Department of Cell & Systems Biology, University of Toronto, Toronto, Canada.
Centre for the Analysis of Genome Evolution & Function, University of Toronto, Toronto, Canada.
PLoS Pathog. 2022 Jul 25;18(7):e1010716. doi: 10.1371/journal.ppat.1010716. eCollection 2022 Jul.
Pseudomonas syringae is a genetically diverse bacterial species complex responsible for numerous agronomically important crop diseases. Individual P. syringae isolates are assigned pathovar designations based on their host of isolation and the associated disease symptoms, and these pathovar designations are often assumed to reflect host specificity although this assumption has rarely been rigorously tested. Here we developed a rapid seed infection assay to measure the virulence of 121 diverse P. syringae isolates on common bean (Phaseolus vulgaris). This collection includes P. syringae phylogroup 2 (PG2) bean isolates (pathovar syringae) that cause bacterial spot disease and P. syringae phylogroup 3 (PG3) bean isolates (pathovar phaseolicola) that cause the more serious halo blight disease. We found that bean isolates in general were significantly more virulent on bean than non-bean isolates and observed no significant virulence difference between the PG2 and PG3 bean isolates. However, when we compared virulence within PGs we found that PG3 bean isolates were significantly more virulent than PG3 non-bean isolates, while there was no significant difference in virulence between PG2 bean and non-bean isolates. These results indicate that PG3 strains have a higher level of host specificity than PG2 strains. We then used gradient boosting machine learning to predict each strain's virulence on bean based on whole genome k-mers, type III secreted effector k-mers, and the presence/absence of type III effectors and phytotoxins. Our model performed best using whole genome data and was able to predict virulence with high accuracy (mean absolute error = 0.05). Finally, we functionally validated the model by predicting virulence for 16 strains and found that 15 (94%) had virulence levels within the bounds of estimated predictions. This study strengthens the hypothesis that P. syringae PG2 strains have evolved a different lifestyle than other P. syringae strains as reflected in their lower level of host specificity. It also acts as a proof-of-principle to demonstrate the power of machine learning for predicting host specific adaptation.
丁香假单胞菌是一种遗传多样性的细菌种复合体，负责许多重要的农业作物疾病。个别丁香假单胞菌分离株根据其分离宿主和相关疾病症状被指定为致病型，并且这些致病型的指定通常被认为反映了宿主特异性，尽管这种假设很少被严格检验。在这里，我们开发了一种快速种子感染测定法来测量 121 种不同丁香假单胞菌分离株对普通豆（Phaseolus vulgaris）的毒力。该集合包括引起细菌性斑点病的丁香假单胞菌 phylogroup 2（PG2）豆类分离株（致病型 syringae）和引起更严重晕斑病的丁香假单胞菌 phylogroup 3（PG3）豆类分离株（致病型 phaseolicola）。我们发现，豆类分离株通常比非豆类分离株在豆类上的毒性更强，并且在 PG2 和 PG3 豆类分离株之间没有观察到明显的毒性差异。然而，当我们比较 PG 内的毒力时，我们发现 PG3 豆类分离株比 PG3 非豆类分离株的毒性显著更高，而 PG2 豆类和非豆类分离株之间的毒力没有显著差异。这些结果表明 PG3 菌株比 PG2 菌株具有更高水平的宿主特异性。然后，我们使用梯度提升机器学习根据全基因组 k-mer、III 型分泌效应物 k-mer 以及 III 型效应物和植物毒素的存在/不存在来预测每个菌株在豆类上的毒力。我们的模型使用全基因组数据表现最佳，能够以高精度（平均绝对误差=0.05）预测毒力。最后，我们通过预测 16 株的毒力来验证模型的功能，发现其中 15 株（94%）的毒力水平在估计预测值的范围内。这项研究加强了这样一种假设，即 PG2 菌株比其他丁香假单胞菌菌株进化出了不同的生活方式，这反映在其较低的宿主特异性上。它还作为一个原理证明，展示了机器学习预测宿主特异性适应的强大功能。
