Multi-omic analysis on the molecular mechanisms of rapid growth in ‘Deqin’ alfalfa after space mutagenesis
Alfalfa ( Medicago sativa L.), known as the “king of forage,” is an excellent forage crop due to its high protein content and balanced nutrition profile . Biomass production is a primary target in breeding new alfalfa varieties, as growth rate directly affects yield and quality . Rapid growth enables alfalfa to accumulate biomass more efficiently, enhancing overall yield and contributing positively to the animal feed supply chain, thereby boosting its economic returns. Furthermore, accelerated growth shortens the growing period, lowering production costs, and maximizing resource use efficiency. Traditionally, the development of new varieties in alfalfa relies on conventional hybridization and selective breeding, now complemented by modern biotechnologies, such as gene editing and transgenic approaches . However, genetic enhancement in self-incompatible autotetraploid alfalfas remains a significant challenge . Space mutagenesis, which involves exposing plants to the special conditions of outer space, such as intense radiation, high vacuum, and microgravity, has emerged as a novel approach for developing new germplasms and varieties . This approach has shown particular promise for plant breeding in China, where approximately 200 new varieties have been developed through space radiation, significantly advancing agricultural innovation . As an autotetraploid, alfalfa faces several breeding challenges, including the aggregation of favorable traits, stability issues, and long breeding cycles (often exceeding 10 years) . Space mutagenesis offers a promising solution by introducing genetic diversity, shortening breeding cycles, and enhancing breeding efficiency. However, its limitations include a low mutation rate, difficulties in directing mutations, and high associated costs . In our previous study, we applied space mutagenesis to ‘Deqin’ alfalfa seeds and observed significant improvements in key agronomic traits, such as high growth rate and biomass accumulation . Notably, the #28 mutant material demonstrated significantly higher growth rates and fresh grass yield compared to other mutagenized materials and the control across both years of cultivation . These morphological changes are likely linked to alterations in physiological processes, potentially involving shifts in phytohormones and energy-related compounds. However, the genetic mechanisms underlying these changes remain poorly understood. To address this, we combined transcriptomics, proteomics, and targeted hormonal analyses to elucidate the processes driving rapid growth in alfalfa, providing valuable insights into the genetic improvement of alfalfa varieties and offering a reference framework for developing new varieties in other crops. 

Appearance characteristic of experimental materials In a previous study, we observed that the mutant material #28 exhibited rapid growth following space mutagenesis (Fig. A-C). Measurements taken 15 days after the first cutting indicated a significant increase in leaf area, fresh grass yield, internode length, and plant height in the mutant, with values showing increases by 231.72%, 129.74%, 37.86%, and 35.21%, respectively, relative to those of the non-mutagenic materials (Fig. D). Content changes in plant hormone components To comprehensively assess hormone levels in mutants following space mutagenesis, we measured hormone concentrations in mutant and non-mutagenic materials. The constructed model demonstrated high goodness of fit (R 2 Y) and predictive ability (Q 2 ), effectively distinguishing between YB and CK groups. Volcano plot analysis revealed significant alterations in 20 hormone components across the leaf, stem, and root tissues. Further analysis revealed that YBL showed significantly higher contents for 14 hormones than did CKL, among which the contents of abscisic acid (ABA), jasmonic acid, 3-indolepropionic acid, and GA3 were 66.9510, 19.4146, 5.0932, and 2.5191 ng/g, respectively, whereas those of CKL were only 22.2605, 9.7879, 3.0009, and 2.2286 ng/g, respectively (Fig. A). In stems, nine hormones were significantly higher in YBS than in CKS, with dihydrojasmonic acid, ABA, GA3, and indole-3-acetic acid (IAA) contributing 31.63% of the total stem hormones (Fig. B). In roots, eight hormones showed significantly elevated levels in YBR compared to control roots (CKR), with significant increases in GA3, ABA, dihydrozeatin riboside, and 3-indolepropionic acid (Fig. C). KEGG analysis of targeted plant hormone metabolomics KEGG enrichment analysis revealed significant involvement of six pathways, including plant hormone signal transduction, alpha-linolenic acid metabolism, zeatin biosynthesis, tryptophan (Trp) metabolism, metabolic pathways, and the biosynthesis of secondary metabolites (Additional file ). Notably, salicylic acid, IAA, jasmonic acid, and dihydrozeatin in the plant hormone signal transduction pathway were significantly enriched in the leaf, stem and root tissues, indicating a pivotal role of this pathway in promoting the rapid growth of the mutant. Transcriptome sequencing and differentially expressed genes (DEGs) identification To investigate gene expression profiles in fast-growing mutants, RNA-seq was conducted on the Illumina platform, generating 18 cDNA libraries (nine each for CK and YB groups). Following sequencing and data filtering, 149.94 Gb clean bases were obtained. For all samples, Q20 values from 97.45 to 98.79%, and Q30 values from 92.89 to 96.23%, suggesting that the sequencing quality was sufficiently high for downstream analyses. Comparison of gene expression profiles between the YB and CK groups identified 2,009, 1,889, and 1,760 upregulated DEGs and 2,082, 2,035, and 1,499 downregulated DEGs in the leaves, stems, and roots, respectively (Fig. ). To further explore the effects of space mutagenesis on hormone biosynthesis in the mutants, hormone-related biosynthetic pathways were analyzed. The analysis resulted in the identification of eight genes in the Trp metabolism pathway with significant expression alterations (Additional file ), including five upregulated and three downregulated DEGs. In the diterpenoid biosynthesis pathway, nine genes encoding proteases showed significant alterations (Additional file ), including one upregulated and eight downregulated genes. For zeatin synthesis, five genes showed alterations (Additional file ), including four upregulated DEGs and one downregulated DEG. The expression levels of nine DEGs were assessed using quantitative real-time polymerase chain reaction (qRT-PCR), and the findings demonstrated a concordance between the qRT-PCR results and transcriptome data (Fig. ). Construction of gene co-expression networks To further explore the regulatory mechanisms underlying mutagenesis and other potential regulatory genes. According to the ranking, we selected the top 10,000 expressed genes and hormone content data for weighted gene co-expression network analysis (WGCNA) analysis. The results yielded 13 modules, including a gray module, containing all genes not grouped into other modules. Module sizes ranged from 41 to 2,964 unigenes (Fig. A). Among the 13 gene co-expression networks, the characteristic genes in the MEpink, MEred, MEturquoise, and MEblue modules showed strong associations with rapid growth, with correlation coefficients (r 2 ) ranging between 0.84 and 0.90 ( P < 0.05; Fig. B). Incorporating functional annotation information, we found that MEpink, MEred, MEturquoise, and MEblue modules contained 29, 51, 63, and 43 genes, respectively. Key genes in the MEred module were RH15, RPL26B, and MEFG2 (Fig. A). The genes identified in the MEturquoise module were RPL5B and RPL38A (Fig. B). Furthermore, PGI1 and NDPK3 (Fig. C) and PSAL, LHCA1, and PNSL3 (Fig. D) were identified as candidate genes in the MEpink and MEblue modules. Further analysis revealed that these genes play a key role in photosynthesis, as well as starch and sucrose metabolism. DEG and differentially expressed metabolite (DEM) analyses between the YB and CK groups Hormones play crucial roles in various plant physiological processes. KEGG enrichment analysis of hormonomics revealed that plant hormone signal transduction might be a primary contributor to the rapid growth of mutants. This finding was further verified through investigations at the hormonal and molecular levels. In the YBL_vs_CKL group, the abundance of auxin-related metabolites, specifically 3-indolecarboxylic, 3-indolepropionic, and 3-indolebutyric acids were significantly elevated. At the transcriptional level, we found 20 DEGs, including 5, 1, and 14 linked to Aux IAA-induced protein (Aux/IAA), auxin-responsive glycoside hydrolase 3, and small-auxin upregulated RNA (SAUR) family proteins, respectively (Fig. A). In the auxin signal transduction pathway of YBS_vs_CKS group, Aux/IAA and SAUR-related genes were also highly expressed. In the GA signal transduction pathway, GA3, GA1, and GA7 contents were increased significantly, while genes related to DELLA proteins were upregulated (Fig. B). Similar results were found for the YBR_vs_CKR group. In the cytokinin signal transduction pathway of YBS_vs_CKS group, the contents of zeatin-riboside and dihydrozeatin riboside were increased significantly, and three genes were highly expressed, namely GRE1, AHP, and A-ARR (Fig. C). To validate the reliability of the data analysis, we sprayed the non-mutagenic materials with 3-indolebutyric acid, zeatin-riboside, and GA7 . The results showed that among the three hormones, GA7 impacted plant height the most. Hence, we sprayed the CK group with 30 ppm of GA7 and the YB group with water. After 7 days, the plant height of the CK group was increased by an average of 9.84 cm, while that of the YB group was increased by an average of 7.86 cm, with no significant differences in plant height (Fig. ). In addition, a similar phenomenon of significant growth was observed after spraying with 3 mg/L zeatin-riboside and 3-indolebutyric acid. Gene ontology (GO) and KEGG analyses of differentially accumulated proteins (DAPs) Using transcriptomics to reflect biological function is associated with limitations, including the inability to fully elucidate protein function and state. To examine the functional and regulatory mechanisms of proteins in the mutants, quantitative proteomics was performed to elucidate their DAPs. Cluster analysis of protein abundance profiles revealed distinct differences between the CK and YB groups (Fig. ). We identified and quantified 8,752 proteins from the samples and selected 479 DAPs using statistical methods. Among them 174 proteins were upregulated, while 305 exhibited downregulation (Fig. A). The 479 DAPs were classified into three main GO domains: biological processes, molecular functions, and cellular components. The top three enriched terms for cellular components were cell wall, symplast, and plasmodesma. Among the molecular functions, the three most abundant terms were hydrolase activity, ATP hydrolysis activity, and catalytic activity. For biological processes, catabolic process, cellular component biogenesis, and cellular stress response were the top three enriched terms (Fig. B). KEGG enrichment analysis showed that Trp metabolism, glutathione metabolism, and plant-pathogen interaction were the top three enriched metabolic pathways (Additional file ). Integrated proteomics and transcriptomics to determine biological processes contributing to rapid growth in mutants We explored gene expression and translation correlation in mutants at the protein and mRNA levels. In leaves, 6,856 genes showed transcriptome/proteome co-expression, and 6,708 showed protein/transcription co-expression. In stems, 7,918 and 7,878 genes showed transcriptome/proteome and proteome/transcriptome co-expressions, respectively. In the roots, 8,044 (transcriptome/proteome) and 7881 (proteome/transcriptome) genes were co-expressed (Fig. A, S5A). The patterns of the DEGs and DAPs were consistent in the third and seventh quadrants, and gene and protein regulations had a positive correlation. There were also cases where DEG and DAP expression patterns were inconsistent, including opposite expression (Quadrants 1 and 9) or unique differential expression (Quadrants 2, 4, 6, and 8) (Fig. B). This is owing to the spatiotemporal inconsistencies in gene transcription and expression, as well as the post-transcriptional modification of RNA. KEGG enrichment analysis of DEGs/DAPs in the third and seventh quadrants highlighted photosynthesis as a significantly enriched pathway in YBL_vs_CKL group (Fig. C). Further analysis identified that 22 genes/proteins involved in various stages of photosynthesis, with high expression of five genes encoding Photosystem II (PS II), Photosystem I (PS I), and chloroplast-related enzymes (Fig. D), consistent with the key candidate genes identified through WGCNA. Certain KEGG terms were selectively enriched in specific quadrants. In the nine-quadrant diagram, the YBR_vs_CKR group genes showed upregulation at both protein and mRNA levels (Fig. B), suggesting that elevated protein content in the roots was at least partially owing to increased transcription and/or translation/synthesis. Among them, energy supply, signal transduction, immune regulation, and metabolic regulation pathways were significantly enriched in the top 20 pathways of the YBR_vs_CKR group (Fig. B). Notably, the 11 genes in carbon metabolism were upregulated (Fig. D), which encode malate dehydrogenase, glyceraldehyde-3-phosphate dehydrogenase, fructose-bisphosphate aldolase, and triosephosphate isomerase. For the YBS_vs_CKS group, an analysis of the top 20 significantly enriched pathways revealed that genes were predominantly enriched in pathways related to energy supply, protein synthesis, signal transduction, immune regulation, and metabolic regulation (Fig. E). We found 13 intersecting genes in the mitogen-activated protein kinase (MAPK) signaling pathway in plants, 3 of which were upregulated (Fig. D), and 10 were downregulated. The upregulated genes encode MAPK, nucleoside diphosphate kinase, and ABA receptor PYL4 ABI1-binding protein. We also found that four pathways were co-enriched in leaves, stems, and roots, with P-values < 0.05, namely, starch and sucrose metabolism, glutathione metabolism, carbon metabolism, and carbon fixation in photosynthetic organisms (Fig. F). Integrated analysis of transcriptomics, targeted plant hormone metabolomics, and proteomics The integration of transcriptomics, proteomics, and metabolomics data revealed that four pathways were associated with DAPs, DEGs, and DEMs, with significant enrichment observed in hormone signal transduction and Trp metabolism across roots, stems, and leaves. Additionally, α-linolenic acid metabolism and zeatin biosynthesis were significantly enriched in stems, while zeatin biosynthesis was significantly enriched in leaves and α-linolenic acid metabolism in roots (Additional file ). In the hormone signal transduction pathways of the leaves, six proteins were significantly enriched, with three being upregulated and three downregulated (Additional file ). Among the upregulated proteins, histidine kinase, a cytokinin receptor histidine kinase that transmits signals via phosphorylation after receiving cytokinin signals, could regulate plant growth and development. Four proteins were significantly enriched in the hormone signal transduction pathways in stems (Additional file ), among which the genes encoding histidine kinase and ABA receptor PYL4 were upregulated. The latter, as a member of the PYR1-like protein family, senses and transmits ABA signals to downstream regulatory factors during plant growth, participating in physiological processes, such as stomatal opening and closure and stress response. Five proteins were enriched in the hormone signal transduction of the roots (Additional file ), among which two were upregulated, which included histidine kinase and pathogenesis-related protein. The latter primarily enhances disease resistance, participates in stress response, promotes cell wall thickening, and exerts antibacterial activity. A correlation analysis between transcription and protein levels for genes involved in hormone signal transduction revealed a stronger correlation in roots and stems compared to leaves (Fig. ). Eight proteins were enriched in the Trp metabolism pathway of the roots, stems, and leaves, which were downregulated (Additional file ). These involved genes encoding acetyl-CoA acetyltransferase, cytosolic Gln amidotransferase subunit A. During the rapid-growth stage, the mutants may have preferentially allocated energy to metabolic pathways that directly support growth and reproduction, whereas the genes in the Trp metabolism pathway may have been downregulated to meet this need. 

In a previous study, we observed that the mutant material #28 exhibited rapid growth following space mutagenesis (Fig. A-C). Measurements taken 15 days after the first cutting indicated a significant increase in leaf area, fresh grass yield, internode length, and plant height in the mutant, with values showing increases by 231.72%, 129.74%, 37.86%, and 35.21%, respectively, relative to those of the non-mutagenic materials (Fig. D). 

To comprehensively assess hormone levels in mutants following space mutagenesis, we measured hormone concentrations in mutant and non-mutagenic materials. The constructed model demonstrated high goodness of fit (R 2 Y) and predictive ability (Q 2 ), effectively distinguishing between YB and CK groups. Volcano plot analysis revealed significant alterations in 20 hormone components across the leaf, stem, and root tissues. Further analysis revealed that YBL showed significantly higher contents for 14 hormones than did CKL, among which the contents of abscisic acid (ABA), jasmonic acid, 3-indolepropionic acid, and GA3 were 66.9510, 19.4146, 5.0932, and 2.5191 ng/g, respectively, whereas those of CKL were only 22.2605, 9.7879, 3.0009, and 2.2286 ng/g, respectively (Fig. A). In stems, nine hormones were significantly higher in YBS than in CKS, with dihydrojasmonic acid, ABA, GA3, and indole-3-acetic acid (IAA) contributing 31.63% of the total stem hormones (Fig. B). In roots, eight hormones showed significantly elevated levels in YBR compared to control roots (CKR), with significant increases in GA3, ABA, dihydrozeatin riboside, and 3-indolepropionic acid (Fig. C). 

