Genome-wide identification of murine interferon genes in microglial-mediated neuroinflammation in Alzheimer's disease

Abstract

Interferons play a major role in microglial-mediated neuroinflammation in Alzheimer's disease (AD). We investigated the interferon transcriptome (AD versus non-AD) using N9 and murine microglia. We identified 64 interferon-related differentially expressed genes (DEG) in LPS-stimulated N9 microglia versus control cells, 26 DEG in microglia from 5XFAD versus wild-type mice, with 13 DEG common to both datasets. Network analyses identified potential key mediators (Cxcl10, Ifit3) of the interferon response in AD. Gene-drug interaction analysis identified therapeutics targeting interferon-related genes. These data characterize the microglial interferon response in AD, providing new targets and therapeutics directed towards interferon-related neuroinflammation in AD.

Keywords: Alzheimer's disease; Interferon; Microglia; Neuroinflammation; Transcriptomics.

Figure 1.. Differentially expressed murine microglial interferon genes in AD.

(A) Scatter plot of interferon DEG (log₂FC > 0.5, FDR-adjusted P-value < 0.05) by RNA-seq in N9 microglia stimulated with LPS (1 μg/ml) for 6 hours versus unstimulated control cells. (B) Scatter plot of interferon DEG (log₂FC > 0.5, P < 0.05) by microarray in microglia isolated from the brains of 5XFAD mice versus wild-type mice (8 months old). For both scatter plots, up-regulated genes are shown in red and down-regulated genes are shown in green. (C) Venn diagram displaying overlap in interferon DEG between the datasets.

Figure 2.. Gene Ontology Biological Process enrichment analysis.

Biological process (BP) analysis of the N9 (A) and mouse (B) microglial interferon DEG was performed using DAVID. Gene Ontology (GO) BP terms with a gene count ≥ 4 and FDR < 0.05 were considered significant. Selected GO BP terms are shown with a complete list available in supplementary tables S3 and S4.

Figure 3.. Ifit3 expression network analyses.

Analyses for Ifit3 positively correlated genes (A) and predicted regulators (B). The gene constellations shown were generated in ImmGen using the “myeloid cells” reference populations option. The predicted regulators were identified using ImmGens Predicted Interferons Network tool. The vertical green line indicates the confidence threshold at 1% FDR.

Figure 4.. Cxcl10 expression network analyses.

Analyses for Cxcl10 positively correlated genes (A) and predicted regulators (B). Genes constellations and predicted regulators were generated using the same tools as used for Ifit3 (Figure 3).

Figure 5.. Gene-drug interactions.

Interactions between therapeutic drugs and the N9 (A) and mouse (B) microglial interferon DEG. Genes identified with drug interactions are shown and the number of drugs associated with each gene is shown above each bar. (C) Experimental validation of selected drugs targeting Cxcl10. N9 microglia were treated with methylprednisolone, zidovudine, and DMSO (control) and stimulated with LPS (1 μg/ml) for 6 hours. Supernatants were collected and assayed for CXCL10 by ELISA. (D) Drug toxicity in N9 microglia treated with methylprednisolone, zidovudine, and DMSO (control) for 8 hours. Cytotoxicity was determined by LDH release in the cell culture supernatants. Data are shown as mean ± SEM. ***P < 0.001, **P < 0.01. ns = not significant.

Figure 6.. Interferon-related cytokines and chemokines induced by LPS in microglia.

Primary microglia were stimulated with LPS (50 ng/ml) for 6 hours. N9 microglia were stimulated with LPS (1 μg/ml) for 6 hours. Supernatants were collected and assayed for CXCL10 secreted by primary (A) and N9 (B) microglia. Supernatants were also assay for IFN-β secreted by primary (C) and N9 (D) microglia. Experiments were performed in duplicate. Results are from three independent experiments. Data are shown as mean ± SEM. ****P ≤ 0.0001. IFN-α was also evaluated, but was below the detection limit of the assay.