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Liu D, Nagana Gowda GA, Jiang Z, Alemdjrodo K, Zhang M, Zhang D, Raftery D. Modeling blood metabolite homeostatic levels reduces sample heterogeneity across cohorts. Proc Natl Acad Sci U S A 2024; 121:e2307430121. [PMID: 38359289 PMCID: PMC10895372 DOI: 10.1073/pnas.2307430121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 12/05/2023] [Indexed: 02/17/2024] Open
Abstract
Blood metabolite levels are affected by numerous factors, including preanalytical factors such as collection methods and geographical sites. These perturbations have caused deleterious consequences for many metabolomics studies and represent a major challenge in the metabolomics field. It is important to understand these factors and develop models to reduce their perturbations. However, to date, the lack of suitable mathematical models for blood metabolite levels under homeostasis has hindered progress. In this study, we develop quantitative models of blood metabolite levels in healthy adults based on multisite sample cohorts that mimic the current challenge. Five cohorts of samples obtained across four geographically distinct sites were investigated, focusing on approximately 50 metabolites that were quantified using 1H NMR spectroscopy. More than one-third of the variation in these metabolite profiles is due to cross-cohort variation. A dramatic reduction in the variation of metabolite levels (90%), especially their site-to-site variation (95%), was achieved by modeling each metabolite using demographic and clinical factors and especially other metabolites, as observed in the top principal components. The results also reveal that several metabolites contribute disproportionately to such variation, which could be explained by their association with biological pathways including biosynthesis and degradation. The study demonstrates an intriguing network effect of metabolites that can be utilized to better define homeostatic metabolite levels, which may have implications for improved health monitoring. As an example of the potential utility of the approach, we show that modeling gender-related metabolic differences retains the interesting variance while reducing unwanted (site-related) variance.
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Affiliation(s)
- Danni Liu
- Department of Statistics, Purdue University, West Lafayette, IN47907
| | - G. A. Nagana Gowda
- Department of Anesthesiology and Pain Medicine, Northwest Metabolomics Research Center, University of Washington, Seattle, WA98109
| | - Zhongli Jiang
- Department of Statistics, Purdue University, West Lafayette, IN47907
| | - Kangni Alemdjrodo
- Department of Statistics, Purdue University, West Lafayette, IN47907
| | - Min Zhang
- Department of Statistics, Purdue University, West Lafayette, IN47907
- Department of Epidemiology and Biostatistics, University of California, Irvine, CA92697
| | - Dabao Zhang
- Department of Statistics, Purdue University, West Lafayette, IN47907
- Department of Epidemiology and Biostatistics, University of California, Irvine, CA92697
| | - Daniel Raftery
- Department of Anesthesiology and Pain Medicine, Northwest Metabolomics Research Center, University of Washington, Seattle, WA98109
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Costa C, De Jesus J, Nikula C, Murta T, Grime GW, Palitsin V, Dartois V, Firat K, Webb R, Bunch J, Bailey MJ. A Multimodal Desorption Electrospray Ionisation Workflow Enabling Visualisation of Lipids and Biologically Relevant Elements in a Single Tissue Section. Metabolites 2023; 13:262. [PMID: 36837881 PMCID: PMC9964958 DOI: 10.3390/metabo13020262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 02/07/2023] [Accepted: 02/09/2023] [Indexed: 02/15/2023] Open
Abstract
The colocation of elemental species with host biomolecules such as lipids and metabolites may shed new light on the dysregulation of metabolic pathways and how these affect disease pathogeneses. Alkali metals have been the subject of extensive research, are implicated in various neurodegenerative and infectious diseases and are known to disrupt lipid metabolism. Desorption electrospray ionisation (DESI) is a widely used approach for molecular imaging, but previous work has shown that DESI delocalises ions such as potassium (K) and chlorine (Cl), precluding the subsequent elemental analysis of the same section of tissue. The solvent typically used for the DESI electrospray is a combination of methanol and water. Here we show that a novel solvent system, (50:50 (%v/v) MeOH:EtOH) does not delocalise elemental species and thus enables elemental mapping to be performed on the same tissue section post-DESI. Benchmarking the MeOH:EtOH electrospray solvent against the widely used MeOH:H2O electrospray solvent revealed that the MeOH:EtOH solvent yielded increased signal-to-noise ratios for selected lipids. The developed multimodal imaging workflow was applied to a lung tissue section containing a tuberculosis granuloma, showcasing its applicability to elementally rich samples displaying defined structural information.
