Effects of Seasonal Climates and MIPS Mutations on Soybean Germination through Multi-Omics Analysis

Effects of Seasonal Climates and MIPS Mutations on Soybean Germination through Multi-Omics Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effects of Seasonal Climates and MIPS Mutations on Soybean Germination through Multi-Omics Analysis Huakun Yu, Longming Zhu, Yuhao Chen, Ping Deng, Bei Liu, Xiaochao Chen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5063011/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Dec, 2024 Read the published version in BMC Plant Biology → Version 1 posted 4 You are reading this latest preprint version Abstract This study delves into the combined effects of seasonal climate variations and MIPS gene mutations on the germination rates of soybean cultivars TW-1 and TW75. Through comprehensive metabolomic and transcriptomic analyses, we identified key KEGG pathways significantly affected by these factors, including starch and sucrose metabolism, lipid metabolism, and amino acid biosynthesis. These pathways were notably disrupted during the spring, leading to an imbalance in metabolic reserves critical for seedling development. Additionally, MIPS gene mutations further altered these pathways, exacerbating the metabolic disturbances. Our results underscore the intricate network of environmental and genetic interactions influencing soybean seed vigor and underscore the importance of understanding these pathways to enhance agricultural resilience and seed quality in fluctuating climates. soybean germination MIPS mutation multi-omics seasonal climate variation low phytic acid Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction The soybean ( Glycine max L. Merr.) stands as a leading oilseed crop worldwide. Its seeds are pivotal, not only significantly bolstering human nutrition with essential plant-derived proteins and fats, but also serving as a critical element in the plant's reproductive cycle by facilitating propagation. Therefore, seed quality is crucial for preserving genetic resources and advancing agricultural yields. Soybean seed development unfolds in three primary stages[ 1 ]: embryonic development featuring embryo growth, cell division, and morphogenesis; maturation characterized by the substantial accumulation of nutritive reserves; and a seed drying phase involving desiccation and dormancy onset. These stages are central to determining seed quality, germination, and viability and coincide with comprehensive changes in gene expression, protein profiles, and metabolite levels, alongside notable metabolic shifts over space and time. The stored nutrition within the seed are the energy source for germination and seedling growth. The quality of soybean seeds stems from a complex interaction between innate genetic factors and external environmental conditions. Environmental factors profoundly and intricately affect plant growth and metabolic functions. The interplay between the soybean's genetics and various climatic stressors, such as temperature fluctuations[ 2 , 3 ], drought stress[ 4 , 5 ], and sunlight exposure, affects soybean physiology in complex and unpredictable ways. While soybeans are generally thermally resilient, the seed development stage is particularly sensitive to extreme temperatures, potentially leading to reduced germination, increased susceptibility to disease, and decreased seed value[ 6 , 7 ]. The genetic blueprint also significantly dictates seed development, where genetic deviations, such as low phytic acid mutations, sometimes align with inferior seed quality. These genetic variations tend to manifest in reduced seed yield and viability at last[ 8 – 12 ]. The work of Meis et al.[ 13 ] illuminated the phenotypic consequences of such genetic alterations, finding that homozygous mips genotypes from the LR 33 lineage display significantly lower field emergence rates compared to wild-type (WT) counterparts. Another important observation from this study was the varying impact of the seeds' origin; those cultivated in temperate climates suffered less in field emergence than those from tropical regions. Low phytic acid mutants associated with the MRP5 gene also exhibit reduced seeds viability[ 14 ]. However, mutations of IPK1 gene, which catalyzes the last step of phytate synthesis, do not lead to the inferior of quality in soybean seeds[ 15 , 16 ]. Genotype play a decisive role in seeds viability among low phytic acid mutants. The MIPS1 gene catalyzes the first step of phytate synthesis, and mutations in this gene result in substantial reduction in phytic acid content, also affecting the metabolism of the raffinose in soybean seeds[ 9 , 10 ]. A large number of studies have been conducted on the metabolic mechanisms of low seed emergence in low phytic acid seeds[ 17 – 19 ] and on improving the emergence of low phytic acid varieties[ 20 , 21 ]. In our previous study[ 22 – 24 ], the TW-1, a mutation of Taiwan75, exhibited a notably different field emergence rate compared to its parent variety, Taiwan75. We have previously investigated the differential expression profiles of these mutations during seed germination through comprehensive proteomic and transcriptomic analyses. However, the impact of parental traits on the F1 generation's subsequent germination remains largely unexplored. By examining how parental characteristics affect the germination potential of the F1 generation, we can gain valuable insights into the genetic mechanisms of seed development and agronomic practices that could enhance seed quality and field performance. Metabolomics has emerged as a powerful tool in delineating metabolic processes across different crops, soybeans included[ 25 ]. Metabolomic studies employing liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-MS have yielded critical insights into the metabolic alterations in stress-exposed seeds. The evolution of next-generation sequencing technologies has paved the way for new omics fields like transcriptomics[ 26 , 27 ], genomics[ 28 ], and proteomics[ 23 ]. Combining metabolomics with transcriptomics in multi-omics analyses has become a prevalent method for investigating the complex interactions within organisms, shedding light on the regulatory networks that control metabolic pathways[ 29 – 31 ]. In this study, we leveraged transcriptomic and metabolomic analyses to map the regulatory network involved in mips1 mutant soybean seed development. Our exploration of the seasonal and genetic variation impacts on field emergence rates aimed to clarify the relationship between mips1 gene expression and metabolic profiles in relation to soybean mutant seeds germination. This research contributes to a more profound and comprehensive understanding of the relation between seasonal and mips1 genetic variables that influence the accumulation of germination-promoting factors in soybean seeds. 2 Materials and methods 2.1 Plant materials and growth conditions The experiment utilized low phytic acid soybean mutant lines Gm-lpa -TW75-1 (referred to as TW-1) and its corresponding wild-type parental variety, Taiwan 75 (referred to as TW75). The TW-1 mutant line was developed through gamma irradiation. TW75 is a widely cultivated vegetable soybean variety in Zhejiang Province. For comparative analysis, seeds were harvested from plants grown in adjacent plots within the same field during the spring and autumn seasons of 2020 at the Experimental Farm of the Zhejiang Academy of Agricultural Sciences in Hangzhou, Zhejiang. Sampling was performed at three developmental stages: the early stage (ES) at 15–20 days after flowering (DAF), the middle stage (MS) at the R6 stage, and the late stage (LS) at the R8 stage, for both spring and autumn. All samples were immediately frozen and stored at -80°C until further metabolite analysis and RNA extraction. 2.2 Sample preparation and metabolomic analysis by LC-MS Sample preparation. A precise 30 mg sample was placed into a 1.5 mL Eppendorf tube containing steel balls. Internal standards, including 20 µL of 2-chloro-l-phenylalanine (0.3 mg/mL) and 20 µL of Lyso PC17:0 (0.01 mg/mL) dissolved in methanol, were added. Subsequently, 1 mL of a methanol-water mixture (7:3, vol/vol) was introduced to each tube. The samples were then subjected to a series of treatments: first, a 2-minute freeze at -20°C, followed by grinding at 60 Hz for 2 minutes, vortexing, 30-minute ultrasonication at room temperature, and another 20-minute freeze at -20°C. After centrifugation at 13,000 rpm and 4°C for 10 minutes, 300 µL of the supernatant was dried in a freeze-concentration centrifugal dryer. The dried residue was reconstituted with 400 µL of a methanol-water mixture (1:4, vol/vol), vortexed for 30 seconds, and ultrasonicated for 2 minutes. Following another centrifugation under the same conditions, 150 µL of the supernatant was syringe-filtered through a 0.22 µm microfilter and transferred to LC vials, which were subsequently stored at -80°C until LC-MS analysis. Quality control (QC) samples were generated by pooling aliquots of all the samples. LC-MS analysis. we employed an ACQUITY UHPLC system coupled with an AB SCIEX Triple TOF 5600 System for metabolic profiling in both ESI positive and negative ion modes. Chromatographic separation was carried out on an ACQUITY UPLC BEH C18 column using a binary gradient elution system comprising solvent (A) water with 0.1% formic acid (v/v) and solvent (B) acetonitrile and methanol (2:3, vol/vol, with 0.1% formic acid). The gradient protocol was as follows: starting with 1% B, increasing to 30% B at 1 minute, 60% B at 2.5 minutes, reaching 90% B at 6.5 minutes, holding at 100% B from 8.5 to 10.7 minutes, returning to 1% B at 10.8 minutes and maintaining until 13 minutes. The flow rate was set at 0.4 mL/min with a column temperature of 45°C. Samples were maintained at 4°C during analysis, with an injection volume of 1 µL. Data acquisition was conducted using full scan mode over an m/z range of 50 to 1000, combined with IDA mode. Mass spectrometry parameters were set as follows: ion source temperature at 115°C for both positive and negative modes; capillary voltages at 2500 V (+) and 2500 V (−); declustering potential at 40 V (+) and 40 V (−); collision energy at 4 eV (+) and 4 eV (−); desolvation temperature at 450°C for both modes; desolvation gas flow at 900 L/h for both modes; with a scan time of 0.2 seconds and interscan delay of 0.02 seconds. QC samples were interspersed throughout the run, with one inserted every 10 samples to facilitate the assessment of analytical repeatability. 2.3 Sample preparation and metabolomic analysis by GC-MS Sample preparation. A 60 mg sample was accurately weighed and transferred into a 1.5 mL Eppendorf tube. To each sample, 360 µL of cold methanol and 40 µL of a 0.3 mg/mL solution of 2-chloro-l-phenylalanine in methanol, serving as an internal standard, were added. The samples were then chilled at -20°C for 2 minutes and ground at 60 Hz for another 2 minutes. After this, the samples were subjected to ultrasonication at room temperature for 30 minutes. This was followed by the addition of 200 µL of chloroform and vortex mixing. An additional 400 µL of water was added and the mixture was vortexed again. The samples underwent a second round of ultrasonication at room temperature for 30 minutes, followed by centrifugation at 12,000 rpm for 10 minutes at 4°C. A QC sample was prepared by pooling aliquots from all the samples. A 200 µL portion of the supernatant was then transferred to a glass sampling vial and vacuum-dried at room temperature. The dry sample was reconstituted with 80 µL of a 15 mg/mL methoxylamine hydrochloride solution in pyridine, vortexed for 2 minutes, and incubated at 37°C for 90 minutes. Subsequently, 80 µL of BSTFA (with 1% TMCS) and 20 µL of n-hexane were added. The sample was vortexed for another 2 minutes and derivatized at 70°C for 60 minutes. The samples were then left to equilibrate at room temperature for 30 minutes prior to GC-MS analysis. GC-MS analysis. The derivatized samples were analyzed using an Agilent 7890B gas chromatograph coupled with a 5977A MSD system. A DB-5MS fused-silica capillary column was employed for derivative separation. Helium of high purity (> 99.999%) served as the carrier gas at a flow rate of 1 mL/min. The injection volume was 1 µL in splitless mode, with the injector temperature set at 260°C. The initial oven temperature was 60°C and was programmed to increase to 305°C utilizing a multi-step temperature gradient. The MS quadrupole and ion source temperatures were maintained at 150°C and 230°C, respectively, with an electron impact energy of 70 eV. The mass spectral data was collected in full-scan mode, scanning from m/z 50 to 500. To ensure analytical consistency, QC samples were injected at regular intervals after every 10 sample analyses. 