When two negatives make a positive: the favorable impact of the combination of abiotic stress and pathogen infection on plants.

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. Various factors that govern the positive influence of combined abiotic stress and pathogen infection on plant performance are also discussed. In addition, we 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.

Stress combinations and their interactions in plants database: a one-stop resource on combined stress responses in plants.

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.

Terzyme: a tool for identification and analysis of the plant terpenome.

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.

EssOilDB: a database of essential oils reflecting terpene composition and variability in the plant kingdom.

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/

The chickpea genomic web resource: visualization and analysis of the desi-type Cicer arietinum nuclear genome for comparative exploration of legumes.

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.