Specific pathogen-free (SPF) 6- to 8-week-old male and female C57BL/6 mice were purchased from Beijing Charles River Laboratory (Beijing, China). Type I IFNs receptor-deficient (IFNAR1^−/−) mice in a C56BL/6 background were originally obtained from The Jackson Laboratory. The animal work in this study was carried out in strict accordance with the Guide for the Care and Use of Laboratory Animals of the People’s Republic of China. All protocols were reviewed and approved by the Committee on Ethics of the Affiliated Hospital of Yangzhou University (2023-YKL02-G022).

The B. pertussis strain B.p-L1 used in this study was recently isolated from a patient in Yunnan Province, China (19, 20). The polymorphisms in the PT promoter (ptxP), PT subunit 1 (ptxA), and pertactin (prn) were assessed by DNA sequencing. The genotype of B.p-L1 was ptxP3/ptxA1/prn2, consistent with the current epidemic strains. For B. pertussis challenge experiments, B. pertussis strain B.p-L1 was grown on Bordet–Gengou (B-G) and Regan–Lowe plates prepared as described previously (19). Briefly, after 2 days of growth at 37°C, the bacteria were scraped off B-G blood agar plates, resuspended in phosphate-buffered saline (PBS), diluted to a concentration of 10¹¹ CFU/mL via a turbidimetric method, and used for aerosol challenge. For the culture of bacteria from tissues, Regan–Lowe plates (Oxord) supplemented with 10% defibrinated sheep blood and 40 μg/mL cephalexin (Oxord) were used.

To investigate the transcriptional dynamics of murine nasal turbinates infected with B. pertussis, 6–8-week-old C57BL/6 mice (equal numbers of males and females) were infected with B. pertussis (10¹¹ CFU/mL) via the aerosol method as described previously (19). Animals were selected for euthanization on day 1 (prechallenge, pre) and at 2 h and 2, 4, 7, and 11 days postinfection (hpi or dpi) for tissue collection.

To investigate the role of type I IFNs in B. pertussis infection, 6–8-week-old C57BL/6 mice (wild type, WT) and type I IFNs receptor-deficient (IFNAR1^−/−) mice were also infected with B. pertussis (10¹¹ CFU/mL) via the aerosol method as described previously (19). Animals were euthanized on day 1 (pre), 2 hpi and 2, 4, 7, 14, 21, and 28 dpi for tissue collection.

Nasal turbinates were collected on day 1 (pre), 2 hpi and 2, 4, 7, and 11 dpi and homogenized with TRIzol reagent (Invitrogen). Total RNA was extracted with chloroform/isopropanol, followed by purification via the RNeasy Mini Kit (Qiagen). Library construction and sequencing were performed as described previously (19, 21). Briefly, the mRNA was isolated and purified from total RNA via oligo(dT)-attached magnetic beads. The purified mRNA was subsequently fragmented into small pieces for cDNA synthesis. The quality of the cDNA library was assessed via an Agilent Technologies 2100 Bioanalyzer and qPCR. Paired-end sequencing with a read length of 100 bases was performed on the BGIseq500 platform. The raw data were filtered with SOAPnuke software (22). The clean data were mapped to the Mus-musculus_GRCm38.p6 reference genome with hierarchical indexing for spliced alignment of transcripts (HISAT) software (23). Fragments per kilobase of transcript per million mapped reads (FPKM) of each gene was calculated based on the length of the gene and RSEM (v1.2.12) were used to quantitative analysis (24). Before differentially expressed (DE) genes analysis, we conducted a Principal Component Analysis (PCA) analysis on raw counts using the factoextra package (v 1.0.7) (https://CRAN.R-project.org/package=factoextra) and the FactoMineR package (v 2.11) to verify the batch effect among the samples ( Supplementary Figure S1 , Supplementary Table S2 ). Differentially expressed (DE) genes were acquired using the Limma (v 3.48.3) package (p.adj < 0.05, |log2FC| > 1) (25). Venn diagram was drawn with Jvenn (http://jvenn.toulouse.inra.fr/app/example.html). Heatmaps were drawn with the heatmap (v1.0.12) R package. The enriched Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the DE genes were analyzed via ClusterProfiler (4.0.5) in RStudio (26). And the bubbleplots were made and analyzed through R packages (27). To identify patterns of gene expression changes over the course of infection, the data were analyzed with the Short Time-series Express Miner (STEM) tool developed by Jason Ernst and Ziv Bar-Joseph (28).

