradiate them with 300 Gy of gamma rays at a dose rate of 1 Gy/min for 5 h. After irradiation, disinfect the seeds with sodium hypochlorite, then peel off the seed shells, germinate at 30 ℃ in the dark, and cut off the seed embryo tissue after 3 days. Prepare protoplast suspension using the same method as above.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003escRNA-seq\u003c/h2\u003e\u003cp\u003eCombine gel beads containing barcode information with the mixture of cells and enzymes, and then wrap them with oil surfactant droplets in the microfluidic \u0026ldquo;double cross\u0026rdquo; system to form Gel Beads In Emotions (GEMs). GEMs flow into the reservoir and are collected. The gel beads dissolve and release Barcode sequences, reverse transcribe cDNA fragments, and label the samples. The gel beads were broken and the oil drops were broken, and the cDNA was used as the template for PCR amplification. Mix all GEMs products and construct a standard sequencing library. Perform high-throughput sequencing on the constructed library using Illumina NovaSeq X Plus dual end sequencing mode.The reads were aligned to the reference genome using CellRanger (v2.0.0) and annotated to specific genes. After UMIs were corrected and counted, an unfiltered feature-barcode matrix was obtained. The cells and noncells in the data were identified and distinguished. Gene quantification was performed using UMI counts to obtain cell-gene expression profiles. The expression matrix was imported into Seurat (v4.2.0), and multicell filtering was conducted using DoubletFinder (v3). Data integration and batch effect correction were performed using Harmony (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/immunogenomics/harmony\u003c/span\u003e\u003cspan address=\"https://github.com/immunogenomics/harmony\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The cell subcluster classification results were subjected to uniform manifold approximation and projection (UMAP) for nonlinear clustering to visualize the different single-cell subclusters.\u003c/p\u003e\u003cp\u003ePAGA first inputs the expression matrix into Scanpy (v1.6.0), then performs dimensionality reduction transformation and maps it to a low dimensional scatter plot. The cells are then divided into several subgroups and represented in the form of nodes based on topological structure, with the strength of connectivity represented by the thickness of lines. For pseudotime analysis, the gene expression matrix was input into Monocle2 (v2.26.0), and cells were arranged into a differentiation trajectory containing branches and nodes. The cell cluster with the most primitive differentiation state was defined as the cell cluster with the lowest pseudotime value in the trajectory, and pseudotime values for all cells were calculated. Negative binomial generalized linear models were fitted for the two branches, and differential genes dependent on different branches were identified and tested by comparing the two models. The screening criterion was set as an FDR\u0026thinsp;\u0026lt;\u0026thinsp;1e-7.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eSingle-cell variant site mapping\u003c/h2\u003e\u003cp\u003eVartrix (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/10\u0026times;Genomics/vartrix\u003c/span\u003e\u003cspan address=\"https://github.com/10\u0026times;Genomics/vartrix\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with default parameters was used with VCF variant files, BAM files, genomic variant data, and single-cell matrix files as input and genomic variant site information, the row number of barcodes in barcodes.tsv, and the corresponding barcode (cell) variant detection results as output. A UMAP plot was generated for visualization of the aforementioned results.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eeQTL and meQTL\u003c/h2\u003e\u003cp\u003eGenomic mutation sites were jointly analysed with transcriptome gene expression levels for expression quantitative trait loci (eQTL) analysis, and genomic mutation sites are jointly analysed with DMCs for methylation quantitative trait loci (meQTL) analysis. The qqnorm() function in R software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for normalization, and the normalized values were used as the final values. The peer (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/PMBio/peer/wiki/Tutorial\u003c/span\u003e\u003cspan address=\"https://github.com/PMBio/peer/wiki/Tutorial\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) tool was employed to check for hidden effects and compute the matrix. The GLM from MatrixEQTL (v2.3) was utilized for the eQTL and meQTL analyses, with a significance threshold of 0.01; pairs with p values below this threshold were considered significant. In the genome-wide analysis of the annotated genes, the majority of the eQTL and meQTL distances were within 10,000 Kb, with this distance used to distinguish between cis- and trans-association signals.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eDAP-seq\u003c/h2\u003e\u003cp\u003eMix the DNA of 10 CK samples after quality inspection in equal amounts, then crush them into short fragments using ultrasonic technology. After end repair and 3\u0026rsquo; end addition, connect them to Illumina sequencing adapters and select 100\u0026ndash;300 bp DNA fragments for PCR amplification. Finally, a qualified library for sequencing was obtained. The coding sequence (CDS) of SVT1 was subsequently cloned and inserted into a plasmid containing the Halo tag, followed by in vitro expression. The purified SVT1 protein was incubated with the Hangjuxiangsimiao DNA library, and the DNA fragments bound to SVT1 were extracted. Sequencing was performed using the Illumina platform. Reads were statistically analysed using deepTools (v3.2.0), with a window size of 50 bp, and the average read depth within each window was calculated. MACS2 (v2.1.2) was used for peak calling across the genome with a threshold of a q value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and the genomic positions and sequences of the peaks were analysed to identify peak-associated genes. All peaks within the group were then merged, and unified peak data were output. The ChIPseeker (v1.16.1) R package was used to annotate the peak-associated genes.