Δ9-tetrahydrocannabinol impairs the inflammatory response to influenza infection: role of antigen-presenting cells and the cannabinoid receptors 1 and 2

Abstract

Δ(9)-tetrahydrocannabinol (Δ(9)-THC) has potent immune modulatory properties and can impair pathogen-induced immune defenses, which in part have been attributed to ligation of the cannabinoid receptors 1 (CB(1)) and 2 (CB(2)). Most recently, dendritic cells (DC) were identified for their potential to enhance influenza-induced immunopathology in mice lacking CB(1) and CB(2) (CB(1) (-/-)CB(2) (-/-)). This study focused on the modulation of the inflammatory immune response to influenza by Δ(9)-THC and the role of CB(1) and/or CB(2) as receptor targets for Δ(9)-THC. C57Bl/6 (wild type) and CB(1) (-/-)CB(2) (-/-) mice were administered Δ(9)-THC (75 mg/kg) surrounding the intranasal instillation of A/PR/8/34 influenza virus. Three days post infection (dpi), Δ(9)-THC broadly decreased expression levels of mRNA induced by the innate immune response to influenza, suppressed the percentage of interferon-gamma (IFN-γ)-producing CD4(+) and interleukin-17-producing NK1.1(+) cells, and reduced the influx of antigen-presenting cells (APC), including inflammatory myeloid cells and monocytes/macrophages, into the lung in a CB(1)- and/or CB(2)-dependent manner. Δ(9)-THC had little effect on the expression of CD86, major histocompatibility complex I (MHC I), and MHC II by APC isolated from the lung. In vitro studies demonstrated that lipopolysaccharide (LPS)-induced maturation was suppressed by Δ(9)-THC in bone marrow-derived DC (bmDC). Furthermore, antigen-specific IFN-γ production by CD8(+) T cells after coculture was reduced by Δ(9)-THC treatment of bmDC in a CB(1)- and/or CB(2)-dependent manner. Collectively, these studies suggest that Δ(9)-THC potently suppresses myeloid cell immune function, in a manner involving CB(1) and/or CB(2), thereby impairing immune responses to influenza infection.

Fig. 1.

Reduction of gene expression associated with influenza infection as a result of Δ⁹-THC treatment. Mice (n = 4) were treated with CO VH or Δ⁹-THC (75mg/kg) for 5 consecutive days surrounding the intranasal instillation of influenza (PR8) or saline (SAL). Lung RNA was isolated 3 dpi and converted into cDNA, and gene expression levels were analyzed using a TaqMan low density gene array. Fold change values were normalized with Blom transformation, log transformed, and mean and median were centered across genes with cluster and visualized using Treeview. Shown are genes differentially regulated by at least 1.5-fold between CO and Δ⁹-THC samples, with a value of at least p ≤ 0.1 in SAL (A) and PR8-instilled (B) groups 3 dpi. Data were analyzed using factorial ANOVA in SAS version 9.1.3 as described in the Materials and Methods section, and p values are indicated in the left most column and represent statistical differences as a result of Δ⁹-THC treatment.

Fig. 2.

Δ⁹-THC does not alter BALF cell counts or composition or total lung cells after influenza infection. (A) BALF was isolated by flushing lungs at 3 dpi twice with 0.9ml SAL (n = 5). Total cell numbers were counted using a hemacytometer, then BALF was centrifuged onto slides, dried, and differential cell counts were performed after Diff-Quick staining of slides. Shown are cell number per milliliters of differentially stained cells out of the total BALF. Statistical analyses indicate comparisons between total cells obtained from the BALF. As indicated on the right of the Figure, Mac/MФ and neutrophils are in the order of 10⁵, whereas eosinophils are in the order of 10⁴ cells/ml. (B) Total cells were isolated from mechanically disrupted lung tissue and counted using a Coulter Counter. Data were analyzed using an ANOVA comparing total cells as indicated by the horizontal bar: +++ (p ≤ 0.001), difference between SAL and PR8; ### (p ≤ 0.001), difference between WT and CB₁ ^−/−CB₂ ^−/−.

Fig. 3.

No change in percent lymphocyte composition in lungs of mice treated with Δ⁹-THC. Lungs (n = 5) were mechanically disrupted at 3 dpi, and single cell suspensions were obtained. Cells were stained with fluorescently labeled antibodies for surface markers CD4, CD8, NK1.1 and analyzed by flow cytometry. (A) Cells were gated on single cells, size, CD4⁺, CD8⁺, or CD4^-CD8^-NK1.1⁺. (B) The percent of cells in the lung with respect to prior gate is shown for each treatment group (n = 5). Data were analyzed using Kruskal-Wallis’ test for nonparametric data: +++ (p ≤ 0.001), difference between SAL and PR8; # (p ≤ 0.05), ## (p ≤ 0.01), difference between WT and CB₁ ^−/−CB₂ ^−/−.

Fig. 4.

Δ⁹-THC decreased IFN-γ production in CD4⁺ cells after influenza infection. At 3 dpi, lungs (n = 5) were mechanically disrupted, and single cell suspensions were restimulated in vitro with PMA/Io (40nM/0.5µM) to induce cytokine secretion and in the presence of Brefeldin A in 2% serum RPMI for 5h to allow for intracellular accumulation of cytokines. After restimulation, cells were stained for CD4, CD8, and NK1.1 surface expression. On the day of flow cytometric analysis, cells were stained for intracellular IFN-γ and analyzed for fluorescence intensity. Data were analyzed using Kruskal-Wallis’ test for nonparametric data: *(p ≤ 0.05), difference between CO and Δ⁹-THC; ++ (p ≤ 0.01), difference between SAL and PR8; ## (p ≤ 0.01), ### (p ≤ 0.001), difference between WT and CB₁ ^−/−CB₂ ^−/−. Shown are samples concatenated (n = 5) within treatment groups.

