Restoration of virulence in the attenuated Candid#1 vaccine virus requires reversion at both positions 168 and 427 in the envelope glycoprotein GPC.

Live-attenuated virus vaccines provide long-lived protection against viral disease but carry inherent risks of residual pathogenicity and genetic reversion. The live-attenuated Candid#1 vaccine was developed to protect Argentines against lethal infection by the Argentine hemorrhagic fever arenavirus, Junín virus. Despite its safety and efficacy in Phase III clinical study, the vaccine is not licensed in the US, in part due to concerns regarding the genetic stability of attenuation. Previous studies had identified a single F427I mutation in the transmembrane domain of the Candid#1 envelope glycoprotein GPC as the key determinant of attenuation, as well as the propensity of this mutation to revert upon passage in cell culture and neonatal mice. To ascertain the consequences of this reversion event, we introduced the I427F mutation into recombinant Candid#1 (I427F rCan) and investigated the effects in two validated small-animal models: in mice expressing the essential virus receptor (human transferrin receptor 1; huTfR1) and in the conventional guinea pig model. We report that I427F rCan displays only modest virulence in huTfR1 mice and appears attenuated in guinea pigs. Reversion at another attenuating locus in Candid#1 GPC (T168A) was also examined, and a similar pattern was observed. By contrast, virus bearing both revertant mutations (A168T+I427F rCan) approached the lethal virulence of the pathogenic Romero strain in huTfR1 mice. Virulence was less extreme in guinea pigs. Our findings suggest that genetic stabilization at both positions is required to minimize the likelihood of reversion to virulence in a second-generation Candid#1 vaccine.IMPORTANCELive-attenuated virus vaccines, such as measles/mumps/rubella and oral poliovirus, provide robust protection against disease but carry with them the risk of genetic reversion to the virulent form. Here, we analyze the genetics of reversion in the live-attenuated Candid#1 vaccine that is used to protect against Argentine hemorrhagic fever, an often-lethal disease caused by the Junín arenavirus. In two validated small-animal models, we find that restoration of virulence in recombinant Candid#1 viruses requires back-mutation at two positions specific to the Candid#1 envelope glycoprotein GPC, at positions 168 and 427. Viruses bearing only a single change showed only modest virulence. We discuss strategies to genetically harden Candid#1 GPC against these two reversion events in order to develop a safer second-generation Candid#1 vaccine virus.

Comparative miRNome and transcriptome analyses reveal the expression of novel miRNAs in the panicle of rice implicated in sustained agronomic performance under terminal drought stress.

MAIN CONCLUSION: Differential expression of 128 known and 111 novel miRNAs in the panicle of Nagina 22 under terminal drought stress targeting transcription factors, stress-associated genes, etc., enhances drought tolerance and helps sustain agronomic performance under terminal drought stress. Drought tolerance is a complex multigenic trait, wherein the genes are fine-tuned by coding and non-coding components in mitigating deleterious effects. MicroRNA (miRNA) controls gene expression at post-transcriptional level either by cleaving mRNA (transcript) or by suppressing its translation. miRNAs are known to control developmental processes and abiotic stress tolerance in plants. To identify terminal drought-responsive novel miRNA in contrasting rice cultivars, we constructed small RNA (sRNA) libraries from immature panicles of drought-tolerant rice [Nagina 22 (N 22)] and drought-sensitive (IR 64) cultivars grown under control and terminal drought stress. Our analysis of sRNA-seq data resulted in the identification of 169 known and 148 novel miRNAs in the rice cultivars. Among the novel miRNAs, 68 were up-regulated while 43 were down-regulated in the panicle of N 22 under stress. Interestingly, 31 novel miRNAs up-regulated in N 22 were down-regulated in IR 64, whereas 4 miRNAs down-regulated in N 22 were up-regulated in IR 64 under stress. To detect the effects of miRNA on mRNA expression level, transcriptome analysis was performed, while differential expression of miRNAs and their target genes was validated by RT-qPCR. Targets of the differentially expressed miRNAs include transcription factors and stress-associated genes involved in cellular/metabolic/developmental processes, response to abiotic stress, programmed cell death, photosynthesis, panicle/seed development, and grain yield. Differential expression of the miRNAs could be validated in an independent set of the samples. The findings might be useful in genetic improvement of drought-tolerant rice.

deepHPI: a comprehensive deep learning platform for accurate prediction and visualization of host-pathogen protein-protein interactions.

