Metabolomics:Open Access

Traditional chemotherapy is the first treatment option for the majority of cancer patients, but due to harsh and toxic side effects, more targeted therapies are needed. T-oligo is an oligonucleotide homologous to the 3’ overhang of the telomere. It induces several DNA damage and anti-cancer responses similar to experimental telomere loop disruption, including senescence, apoptosis and differentiation in malignant cells. To explore T-oligo’s anticancer potential, a panel of 6 malignant melanoma cell lines was treated with T-oligo. Melanoma cell lines with functional p53 or p73 exhibited cell death ranging from 11.80% to 31.73% after T-oligo treatment, with MU and MM-AN melanoma cells expressing the maximum response. There was no significant response in p53 and p73 null RPM-EP cells. Based on these results, MM-AN cells were chosen as a model system to study T-oligo’s effects in vitro and in vivo. To further elucidate its mechanism of action, pro-apoptotic and differentiation markers typically up regulated in responsive melanoma cell lines were studied in RPM-EP cells. FACS analysis and immune fluorescence studies confirmed uptake of fluorescein labeled T-oligo in both cell lines. Western blotting and confocal microscopy studies indicated up regulation of YH2AX after T-oligo treatment in MM-AN cells. In RPM-EP cells, expression of p73 and TRP-1 was not detected, nor was there up regulation of E2F1 and Tyrosinase. For in vivo experiments, SCID mice were injected with MM-AN cells to form tumors on their flanks, which were later treated with T-oligo and complementary oligo using Alzet pumps. Results demonstrated a 98% reduction in tumor size, as well as up regulation of differentiation markers important for anti-tumor immune responses. This study provides novel evidence which further establishes p53/p73 as crucial downstream signalling proteins and important players in T-oligo mediated anti-cancer effects in melanoma. Our results clearly demonstrate that T-oligo may be an effective and novel therapeutic for melanoma.

Background: In this study, we compare methods that can be used to relate a phenotypic trait of interest to an ~omics data set, where the number or variables outnumbers by far the number of samples.

Methods: We apply univariate regression and different regularized multiple regression methods: ridge regression (RR), LASSO, elastic net (EN), principal components regression (PCR), partial least squares regression (PLS), sparse partial least squares regression (SPLS), support vector regression (SVR) and random forest regression (RF). These regression methods were applied to a data set from a potato mapping population, where we predict potato flesh colour from a metabolomics data set.

Results: We compare the methods in terms of the mean square error of prediction of the trait, goodness of fit of the models, and the selection and ranking of the metabolites. In terms of the prediction error, elastic net performed better than the other methods. Different numbers of variables are selected by the methods that allow variable selection but seven variables were in common between LASSO, EN and SPLS. SPLS performed better than EN with respect to the selection of grouped correlated variables.

Conclusions: Four out of these seven variables selected by LASSO, EN, SPLS were putatively identified as carotenoid derived compounds; since the carotenoid pathway is important for flesh colour of potato, this indicates that meaningful compounds are selected. We developed a web application that can perform all the described methods, and that includes a double cross validation for optimization of the methods and for proper estimation of the prediction error.

Background: Biochemical determinations of human postmortem interval have focused on acute biomarkers of alterations in metabolites and in microbial generation of amino acids, organic acids, and volatile amines and acids. The need for a reliable biomarker of tissue deterioration on a longer time scale is clear. To determine if structural glycerophospholipids might be of value in this regard, we undertook a lipidomics evaluation of human skeletal muscle at several intervals postmortem.

Methods: Human anterior quadriceps muscle was excised at 1, 9, and 24 days postmortem. Tissue was extracted with tert butyl methylether and methanol and the extracts submitted to shotgun lipidomics analysis.

Results: Sterol sulfates, very-long-chain fatty acids, choline plasmalogens, ethanolamine plasmalogens, and phosphatidylglycerols all were found to decline over the 24 day postmortem period. Free fatty acids were found to increase over the same time period.

Conclusion: Tissue degradation over time postmortem appears to be reliably monitored by the decline in complex structural glycerophospholipids. The contribution of microbes to this degradation remains to be defined. These preliminary data support further evaluations of this lipidomics approach.

Objective: To investigate the feasibility of 1 H-NMR spectroscopy for metabolic profiling of human saliva samples and to determine whether the concentration of certain salivary metabolites, mainly representing small organic acids and amino acids, differ between patients with primary Sjögren´s syndrome (pSS) and healthy controls. Methods: Stimulated whole-mouth saliva (SWMS) was collected from female pSS patients (n =15, all fulfilling the revised European Community proposed criteria). Salivary flow rate was immediately determined, samples were then frozen and subsequently analyzed by 1 H-NMR spectroscopy in comparison with samples collected from healthy individuals (n=15). Results: From each sample, up to 24 metabolites could be identified and quantified. Choline and taurine concentrations were very significantly higher in the pSS patients compared to healthy controls (p<0.001), but their concentrations correlated negatively with salivary flow rate. Alanine and glycine concentrations were significantly higher (p=0.004, p=0.007, respectively), whereas butyrate (p= 0.034), phenylalanine (p=0.026) and proline (p=0.032) were only slightly higher in pSS saliva samples than in controls. Conclusions: NMR spectroscopy has a potential for quantitative metabolic profiling of saliva samples. NMR spectroscopy is suitable for the analysis of NAAs in saliva and it can bypass the other methods, which are normally suitable for analysis of just one metabolite.