TIGIT/SHIP-1/RelB regulating Th1 inflammation in smoking induced COPD

Junyi Ke; Siyu Lei; Zhixiong He; Qinglan Li; Hongchun Huang; Shu Huang; Shiya Lin; Yinying Li; Lixi Lu; Yuanlan Wu; Luxi Lei; Minchao Duan

doi:10.1186/s12931-025-03400-9

. 2025 Nov 19;26:325. doi: 10.1186/s12931-025-03400-9

TIGIT/SHIP-1/RelB regulating Th1 inflammation in smoking induced COPD

Junyi Ke ^1,^2,^#, Siyu Lei ^1,^2,^#, Zhixiong He ¹, Qinglan Li ¹, Hongchun Huang ¹, Shu Huang ¹, Shiya Lin ¹, Yinying Li ¹, Lixi Lu ¹, Yuanlan Wu ¹, Luxi Lei ¹, Minchao Duan ^1,^2,^✉

PMCID: PMC12628621 PMID: 41257864

Abstract

Background

The airway lesions in COPD are mediated by a variety of inflammatory cells, among which the Th1-dominated delayed-type hypersensitivity response can also cause irreversible damage to normal airways. The immune checkpoint TIGIT and its downstream phosphatase SHIP-1 play a crucial role in coordinating the immune response of CD4 + T cells by regulating the phosphoinositide pathway.

Methods

This study evaluates the expression patterns of TIGIT/SHIP-1 in CD4 + T cells within the context of smoking-induced COPD and investigates the mechanisms by which TIGIT/SHIP-1 affects CD4 + T cells, also the downstream signaling changes regulating Th1 inflammation. In smoking COPD, we established a research framework encompassing clinical, animal, cellular levels including flow cytometry, immunofluorescence, and chromatin immunoprecipitation.

Results

We found that CD4 + T cells in smoking COPD exhibit upregulated expression of TIGIT/SHIP-1, and specific knockout of CD4-Tigit and short-term in vivo inhibition of SHIP-1 significantly elevated Th1 levels in emphysematous mice. Through single-cell and bulk-RNA bioinformatics analysis, we identified RelB that regulates COPD-Th1 inflammation by regulates TBX21 in transcriptional level and confirmed its excessive activation in COPD-Th1 inflammation. The PI3K/AKT signaling, acting downstream of TIGIT/SHIP-1, influences the activation of RelB in Th1 cells.

Conclusions

In smoking-induced COPD, TIGIT/SHIP-1 affects the activation of RelB through the PI3K/AKT pathway, thereby regulating the expression of Th1. It offers insights underlying the immune mechanism of COPD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-025-03400-9.

Keywords: COPD, Th1, TIGIT, SHIP-1, RelB

Introduction

Chronic Obstructive Pulmonary Disease (COPD) is a chronic respiratory disease characterized primarily by chronic progressive airway inflammation and emphysema, which poses a significant burden on healthcare systems worldwide [1]. Among the various causes of COPD, smoking is considered the most relevant factor and is closely associated with the dysregulation of airway immune inflammation in COPD, with abnormal CD4 + T lymphocyte inflammation being one of the key aspects [2]. The activation and differentiation of CD4 + T cell subsets are co-regulated by signaling pathways and transcription factors, and this series of complex processes ultimately determines the fate of CD4 + T cells [3]. Tobacco smoke contains a variety of complex exogenous signals that can activate both the innate and adaptive immune responses in the airways through multiple pathways, promoting the differentiation of CD4 + T cells into pro-inflammatory type 1 helper (Th1) or type 17 helper (Th17) CD4 + T cells, thereby exacerbating airway inflammation and leading to lung damage [4, 5].

Th1 is an important pro-inflammatory CD4 + T cell subset that primarily mediates delayed-type hypersensitivity reactions for microbial killing, which is abnormally activated in the airways of COPD [6]. Th1 can coordinate the accumulation of macrophages in the airways, assist neutrophils in secreting elastase, and directly disrupt the integrity of airway epithelium by secreting IFN-γ, making it one of the core components of airway inflammation in COPD [7–9]. The T-box transcription factor T-bet is a key transcription factor for Th1, specifically regulating Th1 differentiation by encoding the IFNG gene and inhibiting the expression of other transcription factors [10]. The activation of the PI3K/AKT pathway serves as an intrinsic driving force for the activation and differentiation of CD4 + T cells, endowing CD4 + T cells with the Th1 phenotype at the transcriptional level by influencing T-bet expression [11]. However, the specific molecular mechanisms underlying the PI3K/AKT activation and regulation of T-bet in CD4 + T cells in COPD remain unclear.

T Cell Immunoglobulin and ITIM domain (TIGIT) is a negative immune checkpoint predominantly expressed on NK cells and T cells. It is primarily composed of an extracellular IgV domain, an type I transmembrane domain, and an intracellular domain that includes ITIM and ITT motifs. The extracellular structure of TIGIT can phosphorylate its intracellular ITIM motifs through binding with corresponding ligands such as CD155 and CD112, which subsequently recruit SH2-containing inositol phosphatase-1 (SHIP-1) and dephosphorylate key molecules of the PI3K signaling pathway. This process blocks the activation of the PI3K/AKT pathway and affects the maturation and differentiation of T cells [12–14]. However, there has been limited research on whether TIGIT/SHIP-1 mediates CD4 + T cell inflammation in the airways of COPD by influencing the PI3K/AKT pathway.

In this study, we demonstrated the high expression of TIGIT in CD4 + T cells from COPD patients and mice, revealing that the TIGIT/SHIP-1 axis primarily exerts an inhibitory effect on Th1 inflammation in COPD. Mechanistically, the TIGIT/SHIP-1 axis in CD4 + T cells inhibits the activation of the PI3K/AKT signaling pathway, thereby blocking the activation of the key molecule RelB in the non-classical NFκB pathway and its regulatory effect on T-bet at the transcriptional level, thus suppressing Th1 inflammation in COPD. These findings establish TIGIT as a key regulatory protein in the differentiation of CD4 + T cells towards Th1, emphasizing the central role of the TIGIT/SHIP-1/RelB axis in smoking-related COPD-Th1 inflammation, and suggest that RelB may serve as a potential therapeutic target for treating smoking-induced COPD inflammation.

Materials and methods

Human subjects

Patients were classified into Control, Smoker, and COPD groups based on their smoking history and pulmonary function tests. All enrolled patients underwent wedge resection of lung nodules. Lung tissue was collected from a site 5 cm away from the nodule margin, and after thorough washing with sterile PBS to remove blood contaminants, the lung tissue was minced and incubated with approximately 3 ml of RPMI-1640 culture medium. The tissue was then digested with 1 mg/ml of Type I collagenase and 0.5 mg/ml of DNase I in a 37 °C shaking incubator. The digestion solution was filtered through a 100 μm metal mesh, and red blood cells were removed using a red blood cell lysis solution. The mixture was centrifuged at 4 °C for 5 min at 300 g, and the resulting cell pellet was resuspended in pre-cooled PBS to obtain a single-cell suspension. All enrolled patients provided informed consent, and the study received ethical approval from the Ethics Committee of Wuming Hospital, Guangxi Medical University, approval number: GXMU-WM-2025 (17).

Mice

Wild-type C57BL/6J mice were obtained from the Animal Experiment Center of Guangxi Medical University, while Tigit^flox/flox (Tigit-Flox) and Tigit^flox/floxCD4-Cre (Tigit-Cre) mice were sourced from Suzhou Cyagen Biotechnology Co., Ltd. The model of emphysema in mice was established following the previous methods of our research group [15]. Wild-type mice at 6 weeks of age were randomly divided into the air exposure group (Air) and the tobacco exposure group (CS). The CS mice underwent tobacco exposure for 30 min, four times daily, five days a week, for a total of 24 weeks to construct the emphysema model. Knockout mice and control groups were also subjected to tobacco exposure at 6 weeks of age. Mice were anesthetized with isoflurane gas, and the right ventricle was perfused with pre-cooled PBS to flush the lungs. The lung tissues were then harvested, and surrounding fat and connective tissues were removed. The lung tissues were minced and incubated with approximately 3 ml of RPMI-1640 medium and 1 mg/ml of type IV collagenase on a 37 °C shaking incubator for complete digestion. The digested solution was filtered through a 100 μm metal mesh and red blood cell lysis solution was used to remove red blood cells. The mixture was centrifuged at 4 °C at 300 g for 5 min, and the resulting cell pellet was resuspended in pre-cooled PBS to obtain a single-cell suspension. All animal experiments were approved by the Animal Ethics Committee of Guangxi Medical University, approval number: GXMU-202,312,008.

3-AC and (-)-DHMEQ inhibiting experiments in vivo

In parallel experiments, 3-AC was dissolved in a 0.3% Klucel/H2O solution. For the established emphysema model, mice received an intraperitoneal injection of 26.4 mg/kg of the 3-AC solution and 100 µl of the 0.3% Klucel/H2O solution, respectively, for five consecutive days [16], during which the mice were continuously exposed to tobacco. (-)-DHMEQ was dissolved in a DMSO solution, and wild-type mice exposed to tobacco for 20 weeks received an intraperitoneal injection of 4 mg/kg of the (-)-DHMEQ solution and 100 µl of the pure DMSO solution three times a week for four weeks [17], while maintaining continuous tobacco exposure.

Histology and morphometry of mice lungs

Two researchers measured and evaluated mouse lung pathological sections under blind conditions. They randomly selected 10 fields of view at a magnification of 200x and quantified the degree of pulmonary emphysema in mice by calculating the average alveolar interspace (Lm), using the same method as in previous studies conducted by our research group [6].

