journal article
LitStream Collection
Home
Jeppesen, Micah J.; Powers, Robert
doi: 10.1002/mrc.5350pmid: 37005774
Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non‐destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.
Chen, Xi; Bertho, Gildas; Caradeuc, Cédric; Giraud, Nicolas; Lucas‐Torres, Covadonga
doi: 10.1002/mrc.5356pmid: 37157858
NMR is one of the most powerful techniques for the analysis of biological samples in the field of metabolomics. However, the high complexity of fluids, tissues, or other biological materials taken from living organisms is still a challenge for state‐of‐the‐art pulse sequences, thereby limiting the detection, the identification, and the quantification of metabolites. In this context, the resolution enhancement provided by broadband homonuclear decoupling methods, which allows for simplifying 1H multiplet patterns into singlets, has placed this so‐called pure shift technique as a promising approach to perform metabolic profiling with unparalleled level of detail. In recent years, the many advances achieved in the design of pure shift experiments has paved the way to the analysis of a wide range of biological samples with ultra‐high resolution. This review leads the reader from the early days of the main pure shift methods that have been successfully developed over the last decades to address complex samples, to the most recent and promising applications of pure shift NMR to the field of NMR‐based metabolomics.
Jagtap, Anil P.; Mamone, Salvatore; Glöggler, Stefan
doi: 10.1002/mrc.5402pmid: 37821237
Enhancing magnetic resonance signal via hyperpolarization techniques enables the real‐time detection of metabolic transformations even in vivo. The use of para‐hydrogen to enhance 13C‐enriched metabolites has opened a rapid pathway for the production of hyperpolarized metabolites, which usually requires specialized equipment. Metabolite precursors that can be hyperpolarized and converted into metabolites at any given field would open up opportunities for many labs to make use of this technology because already existing hardware could be used. We report here on the complete synthesis and hyperpolarization of suitable precursor molecules of the side‐arm hydrogenation approach. The better accessibility to such side‐arms promises that the para‐hydrogen approach can be implemented in every lab with existing two channel NMR spectrometers for 1H and 13C independent of the magnetic field.
Rout, Manoj; Lipfert, Matthias; Lee, Brian L.; Berjanskii, Mark; Assempour, Nazanin; Fresno, Rosa Vazquez; Cayuela, Arnau Serra; Dong, Ying; Johnson, Mathew; Shahin, Honeya; Gautam, Vasuk; Sajed, Tanvir; Oler, Eponine; Peters, Harrison; Mandal, Rupasri; Wishart, David S.
doi: 10.1002/mrc.5371
Lee, Brian L.; Rout, Manoj; Mandal, Rupasri; Wishart, David S.
doi: 10.1002/mrc.5372pmid: 37265043
We report the development of a software program, called MagMet‐F, that automates the processing and quantification of 1D 1H NMR of human fecal extracts. To optimize the program, we identified 82 potential fecal metabolites using 1D 1H NMR of six human fecal extracts using manual profiling and a literature review of known fecal metabolites. We acquired pure versions of those metabolites and then acquired their 1D 1H NMR spectra at 700 MHz to generate a fecal metabolite spectral library for MagMet‐F. The fitting of these metabolites by MagMet‐F was iteratively optimized to replicate manual profiling. We validated MagMet‐F's automated profiling using a test set of six fecal extracts. It correctly identified 80% of the compounds and quantified those within <20% of the values determined by manual profiling using Chenomx. We also compared MagMet‐F's profiling performance to two other open‐access NMR profiling tools, Bayesil and Batman. MagMet‐F outperformed both. Bayesil repeatedly overestimated metabolite concentrations by 10% to 40% while Batman was unable to properly quantify any compounds and took 10–20× longer. We have implemented MagMet‐F as a freely accessible web server to enable automated, fast and convenient 1D 1H NMR spectral profiling of fecal samples. MagMet‐F is available at https://www.magmet.ca.
