Department of Bioinformatics (BiGCaT), NUTRIM, Maastricht University, Maastricht, The Netherlands.
Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.
Orphanet J Rare Dis. 2023 Apr 26;18(1):95. doi: 10.1186/s13023-023-02683-9.
Inherited Metabolic Disorders (IMDs) are rare diseases where one impaired protein leads to a cascade of changes in the adjacent chemical conversions. IMDs often present with non-specific symptoms, a lack of a clear genotype-phenotype correlation, and de novo mutations, complicating diagnosis. Furthermore, products of one metabolic conversion can be the substrate of another pathway obscuring biomarker identification and causing overlapping biomarkers for different disorders. Visualization of the connections between metabolic biomarkers and the enzymes involved might aid in the diagnostic process. The goal of this study was to provide a proof-of-concept framework for integrating knowledge of metabolic interactions with real-life patient data before scaling up this approach. This framework was tested on two groups of well-studied and related metabolic pathways (the urea cycle and pyrimidine de-novo synthesis). The lessons learned from our approach will help to scale up the framework and support the diagnosis of other less-understood IMDs.
Our framework integrates literature and expert knowledge into machine-readable pathway models, including relevant urine biomarkers and their interactions. The clinical data of 16 previously diagnosed patients with various pyrimidine and urea cycle disorders were visualized on the top 3 relevant pathways. Two expert laboratory scientists evaluated the resulting visualizations to derive a diagnosis.
The proof-of-concept platform resulted in varying numbers of relevant biomarkers (five to 48), pathways, and pathway interactions for each patient. The two experts reached the same conclusions for all samples with our proposed framework as with the current metabolic diagnostic pipeline. For nine patient samples, the diagnosis was made without knowledge about clinical symptoms or sex. For the remaining seven cases, four interpretations pointed in the direction of a subset of disorders, while three cases were found to be undiagnosable with the available data. Diagnosing these patients would require additional testing besides biochemical analysis.
The presented framework shows how metabolic interaction knowledge can be integrated with clinical data in one visualization, which can be relevant for future analysis of difficult patient cases and untargeted metabolomics data. Several challenges were identified during the development of this framework, which should be resolved before this approach can be scaled up and implemented to support the diagnosis of other (less understood) IMDs. The framework could be extended with other OMICS data (e.g. genomics, transcriptomics), and phenotypic data, as well as linked to other knowledge captured as Linked Open Data.
遗传性代谢疾病（IMD）是一种罕见疾病，其中一种受损蛋白会导致相邻化学转化的级联变化。IMD 常表现为非特异性症状、缺乏明确的基因型-表型相关性和新生突变，从而使诊断变得复杂。此外，一种代谢转化的产物可能是另一种途径的底物，从而掩盖生物标志物的识别，并导致不同疾病的生物标志物重叠。代谢生物标志物与相关酶之间的连接可视化可能有助于诊断过程。本研究的目的是在扩大这种方法之前，提供一个将代谢相互作用知识与实际患者数据相结合的概念验证框架。该框架在两个研究充分且相关的代谢途径（尿素循环和嘧啶从头合成）组上进行了测试。我们方法的经验教训将有助于扩大框架并支持其他了解较少的 IMD 的诊断。
我们的框架将文献和专家知识整合到机器可读的途径模型中，包括相关的尿液生物标志物及其相互作用。16 名先前诊断为各种嘧啶和尿素循环障碍的患者的临床数据在 3 个最相关的途径上进行了可视化。两名专家实验室科学家根据产生的可视化结果进行诊断。
概念验证平台为每个患者产生了不同数量的相关生物标志物（5 到 48 个）、途径和途径相互作用。对于我们提出的框架和当前代谢诊断管道，两位专家对所有样本的结论相同。对于 9 个患者样本，在没有临床症状或性别的知识的情况下做出了诊断。对于其余 7 个病例，有 4 个解释指向一组疾病，而 3 个病例由于现有数据而无法诊断。要对这些患者进行诊断，除了生化分析外，还需要进行额外的测试。
提出的框架展示了如何将代谢相互作用知识与临床数据集成在一个可视化中，这对于未来分析困难患者病例和非靶向代谢组学数据可能具有重要意义。在开发该框架过程中发现了几个挑战，在该方法能够扩大规模并实施以支持其他（了解较少） IMD 的诊断之前，应解决这些挑战。该框架可以通过其他 OMICS 数据（例如基因组学、转录组学）和表型数据进行扩展，并与作为链接开放数据捕获的其他知识链接。
