Choline

Plasma samples and associated clinical study data were identified in patients referred for cardiac evaluation at a tertiary care center. All subjects gave written informed consent and the Institutional Review Board of the Cleveland Clinic approved all study protocols. Unbiased metabolic profiling was performed using liquid chromatography coupled to electrospray ionization mass spectrometry (LC/MS). Target analyte structural identification was achieved using a combination of LC/MS/MS, LC-MSⁿ, multinuclear NMR, gas chromatography-mass spectrometry, and choline isotope tracer feeding studies in mice as outlined in Methods. Statstical analyses were performed using R (version 2.10.1)36 . Intestinal microflora was suppressed by supplementation of drinking water with a cocktail of broad spectrum antibiotics37 (link). Germ-free mice were purchased from Taconic SWGF. QTL analyses to identify atherosclerosis related genes were performed on F2 mice generated by crossing atherosclerosis prone C57BL/6J.apoe−/− mice and atherosclerosis resistant C3H/HeJ.apoe−/− mice38 (link). mRNA expression was assayed by Microarray Analysis and Real Time PCR. Aortic root lesion area in mice was quantified by microscopy after staining39 (link). Mouse peritoneal macrophages were collected by lavage for foam cell quantification and cholesterol accumulation assay. Surface protein levels of scavenger receptors, CD36, SR-A1, were determined by flow cytometry.

An important advantage of computational metabolomics lies in the use of correlations among ion signals to aid in determination of chemical identity. Metabolites are interconnected by a series of biochemical reactions, and this network of metabolites is organized in a hierarchical manner such that many small modules combine to form larger modules.^{56 (link),57} Correlation-based network and modularity analysis is one approach to elucidate the association structure of metabolites. Although there are several mechanisms that could lead to correlations between metabolites, the association structure can be used to identify ions derived from the same metabolite,^{58 (link)–60 (link)} identify biotransformations,^{61 (link)} and detect associations between environmental exposures and endogenous metabolites.^{15 (link)}For high abundance unidentified chemicals, multiple spectral features arising from a single chemical provide valuable structural information to characterize a chemical. A network of ions where a pair of ions is linked if their correlation exceeds the significance threshold, e.g., |r| > 0.8, can be generated to identify isotopes, adducts, and in-source fragments associated with a chemical (Figure 4). A similar approach can be used to identify biotransformations and other related metabolites.^{60 (link)} Metabolome-wide association studies (MWAS) allow identification of associations between a specific target variable, e.g., cotinine levels in individuals, and metabolic profiles.^{8 (link),62 (link)–64 (link)} In an MWAS, statistical tests are performed for association of a parameter (e.g., disease biomarker, chemical, or other measured parameter) with each m/z feature to test for significance of association. Application of targeted MWAS using correlation-based criteria identified choline-related metabolites and demonstrated similarity between correlation patterns of choline in different species (Figure 5).^{64 (link)}Correlation-based network analysis can also facilitate identification of in-source fragments. Gas-chromatography–mass spectrometry with electron ionization sources results in a large number of characteristic spectra indicative of chemical functional groups and structure.^{61 (link),65} Electrospray ionization can produce in-source fragmentation (e.g., loss of NH₃, H₂O, CHOOH, etc.) from electrical potentials or heat applied in the ion source.^{66 (link),67 (link)} Because in-source fragments can mimic accurate masses of other common metabolites, computational methods that identify adducts, isotopes, and in-source fragments (based on clustering of highly correlated coeluting ions) increases the ability to correctly assign chemical identities. An example is the in-source formation of pyroglutamate from glutamine or glutamate.^{68 (link)} The identification of in-source fragments requires consideration of chromatographic conditions to separate possible coeluting chemicals, as well as ion source conditions. When using soft ionization techniques, in-source fragmentation is only commonly observed for highly abundant metabolites, many low abundance chemicals will generate only a single detectable signal.^{3 (link),18 (link)} To ensure detected, unannotated ions are unique chemicals, it is important to perform targeted MWAS to exclude the possibility of a signal originating from source fragments, adducts, and/or isotopes. To increase confidence of chemical identification, alternative detection methods with increased sensitivity for unknown chemicals and methods for defining unknown ions will be needed.
In addition to characterizing ions arising from known chemicals, MWAS using univariate and multivariate approaches can be used to generate hypotheses about biochemical roles of features with no database matches. This process uses targeted MWAS with validated metabolites or xMWAS, where “x” corresponds to other–omes (transcriptome, microbiome, genome, etc.). Krumsiek et al. used a systems-level approach where they combined genome-wide association analysis, knowledge-based pathway information, and metabolic networks to predict the identity of unknown metabolites.^{69 (link)} Other studies have used integrative methods based on partial least-squares regression (PLS) to determine correlations between the metabolome and the transcriptome,⁷⁰ proteome,^{71 (link)} and microbiome.^{72 (link)} These methods combined with pathway and literature based information can provide alternative approaches for generating hypotheses about chemical identity, particularly for low abundance chemicals.

Though the vast majority of recent MRI studies of white matter have focused on diffusion, MT or relaxometry, there are other techniques that may provide complementary information. One of the oldest methods is MR spectroscopy, which may be used to characterize specific metabolites in the tissue including NAA (N-acetylaspartate), creatine, choline and neurotransmitters like GABA and glutamine/glutamate. Each of these metabolites reflects different physiological processes and have unique spectral signatures. Of significant interest in white matter is NAA, which is a marker of the presence, density and health of neurons including the axonal processes. In fact, NAA may be one of the most specific markers of healthy axons and, as such, it is surprising that it is not used more widely for the investigation of white matter in the brain. This may be due in part to the fact that MR spectroscopy is extremely sensitive to the homogeneity of the magnetic field, which makes it challenging to apply in areas near air or bone interfaces. The concentrations of the metabolites are also in the micromolar range (compare with multiple molar for water), thus, large voxels must be used and the acquisition speed is slow. Therefore, MR spectroscopy studies are often limited by poor coverage, poor resolution, and long scan times.
The recent push towards ever higher magnetic fields makes quantitative MRI methods more challenging. Imaging distortions in DTI studies increase proportional to the field strength. The RF power deposition (SAR – specific absorption rate) increases quadratically with the magnetic field strength, which limits the application of MT pulses and can also limit the flip angles used in steady state imaging. However, susceptibility weighted imaging is one method that greatly benefits from higher magnetic field strengths. Recent studies have observed interesting contrast in white matter tracts as a function of orientation and degree of myelination (Liu et al., 2011 ). Stunning images of white matter tracts have recently been obtained in ex vivo brain specimens (Sati et al., 2011 ). Techniques for characterizing white matter in the human brain are only beginning to be developed.
Other white matter cellular components are the glia, which include oligodendrocytes, astrocytes, and microglia. In general, there are no specific markers of changes in either oligodendrocytes or astrocytes. Recent evidence suggests that hypointense white matter lesions on T1w imaging may indicate reactive astrocytes (Sibson et al., 2008 (link)). Increases in microglia often accompany inflammation, which can be detected using contrast agents, either gadolinium or superparamagnetic iron oxide (SPIO) particles. Recent studies have suggested that SPIO particles are preferentially taken up by macrophages in inflammatory regions. The impact of these contrast agents on other quantitative MRI measures have not (Oweida et al., 2004 (link)) been widely studied, thus multimodal imaging studies must be designed carefully.