Metabolic phenotypes are often governed by complex genetic architectures involving both additive and non-additive effects. However, the extent to which epistatic interactions contribute to the pathway-level regulation of plant metabolism remains unclear. In this study, we investigated the genetic architecture of flavonoid-related metabolites using metabolomic and genomic data from 200 soybean accessions cultivated under multiple environmental conditions. Broad-sense heritability estimates revealed that many metabolites were under strong genetic control, particularly flavonoid-related metabolites. Principal component analysis-based metabolome-wide genome-wide association studies identified four major loci associated with flavonoid metabolic variation, including a locus corresponding to flavonoid 3′-hydroxylase. Conditional analyses based on multilocus genetic backgrounds demonstrated that the effects of downstream loci were highly dependent on upstream genotypes. In particular, single-nucleotide polymorphism effects were frequently detectable only in specific allelic backgrounds defined by the major flavonoid 3′-hydroxylase locus, consistent with strong epistatic interactions among loci. Bayesian network analyses further supported a hierarchical genetic structure consistent with upstream regulation of downstream loci across the flavonoid biosynthetic pathway. These results demonstrate that highly heritable metabolic phenotypes can be controlled by a few loci exhibiting both additive and context-dependent non-additive effects. Our findings provide evidence that pathway-level metabolic diversity in soybean is generated through hierarchical and epistatic genetic control involving a limited set of key loci.

Legume–cereal intercropping is widely recognised for increasing soil nitrogen (N) stocks, yet the microbial mechanisms driving this N retention remain poorly understood. In this study, we focus on how microbial community genomic traits and soil extractable nutrient stoichiometry impact on soil N accumulation. We conducted a five year maize–peanut field experiment under three N fertilization levels (0, 150, 300 kg N ha⁻1 yr⁻1), we examined soil organic N fractions, microbial community-averaged genomic traits (genome size and Guanine-Cytosine content), and soil extractable nutrient stoichiometry and their relationships. Intercropped peanut soils accumulated significantly more total N than monocultures, primarily through reduced amino acid N and elevated recalcitrant fractions (non-hydrolysable N and unidentified hydrolysable N). This enhanced N stabilisation was closely linked to significantly smaller bacterial community-averaged genome sizes in peanut (versus maize) soils and to lower extractable N:carbon (C), N: phosphorus (P), and N: potassium (K) ratios, indicating a relief of microbial C, P, and K limitation. Nitrogen fertilization enlarged bacterial genome size in most treatments, consistent with induced co-limitation by non-N nutrients. Community-averaged genomic traits, especially bacterial genome size, combined with extractable nutrient stoichiometry, showed strong relationships with soil N fractions and provided substantially high predictive power (across several machine-learning algorithms). These findings reveal that maize–peanut intercropping promotes long-term soil N retention through shifts in microbial life-history traits and nutrient stoichiometry, offering new trait-based predictors of N cycling in low-input agroecosystems.

Microbiomes are increasingly recognized as key to addressing global challenges in health and sustainability, as they can provide emergent biological functions unattainable with single microbial species. However, microbial communities occasionally undergo abrupt shifts in species composition despite their intrinsic steadiness, making it difficult to maintain highly functional microbiome states. Here, we outline emerging statistical frameworks that integrate ecological stability theory with empirical analyses of microbiome structure and function. Approaches inspired by the concept of "stability landscapes" now enable inference of how the relationship between community structure and assembly potential changes along environmental gradients. Such empirical analyses offer bird's-eye perspectives for maintaining or restoring community states with desirable microbiome functions. Moreover, identifying the attractors of microbiome dynamics facilitates forecasting of abrupt transitions into dysfunctional states (i.e. dysbiosis). Bridging classic ecological theory and empirical microbiome analyses will deepen our understanding of the principles governing species-rich community assembly, expanding the scope of microbiome-based solutions across medical, industrial, agricultural, and environmental sciences.