Host-pathogen protein interactions (HPPIs) play vital roles in many biological processes and are directly involved in infectious diseases. With the outbreak of more frequent pandemics in the last couple of decades, such as the recent outburst of Covid-19 causing millions of deaths, it has become more critical to develop advanced methods to accurately predict pathogen interactions with their respective hosts. During the last decade, experimental methods to identify HPIs have been used to decipher host-pathogen systems with the caveat that those techniques are labor-intensive, expensive and time-consuming. Alternatively, accurate prediction of HPIs can be performed by the use of data-driven machine learning. To provide a more robust and accurate solution for the HPI prediction problem, we have developed a deepHPI tool based on deep learning. The web server delivers four host-pathogen model types: plant-pathogen, human-bacteria, human-virus and animal-pathogen, leveraging its operability to a wide range of analyses and cases of use. The deepHPI web tool is the first to use convolutional neural network models for HPI prediction. These models have been selected based on a comprehensive evaluation of protein features and neural network architectures. The best prediction models have been tested on independent validation datasets, which achieved an overall Matthews correlation coefficient value of 0.87 for animal-pathogen using the combined pseudo-amino acid composition and conjoint triad (PAAC_CT) features, 0.75 for human-bacteria using the combined pseudo-amino acid composition, conjoint triad and normalized Moreau-Broto feature (PAAC_CT_NMBroto), 0.96 for human-virus using PAAC_CT_NMBroto and 0.94 values for plant-pathogen interactions using the combined pseudo-amino acid composition, composition and transition feature (PAAC_CTDC_CTDT). Our server running deepHPI is deployed on a high-performance computing cluster that enables large and multiple user requests, and it provides more information about interactions discovered. It presents an enriched visualization of the resulting host-pathogen networks that is augmented with external links to various protein annotation resources. We believe that the deepHPI web server will be very useful to researchers, particularly those working on infectious diseases. Additionally, many novel and known host-pathogen systems can be further investigated to significantly advance our understanding of complex disease-causing agents. The developed models are established on a web server, which is freely accessible at http://bioinfo.usu.edu/deepHPI/.

deepNEC: a novel alignment-free tool for the identification and classification of nitrogen biochemical network-related enzymes using deep learning.

Nitrogen is essential for life and its transformations are an important part of the global biogeochemical cycle. Being an essential nutrient, nitrogen exists in a range of oxidation states from +5 (nitrate) to -3 (ammonium and amino-nitrogen), and its oxidation and reduction reactions catalyzed by microbial enzymes determine its environmental fate. The functional annotation of the genes encoding the core nitrogen network enzymes has a broad range of applications in metagenomics, agriculture, wastewater treatment and industrial biotechnology. This study developed an alignment-free computational approach to determine the predicted nitrogen biochemical network-related enzymes from the sequence itself. We propose deepNEC, a novel end-to-end feature selection and classification model training approach for nitrogen biochemical network-related enzyme prediction. The algorithm was developed using Deep Learning, a class of machine learning algorithms that uses multiple layers to extract higher-level features from the raw input data. The derived protein sequence is used as an input, extracting sequential and convolutional features from raw encoded protein sequences based on classification rather than traditional alignment-based methods for enzyme prediction. Two large datasets of protein sequences, enzymes and non-enzymes were used to train the models with protein sequence features like amino acid composition, dipeptide composition (DPC), conformation transition and distribution, normalized Moreau-Broto (NMBroto), conjoint and quasi order, etc. The k-fold cross-validation and independent testing were performed to validate our model training. deepNEC uses a four-tier approach for prediction; in the first phase, it will predict a query sequence as enzyme or non-enzyme; in the second phase, it will further predict and classify enzymes into nitrogen biochemical network-related enzymes or non-nitrogen metabolism enzymes; in the third phase, it classifies predicted enzymes into nine nitrogen metabolism classes; and in the fourth phase, it predicts the enzyme commission number out of 20 classes for nitrogen metabolism. Among all, the DPC + NMBroto hybrid feature gave the best prediction performance (accuracy of 96.15% in k-fold training and 93.43% in independent testing) with an Matthews correlation coefficient (0.92 training and 0.87 independent testing) in phase I; phase II (accuracy of 99.71% in k-fold training and 98.30% in independent testing); phase III (overall accuracy of 99.03% in k-fold training and 98.98% in independent testing); phase IV (overall accuracy of 99.05% in k-fold training and 98.18% in independent testing), the DPC feature gave the best prediction performance. We have also implemented a homology-based method to remove false negatives. All the models have been implemented on a web server (prediction tool), which is freely available at http://bioinfo.usu.edu/deepNEC/.

In silico prediction of host-pathogen protein interactions in melioidosis pathogen Burkholderia pseudomallei and human reveals novel virulence factors and their targets.

