Placenta Specific 8 Suppresses IL-18 Production through Regulation of Autophagy and Is Associated with Adult Still Disease

Adult Still disease (ASD) is a rare multisystemic autoinflammatory disorder of unknown etiology. The clinical features correlate with systemic manifestations, such as a high spiking fever, arthralgia or arthritis, and an evanescent salmon-pink maculopapular skin rash (1, 2). Although the pathogenesis of ASD is not clear, the interplay of viral infections, genetic factors, and immune dysregulation, including cytokine-mediated inflammation and enhanced apoptosis, may contribute to the development of this disease (3).

Previous reports investigated the pathogenic roles of several proinflammatory cytokines in ASD, including IL-1β, IL-6, IL-18, TNF-α, and IFN-β (3). Among these cytokines, IL-18 is thought to be the upstream initiator of the proinflammatory cytokine cascades, and serum IL-18 levels are particularly elevated in ASD, unlike in other inflammatory conditions (4–8). Furthermore, higher levels of IL-18 correlate with ASD activity (6, 8). Thus, IL-18 is thought to be one of the most important factors in the pathogenesis of ASD. However, there is little or no information on the regulatory process involved in the production of IL-18 and other inflammatory cytokines in ASD.

Activation of myeloid cells, including monocytes, macrophages, dendritic cells, and neutrophils, is the hallmark of ASD. Especially, several markers and cytokines that reflect macrophage activation correlate with ASD activity (3). In contrast, activated monocytes are known to produce IL-6, TNF-α, IL-1β, and IL-18 through several stimuli including viral and bacterial components (9, 10). In patients with ASD, uncontrolled activation of monocytes could induce cytokine storms and several systemic disorders. However, the genes involved in the regulation of monocyte activation remain unidentified in ASD. Therefore, identification of specific genes in monocytes could help in the assessment of disease activity and severity and could be useful as therapeutic targets in ASD.

The present study was designed to clarify the specific genes involved in the progression of ASD. Using DNA microarray analysis, we identified placenta specific 8 (PLAC8) mRNA to be significantly upregulated in monocytes from patients with active ASD. PLAC8 is a small protein known to be highly expressed in giant trophoblasts and the spongiotrophoblast layer of the mouse placenta (11, 12). In humans, PLAC8 is expressed in dendritic cells, myeloid cells, lymphoid cells, and epithelial cells in lungs and intestines (13, 14). Multiple functions of PLAC8 have been demonstrated in the immune system, such as adipocyte differentiation, infectious disease, diabetes, cancers, and autophagy (14–18). However, little is known about the role of PLAC8 in monocytes and how it is involved in inflammatory conditions. The present study provides data on the function of PLAC8 in monocytes and sheds light on the role of PLAC8 in the pathogenesis of ASD.

All subjects were examined at the University of Tsukuba Hospital, and each provided a signed consent form for participation in the study. The study was approved by the ethics committee of the University of Tsukuba. Monocytes or plasma samples were collected from patients with inactive ASD (n = 14, three males and 11 females; the mean ± SD of age was 44.7 ± 13.8 y) and active ASD (n = 10, three males and seven females; the mean ± SD of age was 49.5 ± 17.0 y). All active-ASD patients satisfied Yamaguchi’s (19) criteria. We divided patients with ASD into active and inactive groups according to clinical manifestations (Supplemental Table I). Monocytes of the disease control subjects were collected from rheumatoid arthritis (RA) (n = 8, one male and seven females; the mean ± SD of age was 50.0 ± 18.3 y; the mean ± SD of Disease Activity Score in 28 joints as measured by C-reactive protein [CRP] was 4.1 ± 1.1; steroid use, n = 6; synthetic disease-modifying antirheumatic drug use, n = 6; immunosuppressive drug [ISD] use, n = 1; biological agent use, n = 1); Sjögren syndrome (SS) (n = 5, five females; the mean ± SD of age was 56.0 ± 15.1 y; steroid use, n = 3); systemic lupus erythematosus (SLE) (n = 6, one male and five females; the mean ± SD of age was 46.3 ± 16.3 y; steroid use, n = 6; hydroxychloroquine use, n = 3; ISD use, n = 6); and polymyositis/dermatomyositis (PM/DM) (n = 5, one male and four females; the mean ± SD of age was 55.8 ± 8.73 y; steroid use, n = 5; ISD use, n = 4). All RA patients satisfied the American College of Rheumatology classification criteria (20). All the patients with SS were diagnosed by rheumatologists according to the 1999 Japanese Ministry of Health criteria for diagnosis of SS (21). All the patients with SLE fulfilled the 1997 American College of Rheumatology classification criteria (22). All the patients with PM/DM fulfilled the Bohan and Peter criteria (23). Healthy control (HC) subjects matched for age were recruited as the control group.