KEGG enrichment analysis revealed significant involvement of six pathways, including plant hormone signal transduction, alpha-linolenic acid metabolism, zeatin biosynthesis, tryptophan (Trp) metabolism, metabolic pathways, and the biosynthesis of secondary metabolites (Additional file ). Notably, salicylic acid, IAA, jasmonic acid, and dihydrozeatin in the plant hormone signal transduction pathway were significantly enriched in the leaf, stem and root tissues, indicating a pivotal role of this pathway in promoting the rapid growth of the mutant. 

To investigate gene expression profiles in fast-growing mutants, RNA-seq was conducted on the Illumina platform, generating 18 cDNA libraries (nine each for CK and YB groups). Following sequencing and data filtering, 149.94 Gb clean bases were obtained. For all samples, Q20 values from 97.45 to 98.79%, and Q30 values from 92.89 to 96.23%, suggesting that the sequencing quality was sufficiently high for downstream analyses. Comparison of gene expression profiles between the YB and CK groups identified 2,009, 1,889, and 1,760 upregulated DEGs and 2,082, 2,035, and 1,499 downregulated DEGs in the leaves, stems, and roots, respectively (Fig. ). To further explore the effects of space mutagenesis on hormone biosynthesis in the mutants, hormone-related biosynthetic pathways were analyzed. The analysis resulted in the identification of eight genes in the Trp metabolism pathway with significant expression alterations (Additional file ), including five upregulated and three downregulated DEGs. In the diterpenoid biosynthesis pathway, nine genes encoding proteases showed significant alterations (Additional file ), including one upregulated and eight downregulated genes. For zeatin synthesis, five genes showed alterations (Additional file ), including four upregulated DEGs and one downregulated DEG. The expression levels of nine DEGs were assessed using quantitative real-time polymerase chain reaction (qRT-PCR), and the findings demonstrated a concordance between the qRT-PCR results and transcriptome data (Fig. ). 

To further explore the regulatory mechanisms underlying mutagenesis and other potential regulatory genes. According to the ranking, we selected the top 10,000 expressed genes and hormone content data for weighted gene co-expression network analysis (WGCNA) analysis. The results yielded 13 modules, including a gray module, containing all genes not grouped into other modules. Module sizes ranged from 41 to 2,964 unigenes (Fig. A). Among the 13 gene co-expression networks, the characteristic genes in the MEpink, MEred, MEturquoise, and MEblue modules showed strong associations with rapid growth, with correlation coefficients (r 2 ) ranging between 0.84 and 0.90 ( P < 0.05; Fig. B). Incorporating functional annotation information, we found that MEpink, MEred, MEturquoise, and MEblue modules contained 29, 51, 63, and 43 genes, respectively. Key genes in the MEred module were RH15, RPL26B, and MEFG2 (Fig. A). The genes identified in the MEturquoise module were RPL5B and RPL38A (Fig. B). Furthermore, PGI1 and NDPK3 (Fig. C) and PSAL, LHCA1, and PNSL3 (Fig. D) were identified as candidate genes in the MEpink and MEblue modules. Further analysis revealed that these genes play a key role in photosynthesis, as well as starch and sucrose metabolism. 

Hormones play crucial roles in various plant physiological processes. KEGG enrichment analysis of hormonomics revealed that plant hormone signal transduction might be a primary contributor to the rapid growth of mutants. This finding was further verified through investigations at the hormonal and molecular levels. In the YBL_vs_CKL group, the abundance of auxin-related metabolites, specifically 3-indolecarboxylic, 3-indolepropionic, and 3-indolebutyric acids were significantly elevated. At the transcriptional level, we found 20 DEGs, including 5, 1, and 14 linked to Aux IAA-induced protein (Aux/IAA), auxin-responsive glycoside hydrolase 3, and small-auxin upregulated RNA (SAUR) family proteins, respectively (Fig. A). In the auxin signal transduction pathway of YBS_vs_CKS group, Aux/IAA and SAUR-related genes were also highly expressed. In the GA signal transduction pathway, GA3, GA1, and GA7 contents were increased significantly, while genes related to DELLA proteins were upregulated (Fig. B). Similar results were found for the YBR_vs_CKR group. In the cytokinin signal transduction pathway of YBS_vs_CKS group, the contents of zeatin-riboside and dihydrozeatin riboside were increased significantly, and three genes were highly expressed, namely GRE1, AHP, and A-ARR (Fig. C). To validate the reliability of the data analysis, we sprayed the non-mutagenic materials with 3-indolebutyric acid, zeatin-riboside, and GA7 . The results showed that among the three hormones, GA7 impacted plant height the most. Hence, we sprayed the CK group with 30 ppm of GA7 and the YB group with water. After 7 days, the plant height of the CK group was increased by an average of 9.84 cm, while that of the YB group was increased by an average of 7.86 cm, with no significant differences in plant height (Fig. ). In addition, a similar phenomenon of significant growth was observed after spraying with 3 mg/L zeatin-riboside and 3-indolebutyric acid. 

Using transcriptomics to reflect biological function is associated with limitations, including the inability to fully elucidate protein function and state. To examine the functional and regulatory mechanisms of proteins in the mutants, quantitative proteomics was performed to elucidate their DAPs. Cluster analysis of protein abundance profiles revealed distinct differences between the CK and YB groups (Fig. ). We identified and quantified 8,752 proteins from the samples and selected 479 DAPs using statistical methods. Among them 174 proteins were upregulated, while 305 exhibited downregulation (Fig. A). The 479 DAPs were classified into three main GO domains: biological processes, molecular functions, and cellular components. The top three enriched terms for cellular components were cell wall, symplast, and plasmodesma. Among the molecular functions, the three most abundant terms were hydrolase activity, ATP hydrolysis activity, and catalytic activity. For biological processes, catabolic process, cellular component biogenesis, and cellular stress response were the top three enriched terms (Fig. B). KEGG enrichment analysis showed that Trp metabolism, glutathione metabolism, and plant-pathogen interaction were the top three enriched metabolic pathways (Additional file ). 

We explored gene expression and translation correlation in mutants at the protein and mRNA levels. In leaves, 6,856 genes showed transcriptome/proteome co-expression, and 6,708 showed protein/transcription co-expression. In stems, 7,918 and 7,878 genes showed transcriptome/proteome and proteome/transcriptome co-expressions, respectively. In the roots, 8,044 (transcriptome/proteome) and 7881 (proteome/transcriptome) genes were co-expressed (Fig. A, S5A). The patterns of the DEGs and DAPs were consistent in the third and seventh quadrants, and gene and protein regulations had a positive correlation. There were also cases where DEG and DAP expression patterns were inconsistent, including opposite expression (Quadrants 1 and 9) or unique differential expression (Quadrants 2, 4, 6, and 8) (Fig. B). This is owing to the spatiotemporal inconsistencies in gene transcription and expression, as well as the post-transcriptional modification of RNA. KEGG enrichment analysis of DEGs/DAPs in the third and seventh quadrants highlighted photosynthesis as a significantly enriched pathway in YBL_vs_CKL group (Fig. C). Further analysis identified that 22 genes/proteins involved in various stages of photosynthesis, with high expression of five genes encoding Photosystem II (PS II), Photosystem I (PS I), and chloroplast-related enzymes (Fig. D), consistent with the key candidate genes identified through WGCNA. Certain KEGG terms were selectively enriched in specific quadrants. In the nine-quadrant diagram, the YBR_vs_CKR group genes showed upregulation at both protein and mRNA levels (Fig. B), suggesting that elevated protein content in the roots was at least partially owing to increased transcription and/or translation/synthesis. Among them, energy supply, signal transduction, immune regulation, and metabolic regulation pathways were significantly enriched in the top 20 pathways of the YBR_vs_CKR group (Fig. B). Notably, the 11 genes in carbon metabolism were upregulated (Fig. D), which encode malate dehydrogenase, glyceraldehyde-3-phosphate dehydrogenase, fructose-bisphosphate aldolase, and triosephosphate isomerase. For the YBS_vs_CKS group, an analysis of the top 20 significantly enriched pathways revealed that genes were predominantly enriched in pathways related to energy supply, protein synthesis, signal transduction, immune regulation, and metabolic regulation (Fig. E). We found 13 intersecting genes in the mitogen-activated protein kinase (MAPK) signaling pathway in plants, 3 of which were upregulated (Fig. D), and 10 were downregulated. The upregulated genes encode MAPK, nucleoside diphosphate kinase, and ABA receptor PYL4 ABI1-binding protein. We also found that four pathways were co-enriched in leaves, stems, and roots, with P-values < 0.05, namely, starch and sucrose metabolism, glutathione metabolism, carbon metabolism, and carbon fixation in photosynthetic organisms (Fig. F). 

