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ARS Home » Plains Area » Fargo, North Dakota » Edward T. Schafer Agricultural Research Center » Weed and Insect Biology Research » Research » Publications at this Location » Publication #422656
Research Project: Biology of Weed-Crop Interactions to Improve Weed Management Strategies in Northern Agro-ecosystems

Location: Weed and Insect Biology Research

Title: RNAseq analysis of the below-ground interactions between canola and kochia

Author
item TANVIR, FARHAN - North Dakota State University
item Anderson, James
item PATTERSON, ERIC - Michigan State University
item RAHMAN, MUKHLESUR - North Dakota State University
item Horvath, David

Submitted to: Weed Science Society of America Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 1/16/2025
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Canola (Brassica napus) growers in the Northern Great Plains have concerns about herbicide resistant kochia (Kochia scoparia), as observations of kochia in canola fields are increasing. However, the actual impact and interactions between kochia on canola are not well understood. To obtain better insights on interactions, canola was grown with or without kochia under both field and greenhouse conditions. Although increasing canola densities greatly impacted kochia growth, and impacted growth of canola itself when grown at increasing densities, kochia had little impact on the growth of canola - even at high densities. To further investigate this phenomenon, root samples from kochia growing by itself or with 2, 4, or 6 canola plants, as well as a single canola plant growing by itself or with 2, 4, or 6 kochia plants were collected for RNAseq analysis. The experimental design was a random complete block with three replicates for each of the 8 treatments, and the experiment was run twice. RNAseq was run on each experiment independently and reads were mapped to either the canola genome or the preliminary kochia genome developed by the International Weed Genomics Consortium using HiSAT2 in SciNet. Differential expression was determined by running featureCounts on the resulting BAM files in the CyVerse Discovery Environment followed by DEseq analysis in R. Overlap in lists and expression trends (up or down with increasing numbers of competitors) of genes between experiments were assessed. These studies are ongoing, and the most up to date results and implications of the gene expression studies will be presented and discussed.