Glutathione Fine-Tunes the Innate Immune Response toward Antiviral Pathways in a Macrophage Cell Line Independently of Its Antioxidant Properties

Abstract

Glutathione (GSH), a major cellular antioxidant, is considered an inhibitor of the inflammatory response involving reactive oxygen species (ROS). However, evidence is largely based on experiments with exogenously added antioxidants/reducing agents or pro-oxidants. We show that depleting macrophages of 99% of GSH does not exacerbate the inflammatory gene expression profile in the RAW264 macrophage cell line or increase expression of inflammatory cytokines in response to the toll-like receptor 4 (TLR4) agonist lipopolysaccharide (LPS); only two small patterns of LPS-induced genes were sensitive to GSH depletion. One group, mapping to innate immunity and antiviral responses (Oas2, Oas3, Mx2, Irf7, Irf9, STAT1, il1b), required GSH for optimal induction. Consequently, GSH depletion prevented the LPS-induced activation of antiviral response and its inhibition of influenza virus infection. LPS induction of a second group of genes (Prdx1, Srxn1, Hmox1, GSH synthase, cysteine transporters), mapping to nrf2 and the oxidative stress response, was increased by GSH depletion. We conclude that the main function of endogenous GSH is not to limit inflammation but to fine-tune the innate immune response to infection.

Keywords: TLR4; antiviral immunity; glutathione; inflammation; influenza; innate immunity; macrophages; redox regulation.

Figure 1

GSH + GSSG levels and ROS production in LPS-treated cells with or without BSO. Cells were pretreated with 120 µM BSO for 24 h, followed by 10 ng/ml LPS for 2 h. Total GSH + GSSG and GSSG levels measured in the cell lysates are the mean ± SD of six biological replicates from three independent experiments and are expressed as nmoles/mg protein. ROS production is expressed as signal of BMPO adduct, mean ± SD of six biological replicates from six independent experiments *P < 0.05 and ***P < 0.001 vs control by two-tailed Student’s t-test. BMPO, 5-tert-butoxycarbonyl-5-methyl-1-pyrroline N-oxide; BSO, buthionine sulfoximine; GSH, glutathione; GSSG, glutathione disulfide; LPS, lipopolysaccharide; ROS, reactive oxygen species.

Figure 2

Effect of GSH depletion on LPS-induced changes in gene expression profile. The dataset was filtered to select transcripts that were affected by LPS (LPS vs control, first step), then by BSO (BSO + LPS vs LPS alone, second step). Cutoff for selection was 1.5-fold change and P < 0.05, using the Student’s t-test. Significantly different expressed genes were further selected by using one-way ANOVA, followed by a correction for multiple comparisons by controlling the false discovery rate with the two-stage step-up method of Benjamini, Krieger, and Yekutieli. The number of transcripts resulting from filtering is indicated and color coded (red, increased; green, decreased). ANOVA, analysis of variance; BSO, buthionine sulfoximine; GSH, glutathione; LPS, lipopolysaccharide.

Figure 3

Patterns of gene expression. Cluster heat maps of transcripts in Groups 1 and 2. For each sample (3 BSO, 3 LPS, 3 BSO + LPS), the log₂ fold change in gene expression vs the mean of three controls is represented. LPS, lipopolysaccharide; BSO, buthionine sulfoximine.

Figure 4

Enriched functional categories in the four groups of genes differentially regulated by LPS and BSO. The lists of genes in the four groups at 2 and 6 h were combined and the overrepresented GO biological process (GO:BP) categories (white bars) and KEGG pathways (gray bars) were obtained by DAVID analysis. All categories identified by DAVID for Groups 1–4 are reported. For Group 2, DAVID returned 51 categories and only the top 15 (ordered by EASE score, a modified Fisher’s exact test) are reported. BSO, buthionine sulfoximine; GO:BP, gene ontology biological process; LPS, lipopolysaccharide.

