Tarun Pal

Erratum to: Mining NGS transcriptomes for miRNAs and dissecting their role in regulating growth, development, and secondary metabolites production in different organs of a medicinal herb, Picrorhiza kurroa.

This study is the first endeavor on mining of miRNAs and analyzing their involvement in development and secondary metabolism of an endangered medicinal herb Picrorhiza kurroa (P. kurroa ). miRNAs are ubiquitous non-coding RNA species that target complementary sequences of mRNA and result in either translational repression or target degradation in eukaryotes. The role of miRNAs has not been investigated in P. kurroa which is a medicinal herb of industrial value due to the presence of secondary metabolites, picroside-I and picroside-II. Computational identification of miRNAs was done in 6 transcriptomes of P. kurroa generated from root, shoot, and stolon organs varying for growth, development, and culture conditions. All available plant miRNA entries were retrieved from miRBase and used as backend datasets to computationally identify conserved miRNAs in transcriptome data sets. Total 18 conserved miRNAs were detected in P. kurroa followed by target prediction and functional annotation which suggested their possible role in controlling various biological processes. Validation of miRNA and expression analysis by qRT-PCR and 5′ RACE revealed that miRNA-4995 has a regulatory role in terpenoid biosynthesis ultimately affecting the production of picroside-I. miR-5532 and miR-5368 had negligible expression in field-grown samples as compared to in vitro-cultured samples suggesting their role in regulating P. kurroa growth in culture conditions. The study has thus identified novel functions for existing miRNAs which can be further validated for their potential regulatory role.

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.

NGS Transcriptomes and Enzyme Inhibitors Unravel Complexity of Picrosides Biosynthesis in Picrorhiza kurroa Royle ex. Benth

Picrorhiza kurroa is an important medicinal herb valued for iridoid glycosides, Picroside-I (P-I) and Picroside-II (P-II), which have several pharmacological activities. Genetic interventions for developing a picroside production platform would require knowledge on biosynthetic pathway and key control points, which does not exist as of today. The current study reports that geranyl pyrophosphate (GPP) moiety is mainly contributed by the non-mevalonate (MEP) route, which is further modified to P-I and P-II through phenylpropanoid and iridoid pathways, in total consisting of 41 and 35 enzymatic steps, respectively. The role of the MEP pathway was ascertained through enzyme inhibitors fosmidomycin and mevinolin along with importance of other integrating pathways using glyphosate, aminooxy acetic acid (AOA) and actinomycin D, which overall resulted in 17%-92% inhibition of P-I accumulation. Retrieval of gene sequences for enzymatic steps from NGS transcriptomes and their expression analysis vis-à-vis picrosides content in different tissues/organs showed elevated transcripts for twenty genes, which were further shortlisted to seven key genes, ISPD, DXPS, ISPE, PMK, 2HFD, EPSPS and SK, on the basis of expression analysis between high versus low picrosides content strains of P. kurroa so as to eliminate tissue type/ developmental variations in picrosides contents. The higher expression of the majority of the MEP pathway genes (ISPD, DXPS and ISPE), coupled with higher inhibition of DXPR enzyme by fosmidomycin, suggested that the MEP route contributed to the biosynthesis of P-I in P. kurroa. The outcome of the study is expected to be useful in designing a suitable genetic intervention strategy towards enhanced production of picrosides. Possible key genes contributing to picroside biosynthesis have been identified with potential implications in molecular breeding and metabolic engineering of P. kurroa.

DRPPP: A machine learning based tool for prediction of disease resistance proteins in plants

Plant disease outbreak is increasing rapidly around the globe and is a major cause for crop loss worldwide. Plants, in turn, have developed diverse defense mechanisms to identify and evade different pathogenic microorganisms. Early identification of plant disease resistance genes (R genes) can be exploited for crop improvement programs. The present prediction methods are either based on sequence similarity/domain-based methods or electronically annotated sequences, which might miss existing unrecognized proteins or low similarity proteins. Therefore, there is an urgent need to devise a novel machine learning technique to address this problem. In the current study, a SVM-based tool was developed for prediction of disease resistance proteins in plants. All known disease resistance (R) proteins (112) were taken as a positive set, whereas manually curated negative dataset consisted of 119 non-R proteins. Feature extraction generated 10,270 features using 16 different methods. The ten-fold cross validation was performed to optimize SVM parameters using radial basis function. The model was derived using libSVM and achieved an overall accuracy of 91.11% on the test dataset. The tool was found to be robust and can be used for high-throughput datasets. The current study provides instant identification of R proteins using machine learning approach, in addition to the similarity or domain prediction methods.

Comparative transcriptome analysis in different tissues of a medicinal herb, Picrorhiza kurroa pinpoints transcription factors regulating picrosides biosynthesis.

