Untargeted metabolomics reveals the effect of lovastatin on steroid-induced necrosis of the femoral head in rabbits

Abstract PURPOSE Lovastatin is an important medicine and it shows a significant effect against glucocorticoid-induced necrosis of the femoral head. This study aimed to investigate the effect of lovastatin on preventing necrosis of the femoral head of by serum metabolomics strategy. METHODS Adult healthy adult Japanese white rabbits were divided into three groups: control group, model group and drug group. The pathologic changes of femoral head were assessed with magnetic resonance imaging and microscope. Metabolomics based on UHPLC-MS/MS was used to analyze the collected serum sample. Data were analyzed using principal component analysis (PCA), partial least squares-discriminate analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). All potential metabolites were identified by comparing with HMDB database, metlin database, lipid maps and chemspider database. RESULTS 11 potential biomarkers were noted and identified as potential biomarkers. The change of biomarkers suggested that lovastatin on preventing necrosis of the femoral head may affect glycerophospholipid metabolism, linoleic acid metabolism, sphingolipid metabolism, alpha-Linolenic acid metabolism, pyrimidine metabolism, arachidonic acid metabolism. CONCLUSION The study suggested that lovastatin could prevent the glucocorticoid-induced necrosis of the femoral head of rabbits. The possible reasons were closely associated with adjusting the lipid metabolism, inhibiting adipogenesis and delaying the osteocyte apoptosis.

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Untargeted metabolomics reveals the effect of lovastatin on steroid-induced necrosis of the femoral head in rabbits

10.21203/rs.3.rs-16772/v3 ◽

2020 ◽

Author(s):

Xiangnan Ren ◽

Zixing Shao ◽

Wu Fan ◽

Zixuan Wang ◽

Kaiyun Chen ◽

...

Keyword(s):

Femoral Head ◽

Least Squares ◽

Partial Least Squares ◽

Acid Metabolism ◽

Arachidonic Acid Metabolism ◽

Sphingolipid Metabolism ◽

Control Group ◽

Group Model ◽

Alpha Linolenic Acid ◽

Potential Biomarkers

Abstract PURPOSE Lovastatin is an important medicine and it shows a significant effect against glucocorticoid-induced necrosis of the femoral head. This study aimed to investigate the effect of lovastatin on preventing necrosis of the femoral head of by serum metabolomics strategy. METHODS Adult healthy adult Japanese white rabbits were divided into three groups: control group, model group and drug group. The pathologic changes of femoral head were assessed with magnetic resonance imaging and microscope. Metabolomics based on UHPLC-MS/MS was used to analyze the collected serum sample. Data were analyzed using principal component analysis (PCA), partial least squares-discriminate analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). All potential metabolites were identified by comparing with HMDB database, metlin database, lipid maps and chemspider database. RESULTS 11 potential biomarkers were noted and identified as potential biomarkers. The change of biomarkers suggested that lovastatin on preventing necrosis of the femoral head may affect glycerophospholipid metabolism, linoleic acid metabolism, sphingolipid metabolism, alpha-Linolenic acid metabolism, pyrimidine metabolism, arachidonic acid metabolism. CONCLUSION The study suggested that lovastatin could prevent the glucocorticoid-induced necrosis of the femoral head of rabbits. The possible reasons were closely associated with adjusting the lipid metabolism, inhibiting adipogenesis and delaying the osteocyte apoptosis.

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Untargeted metabolomics reveals the effect of lovastatin on steroid-induced necrosis of the femoral head in rabbits

Journal of Orthopaedic Surgery and Research ◽

10.1186/s13018-020-02026-5 ◽

2020 ◽

Vol 15 (1) ◽

Author(s):

Xiangnan Ren ◽

Zixing Shao ◽

Wu Fan ◽

Zixuan Wang ◽

Kaiyun Chen ◽

...