ABSTRACT Soils harbor immense biosynthetic gene cluster (BGC) diversity that mediates microbial interactions, yet this potential remains unevenly mapped and poorly characterized across diverse bacterial lineages. Ktedonobacteria (phylum Chloroflexota) are an actinomycete-like lineage widely distributed in terrestrial soils, including oligotrophic volcanic deposits; however, their secondary metabolism and genome architecture remain poorly characterized. Here, we integrate targeted cultivation from volcanic soils at Mount Zao (Japan) with genome-resolved metagenomics and comparative analysis of public genomes to examine biosynthetic potential across 183 ktedonobacterial genomes. We identified 1,546 BGCs and grouped them into 1,162 non-redundantgene-cluster families (GCFs) using antiSMASH and BiG-SLiCE. Nearly one quarter of genomes encoded≥10 distinct GCFs, and several family-level clades exhibited high GCF richness that approached that of Streptomyces within our data set, highlighting a putatively biosynthetically rich yet underexplored soil bacterial lineage. Most ktedonobacterial BGCs were highly divergent from current reference collections and exhibited unusually low intra-genomic redundancy, suggesting broad putative chemical diversity. Long-read assemblies from 10 cultured strains revealed recurrent 1.6–3.5 Mb ECE-like contigs with chromid-like features, but distinct maintenance features. These replicons were consistently enriched in BGCs and mobility-associated genes, with mobility loci concentrated near BGC boundaries. Collectively, our results expand the phylogenetic landscape of soil biosynthetic diversity and highlight ECE-like contigs as major genomic reservoirs for secondary metabolism in Ktedonobacteria.IMPORTANCESoil bacteria produce many of the small molecules that become medicines and help microbes interact with each other. Yet most of this chemical diversity remains unexplored because many soil lineages are difficult to cultivate and remain genomically underrepresented. Much of what we know comes from well-studied groups such as actinomycetes, leaving many soil lineages largely unexplored. We analyzed 183 genomes from Ktedonobacteria, an actinomycete-like group within the phylum Chloroflexota that is widespread in terrestrial soils, including nutrient-poor volcanic deposits. We uncovered a large and diverse set of gene clusters predicted to produce secondary metabolites, many of which lack close counterparts in current reference collections. We also show that these clusters are concentrated on large ECE-like contigs with chromid-like features, pointing to a dedicated genomic reservoir that can accumulate and reshuffle biosynthetic traits. Our results expand the known sources of soil biosynthetic diversity and provide a foundation for future cultivation and functional characterization of Ktedonobacteria metabolites.

Intermediate omics traits, which mediate the effects of genetic variation on phenotypic traits, are increasingly recognized as valuable components of genetic evaluation. In particular, rhizosphere microbiota play a crucial role in plant health and productivity; however, their complex interactions with host genetics remain challenging to model. Although two-step modeling frameworks have been proposed to integrate intermediate omics traits into phenotype prediction, existing approaches do not incorporate nonlinear relationships between different omics layers. To address this, we have proposed a two-step phenotype prediction framework that integrates genomic, rhizosphere microbiome, and metabolome (meta-metabolome) data, while explicitly capturing omics-omics nonlinearities. The first step is to predict meta-metabolome traits from genetic and microbial features, thus effectively isolating them from the environmental noise. In this process, intermediate "proxy" omics traits are generated as general biological information to provide robust models. The second step utilizes this "proxy" to enhance the accuracy of the phenotype prediction. We compared a linear mixed model (Best Linear Unbiased Prediction, BLUP) and a nonlinear model (Random Forest, RF) at each step, as demonstrated through simulations and empirical analysis of a multi-omics soybean dataset in which nonlinear modeling captures intricate omics interactions. Notably, our approach enables phenotype prediction without requiring the original meta-metabolome data used in model training, thereby reducing reliance on costly omics measurements. This framework integrates intermediate omics traits into genomic prediction to improve prediction accuracy and provide solutions for deeper insights into plant-microbiome interactions.

