Location: Weed and Insect Biology Research
Project Number: 3060-21220-033-012-T
Project Type: Trust Fund Cooperative Agreement
Start Date: Jul 1, 2021
End Date: Jun 30, 2024
Objective:
1) Complete sequencing of libraries from time course studies on the impact of inter or intra-species competition on the corn root transcriptome. 2) Develop and use machine learning algorithms to identify gene expression patterns associated with competition, and identify gene networks by which corn perceives and alters its expression when presented with inter -or intra-species competition. 3) Develop testable hypotheses for altering perception of competition to reduce corn yield losses.
Approach:
We will complete RNA extractions and sequencing library construction for corn competing with red root pigweed and for corn competing with other corn plants (we have already collected corn root tissue from two separate experimental runs each with three replicates per time point for corn exposed to competition or not exposed to competition at 0, 1, 2, 3, 7, and 14 days, and have already quantified the gene expression data from similar experiments with corn competing with winter canola). These libraries will be sequenced and processed according to best practices, and then expression of individual corn genes from each sample will be determined - also according to best practices. The resulting data will be analyzed using machine learning algorithms needed to identify temporally regulated clusters of genes that are responsive to our cover-crop (winter canola), weed (red root pigweed), or high-density planting (corn with corn) interference. These clusters will then be further analyzed using pattern discerning artificial intelligence technologies to identify likely regulatory genes controlling the later changes in gene expression. Such regulatory genes will be targeted for future research and manipulation. Additionally, the function of genes in the observed clusters will be analyzed to determine the specific physiological and developmental processes impacted by weed presence or high-density planting conditions using gene set and subnetwork enrichment analysis.
