1) these variants can only be classified as (likely) pathogenic when genomic, transcriptomic, proteomic, metabolomic, and/or fluxomic data are integrated through pathway or network analysis. Furthermore, genetic profiles of patients can also contain variants of uncertain significance (Fig. This technique has been found less sensitive and specific as compared to metabolic measurements in newborn screening. Methods to detect genetic variants (WES) are useful for the diagnosis of specific classes of IMDs where few or no specific metabolic biomarkers exist (e.g. The processed data is often linked to existing database knowledge to arrive at a diagnosis. targeted metabolite assays, Whole Exome Sequencing (WES)), which all require data processing and interpretation. After the sample has been collected and processed, several types of analysis can be performed (e.g. The current diagnostic process starts with a metabolic pediatrician, who based on the phenotype of a patient can request biochemical analyses on a patient sample (e.g. A timely and accurate diagnosis of IMDs, currently based on both symptoms and biomarkers measured in various bodily fluids, is required to initiate therapies, which are sparsely available. These disorders are classified as Inherited Metabolic Disorders (IMDs) or Inborn Errors of Metabolism. Figure 1 presents a schematic of the disturbed biochemical reactions based on one impaired protein, leading to an altered phenotype. Malfunctioning of any of these enzymes often results in a lack of or (potentially) toxic levels of metabolites, as well as affecting other (downstream) pathways. Many enzymes are critically involved in the synthesis, degradation, and transport of molecules in metabolic processes. genomics, transcriptomics), and phenotypic data, as well as linked to other knowledge captured as Linked Open Data. The framework could be extended with other OMICS data (e.g. 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 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. Diagnosing these patients would require additional testing besides biochemical analysis. 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. For nine patient samples, the diagnosis was made without knowledge about clinical symptoms or sex. The two experts reached the same conclusions for all samples with our proposed framework as with the current metabolic diagnostic pipeline. The proof-of-concept platform resulted in varying numbers of relevant biomarkers (five to 48), pathways, and pathway interactions for each patient. Two expert laboratory scientists evaluated the resulting visualizations to derive a diagnosis. The clinical data of 16 previously diagnosed patients with various pyrimidine and urea cycle disorders were visualized on the top 3 relevant pathways. Our framework integrates literature and expert knowledge into machine-readable pathway models, including relevant urine biomarkers and their interactions. The lessons learned from our approach will help to scale up the framework and support the diagnosis of other less-understood IMDs. This framework was tested on two groups of well-studied and related metabolic pathways (the urea cycle and pyrimidine de-novo synthesis). 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. Visualization of the connections between metabolic biomarkers and the enzymes involved might aid in the diagnostic process. Furthermore, products of one metabolic conversion can be the substrate of another pathway obscuring biomarker identification and causing overlapping biomarkers for different disorders. IMDs often present with non-specific symptoms, a lack of a clear genotype–phenotype correlation, and de novo mutations, complicating diagnosis. Inherited Metabolic Disorders (IMDs) are rare diseases where one impaired protein leads to a cascade of changes in the adjacent chemical conversions.
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