Nagana Gowda, G. A.; Pascua, Vadim; Lusk, John A.; Hong, Natalie N.; Guo, Lin; Dong, Jiyang; Sweet, Ian R.; Raftery, Daniel
doi: 10.1002/mrc.5341pmid: 36882950
Investigation of mitochondrial metabolism is gaining increased interest owing to the growing recognition of the role of mitochondria in health and numerous diseases. Studies of isolated mitochondria promise novel insights into the metabolism devoid of confounding effects from other cellular organelles such as cytoplasm. This study describes the isolation of mitochondria from mouse skeletal myoblast cells (C2C12) and the investigation of live mitochondrial metabolism in real‐time using isotope tracer‐based NMR spectroscopy. [3‐13C1]pyruvate was used as the substrate to monitor the dynamic changes of the downstream metabolites in mitochondria. The results demonstrate an intriguing phenomenon, in which lactate is produced from pyruvate inside the mitochondria and the results were confirmed by treating mitochondria with an inhibitor of mitochondrial pyruvate carrier (UK5099). Lactate is associated with health and numerous diseases including cancer and, to date, it is known to occur only in the cytoplasm. The insight that lactate is also produced inside mitochondria opens avenues for exploring new pathways of lactate metabolism. Further, experiments performed using inhibitors of the mitochondrial respiratory chain, FCCP and rotenone, show that [2‐13C1]acetyl coenzyme A, which is produced from [3‐13C1]pyruvate and acts as a primary substrate for the tricarboxylic acid cycle in mitochondria, exhibits a remarkable sensitivity to the inhibitors. These results offer a direct approach to visualize mitochondrial respiration through altered levels of the associated metabolites.
Jenne, Amy; Soong, Ronald; Gruschke, Oliver; Bastawrous, Monica; Monks, Patricia; Moloney, Cara; Brougham, Dermot F.; Busse, Falko; Bermel, Wolfgang; Courtier‐Murias, Denis; Wu, Bing; Simpson, Andre
Showing 1 to 10 of 13 Articles
Nuclear magnetic resonance (NMR) spectral analysis of biofluids can be a time‐consuming process, requiring the expertise of a trained operator. With NMR becoming increasingly popular in the field of metabolomics, there is a growing need to change this paradigm and to automate the process. Here we introduce MagMet, an online web server, that automates the processing and quantification of 1D 1H NMR spectra from biofluids—specifically, human serum/plasma metabolites, including those associated with inborn errors of metabolism (IEM). MagMet uses a highly efficient data processing procedure that performs automatic Fourier Transformation, phase correction, baseline optimization, chemical shift referencing, water signal removal, and peak picking/peak alignment. MagMet then uses the peak positions, linewidth information, and J‐couplings from its own specially prepared standard metabolite reference spectral NMR library of 85 serum/plasma compounds to identify and quantify compounds from experimentally acquired NMR spectra of serum/plasma. MagMet employs linewidth adjustment for more consistent quantification of metabolites from higher field instruments and incorporates a highly efficient data processing procedure for more rapid and accurate detection and quantification of metabolites. This optimized algorithm allows the MagMet webserver to quickly detect and quantify 58 serum/plasma metabolites in 2.6 min per spectrum (when processing a dataset of 50–100 spectra). MagMet's performance was also assessed using spectra collected from defined mixtures (simulating other biofluids), with >100 previously measured plasma spectra, and from spiked serum/plasma samples simulating known IEMs. In all cases, MagMet performed with precision and accuracy matching the performance of human spectral profiling experts. MagMet is available at http://magmet.ca.
doi: 10.1002/mrc.5315pmid: 36137948
Superparamagnetic iron oxide nanoparticles (SPIONs) are a contaminant of emerging interest, often used in the medical field as an imaging contrast agent, with additional uses in wastewater treatment and as food additives. Although the use of SPIONs is increasing, little research has been conducted on the toxic impacts to living organisms beyond traditional lethal concentration endpoints. Daphnia magna are model organisms for aquatic toxicity testing with a well understood metabolome and high sensitivity to SPIONs. Thus, as environmental concentrations continue to increase, it is becoming critical to understand their sub‐lethal toxicity. Due to the paramagnetic nature of SPIONs, a range of potential nuclear magnetic resonance spectroscopy (NMR) experiments are possible, offering the potential to probe the physical location (via imaging), binding (via relaxation weighted spectroscopy), and the biochemical pathways impacted (via in vivo metabolomics). Results indicate binding to carbohydrates, likely chitin in the exoskeleton, along with a decrease in energy metabolites and specific biomarkers of oxidative stress. The holistic NMR framework used here helps provide a more comprehensive understanding of SPIONs impacts on D. magna and showcases NMR's versatility in providing physical, chemical, and biochemical insights.