Soybean is a crucial crop for the Brazilian economy, but it faces challenges from the biotrophic fungus Phakopsora pachyrhizi, which causes Asian Soybean Rust (ASR). In this study, we aimed to identify SNPs associated with resistance within the Rpp1 locus, which is effective against Brazilian ASR populations. We employed GWAS and re-sequencing analyzes to pinpoint SNP markers capable of differentiating between soybean accessions harboring the Rpp1, Rpp1-b and other alternative alleles in the Rpp1 locus and from susceptible soybean cultivars. Seven SNP markers were found to be associated with ASR resistance through GWAS, with three of them defining haplotypes that efficiently distinguished the accessions based on their ASR resistance and source of the Rpp gene. These haplotypes were subsequently validated using a bi-parental population and a diverse set of Rpp sources, demonstrating that the GWAS markers co-segregate with ASR resistance. We then examined the presence of these haplotypes in a diverse set of soybean genomes worldwide, finding a few new potential sources of Rpp1/Rpp1-b. Further genomic sequence analysis revealed nucleotide differences within the genes present in the Rpp1 locus, including the ULP1-NBS-LRR genes, which are potential R gene candidates. These results provide valuable insights into ASR resistance in soybean, thus helping the development of resistant soybean varieties through genetic breeding programs.

Asian soybean rust (ASR), caused by Phakopsora pachyrhizi, is a devastating disease that is present in all major soybean-producing regions. The limited availability of resistant germplasm has resulted in a scarcity of commercial soybean cultivars that are resistant to the disease. To date, only the Chinese soybean landrace SX6907 has demonstrated an immune response to ASR. In this study, we present the isolation and characterization of Rpp6907-7 and Rpp6907-4, a gene pair that confer broad-spectrum resistance to ASR. Rpp6907-7 and Rpp6907-4 encode atypic nucleotide-binding leucine-rich repeat (NLR) proteins that are found to be required for NLR-mediated immunity. Genetic analysis shows that only Rpp6907-7 confers resistance, while Rpp6907-4 regulates Rpp6907-7 signaling activity by acting as a repressor in the absence of recognized effectors. Our work highlights the potential value of using Rpp6907 in developing resistant soybean cultivars.

UNASSIGNED: Asian soybean rust is a highly aggressive leaf-based disease triggered by the obligate biotrophic fungus Phakopsora pachyrhizi which can cause up to 80% yield loss in soybean. The precise image segmentation of fungus can characterize fungal phenotype transitions during growth and help to discover new medicines and agricultural biocides using large-scale phenotypic screens.
UNASSIGNED: The improved Mask R-CNN method is proposed to accomplish the segmentation of densely distributed, overlapping and intersecting microimages. First, Res2net is utilized to layer the residual connections in a single residual block to replace the backbone of the original Mask R-CNN, which is then combined with FPG to enhance the feature extraction capability of the network model. Secondly, the loss function is optimized and the CIoU loss function is adopted as the loss function for boundary box regression prediction, which accelerates the convergence speed of the model and meets the accurate classification of high-density spore images.
UNASSIGNED: The experimental results show that the mAP for detection and segmentation, accuracy of the improved algorithm is improved by 6.4%, 12.3% and 2.2% respectively over the original Mask R-CNN algorithm.
UNASSIGNED: This method is more suitable for the segmentation of fungi images and provide an effective tool for large-scale phenotypic screens of plant fungal pathogens.

Soybean rust is an economically significant disease caused by the fungus Phakopsora pachyrhizi that negatively impacts soybean (Glycine max [L.] Merr.) production throughout the world. Susceptible plants infected by P. pachyrhizi develop tan-colored lesions on the leaf surface that give rise to funnel-shaped uredinia as the disease progresses. While most soybean germplasm is susceptible, seven genetic loci (Rpp1 to Rpp7) that provide race-specific resistance to P. pachyrhizi (Rpp) have been identified. Rpp3 was first discovered and characterized in the soybean accession PI 462312 (Ankur), and it was also determined to be one of two Rpp genes present in PI 506764 (Hyuuga). Genetic crosses with PI 506764 were later used to fine-map the Rpp3 locus to a 371-kb region on chromosome 6. The corresponding region in the susceptible Williams 82 (Wm82) reference genome contains several homologous nucleotide binding site-leucine rich repeat (NBS-LRR) genes. To identify Rpp3, we designed oligonucleotide primers to amplify Rpp3 candidate (Rpp3C) NBS-LRR genes at this locus from PI 462312, PI 506764, and Wm82 using polymerase chain reaction (PCR). Five Rpp3C genes were identified in both Rpp3-resistant soybean lines, and co-silencing these genes compromised resistance to P. pachyrhizi. Gene expression analysis and sequence comparisons of the Rpp3C genes in PI 462312 and PI 506764 suggest that a single candidate gene, Rpp3C3, is responsible for Rpp3-mediated resistance. [Formula: see text] The author(s) have dedicated the work to the public domain under the Creative Commons CC0 \"No Rights Reserved\" license by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law, 2024.

