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Abstract INTRODUCTION:
Pompe disease is a rare, progressive, neuromuscular genetic disorder caused by the enzyme deficiency of acid α-glucosidase (GAA gene), leading to abnormal glycogen accumulation in lysosomes, and progressively to skeletal muscle dysfunction (1). Urinary tetrasaccharides (Glcα1-6Glcα1-4Glcα1-4Glc) (Glc4) are upregulated, especially in patients with Infantile Onset Pompe disease (IOPD) (2). Urine Glc4 is a biomarker useful for evaluating Pompe disease patients, including monitoring and follow up of the treatment by enzyme replacement therapy (ERT) (3). Still, Glc4 is not always elevated in all patients due to marked phenotypic/genotypic heterogeneity of the disease, especially for patients with Late Onset Pompe disease (LOPD)(2-3). Nevertheless, altered metabolic pathways in Pompe disease offer new promising perspectives to find alternative or complementary biomarkers for Pompe disease (4).
OBJECTIVES:
Semi-targeted mass spectrometry metabolomic approaches have been devised to discover new biomarkers in biological fluids (urine and plasma) from untreated IOPD and LOPD patients compared to age- and sex-matched controls.
METHODS:
Urine samples were normalized by specific gravity before the extraction due to the hydration status of participants. A liquid-liquid extraction approach was used to separate saccharides (polar layer) and lipids (organic layer) using MTBE/H2O/MeOH (2.6/2.0/2.4). Extracted samples from untreated patients (8 LOPD in urine; 12 IOPD, and 13 LOPD in plasma) were compared to age- and sex-matched control samples using a quadrupole-time-of-flight mass spectrometer (Q-TOF, Xevo G2-XS, Waters Corp.) combined to ultra-performance liquid chromatography (UPLC, Acquity I-Class, Waters Corp.). Separated polar and organic layers were evaporated to dryness under a nitrogen stream and were respectively injected for the metabolomic and lipidomic studies. Compounds were separated by liquid chromatography with a BEH Amide 1.7 µm, 2.1x100 mm column (Waters Corp.) for the metabolomic study and with a BEH C18 1.7 µm, 2.1x100 mm column (Waters Corp.) for the lipidomic study. A pooled quality control (QC) was prepared by mixing the same amount of each sample before injection to verify the data quality and the instrument performance. Based on QC multiple injections within the analysis, features detected were filtered to consider only detected compounds with relative standard deviation ≤ 20%, which were 3 times more abundant than the noise signal. Two criteria were used for defining differential metabolites between healthy controls (CTRLs) and patients: minimum fold change (≥2) and ANOVA p-value (≤0.05). Extracted samples were analyzed in positive (+) and negative (-) electrospray ionization (ESI) combined with the MSE acquisition mode to visualize the fragmentation profile of the precursor ion simultaneously. This mode facilitated the identification of compounds using MS spectra banks such as HMDB, LipidBlast, MassBank, Chem spider, KEGG, etc. Statistical and computational software (Progenesis QI, Waters Corp. and Metaboanalyst 6.0) were used to discriminate and confirm potential biomarkers.
PRELIMINARY RESULTS:
Preliminary results of the metabolomic study in urine showed at least 132 compounds in ESI + and 79 compounds in ESI - that were significantly different in the LOPD group compared to the CTRL group, while 519 compounds in ESI+ and 42 compounds in ESI - were flagged in the lipidomic study. It is noteworthy to mention that Glc4 was not marked as a significant compound in our LOPD cohort. This result will be further investigated. Principal component analysis (PCA) was used to visualize the quality of the analysis and to validate the calculated variance importance value (VIP) with the orthogonal partial least squares discriminant analysis (OPLS-DA). PCA results showed a good separation between the LOPD and CTRL groups in ESI +, but not in ESI -. Therefore, only ESI + results were analyzed using supervised multivariance analysis to ensure conclusive results. Volcano plot analyses showed the most discriminant compounds, revealing at least 50 upregulated and 52 downregulated potential biomarkers in LOPD patients while combining the metabolomic and lipidomic studies. Structural identification is on-going by confirming the fragmentation profile by MRM-TOF mode. The analysis of plasma samples will follow.
CONCLUSIONS:
Recruitment of untreated patients is challenging for Pompe disease studies due to the worldwide low incidence of 1:20,000 to 1:200,000. However, preliminary results from our untreated patient cohort show that some urinary compounds other than Glc4 seem dysregulated in LOPD patients compared to CTRLs. Identification of these dysregulated compounds will be done. A similar approach will be carried out with plasma samples from LOPD and IOPD patients. All significant identified compounds will be quantified by tandem mass spectrometry to evaluate correlations with clinical information.
REFERENCES:
1. Peruzzo P., Pavan E., and Dardis A. Molecular genetics of Pompe disease: a comprehensive overview. Ann Transl Med, 2019. 7(13):278.
2. Chien Y.H., Goldstein J.L., Hwu W.L.,et al. Baseline Urinary Glucose Tetrasaccharide Concentrations in Patients with Infantile- and Late-Onset Pompe Disease Identified by Newborn Screening. JIMD Rep, 2015. 19:67-73.
3. Young S.P., Piraud M., Goldstein J.L.,et al. Assessing disease severity in Pompe disease: the roles of a urinary glucose tetrasaccharide biomarker and imaging techniques. Am J Med Genet C Semin Med Genet, 2012.160C(1):50-8.
4. Gómez-Cebrián N., Gras-Colomer E., Poveda Andrés J.L., Pineda-Lucena A., and Puchades-Carrasco L. Omics-Based Approaches for the Characterization of Pompe Disease Metabolic Phenotypes. Biology (Basel), 2023.12(9):1159. |
= Discovery stage. (53.14%, 2025)
= Translation stage. (22.33%, 2025)
= Clinically available. (24.53%, 2025)