Keyword(s):

Femoral Head ◽

Least Squares ◽

Partial Least Squares ◽

Acid Metabolism ◽

Mass Spectrometry Analysis ◽

Sphingolipid Metabolism ◽

Control Group ◽

Group Model ◽

Alpha Linolenic Acid ◽

Potential Biomarkers

Abstract Purpose Lovastatin is an important medicine and it shows a significant effect against glucocorticoid-induced necrosis of the femoral head. This study aimed to investigate the effect of lovastatin on preventing necrosis of the femoral head of by serum metabolomics strategy. Methods Adult healthy adult Japanese white rabbits were divided into three groups: control group, model group, and drug group. The pathologic changes of femoral head were assessed with magnetic resonance imaging and microscope. Metabolomics based on ultra-high performance liquid chromatography tandem mass spectrometry analysis was used to analyze the collected serum sample. Data were analyzed using principal component analysis, partial least squares-discriminate analysis, and orthogonal partial least squares-discriminant analysis. All potential metabolites were identified by comparing with human metabolome database, Metlin database, lipid maps, and chemspider database. Results Eleven potential biomarkers were noted and identified as potential biomarkers. The change of biomarkers suggested that lovastatin on preventing necrosis of the femoral head may affect glycerophospholipid metabolism, linoleic acid metabolism, sphingolipid metabolism, alpha-linolenic acid metabolism, pyrimidine metabolism, and arachidonic acid metabolism. Conclusion The study suggested that lovastatin could prevent the glucocorticoid-induced necrosis of the femoral head of rabbits. The possible reasons were closely associated with adjusting the lipid metabolism, inhibiting adipogenesis, and delaying the osteocyte apoptosis.

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Metabolomic Study of Collagen-Induced Arthritis in Rats and the Interventional Effects of Huang-Lian-Jie-Du-Tang, a Traditional Chinese Medicine

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2013/439690 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 19

Author(s):

Rongcai Yue ◽

Ling Zhao ◽

Yaohua Hu ◽

Peng Jiang ◽

Shuping Wang ◽

...

Keyword(s):

Arachidonic Acid ◽

Fatty Acid ◽

Chinese Medicine ◽

Traditional Chinese Medicine ◽

Acid Metabolism ◽

Arachidonic Acid Metabolism ◽

Control Group ◽

Plasma Samples ◽

Collagen Induced Arthritis ◽

Potential Biomarkers

Huang-Lian-Jie-Du-Tang (HLJDT) is a traditional Chinese medicine (TCM) with anti-inflammatory activity. The present study used a metabolomic approach based on LC-Q-TOF-MS to profile rheumatoid-arthritis- (RA-) related metabolic changes and to investigate the interventional mechanisms of HLJDT in collagen-induced arthritis rats. Forty male Wistar rats were randomly divided into five groups: (1) a model group, (2) a normal control group, (3) a dexamethasone group, (4) a HLJDT group, and (5) a group that received 13 components of HLJDT. Plasma samples were collected 8, 15, and 22 days after the rats were injected with bovine type II collagen. By combining variable importance in the projection values with partial least squares discriminant analysis, 18 potential biomarkers were identified in the plasma samples. The biomarkers were primarily involved in glycerophospholipid metabolism, fatty acid metabolism, tryptophan metabolism, linoleic acid metabolism, phenylalanine metabolism, purine metabolism, arachidonic acid metabolism, and bile acid biosynthesis. Using the potential biomarkers as a screening index, the results suggest that HLJDT can potentially reverse the process of RA by partially regulating fatty acid oxidation and arachidonic acid metabolism. This study demonstrates that a metabolomic strategy is useful for identifying potential RA biomarkers and investigating the underlying mechanisms of a TCM in RA treatment.

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Plasma Lipidomics Profiling Reveals Biomarkers for Papillary Thyroid Cancer Diagnosis

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.682269 ◽

2021 ◽

Vol 9 ◽

Author(s):

Nan Jiang ◽

Zhenya Zhang ◽

Xianyang Chen ◽

Guofen Zhang ◽

Ying Wang ◽

...