Plant roots are hotspots for interactions with soil microbes, where a characteristic bacterial community structure is formed. Plant specialized metabolites often play pivotal roles in this assembly process. However, the molecular basis underlying root microbiota responses to these bioactive compounds, and how such metabolic interactions shape the assembly of host-specific root microbiota, remain largely unknown. Nicotine is a toxic alkaloid predominantly produced by the genus Nicotiana, and the genus Arthrobacter is known as one of the nicotine-degrading bacteria in the tobacco root microbiota. In this study, we used the tobacco–Arthrobacter interaction system as a model and integrated comparative genomics and experimental genetic manipulation assays to uncover the role of bacterial catabolism capacity for host specialized metabolites in shaping host-specific root microbiota. Nicotine catabolism genes are uniquely found in the Arthrobacter strains derived from nicotine-containing environments, and this restricted gene distribution is driven by a plasmid-mediated horizontal gene transfer. To assess the ecological consequences of this genomic adaptation in Arthrobacter fitness in tobacco roots, we characterized the nicotine utilization ability of Arthrobacter and conducted adaptation assays under in planta conditions using genetically manipulated Arthrobacter strains and tobacco mutants impaired in nicotine catabolism and biosynthesis, respectively. Nicotine improves Arthrobacter colonization of tobacco roots through a catabolism-dependent mechanism. Bacterial community analysis using a synthetic community approach further demonstrated that this metabolic adaptation enhances Arthrobacter fitness within tobacco root microbiota. Our findings illustrated that bacterial catabolic capacity toward host-derived plant specialized metabolites is key for successful root colonization. This metabolic adaptation is driven by plasmid-mediated horizontal gene transfer and ultimately shapes the structure of the root microbiota community.

Soils harbour immense biosynthetic gene cluster (BGC) diversity that can mediate microbial interactions, yet this potential is still mapped unevenly across the tree of life. Ktedonobacteria , a class of actinomycete-like bacteria within phylum Chloroflexota , are widespread in terrestrial environments and repeatedly dominate pioneer communities in extremely oligotrophic volcanic bare-ground soils; however, their secondary metabolism and genome architecture remain poorly characterised. Here, we integrate targeted cultivation using volcanic soils from Mount Zao with genome-resolved metagenomics and public genomes to analyse 183 ktedonobacterial genomes. Using antiSMASH and BiG-SLiCE, we identified 1,546 BGCs comprising 1,162 non-redundant gene-cluster families (GCFs). In our dataset, nearly one quarter of genomes encode >10 distinct GCFs, and several family-level clades show mean GCF counts comparable to those in genus Streptomyces. Most ktedonobacterial BGCs are highly divergent from reference collections and exhibit unusually low intra-genomic redundancy, suggesting broad, underexplored chemotypes. Long-read assemblies from ten strains reveal recurrent 1.6 - 3.5 Mb chromid-like secondary replicons with chromosome-like composition but distinct maintenance signatures. These replicons are consistently enriched in BGCs and mobility-associated genes, with mobility loci concentrated near BGC boundaries. Collectively, our results expand the current knowledge of the phylogenetic landscape of soil biosynthetic diversity and highlight chromid-like secondary replicons as major genomic reservoirs for specialised metabolism in Ktedonobacteria . ### Competing Interest Statement The authors have declared no competing interest. JSPS KAKENHI, JP25K22374, JP25K01112 The Institute for Fermentation, G-2024-1-019

BACKGROUND:Plant-microbe interactions in the rhizosphere are central to plant growth, nutrient acquisition, and stress resilience. Although multi-omics approaches enable comprehensive profiling of different biological layers, integrating these data to understand the mechanisms underlying plant-microbe symbiosis, particularly under drought stress, remains a challenge. RESULTS:Genomic, metabolomic, and microbiome data from 198 soybean accessions grown under both control and drought conditions were integrated to identify environment-specific predictive features of the plant phenotypes. We compared best linear unbiased prediction (BLUP), genome-wide association study (GWAS), and a nonlinear machine learning model to evaluate their ability to detect informative features. The machine learning models provided flexible variable selection and outperformed linear models in capturing nonlinear dependencies. Model interpretation using SHapley Additive exPlanations (SHAP) indicated that the isoflavone derivative, daidzin, and the drought-tolerant Candidatus Nitrosocosmicus, were major contributors to phenotypic variation, specifically under drought stress. SHAP-based interaction networks indicated cross-omics links, including connections between daidzin, gamma-aminobutyric acid (GABA), and Paenibacillus. CONCLUSION:The proposed interpretable machine learning approach for plant phenotype prediction identified multi-omics biomarkers and interactions, providing insights into plant adaptation to drought stress through environment-dependent rhizosphere networks and symbiotic associations.

