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Transcriptomic analysis of the effects of Toll-like receptor 4 and its ligands on the gene expression network of hepatic stellate cells

Overview of attention for article published in Fibrogenesis & Tissue Repair, February 2016
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Title
Transcriptomic analysis of the effects of Toll-like receptor 4 and its ligands on the gene expression network of hepatic stellate cells
Published in
Fibrogenesis & Tissue Repair, February 2016
DOI 10.1186/s13069-016-0039-z
Pubmed ID
Authors

Yangyang Ouyang, Jinsheng Guo, Chenzhao Lin, Jie Lin, Yirong Cao, Yuanqin Zhang, Yujin Wu, Shiyao Chen, Jiyao Wang, Luonan Chen, Scott L. Friedman

Abstract

Intact Toll-like receptor 4 (TLR4) has been identified in hepatic stellate cells (HSCs), the primary fibrogenic cell type in liver. Here, we investigated the impact of TLR4 signaling on the gene expression network of HSCs by comparing the transcriptomic changes between wild-type (JS1) and TLR4 knockout (JS2) murine HSCs in response to two TLR4 ligands, lipopolysacchride (LPS), or high-mobility group box 1 (HMGB1). Whole mouse genome microarray was performed for gene expression analysis. Gene interaction and co-expression networks were built on the basis of ontology and pathway analysis by Kyoto Encyclopedia of Genes and Genomes (KEGG). Gene expression profiles are markedly different between Wild type (JS1) and TLR4 knockout (JS2) HSCs under basal conditions or following stimulation with LPS or HMGB1. The differentially expressed genes between TLR4 intact and null HSCs were enriched in signaling pathways including p53, mTOR, NOD-like receptor, Jak-STAT, chemokine, focal adhesion with some shared downstream kinases, and transcriptional factors. Venn analysis revealed that TLR4-dependent, LPS-responsive genes were clustered into pathways including Toll-like receptor and PI3K-Akt, whereas TLR4-dependent, HMGB1-responsive genes were clustered into pathways including metabolism and phagosome signaling. Genes differentially expressed that were categorized to be TLR4-dependent and both LPS- and HMGB1-responsive were enriched in cell cycle, ubiquitin mediated proteolysis, and mitogen-activated protein kinase (MAPK) signaling pathways. TLR4 mediates complex gene expression alterations in HSCs. The affected pathways regulate a wide spectrum of HSC functions, including inflammation, fibrogenesis, and chemotaxis, as well as cell growth and metabolism. There are common and divergent regulatory signaling downstream of LPS and HMGB1 stimulation via TLR4 on HSCs. These findings emphasize the complex cascades downstream of TLR4 in HSCs that could influence their cellular biology and function.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 17%
Student > Doctoral Student 2 11%
Professor 2 11%
Other 1 6%
Student > Bachelor 1 6%
Other 2 11%
Unknown 7 39%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 22%
Agricultural and Biological Sciences 2 11%
Medicine and Dentistry 2 11%
Immunology and Microbiology 1 6%
Neuroscience 1 6%
Other 1 6%
Unknown 7 39%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 24 February 2016.
All research outputs
#14,251,396
of 22,851,489 outputs
Outputs from Fibrogenesis & Tissue Repair
#54
of 83 outputs
Outputs of similar age
#156,504
of 298,014 outputs
Outputs of similar age from Fibrogenesis & Tissue Repair
#1
of 2 outputs
Altmetric has tracked 22,851,489 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 83 research outputs from this source. They receive a mean Attention Score of 4.4. This one is in the 32nd percentile – i.e., 32% of its peers scored the same or lower than it.
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 298,014 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them