Functional divergence of gene homologs enables species to evolve unique physiological processes, such as nitrogen fixation of nodules in legumes. Nodulation is initiated via rhizobia-induced reactivation of root cortical cells into stem cells, yet the molecular mechanism underlying this process has remained elusive. There are eight florigen homologs in the soybean genome, including GmFT2a and GmFT5a, which are produced in leaves and possibly translocated to roots to promote nodulation. Here, we report the identification of a distinct florigen homolog, GmFT5b, and its partner GmFDL23, both of which are locally upregulated in the root cortex upon rhizobia infection. This GmFT5b-GmFDL23 module directly suppresses GmCLV3-1 expression in the root cortex, thereby alleviating its repression of the stem cell regulator GmWUS1, which further drives cortical cell reprogramming into nodule stem cells. Our findings delineate a florigen-mediated pathway that spatiotemporally controls stem cells in cortex to drive nodule formation.

Calcium-dependent protein kinases (CDPKs) function as key sensors of Ca2+ signals in plants; however, their roles in soybean-rhizobial symbiosis and biological nitrogen fixation remain poorly understood. This study demonstrates that GmCDPK14, a member of the soybean CDPK gene family, is specifically induced following rhizobial infection and is predominantly expressed in primary root tissues and nodules. Functional analyses revealed that GmCDPK14 plays a positive regulatory role in symbiotic nodulation. Loss-of-function mutants showed substantial decreases in nodule number, root dry weight, shoot dry weight, nitrogenase activity, and infection thread formation, whereas overexpression of GmCDPK14 produced the opposite effects. Transcriptomic analysis showed that GmRINRK1, a key symbiotic gene, was significantly downregulated in GmCDPK14-deficient lines. Moreover, overexpression of GmRINRK1 in the Gmcdpk14 mutant background partially rescued the nodulation defects. These results suggest that GmCDPK14 enhances soybean-rhizobium symbiotic nodulation by positively regulating GmRINRK1 expression, offering new insights into the role of CDPKs in controlling legume-rhizobium interactions.

PIN-LIKES (PILS) auxin transport genes play key roles in plant development, but their functions and molecular mechanism in soybean yield remain unclear. Here, we characterized the 44-member soybean GmPILS genes via comprehensive analyses. Phylogenetic analysis classified GmPILS into three subfamilies, with most proteins being hydrophobic, stable, and membrane-localized. Chromosomal distribution showed random scattering across 17 chromosomes, with gene duplication driving family expansion. Expression profiling identified GmPILS36 and GmPILS40 as seed-specific and differentially expressed between cultivated Suinong14 (SN14) and wild ZYD00006 (ZYD06) soybeans. Population genetic analyses revealed GmPILS40 experienced a domestication bottleneck without yield-related superior haplotypes, while GmPILS36 underwent selection during landrace-to-improved variety domestication. A coding region CC/TT natural variation in GmPILS36 (S/A substitution) was significantly associated with seed weight per plant and 100-seed weight, with the TT genotype conferring superior traits. This study provides insights into GmPILS genes' evolution and identifies GmPILS36 as an important candidate gene for further functional study and investigation of the molecular mechanisms regulating soybean yield.

Lodging is a major yield-limiting factor in soybean, but efficient large-scale phenotyping and genetic dissection of this complex trait remain challenging for breeding programs. To bridge this gap, this study developed an integrated, breeding-oriented framework that links UAV-based high-throughput phenotyping with candidate gene identification. Field experiments involving 741 diverse soybean genotypes were conducted over two years, with UAV remote sensing performed at key reproductive stages (from R5 to R7). We identified UAV-derived structural (relative plant height), textural (homogeneity, dissimilarity, correlation), and spectral (NDVI, EVI, NDRE) features as the most sensitive indices for retrieving lodging severity. The fusion of these complementary features, coupled with the XGBoost algorithm, achieved high classification accuracy (0.81–0.92) across genotypes, growth stages, and years. This reliable phenotyping pipeline enabled the precise selection of contrasting genotypes (lodging-resistant vs. lodging-prone) for transcriptomic analysis. Transcriptome sequencing revealed 13,447 differentially expressed genes, with significant enrichment in phenylpropanoid and starch–sucrose metabolic pathways. Moreover, the haplotype analysis within a natural population identified superior allelic variants of two candidate genes (Glyma.19G249100 and Glyma.05G142200) significantly associated with soybean lodging resistance. This work can effectively bridge the gap between scalable field phenotyping and the discovery of functionally validated breeding targets, providing an efficient and translational framework to accelerate the development of lodging-resistant soybean varieties.

