Plant Communications

Asian rice is one of the worlds most widely cultivated crops. Large-scale resequencing analyses have been undertaken to explore the domestication and de-domestication genomic history of Asian rice, but the evolution of rice is still under debate. Here, we construct a syntelog-based rice pan-genome by integrating and merging 74 high-accuracy genomes based on long-read sequencing, encompassing all ecotypes and taxa of Oryza sativa and Oryza rufipogon. Analyses of syntelog groups illustrate subspecies divergence in gene presence-and-absence and haplotype composition and identify massive genomic regions putatively introgressed from ancient Geng/japonica to ancient Xian/indica or its wild ancestor, including almost all well-known domestication genes and a 4.5-Mb centromere-spanning block, supporting a single domestication event in rice. Genomic comparisons between weedy and cultivated rice highlight the contribution from wild introgression to the emergence of de-domestication syndromes in weedy rice. This work highlights the significance of inter-taxa introgression in shaping diversification and divergence in rice evolution and provides an exploratory attempt by utilizing the advantages of pan-genomes in evolutionary studies.

Phosphorus (P) is one of essential macronutrients for plant growth and development. Rapid Alkalization Factors (RALFs) play crucial roles in plant responses to nutrient stresses, however, the functions of Glycine max RALFs (GmRALFs) under low P (LP) stress remain elusive. In this study, we first identified 27 GmRALFs in soybean, then we revealed that GmRALF10, GmRALF11, and GmRALF22 are induced in both roots and leaves, while only GmRALF5, GmRALF6, and GmRALF25 are up-regulated in leaves in LP conditions. Furthermore, GmRALF22 was found to be the target gene of transcription factor GmPHR1, which binds the P1BS cis-element in the promoter of GmRALF22 via electrophoretic mobility shift assay (EMSA) and DUAL-LUC experiment. Colonization of Bacillus subtilis that deliver GmRALF22 increases the expression of high affinity phosphate (Pi) transporter gene GmPT2, GmPT11, GmPT13 and GmPT14, thus increases the total amount of dry matter and soluble Pi in soybean. RNA-sequencing uncovered that GmRALF22 alleviates LP stress by regulating the expression of JA-, SA- and immune-related genes. Finally, we verified that GmRALF22 is dependent on FERONIA to promote Arabidopsis primary root growth under LP conditions. In summary, GmPHR1-GmRALF22 module positively regulates soybeans tolerance to LP. HighlightsO_LISoybean genome has 27 GmRALFs. GmRALF5, GmRALF6, GmRALF10, GmRALF11, GmRALF22 and GmRALF25 are induced in low phosphorus (LP). C_LIO_LIGmPHR1 directly regulate the transcription of GmRALF22 via binding the promoter P1BS cis-element. C_LIO_LISecretion of GmRALF22 protein by Bacillus subtilis promoted soybean growth under LP conditions by improving soybean P nutrition through increased expression of high affinity phosphate (Pi) transporter gene. C_LIO_LIGmRALF22 regulates soybean P nutrition by JA-, SA- and immune-related genes expression at transcriptome level. That application of GmRALF22 promoted primary root growth in Arabidopsis in LP is dependent on FER receptor. C_LI

Translating biological knowledge from Arabidopsis to crop species is important to advance agriculture and secure food production in the face of dwindling fertilizer resources, biotic and abiotic stresses. However, it is often not trivial to identify functional homologs (orthologs) of Arabidopsis genes in crops. Combining sequence and expression data can improve the correct prediction of orthologs. Here, we conducted a large-scale RNA sequencing based transcriptomics study of Arabidopsis, Medicago, Brachypodium and Setaria grown side-by-side in Phosphorus (P)-sufficient and P-limited conditions to generate comparable transcriptomics datasets. Comparison of top 200 P-limitation induced genes in Arabidopsis revealed that [~]80% of these genes have identifiable close homologs in the other three species but only [~]50% retain their P-limitation response in the legume and grasses. Most of the hallmark genes of the P-starvation response were found conserved in all four species. This study reveals many known, novel, unannotated, conserved and species-specific regulations of the transcriptional P-starvation response. Identification and experimental verification of expressologs by independent RT-qPCR for P-limitation marker genes in Prunus showed the usefulness of comparative transcriptomics in pinpointing the functional orthologs in diverse crop species. This study provides an unprecedented resource for functional genomics and translational research to create P-efficient crops. HIGHLIGHTComparative transcriptomics reveals novel, known, conserved and specific transcriptome responding to Phosphorus limitation in Arabidopsis, Medicago, Brachypodium and Setaria to facilitate translational research in crops.