Monocytes (CD14⁺ cells) were isolated from HC (n = 3, one male and two females; the mean ± SD of age was 51.0 ± 7.9 y), inactive-ASD (n = 3, one male and two females; the mean ± SD of age was 48.7 ± 10.7 y), and active-ASD patients (n = 3, one male and two females; the mean ± SD of age was 48.7 ± 10.7 y) by MACS (Miltenyi Biotec, Bergisch Gladbach, Germany). In ASD, monocytes were isolated from the same patients in active and inactive stages (the mean ± SD interval times of active and inactive was 4.33 ± 1.15 mo). The purity of MACS-isolated CD14⁺ cells was >99%. Total RNA was extracted from each cell using Isogen (Nippon Gene, Tokyo, Japan) and then purified using the RNeasy Micro Kit (Qiagen, Venlo, the Netherlands) according to the instructions provided by the manufacturer. Total RNA samples were prepared and processed for microarray analysis using a GeneChip Human Genome U133 Plus 2.0 Array (Affymetrix) according to the standard protocol supplied by the manufacturer. All of the microarray data are Minimum Information about a Microarray Experiment–compliant and have been deposited in a Minimum Information about Microarray Experiment–compliant database, the National Center for Biotechnology Information Gene Expression Omnibus (accession no. GSE113645; http://www.ncbi.nlm.nih.gov/geo/), as detailed on the Functional Genomics Data Society Web site (http://www.fged.org/projects/miame/). The obtained microarray data were quantified with the Factor Analysis for Robust Microarray Summarization (FARMS) algorithm (24) using the statistical language R (http://www.r-project.org/) (25) and Bioconductor (http://www.bioconductor.org/) (26). Global gene expression profiles of all samples were evaluated with principal component analysis (PCA) (27) using the promp() function in R. To compare the genes upregulated in active ASD relative to inactive ASD, the Significance Analysis of Microarrays (SAM) (28) was applied to the FARMS-normalized paired data with modification. In brief, considering the dynamic range of changes in gene expression, using the weight term of the weighted average difference (29) statistic, the modified SAM (mSAM) statistic for the ith probe set, mSAM(i), is calculated simply as

where wi is relative average log signal intensity of the ith probe set (i.e., weight). Then we rank the probe sets according to the mSAM statistic, and among the top-ranked 600 probe sets (approximately top 1% of the all probe sets on the GeneChip Human Genome 2.0 Array), probe sets with p < 0.05 were regarded as differentially expressed. To compare the differentially expressed probe sets between active ASD and HC, the rank products method (30) was applied to the FARMS-normalized data. The probe sets with a false discovery rate <0.05 were regarded to have different expression levels between the two groups (i.e., differentially expressed). Then, we picked up active-ASD–specific, highly expressed probe sets as commonly appeared in both differentially expressed probe sets. Gene annotation enrichment analysis of these active-ASD–specific, highly expressed probe sets was performed according to Gene Ontology (GO) annotation using the Database for Annotation, Visualization and Integrated Discovery Web tool (https://david.ncifcrf.gov) (31). The hierarchical chart of GO terms was constructed using the Web tool QuickGO (https://www.ebi.ac.uk/QuickGO/) (32).