Six new dihydroisoflavones (1-6) along with eight congeners (7-9, 11-15) as well as a known chalcone derivative (10) were isolated from Polygonatum sibiricum. To enhance the specificity and efficiency of the separation process, a combination of MSDIAL and MassQL techniques was used alongside column chromatography with silica gel, ODS, and preparative HPLC. The structural elucidation of the compounds was accomplished via thorough spectral characterisation, encompassing techniques such as 1D and 2D NMR, HR-ESI-MS, IR, UV and ECD. These findings were further validated by comparing them with data from existing literature. Furthermore, the neuroprotective activity of all purified compounds was assessed using a PC12 cell model subjected to H₂O₂ induction. The results of our study indicated that compounds 2, 3, 5, 11, and 12 significantly alleviated H₂O₂-induced damage in PC12 cells, increasing cell viability to approximately 74%, 76%, 79%, 76%, and 72%, respectively, at 50 μM without exhibiting cytotoxicity.

Viscum coloratum (Kom.) Nakai, a traditional Chinese medicine for rheumatoid arthritis, faces emergency challenges in clinical quality control due to inconsistent formulations and the lack of standardized evaluation criteria. To resolve this issue, this study established a systematic quality control framework for Viscum coloratum (Kom.) Nakai based on the concept of quality markers (Q-markers) in traditional Chinese medicine (TCM). Following TCM Q-markers principles, 10 potential candidates identified in our previous study were selected based on the key criteria, including effective separation and high abundance. A comprehensive five-dimensional evaluation system, for assessing reproducibility, content, stability, relevance, and effectiveness, was developed using an integrated analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) methodologies. Data normalization and weighted scoring facilitated the construction of a "spider-web" model for multicriteria decision-making, with a relative proximity threshold (≥ 0.409) applied to identify the most suitable Q-markers. Finally, through this approach, four candidates were ultimately selected as Q-markers for Viscum coloratum (Kom.) Nakai: viscumneoside III (VIS), homoeriodictyol-7-O-β-glucoside (HOG), oleanolic acid (OLA), and chlorogenic Acid (CHL). This systematic approach enhances Viscum coloratum (Kom.) Nakai quality control and provides insights for TCM standardization.

Accurate and efficient soybean yield prediction is essential for ensuring food security and optimizing agricultural management. Remote-sensing approaches based on a single phenological stage or sensor may not fully capture the physiological and structural dynamics associated with soybean yield formation, thereby constraining prediction accuracy under field-level spatial heterogeneity. This study therefore developed and evaluated an integrated multi-source and multi-temporal UAV-based framework for plot-scale soybean yield prediction under production-field conditions. A UAV platform equipped with multispectral, RGB, and LiDAR sensors was used to extract multidimensional remote-sensing features at four key growth stages: beginning pod, full seed, beginning maturity, and full maturity. By integrating spectral indices, texture metrics, and three-dimensional canopy structural parameters, seven machine-learning algorithms—Ridge, LASSO, Random Forest, MLP, LightGBM, XGBoost, and CatBoost—were evaluated under single-temporal and multi-temporal scenarios. Nonlinear tree-based ensemble models generally achieved higher predictive accuracy than the linear models. On the independent test set, CatBoost performed best under the full multi-temporal, three-sensor fusion scenario, with an R2 of 0.816 and an RMSE of 245 kg ha−1. Among individual growth stages, the full seed stage (R6) produced the highest single-stage prediction accuracy. Three-sensor fusion improved prediction relative to the single-source multispectral scheme, and integrating features across phenological stages further improved performance by representing cumulative crop-growth dynamics. The proposed framework provides methodological support for UAV-based precision management and data-driven decision-making in soybean production.