Figure 5

TF binding profile overrepresented in Group 1 (A) and Group 2 (B) transcripts, showing the Fisher score plotted against GC composition (1 = 100%). The threshold (dashed line) is set to the mean + 1 SD. The number in parentheses indicates the number of transcripts that map to each TF. TF, transcription factor.

Figure 6

PCR validation of the microarray data at 2 h. Results for seven genes (il1b, Irf9, Mx2, Il4i1, Srxn1, Oas2, Socs1), comparing expression data from microarrays (N = 3 biological replicates; left graphs) with results from PCR analysis using all the four replicates of the RNA from the same experiment (N = 4 biological replicates; middle graphs) and RNA from an independent experiment (N = 4 biological replicates; right graphs). Data are expressed as fold change vs one of the respective control samples. For each experimental group, the mean is also shown. *P < 0.05, **P < 0.01, and ***P < 0.001 by two-tailed Student’s t-test. PCR, polymerase chain reaction.

Figure 6

PCR validation of the microarray data at 2 h. Results for seven genes (il1b, Irf9, Mx2, Il4i1, Srxn1, Oas2, Socs1), comparing expression data from microarrays (N = 3 biological replicates; left graphs) with results from PCR analysis using all the four replicates of the RNA from the same experiment (N = 4 biological replicates; middle graphs) and RNA from an independent experiment (N = 4 biological replicates; right graphs). Data are expressed as fold change vs one of the respective control samples. For each experimental group, the mean is also shown. *P < 0.05, **P < 0.01, and ***P < 0.001 by two-tailed Student’s t-test. PCR, polymerase chain reaction.

Figure 7

PCR validation of the microarray data at 6 h. Results for four genes (Prdx1, Nos2, Ptgs2, Slc7a11), comparing expression data from microarrays (N = 3 biological replicates; left graphs) with results from PCR analysis using all the four replicates of the RNA from the same experiment (N = 4 biological replicates; middle graphs) and RNA from an independent experiment (N = 4 biological replicates; right graphs). Data are expressed as fold change vs one of the respective control samples. For each experimental group, the mean is also shown. *P < 0.05, **P < 0.01, and ***P < 0.001 by two-tailed Student’s t-test. PCR, polymerase chain reaction.

Figure 8

LPS activation of antiviral innate immunity is dependent on GSH. (A) Western blot for influenza virus proteins in RAW cells infected with PR8 or uninfected, after LPS treatment, with and without GSH depletion. β-Actin was used as loading control. Representative of two Western blots (N = 2 biological replicates). (B) Levels of NP viral protein in RAW cells pretreated with LPS, with and without GSH depletion. Representative of four Western blots (N = 8 biological replicates from two independent experiments). (C) Densitometric analysis expressed as the mean ± SD of the ratio NP/β-Actin from eight biological replicates from two independent experiments for a total of four Western blots. *P < 0.05, **P < 0.01, and ***P < 0.001 by two-tailed Student’s t-test. GSH, glutathione; LPS, lipopolysaccharide; NP, nucleoprotein.

Figure 9

Effect of NAC and menadione on Group 1 (left) and Group 2 (right) genes. Cells were treated with 5 mM NAC for 1 h and then stimulated with 10 ng/ml LPS for further 2 h. Menadione (Men) was added at 10 µM for 2 h. Gene expression was measured by qPCR. Data are expressed as fold change vs one of the control samples, and are the mean ± SD of six biological replicates from two independent experiments. **P < 0.01 and ***P < 0.001 vs control; Δ P < 0.01 vs LPS by two-tailed Student’s t-test. LPS, lipopolysaccharide; NAC, N-acetyl-l-cysteine; qPCR, quantitative polymerase chain reaction.

Figure 10

GSH fine-tuning of TLR4 signaling. LPS triggers TLR4 to induce gene expression of inflammatory cytokines, antioxidant genes, and antiviral/immunity pathways. GSH orients the TLR4-mediated changes in gene expression profile toward activation of host defense. GSH, glutathione; LPS, lipopolysaccharide; TLR4, toll-like receptor 4.