Quantitative PCR (qPCR) was also conducted for validation of upregulated differentially expressed genes in active-ASD patients identified by cDNA microarray analysis. Total RNA was extracted from monocytes isolated from active-ASD patients (n = 7, three males and four females; the mean ± SD of age was 53.3 ± 16.4 y), inactive-ASD patients (n = 10, two males and eight females; the mean ± SD of age was 45.5 ± 13.6 y), and HC (n = 8, three males and five females; the mean ± SD of age was 50.1 ± 8.43 y), which included subjects analyzed by DNA microarray. The mRNA expression level of the target gene was examined by qPCR using the 7500 Real-Time PCR System (Applied Biosystems, Foster City, CA) with the SYBR Green PCR kit (Takara Bio). Predesigned primers specific for the targeted genes (Takara Bio) were used for qPCR. The human GAPDH was also examined as an internal control. The levels of gene expression were calculated from the standard curve and expressed relative to GAPDH gene expression. The primers of 13 differentially expressed genes used for the RT-PCR were as follows: clusterin (CLU)-forward, 5′-CTCCCACTAGGGATGCAGATG-3′; CLU-reverse, 5′-CACAAACAGCAGCAGAGTCTTCA-3′; Fc fragment of IgG high affinity Ib (FCGR1B)–forward, 5′-GTCCTTAAGCACAGCCCTGA-3′; FCGR1B-reverse, 5′-AAACATTATTCCCACTGCCAGA-3′; PLAC8-forward, 5′-CGTTGTGACCCAACCTGGAG-3′; PLAC8-reverse, 5′-TCCACACAGACAGCATTCATTCATA-3′; TLR1-forward, 5′-CTCCCAACTTTGTCCAGAGTGAA-3′; TLR1-reverse, 5′-TTCCAGCAAGATCAGGATTAAGCTA-3′; S100 calcium binding protein A12 (S100A12) –forward, 5′-TCTCTAAGGGTGAGCTGAAGCA-3′; S100A12-reverse, 5′-CAATGGCTACCAGGGATATGAA-3′; CD55 molecule (CD55)–forward, 5′-TGTCTGGGTCATCCCACATTTC-3′; CD55-reverse, 5′-ATGGTTACTAGCGTCCCAAGCA-3′; Pim-1 proto-oncogene (PIM1)–forward, 5′-GGACAGTGCTTGATACAGGAACAAC-3′; PIM1-reverse, 5′-CCCGGGATATTTCAGAGTCCAG-3′; BCL2-related protein A1 (BCL2A1)–forward, 5′-GCCAGCTCAAGACTTTGCTCTC-3′; BCL2A1-reverse, 5′-GGACCTGATCCAGGTTGTGGTA-3′; superoxide dismutase 2 (SOD2)–forward, 5′-CGGCCTACGTGAACAACCTG-3′; SOD2-reverse, 5′-GCTATGATTGATATGACCACCACCA-3′; phospholipid scramblase 1 (PLSCR1)–forward, 5′-AAATAAGTGGTCCATGTGTTGTGTG-3′; PLSCR1-reverse, 5′-GTTATCAGCGTCTGTAAATGCCTCT-3′; cytochrome P450 family 1 subfamily B polypeptide 1 (CYP1B1)–forward, 5′-ACGTACCGGCCACTATCACTGAC-3′; CYP1B1-reverse, 5′-TGATCCAATTCTGCCTGCACTC-3′; STEAP family member 4 (STEAP4)–forward, 5′-AAGCAATTCATGAAGCCTGAAGC-3′; STEAP4-reverse, 5′-GGCCAAAGGTCAGGTCTGGA-3′; IL-1 receptor antagonist (IL1RN)–forward, 5′-TGCTGCAGTCACAGAATGGAAA-3′; IL1RN-reverse, 5′-AAGGTCTTCTGGTTAACATCCCAGA-3′; GAPDH-forward, 5′-GCACCGTCAAGGCTGAGAAC-3′; GAPDH-reverse, 5′-TGGTGAAGACGCCAGTGGA-3′.

Monocytes (CD14⁺ cells) were isolated from PBMCs by MACS (Miltenyi Biotec). The isolated cells were stimulated with LPS (1 μg/ml; Sigma, St. Louis, MO), IL-1β (10 ng/ml; BioLegend, San Diego, CA), IL-6 (10 ng/ml; BioLegend), or TNF-α (10 ng/ml; BioLegend) for 3, 6, 12, 24, and 48 h. Then, total RNA was extracted from each cell using Isogen (NIPPON GENE), and PLAC8 mRNA was exacted by qPCR. At 12 and 24 h after LPS stimulation, the cell lysate and culture supernatant were collected for Western blotting.

The pLVSIN-AcGFP1-N1 vector (Takara Bio) was used to construct PLAC8-overexpressing cells. The DNA sequence of human PLAC8 (NM_001130715.1) was obtained from human monocytes by RT-PCR and inserted into the vector by In-Fusion HD Cloning Kit (Takara Bio). The primers of PLAC8 used for the RT-PCR were as follows: PLAC8-forward, 5′-GAACTCAGATCTCGAAAAATGCAAGCTCAGGCGC-3′; and PLAC8-reverse, 5′-CATGACCGGTGGATCGAAAGTACGCATGGCTCTC-3′. As a control, we generated a lentiviral vector that expressed only AcGFP1 (mock). Lentiviruses were prepared by transfecting the plasmid into Lenti-X293T packaging cells, which facilitate optimal lentivirus production. Lentiviral particles were obtained according to the instructions supplied by the manufacturer and concentrated using the Lenti-X Concentrator kit (Takara Bio). The THP-1 cells, which are a human monocytic cell line (RBRC-RCB1189; RIKEN BioResource Research Center), were transduced with these lentiviral particles with polybrene (16 μg/ml) by centrifugation for 75 min at 1200 × g at 32°C. Mock- and PLAC8-transducted THP-1 cells were selected by puromycin (0.5 μg/ml). For AcGFP1 detection, these cells were fixed with smear gel (Genostaff, Tokyo, Japan), and AcGFP1 was detected using a fluorescence microscope (Fluoview FV10i; Olympus, Tokyo, Japan).

