Time-Course RNAseq Reveals Exserohilum turcicum Effectors and Pathogenicity Determinants

Combination of linkage and association mapping with genomic prediction to infer QTL regions associated with gray leaf spot and northern corn leaf blight resistance in tropical maize

Among the diseases threatening maize production in Africa are gray leaf spot (GLS) caused by Cercospora zeina and northern corn leaf blight (NCLB) caused by Exserohilum turcicum. The two pathogens, which have high genetic diversity, reduce the photosynthesizing ability of susceptible genotypes and, hence, reduce the grain yield. To identify population-based quantitative trait loci (QTLs) for GLS and NCLB resistance, a biparental population of 230 lines derived from the tropical maize parents CML511 and CML546 and an association mapping panel of 239 tropical and sub-tropical inbred lines were phenotyped across multi-environments in western Kenya. Based on 1,264 high-quality polymorphic single-nucleotide polymorphisms (SNPs) in the biparental population, we identified 10 and 18 QTLs, which explained 64.2% and 64.9% of the total phenotypic variance for GLS and NCLB resistance, respectively. A major QTL for GLS, qGLS1_186 accounted for 15.2% of the phenotypic variance, while qNCLB3_50 explained the most phenotypic variance at 8.8% for NCLB resistance. Association mapping with 230,743 markers revealed 11 and 16 SNPs significantly associated with GLS and NCLB resistance, respectively. Several of the SNPs detected in the association panel were co-localized with QTLs identified in the biparental population, suggesting some consistent genomic regions across genetic backgrounds. These would be more relevant to use in field breeding to improve resistance to both diseases. Genomic prediction models trained on the biparental population data yielded average prediction accuracies of 0.66-0.75 for the disease traits when validated in the same population. Applying these prediction models to the association panel produced accuracies of 0.49 and 0.75 for GLS and NCLB, respectively. This research conducted in maize fields relevant to farmers in western Kenya has combined linkage and association mapping to identify new QTLs and confirm previous QTLs for GLS and NCLB resistance. Overall, our findings imply that genetic gain can be improved in maize breeding for resistance to multiple diseases including GLS and NCLB by using genomic selection.

Variation in the morphology and effector profiles of Exserohilum turcicum isolates associated with the Northern Corn Leaf Blight of maize in Nigeria

BACKGROUND

Maize production in lowland agro-ecologies in West and Central Africa is constrained by the fungus Exserohilum turcicum, causal agent of Northern Corn Leaf Blight (NCLB). Breeding for resistance to NCLB is considered the most effective management strategy. The strategy would be even more effective if there is adequate knowledge of the characteristics of E. turcicum in a target region. Maize leaves showing NCLB symptoms were collected during field surveys in three major maize growing areas in Nigeria: Ikenne, Ile-Ife, and Zaria during 2018/2019 and 2019/2020 growing seasons to characterize E. turcicum populations interacting with maize using morphological and molecular criteria.

RESULTS

A total of 217 E. turcicum isolates were recovered. Most of the isolates (47%) were recovered from the Ikenne samples while the least were obtained from Zaria. All isolates were morphologically characterized. A subset of 124 isolates was analyzed for virulence effector profiles using three primers: SIX13-like, SIX5-like, and Ecp6. Inter- and intra-location variations among isolates was found in sporulation, growth patterns, and presence of the effectors. Candidate effector genes that condition pathogenicity and virulence in E. turcicum were found but not all isolates expressed the three effectors.

CONCLUSION

Morphological and genetic variation among E. turcicum isolates was found within and across locations. The variability observed suggests that breeding for resistance to NCLB in Nigeria requires selection for quantitative resistance to sustain the breeding efforts.

Occurrence and Distribution of Physiological Races of Exserohilum turcicum in Maize-Growing Regions of Mexico

Turcicum leaf blight (TLB) is a common foliar disease of maize in Mexico that is caused by the fungal pathogen Exserohilum turcicum. The most effective management strategy against TLB is monogenic race-specific resistance. Among the 140 E. turcicum isolates from symptomatic leaves collected from maize fields in Mexico, 100 were obtained from tropical (Veracruz) and temperate areas (Estado de México) between 2010 and 2019, and 40 isolates were obtained from tropical (Sinaloa, Tamaulipas, Veracruz, and Chiapas), subtropical (Nayarit, Jalisco, and Guanajuato), and temperate areas (Estado de Mexico, Hidalgo, and Puebla) collected in 2019. All the isolates caused TLB symptoms on the positive control (ht4), showing that they were all pathogenic. Six physiological races of E. turcicum (2, 3, 23, 3N, 23N, and 123N) were identified based on resistant or susceptible responses displayed by five maize differential genotypes (A619Ht1, A619Ht2, A619Ht3, B68HtN, and A619ht4). The most common was race 23, accounting for 68% of the isolates, followed by races 23N, 123N, 3, 2, and 3N at 15, 8, 6, 2, and 1%, respectively. Race 123N was able to infect the greatest number of maize differential genotypes used in the study. Race 123N was detected in Sinaloa and Estado de México. Race 3 was detected in Nayarit and Jalisco. Race 2 was detected in Jalisco, Estado de México, and Veracruz, and race 3N was detected in Tamaulipas. Race 23 was equally dominant in the tropical, subtropical, and temperate regions, while race 123N was more common in the tropical environment, and race 23N was more common in the tropical and temperate environments. There was no evidence for shifts in the races between 2010 and 2019.

