MSACL 2024 Abstract

Introduction:
Dyskeratosis congenita (DC) is a rare genetic disorder primarily characterized by abnormal skin pigmentation, nail dystrophy, and mucosal leukoplakia[1]. This condition is also associated with a high risk of bone marrow failure and cancer. DC is typically caused by mutations in genes that are crucial for maintaining the length of telomeres, leading to premature aging of cells and tissues.
We have previously reported that DC patients display a striking upregulation of lactate and pyruvate in plasma samples with a normal lactate/pyruvate ratio, and normal glucose levels. This is indicative of reduced pyruvate dehydrogenase complex activity and a shift of systemic metabolism towards glycolysis. This metabolite profile is strikingly similar to human chronological ageing[2]. However, a plasma metabolic signature of DC has not previously been reported. In this study, we used a liquid-chromatography mass-spectrometry (LC-MS) based approach to perform a metabolic profiling of plasma samples from DC patients.

DC represents a rare cohort, and conducting metabolomic analyses on real-world human samples exhibiting premature aging may facilitate a deeper comprehension of the aging process at the metabolic level.

Methods:
Plasma samples were collected from 29 DC patients and 30 matched controls. The plasma samples were extracted based on the methanol/chloroform method[3] with the aqueous layer containing polar metabolites subsequently subjected to LC-MS analysis. To ensure high-quality data and to enhance the coverage of metabolites, a combination of broad-coverage, targeted reversed-phase LC-MS assays was employed for the metabolic profiling of these samples. The employed assays included an ion-pairing chromatography which improves retention of negatively charged compounds, and a pre-column derivatization (using 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate) which targets amino acids and amine-containing compounds.

Results:
In total, 146 metabolites were detected after quality filtering. Of these, thirty-one showed significant differences with a cutoff of P < 0.01; following correction for false discovery rate (1%) and Bonferroni correction, 16 and 11 of these metabolites remained significant, respectively.
An unsupervised multivariate analysis (principal components analysis, PCA) revealed a clear difference between the healthy controls and DC patient samples. Furthermore, a supervised linear discriminant analysis (LDA) proved to be an excellent classifier, with an area under the receiver operating characteristic (ROC) curve of 0.98.

Particularly noteworthy, two nucleotide sugars, which not only exhibited highly significant P values but also the largest fold changes among the detected metabolites. More interestingly, these nucleotide sugars demonstrated a significantly high correlation with age-associated leukocyte telomere length (AALTL).
In addition to the nucleotide sugars, several other significant metabolites identified in this study have been linked with other telomeropathies, such as ataxia telangiectasia, and various age-related conditions including frailty, sarcopenia, collagen degradation, and neurodegenerative diseases

Conclusion:
Our findings could serve as an additional diagnostic aid for DC and potentially for other telomeropathies with germline telomerase mutations. The identified DC plasma signature may be useful in monitoring the effects of telomerase activators like TA-65 and Danazol, as well as other interventions targeting telomeres or senescent cells. Furthermore, these results provide a starting point for biological experiments aimed at enhancing our understanding of the aging process.

References:

1. Tummala, H., A. Walne, and I. Dokal, The biology and management of dyskeratosis congenita and related disorders of telomeres. Expert Rev Hematol, 2022. 15(8): p. 685-696.
2. James, E.N., et al., Dyskeratosis Congenita Links Telomere Attrition to 
Age-Related Systemic Energetics. J Gerontol A Biol Sci Med Sci, 2023. 78(5): p. 780-789.
3. Le Belle, J.E., et al., A comparison of cell and tissue extraction techniques using high-resolution 1H-NMR spectroscopy. NMR Biomed, 2002. 15(1): p. 37-44.