Dataset Information

A proteomic approach for the diagnosis of bacterial meningitis.

ABSTRACT: BACKGROUND: The discrimination of bacterial meningitis (BM) versus viral meningitis (VM) shapes up as a problem, when laboratory data are not equivocal, in particular, when Gram stain is negative. METHODOLOGY/PRINCIPAL FINDINGS: With the aim to determine reliable marker for bacterial or viral meningitis, we subjected cerebrospinal fluid (CSF) to a quantitative proteomic screening. By using a recently established 2D-DIGE protocol which was adapted to the individual CSF flow, we compared a small set of patients with proven BM and VM. Thereby, we identified six potential biomarkers out of which Prostaglandin-H2 D-isomerase was already described in BM, showing proof of concept. In the subsequent validation phase on a more comprehensive collective of 80 patients, we could validate that in BM high levels of glial fibrillary acidic protein (GFAP) and low levels of soluble amyloid precursor protein alpha/beta (sAPPalpha/beta) are present as possible binding partner of Fibulin-1. CONCLUSIONS/SIGNIFICANCE: We conclude that our CSF flow-adapted 2D-DIGE protocol is valid especially in comparing samples with high differences in total protein and suppose that GFAP and sAPPalpha/beta have a high potential as additional diagnostic markers for differentiation of BM from VM. In the clinical setting, this might lead to an improved early diagnosis and to an individual therapy.

SUBMITTER: Jesse S

PROVIDER: S-EPMC2851643 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A proteomic approach for the diagnosis of bacterial meningitis.

Jesse Sarah S Steinacker Petra P Lehnert Stefan S Sdzuj Martin M Cepek Lukas L Tumani Hayrettin H Jahn Olaf O Schmidt Holger H Otto Markus M

PloS one 20100408 4

<h4>Background</h4>The discrimination of bacterial meningitis (BM) versus viral meningitis (VM) shapes up as a problem, when laboratory data are not equivocal, in particular, when Gram stain is negative.<h4>Methodology/principal findings</h4>With the aim to determine reliable marker for bacterial or viral meningitis, we subjected cerebrospinal fluid (CSF) to a quantitative proteomic screening. By using a recently established 2D-DIGE protocol which was adapted to the individual CSF flow, we compa ...[more]

PMID: 20386697

Similar Datasets

Project description:BackgroundThe differential diagnosis between tuberculous meningitis (TBM) and bacterial meningitis (BM) remains challenging in clinical practice. This study aimed to establish a diagnostic model that could accurately distinguish TBM from BM.MethodsPatients with TBM or BM were recruited between January 2017 and January 2021 at Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort). The detection for indicators involved in cerebrospinal fluid (CSF) and T-SPOT assay were performed simultaneously. Multivariate logistic regression was used to create a diagnostic model.ResultsA total of 174 patients (76 TBM and 98 BM) and another 105 cases (39 TBM and 66 BM) were enrolled from Qiaokou cohort and Caidian cohort, respectively. Significantly higher level of CSF lymphocyte proportion while significantly lower levels of CSF chlorine, nucleated cell count, and neutrophil proportion were observed in TBM group when comparing with those in BM group. However, receiver operating characteristic (ROC) curve analysis showed that the areas under the ROC curve (AUCs) produced by these indicators were all under 0.8. Meanwhile, tuberculosis-specific antigen/phytohemagglutinin (TBAg/PHA) ratio yielded an AUC of 0.889 (95% CI, 0.840-0.938) in distinguishing TBM from BM, with a sensitivity of 68.42% (95% CI, 57.30%-77.77%) and a specificity of 92.86% (95% CI, 85.98%-96.50%) when a cutoff value of 0.163 was used. Consequently, we successfully established a diagnostic model based on the combination of TBAg/PHA ratio, CSF chlorine, CSF nucleated cell count, and CSF lymphocyte proportion for discrimination between TBM and BM. The established model showed good performance in differentiating TBM from BM (AUC: 0.949; 95% CI, 0.921-0.978), with 81.58% (95% CI, 71.42%-88.70%) sensitivity and 91.84% (95% CI, 84.71%-95.81%) specificity. The performance of the diagnostic model obtained in Qiaokou cohort was further validated in Caidian cohort. The diagnostic model in Caidian cohort produced an AUC of 0.923 (95% CI, 0.867-0.980) with 79.49% (95% CI, 64.47%-89.22%) sensitivity and 90.91% (95% CI, 81.55%-95.77%) specificity.ConclusionsThe diagnostic model established based on the combination of four indicators had excellent utility in the discrimination between TBM and BM.

Dataset Information

A proteomic approach for the diagnosis of bacterial meningitis.

Publications

A proteomic approach for the diagnosis of bacterial meningitis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets