Regular articleEvidence of altered phosphatidylcholine metabolism in Alzheimer's disease
Introduction
It is estimated that there are 24 million persons worldwide with Alzheimer's disease (AD), a figure that is expected to double every 20 years until at least 2040 (Mayeux and Stern, 2012). The size of the affected population and the nature of the disease poses a huge challenge to patient health and care organizations around the world. However, the full physiological mechanism of AD is yet to be fully elucidated, and there is thus a need to identify both disease-relevant pathways for targeted treatment as well as molecular markers to aid in clinical diagnosis and monitoring disease progression (Jack et al., 2011).
Previous research has indicated that lipid molecules play a role in AD, and these have frequently been reported at abnormal concentrations in AD tissue (Bradley et al., 2012, Mangialasche et al., 2012, Wang et al., 2012). Many of these prior studies were conducted using a targeted experimental design, in which known pathways of disease were investigated in a focused manner, based on previous hypotheses of disease pathogenesis. Such targeted approaches have provided evidence suggesting a link between AD and high-density lipoproteins (HDLs) and related proteins in plasma (Di Paolo and Kim, 2011, Han et al., 2011, Lovestone et al., 1996, Orešič et al., 2011, Thambisetty et al., 2010, Whiley and Legido-Quigley, 2011). In addition, the lipoproteins apolioprotein E (ApoE) and apolipoprotein J (ApoJ) have been linked to AD via both genetic and proteomic studies (Shi et al., 2012, Thambisetty et al., 2010). ApoJ has been found as a component of HDL and is thought to be a chaperone of amyloid protein, a protein known to be heavily involved in the pathology of AD (Hye et al., 2006, Thambisetty et al., 2010).
An alternative to such targeted discovery is the use of non-targeted small-molecule analysis, commonly termed metabolomics. In contrast to targeted studies, metabolomics attempts to analyze an expansive range of lipids and small metabolites. In metabolomics studies, small-molecule (size <1000–1500 Da) fingerprints are collected, and subsequent data mining can provide unexpected leads into the biochemistry of the disease. The range of molecules studied depends on the analytical platform used and on the applied methodology, with certain combinations of techniques achieving higher specificities (Martin et al., 2007, Whiley et al., 2012).
Non-targeted analysis of AD has previously been reported (Greenberg et al., 2009, Han et al., 2011, Orešič et al., 2011). The majority of these previous studies focus on plasma samples, analyzed either by liquid chromatography–mass spectrometry (LC-MS) (Greenberg et al., 2009, Orešič et al., 2011) or direct infusion mass spectrometry (DIMS) (Han et al., 2011). Interestingly, the significant molecular features identified in these previous works were lipid molecules. One of these studies reported an increase in ceramide (CM16) levels and a decrease in sphingolipid (SP16) levels in the plasma of AD patients (Han et al., 2011). Another investigation identified a phosphatidylcholine (PC), PC 16:0/16:0, as 1 of a cluster of 3 metabolites thought to be predictive markers of AD development in individuals with mild cognitive impairment (MCI) (Orešič et al., 2011). The third example reported a number of bile acids (GCA, GDCA, and GCDCA) that increase in MCI and AD plasma (Greenberg et al., 2009). The latter publication went on to recommend further investigation into the lipid fraction of the AD metabolome.
With these previous results in mind, we developed a non-targeted lipidomics to investigate plasma lipid species in AD. In the study described here, an initial metabolite screen involving LC-MS and nuclear magnetic resonance (NMR) profiling was performed, and the resultant data were analyzed using multivariate statistical modeling. The results of this “screen” phase indicated that 3 lipid phosphatidylcholine molecules (PC16:0/20:5, PC16:0/22:6, and 18:0/22:6) significantly decrease in AD plasma compared to controls. We then performed a multiplatform “validation”, designed to both confirm the findings, as well as provide further biological reasoning regarding the changes observed. Fig. 1 illustrates the overall study design and the individual analytical stages incorporated into each phase (screen and validation).
Section snippets
Sample cohorts
Plasma for the 2 experimental phases (screen and validation) was collected from 2 clinical cohorts, the AddNeuroMed cohort and the King's College London Dementia Case Register (DCR). Ethical approval was awarded for all cohorts in the corresponding centers of collection. The cohorts are described in full in the Supplementary Information section and are summarized in Supplementary Table S1. Further details regarding sample collection and the AddNueroMed and DCR cohorts can be found elsewhere (
Screen results
The initial phase of the study was divided into 2 experiments, incorporating both LC-MS and NMR technologies. LC-MS and NMR analysis was performed on 2 separate sample sets each consisting of 35 age- and sex-matched samples (Supplementary Table S1). After this, multivariate data modeling was completed, including principal component analysis (PCA) and OPLS-DA. Models created from the NMR data revealed no significant metabolite changes attributable to disease type.
