Open Access Research

Genomic transcriptional profiling identifies a candidate blood biomarker signature for the diagnosis of septicemic melioidosis

Rungnapa Pankla12, Surachat Buddhisa1, Matthew Berry3, Derek M Blankenship4, Gregory J Bancroft5, Jacques Banchereau2, Ganjana Lertmemongkolchai1* and Damien Chaussabel2*

Author Affiliations

1 Department of Clinical Immunology, Centre for Research and Development of Medical Diagnostic Laboratories, Faculty of Associated Medical Sciences, Khon Kaen University, 123 Mittraparp Road, Khon Kaen, 40002, Thailand

2 Baylor-National Institute of Allergy and Infectious Diseases (NIAID), Cooperative Center for Translational Research on Human Immunology and Biodefense, Baylor Institute for Immunology Research and Baylor Research Institute, 3434 Live Oak St, Dallas, Texas, 75204, USA

3 Division of Immunoregulation, National Institute for Medical Research, The Ridgeway, Mill Hill, London, NW7 1AA, UK

4 Institute for Health Care Research and Improvement, Baylor Health Care System, 8080 N. Central Expressway Suite 500, Dallas, Texas, 75206, USA

5 Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel St, London, WC1E 7HT, UK

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Genome Biology 2009, 10:R127  doi:10.1186/gb-2009-10-11-r127

Published: 10 November 2009

Additional files

Additional data file 1:

Specific information regarding individual patients enrolled in this study.

Format: XLSX Size: 23KB Download file

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Additional data file 2:

Figure S1 shows the results from a PCA based on 2,785 genes that passed the filtering criteria of 2-fold change and 200 differences from the raw intensity of individual patients when compared to the median intensity across all samples. Figure S2 represents the Gene Ontology term enrichment analysis of 2,785 transcripts forming the unsupervised hierarchical clustering heatmap shown in Figure 2. Figure S3 shows genes that are differentially expressed between septic patients and uninfected controls. Figure S4 shows blood transcriptional expression profiles of neutrophil-related genes in patients with sepsis when compared to uninfected controls. Figure S5 shows linear regression and correlation coefficients of the expression signals obtained from qPCR and microarray analyses. Table S1 lists the hematological data from all patients. Table S2 lists the genes with significant differences in expression between patients with sepsis and uninfected controls. Table S3 lists predictor genes that differentiate septic patients from non-infected controls. Table S4 shows the summary of class prediction analysis.

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