Enhancing stress tolerance using a synthetic phase-dependent stress response.
Circadian biologists have associated large portions of the transcriptome with temporal regulation by the circadian clock. One consequence of this internal timekeeping in plants is time of day dependent responses to abiotic and biotic stress. Given the energy costs associated with mounting a defense response it is not surprising that plants restrict the response to the time when its effectiveness is maximal. Rather than thinking of stress response as simply on or off, we focus on fine-tuning the timing of the response in a more 'turn up or down' approach. Adjusting the amplitude of the response within the normal phase domain and maintaining the timing of essential growth processes has the potential to enhance stress tolerance without a reduction in fitness.
Integration of computational tools to explore the diversity of temporal regulation in plant specialized metabolism.
Plants produce an amazing diversity of specialized metabolites (SM) that offer many benefits to human society. SMs are essential for pharmaceutical products and non-medicinal applications in the chemical industry, food additives, dyes, perfumes, cosmetics, and nutraceuticals. These products offer the potential to increase the return on investment of current biofuel crops by providing high-value co-products. While many specialized metabolic enzymes have been characterized, their spatial and temporal regulation is less understood creating a challenge for engineering and optimizing metabolite levels. Understanding how diverse plants differentially regulate the production of the products arising from the same SM pathway will enable researchers to engineer such plants with greater reliability. The goal of this project is to build a computational tool in KBase that would enable researchers to integrate transcriptome data with metabolic networks of general and specialized metabolism for different plant species. To do so, we will focus on the glucosinolate (GSLs) class of SMs within the plant order Brassicales.
PI: Kathleen Greenham
Co-PI: Samuel Seaver
Mining spatial and single-cell transcriptomics to understand cell locality and heterogeneity in tissues.
Recently developed spatially-resolved RNA sequencing with single-cell RNA sequencing has facilitated the mapping of cell identities and localizations in a tissue by capturing mRNA expressions at their spatial location. The complexity of data analytics has also escalated, pushing the need for new computational methods to reveal the spatial characteristics of single-cell transcriptomes to better understand tissue specific functions. The goal of this work is to develop novel machine learning methods to address the challenges in analyzing spatial and single-cell transcriptomics data with a focus on spatial cell heterogeneity of ovarian cancer and circadian rhythms in Arabidopsis thaliana.
PI: Rui Kuang
Co-PI: Kathleen Greenham, Jeremy Chien