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Ge X, Chen J, Gu J, Yi W, Xu S, Tan L, Liu T. Metabolomic analysis of hydroxycinnamic acid inhibition on Saccharomyces cerevisiae. Appl Microbiol Biotechnol 2024; 108:165. [PMID: 38252275 PMCID: PMC10803543 DOI: 10.1007/s00253-023-12830-8] [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: 03/24/2023] [Revised: 09/23/2023] [Accepted: 10/03/2023] [Indexed: 01/23/2024]
Abstract
Ferulic acid (FA) and p-coumaric acid (p-CA) are hydroxycinnamic acid inhibitors that are mainly produced during the pretreatment of lignocellulose. To date, the inhibitory mechanism of hydroxycinnamic acid compounds on Saccharomyces cerevisiae has not been fully elucidated. In this study, liquid chromatography-mass spectrometry (LC-MS) and scanning electron microscopy (SEM) were used to investigate the changes in S. cerevisiae cells treated with FA and p-CA. In this experiment, the control group was denoted as group CK, the FA-treated group was denoted as group F, and the p-CA-treated group was denoted as group P. One hundred different metabolites in group F and group CK and 92 different metabolites in group P and group CK were selected and introduced to metaboanalyst, respectively. A total of 38 metabolic pathways were enriched in S. cerevisiae under FA stress, and 27 metabolic pathways were enriched in S. cerevisiae under p-CA stress as identified through Kyoto Encyclopaedia of Genes and Genomes (KEGG) analysis. The differential metabolites involved included S-adenosine methionine, L-arginine, and cysteine, which were significantly downregulated, and acetyl-CoA, L-glutamic acid, and L-threonine, which were significantly upregulated. Analysis of differential metabolic pathways showed that the differentially expressed metabolites were mainly related to amino acid metabolism, nucleotide metabolism, fatty acid degradation, and the tricarboxylic acid cycle (TCA). Under the stress of FA and p-CA, the metabolism of some amino acids was blocked, which disturbed the redox balance in the cells and destroyed the synthesis of most proteins, which was the main reason for the inhibition of yeast cell growth. This study provided a strong scientific reference to improve the durability of S. cerevisiae against hydroxycinnamic acid inhibitors. KEY POINTS: • Morphological changes of S. cerevisiae cells under inhibitors stress were observed. • Changes of the metabolites in S. cerevisiae cells were explored by metabolomics. • One of the inhibitory effects on yeast is due to changes in the metabolic network.
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Affiliation(s)
- Xiaoli Ge
- State Key Laboratory of Biobased Material and Green Papermaking, Qilu University of Technology, Shandong Academy of Sciences, Jinan, 250353, China
| | - Junxiao Chen
- State Key Laboratory of Biobased Material and Green Papermaking, Qilu University of Technology, Shandong Academy of Sciences, Jinan, 250353, China
| | - Jie Gu
- State Key Laboratory of Biobased Material and Green Papermaking, Qilu University of Technology, Shandong Academy of Sciences, Jinan, 250353, China
| | - Wenbo Yi
- State Key Laboratory of Biobased Material and Green Papermaking, Qilu University of Technology, Shandong Academy of Sciences, Jinan, 250353, China
| | - Shujie Xu
- State Key Laboratory of Biobased Material and Green Papermaking, Qilu University of Technology, Shandong Academy of Sciences, Jinan, 250353, China
| | - Liping Tan
- State Key Laboratory of Biobased Material and Green Papermaking, Qilu University of Technology, Shandong Academy of Sciences, Jinan, 250353, China.
- Department of Bioengineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, 250353, China.
| | - Tongjun Liu
- State Key Laboratory of Biobased Material and Green Papermaking, Qilu University of Technology, Shandong Academy of Sciences, Jinan, 250353, China.
- Department of Bioengineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, 250353, China.
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Lin G, Dong L, Cheng KK, Xu X, Wang Y, Deng L, Raftery D, Dong J. Differential Correlations Informed Metabolite Set Enrichment Analysis to Decipher Metabolic Heterogeneity of Disease. Anal Chem 2023; 95:12505-12513. [PMID: 37557184 DOI: 10.1021/acs.analchem.3c02246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/11/2023]
Abstract
Metabolic pathways are regarded as functional and basic components of the biological system. In metabolomics, metabolite set enrichment analysis (MSEA) is often used to identify the altered metabolic pathways (metabolite sets) associated with phenotypes of interest (POI), e.g., disease. However, in most studies, MSEA suffers from the limitation of low metabolite coverage. Random walk (RW)-based algorithms can be used to propagate the perturbation of detected metabolites to the undetected metabolites through a metabolite network model prior to MSEA. Nevertheless, most of the existing RW-based algorithms run on a general metabolite network constructed based on public databases, such as KEGG, without taking into consideration the potential influence of POI on the metabolite network, which may reduce the phenotypic specificities of the MSEA results. To solve this problem, a novel pathway analysis strategy, namely, differential correlation-informed MSEA (dci-MSEA), is proposed in this paper. Statistically, differential correlations between metabolites are used to evaluate the influence of POI on the metabolite network, so that a phenotype-specific metabolite network is constructed for RW-based propagation. The experimental results show that dci-MSEA outperforms the conventional RW-based MSEA in identifying the altered metabolic pathways associated with colorectal cancer. In addition, by incorporating the individual-specific metabolite network, the dci-MSEA strategy is easily extended to disease heterogeneity analysis. Here, dci-MSEA was used to decipher the heterogeneity of colorectal cancer. The present results highlight the clustering of colorectal cancer samples with their cluster-specific selection of differential pathways and demonstrate the feasibility of dci-MSEA in heterogeneity analysis. Taken together, the proposed dci-MSEA may provide insights into disease mechanisms and determination of disease heterogeneity.
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Affiliation(s)
- Genjin Lin
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
| | - Liheng Dong
- School of Computing and Data Science, Xiamen University Malaysia, 43600 Sepang, Malaysia
| | - Kian-Kai Cheng
- Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia
| | - Xiangnan Xu
- School of Business and Economics, Humboldt-Universität zu Berlin, Berlin 10099, Germany
| | - Yongpei Wang
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
| | - Lingli Deng
- Department of Information Engineering, East China University of Technology, Nanchang 330013, China
| | - Daniel Raftery
- Northwest Metabolomics Research Center, University of Washington, Seattle, Washington 98109, United States
| | - Jiyang Dong
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
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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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Singh R, Thakur L, Kumar A, Singh S, Kumar S, Kumar M, Kumar Y, Kumar N. Comparison of freeze-thaw and sonication cycle-based methods for extracting AMR-associated metabolites from Staphylococcus aureus. Front Microbiol 2023; 14:1152162. [PMID: 37180233 PMCID: PMC10174324 DOI: 10.3389/fmicb.2023.1152162] [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: 01/27/2023] [Accepted: 04/10/2023] [Indexed: 05/16/2023] Open
Abstract
Emerging antimicrobial resistance (AMR) among Gram-positive pathogens, specifically in Staphylococcus aureus (S. aureus), is becoming a leading public health concern demanding effective therapeutics. Metabolite modulation can improve the efficacy of existing antibiotics and facilitate the development of effective therapeutics. However, it remained unexplored for drug-resistant S. aureus (gentamicin and methicillin-resistant), primarily due to the dearth of optimal metabolite extraction protocols including a protocol for AMR-associated metabolites. Therefore, in this investigation, we have compared the performance of the two most widely used methods, i.e., freeze-thaw cycle (FTC) and sonication cycle (SC), alone and in combination (FTC + SC), and identified the optimal method for this purpose. A total of 116, 119, and 99 metabolites were identified using the FTC, SC, and FTC + SC methods, respectively, leading to the identification of 163 metabolites cumulatively. Out of 163, 69 metabolites were found to be associated with AMR in published literature consisting of the highest number of metabolites identified by FTC (57) followed by SC (54) and FTC + SC (40). Thus, the performances of FTC and SC methods were comparable with no additional benefits of combining both. Moreover, each method showed biasness toward specific metabolite(s) or class of metabolites, suggesting that the choice of metabolite extraction method shall be decided based on the metabolites of interest in the investigation.
