Current Metabolomics最新文献

Background: Principal Component Analysis (PCA) and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) are powerful statistical modeling tools that provide insights into separations between experimental groups based on high-dimensional spectral measurements from NMR, MS or other analytical instrumentation. However, when used without validation, these tools may lead investigators to statistically unreliable conclusions. This danger is especially real for Partial Least Squares (PLS) and OPLS, which aggressively force separations between experimental groups. As a result, OPLS-DA is often used as an alternative method when PCA fails to expose group separation, but this practice is highly dangerous. Without rigorous validation, OPLS-DA can easily yield statistically unreliable group separation.

Methods: A Monte Carlo analysis of PCA group separations and OPLS-DA cross-validation metrics was performed on NMR datasets with statistically significant separations in scores-space. A linearly increasing amount of Gaussian noise was added to each data matrix followed by the construction and validation of PCA and OPLS-DA models.

Results: With increasing added noise, the PCA scores-space distance between groups rapidly decreased and the OPLS-DA cross-validation statistics simultaneously deteriorated. A decrease in correlation between the estimated loadings (added noise) and the true (original) loadings was also observed. While the validity of the OPLS-DA model diminished with increasing added noise, the group separation in scores-space remained basically unaffected.

Conclusion: Supported by the results of Monte Carlo analyses of PCA group separations and OPLS-DA cross-validation metrics, we provide practical guidelines and cross-validatory recommendations for reliable inference from PCA and OPLS-DA models.

Background: Metabolomics aims to characterize the metabolic phenotype and metabolic pathways utilized by microorganisms or other cellular systems. A crucial component to metabolomics research as it applies to microbial metabolism is the development of robust and reproducible methods for extraction of intracellular metabolites. The goal is to extract all metabolites in a non-biased and consistent manner; however, most methods used thus far are targeted to specific metabolite classes and use harsh conditions that may contribute to metabolite degradation. Metabolite extraction methodologies need to be optimized for each microorganism of interest due to different cellular characteristics contributing to lysis resistance.

Methods: Three cell pellet wash solutions were compared for the potential to influence intracellular metabolite leakage of P. aeruginosa. We also compared four different extraction methods using (i) methanol:chloroform (2:1); (ii) 50% methanol; (iii) 100% methanol; or (iv) 100% water to extract intracellular metabolites from P. aeruginosa planktonic and biofilm cultures.

Results: Intracellular metabolite extraction efficiency was found to be dependent on the extraction method and varies between microbial modes of growth. Methods using the 60% methanol wash produced the greatest amount of intracellular material leakage. Quantification of intracellular metabolites via ¹H NMR showed that extraction protocols using 100% water or 50% methanol achieved the greatest extraction efficiencies, while addition of sonication to facilitate cell lysis to the 50% methanol extraction method resulted in at least a two-fold increase in signal intensities for approximately half of the metabolites identified. Phosphate buffered saline (PBS) was determined to be the most appropriate wash solution, yielding little intracellular metabolite leakage from cells.

Conclusion: We determined that washing in 1X PBS and extracting intracellular metabolites with 50% methanol is the most appropriate metabolite extraction protocol because (a) leakage is minimal; (b) a broad range of metabolites present at sufficiently high concentrations is detectable by NMR; and (c) this method proved suitable for metabolite extraction of both planktonic and biofilm P. aeruginosa cultures.

Cancer is a metabolic disease. Cancer cells, being highly proliferative, show significant alterations in metabolic pathways such as glycolysis, respiration, the tricarboxylic acid (TCA) cycle, oxidative phosphorylation, lipid metabolism, and amino acid metabolism. Metabolites like peptides, nucleotides, products of glycolysis, the TCA cycle, fatty acids, and steroids can be an important read out of disease when characterized in biological samples such as tissues and body fluids like urine, serum, etc. The cancer metabolome has been studied since the 1960s by analytical techniques such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. Current research is focused on the identification and validation of biomarkers in the cancer metabolome that can stratify high-risk patients and distinguish between benign and advanced metastatic forms of the disease. In this review, we discuss the current state of prostate cancer metabolomics, the biomarkers that show promise in distinguishing indolent from aggressive forms of the disease, the strengths and limitations of the analytical techniques being employed, and future applications of metabolomics in diagnostic imaging and personalized medicine of prostate cancer.