Plasma metabolomics reveals distinct biological and diagnostic signatures for melioidosis

Rationale: The global burden of sepsis is greatest in low-resource settings. Melioidosis, infection with the Gram-negative bacterium Burkholderia pseudomallei, is a frequent cause of fatal sepsis in endemic tropical regions such as Southeast Asia. Objectives: To investigate whether plasma metabolomics would identify biological pathways specific to melioidosis and yield clinically meaningful biomarkers. Methods: Using a comprehensive approach, differential enrichment of plasma metabolites and pathways were systematically evaluated in patients from a prospective cohort of individuals hospitalized in rural Thailand with infection. Statistical and bioinformatics methods were used to distinguish metabolomic features and processes specific to melioidosis patients, and between fatal and non-fatal cases. Measurements and Main Results: Metabolomic profiling and pathway enrichment analysis of plasma samples of melioidosis (n=175) and non-melioidosis infections (n=75) revealed a distinct immuno-metabolic state among patients with melioidosis, as suggested by excessive tryptophan catabolism in the kynurenine pathway and significantly increased lipid metabolism such as sphingomyelins and ceramide species. We derived a 12-metabolite classifier to distinguish melioidosis from other infections, with an area under the receiver operating characteristic curve of 0.87 in a second validation set of patients. Melioidosis non-survivors (n=94) had a significantly disturbed metabolome compared to survivors (n=81) with increased leucine, isoleucine and valine metabolism, and elevated circulating free fatty acids and acylcarnitines. A limited 8-metabolite panel shows promise as an early prognosticator of mortality in melioidosis. Conclusions: Melioidosis induces a distinct metabolomic state that can be leveraged to distinguish underlying pathophysiological mechanisms leading to increased risk of death. A twelve-metabolite signature accurately differentiates melioidosis from other infections and may have diagnostic applications. This article is open access and distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/

IL-1R2-based biomarker models predict melioidosis mortality independent of clinical data

Introduction: Melioidosis is an often-fatal tropical infectious disease caused by the Gram-negative bacillus Burkholderia pseudomallei, but few studies have identified promising biomarker candidates to predict outcome. Methods: In 78 prospectively enrolled patients hospitalized with melioidosis, six candidate protein biomarkers, identified from the literature, were measured in plasma at enrollment. A multi-biomarker model was developed using least absolute shrinkage and selection operator (LASSO) regression, and mortality discrimination was compared to a clinical variable model by receiver operating characteristic curve analysis. Mortality prediction was confirmed in an external validation set of 191 prospectively enrolled patients hospitalized with melioidosis. Results: LASSO regression selected IL-1R2 and soluble triggering receptor on myeloid cells 1 (sTREM-1) for inclusion in the candidate biomarker model. The areas under the receiver operating characteristic curve (AUC) for mortality discrimination for the IL-1R2 + sTREM-1 model (AUC 0.81, 95% CI 0.72–0.91) as well as for an IL-1R2-only model (AUC 0.78, 95% CI 0.68–0.88) were higher than for a model based on a modified Sequential Organ Failure Assessment (SOFA) score (AUC 0.69, 95% CI 0.56–0.81, p p = 0.03, respectively). In the external validation set, the IL-1R2 + sTREM-1 model (AUC 0.86, 95% CI 0.81–0.92) had superior 28-day mortality discrimination compared to a modified SOFA model (AUC 0.80, 95% CI 0.74–0.86, p p = 0.33). Conclusion: Biomarker models containing IL-1R2 had improved 28-day mortality prediction compared to clinical variable models in melioidosis and may be targets for future, rapid test development

Blood transcriptomics to characterize key biological pathways and identify biomarkers for predicting mortality in melioidosis.

Melioidosis is an often lethal tropical disease caused by the Gram-negative bacillus, Burkholderia pseudomallei. The study objective was to characterize transcriptomes in melioidosis patients and identify genes associated with outcome. Whole blood RNA-seq was performed in a discovery set of 29 melioidosis patients and 3 healthy controls. Transcriptomic profiles of patients who did not survive to 28 days were compared with patients who survived and healthy controls, showing 65 genes were significantly up-regulated and 218 were down-regulated in non-survivors compared to survivors. Up-regulated genes were involved in myeloid leukocyte activation, Toll-like receptor cascades and reactive oxygen species metabolic processes. Down-regulated genes were hematopoietic cell lineage, adaptive immune system and lymphocyte activation pathways. RT-qPCR was performed for 28 genes in a validation set of 60 melioidosis patients and 20 healthy controls, confirming differential expression. IL1R2, GAS7, S100A9, IRAK3, and NFKBIA were significantly higher in non-survivors compared with survivors (P < 0.005) and healthy controls (P < 0.0001). The AUROCC of these genes for mortality discrimination ranged from 0.80-0.88. In survivors, expression of IL1R2, S100A9 and IRAK3 genes decreased significantly over 28 days (P < 0.05). These findings augment our understanding of this severe infection, showing expression levels of specific genes are potential biomarkers to predict melioidosis outcomes