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Owens DJ, Bennett S. An exercise physiologist's guide to metabolomics. Exp Physiol 2024; 109:1066-1079. [PMID: 38358958 PMCID: PMC11215473 DOI: 10.1113/ep091059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 01/25/2024] [Indexed: 02/17/2024]
Abstract
The field of exercise physiology has undergone significant technological advancements since the pioneering works of exercise physiologists in the early to mid-20th century. Historically, the ability to detect metabolites in biofluids from exercising participants was limited to single-metabolite analyses. However, the rise of metabolomics, a discipline focused on the comprehensive analysis of metabolites within a biological system, has facilitated a more intricate understanding of metabolic pathways and networks in exercise. This review explores some of the pivotal technological and bioinformatic advancements that have propelled metabolomics to the forefront of exercise physiology research. Metabolomics offers a unique 'fingerprint' of cellular activity, offering a broader spectrum than traditional single-metabolite assays. Techniques, including mass spectrometry and nuclear magnetic resonance spectroscopy, have significantly improved the speed and sensitivity of metabolite analysis. Nonetheless, challenges persist, including study design and data interpretation issues. This review aims to serve as a guide for exercise physiologists to facilitate better research design, data analysis and interpretation within metabolomics. The potential of metabolomics in bridging the gap between genotype and phenotype is emphasised, underscoring the critical importance of careful study design and the selection of appropriate metabolomics techniques. Furthermore, the paper highlights the need to deeply understand the broader scientific context to discern meaningful metabolic changes. The emerging field of fluxomics, which seeks to quantify metabolic reaction rates, is also introduced as a promising avenue for future research.
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Affiliation(s)
- Daniel J. Owens
- Research Institute of Sport and Exercise Science (RISES)Liverpool John Moores UniversityLiverpoolUK
| | - Samuel Bennett
- Center for Biological Clocks Research, Department of BiologyTexas A&M UniversityCollege StationTexasUSA
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2
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Chen K, Alexander LE, Mahgoub U, Okazaki Y, Higashi Y, Perera AM, Showman LJ, Loneman D, Dennison TS, Lopez M, Claussen R, Peddicord L, Saito K, Lauter N, Dorman KS, Nikolau BJ, Yandeau-Nelson MD. Dynamic relationships among pathways producing hydrocarbons and fatty acids of maize silk cuticular waxes. PLANT PHYSIOLOGY 2024; 195:2234-2255. [PMID: 38537616 PMCID: PMC11213258 DOI: 10.1093/plphys/kiae150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 02/06/2024] [Indexed: 06/30/2024]
Abstract
The hydrophobic cuticle is the first line of defense between aerial portions of plants and the external environment. On maize (Zea mays L.) silks, the cuticular cutin matrix is infused with cuticular waxes, consisting of a homologous series of very long-chain fatty acids (VLCFAs), aldehydes, and hydrocarbons. Together with VLC fatty-acyl-CoAs (VLCFA-CoAs), these metabolites serve as precursors, intermediates, and end-products of the cuticular wax biosynthetic pathway. To deconvolute the potentially confounding impacts of the change in silk microenvironment and silk development on this pathway, we profiled cuticular waxes on the silks of the inbreds B73 and Mo17, and their reciprocal hybrids. Multivariate interrogation of these metabolite abundance data demonstrates that VLCFA-CoAs and total free VLCFAs are positively correlated with the cuticular wax metabolome, and this metabolome is primarily affected by changes in the silk microenvironment and plant genotype. Moreover, the genotype effect on the pathway explains the increased accumulation of cuticular hydrocarbons with a concomitant reduction in cuticular VLCFA accumulation on B73 silks, suggesting that the conversion of VLCFA-CoAs to hydrocarbons is more effective in B73 than Mo17. Statistical modeling of the ratios between cuticular hydrocarbons and cuticular VLCFAs reveals a significant role of precursor chain length in determining this ratio. This study establishes the complexity of the product-precursor relationships within the silk cuticular wax-producing network by dissecting both the impact of genotype and the allocation of VLCFA-CoA precursors to different biological processes and demonstrates that longer chain VLCFA-CoAs are preferentially utilized for hydrocarbon biosynthesis.
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Affiliation(s)
- Keting Chen
- Department of Genetics, Development & Cell Biology, Iowa State University, Ames, IA 50011, USA
- Bioinformatics & Computational Biology Graduate Program, Iowa State University, Ames, IA 50011, USA
| | - Liza E Alexander
- Roy J. Carver Department of Biochemistry, Biophysics & Molecular Biology, Iowa State University, Ames, IA 50011, USA
| | - Umnia Mahgoub
- Department of Genetics, Development & Cell Biology, Iowa State University, Ames, IA 50011, USA
| | - Yozo Okazaki
- Metabolomics Research Group, RIKEN Center for Sustainable Resource Science, Yokohama, Kanagawa 230-0045, Japan
- Graduate School of Bioresources, Mie University, Tsu, Mie 514-8507, Japan
| | - Yasuhiro Higashi
- Metabolomics Research Group, RIKEN Center for Sustainable Resource Science, Yokohama, Kanagawa 230-0045, Japan
| | - Ann M Perera
- W.M. Keck Metabolomics Research Laboratory, Iowa State University, Ames, IA 50011, USA
| | - Lucas J Showman
- W.M. Keck Metabolomics Research Laboratory, Iowa State University, Ames, IA 50011, USA
| | - Derek Loneman
- Department of Genetics, Development & Cell Biology, Iowa State University, Ames, IA 50011, USA
| | - Tesia S Dennison
- Department of Plant Pathology & Microbiology, Iowa State University, Ames, IA 50011, USA
- Interdepartmental Genetics & Genomics Graduate Program, Iowa State University, Ames, IA 50011, USA
| | - Miriam Lopez
- Corn Insects and Crop Genetics Research Unit, USDA-ARS, Ames, IA 50011, USA
| | - Reid Claussen
- Department of Genetics, Development & Cell Biology, Iowa State University, Ames, IA 50011, USA
| | - Layton Peddicord
- Department of Plant Pathology & Microbiology, Iowa State University, Ames, IA 50011, USA
- Interdepartmental Genetics & Genomics Graduate Program, Iowa State University, Ames, IA 50011, USA
| | - Kazuki Saito
- Metabolomics Research Group, RIKEN Center for Sustainable Resource Science, Yokohama, Kanagawa 230-0045, Japan
| | - Nick Lauter
- Department of Plant Pathology & Microbiology, Iowa State University, Ames, IA 50011, USA
- Interdepartmental Genetics & Genomics Graduate Program, Iowa State University, Ames, IA 50011, USA
- Corn Insects and Crop Genetics Research Unit, USDA-ARS, Ames, IA 50011, USA
| | - Karin S Dorman
- Department of Genetics, Development & Cell Biology, Iowa State University, Ames, IA 50011, USA
- Bioinformatics & Computational Biology Graduate Program, Iowa State University, Ames, IA 50011, USA
- Department of Statistics, Iowa State University, Ames, IA 50011, USA
| | - Basil J Nikolau
- Bioinformatics & Computational Biology Graduate Program, Iowa State University, Ames, IA 50011, USA
- Roy J. Carver Department of Biochemistry, Biophysics & Molecular Biology, Iowa State University, Ames, IA 50011, USA
- Interdepartmental Genetics & Genomics Graduate Program, Iowa State University, Ames, IA 50011, USA
- Center for Metabolic Biology, Iowa State University, Ames, IA 50011, USA
| | - Marna D Yandeau-Nelson
- Department of Genetics, Development & Cell Biology, Iowa State University, Ames, IA 50011, USA
- Bioinformatics & Computational Biology Graduate Program, Iowa State University, Ames, IA 50011, USA
- Interdepartmental Genetics & Genomics Graduate Program, Iowa State University, Ames, IA 50011, USA
- Center for Metabolic Biology, Iowa State University, Ames, IA 50011, USA
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Chang C, Roh YS, Du M, Kuo YC, Zhang Y, Hardy M, Gahler R, Solnier J. Differences in Metabolite Profiles of Dihydroberberine and Micellar Berberine in Caco-2 Cells and Humans-A Pilot Study. Int J Mol Sci 2024; 25:5625. [PMID: 38891813 PMCID: PMC11171481 DOI: 10.3390/ijms25115625] [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: 04/15/2024] [Revised: 05/17/2024] [Accepted: 05/20/2024] [Indexed: 06/21/2024] Open
Abstract
We investigated the pharmacokinetic pathway of berberine and its metabolites in vitro, in Caco-2 cells, and in human participants following the administration of dihydroberberine (DHB) and micellar berberine (LipoMicel®, LMB) formulations. A pilot trial involving nine healthy volunteers was conducted over a 24 h period; blood samples were collected and subjected to Ultra High-Performance Liquid Chromatography-High Resolution Mass Spectrometry (UHPLC-HRMS) analyses to quantify the concentrations of berberine and its metabolites. Pharmacokinetic correlations indicated that berberrubine and thalifendine follow distinct metabolic pathways. Additionally, jatrorrhizine sulfate appeared to undergo metabolism differently compared to the other sulfated metabolites. Moreover, berberrubine glucuronide likely has a unique metabolic pathway distinct from other glucuronides. The human trial revealed significantly higher blood concentrations of berberine metabolites in participants of the DHB treatment group compared to the LMB treatment group-except for berberrubine glucuronide, which was only detected in the LMB treatment group. Similarly, results from in vitro investigations showed significant differences in berberine metabolite profiles between DHB and LMB. Dihydroberberine, dihydroxy-berberrubine/thalifendine and jatrorrhizine sulfate were detected in LMB-treated cells, but not in DHB-treated cells; thalifendine and jatrorrhizine-glucuronide were detected in DHB-treated cells only. While DHB treatment provided higher blood concentrations of berberine and most berberine metabolites, both in vitro (Caco-2 cells) and in vivo human studies showed that treatment with LMB resulted in a higher proportion of unmetabolized berberine compared to DHB. These findings suggest potential clinical implications that merit further investigation in future large-scale trials.
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Affiliation(s)
- Chuck Chang
- ISURA, Clinical Research, Burnaby, BC V3N 4S9, Canada; (C.C.); (Y.S.R.); (M.D.); (Y.C.K.); (Y.Z.)
| | - Yoon Seok Roh
- ISURA, Clinical Research, Burnaby, BC V3N 4S9, Canada; (C.C.); (Y.S.R.); (M.D.); (Y.C.K.); (Y.Z.)
| | - Min Du
- ISURA, Clinical Research, Burnaby, BC V3N 4S9, Canada; (C.C.); (Y.S.R.); (M.D.); (Y.C.K.); (Y.Z.)
| | - Yun Chai Kuo
- ISURA, Clinical Research, Burnaby, BC V3N 4S9, Canada; (C.C.); (Y.S.R.); (M.D.); (Y.C.K.); (Y.Z.)
| | - Yiming Zhang
- ISURA, Clinical Research, Burnaby, BC V3N 4S9, Canada; (C.C.); (Y.S.R.); (M.D.); (Y.C.K.); (Y.Z.)
| | - Mary Hardy
- Academy of Integrative and Holistic Medicine, San Diego, CA 92037, USA;
| | | | - Julia Solnier
- ISURA, Clinical Research, Burnaby, BC V3N 4S9, Canada; (C.C.); (Y.S.R.); (M.D.); (Y.C.K.); (Y.Z.)
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Wang X, Jahagirdar S, Bakker W, Lute C, Kemp B, van Knegsel A, Saccenti E. Discrimination of Lipogenic or Glucogenic Diet Effects in Early-Lactation Dairy Cows Using Plasma Metabolite Abundances and Ratios in Combination with Machine Learning. Metabolites 2024; 14:230. [PMID: 38668358 PMCID: PMC11052284 DOI: 10.3390/metabo14040230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 04/05/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
During early lactation, dairy cows have a negative energy balance since their energy demands exceed their energy intake: in this study, we aimed to investigate the association between diet and plasma metabolomics profiles and how these relate to energy unbalance of course in the early-lactation stage. Holstein-Friesian cows were randomly assigned to a glucogenic (n = 15) or lipogenic (n = 15) diet in early lactation. Blood was collected in week 2 and week 4 after calving. Plasma metabolite profiles were detected using liquid chromatography-mass spectrometry (LC-MS), and a total of 39 metabolites were identified. Two plasma metabolomic profiles were available every week for each cow. Metabolite abundance and metabolite ratios were used for the analysis using the XGboost algorithm to discriminate between diet treatment and lactation week. Using metabolite ratios resulted in better discrimination performance compared with the metabolite abundances in assigning cows to a lipogenic diet or a glucogenic diet. The quality of the discrimination of performance of lipogenic diet and glucogenic diet effects improved from 0.606 to 0.753 and from 0.696 to 0.842 in week 2 and week 4 (as measured by area under the curve, AUC), when the metabolite abundance ratios were used instead of abundances. The top discriminating ratios for diet were the ratio of arginine to tyrosine and the ratio of aspartic acid to valine in week 2 and week 4, respectively. For cows fed the lipogenic diet, choline and the ratio of creatinine to tryptophan were top features to discriminate cows in week 2 vs. week 4. For cows fed the glucogenic diet, methionine and the ratio of 4-hydroxyproline to choline were top features to discriminate dietary effects in week 2 or week 4. This study shows the added value of using metabolite abundance ratios to discriminate between lipogenic and glucogenic diet and lactation weeks in early-lactation cows when using metabolomics data. The application of this research will help to accurately regulate the nutrition of lactating dairy cows and promote sustainable agricultural development.
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Affiliation(s)
- Xiaodan Wang
- Adaptation Physiology Group, Department of Animal Sciences, Wageningen University & Research, 6700 AH Wageningen, The Netherlands; (X.W.); (B.K.); (A.v.K.)
