Public Abstract

DE-SC0020358: Discovering Innovations in Stress Tolerance through Comparative Gene Regulatory Network Analysis and Cell-Type Specific Expression Maps

Award Status: Expired

Institution: Board of Trustees of the Leland Stanford Junior University, Redwood City, CA
UEI: HJD6G4D6TJY5
DUNS: 009214214

Most Recent Award Date: 07/29/2022
Number of Support Periods: 3
PM: Rabinowicz, Pablo

Current Budget Period: 09/15/2021 - 09/14/2023
Current Project Period: 09/15/2019 - 09/14/2023
PI: Dinneny, Jose

Supplement Budget Period: N/A

Public Abstract

Discovering innovations in stress tolerance through comparative gene regulatory network analysis and cell-type specific expression maps. José R. Dinneny, Stanford University (Principal Investigator) Maheshi Dassanayake, Louisiana State University (Co-Investigator) Song Li, Virginia Polytech and State University (Co-Investigator) Dong-Ha Oh, Louisiana State University (Co-Investigator) John Schiefelbein, University of Michigan (Co-Investigator) The library of life is composed of genomes. Our ability to read and interpret the texts of this library has been fostered through the use of experimental model organisms that are readily studied. Over the past 100 years the fruit fly, yeast cell and Arabidopsis weed have contributed greatly to our understanding of the biological processes that unite organisms on Earth. Today, however, genomes of organisms that are difficult to culture, rare or even extinct exist in this library of life. While their genome sequences have been unlocked, the nature of the genes contributing to this fascinating diversity in physiology and development is currently hidden due to a lack of methods available to extract significant functional meaning. Which genes allow a cactus to survive the desert heat, or a sea grass plant to grow in ocean water, or some ferns to tolerate desiccation? In agriculture, domestication has led to the breeding of rapidly growing cultivars that perform well when the climate cooperates, but often fail when water or nutrients are limiting. Planned cultivation of bioenergy crops on soils of poor quality is necessary so as not to compete with other agricultural sectors, yet these environments will dramatically impact biomass accumulation. If the innovations that nature has selected for across plant species can be discovered, it may be possible to address these challenges and improve the sustainability of agriculture in ways that are simply impossible by traditional breeding. Innovations in gene function allow wild plants to inhabit environments that are commonly stressful to domesticated crops. The goal of this research program is to identify such innovations by defining the regulatory and physiological context that genes function in across 11 sequenced Brassicaceae genomes, including bioenergy crops and crop wild relatives, using recent advances in single-cell RNA sequencing (scRNA-seq) and DNA affinity purification sequencing (DAP-seq) technologies. Furthermore, machine-learning algorithms will utilize the evolutionary history of gene duplication events and functional genomics data to identify innovations in gene function associated with growth control under environmental stress. Putative genes associated with extremophyte resilience will be introduced into stress sensitive species to test whether extremophyte physiological properties can be transferred to naïve genomes. These studies will utilize a molecular genetic model that can be rapidly characterized using scalable and open source phenomics systems. Through our investigation, we will establish an experimental and data analytics pipeline that will be broadly applicable to the study of gene function at the plant family level and result in a road map for improving plant traits for bioenergy and beyond.

Discovering innovations in stress tolerance through comparative gene regulatory network analysis
and cell-type specific expression maps.
José R. Dinneny, Stanford University (Principal Investigator)
Maheshi Dassanayake, Louisiana State University (Co-Investigator)
Song Li, Virginia Polytech and State University (Co-Investigator)
Dong-Ha Oh, Louisiana State University (Co-Investigator)
John Schiefelbein, University of Michigan (Co-Investigator)

The library of life is composed of genomes. Our ability to read and interpret the texts of this library has been fostered through the use of experimental model organisms that are readily studied. Over the past 100 years the fruit fly, yeast cell and Arabidopsis weed have contributed greatly to our understanding of the biological processes that unite organisms on Earth. Today, however, genomes of organisms that are difficult to culture, rare or even extinct exist in this library of life. While their genome sequences have been unlocked, the nature of the genes contributing to this fascinating diversity in physiology and development is currently hidden due to a lack of methods available to extract significant functional meaning. Which genes allow a cactus to survive the desert heat, or a sea grass plant to grow in ocean water, or some ferns to tolerate desiccation? In agriculture, domestication has led to the breeding of rapidly growing cultivars that perform well when the climate cooperates, but often fail when water or nutrients are limiting. Planned cultivation of bioenergy crops on soils of poor quality is necessary so as not to compete with other agricultural sectors, yet these environments will dramatically impact biomass accumulation. If the innovations that nature has selected for across plant species can be discovered, it may be possible to address these challenges and improve the sustainability of agriculture in ways that are simply impossible by traditional breeding.

Innovations in gene function allow wild plants to inhabit environments that are commonly stressful to domesticated crops. The goal of this research program is to identify such innovations by defining the regulatory and physiological context that genes function in across 11 sequenced Brassicaceae genomes, including bioenergy crops and crop wild relatives, using recent advances in single-cell RNA sequencing (scRNA-seq) and DNA affinity purification sequencing (DAP-seq) technologies. Furthermore, machine-learning algorithms will utilize the evolutionary history of gene duplication events and functional genomics data to identify innovations in gene function associated with growth control under environmental stress. Putative genes associated with extremophyte resilience will be introduced into stress sensitive species to test whether extremophyte physiological properties can be transferred to naïve genomes. These studies will utilize a molecular genetic model that can be rapidly characterized using scalable and open source phenomics systems. Through our investigation, we will establish an experimental and data analytics pipeline that will be broadly applicable to the study of gene function at the plant family level and result in a road map for improving plant traits for bioenergy and beyond.