2.4 Metabolomic data processing The LC-MS raw data were processed using Progenesis QI software, with a precursor tolerance of 5 ppm and a fragment tolerance of 10 ppm. The retention time (RT) tolerance was set to 0.02 minutes. Peak alignment was conducted without relying on internal standard detection parameters, isotopic peaks were excluded, and a noise elimination threshold was established at a level of 10.00. The cut-off for minimum intensity was set to 15% of the base peak intensity. The data was compiled into an Excel file, containing three-dimensional datasets that included m/z values, peak RT, and peak intensities, with RT–m/z pairs serving as unique identifiers for each ion. Peaks that were not detected in over 50% of the samples were excluded from the dataset. The internal standard was employed for data quality control, ensuring reproducibility. Metabolites were identified by progenesis QI (Waters Corporation, Milford, USA) Data Processing Software, based on public databases such as http://www.hmdb.ca/ ; http://www.lipidmaps.org/ and self-built databases. For GC-MS data, the AnalysisBaseFileConverter software was utilized to convert raw data from the .D format to .abf files. These files were then imported into MD-DIAL for data processing. Metabolite annotation was done using the LUG database, specifically designed for untargeted GC-MS analysis. Following this, a 'raw data array' was compiled, including sample information, peak names or retention times and m/z values, and peak intensities. This array was filtered to remove internal standards and any known pseudo-positive peaks resulting from background noise, column bleed, or the BSTFA derivatization process. Peaks with a relative standard deviation (RSD) above 0.3 were discarded. The remaining peak areas were normalized according to retention time periods, using multiple internal standards to adjust for any variations in peak intensity. 2.5 Transcriptome sequencing To isolate total RNA, we employed the Trizol reagent kit (Invitrogen, Carlsbad, CA) following the manufacturer's protocol. The integrity and quality of the RNA were assessed using the Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA) in conjunction with RNase-free agarose gel electrophoresis. Subsequent to DNA digestion with DNase, eukaryotic mRNA was isolated using Oligo(dT) beads. This mRNA was then fragmented in a buffer solution and reverse-transcribed into cDNA with random hexamer primers. To produce the second-strand cDNA, we used a combination of RNase H, DNA Polymerase I, dNTPs, and reaction buffer. The double-stranded cDNA was then purified using the QiaQuick PCR extraction kit (Qiagen, Venlo, The Netherlands), and further processed through end-repair, poly(A) tailing, and adapter ligation for Illumina sequencing. The adapter-ligated fragments were size-selected via agarose gel electrophoresis, PCR-amplified, and sequenced on an Illumina HiSeq 2500 platform by Gene Denovo Biotechnology Co. 2.6 Bioinformatics analysis Raw reads from the sequencing process were pre-processed using Fastp version 0.18.0 to remove low-quality sequences. The resulting clean reads were then aligned against the Nipponbare ribosomal RNA (rRNA) database using Bowtie 2 version 2.2.8 for rRNA removal. An index of the reference genome was generated, and HISAT2 version 2.2.4 was employed for the alignment of the cleaned and paired-end reads to the rice reference genome. Expression levels were quantified by calculating FPKM (Fragments Per Kilobase of exon per Million mapped fragments) for each transcript region. Differential expression analysis among various samples was performed using DESeq2 version 1.44.0. 2.7 Statistical analysis To assess the correlation between replicates, a principal component analysis (PCA) was conducted using the R package gmodels version 2.19.1. Metabolites with a Variable Importance in Projection (VIP) score exceeding 1, a statistically significant P-value less than 0.05 (Mann-Whitney U test), and an absolute log2 fold change (|log2 FC|) greater than 1 were categorized as differentially accumulated metabolites (DAMs). To gain further insights into the biological significance of the DAMs, an enrichment analysis was performed using the MetaboAnalyst platform ( www.metaboanalyst.ca ). Differentially expressed genes (DEGs) were identified as those with an absolute log2 fold change (|log2 FC|) greater than 1 and a P-value less than 0.05 (Mann-Whitney U test). These DEGs were then subjected to enrichment analyses using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases. Correlation and cluster analyses were performed using the R package ComplexHeatmap version 2.20.0 to explore the relationships and patterns of co-expression among the genes of interest. 3 Results 3.1 Metabolome and transcriptome profiling Previous research has extensively explored the agronomic traits of the soybean genotypes TW75 and TW-1[ 22 – 24 ]. It was observed that field emergence rates for these varieties were influenced by a complex interaction between their genetic constitution ( mips1 mutation) and environmental factors (specifically temperature variation associated with seasonal changes). Notably, seeds collected during autumn in Hangzhou demonstrated high field emergence rates for both parental and mutant lines, reaching around 85%. In contrast, seeds from spring season exhibited significantly lower emergence rates, with the parental genotype TW75 achieving 45% and the mutant TW-1 reaching only 25%. These results highlight the significant impact of seasonal variations on seed germination rates and suggest that mutations in the mips1 gene may affect field emergence differently under varying environmental conditions. In our detailed metabolic analysis using LC-MS and GC-MS techniques, we identified a total of 479 unique metabolites, with the majority (419) detected through GC-MS and the remaining 60 via LC-MS. Principal component analysis (PCA) effectively differentiated the sample groups (Fig. 1 A), capturing 55.24% of the variance with the first two principal components. Distinctions among groups were primarily driven by developmental stages and seasonal changes rather than mips1 mutations. The meticulous correlation analysis confirmed unique metabolic profiles for each sample group (Fig. 1 B), particularly when classified by developmental stage and seasonal context. A key finding was the significant metabolic differentiation observed at the late developmental stage, suggesting a vital role for metabolic diversity in affecting germination success. In terms of transcriptomics, the assembly and sequencing of RNA libraries for TW75 and TW-1 yielded a total of 1,088,044,580 clean reads across the variants and seasons. PCA revealed tight clustering of biological replicates (Fig. 1 C), accounting for 75.15% of the variation, primarily due to the developmental stage. This indicated notable gene expression differences, especially evident in the late developmental phase. Moreover, intricate correlation analysis highlighted distinct seasonal expression patterns within the same developmental stages (Fig. 1 D), with a clear distinction between the early and middle stages compared to the late stage. Additionally, the metabolic profiles and transcriptional profiles of parental and mutant soybeans differed to some extent at the same developmental stage and the same season, with this difference being more pronounced in the spring. These findings emphasize the complex relationship between gene expression and environmental conditions. 3.2 Seasonal Variation in Differentially Accumulated Metabolites (DAMs) Amidst pronounced seasonal differences in field emergence rates and metabolite profiles, we conducted metabolite analyses across three developmental stages between spring and autumn to pinpoint the DAMs. As the stages progressed, both TW75 and TW-1 exhibited an increase in DAMs, with a marked rise in up-regulated metabolites (Fig. 2 A). During the late stage, TW75 exhibited 203 DAMs (24 down-regulated and 179 up-regulated), while TW-1 exhibited 208 DAMs (20 down-regulated and 188 up-regulated) between the two seasons. Notably, 147 metabolites were consistently regulated across both soybean varieties, potentially driving the seasonal shifts in field emergence rates. These DAMs were categorized into over ten subclasses, primarily including amino acids, peptides, and analogues, carbohydrates and their conjugates, and fatty acids and conjugates (Fig. 2 B). The top-enriched KEGG pathways (Fig. 2 C) included arginine biosynthesis, glutathione metabolism, the pentose phosphate pathway, arginine and proline metabolism, and pyrimidine metabolism—mainly connected to amino acids and energy metabolism. These pathways, especially prevalent in the late stage, are believed to significantly influence the germination process. Dynamic changes in seed composition during the developmental and maturation phases significantly affect seed quality, highlighting the importance of understanding stage-specific DAMs alterations during seed development in TW75 and TW-1 (Fig. 3 ). The DAMs were grouped into four clusters, each with a distinct profile. A significant proportion of amino acids, peptides, and analogues (69.44%) fell into cluster 1 and 3, both showing a decline in autumn. However, during spring, cluster 1's metabolite levels were considerably higher at every stage, whereas cluster 3 displayed a rising trend, diverging from the autumnal patterns. These trends underscore the critical role of free amino acids in seed maturation. The differential accumulation of these compounds from the middle to late stages may distinctly influence seed quality. Cluster 2 contained a diverse mixture of subclasses, most notably carbohydrates and their conjugates (53.33%), fatty acids and conjugates (44.44%), glycerophosphocholines (70.00%), and alcohols and polyols (100.00%). In this cluster, metabolite levels in spring surged during the late stage while remaining consistent throughout the developmental stages in autumn. The differential build-up of amino acids, carbohydrates, and lipids in this cluster is vital for the germination of soybean seeds, particularly regarding energy metabolism. This variation may contribute to the superior emergence rates seen in autumn-harvested seeds. Additionally, DAMs such as scyllo-inositol and inositol-4-monophosphate in cluster 2, linked to inositol phosphate metabolism, warrant further exploration for their roles in these observed phenomena. 3.3 Seasonal Expression Discrepancy of Genes The distinctive patterns of gene expression observed during different seasons reflected variations in field emergence rates, prompting a detailed analysis of differentially expressed genes (DEGs) (Fig. 4 ). In the ES, a remarkable count of 639 DEGs was identified (551 up-regulated and 88 down-regulated), increasing to 3,071 DEGs (2,167 up-regulated and 904 down-regulated) in MS, and decreasing to 1,636 DEGs (771 up-regulated and 865 down-regulated) in LS of development (Fig. 4 A). A peak in the quantity of DEGs was noted during the middle phase when comparing the spring and autumn periods. These DEGs, numbering 639, 3,071, and 1,636 for each respective stage, were systematically categorized within the Gene Ontology (GO) framework, encompassing three domains: biological processes, molecular functions, and cellular components (Fig. 4 B). The most substantial and enriched category across all three stages pertained to cellular components, specifically cell and cell part (312, 48.82% in ES; 1,265, 41.19% in MS; 747, 52.02% in LS), with the ES also emphasizing intracellular components (289, 45.22%) and binding functions (281, 43.97%). The MS gene expressions were marked by single-organism processes (668, 21.75%), transferase activities (527, 17.16%), and small molecule interactions (454, 14.78%). In contrast, the LS was dominated by intracellular components (678, 47.21%) and organelle-specific genes (521, 36.28%). A distinct shift in GO terms was evident, with the MS favoring cell membrane-related processes and the ES and LS focusing on intracellular metabolic functions. The radial enrichment diagrams (Fig. 4 D-F) illustrate the top 20 enriched KEGG pathways on the periphery, the number of genes associated with each pathway and their significance values on the second tier, the regulation status of the genes on the third, and the pathway enrichment ratios at the core. Early-stage development was characterized by pathways such as motor protein activity (map04814), photosynthesis antenna proteins (map00196), ATP-dependent chromatin restructuring (map03082), circadian rhythm regulation in plants (map04712), and homologous recombination (map03440). As development advanced, the middle stage's pathways shifted towards plant-pathogen interaction (map04626), metabolism of amino and nucleotide sugars (map00520), plant-specific MAPK signaling (map04016), nucleotide sugar biosynthesis (map01250), and galactose utilization (map00052). The later stage was enriched with pathways critical for protein processing in the endoplasmic reticulum (map04141), arginine construction (map00220), glutathione pathways (map00480), glycerolipid metabolism (map00561), and the breakdown of 2-oxocarboxylic acids (map01210). Further enrichments were seen in stress-related metabolic pathways during the middle and late stages, in addition to those involving amino acids previously identified in the analysis of DAMs. Notably, the DEGs across these stages were predominantly responsible for coding 9 key transcription factors (TFs, Fig. 4 C)—AP2/ERF, WRKY, bHLH, MYB, HSF, bZIP, TCP, GATA, and NAC. These TFs play a critical role in regulating gene expression essential for initiating and promoting germination. The expression trends of HSF, in particular, point to the critical impact of thermal dynamics on gene expression throughout the development stages, highlighting the complex regulatory mechanisms that influence germination in soybean seeds. 