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Article Abstract:
Molecules. 2024 May 6;29(9):2155. doi: 10.3390/molecules29092155.
ABSTRACT
The present study investigates the chemical composition variances among Pinelliae Rhizoma, a widely used Chinese herbal medicine, and its common adulterants including Typhonium flagelliforme, Arisaema erubescens, and Pinellia pedatisecta. Utilizing the non-targeted metabolomics technique of employing UHPLC-Q-Orbitrap HRMS, this research aims to comprehensively delineate the metabolic profiles of Pinelliae Rhizoma and its adulterants. Multivariate statistical methods including PCA and OPLS-DA are employed for the identification of differential metabolites. Volcano plot analysis is utilized to discern upregulated and downregulated compounds. KEGG pathway analysis is conducted to elucidate the differences in metabolic pathways associated with these compounds, and significant pathway enrichment analysis is performed. A total of 769 compounds are identified through metabolomics analysis, with alkaloids being predominant, followed by lipids and lipid molecules. Significant differential metabolites were screened out based on VIP > 1 and p-value < 0.05 criteria, followed by KEGG enrichment analysis of these differential metabolites. Differential metabolites between Pinelliae Rhizoma and Typhonium flagelliforme, as well as between Pinelliae Rhizoma and Pinellia pedatisecta, are significantly enriched in the biosynthesis of amino acids and protein digestion and absorption pathways. Differential metabolites between Pinelliae Rhizoma and Arisaema erubescens are mainly enriched in tyrosine metabolism and phenylalanine metabolism pathways. These findings aim to provide valuable data support and theoretical references for further research on the pharmacological substances, resource development and utilization, and quality control of Pinelliae Rhizoma.
Keywords: Pinelliae Rhizoma, Chinese herbal medicine, metabolomics, differential metabolites, alkaloids, lipids, amino acids, protein digestion, biosynthesis, tyrosine metabolism.
Section Title: {'metabolomics': '## Batch effects visualization'}
One-sentence Summary: This study utilizes non-targeted metabolomics to identify and compare the chemical composition of Pinelliae Rhizoma and its common adulterants, providing valuable data for further research on its pharmacological substances and quality control.
DOI: doi:10.3390/molecules29092155
Article Abstract:
Metabolomics. 2024 May 9;20(3):53. doi: 10.1007/s11306-024-02113-2.
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).
Keywords: Metabolomics, Metformin, Lipidomics, Plasma, Animal models, Human subjects, Organic cation transporters, Tissues, Lipid alterations, Tissue concentrations
Section Title: {'Metabolomics': '## Batch effects visualization'}
One-sentence Summary: This study aims to characterize the effects of metformin on lipidomics in non-diabetic male mice, with a focus on tissue-specific changes and the potential mechanisms of action.
DOI: doi:10.1007/s11306-024-02113-2
BACKGROUND: Metabolomics is nowadays considered one the most powerful analytical for the discovery of metabolic dysregulations associated with the insurgence of cancer, given the reprogramming of the cell metabolism to meet the bioenergetic and biosynthetic demands of the malignant cell. Notwithstanding, several challenges still exist regarding quality control, method standardization, data processing, and compound identification. Therefore, there is a need for effective and straightforward approaches for the untargeted analysis of structurally related classes of compounds, such as acylcarnitines, that have been widely investigated in prostate cancer research for their role in energy metabolism and transport and β-oxidation of fatty acids.
RESULTS: In the present study, an innovative analytical platform was developed for the straightforward albeit comprehensive characterization of acylcarnitines based on high-resolution mass spectrometry, Kendrick mass defect filtering, and confirmation by prediction of their retention time in reversed-phase chromatography. In particular, a customized data processing workflow was set up on Compound Discoverer software to enable the Kendrick mass defect filtering, which allowed filtering out more than 90 % of the initial features resulting from the processing of 25 tumoral and adjacent non-malignant prostate tissues collected from patients undergoing radical prostatectomy. Later, a partial least square-discriminant analysis model validated by repeated double cross-validation was built on the dataset of 74 annotated acylcarnitines, with classification rates higher than 93 % for both groups, and univariate statistical analysis helped elucidate the individual role of the annotated metabolites.
SIGNIFICANCE: Hydroxylation of short- and medium-chain minor acylcarnitines appeared to be a significant variable in describing tissue differences, suggesting the hypothesis that the neoplastic growth is linked to oxidation phenomena on selected metabolites and reinforcing the need for effective methods for the annotation of minor metabolites.
Keywords: Metabolomics, Cancer, Acylcarnitines, High-resolution mass spectrometry, Kendrick mass defect filtering, Compound Discoverer software, Partial least square-discriminant analysis, Hydroxylation, Minor metabolites, Neoplastic growth.
