Amesuviridae: a new family of plant-infecting viruses in the phylum Cressdnaviricota, realm Monodnaviria.

The phylum Cressdnaviricota comprises viruses with single-stranded, circular DNA genomes that encode an HUH-type endonuclease (known as Rep). The phylum includes two classes, eight orders, and 11 families. Here, we report the creation of a twelfth family in the order Mulpavirales, class Arfiviricetes of the phylum Cressdnaviricota. The family Amesuviridae comprises viruses that infect plants and is divided into two genera: Temfrudevirus, including the species Temfrudevirus temperatum (with temperate fruit decay-associated virus as a member), and Yermavirus, including the species Yermavirus ilicis (with yerba mate-associated circular DNA virus as a member). Both viruses encode Rep proteins with HUH endonuclease and SH3 superfamily helicase domains. Phylogenetic analysis indicates that the replicative module of amesuviruses constitutes a well-supported monophyletic clade related to Rep proteins from viruses in the order Mulpavirales. Furthermore, both viruses encode a single capsid protein (CP) related to geminivirus CPs. Phylogenetic incongruence between the replicative and structural modules of amesuviruses suggests a chimeric origin resulting from remote recombination events between ancestral mulpavirales and geminivirids. The creation of the family Amesuviridae has been ratified by the International Committee on Taxonomy of Viruses (ICTV).

A systematic review and repeatability study on the use of deep learning for classifying and detecting tuberculosis bacilli in microscopic images.

Tuberculosis (TB) is among the leading causes of death worldwide from a single infectious agent. This disease usually affects the lungs (pulmonary TB) and can be cured in most cases with a quick diagnosis and proper treatment. Microscopic sputum smear is widely used to diagnose and manage pulmonary TB. Despite being relatively fast and low cost, it can be exhausting because it depends on manually counting TB bacilli (Mycobacterium tuberculosis) in microscope images. In this context, different Deep Learning (DL) techniques are proposed in the literature to assist in performing smear microscopy. This article presents a systematic review based on the PRISMA procedure, which investigates which DL techniques can contribute to classifying TB bacilli in microscopic images of sputum smears using the Ziehl-Nielsen method. After an extensive search and a careful inclusion/exclusion procedure, 28 papers were selected from a total of 400 papers retrieved from nine databases. Based on these articles, the DL techniques are presented as possible solutions to improve smear microscopy. The main concepts necessary to understand how such techniques are proposed and used are also presented. In addition, replication work is also carried out, verifying reproducibility and comparing different works in the literature. In this review, we look at how DL techniques can be a partner to make sputum smear microscopy faster and more efficient. We also identify some gaps in the literature that can guide which issues can be addressed in other works to contribute to the practical use of these methods in laboratories.

MicroRNAs expression profiles in early responses to different levels of water deficit in Setaria viridis.

Water deficit is a major constraint for crops of economic importance in almost all agricultural regions. However, plants have an active defense system to adapt to these adverse conditions, acting in the reprogramming of gene expression responsible for encoding microRNAs (miRNAs). These miRNAs promote the regulation to the target gene expression by the post-transcriptional (PTGS) and transcriptional gene silencing (TGS), modulating several pathways including defense response to water deficit. The broader knowledge of the miRNA expression profile and its regulatory networks in response to water deficit can provide evidence for the development of new biotechnological tools for genetic improvement of several important crops. In this study, we used Setaria viridis accession A10.1 as a C4 model plant to widely investigate the miRNA expression profile in early responses to different levels of water deficit. Ecophysiological studies in Setaria viridis under water deficit and after rewatering demonstrated a drought tolerant accession, capable of a rapid recovery from the stress. Deep small RNA sequencing and degradome studies were performed in plants submitted to drought to identify differentially expressed miRNA genes and their predicted targets, using in silico analysis. Our findings showed that several miRNAs were differentially modulated in response to distinctive levels of water deficit and after rewatering. The predicted mRNA targets mainly corresponded to genes related to cell wall remodeling, antioxidant system and drought-related transcription factors, indicating that these genes are rapidly regulated in early responses to drought stress. The implications of these modulations are extensively discussed, and higher-effect miRNAs are suggested as major players for potential use in genetic engineering to improve drought tolerance in economically important crops, such as sugarcane, maize, and sorghum. Supplementary Information: The online version contains supplementary material available at 10.1007/s12298-022-01226-z.

