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Abstract Familial dysautonomia (FD) is a rare genetic neurologic disorder caused by impaired neuronal development and progressive degeneration of both the peripheral and central nervous systems. FD is monogenic, with >99.4% of patients sharing an identical point mutation in the elongator acetyltransferase complex subunit 1 (ELP1) gene, providing a relatively simple genetic background in which to identify modifiable factors that influence pathology. Gastrointestinal symptoms and metabolic deficits are common among FD patients, which supports the hypothesis that the gut microbiome and metabolome are altered and dysfunctional compared to healthy individuals. Here we show significant differences in gut microbiome composition (16 S rRNA gene sequencing of stool samples) and NMR-based stool and serum metabolomes between a cohort of FD patients (~14% of patients worldwide) and their cohabitating, healthy relatives. We show that key observations in human subjects are recapitulated in a neuron-specificElp1-deficient mouse model, and that cohousing mutant and littermate control mice ameliorates gut microbiome dysbiosis, improves deficits in gut transit, and reduces disease severity. Our results provide evidence that neurologic deficits in FD alter the structure and function of the gut microbiome, which shifts overall host metabolism to perpetuate further neurodegeneration.
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This content will become publicly available on April 1, 2026
Diagnostic and prognostic perspectives of Fabry disease via fiber evanescent wave spectroscopy advanced by machine learning
Fabry disease (FD) is a rare disorder resulting from a genetic mutation characterized by the accumulation of sphingolipids in various cells throughout the human body, leading to progressive and irreversible organ damage, particularly in males. Genetically-determined deficiency or reduced activity of the enzyme (alpha – Galactosidase; α-Gal) leads to the accumulation of sphingolipids in the lysosomes of various cell types, including the heart, kidneys, skin, eyes, central nervous system, and digestive system, triggering damage, leading to the failure of vital organs, and resulting in progressive disability and premature death. FD diagnostics currently depend on costly and time-intensive genetic tests and enzymatic analysis, often leading to delayed or inaccurate diagnoses, which contribute to rapid disease progression. In this research, midinfrared Fiber Evanescent Wave Spectroscopy (FEWS) supported by statistical analysis and Machine Learning (ML) algorithms is shown to be an innovative and reliable method to detect globotriaosylsphingosine (Lyso-Gb3) FD biomarker in urine and serum samples by monitoring infrared spectra alone. ML showed a high selectivity for FD in the spectral range of Amide A and Amide I in blood serum, and α-D-galactosyl residues of glycosphingolipids in urine. The developed approach offers a promising, cost-effective express diagnostic tool sensitive enough for early FD detection and monitoring.
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- Award ID(s):
- 2106457
- PAR ID:
- 10567277
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Biosensors and Bioelectronics
- Volume:
- 273
- Issue:
- C
- ISSN:
- 0956-5663
- Page Range / eLocation ID:
- 117139
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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