Naive CD4 + T cells isolation and culture/co-culture

CD44-CD62L + CD4 + T naive (Tn) cells were isolated from the spleens of mice in the Air and CS groups using the initial CD4 + T cell isolation kit (Miltenyi Biotech, 130-104−453). The purity of the isolated CD4 + Tn cells was confirmed to be 90% by flow cytometry. CD4 + Tn cells were cultured in a Th1 polarization environment [anti-CD3 mAb (5 µg/ml), anti-CD28 mAb (3 µg/ml), recombinant IL-2 (10 ng/ml), recombinant IL-12 (10 ng/ml), anti-IL-4 mAb (10 µg/ml)] for four days, with a partial medium change on the third day [6]. The cells were then collected and further processed. Some drugs were co-cultured with CD4 + Tn cells: LY294002 (0 µM, 15 µM, 25 µM, 35 µM) for 24 h [18], recombinant CD155 protein (1 µg/ml) for 24 h [19], 3-AC (10 µM) for 24 h [20], and (-)-DHMEQ (10 µg/ml) for 24 h [17]. In addition, CD4 + Tn cells were cultured for 4 days under Th17 polarization conditions [anti-CD3 mAb (5 µg/ml), anti-CD28 mAb (3 µg/ml), recombinant IL-1β (10 ng/ml), recombinant IL-6 (20 ng/ml), recombinant IL-23 (20 ng/ml), recombinant TGF-β (2 ng/ml), anti-IL-4 mAb (10 µg/ml), anti-IFN-γ mAb (10 µg/ml)] [15]. LY294002 (35 µM) and recombinant CD155 protein (1 µg/ml) were also co-cultured under Th17 polarization conditions for 24 h.

Flow cytometry

Single-cell suspensions were surface stained with the following anti-human or anti-mouse antibodies at 4 °C for 30 min: CD4, CD44, CD62L, and TIGIT. For cytokine staining, the single-cell suspensions were stimulated with PMA (25 ng/ml), ionomycin (1 µg/ml), and BFA (2 µl/ml) in a 37 °C, 5% CO2 incubator for 4 h. Subsequently, they were surface stained with the following anti-human or anti-mouse antibodies at 4 °C for 30 min: CD4 and TIGIT. After staining, the cells were treated with staining-permeabilization buffer for 20 min, followed by cytokine staining with the following anti-human or anti-mouse antibodies for 30 min: IFN-γ, TNF-α, and IL-17 A. For phosphorylated proteins, 1 ml of pre-warmed BD Phosflow™ Fix Buffer I was used to treat the cells in a 37 °C, 5% CO2 incubator for 15 min. The cells were then centrifuged at 300 g for 5 min at 4 °C to obtain a cell pellet. Following this, 200 µl of pre-cooled BD Phosflow™ Perm Buffer III was added to the cells and treated in the dark at 4 °C for 30 min. After another centrifugation, the cells were resuspended in pre-cooled PBS. The treated cells were stained with the following anti-mouse antibodies for 60 min: pPI3K, pAKT1, pRelB, IFN-γ, T-bet in vitro or CD3E, CD8A, pPI3K, pAKT1, pRelB, IFN-γ, T-bet in vivo. Data were collected using a BD FACS Canto and analyzed with Flowjo software.

Immunofluorescent staining

For dual immunofluorescence staining, mouse lung paraffin sections were subjected to deparaffinization and rehydration, followed by antigen retrieval using 1x EDTA. The retrieved sections were then blocked with 3% BSA for 30 min, and subsequently incubated overnight at 4 °C with a primary antibody mixture of CD4 (1:200) and SHIP-1 (1:100). After incubation, the sections were washed and incubated at room temperature in the dark for 50 min with a fluorescently labeled secondary antibody mixture of CY3 (1:300) and Alexa Fluor 488 (1:400). For the three-color immunofluorescence, TSA technology was employed for staining [21]. The sections underwent deparaffinization, rehydration, and antigen retrieval, followed by blocking with 3% hydrogen peroxide and 3% BSA for 25 min and 30 min, respectively. After treatment, the CD4 primary antibody (1:1000) was added and incubated overnight at 4 °C. After washing, the corresponding HRP goat anti-rabbit IgG secondary antibody (1:500) was added for a 50-minute incubation at room temperature. Following incubation, the sections were washed with PBS and the corresponding TSA (IF555) was added. After completing the CD4 staining, the sections were placed in citrate retrieval solution and subjected to microwave treatment. Following this, BSA blocking was performed, and pRelB (1:2000) staining was conducted, followed by repeating the aforementioned steps for T-bet (1:2000) staining. The corresponding secondary antibodies for pRelB and T-bet were HRP goat anti-rabbit IgG (1:500), and the corresponding TSAs were IF488 and IF647, respectively. The stained slices were imaged using a fluorescence inverted microscope system (Life Technologies, USA), and data analysis was performed using the JACoP plugin of ImageJ software.

For CD4 + T cells, we performed embedding, sectioning, and staining on CD4 + Tn extracted from mouse spleens and CD4 + T cells cultured for 4 days under Th1 polarization. RelB (1:5000) was incubated with the sections overnight at 4 °C. After incubation, HRP-conjugated goat anti-rabbit IgG and TSA dye were added. The processed sections were washed with antigen retrieval solution and subjected to microwave heat treatment, followed by incubation with pRelB (1:4000) and repeating the above steps. The treated cell sections were imaged using a confocal fluorescence microscope.

Chromatin Immunoprecipitation

According to the manufacturer’s instructions, the ChIP kit (Cell Signaling Technology, 9005 S) was used to formaldehyde crosslink CD4 + T cells that had been polarized to Th1. Subsequently, cells were lysed, and the DNA was fragmented into 100–500 bp segments using the Covaris M220 ultrasonicator, with 10 µl extracted as 2% input. Immunoprecipitation was performed using 10 µl of ChIP-grade RelB antibody (Cell Signaling Technology, 10544 S) and 2 µl of the corresponding anti-rabbit IgG antibody. qRT-PCR was conducted on the ChIP products using primers targeting the TBX21 promoter binding site (Forward: ACTTCTGATCCTCTACCAACCCTC, Reverse: CATACACCCACGCTTCTGGTT). The PCR products were electrophoresed on a 1% agarose gel.

Single-cell and bulk sequencing bioinformatic analysis

Download the COPD-related sequencing datasets GSE173896 and GSE76925 from the GEO database, and analyze the datasets using R version 4.5.0 and R Studio software. The Seurat package is utilized to annotate the CD4 + T cells in the GSE173896 dataset, while the monocle package is employed for pseudotime analysis of CD4 + T cells. Differential gene analysis of Th1 cells between the COPD and Control groups is conducted, followed by KEGG enrichment analysis of the identified differential genes. The upregulated differential genes are intersected with the TBX21 transcription factor dataset downloaded from the KnockTF V2 website (https://bio.liclab.net/KnockTFv2/index.php), resulting in the key transcription factor RELB. Subsequently, the COPD sequencing data from GSE76925 is divided into high and low groups based on RELB expression levels, and differential gene analysis along with immune infiltration analysis is performed between the two groups. Finally, GSEA enrichment analysis is conducted based on the differential genes.

Western blot

CD4 + T cells were collected after Th1 polarization and washed three times with PBS before being resuspended in PierceTM RIPA lysis extraction buffer. The protein concentration of the cell lysate was measured using the PierceTM BCA Protein Assay Kit according to the manufacturer’s instructions. Samples were diluted with LDS sample buffer, boiled at 95 °C for 15 min, and equal amounts of protein were loaded onto 10% SDS–polyacrylamide gels, followed by transfer to polyvinylidene difluoride membranes. The CozyHi prestained protein ladder was used as a protein size marker. The membranes were blocked in PBS-Tween-20 containing 5% BSA for 1 h, and then incubated overnight with primary antibodies against AKT1 (1:500), pAKT1 (1:1000), and GAPDH (1:3000). After incubation, the membranes were washed and incubated with HRP-conjugated secondary antibodies. Following this, ECL solution was added to the membranes, and imaging was performed using appropriate instrumentation.

Statistical analysis

All data are presented as mean ± standard deviation (mean ± SD), and statistical analysis and graphing were performed using GraphPad Prism 9.5 software. Depending on whether the data followed a normal distribution, comparisons between two groups were conducted using unpaired Student’s t-test and Mann-Whitney test, while comparisons among multiple groups were performed using Kruskal-Wallis one-way analysis of variance. Correlation analysis was conducted using the Pearson method. A P value of less than 0.05 was considered statistically significant.

Results

TIGIT is highly expressed in CD4 + T cells in the lungs of smokers with COPD and in tobacco-exposed mice

Our previous research confirmed that the levels of Th1 (IFN-γ + CD4 + T cells) in the lungs of COPD mice exposed to chronic tobacco smoke are significantly elevated compared to mice exposed to ambient air [6]. To further validate the impact of smoking on pro-inflammatory CD4 + T cells in the lungs of COPD patients using clinical samples, we collected lung tissues from non-smoking individuals with normal lung function (Control), smoking individuals with normal lung function (Smoker), and smoking COPD patients at Wuming Hospital affiliated with Guangxi Medical University for flow cytometry analysis. The baseline clinical data of each patient is presented in Table 1. The results showed that the COPD group exhibited a significant increase in lung Th1 and Th17 (IL-17 A + CD4 + T cells) infiltration compared to the Control group (Fig. 1. A-C), while there was no significant difference in the levels of TNF-α + CD4 + T cells (SF 1. A-B).

Table 1.

Clinical characteristics of participants in lung tissues study group (means± SD)

Characteristics	Nonsmokers (n = 11)	Healthy Smokers (n = 18)	COPD (n = 16)
Age (years)	56.82 ± 12.31	56.44 ± 8.18	65.56 ± 6.79
Gender, Male (Female)	9 (2)	18 (0)	16 (0)
Smoking index	0	462.2 ± 229.8	531.3 ± 262.6
Smoking status, current (former)	0 (0)	17 (1)	16 (0)
FEV1/FVC (%)	81.45 ± 4.58	79.68 ± 4.52	64.51 ± 4.41
FEV1.pred (%)	113.40 ± 10.75	108.20 ± 15.24	82.07 ± 13.38
DLCO (%)	105.80 ± 10.77	86.90 ± 20.92	90.12 ± 21.18
GOLD stage (numbers)
I			10
II			6
III			0
IV			0
Inhaled steroids (numbers)			0

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TIGIT is upregulated on T cells in most diseases characterized by active inflammatory responses [22–24]. To determine the expression pattern of TIGIT in CD4 + T cells of COPD patients, we utilized flow cytometry to assess the expression of TIGIT + CD4 + T cells in lung tissues of these patients. We observed a significant increase in the proportion of TIGIT + CD4 + T cells in smoking COPD patients compared to the Control and Smoker groups (Fig. 1. D), indicating that the expression pattern of TIGIT in CD4 + T cells of smoking COPD patients is similar to that observed in most inflammatory diseases. Furthermore, by examining the expression of TIGIT in Th1 and Th17 cells, we found that smoking COPD patients exhibited higher expression of TIGIT + Th1 and TIGIT + Th17 compared to the Control group (SF 1. C-E). We further analyzed the relationship between the proportion of TIGIT + CD4 + T cells in the smoking COPD group and pulmonary function test results; however, there was no significant correlation between TIGIT expression and DLCO%, FEV1.pp, or FEV1/FVC (SF (1) F).

