1
|
Scheidemantle G, Duan L, Lodge M, Cummings MJ, Hilovsky D, Pham E, Wang X, Kennedy A, Liu X. Data-dependent and -independent acquisition lipidomics analysis reveals the tissue-dependent effect of metformin on lipid metabolism. Metabolomics 2024; 20:53. [PMID: 38722395 PMCID: PMC11145978 DOI: 10.1007/s11306-024-02113-2] [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: 01/04/2023] [Accepted: 03/22/2024] [Indexed: 05/21/2024]
Abstract
INTRODUCTION Despite the well-recognized health benefits, the mechanisms and site of action of metformin remains elusive. Metformin-induced global lipidomic changes in plasma of animal models and human subjects have been reported. However, there is a lack of systemic evaluation of metformin-induced lipidomic changes in different tissues. Metformin uptake requires active transporters such as organic cation transporters (OCTs), and hence, it is anticipated that metformin actions are tissue-dependent. In this study, we aim to characterize metformin effects in non-diabetic male mice with a special focus on lipidomics analysis. The findings from this study will help us to better understand the cell-autonomous (direct actions in target cells) or non-cell-autonomous (indirect actions in target cells) mechanisms of metformin and provide insights into the development of more potent yet safe drugs targeting a particular organ instead of systemic metabolism for metabolic regulations without major side effects. OBJECTIVES To characterize metformin-induced lipidomic alterations in different tissues of non-diabetic male mice and further identify lipids affected by metformin through cell-autonomous or systemic mechanisms based on the correlation between lipid alterations in tissues and the corresponding in-tissue metformin concentrations. METHODS A dual extraction method involving 80% methanol followed by MTBE (methyl tert-butyl ether) extraction enables the analysis of free fatty acids, polar metabolites, and lipids. Extracts from tissues and plasma of male mice treated with or without metformin in drinking water for 12 days were analyzed using HILIC chromatography coupled to Q Exactive Plus mass spectrometer or reversed-phase liquid chromatography coupled to MS/MS scan workflow (hybrid mode) on LC-Orbitrap Exploris 480 mass spectrometer using biologically relevant lipids-containing inclusion list for data-independent acquisition (DIA), named as BRI-DIA workflow followed by data-dependent acquisition (DDA), to maximum the coverage of lipids and minimize the negative effect of stochasticity of precursor selection on experimental consistency and reproducibility. RESULTS Lipidomics analysis of 6 mouse tissues and plasma allowed a systemic evaluation of lipidomic changes induced by metformin in different tissues. We observed that (1) the degrees of lipidomic changes induced by metformin treatment overly correlated with tissue concentrations of metformin; (2) the impact on lysophosphatidylcholine (lysoPC) and cardiolipins was positively correlated with tissue concentrations of metformin, while neutral lipids such as triglycerides did not correlate with the corresponding tissue metformin concentrations; (3) increase of intestinal tricarboxylic acid (TCA) cycle intermediates after metformin treatment. CONCLUSION The data collected in this study from non-diabetic mice with 12-day metformin treatment suggest that the overall metabolic effect of metformin is positively correlated with tissue concentrations and the effect on individual lipid subclass is via both cell-autonomous mechanisms (cardiolipins and lysoPC) and non-cell-autonomous mechanisms (triglycerides).
Collapse
Affiliation(s)
- Grace Scheidemantle
- Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC, 27695, USA
| | - Likun Duan
- Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC, 27695, USA
| | - Mareca Lodge
- Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC, 27695, USA
| | - Magdalina J Cummings
- Department of Animal Science, North Carolina State University, Raleigh, NC, 27695, USA
- The Comparative Medicine Institute, North Carolina State University, Raleigh, NC, 27695, USA
| | - Dalton Hilovsky
- Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC, 27695, USA
| | - Eva Pham
- Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC, 27695, USA
| | - Xiaoqiu Wang
- Department of Animal Science, North Carolina State University, Raleigh, NC, 27695, USA
- The Comparative Medicine Institute, North Carolina State University, Raleigh, NC, 27695, USA
| | - Arion Kennedy
- Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC, 27695, USA
| | - Xiaojing Liu
- Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC, 27695, USA.
