PlantNLRatlas: a comprehensive dataset of full- and partial-length NLR resistance genes across 100 chromosome-level plant genomes

RiceReceptor: The Cell-Surface and Intracellular Immune Receptors of the Oryza Genus

INTRODUCTION

Rice, a cornerstone of global food security, faces escalating demands for enhanced yield and disease resistance. We collected 300 high-quality genomes, representing both cultivated (Oryza sativa indica, O. sativa japonica, and O. sativa aus) and wild species (O. rufipogon, O. glaberrima, and O. barthii).

METHODS

Leveraging HMMER, NLR-Annotator, and OrthoFinder, we systematically identified 148,077 leucine-rich repeat (LRR) and 143,459 nucleotide-binding leucine-rich repeat (NLR) genes, with LRR receptor-like kinases (LRR-RLKs) dominating immune receptor proportions, followed by coiled-coil domain containing (CNL)-type NLRs and LRR receptor-like proteins (LRR-RLPs).

RESULTS

Benchmarking Universal Single-Copy Orthologs (BUSCO) assessments confirmed robust genome quality (average score: 94.78). Strikingly, 454 TIR-NB-LRR (TNL) genes-typically rare in monocots-were detected, challenging prior assumptions. Phylogenetic analysis with Arabidopsis TNLs highlighted five O. glaberrima genes clustering with dicot TNLs; these genes featured truncated PLN03210 motifs fused to nucleotide-binding adaptor shared by APAF-1, R proteins, and CED-4 (NB-ARC) and LRR domains.

CONCLUSIONS

By bridging structural genomics, evolutionary dynamics, and domestication-driven adaptation, this work provides a foundation for targeted breeding strategies and advances functional studies of plant immunity in rice.

The resistance awakens: Diversity at the DNA, RNA, and protein levels informs engineering of plant immune receptors from Arabidopsis to crops

Plants rely on germline-encoded, innate immune receptors to sense pathogens and initiate the defense response. The exponential increase in quality and quantity of genomes, RNA-seq datasets, and protein structures has underscored the incredible biodiversity of plant immunity. Arabidopsis continues to serve as a valuable model and theoretical foundation of our understanding of wild plant diversity of immune receptors, while expansion of study into agricultural crops has also revealed distinct evolutionary trajectories and challenges. Here, we provide the classical context for study of both intracellular nucleotide-binding, leucine-rich repeat receptors and surface-localized pattern recognition receptors at the levels of DNA sequences, transcriptional regulation, and protein structures. We then examine how recent technology has shaped our understanding of immune receptor evolution and informed our ability to efficiently engineer resistance. We summarize current literature and provide an outlook on how researchers take inspiration from natural diversity in bioengineering efforts for disease resistance from Arabidopsis and other model systems to crops.

In silico prediction method for plant Nucleotide-binding leucine-rich repeat- and pathogen effector interactions

Plant Nucleotide-binding leucine-rich repeat (NLR) proteins play a crucial role in effector recognition and activation of Effector triggered immunity following pathogen infection. Genome sequencing advancements have led to the identification of a myriad of NLRs in numerous agriculturally important plant species. However, deciphering which NLRs recognize specific pathogen effectors remains challenging. Predicting NLR-effector interactions in silico will provide a more targeted approach for experimental validation, critical for elucidating function, and advancing our understanding of NLR-triggered immunity. In this study, NLR-effector protein complex structures were predicted using AlphaFold2-Multimer for all experimentally validated NLR-effector interactions reported in literature. Binding affinities- and energies were predicted using 97 machine learning models from Area-Affinity. We show that AlphaFold2-Multimer predicted structures have acceptable accuracy and can be used to investigate NLR-effector interactions in silico. Binding affinities for 58 NLR-effector complexes ranged between -8.5 and -10.6 log(K), and binding energies between -11.8 and -14.4 kcal/mol-1, depending on the Area-Affinity model used. For 2427 "forced" NLR-effector complexes, these estimates showed larger variability, enabling identification of novel NLR-effector interactions with 99% accuracy using an Ensemble machine learning model. The narrow range of binding energies- and affinities for "true" interactions suggest a specific change in Gibbs free energy, and thus conformational change, is required for NLR activation. This is the first study to provide a method for predicting NLR-effector interactions, applicable to all pathosystems. Finally, the NLR-Effector Interaction Classification (NEIC) resource can streamline research efforts by identifying NLRs important for plant-pathogen resistance, advancing our understanding of plant immunity.

