skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Shifting-corrected regularized regression for 1 H NMR metabolomics identification and quantification
Summary The process of identifying and quantifying metabolites in complex mixtures plays a critical role in metabolomics studies to obtain an informative interpretation of underlying biological processes. Manual approaches are time-consuming and heavily reliant on the knowledge and assessment of nuclear magnetic resonance (NMR) experts. We propose a shifting-corrected regularized regression method, which identifies and quantifies metabolites in a mixture automatically. A detailed algorithm is also proposed to implement the proposed method. Using a novel weight function, the proposed method is able to detect and correct peak shifting errors caused by fluctuations in experimental procedures. Simulation studies show that the proposed method performs better with regard to the identification and quantification of metabolites in a complex mixture. We also demonstrate real data applications of our method using experimental and biological NMR mixtures.  more » « less
Award ID(s):
1660921
PAR ID:
10366866
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Oxford University Press
Date Published:
Journal Name:
Biostatistics
Volume:
24
Issue:
1
ISSN:
1468-4357
Format(s):
Medium: X Size: p. 140-160
Size(s):
p. 140-160
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; (, NMR in Biomedicine)
    Abstract Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for quantitative metabolomics; however, quantification of metabolites from NMR data is often a slow and tedious process requiring user input and expertise. In this study, we propose a neural network approach for rapid, automated lipid identification and quantification from NMR data. Multilayered perceptron (MLP) networks were developed with NMR spectra as the input and lipid concentrations as output. Three large synthetic datasets were generated, each with 55,000 spectra from an original 30 scans of reference standards, by using linear combinations of standards and simulating experimental‐like modifications (line broadening, noise, peak shifts, baseline shifts) and common interference signals (water, tetramethylsilane, extraction solvent), and were used to train MLPs for robust prediction of lipid concentrations. The performances of MLPS were first validated on various synthetic datasets to assess the effect of incorporating different modifications on their accuracy. The MLPs were then evaluated on experimentally acquired data from complex lipid mixtures. The MLP‐derived lipid concentrations showed high correlations and slopes close to unity for most of the quantified lipid metabolites in experimental mixtures compared with ground‐truth concentrations. The most accurate, robust MLP was used to profile lipids in lipophilic hepatic extracts from a rat metabolomics study. The MLP lipid results analyzed by two‐way ANOVA for dietary and sex differences were similar to those obtained with a conventional NMR quantification method. In conclusion, this study demonstrates the potential and feasibility of a neural network approach for improving speed and automation in NMR lipid profiling and this approach can be easily tailored to other quantitative, targeted spectroscopic analyses in academia or industry. 
    more » « less
  2. ; ; ; ; (, Journal of Ecology)
    Abstract An increasing number of ecological studies have used chemical diversity as a functionally relevant, scalable measure of phytochemical mixtures, demanding more rigorous attention to how chemical diversity is estimated. Most studies have focused on the composition of phytochemical mixtures and have largely ignored structural concerns, which may have greater importance for ecological function. Here, we explore the development of structural complexity and compositional diversity resulting from different biotic and abiotic interactions inPiper kelleyiTepe (Piperaceae). We also describe how variation in structural complexity and compositional diversity differs between two congeners,P. kelleyiandP.reticulatum. To better interpret these results, we have developed a framework for interpreting these dimensions of chemical diversity in phytochemical mixtures.We used the tropical shrub,P.kelleyi, as a model system to examine interactions between ecological factors and dimensions of phytochemical diversity. We also compared compositional diversity and metabolic complexity inP. kelleyiandP. reticulatumusing liquid chromatography and1H NMR, respectively, to examine trade‐offs between compositional diversity and structural complexity. A framework is provided to generate meaningful estimates of the structural complexity of phytochemical mixtures as measured by1H NMR.Piperis an abundant plant genus that supports diverse insect communities throughout the tropics. Subtle changes in understorey forest light were associated with increases in herbivory that directly increased compositional diversity and indirectly decreased structural complexity inP. kelleyi. This was attributed to the production of oxidation products resulting from herbivory‐driven decomposition of structurally complex defence compounds. This type of complex result would remain undetected using standard chemical ecology approaches and accounts for the detailed molecular changes that are likely to affect species interactions.Synthesis. Our quantitative framework provides a method for considering trade‐offs between structural complexity and compositional diversity and the interpretation of analytical approaches for each. This methodology will provide new theoretical insights and a more sophisticated model for examining the ecology and evolution of chemically mediated interactions. 
