Investigation of a derived adverse outcome pathway (AOP) network for endocrine-mediated perturbations

不良结局途径 计算机科学 工作流程 生物 计算生物学 数据库
作者
Janani Ravichandran,Bagavathy Shanmugam Karthikeyan,Areejit Samal
出处
期刊:Science of The Total Environment [Elsevier BV]
卷期号:826: 154112-154112 被引量:27
标识
DOI:10.1016/j.scitotenv.2022.154112
摘要

An adverse outcome pathway (AOP) is a compact representation of the available mechanistic information on observed adverse effects upon environmental exposure. Sharing of events across individual AOPs has led to the emergence of AOP networks. Since AOP networks are expected to be functional units of toxicity prediction, there is current interest in their development tailored to specific research question or regulatory problem. To this end, we have developed a detailed workflow to construct an endocrine-relevant AOP (ED-AOP) network based on the existing information available in AOP-Wiki. We propose a cumulative weight of evidence (WoE) score for each ED-AOP based on the WoE scores assigned to key event relationships (KERs) by AOP-Wiki, revealing gaps in AOP development. Connectivity analysis of the ED-AOP network comprising 48 AOPs reveals 7 connected components and 12 isolated AOPs. Subsequently, we apply standard network measures to perform an in-depth analysis of the two largest connected components of the ED-AOP network. Notably, the graph-theoretic analyses led to the identification of important events including points of convergence or divergence in the ED-AOP network. These findings can suggest potential adverse outcomes and facilitate the development of new endpoints or assays for chemical risk assessment. Detailed analysis of the largest component in the ED-AOP network gives insights on the systems-level perturbations caused by endocrine disruption, emergent paths, and stressor-event associations. In sum, the derived ED-AOP network can provide a broader view of the biological events disrupted by endocrine disruption, as well as facilitate better risk assessment of environmental chemicals.

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
深情安青应助叶破茧采纳,获得10
刚刚
刚刚
dfgv完成签到,获得积分10
刚刚
1秒前
1秒前
意林完成签到,获得积分10
1秒前
小欣发布了新的文献求助10
2秒前
2秒前
2秒前
脑洞疼应助科研通管家采纳,获得10
2秒前
搜集达人应助科研通管家采纳,获得10
2秒前
Owen应助俭朴琦采纳,获得10
3秒前
小二郎应助科研通管家采纳,获得10
3秒前
凌爽发布了新的文献求助10
3秒前
隐形曼青应助科研通管家采纳,获得10
3秒前
光亮友安发布了新的文献求助10
3秒前
搜集达人应助科研通管家采纳,获得10
3秒前
伶俐妙海应助科研通管家采纳,获得50
3秒前
sagitar应助科研通管家采纳,获得20
3秒前
Lucas应助科研通管家采纳,获得10
3秒前
CipherSage应助科研通管家采纳,获得10
3秒前
小小旭呀完成签到,获得积分10
3秒前
Owen应助科研通管家采纳,获得10
3秒前
3秒前
大力熊猫完成签到,获得积分20
4秒前
4秒前
研友_VZG7GZ应助科研通管家采纳,获得10
4秒前
4秒前
烟花应助科研通管家采纳,获得10
4秒前
4秒前
4秒前
4秒前
4秒前
ding应助科研通管家采纳,获得10
4秒前
4秒前
Zhailin完成签到,获得积分10
4秒前
隐形曼青应助科研通管家采纳,获得10
4秒前
4秒前
4秒前
青青完成签到,获得积分10
4秒前
高分求助中
Principles of Economics, 11th Edition 10000
University Physics with Modern Physics, 16th edition 10000
(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
Molecular Mechanisms of Photosynthesis, 4th Edition 1000
Organic Reactions, Volume 116 1000
Matrix Methods in Data Mining and Pattern Recognition 510
Social Skills Improvement System-Rating Scales--Chinese Version 500
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 7254114
求助须知:如何正确求助?哪些是违规求助? 8876081
关于积分的说明 18740900
捐赠科研通 6934737
什么是DOI,文献DOI怎么找? 3200042
关于科研通互助平台的介绍 2374745
邀请新用户注册赠送积分活动 2174843