Based on clinical research, we continued to construct a COPD pathological mouse model using a 24-week tobacco smoke exposure method (Fig. 1. E). The lungs of this batch of tobacco smoke-exposed mice (CS) and air-exposed mice (Air) were obtained for HE staining. We found that the alveolar structure in CS mice was significantly damaged, and Lm was markedly elevated (Fig. 1. F-G). Flow cytometry analysis of the lungs from Air and CS mice revealed that chronic tobacco smoke exposure promoted the activation of CD4 + T cells in the lungs, as evidenced by a significant increase in the proportion of central memory CD4 + T cells (CD4 + Tcm) and effector memory CD4 + T cells (CD4 + Tem) in CS mice, while the proportion of naive CD4 + T cells (CD4 + Tn) significantly decreased (SF (2) A-B). Further analysis of the pro-inflammatory subtypes Th1, Th17, and TNF-α + CD4 + T cells revealed that the ratio of Th1 and Th17 cells was significantly increased in the CS mice, whereas there was no significant change in the proportion of TNF-α + CD4 + T cells. Similarly, TIGIT + CD4 + T cells, TIGIT + Th1, and TIGIT + Th17 were highly expressed in the lungs of CS mice, while there was no difference in the proportion of TIGIT + TNF-α + CD4 + T cells between the two groups. These results are largely consistent with our findings in clinical samples (Fig. 1. H-L, SF 2. E-I). Additionally, we found that the expression of TIGIT in CD4 + Tcm and CD4 + Tem was elevated in CS mice, while there was no significant difference in TIGIT + CD4 + Tn between the Air and CS mice (SF 2. C-D). In summary, our results indicate that TIGIT expression is consistently upregulated in CD4 + T cells, Th1, and Th17 in the lungs of smoking COPD patients and tobacco smoke-exposed mice.

Furthermore, we conducted a flow cytometry analysis focusing on the expression levels of Th1, Th17, TIGIT + CD4 + T cells, and TIGIT + Th1 in female and male COPD patients. Notably, the female COPD patients had no history of smoking but had a long-term exposure to biomass. Our findings indicate that there were no significant differences in the expression levels of Th1, Th17, TIGIT + CD4 + T cells, and TIGIT + Th1 between the two genders of COPD patients (SF 6. A-H).

TIGIT primarily affects Th1 inflammation in the lungs of smoking-related COPD

TIGIT binds to its ligands and is activated, recruiting and activating enzyme proteins with phosphatase activity through intracellular domains containing ITIM and ITT motifs to transmit inhibitory signals [14]. The high expression of TIGIT in inflammatory diseases is considered a protective mechanism that limits excessive inflammation [15]. Therefore, we investigated the potential impact of TIGIT on pro-inflammatory CD4 + T cells in the lungs of smokers with COPD. We found that the proportion of TIGIT + CD4 + T cells was significantly positively correlated with the ratios of Th1 and Th17 cells in both CS mice and COPD patients (Fig. 2. A), with a stronger correlation between TIGIT + CD4 + T cells and Th1 ratios (R2 = 0.59 in CS mice and R2 = 0.5 in COPD patients).

Fig. 2 — Specific knockout of CD4-Tigit significantly enhances Th1 inflammation in the lungs of CS mice. A Correlation analysis of the proportions of Th1 and Th17 cells in the lungs of COPD and CS mice with the proportion of TIGIT + CD4 + T cells (COPD: n = 16, CS: n = 10). B Construction of pathological models of pulmonary emphysema in Tigit-Cre and Tigit-Flox mice following tobacco exposure. C Representative images of H&E staining of lung tissue sections from CS-Tigit-Cre and CS-Tigit-Flox mice. D Comparison of Lm values in the morphological analysis of lung tissues from CS-Tigit-Cre and CS-Tigit-Flox mice (n = 5). E-G Representative flow cytometry analysis images and comparative bar graphs of Th1 and Th17 cells in the lungs of CS-Tigit-Cre and CS-Tigit-Flox mice (n = 8). H, I Representative flow cytometry analysis images and comparative bar graphs of CD4 + Tn, CD4 + Tcm, and CD4 + Tem cells in the lungs of CS-Tigit-Cre and CS-Tigit-Flox mice (n = 8). Data are representative of three independent experiments and are presented as medians. *P < 0.05, ***P < 0.001

In this study, we utilized the Tigit^flox/floxCd4-Cre mouse model and its control to establish a chronic tobacco smoke exposure-induced COPD pathological model (Fig. 2. B). By obtaining lung tissue sections from both mouse strains and performing HE staining, we observed that the CS-Tigit-Cre mice exhibited more severe alveolar structural destruction compared to the CS-Tigit-Flox mice, with a more significant increase in Lm (Fig. 2. C-D). Flow cytometric analysis of the lung tissues from both groups revealed that, following the gene-specific knockout of Tigit in Cd4, the expression level of TIGIT in CD4 + T cells of CS-Tigit-Cre mice was significantly reduced (SF 3. A, C). Furthermore, we analyzed the activation status of CD4 + T cells and the expression levels of pro-inflammatory CD4 + T cells in the lungs of CS-Tigit-Cre and CS-Tigit-Flox mice. We found no significant changes in the proportions of CD4 + Tcm, CD4 + Tem, and CD4 + Tn cells between the two groups, and the ratios of Th17 and TNF-α + CD4 + T cells also showed no statistical differences. However, the infiltration level of Th1 cells in the lungs of CS-Tigit-Cre mice was markedly increased (Fig. 2. E-I, SF 3. A, D). Previous studies have reported that Th1 can both activate the inflammation of macrophages and neutrophils and damage the integrity of airway epithelium through the secretion of IFN-γ, which is an important component of chronic airway inflammation in COPD [7–9]. Our data indicate that the absence of TIGIT amplifies the stimulatory effect of chronic tobacco smoke on the differentiation of CD4 + T cells into the pro-inflammatory Th1 subtype, further disrupting the alveolar structure, contributing to the release of autoimmune inflammatory responses, and promoting immune pathology.

TIGIT modulates pulmonary Th1 inflammation in smoking-related COPD through SHIP-1

Our previous research elucidated the unique regulatory role of TIGIT on Th1 in smoking-induced COPD, which relies on the hydrolysis of key molecular phosphorylation in the cellular pathway by SHIP-1 [5]. To clarify whether the TIGIT/SHIP-1 axis exists and influences Th1 inflammation in COPD, we further investigated the expression of SHIP-1 in the lungs of Air and CS mice. Through dual immunofluorescence staining, we found that chronic tobacco smoke exposure promoted the expression of SHIP-1 in CD4 + T cells in the lungs of mice (Fig. 3. A-B). Studies have indicated that T cell immune tolerance exists in SHIP-1 knockout mice [25]. To determine whether potential tolerance effects would impact our results, we assessed the expression of SHIP-1 in lung CD4 + T cells of Air, CS-Tigit-Cre, and CS-Tigit-Flox mice. The results indicated that the absence of TIGIT significantly affected the expression of SHIP-1 in CD4 + T cells in the lungs of mice, but the expression level of SHIP-1 in CD4 + T cells from CS-Tigit-Cre mice did not show a significant difference compared to Air mice. Therefore, there would be no immune tolerance response due to reduced SHIP-1 expression in CS-Tigit-Cre mice (Fig. 3. C-D).

Fig. 3 — The impact of the TIGIT/SHIP-1 axis on pulmonary Th1 inflammation in CS mice. A, B Co-localization analysis of SHIP-1 and CD4 in the lungs of Air and CS mice using dual immunofluorescence staining (n = 7). C, D Co-localization analysis of SHIP-1 and CD4 in the lungs of Air, CS-Tigit-Cre, and CS-Tigit-Flox mice using dual immunofluorescence staining (n = 5). E Schematic representation of the SHIP-1 inhibitory tobacco exposure emphysema pathological mouse model. F-H Representative flow cytometry analysis images and statistical bar graphs of pulmonary Th1 and Th17 in CS + 3-AC and CS + Vehicle mice (n = 7). Data are representative of three independent experiments and are presented as medians. *P < 0.05, **P < 0.01

To clarify the impact of SHIP-1 on the activation and function of CD4 + T cells, we administered the specific inhibitor of SHIP-1, 3-AC (26.5 mg/kg), and a vehicle control via intraperitoneal injection for five consecutive days to CS mice [26]. The lungs were then harvested for flow cytometry analysis (Fig. 3. E). The results indicated that there were no significant differences in the proportions of CD4 + Tcm, CD4 + Tem, and CD4 + Tn, as well as in the proportions of Th17 and TNF-α + CD4 + T cells between the lungs of CS + 3-AC and CS + Vehicle mice. However, the expression of Th1 was significantly elevated in the SHIP-1 inhibited CS mice (Fig. 3. F-H, SF 3. B, E-F). This result is consistent with our findings in CS-Tigit knockout mice. These results suggest that the absence of TIGIT affects the expression of SHIP-1 in CD4 + T cells, while the inhibition of SHIP-1 significantly promotes the differentiation of Th1 cells, indicating that the TIGIT/SHIP-1 axis negatively regulates Th1 inflammation in CS mice.

