Browsing by Author "Priya, Piyush"

Now showing 1 - 10 of 10

An artificial neural network–based deep learning model to predict combined stress impact and interaction in plants
(John Wiley & Sons, 2026) Priya, Piyush; Pandey, Prachi; Jain, Rubi; Kandpal, Manu; Jain, Shradha; Chaudhury, Rim; Ramegowda, Venkategowda; Senthil-Kumar, Muthappa
Premise: Plants are frequently exposed to combinations of abiotic and biotic stresses that pose a greater threat to yield and productivity than individual stresses. However, knowledge of the impact of many stress combinations in numerous plants is limited due to the lack of experimental data, which could take decades to generate. To overcome this limitation, we utilized existing literature data from various plant species and stress combinations to derive biological inferences, thereby gaining a comprehensive understanding of plant responses through a computational tool. Methods: Public databases were used to gather literature on the impact of various abiotic and biotic stress combinations. Then, a composite artificial neural network (ANN)–based multi-target classification and regression deep learning model was developed using machine learning algorithms. Results: The model predicted the impact of stress interactions in plants, including the morphological parameters affected and percentage changes in those parameters, with an overall accuracy of 76.33%. Predicted reductions in yield were validated in rice under combined drought and heat stress. Discussion: The ANN-based model developed in this study is a valuable resource for plant researchers seeking to understand the impact of stress combinations. The tool can make use of multivariate and complex combined stress datasets.
The chickpea genomic web resource: visualization and analysis of the desi-type Cicer arietinum nuclear genome for comparative exploration of legumes
(BioMed Central Ltd, 2014) Misra, Gopal; Priya, Piyush; Bandhiwal, Nitesh; Bareja, Neha; Jain, Mukesh; Bhatia, Sabhyata; Chattopadhyay, Debasis; Tyagi, Akhilesh K.; Yadav, Gitanjali
Background: Availability of the draft nuclear genome sequences of small-seeded desi-type legume crop Cicer arietinum has provided an opportunity for investigating unique chickpea genomic features and evaluation of their biological significance. The increasing number of legume genome sequences also presents a challenge for developing reliable and information-driven bioinformatics applications suitable for comparative exploration of this important class of crop plants. Results: The Chickpea Genomic Web Resource (CGWR) is an implementation of a suite of web-based applications dedicated to chickpea genome visualization and comparative analysis, based on next generation sequencing and assembly of Cicer arietinum desi-type genotype ICC4958. CGWR has been designed and configured for mapping, scanning and browsing the significant chickpea genomic features in view of the important existing and potential roles played by the various legume genome projects in mutant mapping and cloning. It also enables comparative informatics of ICC4958 DNA sequence analysis with other wild and cultivated genotypes of chickpea, various other leguminous species as well as several non-leguminous model plants, to enable investigations into evolutionary processes that shape legume genomes. Conclusions: CGWR is an online database offering a comprehensive visual and functional genomic analysis of the chickpea genome, along with customized maps and gene-clustering options. It is also the only plant based web resource supporting display and analysis of nucleosome positioning patterns in the genome. The usefulness of CGWR has been demonstrated with discoveries of biological significance made using this server. The CGWR is compatible with all available operating systems and browsers, and is available freely under the open source license at http://www.nipgr.res.in/CGWR/home.php.
