Detecting and dissecting signaling crosstalk via the multilayer network integration of signaling and regulatory interactions.
Detecting and dissecting signaling crosstalk via the multilayer network integration of signaling and regulatory interactions.
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DOI：
10.1093/nar/gkad1035
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发表时间：
2024-01-11
影响因子：
14.9
通讯作者：
Halu, Arda
中科院分区：
文献类型：
--
作者：
Martini, Leonardo;Baek, Seung Han;Lo, Ian;Raby, Benjamin A.;Silverman, Edwin K.;Weiss, Scott T.;Glass, Kimberly;Halu, Arda
细胞反应的多样性源于信号传导和转录调控相互作用的复杂网络中信号传导途径的通信或串扰。理解全球范围内串扰的各种机制需要非目标计算方法。我们提出了一个基于网络的统计方法，MuXTalk，使用高维边称为多链路建模的独特方式，信号和监管的相互作用可以接口。我们表明，信号调节接口主要位于中间区域之间的信号通路发生串扰，和多链路可以区分不同的信号转录机制。使用统计上过度代表的多链路作为代理的串扰，我们推断60个信令通路之间的串扰，扩展目前可用的串扰数据库超过五倍。MuXTalk在模型性能指标方面超越了现有方法，识别了手动管理工作的补充，并确定了串扰的潜在介质。此外，它适应固有的上下文依赖性的串扰，允许未来的应用细胞类型和疾病特异性的串扰。
The versatility of cellular response arises from the communication, or crosstalk, of signaling pathways in a complex network of signaling and transcriptional regulatory interactions. Understanding the various mechanisms underlying crosstalk on a global scale requires untargeted computational approaches. We present a network-based statistical approach, MuXTalk, that uses high-dimensional edges called multilinks to model the unique ways in which signaling and regulatory interactions can interface. We demonstrate that the signaling-regulatory interface is located primarily in the intermediary region between signaling pathways where crosstalk occurs, and that multilinks can differentiate between distinct signaling-transcriptional mechanisms. Using statistically over-represented multilinks as proxies of crosstalk, we infer crosstalk among 60 signaling pathways, expanding currently available crosstalk databases by more than five-fold. MuXTalk surpasses existing methods in terms of model performance metrics, identifies additions to manual curation efforts, and pinpoints potential mediators of crosstalk. Moreover, it accommodates the inherent context-dependence of crosstalk, allowing future applications to cell type- and disease-specific crosstalk.
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影响因子：
--
作者：
Fazekas D;Koltai M;Türei D;Módos D;Pálfy M;Dúl Z;Zsákai L;Szalay-Bekő M;Lenti K;Farkas IJ;Vellai T;Csermely P;Korcsmáros T
通讯作者：
Korcsmáros T
影响因子：
7
作者：
Donato M;Xu Z;Tomoiaga A;Granneman JG;Mackenzie RG;Bao R;Than NG;Westfall PH;Romero R;Draghici S
通讯作者：
Draghici S
影响因子：
14.9
作者：
Csabai L;Fazekas D;Kadlecsik T;Szalay-Bekő M;Bohár B;Madgwick M;Módos D;Ölbei M;Gul L;Sudhakar P;Kubisch J;Oyeyemi OJ;Liska O;Ari E;Hotzi B;Billes VA;Molnár E;Földvári-Nagy L;Csályi K;Demeter A;Pápai N;Koltai M;Varga M;Lenti K;Farkas IJ;Türei D;Csermely P;Vellai T;Korcsmáros T
通讯作者：
Korcsmáros T
影响因子：
3.7
作者：
Arkun, Yaman
通讯作者：
Arkun, Yaman
影响因子：
4.6
作者：
Haynes, Winston A.;Tomczak, Aurelie;Khatri, Purvesh
通讯作者：
Khatri, Purvesh