Chengdao Li

Phosphorus is an essential macronutrient for all living organisms. Although it is abundant in the Earth’s crust, inorganic phosphate (Pi)—accessible to plants—is often limited due to the soil’s physicochemical properties. To cope with Pi scarcity, plants have evolved a complex phosphate starvation response network centered around phosphate starvation response proteins (PHRs) to regulate cellular Pi homeostasis. However, a comprehensive understanding of physiological and molecular functions of PHRs in barley (Hordeum vulgare) remains elusive. In this study, we identified two homologous PHRs in barley, with HvPHR1 exhibiting higher expression across all developmental stages, suggesting a dominant role. Overexpression of HvPHR1 resulted in necrotic symptoms in mature leaf tips, which correlated with excessive Pi accumulation in leaves. Transcriptome analysis revealed 732 and 307 significantly differentially expressed genes in the roots and leaves, respectively, in the HvPHR1-overexpressing transgenic line grown under Pi-sufficient conditions. These genes were primarily associated with phosphate starvation responses and phosphate ion homeostasis. Using published exome resequencing data, we identified 12 SNPs in the CDS, introns, and 3’ UTR of HvPHR1, which were classified into four main haplotypes. Allele frequency analysis revealed that HvPHR1 underwent artificial selection during barley domestication. Furthermore, the nonsynonymous mutation of HvPHR1 did not affect its nuclear localization or transcriptional activation activity. These findings enhance our understanding of the vital role of HvPHR1 in maintaining Pi homeostasis in barley. [Display omitted]

To exploit allelic variation in Hordeum vulgare subsp. spontaneum, the Wild Barley Diversity Collection was subjected to paired-end Illumina sequencing at ∼9X depth and evaluated for several agronomic traits. We discovered 240.2 million single nucleotide polymorphisms (SNPs) after alignment to the Morex V3 assembly and 24.4 million short (1-50 bp) insertions and deletions. A genome-wide association study of lemma color identified one marker-trait association (MTA) on chromosome 1H close to HvBlp, the cloned gene controlling black lemma. Four MTAs were identified for seedling stem rust resistance, including two novel loci on chromosomes 1H and 6H and one co-locating to the complex RMRL1-RMRL2 locus on 5H. The whole-genome sequence data described herein will facilitate the identification and utilization of new alleles for barley improvement.

Secondary metabolites (SMs) are crucial for plant survival and adaptation and play multiple roles in mediating ecological interactions, such as defense and stress tolerance. Specialized transporters relocate SMs from synthesis sites to defense tissues or storage organs. The spatiotemporal distribution of defense-related SMs is a key determinant of plant fitness. However, the accumulation of anti-nutritional SMs in crop seeds or fruits may pose health risks to humans and livestock. Recent advances have highlighted the significant role of SM transporters in optimizing the allocation of metabolites. This review explores the transport mechanisms for both defense and anti-nutritional SMs, focusing on long-distance transporters that regulate source-sink dynamics and their potential implications in agricultural biotechnology. We highlight innovative approaches to manipulating transporter activities, ranging from multi-omics integration to precision engineering, and discuss how these tools can be used to design crops with enhanced defense capacity, increased levels of beneficial compounds, and more palatable seeds and fruits. We explore the technologies and frameworks for the discovery and characterization of long-distance transporters of SMs for crop improvement. Transporter-focused frameworks offer a promising solution to global agricultural challenges and present exciting opportunities for advancing crop improvement in the context of global food supply.

Phosphorus (P) is a critical element that limits plant growth in agricultural and natural ecosystems, and its deficiency can significantly reduce wheat yield. We systematically evaluated the response of 296 natural wheat populations to low phosphate (Pi) stress at the seedling stage. Using genome-wide association studies with 190 892 single-nucleotide polymorphism markers, we identified 580 marker-trait associations that exhibited a significant association with low-Pi tolerance coefficients for 18 P use efficiency (PUE) related traits. This analysis revealed 44 multi-environment stable quantitative trait loci (QTLs) and 904 candidate genes. By integrating root transcriptome data from low-Pi tolerant and sensitive genotypes under low-Pi treatment for 3 days, 14 days, and post-Pi resupply for 4 days, we performed weighted gene co-expression network analysis (WGCNA) to identify specific modules associated with PUE. Functional annotation and enrichment analysis identified four hub genes (TraesCS2A03G0333400, TraesCS2A03G0335700, TraesCS4B03G0787300 and TraesCS7D03G0752400) linked to PUE, among which TraesCS4B03G0787300 (TaERF112), a candidate gene for the stable QTL qRDW4B.1. Further validation through expression analysis and gene knockout experiments confirmed that TaERF112 positively regulates low-Pi tolerance in wheat seedlings. This study provides novel insights into the genetic and molecular basis of wheat PUE, offering a foundation for breeding P-efficient wheat varieties that enhance agricultural sustainability.