TIGIT/SHIP-1 modulates the activation of PI3K/AKT further to regulate Th1 inflammation

TIGIT exerts its immunosuppressive effect by promoting the phosphatase function of SHIP-1, which terminates the activation of signaling pathways such as PI3K/AKT, MAPK, and NFκB at an early stage [27]. Notably, the activation of the PI3K/AKT signaling pathway is closely associated with Th1 polarization in autoimmune diseases [28, 29]. To investigate the activation of the PI3K/AKT signaling pathway in CD4 + T cells in smoking-induced COPD, we analyzed the proportion of pAKT1 + CD4 + T cells and pPI3K + CD4 + T cells in the lungs of Air and CS groups of mice using flow cytometry. The results showed that the expression levels of pAKT1 + CD4 + T cells and pPI3K + CD4 + T cells in CS mice were significantly increased (Fig. 4. A-D). We also used WB to detect the expression levels of AKT1 and pAKT1 in two groups of cells. Similarly, the expression level of pAKT1 in the Th1 cells of the CS group was significantly higher than that in the Air group (SF 7. C, D). Additionally, we also validated the activation level of the PI3K/AKT signaling pathway in CS-CD4 + T cells at the in vitro level. We used immunomagnetic beads to negatively select CD4 + Tn cells from the spleens of Air and CS mice and cultured them under Th1 polarization conditions for 96 h (Fig. 4. E). The cells were then collected and subjected to flow cytometry analysis. The results indicate that CD4 + Tn cells derived from CS mice exhibit higher levels of IFN + CD4 + T cell expression after Th1 polarization. Similarly, the levels of pPI3K + CD4 + T cells and pAKT1 + CD4 + T cells are significantly elevated in CS-Th1. However, the proportion of TIGIT + CD4 + T cells does not show a significant difference between the two groups of cells (Fig. 4. H-I). Therefore, we further examined the expression patterns of TIGIT + Th1 cells in both groups, and the results demonstrate that the proportion of TIGIT + Th1 cells is markedly upregulated in CS-Th1 (Fig. 4. J-K).

Fig. 4 — PI3K/AKT signaling enhances the activation of CD4 + T cells in CS mice and promotes activation in a Th1 polarizing environment. A-D Flow cytometric analysis of pPI3K and pAKT1 in lung CD4 + T cells from Air and CS mice, with representative images and statistical analysis bar graphs (n = 6). E Schematic diagram of the initial CD4 + T cell isolation and culture model. F-I Flow cytometric analysis of IFN-γ, pPI3K, pAKT1, and TIGIT in initially cultured CD4 + Tn cells after in vitro Th1 polarization, with representative images and statistical analysis bar graphs (n = 3). J-K Flow cytometric analysis of TIGIT and IFN-γ double-positive cells in initially cultured CD4 + Tn cells after in vitro Th1 polarization, with representative images and statistical analysis bar graphs (n = 3). Data are representative of three independent experiments and are presented as medians. *P < 0.05, **P < 0.01, ****P < 0.0001

To validate the hypothesis that the PI3K/AKT signaling pathway influences COPD-Th1 inflammation, we treated CD4 + Tn cells, isolated from the spleens of CS mice and cultured under Th1 polarization conditions, with varying concentrations of the broad-spectrum PI3K inhibitor LY294002 (0 µM, 15 µM, 25 µM, 35 µM). Flow cytometry analysis revealed a concentration-dependent decrease in the expression levels of pPI3K following co-culture with LY294002. However, treatment with 15 µM and 25 µM LY294002 did not significantly reduce the expression of pAKT1 and IFN-γ. A significant decrease in the proportion of pAKT1 + CD4 + T cells and IFN-γ + CD4 + T cells was observed when the concentration of LY294002 reached 35 µM (Fig. 5. A-B). Furthermore, to verify the negative impact of the TIGIT/SHIP-1 axis on the activation of the PI3K/AKT signaling pathway in CD4 + T cells and Th1 differentiation, we co-cultured CD4 + Tn cells with the high-affinity ligand CD155 recombinant protein and CD155 + 3-AC under Th1 polarization conditions in vitro [19, 20]. Consistent with our expected results, the application of recombinant CD155 protein in vitro effectively inhibits the phosphorylation of the PI3K/AKT signaling pathway in CD4 + T cells and reduces the proportion of Th1 cells. However, after the inhibition of SHIP-1 by 3-AC, the suppressive effect of CD155 on PI3K/AKT signaling activation and Th1 differentiation is reversed (Fig. 5. C-D). These results indicate that the TIGIT/SHIP-1 axis negatively regulates the level of COPD-Th1 inflammation by blocking the activation of the PI3K/AKT signaling pathway.

Fig. 5 — In vitro cell culture reveals that TIGIT/SHIP-1 regulates Th1 differentiation by influencing the activation of the PI3K/AKT signaling pathway. (A, B) After co-culturing CD4 + T cells with different concentrations of the PI3K inhibitor LY294002 under Th1 polarization conditions, flow cytometry analysis of IFN-γ, pPI3K, and pAKT1 is shown with representative graphs and statistical analysis dot plots (n = 3). C-D Following co-culture of CD4 + T cells with the TIGIT ligand CD155 recombinant protein and CD155 + 3-AC under Th1 polarization conditions, flow cytometry analysis of IFN-γ, pPI3K, and pAKT1 is shown with representative graphs and statistical analysis dot plots (n = 3). Data are representative of three independent experiments and are presented as medians. *P < 0.05, **P < 0.01, ***P < 0.001

We also investigated the impact of TIGIT/PI3K signaling on Th17 cells in smoking mice. CD4 + Tn cells were co-cultured under Th17 polarization conditions with the TIGIT ligand CD155 and the PI3K inhibitor LY294002. After culturing, the cells were analyzed by flow cytometry. We found that LY294002 significantly inhibited the expression of Th17, whereas, unlike Th1, the recombinant CD155 protein did not significantly suppress the expression level of Th17 (SF 7. A, B). We also examined the expression of the key transcription factor RORγt in CD4 + T cells. Interestingly, LY294002 also exhibited a strong inhibitory effect on RORγt, while CD155 had no impact on RORγt expression (SF 7. A, B).

RelB is a key transcriptional regulator in COPD-Th1 inflammation

Our previous research found that T-bet (encoded by TBX21) is an important transcription factor regulating Th1 differentiation in smoking-induced COPD mice [30]. Additionally, we reported the regulatory role of the TIGIT/SHIP-1/PI3K/AKT axis in COPD-Th1 inflammation. To further explore the connection between the two and the potential molecular mechanisms, we downloaded the single-cell sequencing dataset GSE173896 of COPD and control samples from the GEO database. Using the harmony package and Seurat package in R 4.4.1, we performed quality control, normalization, merging, batch effect removal, and clustering on the data. Based on the differentially expressed genes of cell populations, we annotated each cell group using CellMarker2.0 [31]. Overall, the single-cell profile was defined as 18 cell groups, from which we extracted ‘CD4 + T cells’ and further annotated them into 7 subgroups (Fig. 6. A-D). Among these, cell groups primarily expressing genes such as ‘IFNG, HSPA1A, GZMH’ were defined as Th1, while those expressing genes like ‘KLF2, SELL, MAL’ were defined as Tn. Pseudotime analysis of Th1 and Tn revealed that Th1 occupies the end of the pseudotemporal developmental trajectory, which is consistent with the trajectory of CD4 + Tn activating and differentiating into Th1 (SF 4. A-B). Next, we conducted differential gene analysis and KEGG enrichment analysis of the Th1 subpopulation in Control and COPD samples. We found that the differentially expressed genes upregulated in the COPD group were primarily enriched in ‘Th1 and Th2 cell differentiation’. Furthermore, we downloaded a dataset of transcription factors that bind to TBX21 based on CHIP-seq results from the KnockTF 2.0 database [32], and intersected it with the upregulated differentially expressed genes, resulting in the identification of the unique differentially expressed gene RELB (Fig. 6. E-H).

Fig. 6 — RELB is an important transcriptional regulator of the COPD-Th1 inflammation. A t-SNE plot of cell annotations from the GSE173896 dataset. B t-SNE plot of extracted CD4 + T cell subpopulations from the GSE173896 dataset. C Heatmap of differential gene expression across CD4 + T cell clusters. D t-SNE plot of annotated groups of CD4 + T cells from the GSE173896 dataset. E Volcano plot of differential genes in Th1 subpopulations between the COPD and Control groups. F, G KEGG enrichment analysis results for differential genes in Th1; F represents upregulated genes, while G represents downregulated genes. H Venn diagram of upregulated differential genes in Th1 related to the transcription factor T-bet

To further clarify the potential role of RELB in COPD, we downloaded the dataset GSE76925 from the GEO database, which contains sequencing samples from 111 COPD patients. The 111 COPD samples were categorized into ‘RELB-High’ and ‘RELB-Low’ groups based on the expression levels of RELB. Differential gene analysis between the two groups was performed using the Limma package (SF 3. G, SF 4. C). GSEA enrichment analysis of the differentially expressed genes revealed that the main enriched pathways for the upregulated genes in the RELB-High group were ‘Th1 and Th2 cell differentiation’, ‘T cell receptor signaling pathway’, and ‘NF-Kappa B signaling pathway’ (Fig. 7. A-C). Subsequently, we conducted immune infiltration analysis on the RELB-High and RELB-Low groups using the Cibersort algorithm. The results indicated that ‘T cells CD4 naive’ were significantly upregulated in the RELB-Low group, while ‘T cells CD4 memory activated’ were significantly upregulated in the RELB-High group (SF 4. D-E). Single-gene immune infiltration analysis of COPD samples revealed a significant positive correlation between RELB and “T cells CD4 memory activated,” while showing a notable negative correlation with “T cells CD4 naive” and “T cells CD4 memory resting” (SF 4. F). Furthermore, correlation analysis of the expression levels of RELB and key Th1 genes CD4 and IFNG in COPD samples demonstrated a significant positive correlation between RELB and both CD4 and IFNG (SF 4. G-H).

