Current Research Projects

Soil microorganisms play vital roles in plant growth, development, immunity, and adaptation to the large number of environmental conditions found in nature. Our lab demonstrated that the microbiome profile of the Arabidopsis endosphere (the microbes inhabiting the interior of the root) varies across the day/night cycle. This promising result raises many exciting questions that we want to answer through this project using metagenomic, metatranscriptomic, and physiological approaches:

  • What is the role of the plant circadian clock on the composition of the root microbiome?
  • What are the molecular mechanisms underlying this regulation?
  • What are the molecular mechanisms by which certain microbes interact with plants?
  • How did certain microbes evolve to establish this interaction with plants?  
  • What are the effects of these interactions on plant growth and development? 

Ancient records as early as Mesopotamia (2600 B.C.) show humans using plant secondary metabolites (commonly known as medicinal plants) for the treatment of diseases and illnesses. With the current advancements in technology and chemical methodologies, the scientific community is actively identifying structurally and functionally diverse secondary plant metabolites. Some of the unique biological activities of these plant metabolites have aided in drug discovery. In addition, plant secondary metabolites are routinely used in food flavors, fragrances, and as insecticides and dyes. We use high-throughput transcriptome and metabolome analysis to explore and identify biosynthetic pathways. 

We currently have ongoing projects with:
Hyptis species
Fabiana species
Escallonia species
Thymus species

Climate change scenarios for much of the southwestern U.S. include drier and more variable rainfall patterns compared to the last century, with unknown consequences for plant communities. Land managers and restoration practitioners, who are charged with restoring vast areas of natural landscapes that have been damaged or destroyed by human activity and by climate change, must re-assemble vegetation communities that are appropriate for future drier conditions. Genomic tools can elucidate the molecular mechanisms underlying plant responses to changing environmental conditions. We use a combination of transcriptomic, metabolomic, and physiological analyses to understand the mechanisms by which diverse plant communities experience high intra- and inter-annual variation in rainfall and how individual species and whole ecosystems adapt to the challenges of climate change.

Salvia hispanica L. (commonly known as chia) is an annual self-pollinated species within the mint family Lamiaceae. Chia is gaining popularity worldwide and specially in US as a healthy oil and food supplement for human and animal consumption due to its favorable oil composition, and high protein, fiber, and antioxidant contents. Despite these benefits and its growing public demand, very limited breeding efforts have yet been directed towards improving the varieties currently cultivated. This is due to the lack of genomic resources and molecular markers associated with the desirable agronomic traits. The objective of this project is to generate molecular markers and breed new climate-resilient chia varieties capable of withstanding climate change stresses. We have breeding populations for drought tolerance, pest/pathogen resistance, high productivity, and nutrient content.
Biopesticides are competitive subclass of pesticides that naturally present in organisms or compounds that suppress the growth and proliferation of pests’ or pathogen’s population by diverse mechanisms of action. Biopesticides are attractive environmentally friendly alternative to synthetic pesticides. They can be biodegradable, specific in action (harmless to non-target organisms), and also possess the ability to counter pest and pathogen resistance issues caused by synthetic pesticides. Recent advances in Drug-Target Interaction (DTI) analysis through Deep Learning based molecular modeling and prediction toolkits, it is possible to virtually screen millions of molecules against specific targets. In this project, we are using Deep learning to identify natural products that bind to and deactivate proteins important for plant pathogen/pest virulence.   
We use high-throughput transcriptome sequencing of prostate cancer samples at different stages of progression to advance our understanding of the disease. By comprehensively analyzing gene expression patterns, we identify key molecular changes that occur as the cancer progresses. This knowledge is essential for developing targeted therapies, improving diagnostic tools, and ultimately enhancing patient outcomes. Accurately identifying the cancer stage through this method is vital for providing appropriate treatments, which can significantly improve patient survival rates. As a separate project, we extract plant secondary metabolites from diverse and endangered plant species to identify potential anti-cancer activities and use transcriptome sequencing to elucidate the mode of action of promising candidates. Plants are a rich source of secondary metabolites. Secondary metabolites are mostly small organic molecules, produced by plants and other organisms, that are not essential for growth, development and reproduction but help the organisms adapt to their environment. Such secondary metabolites, broadly classified as terpenoids, phenolics, and alkaloids, exhibit a broad spectrum of bioactivities, including antitumor activity.  
The discovery of novel therapeutic compounds through de novo drug design represents a critical challenge in the field of pharmaceutical research. Traditional drug discovery approaches are often resource-intensive and time-consuming, leading researchers to explore innovative methods that harness the power of deep learning and reinforcement learning techniques. In this project, we developed a novel drug design approach called drugAI that leverages the Encoder–Decoder Transformer architecture in tandem with Reinforcement Learning via a Monte Carlo Tree Search (RL-MCTS) to expedite the process of drug discovery while ensuring the production of valid small molecules with drug-like characteristics and strong binding affinities towards their targets. We are looking for PhD student and/or postdoc with background in Machine Learning to further improve the drugAI algorithm.
In a separate, more recent project, we are interested in harnessing the latest advancements in machine learning to improve our capacity to predict crop yields by integrating genomic and environmental data. By utilizing sophisticated algorithms and models, we aim to analyze vast datasets that include genetic information of crops and various environmental factors. Ultimately, our goal is to provide more accurate and timely forecasts, which can help farmers make informed decisions about planting, irrigation, and harvesting, thereby optimizing productivity and sustainability in agriculture.
Soil microorganisms play vital roles in plant growth, development, immunity, and adaptation to the large number of environmental conditions found in nature. Our lab demonstrated that the microbiome profile of the Arabidopsis endosphere (the microbes inhabiting the interior of the root) varies across the day/night cycle. This promising result raises many exciting questions that we want to answer through this project using metagenomic, metatranscriptomic, and physiological approaches:
  • What is the role of the plant circadian clock on the composition of the root microbiome?
  • What are the molecular mechanisms underlying this regulation?
  • What are the molecular mechanisms by which certain microbes interact with plants?
  • How did certain microbes evolve to establish this interaction with plants?  
  • What are the effects of these interactions on plant growth and development? 
Salvia hispanica L. (commonly known as chia) is an annual self-pollinated species within the mint family Lamiaceae. Chia is gaining popularity worldwide and specially in US as a healthy oil and food supplement for human and animal consumption due to its favorable oil composition, and high protein, fiber, and antioxidant contents. Despite these benefits and its growing public demand, no breeding efforts have yet been directed towards improving the varieties currently cultivated. This is due to the lack of genomic resources and molecular markers associated with the desirable agronomic traits. The objective of this project is to generate resources for the future development of high-density genetic map in S. hispanica