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.

A long and stressful day: Photoperiod shapes aluminium tolerance in plants.

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.

A plant-specific syntaxin-6 protein contributes to the intracytoplasmic route for the begomovirus CabLCV.

Because of limited free diffusion in the cytoplasm, viruses must use active transport mechanisms to move intracellularly. Nevertheless, how the plant single-stranded DNA begomoviruses hijack the host intracytoplasmic transport machinery to move from the nucleus to the plasmodesmata remains enigmatic. Here, we identified nuclear shuttle protein (NSP)-interacting proteins from Arabidopsis (Arabidopsis thaliana) by probing a protein microarray and demonstrated that the cabbage leaf curl virus NSP, a facilitator of the nucleocytoplasmic trafficking of viral (v)DNA, interacts in planta with an endosomal vesicle-localized, plant-specific syntaxin-6 protein, designated NSP-interacting syntaxin domain-containing protein (NISP). NISP displays a proviral function, unlike the syntaxin-6 paralog AT2G18860 that failed to interact with NSP. Consistent with these findings, nisp-1 mutant plants were less susceptible to begomovirus infection, a phenotype reversed by NISP complementation. NISP-overexpressing lines accumulated higher levels of vDNA than wild-type. Furthermore, NISP interacted with an NSP-interacting GTPase (NIG) involved in NSP-vDNA nucleocytoplasmic translocation. The NISP-NIG interaction was enhanced by NSP. We also showed that endosomal NISP associates with vDNA. NISP may function as a docking site for recruiting NIG and NSP into endosomes, providing a mechanism for the intracytoplasmic translocation of the NSP-vDNA complex toward and from the cell periphery.

Intra-host evolution of the ssDNA virus tomato severe rugose virus (ToSRV).

To evaluate and quantify the evolutionary dynamics of the bipartite begomovirus tomato severe rugose virus (ToSRV) in a cultivated and a non-cultivated host, plants of tomato and Nicandra physaloides were biolistically inoculated with an infectious clone and systemically infected leaves were sampled at 30, 75 and 120 days after inoculation. Total DNA was extracted and sequenced in the Illumina HiSeq 2000 platform. The datasets were trimmed with the quality score limit set to 0.01, and the assembly was performed using the infectious clone sequence as reference. SNPs were filtered using a minimum p-value of 0.001 and the sum frequencies were used to calculate the deviation from the original clone sequence. Nucleotide substitution rates were calculated for the two DNA components in both hosts: 1.73 × 10-3 and 3.07 × 10-4 sub/site/year for the DNA-A and DNA-B, respectively, in N. physaloides, and 8.05 × 10-4 and 7.02 × 10-5 sub/site/year the for DNA-A and DNA-B, respectively, in tomato. These values are in the same range of those estimated for viruses with single-stranded RNA genomes and for other begomoviruses. Strikingly, the number of substitutions decreased over time, suggesting the presence of bottlenecks during systemic infection. Determination of Shannon's entropy indicated different patterns of variation in the DNA-A and the DNA-B, suggesting distinct evolutionary forces acting upon each component.

A Begomovirus Nuclear Shuttle Protein-Interacting Immune Hub: Hijacking Host Transport Activities and Suppressing Incompatible Functions.

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.

Comparative genomics of Staphylococcus aureus associated with subclinical and clinical bovine mastitis.

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.

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.

A WW Domain-Containing Protein Forms Immune Nuclear Bodies against Begomoviruses.

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.