The integration of transcriptomics, proteomics, and metabolomics data revealed that four pathways were associated with DAPs, DEGs, and DEMs, with significant enrichment observed in hormone signal transduction and Trp metabolism across roots, stems, and leaves. Additionally, α-linolenic acid metabolism and zeatin biosynthesis were significantly enriched in stems, while zeatin biosynthesis was significantly enriched in leaves and α-linolenic acid metabolism in roots (Additional file ). In the hormone signal transduction pathways of the leaves, six proteins were significantly enriched, with three being upregulated and three downregulated (Additional file ). Among the upregulated proteins, histidine kinase, a cytokinin receptor histidine kinase that transmits signals via phosphorylation after receiving cytokinin signals, could regulate plant growth and development. Four proteins were significantly enriched in the hormone signal transduction pathways in stems (Additional file ), among which the genes encoding histidine kinase and ABA receptor PYL4 were upregulated. The latter, as a member of the PYR1-like protein family, senses and transmits ABA signals to downstream regulatory factors during plant growth, participating in physiological processes, such as stomatal opening and closure and stress response. Five proteins were enriched in the hormone signal transduction of the roots (Additional file ), among which two were upregulated, which included histidine kinase and pathogenesis-related protein. The latter primarily enhances disease resistance, participates in stress response, promotes cell wall thickening, and exerts antibacterial activity. A correlation analysis between transcription and protein levels for genes involved in hormone signal transduction revealed a stronger correlation in roots and stems compared to leaves (Fig. ). Eight proteins were enriched in the Trp metabolism pathway of the roots, stems, and leaves, which were downregulated (Additional file ). These involved genes encoding acetyl-CoA acetyltransferase, cytosolic Gln amidotransferase subunit A. During the rapid-growth stage, the mutants may have preferentially allocated energy to metabolic pathways that directly support growth and reproduction, whereas the genes in the Trp metabolism pathway may have been downregulated to meet this need. 

Hormone biosynthesis and signal transduction in rapid-growth alfalfa mutants Transcriptomic analysis across the leaf, stem, and root tissues in the mutant identified 49,095 genes, forming a substantial genetic foundation for the growth and development of the mutants. Twenty-two genes related to the Trp metabolism, diterpenoid biosynthesis, and zeatin biosynthesis pathways showed significant expression changes. Notably, genes encoding indole-3-pyruvate (IPA) monooxygenase YUCCA and tryptophan aminotransferase-related protein (TAR) were significantly upregulated. IAA, the primary natural auxin in plants, is vital for diverse developmental processes. In auxin biosynthesis, Trp is converted to IPA by an amino acid transferase of the TAA family, followed by the conversion of IPA to IAA by flavin monooxygenases of the YUC family. This two-step conversion is central to auxin production and is crucial in plant growth and development . TAR enzymes (TAR1-4) are crucial in this pathway, participating in IPA synthesis . In the GA biosynthesis pathway, we observed the upregulation of genes encoding GA 20 oxidase 1 and the downregulation of GA 2-beta-dioxygenase. GA 20 oxidase 1 is involved in consecutive steps during the later stages of GA biosynthesis. A GA 20 oxidase 1 mutant isolated from rice exhibited excessive internode elongation, and its morphology was approximately twice as large as that of its wild-type parent . Moreover, GA 2-beta-dioxygenase overexpression catalyzes GA inactivation, reducing the content of active GAs in plants, further suppressing cellular elongation and division, and decreasing plant height . In the zeatin biosynthesis pathway, the genes encoding zeatin O-glucosyltransferase (OscZOG1) and adenylate isopentenyltransferase (IPT) were significantly upregulated. By regulating the gene expression of OscZOG1, altering the zeatin O-glucosylation of rice is possible, regulating key agronomic traits in rice, while the overexpression of this gene stimulates lateral root growth . In flowering plants, IPTs are key enzymes for initiating cytokinin biosynthesis . The disruption of IPT genes in plants and non-plant organisms leads to a sharp drop in cytokinin levels and diminishes the shoot growth of Arabidopsis thaliana . These genes related to hormone synthesis constitute a significant genetic basis for the growth and development of the mutants. Furthermore, using the biological tool WGCNA, we identified four gene modules significantly associated with rapid growth, from which ten key candidate genes were screened. These genes may be pivotal in regulating photosynthetic efficiency, protein synthesis, and energy metabolism. Extensive research suggests that plant hormones are central to regulating plant growth and development and the response to environmental stress . Plants swiftly detect subtle changes in their concentrations and hence trigger a series of precise physiological responses. These response mechanisms are closely associated with the Aux/IAA family, SAUR gene family, and auxin-responsive Gretchen Hagen 3 (GH3) family members. In this study, mutants showed a higher Aux/IAA content than the non-mutagenic materials did, and the gene encoding IAA acid-amido synthetase GH3.6 showed significantly enhanced expression. GH3.6 is a significant member of the GH3 family and encodes a protein that binds amino acids to auxins. This process regulates the bioactivity and spatial distribution of auxins and finely modulates intracellular hormone levels through feedback mechanisms to maintain auxin homeostasis . Nine genes in the SAUR family were upregulated and primarily encode auxin-induced and auxin-responsive proteins. Previous studies have shown that cell polarity, a cornerstone of prokaryotic and eukaryotic development, serves as a precondition for cell specialization and functional differentiation. The nanoclustering phenomenon of auxin-induced signaling protein plays a pivotal role in regulating cell polarity formation, exerting a profound impact on plant growth and morphological construction . In a study on Vaccinium bracteatum , Gao et al. identified 14 auxin-responsive factor genes and 12 IAA genes showing significant differences in expression during fruit development. In contrast, the synergistic effect of these genes demonstrated their regulatory role in fruit ripening . Cytokinins have long been recognized as crucial promoters of plant growth and are extensively involved in regulating multiple key steps in plant growth and development, including phloem activity, chloroplast synthesis, response to nutrient uptake and senescence, and shoot and root proliferation . In an in-depth exploration of the biological characteristics of A. thaliana , the CRE1 gene (a cytokinin signaling component encoding a histidine kinase) activated complex downstream signaling cascade reactions, regulating plant growth and development . The A-ARR gene is a core regulatory factor in the cytokinin signaling pathway, and changes in its expression directly affect the formation and development of floral organs . Furthermore, the multistep phosphorelay mechanism is common in plants, which is closely related to the fine-grained regulation of growth, development, and hormone response and participates in the response to environmental stress, such as osmotic stress. This mechanism relies on a multi-component system that includes histidine kinases, histidine-containing phosphotransfer proteins (HPs), and response regulators (RRs), where HPs serve as “phosphate bridges” that transfer phosphate groups from HKs to RRs, significantly enriching the level and complexity signal transduction . Notably, the two-component signaling system, a signal transduction system originally discovered in bacteria, also plays a crucial role in plants. This is particularly observed in the cytokinin signal transduction pathway, where AHP2, AHP3, and AHP5 are key factors located downstream of the two-component cytokinin signaling pathway and actively promote the normal development of A. thaliana female gametophytes . Our findings indicate that compared with those of non-mutagenic materials, mutant plants showed significantly upregulated expression levels of the CER1, AHP, and A-ARR genes, implying that these genes are essential for driving the rapid growth of the mutants. This is consistent with previous results on citrus, where upregulation trends were observed in the transcription level of genes related to cytokinin signaling in citrus axillary buds after topping . The central role of GA in promoting cell elongation is directly associated with the final formation of plant morphology and is particularly significant under specific conditions wherein cell fluidity is restricted, such as cell wall hardening or increased environmental pressure. This adaptive regulatory mechanism promotes the growth and development of plants. The role of GA in the growth and development of A. thaliana has been extensively characterized at the phenotypic level and through genetic analysis. Similar to its role in other plants, GA promotes germination, shoot elongation, and flowering in A. thaliana . Although many plant hormones contribute to controlling plant height, GA is the most significant regulatory factor, with a crucial role in agricultural production . Previous studies have shown that genetic mutations in GA biosynthesis, metabolism, and signaling cascades may affect plant height in rice . In this study, we found that the expression of the GA signaling inhibitor DELLA was upregulated and that of GID2 was downregulated; however, GA metabolites showed significantly increased content. Hence, the inhibitory effect of DELLA on the GA signaling pathway may have been partially or completely counteracted. This is similar to the results of Li et al. on the regulation of plant height in rice by GA signal transduction . The mutually facilitatory relationship among hormones maintains the normal growth and development of plants. The interactions between auxins and cytokinins exert regulatory effects on various growth and developmental processes in plants . Auxins and GAs interact to promote cell division and expansion, regulating the development of the reproductive organs . Our findings revealed that auxins, cytokinins, GAs, and other plant hormones may interact to regulate the rapid growth of the mutants. Photosynthesis Photosynthesis, the foundation of energy supply for most of the lives on earth , is crucial for plant growth and development. Liu et al. found that the high photosynthesis levels in the leaves of Mikania micrantha facilitated its rapid growth . Other studies have demonstrated that Moso bamboo carries abundant light-responsive elements in its promoters, which may be related to its efficient photosynthesis during growth, promoting its rapid growth . In this study, we found that genes encoding PS I, PS II, and chloroplast-related enzymes were highly expressed. PS II is a crucial photosynthesis component responsible for splitting water and releasing oxygen. Its high expression levels imply that the plant captures light energy more effectively and drives water photolysis, producing more oxygen and electrons. These electrons are later involved in the electron transport chain of photosynthesis and promote the synthesis of adenosine triphosphate (ATP) and reduced nicotinamide adenine dinucleotide phosphate (NADPH) . The light-dependent reactions during photosynthesis also drive the linear and cyclic electron flows around PS I. The linear electron flow produces ATP and NADPH, whereas the PS I cyclic electron flow produces only ATP. PS I is responsible for the linear and cyclic electron flows, while the latter is critical to balancing the ATP/NADPH ratio and protecting the photosynthetic system from damage . Chloroplasts are the primary site for photosynthesis in plants, and they contain numerous proteins and pigments related to photosynthesis . The high expression of chloroplast-related genes implies enhancements in the quantity, size, or function of chloroplasts. Therefore, the elevated expression of genes related to PS II, PS I, and chloroplasts is a crucial strategy utilized by the mutants to optimize and enhance photosynthesis. These genes contributed to improving photosynthetic efficiency and capacity and positively impacted the rapid growth and development of the mutants. Carbon fixation is an essential primary metabolic process in plants and includes pathways such as the Calvin cycle, pyruvate metabolism, glycolysis, tricarboxylic acid cycle, and fatty acid synthesis. It is closely associated with essential metabolites, including lipids and carbohydrates . Our integrated transcriptomics and proteomics revealed that carbon fixation in photosynthetic organisms is significantly enriched in the roots, stems, and leaves. This finding is similar to the results observed in Sesuvium portulacastrum L under low-temperature stress . During the growth of rice plants, the Calvin cycle forms the core of carbon fixation in photosynthesis. Rice converts carbon dioxide into organic matter, such as glucose, via the Calvin cycle in chloroplasts, providing the plant with the required energy and carbon sources . Therefore, carbon fixation in photosynthetic organisms constituted the response to rapid growth in the mutants. 

