The FH strain of M. pneumoniae (ATCC 15531) was acquired from the American Type Culture Collection (ATCC) in Rockville, MD, United States, and was grown in a broth resembling pleuropneumonia-like organisms (PPLO) by BD Biosciences, located in the USA. The M. pneumoniae was then cultured at a temperature of 37°C for a minimum of 7 days until there was a noticeable shift in the color of the medium from red to a yellowish-orange hue.

M. pneumoniae was quantified using Color-changing units (CCU). Twelve sterile EP tubes were labeled with numbers 1 to 12. Each EP tube was filled with 900 μL of Mycoplasma complete medium. Tube 1 received 100 μL of the Mycoplasma solution to be tested, which was then mixed. Then, 100 μL of the mixed liquid in tube 1 was transferred to tube 2, and this process was repeated until tube 11. Tube 12 served as the negative control. The 12 EP tubes were incubated in a 5% CO₂ incubator at 37°C for 7–10 days. The last tube to turn yellow indicated the M. pneumoniae CCU. After centrifugation at 10,000 × g for 10 min, the supernatant was discarded, and the precipitated bacteria were suspended and washed once with sterile PBS solution. After another round of centrifugation, the supernatant was discarded, and the bacteria were left to precipitate for further use.

E. coli BL21 (DE3) was maintained by the laboratory. The LRR protein was tagged with a GST tag, while DUF16 was tagged with a His tag. The following procedure was used to purify GST-LRR, GST-Tag and His-DUF16 proteins. The appropriate plasmid was introduced into E. coli BL21 (DE3) by transformation, and the expression of the mutant protein was induced by adding IPTG to 0.6 mM. The cells were grown at 37°C for 12 h and harvested. The cell pellet was resuspended in buffer PBS. The cells were lysed by sonication, and GST-LRR, GST-Tag or His-DUF16 was purified over an BeaverBeads^™ GSH and Ni-NTA column (beaverbio, China) according to the manufacturer’s instructions. The eluted fractions were separated on a 12% SDS-PAGE to confirm the purity of the protein.

293 T cells and RAW264.7 monocytes from the Chinese Academy of Sciences cell bank in Shanghai, China were utilized in our research after confirming their mycoplasma contamination-free status. Specifically, RAW264.7 monocytes and 293 T cells were maintained in DMEM medium (HyClone, United States) supplemented with 10% FBS and 1% penicillin–streptomycin at 37°C with 5% CO2. Upon reaching the logarithmic growth phase, macrophages were seeded in 6-well plates at a concentration of 1 × 10⁶/ml and allowed to proliferate overnight. Subsequently, the cells were exposed to varying concentrations of Mp or recombinant proteins at designated time points.

The Cell Counting Kit-8 (CCK-8, KeyGEN BioTECH, Nanjing, China) assay was employed to assess cell proliferation, in accordance with the instructions provided by the manufacturer. RAW264.7 cells (8,000 per well) were plated in 96-well dishes, utilizing the same techniques outlined earlier for MФ differentiation. To establish the multiplicity of infection (MOI), MФs were exposed to varying MOI levels of 0, 10, 20, 40, 80, and 160 over a 24-h period after infection with M. pneumoniae. To evaluate Mp’s impact on MФs, cells were exposed to M. pneumoniae at different time points ranging from 0 to 96 h, based on the results of the MOI analysis from the preceding step. Cell viability was gaged following the manufacturer’s instructions for the cell counting kit-8, with absorbance readings taken at 490 nm using a fluorescence microplate reader (PerkinElmer, United States).

RNA extraction was carried out with the Trizol reagent kit according to the manufacturer’s instructions. The RNA quality was assessed using the Agilent 2100 Bioanalyzer and validated by RNase-free agarose gel electrophoresis. Subsequently, eukaryotic mRNA was isolated using Oligo (dT) beads and then fragmented into shorter segments with fragmentation buffer before being reverse transcribed into cDNA using the NEB Next Ultra RNA Library Prep Kit for Illumina. The resulting double-stranded cDNA fragments underwent end repair, addition of an A base, and ligation to Illumina sequencing adapters. After purification with AMPure XP Beads, the ligated fragments were size-selected via agarose gel electrophoresis and amplified by PCR. The cDNA library obtained was then sequenced on an Illumina Novaseq6000 platform by Gene Denovo Biotechnology Co. in Guangzhou, China.