Fig. 7 — RelB activation in CD4 + T cells of CS mice enhances activation in a Th1 polarizing environment. A-C GSEA enrichment analysis of RelB in Th1 and Th2 differentiation, T cell receptor signaling pathway, and NF-Kappa B signaling pathway from GSE76925. D, E Flow cytometry analysis representative plots and statistical analysis bar graphs of pRelB and T-bet in CD4 + T cells from Air and CS mice lungs (n = 6). G, H Flow cytometry analysis representative plots and statistical analysis bar graphs of pRelB and T-bet after in vitro Th1 polarization of naive CD4 + T cells (n = 3). Data are representative of three independent experiments and are presented as medians. *P < 0.05, **P < 0.01

RelB is a major component of the non-classical NFκB pathway, capable of forming heterodimers with p50 or p52, which persistently activate and translocate to the nucleus to regulate gene expression [33]. Since only activated RelB possesses transcriptional regulatory functions, we primarily focused on the expression pattern of pRelB in COPD. We first assessed the expression levels of pRelB and T-bet in CD4 + T cells from Air and CS mice at the in vivo level. Flow cytometry revealed that the expression levels of pRelB and T-bet in CD4 + T cells from CS mice were significantly higher than those from Air mice (Fig. 7. D-F). In vitro studies indicated that CD4 + Tn derived from CS mice exhibited higher expression levels of pRelB and T-bet compared to CD4 + Tn from Air mice when cultured under Th1 polarization conditions (Fig. 7. G, H), which is largely consistent with our findings from single-cell sequencing. Additionally, we clarified the regulatory role of RelB on T-bet at the transcriptional level through our database. The function of RelB as a transcription factor requires an interaction with T-bet. To investigate this, we performed triple immunofluorescence staining for CD4, pRelB, and T-bet on lung tissue sections from CS mice and conducted a colocalization analysis. The results indicated a possible spatial colocalization of pRelB and T-bet in the lungs of CS mice. Given that T-bet is primarily expressed in the nucleus, this finding also indirectly suggests the nuclear translocation of pRelB (Fig. 8. A-B), which serves as the spatial basis for RelB’s regulation of T-bet expression. Subsequently, our ChIP experiments provided direct evidence that RelB interacts directly with sites on the TBX21 promoter, thereby regulating T-bet expression (Fig. 8. C-D). Finally, we used immunofluorescence to assess the nuclear translocation of RelB. We examined the expression of RelB and pRelB in freshly isolated CD4 + T naive (Tn) cells and in CD4 + T cells cultured after Th1 polarization, and imaged them using confocal microscopy. There was almost no expression of pRelB in the nuclei of CD4 + Tn cells, whereas pRelB was observed in polarized CD4 + T cells, there was a more obvious fluorescence co-localization between pRelB and nuclei in polarized CD4 + T cells, confirming that RelB undergoes phosphorylation and nuclear translocation during the Th1 polarization process (SF 7. E-F).

Fig. 8 — RelB is a key transcription factor regulating T-bet in CD4 + T cells, while TIGIT/SHIP-1 can influence the expression levels of pRelB and T-bet in CD4 + T cells through the PI3K/AKT signaling pathway. A, B Co-localization analysis of SHIP-1 and CD4 in double immunofluorescence staining of lung tissues from Air and CS mice (n = 3). C Electrophoresis of DNA binding sites in agarose gel. D PCR results analysis of TBX21 precipitated by anti-RelB antibody and IgG (n = 3). E, F Flow cytometry analysis of pRelB and T-bet after co-culturing CD4 + T cells with different concentrations of the PI3K inhibitor LY294002 under Th1 polarization conditions, with representative images and statistical analysis dot plots (n = 3). G, H Flow cytometry analysis of pRelB and T-bet after co-culturing CD4 + T cells with the TIGIT ligand CD155 recombinant protein, CD155 + 3-AC under Th1 polarization conditions, with representative images and statistical analysis dot plots (n = 3). Data are representative of three independent experiments and are presented as medians. *P < 0.05, **P < 0.01, ***P < 0.001

TIGIT/SHIP-1 Inhibition of the PI3K/AKT pathway impedes the activation of RelB

Our previous research has confirmed the unique role of the TIGIT/SHIP-1/PI3K/AKT axis in COPD-Th1 inflammation. Therefore, does the TIGIT/SHIP-1/PI3K/AKT axis also affect the expression of pRelB and T-bet in COPD-Th1 inflammation? We continued to treat mouse CD4 + Tn cells with different concentrations of LY294002 under in vitro Th1 polarization conditions. The results showed that when the concentration of LY294002 reached 35 µM, the expression levels of pRelB and T-bet in CD4 + T cells significantly decreased (Fig. 8. E-F). Similarly, the application of CD155 alone effectively inhibited the expression of pRelB and T-bet in CD4 + T cells, but the application of 3-AC on the basis of CD155 could reverse the inhibitory effects on pRelB + CD4 + T cells and T-bet + CD4 + T cells (Fig. 8. G-H). Combining our previous findings on the inhibitory effect of TIGIT/SHIP-1 on the PI3K/AKT signaling pathway, we believe that the activation of RelB in CD4 + T cells is regulated by the inhibition of TIGIT/SHIP-1 on the activation of the PI3K/AKT signaling pathway, which in turn affects the expression of T-bet in CD4 + T cells.

The use of (-)-DHMEQ can effectively control Th1 inflammation in COPD

Due to the lack of specific blockers targeting RelB, we chose to use the potent NFκB inhibitor (-)-DHMEQ to validate its inhibitory effect on pRelB in CD4 + T cells and further explore the role of RelB/T-bet in COPD-Th1 inflammatory conditions. Under the conditions of in vitro Th1 polarization culture, CD4 + Tn cells derived from CS mice were treated with (-)-DHMEQ (10 µg/ml). After 16 h, the cells were collected for flow cytometry analysis [17]. We found that treatment with (-)-DHMEQ significantly reduced the proportion of pRelB + CD4 + T cells, indicating its blocking effect on RelB in CD4 + T cells in vitro. Furthermore, the expression levels of T-bet + CD4 + T cells and IFN-γ + CD4 + T cells were also significantly downregulated (Fig. 9.A-D). Based on this, we established two experimental groups: CS mice and CS+(-)-DHMEQ mice, to study COPD-Th1 inflammation in vivo. The results showed that (-)-DHMEQ could also inhibit the expression of pRelB + CD4 + T cells in vivo and significantly suppress the expression ratios of IFN-γ and T-bet in lung CD4 + T cells (Fig. 9. E-I). In contrast to the results we observed in the gene-edited animals and the SHIP-1 experimental group, (-)-DHMEQ significantly inhibited the activation of CD4 + T cells and the expression of Th17 in vivo, while also notably increasing the proportion of naive CD4 + T cells in the lungs.

Fig. 9 — The inhibition of RelB activation by (-)-DHMEQ significantly suppresses the expression of Th1. A-D Following co-culture of CD4 + T cells with the RelB inhibitor (-)-DHMEQ under Th1 polarization conditions, representative flow cytometry analysis and statistical violin plots of pRelB, T-bet, and IFN-γ are shown (n = 4). E-G Flow cytometry analysis of pRelB, T-bet, and IFN-γ in lung CD4 + T cells from CS+(-)-DHMEQ and CS + Vehicle mice, along with representative plots and statistical violin plots (n = 7). Data are representative of three independent experiments and are presented as medians. *P < 0.05, **P < 0.01, ****P < 0.0001

Discussion

In this study, we investigated the role of CD4 + T cell TIGIT deficiency in COPD using CD4 + T cell TIGIT-deficient mice. We conducted both in vivo and in vitro experiments to explore the functional impact of TIGIT on CD4 + T cell inflammation and its potential mechanisms. Our findings reveal a novel role for the negative immune checkpoint TIGIT, which, by binding to its ligand CD155, exerts an inhibitory effect on the PI3K/AKT pathway through SHIP-1 in CD4 + T cell inflammation associated with COPD. This, in turn, affects the transcriptional regulation of T-bet by RelB, ultimately leading to a diminished capacity of CD4 + T cells to differentiate into Th1 cells. Our research indicates that the TIGIT/SHIP-1/RelB axis is an important molecular mechanism regulating COPD-Th1 inflammation.

Negative immune checkpoints, including TIGIT, are a series of molecules that inhibit the activity of immune cells [34]. They serve as brakes to control immune inflammation and are involved in regulating various processes such as the release of cytokines, activation, exhaustion, and apoptosis of immune cells, thereby playing a role in limiting autoimmune diseases or excessive inflammatory responses [35]. In this study, we focus on TIGIT due to its specific expression on NK cells and T cells [12], which is significant for our understanding of CD4 + T cell inflammation in COPD. We found that the expression level of TIGIT is significantly elevated in CD4 + T cells from both smoking COPD patients and CS mice compared to normal individuals and Air mice, a pattern that is consistent with most autoimmune diseases [36]. Furthermore, in COPD patients or CS mice, the proportion of TIGIT + CD4 + T cells is significantly positively correlated with the proportion of pro-inflammatory CD4 + T cell subsets (Th1/Th17). Reports have indicated that Tigit knockout mice in a stable state do not exhibit spontaneous pathological phenomena, highlighting the distinction between TIGIT and PD-1 or CTLA-4 [34]. To clarify the role of TIGIT in CD4 + T cell inflammation in COPD and its impact on the pathological features of COPD, we generated a cohort of Tigit-Cd4 knockout mice and control mice, followed by chronic exposure to tobacco smoke. The results revealed that CS-Tigit-Cre mice displayed more pronounced pathological characteristics of pulmonary emphysema, with more significant destruction of alveolar structures. However, we observed excessive Th1 inflammation only in the lungs of CS-Tigit-Cre mice, emphasizing the unique regulatory role of TIGIT in COPD-Th1 inflammation and the influence of Th1 inflammation on the pathological alterations associated with smoking-induced COPD.

The inhibitory function of TIGIT on immune cells is partially mediated by SHIP-1. SHIP-1 is a phosphoinositide 3-phosphatase that, when activated by TIGIT, hydrolyzes the phosphate groups of key intracellular signaling molecules, thereby inhibiting the activation of signaling pathways [13]. SHIP-1 has been extensively studied in inflammatory diseases, yet its role in these conditions remains controversial. In the ileal region of SHIP-1-deficient mice, typical characteristics of inflammatory bowel disease are observed, and activation of SHIP-1 can alleviate corneal inflammatory responses and angiogenesis in conjunctivitis mice [37, 38]. However, the absence of SHIP-1 leads to a decrease in CD4 + T cell infiltration in the small intestine and downregulation of effector CD4 + T cell expression [25]. Interestingly, CD4 + T cells in the conjunctival tissues of patients with conjunctivitis show high expression of SHIP-1, and inhibition of SHIP-1 can reduce the infiltration of CD4 + T cells in conjunctival tissues [38]. Our results indicate that the expression level of SHIP-1 in CD4 + T cells from the lungs of CS mice is significantly elevated, which is consistent with the expression pattern of TIGIT. To further verify the impact of SHIP-1 on COPD-CD4 + T cell inflammation, we treated CS mice with the specific SHIP-1 inhibitor 3-AC, and the results were consistent with those from CS-Tigit-Cre, showing that treatment with 3-AC significantly upregulated the Th1 levels in CS mice.