The aerobic, Gram-negative motile bacillus, Burkholderia pseudomallei is a facultative intracellular bacterium causing melioidosis, a critical disease of public health importance, which is widely endemic in the tropics and subtropical regions of the world. Melioidosis is associated with high case fatality rates in animals and humans; even with treatment, its mortality is 20-50%. It also infects plants and is designated as a biothreat agent. B. pseudomallei is pathogenic due to its ability to invade, resist factors in serum and survive intracellularly. Despite its importance, to date only a few effector proteins have been functionally characterized, and there is not much information regarding the host-pathogen protein-protein interactions (PPI) of this system, which are important to studying infection mechanisms and thereby develop prevention measures. We explored two computational approaches, the homology-based interolog and the domain-based method, to predict genome-scale host-pathogen interactions (HPIs) between two different strains of B. pseudomallei (prototypical, and highly virulent) and human. In total, 76 335 common HPIs (between the two strains) were predicted involving 8264 human and 1753 B. pseudomallei proteins. Among the unique PPIs, 14 131 non-redundant HPIs were found to be unique between the prototypical strain and human, compared to 3043 non-redundant HPIs between the highly virulent strain and human. The protein hubs analysis showed that most B. pseudomallei proteins formed a hub with human dnaK complex proteins associated with tuberculosis, a disease similar in symptoms to melioidosis. In addition, drug-binding and carbohydrate-binding mechanisms were found overrepresented within the host-pathogen network, and metabolic pathways were frequently activated according to the pathway enrichment. Subcellular localization analysis showed that most of the pathogen proteins are targeting human proteins inside cytoplasm and nucleus. We also discovered the host targets of the drug-related pathogen proteins and proteins that form T3SS and T6SS in B. pseudomallei. Additionally, a comparison between the unique PPI patterns present in the prototypical and highly virulent strains was performed. The current study is the first report on developing a genome-scale host-pathogen protein interaction networks between the human and B. pseudomallei, a critical biothreat agent. We have identified novel virulence factors and their interacting partners in the human proteome. These PPIs can be further validated by high-throughput experiments and may give new insights on how B. pseudomallei interacts with its host, which will help medical researchers in developing better prevention measures.

Transcriptome and Physio-Biochemical Profiling Reveals Differential Responses of Rice Cultivars at Reproductive-Stage Drought Stress.

Drought stress severely affects the growth and development of rice, especially at the reproductive stage, which results in disturbed metabolic processes, reduced seed-set/grain filling, deteriorated grain quality, declined productivity, and lower yield. Despite the recent advances in understanding the responses of rice to drought stress, there is a need to comprehensively integrate the morpho-physio-biochemical studies with the molecular responses/differential expression of genes and decipher the underlying pathways that regulate the adaptability of rice at various drought-sensitive growth stages. Our comparative analysis of immature panicle from a drought-tolerant (Nagina 22) and a drought-sensitive (IR 64) rice cultivar grown under control (well-watered) and water-deficit/drought stress (treatment, imposed at the reproductive stage) conditions unraveled some novel stress-responsive genes/pathways responsible for reproductive-stage drought stress tolerance. The results revealed a more important role of upregulated (6706) genes in the panicle of N 22 at reproductive-stage drought stress compared to that (5590) in IR 64. Functional enrichment and MapMan analyses revealed that majority of the DEGs were associated with the phytohormone, redox signalling/homeostasis, secondary metabolite, and transcription factor-mediated mitigation of the adverse effects of drought stress in N 22. The upregulated expression of the genes associated with starch/sucrose metabolism, secondary metabolites synthesis, transcription factors, glutathione, linoleic acid, and phenylalanine metabolism in N 22 was significantly more than that in the panicle of IR 64. Compared to IR 64, 2743 genes were upregulated in N 22 under control conditions, which further increased (4666) under drought stress in panicle of the tolerant cultivar. Interestingly, we observed 6706 genes to be upregulated in the panicle of N 22 over IR 64 under drought and 5814 genes get downregulated in the panicle of N 22 over IR 64 under the stress. In addition, RT-qPCR analysis confirmed differential expression patterns of the DEGs. These genes/pathways associated with the reproductive-stage drought tolerance might provide an important source of molecular markers for genetic manipulation of rice for enhanced drought tolerance.

PredHPI: an integrated web server platform for the detection and visualization of host-pathogen interactions using sequence-based methods.