Chickpea has become an increasingly popular healthy food worldwide. Aluminum (Al) toxicity is a major hurdle for chickpea cultivation and yield improvement in acidic soils. However, the genetic mechanism of Al-tolerance in chickpea remains poorly understood. Here, we performed a large-scale hydroponics screening and SNP chip array genotyping of 1154 diverse chickpea accessions. Root lengths after 6 days cultivation under hydroponics in control (T0: pH 4.2) and Al treatment (T1: pH4.2, 15/20 μM Al3+) were measured. Root tolerance index (RTI = T1/T0) ranking revealed significant variations in chickpea Al-tolerance, with common Australian chickpea cultivars positioned in the low to medium range. Genome-wide association analyses revealed eight QTLs on chromosomes ca1 (CaAlt1-1), ca3 (CaAlt3-1), ca4 (CaAlt4-1, CaAlt4-2), ca5(CaAlt5-1), ca6 (CaAlt6-1), and ca7 (CaAlt7-1, CaAlt7-2) associated with T1, implying a multigenic genetic basis for Al-tolerance in chickpea. Specifically, CaAlt7-2 was associated with both T1 and RTI, whilst CaAlt4-2 was detected for T1 uniquely in the HatTrick x CudiB22C population. Al- tolerant and sensitive haplotypes for the identified QTLs were also identified. Organic acid transporter genes CaMATE2, CaMATE4, and CaALMT1 were found in proximal genomic regions to CaAlt7-2, CaAlt4-1, and CaAlt6-1, respectively. Further qRT-PCR in parental chickpea lines (HatTrick, Slasher, Gunas, CudiB) confirmed that CaMATE2 and CaMATE4 were strongly induced upon Al treatment. Interestingly, CaMATE2 was preferentially expressed in the upper part of the root, whilst CaMATE4 preferentially in the root tips, implying a potential complementary role in Al resistance. Their direct roles in Al tolerance and the potential alternative candidate genes near the QTLs require further investigation. This first report of QTLs on Al-tolerance in chickpea has substantially advanced our understanding of the genetic basis of Al tolerance in chickpea and will facilitate the rapid breeding of Al-tolerant chickpea cultivars for previously un-accessible acidic soils.

Background
Grain colour is an important quality trait affecting the market value and consumer preference. Chickpeas with black-coloured seed coat is known for their beneficial high antioxidant and fibber content, yet the underlying molecular basis remains poorly understood.

Results
Here, we examined the grain colour trait of a panel of 261 diverse desi chickpea (Cicer arietinum) accessions and specially characterized the development of the black seed coat. We showed that the black colouration emerged on embryo tips at 30 days after flowering (DAF) and expanded to whole grain at 35 DAF. Genome-wide association analyses revealed a single major genetic locus CaBlk3-1 on chromosome Ca3 controlling black seed coat. Candidate gene screening within 0.5 Mb upstream and downstream of CaBlk3-1 identified a single MYB-encoding gene CaMYB114 related to anthocyanin biosynthesis. Phylogeny analyses showed that CaMYB114 was clustered with Arabidopsis MYB90, MYB113, MYB114, consistent with their role in anthocyanin production. Subsequent qRT-PCR analyses suggested that CaMYB114 was abundantly transcribed in black genotypes but weakly in the brown genotypes at 35 DAF, closely linked with black colour development. Genetic variation analyses of CaMYB114 identified a 12-bp deletion containing a GAGA motif in the 5UTR region of black chickpea genotype. A gene-specific marker targeting this deletion was developed to validate its link with the black seed coat in a larger chickpea germplasm collection.

Conclusions
We identified a single major QTL and the underlying candidate gene CaMYB114 closely associated with the black seed coat trait in chickpea. Our study has greatly improved our understanding of the genetic basis of chickpea black seed and will unlock the potential for breeding new chickpeas with desired grain colour to meet various market requirements.