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Affiliation(s)
- Catia Costa
- University of Surrey Ion Beam Centre, Guildford GU2 7XH, UK
| | - Janella De Jesus
- Department of Chemistry, University of Surrey, Guildford GU2 7XH, UK
- The National Physical Laboratory, Teddington TW11 0LW, UK
| | - Chelsea Nikula
- The National Physical Laboratory, Teddington TW11 0LW, UK
| | - Teresa Murta
- The National Physical Laboratory, Teddington TW11 0LW, UK
| | | | | | - Véronique Dartois
- Center for Discovery and Innovation, Hackensack Meridian School of Medicine, Nutley, NJ 07110, USA
| | - Kaya Firat
- Center for Discovery and Innovation, Hackensack Meridian School of Medicine, Nutley, NJ 07110, USA
| | - Roger Webb
- University of Surrey Ion Beam Centre, Guildford GU2 7XH, UK
| | | | - Melanie J. Bailey
- University of Surrey Ion Beam Centre, Guildford GU2 7XH, UK
- Department of Chemistry, University of Surrey, Guildford GU2 7XH, UK
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3
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GC-MS Techniques Investigating Potential Biomarkers of Dying in the Last Weeks with Lung Cancer. Int J Mol Sci 2023; 24:ijms24021591. [PMID: 36675106 PMCID: PMC9867309 DOI: 10.3390/ijms24021591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 12/27/2022] [Accepted: 01/06/2023] [Indexed: 01/15/2023] Open
Abstract
Predicting when a patient with advanced cancer is dying is a challenge and currently no prognostic test is available. We hypothesised that a dying process from cancer is associated with metabolic changes and specifically with changes in volatile organic compounds (VOCs). We analysed urine from patients with lung cancer in the last weeks of life by headspace gas chromatography mass spectrometry. Urine was acidified or alkalinised before analysis. VOC changes in the last weeks of life were identified using univariate, multivariate and linear regression analysis; 12 VOCs increased (11 from the acid dataset, 2 from the alkali dataset) and 25 VOCs decreased (23 from the acid dataset and 3 from the alkali dataset). A Cox Lasso prediction model using 8 VOCs predicted dying with an AUC of 0.77, 0.78 and 0.85 at 30, 20 and 10 days and stratified patients into a low (median 10 days), medium (median 50 days) or high risk of survival. Our data supports the hypothesis there are specific metabolic changes associated with the dying. The VOCs identified are potential biomarkers of dying in lung cancer and could be used as a tool to provide additional prognostic information to inform expert clinician judgement and subsequent decision making.