MOTIVATION: Understanding the mechanisms underlying infectious diseases is fundamental to develop prevention strategies. Host-pathogen interactions (HPIs) are actively studied worldwide to find potential genomic targets for the development of novel drugs, vaccines and other therapeutics. Determining which proteins are involved in the interaction system behind an infectious process is the first step to develop an efficient disease control strategy. Very few computational methods have been implemented as web services to infer novel HPIs, and there is not a single framework which combines several of those approaches to produce and visualize a comprehensive analysis of HPIs. RESULTS: Here, we introduce PredHPI, a powerful framework that integrates both the detection and visualization of interaction networks in a single web service, facilitating the apprehension of model and non-model host-pathogen systems to aid the biologists in building hypotheses and designing appropriate experiments. PredHPI is built on high-performance computing resources on the backend capable of handling proteome-scale sequence data from both the host as well as pathogen. Data are displayed in an information-rich and interactive visualization, which can be further customized with user-defined layouts. We believe PredHPI will serve as an invaluable resource to diverse experimental biologists and will help advance the research in the understanding of complex infectious diseases. AVAILABILITY AND IMPLEMENTATION: PredHPI tool is freely available at http://bioinfo.usu.edu/PredHPI/. SUPPLEMENTARY INFORMATION: Sup plementary data are available at Bioinformatics online.

Deciphering the Host-Pathogen Interactome of the Wheat-Common Bunt System: A Step towards Enhanced Resilience in Next Generation Wheat.

Common bunt, caused by two fungal species, Tilletia caries and Tilletia laevis, is one of the most potentially destructive diseases of wheat. Despite the availability of synthetic chemicals against the disease, organic agriculture relies greatly on resistant cultivars. Using two computational approaches-interolog and domain-based methods-a total of approximately 58 M and 56 M probable PPIs were predicted in T. aestivum-T. caries and T. aestivum-T. laevis interactomes, respectively. We also identified 648 and 575 effectors in the interactions from T. caries and T. laevis, respectively. The major host hubs belonged to the serine/threonine protein kinase, hsp70, and mitogen-activated protein kinase families, which are actively involved in plant immune signaling during stress conditions. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the host proteins revealed significant GO terms (O-methyltransferase activity, regulation of response to stimulus, and plastid envelope) and pathways (NF-kappa B signaling and the MAPK signaling pathway) related to plant defense against pathogens. Subcellular localization suggested that most of the pathogen proteins target the host in the plastid. Furthermore, a comparison between unique T. caries and T. laevis proteins was carried out. We also identified novel host candidates that are resistant to disease. Additionally, the host proteins that serve as transcription factors were also predicted.

TritiKBdb: A Functional Annotation Resource for Deciphering the Complete Interaction Networks in Wheat-Karnal Bunt Pathosystem.

The study of molecular interactions, especially the inter-species protein-protein interactions, is crucial for understanding the disease infection mechanism in plants. These interactions play an important role in disease infection and host immune responses against pathogen attack. Among various critical fungal diseases, the incidences of Karnal bunt (Tilletia indica) around the world have hindered the export of the crops such as wheat from infected regions, thus causing substantial economic losses. Due to sparse information on T. indica, limited insight is available with regard to gaining in-depth knowledge of the interaction mechanisms between the host and pathogen proteins during the disease infection process. Here, we report the development of a comprehensive database and webserver, TritiKBdb, that implements various tools to study the protein-protein interactions in the Triticum species-Tilletia indica pathosystem. The novel 'interactomics' tool allows the user to visualize/compare the networks of the predicted interactions in an enriched manner. TritiKBdb is a user-friendly database that provides functional annotations such as subcellular localization, available domains, KEGG pathways, and GO terms of the host and pathogen proteins. Additionally, the information about the host and pathogen proteins that serve as transcription factors and effectors, respectively, is also made available. We believe that TritiKBdb will serve as a beneficial resource for the research community, and aid the community in better understanding the infection mechanisms of Karnal bunt and its interactions with wheat. The database is freely available for public use at http://bioinfo.usu.edu/tritikbdb/.

LegumeSSRdb: A Comprehensive Microsatellite Marker Database of Legumes for Germplasm Characterization and Crop Improvement.

