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Metabolite identification by nuclear...

Bingol, Ahmet Kerem.

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Metabolite identification by nuclear magnetic resonance spectroscopy.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Metabolite identification by nuclear magnetic resonance spectroscopy./
Author:	Bingol, Ahmet Kerem.
Description:	117 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.
Contained By:	Dissertation Abstracts International74-10B(E).
Subject:	Biophysics, General. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3564836
ISBN:	9781303140891

Metabolite identification by nuclear magnetic resonance spectroscopy.
Bingol, Ahmet Kerem.

Metabolite identification by nuclear magnetic resonance spectroscopy. - 117 p.

Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.

Thesis (Ph.D.)--The Florida State University, 2013.

The metabolic makeup of a biological system is a key determinant of its biological state providing detailed insights into its function. Identification and quantification of the metabolites in a system form critical components of metabolomics. Nuclear magnetic resonance (NMR) spectroscopy is a unique tool for this purpose providing a wealth of atomic-detail information without requiring extensive fractionation of samples. So far, a majority of NMR metabolomics studies have been performed by using 1D NMR techniques because of the short duration of the experiments. The drawback of 1D NMR is the high occurrence of peak overlaps that impairs metabolite identification and quantification. The use of multidimensional NMR techniques can resolve peak overlaps and provide connectivity information of atoms within molecules, thereby outweighing the longer measurement times. In this thesis, we introduce novel approaches to identify metabolites by using multidimensional NMR spectroscopy. Our main approach consists of two major steps. In the first step, the metabolite mixture is deconvoluted into its individual components and in the second step; each individual component is analyzed by using its NMR spectrum. In order to achieve fast, robust and (semi-)automated deconvolution, DeCoDeC technique is introduced and applied to a variety of 1H and 13C TOCSY based NMR spectra. Deconvoluted TOCSY traces are directly queried in metabolite databanks for identification. Since many metabolites are not present in metabolite databanks, we developed a strategy to extract their carbon backbone structures (topology), which is a prerequiste for de novo structure determination. This led to the determination of 112 topologies of unique metabolites in E. coli from a single sample that constitutes the "topolome" of a cell. The topolome is dominated by carbon topologies of carbohydrates (34.8%) and amino acids (45.5%) that can constitute building blocks of more complex structures. Furthermore, since databanks are designed to query 1D NMR spectrum, querying of TOCSY traces against 1D NMR spectra in databanks resulted in imperfect matches. To overcome this, we created a customized 13CTOCSY database, which substantially improved the accuracy of database query of 13C TOCSY traces. Together these new tools open up the prospect to enable routine yet accurate analysis of an increasingly complex and diverse range of molecular solutions including metabolomics samples.

ISBN: 9781303140891Subjects--Topical Terms:

1019105
Biophysics, General.

Metabolite identification by nuclear magnetic resonance spectroscopy.
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100 1 $a Bingol, Ahmet Kerem. $3 2104647
245 1 0 $a Metabolite identification by nuclear magnetic resonance spectroscopy.
300 $a 117 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.
500 $a Adviser: Rafael Bruschweiler.
502 $a Thesis (Ph.D.)--The Florida State University, 2013.
520 $a The metabolic makeup of a biological system is a key determinant of its biological state providing detailed insights into its function. Identification and quantification of the metabolites in a system form critical components of metabolomics. Nuclear magnetic resonance (NMR) spectroscopy is a unique tool for this purpose providing a wealth of atomic-detail information without requiring extensive fractionation of samples. So far, a majority of NMR metabolomics studies have been performed by using 1D NMR techniques because of the short duration of the experiments. The drawback of 1D NMR is the high occurrence of peak overlaps that impairs metabolite identification and quantification. The use of multidimensional NMR techniques can resolve peak overlaps and provide connectivity information of atoms within molecules, thereby outweighing the longer measurement times. In this thesis, we introduce novel approaches to identify metabolites by using multidimensional NMR spectroscopy. Our main approach consists of two major steps. In the first step, the metabolite mixture is deconvoluted into its individual components and in the second step; each individual component is analyzed by using its NMR spectrum. In order to achieve fast, robust and (semi-)automated deconvolution, DeCoDeC technique is introduced and applied to a variety of 1H and 13C TOCSY based NMR spectra. Deconvoluted TOCSY traces are directly queried in metabolite databanks for identification. Since many metabolites are not present in metabolite databanks, we developed a strategy to extract their carbon backbone structures (topology), which is a prerequiste for de novo structure determination. This led to the determination of 112 topologies of unique metabolites in E. coli from a single sample that constitutes the "topolome" of a cell. The topolome is dominated by carbon topologies of carbohydrates (34.8%) and amino acids (45.5%) that can constitute building blocks of more complex structures. Furthermore, since databanks are designed to query 1D NMR spectrum, querying of TOCSY traces against 1D NMR spectra in databanks resulted in imperfect matches. To overcome this, we created a customized 13CTOCSY database, which substantially improved the accuracy of database query of 13C TOCSY traces. Together these new tools open up the prospect to enable routine yet accurate analysis of an increasingly complex and diverse range of molecular solutions including metabolomics samples.
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