Drought Responsive Putative Marker-Trait Association in Tall Fescue as Influenced by the Presence of a Novel Endophyte.

Tall fescue (Festuca arundinacea Schreb.) is one of the most important cool-season perennial obligatory outcrossing forage grasses in the United States. The production and persistence of tall fescue is significantly affected by drought in the south-central United States. Shoot-specific endophyte (Epichloë coenophiala)-infected tall fescue showed superior performance under both biotic and abiotic stress conditions. We performed a genome-wide association analysis using clonal pairs of novel endophyte AR584-positive (EP) and endophyte-free (EF) tall fescue populations consisting of 205 genotypes to identify marker-trait associations (MTAs) that contribute to drought tolerance. The experiment was performed through November 2014 to June 2018 in the field, and phenotypic data were taken on plant height, plant spread, plant vigor, and dry biomass weight under natural summer conditions of sporadic drought. Genotyping-by-sequencing of the population generated 3,597 high quality single nucleotide polymorphisms (SNPs) for further analysis. We identified 26 putative drought responsive MTAs (17 specific to EP, eight specific to EF, and one in both EP and EF populations) and nine of them (i.e., V.ep_10, S.ef_12, V.ep_27, HSV.ef_31, S.ep_30, SV.ef_32, V.ep_68, V.ef_56, and H.ef_57) were identified within 0.5 Mb region in the tall fescue genome (44.5-44.7, 75.3-75.8, 77.5-77.9 and 143.7-144.2 Mb). Using 26 MTAs, 11 tall fescue genotypes were selected for subsequent study to develop EP and EF drought tolerant tall fescue populations. Ten orthologous genes (six for EP and four for EF population) were identified in Brachypodium genome as potential candidates for drought tolerance in tall fescue, which were also earlier reported for their involvement in abiotic stress tolerance. The MTAs and candidate genes identified in this study will be useful for marker-assisted selection in improving drought tolerance of tall fescue as well opening avenue for further drought study in tall fescue.

Virus-induced gene silencing database for phenomics and functional genomics in Nicotiana benthamiana.

Virus-induced gene silencing (VIGS) is an important forward and reverse genetics method for the study of gene function in many plant species, especially Nicotiana benthamiana. However, despite the widespread use of VIGS, a searchable database compiling the phenotypes observed with this method is lacking. Such a database would allow researchers to know the phenotype associated with the silencing of a large number of individual genes without experimentation. We have developed a VIGS phenomics and functional genomics database (VPGD) that has DNA sequence information derived from over 4,000 N. benthamiana VIGS clones along with the associated silencing phenotype for approximately 1,300 genes. The VPGD has a built-in BLAST search feature that provides silencing phenotype information of specific genes. In addition, a keyword-based search function could be used to find a specific phenotype of interest with the corresponding gene, including its Gene Ontology descriptions. Query gene sequences from other plant species that have not been used for VIGS can also be searched for their homologs and silencing phenotype in N. benthamiana. VPGD is useful for identifying gene function not only in N. benthamiana but also in related Solanaceae plants such as tomato and potato. The database is accessible at http://vigs.noble.org.

The Vigna unguiculata Gene Expression Atlas (VuGEA) from de novo assembly and quantification of RNA-seq data provides insights into seed maturation mechanisms.

Legume research and cultivar development are important for sustainable food production, especially of high-protein seed. Thanks to the development of deep-sequencing technologies, crop species have been taken to the front line, even without completion of their genome sequences. Black-eyed pea (Vigna unguiculata) is a legume species widely grown in semi-arid regions, which has high potential to provide stable seed protein production in a broad range of environments, including drought conditions. The black-eyed pea reference genotype has been used to generate a gene expression atlas of the major plant tissues (i.e. leaf, root, stem, flower, pod and seed), with a developmental time series for pods and seeds. From these various organs, 27 cDNA libraries were generated and sequenced, resulting in more than one billion reads. Following filtering, these reads were de novo assembled into 36 529 transcript sequences that were annotated and quantified across the different tissues. A set of 24 866 unique transcript sequences, called Unigenes, was identified. All the information related to transcript identification, annotation and quantification were stored into a gene expression atlas webserver (http://vugea.noble.org), providing a user-friendly interface and necessary tools to analyse transcript expression in black-eyed pea organs and to compare data with other legume species. Using this gene expression atlas, we inferred details of molecular processes that are active during seed development, and identified key putative regulators of seed maturation. Additionally, we found evidence for conservation of regulatory mechanisms involving miRNA in plant tissues subjected to drought and seeds undergoing desiccation.

