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Ross IL, Beardslee JA, Steil MM, Chihanga T, Kennedy MA. Statistical considerations and database limitations in NMR-based metabolic profiling studies. Metabolomics 2023; 19:64. [PMID: 37378680 DOI: 10.1007/s11306-023-02027-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023]
Abstract
INTRODUCTION Interpretation and analysis of NMR-based metabolic profiling studies is limited by substantially incomplete commercial and academic databases. Statistical significance tests, including p-values, VIP scores, AUC values and FC values, can be largely inconsistent. Data normalization prior to statistical analysis can cause erroneous outcomes. OBJECTIVES The objectives were (1) to quantitatively assess consistency among p-values, VIP scores, AUC values and FC values in representative NMR-based metabolic profiling datasets, (2) to assess how data normalization can impact statistical significance outcomes, (3) to determine resonance peak assignment completion potential using commonly used databases and (4) to analyze intersection and uniqueness of metabolite space in these databases. METHODS P-values, VIP scores, AUC values and FC values, and their dependence on data normalization, were determined in orthotopic mouse model of pancreatic cancer and two human pancreatic cancer cell lines. Completeness of resonance assignments were evaluated using Chenomx, the human metabolite database (HMDB) and the COLMAR database. The intersection and uniqueness of the databases was quantified. RESULTS P-values and AUC values were strongly correlated compared to VIP or FC values. Distributions of statistically significant bins depended strongly on whether or not datasets were normalized. 40-45% of peaks had either no or ambiguous database matches. 9-22% of metabolites were unique to each database. CONCLUSIONS Lack of consistency in statistical analyses of metabolomics data can lead to misleading or inconsistent interpretation. Data normalization can have large effects on statistical analysis and should be justified. About 40% of peak assignments remain ambiguous or impossible with current databases. 1D and 2D databases should be made consistent to maximize metabolite assignment confidence and validation.
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Affiliation(s)
- Imani L Ross
- Department of Chemistry and Biochemistry, University of California, San Diego, CA, 92093, USA
| | - Julie A Beardslee
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH, 45056, USA
| | - Maria M Steil
- Division of Plastic Surgery, University of Texas Medical Branch, Galveston, TX, 77555, USA
| | - Tafadzwa Chihanga
- Division of Oncology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
| | - Michael A Kennedy
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH, 45056, USA.
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Judge MT, Ebbels TMD. Problems, principles and progress in computational annotation of NMR metabolomics data. Metabolomics 2022; 18:102. [PMID: 36469142 PMCID: PMC9722819 DOI: 10.1007/s11306-022-01962-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 11/18/2022] [Indexed: 12/08/2022]
Abstract
BACKGROUND Compound identification remains a critical bottleneck in the process of exploiting Nuclear Magnetic Resonance (NMR) metabolomics data, especially for 1H 1-dimensional (1H 1D) data. As databases of reference compound spectra have grown, workflows have evolved to rely heavily on their search functions to facilitate this process by generating lists of potential metabolites found in complex mixture data, facilitating annotation and identification. However, approaches for validating and communicating annotations are most often guided by expert knowledge, and therefore are highly variable despite repeated efforts to align practices and define community standards. AIM OF REVIEW This review is aimed at broadening the application of automated annotation tools by discussing the key ideas of spectral matching and beginning to describe a set of terms to classify this information, thus advancing standards for communicating annotation confidence. Additionally, we hope that this review will facilitate the growing collaboration between chemical data scientists, software developers and the NMR metabolomics community aiding development of long-term software solutions. KEY SCIENTIFIC CONCEPTS OF REVIEW We begin with a brief discussion of the typical untargeted NMR identification workflow. We differentiate between annotation (hypothesis generation, filtering), and identification (hypothesis testing, verification), and note the utility of different NMR data features for annotation. We then touch on three parts of annotation: (1) generation of queries, (2) matching queries to reference data, and (3) scoring and confidence estimation of potential matches for verification. In doing so, we highlight existing approaches to automated and semi-automated annotation from the perspective of the structural information they utilize, as well as how this information can be represented computationally.
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Affiliation(s)
- Michael T Judge
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, 131 Sir Alexander Fleming Building, South Kensington Campus, London, UK
| | - Timothy M D Ebbels
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, 131 Sir Alexander Fleming Building, South Kensington Campus, London, UK.
