Automatic Identification of Potential Cellular Metabolites for Untargeted NMR Metabolomics

ABSTRACT An organism's metabolic profile provides vital information pertaining to its physiology or pathology. To monitor these biochemical changes, Nuclear Magnetic Resonance (NMR) spectroscopy has found success in non‐invasively observing metabolite changes within intact samples in an untarge...

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Veröffentlicht in:NMR in biomedicine Jg. 38; H. 10; S. e70131 - n/a
Hauptverfasser: Chen, Jiashang, Rao, Angela, Ghosh Biswas, Rajshree, Zhang, Ella J., Zhou, Jonathan Xin, Zhang, Evan, Kobus, Zuzanna, Kobus, Marta, Su, Li, Christiani, David C., Wishart, David S., Cheng, Leo L.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Wiley Subscription Services, Inc 01.10.2025
Schlagworte:
Alzheimer's disease
Amino acids
automated metabolite identification
Automation
Biological samples
Carbohydrates
Dementia disorders
Disease control
Humans
Lipids
Lung cancer
Magnetic Resonance Spectroscopy
Metabolites
Metabolome
Metabolomics
Metabolomics - methods
Neurodegenerative diseases
NMR
NMR spectroscopy
Nuclear magnetic resonance
nuclear magnetic resonance spectroscopy
Physiology
Protons
python program
Saliva
Spectroscopy
Spectrum analysis
nuclear magnetic resonance spectroscopy
python program
automated metabolite identification
ISSN:0952-3480, 1099-1492, 1099-1492
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:ABSTRACT An organism's metabolic profile provides vital information pertaining to its physiology or pathology. To monitor these biochemical changes, Nuclear Magnetic Resonance (NMR) spectroscopy has found success in non‐invasively observing metabolite changes within intact samples in an untargeted manner. However, biological samples are chemically complex, comprised of many different constituents (amino acids, carbohydrates, and lipids) at varying concentrations depending on physiological and pathological conditions. Due to the narrow spectral window of proton NMR, compound resonance frequencies can often overlap, making the identification and monitoring of metabolites difficult and time consuming, particularly when dealing with large numbers of samples. Here, we introduce a Python program (ROIAL‐NMR) to systematically identify potential metabolites from defined proton NMR spectral regions‐of‐interest (ROIs), which are identified from complex biological samples (i.e., human serum, saliva, sweat, urine, CSF, and tissues) using the Human Metabolome Database (HMDB) as a reference platform. Briefly, for disease‐versus‐control studies, the program considers disease types and utilizes study‐defined ROIs together with their differing intensity levels, according to sample types, in differentiating disease from control to propose potential metabolites represented by these ROIs in an output table. In this report, we illustrate the utility of the program with one of our recent studies, where we measured proton NMR spectra of serum samples taken from lung cancer (LC) patients, with and without Alzheimer's disease and related dementia (ADRD). The program successfully identified 88 metabolites, with 66 differentiating LC from control patients, and 80 distinguishing LC patients with ADRD from those without ADRD to provide important information regarding pathophysiology in complex biological samples. In complex biological samples, NMR signal overlap can make identifying/monitoring metabolites of interest difficult, particularly within high volumes of data. We introduce a user‐friendly Python program, termed ROIAL‐NMR to systematically identify potential metabolites from defined NMR spectral regions‐of‐interest (ROIs) using the Human Metabolome Database (HMDB). This program considers disease and sample types, utilizes study‐defined ROIs together with statistical analysis and trends to differentiate disease from control and propose potential metabolites in a categorized output table, providing important information regarding pathophysiology.
Bibliographie:Jiashang Chen, Angela Rao, Rajshree Ghosh Biswas, Ella J. Zhang contributed equally.
Funding
This work was supported by the National Institute on Aging, 10.13039/100000049, R01 AG070257, the National Cancer Institute, 10.13039/100000054, CA273010, and the Canadian Institutes of Health Research, 10.13039/501100000024, MFE‐194064.
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ISSN:0952-3480
1099-1492
1099-1492
DOI:10.1002/nbm.70131