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

Pankla R, Buddhisa S, Berry M, Blankenship DM, Bancroft GJ, Banchereau J, Lertmemongkolchai G, Chaussabel D - Genome Biol. (2009)

Bottom Line: Better diagnostic tests are therefore needed to improve therapeutic efficacy and survival rates.This finding was confirmed in 2 independent validation sets, which gave high prediction accuracies of 78% and 80%, respectively.This signature was significantly enriched in genes coding for products involved in the MHC class II antigen processing and presentation pathway.

View Article: PubMed Central - HTML - PubMed

Affiliation: 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. Vrungnapa@gmail.com

ABSTRACT

Background: Melioidosis is a severe infectious disease caused by Burkholderia pseudomallei, a Gram-negative bacillus classified by the National Institute of Allergy and Infectious Diseases (NIAID) as a category B priority agent. Septicemia is the most common presentation of the disease with a 40% mortality rate even with appropriate treatments. Better diagnostic tests are therefore needed to improve therapeutic efficacy and survival rates.

Results: We have used microarray technology to generate genome-wide transcriptional profiles (>48,000 transcripts) from the whole blood of patients with septicemic melioidosis (n = 32), patients with sepsis caused by other pathogens (n = 31), and uninfected controls (n = 29). Unsupervised analyses demonstrated the existence of a whole blood transcriptional signature distinguishing patients with sepsis from control subjects. The majority of changes observed were common to both septicemic melioidosis and sepsis caused by other infections, including genes related to inflammation, interferon-related genes, neutrophils, cytotoxic cells, and T-cells. Finally, class prediction analysis identified a 37 transcript candidate diagnostic signature that distinguished melioidosis from sepsis caused by other organisms with 100% accuracy in a training set. This finding was confirmed in 2 independent validation sets, which gave high prediction accuracies of 78% and 80%, respectively. This signature was significantly enriched in genes coding for products involved in the MHC class II antigen processing and presentation pathway.

Conclusions: Blood transcriptional patterns distinguish patients with septicemic melioidosis from patients with sepsis caused by other pathogens. Once confirmed in a large scale trial this diagnostic signature might constitute the basis of a differential diagnostic assay.

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Related in: MedlinePlus

Unsupervised hierarchical clustering of blood transcriptional profiles of septic patients. Transcripts with 2-fold over- or under-expression compared with the median of all samples and differential expression values greater than 200 from the median for each gene in at least 2 samples in the training set were selected for unsupervised analysis (n = 2,785 transcripts). (a) A heatmap resulting from hierarchical clustering of transcripts and conditions (subjects) was generated for the training set. (b) The same gene tree of these 2,785 transcripts was then used to generate a heatmap for the first independent test set (test set 1) using hierarchical clustering of conditions as before. The color conventions for heatmaps are as follows: red indicates over-expressed transcripts; blue represents underexpressed transcripts; and yellow indicates transcripts that do not deviate from the median. Study group is marked as follows: patients with melioidosis are indicated by pink rectangles; patients with sepsis due to other organisms by green rectangles; uninfected controls who recovered from melioidosis by black rectangles; T2D patients by purple rectangles; and healthy donors by blue rectangles. This unsupervised hierarchical clustering of blood transcriptional profiles was observed to segregate into five distinct regions in both training (regions R1 to R5) and test sets (regions R6 to R10).
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Figure 2: Unsupervised hierarchical clustering of blood transcriptional profiles of septic patients. Transcripts with 2-fold over- or under-expression compared with the median of all samples and differential expression values greater than 200 from the median for each gene in at least 2 samples in the training set were selected for unsupervised analysis (n = 2,785 transcripts). (a) A heatmap resulting from hierarchical clustering of transcripts and conditions (subjects) was generated for the training set. (b) The same gene tree of these 2,785 transcripts was then used to generate a heatmap for the first independent test set (test set 1) using hierarchical clustering of conditions as before. The color conventions for heatmaps are as follows: red indicates over-expressed transcripts; blue represents underexpressed transcripts; and yellow indicates transcripts that do not deviate from the median. Study group is marked as follows: patients with melioidosis are indicated by pink rectangles; patients with sepsis due to other organisms by green rectangles; uninfected controls who recovered from melioidosis by black rectangles; T2D patients by purple rectangles; and healthy donors by blue rectangles. This unsupervised hierarchical clustering of blood transcriptional profiles was observed to segregate into five distinct regions in both training (regions R1 to R5) and test sets (regions R6 to R10).

Mentions: This set of 2,785 transcripts was used in an unsupervised hierarchical clustering analysis where transcripts are ordered horizontally and samples (conditions) vertically, according to similarities in expression patterns (Figure 2a). The resulting heatmap reveals the molecular heterogeneity of this sample set. The molecular classification obtained through hierarchical clustering is then compared with phenotypic classification of the samples: out of the ten uninfected controls, nine samples were clustered together on a branch of the condition tree (region R1) that is distinct from that of septicemic patients (regions R2, R4, and R5). One outlying uninfected control clustered together with septicemic patients (sample R001 in region R3). The expression pattern for this outlying sample appeared nonetheless distinct from that of septicemia and it was excluded from subsequent class comparison analyses.


