Shuffled data on clinical stats of infection.

Most viral diseases display a variable clinical outcome due to differences in virus strain virulence and/or individual host susceptibility to infection. Understanding the biological mechanisms differentiating a viral infection displaying severe clinical manifestations from its milder forms can provide the intellectual framework toward therapies and early prognostic markers. This is especially true in arbovirus infections, where most clinical cases are present as mild febrile illness. Here, we used a naturally occurring vector-borne viral disease of ruminants, bluetongue, as an experimental system to uncover the fundamental mechanisms of virus-host interactions resulting in distinct clinical outcomes. As with most viral diseases, clinical symptoms in bluetongue can vary dramatically. We reproduced experimentally distinct clinical forms of bluetongue infection in sheep using three bluetongue virus (BTV) strains (BTV-1IT2006, BTV-1IT2013 and BTV-8FRA2017). Infected animals displayed clinical signs varying from clinically unapparent, to mild and severe disease. We collected and integrated clinical, haematological, virological, and histopathological data resulting in the analyses of 332 individual parameters from each infected and uninfected control animal. We subsequently used machine learning to select the key viral and host processes associated with disease pathogenesis. We identified and experimentally validated five different fundamental processes affecting the severity of bluetongue: (i) virus load and replication in target organs, (ii) modulation of the host type-I IFN response, (iii) pro-inflammatory responses, (iv) vascular damage, and (v) immunosuppression. Overall, we showed that an agnostic machine learning approach can be used to prioritise the different pathogenetic mechanisms affecting the disease outcome of an arbovirus infection.

多数病毒性疾病的临床转归存在差异，这源于病毒毒株毒力的差异，以及宿主个体对感染的易感性各不相同。阐明区分重症与轻症病毒性感染的生物学机制，可为治疗手段开发与早期预后标志物研发提供理论框架。这一点在虫媒病毒（arbovirus）感染中尤为突出：该类感染的多数临床病例仅表现为轻症发热性疾病。
本研究以反刍动物的天然虫媒病毒性疾病——蓝舌病（bluetongue）为实验模型，旨在揭示导致不同临床转归的病毒-宿主互作核心机制。与多数病毒性疾病类似，蓝舌病的临床症状差异显著。本研究使用3株蓝舌病病毒（bluetongue virus, BTV）毒株（BTV-1IT2006、BTV-1IT2013与BTV-8FRA2017），在绵羊中成功复刻出实验性蓝舌病感染的不同临床表型。感染动物的临床症状从无明显临床表现，到轻症乃至重症各不相同。研究人员收集并整合了临床、血液学、病毒学与组织病理学数据，对每只感染动物与未感染对照动物的332项独立参数开展分析。随后本研究借助机器学习筛选出与疾病发病机制相关的关键病毒与宿主过程。本研究鉴定并通过实验验证了5种影响蓝舌病严重程度的核心过程：(1) 靶器官中的病毒载量与复制；(2) 宿主I型干扰素（type-I IFN）应答的调控；(3) 促炎应答；(4) 血管损伤；(5) 免疫抑制。
综上，本研究证实，无偏倚机器学习方法可用于排序影响虫媒病毒感染疾病转归的不同发病机制。