Multi-omics driven biomarker discovery and pathological insights into Pseudomonas aeruginosa pneumonia

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.

Keywords: Pseudomonas aeruginosa; Biomarker; Multi-omics; Neutrophil extracellular traps; Oxidative stress.

Fig. 1

Integrated Multi-Omics analysis for P. aeruginosa pneumonia Investigation

Fig. 2

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

Fig. 3

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

Fig. 4

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

Fig. 5

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

Fig. 6

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

Fig. 7

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

Fig. 8

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