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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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Candidate blood transcriptional markers discriminate sepsis due to B. pseudomallei from sepsis due to other organisms. (a) Patients with sepsis in R5 of the training set (comprising eight patients with melioidosis (pink rectangles) and six patients with sepsis caused by other organisms (green rectangles)) were subjected to class prediction analysis (K-nearest neighbors (kNN)) using the leave-one-out cross-validation scheme. This algorithm identified 37 classifiers that discriminated samples with 100% accuracy in the training set. (b) Independent validation of the 37 predictors was performed with the equivalent region R9 in test set 1, including 11 patients with melioidosis (pink) and 7 patients with sepsis caused by other organisms (green). The predictors correctly classified 14 of the 18 samples (78% accuracy).
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Figure 6: Candidate blood transcriptional markers discriminate sepsis due to B. pseudomallei from sepsis due to other organisms. (a) Patients with sepsis in R5 of the training set (comprising eight patients with melioidosis (pink rectangles) and six patients with sepsis caused by other organisms (green rectangles)) were subjected to class prediction analysis (K-nearest neighbors (kNN)) using the leave-one-out cross-validation scheme. This algorithm identified 37 classifiers that discriminated samples with 100% accuracy in the training set. (b) Independent validation of the 37 predictors was performed with the equivalent region R9 in test set 1, including 11 patients with melioidosis (pink) and 7 patients with sepsis caused by other organisms (green). The predictors correctly classified 14 of the 18 samples (78% accuracy).

Mentions: We focused our biomarker discovery efforts on the prototypical signatures of sepsis established in both training and test sets. Samples clustering in R5 were used for the discovery of a diagnostic signature that distinguishes sepsis caused by B. pseudomallei from sepsis caused by other organisms. Class prediction identified a set of 37 classifiers that separated samples from the training set (R5; n = 14) with 100% accuracy in a leave-one-out cross-validation scheme (Figure 6a; K-nearest neighbors at cutoff P-value ratio = 0.9 and number of neighbors = 5). Next, the performance of this set of 37 candidate markers was evaluated independently. Samples from region R9 (n = 18) were classified with 78% accuracy (82% sensitivity and 71% specificity; Figure 6b; K-nearest neighbors), with two melioidosis samples and two samples from patients with other infection being incorrectly classified (Table S4 in Additional data file 2). The transcripts forming this candidate biomarker signature are listed in Table 5, with 33 transcripts found to be over-expressed in patients with septicemic melioidosis and 4 underexpressed (IQWD1, OLR1, AGPAT9, and ZNF281). Antigen processing and presentation is the strongest functional association identified for this set of 37 classifiers (P = 1 × 10-11, Fischer's exact test; Figure 7a). Some of the transcripts encode antigen processing and presentation (PSMB8, CD74) via major histocompatibility complex (MHC) class II molecules (HLA-DMA, HLA-DMB, HLA-DRA, HLA-DRB2, and HLA-DPA1), and the proteasome complex in the ubiquitin-proteasome system (UBE2L3, PSME2, PSMB2, and PSMB5) (Figure 7b). Some of the remaining transcripts are involved in proteolysis (LAP3, CFH, and OLR1), the inflammatory response (APOL3 and AIF1), apoptosis and programmed cell death (SEPT4, ELMO2, and ZAK), cellular metabolic processes (ZAK, ZNF281, SSB, WARS, MSRB2, MTHFD2, DUSP3, and ASPHD2), or protein transport (STX11). RARRES3 is involved in negative regulation of cellular process, LGALS3BP is related to the immune response, and MAPBPIP is associated with the activation of MAPKK activity. Finally, the list also includes genes that have not previously been associated with the immune response (IQWD1, FAM26F, C16orf75, AGPAT9, and C19orf12).


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)

Candidate blood transcriptional markers discriminate sepsis due to B. pseudomallei from sepsis due to other organisms. (a) Patients with sepsis in R5 of the training set (comprising eight patients with melioidosis (pink rectangles) and six patients with sepsis caused by other organisms (green rectangles)) were subjected to class prediction analysis (K-nearest neighbors (kNN)) using the leave-one-out cross-validation scheme. This algorithm identified 37 classifiers that discriminated samples with 100% accuracy in the training set. (b) Independent validation of the 37 predictors was performed with the equivalent region R9 in test set 1, including 11 patients with melioidosis (pink) and 7 patients with sepsis caused by other organisms (green). The predictors correctly classified 14 of the 18 samples (78% accuracy).
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Figure 6: Candidate blood transcriptional markers discriminate sepsis due to B. pseudomallei from sepsis due to other organisms. (a) Patients with sepsis in R5 of the training set (comprising eight patients with melioidosis (pink rectangles) and six patients with sepsis caused by other organisms (green rectangles)) were subjected to class prediction analysis (K-nearest neighbors (kNN)) using the leave-one-out cross-validation scheme. This algorithm identified 37 classifiers that discriminated samples with 100% accuracy in the training set. (b) Independent validation of the 37 predictors was performed with the equivalent region R9 in test set 1, including 11 patients with melioidosis (pink) and 7 patients with sepsis caused by other organisms (green). The predictors correctly classified 14 of the 18 samples (78% accuracy).
Mentions: We focused our biomarker discovery efforts on the prototypical signatures of sepsis established in both training and test sets. Samples clustering in R5 were used for the discovery of a diagnostic signature that distinguishes sepsis caused by B. pseudomallei from sepsis caused by other organisms. Class prediction identified a set of 37 classifiers that separated samples from the training set (R5; n = 14) with 100% accuracy in a leave-one-out cross-validation scheme (Figure 6a; K-nearest neighbors at cutoff P-value ratio = 0.9 and number of neighbors = 5). Next, the performance of this set of 37 candidate markers was evaluated independently. Samples from region R9 (n = 18) were classified with 78% accuracy (82% sensitivity and 71% specificity; Figure 6b; K-nearest neighbors), with two melioidosis samples and two samples from patients with other infection being incorrectly classified (Table S4 in Additional data file 2). The transcripts forming this candidate biomarker signature are listed in Table 5, with 33 transcripts found to be over-expressed in patients with septicemic melioidosis and 4 underexpressed (IQWD1, OLR1, AGPAT9, and ZNF281). Antigen processing and presentation is the strongest functional association identified for this set of 37 classifiers (P = 1 × 10-11, Fischer's exact test; Figure 7a). Some of the transcripts encode antigen processing and presentation (PSMB8, CD74) via major histocompatibility complex (MHC) class II molecules (HLA-DMA, HLA-DMB, HLA-DRA, HLA-DRB2, and HLA-DPA1), and the proteasome complex in the ubiquitin-proteasome system (UBE2L3, PSME2, PSMB2, and PSMB5) (Figure 7b). Some of the remaining transcripts are involved in proteolysis (LAP3, CFH, and OLR1), the inflammatory response (APOL3 and AIF1), apoptosis and programmed cell death (SEPT4, ELMO2, and ZAK), cellular metabolic processes (ZAK, ZNF281, SSB, WARS, MSRB2, MTHFD2, DUSP3, and ASPHD2), or protein transport (STX11). RARRES3 is involved in negative regulation of cellular process, LGALS3BP is related to the immune response, and MAPBPIP is associated with the activation of MAPKK activity. Finally, the list also includes genes that have not previously been associated with the immune response (IQWD1, FAM26F, C16orf75, AGPAT9, and C19orf12).

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