NRG1 functions downstream of EDS1 to regulate TIR-NLR-mediated plant immunity in Nicotiana benthamiana.

Effector-triggered immunity (ETI) in plants involves a large family of nucleotide-binding leucine-rich repeat (NLR) immune receptors, including Toll/IL-1 receptor-NLRs (TNLs) and coiled-coil NLRs (CNLs). Although various NLR immune receptors are known, a mechanistic understanding of NLR function in ETI remains unclear. The TNL Recognition of XopQ 1 (Roq1) recognizes the effectors XopQ and HopQ1 from Xanthomonas and Pseudomonas, respectively, which activates resistance to Xanthomonas euvesicatoria and Xanthomonas gardneri in an Enhanced Disease Susceptibility 1 (EDS1)-dependent way in Nicotiana benthamiana In this study, we found that the N. benthamiana N requirement gene 1 (NRG1), a CNL protein required for the tobacco TNL protein N-mediated resistance to tobacco mosaic virus, is also essential for immune signaling [including hypersensitive response (HR)] triggered by the TNLs Roq1 and Recognition of Peronospora parasitica 1 (RPP1), but not by the CNLs Bs2 and Rps2, suggesting that NRG1 may be a conserved key component in TNL signaling pathways. Besides EDS1, Roq1 and NRG1 are necessary for resistance to Xanthomonas and Pseudomonas in N. benthamiana NRG1 functions downstream of Roq1 and EDS1 and physically associates with EDS1 in mediating XopQ-Roq1-triggered immunity. Moreover, RNA sequencing analysis showed that XopQ-triggered gene-expression profile changes in N. benthamiana were almost entirely mediated by Roq1 and EDS1 and were largely regulated by NRG1. Overall, our study demonstrates that NRG1 is a key component that acts downstream of EDS1 to mediate various TNL signaling pathways, including Roq1 and RPP1-mediated HR, resistance to Xanthomonas and Pseudomonas, and XopQ-regulated transcriptional changes in N. benthamiana

Prediction of effector protein structures from fungal phytopathogens enables evolutionary analyses

Elucidating the similarity and diversity of pathogen effectors is critical to understand their evolution across fungal phytopathogens. However, rapid divergence that diminishes sequence similarities between putatively homologous effectors has largely concealed the roots of effector evolution. Here we modelled the structures of 26,653 secreted proteins from 14 agriculturally important fungal phytopathogens, six non-pathogenic fungi and one oomycete with AlphaFold 2. With 18,000 successfully predicted folds, we performed structure-guided comparative analyses on two aspects of effector evolution: uniquely expanded sequence-unrelated structurally similar (SUSS) effector families and common folds present across the fungal species. Extreme expansion of lineage-specific SUSS effector families was found only in several obligate biotrophs, Blumeria graminis and Puccinia graminis. The highly expanded effector families were the source of conserved sequence motifs, such as the Y/F/WxC motif. We identified new classes of SUSS effector families that include known virulence factors, such as AvrSr35, AvrSr50 and Tin2. Structural comparisons revealed that the expanded structural folds further diversify through domain duplications and fusion with disordered stretches. Putatively sub- and neo-functionalized SUSS effectors could reconverge on regulation, expanding the functional pools of effectors in the pathogen infection cycle. We also found evidence that many effector families could have originated from ancestral folds conserved across fungi. Collectively, our study highlights diverse effector evolution mechanisms and supports divergent evolution as a major force in driving SUSS effector evolution from ancestral proteins

Computational Structural Genomics Unravels Common Folds and Novel Families in the Secretome of Fungal Phytopathogen Magnaporthe oryzae.

Structural biology has the potential to illuminate the evolution of pathogen effectors and their commonalities that cannot be readily detected at the primary sequence level. Recent breakthroughs in protein structure modeling have demonstrated the feasibility to predict the protein folds without depending on homologous templates. These advances enabled a genome-wide computational structural biology approach to help understand proteins based on their predicted folds. In this study, we employed structure prediction methods on the secretome of the destructive fungal pathogen Magnaporthe oryzae. Out of 1,854 secreted proteins, we predicted the folds of 1,295 proteins (70%). We showed that template-free modeling by TrRosetta captured 514 folds missed by homology modeling, including many known effectors and virulence factors, and that TrRosetta generally produced higher quality models for secreted proteins. Along with sensitive homology search, we employed structure-based clustering, defining not only homologous groups with divergent members but also sequence-unrelated structurally analogous groups. We demonstrate that this approach can reveal new putative members of structurally similar MAX effectors and novel analogous effector families present in M. oryzae and possibly in other phytopathogens. We also investigated the evolution of expanded putative ADP-ribose transferases with predicted structures. We suggest that the loss of catalytic activities of the enzymes might have led them to new evolutionary trajectories to be specialized as protein binders. Collectively, we propose that computational structural genomics approaches can be an integral part of studying effector biology and provide valuable resources that were inaccessible before the advent of machine learning-based structure prediction.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license

