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Quantification of CSF chemokines and cytokines allows for rapid laboratory detection of CNS infections and further discrimination between viral and non-viral pathogens

机译：CsF趋化因子和细胞因子的定量允许快速实验室检测CNs感染并进一步区分病毒和非病毒病原体

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Background: Prompt diagnosis of central nervous system (CNS) disease is critical to guide intervention and appropriate therapy. Development of novel laboratory approaches to rapidly classify acute-onset CNS disease is in great demand. Serious microbial pathogens, especially viruses, are quickly expanding beyond their historic geographic range and may not even be considered in the clinician’s differential diagnosis. Unlike bacterial cultures, current viral testing targets a limited number of viruses. Additionally, despite diversity in etiology, signs and symptoms of both infectious and non-infectious CNS disorders can be remarkably similar, which can confuse the clinical picture and delay treatment. Bacterial, viral, fungal and protozoan CNS pathogens are sensed by pattern recognition receptors of the immune system, stimulating immediate release of measurable levels of chemokines and cytokines into the CSF. Our objective is to use pathogen-specific chemokine/ cytokine profiles to classify CNS disease as infectious versus non-infectious and further discriminate between viral and non-viral infections.Methods: Levels (pg/ml) of chemokines and cytokines were determined in the CSF of 45 patients with documented infectious meningitis or meningoencephalitis (mean age 19.2 years) and in the CSF of 25 patients who were negative for CNS infection (mean age 27.4 years). MILLIPLEX MAP Human Cytokine/Chemokine Magnetic Bead Panels (Millipore) were used to measure CSF chemokines and cytokines levels (pg/ml). Innate immune analytes quantified included IP-10 (CXCL10), IFNg, IL-15, MDC (CCL22), MCP-1 (CCL2), Fractalkine, and FLT3L. Samples were analyzed in duplicate by a FlexMAP 3D (Luminex). Standard curves were generated for each cytokine and median fluorescent intensities were transformed into concentrations by 5-point, non-linear regression. For univariate analysis, comparisons between groups were made using the Mann-Whitney test. We utilized receiver operating characteristic (ROC) curve analysis to calculate areas under the ROC curve (AUC) for each analyte to access the utility of chemokine/cytokine levels as discriminating tests. The ROC generated sensitivity and specificity values were then used to determine clinically optimal cutoff values for the informative analytes.Results: Univariate analysis utilizing Mann-Whitney tests demonstrated that median values (pg/ml) of IP-10 (CXCL10), IFNg, IL-15, MDC (CCL22), MDC (CCL22), MCP-1 (CCL2), Fractalkine, and FLT3L were all significantly higher in CSF from patients with infectious brain disorders than in CSF from patients with non-infectious disorders (p-value u3c 0.05). MDC (CCL22) demonstrated statistical significance, when comparing viral infections versus non-viral infections (with the non-viral infection group having higher analyte levels). IP10 (CXCL10) can reliably distinguish between an infectious versus non-infectious CNS process (AUC 0.9778) with an optimal cut-off value of 2023 pg/ml (sensitivity, specificity; 93.0%, 92.0%). In the infectious group, MDC (CCL22) can reliably differentiate between viral and non-viral CNS infection (AUC 0.9545) with an optimal cut-off value of 194 pg/ml (sensitivity, specificity; 91.67%, 87.88%).Conclusion: CSF levels (pg/ml) of IP-10 (CXCL10) can reliably distinguish infectious versus noninfectious CNS disorders, and in the infectious group, MDC (CCL22) can reliably distinguish between viral and non-viral CNS infections. These results suggest that CSF chemokine/cytokine quantification can serve as a useful laboratory tool for the rapid triage of CNS diseases to help guide prompt therapy and further testing.
机译：背景：及时诊断中枢神经系统（CNS）疾病对于指导干预和适当的治疗至关重要。迫切需要开发新颖的实验室方法以快速分类急性发作的中枢神经系统疾病。严重的微生物病原体，尤其是病毒，正在迅速扩展到其历史地理范围之外，甚至可能不在临床医生的鉴别诊断中考虑。与细菌培养不同，当前的病毒检测针对的病毒数量有限。此外，尽管病因学上存在差异，但传染性和非传染性中枢神经系统疾病的体征和症状可能非常相似，这可能使临床状况混乱并延误治疗。免疫系统的模式识别受体可检测细菌，病毒，真菌和原生动物中枢神经系统病原体，刺激可测量水平的趋化因子和细胞因子立即释放到脑脊液中。我们的目标是使用病原体特异性趋化因子/细胞因子概况将中枢神经系统疾病分类为感染性与非感染性，并进一步区分病毒感染和非病毒感染。记录的45例感染性脑膜炎或脑膜脑炎患者（平均年龄19.2岁）和25例中枢神经系统感染阴性（平均年龄27.4岁）的脑脊液中。 MILLIPLEX MAP人类细胞因子/趋化因子磁珠板（Millipore）用于测量CSF趋化因子和细胞因子水平（pg / ml）。定量的先天免疫分析物包括IP-10（CXCL10），IFNg，IL-15，MDC（CCL22），MCP-1（CCL2），Fractalkine和FLT3L。样品通过FlexMAP 3D（Luminex）一式两份进行分析。为每种细胞因子生成标准曲线，并通过5点非线性回归将中值荧光强度转换为浓度。对于单变量分析，使用Mann-Whitney检验进行组之间的比较。我们利用接收器工作特征（ROC）曲线分析来计算每种分析物在ROC曲线（AUC）下的面积，以获取趋化因子/细胞因子水平的效用作为区分测试。然后，将ROC产生的灵敏度和特异性值用于确定信息量丰富的分析物的临床最佳临界值。结果：使用Mann-Whitney试验进行的单变量分析表明，IP-10（CXCL10），IFNg，IL的中位数（pg / ml） -15，感染性脑疾病患者的CSF中的MDC（CCL22），MDC（CCL22），MCP-1（CCL2），Fractalkine和FLT3L均显着高于非感染性疾病患者的CSF中（p值 u3c 0.05）。当比较病毒感染与非病毒感染（非病毒感染组的分析物水平较高）时，MDC（CCL22）具有统计学意义。 IP10（CXCL10）可以可靠地区分传染性和非传染性中枢神经系统过程（AUC 0.9778），其最佳临界值为2023 pg / ml（敏感性，特异性； 93.0％，92.0％）。在感染组中，MDC（CCL22）可以可靠地区分病毒和非病毒中枢神经系统感染（AUC 0.9545），最佳临界值为194 pg / ml（敏感性，特异性; 91.67％，87.88％）。 IP-10（CXCL10）的CSF水平（pg / ml）可以可靠地区分感染性和非感染性CNS疾病，在感染组中，MDC（CCL22）可以可靠地区分病毒性和非病毒性CNS感染。这些结果表明，脑脊液趋化因子/细胞因子的定量可以作为快速分类中枢神经系统疾病的有用实验室工具，以帮助指导及时治疗和进一步测试。

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