Deep Learning Diagnostics of Gray Leaf Spot in Maize under Mixed Disease Field Conditions

Maize yields worldwide are limited by foliar diseases that could be fungal, oomycete, bacterial, or viral in origin. Correct disease identification is critical for farmers to apply the correct control measures, such as fungicide sprays. Deep learning has the potential for automated disease classification from images of leaf symptoms. We aimed to develop a classifier to identify gray leaf spot (GLS) disease of maize in field images where mixed diseases were present (18,656 images after augmentation). In this study, we compare deep learning models trained on mixed disease field images with and without background subtraction. Performance was compared with models trained on PlantVillage images with single diseases and uniform backgrounds. First, we developed a modified VGG16 network referred to as “GLS_net” to perform binary classification of GLS, which achieved a 73.4% accuracy. Second, we used MaskRCNN to dynamically segment leaves from backgrounds in combination with GLS_net to identify GLS, resulting in a 72.6% accuracy. Models trained on PlantVillage images were 94.1% accurate at GLS classification with the PlantVillage testing set but performed poorly with the field image dataset (55.1% accuracy). In contrast, the GLS_net model was 78% accurate on the PlantVillage testing set. We conclude that deep learning models trained with realistic mixed disease field data obtain superior degrees of generalizability and external validity when compared to models trained using idealized datasets.

Comparative genomic analysis reveals cellulase plays an important role in the pathogenicity of Setosphaeria turcica f. sp. zeae

Setosphaeria turcica f. sp. zeae and S. turcica f. sp. sorghi, the two formae speciales of S. turcica, cause northern leaf blight disease of corn and sorghum, respectively, and often cause serious economic losses. They have obvious physiological differentiation and show complete host specificity. Host specificity is often closely related to pathogen virulence factors, including secreted protein effectors and secondary metabolites. Genomic sequencing can provide more information for understanding the virulence mechanisms of pathogens. However, the complete genomic sequence of S. turcica f. sp. sorghi has not yet been reported, and no comparative genomic information is available for the two formae speciales. In this study, S. turcica f. sp. zeae was predicted to have fewer secreted proteins, pathogen-host interaction (PHI) genes and carbohydrate-active enzymes (CAZys) than S. turcica f. sp. sorghi. Fifteen and 20 polyketide synthase (PKS) genes were identified in S. turcica f. sp. zeae and S. turcica f. sp. sorghi, respectively, which maintained high homology. There were eight functionally annotated effector protein-encoding genes specifically in S. turcica f. sp. zeae, among which the encoding gene StCEL2 of endo-1, 4-β-D-glucanase, an important component of cellulase, was significantly up-regulated during the interaction process. Finally, gluconolactone inhibited cellulase activity and decreased infection rate and pathogenicity, which indicates that cellulase is essential for maintaining virulence. These findings demonstrate that cellulase plays an important role in the pathogenicity of S. turcica f. sp. zeae. Our results also provide a theoretical basis for future research on the molecular mechanisms underlying the pathogenicity of the two formae speciales and for identifying any associated genes.

The Secreted Ribonuclease SRE1 Contributes to Setosphaeria turcica Virulence and Activates Plant Immunity

During the plant infection process, pathogens can secrete several effectors. Some of the effectors are well-known for their roles in regulating plant immunity and promoting successful pathogen colonization. However, there are few studies on the ribonuclease (RNase) effectors secreted by fungi. In the present study, we discovered a secretable RNase (SRE1) in the secretome of Setosphaeria turcica that was significantly upregulated during the early stages of S. turcica infection in maize. Knockdown of SRE1 significantly reduced the virulence of S. turcica. SRE1 can induce cell death in maize and Nicotiana benthamiana. However, unlike the conventional hypersensitive response (HR) caused by other effectors, SRE1 is not dependent on its signal peptide (SP) or plant receptor kinases (such as BAK1 and SOBIR1). SRE1-induced cell death depends upon its enzymatic activity and the N-terminal β-hairpin structure. SRE1 relies on its N-terminal β-hairpin structure to enter cells, and then degrades plant's RNA through its catalytic activity causing cytotoxic effects. Additionally, SRE1 enhances N. benthamiana's resistance to pathogenic fungi and oomycetes. In summary, SRE1 promotes the virulence of S. turcica, inducing plant cell death and activating plant immune responses.