Microsatellites, or simple sequence repeats (SSRs), are polymorphic loci that play a major role as molecular markers for genome analysis and plant breeding. The legume SSR database is a webserver which contains simple sequence repeats (SSRs) from genomes of 13 legume species. A total of 3,706,276 SSRs are present in the database, 698,509 of which are genic SSRs, and 3,007,772 are non-genic. This webserver is an integrated tool to perform end-to-end marker selection right from generating SSRs to designing and validating primers, visualizing the results and blasting the genomic sequences at one place without juggling between several resources. The user-friendly web interface allows users to browse SSRs based on the genomic region, chromosome, motif type, repeat motif sequence, frequency of motif, and advanced searches allow users to search based on chromosome location range and length of SSR. Users can give their desired flanking region around repeat and obtain the sequence, they can explore the genes in which the SSRs are present or the genes between which the SSRs are bound design custom primers, and perform in silico validation using PCR. An SSR prediction pipeline is implemented where the user can submit their genomic sequence to generate SSRs. This webserver will be frequently updated with more species, in time. We believe that legumeSSRdb would be a useful resource for marker-assisted selection and mapping quantitative trait loci (QTLs) to practice genomic selection and improve crop health. The database can be freely accessed at http://bioinfo.usu.edu/legumeSSRdb/.

alfaNET: A Database of Alfalfa-Bacterial Stem Blight Protein-Protein Interactions Revealing the Molecular Features of the Disease-causing Bacteria.

Alfalfa has emerged as one of the most important forage crops, owing to its wide adaptation and high biomass production worldwide. In the last decade, the emergence of bacterial stem blight (caused by Pseudomonas syringae pv. syringae ALF3) in alfalfa has caused around 50% yield losses in the United States. Studies are being conducted to decipher the roles of the key genes and pathways regulating the disease, but due to the sparse knowledge about the infection mechanisms of Pseudomonas, the development of resistant cultivars is hampered. The database alfaNET is an attempt to assist researchers by providing comprehensive Pseudomonas proteome annotations, as well as a host-pathogen interactome tool, which predicts the interactions between host and pathogen based on orthology. alfaNET is a user-friendly and efficient tool and includes other features such as subcellular localization annotations of pathogen proteins, gene ontology (GO) annotations, network visualization, and effector protein prediction. Users can also browse and search the database using particular keywords or proteins with a specific length. Additionally, the BLAST search tool enables the user to perform a homology sequence search against the alfalfa and Pseudomonas proteomes. With the successful implementation of these attributes, alfaNET will be a beneficial resource to the research community engaged in implementing molecular strategies to mitigate the disease. alfaNET is freely available for public use at http://bioinfo.usu.edu/alfanet/.

GreeningDB: A Database of Host-Pathogen Protein-Protein Interactions and Annotation Features of the Bacteria Causing Huanglongbing HLB Disease.

The Citrus genus comprises some of the most important and commonly cultivated fruit plants. Within the last decade, citrus greening disease (also known as huanglongbing or HLB) has emerged as the biggest threat for the citrus industry. This disease does not have a cure yet and, thus, many efforts have been made to find a solution to this devastating condition. There are challenges in the generation of high-yield resistant cultivars, in part due to the limited and sparse knowledge about the mechanisms that are used by the Liberibacter bacteria to proliferate the infection in Citrus plants. Here, we present GreeningDB, a database implemented to provide the annotation of Liberibacter proteomes, as well as the host-pathogen comparactomics tool, a novel platform to compare the predicted interactomes of two HLB host-pathogen systems. GreeningDB is built to deliver a user-friendly interface, including network visualization and links to other resources. We hope that by providing these characteristics, GreeningDB can become a central resource to retrieve HLB-related protein annotations, and thus, aid the community that is pursuing the development of molecular-based strategies to mitigate this disease's impact. The database is freely available at http://bioinfo.usu.edu/GreeningDB/ (accessed on 11 August 2021).

Insights into the Host Specificity of a New Oomycete Root Pathogen, Pythium brassicum P1: Whole Genome Sequencing and Comparative Analysis Reveals Contracted Regulation of Metabolism, Protein Families, and Distinct Pathogenicity Repertoire.