3.4 Impact of mips1 Mutations on Field Emergence Rates TW-1, a soybean mutant line recognized for its low phytic acid levels, has shown variable field emergence rates during the spring season. This variability suggests that the inositol phosphate metabolism pathway plays a pivotal regulatory role in the seed maturation process. A detailed investigation compared the metabolite levels and gene expression profiles between the mutant and wild-type soybeans to elucidate the genetic factors affecting spring emergence (Fig. 5 ). The study revealed dynamic changes in DAMs, starting with an early count of 40 DAMs (30 up-regulated and 10 down-regulated), shifting to 33 DAMs (24 up-regulated and 9 down-regulated), and peaking at 79 DAMs (50 up-regulated and 29 down-regulated) by the LS of development (Fig. 5 A). Comparative analysis indicated significantly higher counts of DAMs between seasons than between genotypes. Despite this, there was a notable commonality in the DAMs, including fatty acids and their conjugates, carbohydrates, carbohydrate conjugates, as well as amino acids, peptides, and analogues (Fig. 5 B). This suggests that both seasonal variation and mips1 genetic variation may share a similar mechanism underlying reduced soybean emergence, leading to the lowest seed germination rate for TW-1 in spring. However, the regulatory mechanisms during seed development need further discussion. An upward trend was observed in the number of DEGs between TW75-s and TW-1-s, beginning with 232 DEGs (130 up-regulated and 102 down-regulated) in the ES, escalating to 1,204 DEGs (746 up-regulated and 458 down-regulated) in the MS, and culminating in 2,028 DEGs (1,027 up-regulated and 1,001 down-regulated) by the LS (Fig. 5 A). Pathway enrichment analysis of these DEGs highlighted their significant participation in key metabolic pathways, including carbon metabolism, glycolysis/gluconeogenesis, galactose metabolism, and pyruvate metabolism. The inositol phosphate metabolism pathway was especially prominent among the DEGs, emphasizing its potential regulatory importance (Fig. 5 C). The DEGs between TW75-s and TW-1-s demonstrated marked enrichment in energy metabolism pathways and pathways related to amino acid metabolism. An examination of transcription factors (TFs) revealed that the DEGs predominantly encoded AP2/ERF, MYB, bZIP, WRKY, and bHLH transcription factors. This pattern of TF encoding mirrored the differential expression of transcription factors noted between the spring and autumn seasons (Fig. 5 D), suggesting a preservation of regulatory themes within the seasonal transcriptome. 3.5 Correlation Network of Seasonal and Genetic Factors The genes in enriched pathways, transcription factors (TFs) in differentially expressed genes (DEGs), and metabolites in differentially accumulated metabolites (DAMs) were screened based on their relative contents/fpkm values and P values. These elements were then classified using heatmaps and correlation networks (Fig. 6 A). The investigation differentiated between seasonal influences—contrasts observed between autumn and spring samples—and genetic contributors—disparities between the TW75-s and TW-1-s variants. Among the transcription factors surveyed, families such as AP2/ERF, WRKY, bHLH, MYB, and HSF stood out as potential modulators of field emergence rates. The expression profiles of these TFs were predominantly dictated by seasonal changes, with only a subset being influenced by genetic factors. In pathway analysis, key metabolic routes—glutathione metabolism, galactose metabolism, inositol phosphate metabolism, and carbon metabolism—were found to be significantly responsive to both seasonal and genetic elements. Glutathione metabolism, in particular, was identified as a primary determinant for the observed decrease in seed emergence during spring. Moreover, carbon metabolism genes were consistently implicated under both seasonal and genetic factors, suggesting a compounded effect that potentially leads to further diminished field emergence rates in the TW-1-s line. Furthermore, we conducted a correlation analysis to investigate the relationship between field emergence rates and the expression of TF genes, as well as DEGs associated with carbon and glutathione metabolism pathways during specific developmental stages. This analysis revealed a strong correlation (Fig. 6 B), indicating that variations in field emergence rates might be closely linked to the regulatory roles of specific TFs during the seed development phase. These transcription factors are likely critical in fine-tuning gene expression within key metabolic pathways, thereby influencing the germination process and initial seedling growth. 4 Discussion In our study, we observed that both climatic variability and mutations in the mips1 gene have a pronounced impact on the field emergence rates of soybean cultivars TW-1 and TW-75, which is consistent with previous reports about low phytic acid mutants[ 11 , 13 , 24 ]. We proposed that the cooler and wetter conditions typical of spring in Hangzhou may compromise seed integrity, and suggested that mips1 mutations, together with subsequent metabolic pathways, could intensify this decline in seed emergence. The quality of soybean seeds was clearly influential on germination rates; however, the specific factors and mechanisms that account for seasonal variation, mips1 mutation and their synergistic effect in soybean germination are not yet fully understood. To shed light on this, we analyzed mips1 mutant and wild type seed samples from both seasons using metabolomic and transcriptomic approaches, aiming to pinpoint DAMs and DEGs, and thereby illuminate the physiological and biochemical changes that accompany seasonal shifts. During seed maturation, a vital phase of development, we observed significant changes of nutritive reserves. Notably, the seeds in spring exhibited a marked increase in metabolites such as amino acids, peptides, carbohydrates, and fatty acids, particularly in the late maturation stage. This high concentration of these small molecular metabolites could indicate an untimely initiation of metabolic processes typical of germination and seedling growth, resulting in premature energy depletion. Macromolecular substances, namely starch, lipids and proteins, are essential nutrition for seed germination[ 32 ]. The disruption of the seed development process appears to lead to a metabolic imbalance, undermining the seed's germinative capacity. Starch is a pivotal energy store within the endosperm, broken down by amylases from the aleurone layer at the onset of germination[ 33 ]. The increased levels of carbohydrates in spring-harvested seeds may suggest premature starch degradation, potentially affecting energy availability during early germination stages. Moreover, given the critical role of lipids during germination, our observation of increased phosphatidylcholine and glycerophosphocholine levels in the late maturation stage of seeds harvested in spring indicates significant membrane synthesis activity. These elevated lipid levels are often associated with stress responses, as plants frequently alter the selective permeability of membranes to adopt to abiotic stress[ 34 ]. This stress response could divert resources from other vital processes, potentially leading to reduced field emergence rates. Notably, phytic acid is the main storage form of phosphorus in plant seeds[ 9 , 10 , 15 ]. In TW-1-s, the synthesis of large amounts of phosphatidylcholine and lysophosphatidylcholine may further affected phosphorus storage, leading to reduced seed quality. The metabolome dynamics also extend to amino acids and peptides. During germination, these compounds are released through protein hydrolysis, enhancing nutrient accessibility[ 35 ]. Typically, free amino acids decrease, and total amino acids (including those incorporated into seed-storage proteins) increase in the late developmental stage of seeds[ 1 ]. Our comparative analysis revealed a 4.88-fold increase in these metabolites in spring. Notably, the free amino acids identified as DAMs play fundamental roles in the seed's central metabolism, serving not only as building blocks for storage proteins but also as precursors for a myriad of other metabolites, including phytohormones and protective secondary metabolites. The metabolome analysis revealed the accumulation of significantly higher levels of stress-related primary metabolites, such as soluble amino acids, soluble sugars, and TCA cycle intermediates, in spring. This indicates that the climatic stress in spring decreased seed quality and further reduced field emergence rates. The climate-related stress that result in varying seed quality, as revealed by metabolomic analysis, are the direct causes of the low emergence rate. Further transcriptomic analysis combined with metabolic analysis elucidated the underlying regulatory mechanisms. The transcriptomic data exhibited patterns parallel to these metabolic trends, particularly in pathways related to glycerolipid metabolism, glutathione metabolism, carbon metabolism, and alanine, aspartate, and glutamate metabolism. Notably, genes involved in glutathione metabolism, known to preserve seed longevity and regulate dormancy[ 36 – 38 ], displayed distinct differential expression between the spring and autumn seasons. Our findings indicated a significant association between glutathione metabolism and carbon metabolism, which could be a crucial factor contributing to the observed differences in field emergence between the spring and autumn seasons (Fig. 7 ). Examining the differences between TW75-s and TW-1-s, we identified both similarities and distinctions in DAMs and DEGs in response to seasonal variations. These differences suggest that the mips1 gene may be involved in the seasonally induced stress response. The reduction in MIPS activity can lead to a decrease in myo-inositol levels, consequently impeding the production of important oligosaccharides(Fig. 8 ). Additionally, phytic acid synthesis includes both lipid-independent and lipid-dependent pathways[ 9 , 10 , 12 ]. MIPS1 gene is involved in the first step of phytic acid synthesis and affects both pathways, with the lipid-dependent pathway being associated with cell membrane synthesis[ 39 ]. Disturbances in phosphorus and lipid metabolism affected the synthesis of cell membranes under abiotic stress, and thus TW-1 produces lower seed quality in spring than TW75. Furthermore, transcription factors from the AP2/ERF, WRKY, bHLH, MYB, and HSF families play significant roles in regulating seed development and quality, influenced by both seasonal and genetic factors. For instance, the AP2/ERF family, implicated in water absorption and abscisic acid signaling, likely affects the seeds' ability to cope with drought and water-related stresses, influencing emergence rates[ 40 ]. The bHLH family influences germination response to temperature, which could explain varied emergence rates under different seasonal temperature conditions[ 41 ]. The MYB family, involved in stress responses, likely plays a role in the seeds' resilience to environmental stresses, including salinity and drought[ 42 ]. Lastly, the HSF family emphasizes the role of temperature in stress responses, particularly in the activation of heat shock proteins during high-temperature conditions[ 43 ]. Our findings underscore the multifaceted influences on field emergence, rooted in seed development impacted by both seasonal and genetic factors. Stress-induced metabolic imbalances and mutations in the MIPS gene contribute to reduced seed quality and emergence rates. Transcription factors likely play a critical role in modulating these metabolic processes. Both the wild-type and mutant seeds were sensitive to seasonally induced stress; however, the mutant TW-1 exhibited a higher sensitivity, leading to significantly lower field emergence rates in the spring compared to TW75. The MIPS gene is associated with stress resistance, potentially due to its role in regulating cell membrane synthesis through phosphorus metabolism. Despite these insights, the intricate nature of seeds and the variability of climate conditions necessitate tailored optimization strategies. Future research will further investigate the germination rates of various MIPS mutants, aiming to identify optimal seeds and conditions for germination, with the overarching goal of enhancing germination rates to improve the quality of soybean varieties. 