At four weeks after infection, B. pertussis-specific serum IgG titers were measured via ELISA as previously described (19). The components of pertussis antigens Pertussis toxoid (PT), filamentous hemagglutinin (FHA) and Pertactin (PRN) were purified by column chromatography as described previously (29). Specifically, PT, FHA, or PRN at 3 μg/mL were coated on microplates (96-well) overnight at 4°C. The plates were washed with wash buffer (0.05% (v/v) polysorbate 20 in PBS) once and then blocked with 3% (w/v) bovine serum albumin (BSA, Abcam) in wash buffer for 2 h at 37°C. The plates were subsequently washed and incubated with serially diluted mouse sera for 1 h at 37°C. After washing, the plates were incubated with horseradish peroxidase (HRP)-labeled sheep anti-mouse IgG (Jackson ImmunoResearch, USA) antibody for 1 h at 37°C. All the ELISA plates were developed with tetramethylbenzidine (TMB; Solarbio, CHN) to generate a colorimetric reaction, and the reaction was terminated with 2 mmol/L sulfuric acid. The absorbance of the plates at 450 nm was read. Endpoint titers were determined as the dilution that exhibited an optical density exceeding ≥ 2.1 times the background level (secondary antibody alone).

Cytokine levels in the bronchoalveolar lavage fluid (BALF) and the supernatant of cultured cells were measured with an ELISA kit according to the manufacturer’s recommendation. BALF was collected by flushing with 1 mL of PBS back and forth three times via a catheter and syringe as previously described (19). BALF samples were centrifuged at 800 × g for 10 min to obtain a cell pellet (for flow cytometry analysis) and supernatant (for cytokine analysis). The levels of TNF-α (Cat# VAL609, Novus Biologicals), IL-1β (Cat# SMLB00C, R&D Systems), IL-6 (Cat# VAL604, Novus Biologicals), and IFN-β (Cat# VAL612, Novus Biologicals) in the BALF were quantified. And the BALF albumin levels were measured by a mouse-specific albumin ELISA kit (ab207620, Abcam) according to the manufacturer’s instructions. To detect the cytokine levels in the supernatants of cultured splenic or pulmonary lymphocytes, the cells were cultured at a concentration of 2 × 10⁶/mL and stimulated with the antigens PT (2 μg/mL), FHA (2 μg/mL), and PRN (2 μg/mL). The supernatants were removed after 3 days and stored at -80°C before testing. IFN-γ (Cat# VAL607, Novus Biologicals) levels were measured for Th1 responses; IL-17A (Cat# VAL610, Novus Biologicals) levels were tested for Th17 responses; and IL-4 (Cat# M4000B, R&D Systems) levels were detected for Th2 responses.

Single-cell suspensions were obtained from the BALF as described above. The BALF was centrifuged at 800 × g for 10 min to obtain a cell pellet, which was then washed twice with PBS. The cells were incubated with an anti-CD16/CD32 Fc block. Then, the cells were incubated with 7-AAD (BD Bioscience), followed by surface staining with fluorochrome-conjugated anti-mouse antibodies for the following markers. The neutrophils were identified as CD45+LY6G+ cells. All samples were acquired on a flow cytometer (Beckman), and the data were analyzed with FlowJo software (TreeStar).

Total RNA was extracted from nasal turbinates using TRIzol reagent (Invitrogen, USA) according to the manufacturer’s instructions. One microgram of RNA was used as the template to synthesize first-strand cDNA via a PrimeScriptTM RT kit (Accurate Biotechnology, CHN). cDNA was subsequently used as the template for real-time PCR using a SYBR Green Premix Pro Taq HS qPCR Kit (Accurate Biotechnology, CHN). The total reaction system was 20 μL, and the gene amplification process was performed with an Applied Biosystems 7500 Fast Real-Time PCR System (Life Technologies). The RNA expression of target genes was calculated via the 2^-ΔΔCt method on the basis of the housekeeping gene GAPDH. Every sample was analyzed three times in parallel. The primers used for the target genes are listed in Supplementary Table S1 .

For histopathological analysis, lung tissues from necropsied mice were fixed in 10% neutral buffered formalin, embedded in paraffin, and sectioned at 3–5 μm. Then, the sections were stained with hematoxylin and eosin (H&E) after dehydration. The pathological sections were observed and photographed under a microscope (Leica, Germany).

The data were graphed and analyzed via GraphPad Prism version 8.0 (GraphPad Software, Inc.). The results are presented as the means ± SEMs or GMTs and corresponding 95% confidence intervals (CIs). Unpaired Student’s t tests were applied to assess statistical significance. Differences were considered significant at P ≤ 0.05.

Differential expression analysis was performed for a detailed analysis of the mouse nasal turbinates to B. pertussis infection at 2 h and 2, 4, 7 and 11 days after infection (hpi and dpi). Genes exhibiting significant changes in expression were identified via pairwise comparisons between each timepoint and the preinfection timepoint (2 hpi vs. pre, 2 dpi vs. pre, 4 dpi vs. pre, 7 dpi vs. pre, and 11 vs. pre). The total numbers of DE genes in these comparisons were 304 (266 upregulated, 38 downregulated), 255 (247 upregulated, 8 downregulated), 465 (462 upregulated, 3 downregulated), 1120 (920 upregulated, 190 downregulated), and 1576 (1360 upregulated, 216 downregulated) ( Figure 1A , Supplementary Table S2 ). A direct comparison of the DE genes at each timepoint revealed that the nasal turbinate response to infection at 11 dpi was vastly different from the response at the early phase ( Figure 1B ). Among the 1576 DE genes at 11 dpi, 37% (501 genes) were unique to that of other detection timepoints. In contrast, 24% of the genes at 2 hpi (73 genes), 5% of the genes at 2 dpi (13 genes), 8% of the genes at 4 dpi (37 genes), and 5% of the genes at 7 dpi (53 genes) were differentially expressed only at that specific timepoint. To our interest, 74 genes (e.g., Ccl5, Ccl2, Isg15, Ifitm3, Cd40, Cd53, and et al.) were consistently up- or downregulated at all 5 timepoints ( Figure 1B , Supplementary Figure S2 ).

To detect differences in gene expression over time, we conducted differential expression analysis across timepoints. Comparisons of successive timepoints revealed that the largest large number of genes exhibited changes in expression during the 2 hpi to 2 dpi transition (557 DE genes, 182 genes upregulated, 375 genes downregulated), followed by the 4 dpi to 7 dpi transition (448 DE genes, 114 genes upregulated, 334 genes downregulated), and relatively small changes between 2 and 4 dpi (52 genes, 33 genes upregulated, 19 genes downregulated) and between 7 and 11 dpi (206 genes, 19 genes upregulated, 187 genes downregulated) ( Figure 1A , Supplementary Table S2 ). These findings suggest a strong initial response of the murine nasal turbinates to B. pertussis infection.

To obtain an overview of the processes influenced by B. pertussis infection, we conducted GO and KEGG functional enrichment analyses of DE genes associated with the nasal turbinates at five timepoints. The top 10 results from each analysis were selected for visualization. GO enrichment analysis revealed that B. pertussis infection-upregulated genes detected at 2 hpi were enriched predominantly to GO terms related to inflammation and innate immunity, notably “response to lipopolysaccharide (LPS)”, “response to interferon-gamma”, “response to interferon-beta”, “neutrophil migration”, “leukocyte migration”, and “cytokine-mediated signaling pathways” ( Figure 2A , Supplementary Table S3 ). However, at 2 and 4 dpi, the transcripts were enriched mainly in the GO terms related to immune regulation, including “regulation of immune effector process” and “antigen processing and presentation” ( Figure 2A , Supplementary Table S3 ). Specifically, response to “interferon-gamma” and “interferon-beta” were enriched during the early stages of infection (2 hpi, 2 dpi, and 4 dpi). The DE genes upregulated at 7 dpi and 11 dpi were enriched predominantly to GO terms related to adaptive immunity, such as “regulation of T-cell activation” and the “regulation of leukocyte cell–cell adhesion” ( Figure 2A , Supplementary Table S3 ). In contrast to the upregulated DE genes, a small number of downregulated genes were enriched in the early stages of infection (2 hpi and 4 dpi), and no pathways were enriched by GO enrichment analysis at 2 dpi ( Supplementary Figure S3A , Supplementary Table S3 ). The GO terms enriched among genes downregulated in the later stages of infection were “fatty acid metabolic process”, “cellular response to xenobiotic stimulus”, “carboxylic acid biosynthetic process”, and so on ( Supplementary Figure S3A , Supplementary Table S3 ).

