OBIF: an omics-based interaction framework to reveal molecular drivers of synergy

Author:

Pantaleón García Jezreel1ORCID,Kulkarni Vikram V12,Reese Tanner C13,Wali Shradha12,Wase Saima J1,Zhang Jiexin4,Singh Ratnakar56,Caetano Mauricio S1,Kadara Humam7,Moghaddam Seyed Javad12ORCID,Johnson Faye M5,Wang Jing4,Wang Yongxing1,Evans Scott E12

Affiliation:

1. Department of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA

2. MD Anderson Cancer Center UT Health Graduate School of Biomedical Sciences, Houston, TX 77030, USA

3. Rice University, Houston, TX 77005, USA

4. Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

5. Department of Thoracic, Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

6. Department of Comparative Biosciences, University of Illinois at Urbana-Champaign, Urbana, IL 61802, USA

7. Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

Abstract

Abstract Bioactive molecule library screening may empirically identify effective combination therapies, but molecular mechanisms underlying favorable drug–drug interactions often remain unclear, precluding further rational design. In the absence of an accepted systems theory to interrogate synergistic responses, we introduce Omics-Based Interaction Framework (OBIF) to reveal molecular drivers of synergy through integration of statistical and biological interactions in synergistic biological responses. OBIF performs full factorial analysis of feature expression data from single versus dual exposures to identify molecular clusters that reveal synergy-mediating pathways, functions and regulators. As a practical demonstration, OBIF analyzed transcriptomic and proteomic data of a dyad of immunostimulatory molecules that induces synergistic protection against influenza A and revealed unanticipated NF-κB/AP-1 cooperation that is required for antiviral protection. To demonstrate generalizability, OBIF analyzed data from a diverse array of Omics platforms and experimental conditions, successfully identifying the molecular clusters driving their synergistic responses. Hence, unlike existing synergy quantification and prediction methods, OBIF is a phenotype-driven systems model that supports multiplatform interrogation of synergy mechanisms.

Funder

National Institutes of Health

University of Texas System

Consejo Nacional de Ciencia y Tecnología

National Cancer Institute

National Heart, Lung, and Blood Institute

Publisher

Oxford University Press (OUP)

Subject

General Medicine

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3