Far-Go

Diets from each experimental and within each phase were sampled from 9 different locations, a total of 2 kg per diet. Sub-samples of 200 g of each diet and phase were sent to the North Dakota State University Veterinary Diagnostic Laboratory (Fargo, ND, USA) for mycotoxin analysis, and 300 g of each diet were sent to the North Carolina Department of Agriculture (Raleigh, NC, USA) for proximate analysis. The CON had detectable deoxynivalenol levels because corn DDGS used in CON formulation had deoxynivalenol contamination. Nevertheless, MT had 1.9 mg/kg more deoxynivalenol than CON, close to the planned concentration of 2 mg/kg (Table 3).
Serum samples were submitted for a biochemical profile at Antech Diagnostic Laboratory (Cary, NC, USA). Antioxidant status and immune markers were evaluated in mid-jejunal mucosa by quantifying protein carbonyls (STA-310, Cell Biolabs, Inc., San Diego, CA, USA), malondialdehydes (STA-330, Cell Biolabs, Inc., USA), total glutathione (STA-312, Cell Biolabs, Inc., USA), tumor necrosis factor-alpha (PTA00, R&D Systems, Inc., Minneapolis, MN, USA), and interleukin-8 (P8000, R&D Systems, Inc., USA). The manufacturer’s manual for each kit was followed in the laboratory assays of protein carbonyls, malondialdehydes, and total protein according to procedures described by Zhao and Kim [26 (link)]. Measurements of total glutathione, tumor necrosis factor-α, and interleukin-8 followed the procedures as described by Holanda et al [24 (link)].
Transversal sections of 0.5 cm were transferred to 70% ethanol after 14 days and sent to the North Carolina State University Histopathology Laboratory (College of Veterinary Medicine, Raleigh, NC, USA), where samples were included in paraffin, microtomed, and stained for Ki-67 antigen by immunohistochemistry before assembling of histological slides [8 (link)]. Histological evaluation of gut morphology was performed by one single evaluator recording villus height and width, crypt depth, and for calculating villus height:crypt depth ratio, and the proportion of proliferating cells to the total number of cells in the crypt using the Image JS tool [27 (link)] in ten pictures for each experimental unit (pig) according to the measurements described by Holanda and Kim [12 (link)]. Measurements of villus height and width were used to calculate the average mid-jejunal surface area for each villus by using the following formula [28 (link)]:

For each accession, genomic DNA was extracted from the same plant used to increase the seeds evaluated in this study. DNA extractions were performed at the USDA-ARS Small Grains and Potato Germplasm Research Unit using the CTAB protocol (Stewart and Via 1993 (link)). The DNA was precipitated by adding isopropanol, followed by washing of the pellet with ice-cold 70% ethanol, and resuspension in 200 µL of Tris HCl ethylenediaminetetraacetic acid (pH 8.0).
Genotyping was carried out at the USDA-ARS genotyping laboratory at Fargo, North Dakota, using the Infinium wheat SNP 9K iSelect assay from the Illumina platform (Illumina Inc., San Diego, CA) developed by the International Wheat SNP Consortium (Cavanagh et al. 2013 (link)). The raw Illumina SNP data were processed with the GenomeStudio v2011.1 software (Illumina). The array yielded 5234 scorable SNP markers. The polymorphic SNPs were ordered according to the scaled map positions of the hexaploid wheat 9K SNP consensus map (Cavanagh et al. 2013 (link)). The arm orientation of chromosomes 4A, 5A, and 5B presented here is in opposite orientation to the published consensus map (Cavanagh et al. 2013 (link)).
The dataset was filtered using a 10% cutoff for missing data in either loci or accessions (23 accessions were eliminated). On the basis of the filtered SNP data, a triangular identity-by-state genetic similarity matrix (Kang et al. 2008 (link)) was then obtained for all possible pairs of accessions. For groups of accessions with ≥0.99 genetic similarity, only one representative accession (the one with the lowest number of missing data) was retained per group. After applying these filtering criteria, a total of 875 accessions were retained for the GWAS. Only SNPs with minor allele frequency (MAF) ≥0.10 (i.e., minor allele present in at least 87 accessions) were considered for GWAS. Of the 4585 SNPs that satisfied this criterion, 4374 were positioned on the consensus map. Low-frequency SNPs were discarded to focus on SNPs with greater statistical power (Turner et al. 2011 ). The downside of this approach is the potential loss of true resistance loci present at low frequency (increase in false negatives). In this study we prioritized the reliability of the detected QTL over the sensitivity of the analyses.
Molecular markers tightly associated to two well-characterized loci conferring resistance to multiple pathogens were included as internal controls. The diagnostic KaspLr34 assay (http://www.cerealsdb.uk.net/cerealgenomics/CerealsDB/SNPs/Documents/MAS, = wMAS000003) was designed around a 3-bp indel in exon 11 of the Lr34/Yr18 gene (Lagudah et al. 2009 (link)). Marker csSNP856 (=Kasp856) is tightly linked to the Lr67/Yr46 locus but is not diagnostic for the resistance gene (Forrest et al. 2014 ). Data for these two control markers are summarized in File S3.

The artemisinin control solution was used to prepare a series of control solutions for the peak area measurement and recording. Linear regression was conducted with the mass concentration of the reference substance (μg/mL) as the x-coordinate (x) and the peak area as the y-coordinate (y) and was forced to go through the far point. The standard curve equation was y = 317.42x, r = 0.99999. The results showed that the concentration of artemisinin had a good linear correlation with the peak area within the range of 150.76–3015.2 μg/mL.

For GO enrichment analysis on the biological processes domain, the hypergeometric test in the GOstats package (version 2.56.0) was applied. The analysis was restricted to gene sets containing 5–1,000 genes. Significant pathways were filtered by applying P value <0.05 and gene count/term >10. Further selection of relevant GO terms was based on sorting terms on their OddsRatios (the ratio of a GO term in the differently expressed genes list to the occurrence of this GO term in a universal gene list, obtained from org.Mm.eg.db [version 3.13.0]). In addition, GO term results were screened for enrichment of terms related to immune regulation. Selected top pathways were visualized using ggplot2 (version 3.3.5).
A web application for data searching and visualization was generated using the shiny package of Rstudio (https://shiny.rstudio.com), and the package ShinyCell for database creation (Ouyang et al., 2021 (link)).