Co-option of gene regulatory networks leads to the acquisition of new cell types and tissues. Stomata, valves formed by guard cells (GCs), are present in most land plants and regulate CO2 exchange. The transcription factor (TF) FAMA globally regulates GC differentiation. In the Brassicales, FAMA also promotes the development of idioblast myrosin cells (MCs), another type of specialized cell along the vasculature essential for Brassicales-specific chemical defences. Here we show that in Arabidopsis thaliana, FAMA directly induces the TF gene WASABI MAKER (WSB), which triggers MC differentiation. WSB and STOMATAL CARPENTER 1 (SCAP1, a stomatal lineage-specific direct FAMA target), synergistically promote GC differentiation. wsb mutants lacked MCs and the wsb scap1 double mutant lacked normal GCs. Evolutionary analyses revealed that WSB is conserved across stomatous angiosperms. We propose that the conserved and reduced transcriptional FAMA-WSB module was co-opted before evolving to induce MC differentiation.

Background: Plant roots are hotspots for interactions with soil microbes, where a characteristic bacterial community structure is formed. Plant specialized metabolites often play pivotal roles in this assembly process. However, the molecular basis underlying root microbiota responses to these bioactive compounds, and how such metabolic interactions shape the assembly of host-specific root microbiota, remain largely unknown. Nicotine is a toxic alkaloid predominantly produced by the genus Nicotiana, and the genus Arthrobacter is known as one of the nicotine-degrading bacteria in the tobacco root microbiota. In this study, we used the tobacco-Arthrobacter interaction system as a model and integrated comparative genomics and experimental genetic manipulation assays to uncover the role of bacterial catabolism capacity for host specialized metabolites in shaping host-specific root microbiota. Results: Nicotine catabolism genes are uniquely found in the Arthrobacter strains derived from nicotine-containing environments, and this restricted gene distribution is driven by a plasmid-mediated horizontal gene transfer. To assess the ecological consequences of this genomic adaptation in Arthrobacter fitness in tobacco roots, we conducted adaptation assays under both in vitro and in planta conditions using genetically manipulated Arthrobacter and tobacco mutants, which are impaired in nicotine catabolism and biosynthesis, respectively. Nicotine improves Arthrobacter colonization to the tobacco roots through both catabolism-dependent and catabolism-independent mechanisms. Bacterial community analysis using a synthetic community approach further demonstrated that these metabolic interactions, mediated by tobacco nicotine biosynthesis and its catabolism by Arthrobacter, jointly affect root microbiota composition. Conclusions: Our findings illustrated that bacterial catabolic capacity toward host-derived plant specialized metabolites is key for successful root colonization. This metabolic adaptation is driven by plasmid-mediated horizontal gene transfer and ultimately shapes the structure of the overall root microbiota community. ### Competing Interest Statement The authors have declared no competing interest. Japan Society for the Promotion of Science, Research Fellowship for Young Scientists PD, 22KJ3147 Japan Society for the Promotion of Science, KAKENHI grants, 22K21367 Mayekawa Houonkai Foundation, https://ror.org/05qkcsb49 Humanosphere Science Research of RISH RIKEN TRIP initiative

