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.

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 Genome-Wide Analysis of MicroRNAs and Their Target Genes in Roots of Contrasting Indica Rice Cultivars under Reproductive-Stage Drought.

Recurrent occurrence of drought stress in varying intensity has become a common phenomenon in the present era of global climate change, which not only causes severe yield losses but also challenges the cultivation of rice. This raises serious concerns for sustainable food production and global food security. The root of a plant is primarily responsible to perceive drought stress and acquire sufficient water for the survival/optimal growth of the plant under extreme climatic conditions. Earlier studies reported the involvement/important roles of microRNAs (miRNAs) in plants' responses to environmental/abiotic stresses. A number (738) of miRNAs is known to be expressed in different tissues under varying environmental conditions in rice, but our understanding of the role, mode of action, and target genes of the miRNAs are still elusive. Using contrasting rice [IR-64 (reproductive-stage drought sensitive) and N-22 (drought-tolerant)] cultivars, imposed with terminal (reproductive-stage) drought stress, we demonstrate differential expression of 270 known and 91 novel miRNAs in roots of the contrasting rice cultivars in response to the stress. Among the known miRNAs, osamiR812, osamiR166, osamiR156, osamiR167, and osamiR396 were the most differentially expressed miRNAs between the rice cultivars. In the root of N-22, 18 known and 12 novel miRNAs were observed to be exclusively expressed, while only two known (zero novels) miRNAs were exclusively expressed in the roots of IR-64. The majority of the target gene(s) of the miRNAs were drought-responsive transcription factors playing important roles in flower, grain development, auxin signaling, root development, and phytohormone-crosstalk. The novel miRNAs identified in this study may serve as good candidates for the genetic improvement of rice for terminal drought stress towards developing climate-smart rice for sustainable food production.

ranchSATdb: A Genome-Wide Simple Sequence Repeat (SSR) Markers Database of Livestock Species for Mutant Germplasm Characterization and Improving Farm Animal Health.

Microsatellites, also known as simple sequence repeats (SSRs), are polymorphic loci that play an important role in genome research, animal breeding, and disease control. Ranch animals are important components of agricultural landscape. The ranch animal SSR database, ranchSATdb, is a web resource which contains 15,520,263 putative SSR markers. This database provides a comprehensive tool for performing end-to-end marker selection, from SSRs prediction to generating marker primers and their cross-species feasibility, visualization of the resulting markers, and finding similarities between the genomic repeat sequences all in one place without the need to switch between other resources. The user-friendly online interface allows users to browse SSRs by genomic coordinates, repeat motif sequence, chromosome, motif type, motif frequency, and functional annotation. Users may enter their preferred flanking area around the repeat to retrieve the nucleotide sequence, they can investigate SSRs present in the genic or the genes between SSRs, they can generate custom primers, and they can also execute in silico validation of primers using electronic PCR. For customized sequences, an SSR prediction pipeline called miSATminer is also built. New species will be added to this website's database on a regular basis throughout time. To improve animal health via genomic selection, we hope that ranchSATdb will be a useful tool for mapping quantitative trait loci (QTLs) and marker-assisted selection. The web-resource is freely accessible at https://bioinfo.usu.edu/ranchSATdb/.

Deciphering the complete human-monkeypox virus interactome: Identifying immune responses and potential drug targets.

Monkeypox virus (MPXV) is a dsDNA virus, belonging to Poxviridae family. The outbreak of monkeypox disease in humans is critical in European and Western countries, owing to its origin in African regions. The highest number of cases of the disease were found in the United States, followed by Spain and Brazil. Understanding the complete infection mechanism of diverse MPXV strains and their interaction with humans is important for therapeutic drug development, and to avoid any future epidemics. Using computational systems biology, we deciphered the genome-wide protein-protein interactions (PPIs) between 22 MPXV strains and human proteome. Based on phylogenomics and disease severity, 3 different strains of MPXV: Zaire-96-I-16, MPXV-UK_P2, and MPXV_USA_2022_MA001 were selected for comparative functional analysis of the proteins involved in the interactions. On an average, we predicted around 92,880 non-redundant PPIs between human and MPXV proteomes, involving 8014 host and 116 pathogen proteins from the 3 strains. The gene ontology (GO) enrichment analysis revealed 10,624 common GO terms in which the host proteins of 3 strains were highly enriched. These include significant GO terms such as platelet activation (GO:0030168), GABA-A receptor complex (GO:1902711), and metalloendopeptidase activity (GO:0004222). The host proteins were also significantly enriched in calcium signaling pathway (hsa04020), MAPK signaling pathway (hsa04010), and inflammatory mediator regulation of TRP channels (hsa04750). These significantly enriched GO terms and KEGG pathways are known to be implicated in immunomodulatory and therapeutic role in humans during viral infection. The protein hubs analysis revealed that most of the MPXV proteins form hubs with the protein kinases and AGC kinase C-terminal domains. Furthermore, subcellular localization revealed that most of the human proteins were localized in cytoplasm (29.22%) and nucleus (26.79%). A few drugs including Fostamatinib, Tamoxifen and others were identified as potential drug candidates against the monkeypox virus disease. This study reports the genome-scale PPIs elucidation in human-monkeypox virus pathosystem, thus facilitating the research community with functional insights into the monkeypox disease infection mechanism and augment the drug development.