Identifying nucleotide-binding leucine-rich repeat receptor and pathogen effector pairing using transfer-learning and bilinear attention network

MOTIVATION

Nucleotide-binding leucine-rich repeat (NLR) family is a class of immune receptors capable of detecting and defending against pathogen invasion. They have been widely used in crop breeding. Notably, the correspondence between NLRs and effectors (CNE) determines the applicability and effectiveness of NLRs. Unfortunately, CNE data is very scarce. In fact, we've found a substantial 91 291 NLRs confirmed via wet experiments and bioinformatics methods but only 387 CNEs are recognized, which greatly restricts the potential application of NLRs.

RESULTS

We propose a deep learning algorithm called ProNEP to identify NLR-effector pairs in a high-throughput manner. Specifically, we conceptualized the CNE prediction task as a protein-protein interaction (PPI) prediction task. Then, ProNEP predicts the interaction between NLRs and effectors by combining the transfer learning with a bilinear attention network. ProNEP achieves superior performance against state-of-the-art models designed for PPI predictions. Based on ProNEP, we conduct extensive identification of potential CNEs for 91 291 NLRs. With the rapid accumulation of genomic data, we expect that this tool will be widely used to predict CNEs in new species, advancing biology, immunology, and breeding.

AVAILABILITY AND IMPLEMENTATION

The ProNEP is available at http://nerrd.cn/#/prediction. The project code is available at https://github.com/QiaoYJYJ/ProNEP.

Ripening and rot: How ripening processes influence disease susceptibility in fleshy fruits

Fleshy fruits become more susceptible to pathogen infection when they ripen; for example, changes in cell wall properties related to softening make it easier for pathogens to infect fruits. The need for high-quality fruit has driven extensive research on improving pathogen resistance in important fruit crops such as tomato (Solanum lycopersicum). In this review, we summarize current progress in understanding how changes in fruit properties during ripening affect infection by pathogens. These changes affect physical barriers that limit pathogen entry, such as the fruit epidermis and its cuticle, along with other defenses that limit pathogen growth, such as preformed and induced defense compounds. The plant immune system also protects ripening fruit by recognizing pathogens and initiating defense responses involving reactive oxygen species production, mitogen-activated protein kinase signaling cascades, and jasmonic acid, salicylic acid, ethylene, and abscisic acid signaling. These phytohormones regulate an intricate web of transcription factors (TFs) that activate resistance mechanisms, including the expression of pathogenesis-related genes. In tomato, ripening regulators, such as RIPENING INHIBITOR and NON_RIPENING, not only regulate ripening but also influence fruit defenses against pathogens. Moreover, members of the ETHYLENE RESPONSE FACTOR (ERF) family play pivotal and distinct roles in ripening and defense, with different members being regulated by different phytohormones. We also discuss the interaction of ripening-related and defense-related TFs with the Mediator transcription complex. As the ripening processes in climacteric and non-climacteric fruits share many similarities, these processes have broad applications across fruiting crops. Further research on the individual contributions of ERFs and other TFs will inform efforts to diminish disease susceptibility in ripe fruit, satisfy the growing demand for high-quality fruit and decrease food waste and related economic losses.

Conifers Concentrate Large Numbers of NLR Immune Receptor Genes on One Chromosome

Nucleotide-binding domain and leucine-rich repeat (NLR) immune receptor genes form a major line of defense in plants, acting in both pathogen recognition and resistance machinery activation. NLRs are reported to form large gene clusters in limber pine (Pinus flexilis), but it is unknown how widespread this genomic architecture may be among the extant species of conifers (Pinophyta). We used comparative genomic analyses to assess patterns in the abundance, diversity, and genomic distribution of NLR genes. Chromosome-level whole genome assemblies and high-density linkage maps in the Pinaceae, Cupressaceae, Taxaceae, and other gymnosperms were scanned for NLR genes using existing and customized pipelines. The discovered genes were mapped across chromosomes and linkage groups and analyzed phylogenetically for evolutionary history. Conifer genomes are characterized by dense clusters of NLR genes, highly localized on one chromosome. These clusters are rich in TNL-encoding genes, which seem to have formed through multiple tandem duplication events. In contrast to angiosperms and nonconiferous gymnosperms, genomic clustering of NLR genes is ubiquitous in conifers. NLR-dense genomic regions are likely to influence a large part of the plant's resistance, informing our understanding of adaptation to biotic stress and the development of genetic resources through breeding.