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, 6700 EJ Wageningen, The Netherlands;
- Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Sanjeevan Jahagirdar
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, 6700 EJ Wageningen, The Netherlands;
| | - Wouter Bakker
- Division of Toxicology, Wageningen University & Research, 6700 EA Wageningen, The Netherlands;
| | - Carolien Lute
- Adaptation Physiology Group, Department of Animal Sciences, Wageningen University & Research, 6700 AH Wageningen, The Netherlands; (X.W.); (B.K.); (A.v.K.)
| | - Bas Kemp
- Adaptation Physiology Group, Department of Animal Sciences, Wageningen University & Research, 6700 AH Wageningen, The Netherlands; (X.W.); (B.K.); (A.v.K.)
| | - Ariette van Knegsel
- Adaptation Physiology Group, Department of Animal Sciences, Wageningen University & Research, 6700 AH Wageningen, The Netherlands; (X.W.); (B.K.); (A.v.K.)
| | - Edoardo Saccenti
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, 6700 EJ Wageningen, The Netherlands;
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Ma G, Kang J, Yu T. Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes. Brief Bioinform 2024; 25:bbae141. [PMID: 38581417 PMCID: PMC10998539 DOI: 10.1093/bib/bbae141] [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: 12/06/2023] [Revised: 02/28/2024] [Accepted: 03/13/2024] [Indexed: 04/08/2024] Open
Abstract
Untargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application, given its ability to depict the global metabolic pattern in biological samples. However, the data are noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the uncertainty for most features. The existence of the uncertainty causes major difficulty in downstream functional analysis. To address these issues, we develop a novel approach for Bayesian Analysis of Untargeted Metabolomics data (BAUM) to integrate previously separate tasks into a single framework, including matching uncertainty inference, metabolite selection and functional analysis. By incorporating the knowledge graph between variables and using relatively simple assumptions, BAUM can analyze datasets with small sample sizes. By allowing different confidence levels of feature-metabolite matching, the method is applicable to datasets in which feature identities are partially known. Simulation studies demonstrate that, compared with other existing methods, BAUM achieves better accuracy in selecting important metabolites that tend to be functionally consistent and assigning confidence scores to feature-metabolite matches. We analyze a COVID-19 metabolomics dataset and a mouse brain metabolomics dataset using BAUM. Even with a very small sample size of 16 mice per group, BAUM is robust and stable. It finds pathways that conform to existing knowledge, as well as novel pathways that are biologically plausible.
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Affiliation(s)
- Guoxuan Ma
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jian Kang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Tianwei Yu
- Shenzhen Research Institute of Big Data, School of Data Science, The Chinese University of Hong Kong - Shenzhen (CUHK-Shenzhen), Shenzhen, Guangdong 518172, China
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Boubnovski Martell M, Linton-Reid K, Hindocha S, Chen M, Moreno P, Álvarez-Benito M, Salvatierra Á, Lee R, Posma JM, Calzado MA, Aboagye EO. Deep representation learning of tissue metabolome and computed tomography annotates NSCLC classification and prognosis. NPJ Precis Oncol 2024; 8:28. [PMID: 38310164 PMCID: PMC10838282 DOI: 10.1038/s41698-024-00502-3] [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: 06/21/2023] [Accepted: 01/04/2024] [Indexed: 02/05/2024] Open
Abstract
The rich chemical information from tissue metabolomics provides a powerful means to elaborate tissue physiology or tumor characteristics at cellular and tumor microenvironment levels. However, the process of obtaining such information requires invasive biopsies, is costly, and can delay clinical patient management. Conversely, computed tomography (CT) is a clinical standard of care but does not intuitively harbor histological or prognostic information. Furthermore, the ability to embed metabolome information into CT to subsequently use the learned representation for classification or prognosis has yet to be described. This study develops a deep learning-based framework -- tissue-metabolomic-radiomic-CT (TMR-CT) by combining 48 paired CT images and tumor/normal tissue metabolite intensities to generate ten image embeddings to infer metabolite-derived representation from CT alone. In clinical NSCLC settings, we ascertain whether TMR-CT results in an enhanced feature generation model solving histology classification/prognosis tasks in an unseen international CT dataset of 742 patients. TMR-CT non-invasively determines histological classes - adenocarcinoma/squamous cell carcinoma with an F1-score = 0.78 and further asserts patients' prognosis with a c-index = 0.72, surpassing the performance of radiomics models and deep learning on single modality CT feature extraction. Additionally, our work shows the potential to generate informative biology-inspired CT-led features to explore connections between hard-to-obtain tissue metabolic profiles and routine lesion-derived image data.
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Affiliation(s)
| | | | - Sumeet Hindocha
- Early Diagnosis and Detection Centre, National Institute for Health and Care Research Biomedical Research Centre at the Royal Marsden and Institute of Cancer Research, London, SW3 6JJ, UK
| | - Mitchell Chen
- Imperial College London Hammersmith Campus, London, SW7 2AZ, UK
| | - Paula Moreno
- Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, 14004, Spain
- Departamento de Cirugía Toráxica y Trasplante de Pulmón, Hospital Universitario Reina Sofía, Córdoba, 14014, Spain
| | - Marina Álvarez-Benito
- Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, 14004, Spain
- Unidad de Radiodiagnóstico y Cáncer de Mama, Hospital Universitario Reina Sofía, Córdoba, 14004, Spain
| | - Ángel Salvatierra
- Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, 14004, Spain
- Unidad de Radiodiagnóstico y Cáncer de Mama, Hospital Universitario Reina Sofía, Córdoba, 14004, Spain
| | - Richard Lee
- Early Diagnosis and Detection Centre, National Institute for Health and Care Research Biomedical Research Centre at the Royal Marsden and Institute of Cancer Research, London, SW3 6JJ, UK
- National Heart and Lung Institute, Imperial College London, Guy Scadding Building, Dovehouse Street, London, SW3 6LY, UK
| | - Joram M Posma
- Imperial College London Hammersmith Campus, London, SW7 2AZ, UK
| | - Marco A Calzado
- Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, 14004, Spain.
- Departamento de Biología Celular, Fisiología e Inmunología, Universidad de Córdoba, Córdoba, 14014, Spain.
| | - Eric O Aboagye
- Imperial College London Hammersmith Campus, London, SW7 2AZ, UK.
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Ferreira LC, Santana FM, Scagliusi SMM, Beckmann M, Mur LAJ. Induced responses to the wheat pathogen: Tan Spot-(Pyrenophora tritici-repentis) in wheat (Triticum aestivum) focus on changes in defence associated and sugar metabolism. Metabolomics 2024; 20:19. [PMID: 38296869 PMCID: PMC10830751 DOI: 10.1007/s11306-023-02084-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 12/19/2023] [Indexed: 02/02/2024]
Abstract
INTRODUCTION Tan Spot (TS) disease of wheat is caused by Pyrenophora tritici-repentis (Ptr), where most of the yield loss is linked to diseased flag leaves. As there are no fully resistant cultivars available, elucidating the responses of wheat to Ptr could inform the derivation of new resistant genotypes. OBJECTIVES The study aimed to characterise the flag-leaf metabolomes of two spring wheat cultivars (Triticum aestivum L. cv. PF 080719 [PF] and cv. Fundacep Horizonte [FH]) following challenge with Ptr to gain insights into TS disease development. METHODS PF and FH plants were inoculated with a Ptr strain that produces the necrotrophic toxin ToxA. The metabolic changes in flag leaves following challenge (24, 48, 72, and 96 h post-inoculation [hpi]) with Ptr were investigated using untargeted flow infusion ionisation-high resolution mass spectroscopy (FIE-HRMS). RESULTS Both cultivars were susceptible to Ptr at the flag-leaf stage. Comparisons of Ptr- and mock-inoculated plants indicated that a major metabolic shift occurred at 24 hpi in FH, and at 48 hpi in PF. Although most altered metabolites were genotype specific, they were linked to common pathways; phenylpropanoid and flavonoid metabolism. Alterations in sugar metabolism as well as in glycolysis and glucogenesis pathways were also observed. Pathway enrichment analysis suggested that Ptr-triggered alterations in chloroplast and photosynthetic machinery in both cultivars, especially in FH at 96 hpi. In a wheat-Ptr interactome in integrative network analysis, "flavone and flavonol biosynthesis" and "starch and sucrose metabolism" were targeted as the key metabolic processes underlying PF-FH-Ptr interactions. CONCLUSION These observations suggest the potential importance of flavone and flavonol biosynthesis as well as bioenergetic shifts in susceptibility to Ptr. This work highlights the value of metabolomic approaches to provide novel insights into wheat pathosystems.
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Affiliation(s)
- Larissa Carvalho Ferreira
- Department of Life Sciences, Aberystwyth University, Aberystwyth, UK
- Everglades Research and Education Center, University of Florida, Belle Glade, FL, USA
| | | | | | - Manfred Beckmann
- Department of Life Sciences, Aberystwyth University, Aberystwyth, UK
| | - Luis A J Mur
- Department of Life Sciences, Aberystwyth University, Aberystwyth, UK.
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Meng W, Pan H, Sha Y, Zhai X, Xing A, Lingampelly SS, Sripathi SR, Wang Y, Li K. Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases. Metabolites 2024; 14:93. [PMID: 38392985 PMCID: PMC10890086 DOI: 10.3390/metabo14020093] [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: 12/15/2023] [Revised: 01/17/2024] [Accepted: 01/25/2024] [Indexed: 02/25/2024] Open
Abstract
The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications.
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Affiliation(s)
- Weiyu Meng
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Hongxin Pan
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Yuyang Sha
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Xiaobing Zhai
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Abao Xing
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | | | - Srinivasa R Sripathi
- Henderson Ocular Stem Cell Laboratory, Retina Foundation of the Southwest, Dallas, TX 75231, USA
| | - Yuefei Wang
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Kefeng Li
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
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Zhao Q, Chen Y, Huang W, Zhou H, Zhang W. Drug-microbiota interactions: an emerging priority for precision medicine. Signal Transduct Target Ther 2023; 8:386. [PMID: 37806986 PMCID: PMC10560686 DOI: 10.1038/s41392-023-01619-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 07/20/2023] [Accepted: 08/24/2023] [Indexed: 10/10/2023] Open
Abstract
Individual variability in drug response (IVDR) can be a major cause of adverse drug reactions (ADRs) and prolonged therapy, resulting in a substantial health and economic burden. Despite extensive research in pharmacogenomics regarding the impact of individual genetic background on pharmacokinetics (PK) and pharmacodynamics (PD), genetic diversity explains only a limited proportion of IVDR. The role of gut microbiota, also known as the second genome, and its metabolites in modulating therapeutic outcomes in human diseases have been highlighted by recent studies. Consequently, the burgeoning field of pharmacomicrobiomics aims to explore the correlation between microbiota variation and IVDR or ADRs. This review presents an up-to-date overview of the intricate interactions between gut microbiota and classical therapeutic agents for human systemic diseases, including cancer, cardiovascular diseases (CVDs), endocrine diseases, and others. We summarise how microbiota, directly and indirectly, modify the absorption, distribution, metabolism, and excretion (ADME) of drugs. Conversely, drugs can also modulate the composition and function of gut microbiota, leading to changes in microbial metabolism and immune response. We also discuss the practical challenges, strategies, and opportunities in this field, emphasizing the critical need to develop an innovative approach to multi-omics, integrate various data types, including human and microbiota genomic data, as well as translate lab data into clinical practice. To sum up, pharmacomicrobiomics represents a promising avenue to address IVDR and improve patient outcomes, and further research in this field is imperative to unlock its full potential for precision medicine.
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Affiliation(s)
- Qing Zhao
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, PR China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, 110 Xiangya Road, Changsha, 410078, PR China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, 110 Xiangya Road, Changsha, 410078, PR China
- National Clinical Research Center for Geriatric Disorders, 87 Xiangya Road, Changsha, 410008, PR China
| | - Yao Chen
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, PR China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, 110 Xiangya Road, Changsha, 410078, PR China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, 110 Xiangya Road, Changsha, 410078, PR China
- National Clinical Research Center for Geriatric Disorders, 87 Xiangya Road, Changsha, 410008, PR China
| | - Weihua Huang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, PR China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, 110 Xiangya Road, Changsha, 410078, PR China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, 110 Xiangya Road, Changsha, 410078, PR China
- National Clinical Research Center for Geriatric Disorders, 87 Xiangya Road, Changsha, 410008, PR China
| | - Honghao Zhou
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, PR China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, 110 Xiangya Road, Changsha, 410078, PR China
- Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, 110 Xiangya Road, Changsha, 410078, PR China
- National Clinical Research Center for Geriatric Disorders, 87 Xiangya Road, Changsha, 410008, PR China
| | - Wei Zhang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, PR China.
- The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, PR China.
- The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, 510080, PR China.
- Central Laboratory of Hunan Cancer Hospital, Central South University, 283 Tongzipo Road, Changsha, 410013, PR China.
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10
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Jacobson DK, Peterson AC, Qvarnstrom Y, Barratt JL. Novel insights on the genetic population structure of human-infecting Cyclospora spp. and evidence for rapid subtype selection among isolates from the USA. CURRENT RESEARCH IN PARASITOLOGY & VECTOR-BORNE DISEASES 2023; 4:100145. [PMID: 37841306 PMCID: PMC10569985 DOI: 10.1016/j.crpvbd.2023.100145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 09/14/2023] [Accepted: 09/19/2023] [Indexed: 10/17/2023]
Abstract
Human-infecting Cyclospora was recently characterized as three species, two of which (C. cayetanensis and C. ashfordi) are currently responsible for all known human infections in the USA, yet much remains unknown about the genetic structure within these two species. Here, we investigate Cyclospora genotyping data from 2018 through 2022 to ascertain if there are temporal patterns in the genetic structure of Cyclospora parasites that cause infections in US residents from year to year. First, we investigate three levels of genetic characterization: species, subpopulation, and strain, to elucidate annual trends in Cyclospora infections. Next, we determine if shifts in genetic diversity can be linked to any of the eight loci used in our Cyclospora genotyping approach. We observed fluctuations in the abundance of Cyclospora types at the species and subpopulation levels, but no significant temporal trends were identified; however, we found recurrent and sporadic strains within both C. ashfordi and C. cayetanensis. We also uncovered major shifts in the mitochondrial genotypes in both species, where there was a universal increase in abundance of a specific mitochondrial genotype that was relatively abundant in 2018 but reached near fixation (was observed in over 96% of isolates) in C. ashfordi by 2022. Similarly, this allele jumped from 29% to 82% relative abundance of isolates belonging to C. cayetanensis. Overall, our analysis uncovers previously unknown temporal-genetic patterns in US Cyclospora types from 2018 through 2022 and is an important step to presenting a clearer picture of the factors influencing cyclosporiasis outbreaks in the USA.