Monocytes were isolated from PBMCs by MACS (Miltenyi Biotec), and these cells were plated at a density of 3 × 10⁵ cells per well in 96-well plates. These cells were primed with LPS (1 μg/ml) for 12 h in RPMI 1640 (Sigma-Aldrich, St. Louis, MO) supplemented with 10% FBS (Sigma-Aldrich) and 100 U/ml penicillin–streptomycin (Life Technologies). The culture medium was subsequently replaced with Opti-MEM (Invitrogen), and the cells were stimulated with nigericin (5 μM; MilliporeSigma) for 6 h. Primary THP-1, mock–, and PLAC8-overexpressing THP-1 (PLAC8–THP-1) cells were plated at a density of 5 × 10⁵ cells per well in 48-well plates. These cells were primed with LPS (1 μg/ml) for 4 h in RPMI 1640 (Sigma-Aldrich) supplemented with 10% FBS (Sigma-Aldrich) and 100 U/ml penicillin–streptomycin (Life Technologies). The culture medium was subsequently replaced with Opti-MEM (Invitrogen), and the cells were stimulated with nigericin (5 μM; MilliporeSigma) for 4 h. For inhibition of autophagy, the THP-1 cells were preincubated with 3-methyladenine (3-MA; Santa Cruz Biotechnology, Dallas, TX) for 1 h before LPS or nigericin stimulation. For induction of autophagy, THP-1 cells were stimulated with rapamycin (LC Laboratories, Woburn, MA) for 1 h after LPS stimulation. The cell lysate and culture supernatants were used for ELISA and Western blotting. For detection of cell death, mock– and PLAC8–THP-1 cells were stained by propidium iodide solution (BioLegend). The stained cells were analyzed on a BD FACSVerse flow cytometer (Becton Dickinson, Mountain View, CA), and data were processed using FlowJo software (Tree Star, Ashland, OR).

ELISA was used to measure the concentrations of IL-1β (R&D Systems, Minneapolis, MN), IL-18 (Medical and Biological Laboratories [MBL], Nagoya, Japan), and PLAC8 (MyBioSource, San Diego, CA) in the culture supernatant and plasma. The levels of IL-1β, IL-6, and TNF-α in the plasma samples were measured using the BD Cytometric Bead Array (BD Biosciences, San Jose, CA). The assays were performed in accordance with the manufacturer’s instructions. The samples were evaluated using a BD FACSVerse flow cytometer (Becton Dickinson) and analyzed using FCAP Array Software (BD Biosciences).

Cells were lysed with a cell lysis buffer (50 mM Tris-HCl, 280 mM NaCl, 0.5% NP-40, 0.2 mM EDTA, 10% glycerol, and protease inhibitor mixture [Cell Signaling Technology, MA]) and centrifuged at 15,000 rpm at 4°C for 10 min. Protein concentrations were measured using the BCA Protein Assay Reagent Kit (Pierce, Rockford, IL) and then boiled in SDS sample buffer. The cell culture supernatant was concentrated with methanol/chloroform, and the pellets were lysed in the SDS sample buffer. Anti-PLAC8 Abs (1/1000; Cell Signaling Technology), anti–IL-1β Abs (1/500; Cell Signaling Technology), anti-pro–IL-18 (1/500; MBL), anti–caspase-1 (1/500; Cell Signaling Technology), anti-LC3 Abs-HRP-DirecT (1/1000; MBL), anti–β-actin Abs (1/3000; Sigma-Aldrich), anti-mouse IgG HRP conjugate (Cell Signaling Technology), and goat anti-rabbit IgG HRP conjugate (Bio-Rad, Hercules, CA) were used for immunoblotting.

All values are expressed as mean ± SD. The Mann–Whitney U test, Kruskal–Wallis test, and Student t test were used to compare two or more independent continuous variables. Correlation between two continuous variables was assessed by the Spearman rank correlation coefficient. The p values <0.05 were considered significant. All data were analyzed using the Statistical Package for Social Sciences (SPSS version 21; IBM, Chicago, IL).