Momilactone A, an important plant labdane-related diterpenoid, functions as a phytoalexin against pathogens and an allelochemical against neighboring plants. The genes involved in biosynthesis of momilactone A are found in clusters, i.e., MABGCs (Momilactone A biosynthetic gene clusters), in the rice and barnyardgrass genomes. How MABGCs originate and evolve is still not clear. Here, we integrated results from comprehensive phylogeny and comparative genomic analyses of the core genes of MABGC-like clusters and MABGCs in 40 monocot plant genomes, providing convincing evidence for the birth and evolution of MABGCs in grass species. The MABGCs found in the PACMAD clade of the core grass lineage (including Panicoideae and Chloridoideae) originated from a MABGC-like cluster in Triticeae (BOP clade) via horizontal gene transfer (HGT) and followed by recruitment of MAS and CYP76L1 genes. The MABGCs in Oryzoideae originated from PACMAD through another HGT event and lost CYP76L1 afterwards. The Oryza MABGC and another Oryza diterpenoid cluster c2BGC are two distinct clusters, with the latter being originated from gene duplication and relocation within Oryzoideae. Further comparison of the expression patterns of the MABGC genes between rice and barnyardgrass in response to pathogen infection and allelopathy provides novel insights into the functional innovation of MABGCs in plants. Our results demonstrate HGT-mediated origination of MABGCs in grass and shed lights into the evolutionary innovation and optimization of plant biosynthetic pathways.

Artemisia annua is renowned for producing artemisinin, a compound that revolutionized malaria treatment and holds therapeutic promise for other diseases, including cancer and diabetes. However, low natural yields of artemisinin remain a major bottleneck, necessitating a deeper understanding of the genetic and regulatory networks involved in its biosynthesis. Although several transcriptomic studies on A. annua exist, they are often limited in scope, and a comprehensive, tissue-resolved gene expression resource has been lacking. Here, we present the Artemisia Database (Artemisia-DB)--a high-resolution expression atlas constructed from an extensive integration of publicly available RNA-seq datasets. The database provides transcript- and gene-level abundance estimates across major tissues and includes functional annotations such as Gene Ontology (GO) terms, KEGG pathways, and InterPro domains. As a case study, we investigated the coexpression profile of HMGR (3-hydroxy-3-methylglutaryl- CoA reductase), a key enzyme in the mevalonate pathway and an early step in artemisinin biosynthesis. Coexpression analysis in leaf tissue revealed a subset of Auxin Response Factor (ARF) transcription factors strongly correlated to HMGR. This finding suggests a potential regulatory link between auxin signaling and artemisinin biosynthesis, providing new hypotheses for functional validation. Artemisia-DB is freely accessible at https://artemisia-db.com and offers an interactive interface for exploring expression data, functional annotations, transcription factors, CRISPR targets, and more. By combining high-quality transcriptome data with regulatory and functional insights, Artemisia-DB serves as a valuable resource for the plant research community and facilitates deeper investigation into the transcriptional dynamics and specialized metabolism of A. annua.

Brassica napus (canola) is a significant contributor to the worlds oil production and is cultivated across continents, yet acidic soils with Al3+ and Mn2+ toxicities limit its production. The genetic determinants underlying acidic soil tolerance in canola are unknown and require to be uncovered for canola breeding and production. Here, through comprehensive phenotyping, whole genome resequencing, and genome-wide association analysis, we identified three QTLs for tolerance to Mn2+ toxicity on chromosomes A09, C03, and C09. Allelism tests between four tolerance sources confirmed that at least one locus on A09 controls Mn2+ tolerance in B. napus. Integrated analysis of genomic and expression QTL and Mn2+ tolerance data reveals that BnMTP8.A09, in conjunction with BnMATE.C03, BnMTP8.C04 and BnMTP8.C08, play a central role in conferring Mn2+ tolerance in B. napus. Gene expression analysis revealed a high correlation (R2 = 0.74) between Mn2+ tolerance and the BnMTP8.A09 expression. Yeast complementation assays show that BnMTP8.A09 can complement manganese-hypersensitive yeast mutant strain PMR1{Delta} and restore Mn2+ tolerance to wild-type levels. Inductively coupled plasma mass spectrometry revealed that Mn2+ tolerant accessions accumulate less Mn in the shoots compared to Mn2+ sensitives, suggesting that the BnMTP8.A09 transporter likely sequesters Mn2+ into the tonoplast. Taken together, our research unveils the genetic architecture of Mn2+ tolerance and identifies BnMTP8.A09 as a major gene imparting tolerance to Mn2+ toxicity in B. napus.

