Age-Specific Pyroptosis Biomarker Analysis and Non-Pharmacological Intervention in Acute Respiratory Distress Syndrome

Summary

This protocol outlines an integrated bioinformatic and machine-learning workflow to identify age-stratified pyroptosis biomarkers in acute respiratory distress syndrome (ARDS), coupled with an in vivo methodology using an LPS-induced rat model to experimentally validate the preliminary efficacy of spectrum energy water and far-infrared radiation.

Abstract

Acute respiratory distress syndrome (ARDS) is a life-threatening inflammatory lung disorder with high morbidity and mortality, in which pyroptosis has emerged as a pivotal pathogenic mechanism. Considering that age-related immune and molecular differences may influence pyroptosis and therapeutic response, this study aimed to identify age-specific pyroptosis-associated biomarkers and evaluate the therapeutic potential of spectrum energy water (SEW) combined with far-infrared radiation (FIR). The protocol's comprehensive workflow encompasses transcriptomic dataset processing, age stratification, machine learning, and immune infiltration analysis to identify age-specific hub genes. This computational phase is followed by in vivo experimental validation using an LPS-induced ARDS rat model, employing histological assessment, ELISA, and Western blotting to rigorously evaluate the SEW+FIR intervention and verify hub gene expression. Sixteen pyroptosis-associated genes were identified, among which AXL and GSDME were predominantly associated with ARDS severity in older patients, whereas SPP1 was more relevant in younger individuals. Distinct immune signatures were observed, with M2 macrophage enrichment and immunosuppression in older patients, contrasted by pro-inflammatory activation in younger ones. Functional analyses implicated these genes in metabolic, inflammatory, and immune regulatory pathways. In vivo, SEW+FIR treatment alleviated lung injury, suppressed the production of inflammatory cytokines (IL-1β, IL-18, IL-6, TNF-α), and modulated the expression of AXL, GSDME, and SPP1. Collectively, these findings underscore age-dependent differences in pyroptosis-related mechanisms in ARDS and identify AXL, GSDME, and SPP1 as preliminary biomarker candidates that warrant further clinical validation. In the current murine experimental model, SEW+FIR demonstrated protective effects by alleviating systemic inflammation and modulating pyroptosis-related signaling, providing foundational in vivo support for its potential as a non-pharmacological adjunctive strategy.

Introduction

Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) is a severe respiratory disease characterized by an acute onset of severe hypoxemia and non-cardiogenic pulmonary edema, primarily resulting from diffuse alveolar damage¹. It can be triggered by a range of pulmonary and extrapulmonary insults. Pulmonary causes include infectious pneumonia, aspiration of gastric contents, and severe thoracic trauma, while extrapulmonary triggers arise from systemic inflammatory responses secondary to non-pulmonary insults, such as sepsis, pancreatitis, non-thoracic trauma, severe burns, massive transfusion, and reperfusion injury following lung transplantation or thrombectomy^1,2. Despite advances in supportive care, ARDS remains a major clinical challenge in critical care medicine, with high morbidity and mortality rates worldwide³. Therefore, understanding the pathogenesis of ALI/ARDS is crucial for developing effective treatments.

Pyroptosis, an inflammatory form of programmed cell death that has been extensively characterized in the context of tumor suppression and oncological therapies⁴, is now recognized as a pivotal player in ARDS pathophysiology⁵. Unlike apoptosis, pyroptosis is mediated by inflammasome activation and subsequent cleavage of gasdermin D (GSDMD), leading to membrane pore formation, cell lysis, and the release of pro-inflammatory cytokines such as interleukin-1β (IL-1β) and interleukin-18 (IL-18)⁶. In ARDS, excessive pyroptosis activation in alveolar macrophages, epithelial cells, and endothelial cells contributes to an exaggerated inflammatory response, alveolar-capillary barrier disruption, pulmonary edema, and progression of hypoxemia⁷. Growing evidence suggests that targeting key regulators in the pyroptosis pathway may represent a novel strategy to attenuate inflammation and improve clinical outcomes in ARDS^8,9. However, a critical knowledge gap remains regarding the impact of aging—a key determinant of ARDS susceptibility and prognosis—on the pyroptotic landscape. Current studies often treat ARDS as a monolithic condition, overlooking the potential for age-specific molecular drivers that could dictate personalized therapeutic responses.

