Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): an optimized statistical approach for clustering of ¹H NMR spectral data to reduce interference and enhance robust biomarkers selection.

We propose a novel statistical approach to improve the reliability of (1)H NMR spectral analysis in complex metabolic studies. The Statistical HOmogeneous Cluster SpectroscopY (SHOCSY) algorithm aims to reduce the variation within biological classes by selecting subsets of homogeneous (1)H NMR spectra that contain specific spectroscopic metabolic signatures related to each biological class in a study. In SHOCSY, we used a clustering method to categorize the whole data set into a number of clusters of samples with each cluster showing a similar spectral feature and hence biochemical composition, and we then used an enrichment test to identify the associations between the clusters and the biological classes in the data set. We evaluated the performance of the SHOCSY algorithm using a simulated (1)H NMR data set to emulate renal tubule toxicity and further exemplified this method with a (1)H NMR spectroscopic study of hydrazine-induced liver toxicity study in rats. The SHOCSY algorithm improved the predictive ability of the orthogonal partial least-squares discriminatory analysis (OPLS-DA) model through the use of "truly" representative samples in each biological class (i.e., homogeneous subsets). This method ensures that the analyses are no longer confounded by idiosyncratic responders and thus improves the reliability of biomarker extraction. SHOCSY is a useful tool for removing irrelevant variation that interfere with the interpretation and predictive ability of models and has widespread applicability to other spectroscopic data, as well as other "omics" type of data

Consensus-Phenotype Integration of Transcriptomic and Metabolomic Data Implies a Role for Metabolism in the Chemosensitivity of Tumour Cells

Using transcriptomic and metabolomic measurements from the NCI60 cell line panel, together with a novel approach to integration of molecular profile data, we show that the biochemical pathways associated with tumour cell chemosensitivity to platinum-based drugs are highly coincident, i.e. they describe a consensus phenotype. Direct integration of metabolome and transcriptome data at the point of pathway analysis improved the detection of consensus pathways by 76%, and revealed associations between platinum sensitivity and several metabolic pathways that were not visible from transcriptome analysis alone. These pathways included the TCA cycle and pyruvate metabolism, lipoprotein uptake and nucleotide synthesis by both salvage and de novo pathways. Extending the approach across a wide panel of chemotherapeutics, we confirmed the specificity of the metabolic pathway associations to platinum sensitivity. We conclude that metabolic phenotyping could play a role in predicting response to platinum chemotherapy and that consensus-phenotype integration of molecular profiling data is a powerful and versatile tool for both biomarker discovery and for exploring the complex relationships between biological pathways and drug response

Microbial modulation of host body composition and plasma metabolic profile

The gut microbiota is a critical mediator of nutrition and disease risk. Like most complex traits, the microbiome is under genetic regulation and differs between inbred strains of mice. We tested the effect of fecal microbiota transplantation (FMT) on obesity, and plasma glucose. For this study, we collected microbiota from 2 inbred strains of mice which differ in adiposity and glucose tolerance, C57BL/6J and WSB/EiJ. C57BL/6J female mice (n = 18) were first treated with antibiotics for 4 weeks to ablate the microbiota. Following ablation, the mice were transplanted with microbiota from a C57BL/6J or a WSB/EiJ mouse and clinical traits and plasma metabolomic profiles were interrogated at 2- and 4-weeks post-transplantation. Unexpectedly, the mice receiving WSB/EiJ microbiota increased adiposity but decreased plasma glucose. Metabolomic and 16S microbiota profiling indicated broad metabolic changes occurred during and after FMT. Detailed analysis of these interactions demonstrated specific microbiota-host metabolite interactions which may alter disease susceptibility

Metabolomics Analysis of Hormone-Responsive and Triple-Negative Breast Cancer Cell Responses to Paclitaxel Identify Key Metabolic Differences

