RANCM: a new ranking scheme for assigning confidence levels to metabolite assignments in NMR-based metabolomics studies
- 288 Downloads
The Metabolomics Standards Initiative has recommended four categories for metabolite assignments in NMR-based metabolic profiling studies. The “putatively annotated compound” category is most commonly reported by metabolomics investigators. However, there is significant ambiguity in reliability of “putatively annotated compound” assignments, which can range from low confidence made on minimal corroborating data to high confidence made on substantial corroborating data.
To introduce a new ranking system, Rank and AssigN Confidence to Metabolites (RANCM), to assign confidence levels to “putatively annotated compound” assignments in NMR-based metabolic profiling studies.
The ranking system was constructed with three confidence levels ranging from Rank 1 for the lowest confidence assignment level to Rank 3 for the highest confidence assignment level. A decision tree was constructed to guide rank selection for each metabolite assignment.
Examples are provided from experimental data demonstrating how to use the decision tree to make confidence level assignments to “putatively annotated compounds” in each of the three rank levels. A standard Excel sheet template is provided to facilitate decision-making, documentation and submission to data repositories.
RANCM is intended to reduce the ambiguity in “putatively annotated compound” assignments, to facilitate effective communication of the degree of confidence in “putatively annotated compound” assignments, and to make it easier for non-experts to evaluate the significance and reliability of NMR-based metabonomics studies. The system is straightforward to implement, based on the most common datasets collected in NMR-based metabolic profiling studies, and can be used with equal rigor and significance with any set of NMR datasets.
KeywordsNuclear magnetic resonance NMR Metabolomics Metabonomics
The research was conducted with the support of Miami University. The instrumentation used in this work was obtained with the support of Miami University and the Ohio Board of Regents with funds used to establish the Ohio Eminent Scholar Laboratory where the work was performed.
MAK conceived the ranking scheme. WCJ tested and contributed to the development of the new ranking scheme. WJC and MAK wrote the manuscript. WCJ and MAK read and approved the manuscript.
Compliance with ethical standards
Conflict of interest Statements
William C Joesten declares that he has no conflict of interest. Michael A. Kennedy declares that he has no conflict of interest.
Research involving Human Participants and/or Animals. This study did not involve the use of human participants. All procedures involving mice were approved by both the ethics committee and the Institutional Animal Care and Use Committee at Miami University (Animal Welfare Assurance Number: D16-00100). The protocol approved by the Miami University IACUC was assigned Project Number 898.
- Bingol, K., Bruschweiler-Li, L., Li, D. W., & Bruschweiler, R. (2014). Customized metabolomics database for the analysis of NMR H-1-H-1 TOCSY and C-13-H-1 HSQC-TOCSY spectra of complex mixtures. Analytical Chemistry, 86, 5494–5501. https://doi.org/10.1021/ac500979g.CrossRefPubMedPubMedCentralGoogle Scholar
- Chihanga, T., Ma, Q., Nicholson, J. D., Ruby, H. N., Edelmann, R. E., Devarajan, P., et al. (2018b). NMR spectroscopy and electron microscopy identification of metabolic and ultrastructural changes to the kidney following ischemia-reperfusion injury. American Journal of Physiology-Renal Physiology, 314, F154–F166. https://doi.org/10.1152/ajprenal.00363.2017.CrossRefPubMedGoogle Scholar
- Chihanga, T., Ruby, H. N., Ma, Q., Bashir, S., Devarajan, P., & Kennedy, M. A. (2018c). NMR-based urine metabolic profiling and immunohistochemistry analysis of nephron changes in a mouse model of hypoxia-induced acute kidney injury. American Journal of Physiology-Renal Physiology. https://doi.org/10.1152/ajprenal.00500.2017.CrossRefPubMedGoogle Scholar
- Cloarec, O., Dumas, M. E., Craig, A., Barton, R. H., Trygg, J., Hudson, J., et al. (2005). Statistical total correlation spectroscopy: An exploratory approach for latent biomarker identification from metabolic 1H NMR data sets. Analytical Chemistry, 77, 1282–1289. https://doi.org/10.1021/ac048630x.CrossRefPubMedGoogle Scholar
- Dona, A. C., Kyriakides, M., Scott, F., Shephard, E. A., Varshavi, D., Veselkov, K., et al. (2016). A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments. Computational and Structural Biotechnology Journal, 14, 135–153. https://doi.org/10.1016/j.csbj.2016.02.005.CrossRefPubMedPubMedCentralGoogle Scholar