Enrichment analysis is a common method used in omics research to gain insight into the functional tendencies of a gene set. Two popular methods for enrichment analysis include gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. GO, established by the Gene Ontology Consortium, is a database aimed at defining the functions of gene products. Through GO enrichment analysis, researchers can assess how enriched differential genes are in terms of specific GO terms, with more significant enrichments represented by darker colors. On the other hand, KEGG, founded in 1995, is a comprehensive database that integrates information on genomics, chemicals, and systemic functionalities. This database enables the prediction of protein interaction networks involved in various cellular processes. By performing KEGG pathway enrichment analysis, researchers can annotate the functions of differentially expressed genes and gain insights into the relevant pathways and functions associated with these genes.

Small interference reagents, including siNC and siNOD2, were procured from Guangzhou Ruibo Biotechnology Co., Ltd. siNC serves as a universal negative control for siNOD2, with a sequence that shows no similarity to the human, rat, and mouse transcriptomes. The specific sequence details of siNC have not been made public by the company at this moment. The target sequence of siNOD2#1 is: GCAACAGCGTGGGTGATAA, the target sequence of siNOD2#2 is: GCACAGAGTTGCAACTGAA, and the target sequence of siNOD2#3 is: GCGAGCACTTCCATTCCAT. RAW264.7 cells underwent transfection with siNOD2-1, siNOD2-2, siNOD2-3 (50 nM) or a negative control siRNA (si-NC) (50 nM) using Lipofectamine™ 2000 Transfection Reagent (Invitrogen Inc., Carlsbad, CA, United States). The cells were seeded in a 6-well plate per group and placed in an incubator at 37°C with 5% CO2 until reaching 80% confluence. The cell transfection was carried out in strict adherence to the Lipofectamine 2000 Transfection Reagent operational guidelines. The efficacy of knockdown was validated through western blot analysis 24 h post transfection.

The cell supernatants were centrifuged at 12,000 × g and 4°C for 5 min to remove any cellular debris before being analyzed using mouse IL-1β, TNF-α, IL-6, IL-8, and IL-8 ELISA kits from Boster in China following the instructed procedures. The absorbances of the samples were measured at 450 nm using a fluorescence microplate reader made by PerkinElmer in the United States.

Primer sequences were designed using the PCR-based Accurate Synthesis (PAS) method, incorporating the protective base synthesis gene LRR at both ends. The recombinant plasmid pGEX-4 T-1-LRR resulted from ligating the primers within the EcoRI (GAATTC)-XhoI (CTCGAG) sites of the pGEX-4 T-1 vector. Insertion of the full-length NOD2-LRR domain from mice into the pGEX-4 T-1 vector enabled protein production in Escherichia coli. Transformation of the resulting plasmid DNA into BL21 (DE3) cells (TIANGEN BIOTECH (BEIJING) Co., LTD.) was followed by induction of protein expression at 16°C for 12 h upon addition of isopropyl-β-D-thiogalactoside (IPTG) to a final concentration of 0.1 mM. Subsequent steps included cell collection by centrifugation, washing, and storage at −80°C. Protein purification involved incubating supernatants with glutathione-Sepharose resin (Amersham Pharmacia, Piscataway, NJ) for 2 h, followed by centrifugation at 500 rpm for 2 min at 4°C. The resin was rinsed with a buffer containing 1 mM PMSF, 1% Triton, 50 mM Tris–HCl, and 100 mM NaCl, followed by elution of proteins using 15 mM glutathione. The proteins were validated through SDS-PAGE analysis and Western blotting with the anti-GST antibody from Protenintech. To assess binding, 500 μL of the refined GST-LRR extract was combined with Tris-NaCl buffer-washed glutathione-Sepharose resin. As a control, GST protein was utilized. M. pneumoniae whole cell lysates were mixed with the resin and left to incubate overnight with gentle rotation at 4°C. The resin was then collected and washed thrice using Tris-NaCl buffer. Following a brief boiling, SDS-containing gel loading buffer (100 μL) was introduced to the resin. Samples (10 μL) were subjected to SDS-PAGE electrophoresis, protein silver staining, and Western blotting employing anti-GST antibodies.

