Abstract Background Pseudomonas aeruginosa (P. aeruginosa) is a leading cause of hospital-acquired pneumonia, contributing significantly to morbidity and mortality, especially in immunocompromised patients. Understanding the molecular mechanisms underlying this infection is crucial for developing targeted therapeutic strategies. This study aims to elucidate the local and systemic pathways and biomarkers involved in the pathogenesis of P. aeruginosa pneumonia through an integrated multi-omics approach. Methods We performed a comprehensive proteomic and metabolomic analysis on clinical samples from patients diagnosed with P. aeruginosa pneumonia, including both bronchoalveolar lavage fluid (BALF) and serum to capture local and systemic host responses. Data were analyzed using advanced statistical techniques to identify differentially expressed proteins and metabolites. Pathway enrichment analysis was performed to highlight significant biological processes associated with the infection. Results Our findings revealed a significant upregulation of biomarkers associated with neutrophil extracellular traps (NETs) and oxidative stress, underscoring their pivotal roles in immune response and inflammatory pathology. Key proteins such as LCN2, CALR, and TPI1 were identified as central players in NET formation and oxidative stress pathways. Our integrated approach uniquely highlights the simultaneous local and systemic impact of NETs and oxidative stress. Additionally, by analyzing both BALF and serum, we observed distinct disruptions in metabolic pathways, particularly those related to amino acid metabolism and energy production, suggesting a bioenergetic crisis in response to infection. The combined analysis revealed key interactions between local and systemic immune responses, indicating a reprogramming of host energy pathways to meet the heightened immune demands, contributing to disease progression. Conclusion This study provides a comprehensive understanding of the molecular mechanisms driving P. aeruginosa pneumonia by uniquely integrating BALF and serum analyses to explore both local and systemic host responses. Our findings highlight the dual role of NETs in both pathogen containment and tissue damage, as well as the metabolic reprogramming required to sustain immune activity. The identification of key biomarkers and disrupted pathways presents promising targets for therapeutic intervention, with the potential to refine diagnostic precision and improve patient outcomes. Clinical trial number Not applicable. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-025-11119-7. Keywords: Pseudomonas aeruginosa, Neutrophil extracellular traps, Oxidative stress, Multi-omics, Biomarker Background Pseudomonas aeruginosa (P. aeruginosa), a ubiquitous opportunistic pathogen, is a leading cause of healthcare-associated infections, particularly hospital-acquired pneumonia, and poses significant challenges in clinical practice [[42]1]. This pathogen inflicts considerable pulmonary tissue damage, leading to cell death, impaired gas exchange, and a strong immune response [[43]2]. Despite extensive research on P. aeruginosa [[44]3], the understanding of how this infection impacts both local lung environments and systemic immune responses remains limited. Most studies have focused on either the localized effects within the lungs or the broader systemic immune activation, but few have comprehensively explored the dynamic interaction between the two [[45]3, [46]4]. This reveals a critical gap in our knowledge of P. aeruginosa’s full pathogenesis and the mechanisms underlying host susceptibility. Addressing these dual impacts is essential for developing more effective therapeutic strategies and improving clinical outcomes. Neutrophils play a key role in the initial defense against bacterial infections [[47]5], and airway neutrophilia is a hallmark of many respiratory diseases, including pneumonia [[48]6]. A central feature of neutrophil-mediated immunity is the formation of neutrophil extracellular traps (NETs), which consist of nuclear chromatin, mitochondrial DNA and neutrophil granule proteins [[49]7]. While NETs are essential for controlling bacterial infections, recent studies have highlighted their dual role: NETs can exacerbate tissue damage and inflammation, complicating the pathology of bacterial pneumonia [[50]8]. Under certain conditions, an excessive or dysregulated immune response can drive NETs from a protective to a pathogenic role. This process, known as NETosis, represents a distinct form of neutrophil cell death that differs from apoptosis and necrosis [[51]9]. Oxidative stress has also emerged as a key factor in the pathogenesis of bacterial infections [[52]10]. An imbalance between reactive oxygen species (ROS) production and antioxidant defenses results in cellular damage [[53]2]. During P. aeruginosa infection, antioxidant mechanisms such as superoxide dismutase and glutathione peroxidase are often impaired, contributing to this imbalance [[54]11]. Notably, oxidative stress not only exacerbates inflammation but also plays a central role in promoting NET formation [[55]12]. Mechanistically, ROS drive chromatin decondensation, activate peptidyl arginine deiminase 4 (PAD4), and facilitate chromatin release, which are all critical steps in NET formation [[56]13]. However, excessive NET release can lead to microvascular dysfunction, thromboinflammation, and direct cellular injury [[57]14]. These insights into the interplay between oxidative stress and NETosis highlight their dual role in immune responses and reveal potential therapeutic targets for mitigating collateral damage in P. aeruginosa infections. Although research into P. aeruginosa markers has significantly advanced in recent years, many gaps remain in our understanding of the specific biomarkers that drive disease pathogenesis, particularly those involved in immune dysregulation and NETosis. Recent studies have identified a number of P. aeruginosa biomarkers, such as outer membrane vesicles (OMVs), flagellin, and alginate, that contribute to immune evasion and virulence [[58]15, [59]16]. These markers are also implicated in the modulation of the host immune response, with several studies showing their association with neutrophil recruitment and ROS production [[60]17]. However, more research is needed to identify novel biomarkers that can be used for early diagnosis and therapeutic intervention. The advent of multi-omics technologies has revolutionized infectious disease research by enabling comprehensive analyses of proteomic and metabolomic changes during infection [[61]18, [62]19]. Specifically, the application of proteomics and metabolomics to P. aeruginosa has uncovered key insights into its virulence mechanisms and metabolic adaptations in response to host environments. Proteomic studies have identified several critical proteins involved in pathogenesis, such as AlgU and LasA, which play significant roles in biofilm formation and antibiotic resistance [[63]20, [64]21]. Additionally, proteins like PqsR, a quorum-sensing regulator, are essential for controlling the production of virulence factors and metabolites that facilitate P. aeruginosa’s persistence in chronic infections [[65]22]. On the metabolomic side, studies have focused on the identification of specific metabolites, including pyocyanin and quorum sensing molecules (e.g., C4-HSL and 3-oxo-C12-HSL), which serve not only as virulence factors but also as potential biomarkers for infection [[66]23, [67]24]. These metabolites are involved in regulating bacterial communication and influencing host immune responses. However, while considerable progress has been made in understanding the proteomic and metabolomic landscape of P. aeruginosa itself, research on the host’s proteomic and metabolomic response to infection, particularly in the context of pneumonia, remains limited. In the case of pneumonia, the host’s immune response, lung epithelial cell metabolism, and respiratory processes are crucial to understanding disease progression. For instance, few studies have integrated host-specific biomarkers such as acute-phase proteins or metabolites involved in inflammation in the multi-omics analysis of lung infections caused by P. aeruginosa. As such, there is an urgent need to expand research into the host’s proteomic and metabolomic changes during infection, especially in the context of pneumonia. Unlike traditional methods, multi-omics approaches capture multiple layers of biological data, offering a more integrated view of host-pathogen interactions and providing new opportunities for identifying novel biomarkers and therapeutic targets. The combination of proteomics and metabolomics thus holds significant promise for advancing our understanding of both P. aeruginosa pathogenesis and the host response, which could lead to better diagnostic tools and more targeted therapies for pneumonia and other P. aeruginosa infections [[68]25]. In this study, we employed