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Preprint WatchModerateOctober 31st, 2026

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

Zhao, Y.; Cheng, H.; Zhou, S.; He, L.; Xiu, C.; Huang, B.; Lu, X.

Integrative multi-omics of 16 public datasets positions the S100A8/A9 to TLR4 interaction as the most amplified cell-cell signalling axis in the host response to Klebsiella pneumoniae infection, at 8.5 times baseline, within a neutrophil inflammatory programme dominated by IL-17, TNF and NF-kappaB signalling.

Moderate contradiction

2 prior failures

Two documented clinical failures match this mechanism, or a single Phase 3 failure is on record.

This preprint elevates the same alarmin to receptor interaction that Claidex documented as the stated rationale of a terminated clinical programme. The S100A8/A9 to TLR4 axis named here as the most amplified signalling channel in Klebsiella infection is the axis the paridiprubart protocol cited when it proposed blocking TLR4 in COVID-19 associated respiratory failure, recorded in paridiprubart-eb05-tlr4-covid19-ards-phase2-3-sponsor-termination, a Phase 2/3 study that reported a covariate-adjusted mortality benefit in 278 patients and was then terminated for business reasons before its registered 586-patient confirmatory stage ran. The second TLR4 entry, tak-242-resatorvid-tlr4-acute-on-chronic-liver-failure-phase2-enrollment-collapse, never reached a readout at all. The claim here is a computational inference from public datasets rather than an intervention, so it strengthens the associative case for the axis without addressing the gap that sank both prior programmes, which is the absence of a completed, confirmatory, published clinical trial of TLR4 blockade in any acute inflammatory syndrome.

Abstract excerpt

Background: 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. Methods: 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; {approx}164,000 cells), untargeted/targeted metabolomics (four datasets; 1,842 molecular features) and Visium spatial transcriptomics (two datasets; {approx}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. Results: Kp infection evoked a coherent 920 gene neutrophil inflammatory response ({approx}520 up-/{approx}400 down-regulated genes) dominated by IL-17/TNF/NF-{kappa}B signalling, neutrophil degranulation, HIF-1-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). Conclusions: 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.

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This is an automated contradiction flag, not an editorial judgment on the preprint's quality. Flags identify where the preclinical literature and the clinical failure record diverge.