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Maize is a globally important staple that is used as food for human and animal consumption, fuel, and other industrial applications. Pathogens affect all stages of the plant life cycle and every plant organ, and lead to significant yield losses. An integrated strategy incorporating cultural and chemical management practices, as well as development of resistant plant varieties, is needed to prevent yield losses due to plant diseases. Large numbers of breeding material must be screened to develop pathogen-resistant maize varieties. Inoculation methods must be high-throughput to accommodate the large screening experiments. Additionally, there needs to be an extensive understanding of the plant–pathogen interaction to use a targeted biotechnology-based approach, which takes advantage of knowledge of the system to engineer resistance. To evaluate germplasm for breeding and biotechnology approaches, inoculation methods must replicate natural infection, and disease severity must be rated consistently to accurately screen germplasm or gather data on pathogens of interest. Here, we review inoculation and rating methods for Gibberella ear rot, seedling blight caused byGlobisporangium ultimumvar.ultimum, and Goss's wilt that are efficient and high-throughput. We also introduce fluorescence microscopy techniques for leaf samples infected withExserohilum turcicum, the causal agent of northern corn leaf blight. These pathogens all cause significant yield losses, and in particular, Gibberella ear rot is associated with the accumulation of harmful mycotoxins. Understanding how pathogens cause disease and how plants defend against attack is a major goal of maize pathology studies and critical for developing integrated management strategies. 

Maize significantly contributes to food and fuel production. Yields can be reduced due to foliar diseases, which reduce photosynthetic leaf area. The bacterial foliar disease Goss's wilt (caused byClavibacter nebraskensis) can cause significant yield losses in susceptible maize varieties.C. nebraskensiscan infect leaves through wounds and colonize the vascular tissue of the leaf. We present a protocol that replicates this process with the use of a “clapper” with pins on one end to create wounds and a sponge soaked in inoculum on the other end, which allows for efficient field inoculations of maize leaves. Disease severity is then rated on a percentage scale multiple times over the season to generate an area under disease progress curve (AUDPC). Genetic host resistance is one of the most effective forms of foliar disease control in maize, as there are few effective forms of chemical control for bacterial diseases that affect maize. Screening for resistance in diverse germplasm, or for fine mapping a specific resistance gene, requires inoculating large populations in the field for obtaining phenotypic data. Our high-throughput protocol allows for large-scale disease evaluations and is useful for finding forms of genetic resistance or to understand plant–pathogen interactions of bacterial foliar pathogens. 

Maize is an important food and fuel crop globally. Ear rots, caused by fungal pathogens, are some of the most detrimental maize diseases, due to reduced grain yield and the production of harmful mycotoxins. Mycotoxins are naturally occurring toxins produced by certain fungal species that can cause acute and chronic health issues in humans and animals that consume mycotoxin-contaminated grain. Pathogens can infect the developing ear through silks, or through wounds in the ears produced by pests. Plants naturally develop genetic resistance to pathogens. The maize genes involved in resistance to the pathogen may be different, depending on whether the ear was infected via silks or wounds. To differentiate between these two forms of resistance, natural infections can be reproduced by injecting inoculum through the silk channel, or by producing wounds using a needle, and introducing inoculum directly onto developing ears. Our protocol describes a technique used to inoculate developing maize ears withFusarium graminearum, one of the fungal species that causes ear rot. We describe both silk channel and side needle inoculation techniques. Our protocol uses a backpack inoculator for both methods of infection, allowing for high-throughput inoculations, which are necessary for large field experiments. After harvest, the ears are visually rated on a percentage of disease scale. The protocol results in quantitative data that can be used for research on elucidating genetic resistance to fungal pathogens to assist breeding selections, and to understand plant–pathogen interactions of ear rots in maize. 

Maize is a globally important grain crop that is important for food and fuel. Northern corn leaf blight, caused byExserohilum turcicum, is an important fungal foliar disease of maize that is highly prevalent and causes yield losses globally. Microscopy can be used to visualize plant–fungal interactions on a cellular level, which enables pathology and genetics studies. Host resistance and isolate aggressiveness can be characterized at different stages of disease development, which enables a more detailed understanding of the pathogenesis process and host–pathogen interactions. Our protocol outlines an efficient, cost-effective method for stainingE. turcicumtissue on inoculated maize leaves and visualizing samples using a compound fluorescence microscope. This protocol uses KOH treatment followed by aniline blue staining, which stains glucans present in plant and fungal cell walls, and samples are visualized using fluorescence microscopy. Quantitative data about fungal structures including the conidia, hyphal structures, and appressoria, the structures formed to push through the plant leaf surface after conidia have germinated, can be obtained from the images generated using this technique. Visualization of these structures can help pathologists understand plant–pathogen interactions for maize andE. turcicum. This method has advantages over other methods because the stain is less toxic than other available stains, samples can be processed in a more high-throughput manner than other protocols, and the required supplies are relatively inexpensive. 