an integrated proteomic and metabolomic approach to investigate host-pathogen interactions in P. aeruginosa pneumonia (Fig. [69]1). By analyzing bronchoalveolar lavage fluid (BALF) and serum samples from affected patients and comparing them to healthy controls, we aimed to elucidate the local and systemic pathophysiological alterations caused by P. aeruginosa. This dual analysis allowed us to identify key proteins and metabolites linked to NET formation and oxidative stress, providing new insights into the mechanisms of P. aeruginosa infection. Fig. 1. [70]Fig. 1 [71]Open in a new tab Integrated Multi-Omics analysis for P. aeruginosa pneumonia Investigation This research encompasses a dual-phase approach: an initial discovery cohort followed by a validation cohort. In each phase, comprehensive proteomic and metabolomic analyses were performed on BALF and serum specimens derived from the cohorts. The aim is to elucidate the underlying mechanisms and pinpoint biomarkers indicative of P. aeruginosa pneumonia, thereby enhancing our understanding and management of this infection. Methods Study design and participant selection We designed this study with two cohorts. The discovery cohort, consisting of 30 patients with P. aeruginosa pneumonia and 20 healthy controls, was used for initial biomarker discovery and pathway analysis through proteomic and metabolomic profiling of BALF and serum samples. The validation cohort, comprising 10 patients with P. aeruginosa pneumonia and 10 healthy controls, was used to confirm the findings, including testing the diagnostic performance of identified biomarkers through statistical and machine learning methods. For each step, the discovery cohort findings informed the design of validation cohort analyses to ensure reproducibility (Fig. [72]1). Patient selection adhered strictly to current management guidelines for P. aeruginosa pneumonia, confirmed through bacterial culture, while control participants were selected based on negative bacterial cultures. To minimize confounding effects from comorbidities or underlying conditions, we applied the following criteria: (1) inclusion required a confirmed diagnosis of P. aeruginosa pneumonia via positive bacterial culture from respiratory samples, with no evidence of coexisting bacterial, viral, or fungal infections; (2) patients with chronic conditions that could influence inflammatory or hematological markers, such as diabetes mellitus, cardiovascular disease, or chronic kidney disease, were excluded based on medical history and clinical evaluation; (3) individuals with immunosuppressive conditions (e.g., HIV infection, active malignancy, or recent chemotherapy) or those who received systemic antibiotics or corticosteroids within 14 days prior to sample collection were also excluded. We conducted a retrospective review of clinical records, including a comprehensive evaluation of symptoms and systemic markers. Comparative analysis between cohorts focused on demographic data, clinical presentations, inflammatory markers (White Blood Cell count [WBC], Neutrophil percentage [NEU%], Lymphocyte percentage [LYM%], Monocyte percentage [MONO%], C-reactive Protein [CRP], and Procalcitonin [PCT]), as well as hematological parameters (Prothrombin Time [PT], Fibrinogen [FIB], Activated Partial Thromboplastin Time [APTT], D-Dimer, and Platelet count [PLT]). BALF and serum samples were collected from all participants for comprehensive proteomic and metabolomic analysis. BALF was obtained by wedging a bronchoscope into the segmental bronchus of the right middle lobe, instilling four aliquots of 0.9% sterile saline, and recollecting the fluid through gentle suction. The recovered BALF was immediately aliquoted and stored at -80 °C for further analysis [[73]26, [74]27]. To validate the identified biomarkers, we employed a structured statistical framework. In the discovery cohort, initial biomarker candidates were identified through differential expression analysis of proteomic and metabolomic data, as detailed in Sect. 2.5 and 2.6. In the validation cohort, the diagnostic performance of these biomarkers was assessed using Receiver Operating Characteristic (ROC) curve analysis to calculate the Area Under the Curve (AUC) with 95% confidence intervals, ensuring high sensitivity and specificity (threshold: AUC > 0.8). Additionally, machine learning techniques, specifically Random Forest (RF) analysis, were applied to enhance predictive modeling and confirm biomarker significance, with feature importance evaluated using Mean Decrease in Gini Index and Mean Decrease in Accuracy metrics. These statistical and machine learning approaches were systematically integrated to provide robust validation of the biomarkers’ diagnostic potential, with further details provided in Sect. 2.5, 2.6, and 2.7. This study complied with the Helsinki Declaration. Ethical approval was obtained from the Ethics Committee of The First Affiliated Hospital of Guangzhou Medical University (approval codes: 2022 No.121 and 2024 No. G-007). All participants provided written informed consent prior to their inclusion in the study. LC-MS analysis for proteomics For liquid chromatography-mass spectrometry (LC-MS) analysis, 5 µL of each sample was denatured using 8 M urea (Sigma-Aldrich, USA) in 100 mM ammonium bicarbonate (Sigma-Aldrich, USA) to a final volume of 50 µL. Proteins were reduced with 10 mM dithiothreitol (DTT, Sigma-Aldrich, USA) at 37 °C for 30 min, followed by alkylation with 50 mM iodoacetamide (IAA, Sigma-Aldrich, USA) in the dark at room temperature. The solution was then diluted with 50 mM ammonium bicarbonate and digested overnight at 37 °C using trypsin (Promega, USA) at a 1:30 enzyme-to-substrate ratio. After digestion, peptides were desalted using a C18 StageTip (Thermo Fisher Scientific, USA), and concentrations were quantified with a Nanodrop One spectrophotometer (Thermo Fisher Scientific, USA) based on absorbance at 280 nm. For LC-MS, peptides were applied to a C18 reversed-phase column (15 cm length, 75 μm ID, 2 μm particle size, Dr. Maisch GmbH, Germany) and eluted over 60 min with a gradient of 2–90% acetonitrile (ACN, Sigma-Aldrich, USA) in 0.1% formic acid (FA, Sigma-Aldrich, USA) at 300 nL/min, optimized for peptide separation and identification. MS data were acquired using a timsTOF Pro2 (Bruker, Germany) in Parallel Accumulation-Serial Fragmentation (PASEF) mode, employing the diaPASEF method for Data-Independent Acquisition (DIA) analysis [[75]28]. The diaPASEF scan covered an m/z range of 400 to 1200, with an ion mobility spectrum of 0.7 to 1.43 V s/cm², using a 32 × 25 Th window scheme with a 100 ms ramp time. DIA MS data were processed using Spectronaut 18 (Biognosys AG, Switzerland) [[76]29], targeting the UniProtKB human protein database. The search parameters included trypsin as the digestion enzyme, allowance for up to two missed cleavages, and stringent mass tolerances for precursor and fragment ions. Carbamidomethylation of cysteines was set as a fixed modification, while potential oxidation of methionine and N-terminal acetylation were set as variable modifications. To ensure data accuracy, interference correction was applied to MS/MS scans, and the false discovery rate (FDR) was controlled to be below 0.01 for both peptide-spectrum match and protein identification during database searching. The experimental data were screened to ensure that at least 50% of the identified proteins within each sample group contained non-missing values. The remaining protein abundance data were then subjected to median-based normalization to correct for inter-sample variability. For any residual missing values, imputation was performed by assigning half of the minimum detected value across all samples. The fully processed dataset was subsequently utilized for downstream statistical analyses. UHPLC-MS/MS analysis for metabolomics Metabolomic profiling began by preparing each sample through the addition of 100 µL of sample to 400 µL of cold methanol (Sigma-Aldrich, USA) (1:1, v/v), followed by vortexing and a 1-hour sonication in an ice bath to ensure complete metabolite dissolution [[77]30–[78]32]. After sonication, the samples were incubated at -20 °C for 1 h to precipitate proteins, then centrifuged at 4 °C and 14,000 g for 20 min to isolate the supernatant. The supernatants were then vacuum-dried using a vacuum concentrator (Thermo Fisher Scientific, USA) in preparation for LC-MS analysis. The metabolomic analysis was performed on an ultra-high-performance liquid chromatography-electrospray ionization-quadrupole-Orbitrap mass spectrometry (UHPLC-ESI-Q-Orbitrap-MS) system, specifically utilizing a Shimadzu Nexera X2 LC-30AD high-performance liquid chromatography (HPLC) system (Shimadzu Corporation, Japan) coupled with a Q-Exactive Plus mass spectrometer (Thermo Fisher Scientific, USA). Chromatographic separation was achieved using an ACQUITY UPLC^® HSS T3 column (2.1 × 100 mm, 1.8 μm particle size, Waters Corporation, USA), with