Major proliferation of transposable elements shaped the genome of the soybean rust pathogen Phakopsora pachyrhizi.

With >7000 species the order of rust fungi has a disproportionately large impact on agriculture, horticulture, forestry and foreign ecosystems. The infectious spores are typically dikaryotic, a feature unique to fungi in which two haploid nuclei reside in the same cell. A key example is Phakopsora pachyrhizi, the causal agent of Asian soybean rust disease, one of the world's most economically damaging agricultural diseases. Despite P. pachyrhizi's impact, the exceptional size and complexity of its genome prevented generation of an accurate genome assembly. Here, we sequence three independent P. pachyrhizi genomes and uncover a genome up to 1.25 Gb comprising two haplotypes with a transposable element (TE) content of ~93%. We study the incursion and dominant impact of these TEs on the genome and show how they have a key impact on various processes such as host range adaptation, stress responses and genetic plasticity.

RLPredictiOme, a Machine Learning-Derived Method for High-Throughput Prediction of Plant Receptor-like Proteins, Reveals Novel Classes of Transmembrane Receptors.

Cell surface receptors play essential roles in perceiving and processing external and internal signals at the cell surface of plants and animals. The receptor-like protein kinases (RLK) and receptor-like proteins (RLPs), two major classes of proteins with membrane receptor configuration, play a crucial role in plant development and disease defense. Although RLPs and RLKs share a similar single-pass transmembrane configuration, RLPs harbor short divergent C-terminal regions instead of the conserved kinase domain of RLKs. This RLP receptor structural design precludes sequence comparison algorithms from being used for high-throughput predictions of the RLP family in plant genomes, as has been extensively performed for RLK superfamily predictions. Here, we developed the RLPredictiOme, implemented with machine learning models in combination with Bayesian inference, capable of predicting RLP subfamilies in plant genomes. The ML models were simultaneously trained using six types of features, along with three stages to distinguish RLPs from non-RLPs (NRLPs), RLPs from RLKs, and classify new subfamilies of RLPs in plants. The ML models achieved high accuracy, precision, sensitivity, and specificity for predicting RLPs with relatively high probability ranging from 0.79 to 0.99. The prediction of the method was assessed with three datasets, two of which contained leucine-rich repeats (LRR)-RLPs from Arabidopsis and rice, and the last one consisted of the complete set of previously described Arabidopsis RLPs. In these validation tests, more than 90% of known RLPs were correctly predicted via RLPredictiOme. In addition to predicting previously characterized RLPs, RLPredictiOme uncovered new RLP subfamilies in the Arabidopsis genome. These include probable lipid transfer (PLT)-RLP, plastocyanin-like-RLP, ring finger-RLP, glycosyl-hydrolase-RLP, and glycerophosphoryldiester phosphodiesterase (GDPD, GDPDL)-RLP subfamilies, yet to be characterized. Compared to the only Arabidopsis GDPDL-RLK, molecular evolution studies confirmed that the ectodomain of GDPDL-RLPs might have undergone a purifying selection with a predominance of synonymous substitutions. Expression analyses revealed that predicted GDPGL-RLPs display a basal expression level and respond to developmental and biotic signals. The results of these biological assays indicate that these subfamily members have maintained functional domains during evolution and may play relevant roles in development and plant defense. Therefore, RLPredictiOme provides a framework for genome-wide surveys of the RLP superfamily as a foundation to rationalize functional studies of surface receptors and their relationships with different biological processes.

Machine learning approaches and their current application in plant molecular biology: A systematic review.

Machine learning (ML) is a field of artificial intelligence that has rapidly emerged in molecular biology, thus allowing the exploitation of Big Data concepts in plant genomics. In this context, the main challenges are given in terms of how to analyze massive datasets and extract new knowledge in all levels of cellular systems research. In summary, ML techniques allow complex interactions to be inferred in several biological systems. Despite its potential, ML has been underused due to complex computational algorithms and definition terms. Therefore, a systematic review to disentangle ML approaches is relevant for plant scientists and has been considered in this study. We presented the main steps for ML development (from data selection to evaluation of classification/prediction models) with a respective discussion approaching functional genomics mainly in terms of pathogen effector genes in plant immunity. Additionally, we also considered how to access public source databases under an ML framework towards advancing plant molecular biology and introduced novel powerful tools, such as deep learning.

The Hemileia vastatrix effector HvEC-016 suppresses bacterial blight symptoms in coffee genotypes with the SH 1 rust resistance gene.

