The overarching theme of our research is investigating the origins of biological complexity using plant specialized metabolism as a model. We perform this research using a variety of model and non-model plant species, and using both experimental and computational approaches. Findings of this research also have real-world applications such as in agriculture, nutrition and medicine.
Characterizing plant metabolic diversity
We are interested in understanding how lineage-specific specialized metabolic phenotypes (eg: acylsugars, alkaloids, latex metabolites etc.) have emerged across various families and orders in the plant kingdom. Families such as Asteraceae, Apiaceae, Orchidaceae, Euphorbiaceae are large families housing thousands of species, however, we know very little about metabolic diversity and metabolic evolution in these lineages. We are using high-throughput technologies such as mass spectrometry, RNA-seq, genomics as well as traditional biochemical and phylogenetic approaches to obtain a better understanding of evolution of chemical novelty in such poorly sampled lineages.
Discovering useful metabolites from plants
There are over a million estimated metabolites produced across different species in the plant kingdom. Many of these metabolites have already found their way into human endeavors such as medicine, nutrition and agriculture. However, there are still substantial opportunities to prospect useful metabolites, understand their biosynthetic pathways, their ecological and translational roles and study how the chemical novelty evolved. We are focusing on a set of hyper-diverse compounds found only in the morning glory family (Convolvulaceae), which are known to have medicinal properties and can exhibit allelopathic effects. A better understanding of the biosynthesis and biological activities of these compounds can support the cause of sustainable agriculture, and at the same time provide novel insights into how this unique pathway originated.
Predictive computational genomics of metabolic complexity
Much of our knowledge on metabolites and metabolic pathways comes from model species such as Arabidopsis, rice and tomato. While the common descent of life ensures this knowledge can be translated to other related species, it is not always the case. Enzyme function can diverge, pathway organization can change and the metabolite pools may differ through evolutionary time. With recent developments in computational algorithms, it is now possible to start modeling many of these processes in silico and create predictive computational models of enzyme function, pathway organization and metabolite pools. We are using different approaches such as comparative genomics, network reconstruction and machine learning to create predictive models of metabolites and their biosynthetic pathways. The strength of our lab lies in being able to seamlessly merge experimental data generation and computational modeling into a single research framework.
Evolution of metabolic complexity
Metabolic pathways in plants are quite dynamic. They are in a constant state of innovation due to gene duplication, transcriptional divergence, enzyme promiscuity etc. How have specific metabolic pathways originated and diversified? What is the role of positive selection and genetic drift in shaping metabolic diversity? How does enzyme promiscuity influence evolution of specialized metabolic pathways? We are investigating these and other allied questions using evolutionary genomic approaches, by performing comparative analyses of plant genomes, transcriptomes and proteomes.
Our long-term interests lie in generating a better understanding of how plant diversity has influenced the human society. Evidently, much of the influence is due to plant metabolites - from macro and micro nutrients to medicinal compounds. We are interested in documenting these influences in a way that enables further research. The PI has previously founded the website Biodiversity of India, which semantically documents various aspects of the flora and fauna found in India. This Wikipedia-like framework can be extended to address specific topics related to plant diversity.