The purpose of this study was to identify genes specifically associated with the active phase of the disease. To examine the upregulated genes in active-ASD patients, monocytes were isolated from PBMCs and analyzed by DNA microarray. Fig. 1A shows a two-dimensional plot of PCA applied to FARMS-normalized data. The gene expression patterns in the three groups (HC, active ASD, and inactive ASD) showed distinct clusters specific to each group. We next identified those genes that showed differences in expression among the three groups. A total of 542 and 570 probe sets were identified as upregulated in active-ASD patients compared with HC and inactive-ASD patients. Furthermore, 82 probe sets (corresponding to 68 unique genes) were upregulated in monocytes from active-ASD patients compared with HC and inactive-ASD patients (Supplemental Table II). We defined these 68 genes as active-ASD–specific, highly expressed genes and subjected them to further analysis.

Supplemental Fig. 1 shows significantly enriched GO terms found within 68 active-ASD–specific, highly expressed genes. With regard to the hierarchical structure of GO, the more specific GO term appears in the deeper hierarchy. Therefore, we focused on two GO terms, “apoptotic mitochondrial changes” and “cytokine-mediated signaling pathway,” among the 14 significantly enriched GO terms within the active-ASD–specific, highly expressed genes. Using this approach, we identified 13 genes from the two GO terms for validation by qPCR. These were CLU, FCGR1B, PLAC8, TLR1, S100A12, CD55, PIM1, BCL2A1, SOD2, PLSCR1, CYP1B1, STEAP4, and IL1RN.

The validation analysis confirmed higher mRNA expression levels of PLAC8 and CLU in active-ASD patients than in HC and inactive-ASD patients (Fig. 1B, p < 0.05, p < 0.05, respectively). In contrast, FCGR1B and S100A12 mRNA expression levels were significantly higher in active-ASD patients than in HC (Fig. 1B, p < 0.05, p < 0.05, respectively). The mRNA expression levels of the other nine genes (STEAP4, PIM1, PLSCR1, BCL2A1, CD55, SOD2, IL1RN, CYP1B1, TLR1) were not significantly different among the three groups.

To determine the disease specificity of PLAC8 and CLU mRNA expression, we compared active-ASD and inactive-ASD patients and patients with other rheumatic diseases, including RA, SS, SLE, and PM/DM. As shown in Fig. 1C, CLU mRNA expression level was also higher in RA than in HC, inactive ASD, SS, SLE, and PM/DM. In contrast, PLAC8 mRNA expression level was higher in active ASD than in HC, inactive ASD, RA, SS, SLE, and PM/DM (p < 0.05, p < 0.05, p < 0.05, p < 0.05, p < 0.05, p < 0.05, respectively). These results indicated that the upregulation of PLAC8 mRNA in monocytes was specific to the patients with active ASD.

Next, we examined the correlation between PLAC8 mRNA expression level in monocytes and serum CRP and ferritin. In patients with ASD, the expression of PLAC8 mRNA was correlated with serum CRP and ferritin levels (Fig. 2A, 2B, r = 0.855, p < 0.01, r = 0.674, p < 0.005, respectively). Further analysis showed significantly higher serum concentration of IL-6, TNF-α, IL-1β, and IL-18 in active ASD than in HC (Fig. 2C, p < 0.05, p < 0.05, p < 0.05, p < 0.05, respectively). In the patients with ASD, plasma IL-6, TNF-α, IL-1β, and IL-18 levels correlated with serum CRP levels (Fig. 2D, r = 0.860, p < 0.01, r = 0.661, p < 0.01, r = 0.664, p < 0.01, r = 0.630, p < 0.01, respectively). In contrast, plasma IL-18 levels, but not IL-1β, IL-6, or TNF-α levels, correlated with serum ferritin levels in ASD patients (Fig. 2E, r = 0.858, p < 0.05). Moreover, serum IL-1β and IL-18 levels correlated with PLAC8 mRNA expression levels in ASD patients (Fig. 2F, r = 0.635, p < 0.05, r = 0.681, p < 0.01, respectively). Thus, these results suggested that the expression levels of PLAC8 mRNA seemed to be an activity or severity marker for ASD.

To confirm the upregulation of PLAC8 in monocytes, we examined the Western blotting analysis. The amount of PLAC8 proteins was significantly increased in active ASD than HC and inactive ASD, similar to the results of mRNA expression levels (Fig. 3A, p < 0.05, p < 0.05, respectively). Next, to determine the mechanism of PLAC8 upregulation in active ASD, primary monocytes isolated from HC were stimulated by LPS, IL-1β, IL-6, and TNF-α in vitro. PLAC8 mRNA expression was significantly increased at 3, 6, 12, and 24 h after LPS stimulation (Fig. 3B, p < 0.05, p < 0.05, p < 0.05, p < 0.05, respectively). After stimulation with TNF-α, PLAC8 mRNA was slightly increased at 3 h, but this increase was transient (Fig. 3B, p < 0.05). In contrast, IL-1β and IL-6 did not affect the expression of PLAC8 mRNA in monocytes.