a mobile phase of 0.1% formic acid in water (Sigma-Aldrich, USA) (A) and 100% acetonitrile (Sigma-Aldrich, USA) (B), flowing at 0.3 mL/min. The gradient program started at 0% B, increased to 48% B over 4 min, then to 100% B over the next 4 min, held at 100% B for 2 min, and returned to 0% B in 0.1 min, followed by a 3-minute re-equilibration phase. For heated electrospray ionization (HESI), the optimized settings were: spray voltage at 3.8 kV for positive mode and 3.2 kV for negative mode, capillary temperature at 320 °C, sheath gas flow at 30 arbitrary units, auxiliary gas flow at 5 arbitrary units, probe heater temperature at 350 °C, and an S-Lens RF level of 50. Full MS scans covered an m/z range of 70–1050 Da, with a resolution of 70,000 for full scans and 17,500 for MS/MS scans at m/z 200. Stepped normalized collision energies (20, 30, 40) were used for fragmentation. Quality control (QC) samples, made from pooled aliquots of all specimens, and blank samples (75% acetonitrile in water, Sigma-Aldrich, USA) were injected every six samples to ensure data integrity and normalization consistency. MS-DIAL software was used for data analysis, including peak alignment, retention time correction, and peak area extraction. Metabolite identification was based on exact mass (tolerance < 10 ppm) and MS/MS spectral data (tolerance < 0.02 Da), using databases such as Human Metabolome Database (HMDB), MassBank, and a proprietary metabolite standard library. Only metabolites detected in over 50% of samples in any group were considered for further analysis, ensuring the reliability and significance of detected metabolites. To ensure comparability across samples and reduce technical variation, the metabolomics dataset was normalized using the sum normalization method [[79]33]. First, data integrity was assessed, and missing values were imputed using the half-minimum method, where any missing value was replaced with half of the lowest detected metabolite abundance within the dataset. Normalization was performed by first computing the total metabolite intensity for each sample column. The mean of these column-wise sums (meanSum) was then calculated across all samples. A scaling factor was determined for each sample by dividing its total metabolite intensity by the meanSum. All metabolite abundance values were subsequently normalized by dividing them by their respective scaling factors, ensuring uniform distribution across the dataset. The final normalized metabolite values were rounded to two decimal places to maintain consistency and precision in downstream analyses. Construction of weighted gene co-expression and immune infiltration analysis To explore the complex genetic interactions and identify potential biomarkers or therapeutic targets for P. aeruginosa pneumonia, we employed the Weighted Gene Co-expression Network Analysis (WGCNA) framework using the “WGCNA” package in R software (version 4.3.2) [[80]34]. This process began with data preprocessing to eliminate outliers, followed by the construction of a correlation matrix. An optimal soft-thresholding power was applied to convert the correlation matrix into an adjacency matrix, which was then transformed into a topological overlap matrix (TOM). Gene modules were identified using average linkage hierarchical clustering based on TOM-based dissimilarity, and modules significantly associated with P. aeruginosa infection were selected for further exploration. For immune cell infiltration analysis, we used the CIBERSORTx platform ([81]https://cibersortx.stanford.edu/) to apply its deconvolution algorithm, quantifying the abundance and proportions of 22 immune cell types from transcriptomic signatures in BALF samples from both healthy individuals and patients with P. aeruginosa pneumonia in the discovery cohort [[82]35]. Proteomics statistical analysis Statistical analysis of proteomics data was performed using Perseus software and R software (version 4.3.2) [[83]36]. The analysis encompassed hierarchical clustering at both the protein and peptide levels, with sequence annotation supported by databases such as UniProtKB/Swiss-Prot, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO). Enrichment analyses for GO terms and KEGG pathways were conducted using Fisher’s exact test, with multiple comparisons controlled via false discovery rate (FDR) correction. GO terms were categorized into biological processes (BP), molecular functions (MF), and cellular components (CC), with pathways deemed statistically significant at a p-value threshold of < 0.05. Additionally, protein-protein interaction (PPI) networks were generated using the STRING database and visualized through Cytoscape software, clarifying the relationships among differentially expressed proteins (DEPs) (|logFC| > 1.5, and p-value < 0.05) in the context of P. aeruginosa infection. A Venn diagram was used to depict the integration of hub genes identified via Weighted Gene Co-expression Network Analysis (WGCNA) and immune infiltration analysis, facilitating the prioritization of key hub proteins for further study. Metabolomics statistical analysis To distinguish between the metabolic profiles of subjects, we constructed models based on partial least squares-discriminant analysis (OPLS-DA). The robustness and predictive accuracy of these models were assessed using permutation tests (n = 200) to ensure against overfitting. Model quality was evaluated based on R2X (cumulative) and R2Y (cumulative) values, which reflect the descriptive power of the model, and Q2 (cumulative) values, which indicate predictive reliability. Ideal models achieved values close to 1 for these metrics. Importantly, for a model to be considered valid, the R2 and Q2 values obtained from permutation tests must not exceed those of the original, non-permuted model, confirming that predictive capabilities are not due to chance. The identification of discriminating metabolites was based on variable importance in projection (VIP) values from the OPLS-DA model, coupled with a two-tailed Student’s t-test applied to normalized data for univariate analysis. Additionally, p-values were determined using one-way analysis of variance (ANOVA) for comparisons involving multiple groups. Fold-change (FC) values were calculated based on the mean values for each patient group, with selection criteria for differentially expressed metabolites (DEMs) set as follows: VIP > 1,|logFC| > 1.5, and p-value < 0.05. Visualization tools such as volcano plots, heat maps, and boxplots were employed to represent these findings, using the ‘heatmap’ and ‘ggplot2’ packages in R software (version 4.3.2). Further, KEGG pathway analysis was performed to explore the biological implications of the identified DEMs, utilizing the Metaboanalyst6.0 web portal ([84]http://www.metaboanalyst.ca/). Statistical analysis Statistical analysis of clinical data Statistical analyses of clinical indices were conducted using SPSS software (version 26.0). Continuous variables were evaluated using the Student’s t-test, while categorical variables were analyzed with Fisher’s exact test. Results were expressed as mean ± standard deviation (SD), and a p-value of < 0.05 was set as the threshold for statistical significance. In addition, correlation analyses were performed using R software (version 4.3.2), enabling a comprehensive investigation of the associations between clinical parameters. Receiver operating characteristic (ROC) curve validation The diagnostic performance of each identified hub gene was evaluated using Receiver Operating Characteristic (ROC) curve analysis with the “pROC” package in R software (version 4.3.2). This analysis estimated the area under the curve (AUC) along with 95% confidence intervals (CI) to assess the accuracy of each biomarker in distinguishing between disease states. Biomarkers with an AUC greater than 0.8 were considered diagnostically relevant, indicating that they have a high capacity for distinguishing P. aeruginosa pneumonia from other conditions. The ROC curve also provides a comprehensive evaluation of sensitivity (true positive rate) and specificity (false positive rate), ensuring that the biomarkers selected not only possess high diagnostic accuracy but are also practical for clinical use. By analyzing these parameters, the ROC curve serves as a reliable measure for the effectiveness of each biomarker in distinguishing disease states, providing a robust validation of their diagnostic potential. Machine learning analysis Random forest (RF) analysis, facilitated by the “RandomForest” package in R, was employed for predictive modeling and to identify significant gene associations [[85]37]. RF is an ensemble learning method that constructs multiple decision trees to enhance model robustness and predictive accuracy. To mitigate overfitting and ensure generalizability, feature selection was based on two importance metrics: Mean Decrease in Gini Index and Mean Decrease in Accuracy. The Gini Index evaluates how well each