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.

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/.

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.

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.

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/.

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/.

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).

Transcriptional profiling of two contrasting genotypes uncovers molecular mechanisms underlying salt tolerance in alfalfa.

Alfalfa is an important forage crop that is moderately tolerant to salinity; however, little is known about its salt-tolerance mechanisms. We studied root and leaf transcriptomes of a salt-tolerant (G03) and a salt-sensitive (G09) genotype, irrigated with waters of low and high salinities. RNA sequencing led to 1.73 billion high-quality reads that were assembled into 418,480 unigenes; 35% of which were assigned to 57 Gene Ontology annotations. The unigenes were assigned to pathway databases for understanding high-level functions. The comparison of two genotypes suggested that the low salt tolerance index for transpiration rate and stomatal conductance of G03 compared to G09 may be due to its reduced salt uptake under salinity. The differences in shoot biomass between the salt-tolerant and salt-sensitive lines were explained by their differential expressions of genes regulating shoot number. Differentially expressed genes involved in hormone-, calcium-, and redox-signaling, showed treatment- and genotype-specific differences and led to the identification of various candidate genes involved in salinity stress, which can be investigated further to improve salinity tolerance in alfalfa. Validation of RNA-seq results using qRT-PCR displayed a high level of consistency between the two experiments. This study provides valuable insight into the molecular mechanisms regulating salt tolerance in alfalfa.

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.

Computational Systems Biology of Alfalfa - Bacterial Blight Host-Pathogen Interactions: Uncovering the Complex Molecular Networks for Developing Durable Disease Resistant Crop.

Medicago sativa (also known as alfalfa), a forage legume, is widely cultivated due to its high yield and high-value hay crop production. Infectious diseases are a major threat to the crops, owing to huge economic losses to the agriculture industry, worldwide. The protein-protein interactions (PPIs) between the pathogens and their hosts play a critical role in understanding the molecular basis of pathogenesis. Pseudomonas syringae pv. syringae ALF3 suppresses the plant's innate immune response by secreting type III effector proteins into the host cell, causing bacterial stem blight in alfalfa. The alfalfa-P. syringae system has little information available for PPIs. Thus, to understand the infection mechanism, we elucidated the genome-scale host-pathogen interactions (HPIs) between alfalfa and P. syringae using two computational approaches: interolog-based and domain-based method. A total of ∼14 M putative PPIs were predicted between 50,629 alfalfa proteins and 2,932 P. syringae proteins by combining these approaches. Additionally, ∼0.7 M consensus PPIs were also predicted. The functional analysis revealed that P. syringae proteins are highly involved in nucleotide binding activity (GO:0000166), intracellular organelle (GO:0043229), and translation (GO:0006412) while alfalfa proteins are involved in cellular response to chemical stimulus (GO:0070887), oxidoreductase activity (GO:0016614), and Golgi apparatus (GO:0005794). According to subcellular localization predictions, most of the pathogen proteins targeted host proteins within the cytoplasm and nucleus. In addition, we discovered a slew of new virulence effectors in the predicted HPIs. The current research describes an integrated approach for deciphering genome-scale host-pathogen PPIs between alfalfa and P. syringae, allowing the researchers to better understand the pathogen's infection mechanism and develop pathogen-resistant lines.

citSATdb: Genome-Wide Simple Sequence Repeat (SSR) Marker Database of Citrus Species for Germplasm Characterization and Crop Improvement.

Microsatellites or simple sequence repeats (SSRs) are popular co-dominant markers that play an important role in crop improvement. To enhance genomic resources in general horticulture, we identified SSRs in the genomes of eight citrus species and characterized their frequency and distribution in different genomic regions. Citrus is the world's most widely cultivated fruit crop. We have implemented a microsatellite database, citSATdb, having the highest number (~1,296,500) of putative SSR markers from the genus Citrus, represented by eight species. The database is based on a three-tier approach using MySQL, PHP, and Apache. The markers can be searched using multiple search parameters including chromosome/scaffold number(s), motif types, repeat nucleotides (1-6), SSR length, patterns of repeat motifs and chromosome/scaffold location. The cross-species transferability of selected markers can be checked using e-PCR. Further, the markers can be visualized using the Jbrowse feature. These markers can be used for distinctness, uniformity, and stability (DUS) tests of variety identification, marker-assisted selection (MAS), gene discovery, QTL mapping, and germplasm characterization. citSATdb represents a comprehensive source of markers for developing/implementing new approaches for molecular breeding, required to enhance Citrus productivity. The potential polymorphic SSR markers identified by cross-species transferability could be used for genetic diversity and population distinction in other species.

Computational identification of maize miRNA and their gene targets involved in biotic and abiotic stresses.