After LPS stimulation with primary monocytes from inactive-ASD patients, PLAC8 mRNA was significantly upregulated, similar to monocytes from HC (Fig. 3C, p < 0.05, p < 0.05, respectively). Furthermore, LPS-induced PLAC8 mRNA expression levels in monocytes were not significantly different between HC and inactive-ASD patients (Fig. 3C). In addition to the change in mRNA expression level, we confirmed that LPS stimulation increased the production of PLAC8 proteins at 12 and 24 h in monocytes (Fig. 3D, p < 0.05, p < 0.05, respectively). Similar to PLAC8 production in cell lysates, soluble PLAC8 was detected in the culture supernatants of LPS-stimulated monocytes (Fig. 3D, p < 0.05, p < 0.05, respectively). However, the amount of soluble PLAC8 proteins in plasma was not significantly different between HC, inactive-ASD, and active-ASD patients (Fig. 3E). These results indicated that PLAC8 was induced by TLR4 signaling and might be involved in several intracellular molecular processes.

We examined the function of PLAC8 in inflammasome-induced production of cytokines, such as IL-1β and IL-18. After LPS stimulation with primary monocytes from HC, IL-1β and IL-18 mRNA levels were significantly increased at 3, 6, and 12 h (Supplemental Fig. 2A, p < 0.05, p < 0.05, p < 0.05, respectively). Furthermore, we confirmed that the combination of LPS and nigericin strongly induced the secretion of IL-1β and IL-18 extracellularly from primary monocytes (Supplemental Fig. 2B). However, the amount of IL-1β and IL-18 production induced by LPS and nigericin was not significantly different between HC and inactive-ASD patients (Supplemental Fig. 2B).

To examine the function of upregulated PLAC8 in the pathway of IL-1β and IL-18 production, we used the human monocytic cell line THP-1. First, we confirmed that the production of IL-1β and IL-18 by THP-1 cells stimulated by LPS and nigericin was similar to primary monocytes (Supplemental Fig. 2C). Stimulation with LPS or nigericin did not induce PLAC8 protein production in THP-1 cells, as confirmed by Western blotting (Supplemental Fig. 2D). Thus, to analyze the upregulation conditions of PLAC8, PLAC8–AcGFP1 fusion proteins were overexpressed in THP-1 cells using the lentivirus infection system (Supplemental Fig. 2E–G). Under steady state, PLAC8 overexpression did not affect the proliferation of these cells (Supplemental Fig. 2H). Stimulation with LPS and nigericin significantly decreased the production of IL-1β and IL-18 in PLAC8–THP-1 relative to the control (mock) THP-1 cells (Fig. 4A, p < 0.05, p < 0.05, respectively).

IL-1β and IL-18 are produced from pro–IL-1β and pro–IL-18 by similar mechanisms through the activation of inflammasome pathways, including pro–caspase-1 and caspase-1. We next examined the expression of pro–IL-1β, pro–IL-18, and pro–caspase-1 in cell lysates and IL-1β and caspase-1 p20/p22 in culture supernatants by Western blotting. As shown in Fig. 4B, the amount of IL-1β was decreased in culture supernatants from PLAC8–THP-1 cells compared with mock–THP-1 cells after LPS and nigericin stimulation. Furthermore, stimulation with LPS decreased the amount of pro–IL-1β and pro–IL-18 in cell lysates of PLAC8–THP-1 cells compared with mock–THP-1 cells (Fig. 4B, p < 0.05, p < 0.05, respectively). In contrast, the amount of pro–caspase-1 in cell lysates and caspase-1 p20/p22 in culture supernatants were not different between mock– and PLAC8–THP-1 cells under each condition (Fig. 4B).

To clarify the inhibitory effects of PLAC8 on IL-1β and IL-18 production, we analyzed the induction of their mRNAs by qPCR. At 0, 1, 2, 3, and 4 h after LPS stimulation, the expression levels of total IL-1β and IL-18 mRNA were not different between mock– and PLAC8–THP-1 cells (Fig. 4C). PLAC8 was localized in the cytoplasm and did not change after activation by LPS (Fig. 4D). Furthermore, the rate of LPS-induced cell death was not different between mock– and PLAC8–THP-1 cells (Fig. 4E). These results suggested that the upregulated PLAC8 suppressed the production of pro–IL-1β and pro–IL-18 proteins independently of these mRNA transcriptions.

