Capillary electrophoresis‐mass spectrometry‐based metabolome analysis of serum and saliva from neurodegenerative dementia patients

Abstract

Despite increasing global prevalence, the precise pathogenesis and terms for objective diagnosis of neurodegenerative dementias remain controversial, and comprehensive understanding of the disease remains lacking. Here, we conducted metabolomic analysis of serum and saliva obtained from patients with neurodegenerative dementias (n = 10), including Alzheimer's disease, frontotemporal lobe dementia, and Lewy body disease, as well as from age-matched healthy controls (n = 9). Using CE-TOF-MS, six metabolites in serum (β-alanine, creatinine, hydroxyproline, glutamine, iso-citrate, and cytidine) and two in saliva (arginine and tyrosine) were significantly different between dementias and controls. Using multivariate analysis, serum was confirmed as a more efficient biological fluid for diagnosis compared to saliva; additionally, 45 metabolites in total were identified as candidate markers that could discriminate at least one pair of diagnostic groups from the healthy control group. These metabolites possibly provide an objective method for diagnosing dementia-type by multiphase screening. Moreover, diagnostic-type-dependent differences were observed in several tricarboxylic acid cycle compounds detected in serum, indicating that some pathways in glucose metabolism may be altered in dementia patients. This pilot study revealed novel alterations in metabolomic profiles between various neurodegenerative dementias, which would contribute to etiological investigations.

Small molecule biomarkers in Alzheimer’s disease

“…Other plasma lipids classes reported to have close relationships with AD included bile acids (Greenberg et al, 2009), LysoPCs and (Trushina et al, 2013b) AD(n = 3) FTD(n = 4) LBD(n = 3) CN(n = 9) Serum CE-MS ↑b-alanine, creatinine, hydroxyproline, glutamine, isocitrate, cytidine (Tsuruoka et al, 2013) AD(n = 12) CN(n = 5) Whole brain MRS ↓N-acetyl-aspartate, g-aminobutyric acid; ↑ glutamate (Klunk et al, 1992) AD ( Changes in levels of uracil, xanthine, uridine, tyrosyl-serine, methylsalsolinol, nonanoylglycine, dopamine-quinone, caproic acid, vanylglycol, histidine, pipecolic acid, hydroxyphophinylpiruvate, creatinine, taurine, C16-sphingosine-1-phosphate, tryptophan, 5 0 -methylthioadenosine (Ibanez et al, 2013) phytosphingosine (sphingolipid precursors) (Li et al, 2010b), among others (Mapstone et al, 2014). Utilizing in-vial dual extraction protocol Godzien et al, 2013), abnormal levels of long chain cholesteryl esters/triglycerides (Proitsi et al, 2015;Proitsi et al, 2017) and omega-3 containing PC molecules (Whiley et al, 2014;Kim et al, 2016) have been shown in two separate study cohorts.…”

“…HILIC-MS has shown disturbance in 22 biochemical pathways, including homeostasis of polyamines and arginine metabolism (Graham et al, 2015), while CE-MS revealed the presence of oxidative stress and defects in energy metabolism (Tsuruoka et al, 2013;Gonzalez-Dominguez et al, 2014a). Utilizing a RP-MS/HILIC-MS multi-platform, Trushina et al, (2013b) identified 154 metabolites with significant changes in AD vs CN subjects (Trushina et al, 2013b) while Armirotti et al, (2014) has employed RP-MS/ HILIC-MS multi-platform to find abnormally high levels of sphingomyelins in AD blood.…”

“…Tsuruoka et al, (2013) analysed saliva metabolic profiles to complement serum metabolic profiles by employing CE-MS. Abnormal levels of arginine and tyrosine in AD saliva samples were found when compared to CN samples as well as FTD and LBD samples, highlighting the abnormal levels of arginine and tyrosine as specific to AD (Tsuruoka et al, 2013). In a study involving urine samples, Cui et al, (2014) employed RP-MS and discovered impaired metabolic pathways involving L-glutamine and 5-L-glutamylglycine in AD.…”

Capillary electrophoresis mass spectrometry based metabolomics

“…Importantly, CE also gives complementary information to HILIC [13], as well as, significantly better peak shape in many cases. It is therefore not surprising that CE-MS has already been considered for multiple metabolomics studies covering several applications, from aging [19], amino acid analysis [20,21] [46][47][48][49][50][51][52][53], CVD [54][55][56][57][58], diabetes [59][60][61][62][63][64][65], lifestyle [61][62][63][64][65][66][67][68][69][70][71], kidney disease [72,73], liver disease [74][75][76][77][78][79][80][81][82], the microbiome [83][84]…”

Neuroproteomics tools in clinical practice

“…Furthermore, different exogenous factors such as inflammatory or metabolic influences can easily effect the composition of saliva and tears leading to poor reproducible results in CNS diseases. The applicability of saliva in biomarker discovery for neurological disorders is extremely limited, to the best of our knowledge, only one study revealed novel alterations in saliva metabolomic profiles between various neurodegenerative disorders [36].…”