| |
Collapse
|
2
|
Xu R, Liu H, Yuan F, Kim S, Kirpich I, McClain CJ, Zhang X. Lipid Wizard: Analysis Software for Comprehensive Two-Dimensional Liquid Chromatography-Mass Spectrometry-Based Lipid Profiling. Anal Chem 2024; 96:5375-5383. [PMID: 38523323 DOI: 10.1021/acs.analchem.3c04419] [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: 03/26/2024]
Abstract
Lipids play a significant role in life activities and participate in the biological system through different pathways. Although comprehensive two-dimensional liquid chromatography-mass spectrometry (2DLC-MS) has been developed to profile lipid abundance changes, lipid identification and quantification from 2DLC-MS data remain a challenge. We created Lipid Wizard, open-source software for lipid assignment and isotopic peak stripping of the 2DLC-MS data. Lipid Wizard takes the peak list deconvoluted from the 2DLC-MS data as input and assigns each isotopic peak to the lipids recorded in the LIPID MAPS database by precursor ion m/z matching. The matched lipids are then filtered by the first-dimension retention time (1D RT), followed by the second-dimension retention time (2D RT), where the 2D RT of each lipid is predicted using an equivalent carbon number (ECN) model. The remaining assigned lipids are used for isotopic peak stripping via an iterative linear regression. The performance of Lipid Wizard was tested using a set of lipid standards and then applied to study the lipid changes in the livers of mice (fat-1) fed with alcohol.
Collapse
Affiliation(s)
- Raobo Xu
- Department of Chemistry, University of Louisville, Louisville, Kentucky 40292, United States
- Alcohol Research Center, University of Louisville, Louisville, Kentucky 40292, United States
- Hepatobiology and Toxicology Center of Biomedical Research Excellence, University of Louisville, Louisville, Kentucky 40292, United States
- Center for Regulatory and Environmental Analytical Metabolomics, University of Louisville, Louisville, Kentucky 40292, United States
| | - Huan Liu
- Department of Computer Science and Engineering, University of Louisville, Louisville, Kentucky 40292, United States
| | - Fang Yuan
- Department of Chemistry, University of Louisville, Louisville, Kentucky 40292, United States
- Alcohol Research Center, University of Louisville, Louisville, Kentucky 40292, United States
- Hepatobiology and Toxicology Center of Biomedical Research Excellence, University of Louisville, Louisville, Kentucky 40292, United States
- Center for Regulatory and Environmental Analytical Metabolomics, University of Louisville, Louisville, Kentucky 40292, United States
| | - Seongho Kim
- Department of Oncology, Wayne State University, Detroit, Michigan 48201, United States
- Biostatistics and Bioinformatics Core, Karmanos Cancer Institute, Wayne State University, Detroit, Michigan 48201, United States
| | - Irina Kirpich
- Alcohol Research Center, University of Louisville, Louisville, Kentucky 40292, United States
- Hepatobiology and Toxicology Center of Biomedical Research Excellence, University of Louisville, Louisville, Kentucky 40292, United States
- Department of Microbiology and Immunology, University of Louisville, Louisville, Kentucky 40292, United States
| | - Craig J McClain
- Alcohol Research Center, University of Louisville, Louisville, Kentucky 40292, United States
- Hepatobiology and Toxicology Center of Biomedical Research Excellence, University of Louisville, Louisville, Kentucky 40292, United States
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky 40292, United States
- Robley Rex Veterans Affairs Medical Center, Louisville, Kentucky 40206, United States
| | - Xiang Zhang
- Department of Chemistry, University of Louisville, Louisville, Kentucky 40292, United States
- Alcohol Research Center, University of Louisville, Louisville, Kentucky 40292, United States
- Hepatobiology and Toxicology Center of Biomedical Research Excellence, University of Louisville, Louisville, Kentucky 40292, United States
- Center for Regulatory and Environmental Analytical Metabolomics, University of Louisville, Louisville, Kentucky 40292, United States
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky 40292, United States
| |
Collapse
|
3
|
Zhu A, Liu M, Tian Z, Liu W, Hu X, Ao M, Jia J, Shi T, Liu H, Li D, Mao H, Su H, Yan W, Li Q, Lan C, Fernie AR, Chen W. Chemical-tag-based semi-annotated metabolomics facilitates gene identification and specialized metabolic pathway elucidation in wheat. THE PLANT CELL 2024; 36:540-558. [PMID: 37956052 PMCID: PMC10896294 DOI: 10.1093/plcell/koad286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 10/19/2023] [Accepted: 10/19/2023] [Indexed: 11/15/2023]
Abstract
The importance of metabolite modification and species-specific metabolic pathways has long been recognized. However, linking the chemical structure of metabolites to gene function in order to explore the genetic and biochemical basis of metabolism has not yet been reported in wheat (Triticum aestivum). Here, we profiled metabolic fragment enrichment in wheat leaves and consequently applied chemical-tag-based semi-annotated metabolomics in a genome-wide association study in accessions of wheat. The studies revealed that all 1,483 quantified metabolites have at least one known functional group whose modification is tailored in an enzyme-catalyzed manner and eventually allows efficient candidate gene mining. A Triticeae crop-specific flavonoid pathway and its underlying metabolic gene cluster were elucidated in further functional studies. Additionally, upon overexpressing the major effect gene of the cluster TraesCS2B01G460000 (TaOMT24), the pathway was reconstructed in rice (Oryza sativa), which lacks this pathway. The reported workflow represents an efficient and unbiased approach for gene mining using forward genetics in hexaploid wheat. The resultant candidate gene list contains vast molecular resources for decoding the genetic architecture of complex traits and identifying valuable breeding targets and will ultimately aid in achieving wheat crop improvement.