5 Conclusion In conclusion, our study underscores the profound influence of seasonal climatic variability and genetic mutations in the MIPS1 gene on the field emergence rates of soybean cultivars TW-1 and TW75. The marked decrease in seed quality and emergence rates, particularly in the spring, highlights the intricate interplay between environmental conditions and seed physiology. Through comprehensive metabolomic and transcriptomic analyses, we identified that stress-induced metabolic imbalances, especially in lipid, carbohydrate, and amino acid metabolism pathways, are pivotal contributors to these observed variations. Specifically, the MIPS1 gene plays a crucial role in regulating cell membrane synthesis via phosphorus metabolism. Mutations in this gene lead to significant changes in metabolic activities, resulting in increased sensitivity to seasonal stressors. Our observations indicated that the mutant TW-1, in particular, exhibited a higher sensitivity to such stress, manifesting in significantly lower field emergence rates compared to TW75. The differential accumulation of metabolites and expression of genes during seed maturation stages further elucidates the physiological and biochemical changes underpinning these seasonal effects. The study also highlights the role of various transcription factors, including those from the AP2/ERF, bHLH, MYB, and HSF families, which are instrumental in modulating seed development and stress responses. These factors influence critical processes such as water absorption, temperature response, and adaptation to abiotic stressors, thereby affecting seed quality and emergence rates. Going forward, it is essential to delve deeper into the regulatory mechanisms involving the MIPS1 gene and its associated metabolic pathways. Future research should aim to identify the optimal genetic traits and environmental conditions that enhance seed quality and germination rates of low phytic acid cultivars. Such insights will be invaluable in developing soybean varieties with improved resilience and performance, ultimately contributing to agricultural sustainability and food security. Our findings lay a solid foundation for future studies targeting the optimization of seed emergence through an integrated approach combining genetic, metabolic, and environmental factors. By advancing our understanding of these intricate interactions, we pave the way for innovative strategies to improve the quality and yield of soybean crops, thereby addressing the challenges posed by climatic variability and enhancing global food production. Declarations Acknowledgements Reviewers are acknowledged for their contribution to the improvement of the manuscript in the revision process. Funding This research was funded by the Zhejiang Provincial Major Agriculture Science and Technology Special, China (Grant No. 2021C02064-5) References Amir R, Galili G, Cohen H: The metabolic roles of free amino acids during seed development . Plant Science 2018, 275 :11-18. Krishnan HB, Kim W-S, Oehrle NW, Smith JR, Gillman JD: Effect of Heat Stress on Seed Protein Composition and Ultrastructure of Protein Storage Vacuoles in the Cotyledonary Parenchyma Cells of Soybean Genotypes That Are Either Tolerant or Sensitive to Elevated Temperatures . International Journal of Molecular Sciences 2020, 21 (13):4775. Chebrolu KK, Fritschi FB, Ye S, Krishnan HB, Smith JR, Gillman JD: Impact of heat stress during seed development on soybean seed metabolome . Metabolomics 2016, 12 (2):28. 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Feng Z, Ding C, Li W, Wang D, Cui D: Applications of metabolomics in the research of soybean plant under abiotic stress . Food Chemistry 2020, 310 :125914. Zhang H, Hu Z, Yang Y, Liu X, Lv H, Song B-H, An Y-qC, Li Z, Zhang D: Transcriptome profiling reveals the spatial-temporal dynamics of gene expression essential for soybean seed development . BMC Genomics 2021, 22 (1):453. Liu H, Li X, Zhang Q, Yuan P, Liu L, King GJ, Ding G, Wang S, Cai H, Wang C et al : Integrating a genome-wide association study with transcriptomic data to predict candidate genes and favourable haplotypes influencing Brassica napus seed phytate . DNA Research 2021, 28 (5):dsab011. Valliyodan B, Ye H, Song L, Murphy M, Shannon JG, Nguyen HT: Genetic diversity and genomic strategies for improving drought and waterlogging tolerance in soybeans . Journal of Experimental Botany 2016, 68 (8):1835-1849. Chaudhary J, Patil GB, Sonah H, Deshmukh RK, Vuong TD, Valliyodan B, Nguyen HT: Expanding Omics Resources for Improvement of Soybean Seed Composition Traits . Frontiers in Plant Science 2015, 6 :1021. Bisht A, Saini DK, Kaur B, Batra R, Kaur S, Kaur I, Jindal S, Malik P, Sandhu PK, Kaur A et al : Multi-omics assisted breeding for biotic stress resistance in soybean . Molecular Biology Reports 2023, 50 (4):3787-3814. Deshmukh R, Sonah H, Patil G, Chen W, Prince S, Mutava R, Vuong T, Valliyodan B, Nguyen HT: Integrating omic approaches for abiotic stress tolerance in soybean . Frontiers in Plant Science 2014, 5 :244. Bellieny-Rabelo D, de Oliveira EAG, Ribeiro EdS, Costa EP, Oliveira AEA, Venancio TM: Transcriptome analysis uncovers key regulatory and metabolic aspects of soybean embryonic axes during germination . Scientific Reports 2016, 6 (1):36009. Manoharlal R, Saiprasad GVS: Assessment of germination, phytochemicals, and transcriptional responses to ethephon priming in soybean [Glycine max (L.) Merrill] . Genome 2019, 62 (12):769-783. Rustgi S, Kakati JP, Jones ZT, Zoong Lwe ZS, Narayanan S: Heat tolerance as a function of membrane lipid remodeling in the major US oilseed crops (soybean and peanut) . Journal of Plant Biochemistry and Biotechnology 2021, 30 (4):652-667. Zhang G, Xu J, Wang Y, Sun X, Huang S, Huang L, Liu Y, Liu H, Sun J: Combined transcriptome and metabolome analyses reveal the mechanisms of ultrasonication improvement of brown rice germination . Ultrasonics Sonochemistry 2022, 91 :106239. Akram S, Siddiqui MN, Hussain BMN, Al Bari MA, Mostofa MG, Hossain MA, Tran L-SP: Exogenous Glutathione Modulates Salinity Tolerance of Soybean [Glycine max (L.) Merrill] at Reproductive Stage . Journal of Plant Growth Regulation 2017, 36 (4):877-888. Cheng MC, Ko K, Chang WL, Kuo WC, Chen GH, Lin TP: Increased glutathione contributes to stress tolerance and global translational changes in Arabidopsis . The Plant Journal 2015, 83 (5):926-939. Koramutla MK, Negi M, Ayele BT: Roles of Glutathione in Mediating Abscisic Acid Signaling and Its Regulation of Seed Dormancy and Drought Tolerance . Genes 2021, 12 (10):1620. Roth MG: Phosphoinositides in constitutive membrane traffic . Physiological Review 2004, 84 (3):699-730. Ma Z, Hu L, Jiang W: Understanding AP2/ERF Transcription Factor Responses and Tolerance to Various Abiotic Stresses in Plants: A Comprehensive Review . International Journal of Molecular Sciences 2024, 25 (2):893. Guo J, Sun B, He H, Zhang Y, Tian H, Wang B: Current Understanding of bHLH Transcription Factors in Plant Abiotic Stress Tolerance . International Journal of Molecular Sciences 2021, 22 (9):4921. Wang X, Niu Y, Zheng Y: Multiple Functions of MYB Transcription Factors in Abiotic Stress Responses . International Journal of Molecular Sciences 2021, 22 (11):6125. Guo M, Liu J-H, Ma X, Luo D-X, Gong Z-H, Lu M-H: The Plant Heat Stress Transcription Factors (HSFs): Structure, Regulation, and Function in Response to Abiotic Stresses . Frontiers in Plant Science 2016, 7 :114. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Dec, 2024 Read the published version in BMC Plant Biology → Version 1 posted Editorial decision: Revision requested 19 Sep, 2024 Editor assigned by journal 18 Sep, 2024 Submission checks completed at journal 18 Sep, 2024 First submitted to journal 10 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5063011","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":356512772,"identity":"bcab17b0-6741-4076-af90-64128687b34b","order_by":0,"name":"Huakun Yu","email":"","orcid":"","institution":"Xianghu Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Huakun","middleName":"","lastName":"Yu","suffix":""},{"id":356512773,"identity":"7f55ab10-18cc-43b1-91cc-9f819b6c123e","order_by":1,"name":"Longming Zhu","email":"","orcid":"","institution":"ZheJiang Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Longming","middleName":"","lastName":"Zhu","suffix":""},{"id":356512774,"identity":"5e7ccdb0-eff7-451f-aaa8-43b5dc0a9a1f","order_by":2,"name":"Yuhao Chen","email":"","orcid":"","institution":"Xianghu Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Yuhao","middleName":"","lastName":"Chen","suffix":""},{"id":356512775,"identity":"183c6e84-f0f1-4bbf-a726-8cc46144f43a","order_by":3,"name":"Ping Deng","email":"","orcid":"","institution":"Xianghu Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Deng","suffix":""},{"id":356512776,"identity":"590b359d-db8f-4c7c-882b-b522c7cd5c8d","order_by":4,"name":"Bei Liu","email":"","orcid":"","institution":"Xianghu Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Bei","middleName":"","lastName":"Liu","suffix":""},{"id":356512777,"identity":"4d1dc509-123a-43d2-81f4-bf44d906564b","order_by":5,"name":"Xiaochao Chen","email":"","orcid":"","institution":"Xianghu Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Xiaochao","middleName":"","lastName":"Chen","suffix":""},{"id":356512778,"identity":"baaf9863-0476-45c2-9e69-c8346c9d04f8","order_by":6,"name":"Fengjie Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBAC9oYDIOoAAz8z88EHRGnhOQDVItnOlmxApBYGiBaD8zxmAsRpYTxjJvFzxx1748MMZgwMNTbRhLUwnDGT7D3zjNnsMEPaA4ZjabkNhLTYA7VI8LYdZgNqOW7A2HCYsBawLX/bDvMYNzO2SRCtRRpoi4QBMzMbsVqOFVvLth02kDjMxmyQQIxfeCQOb7z5tu2wPX//+Y8PPtTYENbCIHECKQITCCoHAf72B0SpGwWjYBSMghEMAO6sP0YCaEE0AAAAAElFTkSuQmCC","orcid":"","institution":"Xianghu Laboratory","correspondingAuthor":true,"prefix":"","firstName":"Fengjie","middleName":"","lastName":"Yuan","suffix":""}],"badges":[],"createdAt":"2024-09-10 08:27:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5063011/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5063011/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-024-05957-x","type":"published","date":"2024-12-23T15:57:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69339272,"identity":"e2624484-829d-4ba0-babf-413518231702","added_by":"auto","created_at":"2024-11-19 10:40:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55235995,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolome and transcriptome profile in different groups. (A) PCA of metabolites detected in different groups. (B) Correlation analysis of metabolites in different groups. (C) PCA of genes expressing in different groups. (D) Correlation analysis of genes in different groups.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5063011/v1/0377fa62a26d7ee9cda092cb.png"},{"id":69339267,"identity":"91225a98-cb36-4e82-9c2d-cb11d69aabe4","added_by":"auto","created_at":"2024-11-19 10:40:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5521440,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially accumulated metabolites (DAMs) between different seasons. (A) Sum of up-, down-, and same-regulated DAMs and between autumn and spring; (B) HMBD subclass categories of same-regulated DAMs in different stages; (C) Most enriched KEGG terms in different stages.