feature splits the data, with higher values indicating greater importance. The accuracy metric measures the impact of excluding a feature on model performance, prioritizing genes that contribute most to classification. The Maximal Information Coefficient (MIC) was employed to capture both linear and nonlinear relationships between gene pairs, providing a comprehensive assessment of gene associations that may not be detected by traditional correlation methods. To assess the significance of feature importance, permutation testing was applied, where class labels were shuffled and the model retrained to confirm that observed features were not due to chance. Hyperparameter tuning was carried out using a grid search, optimizing the number of trees (ntree) between 100 and 1000 and selecting the optimal value based on the lowest out-of-bag (OOB) error. The number of features considered at each split (mtry) was also optimized to balance model complexity and accuracy. Model stability was further evaluated through bootstrapping with 2000 iterations, allowing for the estimation of confidence intervals for accuracy and AUC. Results Clinical biomarker analysis in P. aeruginosa pneumonia Analysis of clinical data from both the discovery and validation cohorts revealed distinct patterns of systemic inflammation and coagulation abnormalities in patients with P. aeruginosa pneumonia compared to healthy controls. In the discovery cohort, patients exhibited a significant elevation in WBC, with a mean of 11.90 × 10^9/L, compared to 6.05 × 10^9/L in controls. NEU% was markedly increased, indicating acute infection, while LYM% was reduced and MONO% increased, reflecting the immune response to the infection (Table [86]1). Table 1. Basic information of discovery cohort Healthy Controls P. aeruginosa pneumonia patients P-value N 20 30 Age 48.50[36.25,55.00] 56.5[40.25,66.00] 0.157  Inflammatory markers WBC,10^9/L 6.05[4.75,7.43] 11.90[11.40,15.35] < 0.05 NEU% 56.15[45.78,61.80] 74.35[66.78,79.65] < 0.05 LYM% 33.40[26.05,45.38] 18.66[11.75,27.36] < 0.05 MONO% 5.90[4.88,6.45] 10.49[5.62,12.42] < 0.05 CRP, mg/dL 5.05[3.88,8.03] 25.30[18.88,30.35] <0.05 PCT, ng/mL 0.03[0.01,0.05] 2.35[1.30,3.35] <0.05  Hematological markers PT, S 12.15[11.63,13.33] 15.45[12.9,16.13] < 0.05 FIB, g/L 2.87[2.39,3.67] 4.95[4.39,5.85] < 0.05 APTT, S 34.00[29.43,39.15] 50.00[38.90,52.43] < 0.05 D-Dimer, µg/L 289.50[189.50,362.75] 644.5[465.00,742.00] < 0.05 [87]Open in a new tab WBC: White blood cell; NEU: Neutrophil; LYM: Lymphocyte; MONO: Monocyte; CRP: C-reactive protein; PCT: Procalcitonin; PT: Prothrombin time; FIB: Fibrinogen; APTT: Activated partial thromboplastin time Inflammatory biomarkers, including CRP and PCT, were significantly elevated in patients. CRP levels averaged 25.30 mg/dL in the patient group, in contrast to 5.05 mg/dL in controls. Similarly, PCT levels were notably higher in patients, with a mean of 2.35 ng/mL versus 0.03 ng/mL in controls. These biomarkers are well-established indicators of bacterial infection severity and systemic inflammation. Additionally, coagulation markers such as PT, FIB, APTT, and D-dimer were substantially altered, reflecting activation of the coagulation cascade—a hallmark of P. aeruginosa pneumonia. The validation cohort confirmed these findings, displaying similar trends across all measured markers, albeit with slight variations in magnitude. For example, the mean WBC count in patients was 11.75 × 10^9/L, slightly lower than that of the discovery cohort, while CRP levels averaged 26.50 mg/dL, slightly higher than in the discovery cohort. Despite these minor variations, the differences between patients and healthy controls remained statistically significant across most parameters, with the exception of MONO% in the validation cohort (Table [88]2). These consistent findings across both cohorts underscore the robustness of these clinical markers in detecting and characterizing the systemic effects of P. aeruginosa pneumonia. Table 2. Basic information of verification cohort Healthy Controls P. aeruginosa pneumonia patients P-value N 10 10 Age 49.50[41.25,61.50] 61[53.5,71.5] 0.082  Inflammatory markers WBC,10^9/L 6.10[5.50,8.00] 11.75[10.48,13.35] < 0.05 NEU% 51.65[46.38,55.70] 74.20[66.75,81.28] < 0.05 LYM% 32.05[28.25,45.10] 17.42[9.01,23.71] < 0.05 MONO% 5.90[4.48,7.13] 8.84[3.25,11.56] 0.144 CRP, mg/dL 5.75[1.48,7.95] 26.50[20.05,30.30] < 0.05 PCT, ng/mL 0.02[0.02,0.03] 2.08[1.07,2.75] < 0.05  Hematological markers PT, S 13.15[11.68,14.05] 15.25[12.85,16.48] < 0.05 FIB, g/L 2.85[2.06,3.55] 4.91[4.24,5.45] < 0.05 APTT, S 34.75[31.03,38.15] 43.30[35.60,52.33] < 0.05 D-Dimer, µg/L 262.00[171.25,382.00] 682.00[478.50,864.00] < 0.05 [89]Open in a new tab WBC: White blood cell; NEU: Neutrophil; LYM: Lymphocyte; MONO: Monocyte; CRP: C-reactive protein; PCT: Procalcitonin; PT: Prothrombin time; FIB: Fibrinogen; APTT: Activated partial thromboplastin time Proteomic insights from BALF identify biomarker candidates In our proteomic analysis of BALF from a discovery cohort of 30 patients with Pseudomonas aeruginosa pneumonia and 20 healthy controls, we identified 93 DEPs: 74 were upregulated, and 19 were downregulated (Fig. [90]2A, B, Supplementary Table [91]S1). Functional categorization and pathway enrichment analyses, utilizing GO and KEGG databases, revealed significant enrichment of DEPs in GO terms related to bacterial defense responses, humoral immune response, oxidative stress, metal ion handling, and antioxidant activities. These processes involved both vesicle and cytoplasmic vesicle lumens. KEGG pathway enrichment further linked these DEPs to processes such as salivary secretion, the renin-angiotensin system, and neutrophil extracellular trap formation (Figs. [92]2C, D and Supplementary Table [93]S2). Fig. 2. [94]Fig. 2 [95]Open in a new tab Insights from Differential Protein Expression and Network Analysis. (A) Principal Component Analysis (PCA) delineates the distinct clustering between two groups within the discovery cohort, illustrating variances in protein expression profiles. (B) Volcano plots illustrate the differentially expressed proteins (DEPs) within the discovery cohort, identifying key proteins of interest based on statistical and biological significance. (C, D) Functional categorization and pathway enrichment analyses of 93 DEPs employing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) highlight critical biological processes and pathways. These include defense response to bacteria, humoral immune response, oxidative stress, metal ion handling, and antioxidant activity within the GO framework, and salivary secretion, the renin-angiotensin system, and neutrophil extracellular trap formation within KEGG pathways. (E) A protein clustering dendrogram displays the modular organization of proteins, with color coding indicating specific modules. (F) Detailed GO and KEGG pathway analyses of the brown module underscore enrichment in acute inflammatory response, acute-phase response, and mechanisms involved in blood microparticle generation, peptidase regulation, and antioxidation. Notable pathways include thyroid hormone synthesis and the complement and coagulation cascades. (G) The turquoise module’s GO and KEGG pathway analyses focus on bacterial defense responses, leukocyte migration, vesicle composition, sulfur compound binding, and antioxidation, with key pathways being neutrophil extracellular trap formation and the IL-17 signaling pathway. (H) Exploration of the yellow module’s GO and KEGG pathways indicates enrichment in processes vital for anatomical and tissue homeostasis, phagocytic vesicle makeup, centriolar satellite functionality, and ribonuclease activity, highlighting pathways involved in calcium reabsorption regulated by endocrine factors and vasopressin-regulated water reabsorption Application of WGCNA and hierarchical clustering to the proteomic data delineated five distinct co-expression modules among the 50 samples (Fig. [96]2E, Supplementary Figure [97]S1). Of these, three modules exhibited statistically significant associations with disease status (p < 0.05) and were selected for in-depth analysis, whereas the remaining two modules, lacking significant correlation (p > 0.05). The first of these significant modules was characterized by pronounced enrichment in proteins mediating acute inflammatory responses, notably those involved in complement activation pathways critical to the host’s early immune reaction (Fig. [98]2F, Supplementary Table [99]S3). A second module was distinguished by proteins facilitating leukocyte migration and neutrophil extracellular trap formation, highlighting core immune defense strategies against P. aeruginosa (Fig. [100]2G, Supplementary Table [101]S4). The third module was marked by proteins associated with phagocytic vesicle composition, indicative of cellular efforts to sustain structural and functional integrity amid infection (Fig. [102]2H, Supplementary Table [103]S5). Module designations and their corresponding color labels are detailed in the Fig. [104]2 legend and Supplementary Table [105]S3-[106]S5 for reference. These findings reflect the host’s efforts to preserve homeostasis in response to P. aeruginosa infection. The focus on oxidative stress pathways and neutrophil extracellular trap formation aligns with clinical observations of increased neutrophil counts (Tables [107]1 and [108]2), further supporting their involvement in the pathogenesis of P. aeruginosa pneumonia. Intersectional analysis of proteomic and network-based approaches In the discovery cohort, a Venn diagram (Fig. [109]3A) illustrates the overlap between DEPs identified through general proteomic analysis and those identified via WGCNA, referred to as P. aeruginosa-associated differentially expressed proteins (PDEPs). These key proteins, APOD, ITIH4, GC, PRR4, PRH1, PRB4, CSTA, TPI1, GSR, AZU1, CTSG, LCN2, CAMP, S100P, RAP1B, and CALR, exhibited significant expression differences between infected patients and healthy controls (Fig. [110]3B). Fig. 3. [111]Fig. 3 [112]Open in a new tab Functional Proteomic Insights and Immunoinfiltration. (A) A Venn diagram delineates the intersection of differentially expressed proteins (DEPs) uncovered through comprehensive analysis and those identified via weighted gene co-expression network analysis (WGCNA), illustrating the core proteins implicated in the infection. (B) Bar graphs depict the comparative expression levels of sixteen pivotal PDEPs between P. aeruginosa-infected patients and healthy controls, highlighting significant differences. (C) A heatmap illustrates the relative abundance of 22 immune cell types across various samples, providing a visual comparison of immune profiles between infected and non-infected individuals. (D) The Wilcoxon test is employed to ascertain significant differences in immune cell infiltration across the samples, identifying specific cell types that are either enriched or depleted in response to P. aeruginosa infection. (E) A correlation matrix assesses interactions among the 22 immune cell types, with positive correlations indicated in red and negative correlations in blue, offering insights into the complex interplay of immune responses. *, P < 0.05. **, P < 0.01.***, P < 0.001 Further functional profiling of these PDEPs using GO and KEGG analyses provided deeper insights into their biological roles and pathway associations (Supplementary Figure [113]S2, Supplementary Table [114]S6). GO categorization highlighted their involvement in key immune processes, such as leukocyte migration and neutrophil activation—critical components of the host’s immune response to bacterial infections. KEGG pathway analysis reinforced their participation in neutrophil extracellular trap formation. The intricate interactions between these GO terms and KEGG pathways, visualized using chord diagrams (Supplementary Figure [115]S3A-C), reveal a complex network of biological processes and signaling pathways. Proteins such as CAMP, AZU1, CTSG, LCN2, and CALR emerged as central players, reflecting their pivotal roles in the immune response to P. aeruginosa infection. The enrichment of PDEPs in pathways related to oxidative stress and neutrophil extracellular trap formation further underscores their essential roles in host defense mechanisms. BALF immune landscape profiling We applied the CIBERSORTx algorithm to comprehensively profile the immune landscape of the BALF expression matrices, identifying distinct variations in immune cell composition in response to P. aeruginosa pneumonia, in the discovery cohort (Fig. [116]3C, Supplementary Table [117]S7). The Wilcoxon test revealed significant differences in eight immune cell types between infected and non-infected cohorts (Fig. [118]3D). The immunological signature of infection was marked by an increased abundance of T cells, NK cells, and neutrophils, along with a notable depletion of B cells, plasma cells, and mast cells. Further analysis of immune cell interactions within the pneumonia-induced environment demonstrated a synchronized increase in both resting and activated memory T cells, as well as NK cells, suggesting coordinated immune activation in response to the infection (Fig. [119]3E). Neutrophils exhibited a negative correlation with resting dendritic cells but were positively associated with CD4 T cells and resting NK cells, indicating a complex interplay between innate and adaptive immune responses in P. aeruginosa pneumonia. In addition, Spearman correlation analysis between PDEPs and immune cells (Supplementary Figure [120]S3D) identified several key proteins that were significantly positively correlated with neutrophil levels, including RAP1B, S100P, LCN2, CALR, TPI1, ITIH4, GC, APOD, GSR, CAMP, PRH1, AZU1, PRB4, CTSG, CSTA, and PRR4. Conversely, these PDEPs displayed a consistent negative correlation with naive B cells, suggesting distinct immune regulatory dynamics in the infected lung environment. Screening and validation of BALF biomarkers In the discovery cohort, STRING analysis identified a network of 16 PDEPs critical to the infection process (Fig. [121]4A). These PDEPs were further assessed using a Random Forest algorithm, which ranked the top 20 Random Forest proteins (RFPs) based on mean decrease in accuracy and mean decrease in the Gini coefficient (Supplementary Figure [122]S4). The intersection of the PDEPs and RFPs identified seven key proteins, ITIH4, APOD, RAP1B, S100P, CALR, LCN2, and TPI1, that emerged as potential diagnostic markers for P. aeruginosa pneumonia (Fig. [123]4B). To ensure the robustness and reliability of the selected biomarkers, we applied an additional filtering step to these seven candidates. Proteins exhibiting more than 30% missing values across the samples were excluded. Following this filtering process, three proteins in discovery cohort, LCN2, CALR, and TPI1, were retained as the final candidate biomarkers for further validation (Supplementary Figure [124]S5). Fig. 4. [125]Fig. 4 [126]Open in a new tab Screening and Validation of BALF Biomarkers. (A) STRING analysis visualizes a network of 16 P. aeruginosa-associated differentially expressed proteins (PDEPs), mapping the intricate interactions among these key proteins. (B) A Venn diagram delineates the intersection of P. aeruginosa-associated differentially expressed proteins (PDEPs) and Random Forest proteins (RFPs) identified in the discovery cohort. (C) Principal Component Analysis (PCA) discriminates between the two groups within the validation cohort, further substantiating the differential protein expression profiles. (D) Volcano plots underscore the significantly differentially expressed proteins (DEPs) in the validation cohort, spotlighting proteins with substantial fold changes and statistical significance. (E) Comparative analysis of seven biomarker expression levels between P. aeruginosa-infected individuals and healthy controls reveals marked disparities, underscoring their potential diagnostic relevance. (F) Diagnostic Receiver Operating Characteristic (ROC) curves for the seven biomarkers affirm their efficacy in differentiating P. aeruginosa-infected from healthy samples. The Area Under the Curve (AUC) for three biomarkers surpasses the 0.8 threshold, showcasing their robust diagnostic potential. *, P < 0.05. **, P < 0.01.***, P < 0.001. TPR, True Positive Rate. FPR, False Positive Rate. CI, Confidence Interval In the validation cohort, these three final biomarkers (LCN2, CALR, and TPI1) demonstrated significant differential expression between individuals infected with P. aeruginosa and healthy controls (Figs. [127]4C-E, Supplementary Table [128]S8). ROC analysis further confirmed the diagnostic accuracy of these specific biomarkers, with LCN2, CALR, and TPI1 each achieving AUC values exceeding 0.8 (Fig. [129]4F). Serum proteome analysis unveils biomarkers and pathophysiological insights Building on the findings from BALF, we extended our investigation to the systemic level by analyzing the effects of P. aeruginosa infection on the serum proteome. This approach revealed significant systemic changes induced by the infection, which are clearly distinguished in the serum proteome. In the discovery cohort, differentially expressed proteins (SDEPs) in the serum are visualized in volcano plots (Fig. [130]5A, B, and Supplementary Table [131]S9). Functional analyses using GO and KEGG pathways revealed involvement in critical biological processes, including the humoral immune response, acute inflammatory responses, and pathways related to neutrophil extracellular trap formation, nitrogen metabolism, and ferroptosis (Figs. [132]5C-E and Supplementary Table [133]S10). Fig. 5. [134]Fig. 5 [135]Open in a new tab Serum-Based Proteomic Analysis and Biomarker Validation in P. aeruginosa Infection. (A) Principal Component Analysis (PCA) visually segregates two groups within the discovery cohort based on serum samples, emphasizing the distinct proteomic landscapes that differentiate P. aeruginosa-infected