Abstract

   This study delves into the combined effects of seasonal climate
   variations and MIPS1 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,
   MIPS1 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.

Supplementary Information

   The online version contains supplementary material available at
   10.1186/s12870-024-05957-x.

   Keywords: Soybean germination, MIPS1 mutation, Multi-omics, Seasonal
   climate variation, Low phytic acid

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 [[36]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
   [[37]2, [38]3], drought stress [[39]4, [40]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 [[41]6, [42]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 [[43]8–[44]12].
   The work of Meis et al. [[45]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 [[46]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 [[47]15, [48]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 [[49]9,
   [50]10]. A large number of studies have been conducted on the metabolic
   mechanisms of low seed emergence in low phytic acid seeds
   [[51]17–[52]19] and on improving the emergence of low phytic acid
   varieties [[53]20, [54]21]. In our previous study [[55]22–[56]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 [[57]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 [[58]26, [59]27], genomics [[60]28], and
   proteomics [[61]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 [[62]29–[63]31].

   Our previous research has extensively explored the agronomic traits of
   the soybean genotypes TW75 and TW-1 [[64]22–[65]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 only 25%.
   These results highlighted 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 the current study, we leveraged transcriptomic and metabolomic
   analyses to map the regulatory network involved in mips1 mutant soybean
   seed development. Our aim was to explore the impacts of seasonal and
   genetic variations on field emergence rates, thereby clarifying the
   relationship between MIPS1 gene expression and metabolic profiles in
   the context of soybean mutant seed germination. This research
   contributes to a more profound and comprehensive understanding of how
   seasonal and genetic factors that influence the accumulation of
   germination-promoting factors in soybean seeds.

Materials and methods

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, China. 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.

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 PC 17:0
   (0.01 mg/mL) dissolved in methanol, were added. Subsequently, 1 mL of a
   methanol-water mixture (7:3, v/v) 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 min,
   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 min, 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, v/v),
   vortexed for 30 s, and ultrasonicated for 2 min. 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, v/v, with 0.1% formic acid). The
   gradient protocol was as follows: starting with 1% B, increasing to 30%
   B at 1 min, 60% B at 2.5 min, reaching 90% B at 6.5 min, holding at
   100% B from 8.5 to 10.7 min, returning to 1% B at 10.8 min and
   maintaining until 13 min. 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 s and interscan delay of 0.02 s.

   QC samples were interspersed throughout the run, with one inserted
   every 10 samples to facilitate the assessment of analytical
   repeatability.

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 min and ground at 60 Hz for another 2 min. After this, the
   samples were subjected to ultrasonication at room temperature for
   30 min. 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 min, followed by
   centrifugation at 12,000 rpm for 10 min 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 min, and incubated at 37 °C for 90 min. Subsequently, 80
   µL of BSTFA (with 1% TMCS) and 20 µL of n-hexane were added, followed
   by adding 10 µL of 11 internal standards (C8/C9/C10/C12/C14/C16 at
   0.8 mg/mL; C18/C20/C22/C24/C26 at 0.4 mg/mL, all prepared in
   chloroform). The sample was vortexed for another 2 min and derivatized
   at 70 °C for 60 min. The samples were then left to equilibrate at room
   temperature for 30 min 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.

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 min. 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 [66]http://www.hmdb.ca/; [67]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.

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.

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 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 soybean 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.

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 ([68]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.

Results

Metabolome and transcriptome profiling

   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. [69]1A), 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. [70]1B), 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.

Fig. 1.

   [71]Fig. 1
   [72]Open in a new tab

   Metabolome 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

   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. [73]1C), 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. [74]1D), 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.

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. To
   be specific, we screened for DAMs by considering VIP values derived
   from PLS-DA, along with R values and log2 FC (Fig. [75]S1). As the
   stages progressed, both TW75 and TW-1 exhibited an increase in DAMs,
   with a marked rise in up-regulated metabolites (Fig. [76]2A). 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. [77]2B). The top-enriched KEGG pathways
   (Fig. [78]2C) 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.

Fig. 2.

   [79]Fig. 2
   [80]Open in a new tab

   Differentially 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

   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. [81]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.

Fig. 3.

   [82]Fig. 3
   [83]Open in a new tab

   Trends and cluster analysis of DAMs across different stages

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 DEGs (Fig. S2). A similar method was employed to
   filter differentially expressed genes (DEGs) using R values and log2
   FC. Further analyze the same regulated DEGs, 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. [84]4A). 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. [85]4B). 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.

Fig. 4.

   [86]Fig. 4
   [87]Open in a new tab

   DAMs 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 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

   The radial enrichment diagrams (Fig. [88]4D-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. [89]4C)—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.

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 TW-1-s and wild-type soybeans
   TW75-s to elucidate the genetic factors affecting spring emergence
   (Fig. [90]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. [91]5A). 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. [92]5B). 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.