Transcriptomic analysis across the leaf, stem, and root tissues in the mutant identified 49,095 genes, forming a substantial genetic foundation for the growth and development of the mutants. Twenty-two genes related to the Trp metabolism, diterpenoid biosynthesis, and zeatin biosynthesis pathways showed significant expression changes. Notably, genes encoding indole-3-pyruvate (IPA) monooxygenase YUCCA and tryptophan aminotransferase-related protein (TAR) were significantly upregulated. IAA, the primary natural auxin in plants, is vital for diverse developmental processes. In auxin biosynthesis, Trp is converted to IPA by an amino acid transferase of the TAA family, followed by the conversion of IPA to IAA by flavin monooxygenases of the YUC family. This two-step conversion is central to auxin production and is crucial in plant growth and development . TAR enzymes (TAR1-4) are crucial in this pathway, participating in IPA synthesis . In the GA biosynthesis pathway, we observed the upregulation of genes encoding GA 20 oxidase 1 and the downregulation of GA 2-beta-dioxygenase. GA 20 oxidase 1 is involved in consecutive steps during the later stages of GA biosynthesis. A GA 20 oxidase 1 mutant isolated from rice exhibited excessive internode elongation, and its morphology was approximately twice as large as that of its wild-type parent . Moreover, GA 2-beta-dioxygenase overexpression catalyzes GA inactivation, reducing the content of active GAs in plants, further suppressing cellular elongation and division, and decreasing plant height . In the zeatin biosynthesis pathway, the genes encoding zeatin O-glucosyltransferase (OscZOG1) and adenylate isopentenyltransferase (IPT) were significantly upregulated. By regulating the gene expression of OscZOG1, altering the zeatin O-glucosylation of rice is possible, regulating key agronomic traits in rice, while the overexpression of this gene stimulates lateral root growth . In flowering plants, IPTs are key enzymes for initiating cytokinin biosynthesis . The disruption of IPT genes in plants and non-plant organisms leads to a sharp drop in cytokinin levels and diminishes the shoot growth of Arabidopsis thaliana . These genes related to hormone synthesis constitute a significant genetic basis for the growth and development of the mutants. Furthermore, using the biological tool WGCNA, we identified four gene modules significantly associated with rapid growth, from which ten key candidate genes were screened. These genes may be pivotal in regulating photosynthetic efficiency, protein synthesis, and energy metabolism. Extensive research suggests that plant hormones are central to regulating plant growth and development and the response to environmental stress . Plants swiftly detect subtle changes in their concentrations and hence trigger a series of precise physiological responses. These response mechanisms are closely associated with the Aux/IAA family, SAUR gene family, and auxin-responsive Gretchen Hagen 3 (GH3) family members. In this study, mutants showed a higher Aux/IAA content than the non-mutagenic materials did, and the gene encoding IAA acid-amido synthetase GH3.6 showed significantly enhanced expression. GH3.6 is a significant member of the GH3 family and encodes a protein that binds amino acids to auxins. This process regulates the bioactivity and spatial distribution of auxins and finely modulates intracellular hormone levels through feedback mechanisms to maintain auxin homeostasis . Nine genes in the SAUR family were upregulated and primarily encode auxin-induced and auxin-responsive proteins. Previous studies have shown that cell polarity, a cornerstone of prokaryotic and eukaryotic development, serves as a precondition for cell specialization and functional differentiation. The nanoclustering phenomenon of auxin-induced signaling protein plays a pivotal role in regulating cell polarity formation, exerting a profound impact on plant growth and morphological construction . In a study on Vaccinium bracteatum , Gao et al. identified 14 auxin-responsive factor genes and 12 IAA genes showing significant differences in expression during fruit development. In contrast, the synergistic effect of these genes demonstrated their regulatory role in fruit ripening . Cytokinins have long been recognized as crucial promoters of plant growth and are extensively involved in regulating multiple key steps in plant growth and development, including phloem activity, chloroplast synthesis, response to nutrient uptake and senescence, and shoot and root proliferation . In an in-depth exploration of the biological characteristics of A. thaliana , the CRE1 gene (a cytokinin signaling component encoding a histidine kinase) activated complex downstream signaling cascade reactions, regulating plant growth and development . The A-ARR gene is a core regulatory factor in the cytokinin signaling pathway, and changes in its expression directly affect the formation and development of floral organs . Furthermore, the multistep phosphorelay mechanism is common in plants, which is closely related to the fine-grained regulation of growth, development, and hormone response and participates in the response to environmental stress, such as osmotic stress. This mechanism relies on a multi-component system that includes histidine kinases, histidine-containing phosphotransfer proteins (HPs), and response regulators (RRs), where HPs serve as “phosphate bridges” that transfer phosphate groups from HKs to RRs, significantly enriching the level and complexity signal transduction . Notably, the two-component signaling system, a signal transduction system originally discovered in bacteria, also plays a crucial role in plants. This is particularly observed in the cytokinin signal transduction pathway, where AHP2, AHP3, and AHP5 are key factors located downstream of the two-component cytokinin signaling pathway and actively promote the normal development of A. thaliana female gametophytes . Our findings indicate that compared with those of non-mutagenic materials, mutant plants showed significantly upregulated expression levels of the CER1, AHP, and A-ARR genes, implying that these genes are essential for driving the rapid growth of the mutants. This is consistent with previous results on citrus, where upregulation trends were observed in the transcription level of genes related to cytokinin signaling in citrus axillary buds after topping . The central role of GA in promoting cell elongation is directly associated with the final formation of plant morphology and is particularly significant under specific conditions wherein cell fluidity is restricted, such as cell wall hardening or increased environmental pressure. This adaptive regulatory mechanism promotes the growth and development of plants. The role of GA in the growth and development of A. thaliana has been extensively characterized at the phenotypic level and through genetic analysis. Similar to its role in other plants, GA promotes germination, shoot elongation, and flowering in A. thaliana . Although many plant hormones contribute to controlling plant height, GA is the most significant regulatory factor, with a crucial role in agricultural production . Previous studies have shown that genetic mutations in GA biosynthesis, metabolism, and signaling cascades may affect plant height in rice . In this study, we found that the expression of the GA signaling inhibitor DELLA was upregulated and that of GID2 was downregulated; however, GA metabolites showed significantly increased content. Hence, the inhibitory effect of DELLA on the GA signaling pathway may have been partially or completely counteracted. This is similar to the results of Li et al. on the regulation of plant height in rice by GA signal transduction . The mutually facilitatory relationship among hormones maintains the normal growth and development of plants. The interactions between auxins and cytokinins exert regulatory effects on various growth and developmental processes in plants . Auxins and GAs interact to promote cell division and expansion, regulating the development of the reproductive organs . Our findings revealed that auxins, cytokinins, GAs, and other plant hormones may interact to regulate the rapid growth of the mutants. 