In recent years, the application of novel biophysical interventions and advanced functional materials—such as SEW and far-infrared radiation (FIR) emitters—has gained traction as non-pharmacological strategies to modulate immune responses and suppress inflammation^10,11. These therapies have been reported to enhance leukocyte proliferation and phagocytic activity, reduce tissue edema, and mitigate endotoxin-induced microcirculatory disturbances—including leukocyte adhesion, erythrocyte aggregation, platelet activation, and endothelial injury^12,13,14. In lipopolysaccharide (LPS)-induced ARDS rats, combined SEW and FIR treatment significantly reduced the levels of IL-1β and IL-18—the signature downstream effectors of the pyroptotic cascade—in both serum and bronchoalveolar lavage fluid (BALF). This evidence strongly suggests that the protective effects of SEW+FIR may be mediated through the suppression of inflammasome-driven cell death. Furthermore, SEW, when applied either independently or as a solvent medium, was found to lower the expression levels of interleukin-8 (IL-8) and tumor necrosis factor-α (TNF-α), while raising the expression levels of interleukin-4 (IL-4) and interleukin-10 (IL-10) in blood serum and ameliorating histopathological lung damage^15,16,17,18.

Building on these observations, we hypothesized that the therapeutic efficacy of combined SEW and FIR (SEW+FIR) in ARDS is predicated on its ability to modulate age-specific pyroptotic pathways, thereby restoring pulmonary immune homeostasis in a targeted manner. Currently, the mainstay of ARDS management relies on mechanical ventilation and limited pharmacological agents such as corticosteroids, which frequently carry risks of ventilator-induced lung injury or systemic immunosuppression¹⁹. Compared to these conventional therapies, SEW+FIR offers a non-invasive, non-pharmacological alternative that may safely attenuate early-stage inflammatory cascades without secondary organ toxicity. Practically, this protocol is most suitable as an early adjunctive intervention for mild-to-moderate ARDS. Its primary limitation is that its efficacy in severe, late-stage fibrotic ARDS remains unproven, and it cannot replace essential life-support measures in a critical care setting. To test our hypothesis, we pioneered an integrated pipeline that bridges age-stratified transcriptomic profiling and machine learning with vivo validation. This approach not only identifies AXL, GSDME, and SPP1 as novel, age-specific therapeutic targets but also provides the first mechanistic evidence for the clinical potential of SEW+FIR as a precision non-pharmacological intervention for ARDS.

Protocol

All animal experimental procedures were strictly performed in accordance with the guidelines approved by the Experimental Animal Welfare and Ethics Committee of Dongzhimen Hospital, Beijing University of Chinese Medicine (Approval No. 19-54), prior to the commencement of the study.

Data source and data processing
Two gene expression datasets (GSE89953 and GSE116560) were retrieved from the Gene Expression Omnibus (GEO) database²⁰. The dataset GSE89953, which includes whole-alveolar macrophage transcriptomic data from ARDS patients across different age groups, was used for differential expression and network analysis. The GSE116560 dataset, which includes clinical information such as mechanical ventilation status, was used for machine learning and prognostic modeling. Additionally, 608 genes associated with pyroptosis were extracted from a comprehensive human gene annotation database, using a correlation score greater than 1 as the screening criterion. Gene expression data were normalized using the limma package in R. Clinical trial number: not applicable.

Identification of DEGs
The patients in the GSE89953 dataset were stratified into two age groups: low-age (<45 years) and high-age (≥45 years). This cutoff was selected based on epidemiological evidence suggesting that the median age of ARDS onset is approximately 45 years²¹. To ensure the robustness of this threshold, sensitivity analyses were conducted using alternative age cutoffs (50 and 55 years). These analyses demonstrated consistent patterns in hub gene identification and module clustering, thereby statistically validating the 45-year cutoff for subsequent downstream analyses. The dataset was normalized using the limma package in R. Differentially expressed genes (DEGs) between age groups were identified using linear modeling with empirical Bayes moderation. Genes with an adjusted P value < 0.05 and |log₂ fold change| ≥ 0.5 were considered statistically significant DEGs. Volcano plots and heatmaps were generated to visualize DEGs using the ggplot2 package in R.

