Proteomic profiling of human plasma extracellular vesicles identifies PF4 and C1R as novel biomarker in sarcopenia

肌萎缩 生物标志物 蛋白质组学 肌肉团 蛋白质组 医学 接收机工作特性 内科学 生物信息学 生物 生物化学 基因
作者
Paula Aparicio,David Navarrete‐Villanueva,Alba Gómez‐Cabello,Tresa López‐Royo,Enrique Santamaría,Joaquín Fernández‐Irigoyen,Karina Ausín,Manuel Arruebo,Víctor Sebastián,Germán Vicente‐Rodríguez,Rosario Osta,Raquel Manzano
出处
期刊:Journal of Cachexia, Sarcopenia and Muscle [Springer Science+Business Media]
卷期号:15 (5): 1883-1897 被引量:13
标识
DOI:10.1002/jcsm.13539
摘要

Abstract Background Sarcopenia, the gradual and generalized loss of muscle mass and function with ageing, is one of the major health problems in older adults, given its high prevalence and substantial socioeconomic implications. Despite the extensive efforts to reach consensus on definition and diagnostic tests and cut‐offs for sarcopenia, there is an urgent and unmet need for non‐invasive, specific and sensitive biomarkers for the disease. Extracellular vesicles (EVs) are present in different biofluids including plasma, whose cargo reflects cellular physiology. This work analysed EV proteome in sarcopenia and robust patients in the search for differentially contained proteins that can be used to diagnose the disease. Methods Plasma small EVs (sEVs) from a total of 29 robust controls (aged 73.4 ± 5.6 years; 11 men and 18 women) and 49 sarcopenic patients (aged 82.3 ± 5.4 years; 15 men and 34 women) aged 65 years and older were isolated and their cargo was analysed by proteomics. Proteins whose concentration in sEVs was different between sarcopenic and robust patients were further validated using ELISA. The concentration of these candidates was correlated to the EWGSOP2 sarcopenia tests for low muscle strength and low physical performance, and receiver operating characteristic (ROC) curve analyses were carried out to evaluate their diagnostic power, sensitivity and specificity. Results Proteomic analysis identified 157 sEVs proteins in both sarcopenic and robust samples. Among them, 48 proteins had never been reported in the ExoCarta nor Vesiclepedia databases. Statistical analysis revealed eight proteins whose concentration was significantly different between groups: PF4 (log2 FC = 4.806), OIT3 (log2 FC = −1.161), MMRN1 (log2 FC = −1.982), MASP1 (log2 FC = −0.627), C1R (log2 FC = 1.830), SVEP1 (log2 FC = 1.295), VCAN (FC = 0.937) and SPTB (log2 FC = 1.243). Among them, platelet factor 4 (PF4) showed the lowest concentration while Complement C1r subcomponent (C1R) increased the most in sarcopenic patients, being these results confirmed by ELISA ( P = 1.07E‐09 and P = 0.001287, respectively). The concentrations of candidate proteins significantly correlated with EWGSOP2 tests currently used. ROC curve analysis showed an area under the curve of 0.8921 and 0.7476 for PF4 and C1R, respectively. Choosing the optimal for PF4, 80% sensitivity and 85.71% specificity was reached while the optimal cut‐off value of C1R would allow sarcopenia diagnosis with 75% sensitivity and 66.67% specificity. Conclusions Our results support the determination of EV PF4 and C1R as plasma diagnostic biomarkers in sarcopenia and open the door to investigate the role of the content of these vesicles in the pathogeny of the disease.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Yoyo完成签到,获得积分10
1秒前
怡然夕阳发布了新的文献求助10
1秒前
2秒前
2秒前
濮阳思远发布了新的文献求助10
2秒前
3秒前
5秒前
5秒前
JhShang发布了新的文献求助10
6秒前
6秒前
6秒前
Flipped发布了新的文献求助10
8秒前
大鱼发布了新的文献求助10
8秒前
风色幻想发布了新的文献求助10
8秒前
9秒前
万能图书馆应助Pt-SACs采纳,获得10
10秒前
XinYang发布了新的文献求助10
10秒前
10秒前
eth发布了新的文献求助10
12秒前
Owen应助钰宁采纳,获得10
12秒前
知识学爆发布了新的文献求助10
12秒前
风清扬发布了新的文献求助10
13秒前
和谐续发布了新的文献求助10
13秒前
夏延完成签到,获得积分20
14秒前
多吉完成签到 ,获得积分10
15秒前
科研通AI6.3应助LL采纳,获得200
15秒前
龙眼完成签到,获得积分10
15秒前
李健应助JhShang采纳,获得10
16秒前
Hello应助文静人达采纳,获得10
16秒前
16秒前
17秒前
19秒前
猪崽崽完成签到,获得积分20
20秒前
知识学爆完成签到,获得积分10
20秒前
20秒前
21秒前
21秒前
Autumn发布了新的文献求助10
22秒前
zc发布了新的文献求助10
22秒前
凉柒完成签到,获得积分10
22秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Picture this! Including first nations fiction picture books in school library collections 1000
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
Photodetectors: From Ultraviolet to Infrared 500
信任代码:AI 时代的传播重构 450
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6356848
求助须知:如何正确求助?哪些是违规求助? 8171489
关于积分的说明 17204834
捐赠科研通 5412652
什么是DOI,文献DOI怎么找? 2864711
邀请新用户注册赠送积分活动 1842216
关于科研通互助平台的介绍 1690446