Soybean (Glycine max L.) is an important crop in Asia, accounting for 17% of global soybean cultivation. However, this crop faces formidable challenges from the devastating foliar disease, Asian Soybean Rust (ASR), caused by Phakopsora pachyrhizi, a biotrophic fungus with a broad host range, causing substantial yield losses (10-100%) in Asia. This comprehensive review consolidates knowledge on ASR, encompassing its impact, historical perspectives, genetic diversity, epidemic drivers, early detection, risk assessment, and sustainable management strategies of ASR in the region. ASR has expanded globally from Asia, reaching Africa and Americas, driven by wind-dispersed urediniospores. Genetic diversity studies reveal the complexity of P. pachyrhizi, with distinct populations exhibiting varying virulence patterns. Factors affecting ASR epidemics in Asia include host susceptibility, landscape connectivity, climate, and environmental conditions. Understanding the interplay of these factors is essential for early intervention and control of ASR in soybean fields. Effectively managing ASR can exploit the utilization of diverse intervention strategies, encompassing disease forecasting, automated early detection, disease resistance, fungicide application, and biological control. A pivotal aspect of successful, sustainable disease management lies in reducing the ASR pathogen virulence and preventing it from developing fungicide resistance, while the highpoint of effectiveness in disease control is attained through a synergistic approach, integrating various strategies. In summary, this comprehensive review provides insights into multifaceted approaches that contribute to the development of sustainable and economically impactful soybean production in the face of the persistent threat of ASR in Asia.

Soybean rust (SBR), caused by the obligate biotrophic fungus Phakopsora pachyrhizi, is a devastating foliar disease threatening soybean production. To date, no commercial cultivars conferring durable resistance to SBR are available. The development of long-lasting SBR resistance has been hindered by the lack of understanding of this complex pathosystem, encompassing challenges posed by intricate genetic structures in both the host and pathogen, leading to a gap in the knowledge of gene-for-gene interactions between soybean and P. pachyrhizi. In this review, we focus on recent advancements and emerging technologies that can be used to improve our understanding of the P. pachyrhizi-soybean molecular interactions. We further explore approaches used to combat SBR, including conventional breeding, transgenic approaches and RNA interference, and how advances in our understanding of plant immune networks, the availability of new molecular tools, and the recent sequencing of the P. pachyrhizi genome could be used to aid in the development of better genetic resistance against SBR. Lastly, we discuss the research gaps of this pathosystem and how new technologies can be used to shed light on these questions and to develop durable next-generation SBR-resistant soybean plants.

The multifaceted role of pathogen-encoded effectors in plant-pathogen interactions is complex and not fully understood. Effectors operate within intricate host environments, interacting with host proteins and other effectors to modulate virulence. The complex interplay between effectors raises the concept of metaeffectors, wherein some effectors regulate the activity of others. While previous research has demonstrated the importance of effector repertoires in pathogen virulence, only a limited number of studies have investigated the interactions between these effectors. This study explores the interactions among Phakopsora pachyrhizi effector candidates (PpECs). P. pachyrhizi haustorial transcriptome analysis identified a collection of predicted PpECs. Among these, PpEC23 was found to interact with PpEC48, prompting further exploration into their potential interaction with other effectors. Here, we utilized a yeast two-hybrid screen to explore protein-protein interactions between PpECs. A split-luciferase complementation assay also demonstrated that these interactions could occur within soybean cells. Interestingly, PpEC48 displayed the ability to interact with several small cysteine-rich proteins (SCRPs), suggesting its affinity for this specific class of effectors. We show that these interactions involve a histidine-rich domain within PpEC48, emphasizing the significance of structural motifs in mediating effector interactions. The unique nature of PpEC48, showing no sequence matches in other organisms, suggests its relatively recent evolution and potential orphan gene status. Our work reveals insights into the intricate network of interactions among P. pachyrhizi effector-effector interactions. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Asian soybean rust (ASR), caused by Phakopsora pachyrhizi, is one of the most destructive foliar diseases that affect soybeans. Developing resistant cultivars is the most cost-effective, environmentally friendly, and easy strategy for controlling the disease. However, the current understanding of the mechanisms underlying soybean resistance to P. pachyrhizi remains limited, which poses a significant challenge in devising effective control strategies. In this study, comparative transcriptomic profiling using one resistant genotype and one susceptible genotype was performed under infected and control conditions to understand the regulatory network operating between soybean and P. pachyrhizi. RNA-Seq analysis identified a total of 6540 differentially expressed genes (DEGs), which were shared by all four genotypes. The DEGs are involved in defense responses, stress responses, stimulus responses, flavonoid metabolism, and biosynthesis after infection with P. pachyrhizi. A total of 25,377 genes were divided into 33 modules using weighted gene co-expression network analysis (WGCNA). Two modules were significantly associated with pathogen defense. The DEGs were mainly enriched in RNA processing, plant-type hypersensitive response, negative regulation of cell growth, and a programmed cell death process. In conclusion, these results will provide an important resource for mining resistant genes to P. pachyrhizi infection and valuable resources to potentially pyramid quantitative resistance loci for improving soybean germplasm.

