Integrative multi-omics decoding of the host response to Klebsiella pneumoniae infection reveals an itaconate-centred metabolic-immune axis and a spatially organised inflammatory programme
This study identifies a significant inflammatory response to Klebsiella pneumoniae infection, highlighting a 30-fold increase in pulmonary itaconate and a ten-gene signature that may help differentiate infected from non-infected states.
Where it sits
this study against the rest of the glutathione (gsh) corpusSummary and findings
This study examined the host response to Klebsiella pneumoniae infection using multi-omics approaches. It reported a 30-fold pulmonary itaconate accumulation and a ten-gene hub signature associated with the inflammatory response. The findings were based on an integration of 16 public datasets covering various modalities.
Abstract
<h4>Background: </h4> Klebsiella pneumoniae (Kp) is a leading cause of hospital and community acquired pneumonia and bacteraemia, and hypervirulent and carbapenem resistant lineages continue to expand worldwide. Although individual omics layers of Kp infection have been profiled, the host-level molecular logic that connects transcriptional, cellular, metabolic and spatial programmes has not been systematically integrated. <h4>Methods:</h4> We curated 16 public datasets spanning four modalities-bulk and dual host pathogen RNA-seq (six datasets; 118 libraries), single-cell RNA-seq (four datasets; ≈164,000 cells), untargeted/targeted metabolomics (four datasets; 1,842 molecular features) and Visium spatial transcriptomics (two datasets; ≈11,400 spots) covering murine lung infection models, hypervirulent and classical Kp strains, carbapenem-resistant isolates and human clinical specimens. Datasets were harmonised through a standardised pipeline (Salmon/DESeq2; Scanpy/Harmony; MetaboAnalyst; RCTD/spacexr), and integrated by WGCNA, GSVA, CellChat, Monocle3, SCENIC, joint-pathway analysis, RCTD deconvolution, and machine-learning feature selection (LASSO, random forest, SVM-RFE, XGBoost) with 1,000x bootstrap internal validation and independent external validation. <h4>Results:</h4> Kp infection evoked a coherent 920 gene neutrophil inflammatory response (≈520 up-/≈400 down-regulated genes) dominated by IL-17/TNF/NF-κB signalling, neutrophil degranulation, HIF-1α-linked glycolysis and itaconate biosynthesis. A turquoise co-expression module (r = 0.86 with infection status) yielded a ten-gene hub signature (IRG1, S100A9, IL1B, CXCL2, SLC7A11, HMOX1, PTGS2, NFKBIA, LST1, FCGR3B). Single-cell profiling resolved twelve major lung lineages and eleven myeloid substates, tracing a Monocle3 trajectory from Cxcr2hi inflammatory neutrophils through an Irg1/Slc7a11hi oxidative phase into Cxcr4hi/Trem2hi suppressive states; CellChat identified S100A8/A9-TLR4 and CXCL2-CXCR2 as the most amplified axes (8.5x and 4.2x). Metabolomics revealed 30-fold pulmonary itaconate accumulation together with succinate and lactate elevation, glucose/glutamine depletion and glutathione collapse, and joint-pathway analysis positioned the TCA-cycle itaconate and ferroptosis modules as the strongest convergent perturbations. Spatial transcriptomics mapped these programmes onto five anatomical niches, with IRG1 expression peaking in the infection core (58% neutrophil content) and declining radially. A ten-gene host-response classification model reached AUC=0.958 in discovery, 0.937 in internal validation and 0.884-0.912 in two independent external cohorts (Brier score 0.071). <h4>Conclusions:</h4> By vertically integrating public multi-omics data, we define an itaconate-centred metabolic-immune axis that couples inflammatory myeloid expansion to spatially segregated metabolic niches, and we deliver a validated host-side molecular signature that discriminates Kp infection from non-infected states.
Background
Klebsiella pneumoniae is a significant pathogen responsible for pneumonia and bacteraemia. Previous studies have profiled individual omics layers of Kp infection, but a comprehensive integration of these layers at the host level has not been performed. This study aims to fill that gap by systematically integrating various omics data to understand the host response to Kp infection.
Methods
The study curated 16 public datasets across four modalities, including bulk and dual host-pathogen RNA-seq, single-cell RNA-seq, metabolomics, and spatial transcriptomics. The total sample included 118 libraries, approximately 164,000 cells, 1,842 molecular features, and around 11,400 spots. Data integration involved various computational methods, including WGCNA and machine learning techniques, with internal and external validation.
Results
The primary endpoint indicated a coherent 920 gene neutrophil inflammatory response, with approximately 520 genes up-regulated and 400 down-regulated. The study reported a turquoise co-expression module with a correlation coefficient of r = 0.86 with infection status. Additionally, a ten-gene host-response classification model achieved an AUC of 0.958 in discovery and 0.937 in internal validation.
Interpretation
The findings suggest a significant inflammatory response to Kp infection, characterized by a notable accumulation of itaconate and a defined gene signature. While the statistical significance of the results is evident, the clinical relevance remains uncertain without direct evidence of patient outcomes. The reliance on mouse models and public datasets may limit the applicability of these findings to broader populations.
Key findings
- Kp infection evoked a coherent 920 gene neutrophil inflammatory response.
- 30-fold pulmonary itaconate accumulation alongside succinate and lactate elevation.
- A ten-gene host-response classification model reached AUC=0.958 in discovery.
- The turquoise co-expression module had r = 0.86 with infection status.
- 58% neutrophil content was observed in the infection core.
Limitations
- Relies on public datasets, which may introduce variability.
- Findings based on mouse models and human clinical specimens.
- Short follow-up duration not reported.
- Potential confounding factors not controlled for.