Revisiting the Soybean GmNAC Superfamily.

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.

Sugar metabolism and developmental stages of rubber tree (Hevea brasiliensis L.) seeds.

Changes in the concentration of sugars and sucrose metabolism enzymes can characterize the developmental stages of a seed. In recalcitrant species such as Hevea brasiliensis L., little is known about these changes. We aimed to evaluate the three main stages of development of rubber tree seeds - histodifferentiation, cell elongation and accumulation of reserves. The activities of acid and neutral invertases (E.C. 3.2.1.26) and sucrose synthase (EC 2.4.1.13), and the concentrations of reducing sugars (RS), total soluble sugars (TSS) and sucrose (Suc) were determined concomitantly with the histochemical and anatomical evaluation of seed structure. Histodifferentiation in rubber tree seeds occurs up to 75 days after anthesis (DAA). The concentration of RS is high and of Suc is low during seed histodifferentiation, which occurs along with a visible increase in the number of cell divisions. After that period, there is an increase in the concentration of Suc (mg g-1 ) and in the number and size of starch granules, and a decrease in the concentration of RS (mg g-1 ). At that point, cell elongation occurs. At 135 DAA, there is an inversion in the concentration of these two sugars and an increase in reserve accumulation. Thus, in seeds of the evaluated clone, the period up to 75 DAA is characterized as the histodifferentiation stage, while from that time up to 120 DAA the cell elongation stage takes place. The final stage of seed maturation and reserve accumulation begins at 135 DAA, and the seed, including the embryo, is completely formed at 175 DAA.

Rama: a machine learning approach for ribosomal protein prediction in plants.

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 .

Fangorn Forest (F2): a machine learning approach to classify genes and genera in the family Geminiviridae.

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 .

Genetic variability and population structure of the New World begomovirus Euphorbia yellow mosaic virus.

The emergence of begomoviruses (whitefly-transmitted viruses classified in the genus Begomovirus, family Geminiviridae) in Brazil probably occurred by horizontal transfer from non-cultivated plants after the introduction of Bemisia tabaci MEAM1. The centre of diversity of Euphorbia heterophylla (Euphorbiaceae) is located in Brazil and Paraguay, where it is an invasive species in soybean and other crops. Reports of possible begomovirus infection of E. heterophylla in Brazil date back to the 1950s. In 2011, Euphorbia yellow mosaic virus (EuYMV) was described in symptomatic plants collected in the Brazilian state of Goiás. Here we assess the genetic variability and population structure of begomoviruses infecting E. heterophylla in samples collected throughout nine Brazilian states from 2009 to 2014. A total of 158 and 57 haplotypes were compared in DNA-A and DNA-B datasets, respectively. Analysis comparing population structure in a large sampled area enabled us to differentiate two subpopulations. Further, the application of discriminant analysis of principal components allowed the differentiation of six subpopulations according to sampling locations and in agreement with phylogenetic analysis. In general, negative selection was predominant in all six subpopulations. Interestingly, we were able to reconstruct the phylogeny based on the information from the 23 sites that contributed most to the geographical structure proposed, demonstrating that these polymorphisms hold supporting information to discriminate between subpopulations. These sites were mapped in the genome and compared at the level of amino acid changes, providing insights into how genetic drift and selection contribute to maintain the patterns of begomovirus population variability from a geographical structuring point of view.

Geminivirus data warehouse: a database enriched with machine learning approaches.

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.

Bioinformatics Analysis of the Receptor-Like Kinase (RLK) Superfamily.

Receptor-like kinases (RLKs) play key roles during development and in responses to the environment. In plant immunity, some members of RLKs function as pattern recognition receptors (PRRs), which, upon recognition of pathogen-associated molecular patterns (PAMP), are recruited into active complexes to induce pathogen-triggered immunity (PTI). In this chapter, we describe the bioinformatics tools and procedures for the identification and phylogenetic classification of RLKs from different plant species as a framework for understanding RLK function in signal transduction and immunity.