EssOilDB: A database of essential oils reflecting terpene composition and variability in the plant kingdom
(Oxford University Press, 2014) Kumari, Sangita; Pundhir, Sachin; Priya, Piyush; Jeena, Ganga; Punetha, Ankita; Chawla, Konika; Jafaree, Zohra Firdos; Mondal, Subhasish; Yadav, Gitanjali
Plant essential oils are complex mixtures of volatile organic compounds, which play indispensable roles in the environment, for the plant itself, as well as for humans. The potential biological information stored in essential oil composition data can provide an insight into the silent language of plants, and the roles of these chemical emissions in defense, communication and pollinator attraction. In order to decipher volatile profile patterns from a global perspective, we have developed the ESSential OIL DataBase (EssOilDB), a continually updated, freely available electronic database designed to provide knowledge resource for plant essential oils, that enables one to address a multitude of queries on volatile profiles of native, invasive, normal or stressed plants, across taxonomic clades, geographical locations and several other biotic and abiotic influences. To our knowledge, EssOilDB is the only database in the public domain providing an opportunity for context based scientific research on volatile patterns in plants. EssOilDB presently contains 123 041 essential oil records spanning a century of published reports on volatile profiles, with data from 92 plant taxonomic families, spread across diverse geographical locations all over the globe. We hope that this huge repository of VOCs will facilitate unraveling of the true significance of volatiles in plants, along with creating potential avenues for industrial applications of essential oils. We also illustrate the use of this database in terpene biology and show how EssOilDB can be used to complement data from computational genomics to gain insights into the diversity and variability of terpenoids in the plant kingdom. EssOilDB would serve as a valuable information resource, for students and researchers in plant biology, in the design and discovery of new odor profiles, as well as for entrepreneurs--the potential for generating consumer specific scents being one of the most attractive and interesting topics in the cosmetic industry. Database URL: http://nipgr.res.in/Essoildb/
IGMAP: An interactive mapping and clustering platform for plants
(Cell Press, 2015) Priya, Piyush; Bandhiwal, Nitesh; Misra, Gopal; Mondal, Subhashish; Yadav, Gitanjali
Next-generation sequencing (NGS) technologies have resulted in a massive surge of high-throughput genomic data, particularly for the plant kingdom, boosting the development of methods for gene family discovery and identification of clustering patterns at genomic scales. Plants are well known for the occurrence of both genic and chromosomal duplications that have resulted in the widespread existence of gene families in this kingdom, apart from being associated with subsequent evolutionary divergence via sub-functionalization or neo- functionalization (Flagel and Wendel, 2009). These divergent mechanisms eventually lead to the formation of gene clusters, which in turn, have been shown to confer selective advantages to the genome including co-inheritance and co- regulation (Fischbach et al., 2008). Although gene clusters have been conventionally understood to be the genetic building blocks of prokaryotic genomes, comparative genomic studies have revealed the presence of functionally related genes that are clustered in lower nematodes, fungi, and several higher eukaryotes (Zorio et al., 1994; Blumenthal, 1998; Lee and Sonnhammer, 2003; Hurst et al., 2004; Thomas, 2006). However, very few data are available on the modularity or clustered linkage of genes in plants, despite the widespread occurrence of duplication events in the kingdom. In this regard, recent reports of biosynthetic modules and clustered organization of genes spotted in several major classes of plant-derived secondary metabolites arising through neo-functionalization and relocation of duplicated or existing genes, have offered an exciting niche (Osbourn, 2010). The presently available approaches for the identification and analysis of plant gene clusters include map-based cloning, forward and reverse genetics, and genome mining. As the wealth of plant genome sequence data continues to increase exponentially, newer methods are going to be required to carry out genome mining in a rational and useful manner.
Quantification of the plant terpenome: predicted versus actual emission potentials
(Springer, 2016) Priya, Piyush; Kumari, Sangita; Yadav, Gitanjali
Plant essential oils are complex mixtures of volatile organic compounds, which play indispensable roles in communication, defense, and adaptive evolution. The complete chemical library produced by a plant is referred to as its terpenome. The potential biological information stored in essential oil composition data can provide an insight into the silent language of plants, as well as roles of terpene emissions in direct and indirect defense, and for playing a crucial role in adaptive evolution. In this work, we have attempted to measure the plant terpenome from a global perspective. One way of measuring the terpenome is to observe and record actual emissions in natural conditions, and this has been in practice for over a century through variously evolving methods of comprehensive GC–MS and HPLC. An alternative method is a knowledge-based prediction of the terpenome, and this method has gained popularity in recent years, with the advent of large-scale genome sequencing technologies. Over the past decade, our laboratory has been involved in compilation and investigation of the plant terpenome using both these methods and this has offered us the opportunity to compare and contrast data from actual and potential emissions, in order to better understand the terpenome and its roles in primary, secondary and adaptive metabolism. We have used emission data in conjunction with genomic data in order to understand how a plant creates the so-called final terpenome, specific to itself, and whether or not plants tap the complete potential for terpene biosynthesis at their disposal according to their genomes. For measuring actual emissions, we have used EssOilDB (the ESSential OIL DataBase), the largest contextual web resource for phytochemicals and for measuring the total plant potential for emissions, we have used TERZYME, an automated algorithm for identification and analysis of genes and proteins involved in isoprenoid biosynthesis.
