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Late infantile neuronal ceroid lipofuscinosis (CLN2 disease) is a rare lysosomal storage disorder caused by a monogenetic deficiency of tripeptidyl peptidase-1 (TPP1). Despite knowledge that lipofuscin is the hallmark disease product, the relevant TPP1 substrate and its role in neuronal physiology/pathology is unknown. We hypothesized that untargeted metabolite profiling of cerebrospinal fluid (CSF) could be used as an effective tool to identify disease-associated metabolic disruptions in CLN2 disease, offering the potential to identify biomarkers that inform on disease severity and progression. Accordingly, a mass spectrometry-based untargeted metabolite profiling approach was employed to differentiate CSF from normal vs. CLN2 deficient individuals. Of 1,433 metabolite features surveyed, 29 linearly correlated with currently employed disease severity scores. With tandem mass spectrometry 8 distinct metabolite identities were structurally confirmed based on retention time and fragmentation pattern matches, vs. standards. These putative CLN2 biomarkers include 7 acetylated species - all attenuated in CLN2 compared to controls. Because acetate is the major bioenergetic fuel for support of mitochondrial respiration, deficient acetylated species in CSF suggests a brain energy defect that may drive neurodegeneration. Targeted analysis of these metabolites in CSF of CLN2 patients offers a powerful new approach for monitoring CLN2 disease progression and response to therapy.
Pubmed ID: 30323181
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Curated collection of human metabolite and human metabolism data which contains records for endogenous metabolites, with each metabolite entry containing detailed chemical, physical, biochemical, concentration, and disease information. This is further supplemented with thousands of NMR and MS spectra collected on purified reference metabolites.
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View all literature mentionsWeb server for statistical, functional and integrative analysis of metabolomics data. Web based tool suite used for metabolomic data processing, normalization, multivariate statistical analysis, and data annotation, biomarker discovery and classification.
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