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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.

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Aluminium (Al), a limiting factor for crop productivity in acidic soils (pH ≤ 5.5), imposes drastic constraints for food safety in developing countries. The major mechanisms that allow plants to cope with Al involve manipulations of organic acids metabolism and DNA-checkpoints. When assumed individually both approaches have been insufficient to overcome Al toxicity. On analysing the centre of origin of most cultivated plants, we hypothesised that day-length seems to be a pivotal agent modulating Al tolerance across distinct plant species. We observed that with increasing distance from the Equator, Al tolerance decreases, suggesting a relationship with the photoperiod. We verified that long-day (LD) species are generally more Al-sensitive than short-day (SD) species, whereas genetic conversion of tomato for SD growth habit boosts Al tolerance. Reduced Al tolerance correlates with DNA-checkpoint activation under LD. Furthermore, DNA-checkpoint-related genes are under positive selection in Arabidopsis accessions from regions with shorter days, suggesting that photoperiod act as a selective barrier for Al tolerance. A diel regulation and genetic diversity affect Al tolerance, suggesting that day-length orchestrates Al tolerance. Altogether, photoperiodic control of Al tolerance might contribute to solving the historical obstacle that imposes barriers for developing countries to reach a sustainable agriculture.

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Begomoviruses (Geminiviridae family) represent a severe constraint to agriculture worldwide. As ssDNA viruses that replicate in the nuclei of infected cells, the nascent viral DNA has to move to the cytoplasm and then to the adjacent cell to cause disease. The begomovirus nuclear shuttle protein (NSP) assists the intracellular transport of viral DNA from the nucleus to the cytoplasm and cooperates with the movement protein (MP) for the cell-to-cell translocation of viral DNA to uninfected cells. As a facilitator of intra- and intercellular transport of viral DNA, NSP is predicted to associate with host proteins from the nuclear export machinery, the intracytoplasmic active transport system, and the cell-to-cell transport complex. Furthermore, NSP functions as a virulence factor that suppresses antiviral immunity against begomoviruses. In this review, we focus on the protein-protein network that converges on NSP with a high degree of centrality and forms an immune hub against begomoviruses. We also describe the compatible host functions hijacked by NSP to promote the nucleocytoplasmic and intracytoplasmic movement of viral DNA. Finally, we discuss the NSP virulence function as a suppressor of the recently described NSP-interacting kinase 1 (NIK1)-mediated antiviral immunity. Understanding the NSP-host protein-protein interaction (PPI) network will probably pave the way for strategies to generate more durable resistance against begomoviruses.

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Many efforts have been made to understand the pathogenesis of bovine mastitis to reduce losses and promote animal welfare. Staphylococcus aureus may cause bovine clinical mastitis, but it is mainly associated with subclinical infection, which is usually persistent and can easily reoccur. Here, we conducted a comparative genomic analysis between strains of S. aureus causing subclinical infection (Sau170, 302, 1269, 1364), previously sequenced by our group, and two well-characterized strains causing clinical mastitis (N305 and RF122) to find differences that could be linked to mastitis outcome. A total of 146 virulence-associated genes were compared and no appreciable differences were found between the bacteria. However, several nonsynonymous single nucleotide polymorphisms (SNPs) were identified in genes present in the subclinical strains when compared to RF122 and N305, especially in genes encoding host immune evasion and surface proteins. The secreted and surface proteins predicted by in silico tools were compared through multidimensional scaling analysis (MDS), revealing a high degree of similarity among the strains. The comparison of orthologous genes by OrthoMCL identified a membrane transporter and a lipoprotein as exclusive of bacteria belonging to the subclinical and clinical groups, respectively. No hit was found in RF122 and N305 for the membrane transporter using BLAST algorithm. For the lipoprotein, sequences of Sau170, 302, 1269, and 1364 with identities between 68-73% were found in the MDS dataset. A conserved region found only in the lipoprotein genes of RF122 and N305 was used for primer design. Although the polymerase chain reaction (PCR) on field isolates of S. aureus did not validate the findings for the transporter, the lipoprotein was able to separate the clinical from the subclinical isolates. These results show that sequence variation among bovine S. aureus, and not only the presence/absence of virulence factors, is an important aspect to consider when comparing isolates causing different mastitis outcomes.