HuCoPIA: An Atlas of Human vs. SARS-CoV-2 Interactome and the Comparative Analysis with Other Coronaviridae Family Viruses.

SARS-CoV-2, a novel betacoronavirus strain, has caused a pandemic that has claimed the lives of nearly 6.7M people worldwide. Vaccines and medicines are being developed around the world to reduce the disease spread, fatality rates, and control the new variants. Understanding the protein-protein interaction mechanism of SARS-CoV-2 in humans, and their comparison with the previous SARS-CoV and MERS strains, is crucial for these efforts. These interactions might be used to assess vaccination effectiveness, diagnose exposure, and produce effective biotherapeutics. Here, we present the HuCoPIA database, which contains approximately 100,000 protein-protein interactions between humans and three strains (SARS-CoV-2, SARS-CoV, and MERS) of betacoronavirus. The interactions in the database are divided into common interactions between all three strains and those unique to each strain. It also contains relevant functional annotation information of human proteins. The HuCoPIA database contains SARS-CoV-2 (41,173), SARS-CoV (31,997), and MERS (26,862) interactions, with functional annotation of human proteins like subcellular localization, tissue-expression, KEGG pathways, and Gene ontology information. We believe HuCoPIA will serve as an invaluable resource to diverse experimental biologists, and will help to advance the research in better understanding the mechanism of betacoronaviruses.

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.

ProFeatX: A parallelized protein feature extraction suite for machine learning.

Machine learning algorithms have been successfully applied in proteomics, genomics and transcriptomics. and have helped the biological community to answer complex questions. However, most machine learning methods require lots of data, with every data point having the same vector size. The biological sequence data, such as proteins, are amino acid sequences of variable length, which makes it essential to extract a definite number of features from all the proteins for them to be used as input into machine learning models. There are numerous methods to achieve this, but only several tools let researchers encode their proteins using multiple schemes without having to use different programs or, in many cases, code these algorithms themselves, or even come up with new algorithms. In this work, we created ProFeatX, a tool that contains 50 encodings to extract protein features in an efficient and fast way supporting desktop as well as high-performance computing environment. It can also encode concatenated features for protein-protein interactions. The tool has an easy-to-use web interface, allowing non-experts to use feature extraction techniques, as well as a stand-alone version for advanced users. ProFeatX is implemented in C++ and available on GitHub at https://github.com/usubioinfo/profeatx. The web server is available at http://bioinfo.usu.edu/profeatx/.

Heat and drought induced transcriptomic changes in barley varieties with contrasting stress response phenotypes.

Drought and heat stress substantially impact plant growth and productivity. When subjected to drought or heat stress, plants exhibit reduction in growth resulting in yield losses. The occurrence of these two stresses together intensifies their negative effects. Unraveling the molecular changes in response to combined abiotic stress is essential to breed climate-resilient crops. In this study, transcriptome profiles were compared between stress-tolerant (Otis), and stress-sensitive (Golden Promise) barley genotypes subjected to drought, heat, and combined heat and drought stress for five days during heading stage. The major differences that emerged from the transcriptome analysis were the overall number of differentially expressed genes was relatively higher in Golden Promise (GP) compared to Otis. The differential expression of more than 900 transcription factors in GP and Otis may aid this transcriptional reprogramming in response to abiotic stress. Secondly, combined heat and water deficit stress results in a unique and massive transcriptomic response that cannot be predicted from individual stress responses. Enrichment analyses of gene ontology terms revealed unique and stress type-specific adjustments of gene expression. Weighted Gene Co-expression Network Analysis identified genes associated with RNA metabolism and Hsp70 chaperone components as hub genes that can be useful for engineering tolerance to multiple abiotic stresses. Comparison of the transcriptomes of unstressed Otis and GP plants identified several genes associated with biosynthesis of antioxidants and osmolytes were higher in the former that maybe providing innate tolerance capabilities to effectively combat hostile conditions. Lines with different repertoire of innate tolerance mechanisms can be effectively leveraged in breeding programs for developing climate-resilient barley varieties with superior end-use traits.

