The Vanderbilt Urologic Infection Repository, a Resource for Personalized Clinical Discovery

摘要-资源项目 尿路感染（UTI）不仅代表最普遍的泌尿系统病理之一，而且也是最常见的泌尿系统疾病之一。 最多样化的。由于传统和新兴的风险因素，常见的临床情况范围 从无症状的菌尿和无并发症的膀胱炎到肾盂肾炎、菌血症和彻底的尿脓毒症。 致病微生物剂同样包括各种各样的机会致病菌，以及另外的致病微生物。 (and显著的）基因型/表型多样性。在分子水平上， 决定泌尿系统健康和UTI之间平衡的宿主-病原体因素仍然没有完全确定。 广义的发病机制模型不能解释现实世界临床诊断中常见的细微差别， 实践中，限制了医生提供基于证据和个性化护理的能力。到 为了应对这些挑战，我们寻求创建范德比尔特泌尿系统感染库（VUIR）：一个庞大的 (but去识别）收集患者特异性临床信息和配对的微生物分离株， 从我们医疗中心的诊断尿培养的常规工作流程中重新利用。作为一个信息专家， 和生物资源，VUIR将建立在独特的基础上，已经在范德比尔特。这些 包括我们的合成衍生物，我们的电子健康记录的匿名镜像，沿着microVU， 通过该倡议，我们诊断实验室的所有无菌现场微生物分离株都被系统地 保留用于学术调查。我们现在建议扩大microVU的活动，包括范德比尔特的强大的 尿液培养物的数量，将库存的微生物（每年数千个）与关键的可搜索参数联系起来 从来源-患者（例如人口统计学、乳腺病学、风险因素）以及更广泛的数据主体 在合成衍生物中作为一个双向的桥梁，VUIR将创造一个机会， 表型，同时提供了大量的野生型微生物菌株 对于下游实验，全部通过人UTI表型分层。一个特别令人兴奋的应用， VUIR涉及全基因组关联研究（GWAS），直接将病原体的遗传特征网络化 宿主的临床特征除了构建VUIR之外，我们还将挖掘存储库， 用于全基因组测序的未被充分代表的微生物靶标。通过机器学习方法， 这些菌株的多重基因组特征将与其宿主的临床参数相关， 强调大肠埃希菌和[1]有症状UTI和[2]无症状UTI的全球表型 菌尿（ASB）。区分这些现象学类别的微生物特征仍然很差 定义-至少是由迄今为止考虑的简化指标-尽管UTI的严格分子定义- vs-ASB对临床医师具有重要的诊断价值，对研究者具有重要的致病价值。鉴于 由于这种复杂性，我们将利用VUIR来协调主机之间的生物信息学和医学信息学数据 和病原体，作为该计划在临床/基础泌尿学和相关领域的广泛实用性的概念验证。

SUMMARY - RESOURCE PROJECT Urinary tract infections (UTIs) represent not only one of the most prevalent urologic pathologies, but also one of the most diverse. With both traditional and emerging risk factors, commonly encountered clinical scenarios range from asymptomatic bacteriuria and uncomplicated cystitis to pyelonephritis, bacteremia, and outright urosepsis. The causative microbial agents likewise include a tremendous variety of opportunistic pathogens, with additional (and significant) genotypic/phenotypic diversity among individual strains of these species. On a molecular level, the host-pathogen factors that dictate the balance between urologic health and UTI remain incompletely defined. Generalized models of pathogenesis fail to account for commonly encountered nuances of real-world clinical practice, limiting the ability of physicians to provide care that is both evidence-based and personalized. To combat these challenges, we seek to create the Vanderbilt Urologic Infection Repository (VUIR): a massive (but de-identified) collection of patient-specific clinical information and paired microbial isolates, repurposed from our Medical Center's routine workflow of diagnostic urine cultures. As both an informatic and biologic resource, the VUIR will build on unique foundations that are already in place at Vanderbilt. These include our Synthetic Derivative, an anonymized mirror of our electronic health records, along with microVU, an initiative through which all sterile-site microbial isolates from our Diagnostic Laboratories are systematically retained for academic inquiry. We now propose to expand microVU activities to include Vanderbilt's formidable volume of urine cultures, linking the banked organisms (many thousand annually) to key searchable parameters from the source-patients (e.g. demographics, symptomatology, risk factors), as well as the broader body of data within the Synthetic Derivative. As a two-way bridge, the VUIR will create an opportunity to parse human phenomes in light of microbiologic results, while providing a tremendous quantity of wild-type microbial strains for downstream experimentation, all stratified by human UTI-phenotypes. One particularly exciting application of the VUIR involves genome-wide association studies (GWAS) that network genetic features of pathogens directly to clinical features of their hosts. In addition to building the VUIR, we will mine the repository to select underrepresented microbial targets for whole-genome sequencing. Through a machine-learning approach, the multi-partite genomic features of these strains will be correlated to their hosts' clinical parameters, with an emphasis on Escherichia coli and the global phenotypes of [1] symptomatic UTI and [2] asymptomatic bacteriuria (ASB). The microbial features that distinguish these phenomenological categories remain poorly defined—at least by the simplified metrics considered to date—although a rigorous molecular definition of UTI- vs-ASB would carry significant diagnostic value for physicians and pathogenetic value for investigators. In light of this complexity, we will utilize the VUIR to harmonize bioinformatic and medical informatic data across host and pathogen, as proof-of-concept for this program's broad utility in clinical/basic urology and allied fields.