RRGPredictor, a set-theory-based tool for predicting pathogen-associated molecular pattern receptors (PRRs) and resistance (R) proteins from plants

Resistify: A Novel NLR Classifier That Reveals Helitron-Associated NLR Expansion in Solanaceae

Nucleotide-binding domain leucine-rich repeat (NLR) proteins are a key component of the plant innate immune system. In plant genomes, NLRs exhibit considerable presence/absence variation and sequence diversity. Recent advances in sequencing technologies have made the generation of high-quality novel plant genome assemblies considerably more straightforward. Accurately identifying NLRs from these genomes is a prerequisite for improving our understanding of NLRs and identifying novel sources of disease resistance. While several tools have been developed to predict NLRs, they are hampered by low accuracy, speed, and availability. Here, the NLR annotation tool Resistify is presented. Resistify is an easy-to-use, rapid, and accurate tool to identify and classify NLRs from protein sequences. Applying Resistify to the RefPlantNLR database demonstrates that it can correctly identify NLRs from a diverse range of species. Applying Resistify in combination with tools to identify transposable elements to a panel of Solanaceae genomes reveals a previously undescribed association between NLRs and Helitron transposable elements.

Dual transcriptomic analysis reveals early induced Castanea defense-related genes and Phytophthora cinnamomi effectors

Phytophthora cinnamomi Rands devastates forest species worldwide, causing significant ecological and economic impacts. The European chestnut (Castanea sativa) is susceptible to this hemibiotrophic oomycete, whereas the Asian chestnuts (Castanea crenata and Castanea mollissima) are resistant and have been successfully used as resistance donors in breeding programs. The molecular mechanisms underlying the different disease outcomes among chestnut species are a key foundation for developing science-based control strategies. However, these are still poorly understood. Dual RNA sequencing was performed in C. sativa and C. crenata roots inoculated with P. cinnamomi. The studied time points represent the pathogen's hemibiotrophic lifestyle previously described at the cellular level. Phytophthora cinnamomi expressed several genes related to pathogenicity in both chestnut species, such as cell wall-degrading enzymes, host nutrient uptake transporters, and effectors. However, the expression of effectors related to the modulation of host programmed cell death (elicitins and NLPs) and sporulation-related genes was higher in the susceptible chestnut. After pathogen inoculation, 1,556 and 488 genes were differentially expressed by C. crenata and C. sativa, respectively. The most significant transcriptional changes occur at 2 h after inoculation (hai) in C. sativa and 48 hai in C. crenata. Nevertheless, C. crenata induced more defense-related genes, indicating that the resistant response to P. cinnamomi is controlled by multiple loci, including several pattern recognition receptors, genes involved in the phenylpropanoid, salicylic acid and ethylene/jasmonic acid pathways, and antifungal genes. Importantly, these results validate previously observed cellular responses for C. crenata. Collectively, this study provides a comprehensive time-resolved description of the chestnut-P. cinnamomi dynamic, revealing new insights into susceptible and resistant host responses and important pathogen strategies involved in disease development.

Bridging the Gap: Combining Genomics and Transcriptomics Approaches to Understand Stylosanthes scabra, an Orphan Legume from the Brazilian Caatinga

Stylosanthes scabra is a scientifically orphaned legume found in the Brazilian Caatinga biome (a semi-arid environment). This work utilized omics approaches to investigate some ecophysiological aspects of stress tolerance/resistance in S. scabra, study its genomic landscape, and predict potential metabolic pathways. Considering its high-confidence conceptual proteome, 1694 (~2.6%) proteins were associated with resistance proteins, some of which were found in soybean QTL regions that confer resistance to Asian soybean rust. S. scabra was also found to be a potential source of terpenes, as biosynthetic gene clusters associated with terpene biosynthesis were identified in its genome. The analysis revealed that mobile elements comprised approximately 59% of the sequenced genome. In the remaining 41% of the sections, some of the 22,681 protein-coding gene families were categorized into two informational groups: those that were specific to S. scabra and those that expanded significantly compared to their immediate ancestor. Biological process enrichment analyses indicated that these gene families play fundamental roles in the adaptation of S. scabra to extreme environments. Additionally, phylogenomic analysis indicated a close evolutionary relationship between the genera Stylosanthes and Arachis. Finally, this study found a high number (57) of aquaporin-encoding loci in the S. scabra genome. RNA-Seq and qPCR data suggested that the PIP subfamily may play a key role in the species' adaptation to water deficit conditions. Overall, these results provide valuable insights into S. scabra biology and a wealth of gene/transcript information for future legume omics studies.