The development of robust biomarkers enables accurate prediction of complex phenotypes. However, the dynamic nature of biomarkers is often underestimated since their quantitative changes during development are directly connected to phenotypic transformations, influencing both crop agronomic traits and human diseases. Here, we performed network analysis of untargeted metabolite profiles to investigate height heterosis in rice, which is dynamic that varies during development and is a key determinant of yield heterosis. We found that the levels of pyruvaldehyde were predictive of height heterosis specific at the seedling stage, while 4-hydroxycinnamic acid positively correlated with height heterosis across four developmental stages. We identified metabolic pathways associated with height heterosis and found that metabolomic changes during the elongation stage had a greater impact than those in other stages. Finally, 11 heterosis-associated pathways were developed into metabolomic biomarkers through random forest analysis, successfully predicting height heterosis in an independent population under different growth conditions. This study elucidates the metabolomic landscape of dynamic height heterosis in rice and develops pathway biomarkers for complex phenotypes, demonstrating robustness across diverse populations, environments, and developmental stages.

The increasing availability of omics data from non-model plant species has created a pressing need for centralized, user-friendly platforms that maximize the utility of these datasets in a FAIR manner. Here, we introduce PlantaeViz, a web-based tool designed for the integration, visualization, and analysis of multi-omics data across a wide range of plant species. PlantaeViz offers advanced functionalities, including gene catalogues built from curated literature, transcriptomic meta-analyses presented as gene expression atlases, gene co-expression and regulatory networks with on-the-fly ontology analyses, cistrome visualization and metabolomics-transcriptomics integration, among other tools, providing a robust framework for hypothesis generation and biological interpretation. Gene Cards applications, tailored to each plant species, provide both community-curated and automatically generated functional annotation information. One of the platforms core features is its big data approach: over 58,000 publicly available SRA transcriptomic samples have been processed and visualized to date. Significant efforts have been made in orthology assessment using multiple layers of evidence, as well as in the automatic classification and standardization of omics metadata through regular expressions and data mining. As a result, around 90% of transcriptomic runs have been successfully classified according to sample tissue. These data have been used to construct gene networks via computationally intensive methods based on diverse algorithms. We present a study case to illustrate the platforms integration and exploratory capabilities. PlantaeViz bridges genomics and functional knowledge between model and non-model plant species and aims to expand its species catalogue of species in the future, democratizing access to large-scale plant omics data. Further developments will include the incorporation of additional data types, and the implementation of new tools to further support plant research across diverse biological contexts.

Abiotic stresses are primary constraints on global crop productivity, reducing yields by up to 80%. While traditional phenotypic sensing detects stress only after physiological symptoms emerge and often fails to discriminate specific stressor types, transcriptomic profiling offers a high-dimensional solution, capturing rapid and sensitive molecular shifts. In this study, we developed AbiOmics, the first end-to-end machine learning pipeline specifically designed to identify and discriminate among multiple stressors. This approach represents a previously undocumented method for stress specification using large-scale transcriptomic big data. We identified 320 stress-specific marker genes using a curated collection of 1,243 transcriptomes of Arabidopsis samples treated with four major abiotic stresses, salt, cold, heat, and drought. A single-layer perceptron model trained on these features achieved 91% accuracy during five-fold cross-validation and 93% accuracy on an independent test set. The model demonstrated an unprecedented capacity to generalize to multi-stress conditions, identifying concurrent signatures in combinatorial salt-and-heat treatments. By integrating marker identification with SHAP-based biological interpretation, AbiOmics provides a rigorously validated diagnostic tool superior to conventional sensing. This framework establishes a high-confidence labeling strategy for AI-driven crop management and precision breeding to mitigate climate change impacts. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=73 SRC="FIGDIR/small/707868v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@573cb5org.highwire.dtl.DTLVardef@152a0b0org.highwire.dtl.DTLVardef@1b389a5org.highwire.dtl.DTLVardef@11c60d_HPS_FORMAT_FIGEXP M_FIG C_FIG