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Affiliation(s)
- David K. Jacobson
- Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Anna C. Peterson
- Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Yvonne Qvarnstrom
- Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Joel L.N. Barratt
- Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, GA, USA
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11
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Choi E, Yoo WJ, Jang HY, Kim TY, Lee SK, Oh HB. Machine learning liquid chromatography retention time prediction model augments the dansylation strategy for metabolite analysis of urine samples. J Chromatogr A 2023; 1705:464167. [PMID: 37348224 DOI: 10.1016/j.chroma.2023.464167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 06/10/2023] [Accepted: 06/15/2023] [Indexed: 06/24/2023]
Abstract
Herein, a standalone software equipped with a graphic user interface (GUI) is developed to predict liquid chromatography mass spectrometry (LC-MS) retention times (RTs) of dansylated metabolites. Dansylation metabolomics strategy developed by Li et al. narrows down a vast chemical space of metabolites into the metabolites containing amines and phenolic hydroxyls. Combined with differential isotope labeling, e.g., 12C-reagent labeled individual samples spiked with a 13C-reagent labeled reference or pooled sample, LC-MS analysis of the dansylated samples enables accurate relative quantification of all labeled metabolites. Herein, the LC-RTs for dansylated metabolites are predicted using an artificial neural network (ANN) machine-learning model. For the ANN modeling, 315 dansylated urine metabolites obtained from the DnsID database are used. The ANN LC-RT prediction model was reliable, with a mean absolute deviation of 0.74 min for the 30 min LC run. In the RT model, a deviation of more than 2 min was observed in only 3.2% of the total 315 metabolites, while a deviation of 1.5 min or more was observed in 11% of the metabolites. Furthermore, it was found that the LC-RT prediction was also reliable even for metabolites containing both amine and phenolic functional groups that can undergo dansylation on either one of the two functional groups, resulting in the generation of two isomeric forms. This RT-prediction model is embedded into a user-friendly GUI and can be used for identifying nontargeted dansylated metabolites with unknown RTs, along with accurate mass measurements. Furthermore, it is demonstrated that the developed software can help identify metabolites from a urine sample of an anonymous healthy pregnant woman.
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Affiliation(s)
- Eunwoo Choi
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
| | - Won Jun Yoo
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
| | - Hwa-Yong Jang
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
| | - Tae-Young Kim
- School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
| | - Sung Ki Lee
- Department of Obstetrics and Gynecology, College of Medicine, Konyang University, Daejeon 35365, Republic of Korea.
| | - Han Bin Oh
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea.
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12
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da Silva Oliveira L, Crnkovic CM, de Amorim MR, Navarro-Vázquez A, Paz TA, Freire VF, Takaki M, Venâncio T, Ferreira AG, de Freitas Saito R, Chammas R, Berlinck RGS. Phomactinine, the First Nitrogen-Bearing Phomactin, Produced by Biatriospora sp. CBMAI 1333. JOURNAL OF NATURAL PRODUCTS 2023; 86:2065-2072. [PMID: 37490470 DOI: 10.1021/acs.jnatprod.3c00383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Metabolomics analyses and improvement of growth conditions were applied toward diversification of phomactin terpenoids by the fungus Biatriospora sp. CBMAI 1333. Visualization of molecular networking results on Gephi assisted the observation of phomactin diversification and guided the isolation of new phomactin variants by applying a modified version of chemometrics based on a fractional factorial design. Consequentially, the first nitrogen-bearing phomactin, phomactinine (1), with a new rearranged carbon skeleton, was isolated and identified. The strategy combining metabolomics and chemometrics can be extended to include bioassay potency, structure novelty, and metabolic diversification connected or not to genomic analyses.
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Affiliation(s)
- Leandro da Silva Oliveira
- Instituto de Química de São Carlos, Universidade de São Paulo, C.P. 780, CEP 13560-970, São Carlos, SP Brazil
| | - Camila M Crnkovic
- Instituto de Química de São Carlos, Universidade de São Paulo, C.P. 780, CEP 13560-970, São Carlos, SP Brazil
- Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, CEP 05508-000, São Paulo, SP Brazil
| | - Marcelo R de Amorim
- Instituto de Química de São Carlos, Universidade de São Paulo, C.P. 780, CEP 13560-970, São Carlos, SP Brazil
| | - Armando Navarro-Vázquez
- Departamento de Química Fundamental, Universidade Federal de Pernambuco Cidade Universitária CEP, 50.740-540 Recife, PE Brazil
| | - Tiago A Paz
- Departamento de Análises Clínicas, Toxicológicas e Bromatológicas, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, CEP 14040-903, Ribeirão Preto, SP Brazil
| | - Vitor F Freire
- Instituto de Química de São Carlos, Universidade de São Paulo, C.P. 780, CEP 13560-970, São Carlos, SP Brazil
| | - Mirelle Takaki
- Instituto de Química de São Carlos, Universidade de São Paulo, C.P. 780, CEP 13560-970, São Carlos, SP Brazil
| | - Tiago Venâncio
- Departamento de Química, Universidade Federal de São Carlos, CEP 13565-905, São Carlos, SP Brazil
| | - Antonio G Ferreira
- Departamento de Química, Universidade Federal de São Carlos, CEP 13565-905, São Carlos, SP Brazil
| | - Renata de Freitas Saito
- Centro de Investigação Translacional em Oncologia, Instituto do Câncer do Estado de São Paulo, Faculdade de Medicina, Universidade de São Paulo, Avenida Dr. Arnaldo, 251 - Cerqueira César, 01246-000, São Paulo, SP Brazil
| | - Roger Chammas
- Centro de Investigação Translacional em Oncologia, Instituto do Câncer do Estado de São Paulo, Faculdade de Medicina, Universidade de São Paulo, Avenida Dr. Arnaldo, 251 - Cerqueira César, 01246-000, São Paulo, SP Brazil
| | - Roberto G S Berlinck
- Instituto de Química de São Carlos, Universidade de São Paulo, C.P. 780, CEP 13560-970, São Carlos, SP Brazil
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13
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Wu Z, Shang X, Liu G, Xie Y. Comparative analysis of flavonoids, polyphenols and volatiles in roots, stems and leaves of five mangroves. PeerJ 2023; 11:e15529. [PMID: 37366424 PMCID: PMC10290835 DOI: 10.7717/peerj.15529] [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: 12/23/2022] [Accepted: 05/19/2023] [Indexed: 06/28/2023] Open
Abstract
Mangrove plants contain a variety of secondary metabolites, including flavonoids, polyphenols, and volatiles, which are important for their survival and adaptation to the coastal environment, as well as for producing bioactive compounds. To reveal differences in these compounds among five mangrove species' leaf, root, and stem, the total contents of flavonoids and polyphenols, types and contents of volatiles were determined, analyzed and compared. The results showed that Avicennia marina leaves contained the highest levels of flavonoids and phenolics. In mangrove parts, flavonoids are usually higher than phenolic compounds. A total of 532 compounds were detected by a gas chromatography-mass spectrometry (GC-MS) method in the leaf, root, and stem parts of five mangrove species. These were grouped into 18 classes, including alcohols, aldehydes, alkaloids, alkanes, etc. The number of volatile compounds in A. ilicifolius (176) and B. gymnorrhiza (172) was lower than in the other three species. The number of volatile compounds and their relative contents differed among all three parts of five mangrove species, where the mangrove species factor had a greater impact than the part factor. A total of 71 common compounds occurring in more than two species or parts were analyzed by a PLS-DA model. One-way ANOVA revealed 18 differential compounds among mangrove species and nine differential compounds among parts. Principal component analysis and hierarchical clustering analysis showed that both unique and common compounds significantly differed in composition and concentration between species and parts. In general, A. ilicifolius and B. gymnorrhiza differed significantly from the other species in terms of compound content, while the leaves differed significantly from the other parts. VIP screening and pathway enrichment analysis were performed on 17 common compounds closely related to mangrove species or parts. These compounds were mainly involved in terpenoid pathways such as C10 isoprenoids and C15 isoprenoids and fatty alcohols. The correlation analysis showed that the content of flavonoids/phenolics, the number of compounds, and the content of some common compounds in mangroves were correlated with their salt and waterlogging tolerance levels. These findings will help in the development of genetic varieties and medicinal utilization of mangrove plants.
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Affiliation(s)
- Zhihua Wu
- Research Institute of Fast-growing Trees, Chinese Academy of Forestry, Zhanjiang, Guangdong, China
- School of Forestry, Nanjing Forestry University, Nanjing, Jiangsu, China
| | - Xiuhua Shang
- Research Institute of Fast-growing Trees, Chinese Academy of Forestry, Zhanjiang, Guangdong, China
| | - Guo Liu
- Research Institute of Fast-growing Trees, Chinese Academy of Forestry, Zhanjiang, Guangdong, China
| | - Yaojian Xie
- Research Institute of Fast-growing Trees, Chinese Academy of Forestry, Zhanjiang, Guangdong, China
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14
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Mutithu DW, Kirwan JA, Adeola HA, Aremu OO, Lumngwena EN, Wiesner L, Skatulla S, Naidoo R, Ntusi NAB. High-Throughput Metabolomics Applications in Pathogenesis and Diagnosis of Valvular Heart Disease. Rev Cardiovasc Med 2023; 24:169. [PMID: 39077521 PMCID: PMC11264134 DOI: 10.31083/j.rcm2406169] [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: 03/11/2023] [Revised: 05/09/2023] [Accepted: 05/18/2023] [Indexed: 07/31/2024] Open
Abstract
High-throughput metabolomics techniques are a useful tool to understand many disease conditions including cardiovascular disease such as valvular heart disease(s) (VHD). VHD involves damage to heart valves, mostly presenting as stenosis, regurgitation or prolapse and can be classified into degenerative, rheumatic, congenital, or prosthetic valve disease. Gaps remain in our understanding of the pathogenesis of the common VHD. It is now fitting to place into perspective the contribution of metabolomics in the mechanism of development, diagnosis, and prognosis of VHD. A structured search for metabolomics studies centred on human VHD was undertaken. Biomarkers associated with the pathogenesis of bicuspid aortic valve disease, mitral valve disease, rheumatic heart disease, and degenerative aortic valve stenosis are reviewed and discussed. In addition, metabolic biomarkers reported to prognosticate patient outcomes of post-valve repair or replacement are highlighted. Finally, we also review the pitfalls and limitations to consider when designing metabolomics studies, especially from a clinician's viewpoint. In the future, reliable and simple metabolic biomarker(s) may supplement the existing diagnostic tools in the early diagnosis of VHD.
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Affiliation(s)
- Daniel W. Mutithu
- Division of Cardiology, Department of Medicine, Faculty of Health Sciences, University of Cape Town and Groote Schuur Hospital, 7925 Cape Town, South Africa
- Cape Heart Institute, Faculty of Health Sciences, University of Cape Town, 7925 Cape Town, South Africa
- Extramural Unit on Intersection of Noncommunicable Diseases and Infectious Diseases, South African Medical Research Council, 7501 Cape Town, South Africa
| | - Jennifer A. Kirwan
- Metabolomics Platform, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany
- Max-Delbrück-Center (MDC) for Molecular Medicine in the Helmholtz Association, 13125 Berlin, Germany
| | - Henry A. Adeola
- Hair and Skin Research Laboratory, Division of Dermatology, Department of Medicine, University of Cape Town, 7925 Cape Town, South Africa
| | - Olukayode O. Aremu
- Division of Cardiology, Department of Medicine, Faculty of Health Sciences, University of Cape Town and Groote Schuur Hospital, 7925 Cape Town, South Africa
- Cape Heart Institute, Faculty of Health Sciences, University of Cape Town, 7925 Cape Town, South Africa
- Extramural Unit on Intersection of Noncommunicable Diseases and Infectious Diseases, South African Medical Research Council, 7501 Cape Town, South Africa
| | - Evelyn N. Lumngwena
- Division of Cardiology, Department of Medicine, Faculty of Health Sciences, University of Cape Town and Groote Schuur Hospital, 7925 Cape Town, South Africa
- Cape Heart Institute, Faculty of Health Sciences, University of Cape Town, 7925 Cape Town, South Africa
- Extramural Unit on Intersection of Noncommunicable Diseases and Infectious Diseases, South African Medical Research Council, 7501 Cape Town, South Africa
- Institute of Infectious Diseases and Molecular Medicine (IIDM), University of Cape Town, 7925 Cape Town, South Africa
| | - Lubbe Wiesner
- Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, 7925 Cape Town, South Africa
| | - Sebastian Skatulla
- Computational Continuum Mechanics Research Group, Department of Civil Engineering, Faculty of Engineering and the Built Environment, University of Cape Town, 7925 Cape Town, South Africa
| | - Richard Naidoo
- Division of Anatomical Pathology, Department of Pathology, University of Cape Town, and National Health Laboratory Services, 7925 Cape Town, South Africa
| | - Ntobeko A. B. Ntusi
- Division of Cardiology, Department of Medicine, Faculty of Health Sciences, University of Cape Town and Groote Schuur Hospital, 7925 Cape Town, South Africa
- Cape Heart Institute, Faculty of Health Sciences, University of Cape Town, 7925 Cape Town, South Africa
- Extramural Unit on Intersection of Noncommunicable Diseases and Infectious Diseases, South African Medical Research Council, 7501 Cape Town, South Africa
- Cape Universities Body Imaging Centre, Faculty of Health Sciences, University of Cape Town, 7925 Cape Town, South Africa
- Wellcome Centre for Infectious Disease Research, Faculty of Health Sciences, University of Cape Town, 7925 Cape Town, South Africa
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15
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Porto VA, da Rocha Júnior ER, Ursulino JS, Porto RS, da Silva M, de Jesus LWO, Oliveira JMD, Crispim AC, Santos JCC, Aquino TMD. NMR-based metabolomics applied to ecotoxicology with zebrafish (Danio rerio) as a prominent model for metabolic profiling and biomarker discovery: Overviewing the most recent approaches. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 868:161737. [PMID: 36693575 DOI: 10.1016/j.scitotenv.2023.161737] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/28/2022] [Accepted: 01/17/2023] [Indexed: 06/17/2023]
Abstract
Metabolomics is an innovative approach used in the medical, toxicological, and biological sciences. As an interdisciplinary topic, metabolomics and its relation with the environment and toxicological research are extensive. The use of substances, such as drugs and pesticides, contributes to the continuous releasing of xenobiotics into the environment, harming organisms and their habitats. In this context, fish are important bioindicators of the environmental condition and have often been used as model species. Among them, zebrafish (Danio rerio) presents itself as a versatile and straightforward option due to its unique attributes for research. Zebrafish proves to be a valuable model for toxicity assays and also for metabolomics profiling by analytical tools. Thus, NMR-based metabolomics associated with statistical analysis can reasonably assist researchers in critical factors related to discovering and validating biomarkers through accurate diagnosis. Therefore, this review aimed to report the studies that applied zebrafish as a model for (eco)toxicological assays and essentially utilized NMR-based metabolomics analysis to assess the biochemical profile and thus suggest the potential biological marker.