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Standage SW, Xu S, Brown L, Ma Q, Koterba A, Lahni P, Devarajan P, Kennedy MA. NMR-based serum and urine metabolomic profile reveals suppression of mitochondrial pathways in experimental sepsis-associated acute kidney injury. Am J Physiol Renal Physiol 2021; 320:F984-F1000. [PMID: 33843271 DOI: 10.1152/ajprenal.00582.2020] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Sepsis-associated acute kidney injury (SA-AKI) is a significant problem in the critically ill that causes increased death. Emerging understanding of this disease implicates metabolic dysfunction in its pathophysiology. This study sought to identify specific metabolic pathways amenable to potential therapeutic intervention. Using a murine model of sepsis, blood and tissue samples were collected for assessment of systemic inflammation, kidney function, and renal injury. Nuclear magnetic resonance (NMR)-based metabolomics quantified dozens of metabolites in serum and urine that were subsequently submitted to pathway analysis. Kidney tissue gene expression analysis confirmed the implicated pathways. Septic mice had elevated circulating levels of inflammatory cytokines and increased levels of blood urea nitrogen and creatinine, indicating both systemic inflammation and poor kidney function. Renal tissue showed only mild histological evidence of injury in sepsis. NMR metabolomic analysis identified the involvement of mitochondrial pathways associated with branched-chain amino acid metabolism, fatty acid oxidation, and de novo NAD+ biosynthesis in SA-AKI. Renal cortical gene expression of enzymes associated with those pathways was predominantly suppressed. Renal cortical fatty acid oxidation rates were lower in septic mice with high inflammation, and this correlated with higher serum creatinine levels. Similar to humans, septic mice demonstrated renal dysfunction without significant tissue disruption, pointing to metabolic derangement as an important contributor to SA-AKI pathophysiology. Metabolism of branched-chain amino acid and fatty acids and NAD+ synthesis, which all center on mitochondrial function, appeared to be suppressed. Developing interventions to activate these pathways may provide new therapeutic opportunities for SA-AKI.NEW & NOTEWORTHY NMR-based metabolomics revealed disruptions in branched-chain amino acid metabolism, fatty acid oxidation, and NAD+ synthesis in sepsis-associated acute kidney injury. These pathways represent essential processes for energy provision in renal tubular epithelial cells and may represent targetable mechanisms for therapeutic intervention.
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Affiliation(s)
- Stephen W Standage
- Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.,Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio
| | - Shenyuan Xu
- Department of Chemistry and Biochemistry, Miami University, Oxford, Ohio
| | - Lauren Brown
- Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Qing Ma
- Division of Nephrology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Adeleine Koterba
- Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Patrick Lahni
- Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Prasad Devarajan
- Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio.,Division of Nephrology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Michael A Kennedy
- Department of Chemistry and Biochemistry, Miami University, Oxford, Ohio
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Torres S, Samino S, Ràfols P, Martins-Green M, Correig X, Ramírez N. Unravelling the metabolic alterations of liver damage induced by thirdhand smoke. ENVIRONMENT INTERNATIONAL 2021; 146:106242. [PMID: 33197790 DOI: 10.1016/j.envint.2020.106242] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 08/16/2020] [Accepted: 10/22/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Thirdhand smoke (THS) is the accumulation of tobacco smoke gases and particles that become embedded in materials. Previous studies concluded that THS exposure induces oxidative stress and hepatic steatosis in liver. Despite the knowledge of the increasing danger of THS exposure, the metabolic disorders caused in liver are still not well defined. OBJECTIVES The aim of this study is to investigate the metabolic disorders caused by THS exposure in liver of male mice and to evaluate the effects of an antioxidant treatment in the exposed mice. METHODS We investigated liver from three mice groups: non-exposed mice, exposed to THS in conditions that mimic human exposure and THS-exposed treated with antioxidants. Liver samples were analyzed using a multiplatform untargeted metabolomics approach including nuclear magnetic resonance (1H NMR), liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) and laser desorption/ionization mass spectrometry imaging (MSI), able to map lipids in liver tissues. RESULTS Our multiplatform approach allowed the annotation of eighty-eight metabolites altered by THS exposure, including amino acids, nucleotides and several types of lipids. The main dysregulated pathways by THS exposure were D-glutamine and D-glutamate metabolism, glycerophospholipid metabolism and oxidative phosphorylation and glutathione metabolism, being the last two related to oxidative stress. THS-exposed mice also presented higher lipid accumulation and decrease of metabolites involved in the phosphocholine synthesis, as well as choline deficiency, which is related to Non-Alcoholic Fatty Liver Disease and steatohepatitis. Interestingly, the antioxidant treatment of THS-exposed mice reduced the accumulation of some lipids, but could not revert all the metabolic alterations, including some related to the impairment of the mitochondrial function. CONCLUSIONS THS alters liver function at a molecular level, dysregulating many metabolic pathways. The molecular evidences provided here confirm that THS is a new factor for liver steatosis and provide the basis for future research in this respect.