Consistent with the GO enrichment analysis, the KEGG enrichment analysis revealed that upregulated DE genes detected at 2 hpi were enriched predominantly to KEGG pathway related to innate immunity and inflammation, like the “Toll-like receptor signaling pathway”, “NOD-like receptor signaling pathway”, “TNF signaling pathway”, “NF−kappa B signaling pathway”, “cytokine−cytokine receptor interactions”, and “chemokine signaling pathway” ( Figure 2B , Supplementary Table S4 ). Interestingly, among the downregulated DE genes at 2 hpi, we did not observe any enrichment of the KEGG pathway ( Supplementary Figure S3B , Supplementary Table S4 ). At 2 dpi, we observed strong positive enrichment of terms related to the “phagosome”, “cell adhesion molecules”, “antigen processing and presentation”, and so on ( Figure 2B , Supplementary Table S4 ). Similarly, “phagosome” and “antigen processing and presentation” were also upregulated at 4 dpi ( Figure 2B , Supplementary Table S4 ). However, a small number of downregulated genes were enriched at 2 dpi and 4 dpi ( Supplementary Figure S3B , Supplementary Table S4 ). At 7 and 11 dpi, significantly upregulated DE genes were mainly enriched in adaptive immunity related pathways such as “Th17 cell differentiation”, “cytokine−cytokine receptor interaction”, “chemokine signaling pathway”, and “antigen processing and presentation signaling pathways”, whereas downregulated DE genes were enriched mainly in “steroid hormone biosynthesis”, “retinol metabolism”, “protein digestion and absorption”, and so on” ( Figure 2B , Supplementary Figure S3B , Supplementary Table S4 ). These functional enrichment analyses suggest that nasal turbinates transcriptional response to B. pertussis infection revealed an acute upregulation of genes related to inflammation and innate immune activation and a later adaptive immune response.

Given that functional enrichment analyses of the transcriptional changes at each dpi relative to baseline does not consider the dynamic and longitudinal patterns of gene expression, we next used Short Time-series Expression Miner (STEM) to identify clusters of genes, the expression of which changes in a similar manner over time. The results of STEM showed that a total of 50 gene expression profiles were identified, including 13 statistically significant gene profiles (P< 0.05, highlighted with a colored background) ( Supplementary Figure S4 ). Notably, the average expression of genes in Cluster 41 (2012 genes), Cluster 14 (520 genes), and Cluster 6 (1485 genes) increased significantly throughout the infection ( Figure 3A ).

To obtain a more detailed understanding of the functions of the genes in these clusters, we performed GO annotation analysis. According to the GO annotations, the top biological process terms enriched among genes in Cluster 41(2012 genes) were related to the innate and adaptive immune responses such as “regulation of cytokine production”, “mononuclear cell differentiation”, “lymphocyte differentiation”, “response to virus”, “response to interferon-gamma”, and “response interferon-beta” ( Figure 3B ). Genes also enriched to GO term “phagocytic vesicle”, “cytosolic ribosome”, “MHC protein complex”, “peptide antigen binding”, and “CD8 receptor binding” ( Figure 3B ). Cluster 14 (520 genes) was comprised of genes that enriched to GO terms indicative of innate immune regulation state such as “leukocyte migration”, “leukocyte chemotaxis”, and “myeloid leukocyte migration” ( Figure 3C ). Interestingly, for the consistently upregulated genes in Cluster 14, enriched cellular component terms and enriched molecular function terms mainly associated with the endoplasmic reticulum, including “endoplasmic reticulum protein-containing complex”, “endoplasmic reticulum-Golgi intermediate compartment”, “endoplasmic reticulum lumen”, and “endoplasmic reticulum lumen” ( Figure 3C ). Finally, upregulated genes of Cluster 6 (1485 genes) were mainly involved in innate inflammatory response such as “mRNA processing”, “I-kappa B kinase/NF-kappa B signaling”, “response to tumor necrosis factor”, “lysosome”, and “early endosome” ( Figure 3D ). These observations indicate changes associated with regulation of a series of genes that participate in the inflammatory response, immune response, and other processes.

