Performing Department
Global Change and Photosynthesis Research Unit
Non Technical Summary
Increasing atmospheric CO2concentrations has the potential to improveagricultural output. However, the photosynthetic gains from rising atmospheric CO2concentrations are frequently much lower than the maximum predicted gains. This discrepancy is the result of a down-regulation of photosynthesis that is connected to plant carbon-nitrogen imbalance when grown at elevated CO2. While this phenomenon occurs in legumes, it is not as severe as severe dueto their ability to exchange carbon for nitrogen with symbiotic soil bacteria. Understanding how legumes sense, integrate, and respond to nitrogen and carbon/energy status through dynamic control of gene expression will enable the development of strategies to achieve maximum crop yield and quality in future climates.To accomplish this, we willbuild gene regulatory networks that combine gene expression datawith epigenomic data measured across nitrogen and CO2treatments. We hypothesize that because epigenetic/chromatin states are known to influence transcription, their inclusion will enhance the accuracy of our predictions and improve our ability to identify key transcription factors involved in coordinating carbon-nitrogen homeostasis. This approach has broad potential in identifying regulatory factors in a broad range of signaling pathways and eukaryotic species,Ultimately, the regulators of carbon nitrogen balanceidentified will be important for breeding and/or developing transgenic lines with increased yield and seed quality, specifically focusing on strategies that will be robust to future climates.
Animal Health Component
0%
Research Effort Categories
Basic
66%
Applied
(N/A)
Developmental
34%
Goals / Objectives
Our aim is to characterize how nitrogen dose signaling pathways are impacted by CO2 concentration and to predict key regulatory transcription factors that integrate carbon and nitrogen status. Our central hypothesis is thatwe will be able to improve the accuracy of inferred gene regulatory network predictions by combining epigenetic data with the gene expression data we measure from a matrix of treatment conditions.The specific objectives to achieve this goal are: 1) Generate data on the physiological, transcriptomic, and epigenetic responses to nitrogen-by-CO2 treatments in two common bean genotypes, 2) Create and evaluate an epigenetic-enhanced network inference tool, and 3) Develop a system to rapidly validate target genes of transcription factors via transient assay in common bean leaves.
Project Methods
Physiological traits will be measured as follows. Photosynthetic traits measured will include CO2 assimilation measured with an infrared gas analyzer (LI-COR), and PSII efficiency, non-photochemical quenching, and other light reaction parameters measured with chlorophyll fluorescence imaging. Pigments, including chlorophylls and carotenoids, will be measured using high-performance liquid chromatography. An elemental analyzer will be used to determine the shoot and root carbonand nitrogen, as well as natural 15N/14N isotope composition. From the isotope abundance, nitrogenderived from the atmosphere (Ndfa) will be calculated by comparing the ratio in fixing lines to non-fixing reference cultivars. Chromatin accessibility (ATAC-seq), histone marks (ChIP-seq), and gene expression (RNA-seq) will be measured on tissue from the same leavesthat wereused to measure physiological traits. These data will be processed and analyzed using standard protocols.New methods for gene regulatory network inference will be developed by modifying existing?software to incorporate epigenetic data. These modifications will include using aninput matrix that includes rows for each of the histone marks (genic region) and open chromatin status (2 kb upstream) for each gene. To predict target gene expression using this matrix, predictions are made for a subset of rows (target genes) and the model is built using only the epigenetic states for the specific gene being predicted. The resulting predicted gene regulatory network that incorporate epigenetic information will be compared to the gene regulatory network generated using the original algorithm and only gene expression data. We will use Kendall's Tau and ranked based overlapmetrics to quantify the impact that the epigenetic landscape has on transcription factor-target predictions.Transient assays as used in Nicotiana benthamiana will be adapted to common bean leaves and optimized for the identification of transcription factor target genes. This will be done by identifying the best Agrobacteriumstrains and inoculation conditions as well as the timing of sampling in order to enrich for direct regulated targets.Progress will be evaluated through the publication of the research in a peer-reviewed journal(s).