patients from healthy controls. (B) Volcano plots showcase significantly serum differentially expressed proteins (SDEPs) within the discovery cohort’s serum samples, identifying proteins of interest based on their fold changes and statistical significance. (C-E) Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses undertake the functional categorization and pathway enrichment of 10 key SDEPs. GO analysis elucidates involvement in the humoral immune response, acute inflammatory response, endocytic vesicle function, glycosaminoglycan binding, and oxygen carrier activity. KEGG pathway analysis delineates involvement in critical biological pathways, such as Neutrophil extracellular trap formation, Nitrogen metabolism, and Ferroptosis, reflecting the metabolic and immunological disruptions induced by P. aeruginosa infection. (F) PCA in the validation cohort (serum) reaffirms the proteomic distinction between infected and healthy groups, validating the findings from the discovery phase. (G) Volcano plots in the validation cohort accentuate the SDEPs, further substantiating their differential expression and potential as biomarkers. (H-J) GO and KEGG analyses undertake the functional categorization and pathway enrichment of the validation cohort Analysis of the validation cohort corroborated the systemic proteomic alterations identified in the discovery cohort, highlighting the consistency of the infection-induced changes (Fig. [136]5F, G). To further assess the functional implications in the validation set, we performed GO and KEGG pathway enrichment analysis on the differentially expressed serum proteins identified in this cohort (Supplementary Table [137]S11). This analysis demonstrated that the significantly enriched pathways were highly consistent with those found in the discovery cohort, primarily relating to acute inflammatory responses and neutrophil extracellular trap formation (Fig. [138]5H-J). BALF metabolomic insights into P. aeruginosa pneumonia Transitioning from proteomic to metabolomic analysis within BALF, we conducted an in-depth investigation into the biochemical impacts of P. aeruginosa infection. In the discovery cohort, using VIP scores from orthogonal partial least squares discriminant analysis (OPLS-DA), we identified metabolites with VIP scores above 1 that also met initial fold change and significance criteria (Supplementary Figure [139]S6A-D). This selection process yielded a set of P. aeruginosa-associated differentially expressed metabolites (PDEMs) (Fig. [140]6A, Supplementary Table [141]S12). Pathway enrichment analysis revealed significant disruptions in key metabolic pathways, including beta-Alanine metabolism, Pyrimidine metabolism, and Phenylalanine, tyrosine, and tryptophan biosynthesis. These findings provide insights into the biochemical underpinnings of P. aeruginosa infection and offer potential targets for therapeutic intervention (Figs. [142]6B, C, Supplementary Table [143]S13). Fig. 6. [144]Fig. 6 [145]Open in a new tab Comprehensive BALF Metabolomics Analysis, Biomarker Screening, and Validation. (A) A Venn diagram filters for metabolites fulfilling specified criteria, categorized as P. aeruginosa-associated differentially expressed metabolites (PDEMs). (B, C) Pathway enrichment analyses of 173 PDEMs using the Kyoto Encyclopedia of Genes and Genomes (KEGG) elucidate involvement in key metabolic pathways such as beta-Alanine metabolism, Pyrimidine metabolism, and the biosynthesis of Phenylalanine, tyrosine, and tryptophan, providing insights into the metabolic disruptions occasioned by P. aeruginosa infection. (D) Diagnostic Receiver Operating Characteristic (ROC) curves for six BALF biomarkers affirm their efficacy in distinguishing between P. aeruginosa-infected and healthy samples. The Area Under the Curve (AUC) for each biomarker exceeds the 0.8 threshold, underscoring their considerable diagnostic utility. TPR, True Positive Rate. FPR, False Positive Rate. CI, Confidence Interval Our stringent metabolomic screening within the discovery cohort identified top metabolites with strong potential as biomarkers for P. aeruginosa infection, as demonstrated by ROC analysis (Supplementary Figure [146]S6E). Metabolites exhibiting more than 30% missing values across the samples were excluded. In the validation phase, these metabolites achieved AUC values exceeding 0.8, confirming their high diagnostic accuracy in distinguishing infected individuals from healthy controls (Fig. [147]6D, Supplementary Figure [148]S7). These results underscore the potential clinical utility of these metabolites in diagnosing P. aeruginosa pneumonia. Metabolic profiling and biomarker validation in P. aeruginosa pneumonia The identification of serum-based metabolic biomarkers for P. aeruginosa pneumonia demonstrates the robustness of our analytical model. In the discovery cohort, using OPLS-DA, we effectively distinguished the metabolic profiles of infected individuals from healthy controls, capturing precise disease-specific metabolic alterations (Supplementary Figure [149]S8A-D). Several significantly altered serum metabolites, including Geranyl Phosphate, Caprylic Acid, and Sinapoyl Aldehyde, were identified based on stringent criteria (Fig. [150]7A, Supplementary Table [151]S14). Pathway impact analysis revealed substantial disruptions in critical metabolic pathways, notably the tricarboxylic acid (TCA) cycle and pyrimidine metabolism, which were significantly perturbed during infection (Figs. [152]7B, C and Supplementary Table [153]S15). In the validation cohort, the diagnostic potential of identified metabolites, such as 3-Hydroxyisobutyric Acid and 2-Methyl-2-pentenoic Acid, was confirmed via ROC curve analysis, with AUC values significantly exceeding 0.8, indicating high diagnostic accuracy (Fig. [154]7D, Supplementary Figure [155]S8E, [156]S9). Fig. 7. [157]Fig. 7 [158]Open in a new tab Comprehensive Serum Metabolomics Analysis, Biomarker Screening, and Validation. (A) A Venn diagram identifies the intersection between metabolites exhibiting Variable Importance in Projection (VIP) scores above 1 and those with significant alterations, characterizing the defined set of SDEMs. (B, C) Pathway enrichment analysis of 189 SDEMs utilizing the Kyoto Encyclopedia of Genes and Genomes (KEGG) identifies pivotal pathways such as Caffeine metabolism, the Citrate cycle (TCA cycle), and the Biosynthesis of unsaturated fatty acids, highlighting the metabolic pathways perturbed by the infection. (D) Diagnostic Receiver Operating Characteristic (ROC) curves for ten serum biomarkers underscore their efficacy in discriminating between P. aeruginosa-infected and healthy samples. The Area Under the Curve (AUC) for all biomarkers surpassing the 0.8 threshold underscores their substantial diagnostic potential. (E) A Venn diagram illustrates shared metabolites between BALF and serum, signaling systemic metabolic alterations triggered by the infection. (F, G) KEGG pathway enrichment analysis of the shared metabolites underscores the consistency of metabolic alterations and the infection’s metabolic footprint. TPR, True Positive Rate. FPR, False Positive Rate. CI, Confidence Interval A comparative analysis identified 33 metabolites that were differentially expressed in both BALF and serum, marking them as systemic indicators of the metabolic response to P. aeruginosa infection (Fig. [159]7E and Supplementary Table [160]S16). KEGG pathway enrichment analysis of these shared metabolites highlighted consistent metabolic alterations and the broad impact of P. aeruginosa infection on host metabolism (Figs. [161]7F, G and Supplementary Table [162]S17). The observed disruptions in the TCA cycle and pyrimidine metabolism pathways deepen our understanding of the metabolic footprint of the infection. Metabolic disruption and biomarker discovery in P. aeruginosa pneumonia Through the innovative application of multi-omics network construction, integrating DEPs and DEMs from BALF identified in the discovery cohort, we have mapped a comprehensive view of the host’s response to P. aeruginosa infection. This network presents an interconnected ensemble of proteins and metabolites, highlighting the complex pathophysiological shifts induced by the infection (Fig. [163]8A). Enrichment analyses of the proteins within this network, using GO and KEGG methodologies, revealed significant associations with biological processes related to cellular responses to toxins and oxidative stress, suggesting activation of detoxification pathways as a defensive mechanism (Fig. [164]8B, Supplementary Table [165]S18). Fig. 8. [166]Fig. 8 [167]Open in a new tab Multi-omics Insights into P. aeruginosa pneumonia. (A) A network diagram showcases the intricate interactions between metabolites and proteins implicated in P. aeruginosa pneumonia, highlighting critical nodes and connections that play potential roles in the disease’s pathogenesis. (B) Biological process (BP) enrichment analysis focuses on the