Pyroptosis-associated gene identification and enrichment analysis
Pyroptosis-related genes were retrieved from the gene annotation database using the keyword “pyroptosis.” The intersection of DEGs and pyroptosis-associated genes was defined as differentially expressed pyroptosis-related genes (DEPGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEPGs were conducted using the clusterProfiler package in R²². The categories of Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) were annotated, and Z-score ≥ 1 and adjusted P values < 0.05 were considered significant.

Weighted gene co-expression network analysis (WGCNA)
To identify gene modules associated with pyroptosis, WGCNA was performed using the WGCNA R package. A signed network was constructed using a soft-threshold power (β) of 26 to ensure scale-free topology²³. Modules were identified via the dynamic tree-cut algorithm with a minimum module size of 30, a deep split of 2, and a merging threshold (cut height) of 0.25. The correlation between module eigengenes and pyroptosis traits was calculated. Gene set variation analysis (GSVA) was performed on selected modules using hallmark gene sets downloaded from MsigDB^24,25.

Machine learning
The GSE116560 dataset was divided into high- and low-age groups using 45 years as the cutoff, and both groups were analyzed using machine learning algorithms. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was implemented using the glmnet package (version 4.1-2) in R, with the optimal penalty parameter (λ) determined by 10-fold cross-validation (1-SE criteria). For the Random Forest (RF) algorithm, 500 trees (ntree = 500) were constructed, and the number of features sampled at each split (mtry) was set to the square root of the total number of predictors to ensure model stability. Overlapping genes from both methods were defined as age-specific signature genes.

Construction and evaluation of diagnostic models
A diagnostic prediction model was constructed based on the identified signature genes. Logistic regression was employed to develop the model, and a nomogram was created to visualize its predictive power. The model's performance was evaluated using a receiver operating characteristic (ROC) curve, and the area under the curve (AUC) was calculated to assess its diagnostic accuracy. Internal validation was performed via bootstrap resampling. Further assessment of model stability and clinical utility was performed using calibration plots and decision curve analysis (DCA).

Immune infiltration analysis
Immune cell composition in high-age and low-age groups was estimated using the CIBERSORT algorithm based on the LM22 signature matrix. The relative proportions of 22 immune cell types were compared between groups. Differential expression of hub genes across immune cell subsets was analyzed using single-sample data and visualized in heatmaps and histograms.

Gene set enrichment analysis (GSEA)
GSEA was performed separately on hub genes from the high- and low-age groups. Gene Set Enrichment Analysis (GSEA) was performed utilizing the Kyoto Encyclopedia of Genes and Genomes (KEGG) gene sets. Genes were ranked based on the signal-to-noise ratio (or fold Change) between high- and low-expression groups. Enrichment and normalized enrichment scores (NES) were then calculated using 1,000 permutations to identify significantly enriched pathways. Pathways with a false discovery rate (FDR) of less than 0.25 and a nominal P value of less than 0.05 were significantly enriched. This analysis was then used to infer the biological pathways that may be regulated by each hub gene.

Experimental animals
Eighteen male SPF Sprague Dawley rats (aged 6 to 7 weeks, 180 g ± 10 g) were utilized in this study. Detailed supplier information is listed in the Table of Materials.

Reagents and instruments
Electromagnetic field-treated water preparation devices and far-infrared emission instruments were utilized for experimental interventions to provide spectrum energy water (SEW) and far-infrared radiation (FIR), respectively. Lipopolysaccharide (LPS) was used to model ARDS. Cytokine levels (IL-1β, IL-18, IL-6, TNF-α) were quantified using specific ELISA kits. Protein expression levels (AXL, SPP1, Caspase-3, GSDME, GAPDH) were assessed using specific primary antibodies and corresponding HRP-conjugated secondary antibodies. Sample processing and analysis were performed using standard laboratory equipment, including a biomicroscope, a microtome, a high-speed centrifuge, an ultra-low-temperature freezer, and a microplate reader. Complete details of all reagents, antibodies, and instruments, along with their respective manufacturers, are provided in the Table of Materials.

