Identification of Niemann-Pick C1 disease biomarkers through sphingolipid profiling

• Main Authors: Martin Fan, Rohini Sidhu, Hideji Fujiwara, Brett Tortelli, Jessie Zhang, Cristin Davidson, Steven U. Walkley, Jessica H. Bagel, Charles Vite, Nicole M. Yanjanin, Forbes D. Porter, Jean E. Schaffer, Daniel S. Ory
• Format: Article
• Language: English
• Published: Elsevier 2013-10-01
• Series: Journal of Lipid Research
• Subjects: metabolomics; neurodegeneration; sphingolipids
• Online Access: http://www.sciencedirect.com/science/article/pii/S0022227520353062

Niemann-Pick type C (NPC)1 is a rare neurodegenerative disease for which treatment options are limited. A major barrier to development of effective treatments has been the lack of validated biomarkers to monitor disease progression or serve as outcome measures in clinical trials. Using targeted metabolomics to exploit the complex lipid storage phenotype that is the hallmark of NPC1 disease, we broadly surveyed Npc1-/- mouse tissues and identified elevated species across multiple sphingolipid classes that increased with disease progression. There was a striking accumulation of sphingoid bases, monohexosylceramides (MCs), and GM2 gangliosides in liver, and sphingoid bases and GM2 and GM3 gangliosides in brain. These lipids were modestly decreased following miglustat treatment, but markedly decreased in response to treatment with 2-hydroxypropyl-β-cyclodextrin (HP-β-CD), two drugs that have shown efficacy in NPC1 animal models. Extending these studies to human subjects led to identification of sphingolipid classes that were significantly altered in the plasma of NPC1 patients. Plasma MCs and ceramides were elevated, whereas sphingoid bases were reduced in NPC1 subjects. Intervention with miglustat in NPC1 patients was accompanied by striking alterations in plasma (reductions in GM1 and GM3 gangliosides) and cerebrospinal fluid (CSF) (increased MCs) sphingolipids. Similar alterations were observed in the CSF from the NPC1 feline model following HP-β-CD treatment. Our findings suggest that these lipid biomarkers may prove useful as outcome measures for monitoring efficacy of therapy in clinical trials.