RFPDR: a random forest approach for plant disease resistance protein prediction

Background

Plant innate immunity relies on a broad repertoire of receptor proteins that can detect pathogens and trigger an effective defense response. Bioinformatic tools based on conserved domain and sequence similarity are within the most popular strategies for protein identification and characterization. However, the multi-domain nature, high sequence diversity and complex evolutionary history of disease resistance (DR) proteins make their prediction a real challenge. Here we present RFPDR, which pioneers the application of Random Forest (RF) for Plant DR protein prediction.

Methods

A recently published collection of experimentally validated DR proteins was used as a positive dataset, while 10x10 nested datasets, ranging from 400-4,000 non-DR proteins, were used as negative datasets. A total of 9,631 features were extracted from each protein sequence, and included in a full dimension (FD) RFPDR model. Sequence selection was performed, to generate a reduced-dimension (RD) RFPDR model. Model performances were evaluated using an 80/20 (training/testing) partition, with 10-cross fold validation, and compared to baseline, sequence-based and state-of-the-art strategies. To gain some insights into the underlying biology, the most discriminatory sequence-based features in the RF classifier were identified.

Results and Discussion

RD-RFPDR showed to be sensitive (86.4 ± 4.0%) and specific (96.9 ± 1.5%) for identifying DR proteins, while robust to data imbalance. Its high performance and robustness, added to the fact that RD-RFPDR provides valuable information related to DR proteins underlying properties, make RD-RFPDR an interesting approach for DR protein prediction, complementing the state-of-the-art strategies.

RefPlantNLR is a comprehensive collection of experimentally validated plant disease resistance proteins from the NLR family

Reference datasets are critical in computational biology. They help define canonical biological features and are essential for benchmarking studies. Here, we describe a comprehensive reference dataset of experimentally validated plant nucleotide-binding leucine-rich repeat (NLR) immune receptors. RefPlantNLR consists of 481 NLRs from 31 genera belonging to 11 orders of flowering plants. This reference dataset has several applications. We used RefPlantNLR to determine the canonical features of functionally validated plant NLRs and to benchmark 5 NLR annotation tools. This revealed that although NLR annotation tools tend to retrieve the majority of NLRs, they frequently produce domain architectures that are inconsistent with the RefPlantNLR annotation. Guided by this analysis, we developed a new pipeline, NLRtracker, which extracts and annotates NLRs from protein or transcript files based on the core features found in the RefPlantNLR dataset. The RefPlantNLR dataset should also prove useful for guiding comparative analyses of NLRs across the wide spectrum of plant diversity and identifying understudied taxa. We hope that the RefPlantNLR resource will contribute to moving the field beyond a uniform view of NLR structure and function.

Bioinformatic tools support decision-making in plant disease management

Food loss due to pathogens is a major concern in agriculture, requiring the need for advanced disease detection and prevention measures to minimize pathogen damage to plants. Novel bioinformatic tools have opened doors for the low-cost rapid identification of pathogens and prevention of disease. The number of these tools is growing fast and a comprehensive and comparative summary of these resources is currently lacking. Here, we review all current bioinformatic tools used to identify the mechanisms of pathogen pathogenicity, plant resistance protein identification, and the detection and treatment of plant disease. We compare functionality, data volume, data sources, performance, and applicability of all tools to provide a comprehensive toolbox for researchers in plant disease management.

