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Bykowski EA, Petersson JN, Dukelow S, Ho C, Debert CT, Montina T, Metz GAS. Identification of Serum Metabolites as Prognostic Biomarkers Following Spinal Cord Injury: A Pilot Study. Metabolites 2023; 13:metabo13050605. [PMID: 37233646 DOI: 10.3390/metabo13050605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/06/2023] [Accepted: 04/24/2023] [Indexed: 05/27/2023] Open
Abstract
The assessment, management, and prognostication of spinal cord injury (SCI) mainly rely upon observer-based ordinal scales measures. 1H nuclear magnetic resonance (NMR) spectroscopy provides an effective approach for the discovery of objective biomarkers from biofluids. These biomarkers have the potential to aid in understanding recovery following SCI. This proof-of-principle study determined: (a) If temporal changes in blood metabolites reflect the extent of recovery following SCI; (b) whether changes in blood-derived metabolites serve as prognostic indicators of patient outcomes based on the spinal cord independence measure (SCIM); and (c) whether metabolic pathways involved in recovery processes may provide insights into mechanisms that mediate neural damage and repair. Morning blood samples were collected from male complete and incomplete SCI patients (n = 7) following injury and at 6 months post-injury. Multivariate analyses were used to identify changes in serum metabolic profiles and were correlated to clinical outcomes. Specifically, acetyl phosphate, 1,3,7-trimethyluric acid, 1,9-dimethyluric acid, and acetic acid significantly related to SCIM scores. These preliminary findings suggest that specific metabolites may serve as proxy measures of the SCI phenotype and prognostic markers of recovery. Thus, serum metabolite analysis combined with machine learning holds promise in understanding the physiology of SCI and aiding in prognosticating outcomes following injury.
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Affiliation(s)
- Elani A Bykowski
- Canadian Centre for Behavioural Neuroscience, Department of Neuroscience, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
- Southern Alberta Genome Sciences Centre, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
| | - Jamie N Petersson
- Canadian Centre for Behavioural Neuroscience, Department of Neuroscience, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
- Southern Alberta Genome Sciences Centre, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
- Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
| | - Sean Dukelow
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Chester Ho
- Division of Physical Medicine and Rehabilitation, University of Alberta, Edmonton, AB T6G 2R7, Canada
| | - Chantel T Debert
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Tony Montina
- Southern Alberta Genome Sciences Centre, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
- Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
| | - Gerlinde A S Metz
- Canadian Centre for Behavioural Neuroscience, Department of Neuroscience, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
- Southern Alberta Genome Sciences Centre, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
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Bykowski EA, Petersson JN, Dukelow S, Ho C, Debert CT, Montina T, Metz GA. Urinary biomarkers indicative of recovery from spinal cord injury: A pilot study. IBRO Neurosci Rep 2021; 10:178-185. [PMID: 33842921 PMCID: PMC8020035 DOI: 10.1016/j.ibneur.2021.02.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 02/15/2021] [Indexed: 12/21/2022] Open
Abstract
Current assessments of recovery following spinal cord injury (SCI) focus on clinical outcome measures. These assessments bear an inherent risk of bias, emphasizing the need for more reliable prognostic biomarkers to measure SCI severity. This study evaluated fluid biomarkers as an objective tool to aid with prognosticating outcomes following SCI. Using a 1H nuclear magnetic resonance (NMR)-based quantitative metabolomics approach of urine samples, the objectives were to determine (a) if alterations in metabolic profiles reflect the extent of recovery of individual SCI patients, (b) whether changes in urine metabolites correlate to patient outcomes, and (c) whether biological pathway analysis reflects mechanisms of neural damage and repair. An inception cohort exploratory pilot study collected morning urine samples from male SCI patients (n=6) following injury and again at 6-months post-injury. A 700 MHz Bruker Avance III HD NMR spectrometer was used to acquire the metabolic signatures of urine samples, which were used to derive metabolic pathways. Multivariate statistical analyses were used to identify changes in metabolic signatures, which were correlated to clinical outcomes in the Spinal Cord Independence Measure (SCIM). Among SCI-induced metabolic changes, biomarkers which significantly correlated to patient SCIM scores included caffeine (R = -0.76, p < 0.01), 3-hydroxymandelic acid (R= -0.85, p < 0.001), L-valine (R = 0.90, p < 0.001; R = -0.64, p < 0.05), and N-methylhydantoin (R = -0.90, p < 0.001). The most affected pathway was purine metabolism. These findings indicate that urinary metabolites reflect SCI lesion severity and recovery and provide potentially prognostic biomarkers of SCI outcome in precision medicine approaches.
