Sign-in   Home    Members    Sitemap    Statistics    About    Contact-us    FAQ    Feedback

nmr 2.0 Blog

NMR applications in metabolics

without comments

“The application of system biology to drug discovery presents a powerful tool to find out toxicity and efficacy problems prior to initiating clinical trials. As a disease state reflects a certain perturbation in the operation of a network at the cellular level, the comparison of these healthy and diseased networks identifies critical intersection points that are associated with disease markers and drug activity.”1 So how can we analyze the states of these networks? One solution is the analysis of the metabolome, a study of the complete set of small molecules along with the associated biological networks within a living cell.

The perturbation of cellular metabolism can be affected by many approaches such as the use of chemical, biological, or environmental factors. For example, the function of an enzyme may be affected by drugs or genetic mutations, measuring changes in metabolite concentrations provides direct information on changes in the cellular activity of the affected enzyme.2 Other factor that may perturb an enzyme’s activity and induce variations in the metabolome include environmental factors such as temperature, time (cell phase), pH, oxygen levels, nutrient concentration or nutrient limitations.

We use NMR to detect these metabolome perturbations by following changes in the intensities of NMR resonances resulting from metabolite concentrations fluxes in cell lysates or biofluids. There are three methods that we are commonly using to follow changes in a series of NMR spectra caused by metabolome perturbations: subtraction of an average 1D 1H NMR spectra collected between two or more different cellular states, the comparison of crosspeak intensity differences between 2D 1H TOCSY or 2D 1H-13C HSQC spectra from cell lysates, and the principal component analysis (PCA) of a collection of 1D 1H NMR spectra from cell lysates.3 The first two approaches allow us to identify the metabolites, and thus the metabolic pathways, that incur a significant change in concentration (≥ 5-fold) as a result of the environmental stimulus. Specifically, wild-type cells under normal growth conditions are compared against cells grown under various environmental conditions or mutant cell lines.

Alternatively, PCA highlights global differences and similarities between NMR spectra obtain from cells grown under these various conditions. Our differential NMR metabolomics method compares NMR spectra from cell lysates collected from wild-type and mutant cells to determine if an environmental factor or drug has the same impact on the metabolome as a genetically inactivated protein. In effect, we are monitoring the cellular mechanism of the drug. Does it demonstrate in vivo efficacy and selectivity? Does it exhibit possible toxic side-effects? In a similar manner, we can follow cellular processes and address system biology questions such as: is there a metabolic signaling pathway that controls biofilm formation?

PCA data is presented as a 2D scores plot where the coordinate axis corresponds to the principal components representing the directions of the two largest variations in the NMR data set (PC1, PC2). PCA reduces a multivariable NMR spectrum into a single point in the 2D scores plot, where similar spectra will cluster together in the plot.1 Thus, NMR spectra collected from cell lysates for a wild-type cell line will cluster distinctly from NMR spectra obtained from mutant cells because the protein target of a drug has been inactivated. The NMR spectra are different because the metabolome for the mutant cells has changed because of the loss of protein activity. Conversely, NMR spectra collected for wild-type cells treated with the drug would be expected to cluster together with NMR spectra obtained from the mutant cells if the drug is active and selective. In this case the two metabolomes would be similar because the same protein was inactivated, either genetically or chemically. Different clustering patterns in the 2D scores plot are observed for inactive drugs, non-selective binding drugs and drugs that exhibit potential toxic side-effects.

You could find out more about our differential NMR metabolomics technique and access our related publications at the Powers’ Group web site ( http://bionmr-c1.unl.edu/).

Reference

1. P. Forgue, S. Halouska, M. Werth, K. Xu, S. Harris and R. Powers (2006) “NMR Metabolic Profiling of Aspergillus nidulans to Monitor Drug and Protein Activity.”, Journal of Proteome Research, 5(8):1916-1923

2. S. Halouska, O. Chacon, R. Fenton, D. Zinniel, R. Barletta, and R. Powers (2007) “Use of NMR Metabolomics to Analyze the Targets of D-cycloserine in Mycobacteria: Role of D-Alanine Racemase.”, Journal of Proteome Research, 6(12):4608-4614.

3. M. R. Sadykov, M. E. Olson, S. Halouska, Y. Zhu, P. D. Fey, R. Powers, and G. A. Somerville (2008) “Tricarboxylic acid cycle dependent regulation of Staphylococcus epidermidis polysaccharide intercellular adhesin synthesis.” Journal of Bacteriology, 130(23):7621-7632


Share

Written by Robert Powers

June 23rd, 2009 at 10:38 am

Posted in NMR