    more » « less
  3. ; ; (, Journal of Student Research)
    Many  biological  macromolecules  rely  on  metal  ions  to  maintain  structural  integrity  and  control  their  regulatory  function. In biological fluids, detection and identification of metal ions requires sensitive analytical tools with clear readouts.  In  this  work,  we  sought  to  investigate  the  potential  of  solution  Nuclear  Magnetic  Resonance  (NMR)  spectroscopy to analyze metal ion solutions and mixtures. To enable 1H NMR detection, we prepared the complexes of  eight  metal  ions  with  the  chelating  agent,  1,2-bis(o-aminophenoxy)ethane-N,N,N′,N′-tetraacetic  acid  (BAPTA).  The 1H NMR spectra were collected for BAPTA samples as a function of metal ion concentrations. The analysis of NMR data revealed that all metal ions with a notable exception of Mg2+ bind BAPTA with high affinities and form complexes with 1:1 metal-to-chelator stoichiometry. Both methylene and aromatic regions of the BAPTA 1H NMR spectra  experience  significant  changes  upon  the  metal  ion  complex  formation.  We  identified  the  spectroscopic  signatures  of  trivalent  and  paramagnetic  ions  and  demonstrated  that  the  binary  Zn2+/Pb2+  metal  ion  mixture  can  be  successfully analyzed by NMR. We conclude that complexation with BAPTA followed by the 1H NMR analysis is a sensitive method to detect and identify both nutritive and xenobiotic metal ions. 
    more » « less
  4. ; (, Metabolites)
    Background: Quantification of metabolites from nuclear magnetic resonance (NMR) spectra in an accurate, high-throughput manner requires effective data processing tools. Neural networks are relatively underexplored in quantitative NMR metabolomics despite impressive speed and throughput compared to more conventional peak-fitting metabolomics software. Methods: This work investigates practices for dataset and model development in the task of metabolite quantification directly from simulated NMR spectra for three neural network models: the multi-layered perceptron, the convolutional neural network, and the transformer. Model architectures, training parameters, and training datasets are optimized before comparing each model on simulated 400-MHz 1H-NMR spectra of complex mixtures with 8, 44, or 86 metabolites to quantify in spectra ranging from simple to highly complex and overlapping peaks. The optimized models were further validated on spectra at 100- and 800-MHz. Results: The transformer was the most effective network for NMR metabolite quantification, especially as the number of metabolites per spectra increased or target concentrations were low or had a large dynamic range. Further, the transformer was able to accurately quantify metabolites in simulated spectra from 100-MHz up to 800-MHz. Conclusions: The methods developed in this work reveal that transformers have the potential to accurately perform fully automated metabolite quantification in real-time and, with further development with experimental data, could be the basis for automated quantitative NMR metabolomics software. 
    more » « less
  5. ; (, Metabolites)
    Background: The introduction of benchtop NMR instruments has made NMR spectroscopy a more accessible, affordable option for research and industry, but the lower spectral resolution and SNR of a signal acquired on low magnetic field spectrometers may complicate the quantitative analysis of spectra. Methods: In this work, we compare the performance of multiple neural network architectures in the task of converting simulated 100 MHz NMR spectra to 400 MHz with the goal of improving the quality of the low-field spectra for analyte quantification. Multi-layered perceptron networks are also used to directly quantify metabolites in simulated 100 and 400 MHz spectra for comparison. Results: The transformer network was the only architecture in this study capable of reliably converting the low-field NMR spectra to high-field spectra in mixtures of 21 and 87 metabolites. Multi-layered perceptron-based metabolite quantification was slightly more accurate when directly processing the low-field spectra compared to high-field converted spectra, which, at least for the current study, precludes the need for low-to-high-field spectral conversion; however, this comparison of low and high-field quantification necessitates further research, comparison, and experimental validation. Conclusions: The transformer method of NMR data processing was effective in converting low-field simulated spectra to high-field for metabolomic applications and could be further explored to automate processing in other areas of NMR spectroscopy. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email