Strigolactones (SLs) are a class of plant hormones essential for tiller development and yield under diverse environmental conditions. Drought is a major limiting factor for rice yields. Although SLs contribute to drought resistance, mechanisms and practical applications of SL pathway in drought acclimation of rice remain poorly understood. Our study shows that short-term dehydration represses SL biosynthesis in rice roots. Genetic assays indicate that disruption of SL biosynthesis or signaling elevates rice drought resistance, whereas SL signaling activation or supplementation with the SL analog GR244DO impairs drought resistance. SLs negatively regulate drought acclimation by promoting degradation of the repressor protein DWARF53 (D53). D53 interacts with the transcription factor OsWRKY31 via its N-terminal domain and suppresses the protein level of OsWRKY31, which binds to and represses transcription of the ZFP36 promoter. ZFP36 encodes a zinc-finger transcription factor that promotes H2O2 scavenging to sustain reactive oxygen species (ROS) homeostasis during drought stress. Notably, the drought-resistant upland rice variety IRAT109 exhibits lower SL levels in root exudates than the lowland rice variety Nipponbare (NP). Genome editing of key components in SL pathway enhances drought resistance in NP, Huazhan (HZ), and IRAT109. The agronomic potential of tuning SL biosynthesis is further supported by the elite D17/HTD1 allele, which weakens SL biosynthesis and improves drought resistance and grain yield in Nekken 2 (NK2) under field conditions. These findings uncover a key mechanism underlying SL-repressed drought acclimation in rice and provide an effective strategy to improve drought resistance in diverse rice varieties amid ongoing climate change.
All organisms need to sense their surroundings and respond. This is especially true for plants, which are rooted to the ground and must contend with daily fluctuations of both above- and belowground environments. Of these, temperature is perhaps the most variable, with shifts throughout the day and microenvironments that affect one part of the plant differently from another (e.g., a leaf in the canopy vs. one in the shade). Heat stress is of particular concern and negatively affects basic cellular processes including protein folding and function, membrane fluidity, and cytoskeletal organization. The resulting dysfunctions impair growth through metabolic imbalances and the generation of reactive oxygen species (ROS) like hydrogen peroxide (H2O2). As such, plants have evolved numerous genetic and cellular mechanisms to sense heat and mitigate the adverse effects. Much progress has been made on understanding thermoprotective mechanisms, but how plants initially sense heat stress is still poorly understood. It is becoming clear, however, that multiple overlapping mechanisms are important (Hayes et al., 2021). For instance, photoreceptors, the circadian clock evening complex, RNA-based temperature switches, epigenetic mechanisms involving histone methylation, and the unfolded protein response can respond to heat and affect plant growth and development. More recent research has demonstrated that chloroplasts (specialized plastids that perform photosynthesis in plants and algae) also play important roles in sensing heat. These organelles are ideal temperature sensors for the cell as photosynthesis is particularly susceptible to heat. Under increased temperatures, it will rapidly produce ROS and other metabolites that can act as stress signaling molecules. In support of this model, two recent studies have revealed novel post-translational mechanisms within chloroplasts that allow cells to monitor heat stress and initiate a response. In the first, it was demonstrated that heat-induction of the toxic metabolite methylglyoxal (MG) affects thermotolerance in Arabidopsis thaliana by modifying the chloroplast protein import machinery, thereby reducing chloroplast function and photosynthesis (Ding et al., 2026)
Dithiol-disulfide exchange of cysteine residues has deep effects on protein conformation, hence on enzyme activity, which is the basis of redox regulation. The redox state of cells, including cellular compartments, is dynamically adjusted by reducing, i.e. NAD(P)H, and oxidizing signals, such as reactive oxygen species (ROS), generated during metabolic activity. Among ROS, H2O2 triggers the oxidation of cysteine thiols and the formation of disulfide bridges, thus having a relevant contribution to redox regulation. Disulfide reduction is catalyzed by thioredoxins (Trxs), small polypeptides with a characteristic structure, the Trx fold, and an active site formed by the WCGPC motif, or variants of this motif. In heterotrophic organisms, Trxs are reduced by NADPH via an NADPH-dependent Trx reductase (NTR), defining a relatively simple two-component system formed by one or two NTRs and, at most, three Trxs. In contrast, photosynthetic organisms harbor complex redox regulatory machinery. Redox regulation is remarkably complex in plant chloroplasts, organelles equipped with more than 20 Trxs, which rely on two sources of reducing power: photosynthetically reduced ferredoxin (FdxRED), via a Fdx-dependent Trx reductase (FTR); and NADPH, the electron donor to NTRC, an enzyme containing NTR and Trx domains, exclusively found in oxygenic photosynthetic organisms. Thus, the long-standing question is why chloroplasts require such complex redox regulatory machinery. Here, we propose a model of chloroplast redox regulation based on the interplay between reducing and oxidizing branches. Moreover, we propose that the complex chloroplast redox regulatory machinery allows the continuous fine-tuning of photosynthetic performance in response to the unpredictable changes of light intensity that plants face in their natural environments.