Infection Strategies and Pathogenicity of Biotrophic Plant Fungal Pathogens

Biotrophic plant pathogenic fungi are widely distributed and are among the most damaging pathogenic organisms of agriculturally important crops responsible for significant losses in quality and yield. However, the pathogenesis of obligate parasitic pathogenic microorganisms is still under investigation because they cannot reproduce and complete their life cycle on an artificial medium. The successful lifestyle of biotrophic fungal pathogens depends on their ability to secrete effector proteins to manipulate or evade plant defense response. By integrating genomics, transcriptomics, and effectoromics, insights into how the adaptation of biotrophic plant fungal pathogens adapt to their host populations can be gained. Efficient tools to decipher the precise molecular mechanisms of rust–plant interactions, and standardized routines in genomics and functional pipelines have been established and will pave the way for comparative studies. Deciphering fungal pathogenesis not only allows us to better understand how fungal pathogens infect host plants but also provides valuable information for plant diseases control, including new strategies to prevent, delay, or inhibit fungal development. Our review provides a comprehensive overview of the efforts that have been made to decipher the effector proteins of biotrophic fungal pathogens and demonstrates how rapidly research in the field of obligate biotrophy has progressed.

Fusarium graminearum Infection Strategy in Wheat Involves a Highly Conserved Genetic Program That Controls the Expression of a Core Effectome

Fusarium graminearum, the main causal agent of Fusarium Head Blight (FHB), is one of the most damaging pathogens in wheat. Because of the complex organization of wheat resistance to FHB, this pathosystem represents a relevant model to elucidate the molecular mechanisms underlying plant susceptibility and to identify their main drivers, the pathogen’s effectors. Although the F. graminearum catalog of effectors has been well characterized at the genome scale, in planta studies are needed to confirm their effective accumulation in host tissues and to identify their role during the infection process. Taking advantage of the genetic variability from both species, a RNAseq-based profiling of gene expression was performed during an infection time course using an aggressive F. graminearum strain facing five wheat cultivars of contrasting susceptibility as well as using three strains of contrasting aggressiveness infecting a single susceptible host. Genes coding for secreted proteins and exhibiting significant expression changes along infection progress were selected to identify the effector gene candidates. During its interaction with the five wheat cultivars, 476 effector genes were expressed by the aggressive strain, among which 91% were found in all the infected hosts. Considering three different strains infecting a single susceptible host, 761 effector genes were identified, among which 90% were systematically expressed in the three strains. We revealed a robust F. graminearum core effectome of 357 genes expressed in all the hosts and by all the strains that exhibited conserved expression patterns over time. Several wheat compartments were predicted to be targeted by these putative effectors including apoplast, nucleus, chloroplast and mitochondria. Taken together, our results shed light on a highly conserved parasite strategy. They led to the identification of reliable key fungal genes putatively involved in wheat susceptibility to F. graminearum, and provided valuable information about their putative targets.

Novel factors contributing to fungal pathogenicity at early stages of Setosphaeria turcica infection

The fungal pathogen Setosphaeria turcica causes leaf blight on maize, which leads to considerable crop losses. However, how S. turcica establishes sustained systemic infection is largely unknown. Here, we report several novel factors contributing to S. turcica pathogenicity, identified using a genomic and transcriptional screen at different stages of S. turcica appressorium development. We identified two cytoskeleton regulators, SLM1 and SLM2, that are crucial for hypha and appressorium development. The SLM1 and SLM2 transcripts accumulated during germling stage but their levels were notably reduced at the appressorium stage. Deletion of SLM2 dramatically affected cell morphology, penetration ability, and pathogenicity. We also identified three different types of S. turcica glycosyl hydrolases that are critical for plant cell wall degradation. Their transcripts accumulated during the appressorium infection stage induced by cellophane and maize leaf. Most importantly, we characterized a novel and specific S. turcica effector, appressorium-coupled effector 1 (StACE1), whose expression is coupled to appressorium formation in S. turcica. This protein is required for maize infection and induces cell death on expression in Nicotiana benthamiana. These observations suggest that the phytopathogen S. turcica is primed in advance with multiple strategies for maize infection, which are coupled to appressorium formation at the early infection stages.