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Wang Z, Niu Y, Vashisth T, Li J, Madden R, Livingston TS, Wang Y. Nontargeted metabolomics-based multiple machine learning modeling boosts early accurate detection for citrus Huanglongbing. HORTICULTURE RESEARCH 2022; 9:uhac145. [PMID: 36061619 PMCID: PMC9433982 DOI: 10.1093/hr/uhac145] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 06/20/2022] [Indexed: 06/15/2023]
Abstract
Early accurate detection of crop disease is extremely important for timely disease management. Huanglongbing (HLB), one of the most destructive citrus diseases, has brought about severe economic losses for the global citrus industry. The direct strategies for HLB identification, such as quantitative real-time polymerase chain reaction (qPCR) and chemical staining, are robust for the symptomatic plants but powerless for the asymptomatic ones at the early stage of affection. Thus, it is very necessary to develop a practical method used for the early detection of HLB. In this study, a novel method combining ultra-high performance liquid chromatography/mass spectrometry (UHPLC/MS)-based nontargeted metabolomics and machine learning (ML) was developed for conducting the early detection of HLB for the first time. Six ML algorithms were selected to build the classifiers. Regularized logistic regression (LR-L2) and gradient-boosted decision tree (GBDT) outperformed with the highest average accuracy of 95.83% to not only classify healthy and infected plants but identify significant features. The proposed method proved to be practical for early detection of HLB, which tackled the shortcomings of low sensitivity in the conventional methods and avoid the problems such as lighting condition interference in spectrum/image recognition-based ML methods. Additionally, the discovered biomarkers were verified by the metabolic pathway analysis and content change analysis, which was remarkably consistent with the previous reports.
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Affiliation(s)
- Zhixin Wang
- Citrus Research & Education Center, Institute of Food and Agricultural Sciences, University of Florida, Lake Alfred, Florida 33850-2299, U.S.A
| | - Yue Niu
- Department of Mathematics, University of Arizona, Tucson, Arizona 85721-0089, U.S.A
| | - Tripti Vashisth
- Citrus Research & Education Center, Institute of Food and Agricultural Sciences, University of Florida, Lake Alfred, Florida 33850-2299, U.S.A
| | - Jingwen Li
- Citrus Research & Education Center, Institute of Food and Agricultural Sciences, University of Florida, Lake Alfred, Florida 33850-2299, U.S.A
| | - Robert Madden
- Citrus Research & Education Center, Institute of Food and Agricultural Sciences, University of Florida, Lake Alfred, Florida 33850-2299, U.S.A
| | - Taylor Shea Livingston
- Citrus Research & Education Center, Institute of Food and Agricultural Sciences, University of Florida, Lake Alfred, Florida 33850-2299, U.S.A
| | - Yu Wang
- Corresponding author: E-mail:
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Pomilio AB, Vitale AA, Lazarowski AJ. Neuroproteomics Chip-Based Mass Spectrometry and Other Techniques for Alzheimer´S Disease Biomarkers – Update. Curr Pharm Des 2022; 28:1124-1151. [DOI: 10.2174/1381612828666220413094918] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 02/25/2022] [Indexed: 11/22/2022]
Abstract
Background:
Alzheimer's disease (AD) is a progressive neurodegenerative disease of growing interest given that there is cognitive damage and symptom onset acceleration. Therefore, it is important to find AD biomarkers for early diagnosis, disease progression, and discrimination of AD and other diseases.
Objective:
To update the relevance of mass spectrometry for the identification of peptides and proteins involved in AD useful as discriminating biomarkers.
Methods:
Proteomics and peptidomics technologies that show the highest possible specificity and selectivity for AD biomarkers are analyzed, together with the biological fluids used. In addition to positron emission tomography and magnetic resonance imaging, MALDI-TOF mass spectrometry is widely used to identify proteins and peptides involved in AD. The use of protein chips in SELDI technology and electroblotting chips for peptides makes feasible small amounts (L) of samples for analysis.
Results:
Suitable biomarkers are related to AD pathology, such as intracellular neurofibrillary tangles; extraneuronal senile plaques; neuronal and axonal degeneration; inflammation and oxidative stress. Recently, peptides were added to the candidate list, which are not amyloid-b or tau fragments, but are related to coagulation, brain plasticity, and complement/neuroinflammation systems involving the neurovascular unit.
Conclusion:
The progress made in the application of mass spectrometry and recent chip techniques is promising for discriminating between AD, mild cognitive impairment, and matched healthy controls. The application of this technique to blood samples from patients with AD has shown to be less invasive and fast enough to determine the diagnosis, stage of the disease, prognosis, and follow-up of the therapeutic response.