LC-MS modeling (Fig. 2B)
Discussion
Here we report a significant reduction of 3 PC species in patients with AD compared with controls, which have not previously been linked to AD (PC16:0/20:5 (p < 0.001), PC16:0/22:6 (p < 0.05), and PC18:0/22:6 p < 0.005). The reduction was observed in 2 different sample sets, using 2 different LC-MS methods (in the screen and validation phases). In the validation result, the overall trend (i.e., control>MCI>AD) suggested a specific PC decline linked to cognition. Three PC species (PC16:0/20:4,
Disclosure statement
The authors declare no conflicts of interest is assotiated with this manuscript.
Acknowledgements
Supported by the UK NHS National Institute of Health Research (NIHR) Biomedical Research Centre (BRC) for mental health at the South London and Maudsley (SLaM), the European Commision (AddNeuroMed) and the Waters Centre of Excellence for Mass Spectrometry at King's College London. All authors have reviewed and contributed to the writing of this manuscript.
References (49)
- et al.
Elevated 4-hydroxyhexenal in Alzheimer's disease (AD) progression
Neurobiol. Aging
(2012) - et al.
Glycerophospholipids and glycerophospholipid-derived lipid mediators: a complex meshwork in Alzheimer's disease pathology
Prog. Lipid Res.
(2011) - et al.
Decreased phospholipase A2 activity in Alzheimer brains
Biol. Psychiatry
(1995) - et al.
Introduction to the recommendations from the National Institute on Aging–Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease
Alzheimers Dement.
(2011) - et al.
Longitudinal 1H MRS changes in mild cognitive impairment and Alzheimer's disease
Neurobiol. Aging
(2007) - et al.
Phosphatidylcholine and choline homeostasis
J. Lipid Res.
(2008) - et al.
Tocopherols and tocotrienols plasma levels are associated with cognitive impairment
Neurobiol. Aging
(2012) - et al.
Phospholipase A2 and arachidonic acid in Alzheimer's disease
Biochim. Biophys. Acta.
(2010) - et al.
Differential roles of phospholipases A2 in neuronal death and neurogenesis: implications for Alzheimer disease
Prog. Neuropsychopharmacol. Biol. Psychiatry
(2010) - et al.
A frontal variant of Alzheimer's disease exhibits decreased calcium-independent phospholipase A2 activity in the prefrontal cortex
Neurochem. Int.
(2000)
Serum fatty acid profiles using GC-MS and multivariate statistical analysis: potential biomarkers of Alzheimer's disease
Neurobiol. Aging
Measurement of phospholipids by hydrophilic interaction liquid chromatography coupled to tandem mass spectrometry: the determination of choline containing compounds in foods
J. Chromatogr. A
APOE genotype effects on Alzheimer's disease onset and epidemiology
J. Mol. Neurosci.
Using biomarkers to improve detection of Alzheimer's disease
Neurodegener. Dis. Manag.
Metabolic signatures of lung cancer in biofluids: NMR-based metabonomics of urine
J. Proteome Res.
Gene expression profiling of 12633 genes in Alzheimer hippocampal CA1: transcription and neurotrophic factor down-regulation and up-regulation of apoptotic and pro-inflammatory signaling
J. Neurosci. Res.
Consensus report of the Working Group on: “Molecular and Biochemical Markers of Alzheimer's Disease”
Neurobiol. Aging
ApoE-directed therapeutics rapidly clear beta-amyloid and reverse deficits in AD mouse models
Science
Phospholipase A2 enzymes: physical structure, biological function, disease implication, chemical inhibition, and therapeutic intervention
Chem. Rev.
Linking lipids to Alzheimer's disease: cholesterol and beyond
Nature. Rev. Neurosci.
Identification of metabolites in human hepatic bile using 800 MHz H-1 NMR spectroscopy, HPLC-NMR/MS and UPLC-MS
Mol. Biosyst.
Identification, transmembrane orientation and biogenesis of the amyloid A4 precursor of Alzheimers-disease
EMBO J.
Decreased phospholipase A2 activity in the brain and in platelets of patients with Alzheimer's disease
Eur. Arch. Psychiatry Clin. Neurosci.
Choline-containing phospholipids in microdissected human Alzheimer's disease brain senile plaque versus neuropil
Bioanalysis
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