Integrated analysis of smRNAome, transcriptome, and degradome data to decipher microRNAs regulating costunolide biosynthesis in Saussurea lappa.

Saussurea lappa (S. lappa) has been known to synthesize medicinally important, costunolide. Due to its immense therapeutic importance, understanding of regulatory mechanism associated with its biosynthesis is crucial. The identification of genes and transcription factors (TFs) in S. lappa, created a clear picture of costunolide biosynthesis pathways. Further to understand the regulation of costunolide biosynthesis by miRNAs, an integrated study of transcriptome, miRNAs, and degradome was performed. Identified candidate miRNAs and associated feed-forward loops (FFLs) illustrates their regulatory role in secondary metabolite biosynthesis. Small RNA and degradome sequencing were performed for leaf and root tissues to determine miRNAs-targets pairs. A total of 711 and 525 such targets were obtained for novel and known miRNAs respectively. This data was used to generate costunolide-specific miRNA-TF-gene interactome to perform systematic analyses through graph theoretical approach. Interestingly, miR171c.1 and sla-miR121 were identified as key regulators to connect and co-regulate both mevalonate and sesquiterpenoid pathways to bio-synthesize costunolide. Tissue-specific FFLs were identified to be involved in costunolide biosynthesis which further suggests the evolutionary co-relation of root-specific networks in synthesis of secondary metabolites in addition to leaf-specific networks. This integrative approach allowed us to determine candidate miRNAs and associated tissue-specific motifs involved in the diversification of secondary metabolites. MiRNAs identified in present study can provide alternatives for bioengineering tool to enhance the synthesis of costunolide and other secondary metabolites in S. lappa.

Identification of Pseudo-R genes in Vitis vinifera and characterization of their role as immunomodulators in host-pathogen interactions.

INTRODUCTION: Duplication events are fundamental to co-evolution in host-pathogen interactions. Pseudogenes (Ψs) are dysfunctional paralogs of functional genes and resistance genes (Rs) in plants are the key to disarming pathogenic invasions. Thus, deciphering the roles of pseudo-R genes in plant defense is momentous. OBJECTIVES: This study aimed to functionally characterize diverse roles of the resistance Ψs as novel gene footprints and as significant gene regulators in the grapevine genome. METHODS: PlantPseudo pipeline and HMM-profiling identified whole-genome duplication-derived (WGD) Ψs associated with resistance genes (Ψ-Rs). Further, novel antifungal and antimicrobial peptides were characterized for fungal associations using protein-protein docking with Erysiphe necator proteins. miRNA and tasiRNA target sites and transcription factor (TF) binding sites were predicted in Ψ-Rs. Finally, differential co-expression patterns in Ψ-Rs-lncRNAs-coding genes were identified using the UPGMA method. RESULTS: 2,746 Ψ-Rs were identified from 31,032 WGD Ψs in the genome of grapevine. 69-antimicrobial and 81-antifungal novel peptides were generated from Ψ-Rs. The putative genic potential was predicted for five novel antifungal peptides which were further characterized by docking against E. necator proteins. 395 out of 527 resistance loci-specific Ψ-Rs were acting as parental gene mimics. Further, to explore the diverse roles of Ψ-Rs in plant-defense, we identified 37,026 TF-binding sites, 208 miRNA, and 99 tasiRNA targeting sites on these Ψ-Rs. 194 Ψ-Rs were exhibiting tissue-specific expression patterns. The co-expression network analysis between Ψs-lncRNA-genes revealed six out of 79 pathogen-responsive Ψ-Rs as significant during pathogen invasion. CONCLUSIONS: Our study provides pathogen responsive Ψ-Rs integral for pathogen invasion, which will offer a useful resource for future experimental validations. In addition, our findings on novel peptide generations from Ψ-Rs offer valuable insights which can serve as a useful resource for predicting novel genes with the futuristic potential of being investigated for their bioactivities in the plant system.

Computational Identification of Potential Multitarget Inhibitors of Nipah Virus by Molecular Docking and Molecular Dynamics.