In this investigation, a total of 1 microgram of peptides were separated and examined utilizing a nano-UPLC (EASYnLC1200) linked to a Q Exactive HFX Orbitrap device (Thermo Fisher Scientific) with a nano electrospray ion source. The process of separation included the utilization of a reversed-phase column (100 micrometers ID ×15 cm, ReprosilPur 120 C18ÀQ, 1.9 micrometers, Dr. Maisch) and mobile solutions containing H2O with 0.1% formic acid, 2% acetonitrile (solvent A) and 80% acetonitrile, 0.1% formic acid (solvent B). The specimen was separated utilizing a 60-min gradient at a flow rate of 300 nanoliters per minute. The B gradient was varied as follows: 2–5% for 2 min, 5–22% for 44 min, 22–45% for 10 min, 45–95% for 2 min, and 95% for 2 min. Acquisition of data was carried out using Data-Dependent Acquisition (DDA) in both profile and positive modes with an Orbitrap analyzer. The resolution for MS1 analysis was set at 120,000 (@200 m/z) over a range of 350–1,600 m/z. In comparison, MS2 analysis was performed at a resolution of 15,000 with a dynamic initial mass. For MS1, the automatic gain control (AGC) target was 3E6 with a maximum injection time (IT) of 50 ms, while for MS2, it was set to 1E5 with a maximum IT of 110 ms. The 20 most intense ions were subjected to HCD fragmentation with a normalized collision energy (NCE) of 27% within an isolation window of 1.2 m/z. Dynamic exclusion was applied with a time window of 45 s, and single-charged and over 6-charged ions were excluded from the DDA process.

The proteins identified were classified and annotated functionally through the utilization of the Gene Ontology (GO) analysis tool within the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Analysis of pathways was carried out with the KEGG pathway database. Furthermore, the software Ingenuity Pathway Analysis (IPA) was used for the analysis of Diseases and Functions.

The Uniprot database¹ was used for genome annotation of 12 differential proteins. The virulence function of 12 proteins was predicted through VirulentPred² online software.

RAW264.7 cells (0.5–2 × 10⁵/well) were seeded in 24-well plates to achieve a confluence of 90–95% at the time of transfection. Each plasmid was diluted separately in 50 μL OptiPro SFM and mixed gently. 2 μL of each Lipofectamine2000 CD (ThermoFisher, United States) reagent was diluted in each tube in 50 μL LOptiPro SFM. The cells were incubated at room temperature for 5 min. After 5 min of incubation, diluted DNA and Lipofectamine 2000CD reagent (total volume per tube = 100 μL) were combined. Mix gently and incubate at room temperature for 20 min (the solution may become cloudy). 100 μL of the complex was added to Wells containing cells and medium. Shake the dish back and forth and mix lightly. Cells were incubated in a CO₂ incubator for 48 h at 37°C before determined protein expression.

The purified protein solution was mixed with 6 × protein loading buffer (obtained from TransGen in China) and subsequently subjected to denaturation by heating at 100°C for a duration of 10 min. The RIPA Lysis buffer was used to lyse total protein from 264.7 cells, following which 40 μg of total proteins were subjected to sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). These proteins were then transferred onto a PVDF nitrocellulose membrane. The next step involved blocking the membrane with a solution of 5% non-fat milk that contained 0.2% Tween-20 in 1× PBS for a period of 1.5 h at room temperature. The membranes were incubated overnight at 4°C with these primary antibodies: NOD2, RIPK2, total NF-κB, Phospho-NF-κB p65 (Ser536), Phospho-IKB alpha p-IKB α (Ser32/Ser36), IKBα, and β-actin (all sourced from Affinity Biosciences, China); Flag-tag, GST-tag, and His-tag (all obtained from Proteintech, China). Subsequently, the membranes underwent incubation with a secondary antibody conjugated with horseradish peroxidase (HRP) for 2 h at room temperature. The protein of interest was then visualized using an enhanced chemiluminescence (ECL) reagent and GE Image Quant LAS 600. The intensity of the bands was measured through densitometric analysis using Image J software. Each experiment was carried out three times to ensure accuracy and reliability.