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5063011/v1/ed39baf92dc8ad824be88095.png"},{"id":69339276,"identity":"fbb4ac92-a6f5-484d-b7e7-6255c2b8d650","added_by":"auto","created_at":"2024-11-19 10:40:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":208574866,"visible":true,"origin":"","legend":"\u003cp\u003eCluster analysis of DAMs in different stages\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5063011/v1/88426c32862a1e760895c285.png"},{"id":69339273,"identity":"000bea62-ed45-4795-bf8e-82a57ff66839","added_by":"auto","created_at":"2024-11-19 10:40:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":52399885,"visible":true,"origin":"","legend":"\u003cp\u003eDAMs and DEGs between spring and autumn in different stages. (A) sum of up-, down- and same-regulated genes between spring and autumn; (B) top 30 enrichment GO terms between between spring and autumn; (C) Transcription factor (TF) families enriched in DEGs between spring and autumn; (D) top 20 KEGG enrichment pathways of ES; (E)top 20 KEGG enrichment pathways of MS; (F) top 20 KEGG enrichment pathways of LS;\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5063011/v1/63e3d2d7eb9f78f5dd3a3105.png"},{"id":69339268,"identity":"e0296294-16d3-4e1c-8e40-51f3d226f189","added_by":"auto","created_at":"2024-11-19 10:40:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":6213208,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential metabolites and genes between TW75-s and TW-1-s. (A) sum of up- and down-regulated DAMs and DEGs between TW75-s and TW-1-s; (B) HMDB subclass of DEGs between TW75-s and TW-1-s; (C) top 30 enrichment KEGG pathways in different stages; (D) TF families enriched in DEGs between TW75-s and TW-1-s.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5063011/v1/4a72df03611d2028b5645f04.png"},{"id":69339274,"identity":"5fd13bbb-92b6-436e-99aa-faf474adfa33","added_by":"auto","created_at":"2024-11-19 10:40:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":137142068,"visible":true,"origin":"","legend":"\u003cp\u003eExpressing trends and correlation of key metabolites and genes. (A) Expressing trends of DEGs of TF families (left) and DEGs and DAMs of key metabolic pathways (right); (B) Correlation network of TF families and genes in pathways.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5063011/v1/8cac9c38587998c65d71f836.png"},{"id":69339270,"identity":"f5315fee-4ed8-4a01-b539-7c203512f923","added_by":"auto","created_at":"2024-11-19 10:40:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":20173357,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression of DEGs and DAMs associated with glutathione metabolism and carbon metabolism in different groups\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5063011/v1/b044caf0ce12f6de4deb02d1.png"},{"id":69339431,"identity":"4dfe89f0-4997-4c56-87a4-b6eb7dd38829","added_by":"auto","created_at":"2024-11-19 10:48:50","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":12526090,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression of DEGs and DAMs associated with inositol phosphate metabolism and galactose metabolism in different groups\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-5063011/v1/ad3dfc7f7622da23b1a1a3b6.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of Seasonal Climates and MIPS Mutations on Soybean Germination through Multi-Omics Analysis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe soybean (\u003cem\u003eGlycine max L.\u003c/em\u003e Merr.) stands as a leading oilseed crop worldwide. Its seeds are pivotal, not only significantly bolstering human nutrition with essential plant-derived proteins and fats, but also serving as a critical element in the plant's reproductive cycle by facilitating propagation. Therefore, seed quality is crucial for preserving genetic resources and advancing agricultural yields.\u003c/p\u003e \u003cp\u003eSoybean seed development unfolds in three primary stages[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]: embryonic development featuring embryo growth, cell division, and morphogenesis; maturation characterized by the substantial accumulation of nutritive reserves; and a seed drying phase involving desiccation and dormancy onset. These stages are central to determining seed quality, germination, and viability and coincide with comprehensive changes in gene expression, protein profiles, and metabolite levels, alongside notable metabolic shifts over space and time. The stored nutrition within the seed are the energy source for germination and seedling growth.\u003c/p\u003e \u003cp\u003eThe quality of soybean seeds stems from a complex interaction between innate genetic factors and external environmental conditions. Environmental factors profoundly and intricately affect plant growth and metabolic functions. The interplay between the soybean's genetics and various climatic stressors, such as temperature fluctuations[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], drought stress[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and sunlight exposure, affects soybean physiology in complex and unpredictable ways. While soybeans are generally thermally resilient, the seed development stage is particularly sensitive to extreme temperatures, potentially leading to reduced germination, increased susceptibility to disease, and decreased seed value[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe genetic blueprint also significantly dictates seed development, where genetic deviations, such as low phytic acid mutations, sometimes align with inferior seed quality. These genetic variations tend to manifest in reduced seed yield and viability at last[\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The work of Meis et al.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] illuminated the phenotypic consequences of such genetic alterations, finding that homozygous \u003cem\u003emips\u003c/em\u003e genotypes from the LR 33 lineage display significantly lower field emergence rates compared to wild-type (WT) counterparts. Another important observation from this study was the varying impact of the seeds' origin; those cultivated in temperate climates suffered less in field emergence than those from tropical regions. Low phytic acid mutants associated with the MRP5 gene also exhibit reduced seeds viability[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, mutations of IPK1 gene, which catalyzes the last step of phytate synthesis, do not lead to the inferior of quality in soybean seeds[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Genotype play a decisive role in seeds viability among low phytic acid mutants. The MIPS1 gene catalyzes the first step of phytate synthesis, and mutations in this gene result in substantial reduction in phytic acid content, also affecting the metabolism of the raffinose in soybean seeds[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A large number of studies have been conducted on the metabolic mechanisms of low seed emergence in low phytic acid seeds[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and on improving the emergence of low phytic acid varieties[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In our previous study[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], the TW-1, a mutation of Taiwan75, exhibited a notably different field emergence rate compared to its parent variety, Taiwan75. We have previously investigated the differential expression profiles of these mutations during seed germination through comprehensive proteomic and transcriptomic analyses. However, the impact of parental traits on the F1 generation's subsequent germination remains largely unexplored. By examining how parental characteristics affect the germination potential of the F1 generation, we can gain valuable insights into the genetic mechanisms of seed development and agronomic practices that could enhance seed quality and field performance.\u003c/p\u003e \u003cp\u003eMetabolomics has emerged as a powerful tool in delineating metabolic processes across different crops, soybeans included[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Metabolomic studies employing liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-MS have yielded critical insights into the metabolic alterations in stress-exposed seeds. The evolution of next-generation sequencing technologies has paved the way for new omics fields like transcriptomics[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], genomics[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and proteomics[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Combining metabolomics with transcriptomics in multi-omics analyses has become a prevalent method for investigating the complex interactions within organisms, shedding light on the regulatory networks that control metabolic pathways[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we leveraged transcriptomic and metabolomic analyses to map the regulatory network involved in \u003cem\u003emips1\u003c/em\u003e mutant soybean seed development. Our exploration of the seasonal and genetic variation impacts on field emergence rates aimed to clarify the relationship between \u003cem\u003emips1\u003c/em\u003e gene expression and metabolic profiles in relation to soybean mutant seeds germination. This research contributes to a more profound and comprehensive understanding of the relation between seasonal and \u003cem\u003emips1\u003c/em\u003e genetic variables that influence the accumulation of germination-promoting factors in soybean seeds.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Plant materials and growth conditions\u003c/h2\u003e\n \u003cp\u003eThe experiment utilized low phytic acid soybean mutant lines \u003cem\u003eGm-lpa\u003c/em\u003e-TW75-1 (referred to as TW-1) and its corresponding wild-type parental variety, Taiwan 75 (referred to as TW75). The TW-1 mutant line was developed through gamma irradiation. TW75 is a widely cultivated vegetable soybean variety in Zhejiang Province. For comparative analysis, seeds were harvested from plants grown in adjacent plots within the same field during the spring and autumn seasons of 2020 at the Experimental Farm of the Zhejiang Academy of Agricultural Sciences in Hangzhou, Zhejiang. Sampling was performed at three developmental stages: the early stage (ES) at 15\u0026ndash;20 days after flowering (DAF), the middle stage (MS) at the R6 stage, and the late stage (LS) at the R8 stage, for both spring and autumn. All samples were immediately frozen and stored at -80\u0026deg;C until further metabolite analysis and RNA extraction.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Sample preparation and metabolomic analysis by LC-MS\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eSample preparation.\u003c/strong\u003e A precise 30 mg sample was placed into a 1.5 mL Eppendorf tube containing steel balls. Internal standards, including 20 \u0026micro;L of 2-chloro-l-phenylalanine (0.3 mg/mL) and 20 \u0026micro;L of Lyso PC17:0 (0.01 mg/mL) dissolved in methanol, were added. Subsequently, 1 mL of a methanol-water mixture (7:3, vol/vol) was introduced to each tube. The samples were then subjected to a series of treatments: first, a 2-minute freeze at -20\u0026deg;C, followed by grinding at 60 Hz for 2 minutes, vortexing, 30-minute ultrasonication at room temperature, and another 20-minute freeze at -20\u0026deg;C. After centrifugation at 13,000 rpm and 4\u0026deg;C for 10 minutes, 300 \u0026micro;L of the supernatant was dried in a freeze-concentration centrifugal dryer. The dried residue was reconstituted with 400 \u0026micro;L of a methanol-water mixture (1:4, vol/vol), vortexed for 30 seconds, and ultrasonicated for 2 minutes. Following another centrifugation under the same conditions, 150 \u0026micro;L of the supernatant was syringe-filtered through a 0.22 \u0026micro;m microfilter and transferred to LC vials, which were subsequently stored at -80\u0026deg;C until LC-MS analysis.\u003c/p\u003e\n \u003cp\u003eQuality control (QC) samples were generated by pooling aliquots of all the samples.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eLC-MS analysis.\u003c/strong\u003e we employed an ACQUITY UHPLC system coupled with an AB SCIEX Triple TOF 5600 System for metabolic profiling in both ESI positive and negative ion modes. Chromatographic separation was carried out on an ACQUITY UPLC BEH C18 column using a binary gradient elution system comprising solvent (A) water with 0.1% formic acid (v/v) and solvent (B) acetonitrile and methanol (2:3, vol/vol, with 0.1% formic acid). The gradient protocol was as follows: starting with 1% B, increasing to 30% B at 1 minute, 60% B at 2.5 minutes, reaching 90% B at 6.5 minutes, holding at 100% B from 8.5 to 10.7 minutes, returning to 1% B at 10.8 minutes and maintaining until 13 minutes. The flow rate was set at 0.4 mL/min with a column temperature of 45\u0026deg;C. Samples were maintained at 4\u0026deg;C during analysis, with an injection volume of 1 \u0026micro;L.