Abstract Foliar diseases of maize are among the most important diseases of maize worldwide. This study focused on 4 major foliar diseases of maize: Goss's wilt, gray leaf spot, northern corn leaf blight, and southern corn leaf blight. QTL mapping for resistance to Goss’s wilt was conducted in 4 disease resistance introgression line populations with Oh7B as the common recurrent parent and Ki3, NC262, NC304, and NC344 as recurrent donor parents. Mapping results for Goss’s wilt resistance were combined with previous studies for gray leaf spot, northern corn leaf blight, and southern corn leaf blight resistance in the same 4 populations. We conducted (1) individual linkage mapping analysis to identify QTL specific to each disease and population; (2) Mahalanobis distance analysis to identify putative multiple disease resistance regions for each population; and 3) joint linkage mapping to identify QTL across the 4 populations for each disease. We identified 3 lines that were resistant to all 4 diseases. We mapped 13 Goss’s wilt QTLs in the individual populations and an additional 6 using joint linkage mapping. All Goss’s wilt QTL had small effects, confirming that resistance to Goss’s wilt is highly quantitative. We report several potentially important chromosomal bins associated with multiple disease resistance including 1.02, 1.03, 3.04, 4.06, 4.08, and 9.03. Together, these findings indicate that disease QTL distribution is not random and that there are locations in the genome that confer resistance to multiple diseases. Furthermore, resistance to bacterial and fungal diseases is not entirely distinct, and we identified lines resistant to both fungi and bacteria, as well as loci that confer resistance to both bacterial and fungal diseases. 

Bacterial leaf streak (BLS) of maize is an emerging foliar disease of maize in the Americas. It is caused by the gram-negative nonvascular bacterium Xanthomonas vasicola pv. vasculorum. There are no chemical controls available for BLS, and thus, host resistance is crucial for managing X. vasicola pv. vasculorum. The objective of this study was to examine the genetic determinants of resistance to X. vasicola pv. vasculorum in maize, as well as the relationship between other defense-related traits and BLS resistance. Specifically, we examined the correlations among BLS severity, severity for three fungal diseases, flg-22 response, hypersensitive response, and auricle color. We conducted quantitative trait locus (QTL) mapping for X. vasicola pv. vasculorum resistance using the maize recombinant inbred line population Z003 (B73 × CML228). We detected three QTLs for BLS resistance. In addition to the disease resistance QTL, we detected a single QTL for auricle color. We observed significant, yet weak, correlations among BLS severity, levels of pattern-triggered immunity response and leaf flecking. These results will be useful for understanding resistance to X. vasicola pv. vasculorum and mitigating the impact of BLS on maize yields. 

Abstract The pattern‐triggered immunity (PTI) response is triggered at the plant cell surface by the recognition of microbe‐derived molecules known as microbe‐ or pathogen‐associated molecular patterns or molecules derived from compromised host cells called damage‐associated molecular patterns. Membrane‐localized receptor proteins, known as pattern recognition receptors, are responsible for this recognition. Although much of the machinery of PTI is conserved, natural variation for the PTI response exists within and across species with respect to the components responsible for pattern recognition, activation of the response, and the strength of the response induced. This review describes what is known about this variation. We discuss how variation in the PTI response can be measured and how this knowledge might be utilized in the control of plant disease and in developing plant varieties with enhanced disease resistance. 

SUMMARY In this study, we characterized a panel of 1264 maize near‐isogenic lines (NILs), developed from crosses between 18 diverse inbred lines and the recurrent parent B73, referred to as nested NILs (nNILs). In this study, 888 of the nNILs were genotyped using genotyping‐by‐sequencing (GBS). Subsequently, 24 of these nNILs, and all the parental lines, were re‐genotyped using a high‐density single nucleotide polymorphism (SNP) chip. A novel pipeline for calling introgressions, which does not rely on knowing the donor parent of each nNIL, was developed based on a hidden Markov model (HMM) algorithm. By comparing the introgressions detected using GBS data with those identified using chip data, we optimized the HMM parameters for analyzing the entire nNIL population. A total of 2969 introgressions were identified across the 888 nNILs. Individual introgression blocks ranged from 21 bp to 204 Mbp, with an average size of 17 Mbp. By comparing SNP genotypes within introgressed segments to the known genotypes of the donor lines, we determined that in about one third of the lines, the identity of the donors did not match expectation based on their pedigrees. We characterized the entire nNIL population for three foliar diseases. Using these data, we mapped a number of quantitative trait loci (QTL) for disease resistance in the nNIL population and observed extensive variation in effects among the alleles from different donor parents at most QTL identified. This population will be of significant utility for dissecting complex agronomic traits and allelic series in maize. 

Quantification of the simultaneous contributions of loci to multiple traits, a phenomenon called pleiotropy, is facilitated by the increased availability of high-throughput genotypic and phenotypic data. To understand the prevalence and nature of pleiotropy, the ability of multivariate and univariate genome-wide association study (GWAS) models to distinguish between pleiotropic and non-pleiotropic loci in linkage disequilibrium (LD) first needs to be evaluated. Therefore, we used publicly available maize and soybean genotypic data to simulate multiple pairs of traits that were either (i) controlled by quantitative trait nucleotides (QTNs) on separate chromosomes, (ii) controlled by QTNs in various degrees of LD with each other, or (iii) controlled by a single pleiotropic QTN. We showed that multivariate GWAS could not distinguish between QTNs in LD and a single pleiotropic QTN. In contrast, a unique QTN detection rate pattern was observed for univariate GWAS whenever the simulated QTNs were in high LD or pleiotropic. Collectively, these results suggest that multivariate and univariate GWAS should both be used to infer whether or not causal mutations underlying peak GWAS associations are pleiotropic. Therefore, we recommend that future studies use a combination of multivariate and univariate GWAS models, as both models could be useful for identifying and narrowing down candidate loci with potential pleiotropic effects for downstream biological experiments.