Navalkar Krupa Arun, Johnston Stephen Albert, Woodbury Neal, Galgiani John N, Magee D Mitchell, Chicacz Zbigniew, Stafford Phillip
Center for Innovations in Medicine, Arizona State University, Tempe, Arizona, USA.
Valley Fever Center for Excellence, University of Arizona, Tucson, Arizona, USA.
Clin Vaccine Immunol. 2014 Aug;21(8):1169-77. doi: 10.1128/CVI.00228-14. Epub 2014 Jun 25.
Valley fever (VF) is difficult to diagnose, partly because the symptoms of VF are confounded with those of other community-acquired pneumonias. Confirmatory diagnostics detect IgM and IgG antibodies against coccidioidal antigens via immunodiffusion (ID). The false-negative rate can be as high as 50% to 70%, with 5% of symptomatic patients never showing detectable antibody levels. In this study, we tested whether the immunosignature diagnostic can resolve VF false negatives. An immunosignature is the pattern of antibody binding to random-sequence peptides on a peptide microarray. A 10,000-peptide microarray was first used to determine whether valley fever patients can be distinguished from 3 other cohorts with similar infections. After determining the VF-specific peptides, a small 96-peptide diagnostic array was created and tested. The performances of the 10,000-peptide array and the 96-peptide diagnostic array were compared to that of the ID diagnostic standard. The 10,000-peptide microarray classified the VF samples from the other 3 infections with 98% accuracy. It also classified VF false-negative patients with 100% sensitivity in a blinded test set versus 28% sensitivity for ID. The immunosignature microarray has potential for simultaneously distinguishing valley fever patients from those with other fungal or bacterial infections. The same 10,000-peptide array can diagnose VF false-negative patients with 100% sensitivity. The smaller 96-peptide diagnostic array was less specific for diagnosing false negatives. We conclude that the performance of the immunosignature diagnostic exceeds that of the existing standard, and the immunosignature can distinguish related infections and might be used in lieu of existing diagnostics.
山谷热（VF）难以诊断，部分原因是其症状与其他社区获得性肺炎的症状相互混淆。确诊诊断通过免疫扩散（ID）检测针对球孢子菌抗原的IgM和IgG抗体。假阴性率可高达50%至70%，5%有症状的患者从未显示出可检测到的抗体水平。在本研究中，我们测试了免疫特征诊断能否解决山谷热的假阴性问题。免疫特征是抗体与肽微阵列上随机序列肽的结合模式。首先使用一个包含10000种肽的微阵列来确定山谷热患者是否可以与其他3个有相似感染情况的队列区分开来。确定山谷热特异性肽后，制作并测试了一个小型的包含96种肽的诊断阵列。将包含10000种肽的阵列和包含96种肽的诊断阵列的性能与ID诊断标准的性能进行了比较。包含10000种肽的微阵列对山谷热样本与其他3种感染的分类准确率为98%。在一个盲法测试集中，它对山谷热假阴性患者的分类灵敏度为100%，而ID的灵敏度为28%。免疫特征微阵列有潜力同时将山谷热患者与其他真菌或细菌感染患者区分开来。同一个包含10000种肽的阵列对山谷热假阴性患者的诊断灵敏度可达100%。较小的包含96种肽的诊断阵列在诊断假阴性方面特异性较低。我们得出结论，免疫特征诊断的性能超过了现有标准，免疫特征可以区分相关感染，并且可能用于替代现有诊断方法。