Fig. 5.

   [93]Fig. 5
   [94]Open in a new tab

   Differential 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 categories 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

   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. [95]5A). 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. [96]5C). 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 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. [97]5D), suggesting a preservation of
   regulatory themes within the seasonal transcriptome.

Correlation network of seasonal and genetic factors

   The genes in enriched pathways, TFs in DEGs, and metabolites in DAMs
   were screened based on their relative contents/fpkm values and P
   values. These elements were then classified using heatmaps and
   correlation networks (Fig. [98]6A). 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.

Fig. 6.

   [99]Fig. 6
   [100]Open in a new tab

   Expressing trends and correlation of key metabolites and
   genes. A Expressing trends of DEGs of TF families (left) and trends
   of DEGs and DAMs of key metabolic pathways (right); B Correlation
   network of TF families and genes in pathways

   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. [101]6B), 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.

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 [[102]11, [103]13, [104]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 harvested
   in spring exhibited a marked increase in metabolites such as amino
   acids, peptides, carbohydrates, and fatty acids, particularly in the
   late maturation stage. These small molecules play crucial roles in
   abiotic stress response. This high concentration of these small
   molecular metabolites suggests a response to abiotic stresses in
   spring, such as temperature fluctuations. Macromolecular substances,
   namely starch, lipids and proteins, are essential nutrition for seed
   germination [[105]32]. This stress-induced metabolic changes lead to
   early energy usage and depletion of seed storage materials, negatively
   impacting seed development and germination.

   Starch is a pivotal energy store within the endosperm, broken down by
   amylases from the aleurone layer at the onset of germination [[106]33].
   The increased levels of carbohydrates in spring-harvested seeds may
   suggest premature starch degradation, potentially affecting energy
   availability during early germination stages. This premature breakdown
   could impact the energy supply necessary for successful germination,
   thus influencing overall seed viability.

   Moreover, given the critical role of lipids during germination, our
   observation of increased phosphatidylcholine and
   lysophosphatidylcholine 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 [[107]34]. Lipids, notably phosphatidylcholine
   and lysophosphatidylcholine, play a crucial role in maintaining the
   stability and fluidity of cell membranes. These lipids act as key
   structural components that enhance cellular integrity, especially
   during the challenging spring conditions characterized by temperature
   fluctuations and desiccation. By strengthening membrane structures,
   they enable cells to endure and recover from these stresses more
   effectively. This stabilization helps preserve essential cellular
   functions and prevents the leakage of vital contents, ensuring that
   cells remain functional even in adverse environmental conditions. 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 [[108]9,
   [109]10, [110]15]. In TW-1-s, the synthesis of large amounts of
   phosphatidylcholine and lysophosphatidylcholine may further affected
   phosphorus storage, leading to reduced seed quality.

   Furthermore, the metabolome dynamics also extend to amino acids and
   peptides. During germination, these compounds are released through
   protein hydrolysis, enhancing nutrient accessibility [[111]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 [[112]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
   [[113]36–[114]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. [115]7).

Fig. 7.

   [116]Fig. 7
   [117]Open in a new tab

   The expression of DEGs and DAMs associated with glutathione metabolism
   and carbon metabolism in different groups

   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
   MIPS1 activity can lead to a decrease in myo-inositol levels,
   consequently impeding the production of important oligosaccharides
   (Fig. [118]8). Additionally, phytic acid synthesis includes both
   lipid-independent and lipid-dependent pathways [[119]9, [120]10,
   [121]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 [[122]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.

Fig. 8.

   [123]Fig. 8
   [124]Open in a new tab

   The expression of DEGs and DAMs associated with inositol phosphate
   metabolism and galactose metabolism in different groups

   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
   [[125]40]. The bHLH family influences germination response to
   temperature, which could explain varied emergence rates under different
   seasonal temperature conditions [[126]41]. The MYB family, involved in
   stress responses, likely plays a role in the seeds’ resilience to
   environmental stresses, including salinity and drought [[127]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 [[128]43]. These TFs are involved in the
   regulation of previously described pathways, such as energy metabolism,
   lipid metabolism, and phosphorus metabolism, in response to abiotic
   stresses. This regulation can ultimately lead to reduced seed quality
   and field emergence rates.

   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 MIPS1
   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 MIPS1 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
   mips1 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.

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. By employing an integrated multi-omics
   approach, which includes both metabolomic and transcriptomic analyses,
   we offer a more comprehensive and nuanced understanding of the complex
   regulatory networks and stress-induced metabolic imbalances,
   particularly in lipid, carbohydrate, and amino acid metabolism
   pathways. This multi-omics strategy surpasses the limitations of
   single-omics approaches by providing a holistic view that reveals the
   interconnectedness of metabolic and gene expression changes, crucial
   for understanding the 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.

Supplementary Information

   [129]Supplementary Material 1.^ (11.2MB, docx)

Acknowledgements