Sifting Through Complex 2-D NMR Data | Chemical & Engineering News
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Web Date: February 23, 2011

Sifting Through Complex 2-D NMR Data

Data Analysis: Technique highlights chemical differences in sets of metabolite data
Department: Science & Technology, Government & Policy
Keywords: NMR, 2-D NMR, metabolomics, nematode, worm, chemical ecology
Parasitic worms such as Panagrellus redivivus secrete many chemicals to regulate their behavior and development.
Credit: Ramadan Ajredini
Parasitic worms such as Panagrellus redivivus secrete many chemicals to regulate their behavior and development.
Credit: Ramadan Ajredini

To understand how an organism's biochemistry relates to biological functions, such as reproduction or cell-to-cell communication, researchers struggle to make sense of complex chemical mixtures of metabolites. Now a study demonstrates a technique to analyze two-dimensional nuclear magnetic resonance data. With it, researchers have compared the metabolic profiles of two species of nematodes (Anal. Chem. DOI: 10.1021/ac102724x).

To study metabolic profiles, researchers have mainly used mass spectrometry, but this technique can't measure metabolite concentrations. Meanwhile, 2-D NMR can characterize the chemical structures of mixtures of metabolites and measure their relative concentrations. Arthur Edison of the University of Florida, Gainesville, and his colleagues have used the method to investigate how nematode worms communicate with one another through the chemicals they secrete. The researchers wanted to know if different nematode species released similar compounds. Nematodes produce tiny amounts of metabolites, often too small for analysis by other techniques. Unfortunately, the researchers didn't have a good method to compare sets of 2-D NMR data from different species.

Edison's team developed a computer algorithm that is similar to those used to align other bioinformatics data such as gene or protein sequences. Although samples from a single species will contain many similar compounds, changes in pH or concentrations of metal ions between samples can shift individual 2-D NMR peaks corresponding to a given compound. The new algorithm groups the 2-D NMR data from multiple species based on which peak patterns are most similar. It finds peaks that correspond to the same compounds and then aligns them across the spectra, narrowing those peaks to limit any overlap with other peaks with similar chemical shifts.

After the alignment step, Edison and his team use an established statistical technique that looks for variation among the aligned data to compare the metabolites from two different species. This technique allows them to ignore compounds that are found in equal amounts in both species and to highlight the differences. The scientists can then scan existing NMR databases to identify many of the compounds within the sample. 

Edison and his colleagues demonstrated their technique by comparing metabolites from two nematode species, Pristionchus pacificus and Panagrellus redivivus. He points out that the technique should work with any 2-D NMR data. The researchers also demonstrated that the technique can distinguish compounds in two different samples of human urine.

These sorts of techniques isolate biologically interesting chemicals like needles from a chemical haystack, says Jean-Luc Wolfender of the University of Geneva. In that respect, he adds, it's "a very interesting paper."

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