Salt stress severely affects soybean yield and cultivation area. A comprehensive understanding of the molecular mechanisms underlying soybean response to salinity is essential for the development of salt-tolerant cultivars. In this study, transcriptome profiling was employed to compare salt-sensitive (D) and salt-tolerant (Q) variety by analyzing the leaf and root tissues under salt stress. A total of 6,276 and 3,278 differentially expressed genes (DEGs) were identified in the leaves and roots of salt-tolerant variety Q, whereas 9,761 and 11,875 DEGs were detected in salt-sensitive variety D, respectively. The regulatory pathways activated under salt stress exhibited significant differences between genotypes and tissues. The plant hormone signal transduction pathway was strongly activated in both tissues of both cultivars, with key genes in multiple hormone pathways demonstrating substantially higher transcriptional upregulation in the salt-tolerant variety Q. Notably, the activation level of the plant-pathogen interaction pathway differed greatly between varieties, with the GmCML gene family accounting for a major proportion of this pathway. Comparative analysis identified GmCML48 as the gene most strongly induced by NaCl, NaHCO3, ABA, and BR treatments. Transgenic overexpression lines displayed significant improvements in growth traits and antioxidant enzyme activities under salt stress, establishing GmCML48 as a key contributor to soybean salt tolerance. Furthermore, Hap_A was identified as a haplotype associated with superior salt tolerance. These findings could advance the understanding of soybean molecular adaptations to saline environments and provide a theoretical basis for targeted breeding of salt-tolerant cultivars.

Rhizobial type Ⅲ effectors (T3Es) contribute to the establishment of symbiotic interactions with legume host plants in conjunction with Nod factors. However, the functions of most rhizobial T3Es, as well as the regulatory and molecular mechanisms underlying their symbiotic effects—particularly in soybean—remain poorly documented. Here, we characterize the function of the T3E nodulation outer protein C (NopC) from the broad-host-range rhizobium Sinorhizobium fredii HH103 in promoting symbiosis in soybean. The NopC genotype influences root nodulation across diverse host germplasms; this effect is further modulated by GmRAC1, which encodes a ROP/RAC family GTPase in soybean. GmRAC1 physically interacts with NopC and subsequently induces expression of the essential symbiotic genes GmNIN2a/2b and GmENOD40. Knockdown of GmNIN2a/2b results in failure of NopC to promote symbiosis, and Gmrac1 mutants develop fewer nodules than the wild type. NopC facilitates multiple stages of infection, whereas the requirement for GmRAC1 is pronounced during infection-thread progression and nodule primordium initiation. Natural variation in the GmRAC1 promoter largely determines the symbiotic contribution of NopC during symbiosis establishment. Elite GmRAC1 haplotypes associated with strong expression were artificially selected during soybean breeding. Transgenic overexpression and elite GmRAC1 haplotypes increase plant height, 100-seed weight, and overall yield. GmRAC1 functions as a key regulator of NopC-mediated symbiosis promotion and offers translational potential for enhancing symbiotic nitrogen fixation in soybean molecular breeding.

Summary statement This study demonstrates that CRISPR/Cas9‐mediated knockout of the cytokinin oxidase gene CKX3a in soybean simultaneously increases both seed number per plant and individual seed weight, resulting in a 52.4% yield increase—comparable to yield enhancements previously observed in Arabidopsis. The increase in seed size is attributed to enhanced cell division. These findings establish CKX3a as a promising genetic target for improving soybean yield.

Ethnopharmacological relevance Fritillaria ussuriensis Maxim. (FUM) is a traditional medicinal plant widely used in Asian countries, renowned for its effects of clearing heat, moistening the lungs, resolving phlegm, relieving cough, and alleviating asthma. In traditional medicinal practice, FUM is often used in combination with herbs such as Ephedra and Apricot Kernel. Among various preparation forms, decoction is the most common, and its traditional application methods hold significant reference value. Despite its extensive clinical application, the specific active components of FUM and its underlying mechanisms in treating asthma remain incompletely understood. Aim of the study To evaluate the therapeutic effects of FUM extract, this study established an OVA-induced mouse asthma model in vivo. Additionally, an in vitro pyroptosis model was constructed by stimulating BEAS-2B cells with LPS and ATP. Materials and methods This study established an in vivo mouse asthma model induced by OVA and constructed an in vitro cell pyroptosis model by stimulating BEAS-2B cells with LPS and ATP to evaluate the therapeutic effects of FUM extract. The prototype components of FUM absorbed into the bloodstream after administration were identified using liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (LC/IM-QTOF-MS) combined with serum pharmacochemistry. Network pharmacology was employed to predict potential targets and pathways of the active components. The effects on relevant protein expression in both in vivo and in vitro models were validated through behavioral observation, pulmonary function testing, HE staining, immunohistochemistry, Western blotting, and immunofluorescence techniques. Molecular docking, molecular dynamics simulations, and cellular thermal shift assays were employed to predict or validate the interactions between key active components and core targets. Results FUM extract significantly alleviated symptoms in OVA-induced asthmatic mice. LC/IM-QTOF-MS analysis identified nine prototype components of FUM absorbed into the bloodstream, mainly steroidal alkaloids. Through network pharmacology and experimental validation, FUM was found to inhibit the activation of the PI3K/AKT/NF-κB pathway in both mouse lung tissue and stimulated cells, while downregulating pyroptosis-related proteins (NLRP3, GSDMD, Caspase-1). Further molecular docking and validation experiments identified peiminine as the key active component, which demonstrated stable binding with the mTOR target. Conclusion This study investigated the active components and potential mechanisms of FUM in asthma using serum pharmacochemistry and systems pharmacology approaches. The novel findings reveal that FUM alleviates cellular pyroptosis and inflammatory responses by inhibiting the NLRP3 inflammasome, thereby exerting its therapeutic effects on asthma.

Pesticide diffusion from soil to other environments is a major source of environmental pollution. Biochar and organic fertilizers are effective amendments for remediating contaminated soils. However, the effects of combined application of biochar and organic fertilizer on pesticide uptake and translocation in soybean remain unclear. Therefore, a pot experiment was conducted to investigate the effects of biochar (5%), organic fertilizer (5%), and their combined application at an equal ratio (biochar 2.5% + organic fertilizer 2.5%) on the degradation and transport of glyphosate (GLY), imidacloprid (IMI), and pyraclostrobin (PYR) in the soil-soybean system. The results showed that the combined application of biochar and organic fertilizer reduced the absorption of three pesticides by soybean, increased the degradation rate of three pesticides, and shortened the half-life by 6.54, 4.07 and 7.87 d compared with control treatment (CK). The combined application of biochar and organic fertilizer reduced the bioavailability of the three pesticides in the soil, with GLY, IMI, and PYR decreasing by 61.76%, 64.52%, and 66.67%, respectively, compared with CK. For microbial diversity, different treatments exhibited distinct effects on various microbial taxa: bacterial diversity was highest under the combined application of biochar and organic fertilizer; fungal diversity was the best under the organic fertilizer application, showing a significant increase of 67.11% compared to CK; and archaeal diversity peaked under biochar application, which increased by 17.32% compared with CK. Furthermore, the combined application of biochar and organic fertilizer altered the composition and quantity of soybean root exudates, enhanced root metabolic activity, and significantly enriched key metabolic pathways including linoleic acid metabolism, flavonoid biosynthesis, phenylpropanoid biosynthesis, and isoflavonoid biosynthesis (< 0.05). The Structural equation modeling (SEM) revealed that biochar and organic fertilizer reduced the uptake of GLY, IMI, and PYR in soybeans by affecting soil microbial diversity, root exudates and bioavailability. In summary, the combined application of biochar and organic fertilizer is an effective strategy for remediating pesticide-contaminated soil and reducing pesticide residues in soybean plants.