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Affiliation(s)
- Rita Singh
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, India
- Jawaharlal Nehru University, Delhi, India
| | - Lovnish Thakur
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, India
- Jawaharlal Nehru University, Delhi, India
| | - Ashok Kumar
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, India
| | - Sevaram Singh
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, India
- Jawaharlal Nehru University, Delhi, India
| | - Shailesh Kumar
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, India
| | - Manoj Kumar
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, India
| | - Yashwant Kumar
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, India
- *Correspondence: Yashwant Kumar,
| | - Niraj Kumar
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, India
- Niraj Kumar,
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Metabolic flux between organs measured by arteriovenous metabolite gradients. EXPERIMENTAL & MOLECULAR MEDICINE 2022; 54:1354-1366. [PMID: 36075951 PMCID: PMC9534916 DOI: 10.1038/s12276-022-00803-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 02/15/2022] [Accepted: 03/04/2022] [Indexed: 12/15/2022]
Abstract
Mammalian organs convert dietary nutrients into circulating metabolites and share them to maintain whole-body metabolic homeostasis. While the concentrations of circulating metabolites have been frequently measured in a variety of pathophysiological conditions, the exchange flux of circulating metabolites between organs is not easily measurable due to technical difficulties. Isotope tracing is useful for measuring such fluxes for a metabolite of interest, but the shuffling of isotopic atoms between metabolites requires mathematical modeling. Arteriovenous metabolite gradient measurements can complement isotope tracing to infer organ-specific net fluxes of many metabolites simultaneously. Here, we review the historical development of arteriovenous measurements and discuss their advantages and limitations with key example studies that have revealed metabolite exchange flux between organs in diverse pathophysiological contexts.
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Deng L, Ma L, Cheng KK, Xu X, Raftery D, Dong J. Sparse PLS-Based Method for Overlapping Metabolite Set Enrichment Analysis. J Proteome Res 2021; 20:3204-3213. [PMID: 34002606 DOI: 10.1021/acs.jproteome.1c00064] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Metabolite set enrichment analysis (MSEA) has gained increasing research interest for identification of perturbed metabolic pathways in metabolomics. The method incorporates predefined metabolic pathways information in the analysis where metabolite sets are typically assumed to be mutually exclusive to each other. However, metabolic pathways are known to contain common metabolites and intermediates. This situation, along with limitations in metabolite detection or coverage leads to overlapping, incomplete metabolite sets in pathway analysis. For overlapping metabolite sets, MSEA tends to result in high false positives due to improper weights allocated to the overlapping metabolites. Here, we proposed an extended partial least squares (PLS) model with a new sparse scheme for overlapping metabolite set enrichment analysis, named overlapping group PLS (ogPLS) analysis. The weight vector of the ogPLS model was decomposed into pathway-specific subvectors, and then a group lasso penalty was imposed on these subvectors to achieve a proper weight allocation for the overlapping metabolites. Two strategies were adopted in the proposed ogPLS model to identify the perturbed metabolic pathways. The first strategy involves debiasing regularization, which was used to reduce inequalities amongst the predefined metabolic pathways. The second strategy is stable selection, which was used to rank pathways while avoiding the nuisance problems of model parameter optimization. Both simulated and real-world metabolomic datasets were used to evaluate the proposed method and compare with two other MSEA methods including Global-test and the multiblock PLS (MB-PLS)-based pathway importance in projection (PIP) methods. Using a simulated dataset with known perturbed pathways, the average true discovery rate for the ogPLS method was found to be higher than the Global-test and the MB-PLS-based PIP methods. Analysis with a real-world metabolomics dataset also indicated that the developed method was less prone to select pathways with highly overlapped detected metabolite sets. Compared with the two other methods, the proposed method features higher accuracy, lower false-positive rate, and is more robust when applied to overlapping metabolite set analysis. The developed ogPLS method may serve as an alternative MSEA method to facilitate biological interpretation of metabolomics data for overlapping metabolite sets.
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Affiliation(s)
- Lingli Deng
- Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System, East China University of Technology, Nanchang 330013, China.,Department of Information Engineering, East China University of Technology, Nanchang 330013, China
| | - Lei Ma
- Department of Information Engineering, East China University of Technology, Nanchang 330013, China
| | - Kian-Kai Cheng
- Innovation Centre in Agritechnology, Universiti Teknologi Malaysia, Muar 84600, Johor, Malaysia
| | - Xiangnan Xu
- School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Daniel Raftery
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington 98109, United States
| | - Jiyang Dong
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
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