Provenance

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charles.hoyt@scai.fraunhofer.de at 2018-04-03 15:19:20
Authors
Causal Biological Networks Database
Contact
CausalBiologicalNetworks.RD@pmi.com
Description
The Treg Signaling network depicts the causal mechanisms that are activated in CD4+ Foxp3+ regulatory T-cells following T-cell receptor (TCR) ligation. Expanding on these processes, the network highlights the chemokines secreted by macrophages and dendritic cells, as well as the cognate T-cell receptors, involved in mediating T-cell recruitment to compromised lung tissue during COPD development.
License
Please cite: - www.causalbionet.com - https://bionet.sbvimprover.com as well as any relevant publications. The sbv IMPROVER project, the website and the Symposia are part of a collaborative project designed to enable scientists to learn about and contribute to the development of a new crowd sourcing method for verification of scientific data and results. The current challenges, website and biological network models were developed and are maintained as part of a collaboration among Selventa, OrangeBus and ADS. The project is led and funded by Philip Morris International. For more information on the focus of Philip Morris International’s research, please visit www.pmi.com.
Number Nodes
50
Number Edges
118
Number Components
5
Network Density
0.0481633
Average Degree
2.36
Number Citations
75
Number BEL Errors
0

Content Statistics

Network Overlap

The node-based overlap between this network and other networks is calculated as the Szymkiewicz-Simpson coefficient of their respective nodes. Up to the top 10 are shown below.

Network Overlap
Th17 Signaling-2.0-Rn v2.0 42%
BEL Framework Large Corpus Document v20170611 40%
Th1-Th2 Signaling-2.0-Rn v2.0 40%
Selventa Protein Families Definitions v20150611 34%
Dendritic Cell Signaling-2.0-Rn v2.0 34%
Cytotoxic T-cell Signaling-2.0-Rn v2.0 32%
Immune Regulation of Tissue Repair-2.0-Rn v2.0 18%
NK Signaling-2.0-Rn v2.0 15%
Treg Signaling-2.0-Hs v2.0 14%
Treg Signaling-2.0-Mm v2.0 14%

Sample Edges

act(p(RGD:Cxcr3), ma(cat)) increases bp(GOBP:"lymphocyte chemotaxis")

neutralization of CXCR3 reduced MIG/CXCL9-induced T lymphocyte proliferation and the number of IFN-gamma-positive spots PubMed:15187119

act(p(RGD:Cxcr3), ma(cat)) increases bp(GOBP:"lymphocyte chemotaxis")

supernatants harvested from stimulated PMN induced migration and rapid integrin-dependent adhesion of CXCR3-expressing lymphocytes; these activities were significantly reduced by neutralizing anti-MIG and anti-IP-10 Abs, PubMed:10202039

p(RGD:Ccl3) directlyIncreases act(p(RGD:Ccr5), ma(cat))

Numerous studies have shown that immature human and mouse blood- and bone marrow-derived DC subsets express a panel of inflammatory chemokine receptors (CCR1-6,8,9, CXCR3,4, CX3CR1) [Table 1 and reviewed in (1-5)]. [Table 1 Chemokine receptors expressed by DC and the functional outcome of receptor ligation}] PubMed:19028258

p(RGD:Cxcl9) directlyIncreases act(p(RGD:Cxcr3), ma(cat))

By using a CXCR3 ligand reporter mouse, we found that stromal cells predominately expressed the chemokine ligand CXCL9 whereas hematopoietic cells expressed CXCL10 in lymph nodes (LNs). Dendritic cell (DC)-derived CXCL10 facilitated T cell-DC interactions in LNs during T cell priming while both chemokines guided intranodal positioning of CD4(+) T cells to interfollicular and medullary zones. Thus, different chemokines acting on the same receptor can function locally to facilitate DC-T cell interactions and globally to influence intranodal positioning, and both functions contribute to Th1 cell differentiation. PubMed:23123063

Sample Nodes

p(RGD:Ccl3)

In-Edges: 11 | Out-Edges: 19 | Explore Neighborhood | Download JSON

p(RGD:Ifng)

In-Edges: 45 | Out-Edges: 79 | Explore Neighborhood | Download JSON

p(RGD:Il6)

In-Edges: 166 | Out-Edges: 99 | Explore Neighborhood | Download JSON

p(RGD:Crebbp)

In-Edges: 7 | Out-Edges: 13 | Explore Neighborhood | Download JSON

p(RGD:Il10)

In-Edges: 26 | Out-Edges: 10 | Explore Neighborhood | Download JSON

About

BEL Commons is developed and maintained in an academic capacity by Charles Tapley Hoyt and Daniel Domingo-Fernández at the Fraunhofer SCAI Department of Bioinformatics with support from the IMI project, AETIONOMY. It is built on top of the open source project, PyBEL. Please feel free to contact us here to give us feedback or report any issues.