Carbon and nitrogen are the two most abundant nutrients in all living things, and their metabolism maintains normal plant growth. However, the molecular mechanism underlying carbon and nitrogen metabolism remains largely unknown. Here, we found that HSP90.6 is involved in the metabolism of carbon and nitrogen. We performed gene cloning and functional characterization of a maize EMS mutant ehsp90.6, whose kernels were small. HSP90.6 encodes heat shock protein 90.6, which has a single-amino acid mutation within its HATPase_c domain. Transcriptome profiling showed that the expression of amino acid biosynthesis- and carbon metabolism-related genes was significantly downregulated in hsp90.6. HSP90.6 is involved in the 26S proteasome degradation pathway, which affects nitrogen recycling to regulate amino acid synthesis; this occurs by interactions between HSP90.6 and the 26S proteasome subunits RPN6 and PBD2 (PRC2). The loss of HSP90.6 significantly reduced the activity of the 26S proteasome, resulting in the accumulation of ubiquitinated proteins and defects in nitrogen recycling. Moreover, HSP90.6 interacted with the 14-3-3 protein GF14-6 to participate in carbon metabolism. Together, these findings revealed that HSP90.6 regulates nutrient metabolism in maize seeds by affecting 26S proteasome-mediated nitrogen recycling and GF14-6-mediated carbon metabolism. One sentence summaryHSP90.6 is involved in nutrient metabolism via 26S proteasome-mediated protein degradation to promote nitrogen recycling and GF14-6 protein-mediated carbon metabolism. The author responsible for the distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (https://academic.oup.com/plcell/pages/General-Instructions) is Weibin Song (songwb@cau.edu.cn). HighlightsO_LIHATPase_c is necessary for HSP90.6 to regulate maize kernel development. C_LIO_LIHSP90.6 is involved in nitrogen recycling via the 26S proteasome degradation pathway. C_LIO_LIHSP90.6 interacts with the 14-3-3 protein GF14-6 to affect carbon metabolism. C_LI IN A NUTSHELLO_ST_ABSBackgroundC_ST_ABSSeeds are the main harvested organs of maize. Understanding the regulatory mechanism of grain filling is helpful to cultivate high-quality and high-yield maize. In the past few years, the regulatory network of grain filling has been explored through multiple means, including transcriptomic, proteomic and functional genomic techniques. Many genes that control grain filling through different mechanisms have been cloned, such as CTLP1 (Choline Transporter-like Protein 1), OS1 (Opaque Endosperm and Small Germ 1), and MN6 (Miniature Seed6). To identify new genes involved in maize grain filling, ethyl methanesulfonate (EMS) was used to induce mutations, and the ehsp90.6 mutant, which exhibited abnormal kernel development, was isolated by bulked segregant analysis RNA sequencing (BSR). QuestionWhy does the single-amino acid mutation of HSP90.6 affect grain size, and how does the loss of HSP90.6 affect grain filling? FindingsA single-amino acid mutant (ehsp90.6) and knockout mutant (hsp90.6) were obtained. We found that HSP90-6 was involved in the regulation of maize grain filling. A single-single amino acid mutation in the HATPase_c domain reduced the ATPase activity of HSP90.6, resulting in smaller grains. The functional loss of HSP90.6 resulted in the expression of amino acid biosynthesis- and carbon metabolism-related genes being significantly downregulated in hsp90.6. We indicated that HSP90.6 is involved in the 26S proteasome degradation pathway, which affects nitrogen recycling to regulate amino acid synthesis by interacting with the 26S proteasome subunits RPN6 and PBD2 (PRC2). Moreover, HSP90.6 was found to interact with the 14-3-3 protein GF14-6 to participate in carbon metabolism. Next stepsTo further verify that the interaction between HSP90.6 and 26S proteasome subunits and GF14-6 affects grain filling, knockout validation of RPN6, PBD2 (PRC2) and GF14-6 will be performed. In addition, since GF14-6 interacts with the phosphorylated proteins, we will determine the phosphorylation site of HSP90.6. Due to the important role of HSP90 family proteins in plant development, there are other regulatory pathways that need to be explored.

