Metabolomics Reveals Amino Acids Contribute to Variation in Response to Simvastatin Treatment

<div><p>Statins are widely prescribed for reducing LDL-cholesterol (C) and risk for cardiovascular disease (CVD), but there is considerable variation in therapeutic response. We used a gas chromatography-time-of-flight mass-spectrometry-based metabolomics platform to evaluate global effects of simvastatin on intermediary metabolism. Analyses were conducted in 148 participants in the Cholesterol and Pharmacogenetics study who were profiled pre and six weeks post treatment with 40 mg/day simvastatin: 100 randomly selected from the full range of the LDL-C response distribution and 24 each from the top and bottom 10% of this distribution (“good” and “poor” responders, respectively). The metabolic signature of drug exposure in the full range of responders included essential amino acids, lauric acid (p<0.0055, q<0.055), and alpha-tocopherol (p<0.0003, q<0.017). Using the HumanCyc database and pathway enrichment analysis, we observed that the metabolites of drug exposure were enriched for the pathway class amino acid degradation (p<0.0032). Metabolites whose change correlated with LDL-C lowering response to simvastatin in the full range responders included cystine, urea cycle intermediates, and the dibasic amino acids ornithine, citrulline and lysine. These dibasic amino acids share plasma membrane transporters with arginine, the rate-limiting substrate for nitric oxide synthase (NOS), a critical mediator of cardiovascular health. Baseline metabolic profiles of the good and poor responders were analyzed by orthogonal partial least square discriminant analysis so as to determine the metabolites that best separated the two response groups and could be predictive of LDL-C response. Among these were xanthine, 2-hydroxyvaleric acid, succinic acid, stearic acid, and fructose. Together, the findings from this study indicate that clusters of metabolites involved in multiple pathways not directly connected with cholesterol metabolism may play a role in modulating the response to simvastatin treatment.</p> <h3>Trial Registration</h3><p>ClinicalTrials.gov <a href="http://clinicaltrials.gov/ct2/show/NCT00451828?term=Cholesterol+and+Pharmacogenetics+study+%28CAP%29&rank=1">NCT00451828</a></p> </div

Pharmacometabolomic Signature of Ataxia SCA1 Mouse Model and Lithium Effects

<div><p>We have shown that lithium treatment improves motor coordination in a spinocerebellar ataxia type 1 (SCA1) disease mouse model (<i>Sca1^154Q/+</i>). To learn more about disease pathogenesis and molecular contributions to the neuroprotective effects of lithium, we investigated metabolomic profiles of cerebellar tissue and plasma from SCA1-model treated and untreated mice. Metabolomic analyses of wild-type and <i>Sca1^154Q/+</i> mice, with and without lithium treatment, were performed using gas chromatography time-of-flight mass spectrometry and BinBase mass spectral annotations. We detected 416 metabolites, of which 130 were identified. We observed specific metabolic perturbations in <i>Sca1^154Q/+</i> mice and major effects of lithium on metabolism, centrally and peripherally. Compared to wild-type, <i>Sca1^154Q/+</i> cerebella metabolic profile revealed changes in glucose, lipids, and metabolites of the tricarboxylic acid cycle and purines. Fewer metabolic differences were noted in <i>Sca1^154Q/+</i> mouse plasma versus wild-type. In both genotypes, the major lithium responses in cerebellum involved energy metabolism, purines, unsaturated free fatty acids, and aromatic and sulphur-containing amino acids. The largest metabolic difference with lithium was a 10-fold increase in ascorbate levels in wild-type cerebella (p<0.002), with lower threonate levels, a major ascorbate catabolite. In contrast, <i>Sca1^154Q/+</i> mice that received lithium showed no elevated cerebellar ascorbate levels. Our data emphasize that lithium regulates a variety of metabolic pathways, including purine, oxidative stress and energy production pathways. The purine metabolite level, reduced in the <i>Sca1^154Q/+</i> mice and restored upon lithium treatment, might relate to lithium neuroprotective properties.</p></div

Correlation matrix illustrating two clusters of compounds correlated with simvastatin response in full range participants.

