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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.

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.

Seed quality plays an important role in the establishment of healthy crop stands. The aim of this study was to identify the mycoflora associated with maize grain collected over two growing seasons, one experiencing severe drought, from smallholder farms in KwaZulu-Natal (KZN) and the Eastern Cape (EC). These are two adjacent provinces in South Africa with many maize-producing smallholder farmers. Asymptomatic maize ears were collected at harvest during the 2014/2015 and 2015/2016 maize growing seasons from farms located in Hlanganani (KZN), Ntabamhlophe (KZN), KwaNxamalala (KZN), Bizana (EC) and Tabankulu (EC). Maize grain was subjected to seed health tests using the agar plate method. The percentage incidence of fungal species isolated from maize grain was determined with species identities confirmed by ITS sequencing. Eleven fungal genera were identified with Fusarium species and Stenocarpella maydis the most prevalent. Fusarium verticillioides, Fusarium graminearum and S. maydis were isolated from all sites in both seasons. No fungal species exhibited a higher incidence in the drought season across all sites. F. graminearum and S. maydis had higher incidences in the wetter season at four and three sites, respectively. F. verticillioides had a greater incidence at the EC sites, particularly the coastal Bizana site. We conclude that local factors that affect the inoculum levels of each species, such as land preparation, previous crop, amount of debris from previous season, fertilizer application, and the micro-environment at field scale had a greater impact than the drought season on the population structure of ear-rot pathogens. The widespread presence of fungi that are potentially mycotoxin-producing in asymptomatic maize grain poses health risks to consumers and is worthy of further investigation.

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.

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

Gray leaf spot (GLS) disease in maize, caused by the fungus Cercospora zeina, is a threat to maize production globally. Understanding the molecular basis for quantitative resistance to GLS is therefore important for food security. We developed a de novo assembly pipeline to identify candidate maize resistance genes. Near-isogenic maize lines with and without a QTL for GLS resistance on chromosome 10 from inbred CML444 were produced in the inbred B73 background. The B73-QTL line showed a 20% reduction in GLS disease symptoms compared to B73 in the field (p = 0.01). B73-QTL leaf samples from this field experiment conducted under GLS disease pressure were RNA sequenced. The reads that did not map to the B73 or C. zeina genomes were expected to contain novel defense genes and were de novo assembled. A total of 141 protein-coding sequences with B73-like or plant annotations were identified from the B73-QTL plants exposed to C. zeina. To determine whether candidate gene expression was induced by C. zeina, the RNAseq reads from C. zeina-challenged and control leaves were mapped to a master assembly of all of the B73-QTL reads, and differential gene expression analysis was conducted. Combining results from both bioinformatics approaches led to the identification of a likely candidate gene, which was a novel allele of a lectin receptor-like kinase named L-RLK-CML that (i) was induced by C. zeina, (ii) was positioned in the QTL region, and (iii) had functional domains for pathogen perception and defense signal transduction. The 817AA L-RLK-CML protein had 53 amino acid differences from its 818AA counterpart in B73. A second “B73-like” allele of L-RLK was expressed at a low level in B73-QTL. Gene copy-specific RT-qPCR confirmed that the l-rlk-cml transcript was the major product induced four-fold by C. zeina. Several other expressed defense-related candidates were identified, including a wall-associated kinase, two glutathione s-transferases, a chitinase, a glucan beta-glucosidase, a plasmodesmata callose-binding protein, several other receptor-like kinases, and components of calcium signaling, vesicular trafficking, and ethylene biosynthesis. This work presents a bioinformatics protocol for gene discovery from de novo assembled transcriptomes and identifies candidate quantitative resistance genes.

Abstract Background Cercospora zeina is a foliar pathogen responsible for maize grey leaf spot in southern Africa that negatively impacts maize production. Plants use a variety of chemical and structural mechanisms to defend themselves against invading pathogens such as C. zeina, including the production of secondary metabolites with antimicrobial properties. In maize, a variety of biotic and abiotic stressors induce the accumulation of the terpenoid phytoalexins, zealexins and kauralexins. Results C. zeina-susceptible line displayed pervasive rectangular grey leaf spot lesions, running parallel with the leaf veins in contrast to C. zeina-resistant line that had restricted disease symptoms. Analysis of the transcriptome of both lines indicated that genes involved in primary and secondary metabolism were up-regualted, and although different pathways were prioritized in each line, production of terpenoid compounds were common to both. Targeted phytoalexin analysis revealed that C. zeina-inoculated leaves accumulated zealexins and kauralexins. The resistant line shows a propensity toward accumulation of the kauralexin B series metabolites in response to infection, which contrasts with the susceptible line that preferentially accumulates the kauralexin A series. Kauralexin accumulation was correlated to expression of the kauralexin biosynthetic gene, ZmAn2 and a candidate biosynthetic gene, ZmKSL2. We report the expression of a putative copalyl diphosphate synthase gene that is induced by C. zeina in the resistant line exclusively. Discussion This study shows that zealexins and kauralexins, and expression of their biosynthetic genes, are induced by C. zeina in both resistant and susceptible germplasm adapted to the southern African climate. The data presented here indicates that different forms of kauralexins accumulate in the resistant and susceptible maize lines in response to C. zeina, with the accumulation of kauralexin B compounds in a resistant maize line and kauralexin A compounds accumulating in the susceptible line.

