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Application of a metabolomic multiplatform to investigate Alzheimer's disease pathogenesis
1  Department of Chemistry, Faculty of Experimental Sciences. University of Huelva, Spain.


Alzheimer’s disease (AD) is the most common neurodegenerative disorder among older people, but nowadays there is no cure mainly because its etiology is still unclear and existing diagnostic tests show great limitations, including low sensitivity and specificity, as well as the impossibility to detect characteristic symptoms at early stages of disease. Thus, the objective of this work was the optimization of metabolomics approaches based on mass spectrometry in order to investigate AD pathogenesis and discover potential biomarkers for diagnosis. With the aim to get a comprehensive metabolome coverage, multiple analytical platforms were developed, including screening procedures based on direct mass spectrometry analysis and hyphenated approaches with orthogonal separation mechanisms such as liquid chromatography, gas chromatography and capillary electrophoresis. The application of these techniques to serum samples from patients suffering from Alzheimer’s disease and mild cognitive impairment enabled the identification of numerous metabolic alterations linked to pathogenesis of this disorder and its progression from pre-clinical stages, including abnormalities in the composition of membrane lipids, deficits in energy metabolism and neurotransmission, and oxidative stress, among others. In turn, these metabolomics perturbations were also observed in multiple biological compartments from the APP/PS1 model, including serum, brain, liver, kidney, spleen and thymus, thus demonstrating the utility of these transgenic mice to model Alzheimer’s disease. The comparison of different brain regions evidenced that the most affected areas are hippocampus and cortex, but other regions were also significantly perturbed to a lesser extent, such as striatum, cerebellum and olfactory bulbs. Furthermore, alterations detected in peripheral organs confirm the systemic nature of this neurodegenerative disorder. Accordingly, it could be concluded that the combination of complementary metabolomics platforms allows studying etiology associated with Alzheimer’s disease in a deeper manner.

Keywords: metabolomics; mass spectrometry; Alzheimer's disease
Comments on this paper
Daniel Raftery
Sample prep
Very nice study. I'm wondering why you decided to perform the metabolite extraction in two steps instead of just one. I would expect that the methanol extraction would also retain a number of lipids. Couldn't you get a cleaner result by just using a single chloroform/methanol extraction?

Thanks, Dan.
Raúl González-Domínguez
Dear Prof. Raftery,

We used a two-step extraction procedure in order to separate very hydrophobic lipids (e.g. triglycerides) from the medium-high polarity metabolome. This allows reducing ion suppression caused by highy abundant lipids when samples are analyzed by DIMS. Moreover, this extraction protocol also simplifies subsequent analysis by LC/GC-MS. Neutral lipids are strongly retained in reversed phase columns, which requires the use of very long elution programs, and they are not readily detectable by GC because of its low volatility. For this reason, "lipophilic extracts" were only analyzed by DIMS in order to obtain a overview of the implication of neutral lipids in AD, while "polar extracts" were subjected to the multi-platform described in this work with the aim to obtain a comprehensive characterization of the serum metabolome.

Thanks for your interest

Daniel Raftery
Statistical Analysis
I had a second question about the biomarkers you found. In the cases where you measured the same metabolite with different platforms, were they consistent? Also, are you planning to combine the different analyses? We have typically found better results when we combine different platforms that can measure different metabolites.
Raúl González-Domínguez
Some metabolites were detected by different platforms (although this is not the usual situation), and findings obtained were quite consistent. For instance, histidine was identified as a discriminant metabolite by DIMS (Anal Bioanal Chem, 2014, 406:7137), GC-MS (J Pharm Biomed Anal, 2015, 107:75), UHPLC-MS (Curr Alzheimer Res, 2016, 13:641) and CE-MS (Electrophoresis, 2014, 35:3321), and fold changes calculated with these techniques were very close, in the range 15-20%.

As you say, better results are obtained when complementary platforms are combined. Thus, in a second phase of this project, we combined LC-MS and GC-MS analysis to investigate metabolic alterations in multiple biological compartments from the APP/PS1 transgenic mice, including serum (Biochimie, 2015, 110:119), different brain regions (Biochim Biophys Acta 2014, 1842:2395), metabolically active organs, i.e. liver and kidneys (Mol BioSyst, 2015, 11:2429), and organs involved in the immune function, i.e. spleen and thymus (Electrophoresis 2015, 36:577).