To investigate the induction of autophagy, the expression of LC3-II, a marker of autophagy, was analyzed by Western blotting. LPS stimulation increased the rate of LC3-II expression in PLAC8–THP-1 cells compared with mock–THP-1 cells (Fig. 5A, p < 0.05). Furthermore, to clarify the effects of autophagy on IL-1β and IL-18 production, THP-1 cells were cultured with LPS, nigericin, and autophagy inhibitor 3-MA. Culture of THP-1 cells simultaneously with LPS, nigericin, and 3-MA significantly increased the production of IL-1β and IL-18 in a 3-MA dose–dependent manner (Fig. 5B). Similar to these results, IL-1β and IL-18 production by THP-1 cells was significantly increased when 3-MA was added at the same time as LPS. However, when 3-MA was added at the same time as nigericin, the production of IL-1β and IL-18 from THP-1 cells did not increase.

Finally, we examined the effects of autophagy on the production of IL-1β and IL-18. As shown in Fig. 5C, 3-MA inhibited autophagy and abrogated the inhibitory effects of PLAC8 on IL-1β and IL-18 production (p < 0.05, p < 0.05, respectively). Similarly, the production of IL-1β in the culture supernatant and pro–IL-1β and pro–IL-18 in the cell lysates was increased by 3-MA dose-dependently in PLAC8–THP-1 cells (Fig. 5D, 5E). In contrast, the expression of caspase-1 in the culture supernatants and pro–caspase-1 in the cell lysates was not increased in the presence of 3-MA (Fig. 5D, 5E). Furthermore, we also confirmed that the production of IL-1β and IL-18 in the culture supernatants (Fig. 6A) and pro–IL-1β and pro–IL-18 in the cell lysates was decreased by induction of autophagy (Fig. 6B). These results indicated that upregulated PLAC8 acts on the synthesis of inactive precursors of IL-1β and IL-18 and suppressed the production of IL-1β and IL-18 through enhanced autophagy.

This study demonstrated that PLAC8 plays a regulatory role in IL-1β and IL-18 production through the regulation of autophagy. The expression of PLAC8 was upregulated in activated monocytes and in monocytes isolated from active-ASD patients. In human PLAC8-overexpressing monocytic cell lines, the production levels of pro–IL-1β and pro–IL-18 were suppressed through the enhancement of autophagy pathways. These results suggest that PLAC8 seems to play a regulatory role in the production of IL-1β and IL-18 (Fig. 7).

ASD is a rare multisystemic autoinflammatory disorder of unknown etiology. Several mediators have been described as activity or severity markers for ASD, such as serum soluble intracellular adhesion molecule-1 (sICAM-1) (33), macrophage migration inhibitory factor (MIF) (34), S100A8/9 (35), S100A12 (36), calprotectin (37), and α2-glycoprotein (LRG) (38). In the current study, we identified high expression of PLAC8 mRNA and PLAC8 proteins in monocytes from active-ASD patients but not in those from inactive-ASD patients. Interestingly, the expression of PLAC8 mRNA in monocytes correlated with serum levels of CRP, ferritin, IL-1β, and IL-18 in patients with ASD. Serum CRP and ferritin are the most frequently used biomarkers for ASD diagnosis and activity (39). Furthermore, serum IL-1β and IL-18 levels also correlated strongly with disease activity and are considered an important pathogenic factor in ASD (6, 8). These results support the notion that the expression of PLAC8 mRNA in monocytes can be potentially a useful marker of ASD activity.

In ASD, an uncontrolled activation of monocytes had been thought to induce the severe clinical features (3). After LPS stimulation, the expression of PLAC8 mRNA and proteins was increased in monocytes from inactive-ASD patients as well as HC. Furthermore, a large amount of IL-1β and IL-18 was also produced from monocytes after LPS and nigericin stimulation. However, the production of PLAC8, IL-1β, and IL-18 from LPS-activated monocytes was not significantly different between HC and inactive-ASD patients. Thus, these results suggested that TLR4 signaling cascade in ASD patients was not different from that in HC.

To determine the molecular mechanisms of PLAC8 in the regulation of IL-1β and IL-18 production in monocytes, we used the human monocytic cell line THP-1. THP-1 cells were well used to examine the intracellular molecular mechanisms instead of primary monocytes (40). After THP-1 cells were stimulated with LPS and nigericin, we detected production of IL-1β and IL-18 similar to that of primary monocytes. In contrast, PLAC8 was not induced by LPS in THP-1 cells, unlike in primary monocytes. Previously, it was known that THP-1 cells, unlike primary monocytes, express low levels of surface CD14, a part of the LPS binding receptor (41). Thus, the low expression of CD14 on THP-1 cells might be insufficient for inducing PLAC8 after LPS stimulation but sufficient for the production of cytokines, such as IL-1β and IL-18.