Ancient records as early as Mesopotamia (2600 B.C.) show humans using plant secondary metabolites (commonly known as medicinal plants) for the treatment of diseases and illnesses. With the current advancements in technology and chemical methodologies, the scientific community is actively identifying structurally and functionally diverse secondary plant metabolites. Some of the unique biological activities of these plant metabolites have aided in drug discovery. In addition, plant secondary metabolites are routinely used in food flavors, fragrances, and as insecticides and dyes. 

We currently have ongoing projects with:
Hyptis species
Fabiana species
Escallonia species
Menodora species

Biopesticides are competitive subclass of pesticides that naturally present in organisms or compounds that suppress the growth and proliferation of pests’ or pathogen’s population by diverse mechanisms of action. Biopesticides are attractive environmentally friendly alternative to synthetic pesticides. They can be biodegradable, specific in action (harmless to non-target organisms), and also possess the ability to counter pest and pathogen resistance issues caused by synthetic pesticides. Recent advances in Drug-Target Interaction (DTI) analysis through Deep Learning based molecular modeling and prediction toolkits, it is possible to virtually screen millions of molecules against specific targets. In this project, we are using Deep learning to identify natural products that bind to and deactivate proteins important for plant pathogen/pest virulence.   

Climate change scenarios for much of the southwestern U.S. include drier and more variable rainfall patterns compared to the last century with unknown consequences for plant communities. Land managers and restoration practitioners, who are charged with restoring vast areas of natural landscapes that have been damaged or destroyed by human activity and by climate change, must re-assemble vegetation communities that are appropriate for future drier conditions. Genomic tools can elucidate the molecular mechanisms underlying plant responses to changing environmental conditions. We propose to examine physiological and transcriptomic responses of threatened species to aid in restoration efforts and ensure the restored areas will be maintained at low cost under future climate conditions.    

Plants are a rich source of secondary metabolites. Secondary metabolites are mostly small organic molecules, produced by plants and other organisms, that are not essential for growth, development and reproduction but help the organisms adapt to their environment. Such secondary metabolites, broadly classified as terpenoids, phenolics, and alkaloids, possess sufficient structural complexity so that their synthesis is difficult or at this time not yet accomplished. More importantly, secondary metabolites exhibit a broad spectrum of bioactivities including antitumor activity.  In this project, we extract plant secondary metabolites from diverse and hard to find plant species for potential anti-cancer activities and use transcriptome sequencing to elucidate the mode of actions of promising candidates. 
Sunflower is a robust solar tracker. During the day, it continuously orients its apex from east to west. Interestingly, at night sunflower gradually reorients towards east in anticipation of the sunrise. My previous work as postdoc in Harmer lab (UC Davis) demonstrated that sunflower solar tracking is regulated by the plant circadian clock which generates anti-phasic growth rate in the east and west side of the stem. During the day (sunrise to sunset), the east side of the sunflower stem grows faster compared to the west side. Conversely, during the night (Sunset to sunrise), the west side of the stem grows faster than the east side. This differential growth pattern on the opposite sides of the stem is what enables heliotropism in sunflowers. The mechanisms by which a single stem achieves different growth rates in different parts is not yet clear. The goal of this project is to understand the mechanisms by which circadian clock so precisely regulates the growth on the opposite sides of the stem. In the field, we will use compounds that specifically inhibit or activate certain signaling pathways in plants to identify the various molecular pathways that play roles in sunflower’s solar tracking behavior.  
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