Photosynthesis, the foundation of energy supply for most of the lives on earth , is crucial for plant growth and development. Liu et al. found that the high photosynthesis levels in the leaves of Mikania micrantha facilitated its rapid growth . Other studies have demonstrated that Moso bamboo carries abundant light-responsive elements in its promoters, which may be related to its efficient photosynthesis during growth, promoting its rapid growth . In this study, we found that genes encoding PS I, PS II, and chloroplast-related enzymes were highly expressed. PS II is a crucial photosynthesis component responsible for splitting water and releasing oxygen. Its high expression levels imply that the plant captures light energy more effectively and drives water photolysis, producing more oxygen and electrons. These electrons are later involved in the electron transport chain of photosynthesis and promote the synthesis of adenosine triphosphate (ATP) and reduced nicotinamide adenine dinucleotide phosphate (NADPH) . The light-dependent reactions during photosynthesis also drive the linear and cyclic electron flows around PS I. The linear electron flow produces ATP and NADPH, whereas the PS I cyclic electron flow produces only ATP. PS I is responsible for the linear and cyclic electron flows, while the latter is critical to balancing the ATP/NADPH ratio and protecting the photosynthetic system from damage . Chloroplasts are the primary site for photosynthesis in plants, and they contain numerous proteins and pigments related to photosynthesis . The high expression of chloroplast-related genes implies enhancements in the quantity, size, or function of chloroplasts. Therefore, the elevated expression of genes related to PS II, PS I, and chloroplasts is a crucial strategy utilized by the mutants to optimize and enhance photosynthesis. These genes contributed to improving photosynthetic efficiency and capacity and positively impacted the rapid growth and development of the mutants. Carbon fixation is an essential primary metabolic process in plants and includes pathways such as the Calvin cycle, pyruvate metabolism, glycolysis, tricarboxylic acid cycle, and fatty acid synthesis. It is closely associated with essential metabolites, including lipids and carbohydrates . Our integrated transcriptomics and proteomics revealed that carbon fixation in photosynthetic organisms is significantly enriched in the roots, stems, and leaves. This finding is similar to the results observed in Sesuvium portulacastrum L under low-temperature stress . During the growth of rice plants, the Calvin cycle forms the core of carbon fixation in photosynthesis. Rice converts carbon dioxide into organic matter, such as glucose, via the Calvin cycle in chloroplasts, providing the plant with the required energy and carbon sources . Therefore, carbon fixation in photosynthetic organisms constituted the response to rapid growth in the mutants. 

In this study, the gene mutations involved in plant hormone biosynthesis and signal transduction pathways significantly increase auxin, gibberellin, and cytokinin levels. The genetic pathways involved in photosynthesis, starch and sucrose metabolism, MAPK signaling pathway – plant, glutathione metabolism, and tryptophan metabolism collectively contribute to the rapid growth of the mutant by facilitating energy supply, protein synthesis, signal transduction, immune regulation, and metabolic regulation. These findings not only offer new insights into the molecular mechanism governing plant growth regulation but also provide crucial targets and strategies for enhancing crop rapid growth through genetic improvement in the future. This holds significant implications for studying plant growth and development. However, further investigation is required to determine whether there are any changes in the SNPs of the mutants. 

