Newton001 Systems Biology of Typhoid Fever

伤寒沙门氏菌和甲型副伤寒沙门氏菌引起的肠热在世界范围内广泛传播。在东南亚尤其严重，每年约有1.22亿新感染，死亡率为1%。由于对疾病发病机制和对感染的免疫反应了解不足、诊断检测不准确以及许可疫苗的效力有限，阻碍了对该病的控制。因此，了解人类宿主对肠道感染的反应对于开发改进的诊断测试和疫苗至关重要。牛津疫苗小组（OVG）最近开发了一种伤寒沙门氏菌和甲型副伤寒沙门氏菌的人体攻击模型。简单地说，人类成年志愿者口服感染了致病性剂量的伤寒沙门氏菌和甲型副伤寒沙门氏菌，并在接下来的几天内密切监测，直到接受抗生素治疗。该模型随后被用于通过在摄入细菌之前为参与者接种疫苗来测试疫苗效力。收集的各种样本提供了丰富的数据集，包括临床和免疫学测量，这对了解疾病发病机制和人类对感染的分子反应是非常宝贵的。与这些大型数据集和不同水平的数据（分子、血清学和临床）相关的是分析和数据整合的挑战，我们通过使用系统生物学方法来解决这个问题。系统生物学/疫苗学是一个跨学科领域，结合了生物学、疫苗和传染病背景下的全系统测量、网络和预测建模。计算建模和多个层面数据（临床、免疫学和分子）的整合，使人们对疾病发病机制和宿主对感染/疫苗反应的生物学机制有了多方面的了解。在这种情况下，特别重要的是调控机制，它由涉及多个转录和遗传成分的复杂网络组成。最近，人们发现长链非编码rna （lncRNAs）通过多种机制在生物过程的调控中发挥着关键作用。整合分析与lncRNAs表达相关的转录特征使我们能够识别与临床和免疫结果相关的分子事件。我们的目标是彻底整合这些不同的数据集，以充分研究人类宿主对感染反应的复杂性。我们认为，为了进一步阐明免疫系统无可争议的复杂性，这种方法是必要的。由于这些样本来自人类宿主和独特的对照实验设计，结果可能会阐明宿主如何对肠道感染作出反应以及伤寒沙门氏菌/甲型副伤寒沙门氏菌如何潜在地调节宿主反应的重要线索。通过与圣保罗大学的计算系统生物学实验室合作，我们将欧洲最大的临床试验小组之一与在系统疫苗学领域处于领先地位的计算生物学家团队结合起来。

Enteric fever caused by Salmonella Typhi and Paratyphi A is wide spread throughout the world. particularly burdensome in South-East Asia, with approximately >22 million new infections resulting in a 1% fatality rate annually. Control of the disease is hindered due to insufficient understanding of disease pathogenesis and immune responses to the infection, inaccurate diagnostic tests and limited efficacy of licensed vaccines. Thus understanding human host-responses to enteric infections is pivotal in developing improved diagnostic tests and vaccines.The Oxford Vaccine Group (OVG) has recently developed a human challenge model for S. Typhi and Paratyphi A. Briefly, human adult volunteers were orally infected with a pathogenic dose of S. Typhi and Paratyphi A and closely monitored the following days until treatment with antibiotics. This model was subsequently used to test vaccine efficacy by vaccinating participants prior to ingestion of the bacteria. The various samples collected provide a rich dataset consisting of clinical and immunological measurements invaluable to understand disease pathogenesis and human molecular responses to infection. Associated with such large datasets and different levels of data (molecular, serological and clinical) is the challenge of analysis and data integration, which we address through using systems biology approaches.Systems biology/vaccinology is an interdisciplinary field that combines systems-wide measurements, networks, and predictive modelling in the context of biology, vaccines and infectious disease. Computational modelling and integration of multiple levels of data (clinical, immunological, and molecular) develops a multi-facetted understanding of disease pathogenesis and biological mechanisms underlying host-responses to infections/vaccination. Particularly important in this context are regulatory mechanisms, which consist of complex networks involving multiple transcriptional and genetic components. Recently, it has become clear that long non-coding RNAs (lncRNAs) play a pivotal role in the regulation of biological processes by a diverse range of mechanisms. Integrative analysis of transcriptional signatures related to expression of lncRNAs allows us to identify molecular events associated with clinical and immunological outcomes.We are aiming to thoroughly integrate these different datasets to fully investigate the intricacy of human host-responses to infections. We believe that this approach is necessary in order to further shed light on the undisputed complexity of the immune system. As these samples are derived from the human host and a uniquely controlled experimental design, the results will likely elucidate important clues as to how the host reacts to enteric infections and how S. Typhi/Paratyphi A potentially modulate the host-response. By partnering with the Computational Systems Biology Lab at the University of Sao Paulo we are combining one of the largest clinical trials groups in Europe with an excellence in vaccine trials with a team of computational biologists leading in the field of systems vaccinology.