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Affiliation(s)
- Viviane Amaral Porto
- Research Group on Therapeutic Strategies, Institute of Chemistry and Biotechnology, Federal University of Alagoas, Maceió, AL, Brazil.
| | | | - Jeferson Santana Ursulino
- Research Group on Therapeutic Strategies, Institute of Chemistry and Biotechnology, Federal University of Alagoas, Maceió, AL, Brazil
| | - Ricardo Silva Porto
- Institute of Chemistry and Biotechnology, Federal University of Alagoas, Maceió, AL, Brazil
| | - Marciliano da Silva
- Laboratory of Applied Animal Morphophysiology, Institute of Biological and Health Sciences, Federal University of Alagoas, Maceió, AL, Brazil
| | - Lázaro Wender Oliveira de Jesus
- Laboratory of Applied Animal Morphophysiology, Institute of Biological and Health Sciences, Federal University of Alagoas, Maceió, AL, Brazil
| | | | - Alessandre Carmo Crispim
- Research Group on Therapeutic Strategies, Institute of Chemistry and Biotechnology, Federal University of Alagoas, Maceió, AL, Brazil
| | | | - Thiago Mendonça de Aquino
- Research Group on Therapeutic Strategies, Institute of Chemistry and Biotechnology, Federal University of Alagoas, Maceió, AL, Brazil
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16
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Tressler CM, Ayyappan V, Nakuchima S, Yang E, Sonkar K, Tan Z, Glunde K. A multimodal pipeline using NMR spectroscopy and MALDI-TOF mass spectrometry imaging from the same tissue sample. NMR IN BIOMEDICINE 2023; 36:e4770. [PMID: 35538020 PMCID: PMC9867920 DOI: 10.1002/nbm.4770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 05/05/2022] [Accepted: 05/07/2022] [Indexed: 06/14/2023]
Abstract
NMR spectroscopy and matrix assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) are both commonly used to detect large numbers of metabolites and lipids in metabolomic and lipidomic studies. We have demonstrated a new workflow, highlighting the benefits of both techniques to obtain metabolomic and lipidomic data, which has realized for the first time the combination of these two complementary and powerful technologies. NMR spectroscopy is frequently used to obtain quantitative metabolite information from cells and tissues. Lipid detection is also possible with NMR spectroscopy, with changes being visible across entire classes of molecules. Meanwhile, MALDI MSI provides relative measures of metabolite and lipid concentrations, mapping spatial information of many specific metabolite and lipid molecules across cells or tissues. We have used these two complementary techniques in combination to obtain metabolomic and lipidomic measurements from triple-negative human breast cancer cells and tumor xenograft models. We have emphasized critical experimental procedures that ensured the success of achieving NMR spectroscopy and MALDI MSI in a combined workflow from the same sample. Our data show that several phospholipid metabolite species were differentially distributed in viable and necrotic regions of breast tumor xenografts. This study emphasizes the power of combined NMR spectroscopy-MALDI imaging to advance metabolomic and lipidomic studies.
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Affiliation(s)
- Caitlin M. Tressler
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Vinay Ayyappan
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Sofia Nakuchima
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Ethan Yang
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kanchan Sonkar
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Zheqiong Tan
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kristine Glunde
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Biological Chemistry, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- The Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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17
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Di Cesare F, Vignoli A, Luchinat C, Tenori L, Saccenti E. Exploration of Blood Metabolite Signatures of Colorectal Cancer and Polyposis through Integrated Statistical and Network Analysis. Metabolites 2023; 13:metabo13020296. [PMID: 36837915 PMCID: PMC9965766 DOI: 10.3390/metabo13020296] [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: 01/16/2023] [Revised: 02/07/2023] [Accepted: 02/12/2023] [Indexed: 02/19/2023] Open
Abstract
Colorectal cancer (CRC), one of the most prevalent and deadly cancers worldwide, generally evolves from adenomatous polyps. The understanding of the molecular mechanisms underlying this pathological evolution is crucial for diagnostic and prognostic purposes. Integrative systems biology approaches offer an optimal point of view to analyze CRC and patients with polyposis. The present study analyzed the association networks constructed from a publicly available array of 113 serum metabolites measured on a cohort of 234 subjects from three groups (66 CRC patients, 76 patients with polyposis, and 92 healthy controls), which concentrations were obtained via targeted liquid chromatography-tandem mass spectrometry. In terms of architecture, topology, and connectivity, the metabolite-metabolite association network of CRC patients appears to be completely different with respect to patients with polyposis and healthy controls. The most relevant nodes in the CRC network are those related to energy metabolism. Interestingly, phenylalanine, tyrosine, and tryptophan metabolism are found to be involved in both CRC and polyposis. Our results demonstrate that the characterization of metabolite-metabolite association networks is a promising and powerful tool to investigate molecular aspects of CRC.
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Affiliation(s)
- Francesca Di Cesare
- Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy
- Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (C.I.R.M.M.P.), 50019 Sesto Fiorentino, Italy
| | - Alessia Vignoli
- Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy
- Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (C.I.R.M.M.P.), 50019 Sesto Fiorentino, Italy
| | - Claudio Luchinat
- Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy
- Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (C.I.R.M.M.P.), 50019 Sesto Fiorentino, Italy
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy
- Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (C.I.R.M.M.P.), 50019 Sesto Fiorentino, Italy
- Correspondence: (L.T.); (E.S.)
| | - Edoardo Saccenti
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, 6708 WE Wageningen, The Netherlands
- Correspondence: (L.T.); (E.S.)
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18
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Glass KA, Germain A, Huang YV, Hanson MR. Urine Metabolomics Exposes Anomalous Recovery after Maximal Exertion in Female ME/CFS Patients. Int J Mol Sci 2023; 24:3685. [PMID: 36835097 PMCID: PMC9958671 DOI: 10.3390/ijms24043685] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 02/06/2023] [Accepted: 02/09/2023] [Indexed: 02/16/2023] Open
Abstract
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease with unknown etiology or effective treatments. Post-exertional malaise (PEM) is a key symptom that distinguishes ME/CFS patients. Investigating changes in the urine metabolome between ME/CFS patients and healthy subjects following exertion may help us understand PEM. The aim of this pilot study was to comprehensively characterize the urine metabolomes of eight female healthy sedentary control subjects and ten female ME/CFS patients in response to a maximal cardiopulmonary exercise test (CPET). Each subject provided urine samples at baseline and 24 h post-exercise. A total of 1403 metabolites were detected via LC-MS/MS by Metabolon® including amino acids, carbohydrates, lipids, nucleotides, cofactors and vitamins, xenobiotics, and unknown compounds. Using a linear mixed effects model, pathway enrichment analysis, topology analysis, and correlations between urine and plasma metabolite levels, significant differences were discovered between controls and ME/CFS patients in many lipid (steroids, acyl carnitines and acyl glycines) and amino acid subpathways (cysteine, methionine, SAM, and taurine; leucine, isoleucine, and valine; polyamine; tryptophan; and urea cycle, arginine and proline). Our most unanticipated discovery is the lack of changes in the urine metabolome of ME/CFS patients during recovery while significant changes are induced in controls after CPET, potentially demonstrating the lack of adaptation to a severe stress in ME/CFS patients.
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Affiliation(s)
| | | | | | - Maureen R. Hanson
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
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19
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Balcázar-Zumaeta CR, Castro-Alayo EM, Cayo-Colca IS, Idrogo-Vásquez G, Muñoz-Astecker LD. Metabolomics during the spontaneous fermentation in cocoa (Theobroma cacao L.): An exploraty review. Food Res Int 2023; 163:112190. [PMID: 36596129 DOI: 10.1016/j.foodres.2022.112190] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 11/14/2022] [Accepted: 11/15/2022] [Indexed: 11/21/2022]
Abstract
Spontaneous fermentation is a process that depends on substrates' physical characteristics, crop variety, and postharvest practices; it induces variations in the metabolites that are responsible for the taste, aroma, and quality. Metabolomics makes it possible to detect key metabolites using chemometrics and makes it possible to establish patterns or identify biomarker behaviors under certain conditions at a given time. Therefore, sensitive and highly efficient analytical techniques allow for studying the metabolomic fingerprint changes during fermentation; which identify and quantify metabolites related to taste and aroma formation of an adequate processing time. This review shows that studying metabolomics in spontaneous fermentation permits the characterization of spontaneous fermentation in different stages. Also, it demonstrates the possibility of modulating the quality of cocoa by improving the spontaneous fermentation time (because of volatile aromatic compounds formation), thus standardizing the process to obtain attributes and quality that will later impact the chocolate quality.
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Affiliation(s)
- César R Balcázar-Zumaeta
- Instituto de Investigación, Innovación y Desarrollo para el Sector Agrario y Agroindustrial de la Región Amazonas (IIDAA), Facultad de Ingeniería y Ciencias Agrarias, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Calle Higos Urco 342-350-356, Chachapoyas, Amazonas, Peru.
| | - Efraín M Castro-Alayo
- Instituto de Investigación, Innovación y Desarrollo para el Sector Agrario y Agroindustrial de la Región Amazonas (IIDAA), Facultad de Ingeniería y Ciencias Agrarias, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Calle Higos Urco 342-350-356, Chachapoyas, Amazonas, Peru.
| | - Ilse S Cayo-Colca
- Facultad de Ingeniería Zootecnista, Agronegocios y Biotecnología, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Calle Higos Urco 342-350-356, Chachapoyas, Amazonas, Peru.
| | - Guillermo Idrogo-Vásquez
- Instituto de Investigación, Innovación y Desarrollo para el Sector Agrario y Agroindustrial de la Región Amazonas (IIDAA), Facultad de Ingeniería y Ciencias Agrarias, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Calle Higos Urco 342-350-356, Chachapoyas, Amazonas, Peru.
| | - Lucas D Muñoz-Astecker
- Instituto de Investigación, Innovación y Desarrollo para el Sector Agrario y Agroindustrial de la Región Amazonas (IIDAA), Facultad de Ingeniería y Ciencias Agrarias, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Calle Higos Urco 342-350-356, Chachapoyas, Amazonas, Peru.
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20
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Dantas R, Brocchi M, Pacheco Fill T. Chemical-Biology and Metabolomics Studies in Phage-Host Interactions. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1439:71-100. [PMID: 37843806 DOI: 10.1007/978-3-031-41741-2_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
For many years, several studies have explored the molecular mechanisms involved in the infection of bacteria by their specific phages to understand the main infection strategies and the host defense strategies. The modulation of the mechanisms involved in the infection, as well as the expression of key substances in the development of the different life cycles of phages, function as a natural source of strategies capable of promoting the control of different pathogens that are harmful to human and animal health. Therefore, this chapter aims to provide an overview of the mechanisms involved in virus-bacteria interaction to explore the main compounds produced or altered as a chemical survival strategy and the metabolism modulation when occurring a host-phage interaction. In this context, emphasis will be given to the chemistry of peptides/proteins and enzymes encoded by bacteriophages in the control of pathogenic bacteria and the use of secondary metabolites recently reported as active participants in the mechanisms of phage-bacteria interaction. Finally, metabolomics strategies developed to gain new insights into the metabolism involved in the phage-host interaction and the metabolomics workflow in host-phage interaction will be presented.
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Affiliation(s)
- Rodolfo Dantas
- Institute of Chemistry, University of Campinas, Campinas, São Paulo, Brazil
| | - Marcelo Brocchi
- Institute of Biology, University of Campinas, Campinas, São Paulo, Brazil
| | - Taícia Pacheco Fill
- Institute of Chemistry, University of Campinas, Campinas, São Paulo, Brazil.
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21
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Salem MA, Wang JY, Al-Babili S. Metabolomics of plant root exudates: From sample preparation to data analysis. FRONTIERS IN PLANT SCIENCE 2022; 13:1062982. [PMID: 36561464 PMCID: PMC9763704 DOI: 10.3389/fpls.2022.1062982] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
Plants release a set of chemical compounds, called exudates, into the rhizosphere, under normal conditions and in response to environmental stimuli and surrounding soil organisms. Plant root exudates play indispensable roles in inhibiting the growth of harmful microorganisms, while also promoting the growth of beneficial microbes and attracting symbiotic partners. Root exudates contain a complex array of primary and specialized metabolites. Some of these chemicals are only found in certain plant species for shaping the microbial community in the rhizosphere. Comprehensive understanding of plant root exudates has numerous applications from basic sciences to enhancing crop yield, production of stress-tolerant crops, and phytoremediation. This review summarizes the metabolomics workflow for determining the composition of root exudates, from sample preparation to data acquisition and analysis. We also discuss recent advances in the existing analytical methods and future perspectives of metabolite analysis.
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Affiliation(s)
- Mohamed A. Salem
- Department of Pharmacognosy and Natural Products, Faculty of Pharmacy, Menoufia University, Menoufia, Egypt
| | - Jian You Wang
- The BioActives Lab, Center for Desert Agriculture, Biological and Environment Science and Engineering (BESE), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Salim Al-Babili
- The BioActives Lab, Center for Desert Agriculture, Biological and Environment Science and Engineering (BESE), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
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22
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Cutler CB, Lucke-Wold B. Commentary: Serum Levels of Myo-inositol Predicts Clinical Outcome 1 Year After Aneurysmal Subarachnoid Hemorrhage. Neurosurgery 2022; 91:e170-e171. [PMID: 36269571 DOI: 10.1227/neu.0000000000002195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 08/22/2022] [Indexed: 12/15/2022] Open
Affiliation(s)
- Christopher B Cutler
- Chicago Medical School, Rosalind Franklin University of Science and Medicine, North Chicago, Illinois, USA
| | - Brandon Lucke-Wold
- Department of Neurosurgery, University of Florida, Gainesville, Florida, USA
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Ye X, Zhu B, Chen Y, Wang Y, Wang D, Zhao Z, Li Z. Integrated Metabolomics and Lipidomics Approach for the Study of Metabolic Network and Early Diagnosis in Cerebral Infarction. J Proteome Res 2022; 21:2635-2646. [PMID: 36264770 DOI: 10.1021/acs.jproteome.2c00348] [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: 11/29/2022]
Abstract
Cerebral infarction (CI) remains a major cause of high mortality and long-term disability worldwide. The exploration of biomarkers and pathogenesis is crucial for the early diagnosis of CI. Although the understanding of metabolic perturbations underlying CI has increased in recent years, the relationship between altered metabolites and disease pathogenesis has only been partially elucidated and requires further investigation. In this study, we performed an integrated metabolomics and lipidomics analysis on 59 healthy subjects and 47 CI patients. Ultimately, 49 metabolite and 68 lipid biomarkers were identified and enriched in 24 disturbed pathways. The metabolic network revealed a significant interaction between altered lipids and other metabolites. Using receiver operating characteristic curve (ROC) analysis, a panel of three polar metabolites and seven lipids was optimized in the training set, which included taurine, oleoylcarnitine, creatinine, PE(22:6/P-18:0), Cer 34:2, GlcCer(d18:0/18:0), DG 44:0, LysoPC(16:0), 22:6-OH/LysoPC, and TAG58:7-FA22:4. Subsequently, a support vector machine (SVM) model was constructed and validated, which showed excellent predictive ability in the validation set. Thereby, the integrated metabolomics and lipidomics approach could contribute to a comprehensive understanding of the metabolic dyshomeostasis associated with the pathogenesis of underlying CI. The present research may promote a deeper understanding and early diagnosis of CI in the clinic. All raw data were deposited in PRIDE (PXD036199).
