Source: AGRICULTURAL RESEARCH SERVICE submitted to NRP
OPTIMIZING NITROGEN-PHOTOSYNTHESIS RELATIONSHIPS FOR FUTURE CLIMATES: IMPROVING PREDICTIVE ACCURACY OF GENE NETWORKS WITH EPIGENETIC STATES
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
ACTIVE
Funding Source
AFRI COMPETITIVE GRANT
Reporting Frequency
Annual
Accession No.
1032112
Grant No.
2024-67013-42312
Cumulative Award Amt.
$299,496.00
Proposal No.
2023-08543
Multistate No.
(N/A)
Project Start Date
Jul 1, 2024
Project End Date
Jun 30, 2026
Grant Year
2024
Program Code
[A1152]- Physiology of Agricultural Plants
Recipient Organization
AGRICULTURAL RESEARCH SERVICE
1815 N University
Peoria,IL 61604
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
(N/A)
Research Effort Categories
Basic
66%
Applied
(N/A)
Developmental
34%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
20114111020100%
Knowledge Area
201 - Plant Genome, Genetics, and Genetic Mechanisms;

Subject Of Investigation
1411 - Beans (fresh, fresh-processed);

Field Of Science
1020 - Physiology;
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).

Progress 07/01/24 to 06/30/25

Outputs
Target Audience:American farmers benefit from having access to crop varieties that are highly productive, more nutrient efficient, and resilient to biotic and abiotic stresses. Identification of the genes that have a positive effect on traits of interest is a major bottleneck to the development of new germplasm. The goal of this research is to develop a computational pipeline that uses both transcriptome and epigenome data to predict genetic regulators that control important crop traits and to develop a transient system in legumesto test these regulators. This computational and experimental pipeline will be useful to academic, government and industry researchers interested in improving a wide-range of traits. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Training Activities: A graduate student supported by this project gained new technical skills for determining chromatin modifications and chromatin accessibility. Additional travel support from the University of Illinois Urbana-Champaign allowed the student to visit the Co-PI at Purdue University for three weeks to learn these techniques. Professional Development: An undergraduate researcher participating in this research is taking doing a summer internship at University of Illinois to prepare them for post-graduation research opportunities. The paid internship supports their salary in the lab as well as provides bi-weekly seminar/workshops in professional development. How have the results been disseminated to communities of interest? Nothing Reported What do you plan to do during the next reporting period to accomplish the goals?In the second and final year of the project, we will complete Objective 1 by finishing the data collection on epigenetic states by sequence chromatin modification ChIP libraries and performing and sequencing ATAC-seq data. This will be done in the first quarter. For Objective 2, the collected data will be analyzed using our new computational pipeline to generate transcriptome only networks and transcriptome + epigenetic networks which will then be assessed based on their ability to predict the expression of target genes in left out sample data. The target genes of transcriptional regulators we identify using this new computational pipeline will be validated using the transient system we are developing.

Impacts
What was accomplished under these goals? The aim of this project is to incorporate epigenetic data into machine learning models that predict gene regulatory networks from transcriptomic data with the goal of improving our ability to predict important genes that control traits of interest. These networks will be validated through the development of transient expression systems we will develop for legumes. This experimental and computational pipeline will be made available to researchers to help identify genes that improve traits of interest to American farmers. In year one of this project, we made substantial progress on Objective 1 by growing common bean lines at three nitrogen doses and two CO2 concentrations and collecting data on physiological traits, gene expression, and epigenetic marks (histone modifications). Samples were also collected to determine chromatin accessibility. In support of Objective 2, we have used previously generated data to develop our computational approach to integrating epigenetic data using existing tools. We have landed on two possible strategies that will be compared. The fist uses epigenetic data as predictors, similar to transcriptional regulators, while the second uses epigenetic data to weight the influence of the transcriptional regulators on target genes. In support of Objective 3, we have optimized protoplast isolation for common bean to obtain millions of healthy cells. We have demonstrated that we can transfect these cells at sufficient rates for future experiments using the pRUBY construct, which gives the cells a distinct red color which can be visualized under the microscope.

Publications