Scientific communication is facilitated by a data-driven, scientifically sound taxonomy that considers the end-user's needs and established successful practice. Previously (Geiser et al. 2013; Phytopathology 103:400-408. 2013), the Fusarium community voiced near unanimous support for a concept of Fusarium that represented a clade comprising all agriculturally and clinically important Fusarium species, including the F. solani Species Complex (FSSC). Subsequently, this concept was challenged by one research group (Lombard et al. 2015 Studies in Mycology 80: 189-245) who proposed dividing Fusarium into seven genera, including the FSSC as the genus Neocosmospora, with subsequent justification based on claims that the Geiser et al. (2013) concept of Fusarium is polyphyletic (Sandoval-Denis et al. 2018; Persoonia 41:109-129). Here we test this claim, and provide a phylogeny based on exonic nucleotide sequences of 19 orthologous protein-coding genes that strongly support the monophyly of Fusarium including the FSSC. We reassert the practical and scientific argument in support of a Fusarium that includes the FSSC and several other basal lineages, consistent with the longstanding use of this name among plant pathologists, medical mycologists, quarantine officials, regulatory agencies, students and researchers with a stake in its taxonomy. In recognition of this monophyly, 40 species recently described as Neocosmospora were recombined in Fusarium, and nine others were renamed Fusarium. Here the global Fusarium community voices strong support for the inclusion of the FSSC in Fusarium, as it remains the best scientific, nomenclatural and practical taxonomic option available.

Genetic resistance is a key strategy for disease management in soybean. Over the last 50 years, soybean germplasm has been phenotyped for resistance to many pathogens, resulting in the development of disease-resistant elite breeding lines and commercial cultivars. While biparental linkage mapping has been used to identify disease resistance loci, genome-wide association studies (GWAS) using high-density and high-quality markers such as single nucleotide polymorphisms (SNPs) has become a powerful tool to associate molecular markers and phenotypes. The objective of our study was to provide a comprehensive understanding of disease resistance in the United States Department of Agriculture Agricultural Research Service Soybean Germplasm Collection by using phenotypic data in the public Germplasm Resources Information Network and public SNP data (SoySNP50K). We identified SNPs significantly associated with disease ratings from one bacterial disease, five fungal diseases, two diseases caused by nematodes, and three viral diseases. We show that leucine-rich repeat (LRR) receptor-like kinases and nucleotide-binding site-LRR candidate resistance genes were enriched within the linkage disequilibrium regions of the significant SNPs. We review and present a global view of soybean resistance loci against multiple diseases and discuss the power and the challenges of using GWAS to discover disease resistance in soybean.

Areas within an agricultural field in the same season often differ in crop productivity despite having the same cropping history, crop genotype, and management practices. One hypothesis is that abiotic or biotic factors in the soils differ between areas resulting in these productivity differences. In this study, bulk soil samples collected from a high and a low productivity area from within six agronomic fields in Illinois were quantified for abiotic and biotic characteristics. Extracted DNA from these bulk soil samples were shotgun sequenced. While logistic regression analyses resulted in no significant association between crop productivity and the 26 soil characteristics, principal coordinate analysis and constrained correspondence analysis showed crop productivity explained a major proportion of the taxa variance in the bulk soil microbiome. Metagenome-wide association studies (MWAS) identified more Bradyrhizodium and Gammaproteobacteria in higher productivity areas and more Actinobacteria, Ascomycota, Planctomycetales, and Streptophyta in lower productivity areas. Machine learning using a random forest method successfully predicted productivity based on the microbiome composition with the best accuracy of 0.79 at the order level. Our study showed that crop productivity differences were associated with bulk soil microbiome composition and highlighted several nitrogen utility-related taxa. We demonstrated the merit of MWAS and machine learning for the first time in a plant-microbiome study.