Animal grouping and modeling
Eighteen Sprague-Dawley rats were randomly assigned to the Control, Model, and SEW+FIR groups, with six rats per group. Each group was weighed and documented daily. The SEW+FIR group received FIR therapy (wavelength 4 µm–14 µm, irradiation distance of 20 cm from the dorsal surface) for 20 min daily in a temperature-controlled environment (22 °C ± 2 °C), while simultaneously receiving SEW at a dose of 1 mL/100 g/d by oral gavage⁷. Distilled water was administered orally to the Control and Model groups at an equivalent dose of 1 mL/100 g/d. SEW and distilled water were administered once daily for 7 days after heating in a 60 °C warm bath. On the seventh day, 6 hours after feeding, the Model and SEW+FIR groups were injected with LPS solution at a dose of 2 mg/kg by weight through the tail vein, whereas the Control group was treated with 0.9% physiological saline at a dose of 2 mg/kg by weight. The modeling technique was considered a mature and stable method for inducing a systemic inflammatory response with a single LPS injection via the tail vein. Lung tissue from the lung pathology in the modeled groups was consistent with ARDS characteristics²⁵. Checkpoint: Successful ARDS induction is indicated by visible lethargy, tachypnea, and a ~10% reduction in body weight within 16 h post-injection²⁶.

Collection of rat-related indicators
Sixteen hours later, all three groups were injected intraperitoneally with 3% pentobarbital sodium at a dose of 30 mg/kg body weight to induce anesthesia. CRITICAL: Depth of anesthesia must be strictly confirmed by the loss of pedal withdrawal reflex prior to any procedural intervention. Furthermore, strict biosafety protocols were maintained; all LPS-contaminated materials, biological fluids, and animal carcasses were disposed of in designated biohazard waste containers for proper incineration. Five milliliters of blood were collected from the abdominal aorta into sterile tubes, and serum was isolated by centrifugation at 1,000 x g for 20 min at 4 °C. The serum was then stored at −80 °C for further analysis. Following thoracotomy and ligation of the right pulmonary hilum, bronchoalveolar lavage fluid (BALF) was obtained by flushing the left lung three times with pre-cooled phosphate-buffered saline (PBS) via an endotracheal cannula. The BALF was then centrifuged at 1,000 x g for 10 min at 4 °C, and the supernatant was stored at −80 °C. The upper lobe of the right lung was removed and cleaned in cold physiological saline to remove the blood. Nine volumes of physiological saline were added relative to the tissue weight, and the sample was minced in an ice bath using ophthalmic scissors. A 10% lung tissue homogenate was prepared using a homogenizer, followed by centrifugation at 700 x g for 15 min at 4 °C. The supernatant was collected and stored at −80 °C for further biochemical analysis. Additionally, a portion of the right lung tissue from each rat was fixed in 4% paraformaldehyde for histological examination.

Observation indicators and detection methods
The inferior lobe of the right lung was processed using standard embedding, tissue slicing, dewaxing, HE staining, color separation, dehydration, and film sealing after fixation in 4% paraformaldehyde. Each group's lung tissues showed pathological alterations observed under a light microscope.

Pathological abnormalities in alveolar architecture and septum, degree of inflammatory cell infiltration, hyperemia, and pulmonary capillary edema were identified under a light microscope. The Department of Pathology at Beijing University of Chinese Medicine assisted with the observation. Histological lung injury score was calculated to assess lung injury as follows: no injury = 0, injury in less than 25% of the field = 1, injury in 25–50% of the field = 2, injury in 50–75% of the field = 3, and injury in more than 75% of the field = 4. Ten fields were randomly selected and assessed by investigators blinded to the grouping.

ELISA was performed on the previously collected BALF supernatant, lung tissue homogenate, and blood serum samples according to the manufacturer's instructions. Briefly, samples were incubated in pre-coated wells at 37 °C for 90 min. After washing five times with wash buffer, biotinylated detection antibodies (1:100 dilution) were applied for 60 min at 37 °C. Following another washing step, HRP-conjugate was added and incubated in the dark for 30 min at 37 °C. Subsequently, absorbance was measured at 450 nm using a microplate reader to calculate sample concentrations.

