Location: Insect Control and Cotton Disease Research
Project Number: 3091-22000-040-007-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Jun 1, 2026
End Date: May 31, 2028
Objective:
The long-term goal of this project is to develop an integrated disease prediction system that combines real-time pathogen quantification, host genetic susceptibility profiles, and microbiome-mediated conduciveness data through machine learning models to provide variety-specific disease risk predictions for cotton growers. To achieve this goal, a comprehensive platform encompassing soil pathogen abundance, host genetic profiles, and microbiota that the host recruits will be considered (host x microbiome x pathogen interactions). Such models will empower growers to make science-based decisions of germplasms with real risk knowledge before planting, leading to proactive disease control measures. To achieve this goal, the project will consist of six objectives: 1) Collect diverse cotton pathogen isolates from diagnostic labs and field sources, followed by whole genome sequencing for comparative genomic analysis; 2) Develop Loop-mediated isothermal amplification (LAMP) assays for Fusarium oxysporum f. sp. vasinfectum race 4 (Fov4), Meloidogyne incognita (root-knot nematode), and Rhizoctonia solani with lateral flow detection; 3) Validate field-deployability of the LAMP assay; 4) Establish pathogen load thresholds linked to yield loss under diverse soil conditions; 5) Conduct pilot genome wide associations study (GWAS) on 50 diverse cotton genotypes to identify host resistance loci for three Fov4 strains; and 6) Perform Metagenome-assembled genomes (MAG)-based metabolomics GWAS (mGWAS) using known suppressive vs. conducive soils to identify host genes controlling differential microbial recruitment.
Approach:
The strain culture collection will be sourced for three major soil-borne cotton pathogens: Fusarium oxysporum f. sp. vasinfectum race 4 (Fov4), Meloidogyne incognita (root-knot nematode), and Rhizoctonia solani. Isolates will be obtained from: 1) existing diagnostic laboratory collections across Texas through the Texas A&M University Plant Diagnostic Clinic. While the main collection will be from Texas, strains from other cotton growing states will also be obtained through the National Plant Diagnostic Network; and 2) fresh field collections from active cotton production fields with confirmed disease pressure. For each pathogen, we will target a total of 50 isolates representing diverse geographic origins, host varieties, and soil types to capture maximum genetic diversity. Whole genome sequencing will be performed using PromethION and Illumina hybrid assembly. The specific regions identified from the genomes will aid in designing LAMP primers. The primer design strategy will utilize LAMP Designer and PrimerExplorer software to design six primers per pathogen targeting conserved sequences within unique genomic regions. Primer optimization will focus on 60-65°C isothermal amplification with specific attention to hairpin structures, primer-dimer formation, and Guanine and Cytosine (GC) content balance. Multiplex LAMP development will be attempted for simultaneous detection of multiple pathogens using different fluorophore labels or distinct lateral flow channels. The lateral flow detection system will employ dual-labeling approach. Quantitative analysis will be achieved through smartphone-based colorimetric analysis using custom apps for field deployment. Field validation will be conducted at the Texas A&M AgriLife Research farms representing diverse soil conditions: 1) Blackland Prairie clay soils (College Station), 2) sandy soils (El Paso), and 3) Gulf Coast clay loam (Corpus Christi). Each location will include replicated field plots with natural pathogen populations and artificially inoculated plots to establish pathogen load gradients. Inoculum preparation will use characterized strains from the genomic analysis with quantified spore/propagule concentrations. Soil sampling will follow a systematic grid pattern with Global Positioning System (GPS) coordinates for spatial analysis, collected at critical growth stages: pre-plant, emergence, squaring, and flowering. Cross-validation with quantitative Polymerase Chain Reaction (qPCR) quantification will establish assay correlation coefficients and develop correction factors for different soil types.