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Extending inherited metabolic disorder diagnostics with biomarker interaction visualizations.

Denise N Slenter1, Irene M G M Hemel2,3, Chris T Evelo2,3

  • 1Department of Bioinformatics (BiGCaT), NUTRIM, Maastricht University, Maastricht, The Netherlands. denise.slenter@maastrichtuniversity.nl.

Orphanet Journal of Rare Diseases
|April 26, 2023
PubMed
Summary

This study introduces a new visualization framework to help diagnose rare inherited metabolic disorders (IMDs) by integrating metabolic pathway knowledge with patient data. The approach aids in identifying biomarkers and diagnosing complex cases more effectively.

Keywords:
Clinical metabolic biomarkersNetwork data analysisPurine and pyrimidine metabolismSemantic web technologiesSystems biologyUrea cycle

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Area of Science:

  • Biochemistry
  • Medical Genetics
  • Computational Biology

Background:

  • Inherited Metabolic Disorders (IMDs) present diagnostic challenges due to non-specific symptoms and complex biomarker interactions.
  • Existing diagnostic methods struggle with overlapping biomarkers and lack of clear genotype-phenotype correlations in IMDs.
  • Visualizing metabolic interactions could significantly aid in the diagnosis of these rare diseases.

Purpose of the Study:

  • To develop and test a proof-of-concept framework for integrating metabolic pathway knowledge with patient data for diagnosing IMDs.
  • To provide a visualization tool that connects metabolic biomarkers and enzyme interactions for diagnostic support.
  • To lay the groundwork for scaling up this approach to aid in diagnosing less-understood IMDs.

Main Methods:

  • Integrated literature and expert knowledge into machine-readable pathway models, including urine biomarkers and their interactions.
  • Applied the framework to visualize clinical data from 16 patients with urea cycle and pyrimidine synthesis disorders on relevant metabolic pathways.
  • Utilized expert laboratory scientists to evaluate the diagnostic accuracy of the visualizations.

Main Results:

  • The framework successfully visualized patient data, highlighting relevant biomarkers and pathway interactions.
  • Diagnostic conclusions derived from the framework matched those from the current diagnostic pipeline for all tested samples.
  • Diagnosis was achieved without clinical symptoms or sex information for nine patients, demonstrating the framework's potential.

Conclusions:

  • The developed framework effectively integrates metabolic interaction knowledge with clinical data for visualization, aiding in diagnosing complex IMD cases.
  • Challenges were identified, requiring resolution before widespread implementation for diagnosing various IMDs.
  • Future extensions could incorporate multi-omics data (genomics, transcriptomics) and phenotypic information for enhanced diagnostic capabilities.