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.

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Management of insects that cause economic damage to yields of soybean mainly rely on insecticide applications. Sources of resistance in soybean plant introductions (PIs) to different insect pests have been reported, and some of these sources, like for the soybean aphid (SBA), have been used to develop resistant soybean cultivars. With the availability of SoySNP50K and the statistical power of genome-wide association studies, we integrated phenotypic data for beet armyworm, Mexican bean beetle (MBB), potato leafhopper (PLH), SBA, soybean looper (SBL), velvetbean caterpillar (VBC), and chewing damage caused by unspecified insects for a comprehensive understanding of insect resistance in the United States Department of Agriculture Soybean Germplasm Collection. We identified significant single nucleotide (SNP) polymorphic markers for MBB, PLH, SBL, and VBC, and we highlighted several leucine-rich repeat-containing genes and myeloblastosis transcription factors within the high linkage disequilibrium region surrounding significant SNP markers. Specifically for soybean resistance to PLH, we found the PLH locus is close but distinct to a locus for soybean pubescence density on chromosome 12. The results provide genetic support that pubescence density may not directly link to PLH resistance. This study offers a novel insight of soybean resistance to four insect pests and reviews resistance mapping studies for major soybean insects.

Sudden death syndrome (SDS) of soybean is caused by a soilborne pathogen, Fusarium virguliforme. Phytotoxins produced by F. virguliforme are translocated from infected roots to leaves, in which they cause SDS foliar symptoms. In this study, additional putative phytotoxins of F. virguliforme were identified, including three secondary metabolites and 11 effectors. While citrinin, fusaric acid, and radicicol induced foliar chlorosis and wilting, Soybean mosaic virus (SMV)-mediated overexpression of F. virguliforme necrosis-inducing secreted protein 1 (FvNIS1) induced SDS foliar symptoms that mimicked the development of foliar symptoms in the field. The expression level of fvnis1 remained steady over time, although foliar symptoms were delayed compared with the expression levels. SMV::FvNIS1 also displayed genotype-specific toxicity to which 75 of 80 soybean cultivars were susceptible. Genome-wide association mapping further identified three single nucleotide polymorphisms at two loci, where three leucine-rich repeat receptor-like protein kinase (LRR-RLK) genes were found. Culture filtrates of fvnis1 knockout mutants displayed a mild reduction in phytotoxicity, indicating that FvNIS1 is one of the phytotoxins responsible for SDS foliar symptoms and may contribute to the quantitative susceptibility of soybean by interacting with the LRR-RLK genes.

Pre-visual detection of crop disease is critical for food security. Field-based spectroscopic remote sensing offers a method to enable timely detection, but still requires appropriate instrumentation and testing. Soybean plants were spectrally measured throughout a growing season to assess the capacity of leaf and canopy level spectral measurements to detect non-visual foliage symptoms induced by Fusarium virguliforme (Fv, which causes sudden death syndrome). Canopy reflectance measurements were made using the Piccolo Doppio dual field-of-view, two-spectrometer (400 to 1630 nm) system on a tractor. Leaf level measurements were obtained, in different plots, using a handheld spectrometer (400 to 2500 nm). Partial least squares discriminant analysis (PLSDA) was applied to the spectroscopic data to discriminate between Fv-inoculated and control plants. Canopy and leaf spectral data allowed identification of Fv infection, prior to visual symptoms, with classification accuracy of 88% and 91% for calibration, 79% and 87% for cross-validation, and 82% and 92% for validation, respectively. Differences in wavelengths important to prediction by canopy vs. leaf data confirm that there are different bases for accurate predictions among methods. Partial least square regression (PLSR) was used on a late-stage canopy level data to predict soybean seed yield, with calibration, cross-validation and validation R 2 values 0.71, 0.59 and 0.62 (p < 0.01), respectively, and validation root mean square error of 0.31 t•ha −1. Spectral data from the tractor mounted system are thus sensitive to the expression of Fv root infection at canopy scale prior to canopy symptoms, suggesting such systems may be effective for precision agricultural research and management.

BackgroundPlant cell wall degrading enzymes (PCWDEs) are a subset of carbohydrate-active enzymes (CAZy) produced by plant pathogens to degrade plant cell walls. To counteract PCWDEs, plants release PCWDEs inhibitor proteins (PIPs) to reduce their impact. Several transgenic plants expressing exogenous PIPs that interact with fungal glycoside hydrolase (GH)11-type xylanases or GH28-type polygalacturonase (PG) have been shown to enhance disease resistance. However, many plant pathogenic Fusarium species were reported to escape PIPs inhibition. Fusarium virguliforme is a soilborne pathogen that causes soybean sudden death syndrome (SDS). Although the genome of F. virguliforme was sequenced, there were limited studies focused on the PCWDEs of F. virguliforme. Our goal was to understand the genomic CAZy structure of F. viguliforme, and determine if exogenous PIPs could be theoretically used in soybean to enhance resistance against F. virguliforme.ResultsF. virguliforme produces diverse CAZy to degrade cellulose and pectin, similar to other necrotorphic and hemibiotrophic plant pathogenic fungi. However, some common CAZy of plant pathogenic fungi that catalyze hemicellulose, such as GH29, GH30, GH44, GH54, GH62, and GH67, were deficient in F. virguliforme. While the absence of these CAZy families might be complemented by other hemicellulases, F. virguliforme contained unique families including GH131, polysaccharide lyase (PL) 9, PL20, and PL22 that were not reported in other plant pathogenic fungi or oomycetes. Sequence analysis revealed two GH11 xylanases of F. virguliforme, FvXyn11A and FvXyn11B, have conserved residues that allow xylanase inhibitor protein I (XIP-I) binding. Structural modeling suggested that FvXyn11A and FvXyn11B could be blocked by XIP-I that serves as good candidate for developing transgenic soybeans. In contrast, one GH28 PG, FvPG2, contains an amino acid substitution that is potentially incompatible with the bean polygalacturonase-inhibitor protein II (PvPGIP2).ConclusionsIdentification and annotation of CAZy provided advanced understanding of genomic composition of PCWDEs in F. virguliforme. Sequence and structural analyses of FvXyn11A and FvXyn11B suggested both xylanases were conserved in residues that allow XIP-I inhibition, and expression of both xylanases were detected during soybean roots infection. We postulate that a transgenic soybean expressing wheat XIP-I may be useful for developing root rot resistance to F. virguliforme.Electronic supplementary materialThe online version of this article (doi:10.1186/s12866-016-0761-0) contains supplementary material, which is available to authorized users.