Prediction of membrane transport proteins and their substrate specificities using primary sequence information.

BACKGROUND: Membrane transport proteins (transporters) move hydrophilic substrates across hydrophobic membranes and play vital roles in most cellular functions. Transporters represent a diverse group of proteins that differ in topology, energy coupling mechanism, and substrate specificity as well as sequence similarity. Among the functional annotations of transporters, information about their transporting substrates is especially important. The experimental identification and characterization of transporters is currently costly and time-consuming. The development of robust bioinformatics-based methods for the prediction of membrane transport proteins and their substrate specificities is therefore an important and urgent task. RESULTS: Support vector machine (SVM)-based computational models, which comprehensively utilize integrative protein sequence features such as amino acid composition, dipeptide composition, physico-chemical composition, biochemical composition, and position-specific scoring matrices (PSSM), were developed to predict the substrate specificity of seven transporter classes: amino acid, anion, cation, electron, protein/mRNA, sugar, and other transporters. An additional model to differentiate transporters from non-transporters was also developed. Among the developed models, the biochemical composition and PSSM hybrid model outperformed other models and achieved an overall average prediction accuracy of 76.69% with a Mathews correlation coefficient (MCC) of 0.49 and a receiver operating characteristic area under the curve (AUC) of 0.833 on our main dataset. This model also achieved an overall average prediction accuracy of 78.88% and MCC of 0.41 on an independent dataset. CONCLUSIONS: Our analyses suggest that evolutionary information (i.e., the PSSM) and the AAIndex are key features for the substrate specificity prediction of transport proteins. In comparison, similarity-based methods such as BLAST, PSI-BLAST, and hidden Markov models do not provide accurate predictions for the substrate specificity of membrane transport proteins. TrSSP: The Transporter Substrate Specificity Prediction Server, a web server that implements the SVM models developed in this paper, is freely available at http://bioinfo.noble.org/TrSSP.

MET-COFEA: a liquid chromatography/mass spectrometry data processing platform for metabolite compound feature extraction and annotation.

In this paper, we present a novel liquid chromatography/mass spectrometry (LC/MS) data processing and analysis platform, MET-COFEA (METabolite COmpound Feature Extraction and Annotation). MET-COFEA detects and clusters chromatographic peak features for each metabolite compound by first comprehensively evaluating retention time and peak shape criteria and then annotating the associations between each peak's observed m/z value with the corresponding metabolite compound's molecular mass. MET-COFEA integrates a series of innovative approaches, including novel mass trace based extracted-ion chromatogram (EIC) extraction, continuous wavelet transform (CWT)-based peak detection, and compound-associated peak clustering and peak annotation algorithms. On the basis of the deduced neutral molecular mass and retention time, we have also developed a new alignment algorithm that uses compound-associated peak groups instead of individual peaks to align the same metabolite compound across samples from different electrospray ionization (ESI) modes, different instruments, even different experimental conditions. MET-COFEA has been systematically tested on a series of LC/MS profiles of mixed standards at different concentrations as well as real untargeted LC/MS plant metabolomics data. We compared the performances of MET-COFEA with the existing publicly available tools at LC/MS peak analysis level and demonstrated its excellent performance in this arena. MET-COFEA is freely available at http://bioinfo.noble.org/manuscript-support/met-cofea/.