cellular response to toxins and oxidative stress, shedding light on the host’s activation of defensive mechanisms against the infection. (C) Enrichment in cellular components (CC) points to the involvement of secretory granules and the sarcoplasmic reticulum, indicating key sites of sub-cellular localization for the pathogenic response. (D) Analysis of molecular functions (MF) reveals the impact of the infection on activities such as lyase activity and iron ion binding, which are essential to understanding the biochemical basis of the disease’s pathogenic mechanisms. (E) Identification of key metabolic pathways disrupted by the infection, such as drug metabolism and the TCA cycle, presents potential points for therapeutic intervention. (F) Pathway impact analysis links the biosynthesis of amino acids, like phenylalanine, to disease progression, emphasizing their utility as potential biomarkers for P. aeruginosa pneumonia At the cellular component level, disruptions in cellular trafficking and signal transduction were particularly notable in the secretory granule lumen and sarcoplasmic reticulum (Fig. [168]8C). Molecular function analysis further identified impairments in lyase activity and iron ion binding, both essential for energy metabolism and oxygen transport, indicating a disruption in metabolic homeostasis (Fig. [169]8D). KEGG pathway analysis of the network’s metabolites (Figs. [170]8E, F and Supplementary Table [171]S19) pinpointed disturbances in key metabolic pathways necessary for maintaining energy balance and nucleotide synthesis, such as the tricarboxylic acid (TCA) cycle and pyrimidine metabolism. Additionally, alterations in beta-alanine metabolism, which is linked to immune functionality and neurotransmitter regulation, and pathways involved in phenylalanine, tyrosine, and tryptophan biosynthesis, critical for protein synthesis and oxidative stress responses, were observed. Discussion Our study provides a comprehensive understanding of P. aeruginosa pneumonia by leveraging an integrated multi-omics approach, using both BALF and serum to explore the local and systemic impacts of the infection. This dual-sample strategy offers novel insights into the complex pathophysiology of P. aeruginosa pneumonia, filling critical gaps in the current understanding of how localized pulmonary infections translate into widespread systemic effects. The ability to capture both the local immune response in the lungs and the broader systemic alterations in the serum is a key strength of our study, offering a more holistic perspective on disease progression and potential therapeutic interventions. One of the most significant findings from our analysis is the upregulation of biomarkers involved in NET formation. These results confirm and extend previous studies that have highlighted NETs as a double-edged sword in bacterial infections, critical for bacterial clearance but also capable of causing tissue damage when overproduced [[172]38]. The delicate balance between NET-mediated bacterial killing and tissue inflammation has clear clinical implications, suggesting that targeted therapies aimed at modulating NET activity could offer a novel approach to managing P. aeruginosa pneumonia. By examining both BALF and serum, our study reveals the systemic repercussions of excessive NET formation, pointing to the need for therapies that can finely tune this response to avoid exacerbating lung injury [[173]38, [174]39]. Furthermore, our research sheds light on the critical interplay between NETs and oxidative stress, a relationship that significantly exacerbates disease severity. The oxidative stress-driven production of ROS activates NET formation through PAD4-mediated chromatin decondensation and release [[175]17]. This mechanistic insight aligns with our findings of heightened leukocyte migration and activation of oxidative stress pathways, illustrating the dynamic and harmful feedback loop between ROS production and NET release in P. aeruginosa infections. Our study not only confirms the importance of oxidative stress in driving NET formation but also highlights its role in the broader immune dysregulation seen in bacterial pneumonia, further emphasizing the potential for therapies that address both NET and oxidative stress pathways. Several key proteins, AZU1, CTSG, RAP1B, LCN2, S100P, CAMP, and ITIH4, emerge as critical mediators of NET formation and oxidative stress response. NETs rely on neutrophils to release DNA, histones, and granule-sourced proteins like AZU1, CTSG, and RAP1B, which form the structural scaffold of these traps [[176]40]. Meanwhile, LCN2 binds iron to prevent bacterial proliferation, S100P modulates immune responses, and CAMP disrupts bacterial membranes, together forming a multifaceted defense mechanism [[177]41–[178]43]. Oxidative stress also plays a pivotal role in regulating NETosis, with NADPH oxidase-mediated ROS production acting as a key signal [[179]44]. Proteins like CAMP and CTSG modulate the neutrophil response to oxidative stress, subtly influencing NET formation dynamics [[180]43, [181]45]. ITIH4, a protein found in secretory granules, may help modulate the inflammatory environment and oxidative balance within infected lung tissue, further underscoring its therapeutic potential [[182]46]. In addition to these functional roles, the identified biomarkers have significant potential for early diagnosis of P. aeruginosa pneumonia. Specifically, proteins like LCN2, CALR and TPI1 have demonstrated strong diagnostic performance in ROC analysis, with AUC values exceeding 0.8. These results suggest their utility as non-invasive biomarkers for distinguishing P. aeruginosa pneumonia from other infections or conditions. This would significantly reduce the need for invasive procedures such as bronchoscopy, which could improve patient comfort and expedite diagnosis. Incorporating these biomarkers into clinical panels could enhance rapid diagnostic assays, enabling early intervention, better patient stratification, and ultimately improved clinical outcomes. Several biomarkers are currently available that can reflect P. aeruginosa infection to some extent. For instance, calprotectin (S100A8/A9), a key marker of neutrophil activation, is often elevated in respiratory infections and strongly associated with inflammation [[183]47]. Additionally, bacterial proteases, including elastase and alkaline protease, contribute significantly to tissue damage and immune evasion, and their levels correlate with infection severity [[184]48–[185]50]. CRP remains a widely used inflammatory marker, with elevated levels typically indicating more severe infection [[186]51]. Furthermore, extracellular vesicles (EVs) released by P. aeruginosa have emerged as promising biomarkers, offering a non-invasive means of detecting the pathogen, as these vesicles carry bacterial proteins and RNA that reflect the pathogen’s presence and activity [[187]15, [188]16]. While these markers hold significant potential, further studies are needed to validate their clinical utility and improve diagnostic accuracy in P. aeruginosa pneumonia. Our investigation also connects NETs to thromboinflammation. In acute lung injury (ALI), elevated histone levels have been linked to edema, alveolar wall thickening, and hemorrhage [[189]52]. DNA, the backbone of NETs, not only maintains NET integrity but also actively participates in thromboinflammation, recruiting platelets, leukocytes, and coagulation factors. This process facilitates thrombin generation via Factor XII- or Factor XI-dependent pathways, as seen in sepsis [[190]53]. In the context of P. aeruginosa pneumonia, similar mechanisms of NET-mediated thromboinflammation have been observed, contributing to the development of microvascular thrombosis and worsening lung injury. Recent studies on bacterial pneumonia highlight the role of NETs in promoting platelet aggregation and coagulation, further exacerbating inflammatory damage and contributing to poor clinical outcomes [[191]54]. The intricate interaction between NETs, platelets, and thrombin not only promotes intravascular coagulation but also intensifies tissue damage observed in bacterial sepsis [[192]55]. Parallel observations in post-mortem examinations of COVID-19 patients’ lungs demonstrate significant neutrophil infiltration and NET deposition, frequently associated with platelet accumulation and microvascular thrombosis [[193]56–[194]58]. These observations align with studies linking elevated cell-free DNA levels with increased D-dimer levels and ARDS severity [[195]59, [196]60]. Our investigation further corroborates these findings, noting pronounced elevations in hematological markers like D-dimer among patients suffering from P. aeruginosa pneumonia. This understanding suggests that NET structures not only immobilize and activate platelets and leukocytes but also initiate a cascade leading to increased endothelial permeability and microvascular obstruction, spotlighting NET inhibition or