Plant materials and growth conditions The seeds of ‘Deqin’ alfalfa were provided by Yunnan Agricultural University. They were domesticated, selected, and bred by Yunnan Agricultural University. ‘Deqin’ alfalfa were then registered and released as ‘Deqin’ alfalfa breed by China National Grass Variety Approval Committee in the year of 2010 . On May 5, 2020, ‘Deqin’ alfalfa seeds were launched into outer space on board the Long March 5B rocket, and returned to Earth on May 8, 2020. During cultivation followed their return, we identified mutant material #28. We propagated both the mutagenized and non-mutagenized materials through cuttings, and based on their physiological characteristics, we harvested them at the stage of fastest growth (15 days) for sequencing analysis. This resulted in two groups and six categories: YBL_vs_CKL (leaf), YBS_vs_CKS (stem), and YBR_vs_CKR (root). Transcriptomic and proteomic analyses were performed with three biological replicates, each consisting of five individual plants, while targeted plant hormone metabolomics analysis included five independent biological replicates. In autumn 2023, the material was propagated by cuttings in pots (16 × 12 × 12.5 cm) containing a soil mixture of humus soil, ordinary local red soil, and vermiculite in a ratio of 2:1:1, with a soil pH of 7.0. The pots were placed in a tunnel greenhouse at the Kunming Institute of Botany, Chinese Academy of Sciences (25°8’17″N, 102°44’20″E, elevation: 1,950 m). The cuttings were grown in a controlled environment with temperatures maintained between 15 and 25 °C, under a 16-hour light/8-hour dark photoperiod. Relative humidity was controlled at approximately 65%. Environmental conditions were carefully monitored using automated climate control systems, and agricultural management practices such as watering and weeding were carried out according to standard protocols. Measurement of parameters related to agronomic traits Plant height: The natural height of the plants, from the highest point to the ground, was measured. Leaf area: Leaves on the third branch from the top were selected to measure the leaf size using a leaf area meter (LI-3000 C Portable Area Meter, LI-COR, Inc., USA). Internode length: The internode distance of the third node from the bottom was measured. Fresh grass yield: Individual plants were weighed 15 days after the first cutting. The average value of ten individual plants was used to measure each trait mentioned above. Targeted plant hormone metabolomics Targeted plant hormone was performed to determine the phytohormone content in the leaves, roots, and stems. The mutant and non-mutagenic materials were collected, immediately frozen in liquid nitrogen, and stored at -80 °C. Five biological replicates were collected for each sample. Samples were cryogenically ground (50 Hz, 30 s, twice) with liquid nitrogen, and approximately 100 mg of each sample (fresh weight) was subjected to extraction with 1,000 µL of extraction solution (methanol: acetonitrile solution, 1:1) containing 2 ng/mL of isotope internal standard mix. After vortex mixing for 60 s and sonication in an ice bath for 10 min, the supernatant was collected and dried in a centrifugal dryer, with the temperature controlled at 4 °C. The samples were reconstituted by adding 90 µL of 50% methanol-water solution, followed by vortex mixing for 1 min, sonication for 120 s, and another vortex mixing for 1 min. After centrifugation at 12,000 × g and 4 °C for 10 min, 80 µL of the supernatant was placed in a new polyethylene tube and recentrifuged. Finally, 70 µL of the supernatant was placed in a sleeved autosampler for ultra-high-performance liquid chromatography-tandem mass spectrometry. Transcriptome sequencing RNA-seq was performed on approximately 1 g of fresh leaf, stem, and root tissue samples from mutant and non-mutagenic materials for total RNA extraction. Total RNA was extracted from frozen samples using the TransZol Plant Kit, and RNA purity and concentration were assessed with a NanoDrop 2000 spectrophotometer (OD260/OD280 ≥ 1.8, Thermo Scientific, USA). RNA integrity was evaluated using the RNA Nano 6000 Assay Kit with the Agilent Bioanalyzer 2100 system (RIN ≥ 7.0, Agilent Technologies, CA, USA). Qualified RNA samples were used to construct strand-specific sequencing libraries, which were generated using the NEBNext ® Ultra™ RNA Library Prep Kit for Illumina. Fastp ( https://github.com/OpenGene/fastp ) was employed for data quality control and filtering. This software detects and removes adapters, corrects bases in the overlap regions of paired-end sequences, trims heads and tails using a sliding window approach, removes polyG/X tails, and preprocesses Unique Molecular Identifiers. Sequencing was conducted on the Illumina HiSeq 4000 platform in triplicates for each sample. The reference genome and gene annotation files for Medicago sativa ( https://figshare.com/articles/dataset/Medicago_sativa_genome_and_annotation_files/12623960 ); accessed July 9, 2020) , which closely align with our sample, were downloaded and used for reads mapping via HISAT2 (Additional file ). Gene expression levels were estimated using fragments per kilobase of transcript per million mapped reads (FPKM). Differential gene expression analysis was conducted with the DESeq2 R software package (version 1.20.1), applying the criteria of |log2-fold change| ≥ 2 and a false discovery rate < 0.05. Functional annotation of DEGs was performed according to GO ( http://www.geneontology.org/ ), KEGG ( http://www.genome.jp/kegg/ ), and National Center for Biotechnology Information (NCBI, https://www.ncbi.nlm.nih.gov/ ) databases. GO and KEGG pathway enrichment analyses of DEGs were performed using R based on the hypergeometric distribution. WGCNA To elucidate genetic correlation patterns, we performed WGCNA based on the transcriptomic data collected from leaves, stems, and roots of the mutant and non-mutagenic materials . Using the FPKM values of the expressed genes, Pearson or Spearman correlation coefficients were used to calculate the expression correlations among different genes and adjacency matrixes were constructed using a soft threshold (power value = 13, scale-free topology fitting index R 2 > 0.8). The link-cut tree method was used to partition different genes into different co-expression modules. The WGCNA network, module partitioning results, and sample trait integrated analysis modules were combined with the between-sample correlation coefficients. Co-expression network maps were plotted using Cytoscape (3.10.2) to visualize the interactive relationships among the genes. Proteomic analysis Data-independent acquisition (DIA) protein quantitative analysis was performed on leaf, stem, and root samples of the mutant and non-mutagenic materials, with three biological replicates per sample. Total proteins were extracted using the trichloroacetic acid/acetone precipitation method , and protein concentration was measured using the Solarbio Bradford Assay Kit (Solarbio, Beijing, China). Based on the measured concentration, 30 µg of protein was obtained from each sample and incubated with lysis buffer and reductive alkylation reagent for enzymatic hydrolysis. Subsequently, protease was added to terminate the reaction. The samples were centrifuged, and the supernatant was obtained for desalting. After treatment with a desalting column and refrigerated centrifugal vacuum concentrator, the samples were reconstituted using mobile phase A and loaded for analysis. For each sample, 500 ng of peptides were analyzed with a nano UPLC (Vanquish Neo) coupled with Orbitrap Astral (Thermo) using a nano-electrospray ion source. Separation occurred on a reversed-phase column (EASY-Spray ™ HPLC, 150 μm × 15 cm, Thermo Scientific, USA). Mobile phases were H 2 O with 0.1% formic acid solution (phase A) and 80% Acetonitrile solution with 0.1% formic acid solution (phase B). Separation of the sample was executed with a 6.9 min gradient. DIA was conducted in profile and positive mode at 240 K resolution, with m/z ranges of 380–980 for MS1 and 150–2000 for MS2. The higher-energy collisional-dissociation method was applied with 25% normalized collision energy and a 2 m/z isolation window. Statistical methods were used to screen for DAPs based on the following screening criteria: Student’s test of chi-squared test P-value < 0.05 and fold change ≤ 0.25 or ≥ 4 . The genes were mapped to nodes in the Medicago sativa database in GO, and DAPs were analyzed using the GO ( http://www.geneontology.org/ ) and KEGG Pathway ( www.kegg.jp/kegg/pathway.html ) databases . Validation of DEGs using qRT-PCR To validate the RNA-seq results, nine DEGs were randomly selected for qRT-PCR analysis. RNA was extracted from 50 to 100 mg of plant samples and reverse-transcribed using the Tsingke Reverse Transcription Kit. Following two-fold dilution, the reverse transcription products were used as qPCR templates, and qRT-PCR was performed with the ArtiCanCEO SYBR qPCR Mix on the ABI QuantStudio 3 PCR system. The reaction system was 20 µL, which included 10 µL of qPCR Mix, 1 µL of cDNA, 0.8 µL each of forward and reverse primers, and 7.4 µL of nuclease-free water. The qRT-PCR reaction conditions were as follows: pre-denaturation at 95 °C for 5 min, followed by 40 cycles of 95 °C for 15 s, 60 °C for 20 s, and 72 °C for 20 s. Three technical replicates were performed for each reaction, and melting curve analysis was conducted to confirm the specificity of the PCR products. Similarly, 18 S rRNA was selected as the internal control. The primer sequences are presented in Additional file . Gene expression levels were calculated using the 2-ΔΔCt method. Statistical analysis Univariate statistical analysis was performed to identify DEGs and quantify changes in the abundance levels of proteins and hormones. Analysis of variance, least significant difference test ( P < 0.05), and correlation analysis were performed using IBM SPSS. Data were normalized using the cor function in R. GO and KEGG enrichment analyses were completed using the Wuhan Generead Biotechnologies Co. Ltd. online tools, which were plotted using Origin. 

The seeds of ‘Deqin’ alfalfa were provided by Yunnan Agricultural University. They were domesticated, selected, and bred by Yunnan Agricultural University. ‘Deqin’ alfalfa were then registered and released as ‘Deqin’ alfalfa breed by China National Grass Variety Approval Committee in the year of 2010 . On May 5, 2020, ‘Deqin’ alfalfa seeds were launched into outer space on board the Long March 5B rocket, and returned to Earth on May 8, 2020. During cultivation followed their return, we identified mutant material #28. We propagated both the mutagenized and non-mutagenized materials through cuttings, and based on their physiological characteristics, we harvested them at the stage of fastest growth (15 days) for sequencing analysis. This resulted in two groups and six categories: YBL_vs_CKL (leaf), YBS_vs_CKS (stem), and YBR_vs_CKR (root). Transcriptomic and proteomic analyses were performed with three biological replicates, each consisting of five individual plants, while targeted plant hormone metabolomics analysis included five independent biological replicates. In autumn 2023, the material was propagated by cuttings in pots (16 × 12 × 12.5 cm) containing a soil mixture of humus soil, ordinary local red soil, and vermiculite in a ratio of 2:1:1, with a soil pH of 7.0. The pots were placed in a tunnel greenhouse at the Kunming Institute of Botany, Chinese Academy of Sciences (25°8’17″N, 102°44’20″E, elevation: 1,950 m). The cuttings were grown in a controlled environment with temperatures maintained between 15 and 25 °C, under a 16-hour light/8-hour dark photoperiod. Relative humidity was controlled at approximately 65%. Environmental conditions were carefully monitored using automated climate control systems, and agricultural management practices such as watering and weeding were carried out according to standard protocols. 