Western blot analysis was performed to assess the expression levels of AXL, SPP1, caspase-1, GSDMD, caspase-3, GSDME, and GAPDH in lung tissue and BALF samples stored at −80°C. The proteins were harvested and lysed according to the instructions in the Protein Extraction Kit. Equal amounts of protein extracts (40 µg) were loaded per lane and resolved by SDS-PAGE. The polypeptides were then separated and transferred to PVDF membranes. The membranes were blocked with 5% non-fat dry milk in TBST for 1 h at room temperature and then incubated overnight at 4 °C with the specific primary antibodies (diluted 1:1000). After washing with TBST three times for 10 min each, the membranes were incubated with the corresponding HRP-conjugated secondary antibodies (diluted 1:5000) in blocking solution at room temperature for 1 h. GAPDH was used as an internal reference protein. Protein bands were visualized using an enhanced chemiluminescence (ECL) kit with an exposure time of 1–5 min, and the results were analyzed using image processing software. The relative expression levels of the target proteins were calculated as the ratio of the target protein to GAPDH.

Statistical analysis
Quantitative indices were expressed as mean ± standard deviation, and statistical analysis was performed using statistical software. The Kruskal–Wallis test or one-way ANOVA was used to compare differences across several groups, depending on whether the data were normally distributed. All statistics were assessed using a two-sided hypothesis test. For analyses involving multiple comparisons, such as differential gene expression and immune cell infiltration profiling, P values were adjusted using the Benjamini-Hochberg false discovery rate (FDR) method. An adjusted P value of 0.05 or lower was regarded as statistically significant. Graphing software was used for charting.

Results

Identification of DEGs in GSE89953 different age groups and corresponding functional enrichment analysis
Raw expression data from the GSE89953 dataset were normalized using the normalizeBetweenArrays function in the limma package (Figure 1A), and boxplots before and after normalization confirmed improved consistency across samples. Differential expression analysis based on the normalized matrix identified 228 DEGs (adjusted p < 0.05 using Benjamini-Hochberg c...

Discussion

Accumulation of inflammatory injury and immune dysregulation is a hallmark of acute respiratory distress syndrome (ARDS), leading to severe pulmonary damage and high mortality. Due to the complex pathogenesis and lack of effective therapies, identifying precise therapeutic targets is critical to improve outcomes. Emerging evidence suggests that pyroptosis, a form of programmed cell death, plays a central role in ARDS progression. Given that age is an important factor influencing the onset and severity of ARDS, we stratif...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Anti-AXL antibody	Abcam	ab215205
Anti-Caspase-3 antibody	Abcam	ab184787
Anti-GAPDH antibody	Abcam	ab8245
Anti-GSDME antibody	Abcam	ab215191
Anti-SPP1 antibody	Abcam	ab307994
Biomicroscope	OLYMPUS, Japan	BH2
FIR instrument	Guangdong JFC Group	JF-802
Goat Anti-Rabbit IgG H&L (HRP)	Abcam	ab6721
GraphPad Prism 10.1.2	GraphPad Software	-
High-speed centrifuge	Thermo, USA	Sorvall ST16R
IL-18 ELISA kit	Jiangsu Meibiao Biotechnology Co., Ltd.	MB-1735B
IL-1β ELISA kit	Jiangsu Meibiao Biotechnology Co., Ltd.	MB-1588B
IL-6 ELISA kit	Jiangsu Meibiao Biotechnology Co., Ltd.	MB-50054A
ImageJ	NIH	-
Lipopolysaccharide (LPS)	Sigma, USA	L6511
Male SPF Sprague Dawley rats (6-7 weeks)	Beijing HFK Bioscience Co., Ltd., China	SCXK(Beijing)2019-008 (License)
Microplate reader (Varioskan Flash)	Thermo Scientific	Varioskan Flash
Microtome	LEICA	RM2235
R software (limma, clusterProfiler, WGCNA, glmnet, randomForest)	R Foundation for Statistical Computing	-
SEW preparation instrument	Guangdong JFC Group	JF-139
SPSS 26.0	IBM	-
Table balance	Shanghai Cany Precision Instruments Co., Ltd.	HC·TP11·10
TNF-α ELISA kit	Jiangsu Meibiao Biotechnology Co., Ltd.	MB-50051A
Ultra-low temperature freezer	Thermo, USA	Forma 900 Series