The role of lectins and HD-ZIP transcription factors in isoprenoid based plant stress responses
(INSA, 2012) Kumari, Sangita; Shridhar, Smriti; Singh, Daljit; Priya, Piyush; Farmer, Rohit; Hundal, Jasreet; Sharma, Priyanka; Bavishi, Krutika; Schrick, Kathrin; Yadav, Gitanjali
It was over half a century ago when the overwhelming array of chemicals found in plants was postulated to be more than just by-products of primary metabolism. Ever since, extensive research has been conducted on plant secondary metabolites which are now known to be the end points of sophisticated survival mechanisms that plants have developed as a response to various kinds of stresses. Stress, defined by its negative effect on the growth and development of an individual, can be internal (metabolic or genetic), external (biotic or abiotic), permanent or acute. To cope, organisms must develop tolerance, resistance or avoidance mechanisms. Isoprenoids, often released as volatiles from plants, constitute the most diverse groups of natural products and play an essential part in plant defense systems, both directly (as emitted volatiles) and indirectly (the principle of inviting friends to feast on foes). Research over the last decade has resulted in a significant improvement in our understanding of the isoprenoid biosynthesis but there remains much to learn about the complex regulatory network controlling the various steps of these pathways and their dynamic co-ordination. Here we identify novel plant proteins and provide a putative role for them in isoprenoid based stress responses, along with insights into future perspectives for research.
Stress combinations and their interactions in plants database: a one-stop resource on combined stress responses in plants
(John Wiley & Sons, 2023) Priya, Piyush; Patil, Mahesh; Pandey, Prachi; Singh, Anupriya; Babu, Vishnu Sudha; Senthil-Kumar, Muthappa
We have developed a compendium and interactive platform, named Stress Combinations and their Interactions in Plants Database (SCIPDb; http://www.nipgr.ac.in/scipdb.php), which offers information on morpho-physio-biochemical (phenome) and molecular (transcriptome and metabolome) responses of plants to different stress combinations. SCIPDb is a plant stress informatics hub for data mining on phenome, transcriptome, trait-gene ontology, and data-driven research for advancing mechanistic understanding of combined stress biology. We analyzed global phenome data from 939 studies to delineate the effects of various stress combinations on yield in major crops and found that yield was substantially affected under abiotic-abiotic stresses. Transcriptome datasets from 36 studies hosted in SCIPDb identified novel genes, whose roles have not been earlier established in combined stress. Integretome analysis under combined drought-heat stress pinpointed carbohydrate, amino acid, and energy metabolism pathways as the crucial metabolic, proteomic, and transcriptional components in plant tolerance to combined stress. These examples illustrate the application of SCIPDb in identifying novel genes and pathways involved in combined stress tolerance. Further, we showed the application of this database in identifying novel candidate genes and pathways for combined drought and pathogen stress tolerance. To our knowledge, SCIPDb is the only publicly available platform offering combined stress-specific omics big data visualization tools, such as an interactive scrollbar, stress matrix, radial tree, global distribution map, meta-phenome analysis, search, BLAST, transcript expression pattern table, Manhattan plot, and co-expression network. These tools facilitate a better understanding of the mechanisms underlying plant responses to combined stresses.
