The kinase-inducible domain interacting (KIX) domain is a highly conserved independently folding three-helix bundle that serves as a docking site for transcription factors, whereupon promoter activation and target specificity are achieved during gene regulation. This docking event is a harbinger of an intricate multi-protein assembly at the transcriptional apparatus and is regulated in a highly precise manner in view of the critical role it plays in multiple cellular processes. KIX domains have been characterized in transcriptional coactivators such as p300/CREB-binding protein and mediator of RNA polymerase II transcription subunit 15, and even recQ protein-like 5 helicases in various organisms. Their targets are often intrinsically disordered regions within the transactivation domains of transcription factors that attain stable secondary structure only upon complexation with KIX. In this article, we review the KIX domain in terms of its sequence and structure and present the various implications of its ability to act as a transcriptional switch, the mechanistic basis of molecular recognition by KIX, its binding specificity, target promiscuity, combinatorial potential and unique mode of regulation via allostery. We also discuss the possible roles of KIX domains in plants and hope that this review will accelerate scientific interest in KIX and pave the way for novel avenues of research on this critical domain.

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/

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. 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. 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.

Forest fragments are characteristic features of many megacities that have survived the urbanisation process and are often represented by unique assemblages of flora and fauna. Such woodlands are representations of nature in the city—often dominated by non-native and invasive species that coexist with resilient native congeners and purposefully introduced flora. These forest fragments also provide significant ecosystem services to urban society and therefore, understanding their compositional patterns is of considerable importance for conservation and management. In this work, we use a complex network approach to investigate species assemblages across six distinct urban forest fragments in the South Delhi Ridge area of the National Capital Territory, India. We generate bipartite ecological networks using conventional vegetation sampling datasets, followed by network partitioning to identify multiple cliques across the six forest fragments. Our results show that urban woodlands primarily form invasive–native associations, and that major invasive species, such as Prosopis juliflora and Lantana camara exclude each other while forming cliques. Our findings have implications for the conservation of these urban forests and highlight the importance of using network approaches in vegetation analysis.

Prions are often considered as molecular memory devices, generating reproducible memory of a conformational change. Prion-like proteins (PrLPs) have been widely demonstrated to be present in plants, but their role in plant stress and memory remains unexplored. In this work, we report the widespread presence of PrLPs in plants through a comprehensive meta-analysis of 39 genomes representing major taxonomic groups. We find diverse functional roles associated with these proteins in various species and term the full complement of PrLPs in a genome as its “prionome.” In particular, we found the rice prionome being significantly enriched in transposons/retrotransposons (Ts/RTRs) and identified over 60 rice PrLPs that were differentially regulated in stress and developmental responses. This prompted us to explore whether and to what extent PrLPs may build stress memory. By integrating the available rice interactome, transcriptome, and regulome data sets, we could find links between stress and memory pathways that would not have otherwise been discernible. Regulatory inferences derived from the superimposition of these data sets revealed a complex network and cross talk between PrLPs, transcription factors (TFs), and the genes involved in stress priming. This integrative meta-analysis connects transient and transgenerational memory mechanisms in plants with PrLPs, suggesting that plant memory may rely upon protein-based signals in addition to chromatin-based epigenetic signals. Taken together, our work provides important insights into the anticipated role of prion-like candidates in stress and memory, paving the way for more focused studies for validating the role of the identified PrLPs in memory acclimation.