The number of measured metabolites sensitive to <i>Htt</i> genotype, diet or to an <i>Htt</i> genotype-diet interaction varies across tissues.

<p>For each tissue, metabolite values were filtered to include only those with at least 3 non-0 values per arm of the diet/genotype study design.</p

Increased lipid concentrations are common in <i>STHdh</i>^Q111/Q111 cell pellets.

<p>A) Strip-plots summarize the logarithm of the concentration ratios of metabolites altered in mutant, compared to wild-type, in cell pellets (left) or growth media (right) at a false-discovery rate of 10% and fold-change cut-off of +/- 30%. Metabolites whose concentrations are increased in mutant samples are indicated with a blue square, while those metabolites that are decreased in mutant samples are shown in a green. B) Normalized lipid peak values in replicate <i>STHdh</i>^+/+ and <i>STHdh</i>^Q111/Q111 cell pellets are depicted in the heat-map with higher concentrations in red and lower concentrations in blue. For comparison across lipid species, LC-MS peak heights were converted to a Z-score by subtracting the mean concentration of each species and dividing the remainder by the standard deviation. Each lipid class includes species with variable chain lengths and number of saturated bonds (columns). With the exception of lysophosphatidylcholine species, most lipid species are increased in mutant cells. C) The distribution of the actual mutant / wild type peak ratios for all measured lipid concentrations is shown in red. To test for lipid accumulation in mutant cells, the same distribution was calculated 1,000 using the same data with permuted genotype labels; those distributions are shown in gray.</p

Coherently increased lipids in striatal cells and striatal tissue from <i>Hdh</i>^{<i>Q111/+</i>} mice.

<p>A. The mutant/wild-type ratio of each lipid species is indicated on the heatmap (red = 0.5, yellow = 1.0 and green = 1.5). In the panel on the left, lipid species (rows, labels omitted for clarity) were sorted by their genotype p-value in the striatum. Sorting in this way reveals a clustering of green boxes near the top of the heatmap, suggesting an enrichment of accumulated lipids in the striatum. In the panel on the right, the same data was sorted by the genotype p-value in the cerebellum—no such clustering is observed. Metabolites nominally significant in a given tissue are outlined in black. The class of each lipid is indicated in the text, with neutral lipid species indicated in bold. B. The mutant/wild-type ratio of each lipid species that was quantified in both striatal tissue and striatal cells is graphed for striatal tissue (horizontal axis) and immortalized striatal cells (vertical axis); values greater than 1 indicate increased concentrations in mutant samples, while values less than 1 indicate decreased concentrations in mutant samples. The ratios in both cells and tissue for each lipid species are indicated by the position of its label. Lipids whose levels are increased in both striatal cells and tissue predominate, and are indicated by the green rectangle. Lipids with inconsistent changes between cells and tissue are in gray rectangles, while only 1 measured lipid species (C18.0.LPC) is decreased in both samples. Lipid species are indicated as CX.Y.Z, where X = total acyl chain length, Y = total number of unsaturated bonds and Z = lipid species. The lipid species graphed are D/TAG = Di-/Tri-glyceride; LPE/C = Lysophosphatidyl-ethanolamine/choline; PC = phosphatidylcholine.</p

Alterations in tissue metabolite levels in response to CAG-expansion in <i>Htt</i> and increased dietary fat.

<p>A) Summary of the number of nominally significant metabolites per tissue (as a percentage of total measured metabolites) for each tissue—heat map color indicates the number of metabolites significant at p < 0.05. B) A relatively limited number of metabolites show concordant (green text) or discordant (red text) concentration changes in response to CAG-expansion in <i>Htt</i> in the striatum and cerebellum. The striatum and cerebellum also show the largest number (4) of overlapping diet-responsive metabolites amongst tissue pairs. C) Determination of genotype using metabolite concentrations using random forests—the error rate for each tissue is shown over 500 iterations. Below, the contribution of individual metabolites to model accuracy is shown for striatum and cerebellum, the two tissues with accurate models. The y-axis indicates the increase in errors in genotype prediction (in %) after permuting the genotype labels for the indicated metabolite. D) The concentrations of lipid species important for genotype prediction in the striatum are increased in the <i>Htt</i>^{<i>Q111/Q7</i>} samples (PC 32:2–49% increase, p = 0.024; CE 18:0–54% increase, p = 0.004; SM 24:0–33% increase, p = 0.02; 65% increase, p = 0.06).</p

Metabolite profiles clearly discriminate tissue types.

