Southern corn rust caused by Puccinia polysora Underw: a review

Southern corn rust (SCR) caused by Puccinia polysora Underw is one of the most devastating maize diseases, resulting in substantial yield losses worldwide. The pathogen is an obligate biotrophic parasite that is difficult to culture on artificial media. In recent years, the disease has become prevalent—both globally and in China—and increasing difficult to control because of its wide distribution, long-distance migration, multiple physiological races and fast evolution, all of which have contributed to a considerable increase in the risks of associated epidemics. In this review, we summarize the current knowledge of P. polysora, with emphasis on its global distribution (particularly in China), life and disease cycle, population genetics, migration, physiological races, resistance genes in maize and management. Understanding the underlying factors and processes in SCR epidemics should facilitate management of the disease and breeding for resistant maize varieties.

De novo transcriptome assembly, polymorphic SSR markers development and population genetics analyses for southern corn rust (Puccinia polysora)

Southern corn rust is a destructive maize disease caused by Puccinia polysora Underw that can lead to severe yield losses. However, genomic information and microsatellite markers are currently unavailable for this disease. In this study, we generated a total of 27,295,216 high-quality cDNA sequence reads using Illumina sequencing technology. These reads were assembled into 17,496 unigenes with an average length of 1015 bp. The functional annotation indicated that 8113 (46.37%), 1933 (11.04%) and 5516 (31.52%) unigenes showed significant similarity to known proteins in the NCBI Nr, Nt and Swiss-Prot databases, respectively. In addition, 2921 (16.70%) unigenes were assigned to KEGG database categories; 4218 (24.11%), to KOG database categories; and 6,603 (37.74%), to GO database categories. Furthermore, we identified 8,798 potential SSRs among 6653 unigenes. A total of 9 polymorphic SSR markers were developed to evaluate the genetic diversity and population structure of 96 isolates collected from Guangdong Province in China. Clonal reproduction of P. polysora in Guangdong was dominant. The YJ (Yangjiang) population had the highest genotypic diversity and the greatest number of the multilocus genotypes, followed by the HY (Heyuan), HZ (Huizhou) and XY (Xinyi) populations. These results provide valuable information for the molecular genetic analysis of P. polysora and related species.

Development of Spectral Disease Indices for Southern Corn Rust Detection and Severity Classification

Southern Corn Rust (SCR) is one of the most destructive diseases in corn production, significantly affecting corn quality and yields globally. Field-based fast, nondestructive diagnosis of SCR is critical for smart agriculture applications to reduce pesticide use and ensure food safety. The development of spectral disease indices (SDIs), based on in situ leaf reflectance spectra, has proven to be an effective method in detecting plant diseases in the field. However, little is known about leaf spectral signatures that can assist in the accurate diagnosis of SCR, and no SDIs-based model has been reported for the field-based SCR monitoring. Here, to address those issues, we developed SDIs-based monitoring models to detect SCR-infected leaves and classify SCR damage severity. In detail, we first collected in situ leaf reflectance spectra (350–2500 nm) of healthy and infected corn plants with three severity levels (light, medium, and severe) using a portable spectrometer. Then, the RELIEF-F algorithm was performed to select the most discriminative features (wavelengths) and two band normalized differences for developing SDIs (i.e., health index and severity index) in SCR detection and severity classification, respectively. The leaf reflectance spectra, most sensitive to SCR detection and severity classification, were found in the 572 nm, 766 nm, and 1445 nm wavelength and 575 nm, 640 nm, and 1670 nm wavelength, respectively. These spectral features were associated with leaf pigment and leaf water content. Finally, by employing a support vector machine (SVM), the performances of developed SCR-SDIs were assessed and compared with 38 stress-related vegetation indices (VIs) identified in the literature. The SDIs-based models developed in this study achieved an overall accuracy of 87% and 70% in SCR detection and severity classification, 1.1% and 8.3% higher than the other best VIs-based model under study, respectively. Our results thus suggest that the SCR-SDIs is a promising tool for fast, nondestructive diagnosis of SCR in the field over large areas. To our knowledge, this study represents one of the first few efforts to provide a theoretical basis for remote sensing of SCR at field and larger scales. With the increasing use of unmanned aerial vehicles (UAVs) with hyperspectral measurement capability, more studies should be conducted to expand our developed SCR-SDIs for SCR monitoring at different study sites and growing stages in the future.