Background 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. Methods 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. Results 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. Conclusion 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.
Background: Pyrimidine (and purine) metabolism provides essential high-energy vehicles, universal throughout all life, which carry both activated metabolites that serve as fuel and building blocks for countless metabolic processes as well as the messenger molecules that steer these processes. Furthermore, these nitrogen-containing structures encode the alphabet needed to construct the genetic code. The pyrimidine pathway overlaps with the Urea Cycle through a common metabolite (carbamoyl phosphate), with the urea cycle being responsible for the production of several amino acids and removing ammonia. Various Inherited Metabolic Disorders (IMDs) disrupt these pathways, with considerably large phenotypic variation in affected patients, hindering diagnosis. Moreover, the biomarkers for these disorders overlap substantially between the IMDs and their underlying biological pathways, interfering with identifying the deficient protein. We developed a framework that allows us to combine clinical and theoretical biomarkers with pathway models through network approaches and semantic web technologies to support the diagnosis of pyrimidine and urea cycle IMDs. Methods: We integrated literature and expert knowledge into machine-readable pathway models for pyrimidine and urea cycle disorders, including disease information and relevant downstream biomarkers. The theoretical change for each biomarker per disease was compiled based on a manual database search. The top three pathways of interest were retrieved through semantic web technologies, by selecting pathways that covered most unique markers as well as showing overlap with the theoretical marker data. These pathways and the corresponding clinical data were visualized through network analysis. Two expert laboratory scientists evaluated our approach by diagnosing a cohort of sixteen previously diagnosed patients with various pyrimidine and urea cycle disorders, based on a visualization of theoretical biomarker overlap with patient data, as well as top three pathways displaying the relationships between the individual biomarkers. Results: The number of relevant biomarkers for each patient varies greatly (five to 48), and likewise the pathways covering most unique biomarkers differ for equivalent disorders. The two experts reached similar conclusions regarding the diagnosis of nine patient samples without knowledge about clinical symptoms or sex. For the remaining seven cases, four interpretations pointed in the direction of a subset of disorders, which could be prioritized for further investigation. Three cases were found to be undiagnosable with the data available. Conclusion: The presented workflow supports the diagnosis of several IMDs of pyrimidine and urea cycle pathways, by directly linking biological pathway knowledge and theoretical biomarker data to clinical cases. This workflow is adaptable to analyze different types of IMDs, difficult patient cases and functional assays in the future. Furthermore, the pathway models can be used as a basis to perform various other types of (omics) data analysis, e.g. transcriptomics, metabolomics, fluxomics. Data Availability: The data used in this study have been deposited at https://github.com/BiGCAT-UM/IMD-PUPY .
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.