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Affiliation(s)
- Alicia B. Pomilio
- Departamento de Bioquímica Clínica, Área Hematología, Hospital de Clínicas “José de San Martín”, Universidad de Buenos Aires, Av. Córdoba 2351, C1120AAF Buenos Aires, Argentina
| | - Arturo A. Vitale
- Departamento de Bioquímica Clínica, Área Hematología, Hospital de Clínicas “José de San Martín”, Universidad de Buenos Aires, Av. Córdoba 2351, C1120AAF Buenos Aires, Argentina
| | - Alberto J. Lazarowski
- Departamento de Bioquímica Clínica, Facultad de Farmacia y Bioquímica, Instituto de Fisiopatología y Bioquímica Clínica (INFIBIOC), Universidad de Buenos Aires, Córdoba 2351, C1120AAF Buenos Aires, Argentina
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He X, Kevlishvili I, Murcek K, Liu P, Star A. [2π + 2π] Photocycloaddition of Enones to Single-Walled Carbon Nanotubes Creates Fluorescent Quantum Defects. ACS NANO 2021; 15:4833-4844. [PMID: 33689301 DOI: 10.1021/acsnano.0c09583] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Single-walled carbon nanotubes (SWCNTs) have been widely applied in biomedical fields such as drug delivery, biosensing, bioimaging, and tissue engineering. Understanding their reactivity with biomolecules is important for these applications. We describe here a photoinduced cycloaddition reaction between enones and SWCNTs. By creating covalent and tunable sp3 defects in the sp2 carbon lattice of SWCNTs through [2π + 2π] photocycloaddition, a bright red-shifted photoluminescence was gradually generated. The photocycloaddition functionalization was demonstrated with various organic molecules bearing an enone functional group, including biologically important oxygenated lipid metabolites. The mechanism of this reaction was studied empirically and using computational methods. Density functional theory calculations were employed to elucidate the identity of the reaction product and understand the origin of different substrate reactivities. The results of this study can enable engineering of the optical and electronic properties of semiconducting SWCNTs and provide understanding into their interactions with the lipid biocorona.
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Affiliation(s)
- Xiaoyun He
- Department of Chemistry, ‡Department of Chemical and Petroleum Engineering, and §Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Ilia Kevlishvili
- Department of Chemistry, ‡Department of Chemical and Petroleum Engineering, and §Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Katherina Murcek
- Department of Chemistry, ‡Department of Chemical and Petroleum Engineering, and §Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Peng Liu
- Department of Chemistry, ‡Department of Chemical and Petroleum Engineering, and §Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Alexander Star
- Department of Chemistry, ‡Department of Chemical and Petroleum Engineering, and §Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
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Untargeted Urinary Metabolomics and Children's Exposure to Secondhand Smoke: The Influence of Individual Differences. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18020710. [PMID: 33467557 PMCID: PMC7830063 DOI: 10.3390/ijerph18020710] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 12/30/2020] [Accepted: 01/09/2021] [Indexed: 12/26/2022]
Abstract
Children’s exposure to secondhand smoke (SHS) is a severe public health problem. There is still a lack of evidence regarding panoramic changes in children’s urinary metabolites induced by their involuntary exposure to SHS, and few studies have considered individual differences. This study aims to clarify the SHS-induced changes in urinary metabolites in preschool children by using cross-sectional and longitudinal metabolomics analyses. Urinary metabolites were quantified by using untargeted ultra high-performance liquid chromatography-mass spectrometry (UPLC(c)-MS/MS). Urine cotinine-measured SHS exposure was examined to determine the exposure level. A cross-sectional study including 17 children in a low-exposure group, 17 in a medium-exposure group, and 17 in a high-exposure group was first conducted. Then, a before–after study in the cohort of children was carried out before and two months after smoking-cessation intervention for family smokers. A total of 43 metabolites were discovered to be related to SHS exposure in children in the cross-sectional analysis (false discovery rate (FDR) corrected p < 0.05, variable importance in the projection (VIP) > 1.0). Only three metabolites were confirmed to be positively associated with children’s exposure to SHS (FDR corrected p < 0.05) in a follow-up longitudinal analysis, including kynurenine, tyrosyl-tryptophan, and 1-(3-pyridinyl)-1,4-butanediol, the latter of which belongs to carbonyl compounds, peptides, and pyridines. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that 1-(3-pyridinyl)-1,4-butanediol and kynurenine were significantly enriched in xenobiotic metabolism by cytochrome P450 (p = 0.040) and tryptophan metabolism (p = 0.030), respectively. These findings provide new insights into the pathophysiological mechanism of SHS and indicate the influence of individual differences in SHS-induced changes in urinary metabolites in children.