TRustDB: A comprehensive bioinformatics resource for understanding the complete Wheat-Stem rust host-pathogen interactome.

The increasing infectious diseases in wheat immensely reduce crop yield and quality, thus affecting global wheat production. The evolution in phytopathogens hinders the understanding of the disease infection mechanisms. TRustDB is an open-access, comprehensive database that is specifically focused on the disease stem rust (also known as black rust) in Triticum aestivum, which is caused by the fungal pathogen Puccinia graminis (Pgt), strains 'Ug99' and '21-0'. The database aims at a broader focus of providing the researchers with comprehensive tools to predict the protein-protein interactions and avail the functional annotations of the proteins involved in the interactions that cause the disease. The network of the predicted interactome can also be visualized on the browser. Various modules for the functional annotations of the host and pathogen proteins such as subcellular localization, functional domains, gene ontology annotations, pathogen orthologs and effector proteins have been implemented. The host proteins that serve as transcription factors, along with the respective Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways are also available, which further enhance the understanding of the disease infection mechanisms and the defense responses of the host. The database is also linked with several other databases such as InterPro, KEGG pathways, Ensembl and National Center for Biotechnology Information (NCBI). TRustDB has a user-friendly web interface, which can be accessed through . Database URL http://bioinfo.usu.edu/trustdb/.

Deciphering the Crosstalk Mechanisms of Wheat-Stem Rust Pathosystem: Genome-Scale Prediction Unravels Novel Host Targets.

Triticum aestivum (wheat), a major staple food grain, is affected by various biotic stresses. Among these, fungal diseases cause about 15-20% of yield loss, worldwide. In this study, we performed a comparative analysis of protein-protein interactions between two Puccinia graminis races (Pgt 21-0 and Pgt Ug99) that cause stem (black) rust in wheat. The available molecular techniques to study the host-pathogen interaction mechanisms are expensive and labor-intensive. We implemented two computational approaches (interolog and domain-based) for the prediction of PPIs and performed various functional analysis to determine the significant differences between the two pathogen races. The analysis revealed that T. aestivum-Pgt 21-0 and T. aestivum-Pgt Ug99 interactomes consisted of ∼90M and ∼56M putative PPIs, respectively. In the predicted PPIs, we identified 115 Pgt 21-0 and 34 Pgt Ug99 potential effectors that were highly involved in pathogen virulence and development. Functional enrichment analysis of the host proteins revealed significant GO terms and KEGG pathways such as O-methyltransferase activity (GO:0008171), regulation of signal transduction (GO:0009966), lignin metabolic process (GO:0009808), plastid envelope (GO:0009526), plant-pathogen interaction pathway (ko04626), and MAPK pathway (ko04016) that are actively involved in plant defense and immune signaling against the biotic stresses. Subcellular localization analysis anticipated the host plastid as a primary target for pathogen attack. The highly connected host hubs in the protein interaction network belonged to protein kinase domain including Ser/Thr protein kinase, MAPK, and cyclin-dependent kinase. We also identified 5,577 transcription factors in the interactions, associated with plant defense during biotic stress conditions. Additionally, novel host targets that are resistant to stem rust disease were also identified. The present study elucidates the functional differences between Pgt 21-0 and Pgt Ug99, thus providing the researchers with strain-specific information for further experimental validation of the interactions, and the development of durable, disease-resistant crop lines.

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/.

WeCoNET: a host-pathogen interactome database for deciphering crucial molecular networks of wheat-common bunt cross-talk mechanisms.