Pythium brassicum P1 Stanghellini, Mohammadi, Förster, and Adaskaveg is an oomycete root pathogen that has recently been characterized. It only attacks plant species belonging to Brassicaceae family, causing root necrosis, stunting, and yield loss. Since P. brassicum P1 is limited in its host range, this prompted us to sequence its whole genome and compare it to those of broad host range Pythium spp. such as P. aphanidermatum and P. ultimum var. ultimum. A genomic DNA library was constructed with a total of 374 million reads. The sequencing data were assembled using SOAPdenovo2, yielding a total genome size of 50.3 Mb contained in 5434 scaffolds, N50 of 30.2 Kb, 61.2% G+C content, and 13,232 putative protein-coding genes. Pythium brassicum P1 had 175 species-specific gene families, which is slightly below the normal average. Like P. ultimum, P. brassicum P1 genome did not encode any classical RxLR effectors or cutinases, suggesting a significant difference in virulence mechanisms compared to other oomycetes. Pythium brassicum P1 had a much smaller proportions of the YxSL sequence motif in both secreted and non-secreted proteins, relative to other Pythium species. Similarly, P. brassicum P1 had the fewest Crinkler (CRN) effectors of all the Pythium species. There were 633 proteins predicted to be secreted in the P. brassicum P1 genome, which is, again, slightly below average among Pythium genomes. Pythium brassicum P1 had only one cadherin gene with calcium ion-binding LDRE and DxND motifs, compared to Pythium ultimum having four copies. Pythium brassicum P1 had a reduced number of proteins falling under carbohydrate binding module and hydrolytic enzymes. Pythium brassicum P1 had a reduced complement of cellulase and pectinase genes in contrast to P. ultimum and was deficient in xylan degrading enzymes. The contraction in ABC transporter families in P. brassicum P1 is suggested to be the result of a lack of diversity in nutrient uptake and therefore host range.

Molecular characterization and expression analysis of the Na+/H+ exchanger gene family in Medicago truncatula.

One important mechanism plants use to cope with salinity is keeping the cytosolic Na+ concentration low by sequestering Na+ in vacuoles, a process facilitated by Na+/H+ exchangers (NHX). There are eight NHX genes (NHX1 through NHX8) identified and characterized in Arabidopsis thaliana. Bioinformatics analyses of the known Arabidopsis genes enabled us to identify six Medicago truncatula NHX genes (MtNHX1, MtNHX2, MtNHX3, MtNHX4, MtNHX6, and MtNHX7). Twelve transmembrane domains and an amiloride binding site were conserved in five out of six MtNHX proteins. Phylogenetic analysis involving A. thaliana, Glycine max, Phaseolus vulgaris, and M. truncatula revealed that each individual MtNHX class (class I: MtNHX1 through 4; class II: MtNHX6; class III: MtNHX7) falls under a separate clade. In a salinity-stress experiment, M. truncatula exhibited ~ 20% reduction in biomass. In the salinity treatment, sodium contents increased by 178 and 75% in leaves and roots, respectively, and Cl- contents increased by 152 and 162%, respectively. Na+ exclusion may be responsible for the relatively smaller increase in Na+ concentration in roots under salt stress as compared to Cl-. Decline in tissue K+ concentration under salinity was not surprising as some antiporters play an important role in transporting both Na+ and K + . MtNHX1, MtNHX6, and MtNHX7 display high expression in roots and leaves. MtNHX3, MtNHX6, and MtNHX7 were induced in roots under salinity stress. Expression analysis results indicate that sequestering Na+ into vacuoles may not be the principal component trait of the salt tolerance mechanism in M. truncatula and other component traits may be pivotal.

Tris(1,3-dichloro-2-propyl)phosphate Induces Genome-Wide Hypomethylation within Early Zebrafish Embryos.

Tris(1,3-dichloro-2-propyl)phosphate (TDCIPP) is a high-production volume organophosphate-based plasticizer and flame retardant widely used within the United States. Using zebrafish as a model, the objectives of this study were to determine whether (1) TDCIPP inhibits DNA methyltransferase (DNMT) within embryonic nuclear extracts; (2) uptake of TDCIPP from 0.75 h postfertilization (hpf, 2-cell) to 2 hpf (64-cell) or 6 hpf (shield stage) leads to impacts on the early embryonic DNA methylome; and (3) TDCIPP-induced impacts on cytosine methylation are localized to CpG islands within intergenic regions. Within this study, 5-azacytidine (5-azaC, a DNMT inhibitor) was used as a positive control. Although 5-azaC significantly inhibited zebrafish DNMT, TDCIPP did not affect DNMT activity in vitro at concentrations as high as 500 µM. However, rapid embryonic uptake of 5-azaC and TDCIPP from 0.75 to 2 hpf resulted in chemical- and chromosome-specific alterations in cytosine methylation at 2 hpf. Moreover, TDCIPP exposure predominantly resulted in hypomethylation of positions outside of CpG islands and within intragenic (exon) regions of the zebrafish genome. Overall, these findings provide the foundation for monitoring DNA methylation dynamics within zebrafish as well as identifying potential associations among TDCIPP exposure, adverse health outcomes, and DNA methylation status within human populations.

Predicting genome-scale Arabidopsis-Pseudomonas syringae interactome using domain and interolog-based approaches.