Then, we generated PLAC8–THP-1 cells. We showed that the production of IL-1β and IL-18 was significantly decreased in PLAC8–THP-1 cells compared with the control THP-1 (mock–THP-1) cells, suggesting that PLAC8 overexpression inhibits IL-1β and IL-18 production in THP-1 cells. In general, it is known that the production of IL-1β and IL-18 is a two-stage process (42). The first step is transcription and translation of the inactive precursors (pro–IL-1β and pro–IL-18), and this process commonly induces the ligation of pattern-recognition receptors, particularly TLRs. The second step is the cleavage of pro–IL-1β and pro–IL-18 into bioactive mature cytokines, and this second process is usually dependent on the activation of pro–caspase-1 to caspase-1 by inflammasome activation. Furthermore, the second process also induces the cell membrane to destroy and then release mature IL-1β, IL-18, or β-actin into the extracellular space (42). Our study showed low production of pro–IL-1β and pro–IL-18 in PLAC8–THP-1 cells compared with mock–THP-1 cells after LPS stimulation. However, the amounts of pro–caspase-1 and caspase-1 were not different between PLAC8–THP-1 and mock–THP-1 cells. Furthermore, the cell survival and mRNA expression levels of total IL-1β and IL-18 were not different between PLAC8–THP-1 and mock–THP-1 cells after LPS stimulation. These findings indicate that the low production of IL-1β and IL-18 in PLAC8–THP-1 cells is independent of caspase-1 mediated by inflammasome activation, suggesting that PLAC8 seems to suppress the pro–IL-1β and pro–IL-18 proteins independently of mRNA transcription. Previously, it was reported that the autophagy might be controlled by IL-1β production, which is independent of inflammasome activation, similar to PLAC8–THP-1 cells. Harris et al. (43) indicated that the autophagy might control the production of IL-1β through the targeting of pro–IL-1β for lysosomal degradation.

Autophagy is a highly conserved homeostatic process for the sequestration and degradation of cytosolic macromolecules, excess or damaged organelles, and certain pathogens (44–46). In contrast, autophagy controls the production of the inflammatory cytokines IL-1β and IL-18 both in vivo and in vitro (43, 47). In the current study, the production levels of IL-1β and IL-18 were significantly increased by 3-MA, an autophagy inhibitor, in a dose-dependent manner. These results suggest that autophagy inhibits LPS-induced IL-1β and IL-18 production, and conversely, the induction of autophagy reduced IL-1β and IL-18 production. Furthermore, we found enhanced autophagy in PLAC8–THP-1 cells. Recent studies demonstrated that PLAC8 is a critical regulator of autophagy in pancreatic cancer cell lines and prostate epithelial cells (48, 49). Whereas the mechanisms of action of PLAC8 in autophagy are not clear at present, it was possible that PLAC8 regulates the autophagic machinery by facilitating autophagosome–lysosome fusion. Moreover, the relationship between PLAC8 and autophagy in cytokine production also remains unknown. In PLAC8–THP-1 cells, the production of pro–IL-1β and pro–IL-18 was upregulated by 3-MA in a dose-dependent manner. These results clearly indicated that the enhancement of autophagy targeted the production of pro–IL-1β and pro–IL-18 proteins. Conversely, we confirmed that the production of pro–IL-1β and pro–IL-18 was suppressed in the condition of enhanced autophagy by rapamycin. Previous studies showed the involvement of the autophagy process in the pathogenesis of various inflammation diseases, such as Crohn disease, infectious diseases, pulmonary arterial hypertension, cystic fibrosis, SLE, and sepsis (50, 51). To our knowledge, the present study is the first to demonstrate the involvement of autophagy in the pathogenesis of ASD.

In conclusion, the current study demonstrated upregulation of PLAC8 in monocytes of active-ASD patients and that the expression of PLAC8 could be a potentially suitable biomarker of ASD activity. Our analyses showed that PLAC8 could play a regulatory role in the production of IL-1β and IL-18 through the enhancement of autophagy. Collectively, our results indicated that two steps might be needed for inhibition of IL-1β and IL-18 production by PLAC8 in primary monocytes. The first step is the upregulation of PLAC8, pro–IL-1β, and pro–IL-18 in monocytes by LPS stimulation. The second step is the inhibition of pro–IL-1β and pro–IL-18 through the enhancement of autophagic machinery by upregulated PLAC8. These findings enhance our understanding of the pathogenesis of ASD and suggest that PLAC8 may be a potentially suitable therapeutic target in ASD patients.

We thank Dr. F. G. Issa for the critical reading of the manuscript.

The authors have no financial conflicts of interest.