Structural and biochemical perspectives in plant isoprenoid biosynthesis
(Springer, 2013) Kumari, Sangita; Priya, Piyush; Misra, Gopal; Yadav, Gitanjali
The isoprenoid family represents one of the most ancient and widespread classes of structurally and functionally rich biomolecules known to man. Although these natural products are synthesized in all organisms, the plant kingdom exhibits tremendous variation in their chemistry and roles, ranging from primary metabolism to secondary metabolism and specialized ecological interactions with the environment. Despite enormous diversity in structure and function, all isoprenoids are derived from the universal C5 precursor isoprene. The isoprenoid biosynthetic pathway has three major stages, viz., (1) synthesis of the isoprene building blocks, followed by their (2) assembly into flexible linear and branched hydrocarbon substrates, which then undergo (3) multistep reaction cascades to generate the vast assortment of isoprenoid end products. One of the most interesting aspects of isoprenoid biosynthesis is its being finely tuned by a multilayered and complex regulatory network, which excellently controls the machinery producing one of the most heterogeneous groups of molecules in plants. Terpene synthases, enzymes of the final stage, are key players in the generation of isoprenoid diversity, catalyzing one of the most complex reactions known to chemistry and biology. Unraveling the mechanism by which a minimal pool of substrates is thus converted into tens of thousands of regiospecific and stereospecific products, is a promising research avenue: This knowledge may be practically used for rational design of novel compounds by metabolic engineering, in order to yield plants with improved nutritional efficacy, stress resistance, bio-pharmaceutical properties etc. This review is an attempt to summarize the biochemical, molecular, physiological, structural, genomic and evolutionary aspects of isoprenoid biosynthesis, providing new insights into how these enzymes utilize various innovative strategies for creation of the so-called final terpenome.
Terzyme: a tool for identification and analysis of the plant terpenome
(BioMed Central Ltd, 2018) Priya, Piyush; Yadav, Archana; Chand, Jyoti; Yadav, Gitanjali
BACKGROUND: Terpenoid hydrocarbons represent the largest and most ancient group of phytochemicals, such that the entire chemical library of a plant is often referred to as its 'terpenome'. Besides having numerous pharmacological properties, terpenes contribute to the scent of the rose, the flavors of cinnamon and the yellow of sunflowers. Rapidly increasing -omics datasets provide an unprecedented opportunity for terpenome detection, paving the way for automated web resources dedicated to phytochemical predictions in genomic data. RESULTS: We have developed Terzyme, a predictive algorithm for identification, classification and assignment of broad substrate unit to terpene synthase (TPS) and prenyl transferase (PT) enzymes, known to generate the enormous structural and functional diversity of terpenoid compounds across the plant kingdom. Terzyme uses sequence information, plant taxonomy and machine learning methods for predicting TPSs and PTs in genome and proteome datasets. We demonstrate a significant enrichment of the currently identified terpenome by running Terzyme on more than 40 plants. CONCLUSIONS: Terzyme is the result of a rigorous analysis of evolutionary relationships between hundreds of characterized sequences of TPSs and PTs with known specificities, followed by analysis of genome-wide gene distribution patterns, ontology based clustering and optimization of various parameters for building accurate profile Hidden Markov Models. The predictive webserver and database is freely available at http://nipgr.res.in/terzyme.html and would serve as a useful tool for deciphering the species-specific phytochemical potential of plant genomes.
When two negatives make a positive: The favorable impact of the combination of abiotic stress and pathogen infection on plants
(Oxford University Press, 2024) Pandey, Prachi; Patil, Mahesh; Priya, Piyush; Senthil-Kumar, Muthappa
Combined abiotic and biotic stresses modify plant defense signaling, leading to either the activation or suppression of defense responses. Although the majority of combined abiotic and biotic stresses reduce plant fitness, certain abiotic stresses reduce the severity of pathogen infection in plants. Remarkably, certain pathogens also improve the tolerance of some plants to a few abiotic stresses. While considerable research focuses on the detrimental impact of combined stresses on plants, the upside of combined stress remains hidden. This review succinctly discusses the interactions between abiotic stresses and pathogen infection that benefit plant fitness. Here, we discuss various factors that govern the positive influence of combined abiotic stress and pathogen infection on plant performance. We also provide a brief overview of the role of pathogens, mainly viruses, in improving plant responses to abiotic stresses. We further highlight the critical nodes in defense signaling that guide plant responses during abiotic stress towards enhanced resistance to pathogens. Studies on antagonistic interactions between abiotic and biotic stressors can uncover candidates in host plant defense that may shield plants from combined stresses.