\u003c/p\u003e\n \u003cp\u003eData acquisition was conducted using full scan mode over an m/z range of 50 to 1000, combined with IDA mode. Mass spectrometry parameters were set as follows: ion source temperature at 115\u0026deg;C for both positive and negative modes; capillary voltages at 2500 V (+) and 2500 V (\u0026minus;); declustering potential at 40 V (+) and 40 V (\u0026minus;); collision energy at 4 eV (+) and 4 eV (\u0026minus;); desolvation temperature at 450\u0026deg;C for both modes; desolvation gas flow at 900 L/h for both modes; with a scan time of 0.2 seconds and interscan delay of 0.02 seconds.\u003c/p\u003e\n \u003cp\u003eQC samples were interspersed throughout the run, with one inserted every 10 samples to facilitate the assessment of analytical repeatability.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Sample preparation and metabolomic analysis by GC-MS\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eSample preparation.\u003c/strong\u003e A 60 mg sample was accurately weighed and transferred into a 1.5 mL Eppendorf tube. To each sample, 360 \u0026micro;L of cold methanol and 40 \u0026micro;L of a 0.3 mg/mL solution of 2-chloro-l-phenylalanine in methanol, serving as an internal standard, were added. The samples were then chilled at -20\u0026deg;C for 2 minutes and ground at 60 Hz for another 2 minutes. After this, the samples were subjected to ultrasonication at room temperature for 30 minutes. This was followed by the addition of 200 \u0026micro;L of chloroform and vortex mixing. An additional 400 \u0026micro;L of water was added and the mixture was vortexed again. The samples underwent a second round of ultrasonication at room temperature for 30 minutes, followed by centrifugation at 12,000 rpm for 10 minutes at 4\u0026deg;C. A QC sample was prepared by pooling aliquots from all the samples. A 200 \u0026micro;L portion of the supernatant was then transferred to a glass sampling vial and vacuum-dried at room temperature. The dry sample was reconstituted with 80 \u0026micro;L of a 15 mg/mL methoxylamine hydrochloride solution in pyridine, vortexed for 2 minutes, and incubated at 37\u0026deg;C for 90 minutes. Subsequently, 80 \u0026micro;L of BSTFA (with 1% TMCS) and 20 \u0026micro;L of n-hexane were added. The sample was vortexed for another 2 minutes and derivatized at 70\u0026deg;C for 60 minutes. The samples were then left to equilibrate at room temperature for 30 minutes prior to GC-MS analysis.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGC-MS analysis.\u003c/strong\u003e The derivatized samples were analyzed using an Agilent 7890B gas chromatograph coupled with a 5977A MSD system. A DB-5MS fused-silica capillary column was employed for derivative separation. Helium of high purity (\u0026gt;\u0026thinsp;99.999%) served as the carrier gas at a flow rate of 1 mL/min. The injection volume was 1 \u0026micro;L in splitless mode, with the injector temperature set at 260\u0026deg;C. The initial oven temperature was 60\u0026deg;C and was programmed to increase to 305\u0026deg;C utilizing a multi-step temperature gradient. The MS quadrupole and ion source temperatures were maintained at 150\u0026deg;C and 230\u0026deg;C, respectively, with an electron impact energy of 70 eV. The mass spectral data was collected in full-scan mode, scanning from m/z 50 to 500. To ensure analytical consistency, QC samples were injected at regular intervals after every 10 sample analyses.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Metabolomic data processing\u003c/h2\u003e\n \u003cp\u003eThe LC-MS raw data were processed using Progenesis QI software, with a precursor tolerance of 5 ppm and a fragment tolerance of 10 ppm. The retention time (RT) tolerance was set to 0.02 minutes. Peak alignment was conducted without relying on internal standard detection parameters, isotopic peaks were excluded, and a noise elimination threshold was established at a level of 10.00. The cut-off for minimum intensity was set to 15% of the base peak intensity. The data was compiled into an Excel file, containing three-dimensional datasets that included m/z values, peak RT, and peak intensities, with RT\u0026ndash;m/z pairs serving as unique identifiers for each ion. Peaks that were not detected in over 50% of the samples were excluded from the dataset. The internal standard was employed for data quality control, ensuring reproducibility.\u003c/p\u003e\n \u003cp\u003eMetabolites were identified by progenesis QI (Waters Corporation, Milford, USA) Data Processing Software, based on public databases such as \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.hmdb.ca/\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.lipidmaps.org/\u003c/span\u003e\u003c/span\u003e and self-built databases.\u003c/p\u003e\n \u003cp\u003eFor GC-MS data, the AnalysisBaseFileConverter software was utilized to convert raw data from the .D format to .abf files. These files were then imported into MD-DIAL for data processing. Metabolite annotation was done using the LUG database, specifically designed for untargeted GC-MS analysis. Following this, a \u0026apos;raw data array\u0026apos; was compiled, including sample information, peak names or retention times and m/z values, and peak intensities. This array was filtered to remove internal standards and any known pseudo-positive peaks resulting from background noise, column bleed, or the BSTFA derivatization process. Peaks with a relative standard deviation (RSD) above 0.3 were discarded. The remaining peak areas were normalized according to retention time periods, using multiple internal standards to adjust for any variations in peak intensity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Transcriptome sequencing\u003c/h2\u003e\n \u003cp\u003eTo isolate total RNA, we employed the Trizol reagent kit (Invitrogen, Carlsbad, CA) following the manufacturer\u0026apos;s protocol. The integrity and quality of the RNA were assessed using the Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA) in conjunction with RNase-free agarose gel electrophoresis. Subsequent to DNA digestion with DNase, eukaryotic mRNA was isolated using Oligo(dT) beads. This mRNA was then fragmented in a buffer solution and reverse-transcribed into cDNA with random hexamer primers. To produce the second-strand cDNA, we used a combination of RNase H, DNA Polymerase I, dNTPs, and reaction buffer. The double-stranded cDNA was then purified using the QiaQuick PCR extraction kit (Qiagen, Venlo, The Netherlands), and further processed through end-repair, poly(A) tailing, and adapter ligation for Illumina sequencing. The adapter-ligated fragments were size-selected via agarose gel electrophoresis, PCR-amplified, and sequenced on an Illumina HiSeq 2500 platform by Gene Denovo Biotechnology Co.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6 Bioinformatics analysis\u003c/h2\u003e\n \u003cp\u003eRaw reads from the sequencing process were pre-processed using Fastp version 0.18.0 to remove low-quality sequences. The resulting clean reads were then aligned against the Nipponbare ribosomal RNA (rRNA) database using Bowtie 2 version 2.2.8 for rRNA removal. An index of the reference genome was generated, and HISAT2 version 2.2.4 was employed for the alignment of the cleaned and paired-end reads to the rice reference genome. Expression levels were quantified by calculating FPKM (Fragments Per Kilobase of exon per Million mapped fragments) for each transcript region. Differential expression analysis among various samples was performed using DESeq2 version 1.44.0.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eTo assess the correlation between replicates, a principal component analysis (PCA) was conducted using the R package gmodels version 2.19.1. Metabolites with a Variable Importance in Projection (VIP) score exceeding 1, a statistically significant P-value less than 0.05 (Mann-Whitney U test), and an absolute log2 fold change (|log2 FC|) greater than 1 were categorized as differentially accumulated metabolites (DAMs). To gain further insights into the biological significance of the DAMs, an enrichment analysis was performed using the MetaboAnalyst platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.metaboanalyst.ca\u003c/span\u003e\u003c/span\u003e). Differentially expressed genes (DEGs) were identified as those with an absolute log2 fold change (|log2 FC|) greater than 1 and a P-value less than 0.05 (Mann-Whitney U test). These DEGs were then subjected to enrichment analyses using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases. Correlation and cluster analyses were performed using the R package ComplexHeatmap version 2.20.0 to explore the relationships and patterns of co-expression among the genes of interest.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Metabolome and transcriptome profiling\u003c/h2\u003e \u003cp\u003ePrevious research has extensively explored the agronomic traits of the soybean genotypes TW75 and TW-1[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. It was observed that field emergence rates for these varieties were influenced by a complex interaction between their genetic constitution (\u003cem\u003emips1\u003c/em\u003e mutation) and environmental factors (specifically temperature variation associated with seasonal changes). Notably, seeds collected during autumn in Hangzhou demonstrated high field emergence rates for both parental and mutant lines, reaching around 85%. In contrast, seeds from spring season exhibited significantly lower emergence rates, with the parental genotype TW75 achieving 45% and the mutant TW-1 reaching only 25%. These results highlight the significant impact of seasonal variations on seed germination rates and suggest that mutations in the \u003cem\u003emips1\u003c/em\u003e gene may affect field emergence differently under varying environmental conditions.\u003c/p\u003e \u003cp\u003eIn our detailed metabolic analysis using LC-MS and GC-MS techniques, we identified a total of 479 unique metabolites, with the majority (419) detected through GC-MS and the remaining 60 via LC-MS. Principal component analysis (PCA) effectively differentiated the sample groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), capturing 55.24% of the variance with the first two principal components. Distinctions among groups were primarily driven by developmental stages and seasonal changes rather than \u003cem\u003emips1\u003c/em\u003e mutations. The meticulous correlation analysis confirmed unique metabolic profiles for each sample group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), particularly when classified by developmental stage and seasonal context. A key finding was the significant metabolic differentiation observed at the late developmental stage, suggesting a vital role for metabolic diversity in affecting germination success.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn terms of transcriptomics, the assembly and sequencing of RNA libraries for TW75 and TW-1 yielded a total of 1,088,044,580 clean reads across the variants and seasons. PCA revealed tight clustering of biological replicates (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), accounting for 75.15% of the variation, primarily due to the developmental stage. This indicated notable gene expression differences, especially evident in the late developmental phase. Moreover, intricate correlation analysis highlighted distinct seasonal expression patterns within the same developmental stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), with a clear distinction between the early and middle stages compared to the late stage. Additionally, the metabolic profiles and transcriptional profiles of parental and mutant soybeans differed to some extent at the same developmental stage and the same season, with this difference being more pronounced in the spring. These findings emphasize the complex relationship between gene expression and environmental conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Seasonal Variation in Differentially Accumulated Metabolites (DAMs)\u003c/h2\u003e \u003cp\u003eAmidst pronounced seasonal differences in field emergence rates and metabolite profiles, we conducted metabolite analyses across three developmental stages between spring and autumn to pinpoint the DAMs. As the stages progressed, both TW75 and TW-1 exhibited an increase in DAMs, with a marked rise in up-regulated metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). During the late stage, TW75 exhibited 203 DAMs (24 down-regulated and 179 up-regulated), while TW-1 exhibited 208 DAMs (20 down-regulated and 188 up-regulated) between the two seasons. Notably, 147 metabolites were consistently regulated across both soybean varieties, potentially driving the seasonal shifts in field emergence rates. These DAMs were categorized into over ten subclasses, primarily including amino acids, peptides, and