A Multi-Platform Proteomic and Peptidomic Investigation of Plasma and Plasma-Derived Extracellular Vesicles in Lesch-Nyhan Disease

LND is a rare, severe X-linked metabolic disorder caused by congenital deficiency of HGPRT. Although the peripheral hyperuricemia associated with HGPRT deficiency can be controlled pharmacologically, the profound neurodevelopmental and behavioral phenotypes remain poorly understood due to a lack of non-invasive tools capable of querying dynamic systemic pathophysiology. Circulating EVs present an unprecedented opportunity, however, capturing low-molecular-weight proteomic and peptidomic signatures from plasma EVs has historically been hindered by major technical bottlenecks. High-abundance soluble plasma proteins such as serum albumin and immunoglobulins obscure low-abundance vesicular cargo, while unoptimized mass spectrometry acquisition protocols and inconsistent bioinformatics workflows lead to high spectral variance. To overcome these limitations, this PhD dissertation details the systematic setup, optimization, and validation of a high-throughput plasma EV isolation, MALDI-TOF MS, and multi-omics platform designed specifically for rare metabolic disease profiling and cross-pathology application. The primary methodological "step-up" introduced in this work centers on an optimized plasma EV isolation protocol tailored to maximize vesicular purity while stripping interfering matrix proteins. Isolated plasma Instrumental parameters on a Bruker UltrafleXtreme MALDI-TOF/TOF mass spectrometer were systematically fine-tuned to optimize. To translate raw, high-throughput mass spectra into high-confidence analytical feature matrices, an automated computational pipeline was constructed in R utilizing the MALDIquant and MALDIquant Foreign frameworks. Using this standardized analytical setup, an integrative multi-omics machine learning framework was built to map the systemic EV peptidomic landscape of LND patients relative to healthy controls. PCA demonstrated clear, intrinsic cohort clustering, proving that HGPRT deficiency alters the circulating EV peptide profile. PLS-DA was subsequently applied to construct predictive classification models. VIP scoring, combined with univariate volcano plot filtering, isolated a discrete panel of high-confidence, dysregulated vesicular peptide markers, offering new insights into the systemic pathological alterations underlying LND. In conclusion, this doctoral dissertation provides the first dedicated, optimized plasma EV peptidomic and multi-omics modeling framework for Lesch-Nyhan Disease. By pioneering methodological step-ups in EV isolation, MALDI-TOF MS acquisition, and automated bioinformatics processing, this research establishes a robust, highly platform suitable for disease biomarker discovery and disease surveillance across rare metabolic disorders and complex oncological diseases.