Section Title: {'Metabolomics': '## Batch effects visualization', 'High-resolution mass spectrometry': '## MS1 MS2 connection'}
One-sentence Summary: An innovative analytical platform was developed for the comprehensive characterization of acylcarnitines in prostate cancer research, using high-resolution mass spectrometry and Kendrick mass defect filtering to identify significant variables such as hydroxylation of minor acylcarnitines.
DOI: doi:10.1016/j.aca.2024.342574
Keywords: Trypanosoma brucei, metabolomics, extraction methods, HILIC LC-HRMS, untargeted analysis, culture, parasites, samples, data analysis, metabolites
Section Title: {'metabolomics': '## Batch effects visualization', 'untargeted analysis': '## Chromatographic retention-related criteria', 'samples': '## Batch effects classification', 'data analysis': '## post hoc data normalization', 'metabolites': '## Unsupervised methods'}
One-sentence Summary: This protocol describes the optimized method for extracting and analyzing Trypanosoma brucei metabolites using HILIC LC-HRMS, providing a valuable tool for understanding the parasite's cellular metabolism and aiding in the discovery of new antitrypanosomal drugs.
DOI: doi:10.1002/cpz1.1043
The text was updated successfully, but these errors were encountered:
Below are the article matching results from the past week:
Article Abstract:
Molecules. 2024 May 6;29(9):2155. doi: 10.3390/molecules29092155.
ABSTRACT
The present study investigates the chemical composition variances among Pinelliae Rhizoma, a widely used Chinese herbal medicine, and its common adulterants including Typhonium flagelliforme, Arisaema erubescens, and Pinellia pedatisecta. Utilizing the non-targeted metabolomics technique of employing UHPLC-Q-Orbitrap HRMS, this research aims to comprehensively delineate the metabolic profiles of Pinelliae Rhizoma and its adulterants. Multivariate statistical methods including PCA and OPLS-DA are employed for the identification of differential metabolites. Volcano plot analysis is utilized to discern upregulated and downregulated compounds. KEGG pathway analysis is conducted to elucidate the differences in metabolic pathways associated with these compounds, and significant pathway enrichment analysis is performed. A total of 769 compounds are identified through metabolomics analysis, with alkaloids being predominant, followed by lipids and lipid molecules. Significant differential metabolites were screened out based on VIP > 1 and p-value < 0.05 criteria, followed by KEGG enrichment analysis of these differential metabolites. Differential metabolites between Pinelliae Rhizoma and Typhonium flagelliforme, as well as between Pinelliae Rhizoma and Pinellia pedatisecta, are significantly enriched in the biosynthesis of amino acids and protein digestion and absorption pathways. Differential metabolites between Pinelliae Rhizoma and Arisaema erubescens are mainly enriched in tyrosine metabolism and phenylalanine metabolism pathways. These findings aim to provide valuable data support and theoretical references for further research on the pharmacological substances, resource development and utilization, and quality control of Pinelliae Rhizoma.
PMID:38731650 | DOI:10.3390/molecules29092155
Keywords: Pinelliae Rhizoma, Chinese herbal medicine, metabolomics, differential metabolites, alkaloids, lipids, amino acids, protein digestion, biosynthesis, tyrosine metabolism.
Section Title: {'metabolomics': '## Batch effects visualization'}
One-sentence Summary: This study utilizes non-targeted metabolomics to identify and compare the chemical composition of Pinelliae Rhizoma and its common adulterants, providing valuable data for further research on its pharmacological substances and quality control.
DOI: doi:10.3390/molecules29092155
Article Abstract:
Metabolomics. 2024 May 9;20(3):53. doi: 10.1007/s11306-024-02113-2.
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).
PMID:38722395 | DOI:10.1007/s11306-024-02113-2
Keywords: Metabolomics, Metformin, Lipidomics, Plasma, Animal models, Human subjects, Organic cation transporters, Tissues, Lipid alterations, Tissue concentrations
Section Title: {'Metabolomics': '## Batch effects visualization'}
One-sentence Summary: This study aims to characterize the effects of metformin on lipidomics in non-diabetic male mice, with a focus on tissue-specific changes and the potential mechanisms of action.
DOI: doi:10.1007/s11306-024-02113-2
Article Abstract:
Anal Chim Acta. 2024 Jun 8;1307:342574. doi: 10.1016/j.aca.2024.342574. Epub 2024 Apr 13.