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eElectrophoretic mobility shift assay (EMSA)\u003c/h2\u003e\u003cp\u003eThe SVT1 was cloned and inserted into the pET-28a-SUMO prokaryotic expression vector. Positive plasmids were transformed into E. coli DE3 competent cells. Positive strains were identified by PCR. Bacterial lysates were sonicated, and the supernatant was collected as the protein sample. After SDS-PAGE, the protein was purified using magnetic beads to obtain the purified SVT1 protein. Three DNA binding probes targeting the promoter regions of the target genes were synthesized (Supplementary Table\u0026nbsp;1), along with mutant probes. After biotin labelling and annealing to form double-stranded DNA, unlabelled binding probes were used as competitive probes. The SUMO protein was incubated with binding probes as a negative control. The SVT1 protein was separately incubated with biotin-labelled binding probes and mutant probes, and differences in migration rates were evaluated for the competition experiments.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eStatistical analyses\u003c/h2\u003e\u003cp\u003eIn statistical analysis, data is presented in the form of mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation with error bars attached. We used a two tailed t-test to determine significant differences between two groups. When analyzing datasets containing three or more experimental groups, we performed one-way ANOVA using IBM SPSS software (v21.0) and combined it with Duncan's multiple range test. When the p-value is less than 0.05, it is considered statistically significant (* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eData supporting the findings of this work are available within this paper and its Supplementary Information files. The de novo sequencing (CRA027830), WGS (CRA027612), RNA-seq (CRA027698), WGBS (CRA027613), MeRIP-seq (CRA027615), scRNA-seq (CRA027871), DAP-seq (CRA027869), scRNA-seq (CRA027764) and WGS (CRA027868) of mutants have been deposited in the Genome Sequence Archive (GSA) at the National Genomics Data Center. Source data are provided with this paper.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key Research and Development Program of China (2022YFD1200703 to T.G.), the Key-Area Research and Development Program of Guangdong Province (2022B0202060006 to H.W.). The work of carrying rice seeds on Chang\u0026apos;e-5 was supported by the third phase project of the lunar exploration of National Space Administration Lunar Exploration and Aerospace Engineering Center.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.G. and K.S. designed the experiment. K.S., J.Z., H.L., W.S., C.Z., Z.H., and J.Z. conducted experimental operation, data collection and analysis. T.G., Z.C., K.S., Q.Z., L.M., J.W., W.X., G.Y., M.H., C.H., D.Z., R.S., C.C., M.Z., P.W., Y.L., J.Z., and H.W. jointly wrote the paper. All the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCho, Y. et al. Cellular and physiological functions of SGR family in gravitropic response in higher plants. \u003cem\u003eJ. Adv. Res.\u003c/em\u003e\u003cstrong\u003e67\u003c/strong\u003e, 43-60 (2025).\u003c/li\u003e\n\u003cli\u003eBarbero, B. B. et al. Arabidopsis telomerase takes off by uncoupling enzyme activity from telomere length maintenance in space. \u003cem\u003eNat. Commun.\u003c/em\u003e\u003cstrong\u003e14\u003c/strong\u003e, 7854 (2023).\u003c/li\u003e\n\u003cli\u003ePrasad, B. et al. 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MAP kinase cascades in plant development and immune signaling. \u003cem\u003eEMBO. Rep.\u003c/em\u003e\u003cstrong\u003e23\u003c/strong\u003e, e53817 (2022).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7211908/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7211908/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe deep-space environment exerts severe stress on plant genome stability, gene expression, epigenetic modification, and cell differentiation. In this study, through multiomics analysis, changes were observed in rice at molecular and cellular levels after deep-space flight, including an increase in genomic variation frequency and mutations with preferences. While overall DNA methylation levels showed no significant changes, the increase in CHG methylation level was correlated with DNA methylation responses. RNA presented significantly elevated m6A modification levels, which positively regulated gene expression. The proportion of mesophyll cells decreased, and 188 genes were identified as affecting the differentiation of mesophyll cells. Integrated multiomics analysis revealed that the NAC family transcription factor SVT1 negatively regulated MAPK pathway genes to suppress differentiation in cells carrying mutations. Overall, this study comprehensively described the molecular map of rice after deep-space flight, and proposed a new mechanism for SVT1 to adapt to deep-space flight by inhibiting the differentiation of mutant cells.\u003c/p\u003e","manuscriptTitle":"Multiomics analysis of the molecular and single-cell responses of rice after deep-space flight on Chang'e-5","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-22 06:37:57","doi":"10.21203/rs.3.rs-7211908/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b7f91c27-25e5-4570-9bcb-3e5cce319ba7","owner":[],"postedDate":"August 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":53498432,"name":"Biological sciences/Plant sciences/Plant stress responses/Abiotic"},{"id":53498433,"name":"Biological sciences/Plant sciences/Plant molecular biology"}],"tags":[],"updatedAt":"2026-05-11T07:46:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-22 06:37:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7211908","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7211908","identity":"rs-7211908","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}