Identifying non-coding regions that control gene expression has become an essential aspect of understanding gene regulatory networks that can play a role in crop improvements such as crop manipulation, stress response, and plant evolution. Transcription Factor (TF)-binding approaches can provide additional valuable insights and targets for reverse genetic approaches such as EMS-induced or natural SNP variant screens or CRISPR editing techniques (e.g. promoter bashing). Here, we present the first ever DAP-seq profiles of three GRAS family TFs (SHR, SCL23, and SCL3) in the crop Sorghum bicolor, Oryza sativa japonica, and Zea mays. The binding behaviors of the three GRAS TFs display unique and shared gene targets and categories of previously characterized DNA-binding motifs as well as novel sequences that could potentially be GRAS family-specific recognition motifs. Additional transcriptomic and chromatin accessibility data further facilitates the identification of root-specific GRAS regulatory targets corresponding to previous studies. These results provide unique insights into the GRAS family of TFs and novel regulatory targets for further molecular characterization.

The natural auxin indole-3-acetic acid (IAA) is a key regulator of many aspects of plant growth and development. Synthetic auxin herbicides mimic the effects of IAA by inducing strong auxinic signaling responses in plants. Synthetic auxins are crucial herbicides in agriculture, made more important by the recent introduction of transgenic synthetic auxin resistant soybean and cotton. Currently, 41 weed species have evolved resistance to synthetic auxin herbicides and, in all but one case, the molecular basis of these resistance mechanisms is unknown. To determine the mechanism of 2,4-D resistance in a Sisymbrium orientale (Indian hedge mustard) weed population, we performed a transcriptome analysis of 2,4-D-resistant (R) and-susceptible (S) genotypes that revealed an in-frame 27-nucleotide deletion removing 9 amino acids in the degron tail (DT) of the auxin co-receptor Aux/IAA2 (SoIAA2). The deletion allele co-segregated with 2,4-D resistance in recombinant inbred lines. Further, this deletion was also detected in several 2,4-D resistant field populations of this species. Arabidopsis transgenic lines expressing the SoIAA2 mutant allele were resistant to 2,4-D and dicamba. The IAA2-DT deletion reduced binding to TIR1 in vitro with both natural and synthetic auxins, causing reduced association and increased dissociation rates. This novel mechanism of synthetic auxin herbicide resistance assigns a new in planta function to the DT region of this Aux/IAA co-receptor for its role in synthetic auxin binding kinetics and reveals a potential biotechnological approach to produce synthetic auxin resistant crops using gene editing.

Intracellular gene transfers (IGTs) between the nucleus and organelles, including plastids and mitochondria, constantly reshapes the nuclear genome during evolution. Despite the substantial contribution of IGTs to genome variation, the dynamic trajectories of IGTs at the pangenomic level remain elusive. Here, we propose a novel approach, IGTminer, to map the evolutionary trajectories of IGTs by collinearity and gene reannotation across multiple genome assemblies. IGTminer was applied to create a nuclear organelle gene (NOG) map across 67 genomes covering 15 Poaceae species, including important crops, revealing the polymorphisms and trajectory dynamics of NOGs. The NOGs produced were verified by experimental evidence and resequencing datasets. We found that most of the NOGs were recently transferred and lineage specific, and that Triticeae species tended to have more NOGs than other Poaceae species. Wheat had a higher retention rate of NOGs than maize and rice, and the retained NOGs were likely involved in the photosynthesis and translation pathways. Large numbers of NOG clusters were aggregated in hexaploid wheat during two rounds of polyploidization and contributed to the genetic diversities among modern wheat varieties. Finally, we proposed a radiocarbon-like model illustrating the transfer and elimination dynamics of NOGs, highlighting the unceasing integration and selective retention of NOGs over evolutionary time. In addition, we implemented an interactive webserver for NOG exploration in Poaceae. In summary, this study provides new resources and clues for the roles of IGTs in shaping inter- and intraspecies genome variation and driving plant genome evolution.

