Laboratory of Evolutionary Genetics, Institute of Biology, University of Neuchâtel, Neuchâtel, Switzerland.
INRAE, UR BIOGER, Université Paris-Saclay, Thiverval-Grignon, France.
PLoS One. 2023 Feb 6;18(2):e0281181. doi: 10.1371/journal.pone.0281181. eCollection 2023.
Crop pathogens pose severe risks to global food production due to the rapid rise of resistance to pesticides and host resistance breakdowns. Predicting future risks requires monitoring tools to identify changes in the genetic composition of pathogen populations. Here we report the design of a microfluidics-based amplicon sequencing assay to multiplex 798 loci targeting virulence and fungicide resistance genes, and randomly selected genome-wide markers for the fungal pathogen Zymoseptoria tritici. The fungus causes one of the most devastating diseases on wheat showing rapid adaptation to fungicides and host resistance. We optimized the primer design by integrating polymorphism data from 632 genomes of the same species. To test the performance of the assay, we genotyped 192 samples in two replicates. Analysis of the short-read sequence data generated by the assay showed a fairly stable success rate across samples to amplify a large number of loci. The performance was consistent between samples originating from pure genomic DNA as well as material extracted directly from infected wheat leaves. In samples with mixed genotypes, we found that the assay recovers variations in allele frequencies. We also explored the potential of the amplicon assay to recover transposable element insertion polymorphism relevant for fungicide resistance. As a proof-of-concept, we show that the assay recovers the pathogen population structure across French wheat fields. Genomic monitoring of crop pathogens contributes to more sustainable crop protection and yields.
作物病原体由于对杀虫剂的抗性迅速增加和宿主抗性的崩溃，对全球粮食生产构成了严重威胁。预测未来的风险需要监测工具来识别病原体种群遗传组成的变化。在这里，我们报告了一种基于微流控的扩增子测序分析的设计，该分析可对 798 个针对毒力和杀菌剂抗性基因的基因座进行多重检测，并随机选择真菌病原体禾谷丝核菌的全基因组标记。该真菌导致小麦上最具破坏性的疾病之一，表现出对杀菌剂和宿主抗性的快速适应。我们通过整合来自同一物种的 632 个基因组的多态性数据来优化引物设计。为了测试该分析的性能，我们在两个重复中对 192 个样本进行了基因分型。分析该分析生成的短读序列数据表明，在大量基因座中扩增的成功率在样本之间相当稳定。该性能在源自纯基因组 DNA 以及直接从感染的小麦叶片中提取的材料的样本之间是一致的。在具有混合基因型的样本中，我们发现该分析可恢复等位基因频率的变化。我们还探索了扩增子分析在恢复与杀菌剂抗性相关的转座元件插入多态性方面的潜力。作为概念验证，我们展示了该分析可恢复法国麦田中的病原体种群结构。对作物病原体的基因组监测有助于实现更可持续的作物保护和产量。
