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Identification of metabolites from 2D (1)H-(13)C HSQC NMR using peak correlation plots.

Öman T, Tessem MB, Bathen TF, Bertilsson H, Angelsen A, Hedenström M, Andreassen T - BMC Bioinformatics (2014)

Bottom Line: For the identification of individual metabolites in metabolomics, correlation or covariance between peaks in (1)H NMR spectra has previously been successfully employed.The identities of these metabolites were confirmed by comparing the correlation plots with reported NMR data, mostly from the Human Metabolome Database.The correlation plots highlight cross-peaks belonging to each individual compound, not limited by long-range magnetization transfer as conventional NMR experiments.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry, Umeå University, Umeå, Sweden. tommy.oman@ltu.se.

ABSTRACT

Background: Identification of individual components in complex mixtures is an important and sometimes daunting task in several research areas like metabolomics and natural product studies. NMR spectroscopy is an excellent technique for analysis of mixtures of organic compounds and gives a detailed chemical fingerprint of most individual components above the detection limit. For the identification of individual metabolites in metabolomics, correlation or covariance between peaks in (1)H NMR spectra has previously been successfully employed. Similar correlation of 2D (1)H-(13)C Heteronuclear Single Quantum Correlation spectra was recently applied to investigate the structure of heparine. In this paper, we demonstrate how a similar approach can be used to identify metabolites in human biofluids (post-prostatic palpation urine).

Results: From 50 (1)H-(13)C Heteronuclear Single Quantum Correlation spectra, 23 correlation plots resembling pure metabolites were constructed. The identities of these metabolites were confirmed by comparing the correlation plots with reported NMR data, mostly from the Human Metabolome Database.

Conclusions: Correlation plots prepared by statistically correlating (1)H-(13)C Heteronuclear Single Quantum Correlation spectra from human biofluids provide unambiguous identification of metabolites. The correlation plots highlight cross-peaks belonging to each individual compound, not limited by long-range magnetization transfer as conventional NMR experiments.

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HSQC correlation plot of phenylacetylglutamine. Correlation plot showing all data points correlating strongly with 7.41 / 131.5 ppm (1H / 13C) (correlation coefficient higher than 0.8). Correlation to long-range cross-peak is circled. Two cross-peaks are marked with “7” due to diastereotopic protons in this position.
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Fig3: HSQC correlation plot of phenylacetylglutamine. Correlation plot showing all data points correlating strongly with 7.41 / 131.5 ppm (1H / 13C) (correlation coefficient higher than 0.8). Correlation to long-range cross-peak is circled. Two cross-peaks are marked with “7” due to diastereotopic protons in this position.

Mentions: Some of the plots contained unexpected additional cross-peaks (found peaks > expected peaks), possibly because of correlation with some unknown metabolite due to similar biological regulation. Other plots had missing correlations, as expected when certain cross-peaks fall into regions with heavy overlap. The presence of phenylacetylglycine in human urine is controversial, with some groups claiming to have identified it by NMR [21], and others claiming it cannot be detected by GC-MS [22]. If NMR-based identification of phenylacetylglycine is based on signals from the benzyl group, it is likely to be mistaken with phenylacetylglutamine, which contains a similar group with overlapping signals. Creating a correlation plot from one of these signals clearly shows cross-peaks indicative of phenylacetylglutamine, and no sign of the expected phenylacetylglycine signal at 3.74/46.2 ppm (1H / 13C) (Figure 3). No 13C NMR data of phenylacetylglutamine could be found from literature, but 1H NMR data is compatible with reported values [19]. Although we cannot disproof small amounts of phenylacetylglycine by our method, it is obvious that phenylacetylglutamine is the dominating of the two in our study. The example also demonstrates how statistical correlation can connect signals from isolated spin systems (benzyl part and amino acid part), not depending on weak/impossible long-range magnetization transfer. This is in contrast to triple rank correlation NMR which is purely based on spin-spin correlation [16].Figure 3


Identification of metabolites from 2D (1)H-(13)C HSQC NMR using peak correlation plots.

