The cultivated apple (Malus domestica Borkh.) is a cross-pollinated perennial fruit tree of great economic importance. Earlier versions of apple reference genomes were unphased, fragmented, and lacked comprehensive insights into the apple's highly heterozygous genome, which impeded advances in genetic studies and breeding programs. In this study, we assembled a haplotype-resolved telomere-to-telomere (T2T) reference genome for the diploid apple cultivar Golden Delicious. Subsequently, we constructed a pangenome based on 12 assemblies from wild and cultivated species to investigate the dynamic changes of functional genes. Our results revealed the gene gain and loss events during apple domestication. Compared with cultivated species, more gene families in wild species were significantly enriched in oxidative phosphorylation, pentose metabolic process, responses to salt, and abscisic acid biosynthesis process. Our analyses also demonstrated a higher prevalence of different types of resistance gene analogs (RGAs) in cultivars than their wild relatives, partially attributed to segmental and tandem duplication events in certain RGAs classes. Structural variations, mainly deletions and insertions, have affected the presence and absence of TIR-NB-ARC-LRR, NB-ARC-LRR, and CC-NB-ARC-LRR genes. Additionally, hybridization/introgression from wild species has also contributed to the expansion of resistance genes in domesticated apples. Our haplotype-resolved T2T genome and pangenome provide important resources for genetic studies of apples, emphasizing the need to study the evolutionary mechanisms of resistance genes in apple breeding.

Members of the leucine-rich repeat (LRR) superfamily play critical roles in multiple biological processes. As the LRR unit sequence is highly variable, accurately predicting the number and location of LRR units in proteins is a highly challenging task in the field of bioinformatics. Existing methods still need to be improved, especially when it comes to similarity-based methods. We introduce our DeepLRR method based on a convolutional neural network (CNN) model and LRR features to predict the number and location of LRR units in proteins. We compared DeepLRR with six existing methods using a dataset containing 572 LRR proteins and it outperformed all of them when it comes to overall F1 score. In addition, DeepLRR has integrated identifying plant disease-resistance proteins (NLR, LRR-RLK, LRR-RLP) and non-canonical domains. With DeepLRR, 223, 191 and 183 LRR-RLK genes in Arabidopsis (Arabidopsis thaliana), rice (Oryza sativa ssp. Japonica) and tomato (Solanum lycopersicum) genomes were re-annotated, respectively. Chromosome mapping and gene cluster analysis revealed that 24.2% (54/223), 29.8% (57/191) and 16.9% (31/183) of LRR-RLK genes formed gene cluster structures in Arabidopsis, rice and tomato, respectively. Finally, we explored the evolutionary relationship and domain composition of LRR-RLK genes in each plant and distributions of known receptor and co-receptor pairs. This provides a new perspective for the identification of potential receptors and co-receptors.

Grape is an important fruit crop, and its production faces significant threat from diseases, resulting in substantial economic loss. Wild grape relatives are valuable resources for the restoration of disease-resistance loci. However, available resistance loci in wild grape genomes remain largely unexplored. In this study, we assembled two phased genomes, including a high resistant Chinese wild grape, Vitis davidii Föex, and a susceptible cultivar, V. vinifera L. cv. “Manicure Finger”. We detected a total of 36,688 structural variations (SVs), with the genes associated with heterozygous SVs showing an enrichment in allele-specific expression (ASE). Furthermore, we identified eight subgroups of R genes and found that 74.2% of R genes overlap with transposable elements (TEs). Among R genes, NBS-type genes exhibit higher expression profiles in the wild grape genome compared with those in the grape cultivar. Additionally, five specific NBS-type R gene clusters were identified in the wild grape genome that are absent in the cultivar. Through genetic mapping, we identified four quantitative trait loci (QTLs) associated with grape white rot resistance based on the Vitis davidii genome, within which six NBS-type R genes exhibit differential expression between wild and cultivated grapes. Overall, our study revealed the landscape of resistance genes in grape genomes, providing valuable genetic resources for further breeding programs.

Background: Against the background of a changing climate, the responses of functional traits of plateau wetland plants to increasing temperatures and CO₂ concentrations need to be understood. Hydraulic traits are the key for plants to maintain their ecological functions and affect their growth and survival. However, few studies have comprehensively considered the response strategies of wetland plants' hydraulic traits to environmental changes in the context of water and matter transport, loss, and retention. According to the latest IPCC prediction results, we performed experiments under increased temperature (2 °C) and CO₂ levels (850 μmol/mol) in an artificial Sealed-top Chamber (STC) to investigate the responses of the hydraulic characteristics of Schoenoplectus tabernaemontani, the dominant species in plateau wetlands in China. Results: Compared with the CK group, net photosynthetic rate, transpiration rate, stomatal length, cuticle thickness, vascular bundle length, vascular bundle width, and vascular bundle area of S. tabernaemontani in the ET group were significantly reduced, whereas stomatal density and vein density increased significantly. Compared with the CK group, the hydraulic traits of S. tabernaemontani in the EC group were reduced considerably in stomatal length and cuticle thickness but increased dramatically in stomatal density, and there were no significant differences between other parameter values and the control group. Net photosynthetic rate was significantly positively correlated with stomatal length, cuticle thickness, and vascular bundle length, and stomatal conductance was significantly positively correlated with cuticle thickness. The transpiration rate was significantly positively correlated with cuticle thickness, epidermal cell area, vascular bundle length, vascular bundle width, and vascular bundle area. Regarding the hydraulic traits, there was a significant negative correlation between stomatal density and stomatal length, or cuticle thickness, and a significant positive correlation between the latter two. The epidermal cell area was significantly positively correlated with epidermal thickness, vascular bundle length, vascular bundle width, and vascular bundle area. Conclusions: Increased temperature and CO₂ levels are not conducive to the photosynthetic activity of S. tabernaemontani. Photosynthetic rate, stomatal density and size, vein density, epidermal structure size, and vascular bundle size play an essential role in the adaptation of this species to changes in temperature and CO₂ concentration. In the process of adaptation, hydraulic traits are not isolated from each other, and there is a functional association among traits. This study provide a scientific basis for the management and protection of plateau wetlands.

Identifying plant disease-resistance genes is essential for understanding the plant immune system and accelerating the breeding of disease-resistant crops. There is a pressing need for a method capable of accurately identifying plant disease-resistance genes on a genome-wide scale. In this study, we propose evolutionary scale modeling for LRR (ESM-LRR), a deep protein language model designed to accurately predict LRR domains, which are substantially variable structures in disease-resistance proteins. ESM-LRR achieved its highest F1 score of 0.80 on a test set using 90% identity as the matching threshold. Building on ESM-LRR, we developed R-Predictor, a plant disease-resistance gene predictor to simultaneously annotate 15 diverse domain topologies, covering characterized resistance genes across the whole genome. R-Predictor integrates 4 modules, each employing superior methods that outperform existing methods (achieving F1 scores of 0.89 for RLKs and 0.88 for NLRs), demonstrating its high accuracy and practicality in annotating plant disease-resistance genes. R-Predictor integrated with gene expression profiles to identify candidate R genes associated with grape gray mold and downy mildew, outperforming existing methods and detecting dozens of candidate R genes. Overall, this study presents a novel approach to advancing our understanding of plant immunity and facilitating crop breeding for disease resistance.