| Title |
An Integrative Framework Reveals Signaling-to-Transcription Events in Toll-like Receptor Signaling
|
|---|---|
| Published in |
Cell Reports, June 2017
|
| DOI | 10.1016/j.celrep.2017.06.016 |
| Pubmed ID | |
| Authors | |
| Abstract |
Building an integrated view of cellular responses to environmental cues remains a fundamental challenge due to the complexity of intracellular networks in mammalian cells. Here, we introduce an integrative biochemical and genetic framework to dissect signal transduction events using multiple data types and, in particular, to unify signaling and transcriptional networks. Using the Toll-like receptor (TLR) system as a model cellular response, we generate multifaceted datasets on physical, enzymatic, and functional interactions and integrate these data to reveal biochemical paths that connect TLR4 signaling to transcription. We define the roles of proximal TLR4 kinases, identify and functionally test two dozen candidate regulators, and demonstrate a role for Ap1ar (encoding the Gadkin protein) and its binding partner, Picalm, potentially linking vesicle transport with pro-inflammatory responses. Our study thus demonstrates how deciphering dynamic cellular responses by integrating datasets on various regulatory layers defines key components and higher-order logic underlying signaling-to-transcription pathways. |
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X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 8 | 35% |
| United Kingdom | 2 | 9% |
| Japan | 2 | 9% |
| Germany | 1 | 4% |
| Chile | 1 | 4% |
| Australia | 1 | 4% |
| Unknown | 8 | 35% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 13 | 57% |
| Members of the public | 8 | 35% |
| Practitioners (doctors, other healthcare professionals) | 1 | 4% |
| Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 78 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 19 | 24% |
| Researcher | 13 | 17% |
| Student > Bachelor | 7 | 9% |
| Student > Doctoral Student | 6 | 8% |
| Student > Master | 6 | 8% |
| Other | 12 | 15% |
| Unknown | 15 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 21 | 27% |
| Agricultural and Biological Sciences | 16 | 21% |
| Immunology and Microbiology | 12 | 15% |
| Engineering | 3 | 4% |
| Chemistry | 2 | 3% |
| Other | 5 | 6% |
| Unknown | 19 | 24% |