OBIF: an omics-based interaction framework to reveal molecular drivers of synergy.
OBIF: an omics-based interaction framework to reveal molecular drivers of synergy.
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DOI：
10.1093/nargab/lqac028
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发表时间：
2022-06
影响因子：
4.6
通讯作者：
Evans SE
中科院分区：
文献类型：
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作者：
Pantaleón García J;Kulkarni VV;Reese TC;Wali S;Wase SJ;Zhang J;Singh R;Caetano MS;Kadara H;Moghaddam SJ;Johnson FM;Wang J;Wang Y;Evans SE
生物活性分子库筛选可以凭经验确定有效的联合治疗，但有利的药物-药物相互作用的分子机制往往仍然不清楚，排除了进一步的合理设计。在没有公认的系统理论来询问协同反应的情况下，我们引入了基于OMIC的相互作用框架（OBIF），通过整合统计和生物相互作用来揭示协同生物反应的分子驱动因素。OBIF对来自单次与双重暴露的特征表达数据进行全因子分析，以识别揭示协同介导途径、功能和调节因子的分子簇。作为一个实际的证明，OBIF分析了免疫刺激分子二联体的转录组和蛋白质组数据，这些分子诱导针对甲型流感的协同保护，并揭示了抗病毒保护所需的意料之外的NF-κB/AP-1合作。为了证明可推广性，OBIF分析了来自各种组学平台和实验条件的数据，成功地识别了驱动其协同反应的分子簇。因此，与现有的协同量化和预测方法不同，OBIF是一个表型驱动的系统模型，支持协同机制的多平台询问。
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.
影响因子：
3.7
作者：
Li OT;Chan MC;Leung CS;Chan RW;Guan Y;Nicholls JM;Poon LL
通讯作者：
Poon LL
影响因子：
--
作者：
Hsu YC;Chiu YC;Chen Y;Hsiao TH;Chuang EY
通讯作者：
Chuang EY