Inhalational exposure to particulate matter (PM) derived from natural or anthropogenic sources alters gene expression in the airways and increases susceptibility to respiratory viral infection. Woodsmoke-derived ambient PM from wildfire events during 2020 was associated with higher COVID-19 case rates in the western US. We hypothesized that exposure to suspensions of woodsmoke particles (WSP) or diesel exhaust particles (DEP) prior to SARS-CoV-2 infection would alter host immune gene expression at the transcript level. Primary human nasal epithelial cells (hNECs) from both sexes were exposed to WSP or DEP (22 μg/cm2) for 2 h, followed by infection with SARS-CoV-2 at a multiplicity of infection of 0.5. Forty-six genes related to SARS-CoV-2 entry and host response were assessed. Particle exposure alone minimally affected gene expression, while SARS-CoV-2 infection alone induced a robust transcriptional response in hNECs, upregulating type I and III interferons, interferon-stimulated genes, and chemokines by 72 h p.i. This upregulation was higher overall in cells from male donors. However, exposure to WSP prior to infection dampened expression of antiviral, interferon, and chemokine mRNAs. Sex-stratification of these results revealed that WSP exposure downregulated gene expression in cells from females more so than males. We next hypothesized that hNECs exposed to particles would have increased apical viral loads compared to unexposed cells. While apical viral load was correlated to expression of host response genes, viral titer did not differ between groups. These data indicate that WSP alter epithelial immune responses in a sex-dependent manner, potentially suppressing host defense to SARS-CoV-2 infection.

Accelerated marker-assisted selection and genomic selection breeding systems require genotyping data to select the best parents for combining beneficial traits. Since 1935, the Pee Dee cotton germplasm enhancement program has developed an important genetic resource for upland cotton (Gossypium hirsutum L.), contributing alleles for improved fiber quality, agronomic performance, and genetic diversity. To date, a detailed genetic survey of the program’s eight historical breeding cycles has yet to be undertaken. The objectives of this study were to evaluate genetic diversity across and within breeding groups, examine population structure, and contextualize these findings relative to the global upland cotton gene pool. The CottonSNP63K array was used to identify 17,441 polymorphic markers in a panel of 114 diverse Pee Dee genotypes. A subset of 4,597 markers was selected to decrease marker density bias. Identity by state (IBS) pairwise distance varied substantially, ranging from 0.55 to 0.97. Pedigree-based estimates of relatedness were not very predictive of observed genetic similarities. Few rare alleles were present, with 99.1% of SNP alleles appearing within the first four breeding cycles. Population structure analysis with principal component analysis, discriminant analysis of principal components, fastSTRUCTURE, and a phylogenetic approach revealed an admixed population with moderate substructure. A small core collection (n < 20) captured 99% of the program’s allelic diversity. Allele frequency analysis indicated potential selection signatures associated with stress resistance and fiber cell growth. The results of this study will steer future utilization of the program’s germplasm resources and aid in combining program-specific beneficial alleles and maintaining genetic diversity.

A diploid potato (Solanum tuberosum L.) recombinant inbred line population was derived from a cross between Solanum chacoense inbred line M6 and S. chacoense accession USDA8380‐1 (80‐1) to identify loci associated with self‐compatibility and Colorado potato beetle resistance. Individuals from the F4 and F5 generations were genotyped on the Illumina Infinium V3 22K Single Nucleotide Polymorphism (SNP) Array and a genetic map constructed. All F5 individuals contain at least one copy of the dominant S‐locus inhibitor (Sli) haplotype; however, not all F5 individuals set fruit. Pollen tubes reached the ovaries of both self‐fruitful and self‐unfruitful plants, indicating that the presence of the dominant Sli allele is not sufficient for selfed fruit and seed production. Loci on chromosomes 3, 5, 6, and 12 were identified as novel targets for self‐fertility improvement. Evaluation of fruit and seed set upon selfing in the F4 generation over two greenhouse seasons revealed environmental influence on self‐fertility. Loci exhibiting residual heterozygosity were found on all chromosomes except chromosomes 3 and 11 in F5 inbreds, but none of the measured self‐fertility traits were correlated with the level of heterozygosity based on SNP genotyping. Four SNPs on chromosome 2 between 22,151,711 and 22,381,719 bp were associated with foliar leptine glycoalkaloid synthesis and Colorado potato beetle resistance in the recombinant inbred line population. Robust inbred lines carrying Colorado potato beetle resistance were developed without field selection during the inbreeding process and beetle resistance was introgressed into diploid breeding lines.

Researchers have used quantitative genetics to map cotton fiber quality and agronomic performance loci, but many alleles may be population or environment-specific, limiting their usefulness in a pedigree selection, inbreeding-based system. Here, we utilized genotypic and phenotypic data on a panel of 80 important historical Upland cotton (Gossypium hirsutum L.) lines to investigate the potential for genomics-based selection within a cotton breeding program’s relatively closed gene pool. We performed a genome-wide association study (GWAS) to identify alleles correlated to 20 fiber quality, seed composition, and yield traits and looked for a consistent detection of GWAS hits across 14 individual field trials. We also explored the potential for genomic prediction to capture genotypic variation for these quantitative traits and tested the incorporation of GWAS hits into the prediction model. Overall, we found that genomic selection programs for fiber quality can begin immediately, and the prediction ability for most other traits is lower but commensurate with heritability. Stably detected GWAS hits can improve prediction accuracy, although a significance threshold must be carefully chosen to include a marker as a fixed effect. We place these results in the context of modern public cotton line-breeding and highlight the need for a community-based approach to amass the data and expertise necessary to launch US public-sector cotton breeders into the genomics-based selection era.