An automated and combinative method for the predictive ranking of candidate effector proteins of fungal plant pathogens

Fungal plant-pathogens promote infection of their hosts through the release of 'effectors'-a broad class of cytotoxic or virulence-promoting molecules. Effectors may be recognised by resistance or sensitivity receptors in the host, which can determine disease outcomes. Accurate prediction of effectors remains a major challenge in plant pathology, but if achieved will facilitate rapid improvements to host disease resistance. This study presents a novel tool and pipeline for the ranking of predicted effector candidates-Predector-which interfaces with multiple software tools and methods, aggregates disparate features that are relevant to fungal effector proteins, and applies a pairwise learning to rank approach. Predector outperformed a typical combination of secretion and effector prediction methods in terms of ranking performance when applied to a curated set of confirmed effectors derived from multiple species. We present Predector ( https://github.com/ccdmb/predector ) as a useful tool for the ranking of predicted effector candidates, which also aggregates and reports additional supporting information relevant to effector and secretome prediction in a simple, efficient, and reproducible manner.

Diversity of Expression Types of Ht Genes Conferring Resistance in Maize to Exserohilum turcicum

Northern corn leaf blight (NCLB) is an important leaf disease in maize (Zea mays) worldwide and is spreading into new areas with expanding maize cultivation, like Germany. Exserohilum turcicum, causal agent of NCLB, infects and colonizes leaf tissue and induces elongated necrotic lesions. Disease control is based on fungicide application and resistant cultivars displaying monogenic resistance. Symptom expression and resistance mechanisms differ in plants carrying different resistance genes. Therefore, histological studies and DNA quantification were performed to compare the pathogenesis of E. turcicum races in maize lines exhibiting compatible or incompatible interactions. Maize plants from the differential line B37 with and without resistance genes Ht1, Ht2, Ht3, and Htn1 were inoculated with either incompatible or compatible races (race 0, race 1 and race 23N) of E. turcicum. Leaf segments from healthy and inoculated plants were collected at five different stages of infection and disease development from penetration (0-1 days post inoculation - dpi), until full symptom expression (14-18 dpi). Symptoms of resistance responses conveyed by the different Ht genes considerably differed between Ht1 (necrotic lesions with chlorosis), Ht2 (chlorosis and small lesions), Ht3 (chlorotic spots) and Htn1 (no lesions or wilt-type lesions). In incompatible interactions, fungal DNA was only detected in very low amounts. At 10 dpi, DNA content was elevated in all compatible interactions. Histological studies with Chlorazol Black E staining indicated that E. turcicum formed appressoria and penetrated the leaf surface directly in both types of interaction. In contrast to incompatible interactions, however, the pathogen was able to penetrate into xylem vessels at 6 dpi in compatible interactions and strongly colonized the mesophyll at 12 dpi, which is considered the crucial process differentiating susceptible from resistant interactions. Following the distinct symptom expressions, resistance mechanisms conferred by Ht1, Ht2, Ht3, and Htn1 genes apparently are different. Lower disease levels and a delayed progress of infection in compatible interactions with resistant lines imply that maize R genes to E. turcicum are associated with or confer additional quantitative resistance.

Benefits of maize resistance breeding and chemical control against northern leaf blight in smallholder farms in South Africa

Maize underpins food security in South Africa. An annual production of more than 10 million tons is a combination of the output of large-scale commercial farms plus an estimated 250 000 ha cultivated by smallholder farmers. Maize leaves are a rich source of nutrients for fungal pathogens. Farmers must limit leaf blighting by fungi to prevent sugars captured by photosynthesis being ‘stolen’ instead of filling the grain. This study aimed to fill the knowledge gap on the prevalence and impact of fungal foliar diseases in local smallholder maize fields. A survey with 1124 plant observations from diverse maize hybrids was conducted over three seasons from 2015 to 2017 in five farming communities in KwaZulu-Natal Province (Hlanganani, Ntabamhlophe, KwaNxamalala) and Eastern Cape Province (Bizana, Tabankulu). Northern leaf blight (NLB), common rust, Phaeosphaeria leaf spot, and grey leaf spot had overall disease incidences of 75%, 77%, 68% and 56%, respectively, indicating high disease pressure in smallholder farming environments. NLB had the highest disease severity (LSD test, p<0.05). A yield trial focused on NLB in KwaZulu-Natal showed that this disease reduced yields in the three most susceptible maize hybrids by 36%, 71% and 72%, respectively. Eighteen other hybrids in this trial did not show significant yield reductions due to NLB, which illustrates the progress made by local maize breeders in disease resistance breeding. This work highlights the risk to smallholder farmers of planting disease-susceptible varieties, and makes recommendations on how to exploit the advances of hybrid maize disease resistance breeding to develop farmer-preferred varieties for smallholder production.