Fig. 5.

Δ⁹-THC decreased IL-17 production in NK1.1⁺ cells after influenza infection. Lungs were mechanically disrupted and restimulated in vitro with PMA/Io as described in the Figure 4 (n = 5). CD4, CD8, and NK1.1 were stained on the surface of isolated cells, and intracellular staining for IL-17 and flow cytometry were performed. Nonparametric percentage data were analyzed using Kruskal-Wallis’ test: *(p ≤ 0.05), difference between CO and Δ⁹-THC; ++ (p ≤ 0.01), difference between NA (SAL) and PR8; # (p ≤ 0.05), ## (p ≤ 0.01), ### (p ≤ 0.001), difference between WT and CB₁ ^−/−CB₂ ^−/−. Displayed are concatenated samples (n = 5) of each treatment group.

Fig. 6.

Δ⁹-THC reduces recruitment of inflammatory myeloid cells and Mac/MФ into the lungs of PR8-infected mice in a CB₁- and/or CB₂-dependent manner. At 3 dpi, lungs (n = 5) were mechanically disrupted. Single cell suspensions obtained from the lung were stained for CD11b, CD11c, Gr-1, MHC I, MHC II, and CD86. CD11b, CD11c, and Gr-1 were used as markers to identify separate cell populations as shown in the gating scheme (A), which were then enumerated by percentage (B). Kruskal-Wallis’ test was used to perform statistics on nonparametric percentage data: ** (p ≤ 0.01), * (p ≤ 0.05), difference between CO and Δ⁹-THC, ++ (p ≤ 0.01), +++ (p ≤ 0.001), difference between SAL and PR8; ## (p ≤ 0.01), difference between WT and CB₁ ^−/−CB₂ ^−/−.

Fig. 7.

Δ⁹-THC does not alter the maturation status of lung-isolated myeloid cells after PR8 infection. Following the gating scheme in Figure 6, MHC I, MHC II, and CD86 expression was determined on cDC (CD11b⁺CD11c⁺Gr-1⁻), pDC (CD11b⁻Cd11c⁺Gr-1⁺), inflammatory myeloid cells (CD11b⁺Cd11c⁺Gr-1⁺), AM (CD11b⁻CD11c⁺Gr-1⁻), and Mac/MФ (CD11b^loCD11c^lo). Bar graphs show mean fluorescence intensities (MFI) for indicated maturation markers (n = 5). Statistical analysis was performed using ANOVA: * (p ≤ 0.01), difference between Δ⁹-THC and VH (CO); + (p ≤ 0.05), ++ (p ≤ 0.01) +++ (p ≤ 0.001), difference between SAL and PR8; # (p ≤ 0.05), ## (p ≤ 0.01), ### (p ≤ 0.001), difference between WT and CB₁ ^−/−CB₂ ^−/−.

Fig. 8.

Δ⁹-THC suppresses TLR-stimulated bmDC maturation independent of CB₁ and CB₂. Bone marrow was used to generate bmDC in the presence of granulocyte-macrophage colony stimulating factor (20ng/ml) for 9 days. bmDC were washed and incubated for 24h in the presence or absence (NA) of LPS (1 µg/ml) or R848 (5 µg/ml) and cotreated with VH (0.1% ethanol) or Δ⁹-THC (10 µM). After incubation, cells were stained for CD11b, CD11c, MHC I, MHC II and CD86 was performed as previously described. (A) Cells obtained after culture were CD11b⁺CD11c⁺, indicating cDC phenotype. (B) Graphs shown are concatenated samples (n = 3). (C) ANOVA tests were performed. ** (p ≤ 0.01), difference between Δ⁹-THC and VH (ethanol); ++ (p ≤ 0.01), +++ (p ≤ 0.001), difference between NA and PR8; ## (p ≤ 0.01), difference between WT and CB₁ ^−/−CB₂ ^−/−. The experiment is representative of three identical repeat experiments.

Fig. 9.

Δ⁹-THC impairs antigen-specific bmDC-elicited T-cell responses. bmDC (NA or TLR-treated) were treated with VH (0.1% ethanol) or Δ⁹-THC (10 µM) and pulsed with the OT-1 TCR-specific peptide SIINFEKL for 1h and washed three times prior to incubation with Cell Trace–labeled OT-1 splenocytes for 4 days. Cells were restimulated with SIINFEKL, stained for CD8 and IFN-γ, and gated as depicted in the scheme to obtain dot plots (A). bmDC and then gated as described in Figure 3A. Dot plots with Cell Trace loss indicating proliferation on the x-axis and CD8 staining on the y-axis are shown. Gate shown indicates proliferation (loss of Cell Trace fluorescence compared with control) and CD8 staining (B) or IFN-γ staining (C). Shown are concatenated samples of each group (n = 3). Kruskal-Wallis tests were performed on samples from Figures 9B and C, shown in D and E, respectively, * (p ≤ 0.05), difference between Δ⁹-THC to VH (ethanol); ++ (p ≤ 0.01), # (p ≤ 0.05), ### (p ≤ 0.001), difference between WT and CB₁ ^−/−CB₂ ^−/−. The experimental data are representative of two identical repeat experiments.