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Affiliation(s)
- Sònia Torres
- Department of Electronic Engineering, Universitat Rovira i Virgili, Tarragona, Catalonia, Spain; Institut d'Investigació Sanitària Pere Virgili, Tarragona, Catalonia, Spain
| | - Sara Samino
- Department of Electronic Engineering, Universitat Rovira i Virgili, Tarragona, Catalonia, Spain; CIBERDEM, Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders, Barcelona, Catalonia, Spain
| | - Pere Ràfols
- Department of Electronic Engineering, Universitat Rovira i Virgili, Tarragona, Catalonia, Spain; CIBERDEM, Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders, Barcelona, Catalonia, Spain
| | - Manuela Martins-Green
- Department of Molecular, Cell and Systems Biology, University of California, Riverside CA 92521, USA
| | - Xavier Correig
- Department of Electronic Engineering, Universitat Rovira i Virgili, Tarragona, Catalonia, Spain; Institut d'Investigació Sanitària Pere Virgili, Tarragona, Catalonia, Spain; CIBERDEM, Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders, Barcelona, Catalonia, Spain
| | - Noelia Ramírez
- Department of Electronic Engineering, Universitat Rovira i Virgili, Tarragona, Catalonia, Spain; Institut d'Investigació Sanitària Pere Virgili, Tarragona, Catalonia, Spain; CIBERDEM, Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders, Barcelona, Catalonia, Spain.
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Charris-Molina A, Riquelme G, Burdisso P, Hoijemberg PA. Consecutive Queries to Assess Biological Correlation in NMR Metabolomics: Performance of Comprehensive Search of Multiplets over Typical 1D 1H NMR Database Search. J Proteome Res 2020; 19:2977-2988. [PMID: 32450699 DOI: 10.1021/acs.jproteome.9b00872] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
NMR-based metabolomics requires proper identification of metabolites to draw conclusions from the system under study. Normally, multivariate data analysis is performed using 1D 1H NMR spectra, and identification of peaks (and then compounds) relevant to the classification is accomplished using database queries as a first step. 1D 1H NMR spectra of complex mixtures often suffer from peak overlap. To overcome this issue, several studies employed the projections of the (tilted and symmetrized) 2D 1H J-resolved (JRES) spectra, p-JRES, which are similar to 1D 1H decoupled spectra. Nonetheless, there are no public databases available that allow searching for chemical shift spectral data for multiplets. We present the Chemical Shift Multiplet Database (CSMDB), built utilizing JRES spectra obtained from the Birmingham Metabolite Library. The CSMDB provides scoring accounting for both matched and unmatched peaks from a query list and the database hits. This input list is generated from a projection of a 2D statistical correlation analysis on the JRES spectra, p-(JRES-STOCSY), being able to compare the multiplets for the matched peaks, in essence, the f1 traces from the JRES-STOCSY spectrum and from the database hit. The inspection of the unmatched peaks for the database hit allows the retrieval of peaks in the query list that have a decreased correlation coefficient due to low intensities. The CSMDB is coupled to "ConQuer ABC", which permits the assessment of biological correlation by means of consecutive queries with the unmatched peaks in the first and subsequent queries.