BACKGROUND: Every year pathogenic organisms cause billions of dollars' worth damage to crops and livestock. In agriculture, study of plant-microbe interactions is demanding a special attention to develop management strategies for the destructive pathogen induced diseases that cause huge crop losses every year worldwide. Pseudomonas syringae is a major bacterial leaf pathogen that causes diseases in a wide range of plant species. Among its various strains, pathovar tomato strain DC3000 (PstDC3000) is asserted to infect the plant host Arabidopsis thaliana and thus, has been accepted as a model system for experimental characterization of the molecular dynamics of plant-pathogen interactions. Protein-protein interactions (PPIs) play a critical role in initiating pathogenesis and maintaining infection. Understanding the PPI network between a host and pathogen is a critical step for studying the molecular basis of pathogenesis. The experimental study of PPIs at a large scale is very scarce and also the high throughput experimental results show high false positive rate. Hence, there is a need for developing efficient computational models to predict the interaction between host and pathogen in a genome scale, and find novel candidate effectors and/or their targets. RESULTS: In this study, we used two computational approaches, the interolog and the domain-based to predict the interactions between Arabidopsis and PstDC3000 in genome scale. The interolog method relies on protein sequence similarity to conduct the PPI prediction. A Pseudomonas protein and an Arabidopsis protein are predicted to interact with each other if an experimentally verified interaction exists between their respective homologous proteins in another organism. The domain-based method uses domain interaction information, which is derived from known protein 3D structures, to infer the potential PPIs. If a Pseudomonas and an Arabidopsis protein contain an interacting domain pair, one can expect the two proteins to interact with each other. The interolog-based method predicts ~0.79M PPIs involving around 7700 Arabidopsis and 1068 Pseudomonas proteins in the full genome. The domain-based method predicts 85650 PPIs comprising 11432 Arabidopsis and 887 Pseudomonas proteins. Further, around 11000 PPIs have been identified as interacting from both the methods as a consensus. CONCLUSION: The present work predicts the protein-protein interaction network between Arabidopsis thaliana and Pseudomonas syringae pv. tomato DC3000 in a genome wide scale with a high confidence. Although the predicted PPIs may contain some false positives, the computational methods provide reasonable amount of interactions which can be further validated by high throughput experiments. This can be a useful resource to the plant community to characterize the host-pathogen interaction in Arabidopsis and Pseudomonas system. Further, these prediction models can be applied to the agriculturally relevant crops.

LacSubPred: predicting subtypes of Laccases, an important lignin metabolism-related enzyme class, using in silico approaches.

BACKGROUND: Laccases (E.C. 1.10.3.2) are multi-copper oxidases that have gained importance in many industries such as biofuels, pulp production, textile dye bleaching, bioremediation, and food production. Their usefulness stems from the ability to act on a diverse range of phenolic compounds such as o-/p-quinols, aminophenols, polyphenols, polyamines, aryl diamines, and aromatic thiols. Despite acting on a wide range of compounds as a family, individual Laccases often exhibit distinctive and varied substrate ranges. This is likely due to Laccases involvement in many metabolic roles across diverse taxa. Classification systems for multi-copper oxidases have been developed using multiple sequence alignments, however, these systems seem to largely follow species taxonomy rather than substrate ranges, enzyme properties, or specific function. It has been suggested that the roles and substrates of various Laccases are related to their optimal pH. This is consistent with the observation that fungal Laccases usually prefer acidic conditions, whereas plant and bacterial Laccases prefer basic conditions. Based on these observations, we hypothesize that a descriptor-based unsupervised learning system could generate homology independent classification system for better describing the functional properties of Laccases. RESULTS: In this study, we first utilized unsupervised learning approach to develop a novel homology independent Laccase classification system. From the descriptors considered, physicochemical properties showed the best performance. Physicochemical properties divided the Laccases into twelve subtypes. Analysis of the clusters using a t-test revealed that the majority of the physicochemical descriptors had statistically significant differences between the classes. Feature selection identified the most important features as negatively charges residues, the peptide isoelectric point, and acidic or amidic residues. Secondly, to allow for classification of new Laccases, a supervised learning system was developed from the clusters. The models showed high performance with an overall accuracy of 99.03%, error of 0.49%, MCC of 0.9367, precision of 94.20%, sensitivity of 94.20%, and specificity of 99.47% in a 5-fold cross-validation test. In an independent test, our models still provide a high accuracy of 97.98%, error rate of 1.02%, MCC of 0.8678, precision of 87.88%, sensitivity of 87.88% and specificity of 98.90%. CONCLUSION: This study provides a useful classification system for better understanding of Laccases from their physicochemical properties perspective. We also developed a publically available web tool for the characterization of Laccase protein sequences (http://lacsubpred.bioinfo.ucr.edu/). Finally, the programs used in the study are made available for researchers interested in applying the system to other enzyme classes (https://github.com/tweirick/SubClPred).