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Aggarwal P, Baker J, Boyd MT, Coyle S, Probert C, Chapman EA. Optimisation of Urine Sample Preparation for Headspace-Solid Phase Microextraction Gas Chromatography-Mass Spectrometry: Altering Sample pH, Sulphuric Acid Concentration and Phase Ratio. Metabolites 2020; 10:metabo10120482. [PMID: 33255680 PMCID: PMC7760603 DOI: 10.3390/metabo10120482] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 11/17/2020] [Accepted: 11/17/2020] [Indexed: 12/21/2022] Open
Abstract
Headspace-solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) can be used to measure volatile organic compounds (VOCs) in human urine. However, there is no widely adopted standardised protocol for the preparation of urine samples for analysis resulting in an inability to compare studies reliably between laboratories. This paper investigated the effect of altering urine sample pH, volume, and vial size for optimising detection of VOCs when using HS-SPME-GC-MS. This is the first, direct comparison of H2SO4, HCl, and NaOH as treatment techniques prior to HS-SPME-GC-MS analysis. Altering urine sample pH indicates that H2SO4 is more effective at optimising detection of VOCs than HCl or NaOH. H2SO4 resulted in a significantly larger mean number of VOCs being identified per sample (on average, 33.5 VOCs to 24.3 in HCl or 12.2 in NaOH treated urine) and more unique VOCs, produced a more diverse range of classes of VOCs, and led to less HS-SPME-GC-MS degradation. We propose that adding 0.2 mL of 2.5 M H2SO4 to 1 mL of urine within a 10 mL headspace vial is the optimal sample preparation prior to HS-SPME-GC-MS analysis. We hope the use of our optimised method for urinary HS-SPME-GC-MS analysis will enhance our understanding of human disease and bolster metabolic biomarker identification.
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Affiliation(s)
- Prashant Aggarwal
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 3BX, UK; (P.A.); (J.B.); (C.P.)
- School of Medicine, Cedar House, University of Liverpool, Liverpool L69 3GE, UK
| | - James Baker
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 3BX, UK; (P.A.); (J.B.); (C.P.)
- School of Medicine, Cedar House, University of Liverpool, Liverpool L69 3GE, UK
| | - Mark T. Boyd
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, Cancer Research Centre, University of Liverpool, Liverpool L3 9TA, UK;
| | - Séamus Coyle
- Palliative Care Institute Liverpool, Cancer Research Centre, University of Liverpool, Liverpool L3 9TA, UK;
- Clatterbridge Cancer Centre, Liverpool L7 8YA, UK
| | - Chris Probert
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 3BX, UK; (P.A.); (J.B.); (C.P.)
| | - Elinor A. Chapman
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 3BX, UK; (P.A.); (J.B.); (C.P.)