A number of genes that confer resistance to coffee leaf rust (SH 1-SH 9) have been identified within the genus Coffea, but despite many years of research on this pathosystem, the complementary avirulence genes of Hemileia vastatrix have not been reported. After identification of H. vastatrix effector candidate genes (HvECs) expressed at different stages of its lifecycle, we established an assay to characterize HvEC proteins by delivering them into coffee cells via the type-three secretion system (T3SS) of Pseudomonas syringae pv. garcae (Psgc). Employing a calmodulin-dependent adenylate cyclase assay, we demonstrate that Psgc recognizes a heterologous P. syringae T3SS secretion signal which enables us to translocate HvECs into the cytoplasm of coffee cells. Using this Psgc-adapted effector detector vector (EDV) system, we found that HvEC-016 suppresses the growth of Psgc on coffee genotypes with the SH 1 resistance gene. Suppression of bacterial blight symptoms in SH 1 plants was associated with reduced bacterial multiplication. By contrast, HvEC-016 enhanced bacterial multiplication in SH 1-lacking plants. Our findings suggest that HvEC-016 may be recognized by the plant immune system in a SH 1-dependent manner. Thus, our experimental approach is an effective tool for the characterization of effector/avirulence proteins of this important pathogen.

A pigeonpea gene confers resistance to Asian soybean rust in soybean.

Asian soybean rust (ASR), caused by the fungus Phakopsora pachyrhizi, is one of the most economically important crop diseases, but is only treatable with fungicides, which are becoming less effective owing to the emergence of fungicide resistance. There are no commercial soybean cultivars with durable resistance to P. pachyrhizi, and although soybean resistance loci have been mapped, no resistance genes have been cloned. We report the cloning of a P. pachyrhizi resistance gene CcRpp1 (Cajanus cajan Resistance against Phakopsora pachyrhizi 1) from pigeonpea (Cajanus cajan) and show that CcRpp1 confers full resistance to P. pachyrhizi in soybean. Our findings show that legume species related to soybean such as pigeonpea, cowpea, common bean and others could provide a valuable and diverse pool of resistance traits for crop improvement.

MpSaci is a widespread gypsy-Ty3 retrotransposon highly represented by non-autonomous copies in the Moniliophthora perniciosa genome.

Transposons are an important source of genetic variation. The phytopathogen Moniliophthora perniciosa shows high level of variability but little is known about the role of class I elements in shaping its genome. In this work, we aimed the characterization of a new gypsy/Ty3 retrotransposon species, named MpSaci, in the M. perniciosa genome. These elements are largely variable in size, ranging from 4 to 15 kb, and harbor direct long terminal repeats (LTRs) with varying degrees of similarity. Approximately, all of the copies are non-autonomous as shifts in the reading frame and stop codons were detected. Only two elements (MpSaci6 and MpSaci9) code for GAG and POL proteins that possess functional domains. Conserved domains that are typically not found in retrotransposons were detected and could potentially impact the expression of neighbor genes. Solo LTRs and several LARDs (large retrotransposon derivative) were detected. Unusual elements containing small sequences with or without interruptions that are similar to gag or different pol domains and presenting LTRs with different levels of similarities were identified. Methylation was observed in MpSaci reverse transcriptase sequences. Distribution analysis indicates that MpSaci elements are present in high copy number in the genomes of C-, S- and L-biotypes of M. perniciosa. In addition, C-biotype isolates originating from the state of Bahia have fragments in common with isolates from the Amazon region and two hybridization profiles related to two chromosomal groups. RT-PCR analysis reveals that the gag gene is constitutively expressed and that the expression is increased at least three-fold with nutrient depravation even though no new insertion were observed. These findings point out that MpSaci collaborated and, even though is primarily represented by non-autonomous elements, still might contribute to the generation of genetic variability in the most important cacao pathogen in Brazil.

Genome-wide analysis of differentially expressed genes during the early stages of tomato infection by a potyvirus.

Plant responses against pathogens cause up- and downward shifts in gene expression. To identify differentially expressed genes in a plant-virus interaction, susceptible tomato plants were inoculated with the potyvirus Pepper yellow mosaic virus (PepYMV) and a subtractive library was constructed from inoculated leaves at 72 h after inoculation. Several genes were identified as upregulated, including genes involved in plant defense responses (e.g., pathogenesis-related protein 5), regulation of the cell cycle (e.g., cytokinin-repressed proteins), signal transduction (e.g., CAX-interacting protein 4, SNF1 kinase), transcriptional regulators (e.g., WRKY and SCARECROW transcription factors), stress response proteins (e.g., Hsp90, DNA-J, 20S proteasome alpha subunit B, translationally controlled tumor protein), ubiquitins (e.g., polyubiquitin, ubiquitin activating enzyme 2), among others. Downregulated genes were also identified, which likewise display identity with genes involved in several metabolic pathways. Differential expression of selected genes was validated by macroarray analysis and quantitative real-time polymerase chain reaction. The possible roles played by some of these genes in the viral infection cycle are discussed.