Dataset Information

Sarcopenic metabolomic profile reflected a sarcopenic phenotype associated with amino acid and essential fatty acid changes

ABSTRACT:

INTRODUCTION: Although sarcopenia greatly affects health and quality of life in older people, its pathophysiological causes are not fully elucidated. To face this challenge, omics technologies can be used. The metabolome gives a vision of the interaction between the genome and the environment through metabolic networks, thus contributing in clarifying the pathophysiology of the sarcopenic phenotype.

OBJECTIVES: The main goal of this study was to compare the plasma metabolome of sarcopenic and non-sarcopenic older people.

METHODS: Cross-sectional study of 20 sarcopenic and 21 non-sarcopenic older subjects with available frozen plasma samples. Non-targeted metabolomic study by ultra-high-performance liquid chromatography-electrospray ionization tandem mass spectrometry (UHPLC-ESI-MS/MS) analysis with later bioinformatics data analysis. Once the significantly different metabolites were identified, the KEGG database was used on them to establish which were the metabolic pathways mainly involved.

RESULTS: From 657 features identified, 210 showed significant differences between the study groups, and 30 had a FoldChangeLog₂ > 2. The most interesting metabolic pathways found with the KEGG database were the biosynthesis of amino acids, arginine and proline metabolism, the biosynthesis of alkaloids derived from ornithine, linoleic acid metabolism, and the biosynthesis of unsaturated fatty acids.

CONCLUSIONS: The study results allowed us to confirm that the concept of 'sarcopenic phenotype' is also witnessed at the plasma metabolite levels. The non-targeted metabolomics study can open a wide view of the sarcopenic features changes at the plasma level, which would be linked to the sarcopenic physiopathological alterations.

INSTRUMENT(S): Liquid Chromatography MS - positive - reverse phase

SUBMITTER: Rafael Opazo

PROVIDER: MTBLS2679 | MetaboLights | 2021-09-29

REPOSITORIES: MetaboLights

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Sarcopenic metabolomic profile reflected a sarcopenic phenotype associated with amino acid and essential fatty acid changes.

Opazo Rafael R Angel Bárbara B Márquez Carlos C Lera Lydia L Cardoso Dos Santos Gustavo R GR Monnerat Gustavo G Albala Cecilia C

Metabolomics : Official journal of the Metabolomic Society 20210908 9

<h4>Introduction</h4>Although sarcopenia greatly affects health and quality of life in older people, its pathophysiological causes are not fully elucidated. To face this challenge, omics technologies can be used. The metabolome gives a vision of the interaction between the genome and the environment through metabolic networks, thus contributing in clarifying the pathophysiology of the sarcopenic phenotype.<h4>Objectives</h4>The main goal of this study was to compare the plasma metabolome of sarc ...[more]

PMID: 34498155

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Project description:Purpose: In this study, we try to investigate the possible signaling pathways involved in the tumorigenesis of fibroblastic and anaplastic meningiomas. We also attempt to investigate EGFL6 gene expression in brain arachnoidal tissues and various tumors and to measure EGFL6 levels in serum samples from healthy people and patients with various tumors by using ELISA. Experimental Design: Differential gene expression profiles between meningiomas and brain arachnoidal tissues were established by using Affymetrix GeneChip Human U133 Plus 2.0 Array. KEGG pathway analysis was performed to identify potential gene pathways that may be involved in the pathogenesis of meningiomas. Quantitative real-time PCR (qRT-PCR) was performed to validate the differentially expressed genes in the KEGG pathways. EGFL6 mRNA levels were also determined in brain arachnoidal tissues, meningiomas, and other tumors by qRT-PCR. EGFL6 levels were measured in serum samples from healthy people and patients with various tumors by using ELISA. Results: Fibroblastic meningioma exhibited upregulated PI3K/Akt and TGFÎ² signaling pathways, and accelerated G1/S progression cell cycle. KEGG analysis also demonstrated that focal adhesion and ECM-receptor interaction pathways were activated in anaplastic meningioma. Benign meningiomas had significantly higher levels of EGFL6 mRNA than brain arachnoidal tissues and atypical and anaplastic meningiomas (P<0.001). EGFL6 gene was also highly expressed in ovarian cancer, but expressed lowly in all other investigated tumors. EGFL6 was hardly detectable in serum samples of healthy people. The mean serum EGFL6 concentration was 675, 118, and 126 pg/ml in patients with benign, atypical, and anaplastic meningiomas respectively. Patients with ovarian cancers also had high serum EGFL6 levels (mean concentration: 617 pg/ml). Patients with all other investigated tumors, however, had low levels of serum EGFL6 with mean concentration less than 240 pg/ml. Conclusion: We proposed that deregulation of cell cycle and PI3K/Akt pathways might play important roles in the tumorigenesis of fibroblastic meningioma. It was also suggested that the activated integrin-mediated signaling pathways were involved in the pathogenesis of anaplastic meningioma. We presented evidence that EGFL6 might serve as a novel serum biomarker for benign meningioma and ovarian cancer. It was also suggested that EGFL6 could help discriminate benignancy or malignancy of meningiomas before surgery or at early time points. Differential gene expression profiles between meningiomas and brain arachnoidal tissues were established by using Affymetrix GeneChip Human U133 Plus 2.0 Array. KEGG pathway analysis was performed to identify potential gene pathways that may be involved in the pathogenesis of meningiomas. Quantitative real-time PCR (qRT-PCR) was performed to validate the differentially expressed genes in the KEGG pathways. EGFL6 mRNA levels were also determined in brain arachnoidal tissues, meningiomas, and other tumors by qRT-PCR. EGFL6 levels were measured in serum samples from healthy people and patients with various tumors by using ELISA.

			Action	DRS
	MTBLS2679	Other
	FILES	Other
	a_MTBLS2679_LC-MS_positive_reverse-phase_metabolite_profiling.txt	Txt
	files-all.json	Other
	i_Investigation.txt	Txt