Gray leaf spot (GLS), caused by the sibling species Cercospora zeina or Cercospora zeae-maydis, is cited as one of the most important diseases threatening global maize production. C. zeina fails to produce cercosporin in vitro and, in most cases, causes large coalescing lesions during maize infection, a symptom generally absent from cercosporin-deficient mutants in other Cercospora spp. Here, we describe the C. zeina cercosporin toxin biosynthetic (CTB) gene cluster. The oxidoreductase gene CTB7 contained several insertions and deletions as compared with the C. zeae-maydis ortholog. We set out to determine whether complementing the defective CTB7 gene with the full-length gene from C. zeae-maydis could confer in vitro cercosporin production. C. zeina transformants containing C. zeae-maydis CTB7 were generated by Agrobacterium tumefaciens–mediated transformation and were evaluated for in vitro cercosporin production. When grown on nitrogen-limited medium in the light—conditions conducive to cercosporin production in other Cercospora spp.—one transformant accumulated a red pigment that was confirmed to be cercosporin by the KOH assay, thin-layer chromatography, and ultra performance liquid chromatography-quadrupole-time-of-flight mass spectrometry. Our results indicated that C. zeina has a defective CTB7, but all other necessary machinery required for synthesizing cercosporin-like molecules and, thus, C. zeina may produce a structural variant of cercosporin during maize infection.

Bacterial wilt caused by Ralstonia solanacearum is a disease of widespread economic importance that affects numerous plant species, including Arabidopsis thaliana. We describe a pathosystem between A. thaliana and biovar 3 phylotype I strain BCCF402 of R. solanacearum isolated from Eucalyptus trees. A. thaliana accession Be-0 was susceptible and accession Kil-0 was tolerant. Kil-0 exhibited no wilting symptoms and no significant reduction in fitness (biomass, seed yield, and germination efficiency) after inoculation with R. solanacearum BCCF402, despite high bacterial numbers in planta. This was in contrast to the well-characterized resistance response in the accession Nd-1, which limits bacterial multiplication at early stages of infection and does not wilt. R. solanacearum BCCF402 was highly virulent because the susceptible accession Be-0 was completely wilted after inoculation. Genetic analyses, allelism studies with Nd-1, and RRS1 cleaved amplified polymorphic sequence marker analysis showed that the tolerance phenotype in Kil-0 was dependent upon the resistance gene RRS1. Knockout and complementation studies of the R. solanacearum BCCF402 effector PopP2 confirmed that the tolerance response in Kil-0 was dependent upon the RRS1–PopP2 interaction. Our data indicate that the gene-for-gene interaction between RRS1 and PopP2 can contribute to tolerance, as well as resistance, which makes it a useful model system for evolutionary studies of the arms race between plants and bacterial pathogens. In addition, the results alert biotechnologists to the risk that deployment of RRS1 in transgenic crops may result in persistence of the pathogen in the field.

Efficient construction of cDNA libraries enriched for differentially expressed transcripts is an important first step in many biological investigations. We present a quantitative procedure for screening cDNA libraries constructed by suppression subtractive hybridization (SSH). The methodology was applied to two independent SSHs from pearl millet and banana. Following two-color cyanin dye labeling and hybridization of subtracted tester with either unsubtracted driver or unsubtracted tester cDNAs to the SSH libraries arrayed on glass slides, two values were calculated for each clone, an enrichment ratio 1 (ER1) and an enrichment ratio 2 (ER2). Graphical representation of ER1 and ER2 enabled the identification of clones that were likely to represent up-regulated transcripts. Normalization of each clone by the SSH process was determined from the ER2 values, thereby indicating whether clones represented rare or abundant transcripts. Differential expression of pearl millet and banana clones identified from both libraries by this quantitative approach was verified by inverse Northern blot analysis.