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Dysregulated immunologic landscape of the early host response in melioidosis
Patpong Rongkard, Lu Xia, Barbara Kronsteiner, Thatcha Yimthin, Rungnapa Phunpang, Adul Dulsuk, Viriya Hantrakun, Gumphol Wongsuvan, Parinya Chamnan, Lara Lovelace-Macon, Emanuele Marchi, Nicholas P.J. Day, Ali Shojaie, Direk Limmathurotsakul, Narisara Chantratita, Paul Klenerman, Susanna J. Dunachie, T. Eoin West, Sina A. Gharib
Patpong Rongkard, Lu Xia, Barbara Kronsteiner, Thatcha Yimthin, Rungnapa Phunpang, Adul Dulsuk, Viriya Hantrakun, Gumphol Wongsuvan, Parinya Chamnan, Lara Lovelace-Macon, Emanuele Marchi, Nicholas P.J. Day, Ali Shojaie, Direk Limmathurotsakul, Narisara Chantratita, Paul Klenerman, Susanna J. Dunachie, T. Eoin West, Sina A. Gharib
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Research Article Immunology Infectious disease

Dysregulated immunologic landscape of the early host response in melioidosis

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Abstract

Melioidosis, a neglected tropical infection caused by Burkholderia pseudomallei, commonly presents as pneumonia or sepsis with mortality rates up to 50% despite appropriate treatment. A better understanding of the early host immune response to melioidosis may lead to new therapeutic interventions and prognostication strategies to reduce disease burden. Whole blood transcriptomic signatures in 164 patients with melioidosis and in 70 patients with other infections hospitalized in northeastern Thailand enrolled within 24 hours following hospital admission were studied. Key findings were validated in an independent melioidosis cohort. Melioidosis was characterized by upregulation of interferon (IFN) signaling responses compared with other infections. Mortality in melioidosis was associated with excessive inflammation, enrichment of type 2 immune responses, and a dramatic decrease in T cell–mediated immunity compared with survivors. We identified and independently confirmed a 5-gene predictive set classifying fatal melioidosis (validation cohort area under the receiver operating characteristic curve 0.83; 95% CI, 0.67–0.99). This study highlights the intricate balance between innate and adaptive immunity during fatal melioidosis and can inform future precision medicine strategies for targeted therapies and prognostication in this severe infection.

Authors

Patpong Rongkard, Lu Xia, Barbara Kronsteiner, Thatcha Yimthin, Rungnapa Phunpang, Adul Dulsuk, Viriya Hantrakun, Gumphol Wongsuvan, Parinya Chamnan, Lara Lovelace-Macon, Emanuele Marchi, Nicholas P.J. Day, Ali Shojaie, Direk Limmathurotsakul, Narisara Chantratita, Paul Klenerman, Susanna J. Dunachie, T. Eoin West, Sina A. Gharib

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Figure 2

Transcriptomic profiling of fatal melioidosis in the Ubon-sepsis discovery cohort.

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Transcriptomic profiling of fatal melioidosis in the Ubon-sepsis discove...
(A) The core data analysis pipeline consists of 4 independent approaches: supervised data analysis, unsupervised data analysis, cell type deconvolution, and predictive classification. (B) Principal component analysis (PCA) of the top 1,000 most variable genes among patients with melioidosis in the Ubon-sepsis discovery cohort. Patients with melioidosis are divided into fatal (red dots, n = 84) and nonfatal (turquoise dots, n = 80) cases. Each ellipse represents 95% CI of bivariate t distribution. (C) Volcano plot of differentially expressed genes between fatal and nonfatal patients with melioidosis in the Ubon-sepsis discovery cohort. Dotted lines define a cut-off of differentially expressed genes based on absolute log2 fold change ≥ 1 (x axis) and adjusted P < 0.05 (y axis). Selected DEGs are labeled. (D) Corresponding overrepresentation analysis (ORA) between fatal and nonfatal patients with melioidosis in the Ubon-sepsis discovery cohort based on Reactome and KEGG gene sets. The top 10 overrepresented pathways are displayed by –log10 (FDR) values. ORA was performed on prefiltered upregulated DEGs based on a cut-off of absolute (log2 fold change ≥ 1) and adjusted P < 0.05. Created with BioRender.com.

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