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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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Related in: MedlinePlus

Real and constructed1H-13C HSQC spectra from post-prostatic palpation urine. To the left (a) is one of the 50 recorded 1H-13C HSQC spectra from post-prostatic palpation urine. To the right (b) is a constructed 1H-13C HSQC spectrum, prepared by merging correlation plots from 23 metabolites. Peaks from 7 metabolites with only one 1H-13C HSQC cross-peak are also included. TMAO appears broad due to phase distortion.
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Fig2: Real and constructed1H-13C HSQC spectra from post-prostatic palpation urine. To the left (a) is one of the 50 recorded 1H-13C HSQC spectra from post-prostatic palpation urine. To the right (b) is a constructed 1H-13C HSQC spectrum, prepared by merging correlation plots from 23 metabolites. Peaks from 7 metabolites with only one 1H-13C HSQC cross-peak are also included. TMAO appears broad due to phase distortion.

Mentions: A representative 1H-13C HSQC spectrum from post-prostatic palpation urine is shown in Figure 2a. The Human Metabolite Database (HMDB) [18] was browsed for urinary metabolites with expected high levels (above 20 μmol/mmol creatinine). When HSQC data was available, correlation plots were produced selecting one of the cross-peaks from the metabolites in question. The Pearson correlation coefficients calculated range from −1 to 1, with 1 meaning a perfect positive correlation. To generate clean plots, only the most highly correlated peaks were shown. In many cases, a cutoff value of 0.9 provided perfect correlation plots, only containing the cross-peaks as expected from the reference. In other cases, some fine tuning of the cutoff was required before a satisfactory plot could be produced. In addition to typical urinary metabolites, post-prostatic palpation urine contains metabolites originating from the prostate. One of these is spermine, which is included in the list of 23 metabolites unambiguously identified by their correlation plots (Table 1).Figure 2


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)

Real and constructed1H-13C HSQC spectra from post-prostatic palpation urine. To the left (a) is one of the 50 recorded 1H-13C HSQC spectra from post-prostatic palpation urine. To the right (b) is a constructed 1H-13C HSQC spectrum, prepared by merging correlation plots from 23 metabolites. Peaks from 7 metabolites with only one 1H-13C HSQC cross-peak are also included. TMAO appears broad due to phase distortion.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig2: Real and constructed1H-13C HSQC spectra from post-prostatic palpation urine. To the left (a) is one of the 50 recorded 1H-13C HSQC spectra from post-prostatic palpation urine. To the right (b) is a constructed 1H-13C HSQC spectrum, prepared by merging correlation plots from 23 metabolites. Peaks from 7 metabolites with only one 1H-13C HSQC cross-peak are also included. TMAO appears broad due to phase distortion.
Mentions: A representative 1H-13C HSQC spectrum from post-prostatic palpation urine is shown in Figure 2a. The Human Metabolite Database (HMDB) [18] was browsed for urinary metabolites with expected high levels (above 20 μmol/mmol creatinine). When HSQC data was available, correlation plots were produced selecting one of the cross-peaks from the metabolites in question. The Pearson correlation coefficients calculated range from −1 to 1, with 1 meaning a perfect positive correlation. To generate clean plots, only the most highly correlated peaks were shown. In many cases, a cutoff value of 0.9 provided perfect correlation plots, only containing the cross-peaks as expected from the reference. In other cases, some fine tuning of the cutoff was required before a satisfactory plot could be produced. In addition to typical urinary metabolites, post-prostatic palpation urine contains metabolites originating from the prostate. One of these is spermine, which is included in the list of 23 metabolites unambiguously identified by their correlation plots (Table 1).Figure 2

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
Related in: MedlinePlus