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Affiliation(s)
- Xinxin Ye
- Department of Chemistry, Capital Normal University, No. 105, West Third Ring Road North, Haidian District, Beijing 100048, P. R. China
| | - Bin Zhu
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, No. 119 South Fourth Ring Road West, Fengtai District, Beijing 100070, P. R. China
| | - Yang Chen
- Department of Chemistry, Capital Normal University, No. 105, West Third Ring Road North, Haidian District, Beijing 100048, P. R. China
| | - Yingfeng Wang
- Department of Chemistry, Capital Normal University, No. 105, West Third Ring Road North, Haidian District, Beijing 100048, P. R. China
| | - Dan Wang
- Department of Chemistry, Capital Normal University, No. 105, West Third Ring Road North, Haidian District, Beijing 100048, P. R. China
| | - Zhigang Zhao
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, No. 119 South Fourth Ring Road West, Fengtai District, Beijing 100070, P. R. China
| | - Zhongfeng Li
- Department of Chemistry, Capital Normal University, No. 105, West Third Ring Road North, Haidian District, Beijing 100048, P. R. China
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24
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Wolfsberger JG, Hunt EC, Bobba SS, Love-Rutledge S, Vogler B. Metabolite quantification: A fluorescence-based method for urine sample normalization prior to 1H-NMR analysis. Metabolomics 2022; 18:80. [PMID: 36261660 DOI: 10.1007/s11306-022-01939-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 09/27/2022] [Indexed: 12/29/2022]
Abstract
INTRODUCTION Metabolomics is a multi-discipline approach to systems biology that provides a snapshot of the metabolic status of a cell, tissue, or organism. Metabolomics uses mass spectroscopy (MS) and nuclear magnetic resonance (NMR) to analyze biological samples for low molecular weight metabolites. OBJECTIVE Normalize urine sample pre-acquisition to perform a targeted quantitative analysis of selected metabolites in rat urine. METHODS Urine samples were provided from rats on a control diet (n = 10) and moderate sucrose diet (n = 8) collected in a metabolic cage during an eight hour fast. Urine from each sample was prepared by two different methods. One sample was a non-normalized sample of 1200 µL and the second sample was a variable volume-normalized to the concentration of urobilin in a standard sample of urine. The urobilin concentration in all samples was determined by fluorescence. Ten metabolites for each non-normalized and normalized urine sample were quantified by integration to an internal standard of DSS. RESULTS Both groups showed an improvement in pH range going from non-normalized to normalized samples. In the group on the control diet, eight metabolites had significant improvement in range, while the remaining two metabolites had insignificant improvement in range comparing the non-normalized sample to the normalized sample. In the group on the moderate sucrose diet all ten metabolites showed significant improvement in range going from non-normalized to normalized samples. CONCLUSIONS These findings describe a pre-acquisition method of urine normalization to adjust for differences in hydration state of each organism. This results in a narrower concentration range in a targeted analysis.
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Affiliation(s)
- James Gerard Wolfsberger
- Chemistry Department, University of Alabama in Huntsville, 301 Sparkman Drive, Huntsville, Alabama, 35899, USA
| | - Emily C Hunt
- Chemistry Department, University of Alabama in Huntsville, 301 Sparkman Drive, Huntsville, Alabama, 35899, USA
| | - Sai Sumedha Bobba
- James Clemens High School, 11306 County Line Rd, Madison, Alabama, 35756, USA
| | - Sharifa Love-Rutledge
- Chemistry Department, University of Alabama in Huntsville, 301 Sparkman Drive, Huntsville, Alabama, 35899, USA
| | - Bernhard Vogler
- Chemistry Department, University of Alabama in Huntsville, 301 Sparkman Drive, Huntsville, Alabama, 35899, USA.
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25
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Labine LM, Oliveira Pereira EA, Kleywegt S, Jobst KJ, Simpson AJ, Simpson MJ. Comparison of sub-lethal metabolic perturbations of select legacy and novel perfluorinated alkyl substances (PFAS) in Daphnia magna. ENVIRONMENTAL RESEARCH 2022; 212:113582. [PMID: 35661729 DOI: 10.1016/j.envres.2022.113582] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/19/2022] [Accepted: 05/26/2022] [Indexed: 06/15/2023]
Abstract
Per- and polyfluoroalkyl substances (PFAS) are a class of pollutants of concern due to their ubiquitous presence, persistence, and toxicity in aquatic environments. Legacy PFAS pollutants such as perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) have been more widely studied in aquatic environments. However, replacement PFAS, such as ammonium perfluoro (2-methyl-3-oxahexanoate; GenX) are increasingly being detected with little known information surrounding their toxicity. Here, Daphnia magna, a model organism for freshwater ecotoxicology was used to compare the acute sub-lethal toxicity of PFOS, PFOA, GenX, and PFAS mixtures. Using liquid chromatography with tandem mass spectrometry (LC-MS/MS), the targeted polar metabolic profile extracted from single Daphnia was quantified to investigate perturbations in the exposure groups versus the unexposed organisms. Multivariate statistical analyses demonstrated significant non-monotonic separation in PFOA, GenX, and PFAS mixture exposures. Sub-lethal exposure to concentrations of PFOS did not lead to significant separation in multivariate analyses. Univariate statistics and pathway analyses were used to elucidate the mode of action of PFAS exposure. Exposure to all individual PFAS led to significant perturbations in many amino acids including cysteine, histidine, tryptophan, glycine, and serine. These perturbations are consistent with biochemical pathway disruptions in the pantothenate and Coenzyme A (CoA) biosynthesis, thiamine metabolism, histidine metabolism, and aminoacyl-tRNA biosynthesis pathways. Overall, the collected metabolomic data is consistent with disruptions in energy metabolism and protein synthesis as the primary mode of action of sub-lethal PFAS exposure. Secondary modes of action among individual pollutant exposures demonstrated that the structural properties (carboxylic acid vs. sulfonic acid group) may play a role in the metabolic perturbations observed. Sub-lethal exposure to PFAS mixtures highlighted a mixed response when compared to the individual pollutants (PFOS, PFOA, and GenX). Overall, this study emphasizes the niche capability of environmental metabolomics to differentiate secondary modes of action from metabolic perturbations in both single pollutant and pollutant mixtures within the same chemical class.
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Affiliation(s)
- Lisa M Labine
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada; Environmental NMR Centre and Department of Physical and Environmental Sciences, University of Toronto Scarborough, 1265 Military Trail, Toronto, ON, M1C 1A4, Canada
| | - Erico A Oliveira Pereira
- Environmental NMR Centre and Department of Physical and Environmental Sciences, University of Toronto Scarborough, 1265 Military Trail, Toronto, ON, M1C 1A4, Canada
| | - Sonya Kleywegt
- Technical Assessment and Standards Development Branch, Ontario Ministry of the Environment, Conservation and Parks, Toronto, ON, Canada, M4V 1M2
| | - Karl J Jobst
- Department of Chemistry, Memorial University of Newfoundland, St. John's, NL, Canada, A1B 3X7
| | - Andre J Simpson
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada; Environmental NMR Centre and Department of Physical and Environmental Sciences, University of Toronto Scarborough, 1265 Military Trail, Toronto, ON, M1C 1A4, Canada
| | - Myrna J Simpson
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada; Environmental NMR Centre and Department of Physical and Environmental Sciences, University of Toronto Scarborough, 1265 Military Trail, Toronto, ON, M1C 1A4, Canada.
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Soil Metabolomics Predict Microbial Taxa as Biomarkers of Moisture Status in Soils from a Tidal Wetland. Microorganisms 2022; 10:microorganisms10081653. [PMID: 36014071 PMCID: PMC9416152 DOI: 10.3390/microorganisms10081653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 08/12/2022] [Accepted: 08/12/2022] [Indexed: 11/16/2022] Open
Abstract
We present observations from a laboratory-controlled study on the impacts of extreme wetting and drying on a wetland soil microbiome. Our approach was to experimentally challenge the soil microbiome to understand impacts on anaerobic carbon cycling processes as the system transitions from dryness to saturation and vice-versa. Specifically, we tested for impacts on stress responses related to shifts from wet to drought conditions. We used a combination of high-resolution data for small organic chemical compounds (metabolites) and biological (community structure based on 16S rRNA gene sequencing) features. Using a robust correlation-independent data approach, we further tested the predictive power of soil metabolites for the presence or absence of taxa. Here, we demonstrate that taking an untargeted, multidimensional data approach to the interpretation of metabolomics has the potential to indicate the causative pathways selecting for the observed bacterial community structure in soils.
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Metabolomics in Team-Sport Athletes: Current Knowledge, Challenges, and Future Perspectives. Proteomes 2022; 10:proteomes10030027. [PMID: 35997439 PMCID: PMC9396992 DOI: 10.3390/proteomes10030027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/31/2022] [Accepted: 08/08/2022] [Indexed: 11/17/2022] Open
Abstract
Metabolomics is a promising tool for studying exercise physiology and exercise-associated metabolism. It has recently been defined with the term “sportomics” due to metabolomics’ capability to characterize several metabolites in several biological samples simultaneously. This narrative review on exercise metabolomics provides an initial and brief overview of the different metabolomics technologies, sample collection, and further processing steps employed for sport. It also discusses the data analysis and its biological interpretation. Thus, we do not cover sample collection, preparation, and analysis paragraphs in detail here but outline a general outlook to help the reader to understand the metabolomics studies conducted in team-sports athletes, alongside endeavoring to recognize existing or emergent trends and deal with upcoming directions in the field of exercise metabolomics in a team-sports setting.
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Advances in Metabolomics-Driven Diagnostic Breeding and Crop Improvement. Metabolites 2022; 12:metabo12060511. [PMID: 35736444 PMCID: PMC9228725 DOI: 10.3390/metabo12060511] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 02/04/2023] Open
Abstract
Climate change continues to threaten global crop output by reducing annual productivity. As a result, global food security is now considered as one of the most important challenges facing humanity. To address this challenge, modern crop breeding approaches are required to create plants that can cope with increased abiotic/biotic stress. Metabolomics is rapidly gaining traction in plant breeding by predicting the metabolic marker for plant performance under a stressful environment and has emerged as a powerful tool for guiding crop improvement. The advent of more sensitive, automated, and high-throughput analytical tools combined with advanced bioinformatics and other omics techniques has laid the foundation to broadly characterize the genetic traits for crop improvement. Progress in metabolomics allows scientists to rapidly map specific metabolites to the genes that encode their metabolic pathways and offer plant scientists an excellent opportunity to fully explore and rationally harness the wealth of metabolites that plants biosynthesize. Here, we outline the current application of advanced metabolomics tools integrated with other OMICS techniques that can be used to: dissect the details of plant genotype–metabolite–phenotype interactions facilitating metabolomics-assisted plant breeding for probing the stress-responsive metabolic markers, explore the hidden metabolic networks associated with abiotic/biotic stress resistance, facilitate screening and selection of climate-smart crops at the metabolite level, and enable accurate risk-assessment and characterization of gene edited/transgenic plants to assist the regulatory process. The basic concept behind metabolic editing is to identify specific genes that govern the crucial metabolic pathways followed by the editing of one or more genes associated with those pathways. Thus, metabolomics provides a superb platform for not only rapid assessment and commercialization of future genome-edited crops, but also for accelerated metabolomics-assisted plant breeding. Furthermore, metabolomics can be a useful tool to expedite the crop research if integrated with speed breeding in future.
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Marie B, Gallet A. Fish metabolome from sub-urban lakes of the Paris area (France) and potential influence of noxious metabolites produced by cyanobacteria. CHEMOSPHERE 2022; 296:134035. [PMID: 35183584 DOI: 10.1016/j.chemosphere.2022.134035] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 02/03/2022] [Accepted: 02/15/2022] [Indexed: 06/14/2023]
Abstract
The recent democratization of high-throughput molecular phenotyping allows the rapid expansion of promising untargeted multi-dimensional approaches (e.g. epigenomics, transcriptomics, proteomics, and/or metabolomics). Indeed, these emerging omics tools, processed for ecologically relevant species, may present innovative perspectives for environmental assessments, that could provide early warning of eco(toxico)logical impairments. In a previous pilot study (Sotton et al., Chemosphere 2019), we explore by 1H NMR the bio-indicative potential of metabolomics analyses on the liver of 2 sentinel fish species (Perca fluviatilis and Lepomis gibbosus) collected in 8 water bodies of the peri-urban Paris' area (France). In the present study, we further investigate on the same samples the high potential of high-throughput UHPLC-HRMS/MS analyses. We show that the LC-MS metabolome investigation allows a clear separation of individuals according to the species, but also according to their respective sampling lakes. Interestingly, similar variations of Perca and Lepomis metabolomes occur locally indicating that site-specific environmental constraints drive the metabolome variations which seem to be influenced by the production of noxious molecules by cyanobacterial blooms in certain lakes. Thus, the development of such reliable environmental metabolomics approaches appears to constitute an innovative bio-indicative tool for the assessment of ecological stress, such as toxigenic cyanobacterial blooms, and aim at being further follow up.
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Affiliation(s)
- Benjamin Marie
- UMR 7245, CNRS/MNHN, Molécules de Communication et Adaptation des Micro-organismes (MCAM), équipe "Cyanobactéries, Cyanotoxines et Environnement", 12 rue Buffon - CP 39, 75231, Paris Cedex 05, France.
| | - Alison Gallet
- UMR 7245, CNRS/MNHN, Molécules de Communication et Adaptation des Micro-organismes (MCAM), équipe "Cyanobactéries, Cyanotoxines et Environnement", 12 rue Buffon - CP 39, 75231, Paris Cedex 05, France
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30
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Multivariate Analysis of Metabolomic and Nutritional Profiles among Children with Autism Spectrum Disorder. J Pers Med 2022; 12:jpm12060923. [PMID: 35743708 PMCID: PMC9224818 DOI: 10.3390/jpm12060923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/19/2022] [Accepted: 05/24/2022] [Indexed: 12/02/2022] Open
Abstract
There have been promising results regarding the capability of statistical and machine-learning techniques to offer insight into unique metabolomic patterns observed in ASD. This work re-examines a comparative study contrasting metabolomic and nutrient measurements of children with ASD (n = 55) against their typically developing (TD) peers (n = 44) through a multivariate statistical lens. Hypothesis testing, receiver characteristic curve assessment, and correlation analysis were consistent with prior work and served to underscore prominent areas where metabolomic and nutritional profiles between the groups diverged. Improved univariate analysis revealed 46 nutritional/metabolic differences that were significantly different between ASD and TD groups, with individual areas under the receiver operator curve (AUROC) scores of 0.6–0.9. Many of the significant measurements had correlations with many others, forming two integrated networks of interrelated metabolic differences in ASD. The TD group had 189 significant correlation pairs between metabolites, vs. only 106 for the ASD group, calling attention to underlying differences in metabolic processes. Furthermore, multivariate techniques identified potential biomarker panels with up to six metabolites that were able to attain a predictive accuracy of up to 98% for discriminating between ASD and TD, following cross-validation. Assessing all optimized multivariate models demonstrated concordance with prior physiological pathways identified in the literature, with some of the most important metabolites for discriminating ASD and TD being sulfate, the transsulfuration pathway, uridine (methylation biomarker), and beta-amino isobutyrate (regulator of carbohydrate and lipid metabolism).