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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.

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The bipartite begomoviruses (Geminiviridae family), which are DNA viruses that replicate in the nucleus of infected cells, encode the nuclear shuttle protein (NSP) to facilitate the translocation of viral DNA from the nucleus to the cytoplasm via nuclear pores. This intracellular trafficking of NSP-DNA complexes is accessorized by the NSP-interacting guanosine triphosphatase (NIG) at the cytosolic side. Here, we report the nuclear redistribution of NIG by AtWWP1, a WW domain-containing protein that forms immune nuclear bodies (NBs) against begomoviruses. We demonstrated that AtWWP1 relocates NIG from the cytoplasm to the nucleus where it is confined to AtWWP1-NBs, suggesting that the NIG-AtWWP1 interaction may interfere with the NIG pro-viral function associated with its cytosolic localization. Consistent with this assumption, loss of AtWWP1 function cuased plants more susceptible to begomovirus infection, whereas overexpression of AtWWP1 enhanced plant resistance to begomovirus. Furthermore, we found that a mutant version of AtWWP1 defective for NB formation was no longer capable of interacting with and relocating NIG to the nucleus and lost its immune function against begomovirus. The antiviral function of AtWWP1-NBs, however, could be antagonized by viral infection that induced either the disruption or a decrease in the number of AtWWP1-NBs. Collectively, these results led us to propose that AtWWP1 organizes nuclear structures into nuclear foci, which provide intrinsic immunity against begomovirus infection.

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The NAC (NAM, ATAF, and CUC) genes encode transcription factors involved with the control of plant morph-physiology and stress responses. The release of the last soybean (Glycine max) genome assembly (Wm82.a2.v1) raised the possibility that new NAC genes would be present in the soybean genome. Here, we interrogated the last version of the soybean genome against a conserved NAC domain structure. Our analysis identified 32 putative novel NAC genes, updating the superfamily to 180 gene members. We also organized the genes in 15 phylogenetic subfamilies, which showed a perfect correlation among sequence conservation, expression profile, and function of orthologous Arabidopsis thaliana genes and NAC soybean genes. To validate our in silico analyses, we monitored the stress-mediated gene expression profiles of eight new NAC-genes by qRT-PCR and monitored the GmNAC senescence-associated genes by RNA-seq. Among ER stress, osmotic stress and salicylic acid treatment, all the novel tested GmNAC genes responded to at least one type of stress, displaying a complex expression profile under different kinetics and extension of the response. Furthermore, we showed that 40% of the GmNACs were differentially regulated by natural leaf senescence, including eight (8) newly identified GmNACs. The developmental and stress-responsive expression profiles of the novel NAC genes fitted perfectly with their phylogenetic subfamily. Finally, we examined two uncharacterized senescence-associated proteins, GmNAC065 and GmNAC085, and a novel, previously unidentified, NAC protein, GmNAC177, and showed that they are nuclear localized, and except for GmNAC065, they display transactivation activity in yeast. Consistent with a role in leaf senescence, transient expression of GmNAC065 and GmNAC085 induces the appearance of hallmarks of leaf senescence, including chlorophyll loss, leaf yellowing, lipid peroxidation and accumulation of H2O2. GmNAC177 was clustered to an uncharacterized subfamily but in close proximity to the TIP subfamily. Accordingly, it was rapidly induced by ER stress and by salicylic acid under late kinetic response and promoted cell death in planta. Collectively, our data further substantiated the notion that the GmNAC genes display functional and expression profiles consistent with their phylogenetic relatedness and established a complete framework of the soybean NAC superfamily as a foundation for future analyses.

