A Decision Support tool for Potato Blackleg Disease (DeS-BL)

马铃薯黑胫病是英国最具破坏性的细菌性植物病原体，每年造成5000万英镑的损失。马铃薯产业的损失。目前的知识认为，疾病是通过铅感染的种薯引起的。然而，我们最近未发表的数据表明，在灌溉后的高土壤湿度下，无病原体种子（微型块茎）生长的植物会出现疾病。最可能的解释是细菌直接从土壤中进入植物并引起疾病;这是以前没有考虑过的。我们还表明，Pba能够殖民其他植物物种（包括作物）的根，可能是土壤中的天然根际栖息的腐殖土。在单独使用Pba的盆栽试验中，我们表明Pba没有从土壤进入植物。然而，当自由生活的线虫（FLN）被添加到土壤中时，茎中的Pba增加了100倍。通过这些和其他发现，我们现在有可能在我们如何管理黑胫病方面做出一步改变。我们将首先通过使用光片和共聚焦激光扫描显微镜，透明土壤和围隔研究来评估FLN作为Pba载体的作用以及感染如何发生，从而解决知识差距。我们还将研究标准灌溉制度的变化如何有助于减少商品作物中的黑胫病水平（灌溉通常过度，以避免在干燥条件下发生的常见疮痂病），以及它如何改变马铃薯根系周围的FLN社区。同样，我们将确定覆盖作物，限制其根上的自然Pba定殖，作为在种植马铃薯之前减少土壤中Pba数量的可能方法。关于马铃薯根部的微生物组以及这些微生物组如何受到影响以有利于或减少Pba的定殖知之甚少。因此，我们将使用鸟枪宏基因组测序和最新的生物信息学工具在灌溉前后对马铃薯微生物组进行分析，重点是果胶杆菌科和更广泛的γ-变形菌。我们还将使用GC-MS来研究根结构和根分泌物成分的变化如何影响这些细菌群的组成，以评估灌溉和覆盖作物的使用是否改变了与马铃薯相关的有益和有害细菌之间的平衡。最后，我们将确定是否在密切相关的非致病性细菌的微生物组中的新型抗菌剂（细菌素）可以作为一种管理选项对Pba.Our最近的建模研究使用苏格兰政府的内部马铃薯检查数据库（SPUDS），表明黑胫病的发病率在全国范围内不会随机发生，但在集群。其原因尚不清楚，但可能是由于几个因素，当确定时，可能有助于种植者管理其作物，例如马铃薯作物分布，天气，土壤类型，土壤湿度，叶片湿度，FLN分布，作物类型和种植前的轮作。使用该项目生成的数据，来自其他近期和历史调查的大量数据以及来自政府和行业的最新数据，我们将使用创新的机器学习方法在全国范围内对这些数据进行建模，以确定Pba发病率在空间和时间上的趋势和驱动因素，并通过此，制作预测模型，以支持开发一套决策支持工具，供利益攸关方在项目期间进行评估，并在其后尽早采用。此外，通过情景测试，我们将量化气候变化对与FLN存在相关的未来黑胫病发生率的预测影响，从而为行业提供可靠和新颖的数据，以支持行业弹性规划。

Blackleg disease of potato caused by P. atrosepticum (Pba) is the most damaging bacterial plant pathogen in the UK, costing £50M p.a. in losses for the potato industry. Current knowledge assumes that disease is caused through Pba-infected seed tubers. However, our recent unpublished data have shown that under high soil moisture following irrigation, disease appears in plants grown from pathogen free seed (minitubers). The most likely explanation is that bacteria enter the plant and cause disease directly from the soil; something not previously considered. We have also shown that Pba is able to colonise roots of other plant species (including crops), possibly as natural rhizosphere-dwelling saprophytes in the soil. In pot trials with Pba alone, we showed there was no movement of Pba from soil into the plant. However, when free-living nematodes (FLN) were added to soil, a 100-fold increase in Pba in stems occurred.Through these and other findings we now have the potential to make a step change in how we manage blackleg. We will address knowledge gaps firstly by using Light Sheet and Confocal Laser Scanning microscopy, transparent soils and mesocosm studies to assess the role of FLN as vectors of Pba and how infection occurs. We will also examine how changes in standard irrigation regimes can help to reduce levels of blackleg in ware crops (where irrigation is often over-applied to avoid common scab disease that occurs in dry conditions), and how it might change FLN communities around potato root systems. Similarly, we will identify cover crops that limit natural Pba colonisation on their roots as a possible way to reduce Pba numbers in soil prior to planting potato. Little is known about the microbiome on potato roots and how these might be influenced to favour or reduce colonisation by Pba. We will therefore characterise the potato microbiome prior to and following irrigation using shotgun metagenomics sequencing and the latest bioinformatics tools, with a focus on the Pectobacteriaceae and wider Gamma-proteobacteria. We will also use GC-MS to examine how changes in root architecture and the constituents of root exudates influence the composition of these bacterial groups, to assess whether the use irrigation and cover crops alter the balance between beneficial and harmful bacteria associated with potato. Finally, we will determine whether novel antimicrobials (bacteriocins) in closely related non-pathogenic bacteria in the microbiome could act as a management option against Pba.Our recent modelling research using the Scottish Government's in-house potato inspections database (SPUDS), shows that blackleg incidence on a national scale does not occur randomly but in clusters. Reason(s) for this remain unclear but could be due to several things that, when identified, may assist growers in managing their crops, e.g. potato crop distribution, weather, soil type, soil moisture, leaf wetness, FLN distribution, crop type and rotation prior to planting. Using data generated from this project, an extensive array of data from other recent and historical investigations and the latest data from government and industry we will model, using innovative machine learning methods, at national scale these data to identify trends and drivers of Pba incidence in both space and time and, through this, produce predictive models to support development of a set of decision support tools for evaluation by stakeholders during the project and early adoption thereafter. Further, through scenario testing, we will quantify the predicted effects of climate change on future blackleg incidence in association with FLN presence thus providing the industry with robust and novel data to underpin sector resilience planning.