<p>A—Linear discriminant analysis was conducted to derive linear combinations of metabolite concentrations that separate tissue types. In this approach, linear combinations of factors (here, metabolites) are constructed that maximally differentiate a factor of interest (here, tissue type). Scatter-plot matrix of the top 3 linear discriminants (linear combinations of variables) constructed reveals that they very effectively separate tissue types; the tissue type of each sample is indicated by text and color. To test the robustness of this finding, the tissue labels of each sample were randomly permuted, and the LDA analysis repeated. Increased scatter suggests that tissue-separation is much less accurate using permuted data, consistent with the hypothesis that inter-tissue variability is lower than intra-tissue variability in these samples. Linear discriminants derived from actual data are shown on the left, those derived from tissue-label permuted data are shown on the right. B—Sample concentrations of metabolites with clear tissue-specific roles; GABA is high in CNS tissues (striatum and cerebellum), and absent in peripheral tissues. The conjugated bile acid taurochenodeoxycholate are present in the liver, and to a smaller extent the plasma, but absent from other tissues.</p

Striatal enrichment of the 10 most genotype-sensitive metabolites in the striatum of <i>Htt</i>^{<i>Q111/+</i>} mice.

<p>Top ranked metabolites by genotype ANOVA F-statistic are included in order of magnitude. The relative integrated peak height of each metabolite in the striatum compared to the liver and cerebellum is indicated by the rank of each metabolite on lists of 243 total metabolites.</p

Unbiased Metabolite Profiling of Schizophrenia Fibroblasts under Stressful Perturbations Reveals Dysregulation of Plasmalogens and Phosphatidylcholines

We undertook an unbiased metabolite profiling of fibroblasts from schizophrenia patients and healthy controls to identify metabolites and pathways that are dysregulated in disease, seeking to gain new insights into the disease biology of schizophrenia and to discover potential disease-related biomarkers. We measured polar and nonpolar metabolites in the fibroblasts under normal conditions and under two stressful physiological perturbations: growth in low-glucose media and exposure to the steroid hormone dexamethasone. We found that metabolites that were significantly different between schizophrenia and control subjects showed separation of the two groups by partial least-squares discriminant analysis methods. This separation between schizophrenia and healthy controls was more robust with metabolites identified under the perturbation conditions. The most significant individual metabolite differences were also found in the perturbation experiments. Metabolites that were significantly different between schizophrenia and healthy controls included a number of plasmalogens and phosphatidylcholines. We present these results in the context of previous reports of metabolic profiling of brain tissue and plasma in schizophrenia. These results show the applicability of metabolite profiling under stressful perturbations to reveal cellular pathways that may be involved in disease biology

Associations of non-lipid metabolite profiles with BMI and other metabolic traits.

<p>Associations of non-lipid metabolite profiles with BMI and other metabolic traits.</p

Association of metabolic traits and TAG length and saturation.

<p>X-axis represents number of double bonds, Y-axis is the beta-coefficient for the change in metabolic trait per 1 standard deviation increase in metabolite. Darker circles are shorter length TAGs, and lighter circles represent greater carbon length TAGs. Panel (A) shows body-mass index, (B) waist circumference, (C) HOMA-IR, (D) fasting glucose, (E) HDL cholesterol, and (F) systolic blood pressure.</p

Metabolites associated with longitudinal changes in fasting glucose.

<p>Metabolites associated with longitudinal changes in fasting glucose.</p