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31
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Resurreccion EP, Fong KW. The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer. Metabolites 2022; 12:metabo12060488. [PMID: 35736421 PMCID: PMC9230859 DOI: 10.3390/metabo12060488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 05/21/2022] [Accepted: 05/24/2022] [Indexed: 02/06/2023] Open
Abstract
Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.
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Affiliation(s)
- Eleazer P. Resurreccion
- Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY 40506, USA;
| | - Ka-wing Fong
- Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY 40506, USA;
- Markey Cancer Center, University of Kentucky, Lexington, KY 40506, USA
- Correspondence: ; Tel.: +1-859-562-3455
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32
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Stefan T, Wu XN, Zhang Y, Fernie A, Schulze WX. Regulatory Modules of Metabolites and Protein Phosphorylation in Arabidopsis Genotypes With Altered Sucrose Allocation. FRONTIERS IN PLANT SCIENCE 2022; 13:891405. [PMID: 35665154 PMCID: PMC9161306 DOI: 10.3389/fpls.2022.891405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 04/11/2022] [Indexed: 06/15/2023]
Abstract
Multi-omics data sets are increasingly being used for the interpretation of cellular processes in response to environmental cues. Especially, the posttranslational modification of proteins by phosphorylation is an important regulatory process affecting protein activity and/or localization, which, in turn, can have effects on metabolic processes and metabolite levels. Despite this importance, relationships between protein phosphorylation status and metabolite abundance remain largely underexplored. Here, we used a phosphoproteomics-metabolomics data set collected at the end of day and night in shoots and roots of Arabidopsis to propose regulatory relationships between protein phosphorylation and accumulation or allocation of metabolites. For this purpose, we introduced a novel, robust co-expression measure suited to the structure of our data sets, and we used this measure to construct metabolite-phosphopeptide networks. These networks were compared between wild type and plants with perturbations in key processes of sugar metabolism, namely, sucrose export (sweet11/12 mutant) and starch synthesis (pgm mutant). The phosphopeptide-metabolite network turned out to be highly sensitive to perturbations in sugar metabolism. Specifically, KING1, the regulatory subunit of SnRK1, was identified as a primary candidate connecting protein phosphorylation status with metabolism. We additionally identified strong changes in the fatty acid network of the sweet11/12 mutant, potentially resulting from a combination of fatty acid signaling and metabolic overflow reactions in response to high internal sucrose concentrations. Our results further suggest novel protein-metabolite relationships as candidates for future targeted research.
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Affiliation(s)
- Thorsten Stefan
- Department of Plant Systems Biology, University of Hohenheim, Stuttgart, Germany
| | - Xu Na Wu
- College for Life Science, Yunnan University, Kunming, China
| | - Youjun Zhang
- Department of Central Metabolism, Max-Planck-Institute of Molecular Plant Physiology, Potsdam, Germany
- Center of Plant System Biology and Biotechnology, Plovdiv, Bulgaria
| | - Alisdair Fernie
- Department of Central Metabolism, Max-Planck-Institute of Molecular Plant Physiology, Potsdam, Germany
- Center of Plant System Biology and Biotechnology, Plovdiv, Bulgaria
| | - Waltraud X. Schulze
- Department of Plant Systems Biology, University of Hohenheim, Stuttgart, Germany
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Shankar A, Sharma KK. Fungal secondary metabolites in food and pharmaceuticals in the era of multi-omics. Appl Microbiol Biotechnol 2022; 106:3465-3488. [PMID: 35546367 PMCID: PMC9095418 DOI: 10.1007/s00253-022-11945-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 04/12/2022] [Accepted: 04/24/2022] [Indexed: 01/16/2023]
Abstract
Fungi produce several bioactive metabolites, pigments, dyes, antioxidants, polysaccharides, and industrial enzymes. Fungal products are also the primary sources of functional food and nutrition, and their pharmacological products are used for healthy aging. Their molecular properties are validated through the use of recent high-throughput genomic, transcriptomic, and metabolomic tools and techniques. Together, these updated multi-omic tools have been used to study fungal metabolites structure and their mode of action on biological and cellular processes. Diverse groups of fungi produce different proteins and secondary metabolites, which possess tremendous biotechnological and pharmaceutical applications. Furthermore, its use and acceptability can be accelerated by adopting multi-omics, bioinformatics, and machine learning tools that generate a huge amount of molecular data. The integration of artificial intelligence and machine learning tools in the era of omics and big data has opened up a new outlook in both basic and applied researches in the area of nutraceuticals and functional food and nutrition. KEY POINTS: • Multi-omic tool helps in the identification of novel fungal metabolites • Intra-omic data from genomics to bioinformatics • Novel metabolites and application in human health.
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Affiliation(s)
- Akshay Shankar
- Laboratory of Enzymology and Recombinant DNA Technology, Department of Microbiology, Maharshi Dayanand University, Rohtak, 124001, Haryana, India
| | - Krishna Kant Sharma
- Laboratory of Enzymology and Recombinant DNA Technology, Department of Microbiology, Maharshi Dayanand University, Rohtak, 124001, Haryana, India.
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Galvão Ferrarini M, Ziska I, Andrade R, Julien-Laferrière A, Duchemin L, César RM, Mary A, Vinga S, Sagot MF. Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations. Front Genet 2022; 13:815476. [PMID: 35281848 PMCID: PMC8905348 DOI: 10.3389/fgene.2022.815476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 01/24/2022] [Indexed: 11/16/2022] Open
Abstract
Motivation: The increasing availability of metabolomic data and their analysis are improving the understanding of cellular mechanisms and how biological systems respond to different perturbations. Currently, there is a need for novel computational methods that facilitate the analysis and integration of increasing volume of available data. Results: In this paper, we present Totoro a new constraint-based approach that integrates quantitative non-targeted metabolomic data of two different metabolic states into genome-wide metabolic models and predicts reactions that were most likely active during the transient state. We applied Totoro to real data of three different growth experiments (pulses of glucose, pyruvate, succinate) from Escherichia coli and we were able to predict known active pathways and gather new insights on the different metabolisms related to each substrate. We used both the E. coli core and the iJO1366 models to demonstrate that our approach is applicable to both smaller and larger networks. Availability:Totoro is an open source method (available at https://gitlab.inria.fr/erable/totoro) suitable for any organism with an available metabolic model. It is implemented in C++ and depends on IBM CPLEX which is freely available for academic purposes.
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Affiliation(s)
- Mariana Galvão Ferrarini
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France.,Univ Lyon, INRAE, INSA-Lyon, BF2I, UMR 203, Villeurbanne, France
| | - Irene Ziska
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France.,INRIA Grenoble Rhône-Alpes, Villeurbanne, France
| | - Ricardo Andrade
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France.,Institute of Mathematics and Statistics (IME), University of São Paulo, São Paulo, Brazil
| | | | - Louis Duchemin
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France
| | | | - Arnaud Mary
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France.,INRIA Grenoble Rhône-Alpes, Villeurbanne, France
| | - Susana Vinga
- INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | - Marie-France Sagot
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France.,INRIA Grenoble Rhône-Alpes, Villeurbanne, France
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Amara A, Frainay C, Jourdan F, Naake T, Neumann S, Novoa-del-Toro EM, Salek RM, Salzer L, Scharfenberg S, Witting M. Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation. Front Mol Biosci 2022; 9:841373. [PMID: 35350714 PMCID: PMC8957799 DOI: 10.3389/fmolb.2022.841373] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 02/18/2022] [Indexed: 01/19/2023] Open
Abstract
Both targeted and untargeted mass spectrometry-based metabolomics approaches are used to understand the metabolic processes taking place in various organisms, from prokaryotes, plants, fungi to animals and humans. Untargeted approaches allow to detect as many metabolites as possible at once, identify unexpected metabolic changes, and characterize novel metabolites in biological samples. However, the identification of metabolites and the biological interpretation of such large and complex datasets remain challenging. One approach to address these challenges is considering that metabolites are connected through informative relationships. Such relationships can be formalized as networks, where the nodes correspond to the metabolites or features (when there is no or only partial identification), and edges connect nodes if the corresponding metabolites are related. Several networks can be built from a single dataset (or a list of metabolites), where each network represents different relationships, such as statistical (correlated metabolites), biochemical (known or putative substrates and products of reactions), or chemical (structural similarities, ontological relations). Once these networks are built, they can subsequently be mined using algorithms from network (or graph) theory to gain insights into metabolism. For instance, we can connect metabolites based on prior knowledge on enzymatic reactions, then provide suggestions for potential metabolite identifications, or detect clusters of co-regulated metabolites. In this review, we first aim at settling a nomenclature and formalism to avoid confusion when referring to different networks used in the field of metabolomics. Then, we present the state of the art of network-based methods for mass spectrometry-based metabolomics data analysis, as well as future developments expected in this area. We cover the use of networks applications using biochemical reactions, mass spectrometry features, chemical structural similarities, and correlations between metabolites. We also describe the application of knowledge networks such as metabolic reaction networks. Finally, we discuss the possibility of combining different networks to analyze and interpret them simultaneously.
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Affiliation(s)
- Adam Amara
- Section of Nutrition and Metabolism, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Clément Frainay
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Fabien Jourdan
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
- MetaboHUB-Metatoul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Thomas Naake
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Steffen Neumann
- Bioinformatics and Scientific Data, Leibniz Institute of Plant Biochemistry, Halle (Saale), Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
| | - Elva María Novoa-del-Toro
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | | | - Liesa Salzer
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, Neuherberg, Germany
| | - Sarah Scharfenberg
- Bioinformatics and Scientific Data, Leibniz Institute of Plant Biochemistry, Halle (Saale), Germany
| | - Michael Witting
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Analytical Food Chemistry, TUM School of Life Sciences, Freising, Germany
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Othibeng K, Nephali L, Myoli A, Buthelezi N, Jonker W, Huyser J, Tugizimana F. Metabolic Circuits in Sap Extracts Reflect the Effects of a Microbial Biostimulant on Maize Metabolism under Drought Conditions. PLANTS (BASEL, SWITZERLAND) 2022; 11:plants11040510. [PMID: 35214843 PMCID: PMC8877938 DOI: 10.3390/plants11040510] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 02/07/2022] [Accepted: 02/08/2022] [Indexed: 05/17/2023]
Abstract
The use of microbial biostimulants in the agricultural sector is increasingly gaining momentum and drawing scientific attention to decode the molecular interactions between the biostimulants and plants. Although these biostimulants have been shown to improve plant health and development, the underlying molecular phenomenology remains enigmatic. Thus, this study is a metabolomics work to unravel metabolic circuits in sap extracts from maize plants treated with a microbial biostimulant, under normal and drought conditions. The biostimulant, which was a consortium of different Bacilli strains, was applied at the planting stage, followed by drought stress application. The maize sap extracts were collected at 5 weeks after emergence, and the extracted metabolites were analyzed on liquid chromatography-mass spectrometry platforms. The acquired data were mined using chemometrics and bioinformatics tools. The results showed that under both well-watered and drought stress conditions, the application of the biostimulant led to differential changes in the profiles of amino acids, hormones, TCA intermediates, phenolics, steviol glycosides and oxylipins. These metabolic changes spanned several biological pathways and involved a high correlation of the biochemical as well as structural metabolic relationships that coordinate the maize metabolism. The hypothetical model, postulated from this study, describes metabolic events induced by the microbial biostimulant for growth promotion and enhanced defences. Such understanding of biostimulant-induced changes in maize sap pinpoints to the biochemistry and molecular mechanisms that govern the biostimulant-plant interactions, which contribute to ongoing efforts to generate actionable knowledge of the molecular and physiological mechanisms that define modes of action of biostimulants.
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Affiliation(s)
- Kgalaletso Othibeng
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (K.O.); (L.N.); (A.M.); (N.B.)
| | - Lerato Nephali
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (K.O.); (L.N.); (A.M.); (N.B.)
| | - Akhona Myoli
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (K.O.); (L.N.); (A.M.); (N.B.)
| | - Nombuso Buthelezi
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (K.O.); (L.N.); (A.M.); (N.B.)
| | - Willem Jonker
- International Research and Development Division, Omnia Group, Johannesburg 2021, South Africa; (W.J.); (J.H.)
| | - Johan Huyser
- International Research and Development Division, Omnia Group, Johannesburg 2021, South Africa; (W.J.); (J.H.)
| | - Fidele Tugizimana
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (K.O.); (L.N.); (A.M.); (N.B.)
- International Research and Development Division, Omnia Group, Johannesburg 2021, South Africa; (W.J.); (J.H.)
- Correspondence: or ; Tel.: +27-011-559-7784
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37
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Hosseinkhani F, Huang L, Dubbelman AC, Guled F, Harms AC, Hankemeier T. Systematic Evaluation of HILIC Stationary Phases for Global Metabolomics of Human Plasma. Metabolites 2022; 12:metabo12020165. [PMID: 35208239 PMCID: PMC8875576 DOI: 10.3390/metabo12020165] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 01/25/2022] [Accepted: 02/07/2022] [Indexed: 12/14/2022] Open
Abstract
Polar hydrophilic metabolites have been identified as important actors in many biochemical pathways. Despite continuous improvement and refinement of hydrophilic interaction liquid chromatography (HILIC) platforms, its application in global polar metabolomics has been underutilized. In this study, we aimed to systematically evaluate polar stationary phases for untargeted metabolomics by using HILIC columns (neutral and zwitterionic) that have been exploited widely in targeted approaches. To do so, high-resolution mass spectrometry was applied to thoroughly investigate selectivity, repeatability and matrix effect at three pH conditions for 9 classes of polar compounds using 54 authentic standards and plasma matrix. The column performance for utilization in untargeted metabolomics was assessed using plasma samples with diverse phenotypes. Our results indicate that the ZIC-c HILIC column operated at neutral pH exhibited several advantages, including superior performance for different classes of compounds, better isomer separation, repeatability and high metabolic coverage. Regardless of the column type, the retention of inorganic ions in plasma leads to extensive adduct formation and co-elution with analytes, which results in ion-suppression as part of the overall plasma matrix effect. In ZIC-c HILIC, the sodium chloride ion effect was particularly observed for amino acids and amine classes. Successful performance of HILIC for separation of plasma samples with different phenotypes highlights this mode of separation as a valuable approach in global profiling of plasma sample and discovering the metabolic changes associated with health and disease.
