Rapid Identification of New Biomarkers for the Classification of GM1 Type 2 Gangliosidosis Using an Unbiased <sup>1</sup>H NMR-Linked Metabolomics Strategy
Biomarkers currently available for the diagnosis, prognosis, and therapeutic monitoring of GM1 gangliosidosis type 2 (GM1T2) disease are mainly limited to those discovered in targeted proteomic-based studies. In order to identify and establish new, predominantly low-molecular-mass biomarkers for thi...
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MDPI AG
2021-03-01
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| Series: | Cells |
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| Online Access: | https://www.mdpi.com/2073-4409/10/3/572 |
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| author | Benita C. Percival Yvonne L. Latour Cynthia J. Tifft Martin Grootveld |
| author_facet | Benita C. Percival Yvonne L. Latour Cynthia J. Tifft Martin Grootveld |
| author_sort | Benita C. Percival |
| collection | DOAJ |
| description | Biomarkers currently available for the diagnosis, prognosis, and therapeutic monitoring of GM1 gangliosidosis type 2 (GM1T2) disease are mainly limited to those discovered in targeted proteomic-based studies. In order to identify and establish new, predominantly low-molecular-mass biomarkers for this disorder, we employed an untargeted, multi-analyte approach involving high-resolution <sup>1</sup>H NMR analysis coupled to a range of multivariate analysis and computational intelligence technique (CIT) strategies to explore biomolecular distinctions between blood plasma samples collected from GM1T2 and healthy control (HC) participants (<i>n</i> = 10 and 28, respectively). The relationship of these differences to metabolic mechanisms underlying the pathogenesis of GM1T2 disorder was also investigated. <sup>1</sup>H NMR-linked metabolomics analyses revealed significant GM1T2-mediated dysregulations in ≥13 blood plasma metabolites (corrected <i>p</i> < 0.04), and these included significant upregulations in 7 amino acids, and downregulations in lipoprotein-associated triacylglycerols and alanine. Indeed, results acquired demonstrated a profound distinctiveness between the GM1T2 and HC profiles. Additionally, employment of a genome-scale network model of human metabolism provided evidence that perturbations to propanoate, ethanol, amino-sugar, aspartate, seleno-amino acid, glutathione and alanine metabolism, fatty acid biosynthesis, and most especially branched-chain amino acid degradation (<i>p</i> = 10<sup>−12</sup>−10<sup>−5</sup>) were the most important topologically-highlighted dysregulated pathways contributing towards GM1T2 disease pathology. Quantitative metabolite set enrichment analysis revealed that pathological locations associated with these dysfunctions were in the order fibroblasts > Golgi apparatus > mitochondria > spleen ≈ skeletal muscle ≈ muscle in general. In conclusion, results acquired demonstrated marked metabolic imbalances and alterations to energy demand, which are consistent with GM1T2 disease pathogenesis mechanisms. |
| first_indexed | 2024-03-09T05:20:21Z |
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| language | English |
| last_indexed | 2024-03-09T05:20:21Z |
| publishDate | 2021-03-01 |
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| series | Cells |
| spelling | doaj.art-9e921bcb578849fcac969f6c63d3eb522023-12-03T12:41:20ZengMDPI AGCells2073-44092021-03-0110357210.3390/cells10030572Rapid Identification of New Biomarkers for the Classification of GM1 Type 2 Gangliosidosis Using an Unbiased <sup>1</sup>H NMR-Linked Metabolomics StrategyBenita C. Percival0Yvonne L. Latour1Cynthia J. Tifft2Martin Grootveld3Leicester School of Pharmacy, De Montfort University, The Gateway, Leicester LE1 9BH, UKDepartment of Pathology, Microbiology, and Immunology, Vanderbilt University, Nashville, TN 37232-0252, USADeputy Clinical Director, National Human Genome Research Institute, Director, National Institutes of Health, Bethesda, MD 