Gumbleton [26] elucidated a specific cellular protective mechanism, whereby the short-term loss of SHIP-1 can lead to functional activation of T cells, while the long-term absence of SHIP-1 signaling results in a decreased response of T cells to maintain tolerance. In our previous studies, we found that the short-term inhibition of SHIP-1 could promote Th1 inflammation in COPD and speculated that there is a potential association between TIGIT and SHIP-1 in the regulation of COPD-Th1 inflammation. In Cd4-Tigit knockout CS mice, we observed a significant downregulation of SHIP-1 expression levels in CD4 + T cells, but no significant difference compared to normal air mice. Thus, the lack of TIGIT indeed leads to a reduction of SHIP-1 in CD4 + T cells in COPD, but it does not significantly drop below normal SHIP-1 levels, resulting in T cells entering a state of tolerance. Therefore, the changes in CD4 + T cells observed in CS-Tigit-Cre are credible. In summary, our findings reveal that TIGIT/SHIP-1 is an important mechanism regulating smoking-related COPD-Th1 inflammation.

It has been reported that the PI3K/AKT signaling pathway influences T cell activation, differentiation, and cytokine secretion through various aspects such as transcription, energy, and metabolism [39]. Targeting the PI3K/AKT pathway can effectively alleviate oxidative stress and inflammatory responses in COPD [40]. We first detected elevated phosphorylation levels of PI3K and AKT in CD4 + T cells from CS mice. Subsequently, under in vitro Th1 polarization conditions, CD4 + T cells derived from CS mice exhibited more pronounced activation of the PI3K/AKT signaling pathway during the Th1 polarization process. Targeted inhibition of PI3K significantly reduced the level of Th1 polarization. The blockade of downstream important signaling pathways by TIGIT/SHIP-1 is a crucial means of exerting its immunosuppressive function, with PI3K/AKT being one of them [13]. We found that the application of the high-affinity ligand CD155 recombinant protein for TIGIT effectively inhibited the activation of PI3K/AKT and the level of Th1 polarization in CD4 + T cells. However, the inhibition effect of TIGIT on CD4 + T cells was reversed after the use of 3-AC to inhibit SHIP-1. Our results indicate for the first time that TIGIT/SHIP-1 inhibits the Th1 differentiation capability of CD4 + T cells by suppressing the activation of PI3K/AKT.

Interestingly, Th17 cells in CS mice also show an upward trend similar to Th1 cells, but the regulatory mechanisms for Th17 appear to differ from those of Th1. The PI3K pathway and its downstream signaling can promote the expression of RORγt through transcriptional regulation and metabolic reprogramming, thereby driving the differentiation of Th17 cells [41]. TIGIT plays an inhibitory role on Th17 in autoimmune diseases [42]. Our results indicate that inhibiting PI3K signaling can reduce the expression of RORγt and the polarization level of Th17; however, activating TIGIT through CD155 does not significantly inhibit the expression of Th17. This is because CD4 + Tn cells derived from CS mice do not suppress the expression of RORγt in vitro upon activation of the CD155/TIGIT axis, reflecting the heterogeneity of Th17 polarization under different disease conditions and the differences in pathways affecting Th1 and Th17 polarization in diseases.

Our previous research reported that the transcription factor T-bet participates in the regulation of COPD Th1 inflammation by affecting the expression of CD4 + T cell-derived IFN-γ [30]. To further elucidate the core mechanisms of COPD-Th1 inflammation, we identified the unique transcription factor gene RELB by integrating single-cell sequencing data from COPD samples with TBX21 transcription factor profiles based on CHIP-seq [32]. RelB is a major component of the non-canonical NFκB pathway and is involved in various biological processes, including inflammation, immune response, and cell growth differentiation [43]. RelB exists in the cytoplasm as a monomer and can activate and enter the nucleus as a transcription factor to regulate gene expression when it forms a dimer with p52 [33]. Sharfe [44] previously reported that patients with RELB deficiency exhibit T cell dysfunction characterized primarily by downregulation of T-bet and IFN-γ expression, emphasizing the important role of RelB in Th1 inflammation. We observed that pRelB and T-bet were indeed upregulated in the lung CD4 + T cells of CS mice, and CD4 + Tn derived from CS mice also showed increased levels of pRelB and T-bet during in vitro Th1 polarization. Furthermore, our immunofluorescence and CHIP results provide indirect and direct evidence that pRelB regulates T-bet expression at the transcriptional level. Our results reveal for the first time the significant role of the non-canonical NFκB pathway in the transcriptional regulation of COPD-Th1 inflammation. In prostate cancer, the inhibition of the PI3K/AKT/IKKα signaling pathway can impede the nuclear translocation of RelB and interfere with the expression of downstream genes [45]. This provides a theoretical basis for establishing a connection between the two regulatory signaling axes of Th1 inflammation in COPD: TIGIT/SHIP-1/PI3K/AKT and pRelB/T-bet. Our in vitro experiments demonstrated that changes in the TIGIT/SHIP-1/PI3K/AKT signaling axis can similarly induce alterations in the expression of pRelB and T-bet, which correlate with the trends in IFN-γ levels. Furthermore, the inhibition of pRelB in vitro resulted in a decrease in the expression of T-bet and IFN-γ in CD4 + T cells, indicating a regulatory relationship between the two signaling axes.

Despite the groundbreaking progress made by negative immune checkpoints in tumor immunotherapy, their application in non-tumor diseases has not yielded ideal results, instead increasing the risk of infections and tumors in patients [46]. RelB, which is influenced by the inhibitory function of TIGIT and plays a crucial role in transmitting cellular signal changes to the nucleus, is an ideal target for the treatment of COPD inflammation. Our research also indicates that inhibiting RelB can significantly reduce CD4 + T cell inflammation in COPD, providing an experimental basis for the application of RelB as a therapeutic target for airway inflammation in COPD.

Our study has several limitations. First, the expression data of the key proteins SHIP-1 and pRelB in COPD patients is lacking. Second, when comparing the expression differences of TIGIT + CD4 + T cells, Th1, and TIGIT + Th1 between male and female COPD patients, the number of female patients included was relatively small, indicating a need to further expand the sample size for additional research. Third, in constructing the tobacco exposure-induced COPD mouse model, we only used histopathological sections of mouse lung tissue to confirm the pathological changes of COPD in the CS mice, but did not assess the lung function of the CS mice. Lastly, the (-)-DHMEQ used in our study is a broad-spectrum NFκB inhibitor, and thus, the results obtained may be biased. In future research, we will design Relb^flox/floxCd4-Cre knockout mice and construct a smoke-induced COPD model, extract the CD4 + Tn with deleted Relb, and perform Th1 polarization to study the expression levels of IFN-γ and T-bet, aiming to accurately demonstrate the role of RelB in Th1 polarization. In summary, our research indicates that TIGIT/SHIP-1 influences the transcriptional regulation of T-bet by inhibiting the PI3K/AKT signaling pathway. The TIGIT/SHIP-1/RelB axis is an important regulatory mechanism in smoking-related COPD-Th1 inflammation, and RelB may serve as an effective target for treating COPD inflammation.

Supplementary Information

Supplementary Material 1.^{(5.2MB, zip)}

Supplementary Material 2.^{(1.1MB, tif)}

Supplementary Material 3.^{(1.1MB, tif)}

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Acknowledgements

Not applicable.

Abbreviations

COPD: Chronic obstructive pulmonary disease
TIGIT: T cell immunoglobulin and ITIM domain
SHIP-1: SH2-containing inositol phosphatase-1
RelB: RelB proto-oncogene
Th1: T helper cell type 1
Th17: T helper cell type 17
CS: Cigarette smoke
3-AC: 3α-Aminocholestane
CD4+ Tn: CD4+ naive T cell
CD4+Tem: CD4+ effector memory T cell
CD4+Tcm: CD4+ central memory T cell
PI3K: Phosphatidyqinositol‐3 kinase
AKT: Protein kinase B
IFN-γ: Interferon-γ
TNF-α: Tumor necrosis factor-α
IL-17A: Interleukin 17A

Authors’ contributions

J.K., and S.L. contributed equally to this work and should be considered co-first authors. J.K., S.L. and M.D. contributed to concept and design. J.K., S.L., and M.D. performed acquisition, analysis, and interpretation of data. J.K., and S.L. drafted the manuscript. Z.H., Q.L., H.H., performed statistical analysis. S.H., S.L., Y.L., L.L., Y.W., and L.L., provided administrative, technical, or material support, J.K., and M.D. performed supervision. All authors reviewed the manuscript.

Funding

This work was supported in part by the National Natural Science Foundation of China (NSFC) (82260011 to M.D.).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

The studies involving human participants were reviewed and approved by Ethics Committee of The Wuming Hospital of Guangxi Medical University, approval number: GXMU-WM-2025 (17). Informed consent was obtained from all participants or their guardians. All animal experiments were conducted with the approval by the Animal Ethics Committee of Guangxi Medical University, approval number: GXMU-202312008.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Junyi Ke and Siyu Lei contributed equally to this work and designated to have co-first authorship.

