The study included 60 individuals, comprising 30 cases with primary DCM and 30 normal individuals (controls) and was conducted between December 2010 and February 2013. Primary DCM was diagnosed based on the guidelines of the World Health Organization (1). Accordingly, various secondary causes of heart failure, including coronary artery disease and valvular diseases, were excluded in order to diminish the potential confounding effects of etiological heterogeneity of heart failure. A control group of normal individuals, defined as asymptomatic age- and gender-matched individuals with normal electrocardiograms and echocardiograms, was included. A normal echocardiogram excluded the diagnosis of DCM in the control individuals. Patients who were receiving anti-inflammatory or immunosuppressive medications, recently treated with antibiotics and other underlying acute or chronic diseases, were all excluded from the present study. To reduce the potential confounding effects of diabetes mellitus, those who had this disease were excluded. The present study was performed in accordance with protocols approved by the Guangxi Medical University Ethics Committee (Guangxi, China) and the participants gave informed consent. The clinical data, including ESR and CRP were also collected. The characteristics of the study subjects are summarized in Table I.

Peripheral blood was harvested with 50 U/ml heparin and diluted with an equal volume of phosphate-buffered saline. PBMCs were isolated using Ficoll-Plaque (Solarbio Science & Technology, Beijing, China) by gradient centrifugation at 300 × g and cells were resuspended with a final concentration of 2×10⁶ in complete RPMI-1640 (Solarbio Science & Technology) supplemented with 100 U/ml penicillin and 100 μg/ml streptomycin, followed by incubation at 37°C with 5% CO₂ for 5 h in the presence of 25 ng/ml phorbol myristate acetate (PMA) 1 μg/ml ionomycin (both from Sigma-Aldrich, St. Louis, MO, USA) and Golgi Plug (1 μl/10⁶ cells; BD Biosciences, San Diego, CA, USA).

The expression of markers on T cells from blood were determined by flow cytometry following surface or intracellular staining with anti-human-specific antibodies. Briefly, following incubation, the PBMCs were stained with fluorescein isothyocyanate-conjugated anti-CD4 monoclonal antibodies (cat no. 555346; BD Biosciences) at room temperature in the dark for 30 min. Next, the cells were stained with allophycocianin-conjugated anti-IFN-γ (cat no. 554702; BD Biosciences), peridinin chlorophyll-CY5.5-conjugated anti-IL-17A (cat no. 560799; BD Biosciences) and phycoerythrin-conjugated anti-IL-22 monoclonal antibodies (cat no. 12-7229-42; eBioscience, San Diego, CA, USA) following fixation and permeabilization. Isotype controls (BD Bioscience) were given to enable correct compensation and confirm the antibody specificity. Stained cells were analyzed by flow cytometric analysis using a FACScan cytometer equipped with CellQuest software (BD Biosciences). Pure Th22 cells were defined as IL-22⁺IL-17⁻ IFN-γ⁻CD4⁺. The Th17 cells were identified as those that were CD4⁺IL-17⁺, and Th1 cells were CD4⁺IFN-γ⁺.

TRIzol reagent (Invitrogen, Carlsbad, CA, USA) was used to isolate the total RNA of PBMCs. The RNA was transcribed into cDNA with a reverse transcription kit (Ferma, Mountain View, CA, USA) according to the manufacturer’s instructions. qPCR was performed for AHR and the housekeeping gene β-actin on an ABI 7500 Real-Time PCR System (Applied Biosystems, Grand Island, NY, USA) using SYBR^® Green. The PCR reactions were cycled 40 times following initial denaturation at 94°C for 3 min with the following parameters: Denaturation at 94°C for 30 sec, annealing at 60°C for 30 sec and extension at 72°C for 60 sec. The primers used were as follows: AHR forward, 5′-ACT CCA CTT CAG CCA CCA TC-3′ and reverse, 3′-ATG GGA CTC GGC ACA ATA AA-5′; β-actin forward, 5′-GCA AGC AGG AGT ATG ACG AG-3′ and reverse, 3′-CAA ATA AAG CCA TGC CAA TC-5′. The relative gene expression of AHR was normalized to the level of β-actin transcripts and quantified by the ΔΔCt method using 7500 System Sequence Detection software (Applied Biosystems, Foster City, CA, USA). All experiments were conducted in triplicate.

Serum was obtained from all subjects by centrifugation at 200 × g and stored at −80°C for the determination of cytokine levels. Serum IL-22 levels were determined with a quantitative sandwich enzyme immunoassay technique following the manufacturer’s instructions (cat no. BMS2047; eBioscience). The levels of BNP were measured using ELISA (cat no. CSB-E07970h; Cusabio, Wuhan, China). The minimum detectable concentration was 5 pg/ml for IL-22 and 23 pg/ml for BNP, respectively. All the samples were assayed in duplicate.