20892-1205, USALeicester School of Pharmacy, De Montfort University, The Gateway, Leicester LE1 9BH, UKBiomarkers currently available for the diagnosis, prognosis, and therapeutic monitoring of GM1 gangliosidosis type 2 (GM1T2) disease are mainly limited to those discovered in targeted proteomic-based studies. In order to identify and establish new, predominantly low-molecular-mass biomarkers for this disorder, we employed an untargeted, multi-analyte approach involving high-resolution <sup>1</sup>H NMR analysis coupled to a range of multivariate analysis and computational intelligence technique (CIT) strategies to explore biomolecular distinctions between blood plasma samples collected from GM1T2 and healthy control (HC) participants (<i>n</i> = 10 and 28, respectively). The relationship of these differences to metabolic mechanisms underlying the pathogenesis of GM1T2 disorder was also investigated. <sup>1</sup>H NMR-linked metabolomics analyses revealed significant GM1T2-mediated dysregulations in ≥13 blood plasma metabolites (corrected <i>p</i> < 0.04), and these included significant upregulations in 7 amino acids, and downregulations in lipoprotein-associated triacylglycerols and alanine. Indeed, results acquired demonstrated a profound distinctiveness between the GM1T2 and HC profiles. Additionally, employment of a genome-scale network model of human metabolism provided evidence that perturbations to propanoate, ethanol, amino-sugar, aspartate, seleno-amino acid, glutathione and alanine metabolism, fatty acid biosynthesis, and most especially branched-chain amino acid degradation (<i>p</i> = 10<sup>−12</sup>−10<sup>−5</sup>) were the most important topologically-highlighted dysregulated pathways contributing towards GM1T2 disease pathology. Quantitative metabolite set enrichment analysis revealed that pathological locations associated with these dysfunctions were in the order fibroblasts > Golgi apparatus > mitochondria > spleen ≈ skeletal muscle ≈ muscle in general. In conclusion, results acquired demonstrated marked metabolic imbalances and alterations to energy demand, which are consistent with GM1T2 disease pathogenesis mechanisms.https://www.mdpi.com/2073-4409/10/3/572GM1 gangliosidosislysosomal storage disordersnuclear magnetic resonance (NMR) analysisNMR-based metabolomicsbiomarkersvalidation |
| spellingShingle | Benita C. Percival Yvonne L. Latour Cynthia J. Tifft Martin Grootveld Rapid Identification of New Biomarkers for the Classification of GM1 Type 2 Gangliosidosis Using an Unbiased <sup>1</sup>H NMR-Linked Metabolomics Strategy Cells GM1 gangliosidosis lysosomal storage disorders nuclear magnetic resonance (NMR) analysis NMR-based metabolomics biomarkers validation |
| title | Rapid Identification of New Biomarkers for the Classification of GM1 Type 2 Gangliosidosis Using an Unbiased <sup>1</sup>H NMR-Linked Metabolomics Strategy |
| title_full | Rapid Identification of New Biomarkers for the Classification of GM1 Type 2 Gangliosidosis Using an Unbiased <sup>1</sup>H NMR-Linked Metabolomics Strategy |
| title_fullStr | Rapid Identification of New Biomarkers for the Classification of GM1 Type 2 Gangliosidosis Using an Unbiased <sup>1</sup>H NMR-Linked Metabolomics Strategy |
| title_full_unstemmed | Rapid Identification of New Biomarkers for the Classification of GM1 Type 2 Gangliosidosis Using an Unbiased <sup>1</sup>H NMR-Linked Metabolomics Strategy |
| title_short | Rapid Identification of New Biomarkers for the Classification of GM1 Type 2 Gangliosidosis Using an Unbiased <sup>1</sup>H NMR-Linked Metabolomics Strategy |
| title_sort | rapid identification of new biomarkers for the classification of gm1 type 2 gangliosidosis using an unbiased sup 1 sup h nmr linked metabolomics strategy |
| topic | GM1 gangliosidosis lysosomal storage disorders nuclear magnetic resonance (NMR) analysis NMR-based metabolomics biomarkers validation |
| url | https://www.mdpi.com/2073-4409/10/3/572 |
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