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Ribosomal proteins (RPs) play a fundamental role within all type of cells, as they are major components of ribosomes, which are essential for translation of mRNAs. Furthermore, these proteins are involved in various physiological and pathological processes. The intrinsic biological relevance of RPs motivated advanced studies for the identification of unrevealed RPs. In this work, we propose a new computational method, termed Rama, for the prediction of RPs, based on machine learning techniques, with a particular interest in plants. To perform an effective classification, Rama uses a set of fundamental attributes of the amino acid side chains and applies a two-step procedure to classify proteins with unknown function as RPs. The evaluation of the resultant predictive models showed that Rama could achieve mean sensitivity, precision, and specificity of 0.91, 0.91, and 0.82, respectively. Furthermore, a list of proteins that have no annotation in Phytozome v.10, and are annotated as RPs in Phytozome v.12, were correctly classified by our models. Additional computational experiments have also shown that Rama presents high accuracy to differentiate ribosomal proteins from RNA-binding proteins. Finally, two novel proteins of Arabidopsis thaliana were validated in biological experiments. Rama is freely available at http://inctipp.bioagro.ufv.br:8080/Rama .

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BACKGROUND: Geminiviruses infect a broad range of cultivated and non-cultivated plants, causing significant economic losses worldwide. The studies of the diversity of species, taxonomy, mechanisms of evolution, geographic distribution, and mechanisms of interaction of these pathogens with the host have greatly increased in recent years. Furthermore, the use of rolling circle amplification (RCA) and advanced metagenomics approaches have enabled the elucidation of viromes and the identification of many viral agents in a large number of plant species. As a result, determining the nomenclature and taxonomically classifying geminiviruses turned into complex tasks. In addition, the gene responsible for viral replication (particularly, the viruses belonging to the genus Mastrevirus) may be spliced due to the use of the transcriptional/splicing machinery in the host cells. However, the current tools have limitations concerning the identification of introns. RESULTS: This study proposes a new method, designated Fangorn Forest (F2), based on machine learning approaches to classify genera using an ab initio approach, i.e., using only the genomic sequence, as well as to predict and classify genes in the family Geminiviridae. In this investigation, nine genera of the family Geminiviridae and their related satellite DNAs were selected. We obtained two training sets, one for genus classification, containing attributes extracted from the complete genome of geminiviruses, while the other was made up to classify geminivirus genes, containing attributes extracted from ORFs taken from the complete genomes cited above. Three ML algorithms were applied on those datasets to build the predictive models: support vector machines, using the sequential minimal optimization training approach, random forest (RF), and multilayer perceptron. RF demonstrated a very high predictive power, achieving 0.966, 0.964, and 0.995 of precision, recall, and area under the curve (AUC), respectively, for genus classification. For gene classification, RF could reach 0.983, 0.983, and 0.998 of precision, recall, and AUC, respectively. CONCLUSIONS: Therefore, Fangorn Forest is proven to be an efficient method for classifying genera of the family Geminiviridae with high precision and effective gene prediction and classification. The method is freely accessible at www.geminivirus.org:8080/geminivirusdw/discoveryGeminivirus.jsp .

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BACKGROUND: The Geminiviridae family encompasses a group of single-stranded DNA viruses with twinned and quasi-isometric virions, which infect a wide range of dicotyledonous and monocotyledonous plants and are responsible for significant economic losses worldwide. Geminiviruses are divided into nine genera, according to their insect vector, host range, genome organization, and phylogeny reconstruction. Using rolling-circle amplification approaches along with high-throughput sequencing technologies, thousands of full-length geminivirus and satellite genome sequences were amplified and have become available in public databases. As a consequence, many important challenges have emerged, namely, how to classify, store, and analyze massive datasets as well as how to extract information or new knowledge. Data mining approaches, mainly supported by machine learning (ML) techniques, are a natural means for high-throughput data analysis in the context of genomics, transcriptomics, proteomics, and metabolomics. RESULTS: Here, we describe the development of a data warehouse enriched with ML approaches, designated geminivirus.org. We implemented search modules, bioinformatics tools, and ML methods to retrieve high precision information, demarcate species, and create classifiers for genera and open reading frames (ORFs) of geminivirus genomes. CONCLUSIONS: The use of data mining techniques such as ETL (Extract, Transform, Load) to feed our database, as well as algorithms based on machine learning for knowledge extraction, allowed us to obtain a database with quality data and suitable tools for bioinformatics analysis. The Geminivirus Data Warehouse (geminivirus.org) offers a simple and user-friendly environment for information retrieval and knowledge discovery related to geminiviruses.

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