Nipah virus (NiV) is a recently emerged paramyxovirus that causes severe encephalitis and respiratory diseases in humans. Despite the severe pathogenicity of this virus and its pandemic potential, not even a single type of molecular therapeutics has been approved for human use. Considering the role of NiV attachment glycoprotein G (NiV-G), fusion glycoprotein (NiV-F), and nucleoprotein (NiV-N) in virus replication and spread, these are the most attractive targets for anti-NiV drug discovery. Therefore, to prospect for potential multitarget chemical/phytochemical inhibitor(s) against NiV, a sequential molecular docking and molecular-dynamics-based approach was implemented by simultaneously targeting NiV-G, NiV-F, and NiV-N. Information on potential NiV inhibitors was compiled from the literature, and their 3D structures were drawn manually, while the information and 3D structures of phytochemicals were retrieved from the established structural databases. Molecules were docked against NiV-G (PDB ID:2VSM), NiV-F (PDB ID:5EVM), and NiV-N (PDB ID:4CO6) and then prioritized based on (1) strong protein-binding affinity, (2) interactions with critically important binding-site residues, (3) ADME and pharmacokinetic properties, and (4) structural stability within the binding site. The molecules that bind to all the three viral proteins (NiV-G ∩ NiV-F ∩ NiV-N) were considered multitarget inhibitors. This study identified phytochemical molecules RASE0125 (17-O-Acetyl-nortetraphyllicine) and CARS0358 (NA) as distinct multitarget inhibitors of all three viral proteins, and chemical molecule ND_nw_193 (RSV604) as an inhibitor of NiV-G and NiV-N. We expect the identified compounds to be potential candidates for in vitro and in vivo antiviral studies, followed by clinical treatment of NiV.

Gauging the trends of pseudogenes in plants.

Pseudogenes, the debilitated parts of ancient genes, were previously scrapped off as junk or discarded genes with no functional significance. Pseudogenes have come under scrutiny for their functionality, since recent studies have unveiled their importance in the regulation of their corresponding parent genes and various biological mechanisms. Despite the enormous occurrence of pseudogenes in plants, the lack of experimental validation has contributed toward their unresolved roles in gene regulation. Contrarily, most of the studies associated with gene regulation have been mainly reported for humans, mice, and other mammalian genomes. Consequently, in order to present a cumulative report on plant-based pseudogenes research, an attempt has been made to assemble multiple studies presenting the pseudogene classification, the prediction and the determination of comparative accuracies of various computational pipelines, and recent trends in analyzing their biological functions, and regulatory mechanisms. This review represents the classical, as well as the recent advances on pseudogene identification and their potential roles in transcriptional regulation, which could possibly invigorate the quality of genome annotation, evolutionary analysis, and complexity surrounding the regulatory pathways in plants. Thus, when the ambiguous boundary girdling the pseudogenes eventually recedes on account of their explicit orchestration role, research in flora would no longer saunter compared to that on fauna.

Advancing from protein interactomes and gene co-expression networks towards multi-omics-based composite networks: approaches for predicting and extracting biological knowledge.

Prediction of biological interaction networks from single-omics data has been extensively implemented to understand various aspects of biological systems. However, more recently, there is a growing interest in integrating multi-omics datasets for the prediction of interactomes that provide a global view of biological systems with higher descriptive capability, as compared to single omics. In this review, we have discussed various computational approaches implemented to infer and analyze two of the most important and well studied interactomes: protein-protein interaction networks and gene co-expression networks. We have explicitly focused on recent methods and pipelines implemented to infer and extract biologically important information from these interactomes, starting from utilizing single-omics data and then progressing towards multi-omics data. Accordingly, recent examples and case studies are also briefly discussed. Overall, this review will provide a proper understanding of the latest developments in protein and gene network modelling and will also help in extracting practical knowledge from them.

Comparative transcriptomics reveals candidate transcription factors involved in costunolide biosynthesis in medicinal plant-Saussurea lappa.

Costunolides, an important sesquiterpene lactone (STL) isolated from Saussurea lappa, are the major pharmaceutical ingredient of various drug formulations. Identification of the genes and transcriptional regulation of costunolide biosynthesis pathway in S. lappa will propose alternatives for engineering enhanced metabolite biosynthesis in plant. Here, we aimed to unravel the transcription factors (TFs) regulating the costunolide biosynthesis. Comparative transcriptome analysis of root and leaf tissues and transcripts were annotated using various in silico tools. Putative transcription factors were identified using PlantTFDB and TF- gene co-expression network was generated followed by clustering using module based analysis to observe their coordinated behaviour. The module 1 was found to be significant based on its enrichment with major pathway genes. Further, promoter cloning determined the cis acting elements in costunolide synthase (SlCOS1) gene which catalyses the final key step of costunolide biosynthesis. Bioinformatics tools were employed to predict the cis regulatory elements, leading to the identification of MYB family of TFs as an interacting partner of SlCOS1 gene. The present study is the pioneer attempt for TF prediction and elucidation of their regulatory role in costunolide synthesis. This will help in future metabolic engineering of the pharmaceutically important STLs and their yield improvement.

Identifying potential entry inhibitors for emerging Nipah virus by molecular docking and chemical-protein interaction network.