Soybean leaf morphology is an important breeding trait that requires large-scale phenotyping in commercial breeding programs. Conventional leaf phenotyping still relies on manual destructive measurements, which are labor-intensive and inefficient. Low-altitude unmanned aerial vehicles (UAVs) have emerged as a high-throughput phenotyping platform, but the retrieval of leaf morphology in densely occluded canopies remains challenging due to complex canopy backgrounds, illumination heterogeneity, and leaf overlap under field conditions. To address this issue, this study developed an integrated UAV framework that couples a YOLOv10-based leaf detection module, a novel structure-adaptive segmentation network (DynamicU), and a regression-based trait prediction model for retrieving a set of leaf morphological parameters across 273 soybean genotypes under field conditions. The YOLOv10 detector reliably localized individual leaves under complex canopy conditions, achieving a mean average precision (mAP@50) of 0.84. Subsequently, the DynamicU network, whose architecture was automatically optimized via Emperor Penguin Optimization, achieved a segmentation accuracy of 97.2% and a mean Intersection over Union of 93.8%, substantially outperforming conventional models. Using random forest regression, the framework retrieved relative leaf shape traits, including length-to-width ratio and dissection index, with markedly higher accuracy (R2=0.97), compared to absolute morphological traits, including leaf length, width, perimeter, and area (R2: 0.76–0.84). Notably, relative leaf shape traits showed positive associations with oil yield per plant and protein yield per plant, supporting their potential as complementary indicators for screening soybean germplasm with differential industrial product output. This end-to-end framework establishes a reliable bridge between UAV remote sensing and leaf-level morphological quantification, advancing high-throughput phenotyping capabilities to support precision breeding in soybean.

As a major commercial legume crop, soybean ranks among the world's most significant sources of edible oil and plant protein. We previously identified a SEIPIN homologue (FA9) at the fatty acid 9 locus that promotes fatty acid accumulation in soybean. To examine the detailed molecular mechanisms by which FA9 regulates lipid metabolism, we performed single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (stRNA-seq) of wild-type and FA9-knockout soybean seeds at the late maturity stage. scRNA-seq analysis identified 26 transcriptional clusters and revealed the spatial distribution of FA9 in seeds, in which the deletion of FA9 altered lipid and storage-related transcriptional programmes. On the basis of single-cell sequencing and immunoprecipitation-mass spectrometry (IP-MS), the vesicle-associated membrane protein (VAMP)-associated protein (VAP) was identified, and subsequent experiments demonstrated that FA9 interacts specifically with VAP via its N-terminal FFAT motif at the endoplasmic reticulum. Seeds of vap knockout (vap-KO1 and vap-KO2) and fa9 vap double knockout (fa9 vap-KO) lines, created by CRISPR-Cas9 gene editing, had higher protein contents and lower total fatty acid contents than wild-type soybean, whereas overexpression of FA9 and VAP enhanced lipid droplet formation in Nicotiana benthamiana. These findings reveal that FA9 interacts with VAP to promote lipid droplet biogenesis and lipid transport, thereby driving fatty acid accumulation in soybean seeds. This research provides new insight into the molecular mechanisms that regulate seed oil synthesis and identifies potential target genes for improvement of soybean oil quality through molecular breeding.