<p>The two clusters were identified in a clustering analysis for the change of all metabolites (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038386#s2" target="_blank">results</a> not shown) according to their pairwise correlations using the MMC algorithm <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038386#pone.0038386-Yao2" target="_blank">[14]</a>). Correlations of metabolites to drug response in LDLC were given in the first row and column, and are rescaled (divided by the largest absolute value of them) to be clearer in the map. The color scheme corresponds to correlation strength as shown by the color bar. Red: Better response, more reduction of the metabolite. Blue: Better response, less reduction or increase of the metabolite. Abbreviations: LDLC, Low-Density Lipoprotein Cholesterol; NIST, National Institute of Standards and Technology.</p

Metabolites significantly altered by simvastatin in poor responders.

*<p>indicates a partially identified compound: pentonic acid is an aldonic acid with five carbons and hexaric acid is an aldonic acid with six carbons.</p

Metabolites with baseline levels significantly different between good and poor responders among extreme range participants.

*<p>indicates a partially identified compound: pentonic acid is an aldonic acid with five carbons and hexaric acid is an aldonic acid with six carbons.</p

Metabolite changes significantly correlated with response to simvastatin treatment in full range participants.

<p>Metabolite changes significantly correlated with response to simvastatin treatment in full range participants.</p

Correlation matrix of metabolites altered by simvastatin in full range participants.

<p>Correlations among metabolites in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038386#pone-0038386-t001" target="_blank">Table 1</a> were obtained by deriving a Spearman’s correlation coefficient between each pair of metabolites. The color scheme corresponds to correlation strength as shown by the color bar. Red: Better response, more reduction of the metabolite. Blue: Better response, less reduction or increase of the metabolite. The metabolites have been rescaled (divided by the largest absolute value of them) to be clearer on the map. Abbreviations: NIST, National Institute of Standards and Technology.</p

OPLSDA of baseline metabolites classifies good and poor responders.

<p>(A) Orthogonal partial least square discriminant analysis was used to classify good and poor responders based on log-transformed baseline concentration of metabolites (R² = 0.87, Q² = 0.31). Good responders are shown in black and poor responders in red. Baseline metabolites were log-transformed and normalized (described in methods). Performance evaluation by 7-fold cross validation yielded the following statistics: prediction accuracy: 74%; sensitivity: 70%; specificity: 79% (not shown). (B) ROC curve of true positive rate (x-axis) versus false positive rate (y-axis) yields an area under the curve (AUC) of 0.84. (C) Baseline metabolites ranked by importance in classifying good and poor responders in the OPLS model. *indicates a partially identified compound: pentonic acid is an aldonic acid with five carbons and hexaric acid is an aldonic acid with six carbons. Abbreviations: VIP, variable importance score; cvSE, standard error derived from cross validation.</p

Effect of lithium treatment on cerebellum metabolome.

<p>Metabolic network of wild-type and <i>Sca1^154Q/+</i> cerebellum phenotypes. <b>A.</b> Wild-type mice. <b>B.</b> SCA1 knock-in mice. Red nodes: Increased metabolite levels under Lithium treatment; blue nodes: decreased levels. Node shades indicate ANOVA significance levels, node size reflect differences in magnitude of regulation. Red lines: reactant pair relationships obtained from the KEGG reaction pair database. Yellow solid lines: chemical similarity >0.5 Tanimoto score (Tanimoto scores range between 0 to 1, where 1 reflects identical structures). Yellow broken lines: chemically closest structure at <0.5 Tanimoto scores. Green circles group significant compounds that changed only in the Wild-type genotype. Orange circles group significant compounds that changed in both genotypes.</p

Metabolites significantly altered by simvastatin in full range participants.

<p>N(superscript): indicates a compound identified by spectral matching to the NIST spectral library.</p