Identification and characterization of plastid-type proteins from sequence-attributed features using machine learning.

BACKGROUND: Plastids are an important component of plant cells, being the site of manufacture and storage of chemical compounds used by the cell, and contain pigments such as those used in photosynthesis, starch synthesis/storage, cell color etc. They are essential organelles of the plant cell, also present in algae. Recent advances in genomic technology and sequencing efforts is generating a huge amount of DNA sequence data every day. The predicted proteome of these genomes needs annotation at a faster pace. In view of this, one such annotation need is to develop an automated system that can distinguish between plastid and non-plastid proteins accurately, and further classify plastid-types based on their functionality. We compared the amino acid compositions of plastid proteins with those of non-plastid ones and found significant differences, which were used as a basis to develop various feature-based prediction models using similarity-search and machine learning. RESULTS: In this study, we developed separate Support Vector Machine (SVM) trained classifiers for characterizing the plastids in two steps: first distinguishing the plastid vs. non-plastid proteins, and then classifying the identified plastids into their various types based on their function (chloroplast, chromoplast, etioplast, and amyloplast). Five diverse protein features: amino acid composition, dipeptide composition, the pseudo amino acid composition, N(terminal)-Center-C(terminal) composition and the protein physicochemical properties are used to develop SVM models. Overall, the dipeptide composition-based module shows the best performance with an accuracy of 86.80% and Matthews Correlation Coefficient (MCC) of 0.74 in phase-I and 78.60% with a MCC of 0.44 in phase-II. On independent test data, this model also performs better with an overall accuracy of 76.58% and 74.97% in phase-I and phase-II, respectively. The similarity-based PSI-BLAST module shows very low performance with about 50% prediction accuracy for distinguishing plastid vs. non-plastids and only 20% in classifying various plastid-types, indicating the need and importance of machine learning algorithms. CONCLUSION: The current work is a first attempt to develop a methodology for classifying various plastid-type proteins. The prediction modules have also been made available as a web tool, PLpred available at http://bioinfo.okstate.edu/PLpred/ for real time identification/characterization. We believe this tool will be very useful in the functional annotation of various genomes.

PHDcleav: a SVM based method for predicting human Dicer cleavage sites using sequence and secondary structure of miRNA precursors.

BACKGROUND: Dicer, an RNase III enzyme, plays a vital role in the processing of pre-miRNAs for generating the miRNAs. The structural and sequence features on pre-miRNA which can facilitate position and efficiency of cleavage are not well known. A precise cleavage by Dicer is crucial because an inaccurate processing can produce miRNA with different seed regions which can alter the repertoire of target genes. RESULTS: In this study, a novel method has been developed to predict Dicer cleavage sites on pre-miRNAs using Support Vector Machine. We used the dataset of experimentally validated human miRNA hairpins from miRBase, and extracted fourteen nucleotides around Dicer cleavage sites. We developed number of models using various types of features and achieved maximum accuracy of 66% using binary profile of nucleotide sequence taken from 5p arm of hairpin. The prediction performance of Dicer cleavage site improved significantly from 66% to 86% when we integrated secondary structure information. This indicates that secondary structure plays an important role in the selection of cleavage site. All models were trained and tested on 555 experimentally validated cleavage sites and evaluated using 5-fold cross validation technique. In addition, the performance was also evaluated on an independent testing dataset that achieved an accuracy of ~82%. CONCLUSION: Based on this study, we developed a webserver PHDcleav (http://www.imtech.res.in/raghava/phdcleav/) to predict Dicer cleavage sites in pre-miRNA. This tool can be used to investigate functional consequences of genetic variations/SNPs in miRNA on Dicer cleavage site, and gene silencing. Moreover, it would also be useful in the discovery of miRNAs in human genome and design of Dicer specific pre-miRNAs for potent gene silencing.

Proceedings of the 2013 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference.

The tenth annual conference of the MidSouth Computational Biology and Bioinformatics Society (MCBIOS 2013), "The 10th Anniversary in a Decade of Change: Discovery in a Sea of Data", took place at the Stoney Creek Inn & Conference Center in Columbia, Missouri on April 5-6, 2013. This year's Conference Chairs were Gordon Springer and Chi-Ren Shyu from the University of Missouri and Edward Perkins from the US Army Corps of Engineers Engineering Research and Development Center, who is also the current MCBIOS President (2012-3). There were 151 registrants and a total of 111 abstracts (51 oral presentations and 60 poster session abstracts).