Predictive metabolomic signatures for safety assessment of three plastic nanoparticles using intestinal organoids

类有机物 代谢组学 毒性 坏死性下垂 细胞生物学 化学 细胞内 代谢途径 生物 生物化学 新陈代谢 程序性细胞死亡 细胞凋亡 生物信息学 有机化学
作者
Lihui Xuan,Jinhua Luo,Can Qu,Peiyu Guo,Wensen Yi,Jingjing Yang,Yuhui Yan,Hua Guan,Ping‐Kun Zhou,Ruixue Huang
出处
期刊:Science of The Total Environment [Elsevier BV]
卷期号:913: 169606-169606 被引量:12
标识
DOI:10.1016/j.scitotenv.2023.169606
摘要

Nanoplastic particles are pervasive environmental contaminants with potential health risks, while mouse intestinal organoids provide accurate in vitro models for studying these interactions. Metabolomics, especially through LC-MS, enables detailed cellular response studies, and there's a novel interest in comparing metabolic changes across nanoparticle species using gut organoids. This study used a mouse intestinal organoid combined with cell model to explore the differences in metabolites and toxicity mechanisms induced by exposure to three nanoplastics (PS, PTFE, and PMMA). The results showed that PS, PTFE, and PMMA exposure reduced mitochondrial membrane potential, intracellular ROS accumulation and oxidative stress, and inhibited the AKT/mTOR signaling pathway. Non-targeted metabolomics results confirmed that three types of nanoplastic particles regulate cellular status by regulating fatty acid metabolism, nucleotide metabolism, necroptosis and autophagy pathways. More importantly, these representative metabolites were further validated in model groups after mouse intestinal organoids and HCT116 cells were exposed to the respective NPs, indicating that organoid metabolomics results can be used to effectively predict toxicity. Untargeted metabolomics is sensitive enough to detect subtle metabolomic changes when functional cellular analysis shows no significant differences. Overall, our study reveals the underlying metabolic mechanism of NPs-induced intestinal organoid toxicity and provides new insights into the possible adverse consequences of NPs.
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
luafu发布了新的文献求助10
1秒前
ynn发布了新的文献求助10
5秒前
科研专家完成签到 ,获得积分10
7秒前
NexusExplorer应助湖以采纳,获得10
8秒前
SciGPT应助琪琪子采纳,获得10
8秒前
SXYYXS完成签到 ,获得积分10
11秒前
luafu完成签到,获得积分10
13秒前
13秒前
13秒前
14秒前
脾气暴躁的小兔完成签到,获得积分10
18秒前
Hu完成签到 ,获得积分20
23秒前
Ava应助晓晓马儿采纳,获得10
24秒前
渝州人完成签到,获得积分10
25秒前
26秒前
27秒前
27秒前
28秒前
汉堡包应助alpv采纳,获得10
29秒前
30秒前
咪咪完成签到,获得积分20
30秒前
方俊驰发布了新的文献求助10
30秒前
liyang999发布了新的文献求助30
30秒前
31秒前
32秒前
32秒前
32秒前
慕青应助潇洒的语蝶采纳,获得10
32秒前
一棵狗芽发布了新的文献求助10
33秒前
方俊驰完成签到,获得积分10
35秒前
科研通AI5应助衫青采纳,获得10
36秒前
36秒前
常冬寒发布了新的文献求助10
37秒前
37秒前
38秒前
asdfqwer应助xhzhao86采纳,获得10
39秒前
40秒前
samantha完成签到 ,获得积分10
41秒前
43秒前
高分求助中
All the Birds of the World 4000
Production Logging: Theoretical and Interpretive Elements 3000
Les Mantodea de Guyane Insecta, Polyneoptera 2000
Machine Learning Methods in Geoscience 1000
Resilience of a Nation: A History of the Military in Rwanda 888
Musculoskeletal Pain - Market Insight, Epidemiology And Market Forecast - 2034 500
Crystal Nonlinear Optics: with SNLO examples (Second Edition) 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3734464
求助须知:如何正确求助?哪些是违规求助? 3278459
关于积分的说明 10009515
捐赠科研通 2995045
什么是DOI,文献DOI怎么找? 1643172
邀请新用户注册赠送积分活动 780986
科研通“疑难数据库(出版商)”最低求助积分说明 749183