Pattern recognition receptors in health and diseases

Pattern recognition receptors (PRRs) are a class of receptors that can directly recognize the specific molecular structures on the surface of pathogens, apoptotic host cells, and damaged senescent cells. PRRs bridge nonspecific immunity and specific immunity. Through the recognition and binding of ligands, PRRs can produce nonspecific anti-infection, antitumor, and other immunoprotective effects. Most PRRs in the innate immune system of vertebrates can be classified into the following five types based on protein domain homology: Toll-like receptors (TLRs), nucleotide oligomerization domain (NOD)-like receptors (NLRs), retinoic acid-inducible gene-I (RIG-I)-like receptors (RLRs), C-type lectin receptors (CLRs), and absent in melanoma-2 (AIM2)-like receptors (ALRs). PRRs are basically composed of ligand recognition domains, intermediate domains, and effector domains. PRRs recognize and bind their respective ligands and recruit adaptor molecules with the same structure through their effector domains, initiating downstream signaling pathways to exert effects. In recent years, the increased researches on the recognition and binding of PRRs and their ligands have greatly promoted the understanding of different PRRs signaling pathways and provided ideas for the treatment of immune-related diseases and even tumors. This review describes in detail the history, the structural characteristics, ligand recognition mechanism, the signaling pathway, the related disease, new drugs in clinical trials and clinical therapy of different types of PRRs, and discusses the significance of the research on pattern recognition mechanism for the treatment of PRR-related diseases.

Structural and Functional Genomics of the Resistance of Cacao to Phytophthora palmivora

Black pod disease, caused by Phytophthora spp., is one of the main diseases that attack cocoa plantations. This study validated, by association mapping, 29 SSR molecular markers flanking to QTL (Quantitative Trait Loci) associated with Phytophthora palmivora Butler (Butler) (PP) resistance, in three local ancient varieties of the Bahia (Comum, Pará, and Maranhão), varieties that have a high potential in the production of gourmet chocolate. Four SSR loci associated with resistance to PP were detected, two on chromosome 8, explaining 7.43% and 3.72% of the Phenotypic Variation (%PV), one on chromosome 2 explaining 2.71%PV and one on chromosome 3 explaining 1.93%PV. A functional domains-based annotation was carried out, in two Theobroma cacao (CRIOLLO and MATINA) reference genomes, of 20 QTL regions associated with cocoa resistance to the pathogen. It was identified 164 (genome CRIOLLO) and 160 (genome MATINA) candidate genes, hypothetically involved in the recognition and activation of responses in the interaction with the pathogen. Genomic regions rich in genes with Coiled-coils (CC), nucleotide binding sites (NBS) and Leucine-rich repeat (LRR) domains were identified on chromosomes 1, 3, 6, 8, and 10, likewise, regions rich in Receptor-like Kinase domain (RLK) and Ginkbilobin2 (GNK2) domains were identified in chromosomes 4 and 6.

Understanding Phytomicrobiome: A Potential Reservoir for Better Crop Management

Recent crop production studies have aimed at an increase in the biotic and abiotic tolerance of plant communities, along with increased nutrient availability and crop yields. This can be achieved in various ways, but one of the emerging approaches is to understand the phytomicrobiome structure and associated chemical communications. The phytomicrobiome was characterized with the advent of high-throughput techniques. Its composition and chemical signaling phenomena have been revealed, leading the way for “rhizosphere engineering”. In addition to the above, phytomicrobiome studies have paved the way to best tackling soil contamination with various anthropogenic activities. Agricultural lands have been found to be unbalanced for crop production. Due to the intense application of agricultural chemicals such as herbicides, fungicides, insecticides, fertilizers, etc., which can only be rejuvenated efficiently through detailed studies on the phytomicrobiome component, the phytomicrobiome has recently emerged as a primary plant trait that affects crop production. The phytomicrobiome also acts as an essential modifying factor in plant root exudation and vice versa, resulting in better plant health and crop yield both in terms of quantity and quality. Not only supporting better plant growth, phytomicrobiome members are involved in the degradation of toxic materials, alleviating the stress conditions that adversely affect plant development. Thus, the present review compiles the progress in understanding phytomicrobiome relationships and their application in achieving the goal of sustainable agriculture.