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Identification of disease-specific motifs in the antibody specificity repertoire via next-generation sequencing

机译:通过下一代测序鉴定抗体特异性库中的疾病特异性基序

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Disease-specific antibodies can serve as highly effective biomarkers but have been identified for only a relatively small number of autoimmune diseases. A method was developed to identify disease-specific binding motifs through integration of bacterial display peptide library screening, next-generation sequencing (NGS) and computational analysis. Antibody specificity repertoires were determined by identifying bound peptide library members for each specimen using cell sorting and performing NGS. A computational algorithm, termed Identifying Motifs Using Next- generation sequencing Experiments (IMUNE), was developed and applied to discover disease- and healthy control-specific motifs. IMUNE performs comprehensive pattern searches, identifies patterns statistically enriched in the disease or control groups and clusters the patterns to generate motifs. Using celiac disease sera as a discovery set, IMUNE identified a consensus motif (QPEQPF[PS]E) with high diagnostic sensitivity and specificity in a validation sera set, in addition to novel motifs. Peptide display and sequencing (Display-Seq) coupled with IMUNE analysis may thus be useful to characterize antibody repertoires and identify disease-specific antibody epitopes and biomarkers.
机译:疾病特异性抗体可以用作高效的生物标志物,但仅针对相对少量的自身免疫疾病进行了鉴定。开发了一种通过整合细菌展示肽文库筛选,下一代测序(NGS)和计算分析来识别疾病特异性结合基序的方法。通过使用细胞分选和进行NGS鉴定每个标本的结合肽库成员来确定抗体特异性库。开发了一种计算算法,称为使用下一代测序实验(IMUNE)识别母题,并将其应用于发现疾病和健康对照组的特定基序。 IMUNE进行全面的模式搜索,识别在统计学上在疾病或对照组中丰富的模式,并对模式进行聚类以生成模式。使用腹腔疾病血清作为发现集,IMUNE鉴定了一个共有基序(QPEQPF [PS] E),除了一个新颖的基序外,它还在一个验证的血清集中具有很高的诊断敏感性和特异性。肽展示和测序(Display-Seq)结合IMUNE分析可用于表征抗体库并鉴定疾病特异性抗体表位和生物标记。

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