Plant height: The natural height of the plants, from the highest point to the ground, was measured. Leaf area: Leaves on the third branch from the top were selected to measure the leaf size using a leaf area meter (LI-3000 C Portable Area Meter, LI-COR, Inc., USA). Internode length: The internode distance of the third node from the bottom was measured. Fresh grass yield: Individual plants were weighed 15 days after the first cutting. The average value of ten individual plants was used to measure each trait mentioned above. 

Targeted plant hormone was performed to determine the phytohormone content in the leaves, roots, and stems. The mutant and non-mutagenic materials were collected, immediately frozen in liquid nitrogen, and stored at -80 °C. Five biological replicates were collected for each sample. Samples were cryogenically ground (50 Hz, 30 s, twice) with liquid nitrogen, and approximately 100 mg of each sample (fresh weight) was subjected to extraction with 1,000 µL of extraction solution (methanol: acetonitrile solution, 1:1) containing 2 ng/mL of isotope internal standard mix. After vortex mixing for 60 s and sonication in an ice bath for 10 min, the supernatant was collected and dried in a centrifugal dryer, with the temperature controlled at 4 °C. The samples were reconstituted by adding 90 µL of 50% methanol-water solution, followed by vortex mixing for 1 min, sonication for 120 s, and another vortex mixing for 1 min. After centrifugation at 12,000 × g and 4 °C for 10 min, 80 µL of the supernatant was placed in a new polyethylene tube and recentrifuged. Finally, 70 µL of the supernatant was placed in a sleeved autosampler for ultra-high-performance liquid chromatography-tandem mass spectrometry. 

RNA-seq was performed on approximately 1 g of fresh leaf, stem, and root tissue samples from mutant and non-mutagenic materials for total RNA extraction. Total RNA was extracted from frozen samples using the TransZol Plant Kit, and RNA purity and concentration were assessed with a NanoDrop 2000 spectrophotometer (OD260/OD280 ≥ 1.8, Thermo Scientific, USA). RNA integrity was evaluated using the RNA Nano 6000 Assay Kit with the Agilent Bioanalyzer 2100 system (RIN ≥ 7.0, Agilent Technologies, CA, USA). Qualified RNA samples were used to construct strand-specific sequencing libraries, which were generated using the NEBNext ® Ultra™ RNA Library Prep Kit for Illumina. Fastp ( https://github.com/OpenGene/fastp ) was employed for data quality control and filtering. This software detects and removes adapters, corrects bases in the overlap regions of paired-end sequences, trims heads and tails using a sliding window approach, removes polyG/X tails, and preprocesses Unique Molecular Identifiers. Sequencing was conducted on the Illumina HiSeq 4000 platform in triplicates for each sample. The reference genome and gene annotation files for Medicago sativa ( https://figshare.com/articles/dataset/Medicago_sativa_genome_and_annotation_files/12623960 ); accessed July 9, 2020) , which closely align with our sample, were downloaded and used for reads mapping via HISAT2 (Additional file ). Gene expression levels were estimated using fragments per kilobase of transcript per million mapped reads (FPKM). Differential gene expression analysis was conducted with the DESeq2 R software package (version 1.20.1), applying the criteria of |log2-fold change| ≥ 2 and a false discovery rate < 0.05. Functional annotation of DEGs was performed according to GO ( http://www.geneontology.org/ ), KEGG ( http://www.genome.jp/kegg/ ), and National Center for Biotechnology Information (NCBI, https://www.ncbi.nlm.nih.gov/ ) databases. GO and KEGG pathway enrichment analyses of DEGs were performed using R based on the hypergeometric distribution. 

To elucidate genetic correlation patterns, we performed WGCNA based on the transcriptomic data collected from leaves, stems, and roots of the mutant and non-mutagenic materials . Using the FPKM values of the expressed genes, Pearson or Spearman correlation coefficients were used to calculate the expression correlations among different genes and adjacency matrixes were constructed using a soft threshold (power value = 13, scale-free topology fitting index R 2 > 0.8). The link-cut tree method was used to partition different genes into different co-expression modules. The WGCNA network, module partitioning results, and sample trait integrated analysis modules were combined with the between-sample correlation coefficients. Co-expression network maps were plotted using Cytoscape (3.10.2) to visualize the interactive relationships among the genes. 

Data-independent acquisition (DIA) protein quantitative analysis was performed on leaf, stem, and root samples of the mutant and non-mutagenic materials, with three biological replicates per sample. Total proteins were extracted using the trichloroacetic acid/acetone precipitation method , and protein concentration was measured using the Solarbio Bradford Assay Kit (Solarbio, Beijing, China). Based on the measured concentration, 30 µg of protein was obtained from each sample and incubated with lysis buffer and reductive alkylation reagent for enzymatic hydrolysis. Subsequently, protease was added to terminate the reaction. The samples were centrifuged, and the supernatant was obtained for desalting. After treatment with a desalting column and refrigerated centrifugal vacuum concentrator, the samples were reconstituted using mobile phase A and loaded for analysis. For each sample, 500 ng of peptides were analyzed with a nano UPLC (Vanquish Neo) coupled with Orbitrap Astral (Thermo) using a nano-electrospray ion source. Separation occurred on a reversed-phase column (EASY-Spray ™ HPLC, 150 μm × 15 cm, Thermo Scientific, USA). Mobile phases were H 2 O with 0.1% formic acid solution (phase A) and 80% Acetonitrile solution with 0.1% formic acid solution (phase B). Separation of the sample was executed with a 6.9 min gradient. DIA was conducted in profile and positive mode at 240 K resolution, with m/z ranges of 380–980 for MS1 and 150–2000 for MS2. The higher-energy collisional-dissociation method was applied with 25% normalized collision energy and a 2 m/z isolation window. Statistical methods were used to screen for DAPs based on the following screening criteria: Student’s test of chi-squared test P-value < 0.05 and fold change ≤ 0.25 or ≥ 4 . The genes were mapped to nodes in the Medicago sativa database in GO, and DAPs were analyzed using the GO ( http://www.geneontology.org/ ) and KEGG Pathway ( www.kegg.jp/kegg/pathway.html ) databases . 

To validate the RNA-seq results, nine DEGs were randomly selected for qRT-PCR analysis. RNA was extracted from 50 to 100 mg of plant samples and reverse-transcribed using the Tsingke Reverse Transcription Kit. Following two-fold dilution, the reverse transcription products were used as qPCR templates, and qRT-PCR was performed with the ArtiCanCEO SYBR qPCR Mix on the ABI QuantStudio 3 PCR system. The reaction system was 20 µL, which included 10 µL of qPCR Mix, 1 µL of cDNA, 0.8 µL each of forward and reverse primers, and 7.4 µL of nuclease-free water. The qRT-PCR reaction conditions were as follows: pre-denaturation at 95 °C for 5 min, followed by 40 cycles of 95 °C for 15 s, 60 °C for 20 s, and 72 °C for 20 s. Three technical replicates were performed for each reaction, and melting curve analysis was conducted to confirm the specificity of the PCR products. Similarly, 18 S rRNA was selected as the internal control. The primer sequences are presented in Additional file . Gene expression levels were calculated using the 2-ΔΔCt method. 

Univariate statistical analysis was performed to identify DEGs and quantify changes in the abundance levels of proteins and hormones. Analysis of variance, least significant difference test ( P < 0.05), and correlation analysis were performed using IBM SPSS. Data were normalized using the cor function in R. GO and KEGG enrichment analyses were completed using the Wuhan Generead Biotechnologies Co. Ltd. online tools, which were plotted using Origin. 

Below is the link to the electronic supplementary material. Supplementary Material 1 Supplementary Material 2 Supplementary Material 3 Supplementary Material 4 Supplementary Material 5 Supplementary Material 6 Supplementary Material 7 Supplementary Material 8 Supplementary Material 9 Supplementary Material 10 Supplementary Material 11 Supplementary Material 12 