Öman T, Tessem MB, Bathen TF, Bertilsson H, Angelsen A, Hedenström M, Andreassen T - BMC Bioinformatics (2014)

HSQC correlation plot of phenylacetylglutamine. Correlation plot showing all data points correlating strongly with 7.41 / 131.5 ppm (1H / 13C) (correlation coefficient higher than 0.8). Correlation to long-range cross-peak is circled. Two cross-peaks are marked with “7” due to diastereotopic protons in this position.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4274720&req=5

Fig3: HSQC correlation plot of phenylacetylglutamine. Correlation plot showing all data points correlating strongly with 7.41 / 131.5 ppm (1H / 13C) (correlation coefficient higher than 0.8). Correlation to long-range cross-peak is circled. Two cross-peaks are marked with “7” due to diastereotopic protons in this position.
Mentions: Some of the plots contained unexpected additional cross-peaks (found peaks > expected peaks), possibly because of correlation with some unknown metabolite due to similar biological regulation. Other plots had missing correlations, as expected when certain cross-peaks fall into regions with heavy overlap. The presence of phenylacetylglycine in human urine is controversial, with some groups claiming to have identified it by NMR [21], and others claiming it cannot be detected by GC-MS [22]. If NMR-based identification of phenylacetylglycine is based on signals from the benzyl group, it is likely to be mistaken with phenylacetylglutamine, which contains a similar group with overlapping signals. Creating a correlation plot from one of these signals clearly shows cross-peaks indicative of phenylacetylglutamine, and no sign of the expected phenylacetylglycine signal at 3.74/46.2 ppm (1H / 13C) (Figure 3). No 13C NMR data of phenylacetylglutamine could be found from literature, but 1H NMR data is compatible with reported values [19]. Although we cannot disproof small amounts of phenylacetylglycine by our method, it is obvious that phenylacetylglutamine is the dominating of the two in our study. The example also demonstrates how statistical correlation can connect signals from isolated spin systems (benzyl part and amino acid part), not depending on weak/impossible long-range magnetization transfer. This is in contrast to triple rank correlation NMR which is purely based on spin-spin correlation [16].Figure 3

Bottom Line: For the identification of individual metabolites in metabolomics, correlation or covariance between peaks in (1)H NMR spectra has previously been successfully employed.The identities of these metabolites were confirmed by comparing the correlation plots with reported NMR data, mostly from the Human Metabolome Database.The correlation plots highlight cross-peaks belonging to each individual compound, not limited by long-range magnetization transfer as conventional NMR experiments.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry, Umeå University, Umeå, Sweden. tommy.oman@ltu.se.

ABSTRACT

Background: Identification of individual components in complex mixtures is an important and sometimes daunting task in several research areas like metabolomics and natural product studies. NMR spectroscopy is an excellent technique for analysis of mixtures of organic compounds and gives a detailed chemical fingerprint of most individual components above the detection limit. For the identification of individual metabolites in metabolomics, correlation or covariance between peaks in (1)H NMR spectra has previously been successfully employed. Similar correlation of 2D (1)H-(13)C Heteronuclear Single Quantum Correlation spectra was recently applied to investigate the structure of heparine. In this paper, we demonstrate how a similar approach can be used to identify metabolites in human biofluids (post-prostatic palpation urine).

Results: From 50 (1)H-(13)C Heteronuclear Single Quantum Correlation spectra, 23 correlation plots resembling pure metabolites were constructed. The identities of these metabolites were confirmed by comparing the correlation plots with reported NMR data, mostly from the Human Metabolome Database.

Conclusions: Correlation plots prepared by statistically correlating (1)H-(13)C Heteronuclear Single Quantum Correlation spectra from human biofluids provide unambiguous identification of metabolites. The correlation plots highlight cross-peaks belonging to each individual compound, not limited by long-range magnetization transfer as conventional NMR experiments.

Show MeSH