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Metabolomics: A Pipeline for Biomarker Discovery in Genetic Diseases

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Clinical Metabolomics Applications in Genetic Diseases

Abstract

Genetic disorders (GD) affect hundreds of millions of lives worldwide, leading to specific molecular perturbations in the metabolic profile within a certain biological matrix. Metabolomic studies use advanced technologies (nuclear magnetic resonance (NMR) and mass spectrometry (MS)) with bioinformatics to identify and quantify a set of small molecules (such as carbohydrates, nucleic acids, amino acids, and lipids) present in a biological system. As metabolites represent the downstream product of gene and protein activity, they most likely reflect the phenotype of an organism at a specific time. Metabolomic studies provide novel insights into the underlying disease pathophysiological mechanisms, evaluate the progress of the disease, and identify unique biomarkers for the prediction of disease and therapeutic outcomes. This chapter highlights the applications of metabolomic techniques for biomarker discovery in GD. It discusses the workflow followed, the methods used for sample analysis and data interpretation, and the major challenges and limitations in applying the metabolomic approach for biomarker discovery in GD. Moreover, the chapter provides an overview of the biomarkers identified in four GD: cystic fibrosis, Down syndrome, sickle cell anemia, and glycogen storage disorders, highlighting the promising role of metabolomics in clinical applications.

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Abbreviations

BALF:

Bronchoalveolar lavage fluid

CF:

Cystic fibrosis

CFTR:

Cystic fibrosis transmembrane conductance regulator

CID:

Collision-induced dissociation

DBS:

Dried blood spot

DDA:

Data-dependent acquisition

DIA:

Data-independent acquisition

EBC:

Exhaled breath condensate

GC:

Gas chromatography

GD:

Genetic disorders

GSDs:

Glycogen storage disorders

LOD:

Limit of detection

LOQ:

Limit of quantification

MS:

Mass spectrometry

NMR:

Nuclear magnetic resonance

OPLS-DA:

Orthogonal partial least squares-discriminant analysis

PCA:

Principal component analysis

PLS-DA:

Partial least squares-discriminant analysis

SCD:

Sickle cell disease

VIP:

Variable importance in projection

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Authors and Affiliations

  1. Department of Pharmaceutical Sciences, School of Pharmacy, The University of Jordan, Amman, Jordan

    Lina A. Dahabiyeh

  2. Department of Medical Laboratory Sciences, Jordan University of Science and Technology, Irbid, Jordan

    Refat M. Nimer

Authors
  1. Lina A. Dahabiyeh
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  2. Refat M. Nimer
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Correspondence to Lina A. Dahabiyeh or Refat M. Nimer .

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Editors and Affiliations

  1. Metabolomics Section, Department of Clinical Genomics, Center for Genome Medicine, King Faisal Specialist Hospital & Research Center (KFSHRC), Riyadh, Saudi Arabia

    Anas M. Abdel Rahman

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Dahabiyeh, L.A., Nimer, R.M. (2023). Metabolomics: A Pipeline for Biomarker Discovery in Genetic Diseases. In: Abdel Rahman, A.M. (eds) Clinical Metabolomics Applications in Genetic Diseases. Springer, Singapore. https://doi.org/10.1007/978-981-99-5162-8_3

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  • DOI: https://doi.org/10.1007/978-981-99-5162-8_3

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