Skip to main content
Contact Info
Núria Amigó
Department of Electronic Engineering, Rovira i Virgili University, Tarragona, Spain

LMWMscale: A New Bioinformatics Tool for Low Molecular Weight Metabolites  Quantification based on 1H-NMR spectroscopy for biomarker discovery

Aim: The use of “omic” approaches to unravel the disease complexity accelerates its understanding,  allowing untargeted approaches necessaries when limited disease knowledge is available especially adept  identifying not previously described altered mechanisms, discovering prognostic biomarkers and  monitoring health status (for COVID-19 and future communicable -and non-communicable- diseases). 

“omic” approaches describing COVID-19 nature demonstrated that early blood molecular changes  induced by SARS-CoV-2 infection are associated with future COVID-19 severity. Early advanced molecular  profiling of patients opens the door developing COVID-19 prognosis tool, to better understand the  physiopathology of the disease and to stratify the risk of complications. 

Nuclear Magnetic Resonace (NMR) metabolomics for biomarker discovery has repeatedly succeed  describing different in nature aetiologies and pathogenesis. Among the extensive NMR-visible amount of  metabolites, the main molecular families associated with disease severity and mortality in COVID-19 (i.e.  lipoproteins, specific lipid families, glycosylated proteins, amino-acids, and other aqueous small  molecules) can be detected in one single fast analysis compatible with clinical requirements. However,  the current metabolomic approaches need higher degree of automation and standardization for clinical  applications.  

The current study presents LMWMscale® Test, an automatic bioinformatics tool for high-throughput  quantitative metabolic profiling based on 1H-Nuclear Magnetic Resonance (1H-NMR), complementing  NMR capability on high-throughput profiling of macromolecular complexes such as lipoprotein and  glycoprotein characterization. 

Materials and Methods: 1H-NMR spectra from different biological matrixes -including 4800 serum, 107  fecal extract, 21 cell culture and 52 tissue samples- were acquired by using an Avance III-500 and Avance III-600 Bruker NMR spectrometers from the Center for Omic Sciences (Reus, Spain). An in-house software  was designed to automatically pre-treat and deconvolute the NMR signal for characterization of low  molecular weight metabolite (LMWM) associated NMR signals.  

Results: The algorithm read and processed 1H-NMR spectra to optimize, phase and baseline correct. The  LMWM-associated regions were selectively batched to isolate, align and deconvolute each signal automatically. The deconvolution approach used Voigt analytical functions (a mixture between lorentzian  and gaussian functions) to reproduce the experimental curve minimizing the fitting error, to quantify the  area of each signal proportional to the metabolite concentration. The deconvolution approach allowed  the quantification of several LWMW signals with complex coupling patterns, even in highly overlapped  spectral regions. The resulting areas were transformed to concentration units by applying specific  conversion factors. Consistency between standard techniques were evaluated for glucose and creatinine among others, the correlation coefficients were R2>0.9. 

Conclusion: The LMWMscale® Test provides automatic quantitative screening of LMWMs present in  biological matrixes from 1H-NMR spectra; that can be coupled with current lipoprotein and glycoprotein  profile methodology in a high-throughput mode for global molecular screening compatible with clinical  and epidemiological requirements.