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38
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Warne RW, Dallas J. Microbiome mediation of animal life histories
via
metabolites and insulin‐like signalling. Biol Rev Camb Philos Soc 2022; 97:1118-1130. [DOI: 10.1111/brv.12833] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 01/05/2022] [Accepted: 01/06/2022] [Indexed: 12/15/2022]
Affiliation(s)
- Robin W. Warne
- School of Biological Sciences Southern Illinois University 1125 Lincoln Dr. Carbondale IL 62901‐6501 U.S.A
| | - Jason Dallas
- School of Biological Sciences Southern Illinois University 1125 Lincoln Dr. Carbondale IL 62901‐6501 U.S.A
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Gill NP, Balasubramanian R, Bain JR, Muehlbauer MJ, Lowe WL, Scholtens DM. Path-level interpretation of Gaussian graphical models using the pair-path subscore. BMC Bioinformatics 2022; 23:12. [PMID: 34986802 PMCID: PMC8729005 DOI: 10.1186/s12859-021-04542-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 12/10/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND : Construction of networks from cross-sectional biological data is increasingly common. Many recent methods have been based on Gaussian graphical modeling, and prioritize estimation of conditional pairwise dependencies among nodes in the network. However, challenges remain on how specific paths through the resultant network contribute to overall 'network-level' correlations. For biological applications, understanding these relationships is particularly relevant for parsing structural information contained in complex subnetworks. RESULTS: We propose the pair-path subscore (PPS), a method for interpreting Gaussian graphical models at the level of individual network paths. The scoring is based on the relative importance of such paths in determining the Pearson correlation between their terminal nodes. PPS is validated using human metabolomics data from the Hyperglycemia and adverse pregnancy outcome (HAPO) study, with observations confirming well-documented biological relationships among the metabolites. We also highlight how the PPS can be used in an exploratory fashion to generate new biological hypotheses. Our method is implemented in the R package pps, available at https://github.com/nathan-gill/pps . CONCLUSIONS: The PPS can be used to probe network structure on a finer scale by investigating which paths in a potentially intricate topology contribute most substantially to marginal behavior. Adding PPS to the network analysis toolkit may enable researchers to ask new questions about the relationships among nodes in network data.
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Affiliation(s)
- Nathan P Gill
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Raji Balasubramanian
- Department of Biostatistics and Epidemiology, University of Massachusetts - Amherst, Amherst, MA, USA
| | - James R Bain
- Sarah W. Stedman Nutrition and Metabolism Center, Duke University Medical Center, Durham, NC, USA.,Duke Molecular Physiology Institute, Durham, NC, USA.,Duke University School of Medicine, Durham, NC, USA
| | - Michael J Muehlbauer
- Sarah W. Stedman Nutrition and Metabolism Center, Duke University Medical Center, Durham, NC, USA.,Duke Molecular Physiology Institute, Durham, NC, USA.,Duke University School of Medicine, Durham, NC, USA
| | - William L Lowe
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
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40
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Profiles of Essential Oils and Correlations with Phenolic Acids and Primary Metabolites in Flower Buds of Magnolia heptapeta and Magnolia denudata var. purpurascens. MOLECULES (BASEL, SWITZERLAND) 2021; 27:molecules27010221. [PMID: 35011454 PMCID: PMC8746637 DOI: 10.3390/molecules27010221] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 12/28/2021] [Accepted: 12/28/2021] [Indexed: 01/07/2023]
Abstract
Magnolia flower buds are a source of herbal medicines with various active compounds. In this study, differences in the distribution and abundance of major essential oils, phenolic acids, and primary metabolites between white flower buds of Magnolia heptapeta and violet flower buds of Magnolia denudata var. purpurascens were characterised. A multivariate analysis revealed clear separation between the white and violet flower buds with respect to primary and secondary metabolites closely related to metabolic systems. White flower buds contained large amounts of monoterpene hydrocarbons (MH), phenolic acids, aromatic amino acids, and monosaccharides, related to the production of isoprenes, as MH precursors, and the activity of MH synthase. However, concentrations of β-myrcene, a major MH compound, were higher in violet flower buds than in white flower buds, possibly due to higher threonine levels and low acidic conditions induced by comparatively low levels of some organic acids. Moreover, levels of stress-related metabolites, such as oxygenated monoterpenes, proline, and glutamic acid, were higher in violet flower buds than in white flower buds. Our results support the feasibility of metabolic profiling for the identification of phytochemical differences and improve our understanding of the correlated biological pathways for primary and secondary metabolites.
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Shilovsky GA, Putyatina TS, Markov AV. Altruism and Phenoptosis as Programs Supported by Evolution. BIOCHEMISTRY. BIOKHIMIIA 2021; 86:1540-1552. [PMID: 34937533 PMCID: PMC8678581 DOI: 10.1134/s0006297921120038] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/17/2021] [Accepted: 09/17/2021] [Indexed: 11/22/2022]
Abstract
Phenoptosis is a programmed death that has emerged in the process of evolution, sometimes taking the form of an altruistic program. In particular, it is believed to be a weapon against the spread of pandemics in the past and an obstacle in fighting pandemics in the present (COVID). However, on the evolutionary scale, deterministic death is not associated with random relationships (for example, bacteria with a particular mutation), but is a product of higher nervous activity or a consequence of established hierarchy that reaches its maximal expression in eusocial communities of Hymenoptera and highly social communities of mammals. Unlike a simple association of individuals, eusociality is characterized by the appearance of non-reproductive individuals as the highest form of altruism. In contrast to primitive programs for unicellular organisms, higher multicellular organisms are characterized by the development of behavior-based phenoptotic programs, especially in the case of reproduction-associated limitation of lifespan. Therefore, we can say that the development of altruism in the course of evolution of sociality leads in its extreme manifestation to phenoptosis. Development of mathematical models for the emergence of altruism and programmed death contributes to our understanding of mechanisms underlying these paradoxical counterproductive (harmful) programs. In theory, this model can be applied not only to insects, but also to other social animals and even to the human society. Adaptive death is an extreme form of altruism. We consider altruism and programmed death as programmed processes in the mechanistic and adaptive sense, respectively. Mechanistically, this is a program existing as a predetermined chain of certain responses, regardless of its adaptive value. As to its adaptive value (regardless of the degree of "phenoptoticity"), this is a characteristic of organisms that demonstrate high levels of kinship, social organization, and physical association typical for higher-order individuals, e.g., unicellular organisms forming colonies with some characteristics of multicellular animals or colonies of multicellular animals displaying features of supraorganisms.
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Affiliation(s)
- Gregory A Shilovsky
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119991, Russia.
- Faculty of Biology, Lomonosov Moscow State University, Moscow, 119234, Russia
- Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, 127051, Russia
| | - Tatyana S Putyatina
- Faculty of Biology, Lomonosov Moscow State University, Moscow, 119234, Russia
| | - Alexander V Markov
- Faculty of Biology, Lomonosov Moscow State University, Moscow, 119234, Russia
- Borissiak Paleontological Institute, Russian Academy of Sciences, Moscow, 117997, Russia
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Gupta S, Sharma U. Metabolomics of neurological disorders in India. ANALYTICAL SCIENCE ADVANCES 2021; 2:594-610. [PMID: 38715858 PMCID: PMC10989583 DOI: 10.1002/ansa.202000169] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 10/31/2021] [Accepted: 11/01/2021] [Indexed: 06/11/2024]
Abstract
Metabolomics is the comprehensive study of the metabolome and its alterations within biological fluids and tissues. Over the years, applications of metabolomics have been explored in several areas, including personalised medicine in diseases, metabolome-wide association studies (MWAS), pharmacometabolomics and in combination with other branches of omics such as proteomics, transcriptomics and genomics. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy are the major analytical techniques widely employed in metabolomics. In addition, MS is coupled with chromatography techniques like gas chromatography (GC) and liquid chromatography (LC) to separate metabolites before analysis. These analytical techniques have made possible identification and quantification of large numbers of metabolites, encompassing characterization of diseases and facilitating a systematic and rational therapeutic strategy based on metabolic patterns. In recent years, the metabolomics approach has been used to obtain a deeper insight into the underlying biochemistry of neurodegenerative disorders and the discovery of biomarkers of clinical implications. The current review mainly focuses on an Indian perspective of metabolomics for the identification of metabolites and metabolic alterations serving as potential diagnostic biomarkers for neurological diseases including acute spinal cord injury, amyotrophic lateral sclerosis, tethered cord syndrome, spina bifida, stroke, Parkinson's disease, glioblastoma and neurological disorders with inborn errors of metabolism.
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Affiliation(s)
- Sangeetha Gupta
- Amity Institute of PharmacyAmity UniversityNoidaUttar PradeshIndia
| | - Uma Sharma
- Department of NMR & MRI FacilityAll India Institute of Medical SciencesNew DelhiIndia
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Chele KH, Steenkamp P, Piater LA, Dubery IA, Huyser J, Tugizimana F. A Global Metabolic Map Defines the Effects of a Si-Based Biostimulant on Tomato Plants under Normal and Saline Conditions. Metabolites 2021; 11:metabo11120820. [PMID: 34940578 PMCID: PMC8709197 DOI: 10.3390/metabo11120820] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 11/10/2021] [Accepted: 11/13/2021] [Indexed: 01/19/2023] Open
Abstract
The ongoing unpredictability of climate changes is exponentially exerting a negative impact on crop production, further aggravating detrimental abiotic stress effects. Several research studies have been focused on the genetic modification of crop plants to achieve more crop resilience against such stress factors; however, there has been a paradigm shift in modern agriculture focusing on more organic, eco-friendly and long-lasting systems to improve crop yield. As such, extensive research into the use of microbial and nonmicrobial biostimulants has been at the core of agricultural studies to improve crop growth and development, as well as to attain tolerance against several biotic and abiotic stresses. However, the molecular mechanisms underlying the biostimulant activity remain enigmatic. Thus, this study is a liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics approach to unravel the hypothetical biochemical framework underlying effects of a nonmicrobial biostimulant (a silicon-based formulation) on tomato plants (Solanum lycopersium) under salinity stress conditions. This metabolomics study postulates that Si-based biostimulants could alleviate salinity stress in tomato plants through modulation of the primary metabolism involving changes in the tricarboxylic acid cycle, fatty acid and numerous amino acid biosynthesis pathways, with further reprogramming of several secondary metabolism pathways such as the phenylpropanoid pathway, flavonoid biosynthesis pathways including flavone and flavanol biosynthesis. Thus, the postulated hypothetical framework, describing biostimulant-induced metabolic events in tomato plants, provides actionable knowledge necessary for industries and farmers to, confidently and innovatively, explore, design, and fully implement Si-based formulations and strategies into agronomic practices for sustainable agriculture and food production.
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Affiliation(s)
- Kekeletso H. Chele
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (K.H.C.); (P.S.); (L.A.P.); (I.A.D.)
| | - Paul Steenkamp
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (K.H.C.); (P.S.); (L.A.P.); (I.A.D.)
| | - Lizelle A. Piater
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (K.H.C.); (P.S.); (L.A.P.); (I.A.D.)
| | - Ian A. Dubery
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (K.H.C.); (P.S.); (L.A.P.); (I.A.D.)
| | - Johan Huyser
- International Research and Development Division, Omnia Group, Ltd., Johannesburg 2021, South Africa;
| | - Fidele Tugizimana
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (K.H.C.); (P.S.); (L.A.P.); (I.A.D.)
- International Research and Development Division, Omnia Group, Ltd., Johannesburg 2021, South Africa;
- Correspondence: ; Tel.: +27-011-559-7784
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44
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Sabatini S, Gastaldelli A. Disparity-filtered differential correlation network analysis: a case study on CRC metabolomics. J Integr Bioinform 2021; 18:jib-2021-0030. [PMID: 34792303 PMCID: PMC8709737 DOI: 10.1515/jib-2021-0030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 10/18/2021] [Indexed: 11/15/2022] Open
Abstract
Differential network analysis has become a widely used technique to investigate changes of interactions among different conditions. Although the relationship between observed interactions and biochemical mechanisms is hard to establish, differential network analysis can provide useful insights about dysregulated pathways and candidate biomarkers. The available methods to detect differential interactions are heterogeneous and often rely on assumptions that are unrealistic in many applications. To address these issues, we develop a novel method for differential network analysis, using the so-called disparity filter as network reduction technique. In addition, we propose a classification model based on the inferred network interactions. The main novelty of this work lies in its ability to preserve connections that are statistically significant with respect to a null model without favouring any resolution scale, as a hard threshold would do, and without Gaussian assumptions. The method was tested using a published metabolomic dataset on colorectal cancer (CRC). Detected hub metabolites were consistent with recent literature and the classifier was able to distinguish CRC from polyp and healthy subjects with great accuracy. In conclusion, the proposed method provides a new simple and effective framework for the identification of differential interaction patterns and improves the biological interpretation of metabolomics data.
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Affiliation(s)
- Silvia Sabatini
- Institute of Clinical Physiology, CNR-Pisa, Via Moruzzi 1, Pisa, Italy.,University of Siena, Siena, Italy
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45
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Mycorrhiza-Induced Alterations in Metabolome of Medicago lupulina Leaves during Symbiosis Development. PLANTS 2021; 10:plants10112506. [PMID: 34834870 PMCID: PMC8617643 DOI: 10.3390/plants10112506] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/12/2021] [Accepted: 10/22/2021] [Indexed: 01/12/2023]
Abstract
The present study is aimed at disclosing metabolic profile alterations in the leaves of the Medicago lupulina MlS-1 line that result from high-efficiency arbuscular mycorrhiza (AM) symbiosis formed with Rhizophagus irregularis under condition of a low phosphorus level in the substrate. A highly effective AM symbiosis was established in the period from the stooling to the shoot branching initiation stage (the efficiency in stem height exceeded 200%). Mycorrhization led to a more intensive accumulation of phosphates (glycerophosphoglycerol and inorganic phosphate) in M. lupulina leaves. Metabolic spectra were detected with GS-MS analysis. The application of complex mathematical analyses made it possible to identify the clustering of various groups of 320 metabolites and thus demonstrate the central importance of the carbohydrate and carboxylate-amino acid clusters. The results obtained indicate a delay in the metabolic development of mycorrhized plants. Thus, AM not only accelerates the transition between plant developmental stages but delays biochemical “maturation” mainly in the form of a lag of sugar accumulation in comparison with non-mycorrhized plants. Several methods of statistical modeling proved that, at least with respect to determining the metabolic status of host-plant leaves, stages of phenological development have priority over calendar age.