A major goal in agriculture is to engineer crops that can maintain yield with less nitrogen (N) fertilizer input. Major orchestrators of plant responses to N include members of the HRS1 HOMOLOG (HHO) family of transcription factors (TFs). However, HHO TFs have been difficult targets for functional studies in planta due to their redundancy. Here, we highlight a unique role for a phylogenetically diverged HHO TF, HHO5, whose expression is regulated in an N-dose dependent fashion and is specifically expressed in phloem. We found that an HHO5 single mutant displays significant misregulation of N-dose dependent genes and plant growth rates. HHO5 is also unique as it displays a dual activator/repressor activity on N-dose dependent gene regulation. HHO5 specifically acts as a direct gene repressor when binding DNA targets. In contrast, genes activated by HHO5 include indirect targets regulated by TFs downstream of HHO5 (TF2s). To validate the influence of HHO5 via its direct TF2s, we used validated TF2 data to build a gene regulatory network that links HHO5-TF2 targets to [~]70% of the N-dose genes regulated by HHO5 in planta. By these means, we define HHO5 as a novel dual activator/repressor of plant N-dose signaling. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/667803v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@11adaa9org.highwire.dtl.DTLVardef@9c221org.highwire.dtl.DTLVardef@a80eceorg.highwire.dtl.DTLVardef@163fcdf_HPS_FORMAT_FIGEXP M_FIG C_FIG

Nitrogen is essential for plant growth, yet its availability often limits agricultural productivity. Some legumes have evolved a unique ability to form symbiotic relationships with nitrogen-fixing soil bacteria called rhizobia, enabling them to thrive in nitrogen-deficient soils. In five legume clades, an exploitive strategy has evolved in which rhizobia undergo Terminal Bacteroid Differentiation (TBD), where the bacteria become larger, polyploid, and have a permeabilized membrane. Terminally differentiated bacteria are associated with higher N2-fixation and, thus, a higher return on investment to the plant. In several members of the IRLC (Inverted Repeat-Lacking Clade) and the Dalbergioid clades of legumes, this differentiation process is triggered by a set of apparently unrelated plant antimicrobial peptides with membrane-damaging activity, known as Nodule-specific Cysteine-Rich (NCR) peptides. However, whether NCR peptides are also implicated in symbiotic TBD in other legume clades and whether they are evolutionarily related remains unknown. Here, to address the molecular identity of NCR peptides and their evolution in different legume clades, we performed inter- and intra-clade comparisons of NCR peptides in representative species of four TBD-inducing legume clades. First, we collected genomic and proteomic data of species for which NCR peptides are known (1523 NCR peptides). We then used sequence similarity-based clustering to regroup the NCR peptides, resulting in over 400 different NCR clusters, each clade-specific. We obtained Hidden Markov Models for each cluster and used them to predict NCR peptides in 21 legume genomes (6 clades), including newly generated deep-sequenced root and nodule RNA-seq data of Indigofera argentea (Indigoferoid clade) and newly assembled high-quality transcriptomes of Lupinus luteus and Lupinus mariae-josephae (Genistoid clade), using tailored gene prediction pipeline and transcriptome matching. This resulted in 3710 NCR peptides in species that induce TBD. To date, the rapid diversification of NCR peptides that reduces the sequence similarities has masked the origin of NCR peptide evolution. We obtained high-confidence structural models for one sequence of each cluster. We performed structure-based clustering and phylogenetics, which resulted in 23 superclusters (14 inter-clade and nine clade-specific) that we represent in a structural distance-based tree. Our study revealed that the evolution of NCR peptides is a mix of divergent and convergent processes within each clade. We further chose nine independently evolved NCR peptides to test in vitro whether they are functional analogs in symbiosis. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=145 HEIGHT=200 SRC="FIGDIR/small/675119v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@8d1698org.highwire.dtl.DTLVardef@c65b98org.highwire.dtl.DTLVardef@a75994org.highwire.dtl.DTLVardef@ea1a73_HPS_FORMAT_FIGEXP M_FIG Overview of the experimental and computational workflow for NCR peptide detection, characterization, and structural analysis. Nodule and root samples from Indigofera argentea (8 weeks post-inoculation) were collected and subjected to RNA extraction, library preparation, and Illumina PE150 sequencing. Raw RNA-seq reads from two Lupinus species were also included (Lupinus luteus and Lupinus mariae-josephae). Bacteroid differentiation of I. argentea was assessed by flow cytometry and confocal microscopy. Transcriptomes were assembled de novo and analyzed for differential gene expression between root and nodule tissues. NCR peptides were identified from them and other legume genomes and transcriptomes using the SPADA pipeline and HMM profiles from NCR clusters of the known NCR peptides. The putative NCR peptides were filtered based on conserved cysteine motifs, length, and nodule expression to build an exhaustive NCR peptide database. 3D structural predictions of NCR clusters were performed using AlphaFold2 (pLDDT >70), followed by structural clustering (Foldseek) and phylogenetic analysis (Foldtree). Functional validation involved flow cytometry and antimicrobial assays (against Eschericha coli, Sinorhizobium meliloti, and Bacillus subtilis), enabling structural and evolutionary characterization of NCR peptides. The green box at the top represents the experimental analysis, the blue box represents the sequence-based computational pipeline, the red box represents the structure-based computational pipeline, and the grey box at the bottom left represents the functional validation and interpretation of the results. C_FIG