Three-dimensional (3D) reconstruction technologies for crops are of significant importance in the context of smart breeding and precision agriculture, as they enable accurate characterization of crop spatial architecture and developmental dynamics. Such capabilities provide essential phenotypic information for the rapid selection of breeding materials and informed agronomic decision-making. A critical requirement for the practical application of crop 3D models is high-accuracy organ-level segmentation. However, the absence of a stage-universal segmentation framework capable of operating across complete soybean growth cycle remains a major bottleneck hindering progress in this field. To address this issue, we propose SOY3DSEG-a high-precision framework based on an improved Point Transformer, designed to support the full developmental spectrum of soybean (V1-R7). The framework incorporates a novel down sampling strategy termed Dynamic Multi-Stage Sampling Strategy (DMSS), alongside multi-scale feature enhancement and a local geometry-aware attention mechanism, enhancing segmentation accuracy and efficiency. Performance evaluations across 12 consecutive soybean growth stages (V1 to R7) indicate that SOY3DSEG achieved an average mean Intersection-over-Union (mIoU) of 93.34 % for stemleaf segmentation-surpassing RandLA-Net, BAAF-Net, PointNet++, and PointConv by over 30 %, and outperforming the baseline Point Transformer by 14.18 %. A moderate accuracy decline appears at R6-R7 due to dense canopies and strong occlusion, yet SOY3DSEG retains clear superiority over the baseline Point Transformer, demonstrating robustness under complex morphology. In cross-crop transfer tests limited to early seedling stages of maize and tomato, the model achieves an mIoU of approximately 99 %, indicating strong earlystage transferability while mature-stage generalization across species remains open for future study. SOY3DSEG thus provides a stage-robust and scalable solution for full-cycle soybean phenotyping and growth monitoring, contributing to precision agricultural practice.

OBJECTIVE:This study aimed to investigate whether phenylpropionamides (PHS) exert therapeutic effects on Parkinson's disease (PD) by targeting autophagy-related pathways, using network pharmacology and in vitro experiments. METHODS:Network pharmacology (NP) analysis and molecular dynamics simulation (MDS) were applied to elucidate the potential mechanisms by which PHS treats PD. Subsequently, SH-SY5Y cells were treated with MPP+ to establish a neurotoxin model. Cell viability was assessed using the CCK-8 assay. Mitochondrial membrane potential (MMP) in SH-SY5Y cells was measured using JC-1 staining. Western blot (WB) was used to detect the expression of Bax, cleaved caspase-3, caspase-3, LC3-II, p62, Beclin-1, AMPK, mTOR, and ULK1 signaling proteins in SH-SY5Y cells. RESULTS:NP analysis suggested that the potential anti-PD effects of PHS were associated with cleaved caspase-3, Bcl-2, mTOR, and Beclin-1. Furthermore, KEGG and PPI analyses demonstrated that PHS may exert anti-PD effects by modulating the AMPK/mTOR/ULK1 autophagy signaling pathway. Molecular docking (MolD) and MDS showed that the key PHS component (Cannabisin I) had a stable interaction with caspase-3, Bcl-2, AMPK, mTOR, ULK1, and Beclin-1. The in vitro experiments showed that PHS suppressed the expression of cleaved caspase-3 and Bax, promoted Bcl-2 expression, activated the autophagy pathway, increased the levels of LC3-II and Beclin-1, increased mitochondrial membrane potential and decreased the levels of p62. Notably, PHS promoted autophagy by increasing AMPK and ULK1 while inhibiting mTOR protein levels. Therefore, PHS may represent a promising candidate for neuroprotective intervention in neurodegenerative disorders. CONCLUSION:This study suggests that PHS may exert anti-PD effects, possibly through triggering autophagy via the AMPK/mTOR/ULK1 signaling pathway.

Biochar is widely recognized as a beneficial soil amendment; however, its potential to mitigate long-term continuous cropping obstacles in soybean systems remains poorly understood. Based on an 11-year field experiment, this study systematically explored the effects of biochar application on soil physical properties, nutrients, hydrological characteristics, erosion resistance, and soybean yield stability. The results demonstrated that long-term continuous soybean cropping led to soil structural degradation, nutrients depletion, increased erosion, reduced soybean yield, and lower water use efficiency. In contrast, biochar application significantly enhanced total soil porosity (TP) and the generalized soil structure index (GSSI), increased the proportion of macroaggregates (>0.25 mm) and pores with diameters >= 0.3 mu m. Furthermore, biochar improved soil hydrological functions by enhancing water retention capacity and hydraulic conductivity, and significantly raised the initial, steady, and mean soil water infiltration rates. Notably, the application of 5.0 tha(-)(1) biochar was the most effective treatment. Compared to the control across years, it increased cumulative soil infiltration within 60 min by 50.26 mm (2015), 52.15 mm (2017), 69.88 mm (2019), 57.75 mm (2021), 55.52 mm (2023), and 67.92 mm (2025), respectively. This treatment also markedly reduced annual runoff and soil erosion, increased soil nutrients (organic carbon, alkali-hydrolyzed nitrogen, available phosphorus, available potassium), promoted soybean growth, and improved water use efficiency and yield stability. Structural equation modeling indicated that biochar primarily enhanced soybean yield by improving soil hydrological properties and reducing soil erosion. These long-term findings highlight that biochar, particularly at 5.0 tha(-)(1) , can effectively alleviate continuous cropping obstacles, providing a theoretical and technical basis for sustainable soybean production.