To date, no targeted therapies are available to treat triple negative breast cancer (TNBC), while other breast cancer subtypes are responsive to current therapeutic treatment. Metabolomics was conducted to reveal differences in two hormone receptor-negative TNBC cell lines and two hormone receptor-positive Luminal A cell lines. Studies were conducted in the presence and absence of paclitaxel (Taxol). TNBC cell lines had higher levels of amino acids, branched-chain amino acids, nucleotides, and nucleotide sugars and lower levels of proliferation-related metabolites like choline compared with Luminal A cell lines. In the presence of paclitaxel, each cell line showed unique metabolic responses, with some similarities by type. For example, in the Luminal A cell lines, levels of lactate and creatine decreased while certain choline metabolites and myo-inositol increased with paclitaxel. In the TNBC cell lines levels of glutamine, glutamate, and glutathione increased, whereas lysine, proline, and valine decreased in the presence of drug. Profiling secreted inflammatory cytokines in the conditioned media demonstrated a greater response to paclitaxel in the hormone-positive Luminal cells compared with a secretion profile that suggested greater drug resistance in the TNBC cells. The most significant differences distinguishing the cell types based on pathway enrichment analyses were related to amino acid, lipid and carbohydrate metabolism pathways, whereas several biological pathways were differentiated between the cell lines following treatment

Comparison of GC-MS and GC×GC-MS in the Analysis of Human Serum Samples for Biomarker Discovery

We compared the performance of gas chromatography time-of-flight mass spectrometry (GC-MS) and comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) for metabolite biomarker discovery. Metabolite extracts from 109 human serum samples were analyzed on both platforms with a pooled serum sample analyzed after every 9 biological samples for the purpose of quality control (QC). The experimental data derived from the pooled QC samples showed that the GC×GC-MS platform detected about three times as many peaks as the GC-MS platform at a signal-to-noise ratio SNR ≥ 50, and three times the number of metabolites were identified by mass spectrum matching with a spectral similarity score <i>R</i>_sim ≥ 600. Twenty-three metabolites had statistically significant abundance changes between the patient samples and the control samples in the GC-MS data set while 34 metabolites in the GC×GC-MS data set showed statistically significant differences. Among these two groups of metabolite biomarkers, nine metabolites were detected in both the GC-MS and GC×GC-MS data sets with the same direction and similar magnitude of abundance changes between the control and patient sample groups. Manual verification indicated that the difference in the number of the biomarkers discovered using these two platforms was mainly due to the limited resolution of chromatographic peaks by the GC-MS platform, which can result in severe peak overlap making subsequent spectrum deconvolution for metabolite identification and quantification difficult

Direct measurement of VOC diffusivities in tree tissues:Impacts on tree-based phytoremediation and plant contamination

Recent discoveries in the phytoremediation of volatile organic compounds (VOCs) show that vapor-phase transport into roots leads to VOC removal from the vadose zone and diffusion and volatilization out of plants is an important fate following uptake. Volatilization to the atmosphere constitutes one fundamental terminal fate processes for VOCs that have been translocated from contaminated soil or groundwater, and diffusion constitutes the mass transfer mechanism to the plant−atmosphere interface. Therefore, VOC diffusion through woody plant tissues, that is, xylem, has a direct impact on contaminant fate in numerous vegetation−VOC interactions, including the phytoremediation of soil vapors and dissolved aqueous-phase contaminants. The diffusion of VOCs through freshly excised tree tissue was directly measured for common groundwater contaminants, chlorinated compounds such as trichloroethylene, perchloroethene, and tetrachloroethane and aromatic hydrocarbons such as benzene, toluene, and methyl tert-butyl ether. All compounds tested are currently being treated at full scale with tree-based phytoremediation. Diffusivities were determined by modeling the diffusive transport data with a one-dimensional diffusive flux model, developed to mimic the experimental arrangement. Wood−water partition coefficients were also determined as needed for the model application. Diffusivities in xylem tissues were found to be inversely related to molecular weight, and values determined herein were compared to previous modeling on the basis of a tortuous diffusion path in woody tissues. The comparison validates the predictive model for the first time and allows prediction for other compounds on the basis of chemical molecular weight and specific plant properties such as water, lignin, and gas contents. This research provides new insight into phytoremediation efforts and into potential fruit contamination for fruit-bearing trees, specifically establishing diffusion rates from the transpiration stream and modeling volatilization along the transpiration path, including the trunk and branches. This work also has importance in other plant−VOC interactions, such as potential uptake from the atmosphere for hydrophobic compounds and also uptake from vapor-phase soil contaminants