analogues, carbohydrates and their conjugates, and fatty acids and conjugates (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The top-enriched KEGG pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) included arginine biosynthesis, glutathione metabolism, the pentose phosphate pathway, arginine and proline metabolism, and pyrimidine metabolism\u0026mdash;mainly connected to amino acids and energy metabolism. These pathways, especially prevalent in the late stage, are believed to significantly influence the germination process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDynamic changes in seed composition during the developmental and maturation phases significantly affect seed quality, highlighting the importance of understanding stage-specific DAMs alterations during seed development in TW75 and TW-1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The DAMs were grouped into four clusters, each with a distinct profile. A significant proportion of amino acids, peptides, and analogues (69.44%) fell into cluster 1 and 3, both showing a decline in autumn. However, during spring, cluster 1's metabolite levels were considerably higher at every stage, whereas cluster 3 displayed a rising trend, diverging from the autumnal patterns. These trends underscore the critical role of free amino acids in seed maturation. The differential accumulation of these compounds from the middle to late stages may distinctly influence seed quality. Cluster 2 contained a diverse mixture of subclasses, most notably carbohydrates and their conjugates (53.33%), fatty acids and conjugates (44.44%), glycerophosphocholines (70.00%), and alcohols and polyols (100.00%). In this cluster, metabolite levels in spring surged during the late stage while remaining consistent throughout the developmental stages in autumn. The differential build-up of amino acids, carbohydrates, and lipids in this cluster is vital for the germination of soybean seeds, particularly regarding energy metabolism. This variation may contribute to the superior emergence rates seen in autumn-harvested seeds. Additionally, DAMs such as scyllo-inositol and inositol-4-monophosphate in cluster 2, linked to inositol phosphate metabolism, warrant further exploration for their roles in these observed phenomena.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Seasonal Expression Discrepancy of Genes\u003c/h2\u003e \u003cp\u003eThe distinctive patterns of gene expression observed during different seasons reflected variations in field emergence rates, prompting a detailed analysis of differentially expressed genes (DEGs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the ES, a remarkable count of 639 DEGs was identified (551 up-regulated and 88 down-regulated), increasing to 3,071 DEGs (2,167 up-regulated and 904 down-regulated) in MS, and decreasing to 1,636 DEGs (771 up-regulated and 865 down-regulated) in LS of development (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). A peak in the quantity of DEGs was noted during the middle phase when comparing the spring and autumn periods. These DEGs, numbering 639, 3,071, and 1,636 for each respective stage, were systematically categorized within the Gene Ontology (GO) framework, encompassing three domains: biological processes, molecular functions, and cellular components (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The most substantial and enriched category across all three stages pertained to cellular components, specifically cell and cell part (312, 48.82% in ES; 1,265, 41.19% in MS; 747, 52.02% in LS), with the ES also emphasizing intracellular components (289, 45.22%) and binding functions (281, 43.97%). The MS gene expressions were marked by single-organism processes (668, 21.75%), transferase activities (527, 17.16%), and small molecule interactions (454, 14.78%). In contrast, the LS was dominated by intracellular components (678, 47.21%) and organelle-specific genes (521, 36.28%). A distinct shift in GO terms was evident, with the MS favoring cell membrane-related processes and the ES and LS focusing on intracellular metabolic functions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe radial enrichment diagrams (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-F) illustrate the top 20 enriched KEGG pathways on the periphery, the number of genes associated with each pathway and their significance values on the second tier, the regulation status of the genes on the third, and the pathway enrichment ratios at the core. Early-stage development was characterized by pathways such as motor protein activity (map04814), photosynthesis antenna proteins (map00196), ATP-dependent chromatin restructuring (map03082), circadian rhythm regulation in plants (map04712), and homologous recombination (map03440). As development advanced, the middle stage's pathways shifted towards plant-pathogen interaction (map04626), metabolism of amino and nucleotide sugars (map00520), plant-specific MAPK signaling (map04016), nucleotide sugar biosynthesis (map01250), and galactose utilization (map00052). The later stage was enriched with pathways critical for protein processing in the endoplasmic reticulum (map04141), arginine construction (map00220), glutathione pathways (map00480), glycerolipid metabolism (map00561), and the breakdown of 2-oxocarboxylic acids (map01210). Further enrichments were seen in stress-related metabolic pathways during the middle and late stages, in addition to those involving amino acids previously identified in the analysis of DAMs.\u003c/p\u003e \u003cp\u003eNotably, the DEGs across these stages were predominantly responsible for coding 9 key transcription factors (TFs, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC)\u0026mdash;AP2/ERF, WRKY, bHLH, MYB, HSF, bZIP, TCP, GATA, and NAC. These TFs play a critical role in regulating gene expression essential for initiating and promoting germination. The expression trends of HSF, in particular, point to the critical impact of thermal dynamics on gene expression throughout the development stages, highlighting the complex regulatory mechanisms that influence germination in soybean seeds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Impact of mips1 Mutations on Field Emergence Rates\u003c/h2\u003e \u003cp\u003eTW-1, a soybean mutant line recognized for its low phytic acid levels, has shown variable field emergence rates during the spring season. This variability suggests that the inositol phosphate metabolism pathway plays a pivotal regulatory role in the seed maturation process. A detailed investigation compared the metabolite levels and gene expression profiles between the mutant and wild-type soybeans to elucidate the genetic factors affecting spring emergence (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The study revealed dynamic changes in DAMs, starting with an early count of 40 DAMs (30 up-regulated and 10 down-regulated), shifting to 33 DAMs (24 up-regulated and 9 down-regulated), and peaking at 79 DAMs (50 up-regulated and 29 down-regulated) by the LS of development (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Comparative analysis indicated significantly higher counts of DAMs between seasons than between genotypes. Despite this, there was a notable commonality in the DAMs, including fatty acids and their conjugates, carbohydrates, carbohydrate conjugates, as well as amino acids, peptides, and analogues (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). This suggests that both seasonal variation and \u003cem\u003emips1\u003c/em\u003e genetic variation may share a similar mechanism underlying reduced soybean emergence, leading to the lowest seed germination rate for TW-1 in spring. However, the regulatory mechanisms during seed development need further discussion.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAn upward trend was observed in the number of DEGs between TW75-s and TW-1-s, beginning with 232 DEGs (130 up-regulated and 102 down-regulated) in the ES, escalating to 1,204 DEGs (746 up-regulated and 458 down-regulated) in the MS, and culminating in 2,028 DEGs (1,027 up-regulated and 1,001 down-regulated) by the LS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Pathway enrichment analysis of these DEGs highlighted their significant participation in key metabolic pathways, including carbon metabolism, glycolysis/gluconeogenesis, galactose metabolism, and pyruvate metabolism. The inositol phosphate metabolism pathway was especially prominent among the DEGs, emphasizing its potential regulatory importance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The DEGs between TW75-s and TW-1-s demonstrated marked enrichment in energy metabolism pathways and pathways related to amino acid metabolism. An examination of transcription factors (TFs) revealed that the DEGs predominantly encoded AP2/ERF, MYB, bZIP, WRKY, and bHLH transcription factors. This pattern of TF encoding mirrored the differential expression of transcription factors noted between the spring and autumn seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), suggesting a preservation of regulatory themes within the seasonal transcriptome.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Correlation Network of Seasonal and Genetic Factors\u003c/h2\u003e \u003cp\u003eThe genes in enriched pathways, transcription factors (TFs) in differentially expressed genes (DEGs), and metabolites in differentially accumulated metabolites (DAMs) were screened based on their relative contents/fpkm values and P values. These elements were then classified using heatmaps and correlation networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The investigation differentiated between seasonal influences\u0026mdash;contrasts observed between autumn and spring samples\u0026mdash;and genetic contributors\u0026mdash;disparities between the TW75-s and TW-1-s variants. Among the transcription factors surveyed, families such as AP2/ERF, WRKY, bHLH, MYB, and HSF stood out as potential modulators of field emergence rates. The expression profiles of these TFs were predominantly dictated by seasonal changes, with only a subset being influenced by genetic factors. In pathway analysis, key metabolic routes\u0026mdash;glutathione metabolism, galactose metabolism, inositol phosphate metabolism, and carbon metabolism\u0026mdash;were found to be significantly responsive to both seasonal and genetic elements. Glutathione metabolism, in particular, was identified as a primary determinant for the observed decrease in seed emergence during spring. Moreover, carbon metabolism genes were consistently implicated under both seasonal and genetic factors, suggesting a compounded effect that potentially leads to further diminished field emergence rates in the TW-1-s line.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, we conducted a correlation analysis to investigate the relationship between field emergence rates and the expression of TF genes, as well as DEGs associated with carbon and glutathione metabolism pathways during specific developmental stages. This analysis revealed a strong correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), indicating that variations in field emergence rates might be closely linked to the regulatory roles of specific TFs during the seed development phase. These transcription factors are likely critical in fine-tuning gene expression within key metabolic pathways, thereby influencing the germination process and initial seedling growth.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn our study, we observed that both climatic variability and mutations in the \u003cem\u003emips1\u003c/em\u003e gene have a pronounced impact on the field emergence rates of soybean cultivars TW-1 and TW-75, which is consistent with previous reports about low phytic acid mutants[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. We proposed that the cooler and wetter conditions typical of spring in Hangzhou may compromise seed integrity, and suggested that \u003cem\u003emips1\u003c/em\u003e mutations, together with subsequent metabolic pathways, could intensify this decline in seed emergence. The quality of soybean seeds was clearly influential on germination rates; however, the specific factors and mechanisms that account for seasonal variation, \u003cem\u003emips1\u003c/em\u003e mutation and their synergistic effect in soybean germination are not yet fully understood. To shed light on this, we analyzed \u003cem\u003emips1\u003c/em\u003e mutant and wild type seed samples from both seasons using metabolomic and transcriptomic approaches, aiming to pinpoint DAMs and DEGs, and thereby illuminate the physiological and biochemical changes that accompany seasonal shifts.