Bacterial and viral diseases significantly reduce soybean (Glycine max) yield and quality. RNA modifications, particularly N6-methyladenosine (m6A), are increasingly recognized as having a regulatory role in plant-pathogen interactions, but the m6A methylome of soybean during viral and bacterial infection has not yet been characterized. Here, we performed transcriptome sequencing and MeRIP-seq (methylated RNA immunoprecipitation followed by high-throughput sequencing) of soybean leaves infected with Soybean mosaic virus (SMV) and/or Pseudomonas syringae pv. glycinea (Psg). In general, m6A peaks were highly enriched near stop codons and in 3 '-UTR regions of soybean transcripts, and m6A methylation was negatively correlated with transcript abundance. Multiple genes showed differential methylation between infected and control plants: 1122 in Psg-infected plants, 539 in SMV-infected plants, and 2269 in co-infected plants; 195 (Psg), 84 (SMV), and 354 (Psg + SMV) of these transcripts were both differentially methylated and differentially expressed. Interestingly, viral infection was predominantly associated with hypermethylation and downregulation, whereas bacterial infection was predominantly associated with hypomethylation and upregulation. GO and KEGG enrichment analysis revealed shared processes likely affected by changes in m6A methylation during bacterial and viral infection, including ATP-dependent RNA helicase activity, RNA binding, and endonuclease activity, as well as specific processes affected by only one pathogen. Our findings shed light on the role of m6A modifications during pathogen infection and highlight potential targets for epigenetic editing to increase the broad-spectrum disease resistance of soybean.

Isoflavonoids, a major class of secondary metabolites predominantly found in legumes, serve as vital defenders against both biotic and abiotic stresses, though their molecular mechanisms in mitigating aluminum (Al) toxicity remain incompletely understood. In this study, we introduced the isoflavone synthase gene isolated from a white clover (TrIFS) into soybean. The resulting transgenic soybeans exhibited a noticeable increase in isoflavone accumulation in various organs and stronger tolerance to aluminum toxicity. The dry weight of roots of two transgenic lines, TL124 and TL129, were heavier than that of the wild type (WT) after 200 µM AlCl3 treatment, by 46.1 % and 38.6 % respectively. Higher seed yield per plant was also obtained in these two transgenic lines by 18.9 % and 18.2 % than in the WT under Al stress, respectively. Additionally, TrIFS transgenic soybeans showed significantly reduced levels of aluminum-induced malondialdehyde (MDA) and reactive oxygen species (ROS). RNA transcriptome analysis revealed that the transcriptional level of GmALDH22A2 (Glyma.09g036000, Wm82.a2.v1) was sharply decreased in the TrIFS transgenic lines under Al treatment. The GmALDH22A2 protein interaction with a respiratory burst oxidase homologue (GmRBOHL, Glyma.10g152200, Wm82.a2.v1) was comfirmed by yeast two hybrid, fluorescence complementation and co-immunoprecipitation assays. Furthermore, this ALDH-RBOH interaction could cause increase in ROS production in tobacco, which suggested that the decreased transcription of GmALDH22A2 in the TrIFS transgenic soybeans would attenuate ROS generation. In summary, we provided new insights in improving Al tolerance through isoflavone enhancement.