Given that we observed persistent upregulation of type I IFNs-related pathways in the analysis, we next analyzed type I IFNs-related gene transcript levels during B. pertussis infection. We focused on the DE genes enriched with the GO terms “type I IFNs production” (GO 0032606) and “response to type I IFNs” (GO 0035456). A heatmap was constructed to visualize the expression levels of type I IFNs-related genes in different stages ( Figures 4A , B ). As expected, B. pertussis infection was associated with increased expression of receptors associated with type I IFNs production, including Tlr7, Tlr8, Tlr9, Dhx58, and Ifih1( Figure 4A ). Similarly, B. pertussis infection was also associated with increased expression of IFNs regulatory factors, IRF1, IRF5, IRF7, IRF8, and IRF9 ( Figure 4A ). Additionally, the DE genes annotated with the enriched GO term “response to type I IFNs” included IFNs receptor (e.g., IFNAR2), immune signaling (e.g., Acod1, Bst2, Aim2, Sting, and Irgm1), IFNs-induced transmembrane (IFITM) proteins (e.g., Ifitm1, Ifitm2, Ifitm3, and Ifitm6), and IFNs-induced guanylate-binding proteins (e.g., Gbp2b, Gbp3, and Gbp6) ( Figure 4B ). Interestingly, Ifitm3, IRF7, ISG15, and Acod1 (also known as immune-responsive gene 1, IRG1) were also among the 74 common genes that were differentially expressed at all 5 detection time points ( Figures 1B , 4C ), indicating that type I IFNs signaling was high expression throughout B. pertussis infection. As expected, the average expression of the Ifitm3, IRF7, ISG15, and Acod1 genes obtained from RNA-seq at different stages continuously increased ( Figure 4C ). Moreover, the expression levels of the four key genes were verified via RT−qPCR, and the relative expression levels were consistent with those in the RNA-seq data ( Figure 4D ). In addition, we selected several genes for validation, including Stat1, Ifnar2, Aim2, and Sting ( Supplementary Figure S5 ). Lastly, we also detected IFN-β levels via RT−qPCR. As expected, we detected significant transcriptional upregulation of IFN-β after B. pertussis infection, which peaked at 2 hpi at approximately 22-fold greater than the level before challenge ( Figure 4E ). These results suggest that B. pertussis infection induces high expression of type I IFNs production- and response-related genes.

B. pertussis infection induces an intense type I IFNs response; therefore, we further investigated whether type I IFNs influences the process of B. pertussis infection. Wild-type (WT) and IFNAR1 KO (IFNAR1^-/-) mice were infected with B. pertussis via aerosol exposure ( Figure 5A ). We first assessed the changes in peripheral WBCs in mice after infection. At 4 dpi, the number of peripheral WBCs in both WT and IFNAR1^-/- mice was greater than that in preinfected mice; however, the total WBC count was significantly lower in IFNAR1^-/- mice than in wild-type mice ( Figure 5B ). Next, we compared the kinetics of B. pertussis clearance over the 2 hpi and 2, 4, 7, 14, 21, and 28 dpi timepoints between WT and IFNAR1^-/- mice. Nasal bacterial colonization in IFNAR1^-/- mice was greater than that in WT mice at 2 hpi but significantly lower than that in WT mice at 7 dpi, and no significant difference was detected at other time points compared with that in the WT group. The areas under the bacterial clearance curve (AUCs) in WT and IFNAR1^-/- mice were 16.87 and 15.49, respectively ( Figure 5C ).

With respect to tracheal infection, tracheal homogenates from WT mice were heavily colonized at 2 hpi after B. pertussis challenge and reached the highest level, 2.0 × 10⁵ CFU/mL, at 7 dpi; then, the number of colonies gradually decreased, with an AUC of 12.19 ( Figure 5D ). However, compared with WT mice, IFNAR1^-/- mice had higher CFU counts at 2 hpi and reached the highest level, 2.3 × 10⁵ CFU/mL, at 4 dpi, with an AUC of 13.35 ( Figure 5D ). We also observed lung colonization of B. pertussis in WT and IFNAR1^-/- mice. Both groups of mice presented similar bacterial colonization and clearance curves without clearance within 28 days, with AUCs of 21.35 and 23.03, respectively ( Figure 5E ).