Nipah Virus (NiV) is a newly emergent paramyxovirus that has caused various outbreaks in Asian countries. Despite its acute pathogenicity and lack of approved therapeutics for human use, there is an urgent need to determine inhibitors against NiV. Hence, this work includes prospection of potential entry inhibitors by implementing an integrative structure- and network-based drug discovery approach. FDA-approved drugs were screened against attachment glycoprotein (NiV-G, PDB: 2VSM), one of the prime targets to inhibit viral entry, using a molecular docking approach that was benchmarked both on CCDC/ASTEX and known NIV-G inhibitor set. The predicted small molecules were prioritized on the basis of topological analysis of the chemical-protein interaction network, which was inferred by integrating the drug-target network, NiV-human interaction network, and human protein-protein interaction network. A total of 17 drugs were predicted to be NiV-G inhibitors using molecular docking studies that were further prioritized to 3 novel leads - Nilotinib, Deslanoside and Acetyldigitoxin - on the basis of topological analysis of inferred chemical-protein interaction network. While Deslanoside and Acetyldigitoxin belong to an already known class of anti-NiV inhibitors, Nilotinib belongs to Benzenoids chemical class that has not been reported hitherto for developing anti-NiV inhibitors. These identified drugs are expected to be successful in further experimental evaluation and therefore could be used for anti-Nipah drug discovery. Apart, we also obtained various insights into the underlying chemical-protein interaction network, based on which several important network nodes were predicted. The applicability of our proposed approach was also demonstrated by prospecting for anti-NiV phytochemicals on an independent dataset.Communicated by Ramaswamy H. Sarma.

Transcriptome and Co-Expression Network Analyses Identify Key Genes Regulating Nitrogen Use Efficiency in Brassica juncea L.

Nitrate is the main source of inorganic nitrogen for plants, which also act as signaling molecule. Present study was aimed to understand nitrate regulatory mechanism in Brassica juncea cultivars, with contrasting nitrogen-use-efficiency (NUE) viz. Pusa Bold (PB, high-NUE) and Pusa Jai Kisan (PJK, low-NUE), employing RNA-seq approach. A total of 4031, 3874 and 3667 genes in PB and 2982, 2481 and 2843 genes in PJK were differentially expressed in response to early, low (0.25 mM KNO3), medium (2 mM KNO3) and high (4 mM KNO3) nitrate treatments, respectively, as compared to control (0 mM KNO3). Genes of N-uptake (NRT1.1, NRT1.8, and NRT2.1), assimilation (NR1, NR2, NiR, GS1.3, and Fd-GOGAT) and remobilization (GDH2, ASN2-3 and ALaT) were highly-upregulated in PB than in PJK in response to early nitrate treatments. We have also identified transcription factors and protein kinases that were rapidly induced in response to nitrate, suggesting their involvement in nitrate-mediated signaling. Co-expression network analysis revealed four nitrate specific modules in PB, enriched with GO terms like, "Phenylpropanoid pathway", "Nitrogen compound metabolic process" and "Carbohydrate metabolism". The network analysis also identified HUB transcription factors like mTERF, FHA, Orphan, bZip and FAR1, which may be the key regulators of nitrate-mediated response in B. juncea.

Chilling Affects Phytohormone and Post-Embryonic Development Pathways during Bud Break and Fruit Set in Apple (Malus domestica Borkh.).

The availability of sufficient chilling during bud dormancy plays an important role in the subsequent yield and quality of apple fruit, whereas, insufficient chilling availability negatively impacts the apple production. The transcriptome profiling during bud dormancy release and initial fruit set under low and high chill conditions was performed using RNA-seq. The comparative high number of differentially expressed genes during bud break and fruit set under high chill condition indicates that chilling availability was associated with transcriptional reorganization. The comparative analysis reveals the differential expression of genes involved in phytohormone metabolism, particularly for Abscisic acid, gibberellic acid, ethylene, auxin and cytokinin. The expression of Dormancy Associated MADS-box, Flowering Locus C-like, Flowering Locus T-like and Terminal Flower 1-like genes was found to be modulated under differential chilling. The co-expression network analysis indentified two high chill specific modules that were found to be enriched for "post-embryonic development" GO terms. The network analysis also identified hub genes including Early flowering 7, RAF10, ZEP4 and F-box, which may be involved in regulating chilling-mediated dormancy release and fruit set. The results of transcriptome and co-expression network analysis indicate that chilling availability majorly regulates phytohormone-related pathways and post-embryonic development during bud break.

Differential Network Analysis Reveals Evolutionary Complexity in Secondary Metabolism of Rauvolfia serpentina over Catharanthus roseus.