Asian soybean rust (ASR), caused by Phakopsora pachyrhizi, is one of the most serious soybean (Glycine max) diseases in tropical and subtropical regions. To facilitate the development of resistant varieties using gene pyramiding, DNA markers closely linked to seven resistance genes, namely, Rpp1, Rpp1-b, Rpp2, Rpp3, Rpp4, Rpp5, and Rpp6, were identified. Linkage analysis of resistance-related traits and marker genotypes using 13 segregating populations of ASR resistance, including eight previously published by our group and five newly developed populations, identified the resistance loci with markers at intervals of less than 2.0 cM for all seven resistance genes. Inoculation was conducted of the same population with two P. pachyrhizi isolates of different virulence, and two resistant varieties, \'Kinoshita\' and \'Shiranui,\' previously thought to only harbor Rpp5, was found to also harbor Rpp3. Markers closely linked to the resistance loci identified in this study will be used for ASR-resistance breeding and the identification of the genes responsible for resistance.

Soybean brown rust (SBR), caused by Phakopsora pachyrhizi, is a devastating fungal disease that threatens global soybean production. This study conducted a genome-wide association study (GWAS) with seven models on a panel of 3,082 soybean accessions to identify the markers associated with SBR resistance by 30,314 high quality single nucleotide polymorphism (SNPs). Then five genomic selection (GS) models, including Ridge regression best linear unbiased predictor (rrBLUP), Genomic best linear unbiased predictor (gBLUP), Bayesian least absolute shrinkage and selection operator (Bayesian LASSO), Random Forest (RF), and Support vector machines (SVM), were used to predict breeding values of SBR resistance using whole genome SNP sets and GWAS-based marker sets. Four SNPs, namely Gm18_57,223,391 (LOD = 2.69), Gm16_29,491,946 (LOD = 3.86), Gm06_45,035,185 (LOD = 4.74), and Gm18_51,994,200 (LOD = 3.60), were located near the reported P. pachyrhizi R genes, Rpp1, Rpp2, Rpp3, and Rpp4, respectively. Other significant SNPs, including Gm02_7,235,181 (LOD = 7.91), Gm02_7234594 (LOD = 7.61), Gm03_38,913,029 (LOD = 6.85), Gm04_46,003,059 (LOD = 6.03), Gm09_1,951,644 (LOD = 10.07), Gm10_39,142,024 (LOD = 7.12), Gm12_28,136,735 (LOD = 7.03), Gm13_16,350,701(LOD = 5.63), Gm14_6,185,611 (LOD = 5.51), and Gm19_44,734,953 (LOD = 6.02), were associated with abundant disease resistance genes, such as Glyma.02G084100, Glyma.03G175300, Glyma.04g189500, Glyma.09G023800, Glyma.12G160400, Glyma.13G064500, Glyma.14g073300, and Glyma.19G190200. The annotations of these genes included but not limited to: LRR class gene, cytochrome 450, cell wall structure, RCC1, NAC, ABC transporter, F-box domain, etc. The GWAS based markers showed more accuracies in genomic prediction than the whole genome SNPs, and Bayesian LASSO model was the ideal model in SBR resistance prediction with 44.5% ~ 60.4% accuracies. This study aids breeders in predicting selection accuracy of complex traits such as disease resistance and can shorten the soybean breeding cycle by the identified markers.