For the qualitative determination of human ANT autoantibody concentrations in serum, an ANT autoantibody ELISA kit was used according to manufacturer’s instructions (cat no. CSB-EQ027458HU; Cusabio, Wuhan, China). In order to calculate the valence of the human ANT autoantibody, the sample well was compared with the control. A cutoff value was defined as the average negative control value plus 0.2. An optical density (OD) sample < the cutoff value was negative, while an OD sample ≥ the cut off value was positive.

Quantitative variables were expressed as the mean ± standard deviation. Differences between two groups were determined using the Newman-Keuls multiple comparison test (q-test). The Pearson’s or Spearman’s correlation test was used for correlation analysis depending on the data distribution. Qualitative variables of ANT were expressed as percentages and differences in ANT distribution between groups were obtained using the χ² test. Analysis was performed using SPSS version 17.0 (SPSS, Inc., Chicago, IL, USA). P<0.05 was considered to indicate a statistically significant difference.

The frequencies of Th22, Th17 and Th1 based on cytokine patterns following in vitro activation by phorbol myristate acetate, ionomycin and monensin were analyzed in short-term cultures. Pure Th22 was defined as CD4⁺IFN-γ⁻IL-17⁻IL-22⁺ T cells to exclude Th1 or Th17 cells. Th17 and Th1 cells were identified as CD4⁺IL-17⁺ T cells and IFN-γ⁺CD4⁺ T cells, respectively. Representative plots of the proportion of Th22, Th17 and Th1 cells in a representative patient with DCM and a healthy control are shown in Fig. 1A–C. As shown in Fig. 2A, the frequencies of Th1 cells were significantly elevated in patients with DCM as compared with the healthy donors (5.29±1.82 vs. 2.81±0.97%; P<0.01). The proportions of the peripheral Th22 cells were significant higher in the DCM group (0.91±0.34%) in comparison with the control group (0.26±0.08%; P<0.01) (Fig. 2B). Similarly, the percentages of the Th17 cells were profoundly increased in the DCM group (2.27±0.59%) as compared with the control group (0.27±0.07%; P<0.01) (Fig. 2C).

Correlations between the frequencies of Th22, Th17 and Th1 cells in patients with DCM were examined. There was a significant positive correlation between numbers of Th22 and Th17 cells, as shown in Fig. 2D (r=0.676 and P<0.001). However, the correlation analysis between the percentages of Th1 and Th22 cells showed no significant association in patients with DCM (r=0.326 and P=0.052) (Fig. 2E).

The mRNA expression of AHR, the key transcription factor directing Th22 lineage commitment, was determined by qPCR to further confirm the results of the present study obtained from flow cytometric analysis. Consistent with the higher proportion of Th22 cells in patients with DCM, the data demonstrated that levels of the relative AHR transcript were notably higher in the PBMCs of patients with DCM as compared with those in the healthy control (1.93±0.4 vs. 1.06±0.16; P<0.01) (Fig. 2F).

Plasma levels of IL-22 were evaluated by ELISA. Of note, the expression of IL-22 did not differ between patients with DCM and healthy controls (19.78±5.9 vs. 21.3±5.4 pg/ml; P=0.303) (Fig. 3A). Furthermore, no significant correlation was identified between Th22 cells and IL-22 levels (r=0.345 and P=0.061; Fig. 3B), nor between Th17 cells and IL-22 (r=0.235 and P=0.211; Fig. 3C). As shown in Fig. 3D, the peripheral Th1 cells did not show a statistical correlation with circulating levels of IL-22 (r=0.221 and P=0.241).

The percentages of ANT-positive individuals among patients with DCM and healthy controls were 40 and 6.7%, respectively (P<0.05). It was investigated whether the levels of Th22 cells and IL-22 in the DCM group (n=30) demonstrated a difference between ANT-positive and -negative patients. Compared with ANT-negative patients with DCM, levels of circulating Th22 cells were increased in ANT-positive patients with DCM (1.04±0.32 vs. 0.69±0.26%; P=0.005; Fig. 4A). However, the levels of plasma IL-22 did not differ between ANT antibody-positive and −negative individuals (21.11±5.76 vs. 17.78±5.76 pg/ml; P=0.132; Fig. 4B).

Finally, the correlation between the percentages of Th22 cells with the levels of laboratory parameters, including BNP, CRP and ESR, were explored. Significantly higher levels of BNP were observed in the DCM group in comparison with the control group (1781.9±1329.8 vs. 78.6±21.4; P<0.05). As shown in Fig. 5A, there was a significant positive correlation between proportion of Th22 cells and BNP levels in patients with DCM (r=0.567 and P<0.01). Additionally, a positive correlation was found between the frequencies of Th22 cells and the expression of CRP (r=0.556 and P<0.01; Fig. 5B). By contrast, Th22 cells did not show a statistically significant correlation with ESR levels (r=−0.186 and P=0.324; Fig. 5C).