degradation as potential strategies for mitigating P. aeruginosa pneumonia severity and enhancing patient outcomes. Pathway enrichment analysis meticulously probes the biological ramifications of metabolic shifts, highlighting the perturbations within pivotal pathways. Comparing these findings with similar studies on other respiratory pathogens, such as Streptococcus pneumoniae and Haemophilus influenzae, suggests that some of these metabolic shifts, particularly those involving amino acid and energy metabolism, may be unique to P. aeruginosa [[197]61, [198]62]. This distinct metabolic reprogramming underscores the pathogen’s adaptive strategies in response to host immune pressures. This includes the disruption of the beta-alanine metabolism pathway, a critical axis for immune response modulation and neurotransmitter regulation [[199]63], as well as the biosynthetic pathways for phenylalanine, tyrosine, and tryptophan, which are indispensable for protein synthesis and mitigating oxidative stress [[200]64]. These findings are consistent with recent studies demonstrating that amino acid metabolism plays a crucial role in modulating immune responses during bacterial infections [[201]65]. Furthermore, the examination of metabolites within this network reveals significant disturbances in pathways integral to sustaining energy equilibrium and nucleotide synthesis, notably the tricarboxylic acid (TCA) cycle and pyrimidine metabolism [[202]66]. The misregulation of these pathways suggests a bioenergetic crisis, necessitating a shift to alternate metabolic strategies to satisfy the increased energy demands of the immune response. Such metabolic reprogramming, a distinctive marker of infection, accentuates the therapeutic potential of targeting metabolic pathways to modulate host defenses. Building on these observations, our study identified several metabolite biomarkers within these disrupted pathways that hold significant diagnostic and therapeutic potential. Using a stringent selection criterion of ROC AUC > 0.8 in both the discovery and validation cohorts, we pinpointed key metabolites, including those involved in the beta-alanine metabolism and the TCA cycle, as reliable indicators of P. aeruginosa infection. These metabolites not only reflect the metabolic reprogramming induced by the pathogen but also provide insights into its ability to adapt to and exploit host immune pressures. The differential expression of proteins and metabolites associated with iron ion binding and peptidase inhibitor activity further underscores the strategic battle over iron, a critical nutrient for both host and pathogen [[203]67]. P. aeruginosa employs multiple sophisticated mechanisms to counter host-imposed nutritional immunity, including the production of siderophores like pyoverdine and pyochelin, which scavenge iron from host proteins, and the use of heme as an iron source [[204]68, [205]69]. This differs from other pathogens such as Staphylococcus aureus and Escherichia coli, emphasizing the versatility of P. aeruginosa in iron acquisition and its ability to thrive in iron-limited environments [[206]70]. However, the dysregulation of iron metabolism can lead to an excess of free iron, catalyzing the formation of highly reactive hydroxyl radicals through the Fenton reaction, thereby exacerbating oxidative stress [[207]71]. This heightened oxidative environment is conducive to the activation of neutrophils and the subsequent release of NETs [[208]72]. These molecular insights suggest promising therapeutic avenues targeting P. aeruginosa’s virulence strategies. The identified biomarkers, AZU1, CTSG, and LCN2, play dual roles in host defense and tissue damage, making them compelling therapeutic targets for Pseudomonas aeruginosa pneumonia; AZU1 and CTSG, integral to NETs formation, suggest modulating NET activity with DNase I to degrade NETs, mitigating microvascular thrombosis and lung injury as demonstrated in sepsis models, or PAD4 inhibitors to suppress excessive NETosis, preserving bacterial clearance while curbing inflammation; LCN2, a regulator of iron homeostasis and oxidative stress, supports the use of antioxidants like N-acetylcysteine to disrupt the ROS-driven NET feedback loop, reducing cellular damage and enhancing pathogen elimination; furthermore, the associated disruptions in beta-alanine metabolism and TCA cycle dysfunction highlight opportunities for metabolic reprogramming, such as supplementation with TCA intermediates (e.g., alpha-ketoglutarate) or beta-alanine modulators, to restore immune balance and alleviate the bioenergetic crisis, offering a multi-pronged, precision-guided therapeutic approach tailored to individual biomarker profiles that could significantly improve outcomes by addressing both the infection and its inflammatory consequences. Despite these advances, our study faces limitations, including the need for broader validation across more diverse patient cohorts to ensure the generalizability of our findings and enhance their clinical applicability across varied populations. While our results have been rigorously validated using a discovery and validation cohort approach, supplemented by statistical and machine learning methods, additional independent validation in diverse demographic and clinical contexts would further solidify the translational potential of our biomarkers. Additionally, while our study links NET formation to oxidative stress, it does not sufficiently address the potential causal pathways or experimental validation of these relationships. Functional assays, such as those investigating the direct role of oxidative stress in NET formation or assessing the impact of inhibiting oxidative pathways on NET release, would be essential to strengthen these claims. Experimental validation through these assays could further elucidate the precise mechanisms and confirm the proposed pathways involved in P. aeruginosa pneumonia. Nevertheless, our study offers several strengths. It is the first to use an integrated multi-omics approach on both BALF and serum, providing a comprehensive view of both local and systemic responses to P. aeruginosa infection. This dual-sample analysis captures the dynamic interplay between localized and systemic immune responses, offering a novel perspective not previously explored. Our findings lay a valuable foundation for future research, guiding efforts towards precision medicine and targeted therapeutic interventions, which are critical for improving patient outcomes in complex infections. Further in-depth studies are needed to unravel the precise mechanisms underlying these responses and validate the therapeutic potential of the identified targets [[209]73]. Our study highlights promising therapeutic pathways, including the modulation of NET activity and oxidative stress, which could significantly improve patient outcomes. While the metabolites identified in this study are promising, their identity should ideally be confirmed through the use of authentic standards in future validation studies, where available, to further strengthen the accuracy of the findings. Future research should explore the interaction between cell death pathways in P. aeruginosa pneumonia and assess the therapeutic efficacy of targeting these pathways. Additionally, investigating the mechanisms underlying metabolic reprogramming and iron acquisition across diverse clinical settings could provide new insights into P. aeruginosa’s adaptability and inform innovative therapeutic strategies. Conclusions This study integrates proteomic and metabolomic analyses of both BALF and serum to comprehensively explore the local and systemic impacts of P. aeruginosa pneumonia. By examining both pulmonary and systemic responses, we identified key roles for NETs and oxidative stress in driving disease progression. Our findings highlight the need for therapeutic strategies that balance NETs activity and oxidative stress to minimize tissue damage while controlling infection. The biomarkers identified in both BALF and serum offer significant potential for improving diagnostic accuracy and developing targeted therapies. This dual-sample approach enhances our understanding of how localized infections trigger systemic consequences and demonstrates the power of multi-omics in revealing complex host-pathogen interactions. Future work should focus on validating these biomarkers and exploring their clinical applications, paving the way for more personalized treatments for P. aeruginosa pneumonia. Electronic supplementary material Below is the link to the electronic supplementary material. [210]12879_2025_11119_MOESM1_ESM.docx^ (1.5MB, docx) Supplementary Material 1: Additional files 2: Supplementary Table S1-S19. The original data and analysis results mentioned in the article. [211]12879_2025_11119_MOESM2_ESM.xlsx^ (258.9KB, xlsx) Supplementary Material 2: Additional files 1: Supplementary Figure S1-S9. Supplementary figures and legends mentioned in the article. Acknowledgements