- Palliative Care Institute Liverpool, Cancer Research Centre, University of Liverpool, Liverpool L3 9TA, UK;
- School of Medical Sciences, Bangor University, Bangor, Gwynedd LL57 2DG, UK
- Correspondence:
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Callaghan NI, Hadipour-Lakmehsari S, Lee SH, Gramolini AO, Simmons CA. Modeling cardiac complexity: Advancements in myocardial models and analytical techniques for physiological investigation and therapeutic development in vitro. APL Bioeng 2019; 3:011501. [PMID: 31069331 PMCID: PMC6481739 DOI: 10.1063/1.5055873] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 12/31/2018] [Indexed: 02/06/2023] Open
Abstract
Cardiomyopathies, heart failure, and arrhythmias or conduction blockages impact millions of patients worldwide and are associated with marked increases in sudden cardiac death, decline in the quality of life, and the induction of secondary pathologies. These pathologies stem from dysfunction in the contractile or conductive properties of the cardiomyocyte, which as a result is a focus of fundamental investigation, drug discovery and therapeutic development, and tissue engineering. All of these foci require in vitro myocardial models and experimental techniques to probe the physiological functions of the cardiomyocyte. In this review, we provide a detailed exploration of different cell models, disease modeling strategies, and tissue constructs used from basic to translational research. Furthermore, we highlight recent advancements in imaging, electrophysiology, metabolic measurements, and mechanical and contractile characterization modalities that are advancing our understanding of cardiomyocyte physiology. With this review, we aim to both provide a biological framework for engineers contributing to the field and demonstrate the technical basis and limitations underlying physiological measurement modalities for biologists attempting to take advantage of these state-of-the-art techniques.
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Affiliation(s)
| | | | | | | | - Craig A. Simmons
- Author to whom correspondence should be addressed: . Present address: Ted Rogers Centre for Heart
Research, 661 University Avenue, 14th Floor Toronto, Ontario M5G 1M1, Canada. Tel.:
416-946-0548. Fax: 416-978-7753
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Tamura Y, Mori T, Nakabayashi R, Kobayashi M, Saito K, Okazaki S, Wang N, Kusano M. Metabolomic Evaluation of the Quality of Leaf Lettuce Grown in Practical Plant Factory to Capture Metabolite Signature. FRONTIERS IN PLANT SCIENCE 2018; 9:665. [PMID: 29997631 PMCID: PMC6030546 DOI: 10.3389/fpls.2018.00665] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Accepted: 04/30/2018] [Indexed: 05/11/2023]
Abstract
Vegetables produce metabolites that affect their taste and nutritional value and compounds that contribute to human health. The quality of vegetables grown in plant factories under hydroponic cultivation, e.g., their sweetness and softness, can be improved by controlling growth factors including the temperature, humidity, light source, and fertilizer. However, soil is cheaper than hydroponic cultivation and the visual phenotype of vegetables grown under the two conditions is different. As it is not clear whether their metabolite composition is also different, we studied leaf lettuce raised under the hydroponic condition in practical plant factory and strictly controlled soil condition. We chose two representative cultivars, "black rose" (BR) and "red fire" (RF) because they are of high economic value. Metabolite profiling by comprehensive gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) resulted in the annotation of 101 metabolites from 223 peaks detected by GC-MS; LC-MS yielded 95 peaks. The principal component analysis (PCA) scatter plot showed that the most distinct separation patterns on the first principal component (PC1) coincided with differences in the cultivation methods. There were no clear separations related to cultivar differences in the plot. PC1 loading revealed the discriminant metabolites for each cultivation method. The level of amino acids such as lysine, phenylalanine, tryptophan, and valine was significantly increased in hydroponically grown leaf lettuce, while soil-cultivation derived leaf lettuce samples contained significantly higher levels of fatty-acid derived alcohols (tetracosanol and hexacosanol) and lettuce-specific sesquiterpene lactones (lactucopicrin-15-oxalate and 15-deoxylactucin-8-sulfate). These findings suggest that the metabolite composition of leaf lettuce is primarily affected by its cultivation condition. As the discriminant metabolites reveal important factors that contribute to the nutritional value and taste characteristics of leaf lettuce, we performed comprehensive metabolite profiling to identify metabolite compositions, i.e., metabolite signature, that directly improve its quality and value.
