Ducrocq Florent, Piutti Séverine, Henychová Alena, Villerd Jean, Laflotte Alexandre, Girardeau Loïc, Grosjean Jérémy, Patzak Josef, Hehn Alain
Université de Lorraine, INRAE, LAE, Nancy, France.
Hop Research Institute Co. Ltd., Žatec, Czech Republic.
PLoS One. 2025 May 6;20(5):e0322330. doi: 10.1371/journal.pone.0322330. eCollection 2025.
Hop (Humulus lupulus L.) is an emblematic industrial crop in the French North East region that developed at the same time as the brewing activity. Presently, this sector, especially microbreweries, are interested in endemic wild hops, which give beer production a local signature. In this study, we investigated the genetic and metabolic diversity of thirty-six wild hops sampled in various ecological environments. These wild accessions were propagated aeroponically and cultivated under uniform conditions (the same soil and the same environmental factors). Our phytochemical approach based on UHPLC-ESI-MS/MS analysis led to the identification of three metabolic clusters based on leaf content and characterized by variations in the contents of twelve specialized metabolites that were identified (including xanthohumol, bitter acids, and their oxidized derivatives). Furthermore, molecular characterization was carried out using sixteen EST-SSR microsatellites, allowing a genetic affiliation of our wild hops with hop varieties cultivated worldwide and wild hops genotyped to date using this method. Genetic proximity was observed for both European wild and hop varieties, especially for Strisselspalt, the historical variety of our region. Finally, our findings collectively assessed the impact of the hop genotype on the chemical phenotype through multivariate regression tree (MRT) analysis. Our results highlighted the 'WRKY 224' allele as a key discriminator between high- and low-metabolite producers. Moreover, the model based on genetic information explained 40% of the variance in the metabolic data. However, despite this strong association, the model lacked predictive power, suggesting that its applicability may be confined to the datasets analyzed.
啤酒花(Humulus lupulus L.)是法国东北部地区一种具有代表性的经济作物,与酿造业同时发展起来。目前,这个行业,尤其是小型啤酒厂,对当地特有的野生啤酒花感兴趣,这些野生啤酒花赋予了啤酒生产一种地方特色。在本研究中,我们调查了在各种生态环境中采集的36份野生啤酒花的遗传和代谢多样性。这些野生种质通过雾培法繁殖,并在统一条件下(相同的土壤和相同的环境因素)种植。我们基于超高效液相色谱-电喷雾串联质谱(UHPLC-ESI-MS/MS)分析的植物化学方法,基于叶片含量鉴定出三个代谢簇,其特征是已鉴定的12种特殊代谢物(包括黄腐酚、苦味酸及其氧化衍生物)含量的变化。此外,使用16个EST-SSR微卫星进行了分子表征,从而使我们的野生啤酒花与世界各地种植的啤酒花品种以及迄今使用该方法进行基因分型的野生啤酒花有了遗传关联。观察到欧洲野生啤酒花和啤酒花品种之间存在遗传亲缘关系,特别是对于我们地区的历史品种施特塞尔斯帕尔(Strisselspalt)。最后,我们的研究结果通过多元回归树(MRT)分析共同评估了啤酒花基因型对化学表型的影响。我们的结果突出了“WRKY 224”等位基因是高代谢物生产者和低代谢物生产者之间的关键区分因素。此外,基于遗传信息的模型解释了代谢数据中40%的方差。然而,尽管有这种强关联,该模型缺乏预测能力,这表明其适用性可能仅限于所分析的数据集。