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. 2012 Oct 26;151(3):658-70.
doi: 10.1016/j.cell.2012.08.043.

Unraveling Inflammatory Responses Using Systems Genetics and Gene-Environment Interactions in Macrophages

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Free PMC article

Unraveling Inflammatory Responses Using Systems Genetics and Gene-Environment Interactions in Macrophages

Luz D Orozco et al. Cell. .
Free PMC article

Abstract

Many common diseases have an important inflammatory component mediated in part by macrophages. Here we used a systems genetics strategy to examine the role of common genetic variation in macrophage responses to inflammatory stimuli. We examined genome-wide transcript levels in macrophages from 92 strains of the Hybrid Mouse Diversity Panel. We exposed macrophages to control media, bacterial lipopolysaccharide (LPS), or oxidized phospholipids. We performed association mapping under each condition and identified several thousand expression quantitative trait loci (eQTL), gene-by-environment interactions, and eQTL "hot spots" that specifically control LPS responses. We used siRNA knockdown of candidate genes to validate an eQTL hot spot in chromosome 8 and identified the gene 2310061C15Rik as a regulator of inflammatory responses in macrophages. We have created a public database where the data presented here can be used as a resource for understanding many common inflammatory traits that are modeled in the mouse and for the dissection of regulatory relationships between genes.

Figures

Figure 1
Figure 1. Environmental, Genetic and GxE interaction effects on gene expression
Expression levels are plotted as the log2(microarray intensity) on the Y-axis, for mouse strains on the X-axis. Each dot represents the levels of a gene for a given strain in control (blue dots) and treated cells (red dots). (A) Hmox1 expression in response to OxPAPC and (B) Hmox1 in response LPS, illustrate environmental effects. (C) Npl levels are influenced by genetic effects. (D) Expression levels of Ifi205 are influenced by GxE interactions. See also Figures S1 and S2.
Figure 2
Figure 2. Genome-wide association of gene expression
Association using microarray expression of macrophages in various conditions. Each dot represents a significant association between a transcript and a SNP. Genomic position of the SNPs and transcripts are shown on the X and Y-axes, respectively. (A) Association in control condition. (B) Association in LPS condition. (C) Association in OxPAPC condition. See also Figure S3, Table S2 and Table S7.
Figure 3
Figure 3. eQTL hotspots
The number of genes mapping to each 2-Mb bin is shown on the Y-axis and the genomic position of the bin is on the X-axis. The horizontal dashed line represents the significance threshold. (A) Hotspots in control eQTL. (B) Hotspots in OxPAPC eQTL. (C) Hotspots in LPS eQTL. (D) Hotspots in LPS gxeQTL. See also Figure S4, Table S3 and Table S4.
Figure 4
Figure 4. Expression levels in LPS condition after knock-down of candidate genes
(A) Microarray expression levels in LPS condition for 273 genes affected by knock-down of the candidate genes Gcsh (siGcsh) and 2310061C15Rik (siC15Rik). For each gene on the Y-axis, expression is plotted as the mean of the siRNAs (X-axis) that significantly affected expression relative to the scramble control on a log2 scale. (B) Microarray expression levels for the genes Il1b, Csf1, Il6, Ccl2 and Serpine1, after knock-down of the candidate gene 2310061C15Rik (C15Rik). Data are presented as mean +/− standard deviation. See also Table S5.
Figure 5
Figure 5. Database plots for Abca1
Sample plots for a given gene of interest that can be obtained from our online database. (A) LPS response of Abca1. (B) Genome-wide association for the expression of Abca1. (C) Relative expression levels among mouse strains of the HMDP in macrophages and different tissues.
Figure 6
Figure 6. Abca1 and LPS-activation regulatory network defined by trans-eQTL
Causal regulatory relationships between genes were defined using LPS trans-eQTL. Novel relationships are shown in red lines, and previously described relationships are in black lines. Dotted lines are previously described relationships which were not identified in the LPS trans-eQTL. See also Figure S5.
Figure 7
Figure 7. Candidate genes in QTL for Atherosclerosis and susceptibility to Salmonella
Ideograms of mouse autosomes showing the position of clinical QTL identified through multiple studies. The peak linkage region is marked with a red, blue, green or black bar. Genes listed for each QTL are cis-eQTL identified in this study. Genes discussed in the text are highlighted in red. (A) Atherosclerosis QTL. (B) QTL for susceptibility to Salmonella thyphimurium.

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