Network-based molecular modeling of physiological behaviors has proven invaluable in the study of complex diseases such as cancer, but these approaches remain largely untested in contexts involving interacting tissues such as autoimmunity. Here, using Alopecia Areata (AA) as a model, we have adapted regulatory network analysis to specifically isolate physiological behaviors in the skin that contribute to the recruitment of immune cells in autoimmune disease. We use context-specific regulatory networks to deconvolve and identify skin-specific regulatory modules with IKZF1 and DLX4 as master regulators (MRs). These MRs are sufficient to induce AA-like molecular states in vitro in three cultured cell lines, resulting in induced NKG2D-dependent cytotoxicity. This work demonstrates the feasibility of a network-based approach for compartmentalizing and targeting molecular behaviors contributing to interactions between tissues in autoimmune disease.
Pharmacology, Toxicology and Pharmaceutical Science
1
3%
Other
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3%
Unknown
5
14%
Attention Score in Context
Attention Score in Context
This research output has an Altmetric Attention Score of 35. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 November 2022.
All research outputs
#1,416,386
of 33,493,714 outputs
Outputs from Cell Systems
#288
of 1,213 outputs
Outputs of similar age
#16,096
of 317,859 outputs
Outputs of similar age from Cell Systems
#6
of 22 outputs
Altmetric has tracked 33,493,714 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,213 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 32.5. This one has done well, scoring higher than 76% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 317,859 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