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Affiliation(s)
- Yoshio Tamura
- Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
- Central Research Institute for Feed and Livestock, National Federation of Agricultural Co-operative Associations, Tsukuba, Japan
| | - Tetsuya Mori
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | - Ryo Nakabayashi
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | | | - Kazuki Saito
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan
- Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, Japan
| | - Seiichi Okazaki
- Keystone Technology, Yokohama, Japan
- Graduate School of Environment and Information Sciences, Yokohama National University, Yokohama, Japan
| | - Ning Wang
- Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
| | - Miyako Kusano
- Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
- RIKEN Center for Sustainable Resource Science, Yokohama, Japan
- *Correspondence: Miyako Kusano,
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11
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Deng L, Gu H, Zhu J, Nagana Gowda GA, Djukovic D, Chiorean EG, Raftery D. Combining NMR and LC/MS Using Backward Variable Elimination: Metabolomics Analysis of Colorectal Cancer, Polyps, and Healthy Controls. Anal Chem 2016; 88:7975-83. [PMID: 27437783 DOI: 10.1021/acs.analchem.6b00885] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) play important roles in metabolomics. The complementary features of NMR and MS make their combination very attractive; however, currently the vast majority of metabolomics studies use either NMR or MS separately, and variable selection that combines NMR and MS for biomarker identification and statistical modeling is still not well developed. In this study focused on methodology, we developed a backward variable elimination partial least-squares discriminant analysis algorithm embedded with Monte Carlo cross validation (MCCV-BVE-PLSDA), to combine NMR and targeted liquid chromatography (LC)/MS data. Using the metabolomics analysis of serum for the detection of colorectal cancer (CRC) and polyps as an example, we demonstrate that variable selection is vitally important in combining NMR and MS data. The combined approach was better than using NMR or LC/MS data alone in providing significantly improved predictive accuracy in all the pairwise comparisons among CRC, polyps, and healthy controls. Using this approach, we selected a subset of metabolites responsible for the improved separation for each pairwise comparison, and we achieved a comprehensive profile of altered metabolite levels, including those in glycolysis, the TCA cycle, amino acid metabolism, and other pathways that were related to CRC and polyps. MCCV-BVE-PLSDA is straightforward, easy to implement, and highly useful for studying the contribution of each individual variable to multivariate statistical models. On the basis of these results, we recommend using an appropriate variable selection step, such as MCCV-BVE-PLSDA, when analyzing data from multiple analytical platforms to obtain improved statistical performance and a more accurate biological interpretation, especially for biomarker discovery. Importantly, the approach described here is relatively universal and can be easily expanded for combination with other analytical technologies.
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Affiliation(s)
- Lingli Deng
- Department of Information Engineering, East China University of Technology , 418 Guanglan Avenue, Nanchang, Jiangxi Province 330013, China.,Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington , 850 Republican Street, Seattle, Washington 98109, United States
| | - Haiwei Gu
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington , 850 Republican Street, Seattle, Washington 98109, United States.,Jiangxi Key Laboratory for Mass Spectrometry and Instrumentation, East China University of Technology , 418 Guanglan Avenue, Nanchang, Jiangxi Province 330013, China
| | - Jiangjiang Zhu
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington , 850 Republican Street, Seattle, Washington 98109, United States
| | - G A Nagana Gowda
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington , 850 Republican Street, Seattle, Washington 98109, United States
| | - Danijel Djukovic
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington , 850 Republican Street, Seattle, Washington 98109, United States
| | - E Gabriela Chiorean
- Department of Medicine, University of Washington , 825 Eastlake Avenue East, Seattle, Washington 98109, United States.,Indiana University Melvin and Bren Simon Cancer Center , 535 Barnhill Drive, Indianapolis, Indiana 46202, United States
| | - Daniel Raftery
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington , 850 Republican Street, Seattle, Washington 98109, United States.,Department of Chemistry, Purdue University , 560 Oval Drive, West Lafayette, Indiana 47907, United States.,Public Health Sciences Division, Fred Hutchinson Cancer Research Center , 1100 Fairview Avenue North, Seattle, Washington 98109, United States
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