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Affiliation(s)
- Elani A. Bykowski
- Canadian Centre for Behavioural Neuroscience, Department of Neuroscience, University of Lethbridge, Lethbridge, Alberta, Canada
- Southern Alberta Genome Sciences Centre, University of Lethbridge, Lethbridge, Alberta, Canada
| | - Jamie N. Petersson
- Canadian Centre for Behavioural Neuroscience, Department of Neuroscience, University of Lethbridge, Lethbridge, Alberta, Canada
- Southern Alberta Genome Sciences Centre, University of Lethbridge, Lethbridge, Alberta, Canada
- Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, Alberta, Canada
| | - Sean Dukelow
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Chester Ho
- Division of Physical Medicine and Rehabilitation, University of Alberta, Edmonton, Alberta, Canada
| | - Chantel T. Debert
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Tony Montina
- Southern Alberta Genome Sciences Centre, University of Lethbridge, Lethbridge, Alberta, Canada
- Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, Alberta, Canada
| | - Gerlinde A.S. Metz
- Canadian Centre for Behavioural Neuroscience, Department of Neuroscience, University of Lethbridge, Lethbridge, Alberta, Canada
- Southern Alberta Genome Sciences Centre, University of Lethbridge, Lethbridge, Alberta, Canada
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Jin H, Moseley HNB. Moiety modeling framework for deriving moiety abundances from mass spectrometry measured isotopologues. BMC Bioinformatics 2019; 20:524. [PMID: 31660850 PMCID: PMC6816163 DOI: 10.1186/s12859-019-3096-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 09/10/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Stable isotope tracing can follow individual atoms through metabolic transformations through the detection of the incorporation of stable isotope within metabolites. This resulting data can be interpreted in terms related to metabolic flux. However, detection of a stable isotope in metabolites by mass spectrometry produces a profile of isotopologue peaks that requires deconvolution to ascertain the localization of isotope incorporation. RESULTS To aid the interpretation of the mass spectroscopy isotopologue profile, we have developed a moiety modeling framework for deconvoluting metabolite isotopologue profiles involving single and multiple isotope tracers. This moiety modeling framework provides facilities for moiety model representation, moiety model optimization, and moiety model selection. The moiety_modeling package was developed from the idea of metabolite decomposition into moiety units based on metabolic transformations, i.e. a moiety model. The SAGA-optimize package, solving a boundary-value inverse problem through a combined simulated annealing and genetic algorithm, was developed for model optimization. Additional optimization methods from the Python scipy library are utilized as well. Several forms of the Akaike information criterion and Bayesian information criterion are provided for selecting between moiety models. Moiety models and associated isotopologue data are defined in a JSONized format. By testing the moiety modeling framework on the timecourses of 13C isotopologue data for uridine diphosphate N-acetyl-D-glucosamine (UDP-GlcNAc) in human prostate cancer LnCaP-LN3 cells, we were able to confirm its robust performance in isotopologue deconvolution and moiety model selection. CONCLUSIONS SAGA-optimize is a useful Python package for solving boundary-value inverse problems, and the moiety_modeling package is an easy-to-use tool for mass spectroscopy isotopologue profile deconvolution involving single and multiple isotope tracers. Both packages are freely available on GitHub and via the Python Package Index.
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Affiliation(s)
- Huan Jin
- Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY, USA
| | - Hunter N B Moseley
- Department of Molecular & Cellular Biochemistry, University of Kentucky, Lexington, KY, USA. .,Markey Cancer Center, University of Kentucky, Lexington, KY, USA. .,Resource Center for Stable Isotope Resolved Metabolomics, University of Kentucky, Lexington, KY, USA. .,Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, USA.