ABSTRACT
BACKGROUND: Metabolomics is nowadays considered one the most powerful analytical for the discovery of metabolic dysregulations associated with the insurgence of cancer, given the reprogramming of the cell metabolism to meet the bioenergetic and biosynthetic demands of the malignant cell. Notwithstanding, several challenges still exist regarding quality control, method standardization, data processing, and compound identification. Therefore, there is a need for effective and straightforward approaches for the untargeted analysis of structurally related classes of compounds, such as acylcarnitines, that have been widely investigated in prostate cancer research for their role in energy metabolism and transport and β-oxidation of fatty acids.
RESULTS: In the present study, an innovative analytical platform was developed for the straightforward albeit comprehensive characterization of acylcarnitines based on high-resolution mass spectrometry, Kendrick mass defect filtering, and confirmation by prediction of their retention time in reversed-phase chromatography. In particular, a customized data processing workflow was set up on Compound Discoverer software to enable the Kendrick mass defect filtering, which allowed filtering out more than 90 % of the initial features resulting from the processing of 25 tumoral and adjacent non-malignant prostate tissues collected from patients undergoing radical prostatectomy. Later, a partial least square-discriminant analysis model validated by repeated double cross-validation was built on the dataset of 74 annotated acylcarnitines, with classification rates higher than 93 % for both groups, and univariate statistical analysis helped elucidate the individual role of the annotated metabolites.
SIGNIFICANCE: Hydroxylation of short- and medium-chain minor acylcarnitines appeared to be a significant variable in describing tissue differences, suggesting the hypothesis that the neoplastic growth is linked to oxidation phenomena on selected metabolites and reinforcing the need for effective methods for the annotation of minor metabolites.
PMID:38719419 | DOI:10.1016/j.aca.2024.342574
Keywords: Metabolomics, Cancer, Acylcarnitines, High-resolution mass spectrometry, Kendrick mass defect filtering, Compound Discoverer software, Partial least square-discriminant analysis, Hydroxylation, Minor metabolites, Neoplastic growth.
Section Title: {'Metabolomics': '## Batch effects visualization', 'High-resolution mass spectrometry': '## MS1 MS2 connection'}
One-sentence Summary: An innovative analytical platform was developed for the comprehensive characterization of acylcarnitines in prostate cancer research, using high-resolution mass spectrometry and Kendrick mass defect filtering to identify significant variables such as hydroxylation of minor acylcarnitines.
DOI: doi:10.1016/j.aca.2024.342574
Article Abstract:
Curr Protoc. 2024 May;4(5):e1043. doi: 10.1002/cpz1.1043.
ABSTRACT
Trypanosoma brucei (Tb) is the causative agent of human African trypanosomiasis (HAT), also known as sleeping sickness, which can be fatal if left untreated. An understanding of the parasite's cellular metabolism is vital for the discovery of new antitrypanosomal drugs and for disease eradication. Metabolomics can be used to analyze numerous metabolic pathways described as essential to Tb. brucei but has some limitations linked to the metabolites' physicochemical properties and the extraction process. To develop an optimized method for extracting and analyzing Tb. brucei metabolites, we tested the three most commonly used extraction methods, analyzed the extracts by hydrophilic interaction liquid chromatography high-resolution mass spectrometry (HILIC LC-HRMS), and further evaluated the results using quantitative criteria including the number, intensity, reproducibility, and variability of features, as well as qualitative criteria such as the specific coverage of relevant metabolites. Here, we present the resulting protocols for untargeted metabolomic analysis of Tb. brucei using (HILIC LC-HRMS). © 2024 Wiley Periodicals LLC. Basic Protocol 1: Culture of Trypanosoma brucei brucei parasites Basic Protocol 2: Preparation of samples for metabolomic analysis of Trypanosoma brucei brucei Basic Protocol 3: LC-HRMS-based metabolomic data analysis of Trypanosoma brucei brucei.
PMID:38706422 | DOI:10.1002/cpz1.1043
Keywords: Trypanosoma brucei, metabolomics, extraction methods, HILIC LC-HRMS, untargeted analysis, culture, parasites, samples, data analysis, metabolites
Section Title: {'metabolomics': '## Batch effects visualization', 'untargeted analysis': '## Chromatographic retention-related criteria', 'samples': '## Batch effects classification', 'data analysis': '## post hoc data normalization', 'metabolites': '## Unsupervised methods'}
One-sentence Summary: This protocol describes the optimized method for extracting and analyzing Trypanosoma brucei metabolites using HILIC LC-HRMS, providing a valuable tool for understanding the parasite's cellular metabolism and aiding in the discovery of new antitrypanosomal drugs.
DOI: doi:10.1002/cpz1.1043
The text was updated successfully, but these errors were encountered: