Novel biomarker combination improves the diagnosis of serious bacterial infections in Malawian children

BMC Medical Genomics (Impact Factor: 2.87). 05/2012; 5(1):13. DOI: 10.1186/1755-8794-5-13
Source: PubMed


High throughput technologies offer insight into disease processes and heightens opportunities for improved diagnostics. Using transcriptomic analyses, we aimed to discover and to evaluate the clinical validity of a combination of reliable and functionally important biomarkers of serious bacterial infection (SBI).

We identified three previously reported biomarkers of infection (neutrophil gelatinase-associated lipocalin (NGAL), granulysin and resistin) and measured gene expression using quantitative real-time PCR. Protein products related to the three transcripts were measured by immunoassays.

Relative gene expression values of NGAL and resistin were significantly increased, and expression of granulysin significantly decreased in cases compared to controls. Plasma concentrations of NGAL and resistin were significantly increased in children with confirmed SBI compared to children with no detectable bacterial infection (NBI), and to controls (287 versus 128 versus 62 ng/ml and 195 versus 90 versus 18 ng/ml, respectively, p < 0.05). Plasma protein concentrations of NGAL and resistin were significantly increased in non-survivors compared to survivors (306 versus 211 and 214 versus 150 ng/ml, p = 0.02). The respective areas under the curve (AUC) for NGAL, resistin and procalcitonin in predicting SBI were 0.79, 0.80 and 0.86, whilst a combination of NGAL, resistin and procalcitonin achieved an AUC of 0.90.

We have demonstrated a unique combination of diagnostic biomarkers of SBI using transcriptomics, and demonstrated translational concordance with the corresponding protein. The addition of NGAL and resistin protein measurement to procalcitonin significantly improved the diagnosis of SBI.

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Available from: Philip J R Day, Dec 18, 2013
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    • "GNLY, KLRG1 and CX3CR1 genes were identified as potential gene biomarkers for the prediction of αβ T lymphocyte (CD4-/CD8+) counts. In humans, it has been shown that GNLY expressed in peripheral blood mononuclear cells (PBMC) is a biomarker for childhood and adolescent tuberculosis [45] and for the diagnosis of serious bacterial infections [46]. CXCR3 is a highly selective chemokine receptor and surface marker for cytotoxic effector lymphocytes, and KLRG1 is a surface marker used to predict the potential of CD8 effector T cells to differentiate into memory cells [47,48]. "
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    ABSTRACT: Immune traits (ITs) are potentially relevant criteria to characterize an individual's immune response. Our aim was to investigate whether the peripheral blood transcriptome can provide a significant and comprehensive view of IT variations in pig. Sixty-day-old Large White pigs classified as extreme for in vitro production of IL2, IL10, IFNgamma and TNFalpha, phagocytosis activity, in vivo CD4-/CD8+ or TCRgammadelta + cell counts, and anti-Mycoplasma antibody levels were chosen to perform a blood transcriptome analysis with a porcine generic array enriched with immunity-related genes. Differentially expressed (DE) genes for in vitro production of IL2 and IL10, phagocytosis activity and CD4-/CD8+ cell counts were identified. Gene set enrichment analysis revealed a significant over-representation of immune response functions. To validate the microarray-based results, a subset of DE genes was confirmed by RT-qPCR. An independent set of 74 animals was used to validate the covariation between gene expression levels and ITs. Five potential gene biomarkers were found for prediction of IL2 (RALGDS), phagocytosis (ALOX12) or CD4-/CD8+ cell count (GNLY, KLRG1 and CX3CR1). On average, these biomarkers performed with a sensitivity of 79% and a specificity of 86%. Our results confirmed that gene expression profiling in blood represents a relevant molecular phenotype to refine ITs in pig and to identify potential biomarkers that can provide new insights into immune response analysis.
    Full-text · Article · Dec 2013 · BMC Genomics
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