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Chen X, Smelter A, Moseley HNB. BaMORC: A Software Package for Accurate and Robust 13C Reference Correction of Protein NMR Spectra. Nat Prod Commun 2019; 14. [PMID: 33936358 DOI: 10.1177/1934578x19849142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
We describe BaMORC, a software package that performs 13C chemical shifts reference correction for either assigned or unassigned peak lists derived from protein NMR spectra. BaMORC provides an intuitive command line interface that allows non-NMR experts to detect and correct 13C chemical shift referencing errors of unassigned peak lists at the very beginning of NMR data analysis, further lowering the bar of expertise required for effective protein NMR analysis. Furthermore, BaMORC provides an application programming interface for integration into sophisticated protein NMR data analysis pipelines, both before and after the protein resonance assignment step.
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Affiliation(s)
- Xi Chen
- Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY 40536-0093, USA.,Department of Statistics, University of Kentucky, Lexington, KY 40536-0093, USA
| | - Andrey Smelter
- Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY 40536-0093, USA.,Center for Environmental and Systems Biochemistry, Lexington, KY 40536-0093, USA
| | - Hunter N B Moseley
- Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY 40536-0093, USA.,Center for Environmental and Systems Biochemistry, Lexington, KY 40536-0093, USA.,Markey Cancer Center, University of Kentucky, Lexington, KY 40536-0093, USA.,Institute for Biomedical Informatics, University of Kentucky, Lexington, KY 40536-0093, USA
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Chen X, Smelter A, Moseley HNB. Automatic 13C chemical shift reference correction for unassigned protein NMR spectra. JOURNAL OF BIOMOLECULAR NMR 2018; 72:11-28. [PMID: 30097912 PMCID: PMC6209040 DOI: 10.1007/s10858-018-0202-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Accepted: 08/01/2018] [Indexed: 05/09/2023]
Abstract
Poor chemical shift referencing, especially for 13C in protein Nuclear Magnetic Resonance (NMR) experiments, fundamentally limits and even prevents effective study of biomacromolecules via NMR, including protein structure determination and analysis of protein dynamics. To solve this problem, we constructed a Bayesian probabilistic framework that circumvents the limitations of previous reference correction methods that required protein resonance assignment and/or three-dimensional protein structure. Our algorithm named Bayesian Model Optimized Reference Correction (BaMORC) can detect and correct 13C chemical shift referencing errors before the protein resonance assignment step of analysis and without three-dimensional structure. By combining the BaMORC methodology with a new intra-peaklist grouping algorithm, we created a combined method called Unassigned BaMORC that utilizes only unassigned experimental peak lists and the amino acid sequence. Unassigned BaMORC kept all experimental three-dimensional HN(CO)CACB-type peak lists tested within ± 0.4 ppm of the correct 13C reference value. On a much larger unassigned chemical shift test set, the base method kept 13C chemical shift referencing errors to within ± 0.45 ppm at a 90% confidence interval. With chemical shift assignments, Assigned BaMORC can detect and correct 13C chemical shift referencing errors to within ± 0.22 at a 90% confidence interval. Therefore, Unassigned BaMORC can correct 13C chemical shift referencing errors when it will have the most impact, right before protein resonance assignment and other downstream analyses are started. After assignment, chemical shift reference correction can be further refined with Assigned BaMORC. These new methods will allow non-NMR experts to detect and correct 13C referencing error at critical early data analysis steps, lowering the bar of NMR expertise required for effective protein NMR analysis.
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Affiliation(s)
- Xi Chen
- Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY, 40356, USA
- Department of Statistics, University of Kentucky, Lexington, KY, 40356, USA
| | - Andrey Smelter
- Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY, 40356, USA
- Markey Cancer Center, University of Kentucky, Lexington, KY, 40356, USA
- Center for Environmental and Systems Biochemistry, University of Kentucky, Lexington, KY, 40356, USA
| | - Hunter N B Moseley
- Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY, 40356, USA.
- Department of Statistics, University of Kentucky, Lexington, KY, 40356, USA.
- Markey Cancer Center, University of Kentucky, Lexington, KY, 40356, USA.
- Center for Environmental and Systems Biochemistry, University of Kentucky, Lexington, KY, 40356, USA.
- Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, 40356, USA.
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