Transcriptional regulation of picrosides biosynthesis, the iridoid glycosides of an endangered medicinal herb, Picrorhiza kurroa, is completely unknown. P. kurroa plants obtained from natural habitat accumulate higher picrosides than in-vitro cultured plants, which necessitates identification of transcription factors (TFs) regulating their differential biosynthesis. The current study investigates complete spectrum of different TF classes in P. kurroa transcriptomes and discerns their association with picrosides biosynthesis. Transcriptomes of differential picroside-I content shoots and picroside-II content roots were mined for seven classes of TFs implicated in secondary metabolism regulation in plants. Key TFs were identified through in silico transcript abundance and qPCR analysis was performed to confirm transcript levels of TFs under study in differential content tissues and genotypes. Promoter regions of key picrosides biosynthetic pathway genes were explored to hypothesize which TFs can possibly regulate target genes. A total of 131, 137, 107, 82 and 101 transcripts encoding different TFs families were identified in PKS-25, PKS-15, PKSS, PKR-25 and PKSR transcriptomes, respectively. ERF-18, bHLH-104, NAC-25, 32, 94 and SUF-4 showed elevated expression in roots (up to 37 folds) and shoots (up to 195 folds) of plants obtained from natural habitat, indicating their role as activators of picrosides biosynthesis whereas, elevated expression of WRKY-17, 40, 71 and MYB-4 in low picrosides content conditions suggested their down-regulatory role. In silico analysis of key picrosides biosynthetic pathway gene promoter regions revealed binding domains for ERF-18, NAC-25, WRKY-40 and MYB-4. Identification of candidate TFs contributing towards picrosides biosynthesis is a pre-requisite for designing appropriate metabolic engineering strategies aimed at enhancing picrosides content in vitro and in vivo.

Comparative transcriptomics uncovers differences in photoautotrophic versus photoheterotrophic modes of nutrition in relation to secondary metabolites biosynthesis in Swertia chirayita

Swertia chirayita is a high-value medicinal herb exhibiting antidiabetic, hepatoprotective, anticancer, antiediematogenic and antipyretic properties. Scarcity of its plant material has necessitated in vitro production of therapeutic metabolites; however, their yields were low compared to field grown plants. Possible reasons for this could be differences in physiological and biochemical processes between plants grown in photoautotrophic versus photoheterotrophic modes of nutrition. Comparative transcriptomes of S. chirayita were generated to decipher the crucial molecular components associated with the secondary metabolites biosynthesis. Illumina HiSeq sequencing yielded 57,460 and 43,702 transcripts for green house grown (SCFG) and tissue cultured (SCTC) plants, respectively. Biological role analysis (GO and COG assignments) revealed major differences in SCFG and SCTC transcriptomes. KEGG orthology mapped 351 and 341 transcripts onto secondary metabolites biosynthesis pathways for SCFG and SCTC transcriptomes, respectively. Nineteen out of 30 genes from primary metabolism showed higher in silico expression (FPKM) in SCFG versus SCTC, possibly indicating their involvement in regulating the central carbon pool. In silico data were validated by RT-qPCR using a set of 16 genes, wherein 10 genes showed similar expression pattern across both the methods. Comparative transcriptomes identified differentially expressed transcription factors and ABC-type transporters putatively associated with secondary metabolism in S. chirayita. Additionally, functional classification was performed using NCBI Biosystems database. This study identified the molecular components implicated in differential modes of nutrition (photoautotrophic vs. photoheterotrophic) in relation to secondary metabolites production in S. chirayita.

Uncovering interconnections between kinases vis-à-vis physiological and biochemical processes contributing to picroside-I biosynthesis in a medicinal herb, Picrorhiza kurroa Royle ex. Benth.

Picroside-I (P-I) is an iridoid glycoside of Picrorhiza kurroa, a perennial medicinal herb native to North-Western Himalayas, used in the preparation of herbal drug formulations. Natural habitat shoots (PKSS) produce significantly higher P-I content as compared to in vitro shoots (PKS-15 and PKS-25). Although temperature and culture conditions are known to play a major role in influencing P-I biosynthesis in different shoots of P. kurroa, the molecular mechanisms behind signal perception of variable environments are completely unknown. Kinases have been considered as key signaling proteins which control cellular processes involved in adaptability under diverse environments, thereby affecting downstream primary and secondary metabolic pathways. The current study investigated the association of kinases with P-I production and shoot biomass in P. kurroa. Transcriptome mining and in silico transcript abundance in three shoot tissues revealed differentially expressed kinases under high and low P-I accumulating conditions. A total of 521, 473 and 346 transcripts encoding kinases were identified in PKS-25, PKS-15 and PKSS tissues, respectively. Gene expression analysis of 43 selected genes in differential P-I content shoot tissues and genotypes revealed key processes regulated by kinases which might be associated with P-I biosynthesis. Expression of 16 kinases genes involved in plant–pathogen interactions, abiotic stress, wounding, hormonal response and carbohydrate metabolism was observed to be up-regulated in high P-I accumulating conditions, indicating their possible role in eliciting P-I biosynthesis in P. kurroa. Analysis of kinases along with genes involved in controlling shoot biomass productivity revealed that auxin response plays a major role in affecting biomass productivity in in vitro shoots of P. kurroa. This study provides a basic understanding of physiological processes affected under variable environmental conditions leading to differential biosynthesis of P-I in P. kurroa.