Sweet sorghum has gained global significance as a versatile crop for food, fodder, and biofuel. Department of Agriculture, USA declared sorghum a sweet alternative for corn and sugarcane for biofuel production. Its cultivated varieties, along with their wild counterparts, contribute to the core genetic pool. We harnessed 223 publicly available RNA-seq datasets from sweet sorghum to construct the superTranscriptome and analyze gene structure. This approach yielded 45,864 Representative Transcript Assemblies (RTA) that showcased intriguing Presence-Absence Variation (PAV) across 15 existing sorghum genomes, even incorporating one wild progenitor. We identified 301 superTranscripts exclusive to sweet sorghum, encompassing elements such as hexokinases, cytochromes, select lncRNAs, and histones. Moreover, this study enriched sweet sorghum annotations with 2,802 newly identified protein-coding genes, including 559 encoding diverse transcription factors (TFs). This study unveiled 10,059 superTranscripts associated with various non-coding RNAs. The Rio variety displayed elevated expression of light-harvesting complexes (LHCs) and reduced expression of Metallothioneins during internode growth, suggesting the influence of photosynthesis and metal ion transport on sugar accumulation. Intriguingly, specific lncRNAs exhibited significant expression shifts in Rio during internode development, possibly implying their role in sugar accumulation. We validated the superTranscriptome against the Sweet Sorghum Reference Genome (SSRG) using Differential Exon Usage (DEU) and Differential Gene Expression (DGE), which yielded superior estimations. This study underscores the superTranscriptomes utility in unraveling fundamental sorghum mechanisms, enhancing genome annotations, and offering a potential alternative to the reference genome. Significance StatementThe comprehensive superTranscriptome of seven sweet sorghum genotypes revealed 45,864 genes, including 28.27% novel ones, predominantly comprising non-coding RNAs. Distributing core, dispensable, and cloud genes across 15 sorghum genomes differentiated common genes from cultivar-specific ones. superTranscriptome enhanced the annotation of 14 sorghum genomes with new genes/exons and effectively utilized RNA-seq data to annotate reference genomes. It identified presence/absence variations and non-coding genes and could be a potential alternative to the reference genome.

O_LIWild crop relatives are valuable genetic resources for improving stress adaptation in cultivated species, but their effective use depends on high-quality reference genomes integrated with phenotypic and molecular datasets. Hordeum erectifolium, a wild relative of barley (H. vulgare), is adapted to intermittent and prolonged drought and saline soils, making it an excellent model for stress-adaptation research. C_LIO_LIWe assembled a chromosome-scale, annotated reference genome of H. erectifolium comprising 3.85 Gbp, and identified 71,475 genes supported by a tissue-specific gene expression atlas. Comparative morphological, physiological, and transcriptomic analyses under water limitation were conducted with cultivated and wild barley. C_LIO_LIH. erectifolium displayed a greater density of leaf veins and sclerenchyma cells, alongside rapid leaf rolling upon dehydration. Genomic comparisons revealed structural variations, independent transposon-driven evolution, and copy number expansions of desiccation-responsive gene families relative to barley. The transcriptional responses of H. erectifolium and barley to water limitation suggested contrasting drought-adaptation strategies: metabolic down-regulation and survival prioritization in H. erectifolium versus maintenance of metabolic activity and competitiveness in barley. C_LIO_LIOur data suggest that H. erectifolium is genetically primed for survival under drought through anatomical adaptations, gene family expansion, efficient shutdown of growth-related metabolism, and rapid recovery upon rehydration. C_LI

