Biology subjects

Rody, H. V. S.

Publications and source records attributed to Rody, H. V. S..

4 recordsLinked to original sources

bioRxiv2024

Acquisition, Evolution, and Diversification of Genomic Islands: A Case Study of a Virulence Gene Cluster in Pseudomonas syringae

Genomic islands are widely distributed among bacteria, facilitating the dissemination of genes relevant to human, animal, and plant health. Herein, we explore the evolutionary history of a genomic island (Tox island) composed of a novel mobile genetic element (GInt) carrying an [~]25 kb gene cluster (Pht cluster) responsible for the biosynthesis of the phytotoxin phaseolotoxin, a virulence factor of the plant pathogen Pseudomonas syringae. The Pht cluster has been acquired, either naked or associated with a GInt, on seven independent occasions by four phylogroups of P. syringae and the distant rhizobacterium Pseudomonas sp. JAI115. The Pht cluster was independently captured by three distinct GInt elements, suggesting specific mechanisms for gene capture. Once acquired, the Tox island tends to be stably maintained, evolving with the genome. An array of molecular analyses delineated the likely evolutionary trajectory of the Pht cluster within P. syringae pv. actinidiae (Psa) and P. amygdali pv. phaseolicola (Pph), involving: 1) acquisition by Pph; 2) transfer of haplotype G to Psa biovar 1; 3) acquisition or replacement by a haplotype of haplogroup D in Psa biovar 1; 4) acquisition of haplotype C by Psa biovar 6; and 5) replacement of the Tox island in Pph by a distantly related GInt. These findings underscore the potential role of phaseolotoxin in bacterial fitness and contribute to our understanding of virulence evolution in plant pathogens. Furthermore, GInts provide a model for studying the evolutionary dynamics of mobile genetic elements and the dissemination of adaptive genes among bacterial pathogens.

microbiology↗

bioRxiv2022

The Necrosis- and Ethylene-inducing peptide 1-like protein (NLP) gene family of the plant pathogen Corynespora cassiicola

Effectors are secreted by plant-associated microorganisms in order to modify the host cell physiology. As effectors, the Necrosis- and Ethylene-inducing peptide 1-like proteins (NLPs) are involded in the early phases of plant infection and may trigger host immune responses. Corynespora cassiicola is a polyphagous plant-pathogen that causes target spot on many agriculturally important crops. Using genome assembly, gene prediction, and proteome annotation tools, we retrieved 135 NLP-encoding genes from proteomes of 44 isolates. We explored the evolutionary history of NLPs using Bayesian phylogeny, gene genealogies, and selection analyses. We accessed the expression profiles of the NLP genes during the early phase of C. cassiicola-soybean interaction. Three NLP effector genes (Cc_NLP1.1, Cc_NLP1.2A, and Cc_NLP1.2B) were maintained in the genomes of all isolates tested. A non-effector NLP gene (Cc_NLP1.3) was found in three isolates that had been originally obtained from soybean. NLP effectors were under different selective constraints: Cc_NLP1.1 was under stronger selective pressure, while Cc_NLP1.2A was under a more relaxed constraint. Meanwhile, Cc_NLP1.2B likely evolved under either positive or balancing selection. Despite highly divergent, the effector NLPs maintain conserved the residues necessary to trigger plant immune responses, suggesting they are potentially functional. Only the Cc_NLP1.1 effector gene was significantly expressed at the early hours of soybean colonization, while Cc_NLP1.2A and Cc_NLP1.2B showed much lower levels of gene expression.

microbiology↗

bioRxiv2022

Genome-wide analysis and evolutionary history of the Necrosis and Ethylene-inducing peptide 1-like protein (NLP) superfamily across the Dothideomycetes class of fungi

Necrosis and Ethylene-inducing peptide 1-like proteins (NLPs) are broadly distributed across bacteria, fungi and oomycetes. Cytotoxic NLPs are usually secreted into the host apoplast where they can induce cell death and trigger plant immune responses in eudicots. To investigate the evolutionary history of the NLPs, we accessed the genomic resources of 79 species from 15 orders of Dothideomycetes. Phylogenetic approaches searched for biased patterns of NLP gene evolution and aimed to provide a phylogenetic framework for the cytotoxic activities of NLPs. Among Dothideomycetes, the NLP superfamily sizes varied, but usually contained from one to six members. Superfamily sizes were higher among pathogenic fungi, with family members that were mostly effector NLPs. Across species, members of the NLP1 family (Type I NLPs) were predominant (84%) over members of the NLP2 family (Type II NLPs). The NLP1 family split into two subfamilies (NLP1.1 and NLP1.2). The NLP1.1 subfamily was broadly distributed across Dothideomycetes. There was strong agreement between the phylogenomics of Dothideomycetes and the phylogenetic tree based on members of the NLP1 subfamilies. To a lesser extent, phylogenomics also agreed with the phylogeny based on members of the NLP2 family. While gene losses seem to have shaped the evolutionary history of NLP2 family, ancient gene duplications followed by descent with modification characterized the NLP1 family. The strongest cytotoxic activities were recorded on NLPs of the NLP1.1 subfamily, suggesting that biased NLP gene retention in this subfamily favored the cytotoxic paralogs.

evolutionary biology↗

bioRxiv2020

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust resistance is a desirable characteristic due to the large economic impact of the disease. Although marker-assisted selection for rust resistance has been successful, the genes involved are still unknown, and the associated regions vary among cultivars, thus restricting methodological generalization. We used genotyping by sequencing of full-sib progeny to relate genomic regions with brown rust phenotypes. We established a pipeline to identify reliable SNPs in complex polyploid data, which were used for phenotypic prediction via machine learning. We identified 14,540 SNPs, which led to a mean prediction accuracy of 50% by using different models. We also tested feature selection algorithms to increase predictive accuracy, resulting in a reduced dataset with more explanatory power for rust phenotypes. Using different feature selection techniques, we achieved accuracy of up to 95% with a dataset of 131 SNPs related to brown rust QTL regions and auxiliary genes. Therefore, our novel strategy has the potential to assist studies of the genomic organization of brown rust resistance in sugarcane.

bioinformatics↗