Keyword(s):

Metabolic Pathways ◽

Acid Metabolism ◽

Normal Subjects ◽

Serum Markers ◽

Arachidonic Acid Metabolism ◽

Proton Nuclear Magnetic Resonance ◽

Papillary Thyroid ◽

Learning Potential ◽

Alpha Linolenic Acid ◽

Potential Biomarkers

The objective of this study was to identify potential biomarkers and possible metabolic pathways of malignant and benign thyroid nodules through lipidomics study. A total of 47 papillary thyroid carcinomas (PTC) and 33 control check (CK) were enrolled. Plasma samples were collected for UPLC-Q-TOF MS system detection, and then OPLS-DA model was used to identify differential metabolites. Based on classical statistical methods and machine learning, potential biomarkers were characterized and related metabolic pathways were identified. According to the metabolic spectrum, 13 metabolites were identified between PTC group and CK group, and a total of five metabolites were obtained after further screening. Its metabolic pathways were involved in glycerophospholipid metabolism, linoleic acid metabolism, alpha-linolenic acid metabolism, glycosylphosphatidylinositol (GPI)—anchor biosynthesis, Phosphatidylinositol signaling system and the metabolism of arachidonic acid metabolism. The metabolomics method based on PROTON nuclear magnetic resonance (NMR) had great potential for distinguishing normal subjects from PTC. GlcCer(d14:1/24:1), PE-NME (18:1/18:1), SM(d16:1/24:1), SM(d18:1/15:0), and SM(d18:1/16:1) can be used as potential serum markers for the diagnosis of PTC.

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1H-NMR spectroscopy identifies potential biomarkers in serum metabolomic signatures for early stage esophageal squamous cell carcinoma

PeerJ ◽

10.7717/peerj.8151 ◽

2019 ◽

Vol 7 ◽

pp. e8151 ◽

Cited By ~ 3

Author(s):

Yan-Yan Liu ◽

Zhong-Xian Yang ◽

Li-Min Ma ◽

Xu-Qing Wen ◽

Huan-Lin Ji ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Discriminant Analysis ◽

Nmr Spectroscopy ◽

Esophageal Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Least Squares ◽

Partial Least Squares ◽

Early Stage ◽

H Nmr ◽

Potential Biomarkers

Background Esophageal squamous cell carcinoma (ESCC) is one of the most prevalent types of upper gastrointestinal malignancies. Here, we used 1H nuclear magnetic resonance spectroscopy (1H-NMR) to identify potential serum biomarkers in patients with early stage ESCC. Methods Sixty-five serum samples from early stage ESCC patients (n = 25) and healthy controls (n = 40) were analysed using 1H-NMR spectroscopy. We distinguished between different metabolites through principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis (OPLS-DA) using SIMCA-P+ version 14.0 software. Receiver operating characteristic (ROC) analysis was conducted to verify potential biomarkers. Results Using OPLS-DA, 31 altered serum metabolites were successfully identified between the groups. Based on the area under the ROC curve (AUROC), and the biomarker panel with AUROC of 0.969, six serum metabolites (α-glucose, choline, glutamine, glutamate, valine, and dihydrothymine) were selected as potential biomarkers for early stage ESCC. Dihydrothymine particularly was selected as a new feasible biomarker associated with tumor occurrence. Conclusions 1H-NMR spectroscopy may be a useful tumour detection approach in identifying useful metabolic ESCC biomarkers for early diagnosis and in the exploration of the molecular pathogenesis of ESCC.

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Sebum: A Window into Dysregulation of Mitochondrial Metabolism in Parkinson’s Disease

10.26434/chemrxiv.11603613.v1 ◽

2020 ◽

Author(s):

Eleanor Sinclair ◽

Drupad Trivedi ◽

Depanjan Sarkar ◽

Caitlin Walton-Doyle ◽

Joy Milne ◽

...

