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Functional metabolomics reveal the role of AHR/GPR35 mediated kynurenic acid gradient sensing in chemotherapy-induced intestinal damage. Acta Pharm Sin B 2021; 11:763-780. [PMID: 33777681 PMCID: PMC7982426 DOI: 10.1016/j.apsb.2020.07.017] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 07/14/2020] [Accepted: 07/15/2020] [Indexed: 12/26/2022] Open
Abstract
Intestinal toxicity induced by chemotherapeutics has become an important reason for the interruption of therapy and withdrawal of approved agents. In this study, we demonstrated that chemotherapeutics-induced intestinal damage were commonly characterized by the sharp upregulation of tryptophan (Trp)−kynurenine (KYN)−kynurenic acid (KA) axis metabolism. Mechanistically, chemotherapy-induced intestinal damage triggered the formation of an interleukin-6 (IL-6)−indoleamine 2,3-dioxygenase 1 (IDO1)−aryl hydrocarbon receptor (AHR) positive feedback loop, which accelerated kynurenine pathway metabolism in gut. Besides, AHR and G protein-coupled receptor 35 (GPR35) negative feedback regulates intestinal damage and inflammation to maintain intestinal integrity and homeostasis through gradually sensing kynurenic acid level in gut and macrophage, respectively. Moreover, based on virtual screening and biological verification, vardenafil and linagliptin as GPR35 and AHR agonists respectively were discovered from 2388 approved drugs. Importantly, the results that vardenafil and linagliptin significantly alleviated chemotherapy-induced intestinal toxicity in vivo suggests that chemotherapeutics combined with the two could be a promising therapeutic strategy for cancer patients in clinic. This work highlights GPR35 and AHR as the guardian of kynurenine pathway metabolism and core component of defense responses against intestinal damage.
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Key Words
- 1-MT, 1-methyl-tryptophan
- AG, AG490
- AHR
- AHR, aryl hydrocarbon receptor
- ARNT, aryl hydrocarbon receptor nuclear translocator
- BCA, bicinchoninic acid
- BSA, bovine serum albumin
- CH, CH223191
- CPT-11, irinotecan
- CYP1A1, cytochrome P450 1A1
- DAI, disease activity index
- DMSO, dimethyl sulfoxide
- DPP-4, dipeptidyl peptidase-4
- DRE, dioxin response elements
- DSS, dextran sulphate sodium
- Dens-Cl, N-diethyl-amino naphthalene-1-sulfonyl chloride
- Dns-Cl, N-dimethyl-amino naphthalene-1-sulfonyl chloride
- ECL, enhanced chemiluminescence
- ELISA, enzyme-linked immunosorbent assay
- ERK1/2, extracellular regulated protein kinases 1/2
- ESI, electrospray ionization
- FBS, fetal bovine serum
- GE, gastric emptying
- GFP, green fluorescence protein
- GI, gastrointestinal transit
- GPR35
- GPR35, G protein-coupled receptor 35
- Gradually sensing
- HE, hematoxylin and eosin
- HRP, horseradish peroxi-dase
- IBD, inflammatory bowel disease
- IDO1, indoleamine 2,3-dioxygenase 1
- IL-6, interleukin-6
- IS, internal standard
- Intestinal toxicity
- JAK2, janus kinase 2
- KA, kynurenic acid
- KAT, kynurenine aminotransferase
- KYN, kynurenine
- Kynurenine pathway
- LC–MS, liquid chromatography–mass spectrometry
- LPS, lipopolysaccharides
- Linag, linagliptin
- MOE, molecular operating environment
- MOI, multiplicity of infection
- MRM, multiple-reaction monitoring
- MTT, thiazolyl blue tetrazolium bromide
- PBS, phosphate buffer saline
- PDB, protein data bank
- PDE5, phosphodiesterase type-5
- PF, PF-04859989
- PMA, phorbol 12-myristate 13-acetate
- PMSF, phenylmethylsulfonyl fluoride
- RIPA, radioimmunoprecipitation
- RPKM, reads per kilobase per million mapped reads
- RPMI 1640, Roswell Park Memorial Institute 1640
- RT-PCR, real-time polymerase chain reaction
- STAT3, signal transducer and activator of transcription 3
- Trp, tryptophan
- VCR, vincristine
- Vard, vardenafil
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Eicher T, Kinnebrew G, Patt A, Spencer K, Ying K, Ma Q, Machiraju R, Mathé EA. Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources. Metabolites 2020; 10:E202. [PMID: 32429287 PMCID: PMC7281435 DOI: 10.3390/metabo10050202] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/07/2020] [Accepted: 05/13/2020] [Indexed: 02/06/2023] Open
Abstract
As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.
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Affiliation(s)
- Tara Eicher
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Computer Science and Engineering Department, The Ohio State University College of Engineering, Columbus, OH 43210, USA
| | - Garrett Kinnebrew
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH 43210, USA;
- Bioinformatics Shared Resource Group, The Ohio State University, Columbus, OH 43210, USA
| | - Andrew Patt
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, NIH, 9800 Medical Center Dr., Rockville, MD, 20892, USA;
- Biomedical Sciences Graduate Program, The Ohio State University, Columbus, OH 43210, USA
| | - Kyle Spencer
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Biomedical Sciences Graduate Program, The Ohio State University, Columbus, OH 43210, USA
- Nationwide Children’s Research Hospital, Columbus, OH 43210, USA
| | - Kevin Ying
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH 43210, USA;
- Molecular, Cellular and Developmental Biology Program, The Ohio State University, Columbus, OH 43210, USA
| | - Qin Ma
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
| | - Raghu Machiraju
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Computer Science and Engineering Department, The Ohio State University College of Engineering, Columbus, OH 43210, USA
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH 43210, USA
- Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA
| | - Ewy A. Mathé
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, NIH, 9800 Medical Center Dr., Rockville, MD, 20892, USA;
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Yan Y, Du Z, Chen C, Li J, Xiong X, Zhang Y, Jiang H. Lysophospholipid profiles of apolipoprotein E-deficient mice reveal potential lipid biomarkers associated with atherosclerosis progression using validated UPLC-QTRAP-MS/MS-based lipidomics approach. J Pharm Biomed Anal 2019; 171:148-157. [PMID: 30999225 DOI: 10.1016/j.jpba.2019.03.062] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 03/25/2019] [Accepted: 03/27/2019] [Indexed: 01/19/2023]
Abstract
Lysophospholipids (Lyso-PLs) are lipid-derived signaling molecules which were demonstrated to have a strong correlation with the progression of atherosclerosis. In this study, we investigated the influence of high-fat diet on Lyso-PL profiles of atherosclerosis-prone apolipoprotein E-deficient (ApoE-/-) mice and wild type C57BL/6 J mice to find out the potential biomarkers associated with atherosclerosis. Firstly, the quantitative profiling method for Lyso-PLs based on ultra-performance liquid chromatography-quadrupole linear ion trap mass spectrometry (UPLC-QTRAP-MS/MS) was established and validated. Secondly, this method was utilized to quantify 169 targeted Lyso-PLs in plasma samples of ApoE-/- mice and wild type C57BL/6 J mice collected at different time points. Finally, 12 of 37 differential Lyso-PLs were identified as more reliable biomarkers by integrating static metabolomics and time-dependent analyses, among which Lyso-PC/15:0, 18:1/Lyso-PI, 22:5/Lyso-PI and 22:4/Lyso-PI were highly correlated with TCand LDL-C levels. Meanwhile, we found that the Lyso-PL profiles of ApoE-/- mice and C57BL/6 J mice were distinguished by altered metabolism of different Lyso-PLs classes, while C57BL/6 J mice fed with high-fat diet and normal diet were discriminated by the content differences of Lyso-PLs with same fatty acid composition. In conclusion, these results provided detailed changes of Lyso-PL profiles associated with atherosclerosis and the differential Lyso-PLs with reasonable change trends may serve as promising biomarkers for atherosclerosis progression.
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Affiliation(s)
- Yingfei Yan
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan, China
| | - Zhifeng Du
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan, China
| | - Chang Chen
- Institute of Life Sciences, Chongqing Medical University, Chongqing, China
| | - Jiaxin Li
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan, China
| | - Xiang Xiong
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan, China
| | - Yang Zhang
- Department of Pharmacy, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China.
| | - Hongliang Jiang
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan, China.
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Guo H, Jiao Y, Wang X, Lu T, Zhang Z, Xu F. Twins labeling-liquid chromatography/mass spectrometry based metabolomics for absolute quantification of tryptophan and its key metabolites. J Chromatogr A 2017; 1504:83-90. [DOI: 10.1016/j.chroma.2017.05.008] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Revised: 05/02/2017] [Accepted: 05/04/2017] [Indexed: 01/22/2023]
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