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<p>The severity of plant diseases caused by phytopathogenic fungi is shaped by both molecular-level virulence factors, ecological infection strategies. This study quantified the variances in disease severity driven by these predictors. To achieve this, we compiled data from experimental studies that compared disease outcomes between wild-type fungal strains and knock out mutants lacking genes suspected to influence pathogenicity. We used <i>Cohen’s d</i> to quantify the effect size between measures of disease severity in these wild-type and mutant pairs and a ridge regression to quantify associations between pathogen traits and effect size. For predictor of disease severity, we found that knock outs of secreted enzymes resulted in smaller disease effect in wild-type strain relative to the mutant strain (scaled estimate = -5.780, <i>p</i> &lt; 0.001), indicating potential redundancy or compensatory effects from other class of enzymes. However, when effector proteins were knocked out, the wild-type strains had significantly higher disease effect compared to the mutant strains (scaled estimate = 7.514, <i>p</i> &lt; 0.001). Furthermore, necrotrophic fungi (scaled estimate = -6.009,<i>p</i> &lt; 0.001) and those targeting roots and vascular tissues (scaled estimate = -2.510, <i>p</i> = 0.004) resulted in smaller disease effects in the wild-type compared to the knockout. In contrast, waterborne fungi (scaled estimate = 5.060, <i>p</i> &lt; 0.001) and those targeting leaves and stems (scaled estimate = 2.656, <i>p</i> = 0.003) exhibited larger disease effects in the wild-type. Combined, our findings suggest that the influence of gene knockouts on disease severity is context-dependent, shaped by fungal lifestyle and infection strategy. This underscores the importance of integrating molecular data with ecological context when predicting or managing plant disease. These results can inform breeding programs and disease management strategies by identifying molecular targets likely to yield robust reductions in pathogenicity.</p>

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Quantifying the influence of genetic factors and life cycle strategies on plant pathogens virulence and disease severity

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月10日

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  • Temitope R. Folorunso,
  • Mekala Sundaram,
  • Neha Potnis,
  • Laurie Stevison,
  • Lori G. Eckhardt,
  • Janna R. Willoughby

摘要

The severity of plant diseases caused by phytopathogenic fungi is shaped by both molecular-level virulence factors, ecological infection strategies. This study quantified the variances in disease severity driven by these predictors. To achieve this, we compiled data from experimental studies that compared disease outcomes between wild-type fungal strains and knock out mutants lacking genes suspected to influence pathogenicity. We used Cohen’s d to quantify the effect size between measures of disease severity in these wild-type and mutant pairs and a ridge regression to quantify associations between pathogen traits and effect size. For predictor of disease severity, we found that knock outs of secreted enzymes resulted in smaller disease effect in wild-type strain relative to the mutant strain (scaled estimate = -5.780, p < 0.001), indicating potential redundancy or compensatory effects from other class of enzymes. However, when effector proteins were knocked out, the wild-type strains had significantly higher disease effect compared to the mutant strains (scaled estimate = 7.514, p < 0.001). Furthermore, necrotrophic fungi (scaled estimate = -6.009,p < 0.001) and those targeting roots and vascular tissues (scaled estimate = -2.510, p = 0.004) resulted in smaller disease effects in the wild-type compared to the knockout. In contrast, waterborne fungi (scaled estimate = 5.060, p < 0.001) and those targeting leaves and stems (scaled estimate = 2.656, p = 0.003) exhibited larger disease effects in the wild-type. Combined, our findings suggest that the influence of gene knockouts on disease severity is context-dependent, shaped by fungal lifestyle and infection strategy. This underscores the importance of integrating molecular data with ecological context when predicting or managing plant disease. These results can inform breeding programs and disease management strategies by identifying molecular targets likely to yield robust reductions in pathogenicity.