Keyword(s):

Acid Metabolism ◽

Matched Control ◽

Enrichment Analysis ◽

Arachidonic Acid Metabolism ◽

Sphingolipid Metabolism ◽

Pathway Enrichment Analysis ◽

Liquid Chromatography Mass Spectrometry ◽

Diagnostic Biomarkers ◽

Pathway Enrichment ◽

Drug Naïve

<p>A metabolomics profiling approach was conducted to identify diagnostic biomarkers of PD from sebum, a non-invasively available biofluid. In this study, we used liquid chromatography-mass spectrometry (LC-MS) to analyse 274 samples from participants (80 drug naïve PD, 138 medicated PD and 56 well matched control subjects) and detected metabolites that could predict PD phenotype. Partial least squares-discriminant analysis (PLS-DA) models based on this sebum metabolome had correct classification rates of 70.4% and 69.7% to distinguish between drug naïve PD and medicated PD from control, respectively. Variable importance in projection (VIP) scores indicate compounds with significance belonged to sphingolipid, triacylglycerol and fatty acid/ester lipid classes. Pathway enrichment analysis showed alterations in lipid metabolism and mitochondrial dysfunction <i>viz.</i> the carnitine shuttle, sphingolipid metabolism and arachidonic acid metabolism. This study unveiled novel diagnostic sebum-based biomarkers for PD, and provides insight towards our current understanding of the pathogenesis of PD.</p>

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A Urinary Metabolomics Analysis Based on UPLC-MS and Effects of Moxibustion in APP/PS1 Mice

Current Alzheimer Research ◽

10.2174/1567205017666201109091759 ◽

2020 ◽

Vol 17 (8) ◽

pp. 753-765

Author(s):

Rui He ◽

Juntian Liu ◽

Chang Huang ◽

Jinyi Liu ◽

Herong Cui ◽

...

Keyword(s):

Amino Acid ◽

Amino Acid Metabolism ◽

Acid Metabolism ◽

Neurodegenerative Disorder ◽

Principal Component ◽

Sucrose Metabolism ◽

Control Group ◽

Starch Metabolism ◽

Moxibustion Group ◽

Potential Biomarkers

Background: Alzheimer's disease (AD) is a common neurodegenerative disorder with the symptoms of cognitive impairment and decreased learning and memory abilities. Metabolomics can reflect the related functional status and physiological and pathological changes in the process of AD. Moxibustion is a unique method in traditional Chinese medicine, which has been used in the treatment and prevention of diseases for thousands of years. Methods: A total of 32 APP/PS1 mice were randomly divided into the model group, moxibustion group, moxa smoke group and smoke-free moxibustion group (n=8/group), using the random number table method, while eight C57BL/6 mice were used as the control group. The five groups were measured for 20 min/day, 6 days/week, for 4 weeks. After 4 weeks’ experiment, all the mice were placed in metabolic cages to collect urine continuously for 24 hours, for UPLC-MS analysis. Results: Principal component analysis (PCA) was used to identify the different metabolites among the five groups, and partial least squares discriminant analysis (PLS-DA) was performed to reveal the effects on the metabolic variance. Sixteen potential biomarkers were identified among the five groups, primarily related to amino acid metabolism, starch metabolism, sucrose metabolism, interconversion of pentose and glucuronate, and aminoacyl biosynthesis. There were 17 differences in the potential metabolites between the control and model groups, involving the metabolism of amino acid, purine, pyrimidine, nicotinic acid and nicotinamide, and biosynthesis of pantothenate and coenzyme A. Fifteen potential biomarkers were identified between the model and moxibustion groups, related to starch metabolism, sucrose metabolism, interconversion of pentose and glucuronate, glyoxylate, dicarboxylate anions and some amino acid metabolism. Conclusion: Moxibustion can regulate the metabolism of substance and energy by improving the synthesis and decomposition of carbohydrates and amino acids in APP/PS1 transgenic AD model mice.

