Population genetic structure and migration patterns of the maize pathogenic fungus, Cercospora zeina in East and Southern Africa

Recent advances in the population biology and management of maize foliar fungal pathogens Exserohilum turcicum, Cercospora zeina and Bipolaris maydis in Africa

Maize is the most widely cultivated and major security crop in sub-Saharan Africa. Three foliar diseases threaten maize production on the continent, namely northern leaf blight, gray leaf spot, and southern corn leaf blight. These are caused by the fungi Exserohilum turcicum, Cercospora zeina, and Bipolaris maydis, respectively. Yield losses of more than 10% can occur if these pathogens are diagnosed inaccurately or managed ineffectively. Here, we review recent advances in understanding the population biology and management of the three pathogens, which are present in Africa and thrive under similar environmental conditions during a single growing season. To effectively manage these pathogens, there is an increasing adoption of breeding for resistance at the small-scale level combined with cultural practices. Fungicide usage in African cropping systems is limited due to high costs and avoidance of chemical control. Currently, there is limited knowledge available on the population biology and genetics of these pathogens in Africa. The evolutionary potential of these pathogens to overcome host resistance has not been fully established. There is a need to conduct large-scale sampling of isolates to study their diversity and trace their migration patterns across the continent.

ZmWAK02 encoding an RD-WAK protein confers maize resistance against gray leaf spot

Gray leaf spot (GLS) caused by Cercospora zeina or C. zeae-maydis is a major maize disease throughout the world. Although more than 100 QTLs resistant against GLS have been identified, very few of them have been cloned. Here, we identified a major resistance QTL against GLS, qRglsSB, explaining 58.42% phenotypic variation in SB12×SA101 BC1 F1 population. By fine-mapping, it was narrowed down into a 928 kb region. By using transgenic lines, mutants and complementation lines, it was confirmed that the ZmWAK02 gene, encoding an RD wall-associated kinase, is the responsible gene in qRglsSB resistant against GLS. The introgression of the ZmWAK02 gene into hybrid lines significantly improves their grain yield in the presence of GLS pressure and does not reduce their grain yield in the absence of GLS. In summary, we cloned a gene, ZmWAK02, conferring large effect of GLS resistance and confirmed its great value in maize breeding.

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.

Population genomic analyses suggest recent dispersal events of the pathogen Cercospora zeina into East and Southern African maize cropping systems

A serious factor hampering global maize production is gray leaf spot disease. Cercospora zeina is one of the causative pathogens, but population genomics analysis of C. zeina is lacking. We conducted whole-genome Illumina sequencing of a representative set of 30 C. zeina isolates from Kenya and Uganda (East Africa) and Zambia, Zimbabwe, and South Africa (Southern Africa). Selection of the diverse set was based on microsatellite data from a larger collection of the pathogen. Pangenome analysis of the C. zeina isolates was done by (1) de novo assembly of the reads with SPAdes, (2) annotation with BRAKER, and (3) protein clustering with OrthoFinder. A published long-read assembly of C. zeina (CMW25467) from Zambia was included and annotated using the same pipeline. This analysis revealed 790 non-shared accessory and 10,677 shared core orthogroups (genes) between the 31 isolates. Accessory gene content was largely shared between isolates from all countries, with a few genes unique to populations from Southern Africa (32) or East Africa (6). There was a significantly higher proportion of effector genes in the accessory secretome (44%) compared to the core secretome (24%). PCA, ADMIXTURE, and phylogenetic analysis using a neighbor-net network indicated a population structure with a geographical subdivision between the East African isolates and the Southern African isolates, although gene flow was also evident. The small pangenome and partial population differentiation indicated recent dispersal of C. zeina into Africa, possibly from 2 regional founder populations, followed by recurrent gene flow owing to widespread maize production across sub-Saharan Africa.

Pathogenicity Variation in Two Genomes of Cercospora Species Causing Gray Leaf Spot in Maize

The gray leaf spots caused by Cercospora spp. severely affect the yield and quality of maize. However, the evolutionary relation and pathogenicity variation between species of the Cercospora genus is largely unknown. In this study, we constructed high-quality reference genomes by nanopore sequencing two Cercospora species, namely, C. zeae-maydis and C. zeina, with differing pathogenicity, collected from northeast (Liaoning [LN]) and southeast (Yunnan [YN]) China, respectively. The genome size of C. zeae-maydis-LN is 45.08 Mb, containing 10,839 annotated genes, whereas that of Cercospora zeina-YN is 42.18 Mb, containing 10,867 annotated genes, of which approximately 86.58% are common in the two species. The difference in their genome size is largely attributed to increased long terminal repeat retrotransposons of 3.8 Mb in total length in C. zeae-maydis-LN. There are 41 and 30 carbohydrate-binding gene subfamilies identified in C. zeae-maydis-LN and C. zeina-YN, respectively. A higher number of carbohydrate-binding families found in C. zeae-maydis-LN, and its unique CBM4, CBM37, and CBM66, in particular, may contribute to variation in pathogenicity between the two species, as the carbohydrate-binding genes are known to encode cell wall-degrading enzymes. Moreover, there are 114 and 107 effectors predicted, with 47 and 46 having unique potential pathogenicity in C. zeae-maydis-LN and C. zeina-YN, respectively. Of eight effectors randomly selected for pathogenic testing, five were found to inhibit cell apoptosis induced by Bcl-2-associated X. Taken together, our results provide genomic insights into variation in pathogenicity between C. zeae-maydis and C. zeina. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

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.