- Guijas, C., Montenegro-Burke, J. R., Domingo-Almenara, X., Palermo, A., Warth, B., Hermann, G., et al. (2018). METLIN: A technology platform for identifying knowns and unknowns. Analytical Chemistry, 90, 3156–3164. https://doi.org/10.1021/acs.analchem.7b04424.CrossRefPubMedPubMedCentralGoogle Scholar
- Haug, K., Salek, R. M., Conesa, P., Hastings, J., de Matos, P., Rijnbeek, M., et al. (2013). MetaboLights—an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Research, 41, D781–D786. https://doi.org/10.1093/nar/gks1004.CrossRefPubMedGoogle Scholar
- Nicholson, J. K., Lindon, J. C., & Holmes, E. (1999). ‘Metabonomics’: Understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica, 29, 1181–1189 doi. https://doi.org/10.1080/004982599238047.CrossRefPubMedGoogle Scholar
- Posma, J. M., Garcia-Perez, I., Heaton, J. C., Burdisso, P., Mathers, J. C., Draper, J., et al. (2017). Integrated analytical and statistical two-dimensional spectroscopy strategy for metabolite identification: Application to dietary biomarkers. Analytical Chemistry, 89, 3300–3309. https://doi.org/10.1021/acs.analchem.6b03324.CrossRefPubMedPubMedCentralGoogle Scholar
- Rohnisch, H. E., Eriksson, J., Mullner, E., Agback, P., Sandstrom, C., & Moazzami, A. A. (2018). AQuA: An automated quantification algorithm for high-throughput nmr-based metabolomics and its application in human plasma. Analytical Chemistry, 90, 2095–2102. https://doi.org/10.1021/acs.analchem.7b04324.CrossRefGoogle Scholar
- Romick-Rosendale, L. E., Goodpaster, A. M., Hanwright, P. J., Patel, N. B., Wheeler, E. T., Chona, D. L., et al. (2009). NMR-based metabonomics analysis of mouse urine and fecal extracts following oral treatment with the broad-spectrum antibiotic enrofloxacin (Baytril). Magnetic Resonance in Chemistry, 47(Suppl 1), 36–46. https://doi.org/10.1002/mrc.2511.CrossRefGoogle Scholar
- Romick-Rosendale, L. E., Legomarcino, A., Patel, N. B., Morrow, A. L., & Kennedy, M. A. (2014). Prolonged antibiotic use induces intestinal injury in mice that is repaired after removing antibiotic pressure: implications for empiric antibiotic therapy. Metabolomics, 10, 8–20. https://doi.org/10.1007/s11306-013-0546-5.CrossRefGoogle Scholar
- Sanchon-Lopez, B., & Everett, J. R. (2016). New Methodology for known metabolite identification in metabonomics/metabolomics: Topological metabolite identification carbon efficiency (tMICE). Journal of Proteome Research, 15, 3405–3419. https://doi.org/10.1021/acs.jproteome.6b00631.CrossRefPubMedGoogle Scholar
- Schmahl, M. J., Regan, D. P., Rivers, A. C., Joesten, W. C., & Kennedy, M. A. (2018). NMR-based metabolic profiling of urine, serum, fecal, and pancreatic tissue samples from the Ptf1a-Cre; LSL-KrasG12D transgenic mouse model of pancreatic cancer. PLoS ONE, 13, e0200658. https://doi.org/10.1371/journal.pone.0200658.CrossRefPubMedPubMedCentralGoogle Scholar
- Smith, C. A., O’Maille, G., Want, E. J., Qin, C., Trauger, S. A., Brandon, T. R., et al. (2005). METLIN—a metabolite mass spectral database. Therapeutic Drug Monitoring, 27, 747–751 doi. https://doi.org/10.1097/01.ftd.0000179845.53213.39.CrossRefPubMedGoogle Scholar
- Sud, M., Fahy, E., Cotter, D., Azam, K., Vadivelu, I., Burant, C., et al. (2016). Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools. Nucleic Acids Research, 44, D463–D470. https://doi.org/10.1093/nar/gkv1042.CrossRefPubMedGoogle Scholar
- Sumner, L. W., Amberg, A., Barrett, D., Beale, M. H., Beger, R., Daykin, C. A., et al. (2007). Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics, 3, 211–221. https://doi.org/10.1007/s11306-007-0082-2.CrossRefPubMedPubMedCentralGoogle Scholar
- van der Hooft, J. J. J., & Rankin, N. (2016). Metabolite identification in complex mixtures using nuclear magnetic resonance spectroscopy. In G. A. Webb (Ed.), Modern magnetic resonance. Glascow: Springer.Google Scholar
- Watanabe, M., Sheriff, S., Lewis, K. B., Cho, J., Tinch, S. L., Balasubramaniam, A., et al. (2012b). Metabolic profiling comparison of human pancreatic ductal epithelial cells and three pancreatic cancer cell lines using NMR based metabonomics. Journal of Molecular Biomarkers and Diagnosis, S3, 1–17.Google Scholar
- Watanabe, M., Sheriff, S., Ramelot, T. A., Kadeer, N., Cho, J., Lewis, K. B., et al. (2011). NMR Based metabonomics study of DAG treatment in a C2C12 mouse skeletal muscle cell line myotube model of burn-injury. International Journal of Peptide Research and Therapeutics, 17, 281–299. https://doi.org/10.1007/s10989-011-9264-x.CrossRefGoogle Scholar