Next-generation sequencing (NGS) transcriptomes reveal association of multiple genes and pathways contributing to secondary metabolites accumulation in tuberous roots of Aconitum heterophyllum Wall.

MAIN CONCLUSION: The transcriptomes of Aconitum heterophyllum were assembled and characterized for the first time to decipher molecular components contributing to biosynthesis and accumulation of metabolites in tuberous roots. Aconitum heterophyllum Wall., popularly known as Atis, is a high-value medicinal herb of North-Western Himalayas. No information exists as of today on genetic factors contributing to the biosynthesis of secondary metabolites accumulating in tuberous roots, thereby, limiting genetic interventions towards genetic improvement of A. heterophyllum. Illumina paired-end sequencing followed by de novo assembly yielded 75,548 transcripts for root transcriptome and 39,100 transcripts for shoot transcriptome with minimum length of 200 bp. Biological role analysis of root versus shoot transcriptomes assigned 27,596 and 16,604 root transcripts; 12,340 and 9398 shoot transcripts into gene ontology and clusters of orthologous group, respectively. KEGG pathway mapping assigned 37 and 31 transcripts onto starch-sucrose metabolism while 329 and 341 KEGG orthologies associated with transcripts were found to be involved in biosynthesis of various secondary metabolites for root and shoot transcriptomes, respectively. In silico expression profiling of the mevalonate/2-C-methyl-D-erythritol 4-phosphate (non-mevalonate) pathway genes for aconites biosynthesis revealed 4 genes HMGR (3-hydroxy-3-methylglutaryl-CoA reductase), MVK (mevalonate kinase), MVDD (mevalonate diphosphate decarboxylase) and HDS (1-hydroxy-2-methyl-2-(E)-butenyl 4-diphosphate synthase) with higher expression in root transcriptome compared to shoot transcriptome suggesting their key role in biosynthesis of aconite alkaloids. Five genes, GMPase (geranyl diphosphate mannose pyrophosphorylase), SHAGGY, RBX1 (RING-box protein 1), SRF receptor kinases and ß-amylase, implicated in tuberous root formation in other plant species showed higher levels of expression in tuberous roots compared to shoots. A total of 15,487 transcription factors belonging to bHLH, MYB, bZIP families and 399 ABC transporters which regulate biosynthesis and accumulation of bioactive compounds were identified in root and shoot transcriptomes. The expression of 5 ABC transporters involved in tuberous root development was validated by quantitative PCR analysis. Network connectivity diagrams were drawn for starch-sucrose metabolism and isoquinoline alkaloid biosynthesis associated with tuberous root growth and secondary metabolism, respectively, in root transcriptome of A. heterophyllum. The current endeavor will be of practical importance in planning a suitable genetic intervention strategy for the improvement of A. heterophyllum.

Mining whole genomes and transcriptomes of Jatropha (Jatropha curcas) and Castor bean (Ricinus communis) for NBS-LRR genes and defense response associated transcription factors.

Jatropha (Jatropha curcas L.) and Castor bean (Ricinus communis) are oilseed crops of family Euphorbiaceae with the potential of producing high quality biodiesel and having industrial value. Both the bioenergy plants are becoming susceptible to various biotic stresses directly affecting the oil quality and content. No report exists as of today on analysis of Nucleotide Binding Site-Leucine Rich Repeat (NBS-LRR) gene repertoire and defense response transcription factors in both the plant species. In silico analysis of whole genomes and transcriptomes identified 47 new NBS-LRR genes in both the species and 122 and 318 defense response related transcription factors in Jatropha and Castor bean, respectively. The identified NBS-LRR genes and defense response transcription factors were mapped onto the respective genomes. Common and unique NBS-LRR genes and defense related transcription factors were identified in both the plant species. All NBS-LRR genes in both the species were characterized into Toll/interleukin-1 receptor NBS-LRRs (TNLs) and coiled-coil NBS-LRRs (CNLs), position on contigs, gene clusters and motifs and domains distribution. Transcript abundance or expression values were measured for all NBS-LRR genes and defense response transcription factors, suggesting their functional role. The current study provides a repertoire of NBS-LRR genes and transcription factors which can be used in not only dissecting the molecular basis of disease resistance phenotype but also in developing disease resistant genotypes in Jatropha and Castor bean through transgenic or molecular breeding approaches.

Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions.

BACKGROUND: Subunit vaccines based on recombinant proteins have been effective in preventing infectious diseases and are expected to meet the demands of future vaccine development. Computational approach, especially reverse vaccinology (RV) method has enormous potential for identification of protein vaccine candidates (PVCs) from a proteome. The existing protective antigen prediction software and web servers have low prediction accuracy leading to limited applications for vaccine development. Besides machine learning techniques, those software and web servers have considered only protein's adhesin-likeliness as criterion for identification of PVCs. Several non-adhesin functional classes of proteins involved in host-pathogen interactions and pathogenesis are known to provide protection against bacterial infections. Therefore, knowledge of bacterial pathogenesis has potential to identify PVCs. RESULTS: A web server, Jenner-Predict, has been developed for prediction of PVCs from proteomes of bacterial pathogens. The web server targets host-pathogen interactions and pathogenesis by considering known functional domains from protein classes such as adhesin, virulence, invasin, porin, flagellin, colonization, toxin, choline-binding, penicillin-binding, transferring-binding, fibronectin-binding and solute-binding. It predicts non-cytosolic proteins containing above domains as PVCs. It also provides vaccine potential of PVCs in terms of their possible immunogenicity by comparing with experimentally known IEDB epitopes, absence of autoimmunity and conservation in different strains. Predicted PVCs are prioritized so that only few prospective PVCs could be validated experimentally. The performance of web server was evaluated against known protective antigens from diverse classes of bacteria reported in Protegen database and datasets used for VaxiJen server development. The web server efficiently predicted known vaccine candidates reported from Streptococcus pneumoniae and Escherichia coli proteomes. The Jenner-Predict server outperformed NERVE, Vaxign and VaxiJen methods. It has sensitivity of 0.774 and 0.711 for Protegen and VaxiJen dataset, respectively while specificity of 0.940 has been obtained for the latter dataset. CONCLUSIONS: Better prediction accuracy of Jenner-Predict web server signifies that domains involved in host-pathogen interactions and pathogenesis are better criteria for prediction of PVCs. The web server has successfully predicted maximum known PVCs belonging to different functional classes. Jenner-Predict server is freely accessible at http://117.211.115.67/vaccine/home.html.

UniDrug-target: a computational tool to identify unique drug targets in pathogenic bacteria.

BACKGROUND: Targeting conserved proteins of bacteria through antibacterial medications has resulted in both the development of resistant strains and changes to human health by destroying beneficial microbes which eventually become breeding grounds for the evolution of resistances. Despite the availability of more than 800 genomes sequences, 430 pathways, 4743 enzymes, 9257 metabolic reactions and protein (three-dimensional) 3D structures in bacteria, no pathogen-specific computational drug target identification tool has been developed. METHODS: A web server, UniDrug-Target, which combines bacterial biological information and computational methods to stringently identify pathogen-specific proteins as drug targets, has been designed. Besides predicting pathogen-specific proteins essentiality, chokepoint property, etc., three new algorithms were developed and implemented by using protein sequences, domains, structures, and metabolic reactions for construction of partial metabolic networks (PMNs), determination of conservation in critical residues, and variation analysis of residues forming similar cavities in proteins sequences. First, PMNs are constructed to determine the extent of disturbances in metabolite production by targeting a protein as drug target. Conservation of pathogen-specific protein's critical residues involved in cavity formation and biological function determined at domain-level with low-matching sequences. Last, variation analysis of residues forming similar cavities in proteins sequences from pathogenic versus non-pathogenic bacteria and humans is performed. RESULTS: The server is capable of predicting drug targets for any sequenced pathogenic bacteria having fasta sequences and annotated information. The utility of UniDrug-Target server was demonstrated for Mycobacterium tuberculosis (H37Rv). The UniDrug-Target identified 265 mycobacteria pathogen-specific proteins, including 17 essential proteins which can be potential drug targets. CONCLUSIONS/SIGNIFICANCE: UniDrug-Target is expected to accelerate pathogen-specific drug targets identification which will increase their success and durability as drugs developed against them have less chance to develop resistances and adverse impact on environment. The server is freely available at http://117.211.115.67/UDT/main.html. The standalone application (source codes) is available at http://www.bioinformatics.org/ftp/pub/bioinfojuit/UDT.rar.