All data were analyzed using GraphPad Prism 8 software. Each experiment was repeated a minimum of 3 times. The data were analyzed using ANOVA and Tukey’s test. And expressed as the mean ± SD. Significance levels were indicated as *p < 0.05, **p < 0.01, ***p < 0.001, and ns for not significant.

In order to investigate the transcriptome of M. pneumoniae-infected macrophages, we conducted experiments to determine the optimal post-infection time and dose using CCK8. Our results indicated a notable decrease in macrophage viability with increasing MOI values, with a significant decrease observed at MOI = 10:1 (Figure 1A). Further analysis showed that after infecting macrophages with M. pneumoniae (MOI = 10:1) for 6 h and 12 h, there was no significant change in macrophage viability. However, as the infection duration increased, macrophage viability began to decrease significantly after 24 h (Figure 1B). The same experimental results were also obtained through fluorescence microscopy (Figures 1C,D). After infecting macrophages with Mycoplasma pneumoniae at a dose of MOI = 10 for 24 h, it was observed that the majority of cells remained viable (green). Consequently, we established an infection dose of MOI = 10 and an infection duration of 24 h as the standardized experimental conditions for further analysis. From three independent experiments, we obtained a total of 277,293,446 raw sequencing, encompassing both M. pneumoniae-infection and uninfected RAW264.7 cells. Our analysis identified 806 differentially expressed genes in response to M. pneumoniae infection based on FDR and log2 fold change criteria (FDR < 0.05 and |log2fc| > 2). KEGG Pathway analysis demonstrated that the top 20 enriched pathways associated with M. pneumoniae infection predominantly involved NOD-like receptor signaling pathway, Toll-like receptor signaling pathway, and other pathways (Figure 1E). To better illustrate the regulatory role of NOD-like receptor signaling pathways in M. pneumoniae infection, we analyzed and identified differential genes enriched in these pathways. We found that the expression of genes such as NOD2, IL-1β, and TNF-α in the NOD-like signaling pathway was up-regulated (Figure 1F). qPCR technology was utilized to confirm the expression of key genes in the NOD2 signaling pathway 24 h post-infection of macrophages with M. pneumoniae. In comparison to the control group, M. pneumoniae infection led to a notable upregulation in the transcription levels of genes like NOD2, RIP2, and TNF-α (see Figure 1G; Supplementary Figure S1). These data collectively suggest the involvement of the NOD2 signaling pathway in the inflammatory response of mouse macrophages induced by M. pneumoniae.

Our Transcriptome results revealed a notable focus on the NOD-like receptor signaling pathway during M. pneumoniae infection. In the NOD-like signaling pathway, NOD1 primarily detects γ-D-glu-meso-diaminopimelic acid (iE-DAP) in the bacterial cell wall, whereas NOD2 primarily recognizes muramyl dipeptide (MDP). Upon activation, NOD1 and NOD2 recruit downstream receptor-interacting serine–threonine protein 2 (RIP2) through CARD-CARD interaction. RIP2, along with E3 ligases cIAP1, cIAP2, and XIAP, form a polyubiquitin scaffold that recruits TAK1 and IKK, leading to NF-κB activation. NF-κB then triggers the expression of inflammatory cytokines and genes involved in nitric oxide (NO) production. The downstream signaling pathways of NOD1 and NOD2 exhibit significant similarities. To verify the activation of NOD2 induced in macrophage inflammation, we assessed the expression of NOD1 and NOD2-related genes in RAW264.7 cells following a 24-h infection of M. pneumonia. Our experimental results demonstrate that M. pneumoniae infection induces a notable upregulation of NOD2, p-RIP2, and p-NF-κB p65 expression in macrophages when compared to uninfected cells (control). This is similar to the effect observed with MDP, a NOD2 activator (Figures 2A–E). Conversely, there was no significant alteration in the expression of NOD1 in macrophages (Figure 2B). These results confirmed that M. pneumoniae can activate NOD2. Small interfering (siRNA)-NOD2 was utilized to downregulate NOD2 expression in order to investigate the impact of NOD2 on inflammation induction in M. pneumoniae-infected cells over a 24-h period. The findings revealed that all three small interfering RNAs successfully achieved NOD2 knockdown, with siNOD2-3 demonstrating the most effective interference effect (Figures 2F,G). Consequently, siNOD2-3 was selected for further experiments. Inflammatory factors TNF-α, IL-6, IL-8, and IL-1β were assessed using ELISA. The results show that both M. pneumoniae and MDP treated RAW264.7 cells showed markedly increased expression of TNF-α, IL-6, IL-8, and IL-1β compared to siRNA-control (siNC) (Figures 2H–K). knock downing NOD2 expression via siRNA significantly suppressed the M. pneumoniae induced the expression of TNF-α, IL-6, IL-8, and IL-1β comparing with siNC+Mp treatment (Figures 2H–K). Thus, these collective findings indicate that M. pneumoniae-induced inflammatory was mediated by the activation of NOD2.