Under iron (Fe)-limiting conditions, Arabidopsis thaliana roots secrete coumarins, phenylpropanoid-derived secondary metabolites that can mobilize sparingly available Fe and shape the composition of the root-associated microbiome. Recent studies show that microbial partners can act on root-secreted coumarins to produce forms with higher Fe-mobilization capacity. Besides providing a mechanistic understanding of the positive interaction of soil microbiota to improved plant Fe nutrition, these findings provide new evidence that the functional boundaries of plant metabolic pathways extend into the rhizosphere.
Improving nitrogen use efficiency (NUE) is essential for sustainable agriculture, yet conventionally measured plant characteristics have limited value as NUE proxies. Here we show that artificial intelligence (AI) can uncover previously unrecognized phenotypic variation associated with NUE, revealing genetic variation that is largely missed by conventional phenotypes. We trained a convolutional neural network (CNN) on 25,080 maize images to learn features that distinguish how plants respond to low- and high-N conditions, achieving 96.7% accuracy. The learned features were defined as deep phenotypes. Compared with conventional phenotypes, deep phenotypes showed greater phenotypic variation and higher heritability, enabling the identification of 523 significant loci compared with 21 for conventional phenotypes. We next investigated candidate genes underlying these loci and used these findings to interpret the learned features. Lower CNN layers primarily reflected visual patterns overlapping with conventional phenotypes, whereas deeper layers encoded additional features associated with N-responsive genetic variation. To validate candidate genes identified by the AI framework, we functionally characterized Liguleless2 (LG2), a basic-leucine zipper (bZIP) transcription factor, and demonstrated that lg2 mutants exhibit enhanced root architecture and increased N uptake efficiency. Field trials of 200 hybrids across diverse N environments further supported the AI findings, with each beneficial allele increasing ear weight by an average of 18 g per plot under low-N conditions. These results show how integrating AI and biology can uncover biologically relevant variation underlying complex traits such as NUE and enhance the interpretability of AI models.
Global warming poses a considerable threat to crop production, making heat stress a pivotal challenge in agriculture. Yet how epitranscriptomic modifications contribute to plant heat stress responses remains to be explored. Here, this study reveals the critical role of phase separation in plant heat stress tolerance and demonstrated that N-acetyltransferase 10 (NAT10), which encodes of the cytosine N4 acetyltransferase protein, contributes to heat resistance. We found that NAT10 interacts with polyadenylate-binding protein (PABP), which contains intrinsically disordered regions (IDRs), thereby facilitating the selective recruitment of ac4C-modified mRNAs into PABP-mediated condensates. Integrative transcriptome-wide analysis, combining ac4C acetylome profiling with SG-enriched transcript sequencing, revealed that detoxification-related mRNAs, including those encoding the cytochrome P450, phenylalanine ammonia-lyase, glutathione S-transferase, and heat shock 70 protein families, preferentially accumulate within these condensates. This accumulation maintains their stability and prevents stress-induced degradation. Conversely, loss of PABP impairs the recruitment of ac4C-modified detoxification-related transcripts into stress granules, thereby promoting their degradation under heat stress. In summary, our findings identify a stress-responsive NAT10-PABP-ac4C axis that promotes phase separation to stabilize ac4C-modified mRNAs under heat stress. By recruiting detoxification-related transcripts into stress granules, this axis ensures mRNA stability and offers insights for enhancing crop resilience under environmental stress.
Strigolactones (SLs) are fundamental phytohormones that suppress tillering, whereas sugars act as both energy source and signaling molecules to promote tillering. However, the molecular interplay between SLs and sugars remains largely underexplored. Here, through iTRAQ-based quantitative proteomic analysis, we identified two highly homologous monosaccharide transporters OsMST3 and OsMST6 showing increased protein abundance in the SLs signaling mutant d3s2-215 and biosynthesis mutant d10XJ1705. Through feeding with fluorescence- or isotope-labelled sugars, we demonstrated that OsMST3/6 functioned as influx transporters of glucose and fructose, facilitating their translocation from leaf blades to tiller buds to promote bud outgrowth. Interestingly, glucose and fructose induced OsMST3/6 expression and polarized plasma membrane localization, a process essential for bud outgrowth, whereas SLs suppressed their expression and counteracted sugars-induced polarized localization. OsTB1, a key transcription factor downstream of SLs signaling, directly bound the OsMST3/6 promoters to inhibit their transcription. Tiller bud outgrowth of the tb1SG0312 mutant was severely inhibited by a non-metabolizable glucose analog (2-Deoxy-D-glucose) and an inhibitor of sugar transporters (CCCP). Moreover, simultaneous knockout of OsMST3/6 rescued the high-tillering phenotype of the d3s2-215, d10XJ1705 and tb1SG0312mutants. Together, we found a novel mechanism by which SLs control tillering by limiting sugars allocation to buds through suppressing monosaccharide transporters OsMST3/6.
