Improved Screening Method for Genetic Resistance to White Mold (Sclerotinia stem rot) in Soybean
Published: 03/18/2021
DOI: doi.org/10.31274/cpn-20210318-1
CPN-5006
Mitchell G. Roth, University of Wisconsin-Madison; Richard W. Webster, University of Wisconsin-Madison; Hannah Reed, University of Wisconsin-Madison; Brian Mueller, University of Wisconsin-Madison; Carol L. Groves, University of Wisconsin-Madison; Megan McCaghey, University of California-Davis; Martin I. Chilvers, Michigan State University; Daren S. Mueller, Iowa State University; Mehdi Kabbage, University of Wisconsin-Madison; and Damon Smith, University of Wisconsin-Madison.
Characteristc white, fuzzy growth and sclerotia indicative of white mold of soybean.
Introduction
Sclerotinia sclerotiorum is a fungus that forms durable resting structures, called sclerotia, that allows it to survive dormant in the soil for up to 8 years (Adams and Ayers, 1979). Under certain environmental conditions, the sclerotia will germinate and form small mushroom-like structures, called apothecia, which can launch spores into the air and onto soybean plants. Often, the environmental conditions for sclerotia germination occur during soybean flowering, so fungal spores commonly land on flowers, colonize them, and infect into the stem. Relying on this natural infection process in soybean breeding programs can complicate research because soybean genotypes vary widely in their flowering times. Even susceptible soybean genotypes can appear resistant if their flowering time is not in sync with sclerotia germination and spore release in the field. A more robust method to test for resistance is to use a direct inoculation method by removing a portion of a soybean petiole and placing the fungus on the cut-petiole (Figure 1, left). This allows S. sclerotiorum to colonize the soybean stem, mimicking a natural infection and allowing breeders to better evaluate physiological or active resistance to white mold in soybeans.
Many university, industry, and government laboratories use a direct inoculation method to screen soybean genotypes for enhanced resistance to white mold. However, each institution tends to use different soybean genotypes as checks or controls, and different rating scales to report levels of susceptibility or resistance. This has led to confusion on how to interpret disease resistance ratings of public and commercial soybean genotypes.
Research goals
Develop a set of soybean genotypes with consistent responses to diverse S. sclerotiorum isolates that can be used as checks across breeding programs
Screen additional soybean genotypes for resistance to white mold with a greenhouse inoculation assay
Encourage soybean breeding programs to adopt this screening protocol to improve the reliability and rate of resistance identification
The research
Resistance to white mold in soybean is challenging to identify using only field experiments for a number of reasons. First, there is a strong influence of environment on S. sclerotiorum‘s life cycle, which can influence white mold severity year to year in the same locations (Peltier et al., 2012). Second, different agronomic traits such as maturity group, lodging, and plant height are highly correlated with disease resistance in field settings (Kim and Diers, 2000), which complicate our understanding of resistance to white mold. Furthermore, genetic resistance to white mold appears to be controlled by many genes, with each gene contributing small amounts of resistance to the disease (Arahana et al., 2001; Vuong et al., 2008). Therefore, improved genetic resistance is still desired by farmers, and actively sought by soybean breeders.
Figure 1. Direct inoculation method to test soybean responses to S. sclerotiorum infection and white mold severity. Symptoms of white mold become apparent as stem lesions, 3 days after inoculation.
Mitchell Roth
Direct inoculation methods facilitate infection of soybean by bringing the fungus in close contact with the soybean plant. A few days post inoculation, the first symptoms of white mold become apparent as a stem lesion (Figure 1, right). Monitoring the length of the developing lesion on the main stem allows researchers to identify soybean genotypes that restrict white mold development. After screening 1,076 soybean genotypes with this method (McCaghey et al., 2017), four genotypes were identified as good candidate genotypes for standardization in a check panel for future screening efforts. To validate the consistency of these four soybean genotypes, each were tested against nine geographically and genetically diverse S. sclerotiorum isolates previously described by Willbur et al. (2017). While each isolate caused different levels of disease, the responses of the four soybean genotypes were consistent, with ‘Dwight’ developing the longest stem lesions, ‘52-82B’ developing the smallest stem lesions, and ‘51-23’ and ‘SSR51-70’ developing intermediate stem lesions (Figure 2).
Figure 2. Soybean genotype (line) responses to diverse S. sclerotiorum isolates. Average stem lesion length over time by soybean genotype across S. sclerotiorum isolates (A), and average stem lesion length over time by unique S. sclerotiorum across soybean genotypes (B). Area under the disease progress curve (AUDPC; a measure of disease intensity over time) by soybean genotype (C) and by unique S. sclerotiorum isolates (D). Data were collected at 7, 11, and 14 days post inoculation, and error bars represent the standard error of the mean. Bars with the same letter above them are statistically similar as determined by Fisher’s LSD (p < 0.05).
These soybean check genotypes produced reproducible results, and were then used to screen eleven additional soybean genotypes with unknown levels of resistance to white mold. The check genotypes displayed expected levels of resistance, and the unknown soybean genotypes were compared with the check genotypes (Figure 3). This screen revealed that five of the 11 genotypes were good candidates for field testing, while the remaining six genotypes were as susceptible as ‘Dwight’, the susceptible check genotype in the panel.
Figure 3. Area under the disease progress curve (AUDPC; a measure of disease intensity over time) values of the four check genotypes (light grey bars) and 11 soybean genotypes with unknown resistance levels to S. sclerotiorum (dark grey bars). All soybean genotypes were challenged with highly aggressive isolate number 20, and data were collected at 7, 11, and 14 days post inoculation and used to generate the AUDPC. Error bars represent the standard error of the mean. Bars with the same letter above them are statistically similar as determined by Fisher’s least significant difference (LSD) test (p < 0.05).
Conclusion
Soybean breeding efforts often generate thousands of new genotypes with different genetic combinations that significantly affect agronomic and disease resistance traits. Screening thousands of genotypes and the check genotypes with nine isolates of S. sclerotiorum is not possible. However, this method is highly valuable in early breeding stages to screen soybean genotypes as potential parents for future crosses to develop resistant varieties. In subsequent stages of the breeding process, the method presented here can be used with one aggressive S. sclerotiorum isolate like isolate 20 (as shown in Figures 2B and 2D), and the four check genotypes to screen the offspring of crosses. This approach can help speed up identification of resistant offspring and allow for comparisons across different soybean breeding programs at all institutions.
References
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This research was based on the following manuscript
Webster, R. W., Roth, M. G., Reed, H., Mueller, B., Groves, C. L., McCaghey, M., Chilvers, M. I., Mueller, D. S., Kabbage, M., and Smith, D. L. 2021. Identification of soybean (Glycine max) check lines for evaluating genetic resistance to Sclerotinia stem rot. Plant Disease. Article / Google Scholar
Acknowledgements
Authors
Mitchell G. Roth, University of Wisconsin-Madison; Richard W. Webster, University of Wisconsin-Madison; Hannah Reed, University of Wisconsin-Madison; Brian Mueller, University of Wisconsin-Madison; Carol L. Groves, University of Wisconsin-Madison; Megan McCaghey, University of California-Davis; Martin I. Chilvers, Michigan State University; Daren S. Mueller, Iowa State University; Mehdi Kabbage, University of Wisconsin-Madison; and Damon Smith, University of Wisconsin-Madison.
Reviewers
Carl Bradley, University of Kentucky; Travis Faske, University of Arkansas; and Kiersten Wise, University of Kentucky.
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