Data-dependent and -independent acquisition lipidomics analysis reveals the tissue-dependent effect of metformin on lipid metabolism.

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).

Data-dependent and -independent acquisition lipidomics analysis reveals the tissue-dependent effect of metformin on lipid metabolism.

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).

Prioritize biologically relevant ions for data-independent acquisition (BRI-DIA) in LC-MS/MS-based lipidomics analysis.

INTRODUCTION: Data-dependent acquisition (DDA) is the most commonly used MS/MS scan method for lipidomics analysis on orbitrap-based instrument. However, MS instrument associated software decide the top N precursors for fragmentation, resulting in stochasticity of precursor selection and compromised consistency and reproducibility. We introduce a novel workflow using biologically relevant lipids to construct inclusion list for data-independent acquisition (DIA), named as BRI-DIA workflow. OBJECTIVES: To ensure consistent coverage of biologically relevant lipids in LC-MS/MS-based lipidomics analysis. METHODS: Biologically relevant ion list was constructed based on LIPID MAPS and lipidome atlas in MS-DIAL 4. Lipids were extracted from mouse tissues and used to assess different MS/MS scan workflow (DDA, BRI-DIA, and hybrid mode) on LC-Orbitrap Exploris 480 mass spectrometer. RESULTS: DDA resulted in more MS/MS events, but the total number of unique lipids identified by three methods (DDA, BRI-DIA, and hybrid MS/MS scan mode) is comparable (580 unique lipids across 44 lipid subclasses in mouse liver). Major cardiolipin molecular species were identified by data generated using BRI-DIA and hybrid methods and allowed calculation of cardiolipin compositions, while identification of the most abundant cardiolipin CL72:8 was missing in data generated using DDA method, leading to wrong calculation of cardiolipin composition. CONCLUSION: The method of using inclusion list comprised of biologically relevant lipids in DIA MS/MS scan is as efficient as traditional DDA method in profiling lipids, but offers better consistency of lipid identification, compared to DDA method. This study was performed using Orbitrap Exploris 480, and we will further evaluate this workflow on other platforms, and if verified by future work, this biologically relevant ion fragmentation workflow could be routinely used in many studies to improve MS/MS identification capacities.