Nuclear magnetic resonance spectroscopy (NMR)-based metabolomics is a powerful method for identifying metabolic perturbations that report on different biological states and sample types. Compared to mass spectrometry, NMR provides robust and highly reproducible quantitative data in a matter of minutes, which makes it very suitable for first-line clinical diagnostics. Although the metabolome is known to provide an instantaneous snap-shot of the biological status of a cell, tissue, and organism, the utilization of NMR in clinical practice is hindered by cumbersome data analysis. Major challenges include high-dimensionality of the data, overlapping signals, variability of resonance frequencies (chemical shift), non-ideal shapes of signals, and low signal-to-noise ratio (SNR) for low concentration metabolites. Existing approaches fail to address these challenges and sample analysis is time-consuming, manually done, and requires considerable knowledge of NMR spectroscopy. Recent developments in the field of sparse methods for machine learning and accelerated convex optimization for high dimensional problems, as well as kernel-based spatial clustering show promise at enabling us to overcome these challenges and achieve fully automated, operator-independent analysis. We are developing two novel, powerful, and automated algorithms that capitalize on these recent developments in machine learning.
In Aim 1, we describe ?NMRQuant? for automated identification and quantification of annotated metabolites irrespective of the chemical shift, low SNR, and signal shape variability.
In Aim 2, we describe ?SPA-STOCSY? for automated de-novo identification of molecular fragments of unknown, non- annotated metabolites. Based on substantial preliminary data, we propose to evaluate these algorithms' sensitivity, specificity, stability, and resistance to noise on phantom, biological, and clinical samples, comparing them to current methods. We will validate the accuracy of analyses by experimental 2D NMR, spike-in, and mass spectrometry. The proposed efforts will produce new NMR analytical software for discovery of both annotated and non-annotated metabolites, substantially improving accuracy and reproducibility of NMR analysis. Such analytical ability would change the existing paradigm of NMR-based metabolomics and provide an even stronger complement to current mass spectrometry-based methods. This approach, once thoroughly validated, will enable NMR to reach wide network of applications in biomedical, pharmaceutical, and nutritional research and clinical medicine.

Public Health Relevance

This project seeks to develop an advanced and automated platform for identifying NMR metabolomics biomarkers of diseases and for fundamental studies of biological systems. When fully developed, these approaches could be used to detect small molecules in the blood or urine, indicative of the onset of various diseases, drug toxicity, or environmental effects on the organism.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM120033-02
Application #
9406318
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Ravichandran, Veerasamy
Project Start
2017-01-01
Project End
2021-12-31
Budget Start
2018-01-01
Budget End
2018-12-31
Support Year
2
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Baylor College of Medicine
Department
Pediatrics
Type
Schools of Medicine
DUNS #
051113330
City
Houston
State
TX
Country
United States
Zip Code
77030
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