To identify the precise protein from M. pneumoniae that activates NOD2, we expressed NOD2 LRR domain tagged it with GST (LRR-GST) using an E. coli expression system (Figure 3A). Purified LRR-GST fusion protein purification magnetic beads were used to pull down the LRR-GST interaction protein from lysed M. pneumoniae. Purified GST was used as control. Silver staining revealed discernible bands in the LRR-GST efflux fluid group (LRR-GST-elution) compared to the control group (GST-elution) (Figure 3B). Subsequent elution protein samples using LC–MS/MS identified a total of 39 M. pneumoniae proteins. Twelve M. pneumoniae proteins that potentially interact with NOD2 were identified in the LRR-GST group (Figure 3C). Information of these proteins regarding the names, subcellular localization, functions, and virulence effects of these proteins was obtained in Table 1. Subcellular localization analysis revealed that out of the 12 differential proteins, four were membrane proteins, five were intracellular proteins, and three had unknown localization. Virulence protein prediction indicated that six proteins have virulence functions. We identified 4 proteins that may activate NOD2 through information such as subcellular localization, virulence function prediction, and biological function of 12 proteins. For further verification, we selected the four most likely proteins: 30S ribosomal protein S17(30S), DUF16 family-like protein (DUF16), P1 adhesin type 2 g2 (P1), and P40/P90 adhesin (P40/P90). We transfected S17(30S), DUF16, P1 and P40/P90 into RAW264.7 (Supplementary Figure S2), and identified the expression of NOD2 in transfected cells, we found that only RAW264.7 cell transfected with DUF16 trigger the overexpression of NOD2 (Figures 3D,E). It indicates that DUF16 might is the specific protein in M. pneumoniae for the activation of NOD2. To further validate the interaction between DUF16 and NOD2, we Co-transfection of 293 T cells with the DUF16 tagged with Flag (Flag-DUF16) and NOD2 tagged with GST. Co-IP revealed the presence of DUF16 only in the presence of NOD2 (Figure 3F). The same results were found in RAW264.7 transfected with DUF16 (Figure 3G). Additionally, co-localization analysis via laser confocal microscopy showed DUF16 and NOD2 co-localizing in the cytoplasm in 293 T cells transfected with DUF16 and NOD2 (Figure 3H). Thus, our findings support that the DUF16 protein of M. pneumoniae interacts with the host cell NOD2.

To study the function of DUF16 protein, we obtained recombinant DUF16 (His-DUF16) protein using the E. coli expression system (Supplementary Figure S3). The concentration of DUF16 treatment in subsequent experiments was determined by assessing the relative cell viability of DUF16-treated macrophages using CCK8. Our results showed that DUF16 significantly reduced the viability of uninfected cells at a concentration of 1,600 ng/mL (Figure 4A). We determined the concentration of DUF16 treatment in subsequent experiments to be 1,600 ng/mL. NOD2 is an intracellular Pattern Recognition Receptor. therefore, we performed colocalization analysis of recombinant DUF16 protein and RAW264.7 cells using immunofluorescence to confirm whether macrophages can phagocytose DUF16. Our results revealed the presence of DUF16 protein (green fluorescence) in the cytoplasm of RAW264.7 cells in the experimental group, while no green fluorescence signal was observed in the control group (Figure 4B). It shown that macrophages are capable of phagocytosing DUF16 protein.

