Advancing the Effect-Directed Identification in Combined Pollution: Using Pathways to Link Effects and Toxicants

鉴定(生物学) 污染 链接(几何体) 化学 环境化学 环境科学 生物 计算机科学 生态学 计算机网络
作者
Jing Guo,Yiwen Luo,Chao Fang,Jinsha Jin,Pu Xia,Bing Wu,Xiaowei Zhang,Hongxia Yu,Hongqiang Ren,Wei Shi
出处
期刊:Environmental Science & Technology [American Chemical Society]
卷期号:58 (42): 18642-18653
标识
DOI:10.1021/acs.est.4c07735
摘要

The difficulty in associating diverse pollutants with mixture effects has led to significant challenges in identifying toxicants in combined pollution. In this study, pathways were used to link effects and toxicants. By pathways evaluated by the concentration-dependent transcriptome, individual effects were extended to molecular mechanisms encompassing 135 pathways corresponding to 6 biological processes. Accordingly, mechanism-based identification of toxicants was achieved by constructing a pathway toxicant database containing 2413 chemical–pathway interactions and identifying pathway active fragments of 72 pathways. The developed method was applied to two different wastewaters, industrial wastewater OB and municipal wastewater HL. Although lethality and teratogenesis were both observed at the individual level, different molecular mechanisms were revealed by pathways, with cardiotoxicity- and genotoxicity-related pathways significantly enriched in OB, and neurotoxicity- and environmental information processing-related pathways significantly enriched in HL. Further suspect and nontargeted screening generated 59 and 86 causative toxicants in OB and HL, respectively, among which 29 toxicants were confirmed, that interacted with over 90% of enriched pathways and contributed over 50% of individual effects. After upgrading treatments based on causative toxicants, consistent removal of toxicants, pathway effects, and individual effects were observed. Mediation by pathways enables mechanism-based identification, supporting the assessment and management of combined pollution.
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
852应助科研通管家采纳,获得10
刚刚
在水一方应助科研通管家采纳,获得10
刚刚
小马甲应助科研通管家采纳,获得10
刚刚
刚刚
英姑应助科研通管家采纳,获得10
刚刚
maox1aoxin应助科研通管家采纳,获得30
1秒前
CipherSage应助科研通管家采纳,获得10
1秒前
激昂的幻梦完成签到,获得积分10
1秒前
1秒前
斯文败类应助科研通管家采纳,获得10
1秒前
wanci应助科研通管家采纳,获得10
1秒前
shouyu29应助科研通管家采纳,获得10
1秒前
传奇3应助科研通管家采纳,获得10
1秒前
w_x_x应助科研通管家采纳,获得10
1秒前
科研通AI2S应助科研通管家采纳,获得10
1秒前
CodeCraft应助科研通管家采纳,获得10
1秒前
田様应助科研通管家采纳,获得10
1秒前
Ava应助科研通管家采纳,获得10
1秒前
Orange应助难过的慕青采纳,获得10
1秒前
丘比特应助科研通管家采纳,获得10
1秒前
Akim应助科研通管家采纳,获得10
1秒前
所所应助科研通管家采纳,获得10
1秒前
酷波er应助科研通管家采纳,获得10
1秒前
NexusExplorer应助科研通管家采纳,获得10
2秒前
科研通AI5应助科研通管家采纳,获得10
2秒前
大模型应助科研通管家采纳,获得10
2秒前
烟花应助科研通管家采纳,获得10
2秒前
2秒前
赘婿应助科研通管家采纳,获得30
2秒前
2秒前
一日不看书智商输给猪完成签到,获得积分10
2秒前
liu123479发布了新的文献求助20
2秒前
小蘑菇应助优秀的枫采纳,获得10
2秒前
Ll发布了新的文献求助10
3秒前
3秒前
SS发布了新的文献求助10
3秒前
workwork发布了新的文献求助10
3秒前
愉快的鞯发布了新的文献求助10
3秒前
zcbb完成签到,获得积分10
4秒前
开朗的西兰花完成签到,获得积分20
4秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527699
求助须知:如何正确求助?哪些是违规求助? 3107752
关于积分的说明 9286499
捐赠科研通 2805513
什么是DOI,文献DOI怎么找? 1539954
邀请新用户注册赠送积分活动 716878
科研通“疑难数据库(出版商)”最低求助积分说明 709759