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Compositional differences between preterm milk of different gestational ages with the term milk: a comparative lipidomic study by LC-MS/MS

10.21203/rs.3.rs-861207/v1 ◽

2021 ◽

Author(s):

Ying-chun Zhao ◽

Ying Zhang ◽

Chun-xue Liu ◽

Dong-yong Yan ◽

Ping Dong

Keyword(s):

Fatty Acid ◽

Acid Metabolism ◽

Biological Effects ◽

Postnatal Growth ◽

Arachidonic Acid Metabolism ◽

Alpha Linolenic Acid ◽

Mature Milk ◽

Lipid Species ◽

Extremely Preterm ◽

Preterm Group

Abstract Background Studies are beginning to emerge on the important biological effects of the milk lipids exerted on the recipient infant, especially on the premature infants. The aim of this study was to comprehensively describe lipidomic differences between preterm milk of different gestational ages with the term milk over the course of lactation. Methods Breast milk samples were collected from 88 mothers giving birth prematurely and 39 mothers delivering at full-term (FT). Lipid profiles were assessed using an LC-MS/MS metabolomics strategy. Orthogonal partial least-squares discriminant analysis (OPLS-DA) and pathway analysis were subsequently performed. Results The OPLS-DA score plots significantly distinguished the lipids in preterm milk of different gestation ages from their counterparts in term milk. The concentrations of 10 out of 43 lipid subclasses were found to be persistently higher in preterm compared to term milk over the course of lactation; the diacylglycerol (DAG) and a bioactive subclass fatty acid ester of hydroxyl fatty acid (FAHFA) contributed the most to the differences. In terms of individual lipid species, the ten highest substances found in very preterm (VPT) colostrum compared to FT colostrum mainly come from the phosphatidylethanolamine class and the DAG species. Lipid species from the free fatty acid and FAHFA classes were significantly higher in either extremely preterm (EPT) or VPT mature milk (variable importance in projection > 1, P < 0.0001 for all). The differential lipids between each preterm group and its term counterpart were predicted to be mainly involved in six metabolic pathways, including glycerophospholipid metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, linoleic acid metabolism, alpha-Linolenic acid metabolism, arachidonic acid metabolism and glycerolipid metabolism. Conclusions The lipids in preterm and term milk showed substantial differences, which may be critical for postnatal growth, as well as the neural and immune development of newborns, especially EPT and VPT.

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Andrographolide Against Lung Cancer-New Pharmacological Insights Based on High-Throughput Metabolomics Analysis Combined with Network Pharmacology

Frontiers in Pharmacology ◽

10.3389/fphar.2021.596652 ◽

2021 ◽

Vol 12 ◽

Author(s):

Wen Luo ◽

Li Jia ◽

Jia-Wen Zhang ◽

Dong-Jie Wang ◽

Qiu Ren ◽

...

Keyword(s):

Lung Cancer ◽

High Throughput ◽

Acid Metabolism ◽

Pharmacological Action ◽

Arachidonic Acid Metabolism ◽

Metabolic Effects ◽

Network Pharmacology ◽

Control Group ◽

Protective Effects ◽

Metabolomics Analysis

Andrographolide (Andro) has known to treat various illnesses such as colds, diarrhea, fever and infectious diseases. However, the effect mechanism of Andro is still unclear. Therefore, we used high-throughput metabolomics analysis to discover biomarkers, metabolic profiles and pathways to reveal the pharmacological action and effective mechanism of Andro against lung cancer. The metabolic effects of Andro on lung cancer animal was explored by ultra-performance liquid chromatography-triple-time of flight/mass spectrometry (UPLC-TOF/MS) analysis. Our results showed that Andro exhibited significant protective effects against lung cancer. Compared with control group, a total of 25 metabolites biomarkers was identified in urine of model animals, which 18 of them were regulated toward the normal direction after Andro treatment, and network pharmacology analysis showed that they were related with 570 proteins. Biological pathways analysis showed that the 11 metabolism pathways were regulated by Andro treatment in lung cancer mouse, and amino acid metabolism and arachidonic acid metabolism have great potential as target pathways for Andro against lung cancer. It revealed that high-throughput metabolomics combined with network pharmacology analysis provides deeply insight into the therapeutic mechanisms of natural product for promoting medicine development and disease treatment.

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