Collapse
Affiliation(s)
- Anting Zhu
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Mengmeng Liu
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Zhitao Tian
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Wei Liu
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Xin Hu
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Min Ao
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Jingqi Jia
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Taotao Shi
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Hongbo Liu
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
| | - Dongqin Li
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
| | - Hailiang Mao
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
| | - Handong Su
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Wenhao Yan
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Qiang Li
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
| | - Caixia Lan
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Department of Root Biology and Symbiosis, Potsdam-Golm 14476, Germany
| | - Wei Chen
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| |
Collapse
|
4
|
Gerhardtova I, Jankech T, Majerova P, Piestansky J, Olesova D, Kovac A, Jampilek J. Recent Analytical Methodologies in Lipid Analysis. Int J Mol Sci 2024; 25:2249. [PMID: 38396926 PMCID: PMC10889185 DOI: 10.3390/ijms25042249] [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: 01/19/2024] [Revised: 02/09/2024] [Accepted: 02/11/2024] [Indexed: 02/25/2024] Open
Abstract
Lipids represent a large group of biomolecules that are responsible for various functions in organisms. Diseases such as diabetes, chronic inflammation, neurological disorders, or neurodegenerative and cardiovascular diseases can be caused by lipid imbalance. Due to the different stereochemical properties and composition of fatty acyl groups of molecules in most lipid classes, quantification of lipids and development of lipidomic analytical techniques are problematic. Identification of different lipid species from complex matrices is difficult, and therefore individual analytical steps, which include extraction, separation, and detection of lipids, must be chosen properly. This review critically documents recent strategies for lipid analysis from sample pretreatment to instrumental analysis and data interpretation published in the last five years (2019 to 2023). The advantages and disadvantages of various extraction methods are covered. The instrumental analysis step comprises methods for lipid identification and quantification. Mass spectrometry (MS) is the most used technique in lipid analysis, which can be performed by direct infusion MS approach or in combination with suitable separation techniques such as liquid chromatography or gas chromatography. Special attention is also given to the correct evaluation and interpretation of the data obtained from the lipid analyses. Only accurate, precise, robust and reliable analytical strategies are able to bring complex and useful lipidomic information, which may contribute to clarification of some diseases at the molecular level, and may be used as putative biomarkers and/or therapeutic targets.
Collapse
Affiliation(s)
- Ivana Gerhardtova
- Institute of Neuroimmunology, Slovak Academy of Sciences, Dubravska cesta 9, SK-845 10 Bratislava, Slovakia
- Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University in Bratislava, Ilkovicova 6, SK-842 15 Bratislava, Slovakia
| | - Timotej Jankech
- Institute of Neuroimmunology, Slovak Academy of Sciences, Dubravska cesta 9, SK-845 10 Bratislava, Slovakia
- Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University in Bratislava, Ilkovicova 6, SK-842 15 Bratislava, Slovakia
| | - Petra Majerova
- Institute of Neuroimmunology, Slovak Academy of Sciences, Dubravska cesta 9, SK-845 10 Bratislava, Slovakia
| | - Juraj Piestansky
- Institute of Neuroimmunology, Slovak Academy of Sciences, Dubravska cesta 9, SK-845 10 Bratislava, Slovakia
- Toxicological and Antidoping Center, Faculty of Pharmacy, Comenius University in Bratislava, Odbojarov 10, SK-832 32 Bratislava, Slovakia
- Department of Galenic Pharmacy, Faculty of Pharmacy, Comenius University in Bratislava, Odbojarov 10, SK-832 32 Bratislava, Slovakia
| | - Dominika Olesova
- Institute of Neuroimmunology, Slovak Academy of Sciences, Dubravska cesta 9, SK-845 10 Bratislava, Slovakia
- Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Dubravska cesta 9, SK-845 05 Bratislava, Slovakia
| | - Andrej Kovac
- Institute of Neuroimmunology, Slovak Academy of Sciences, Dubravska cesta 9, SK-845 10 Bratislava, Slovakia
- Department of Pharmacology and Toxicology, University of Veterinary Medicine and Pharmacy in Kosice, Komenskeho 68/73, SK-041 81 Kosice, Slovakia
| | - Josef Jampilek
- Institute of Neuroimmunology, Slovak Academy of Sciences, Dubravska cesta 9, SK-845 10 Bratislava, Slovakia
- Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University in Bratislava, Ilkovicova 6, SK-842 15 Bratislava, Slovakia
| |
Collapse
|
5
|
