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

Silva, Raner José Santana; Micheli, Fabienne

doi:10.1016/j.ygeno.2020.03.001

Cited by 18 publications

(18 citation statements)

References 52 publications

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“…The files in tabulated TSV format generated by the InterProScan were used as input for the software RRG_Predictor [ 63 ], which makes a search in two steps, the first searches for sequences with one or more domain codes associated with PRR and R genes, and the second step classifies those sequences into one of the 13 resistance gene classes in plants associated with resistance to pathogens, based on the following domain combinations: (1) Proteins with Coiled-coils ( CC ) and nucleotide binding sites (NBS) domains, (2) proteins with CC, NBS, and Leucine-rich repeat (LRR) domains, (3) proteins with Mlo-like resistance proteins, (4) proteins with just NBS domain, (5) proteins with NBS and LRR domains, (6) proteins with Receptor-like Kinase (RLK), (7) proteins with RLK and Ginkbilobin2 (GNK2) domains, (8) proteins with Receptor-like without the Kinase domain, (9) proteins with Resistance to Powdery Mildew 8 ( RPW8 ) NBS, and LRR domains, (10) proteins with Toll/interleukin-1 receptor (TIR) domain, (11) proteins with TIR and NBS domains, (12) proteins with TIR, NBS, and LRR domains, and (13) proteins with Leucine-rich repeat (LRR) domains that do not fit any other class (UNKNOWN).…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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“…For benchmarking using the RefPlantNLR dataset we used DRAGO2 (DRAGO2-API, Osuna-Cruz et al ., 2018), NLGenomeSweeper (v1.2.0, Toda et al ., 2020; dependencies: Python 3.8, NCBI-BLAST+ (v2.11.0+), MUSCLE aligner (v3.8.1551), SAMtools (v1.9-50-g18be38a), bedtools (v2.27.1-9-g5f83cacb), HMMER (v3.3.1), InterProScan (v5.47-82.0), TransDecoder (v5.5.0)), NLR-Annotator (Steuernagel et al ., 2020; dependencies: meme-suite (v5.1.1), NLR-Parser (v3;Steuernagel et al ., 2015), Oracle Java SE Development Kit 11.0.9), RGAugury (Li et al ., 2016; dependencies: CViT, HMMER, InterProScan, ncoils, NCBI-BLAST+, Pfamscan, Phobius), and RRGPredictor (Santana Silva and Micheli, 2020; dependencies: InterProScan) using either amino acid, CDS, and/or the extracted NLR genomic loci as an input. Since NLGenomeSweeper and NLR-Annotator only accept nucleotide input, while RGAugury only accepts amino acid input, we only used RefPlantNLR entries for which CDS was available in the direct comparison.…”

Section: Methodsmentioning

confidence: 99%

“…As an input these tools take either annotated genomic features and transcriptomic data, or alternatively can be run directly on the unannotated genomic sequence. NLR-Parser, RGAugury, RRGPredictor, and DRAGO2 identify transcript and protein sequences that have features of NLRs and are best described as NLR extractors (Steuernagel et al ., 2015; Li et al ., 2016; Osuna-Cruz et al ., 2018; Santana Silva and Micheli, 2020). RGAugury, RRGPredictor, and DRAGO2 also extract other classes of immune-related genes in addition to NLRs.…”

Section: Introductionmentioning

confidence: 99%

RefPlantNLR: a comprehensive collection of experimentally validated plant NLRs

Kourelis

Sakai

Adachi

et al. 2020

Preprint

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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 NLR immune receptors. RefPlantNLR consists of 415 NLRs from 31 genera belonging to 11 orders of flowering plants. We used RefPlantNLR to determine the canonical features of functionally validated plant NLRs. This reference dataset should prove useful for benchmarking NLR annotation tools, guiding comparative analyses of NLRs across the wide spectrum of plant diversity and identifying under-studied taxa. We hope that the RefPlantNLR resource will contribute to moving the field beyond a uniform view of NLR structure and function.

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“… 495 Recently, RRGPredictor, a tool mainly based on text mining and set theory to predict new plant PRRs, makes the identification of PRRs more effective, specific, and sensitive than other available tools. 496 And a random forest-based method was proposed to identify PRRs, which is superior to other machine learning methods for PRRs. This method constructs a benchmark database, uses the amino acid composition and the composition transition distribution to formulate the sequences in the dataset, and then uses the maximum relevance maximum distance to select the best features.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Pattern recognition receptors in health and diseases

2021

Sig Transduct Target Ther

836

515

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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.

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

Cited by 18 publications

References 52 publications

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

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

RefPlantNLR: a comprehensive collection of experimentally validated plant NLRs

Pattern recognition receptors in health and diseases

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