Elucidating gene regulatory networks is a major area of study within plant systems biology. Phenotypic traits are intricately linked to specific gene expression profiles. These expression patterns arise primarily from regulatory connections between sets of transcription factors (TFs) and their target genes. Here, we integrated 46 co-expression networks, 283 protein-DNA interaction (PDI) assays, and 16 million SNPs used to identify expression quantitative trait loci (eQTL) to construct TF-target networks. In total, we analyzed [~]4.6M interactions to generate four distinct types of TF-target networks: co-expression, PDI, trans-eQTL, and cis-eQTL combined with PDIs. To functionally annotate TFs based on their target genes, we implemented three different network integration strategies. We evaluated the effectiveness of each strategy through TF loss-of function mutant inspection and random network analyses. The multi-network integration allowed us to identify transcriptional regulators of several biological processes. Using the topological properties of the fully integrated network, we identified potential functionally redundant TF paralogs. Our findings retrieved functions previously documented for numerous TFs and revealed novel functions that are crucial for informing the design of future experiments. The approach here-described lays the foundation for the integration of multi-omic datasets in maize and other plant systems. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/582075v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@19516e4org.highwire.dtl.DTLVardef@112121eorg.highwire.dtl.DTLVardef@163adaborg.highwire.dtl.DTLVardef@11ebe78_HPS_FORMAT_FIGEXP M_FIG C_FIG

High-throughput sequencing technologies have enabled the generation of high-quality reference genomes for numerous rice cultivars. However, inferring gene functions, associated phenotypes, and causal variants from these sequences remains challenging. The Rice Annotation Project Database (RAP-DB; https://rapdb.dna.affrc.go.jp) is a curated genomic resource that provides comprehensive gene annotations for the reference genome of Oryza sativa ssp. japonica cv. Nipponbare. Since its major update in 2013, gene models and functional annotations have been continuously revised through expert manual curation of newly published literature related to rice genes. As of March 2025, a total of 6,631 transcripts corresponding to 6,371 loci have been curated based on 4,699 peer-reviewed publications. These curated genes are functionally characterized and are frequently associated with agronomic traits, including yield components, stress tolerance, and disease resistance. To support molecular breeding, RAP-DB now provides a curated catalogue of 904 agronomically important loci, including gene symbols, functional descriptions, and associated traits, together with more than 1,000 functionally characterized alleles compiled from the literature. In addition to in-house expert curation, RAP-DB integrates community-curated datasets for major gene families, such as WRKY transcription factors, S-domain receptor-like kinases, and leucine-rich repeat-containing receptors, thereby expanding coverage of key regulatory and defense-related genes. RAP-DB also incorporates reanalyzed RNA sequencing expression profiles alongside microarray-based expression data and co-expression networks, offering gene-centric views of expression patterns across tissues, conditions, and developmental stages. Furthermore, RAP-DB is linked to genome-wide variation datasets from diverse rice varieties through the TASUKE+ genome browser, enabling exploration of allelic diversity across varieties. To enhance annotation quality and long-term sustainability, AI-assisted literature screening and a web-based feedback system have been introduced, allowing users to submit corrections to gene models and report newly characterized genes or relevant publications. Together, these developments strengthen RAP-DB as a primary, literature-based gene annotation resource and provide a practical foundation for molecular breeding in rice.

Plant metabolism is a pillar of our ecosystem, food security, and economy. To understand and engineer plant metabolism, we first need a comprehensive and accurate annotation of all metabolic information across plant species. As a step towards this goal, we previously created the Plant Metabolic Network (PMN), an online resource of curated and computationally predicted information about the enzymes, compounds, reactions, and pathways that make up plant metabolism. Here we report PMN 15, which contains genome-scale metabolic pathway databases of 126 algal and plant genomes, ranging from model organisms to crops to medicinal plants, and new tools for analyzing and viewing metabolism information across species and integrating omics data in a metabolic context. We systematically evaluated the quality of the databases, which revealed that our semi-automated validation pipeline dramatically improves the quality. We then compared the metabolic content across the 126 organisms using multiple correspondence analysis and found that Brassicaceae, Poaceae, and Chlorophyta appeared as metabolically distinct groups. To demonstrate the utility of this resource, we used recently published sorghum transcriptomics data to discover previously unreported trends of metabolism underlying drought tolerance. We also used single-cell transcriptomics data from the Arabidopsis root to infer cell-type specific metabolic pathways. This work shows the continued growth and refinement of the PMN resource and demonstrates its wide-ranging utility in integrating metabolism with other areas of plant biology. One-sentence SummaryThe Plant Metabolic Network is a collection of databases containing experimentally-supported and predicted information about plant metabolism spanning many species.