Oat grain is a traditional human food that is rich in dietary fibre and contributes to improved human health1,2. Interest in the crop has surged in recent years owing to its use as the basis for plant-based milk analogues3. Oat is an allohexaploid with a large, repeat-rich genome that was shaped by subgenome exchanges over evolutionary timescales4. In contrast to many other cereal species, genomic research in oat is still at an early stage, and surveys of structural genome diversity and gene expression variability are scarce. Here we present annotated chromosome-scale sequence assemblies of 33 wild and domesticated oat lines, along with an atlas of gene expression across 6 tissues of different developmental stages in 23 of these lines. We construct an atlas of gene-expression diversity across subgenomes, accessions and tissues. Gene loss in the hexaploid is accompanied by compensatory upregulation of the remaining homeologues, but this process is constrained by subgenome divergence. Chromosomal rearrangements have substantially affected recent oat breeding. A large pericentric inversion associated with early flowering explains distorted segregation on chromosome 7D and a homeologous sequence exchange between chromosomes 2A and 2C in a semi-dwarf mutant has risen to prominence in Australian elite varieties. The oat pangenome will promote the adoption of genomic approaches to understanding the evolution and adaptation of domesticated oats and will accelerate their improvement.Oat grain is a traditional human food that is rich in dietary fibre and contributes to improved human health1,2. Interest in the crop has surged in recent years owing to its use as the basis for plant-based milk analogues3. Oat is an allohexaploid with a large, repeat-rich genome that was shaped by subgenome exchanges over evolutionary timescales4. In contrast to many other cereal species, genomic research in oat is still at an early stage, and surveys of structural genome diversity and gene expression variability are scarce. Here we present annotated chromosome-scale sequence assemblies of 33 wild and domesticated oat lines, along with an atlas of gene expression across 6 tissues of different developmental stages in 23 of these lines. We construct an atlas of gene-expression diversity across subgenomes, accessions and tissues. Gene loss in the hexaploid is accompanied by compensatory upregulation of the remaining homeologues, but this process is constrained by subgenome divergence. Chromosomal rearrangements have substantially affected recent oat breeding. A large pericentric inversion associated with early flowering explains distorted segregation on chromosome 7D and a homeologous sequence exchange between chromosomes 2A and 2C in a semi-dwarf mutant has risen to prominence in Australian elite varieties. The oat pangenome will promote the adoption of genomic approaches to understanding the evolution and adaptation of domesticated oats and will accelerate their improvement.

In precision agriculture, accurate and timely identification of plant disease severity is essential for optimizing crop yield and health. However, current methods often face challenges such as high computational cost and reduced accuracy in resource-constrained environments, limiting their practical use on farms. To address these limitations, we propose RSD-YOLO, an improved YOLOv7-tiny model that integrates a Regularized Xception-based Network (ReXNet), a Slim-Neck module, and a Decoupled Head—together forming the RSD design. We construct a dataset of 1,010 oat leaf images, categorized into five severity levels and annotated by experts. RSD-YOLO achieves 91.6% precision, 90.8% recall, and 88.5% mAP@0.5, significantly outperforming YOLOv7-tiny by up to 10%, while maintaining a computational cost of only 11.2 GFLOPs. Recent studies have applied lightweight models such as EfficientSAM and SwiftFormer for crop health monitoring on drones and edge devices. However, these models often struggle to balance accuracy and efficiency. In contrast, RSD-YOLO achieves higher performance with lower computational cost, making it well-suited for real-time deployment in agricultural environments.

Artificial Intelligence (AI) has emerged as a key driver of precision agriculture, facilitating enhanced crop productivity, optimized resource management, and sustainable farming practices. Also, the expansion of genome sequencing technology has greatly increased crop genomic resources, offering deeper insights into genetic variation and enhancing desirable crop traits for better performance across various environments. Machine learning (ML) and deep learning (DL) algorithms are gaining traction for genotype-to-phenotype prediction, due to their excellence in capturing complex interactions within large, high-dimensional datasets. In this work, we present a new LSTM autoencoder-based model for barley genotype-to-phenotype prediction, specifically targeting flowering time and grain yield estimation. Our model outperformed the other baseline methods, highlighting its effectiveness in handling complex, high-dimensional agricultural datasets and enhancing the accuracy of crop phenotype prediction predictions. This approach has the potential to optimize crop yields and improve management practices.