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01.12.2017 | Research | Ausgabe 1/2017 Open Access

Journal of Neuroinflammation 1/2017

Toll-like receptor 4 in glial inflammatory responses to air pollution in vitro and in vivo

Zeitschrift:
Journal of Neuroinflammation > Ausgabe 1/2017
Autoren:
Nicholas C. Woodward, Morgan C. Levine, Amin Haghani, Farimah Shirmohammadi, Arian Saffari, Constantinos Sioutas, Todd E. Morgan, Caleb E. Finch
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​s12974-017-0858-x) contains supplementary material, which is available to authorized users.

Abstract

Background

Exposure to traffic-related air pollution (TRAP) is associated with accelerated cognitive aging and higher dementia risk in human populations. Rodent brains respond to TRAP with activation of astrocytes and microglia, increased inflammatory cytokines, and neurite atrophy. A role for Toll-like receptor 4 (TLR4) was suggested in mouse TLR4-knockouts, which had attenuated lung macrophage responses to air pollution.

Methods

To further analyze these mechanisms, we examined mixed glial cultures (astrocytes and microglia) for RNA responses to nanoscale particulate matter (nPM; diameter <0.2 μm), a well-characterized nanoscale particulate matter subfraction of TRAP collected from a local freeway (Morgan et al. Environ Health Perspect 2011; 119,1003–1009, 2011). The nPM was compared with responses to the endotoxin lipopolysaccharide (LPS), a classic TLR4 ligand, using Affymetrix whole genome microarray in rats. Expression patterns were analyzed by significance analysis of microarrays (SAM) for fold change and by weighted gene co-expression network analysis (WGCNA) to identify modules of shared responses between nPM and LPS. Finally, we examined TLR4 activation in hippocampal tissue from mice chronically exposed to nPM.

Results

SAM and WGCNA analyses showed strong activation of TLR4 and NF-κB by both nPM and LPS. TLR4 siRNA attenuated TNFα and other inflammatory responses to nPM in vitro, via the MyD88-dependent pathway. In vivo, mice chronically exposed to nPM showed increased TLR4, MyD88, TNFα, and TNFR2 RNA, and decreased NF-κB and TRAF6 RNA TLR4 and NF-κB responses in the hippocampus.

Conclusions

These results show TLR4 activation is integral in brain inflammatory responses to air pollution, and warrant further study of TLR4 in accelerated cognitive aging by air pollution.
Zusatzmaterial
Additional file 1: Gene kME relationship between modules: The two shared modules, enriched by both treatments, had strong inverse correlations between the gene-module specific kMEs, which is the eigengene-based connectivity for a gene within a module (nPM, R = −0.994; LPS, R = −0.968). Each gene is given a kME for every module, and then placed into the module of best fit. Thus, individual RNAs with increased expression had strong inverse associations with the eigengene for the turquoise module, while RNAs with decreased expression had strong inverse associations with the eigengene for the blue module. As examples, TNFα had a kME of +0.93 in the blue module, but −0.91 in the turquoise module; again, the subunits NF-κB1 and NF-κB2 had positive kMEs of +0.91 and +0.96, respectively, in the blue module, but negative kMEs of −0.86 and −0.93, respectively, in the turquoise module. Together this suggests that the turquoise and the blue modules represent a single network, where the turquoise comprises the decreased RNAs in the network and the blue comprises the increased RNAs. (ZIP 43372 kb)
12974_2017_858_MOESM1_ESM.zip
Additional file 2: Figure S1. A, LPS-increased RNAs: clustered into networks. 16 nodes, composing 6 networks and 3 individual nodes, are depicted. B, LPS-decreased RNAs: two single nodes are depicted. Colors denote different networks. Nodes with two colors belong to both networks. Each network has one highlighted node (colored text, chosen by experimenter) that best represents network function. The circle size represents the number of genes enriched in the node. The width of connecting lines represents the strength of connectivity between nodes, as measured by kappa score. (ZIP 347 kb)
12974_2017_858_MOESM2_ESM.zip
Literatur
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