Functional and regulatory conservation of the soybean ER stress-induced DCD/NRP-mediated cell death signaling in plants.

BACKGROUND: The developmental and cell death domain (DCD)-containing asparagine-rich proteins (NRPs) were first identified in soybean (Glycine max) as transducers of a cell death signal derived from prolonged endoplasmic reticulum (ER) stress, osmotic stress, drought or developmentally-programmed leaf senescence via the GmNAC81/GmNAC30/GmVPE signaling module. In spite of the relevance of the DCD/NRP-mediated signaling as a versatile adaptive response to multiple stresses, mechanistic knowledge of the pathway is lacking and the extent to which this pathway may operate in the plant kingdom has not been investigated. RESULTS: Here, we demonstrated that the DCD/NRP-mediated signaling also propagates a stress-induced cell death signal in other plant species with features of a programmed cell death (PCD) response. In silico analysis revealed that several plant genomes harbor conserved sequences of the pathway components, which share functional analogy with their soybean counterparts. We showed that GmNRPs, GmNAC81and VPE orthologs from Arabidopsis, designated as AtNRP-1, AtNRP-2, ANAC036 and gVPE, respectively, induced cell death when transiently expressed in N. benthamiana leaves. In addition, loss of AtNRP1 and AtNRP2 function attenuated ER stress-induced cell death in Arabidopsis, which was in marked contrast with the enhanced cell death phenotype displayed by overexpressing lines as compared to Col-0. Furthermore, atnrp-1 knockout mutants displayed enhanced sensitivity to PEG-induced osmotic stress, a phenotype that could be complemented with ectopic expression of either GmNRP-A or GmNRP-B. In addition, AtNRPs, ANAC036 and gVPE were induced by osmotic and ER stress to an extent that was modulated by the ER-resident molecular chaperone binding protein (BiP) similarly as in soybean. Finally, as putative downstream components of the NRP-mediated cell death signaling, the stress induction of AtNRP2, ANAC036 and gVPE was dependent on the AtNRP1 function. BiP overexpression also conferred tolerance to water stress in Arabidopsis, most likely due to modulation of the drought-induced NRP-mediated cell death response. CONCLUSION: Our results indicated that the NRP-mediated cell death signaling operates in the plant kingdom with conserved regulatory mechanisms and hence may be target for engineering stress tolerance and adaptation in crops.

The Stress-Induced Soybean NAC Transcription Factor GmNAC81 Plays a Positive Role in Developmentally Programmed Leaf Senescence.

The onset of leaf senescence is a highly regulated developmental change that is controlled by both genetics and the environment. Senescence is triggered by massive transcriptional reprogramming, but functional information about its underlying regulatory mechanisms is limited. In the current investigation, we performed a functional analysis of the soybean (Glycine max) osmotic stress- and endoplasmic reticulum (ER) stress-induced NAC transcription factor GmNAC81 during natural leaf senescence using overexpression studies and reverse genetics. GmNAC81-overexpressing lines displayed accelerated flowering and leaf senescence but otherwise developed normally. The precocious leaf senescence of GmNAC81-overexpressing lines was associated with greater Chl loss, faster photosynthetic decay and higher expression of hydrolytic enzyme-encoding GmNAC81 target genes, including the vacuolar processing enzyme (VPE), an executioner of vacuole-triggered programmed cell death (PCD). Conversely, virus-induced gene silencing-mediated silencing of GmNAC81 delayed leaf senescence and was associated with reductions in Chl loss, lipid peroxidation and the expression of GmNAC81 direct targets. Promoter-reporter studies revealed that the expression pattern of GmNAC81 was associated with senescence in soybean leaves. Our data indicate that GmNAC81 is a positive regulator of age-dependent senescence and may integrate osmotic stress- and ER stress-induced PCD responses with natural leaf senescence through the GmNAC81/VPE regulatory circuit.