BACKGROUND: Triticum aestivum is the most important staple food grain of the world. In recent years, the outbreak of a major seed-borne disease, common bunt, in wheat resulted in reduced quality and quantity of the crop. The disease is caused by two fungal pathogens, Tilletia caries and Tilletia laevis, which show high similarity to each other in terms of life cycle, germination, and disease symptoms. The host-pathogen protein-protein interactions play a crucial role in initiating the disease infection mechanism as well as in plant defense responses. Due to the availability of limited information on Tilletia species, the elucidation of infection mechanisms is hampered. RESULTS: We constructed a database WeCoNET ( http://bioinfo.usu.edu/weconet/ ), providing functional annotations of the pathogen proteins and various tools to exploit host-pathogen interactions and other relevant information. The database implements a host-pathogen interactomics tool to predict protein-protein interactions, followed by network visualization, BLAST search tool, advanced 'keywords-based' search module, etc. Other features in the database include various functional annotations of host and pathogen proteins such as gene ontology terms, functional domains, and subcellular localization. The pathogen proteins that serve as effector and secretory proteins have also been incorporated in the database, along with their respective descriptions. Additionally, the host proteins that serve as transcription factors were predicted, and are available along with the respective transcription factor family and KEGG pathway to which they belong. CONCLUSION: WeCoNET is a comprehensive, efficient resource to the molecular biologists engaged in understanding the molecular mechanisms behind the common bunt infection in wheat. The data integrated into the database can also be beneficial to the breeders for the development of common bunt-resistant cultivars.

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/.

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.

Diversity and conservation of plant small secreted proteins associated with arbuscular mycorrhizal symbiosis.

Arbuscular mycorrhizal symbiosis (AMS) is widespread mutualistic association between plants and fungi, which plays an essential role in nutrient exchange, enhancement in plant stress resistance, development of host, and ecosystem sustainability. Previous studies have shown that plant small secreted proteins (SSPs) are involved in beneficial symbiotic interactions. However, the role of SSPs in the evolution of AMS has not been well studied yet. In this study, we performed computational analysis of SSPs in 60 plant species and identified three AMS-specific ortholog groups containing SSPs only from at least 30% of the AMS species in this study and three AMS-preferential ortholog groups containing SSPs from both AMS and non-AMS species, with AMS species containing significantly more SSPs than non-AMS species. We found that independent lineages of monocot and eudicot plants contained genes in the AMS-specific ortholog groups and had significant expansion in the AMS-preferential ortholog groups. Also, two AMS-preferential ortholog groups showed convergent changes, between monocot and eudicot species, in gene expression in response to arbuscular mycorrhizal fungus Rhizophagus irregularis. Furthermore, conserved cis-elements were identified in the promoter regions of the genes showing convergent gene expression. We found that the SSPs, and their closely related homologs, in each of three AMS-preferential ortholog groups, had some local variations in the protein structural alignment. We also identified genes co-expressed with the Populus trichocarpa SSP genes in the AMS-preferential ortholog groups. This first plant kingdom-wide analysis on SSP provides insights on plant-AMS convergent evolution with specific SSP gene expression and local diversification of protein structures.

Increased expression of pro-inflammatory cytokines at the fetal-maternal interface in bovine pregnancies produced by cloning.

PROBLEM: A significant rate of spontaneous abortion is observed in cattle pregnancies produced by somatic cell nuclear transfer (SCNT). Major histocompatibility complex class I (MHC-I) proteins are abnormally expressed on the surface of trophoblast cells from SCNT conceptuses. METHOD OF STUDY: MHC-I homozygous compatible (n = 9), homozygous incompatible (n = 8), and heterozygous incompatible (n = 5) pregnancies were established by SCNT. Eight control pregnancies were established by artificial insemination. Uterine and trophoblast samples were collected on day 35 ±1 of pregnancy, the expression of immune-related genes was examined by qPCR, and the expression of trophoblast microRNAs was assessed by sequencing. RESULTS: Compared to the control group, trophoblast from MHC-I heterozygous incompatible pregnancies expressed increased levels of CD28, CTLA4, CXCL8, IFNG, IL1A, IL2, IL10, IL12B, TBX21, and TNF, while GNLY expression was downregulated. The MHC-I homozygous incompatible treatment group expressed increased levels of IFNG, IL1A, and IL2 while the MHC-I homozygous compatible group did not differentially express any genes compared to the control group. In the endometrium, relative to the control group, MHC-I heterozygous incompatible pregnancies expressed increased levels of CD28, CTLA4, CXCL8, IFNG, IL10, IL12B, and TNF, while GATA3 expression was downregulated. The MHC-I homozygous incompatible group expressed decreased amounts of CSF2 transcripts compared with the control group but did not have abnormal expression of any other immune-related genes. MHC-I incompatible pregnancies had 40 deregulated miRNAs compared to control pregnancies and 62 deregulated microRNAs compared to MHC-I compatible pregnancies. CONCLUSIONS: MHC-I compatibility between the dam and fetus prevented an exacerbated maternal immune response from being mounted against fetal antigens.

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/.

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).