The nectaries of flowers in angiosperms are typically located at the base of the stamens and secrete nectar, composed primarily of sugars and various secondary metabolites, either actively or passively. Although nectaries develop in specific locations and perform specialized functions, the regulatory mechanisms governing their formation are unclear. Through a pseudotime trajectory analysis based on single-nucleus RNA-seq data and newly performed spatial transcriptomics, we uncover a spatiotemporal regulatory network governing nectary development. We establish CRABS CLAW (CRC) as a master regulator that integrates temporal transcriptional regulation with spatial auxin transport, orchestrating nectary development through phytohormone signaling, sugar transport and terpene biosynthesis pathways. CRC interacts to MYB21, MYB24 and MYB57, with the CRC–MYB57 complex binding the 5′ and 3′ regulatory regions of the MACCHI-BOU 4 (MAB4) locus, facilitating the formation of a chromatin loop that enhances MAB4 expression. This loop is necessary for the development of properly sized nectaries. MAB4 reinforces PIN-FORMED 1 (PIN1)-mediated auxin transport to establish auxin maxima at the nectary tip and regulates PIN6 to maintain auxin gradients. These findings reveal how CRC integrates transcriptional regulation and auxin transport to coordinate nectary development, linking metabolic output with organogenesis.

Soil CO2 dynamics play a crucial role in the global carbon cycle, particularly in agricultural soils where management practices influence CO2 dynamics. This study quantified soil CO2 concentrations and fluxes under different fertilization treatments (chemical vs. organic) in a soybean field and evaluated the performance of the SOILCO2 model with different gas diffusivity models. Soil moisture, temperature, and CO2 concentrations were continuously monitored at multiple depths throughout the growing season. The results showed that microbial and root-associated CO2 production were enhanced in the presence of crops and organic matter, with microbial activity significantly increasing the CO2 concentrations, particularly after rainfall events. Gas diffusivity, a critical factor in CO2 transport modeling, was evaluated using the Millington–Quirk (MQ) and Water Linear Reduction (WLR) models. The WLR model with fitted parameter provided better agreement with measured gas diffusivity and observed CO2 profiles as compared to the MQ model. Sensitivity analysis demonstrated that an accurate representation of gas diffusivity is essential for a realistic simulation of soil CO2 behavior. This study highlights the importance of high-resolution field monitoring and model calibration for improving CO2 transport predictions, and supports the use of the WLR model for agricultural soils with varying textures and moisture conditions.

Variability in environmental conditions and farming practices often leads to discrepancies between experimental results and outcomes in farmers' fields. This gap poses a challenge for understanding the effects of agricultural inputs and methods under real-world conditions, particularly in fruit cultivation systems, where large-scale experimental data are limited. In this study, we applied a cohort study approach leveraging data from farmers' fields to investigate the effects of pesticide and fertilizer application methods on fruit quality and soil properties in mandarin orange orchards. Biases arising from differences in covariates among cultivation methods were controlled using the inverse probability weighting (IPW) based on propensity scores. Consequently, compared to local-scale analysis between adjacent fields, the nationwide cohort analysis detected a greater number of significant effects of cultivation methods by utilizing its larger sample size. Through this analysis, we found important insights into the effects of pesticide and fertilizer application methods on plant pathogens, nutritional quality, and soil properties in sustainable cultivation systems of mandarin orange. This study demonstrates that cohort analyses using real-world data have great potential to advance agricultural biotechnology by providing effective feedback from farmers' fields and bridging the gap between scientific research and real-world agriculture.

The facultative parasitic plant Phtheirospermum japonicum forms a specialized organ, the haustorium, to invade its host, Arabidopsis thaliana, establishing a vascular connection via the formation of a xylem bridge. This connection depends on coordinated interactions between the vascular systems of both plants, yet the molecular dynamics of these interactions within the haustorium and the host roots remain elusive. This study aimed to unravel the transcriptomic heterogeneity of haustoria and gene regulatory networks involved in this process by integrating single nucleus RNA sequencing (snRNA-seq) and bulk RNA sequencing (bulk RNA-seq). snRNA-seq identified a total of 7 P. japonicum cell clusters and 4 A. thaliana cell clusters, each with cluster-specific marker genes, allowing for a distinct characterization of vascular cells within the haustorium. Differential gene expression analyses revealed up-regulation of genes associated with xylem formation and auxin transport in both parasites and hosts, suggesting the presence of shared molecular mechanisms facilitating vascular connection. Further gene network analysis combining snRNA-seq and bulk RNA-seq identified conserved homologous genes across both species, indicating potential molecular interactions of vascular-related genes from hosts and parasites. Our study reveals the high heterogeneity of haustorium cells, characterizing the expression profiles of each cell type in haustoria and host roots during haustorium development at single-cell resolution. These findings provide insights into the molecular interactions between parasitic plants and hosts, presenting potential targets for disrupting these interactions to manage parasitic plant infestations in crops.