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10.Zhu X, Zhu J. CD4 t helper cell subsets and related human immunological disorders. Int J Mol Sci. 2020;21(21):8011. 10.3390/ijms21218011. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Chen H, Fu T, Suh WK, et al. CD4 T cells require ICOS-mediated PI3K signaling to increase T-Bet expression in the setting of anti-CTLA-4 therapy. Cancer Immunol Res. 2014;2(2):167–76. 10.1158/2326-6066.CIR-13-0155. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Anderson AC, Joller N, Kuchroo VK. Lag-3, Tim-3, and TIGIT: co-inhibitory receptors with specialized functions in immune regulation. Immunity. 2016;44(5):989–1004. 10.1016/j.immuni.2016.05.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Fathi M, Pustokhina I, Kuznetsov SV, et al. T-cell immunoglobulin and ITIM domain, as a potential immune checkpoint target for immunotherapy of colorectal cancer. IUBMB Life. 2021;73(5):726–38. 10.1002/iub.2461. [DOI] [PubMed] [Google Scholar]
14.Ke J, Huang S, He Z, et al. TIGIT regulates T cell inflammation in airway inflammatory diseases. Inflammation. 2025;48(1):15–24. 10.1007/s10753-024-02045-y. [DOI] [PubMed] [Google Scholar]
15.Qiu S, Zhou G, Ke J, et al. Impairment of Gal-9 and Tim-3 crosstalk between Tregs and Th17 cells drives tobacco smoke-induced airway inflammation. Immunology. 2024;173(1):152–71. 10.1111/imm.13820. [DOI] [PubMed] [Google Scholar]
16.Brooks R, Iyer S, Akada H, et al. Coordinate expansion of murine hematopoietic and mesenchymal stem cell compartments by SHIPi. Stem Cells. 2015;33(3):848–58. [DOI] [PubMed] [Google Scholar]
17.Watanabe M, Ohsugi T, Shoda M, et al. Dual targeting of transformed and untransformed HTLV-1-infected T cells by DHMEQ, a potent and selective inhibitor of NF-kappaB, as a strategy for chemoprevention and therapy of adult T-cell leukemia. Blood. 2005;106(7):2462–71. 10.1182/blood-2004-09-3646. [DOI] [PubMed] [Google Scholar]
18.He C, Li Y, Gan L, et al. Notch signaling regulates Th17 cells differentiation through PI3K/AKT/mTORC1 pathway and involves in the thyroid injury of autoimmune thyroiditis. J Endocrinol Invest. 2024;47(8):1971–86. [DOI] [PubMed] [Google Scholar]
19.Li S, Ding J, Wang Y, et al. CD155/TIGIT signaling regulates the effector function of tumor-infiltrating CD8 + T cell by NF-κB pathway in colorectal cancer. J Gastroenterol Hepatol. 2022;37(1):154–63. 10.1111/jgh.15730. [DOI] [PubMed] [Google Scholar]
20.Muthukuru M, Darveau RP. TLR signaling that induces weak inflammatory response and SHIP1 enhances osteogenic functions. Bone Res. 2014;2:14031. 10.1038/boneres.2014.31. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Taube JM, Roman K, Engle EL, et al. Multi-institutional TSA-amplified multiplexed immunofluorescence reproducibility evaluation (MITRE) study. J Immunother Cancer. 2021;9(7):e002197. [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Zhong X, Xie H, Wang S, et al. TIGIT regulates CD4+ T cell immunity against polymicrobial sepsis. Front Immunol. 2024;15:1290564. 10.3389/fimmu.2024.1290564. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Noel S, Lee K, Gharaie S, et al. Immune checkpoint molecule TIGIT regulates kidney T cell functions and contributes to AKI. J Am Soc Nephrol. 2023;34(5):755–71. 10.1681/ASN.0000000000000063. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Mao L, Hou H, Wu S, et al. TIGIT signalling pathway negatively regulates CD4+ T-cell responses in systemic lupus erythematosus. Immunology. 2017;151(3):280–90. 10.1111/imm.12715. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Park MY, Srivastava N, Sudan R, et al. Impaired T-cell survival promotes mucosal inflammatory disease in SHIP1-deficient mice. Mucosal Immunol. 2014;7(6):1429–39. 10.1038/mi.2014.32. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Gumbleton M, Sudan R, Fernandes S, et al. Dual enhancement of T and NK cell function by pulsatile inhibition of SHIP1 improves antitumor immunity and survival. Sci Signal. 2017;10(500):eaam5353. 10.1126/scisignal.aam5353. [DOI] [PubMed] [Google Scholar]
27.Joller N, Anderson AC, Kuchroo VK. LAG-3, TIM-3, and TIGIT: distinct functions in immune regulation. Immunity. 2024;57(2):206–22. 10.1016/j.immuni.2024.01.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Yu W, Li C, Zhang D, et al. Advances in T cells based on inflammation in metabolic diseases. Cells. 2022;11(22):3554. 10.3390/cells11223554. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Xu S, Chen J, Yue S, et al. Alcohol intake exacerbates experimental autoimmune prostatitis through activating PI3K/AKT/mTOR pathway-mediated Th1 differentiation. Front Immunol. 2025;15:1512456. 10.3389/fimmu.2024.1512456. [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Qiu SL, Duan MC, Liang Y. Cigarette smoke induction of Interleukin-27/WSX-1 regulates the differentiation of Th1 and Th17 cells in a smoking mouse model of emphysema. Front Immunol. 2016;7:553. 10.3389/fimmu.2016.00553. [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Hu C, Li T, Xu Y, et al. CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data. Nucleic Acids Res. 2023;51(D1):D870–6. 10.1093/nar/gkac947. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Feng C, Song C, Song S, et al. KnockTF 2.0: a comprehensive gene expression profile database with knockdown/knockout of transcription (co-)factors in multiple species. Nucleic Acids Res. 2024;52(D1):D183–93. 10.1093/nar/gkad1016. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Chen K, Coonrod EM, Kumánovics A, et al. Germline mutations in NFKB2 implicate the noncanonical NF-κB pathway in the pathogenesis of common variable immunodeficiency. Am J Hum Genet. 2013;93(5):812–24. 10.1016/j.ajhg.2013.09.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Xie W, Fang J, Shan Z, et al. Regulation of autoimmune disease progression by Pik3ip1 through metabolic reprogramming in T cells and therapeutic implications. Sci Adv. 2022;8(39):eabo4250. 10.1126/sciadv.abo4250. [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Blank CU, Haining WN, Held W, et al. Defining ‘T cell exhaustion.’ Nat Rev Immunol. 2019;19(11):665–74. 10.1038/s41577-019-0221-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Joller N, Hafler JP, Brynedal B, et al. Cutting edge: TIGIT has T cell-intrinsic inhibitory functions. J Immunol. 2011;186(3):1338–42. 10.4049/jimmunol.1003081. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Kerr WG, Park MY, Maubert M, et al. Ship deficiency causes crohn’s disease-like ileitis. Gut. 2011;60(2):177–88. 10.1136/gut.2009.202283. [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Qin Q, Wang Y, Huang X, et al. SHIP-1 affects herpetic simplex keratitis prognosis by mediating CD4+ T lymphocytes migration through PI3K signaling and transcription factor KLF2 in the cornea. Antivir Res. 2022;207:105424. 10.1016/j.antiviral.2022.105424. [DOI] [PubMed] [Google Scholar]
39.Xu K, Yin N, Peng M, et al. Glycolysis fuels phosphoinositide 3-kinase signaling to bolster T cell immunity. Science. 2021;371(6527):405–10. 10.1126/science.abb2683. [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Wang C, Zhou J, Wang J, et al. Progress in the mechanism and targeted drug therapy for COPD. Signal Transduct Target Ther. 2020;5(1):248. 10.1038/s41392-020-00345-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Jiang C, Zeng X, Wang J, et al. Andrographolide sulfonate alleviates rheumatoid arthritis by inhibiting glycolysis-mediated activation of PI3K/AKT to restrain Th17 cell differentiation. Chin J Nat Med. 2025;23(4):480–91. 10.1016/S1875-5364(25)60855-7. [DOI] [PubMed] [Google Scholar]
42.Peters K, McDonald T, Muhammad F, et al. Tigit stimulation suppresses autoimmune uveitis by inhibiting Th17 cell infiltration. J Leukoc Biol. 2024;116(5):1054–60. 10.1093/jleuko/qiae116. [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Ryseck RP, Bull P, Takamiya M, et al. RelB, a new rel family transcription activator that can interact with p50-NF-kappa B. Mol Cell Biol. 1992;12(2):674–84. 10.1128/mcb.12.2.674-684.1992. [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Sharfe N, Merico D, Karanxha A, et al. The effects of RelB deficiency on lymphocyte development and function. J Autoimmun. 2015;65:90–100. 10.1016/j.jaut.2015.09.001. [DOI] [PubMed] [Google Scholar]
45.Zhang Y, Xu Z, Ding J, et al. HZ08 suppresses RelB-activated MnSOD expression and enhances radiosensitivity of prostate cancer cells. J Exp Clin Cancer Res. 2018;37(1):174. 10.1186/s13046-018-0849-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Geraud A, Gougis P, Vozy A, et al. Clinical pharmacology and interplay of immune checkpoint agents: a Yin-yang balance. Annu Rev Pharmacol Toxicol. 2021;61:85–112. 10.1146/annurev-pharmtox-022820-093805. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1.^{(5.2MB, zip)}

Supplementary Material 2.^{(1.1MB, tif)}

Supplementary Material 3.^{(1.1MB, tif)}

Supplementary Material 4.^{(1.4MB, tif)}

Data Availability Statement

No datasets were generated or analysed during the current study.

[CR1] 1.Christenson SA, Smith BM, Bafadhel M, et al. Chronic obstructive pulmonary disease. Lancet. 2022;399(10342):2227–42. 10.1016/S0140-6736(22)00470-6. [DOI] [PubMed] [Google Scholar]

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[CR9] 9.Amedei A, Cappon A, Codolo G, et al. The neutrophil-activating protein of Helicobacter pylori promotes Th1 immune responses. J Clin Invest. 2006;116(4):1092–101. 10.1172/JCI27177. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR10] 10.Zhu X, Zhu J. CD4 t helper cell subsets and related human immunological disorders. Int J Mol Sci. 2020;21(21):8011. 10.3390/ijms21218011. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR11] 11.Chen H, Fu T, Suh WK, et al. CD4 T cells require ICOS-mediated PI3K signaling to increase T-Bet expression in the setting of anti-CTLA-4 therapy. Cancer Immunol Res. 2014;2(2):167–76. 10.1158/2326-6066.CIR-13-0155. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR12] 12.Anderson AC, Joller N, Kuchroo VK. Lag-3, Tim-3, and TIGIT: co-inhibitory receptors with specialized functions in immune regulation. Immunity. 2016;44(5):989–1004. 10.1016/j.immuni.2016.05.001. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR13] 13.Fathi M, Pustokhina I, Kuznetsov SV, et al. T-cell immunoglobulin and ITIM domain, as a potential immune checkpoint target for immunotherapy of colorectal cancer. IUBMB Life. 2021;73(5):726–38. 10.1002/iub.2461. [DOI] [PubMed] [Google Scholar]

[CR14] 14.Ke J, Huang S, He Z, et al. TIGIT regulates T cell inflammation in airway inflammatory diseases. Inflammation. 2025;48(1):15–24. 10.1007/s10753-024-02045-y. [DOI] [PubMed] [Google Scholar]