We then examined the histological changes in the lungs of IFNAR1^-/- and WT mice at 4, 7, and 14 dpi. Large amounts of inflammatory cell infiltration, especially polymorphonuclear granulocytes and monocytes, lung interstitial thickening, and severe bronchial obstruction, were observed in the lungs of WT mice after challenge ( Figure 5F ). However, substantially fewer inflammatory cells and relatively slight bronchial obstruction were observed in the lungs of IFNAR1^-/- mice compared with those of WT mice. Histological examinations of lung sections revealed that pathological damage increased with time in both WT and IFNAR1^-/- mice. However, histological section scoring revealed that lung lesion burden and severity were both alleviated in the lungs of infected IFNAR1^-/- mice compared with those of WT mice ( Figure 5F ). In addition, we examined the albumin level in BALF of IFNAR1^-/- and WT mice at 4 dpi. which is another parameter reflecting the lung injury. Albumin levels were significantly reduced in the BALF of IFNAR1^-/-mice when compared with WT mice ( Supplementary Figure S6 ). Collectively, these findings demonstrate that type I IFN signaling exacerbates lung pathology during B. pertussis infection in mice.

Next, we evaluated the effects of type I IFN signaling on the regulation of inflammatory cytokines. We measured the levels of multiple cytokines in the nasal turbinates and bronchoalveolar lavage fluid (BALF) at 2 hpi, 2 dpi, and 4 dpi via RT−qPCR and ELISA, respectively. We detected significantly higher mRNA levels of TNF-α in the turbinates of WT mice than in those of IFNAR1^-/- mice at 2 hpi ( Figure 6A ). In addition, IL-1β gene expression was significantly greater in WT mice than in IFNAR1^-/- mice at 2 hpi and 2 dpi ( Figure 6A ). IL-6 levels were comparable between WT mice and IFNAR1^-/- mice at the indicated timepoints ( Figure 6A ). We also measured the levels of IFN-β and observed that the level of IFN-β was significantly lower in the IFNAR1^-/- mice than in the WT mice ( Figure 6B ).

Next, we measured the protein levels of TNF-α, IL-1β, IL-6, and IFN-β in the BALF. IFN-β was present at very low levels in mice before infection (pre), while TNF-α, IL-1β, and IL-6 were undetectable ( Figures 6C, D ). There were no significant differences in the basal cytokine levels between WT mice and mice that lacked IFNAR1 ( Figures 6C, D ). Following B. pertussis infection, the level of TNF-α increased significantly in both WT mice and IFNAR1^-/- mice, but the level of TNF-α in the BALF was significantly lower in IFNAR1^-/- mice than in WT mice at 2 hpi and 4 dpi ( Figure 6C ). Similarly, the levels of IL-1β and IL-6 were also significantly increased after B. pertussis infection in both WT and IFNAR1^-/- mice. The levels of IL-1β in the two groups were comparable, whereas the level of IL-6 after infection was significantly lower in IFNAR1^-/- mice than in WT mice ( Figure 6C ). IFN-β was induced significantly (>50-fold) in WT mice after infection. IFN-β was induced at a very low level at 2 hpi and was sustained at a similar level on days 2 and 4 postinfection in infected IFNAR1^-/- mice, in contrast to the case in infected WT mice ( Figure 6D ). Furthermore, neutrophils in the BALF were assessed via flow cytometry at 2 hpi, 2 dpi, and 4 dpi. The percentage of CD45+ immune cells and neutrophils (gating strategy shown in Supplementary Figure S7 ) was significantly greater in WT mice than in IFNAR1^-/- mice ( Figure 6E ). Collectively, these results indicate that type I IFN signaling contributes to inflammatory reactions during B. pertussis infection.

To evaluate the effects of type I IFNs during B. pertussis infection on adaptive immune responses during B. pertussis infection, we examined pertussis toxoid (PT), filamentous hemagglutinin (FHA), and pertactin (PRN)-specific antibodies in the serum and cytokines in the supernatant of pulmonary and/or splenic lymphocytes after 3 days of incubation with PT, FHA, and PRN. The results revealed that at 28 dpi, IFNAR1^-/- mice presented levels of PT-, FHA-, and PRN-specific IgG comparable to those of the WT mice ( Figure 7A ). Moreover, the levels of cytokines associated with Th1 responses (IFN-γ), Th17 responses (IL-17A), and Th2 responses (IL-4) were evaluated in the splenocytes of IFNAR1^-/- mice and WT mice, and no differences were detected between the groups ( Figure 7B ). We also did not observe any significant differences in the levels of IFN-γ and IL-17A in the culture supernatants of pulmonary lymphocytes between the groups, but differences in Th2 (IL-4) responses were observed ( Figure 7C ). Overall, these results suggest that there is no significant difference in the antibody responses or Th1 and Th17 responses in either the spleen or lungs between IFNAR1^-/- and WT mice.