Plant interactions with biotic and abiotic stresses are complex and entail changes at the transcriptional, cellular and physiological level. MicroRNAs (miRNAs) are small (∼20-24 nt), non-coding RNAs that play a vital role in wide range of biological processes involved in regulation of gene expression through translation inhibition or degradation of their target mRNAs during stress conditions. Therefore, identification of miRNAs and their targets are of immense value in understanding the regulatory networks triggered during stress. Advancement in computational approaches has opened up ways for the prediction of miRNAs and their possible targets with functional pathways. Our objective was to identify miRNA and their potential targets involved in both biotic and abiotic stresses in maize. A total of 2,019,524 downloaded ESTs from dbEST were processed and trimmed by Seq Clean. The program trashed 264,000 and trimmed 284,979 sequences and the resulting 1,755,534 sequences were submitted for clustering and assembled to RepeatMasker and TGICL. A total of 30 miRNAs were found to hybridize with the potential targets of gene families such as CoA ligase, lipoxygenase 1, Terpenoideyclases, Zn finger, transducing, etc. Ten of the identified miRNAs targeted cytochrome c1 family. Zm_miR23 class targeted 11 different genes. The identified targets are involved in the plant growth and development during biotic and abiotic stresses in maize. These miRNAs may be further used for functional analysis. Furthermore, four and two of the miRNA targets were validated in response to waterlogging tolerance and southern leaf blight resistance, respectively, to understand the miRNA-assisted regulation of target miRNAs. The functional annotation of the predicted targets indicated that these stress-responsive miRNAs regulate cellular function; molecular function and biological process in maize at the post-transcriptional level. The present results have paved way towards better understanding the role of miRNAs in the mechanism of stress tolerance in maize.

Developmental and Molecular Changes Underlying the Vernalization-Induced Transition to Flowering in Aquilegia coerulea (James).

Reproductive success in plants is dependent on many factors but the precise timing of flowering is certainly among the most crucial. Perennial plants often have a vernalization or over-wintering requirement in order to successfully flower in the spring. The shoot apical meristem undergoes drastic developmental and molecular changes as it transitions into inflorescence meristem (IM) identity, which then gives rise to floral meristems (FMs). In this study, we have examined the developmental and gene expression changes underlying the transition from the vegetative to reproductive phases in the basal eudicot Aquilegia coerulea, which has evolved a vernalization response independently relative to other established model systems. Results from both our histology and scanning electron studies demonstrate that developmental changes in the meristem occur gradually during the third and fourth weeks of vernalization. Based on RNAseq data and cluster analysis, several known flowering time loci, including AqFT and AqFL1, exhibit dramatic changes in expression during the fourth week. Further consideration of candidate gene homologs as well as unexpected loci of interest creates a framework in which we can begin to explore the genetic basis of the flowering time transition in Aquilegia.

In Silico Identification and Validation of Potential microRNAs in Kinnow Mandarin (Citrus reticulata Blanco).

MicroRNAs (miRNAs) are a large family of 19-25 nucleotides, regulatory, non-coding RNA molecules that control gene expression by cleaving or inhibiting the translation of target gene transcripts in animals and plants. Despite the important functions of miRNAs related to regulation of plant growth and development processes, metabolism, and abiotic and biotic stresses, little is known about the disease-related miRNA. Here, we present a new pipeline for miRNA analysis using expressed sequence tags (ESTs)-based bioinformatics approach in Kinnow mandarin, a commercially important citrus fruit crop. For this, 56,041 raw EST sequences of Citrus reticulata Blanco were retrieved from EST database in NCBI through step-by-step filtering and processing methods and 130 miRNAs were predicted. Upon blast with Citrus sinensis transcriptome data, these produced potential targets related to disease resistance proteins, pectin lyase-like superfamily proteins, lateral organ boundaries (LOB) domain-containing proteins 11, and protein phosphatase 2C family proteins, protein kinases, dehydrogenases, and methyltransferases. Majority of the predicted miRNAs were of 22, 23, and 24 nucleotides in length. To validate these computationally predicted miRNA, poly(A)-tailed Reverse Transcription-PCR was applied to detect the expression of seven miRNA which showed disease-related potential targets, in citrus greening diseased leaf tissues in comparison to the healthy tissues of Kinnow mandarin. Our study provides information on regulatory roles of these potential miRNAs for the citrus greening disease development, miRNA targets, and would be helpful for future research of miRNA function in citrus.

Identification of miRNAs in C. roseus and their potential targets.

MicroRNAs are small (20-22 nucleotides) none coding, regulatory RNAs, whose pivotal role in gene expression has been associated in number of diseases, therefore prediction of miRNA is an essential yet challenging field. In this study miRNAs of C. roseus are predicted along with their possible target genes. A total of 19,899 ESTs were downloaded from dbEST database and processed and trimmed through SeqClean. Nine sequences were trashed and 31 sequences were trimmed by the program and the resulting sequences were submitted to Repeatmasker and TGICL for clustering and assembly. This contig database was now used to find the putative miRNAs by performing a local BLAST with the miRNAs of B. rapa retrieved from miRBase. The targets were scanned by hybridizing screened ESTs with the UTRs of human using miRanda software. Finally, 7 putative miRNAs were found to hybridize with the various targets of signal transduction and apoptosis that may play significant role in preventing diseases like Leukemia, Arthritis and Alzheimer.