To investigate the pro-inflammatory effects of DUF16 on macrophages through NOD2, we utilized small interfering (siRNA)-NOD2 to downregulate NOD2 expression for a 12-h duration. Subsequently, RAW264.7 cells were treated with DUF16 protein for 24 h. The levels of inflammatory cytokines, including IL-1β, IL-6, IL-8, and TNF-α, were quantified using ELISA (Figures 5A–D). The results demonstrated a significant reduction in the expression of inflammatory factors upon NOD2 suppression compared to the control group. Additionally, DUF16 displayed a notable ability to enhance the expression of inflammatory factors in macrophages, similar to the effects observed with MDP (NOD2 activator). NOD2 knockdown significantly reduced the expression of inflammatory factors induced by DUF16. Interestingly, even when NOD2 was knocked down and cells were treated with DUF16 domain proteins, the secretion of inflammatory factors persisted, suggesting the possible involvement of other pattern recognition receptors such as TLRs. These findings indicate that DUF16 protein can stimulate macrophages to secrete inflammatory factors via NOD2. To investigate the involvement of the NOD2/RIP2/NF-κB signaling pathway in DUF16-induced macrophage inflammation, we utilized siRNA-NOD2 to knock down NOD2. Macrophages were then treated with DUF16 for 24 h, and the expressions of NOD2, p-RIP2, RIP2, p-NF-κB p65, NF-κB p65, p-IKB α, and IKB α were assessed. The results indicated that the DUF16 upregulated the expression of NOD2, p-RIP2, p-NF-κB p65, and p-IKB α in the macrophage NOD2 signaling pathway, while downregulating the expression of IKBα. Moreover, NOD2 knockdown diminished the impact of DUF16 on the elevated expression of p-RIP2, p-NF-κB p65 and p-IKB α compared to the siNC+DUF16 group (Figures 5E–I). These findings suggest that DUF16 elicits cellular inflammatory responses through the NOD2/RIP2/NF-κB signaling pathway.

To identify the specific region where DUF16 interacts with the host protein NOD2 to induce cellular inflammation, we conducted a multiple sequence alignment of various proteins within the DUF16 family using the Clustal Omega tool.³ Our analysis revealed a conserved region in the DUF16 protein family (Supplementary Figure S4). In order to further investigate whether DUF16 activates NOD2 through this conserved structural region, we designed and constructed different truncated versions of DUF16 (Figure 6A). These truncated constructs were then transfected into RAW264.7 cells, and the subsequent expression of NOD2 in the transfected cells was assessed using Western blotting. The results revealed that both Flag-Δ1 (1-90 aa) and Flag-Δ3 (13-276 aa) significantly increased the expression of NOD2 in macrophages compared to RAW264.1 cells transfected with pCDN3.1 (control) (Figures 6B,C). However, transfection of RAW264.7 cells with Flag -Δ2 (91–276 aa) did not induce the expression of NOD2. These findings suggest that the 13-90 fragment of DUF16 is critical for NOD2 activation. To further confirm the key interaction areas between DUF16 and NOD2, Flag-Δ1 (1-90 aa), Flag-Δ2 (91–276 aa), Flag-Δ3 (13-276 aa), and GST-NOD2 were co-transfected into 293 T (Figures 6D,F,H) or RAW264.7 (Figures 6E,G,I) cells, followed by Co-IP verification. The results showed that both Flag-Δ1 (1-90 aa) and Flag-Δ3 (13-276 aa) interacted with NOD2, while Flag-Δ2 (91–276 aa) could not interact with NOD2. These findings indicate that the DUF16 (13-90 aa) region plays a critical role as a phase activator of NOD2 in macrophages.

To further investigate the function of the critical region of DUF 16, we constructed a truncation mutant (Flag-Δa) of this region, as shown in Figure 7A. The truncated construct was then transfected into RAW264.7 cells for 48 h, while M. pneumoniae and DUF16 proteins were used to treat macrophages for 24 h, respectively. Subsequent expression of NOD2 in cells from each treatment group was assessed using Western blotting. The experimental results revealed that both M. pneumoniae and DUF16 significantly increased the expression of TNF-α and IL-1β in macrophages (Figure 7B). In contrast, Flag-Δa did not show the same effect after transfection into macrophages. Additionally, Western blot analysis demonstrated that both M. pneumoniae and DUF16 significantly increased the expression of RIP2 and P-NF-κB p65 in the macrophage NOD2 signaling pathway (Figure 7C). On the other hand, Flag-Δa did not show such an effect. These results indicate that the 13–90 amino acid region plays a critical role in DUF16-induced inflammation.