\u003c/p\u003e \u003cp\u003eDuring seed maturation, a vital phase of development, we observed significant changes of nutritive reserves. Notably, the seeds in spring exhibited a marked increase in metabolites such as amino acids, peptides, carbohydrates, and fatty acids, particularly in the late maturation stage. This high concentration of these small molecular metabolites could indicate an untimely initiation of metabolic processes typical of germination and seedling growth, resulting in premature energy depletion. Macromolecular substances, namely starch, lipids and proteins, are essential nutrition for seed germination[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The disruption of the seed development process appears to lead to a metabolic imbalance, undermining the seed's germinative capacity. Starch is a pivotal energy store within the endosperm, broken down by amylases from the aleurone layer at the onset of germination[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The increased levels of carbohydrates in spring-harvested seeds may suggest premature starch degradation, potentially affecting energy availability during early germination stages. Moreover, given the critical role of lipids during germination, our observation of increased phosphatidylcholine and glycerophosphocholine levels in the late maturation stage of seeds harvested in spring indicates significant membrane synthesis activity. These elevated lipid levels are often associated with stress responses, as plants frequently alter the selective permeability of membranes to adopt to abiotic stress[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This stress response could divert resources from other vital processes, potentially leading to reduced field emergence rates. Notably, phytic acid is the main storage form of phosphorus in plant seeds[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In TW-1-s, the synthesis of large amounts of phosphatidylcholine and lysophosphatidylcholine may further affected phosphorus storage, leading to reduced seed quality. The metabolome dynamics also extend to amino acids and peptides. During germination, these compounds are released through protein hydrolysis, enhancing nutrient accessibility[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Typically, free amino acids decrease, and total amino acids (including those incorporated into seed-storage proteins) increase in the late developmental stage of seeds[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Our comparative analysis revealed a 4.88-fold increase in these metabolites in spring. Notably, the free amino acids identified as DAMs play fundamental roles in the seed's central metabolism, serving not only as building blocks for storage proteins but also as precursors for a myriad of other metabolites, including phytohormones and protective secondary metabolites. The metabolome analysis revealed the accumulation of significantly higher levels of stress-related primary metabolites, such as soluble amino acids, soluble sugars, and TCA cycle intermediates, in spring. This indicates that the climatic stress in spring decreased seed quality and further reduced field emergence rates.\u003c/p\u003e \u003cp\u003eThe climate-related stress that result in varying seed quality, as revealed by metabolomic analysis, are the direct causes of the low emergence rate. Further transcriptomic analysis combined with metabolic analysis elucidated the underlying regulatory mechanisms. The transcriptomic data exhibited patterns parallel to these metabolic trends, particularly in pathways related to glycerolipid metabolism, glutathione metabolism, carbon metabolism, and alanine, aspartate, and glutamate metabolism. Notably, genes involved in glutathione metabolism, known to preserve seed longevity and regulate dormancy[\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], displayed distinct differential expression between the spring and autumn seasons. Our findings indicated a significant association between glutathione metabolism and carbon metabolism, which could be a crucial factor contributing to the observed differences in field emergence between the spring and autumn seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExamining the differences between TW75-s and TW-1-s, we identified both similarities and distinctions in DAMs and DEGs in response to seasonal variations. These differences suggest that the mips1 gene may be involved in the seasonally induced stress response. The reduction in MIPS activity can lead to a decrease in myo-inositol levels, consequently impeding the production of important oligosaccharides(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Additionally, phytic acid synthesis includes both lipid-independent and lipid-dependent pathways[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. MIPS1 gene is involved in the first step of phytic acid synthesis and affects both pathways, with the lipid-dependent pathway being associated with cell membrane synthesis[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Disturbances in phosphorus and lipid metabolism affected the synthesis of cell membranes under abiotic stress, and thus TW-1 produces lower seed quality in spring than TW75.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, transcription factors from the AP2/ERF, WRKY, bHLH, MYB, and HSF families play significant roles in regulating seed development and quality, influenced by both seasonal and genetic factors. For instance, the AP2/ERF family, implicated in water absorption and abscisic acid signaling, likely affects the seeds' ability to cope with drought and water-related stresses, influencing emergence rates[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The bHLH family influences germination response to temperature, which could explain varied emergence rates under different seasonal temperature conditions[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The MYB family, involved in stress responses, likely plays a role in the seeds' resilience to environmental stresses, including salinity and drought[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Lastly, the HSF family emphasizes the role of temperature in stress responses, particularly in the activation of heat shock proteins during high-temperature conditions[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur findings underscore the multifaceted influences on field emergence, rooted in seed development impacted by both seasonal and genetic factors. Stress-induced metabolic imbalances and mutations in the MIPS gene contribute to reduced seed quality and emergence rates. Transcription factors likely play a critical role in modulating these metabolic processes. Both the wild-type and mutant seeds were sensitive to seasonally induced stress; however, the mutant TW-1 exhibited a higher sensitivity, leading to significantly lower field emergence rates in the spring compared to TW75. The MIPS gene is associated with stress resistance, potentially due to its role in regulating cell membrane synthesis through phosphorus metabolism. Despite these insights, the intricate nature of seeds and the variability of climate conditions necessitate tailored optimization strategies. Future research will further investigate the germination rates of various MIPS mutants, aiming to identify optimal seeds and conditions for germination, with the overarching goal of enhancing germination rates to improve the quality of soybean varieties.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn conclusion, our study underscores the profound influence of seasonal climatic variability and genetic mutations in the MIPS1 gene on the field emergence rates of soybean cultivars TW-1 and TW75. The marked decrease in seed quality and emergence rates, particularly in the spring, highlights the intricate interplay between environmental conditions and seed physiology. Through comprehensive metabolomic and transcriptomic analyses, we identified that stress-induced metabolic imbalances, especially in lipid, carbohydrate, and amino acid metabolism pathways, are pivotal contributors to these observed variations.\u003c/p\u003e \u003cp\u003eSpecifically, the MIPS1 gene plays a crucial role in regulating cell membrane synthesis via phosphorus metabolism. Mutations in this gene lead to significant changes in metabolic activities, resulting in increased sensitivity to seasonal stressors. Our observations indicated that the mutant TW-1, in particular, exhibited a higher sensitivity to such stress, manifesting in significantly lower field emergence rates compared to TW75. The differential accumulation of metabolites and expression of genes during seed maturation stages further elucidates the physiological and biochemical changes underpinning these seasonal effects.\u003c/p\u003e \u003cp\u003eThe study also highlights the role of various transcription factors, including those from the AP2/ERF, bHLH, MYB, and HSF families, which are instrumental in modulating seed development and stress responses. These factors influence critical processes such as water absorption, temperature response, and adaptation to abiotic stressors, thereby affecting seed quality and emergence rates.\u003c/p\u003e \u003cp\u003eGoing forward, it is essential to delve deeper into the regulatory mechanisms involving the MIPS1 gene and its associated metabolic pathways. Future research should aim to identify the optimal genetic traits and environmental conditions that enhance seed quality and germination rates of low phytic acid cultivars. Such insights will be invaluable in developing soybean varieties with improved resilience and performance, ultimately contributing to agricultural sustainability and food security. Our findings lay a solid foundation for future studies targeting the optimization of seed emergence through an integrated approach combining genetic, metabolic, and environmental factors. By advancing our understanding of these intricate interactions, we pave the way for innovative strategies to improve the quality and yield of soybean crops, thereby addressing the challenges posed by climatic variability and enhancing global food production.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eReviewers are acknowledged for their contribution to the improvement of the manuscript in the revision process.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Zhejiang Provincial Major Agriculture Science and\u003c/p\u003e\n\u003cp\u003eTechnology Special, China (Grant No. 2021C02064-5)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmir R, Galili G, Cohen H: \u003cstrong\u003eThe metabolic roles of free amino acids during seed development\u003c/strong\u003e. \u003cem\u003ePlant Science \u003c/em\u003e2018, 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Through comprehensive metabolomic and transcriptomic analyses, we identified key KEGG pathways significantly affected by these factors, including starch and sucrose metabolism, lipid metabolism, and amino acid biosynthesis. These pathways were notably disrupted during the spring, leading to an imbalance in metabolic reserves critical for seedling development. Additionally, MIPS gene mutations further altered these pathways, exacerbating the metabolic disturbances. Our results underscore the intricate network of environmental and genetic interactions influencing soybean seed vigor and underscore the importance of understanding these pathways to enhance agricultural resilience and seed quality in fluctuating climates.\u003c/p\u003e","manuscriptTitle":"Effects of Seasonal Climates and MIPS Mutations on Soybean Germination through Multi-Omics Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-19 10:40:45","doi":"10.21203/rs.3.rs-5063011/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-19T22:47:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-19T03:51:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-19T03:50:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2024-09-10T08:26:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bed13b47-8bab-461e-b83d-1fbad797f717","owner":[],"postedDate":"November 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-30T16:05:57+00:00","versionOfRecord":{"articleIdentity":"rs-5063011","link":"https://doi.org/10.1186/s12870-024-05957-x","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2024-12-23 15:57:51","publishedOnDateReadable":"December 23rd, 2024"},"versionCreatedAt":"2024-11-19 10:40:45","video":"","vorDoi":"10.1186/s12870-024-05957-x","vorDoiUrl":"https://doi.org/10.1186/s12870-024-05957-x","workflowStages":[]},"version":"v1","identity":"rs-5063011","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5063011","identity":"rs-5063011","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}