High-dimensional multi-omics microbiome data play an important role in elucidating microbial community interactions with their hosts and environment in critical diseases and ecological changes. Although Bayesian clustering methods have recently been used for the integrated analysis of multi-omics data, no method designed to analyze multi-omics microbiome data has been proposed. In this study, we propose a novel framework called integrative stochastic variational variable selection (I-SVVS), which is an extension of stochastic variational variable selection for high-dimensional microbiome data. The I-SVVS approach addresses a specific Bayesian mixture model for each type of omics data, such as an infinite Dirichlet multinomial mixture model for microbiome data and an infinite Gaussian mixture model for metabolomic data. This approach is expected to reduce the computational time of the clustering process and improve the accuracy of the clustering results. Additionally, I-SVVS identifies a critical set of representative variables in multi-omics microbiome data. Three datasets from soybean, mice, and humans (each set integrated microbiome and metabolome) were used to demonstrate the potential of I-SVVS. The results indicate that I-SVVS achieved improved accuracy and faster computation compared to existing methods across all test datasets. It effectively identified key microbiome species and metabolites characterizing each cluster. For instance, the computational analysis of the soybean dataset, including 377 samples with 16 943 microbiome species and 265 metabolome features, was completed in 2.18 hours using I-SVVS, compared to 2.35 days with Clusternomics and 1.12 days with iClusterPlus. The software for this analysis, written in Python, is freely available at https://github.com/tungtokyo1108/I-SVVS.

Omics data provide a plethora of quantifiable information that can potentially be used to identify biomarkers targeting the physiological processes and ecological phenomena of organisms. However, omics data have not been fully utilized because current prediction methods in biomarker construction are susceptible to data multidimensionality and noise. We developed OmicSense, a quantitative prediction method that uses a mixture of Gaussian distributions as the probability distribution, yielding the most likely objective variable predicted for each biomarker. Our benchmark test using a transcriptome dataset revealed that OmicSense achieves accurate and robust prediction against background noise without overfitting. Weighted gene co-expression network analysis revealed that OmicSense preferentially utilized hub nodes of the network, indicating the interpretability of the method. Application of OmicSense to single-cell transcriptome, metabolome, and microbiome datasets confirmed high prediction performance (r > 0.8), suggesting applicability to diverse scientific fields. Given the recent rapidly expanding availability of omics data, the developed prediction tool OmicSense, can accelerate the use of omics data as a “biosensor” based on an assemblage of potential biomarkers.

Securing a stable food supply and achieving sustainable agricultural production are essential for mitigating future food insecurity. Soil metabolomics is a promising tool for capturing soil status, which is a critical issue for future sustainable food security. This study aims to provide deeper insights into the status of soybean-grown fields under varying soil conditions over three years by employing comprehensive soil volatile organic compound (VOC) profiling, also known as soil volatilomics. Profiling identified approximately 200 peaks in agricultural fields. The soil of soybean-presented plots exhibited markedly higher VOC levels than those of non-soybean plots during the flowering season. Pentanoic acid, 2,2,4-trimethyl-3-carboxyisopropyl, isobutyl ester, a discriminative soil VOC, was identified through multivariate data analysis as a distinctively present VOC in fields with or without soybean plants during the flowering period. Soil VOC profiles exhibited strong correlations with soil-related omics datasets (soil ionome, microbiome, metabolome, and physics) and no significant correlations with root microbiome and rhizosphere chemicals. These findings indicate that soil VOC profiles could serve as a valuable indicator for assessing soil status, thereby supporting efforts to ensure future global food security.