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46
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Di Cesare F, Luchinat C, Tenori L, Saccenti E. Age and sex dependent changes of free circulating blood metabolite and lipid abundances, correlations and ratios. J Gerontol A Biol Sci Med Sci 2021; 77:918-926. [PMID: 34748631 PMCID: PMC9071469 DOI: 10.1093/gerona/glab335] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Indexed: 11/24/2022] Open
Abstract
In this study, we investigated how the concentrations, pairwise correlations and ratios of 202 free circulating blood metabolites and lipids vary with age in a panel of n = 1 882 participants with an age range from 48 to 94 years. We report a statistically significant sex-dependent association with age of a panel of metabolites and lipids involving, in women, linoleic acid, α-linoleic acid, and carnitine, and, in men, monoacylglycerols and lysophosphatidylcholines. Evaluating the association of correlations among metabolites and/or lipids with age, we found that phosphatidylcholines correlations tend to have a positive trend associated with age in women, and monoacylglycerols and lysophosphatidylcholines correlations tend to have a negative trend associated with age in men. The association of ratio between molecular features with age reveals that decanoyl-l-carnitine/lysophosphatidylcholine ratio in women “decrease” with age, while l-carnitine/phosphatidylcholine and l-acetylcarnitine/phosphatidylcholine ratios in men “increase” with age. These results suggest an age-dependent remodeling of lipid metabolism that induces changes in cell membrane bilayer composition and cell cycle mechanisms. Furthermore, we conclude that lipidome is directly involved in this age-dependent differentiation. Our results demonstrate that, using a comprehensive approach focused on the changes of concentrations and relationships of blood metabolites and lipids, as expressed by their correlations and ratios, it is possible to obtain relevant information about metabolic dynamics associated with age.
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Affiliation(s)
- Francesca Di Cesare
- Magnetic Resonance Center (CERM), University of Florence, Via Luigi Sacconi, Sesto Fiorentino, Firenze, Italy
| | - Claudio Luchinat
- Magnetic Resonance Center (CERM), University of Florence, Via Luigi Sacconi, Sesto Fiorentino, Firenze, Italy.,Department of Chemistry "Ugo Schiff", University of Florence, Via della Lastruccia, Sesto Fiorentino, Italy
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM), University of Florence, Via Luigi Sacconi, Sesto Fiorentino, Firenze, Italy.,Department of Chemistry "Ugo Schiff", University of Florence, Via della Lastruccia, Sesto Fiorentino, Italy
| | - Edoardo Saccenti
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng, Wageningen, the Netherlands
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47
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Khoramipour K, Sandbakk Ø, Keshteli AH, Gaeini AA, Wishart DS, Chamari K. Metabolomics in Exercise and Sports: A Systematic Review. Sports Med 2021; 52:547-583. [PMID: 34716906 DOI: 10.1007/s40279-021-01582-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/03/2021] [Indexed: 12/26/2022]
Abstract
BACKGROUND Metabolomics is a field of omics science that involves the comprehensive measurement of small metabolites in biological samples. It is increasingly being used to study exercise physiology and exercise-associated metabolism. However, the field of exercise metabolomics has not been extensively reviewed or assessed. OBJECTIVE This review on exercise metabolomics has three aims: (1) to provide an introduction to the general workflow and the different metabolomics technologies used to conduct exercise metabolomics studies; (2) to provide a systematic overview of published exercise metabolomics studies and their findings; and (3) to discuss future perspectives in the field of exercise metabolomics. METHODS We searched electronic databases including Google Scholar, Science Direct, PubMed, Scopus, Web of Science, and the SpringerLink academic journal database between January 1st 2000 and September 30th 2020. RESULTS Based on our detailed analysis of the field, exercise metabolomics studies fall into five major categories: (1) exercise nutrition metabolism; (2) exercise metabolism; (3) sport metabolism; (4) clinical exercise metabolism; and (5) metabolome comparisons. Exercise metabolism is the most popular category. The most common biological samples used in exercise metabolomics studies are blood and urine. Only a small minority of exercise metabolomics studies employ targeted or quantitative techniques, while most studies used untargeted metabolomics techniques. In addition, mass spectrometry was the most commonly used platform in exercise metabolomics studies, identified in approximately 54% of all published studies. Our data indicate that biomarkers or biomarker panels were identified in 34% of published exercise metabolomics studies. CONCLUSION Overall, there is an increasing trend towards better designed, more clinical, mass spectrometry-based metabolomics studies involving larger numbers of participants/patients and larger numbers of metabolites being identified.
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Affiliation(s)
- Kayvan Khoramipour
- Physiology Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran. .,Department of Physiology and Pharmacology, Medical Faculty, Kerman University of Medical Sciences, Blvd. 22 Bahman, Kerman, Iran.
| | - Øyvind Sandbakk
- Department of Neuromedicine and Movement Science, Centre for Elite Sports Research, Norwegian University of Science and Technology, Trondheim, Norway
| | | | - Abbas Ali Gaeini
- Department of Exercise Physiology, University of Tehran, Tehran, Iran
| | - David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB, T6G 2E9, Canada.,Department of Computing Science, University of Alberta, AB, T6G 2E9, Edmonton, Canada
| | - Karim Chamari
- ASPETAR, Qatar Orthopaedic and Sports Medicine Hospital, Doha, Qatar
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48
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Licari C, Tenori L, Giusti B, Sticchi E, Kura A, De Cario R, Inzitari D, Piccardi B, Nesi M, Sarti C, Arba F, Palumbo V, Nencini P, Marcucci R, Gori AM, Luchinat C, Saccenti E. Analysis of Metabolite and Lipid Association Networks Reveals Molecular Mechanisms Associated with 3-Month Mortality and Poor Functional Outcomes in Patients with Acute Ischemic Stroke after Thrombolytic Treatment with Recombinant Tissue Plasminogen Activator. J Proteome Res 2021; 20:4758-4770. [PMID: 34473513 PMCID: PMC8491161 DOI: 10.1021/acs.jproteome.1c00406] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
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Here, we present
an integrated multivariate, univariate, network
reconstruction and differential analysis of metabolite–metabolite
and metabolite–lipid association networks built from an array
of 18 serum metabolites and 110 lipids identified and quantified through
nuclear magnetic resonance spectroscopy in a cohort of 248 patients,
of which 22 died and 82 developed a poor functional outcome within
3 months from acute ischemic stroke (AIS) treated with intravenous
recombinant tissue plasminogen activator. We explored differences
in metabolite and lipid connectivity of patients who did not develop
a poor outcome and who survived the ischemic stroke from the related
opposite conditions. We report statistically significant differences
in the connectivity patterns of both low- and high-molecular-weight
metabolites, implying underlying variations in the metabolic pathway
involving leucine, glycine, glutamine, tyrosine, phenylalanine, citric,
lactic, and acetic acids, ketone bodies, and different lipids, thus
characterizing patients’ outcomes. Our results evidence the
promising and powerful role of the metabolite–metabolite and
metabolite–lipid association networks in investigating molecular
mechanisms underlying AIS patient’s outcome.
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Affiliation(s)
- Cristina Licari
- Magnetic Resonance Center (CERM), University of Florence, Via Luigi Sacconi 6, Sesto Fiorentino, Florence 50019, Italy
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM), University of Florence, Via Luigi Sacconi 6, Sesto Fiorentino, Florence 50019, Italy.,Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (C.I.R.M.M.P.), Via Luigi Sacconi 6, Sesto Fiorentino, Florence 50019, Italy.,Department of Chemistry, University of Florence, Via della Lastruccia, 3, Sesto Fiorentino, Florence 50019, Italy
| | - Betti Giusti
- Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, Florence 50134, Italy.,Atherothrombotic Diseases Center, Careggi Hospital, Florence, Largo Brambilla 3, Florence 50134, Italy.,Excellence Centre for Research, Transfer and High Education for the Development of DE NOVO Therapies (DENOTHE), University of Florence, Viale Pieraccini 6, Firenze 50139, Italy
| | - Elena Sticchi
- Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, Florence 50134, Italy
| | - Ada Kura
- Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, Florence 50134, Italy.,Atherothrombotic Diseases Center, Careggi Hospital, Florence, Largo Brambilla 3, Florence 50134, Italy
| | - Rosina De Cario
- Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, Florence 50134, Italy
| | - Domenico Inzitari
- Stroke Unit, Careggi University Hospital, Florence 50134, Italy.,Institute of Neuroscience, Italian National Research Council (CNR), Via Madonna del Piano, 10, Sesto Fiorentino, Florence 50019, Italy
| | | | - Mascia Nesi
- Stroke Unit, Careggi University Hospital, Florence 50134, Italy
| | - Cristina Sarti
- NEUROFARBA Department, Neuroscience Section, University of Florence, Largo Brambilla 3, Florence 50134, Italy
| | - Francesco Arba
- Department of Neurology, Careggi University Hospital, Largo Brambilla 3, Florence 50134, Italy
| | - Vanessa Palumbo
- Stroke Unit, Careggi University Hospital, Florence 50134, Italy
| | | | - Rossella Marcucci
- Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, Florence 50134, Italy.,Atherothrombotic Diseases Center, Careggi Hospital, Florence, Largo Brambilla 3, Florence 50134, Italy.,Excellence Centre for Research, Transfer and High Education for the Development of DE NOVO Therapies (DENOTHE), University of Florence, Viale Pieraccini 6, Firenze 50139, Italy
| | - Anna Maria Gori
- Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, Florence 50134, Italy.,Atherothrombotic Diseases Center, Careggi Hospital, Florence, Largo Brambilla 3, Florence 50134, Italy.,Excellence Centre for Research, Transfer and High Education for the Development of DE NOVO Therapies (DENOTHE), University of Florence, Viale Pieraccini 6, Firenze 50139, Italy
| | - Claudio Luchinat
- Magnetic Resonance Center (CERM), University of Florence, Via Luigi Sacconi 6, Sesto Fiorentino, Florence 50019, Italy.,Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (C.I.R.M.M.P.), Via Luigi Sacconi 6, Sesto Fiorentino, Florence 50019, Italy.,Department of Chemistry, University of Florence, Via della Lastruccia, 3, Sesto Fiorentino, Florence 50019, Italy
| | - Edoardo Saccenti
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, Wageningen 6708 WE, the Netherlands
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49
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Oluwagbemigun K, Anesi A, Clarke G, Schmid M, Mattivi F, Nöthlings U. An Investigation into the Temporal Reproducibility of Tryptophan Metabolite Networks Among Healthy Adolescents. Int J Tryptophan Res 2021; 14:11786469211041376. [PMID: 34594109 PMCID: PMC8477685 DOI: 10.1177/11786469211041376] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 08/01/2021] [Indexed: 01/15/2023] Open
Abstract
Tryptophan and its bioactive metabolites are associated with health conditions such as systemic inflammation, cardiometabolic diseases, and neurodegenerative disorders. There are dynamic interactions among metabolites of tryptophan. The interactions between metabolites, particularly those that are strong and temporally reproducible could be of pathophysiological relevance. Using a targeted metabolomics approach, the concentration levels of tryptophan and 18 of its metabolites across multiple pathways was quantified in 24-hours urine samples at 2 time-points, age 17 years (baseline) and 18 years (follow-up) from 132 (52% female) apparently healthy adolescent participants of the DOrtmund Nutritional and Anthropometric Longitudinally Designed (DONALD) Study. In sex-specific analyses, we applied 2 network approaches, the Gaussian graphical model and Bayesian network to (1) explore the network structure for both time-points, (2) retrieve strongly related metabolites, and (3) determine whether the strongly related metabolites were temporally reproducible. Independent of selected covariates, the 2 network approaches revealed 5 associations that were strong and temporally reproducible. These were novel relationships, between kynurenic acid and indole-3-acetic acid in females and between kynurenic acid and xanthurenic acid in males, as well as known relationships between kynurenine and 3-hydroxykynurenine, and between 3-hydroxykynurenine and 3-hydroxyanthranilic acid in females and between tryptophan and kynurenine in males. Overall, this epidemiological study using network-based approaches shed new light into tryptophan metabolism, particularly the interaction of host and microbial metabolites. The 5 observed relationships suggested the existence of a temporally stable pattern of tryptophan and 6 metabolites in healthy adolescent, which could be further investigated in search of fingerprints of specific physiological states. The metabolites in these relationships may represent a multi-biomarker panel that could be informative for health outcomes.
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Affiliation(s)
- Kolade Oluwagbemigun
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Germany
| | - Andrea Anesi
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach, San Michele all’Adige, Italy
| | - Gerard Clarke
- APC Microbiome Ireland, University College Cork, Ireland
- INFANT Research Centre, University College Cork, Ireland
- Department of Psychiatry and Neurobehavioural Science, University College Cork, Ireland
| | - Matthias Schmid
- Department of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, University of Bonn, Germany
| | - Fulvio Mattivi
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach, San Michele all’Adige, Italy
- Department of Cellular, Computational and Integrative Biology – CIBIO, University of Trento, San Michele all’Adige, Italy
| | - Ute Nöthlings
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Germany
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50
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Frizzo R, Bortoletto E, Riello T, Leanza L, Schievano E, Venier P, Mammi S. NMR Metabolite Profiles of the Bivalve Mollusc Mytilus galloprovincialis Before and After Immune Stimulation With Vibrio splendidus. Front Mol Biosci 2021; 8:686770. [PMID: 34540890 PMCID: PMC8447493 DOI: 10.3389/fmolb.2021.686770] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 07/15/2021] [Indexed: 01/26/2023] Open
Abstract
The hemolymph metabolome of Mytilus galloprovincialis injected with live Vibrio splendidus bacteria was analyzed by 1H-NMR spectrometry. Changes in spectral hemolymph profiles were already detected after mussel acclimation (3 days at 18 or 25 °C). A significant decrease of succinic acid was accompanied by an increase of most free amino acids, mytilitol, and, to a smaller degree, osmolytes. These metabolic changes are consistent with effective osmoregulation, and the restart of aerobic respiration after the functional anaerobiosis occurred during transport. The injection of Vibrio splendidus in mussels acclimated at 18°C caused a significant decrease of several amino acids, sugars, and unassigned chemical species, more pronounced at 24 than at 12 h postinjection. Correlation heatmaps indicated dynamic metabolic adjustments and the relevance of protein turnover in maintaining the homeostasis during the response to stressful stimuli. This study confirms NMR-based metabolomics as a feasible analytical approach complementary to other omics techniques in the investigation of the functional mussel responses to environmental challenges.
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Affiliation(s)
- Riccardo Frizzo
- Department of Chemical Sciences, University of Padova, Padova, Italy
| | | | - Tobia Riello
- Department of Chemical Sciences, University of Padova, Padova, Italy
| | - Luigi Leanza
- Department of Biology, University of Padova, Padova, Italy
| | | | - Paola Venier
- Department of Biology, University of Padova, Padova, Italy
| | - Stefano Mammi
- Department of Chemical Sciences, University of Padova, Padova, Italy
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