Post-translational modifications (PTMs) play crucial regulatory roles in plants, orchestrating protein functions to maintain metabolic homeostasis, enable adaptation to dynamic environments, and regulate diverse cellular processes. Thus, identifying PTM sites is essential for elucidating the mechanisms underlying plant growth, development, and stress responses. However, reliable and cost-effective computational approaches for predicting PTM sites in plants remain lacking. Here, we present PlantPTM, an integrated deep-learning framework for predicting nine PTM types in plants. By combining protein language models with evolutionary information, PlantPTM demonstrates robust generalizability across a wide range of PTM types and plant species. Our method achieves state-of-the-art performance, with a mean area under the receiver operating characteristic curve (AUROC) of 0.8640 and a peak AUROC of 0.9699 across the nine PTM types. Notably, PlantPTM maintains strong performance even under low-data scenarios and unseen species. Extensive comparative benchmarks demonstrate that PlantPTM outperforms existing PTM prediction tools by an average of 15.46%, with improvements ranging from 2.90% to 19.08% over the best-performing tools for each PTM type. Furthermore, independent in-house mass spectrometry data confirmed the accuracy of PlantPTM for ubiquitination, acetylation, and N-glycosylation sites, with all AUROC values exceeding 0.8. To facilitate PTM studies in plants, we provide the PlantPTM online service freely available at https://ai4bio.online/PlantPTM.
Improving protein accumulation in maize is essential for sustainable agriculture, yet the regulatory mechanisms governing the intermediate "flow" of organic nitrogen remain elusive. Here, we show that the maize stem acts as a regulatory node for nitrogen allocation. By integrating spatial transcriptomics and metabolomics with quantitative genetics, we demonstrate that a transport-oriented stem program orchestrates the high-protein phenotype of the wild maize accession Ames21814. We identified a major locus, Whole-plant High Protein 10 (WHP10), that encodes a tandemly duplicated cluster of amino acid transporter genes. WHP10 exhibits strong vascular-biased expression, driven by promoter divergence that enhances the wild allele's activity. Functional assays and genetic validation support a model in which the WHP10 cluster facilitates the transport of multiple nitrogen-rich amino acids, thereby contributing to vascular-associated amino acid transport and post-uptake organic-nitrogen partitioning. Our findings establish stem flow as a regulatory layer for protein accumulation and identify WHP10 as a high-value target for precision breeding to enhance whole-plant protein accumulation without compromising grain yield.
UFMylation is a reversible ubiquitin-like modification with an emerging and essential role in mammals, yet its functional mechanisms in plants remain largely unknown. In this study, we systematically characterized the complete UFMylation pathway in Arabidopsis thaliana and demonstrated that its core components-E1 UBA5, E2 UFC1, and E3 UFL1-possessed potent enzymatic activity. UFL1 is an E3 ligase that localizes to both the nucleus and cytoplasm. Expression pattern analyses revealed that UFL1 is strongly upregulated during embryogenesis and progressively accumulates throughout seed maturation. Phenotypic analyses showed that UFL1 overexpression enhances seed dormancy, increases plant sensitivity to abscisic acid (ABA), and significantly delays seed germination and cotyledon greening, identifying UFL1 as a previously unrecognized positive regulator of ABA signaling. Biochemical analyses further demonstrated that UFL1 physically interacts with the bZIP transcription factor ABI5 and mediates its UFMylation, thereby antagonizing ubiquitination and stabilizing ABI5 protein abundance. Genetic evidence further supported that ABI5 acts downstream of UFL1 to regulate seed developmental processes. Collectively, our findings reveal UFMylation as a pivotal regulatory layer in plants and uncover a UFL1-ABI5 signaling module that coordinates seed maturation, dormancy, and germination.
Coping with high light represents a major challenge for plants in nature. Under high light, 1O2 can induce MBS1 to form a low-dynamic condensate, which can effectively shade chloroplasts to avoid photodamage. This mechanism can be used to support breeding crops for both high photoprotection and high photosynthetic light use efficiency.