Evolution of NLR resistance genes with non-canonical N-terminal domains in wild tomato species

Nucleotide-binding and leucine-rich repeat immune receptors (NLRs) provide resistance against diverse pathogens. To create comparative NLR resources, we conducted resistance gene enrichment sequencing (RenSeq) with single-molecule real-time sequencing of PacBio for 18 accessions in Solanaceae, including 15 accessions of five wild tomato species. We investigated the evolution of a class of NLRs, CNLs with extended N-terminal sequences previously named Solanaceae Domain. Through comparative genomic analysis, we revealed that the extended CNLs (exCNLs) anciently emerged in the most recent common ancestor between Asterids and Amaranthaceae, far predating the Solanaceae family. In tomatoes, the exCNLs display exceptional modes of evolution in a clade-specific manner. In the clade G3, exCNLs have substantially elongated their N-termini through tandem duplications of exon segments. In the clade G1, exCNLs have evolved through recent proliferation and sequence diversification. In the clade G6, an ancestral exCNL has lost its N-terminal domains in the course of evolution. Our study provides high-quality NLR gene models for close relatives of domesticated tomatoes that can serve as a useful resource for breeding and molecular engineering for disease resistance. Our findings regarding the exCNLs offer unique backgrounds and insights for future functional studies of the NLRs

RGB Channel Combinations Method for Feature Extraction in Image Analysis

Latest image analysis deep learning algorithms use diverse methods to extract features from images based on the Convolution Neural Network (CNN). CNN has a convolution layer consisting of RGB as three overlapping channels in the feature extraction process, and such architecture enables the backbone network to flow without losing each hue information. Therefore, 3D input data consisting of 3 channels to process the RGB channel consists of a large-scale neural network with many layer blocks. This processing method exhibits high accuracy. However, in terms of practicality, it results in big inefficiencies such as memory overhead and computational overhead. This study proposes the RGB Channel Combinations Method for Feature Extraction in Image Analysis to resolve such inefficiencies. This is a method that converts the RGB value into one tensor structure by combining each weight and bias and makes it possible to pass through the backbone network without damaging hue information. Based on the experiment results, it is confirmed that the accuracy decreased by 1.42% compared to the pre-existing method, but the number of parameters used by the input layer decreased. It is confirmed that the pre-processing used in the proposed method gained an additional computational overhead, but the number of input parameters decreased to 1/3 in the feature extraction stage performed afterward. As the proposed method applies to all image analysis algorithms, its expandability is extremely high and can process a large amount of image data

Diversity of genomic adaptations to the post-fire environment in Pezizales fungi points to crosstalk between charcoal tolerance and sexual development

International audienceWildfires drastically impact the soil environment, altering the soil organic matter, forming pyrolyzed compounds, and markedly reducing the diversity of microorganisms. Pyrophilous fungi, especially the species from the orders Pezizales and Agaricales, are fire-responsive fungal colonizers of post-fire soil that have historically been found fruiting on burned soil and thus may encode mechanisms of processing these compounds in their genomes. Pyrophilous fungi are diverse. In this work, we explored this diversity and sequenced six new genomes of pyrophilous Pezizales fungi isolated after the 2013 Rim Fire near Yosemite Park in California, USA: Pyronema domesticum, Pyronema omphalodes, Tricharina praecox, Geopyxis carbonaria, Morchella snyderi, and Peziza echinospora. A comparative genomics analysis revealed the enrichment of gene families involved in responses to stress and the degradation of pyrolyzed organic matter. In addition, we found that both protein sequence lengths and G + C content in the third base of codons (GC3) in pyrophilous fungi fall between those in mesophilic/nonpyrophilous and thermophilic fungi. A comparative transcriptome analysis of P. domesticum under two conditions - growing on charcoal, and during sexual development - identified modules of genes that are co-expressed in the charcoal and light-induced sexual development conditions. In addition, environmental sensors such as transcription factors STE12, LreA, LreB, VosA, and EsdC were upregulated in the charcoal condition. Taken together, these results highlight genomic adaptations of pyrophilous fungi and indicate a potential connection between charcoal tolerance and fruiting body formation in P. domesticum