Comparative co-expression analysis of multiple species using high-throughput data is an integrative approach to determine the uniformity as well as diversification in biological processes. Rauvolfia serpentina and Catharanthus roseus, both members of Apocyanacae family, are reported to have remedial properties against multiple diseases. Despite of sharing upstream of terpenoid indole alkaloid pathway, there is significant diversity in tissue-specific synthesis and accumulation of specialized metabolites in these plants. This led us to implement comparative co-expression network analysis to investigate the modules and genes responsible for differential tissue-specific expression as well as species-specific synthesis of metabolites. Toward these goals differential network analysis was implemented to identify candidate genes responsible for diversification of metabolites profile. Three genes were identified with significant difference in connectivity leading to differential regulatory behavior between these plants. These genes may be responsible for diversification of secondary metabolism, and thereby for species-specific metabolite synthesis. The network robustness of R. serpentina, determined based on topological properties, was also complemented by comparison of gene-metabolite networks of both plants, and may have evolved to have complex metabolic mechanisms as compared to C. roseus under the influence of various stimuli. This study reveals evolution of complexity in secondary metabolism of R. serpentina, and key genes that contribute toward diversification of specific metabolites.

Phytochemica: a platform to explore phytochemicals of medicinal plants.

Plant-derived molecules (PDMs) are known to be a rich source of diverse scaffolds that could serve as the basis for rational drug design. Structured compilation of phytochemicals from traditional medicinal plants can facilitate prospection for novel PDMs and their analogs as therapeutic agents. Atropa belladonna, Catharanthus roseus, Heliotropium indicum, Picrorhiza kurroa and Podophyllum hexandrum are important Himalayan medicinal plants, reported to have immense therapeutic properties against various diseases. We present Phytochemica, a structured compilation of 963 PDMs from these plants, inclusive of their plant part source, chemical classification, IUPAC names, SMILES notations, physicochemical properties and 3-dimensional structures with associated references. Phytochemica is an exhaustive resource of natural molecules facilitating prospection for therapeutic molecules from medicinally important plants. It also offers refined search option to explore the neighbourhood of chemical space against ZINC database to identify analogs of natural molecules at user-defined cut-off. Availability of phytochemical structured dataset may enable their direct use in in silico drug discovery which will hasten the process of lead identification from natural products under proposed hypothesis, and may overcome urgent need for phytomedicines. Compilation and accessibility of indigenous phytochemicals and their derivatives can be a source of considerable advantage to research institutes as well as industries. DATABASE URL: home.iitj.ac.in/∼bagler/webservers/Phytochemica.

SerpentinaDB: a database of plant-derived molecules of Rauvolfia serpentina.

BACKGROUND: Plant-derived molecules (PDMs) are known to be a rich source of diverse scaffolds that could serve as a basis for rational drug design. Structured compilation of phytochemicals from traditional medicinal plants can facilitate prospection for novel PDMs and their analogs as therapeutic agents. Rauvolfia serpentina is an important medicinal plant, endemic to Himalayan mountain ranges of Indian subcontinent, reported to be of immense therapeutic value against various diseases. DESCRIPTION: We present SerpentinaDB, a structured compilation of 147 R. serpentina PDMs, inclusive of their plant part source, chemical classification, IUPAC, SMILES, physicochemical properties, and 3D chemical structures with associated references. It also provides refined search option for identification of analogs of natural molecules against ZINC database at user-defined cut-off. CONCLUSION: SerpentinaDB is an exhaustive resource of R. serpentina molecules facilitating prospection for therapeutic molecules from a medicinally important source of natural products. It also provides refined search option to explore the neighborhood of chemical space against ZINC database to identify analogs of natural molecules obtained as leads. In a previous study, we have demonstrated the utility of this resource by identifying novel aldose reductase inhibitors towards intervention of complications of diabetes.

Prospecting for novel plant-derived molecules of Rauvolfia serpentina as inhibitors of Aldose Reductase, a potent drug target for diabetes and its complications.

Aldose Reductase (AR) is implicated in the development of secondary complications of diabetes, providing an interesting target for therapeutic intervention. Extracts of Rauvolfia serpentina, a medicinal plant endemic to the Himalayan mountain range, have been known to be effective in alleviating diabetes and its complications. In this study, we aim to prospect for novel plant-derived inhibitors from R. serpentina and to understand structural basis of their interactions. An extensive library of R. serpentina molecules was compiled and computationally screened for inhibitory action against AR. The stability of complexes, with docked leads, was verified using molecular dynamics simulations. Two structurally distinct plant-derived leads were identified as inhibitors: indobine and indobinine. Further, using these two leads as templates, 16 more leads were identified through ligand-based screening of their structural analogs, from a small molecules database. Thus, we obtained plant-derived indole alkaloids, and their structural analogs, as potential AR inhibitors from a manually curated dataset of R. serpentina molecules. Indole alkaloids reported herein, as a novel structural class unreported hitherto, may provide better insights for designing potential AR inhibitors with improved efficacy and fewer side effects.