The amino acid metabolomics signature of differentiating myocardial infarction from strangulation death in mice models

代谢组学 代谢物 免疫印迹 代谢组 氨基酸 氨基酸代谢 化学 内科学 生物 医学 生物化学 色谱法 基因
作者
Song-Jun Wang,Bingrui Liu,Fu Zhang,Xiaorui Su,Yaping Li,Chen-Teng Yang,Zhihua Zhang,Bin Cong
出处
期刊:Scientific Reports [Nature Portfolio]
卷期号:13 (1) 被引量:3
标识
DOI:10.1038/s41598-023-41819-6
摘要

Abstract This study differentiates myocardial infarction (MI) and strangulation death (STR) from the perspective of amino acid metabolism. In this study, MI mice model via subcutaneous injection of isoproterenol and STR mice model by neck strangulation were constructed, and were randomly divided into control (CON), STR, mild MI (MMI), and severe MI (SMI) groups. The metabolomics profiles were obtained by liquid chromatography-mass spectrometry (LC–MS)-based untargeted metabolomics. Principal component analysis, partial least squares-discriminant analysis, volcano plots, and heatmap were used for discrepancy metabolomics analysis. Pathway enrichment analysis was performed and the expression of proteins related to metabolomics was detected using immunohistochemical and western blot methods. Differential metabolites and metabolite pathways were screened. In addition, we found the expression of PPM1K was significantly reduced in the MI group, but the expression of p-mTOR and p-S6K1 were significantly increased (all P < 0.05), especially in the SMI group (P < 0.01). The expression of Cyt-C was significantly increased in each group compared with the CON group, especially in the STR group (all P < 0.01), and the expression of AMPKα1 was significantly increased in the STR group (all P < 0.01). Our study for the first time revealed significant differences in amino acid metabolism between STR and MI.
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
1秒前
33完成签到,获得积分10
1秒前
2秒前
英俊的铭应助star采纳,获得10
2秒前
喜久福发布了新的文献求助20
3秒前
万柏祺完成签到,获得积分10
3秒前
ckgn完成签到,获得积分20
3秒前
村上春树的摩的完成签到 ,获得积分10
3秒前
渡安完成签到 ,获得积分10
4秒前
psj发布了新的文献求助10
4秒前
4秒前
懵懂的明辉完成签到,获得积分10
5秒前
可可咖啡应助文件撤销了驳回
5秒前
zjy发布了新的文献求助10
5秒前
6秒前
zengwei完成签到,获得积分10
7秒前
younghippo发布了新的文献求助10
7秒前
7秒前
Pt-SACs发布了新的文献求助10
7秒前
小蘑菇应助grx采纳,获得10
8秒前
8秒前
ijie完成签到,获得积分10
9秒前
9秒前
Kao应助科研通管家采纳,获得10
9秒前
ming2026应助科研通管家采纳,获得10
9秒前
9秒前
思源应助科研通管家采纳,获得10
10秒前
深情安青应助跳跃靖采纳,获得10
10秒前
Kao应助科研通管家采纳,获得10
10秒前
香蕉觅云应助科研通管家采纳,获得10
10秒前
酷波er应助科研通管家采纳,获得10
10秒前
脑洞疼应助科研通管家采纳,获得10
10秒前
10秒前
华仔应助科研通管家采纳,获得10
10秒前
Hello应助科研通管家采纳,获得10
11秒前
xing_xing应助科研通管家采纳,获得20
11秒前
可乐发布了新的文献求助10
11秒前
英俊的铭应助科研通管家采纳,获得10
11秒前
11秒前
慕青应助科研通管家采纳,获得10
11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
China Pluperfect I: Epistemology of Past and Outside in Chinese Art 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Cosmos as Art Object: Studies in Plato's Timaeus and Other Dialogues 500
What is the Future of Psychotherapy in Digital Age? Technology, AI Bots, and Psychotherapy after Covid 444
Management and the Arts 310
Teaching Social and Emotional Learning in Physical Education 300
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 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7634820
求助须知:如何正确求助?哪些是违规求助? 9208909
关于积分的说明 19750140
捐赠科研通 7202865
什么是DOI,文献DOI怎么找? 3275133
关于科研通互助平台的介绍 2436999
邀请新用户注册赠送积分活动 2272066