[CR15] 15.Qiu S, Zhou G, Ke J, et al. Impairment of Gal-9 and Tim-3 crosstalk between Tregs and Th17 cells drives tobacco smoke-induced airway inflammation. Immunology. 2024;173(1):152–71. 10.1111/imm.13820. [DOI] [PubMed] [Google Scholar]

[CR16] 16.Brooks R, Iyer S, Akada H, et al. Coordinate expansion of murine hematopoietic and mesenchymal stem cell compartments by SHIPi. Stem Cells. 2015;33(3):848–58. [DOI] [PubMed] [Google Scholar]

[CR17] 17.Watanabe M, Ohsugi T, Shoda M, et al. Dual targeting of transformed and untransformed HTLV-1-infected T cells by DHMEQ, a potent and selective inhibitor of NF-kappaB, as a strategy for chemoprevention and therapy of adult T-cell leukemia. Blood. 2005;106(7):2462–71. 10.1182/blood-2004-09-3646. [DOI] [PubMed] [Google Scholar]

[CR18] 18.He C, Li Y, Gan L, et al. Notch signaling regulates Th17 cells differentiation through PI3K/AKT/mTORC1 pathway and involves in the thyroid injury of autoimmune thyroiditis. J Endocrinol Invest. 2024;47(8):1971–86. [DOI] [PubMed] [Google Scholar]

[CR19] 19.Li S, Ding J, Wang Y, et al. CD155/TIGIT signaling regulates the effector function of tumor-infiltrating CD8 + T cell by NF-κB pathway in colorectal cancer. J Gastroenterol Hepatol. 2022;37(1):154–63. 10.1111/jgh.15730. [DOI] [PubMed] [Google Scholar]

[CR20] 20.Muthukuru M, Darveau RP. TLR signaling that induces weak inflammatory response and SHIP1 enhances osteogenic functions. Bone Res. 2014;2:14031. 10.1038/boneres.2014.31. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR21] 21.Taube JM, Roman K, Engle EL, et al. Multi-institutional TSA-amplified multiplexed immunofluorescence reproducibility evaluation (MITRE) study. J Immunother Cancer. 2021;9(7):e002197. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR22] 22.Zhong X, Xie H, Wang S, et al. TIGIT regulates CD4+ T cell immunity against polymicrobial sepsis. Front Immunol. 2024;15:1290564. 10.3389/fimmu.2024.1290564. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR23] 23.Noel S, Lee K, Gharaie S, et al. Immune checkpoint molecule TIGIT regulates kidney T cell functions and contributes to AKI. J Am Soc Nephrol. 2023;34(5):755–71. 10.1681/ASN.0000000000000063. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR24] 24.Mao L, Hou H, Wu S, et al. TIGIT signalling pathway negatively regulates CD4+ T-cell responses in systemic lupus erythematosus. Immunology. 2017;151(3):280–90. 10.1111/imm.12715. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR25] 25.Park MY, Srivastava N, Sudan R, et al. Impaired T-cell survival promotes mucosal inflammatory disease in SHIP1-deficient mice. Mucosal Immunol. 2014;7(6):1429–39. 10.1038/mi.2014.32. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR26] 26.Gumbleton M, Sudan R, Fernandes S, et al. Dual enhancement of T and NK cell function by pulsatile inhibition of SHIP1 improves antitumor immunity and survival. Sci Signal. 2017;10(500):eaam5353. 10.1126/scisignal.aam5353. [DOI] [PubMed] [Google Scholar]

[CR27] 27.Joller N, Anderson AC, Kuchroo VK. LAG-3, TIM-3, and TIGIT: distinct functions in immune regulation. Immunity. 2024;57(2):206–22. 10.1016/j.immuni.2024.01.010. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR28] 28.Yu W, Li C, Zhang D, et al. Advances in T cells based on inflammation in metabolic diseases. Cells. 2022;11(22):3554. 10.3390/cells11223554. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR29] 29.Xu S, Chen J, Yue S, et al. Alcohol intake exacerbates experimental autoimmune prostatitis through activating PI3K/AKT/mTOR pathway-mediated Th1 differentiation. Front Immunol. 2025;15:1512456. 10.3389/fimmu.2024.1512456. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR30] 30.Qiu SL, Duan MC, Liang Y. Cigarette smoke induction of Interleukin-27/WSX-1 regulates the differentiation of Th1 and Th17 cells in a smoking mouse model of emphysema. Front Immunol. 2016;7:553. 10.3389/fimmu.2016.00553. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR31] 31.Hu C, Li T, Xu Y, et al. CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data. Nucleic Acids Res. 2023;51(D1):D870–6. 10.1093/nar/gkac947. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR32] 32.Feng C, Song C, Song S, et al. KnockTF 2.0: a comprehensive gene expression profile database with knockdown/knockout of transcription (co-)factors in multiple species. Nucleic Acids Res. 2024;52(D1):D183–93. 10.1093/nar/gkad1016. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR33] 33.Chen K, Coonrod EM, Kumánovics A, et al. Germline mutations in NFKB2 implicate the noncanonical NF-κB pathway in the pathogenesis of common variable immunodeficiency. Am J Hum Genet. 2013;93(5):812–24. 10.1016/j.ajhg.2013.09.009. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR34] 34.Xie W, Fang J, Shan Z, et al. Regulation of autoimmune disease progression by Pik3ip1 through metabolic reprogramming in T cells and therapeutic implications. Sci Adv. 2022;8(39):eabo4250. 10.1126/sciadv.abo4250. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR35] 35.Blank CU, Haining WN, Held W, et al. Defining ‘T cell exhaustion.’ Nat Rev Immunol. 2019;19(11):665–74. 10.1038/s41577-019-0221-9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR36] 36.Joller N, Hafler JP, Brynedal B, et al. Cutting edge: TIGIT has T cell-intrinsic inhibitory functions. J Immunol. 2011;186(3):1338–42. 10.4049/jimmunol.1003081. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR37] 37.Kerr WG, Park MY, Maubert M, et al. Ship deficiency causes crohn’s disease-like ileitis. Gut. 2011;60(2):177–88. 10.1136/gut.2009.202283. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR38] 38.Qin Q, Wang Y, Huang X, et al. SHIP-1 affects herpetic simplex keratitis prognosis by mediating CD4+ T lymphocytes migration through PI3K signaling and transcription factor KLF2 in the cornea. Antivir Res. 2022;207:105424. 10.1016/j.antiviral.2022.105424. [DOI] [PubMed] [Google Scholar]

[CR39] 39.Xu K, Yin N, Peng M, et al. Glycolysis fuels phosphoinositide 3-kinase signaling to bolster T cell immunity. Science. 2021;371(6527):405–10. 10.1126/science.abb2683. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR40] 40.Wang C, Zhou J, Wang J, et al. Progress in the mechanism and targeted drug therapy for COPD. Signal Transduct Target Ther. 2020;5(1):248. 10.1038/s41392-020-00345-x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR41] 41.Jiang C, Zeng X, Wang J, et al. Andrographolide sulfonate alleviates rheumatoid arthritis by inhibiting glycolysis-mediated activation of PI3K/AKT to restrain Th17 cell differentiation. Chin J Nat Med. 2025;23(4):480–91. 10.1016/S1875-5364(25)60855-7. [DOI] [PubMed] [Google Scholar]

[CR42] 42.Peters K, McDonald T, Muhammad F, et al. Tigit stimulation suppresses autoimmune uveitis by inhibiting Th17 cell infiltration. J Leukoc Biol. 2024;116(5):1054–60. 10.1093/jleuko/qiae116. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR43] 43.Ryseck RP, Bull P, Takamiya M, et al. RelB, a new rel family transcription activator that can interact with p50-NF-kappa B. Mol Cell Biol. 1992;12(2):674–84. 10.1128/mcb.12.2.674-684.1992. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR44] 44.Sharfe N, Merico D, Karanxha A, et al. The effects of RelB deficiency on lymphocyte development and function. J Autoimmun. 2015;65:90–100. 10.1016/j.jaut.2015.09.001. [DOI] [PubMed] [Google Scholar]

[CR45] 45.Zhang Y, Xu Z, Ding J, et al. HZ08 suppresses RelB-activated MnSOD expression and enhances radiosensitivity of prostate cancer cells. J Exp Clin Cancer Res. 2018;37(1):174. 10.1186/s13046-018-0849-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR46] 46.Geraud A, Gougis P, Vozy A, et al. Clinical pharmacology and interplay of immune checkpoint agents: a Yin-yang balance. Annu Rev Pharmacol Toxicol. 2021;61:85–112. 10.1146/annurev-pharmtox-022820-093805. [DOI] [PubMed] [Google Scholar]

PERMALINK

TIGIT/SHIP-1/RelB regulating Th1 inflammation in smoking induced COPD

Junyi Ke

Siyu Lei

Zhixiong He

Qinglan Li

Hongchun Huang

Shu Huang

Shiya Lin

Yinying Li

Lixi Lu

Yuanlan Wu

Luxi Lei

Minchao Duan

Abstract

Background

Methods

Results

Conclusions

Supplementary Information

Introduction

Materials and methods

Human subjects

Mice

3-AC and (-)-DHMEQ inhibiting experiments in vivo

Histology and morphometry of mice lungs

Naive CD4 + T cells isolation and culture/co-culture

Flow cytometry

Immunofluorescent staining

Chromatin Immunoprecipitation

Single-cell and bulk sequencing bioinformatic analysis

Western blot

Statistical analysis

Results

TIGIT is highly expressed in CD4 + T cells in the lungs of smokers with COPD and in tobacco-exposed mice

Table 1.

Fig. 1.

TIGIT primarily affects Th1 inflammation in the lungs of smoking-related COPD

Fig. 2.

TIGIT modulates pulmonary Th1 inflammation in smoking-related COPD through SHIP-1

Fig. 3.

TIGIT/SHIP-1 modulates the activation of PI3K/AKT further to regulate Th1 inflammation

Fig. 4.

Fig. 5.

RelB is a key transcriptional regulator in COPD-Th1 inflammation

Fig. 6.

Fig. 7.

Fig. 8.

TIGIT/SHIP-1 Inhibition of the PI3K/AKT pathway impedes the activation of RelB

The use of (-)-DHMEQ can effectively control Th1 inflammation in COPD

Fig. 9.

Discussion

Supplementary Information

Acknowledgements

Abbreviations

Authors’ contributions

Funding

Data availability

Declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Footnotes

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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