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Ageing- and AAA-associated Differentially Expressed Proteins Identified by Proteomic Analysis

ABSTRACT: We induced AAA in ApoE-/- mice of different ages (10 or 24 weeks) and obtained plasma samples. After the top 14 most abundant proteins were detected, the plasma was analyzed by a proteomic study using the data-dependent acquisition (DDA) technique.

ORGANISM(S): Mus Musculus

SUBMITTER: Yuehong Zheng

PROVIDER: PXD033725 | iProX | Fri May 06 00:00:00 BST 2022

REPOSITORIES: iProX

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Ageing- and AAA-associated differentially expressed proteins identified by proteomic analysis in mice.

Ren Jinrui J Wu Jianqiang J Tang Xiaoyue X Chen Siliang S Wang Wei W Lv Yanze Y Wu Lianglin L Yang Dan D Zheng Yuehong Y

PeerJ 20220525

<h4>Background</h4>Abdominal aortic aneurysm (AAA) is a disease of high prevalence in old age, and its incidence gradually increases with increasing age. There were few studies about differences in the circulatory system in the incidence of AAA, mainly because younger patients with AAA are fewer and more comorbid nonatherosclerotic factors.<h4>Method</h4>We induced AAA in ApoE<sup>-/-</sup> male mice of different ages (10 or 24 weeks) and obtained plasma samples. After the top 14 most abundant p ...[more]

PMID: 35637715

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Six drivers of aging identified among genes differentially expressed with age.

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A comprehensive analysis identified the key differentially expressed circular RNAs and methylation-related function in pheochromocytomas and paragangliomas

Project description:Aim: To investigate the differentially expressed circular RNAs (circRNAs) and their potential functions in pheochromocytomas and paragangliomas (PCC/PGLs). Materials & Methods: We analyzed the expression levels of circRNAs through RNA sequencing in tumor and adjacent normal tissues from 7 PCC/PGL patients. Real-time qPCR was conducted to verify the key candidates identified in the sequencing data. GO and KEGG Pathway analyses were performed to predict the functions of these circRNAs. The circRNA-miRNA-mRNA coding-noncoding gene co-expression (CNC) networks were mapped to show the potential binding relationship between circRNAs and their targets in PCC/PGLs. Results: A total of 367 circRNAs were shown to have differential expression between tumor and normal samples. The top 3 histone methylation-related circRNAs (hsa_circ_0000567, hsa_circ_0002897, hsa_circ_0004473) and their target miRNAs were identified and validated. The top 5 mRNAs, 88 miRNAs, and 132 circRNAs related to the pathogenesis were utilized to map the CNC network, and we observed that the interactions of these candidates with their target miRNAs regulated the procedure of histone-methylation, and further controlled the pathogenesis of PCC/PGLs. Conclusions: Our study is the first to provide the whole profile of differentially expressed circRNAs in PCC/PGLs, and our data indicates that the altered circRNAs may control the pathogenesis of PCC/PGLs by regulating the histone-methylation processes and act as biomarkers.

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