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Affiliation(s)
- Andrés Charris-Molina
- Departamento de Química Inorgánica Analítica y Química Física, Facultad de Ciencias Exactas y Naturales, Ciudad Universitaria, Universidad de Buenos Aires, Buenos Aires C1428EGA, Argentina.,CIBION-CONICET, Centro de Investigaciones en Bionanociencias, NMR Group, Polo Científico Tecnológico, Ciudad Autónoma de Buenos Aires, Buenos Aires C1425FQD, Argentina
| | - Gabriel Riquelme
- Departamento de Química Inorgánica Analítica y Química Física, Facultad de Ciencias Exactas y Naturales, Ciudad Universitaria, Universidad de Buenos Aires, Buenos Aires C1428EGA, Argentina.,CIBION-CONICET, Centro de Investigaciones en Bionanociencias, NMR Group, Polo Científico Tecnológico, Ciudad Autónoma de Buenos Aires, Buenos Aires C1425FQD, Argentina
| | - Paula Burdisso
- Instituto de Biología Molecular y Celular de Rosario (IBR-CONICET), Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario and Plataforma Argentina de Biología Estructural y Metabolómica (PLABEM), Rosario, Santa Fe S2002LRK, Argentina
| | - Pablo A Hoijemberg
- CIBION-CONICET, Centro de Investigaciones en Bionanociencias, NMR Group, Polo Científico Tecnológico, Ciudad Autónoma de Buenos Aires, Buenos Aires C1425FQD, Argentina.,ECyT-UNSAM, 25 de Mayo y Francia, San Martín, Buenos Aires B1650HMP, Argentina
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O'Shea K, Misra BB. Software tools, databases and resources in metabolomics: updates from 2018 to 2019. Metabolomics 2020; 16:36. [PMID: 32146531 DOI: 10.1007/s11306-020-01657-3] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 03/01/2020] [Indexed: 12/24/2022]
Abstract
Metabolomics has evolved as a discipline from a discovery and functional genomics tool, and is now a cornerstone in the era of big data-driven precision medicine. Sample preparation strategies and analytical technologies have seen enormous growth, and keeping pace with data analytics is challenging, to say the least. This review introduces and briefly presents around 100 metabolomics software resources, tools, databases, and other utilities that have surfaced or have improved in 2019. Table 1 provides the computational dependencies of the tools, categorizes the resources based on utility and ease of use, and provides hyperlinks to webpages where the tools can be downloaded or used. This review intends to keep the community of metabolomics researchers up to date with all the software tools, resources, and databases developed in 2019, in one place.
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Affiliation(s)
- Keiron O'Shea
- Institute of Biological, Environmental, and Rural Studies, Aberystwyth University, Ceredigion, Wales, SY23 3DA, UK
| | - Biswapriya B Misra
- Center for Precision Medicine, Department of Internal Medicine, Section of Molecular Medicine, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA.
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Petrova I, Xu S, Joesten WC, Ni S, Kennedy MA. Influence of Drying Method on NMR-Based Metabolic Profiling of Human Cell Lines. Metabolites 2019; 9:metabo9110256. [PMID: 31683565 PMCID: PMC6918379 DOI: 10.3390/metabo9110256] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 10/24/2019] [Accepted: 10/28/2019] [Indexed: 12/12/2022] Open
Abstract
Metabolic profiling of cell line and tissue extracts involves sample processing that includes a drying step prior to re-dissolving the cell or tissue extracts in a buffer for analysis by GC/LC-MS or NMR. Two of the most commonly used drying techniques are centrifugal evaporation under vacuum (SpeedVac) and lyophilization. Here, NMR spectroscopy was used to determine how the metabolic profiles of hydrophilic extracts of three human pancreatic cancer cell lines, MiaPaCa-2, Panc-1 and AsPC-1, were influenced by the choice of drying technique. In each of the three cell lines, 40-50 metabolites were identified as having statistically significant differences in abundance in redissolved extract samples depending on the drying technique used during sample preparation. In addition to these differences, some metabolites were only present in the lyophilized samples, for example, n-methyl-α-aminoisobutyric acid, n-methylnicotimamide, sarcosine and 3-hydroxyisovaleric acid, whereas some metabolites were only present in SpeedVac dried samples, for example, trimethylamine. This research demonstrates that the choice of drying technique used during the preparation of samples of human cell lines or tissue extracts can significantly influence the observed metabolome, making it important to carefully consider the selection of a drying method prior to preparation of such samples for metabolic profiling.
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Affiliation(s)
- Irina Petrova
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA.
| | - Shenyuan Xu
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA.
| | - William C Joesten
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA.
| | - Shuisong Ni
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA.
| | - Michael A Kennedy
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA.
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