Abstract
Introduction (Words remaining 120)Protein turnover analysis provides crucial insights into disease-related alterations in synthesis, degradation, and transport dynamics. Here, we present a novel application of Bayesian hierarchical modeling for simultaneous analysis of protein dynamics in blood plasma and cerebrospinal fluid (CSF) using Stable Isotope Labeling Kinetics (SILK). This approach enables precise quantification of inter-compartmental protein transport, turnover kinetics, and the identification of candidate biomarkers relevant to neurodegenerative diseases.Methods (Words remaining 120)Samples from an initial pilot cohort of four individuals were collected following a SILK protocol using 13C6-leucine infusion over 9 hours, with serial plasma and CSF sampling for 36 and 24 hours, respectively. Proteins were processed using automated SP3 digestion and fractionation, followed by LC-MS analysis on Evosep One coupled to timsTOF HT. Data processing employed Skyline for integration and Bayesian modeling with OpenBUGS for parameter estimation. Kinetic curves for protein incorporation were fitted using two- and three-compartment mathematical models. Analysis will be extended to 18 patients (9 amyloid-positive and 9 amyloid-negative) to evaluate inter-individual variability and subgroup-specific turnover patterns.Preliminary Data or Plenary Speakers Abstract (Words remaining 300)In the pilot study, mass spectrometry identified 65 proteins across plasma and cerebrospinal fluid (CSF), with 30 proteins shared between the two compartments. These proteins demonstrated diverse incorporation profiles, reflecting distinct biological processes. Notably, 15 proteins, including ApoE, PTGDS, and TIMP1, exhibited prolonged incorporation in the CSF, suggesting slower clearance or retention related to central nervous system (CNS) dynamics. Conversely, 12 proteins, such as Clusterin (CLUS), IBP7, and SODE, showed rapid turnover, indicative of efficient clearance mechanisms or active transport across the blood-CSF barrier.Bayesian hierarchical modeling provided precise parameter estimates for synthesis, degradation, and transport rates, with a precision of ±5%. Noise correction within the model improved discrimination between biological variability and experimental artifacts. Approximately 40% of shared proteins, including ApoA1 and IGHG family proteins, exhibited coordinated plasma-CSF turnover, reflecting systemic regulation. However, 60% displayed compartment-specific kinetics, emphasizing the influence of biological barriers or localized synthesis.Key findings include the distinct clearance dynamics of Clusterin, which was rapidly removed from the CSF, and ApoE, which showed sustained incorporation. Proteins such as TIMP1 and AACT demonstrated significant inter-individual variability, suggesting differences in CNS metabolism or peripheral clearance rates. These findings reveal biological barriers as critical regulators of protein turnover and provide insight into disease-related processes.The pilot study also highlighted the potential of SILK-MS to uncover proteins with disease-relevant dynamics, paving the way for the expanded cohort analysis. The upcoming study will focus on 18 patients (9 amyloid-positive and 9 amyloid-negative), with a specific emphasis on amyloid-related proteoforms. This analysis aims to characterize subgroup-specific turnover patterns and refine models of proteostasis. By comparing amyloid-positive and -negative groups, we expect to identify biomarkers and transport alterations linked to neurodegenerative disease pathology.Novel Aspect (Words remaining 20)Establishing to link between plasma and CSF protein dynamics, highlighting turnover differences between amyloid-positive and -negative patients.