Adams VR, Collins LB, Williams TI, Holmes J, Hess P, Atkins HM, Scheidemantle G, Liu X, Lodge M, Johnson AJ, Kennedy A. Myeloid cell MHC I expression drives CD8 + T cell activation in nonalcoholic steatohepatitis. Front Immunol 2024; 14:1302006. [PMID: 38274832 PMCID: PMC10808415 DOI: 10.3389/fimmu.2023.1302006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 11/28/2023] [Indexed: 01/27/2024] Open
Abstract
Background & aims Activated CD8+ T cells are elevated in Nonalcoholic steatohepatitis (NASH) and are important for driving fibrosis and inflammation. Despite this, mechanisms of CD8+ T cell activation in NASH are largely limited. Specific CD8+ T cell subsets may become activated through metabolic signals or cytokines. However, studies in NASH have not evaluated the impact of antigen presentation or the involvement of specific antigens. Therefore, we determined if activated CD8+ T cells are dependent on MHC class I expression in NASH to regulate fibrosis and inflammation. Methods We used H2Kb and H2Db deficient (MHC I KO), Kb transgenic mice, and myeloid cell Kb deficient mice (LysM Kb KO) to investigate how MHC class I impacts CD8+ T cell function and NASH. Flow cytometry, gene expression, and histology were used to examine hepatic inflammation and fibrosis. The hepatic class I immunopeptidome was evaluated by mass spectrometry. Results In NASH, MHC class I isoform H2Kb was upregulated in myeloid cells. MHC I KO demonstrated protective effects against NASH-induced inflammation and fibrosis. Kb mice exhibited increased fibrosis in the absence of H2Db while LysM Kb KO mice showed protection against fibrosis but not inflammation. H2Kb restricted peptides identified a unique NASH peptide Ncf2 capable of CD8+ T cell activation in vitro. The Ncf2 peptide was not detected during fibrosis resolution. Conclusion These results suggest that activated hepatic CD8+ T cells are dependent on myeloid cell MHC class I expression in diet induced NASH to promote inflammation and fibrosis. Additionally, our studies suggest a role of NADPH oxidase in the production of Ncf2 peptide generation.
Collapse
Affiliation(s)
- Victoria R. Adams
- Department of Molecular and Structural Biochemistry, NC State University, Raleigh, NC, United States
| | - Leonard B. Collins
- Molecular Education, Technology and Research Innovation Center (METRIC), NC State University, Raleigh, NC, United States
| | - Taufika Islam Williams
- Molecular Education, Technology and Research Innovation Center (METRIC), NC State University, Raleigh, NC, United States
- Department of Chemistry, NC State University, Raleigh, NC, United States
| | - Jennifer Holmes
- College of Veterinary Medicine, NC State University, Raleigh, NC, United States
| | - Paul Hess
- College of Veterinary Medicine, NC State University, Raleigh, NC, United States
| | - Hannah M. Atkins
- Center for Human Health and Environment, NC State University, Raleigh, NC, United States
- Division of Comparative Medicine, UNC Chapel Hill, Chapel Hill, NC, United States
| | - Grace Scheidemantle
- Department of Molecular and Structural Biochemistry, NC State University, Raleigh, NC, United States
| | - Xiaojing Liu
- Department of Molecular and Structural Biochemistry, NC State University, Raleigh, NC, United States
| | - Mareca Lodge
- Department of Molecular and Structural Biochemistry, NC State University, Raleigh, NC, United States
| | - Aaron J. Johnson
- Department of Immunology, Mayo Clinic, Rochester, MN, United States
| | - Arion Kennedy
- Department of Molecular and Structural Biochemistry, NC State University, Raleigh, NC, United States
| |
Collapse
|
6
|
Fernández Requena B, Nadeem S, Reddy VP, Naidoo V, Glasgow JN, Steyn AJC, Barbas C, Gonzalez-Riano C. LiLA: lipid lung-based ATLAS built through a comprehensive workflow designed for an accurate lipid annotation. Commun Biol 2024; 7:45. [PMID: 38182666 PMCID: PMC10770321 DOI: 10.1038/s42003-023-05680-7] [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: 08/17/2023] [Accepted: 12/06/2023] [Indexed: 01/07/2024] Open
Abstract
Accurate lipid annotation is crucial for understanding the role of lipids in health and disease and identifying therapeutic targets. However, annotating the wide variety of lipid species in biological samples remains challenging in untargeted lipidomic studies. In this work, we present a lipid annotation workflow based on LC-MS and MS/MS strategies, the combination of four bioinformatic tools, and a decision tree to support the accurate annotation and semi-quantification of the lipid species present in lung tissue from control mice. The proposed workflow allowed us to generate a lipid lung-based ATLAS (LiLA), which was then employed to unveil the lipidomic signatures of the Mycobacterium tuberculosis infection at two different time points for a deeper understanding of the disease progression. This workflow, combined with manual inspection strategies of MS/MS data, can enhance the annotation process for lipidomic studies and guide the generation of sample-specific lipidome maps. LiLA serves as a freely available data resource that can be employed in future studies to address lipidomic alterations in mice lung tissue.
Collapse
Affiliation(s)
- Belén Fernández Requena
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660, Boadilla del Monte, España
| | - Sajid Nadeem
- Department of Microbiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Vineel P Reddy
- Department of Microbiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Joel N Glasgow
- Department of Microbiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Adrie J C Steyn
- Department of Microbiology, University of Alabama at Birmingham, Birmingham, AL, USA
- Africa Health Research Institute, Durban, South Africa
- Centers for AIDS Research and Free Radical Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Coral Barbas
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660, Boadilla del Monte, España.
| | - Carolina Gonzalez-Riano
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660, Boadilla del Monte, España.
| |
Collapse
|
7
|
Scheidemantle G, Duan L, Lodge M, Cummings MJ, Hilovsky D, Pham E, Wang X, Kennedy A, Liu X. Data-dependent and -independent acquisition lipidomics analysis reveals the tissue-dependent effect of metformin on lipid metabolism. RESEARCH SQUARE 2023:rs.3.rs-2444456. [PMID: 36711728 PMCID: PMC9882637 DOI: 10.21203/rs.3.rs-2444456/v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Introduction Despite the well-recognized health benefits, the mechanisms and site of action of metformin remains elusive. Metformin-induced global lipidomic changes in plasma of animal models and human subjects have been reported. However, there is a lack of systemic evaluation of metformin-induced lipidomic changes in different tissues. Metformin uptake requires active transporters such as organic cation transporters (OCTs), and hence, it is anticipated that metformin actions are tissue-dependent. In this study, we aim to characterize metformin effects in non-diabetic male mice with a special focus on lipidomics analysis. The findings from this study will help us to better understand the cell-autonomous (direct actions in target cells) or non-cell-autonomous (indirect actions in target cells) mechanisms of metformin and provide insights into the development of more potent yet safe drugs targeting a particular organ instead of systemic metabolism for metabolic regulations without major side effects. Objectives To characterize metformin-induced lipidomic alterations in different tissues of non-diabetic male mice and further identify lipids affected by metformin through cell-autonomous or systemic mechanisms based on the correlation between lipid alterations in tissues and the corresponding in-tissue metformin concentrations. Methods Lipids were extracted from tissues and plasma of male mice treated with or without metformin in drinking water for 12 days and analyzed using MS/MS scan workflow (hybrid mode) on LC-Orbitrap Exploris 480 mass spectrometer using biologically relevant lipids-containing inclusion list for data-independent acquisition (DIA), named as BRI-DIA workflow followed by data-dependent acquisition (DDA), to maximum the coverage of lipids and minimize the negative effect of stochasticity of precursor selection on experimental consistency and reproducibility. Results Lipidomics analysis of 6 mouse tissues and plasma using MS/MS combining BRI-DIA and DDA allowed a systemic evaluation of lipidomic changes induced by metformin in different tissues. We observed that 1) the degrees of lipidomic changes induced by metformin treatment overly correlated with tissue concentrations of metformin; 2) the impact on lysophosphorylcholine and cardiolipins was positively correlated with tissue concentrations of metformin, while neutral lipids such as triglycerides did not correlate with the corresponding tissue metformin concentrations. Conclusion The data collected in this study from non-diabetic mice with 12-day metformin treatment suggest that the overall metabolic effect of metformin is positively correlated with tissue concentrations and the effect on individual lipid subclass is via both cell-autonomous mechanisms (cardiolipins and lysoPC) and non-cell-autonomous mechanisms (triglycerides).
Collapse
|