Nitrosophilus labii HRV44T is a thermophilic chemolithoautotroph possessing clade II type nitrous oxide (N2O) reductase (NosZ) that has an outstanding activity in reducing N2O to dinitrogen gas. Here, we attempt to understand molecular responses of HRV44T to N2O. Time course transcriptome and proteomic mass spectrometry analyses under anaerobic conditions revealed that most of transcripts and peptides related to denitrification were constitutively detected, even in the absence of any nitrogen oxides as electron acceptors. Gene expressions involved in electron transport to NosZ were upregulated within 3 h in response to N2O, rather than upregulation of nos genes. Two genes encoding Crp/Fnr transcriptional regulators observed upstream of nap and nor gene clusters had significant negative correlations with nosZ expression. Statistical path analysis further inferred a significant causal relationship between the gene expression of nosZ and that of one Crp/Fnr regulators. Our findings contribute to understanding the transcriptional regulation in clade II type N2O-reducers.

Plants detect pathogens using cell-surface pattern recognition receptors (PRRs) such as ELONGATION Factor-TU (EF-TU) RECEPTOR (EFR) and FLAGELLIN SENSING 2 (FLS2), which recognize bacterial EF-Tu and flagellin, respectively. These PRRs belong to the leucine-rich repeat receptor kinase (LRR-RK) family and activate the production of reactive oxygen species via the NADPH oxidase RESPIRATORY BURST OXIDASE HOMOLOG D (RBOHD). The PRR-RBOHD complex is tightly regulated to prevent unwarranted or exaggerated immune responses. However, certain pathogen effectors can subvert these regulatory mechanisms, thereby suppressing plant immunity. To elucidate the intricate dynamics of the PRR-RBOHD complex, we conducted a comparative coimmunoprecipitation analysis using EFR, FLS2, and RBOHD in Arabidopsis thaliana. We identified QIAN SHOU KINASE 1 (QSK1), an LRR-RK, as a PRR-RBOHD complex-associated protein. QSK1 downregulated FLS2 and EFR abundance, functioning as a negative regulator of PRR-triggered immunity (PTI). QSK1 was targeted by the bacterial effector HopF2Pto, a mono-ADP ribosyltransferase, reducing FLS2 and EFR levels through both transcriptional and transcription-independent pathways, thereby inhibiting PTI. Furthermore, HopF2Pto transcriptionally downregulated PROSCOOP genes encoding important stress-regulated phytocytokines and their receptor MALE DISCOVERER 1-INTERACTING RECEPTOR-LIKE KINASE 2. Importantly, HopF2Pto requires QSK1 for its accumulation and virulence functions within plants. In summary, our results provide insights into the mechanism by which HopF2Pto employs QSK1 to desensitize plants to pathogen attack.

In angiosperms, the transition from floral-organ maintenance to abscission determines reproductive success and seed dispersion. For petal abscission, cell-fate decisions specifically at the petal-cell base are more important than organ-level senescence or cell death in petals. However, how this transition is regulated remains unclear. Here, we identify a jasmonic acid (JA)-regulated chromatin-state switch at the base of Arabidopsis petals that directs local cell-fate determination via autophagy. During petal maintenance, co-repressors of JA signaling accumulate at the base of petals to block MYC activity, leading to lower levels of ROS. JA acts as an airborne signaling molecule transmitted from stamens to petals, accumulating primarily in petal bases to trigger chromatin remodeling. This allows MYC transcription factors to promote chromatin accessibility for downstream targets, including NAC DOMAIN-CONTAINING PROTEIN102 ( ANAC102 ). ANAC102 accumulates specifically at the petal base prior to abscission and triggers ROS accumulation and cell death via AUTOPHAGY-RELATED GENE s induction. Developmentally induced autophagy at the petal base causes maturation, vacuolar delivery, and breakdown of autophagosomes for terminal cell differentiation. Dynamic changes in vesicles and cytoplasmic components in the vacuole occur in many plants, suggesting JA–NAC-mediated local cell-fate determination by autophagy may be conserved in angiosperms.