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

Pankla R, Buddhisa S, Berry M, Blankenship DM, Bancroft GJ, Banchereau J, Lertmemongkolchai G, Chaussabel D - Genome Biol. (2009)

Unsupervised hierarchical clustering of blood transcriptional profiles of septic patients. Transcripts with 2-fold over- or under-expression compared with the median of all samples and differential expression values greater than 200 from the median for each gene in at least 2 samples in the training set were selected for unsupervised analysis (n = 2,785 transcripts). (a) A heatmap resulting from hierarchical clustering of transcripts and conditions (subjects) was generated for the training set. (b) The same gene tree of these 2,785 transcripts was then used to generate a heatmap for the first independent test set (test set 1) using hierarchical clustering of conditions as before. The color conventions for heatmaps are as follows: red indicates over-expressed transcripts; blue represents underexpressed transcripts; and yellow indicates transcripts that do not deviate from the median. Study group is marked as follows: patients with melioidosis are indicated by pink rectangles; patients with sepsis due to other organisms by green rectangles; uninfected controls who recovered from melioidosis by black rectangles; T2D patients by purple rectangles; and healthy donors by blue rectangles. This unsupervised hierarchical clustering of blood transcriptional profiles was observed to segregate into five distinct regions in both training (regions R1 to R5) and test sets (regions R6 to R10).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3091321&req=5

Figure 2: Unsupervised hierarchical clustering of blood transcriptional profiles of septic patients. Transcripts with 2-fold over- or under-expression compared with the median of all samples and differential expression values greater than 200 from the median for each gene in at least 2 samples in the training set were selected for unsupervised analysis (n = 2,785 transcripts). (a) A heatmap resulting from hierarchical clustering of transcripts and conditions (subjects) was generated for the training set. (b) The same gene tree of these 2,785 transcripts was then used to generate a heatmap for the first independent test set (test set 1) using hierarchical clustering of conditions as before. The color conventions for heatmaps are as follows: red indicates over-expressed transcripts; blue represents underexpressed transcripts; and yellow indicates transcripts that do not deviate from the median. Study group is marked as follows: patients with melioidosis are indicated by pink rectangles; patients with sepsis due to other organisms by green rectangles; uninfected controls who recovered from melioidosis by black rectangles; T2D patients by purple rectangles; and healthy donors by blue rectangles. This unsupervised hierarchical clustering of blood transcriptional profiles was observed to segregate into five distinct regions in both training (regions R1 to R5) and test sets (regions R6 to R10).
Mentions: This set of 2,785 transcripts was used in an unsupervised hierarchical clustering analysis where transcripts are ordered horizontally and samples (conditions) vertically, according to similarities in expression patterns (Figure 2a). The resulting heatmap reveals the molecular heterogeneity of this sample set. The molecular classification obtained through hierarchical clustering is then compared with phenotypic classification of the samples: out of the ten uninfected controls, nine samples were clustered together on a branch of the condition tree (region R1) that is distinct from that of septicemic patients (regions R2, R4, and R5). One outlying uninfected control clustered together with septicemic patients (sample R001 in region R3). The expression pattern for this outlying sample appeared nonetheless distinct from that of septicemia and it was excluded from subsequent class comparison analyses.

Bottom Line: Better diagnostic tests are therefore needed to improve therapeutic efficacy and survival rates.This finding was confirmed in 2 independent validation sets, which gave high prediction accuracies of 78% and 80%, respectively.This signature was significantly enriched in genes coding for products involved in the MHC class II antigen processing and presentation pathway.

View Article: PubMed Central - HTML - PubMed

Affiliation: 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. Vrungnapa@gmail.com

ABSTRACT

Background: Melioidosis is a severe infectious disease caused by Burkholderia pseudomallei, a Gram-negative bacillus classified by the National Institute of Allergy and Infectious Diseases (NIAID) as a category B priority agent. Septicemia is the most common presentation of the disease with a 40% mortality rate even with appropriate treatments. Better diagnostic tests are therefore needed to improve therapeutic efficacy and survival rates.

Results: We have used microarray technology to generate genome-wide transcriptional profiles (>48,000 transcripts) from the whole blood of patients with septicemic melioidosis (n = 32), patients with sepsis caused by other pathogens (n = 31), and uninfected controls (n = 29). Unsupervised analyses demonstrated the existence of a whole blood transcriptional signature distinguishing patients with sepsis from control subjects. The majority of changes observed were common to both septicemic melioidosis and sepsis caused by other infections, including genes related to inflammation, interferon-related genes, neutrophils, cytotoxic cells, and T-cells. Finally, class prediction analysis identified a 37 transcript candidate diagnostic signature that distinguished melioidosis from sepsis caused by other organisms with 100% accuracy in a training set. This finding was confirmed in 2 independent validation sets, which gave high prediction accuracies of 78% and 80%, respectively. This signature was significantly enriched in genes coding for products involved in the MHC class II antigen processing and presentation pathway.

Conclusions: Blood transcriptional patterns distinguish patients with septicemic melioidosis from patients with sepsis caused by other pathogens. Once confirmed in a large scale trial this diagnostic signature might constitute the basis of a differential diagnostic assay.

Show MeSH
Related in: MedlinePlus