Istituto di Chimica del Riconoscimento Molecolare, ICRM, CNR. Via Mario Bianco 9, 20131, Milano (Italy).
Cystic Fibrosis Microbiology Laboratory, Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico, via San Barnaba 8, 20122, Milano (Italy).
Sci Rep. 2016 Sep 12;6:32873. doi: 10.1038/srep32873.
Efficient diagnosis of emerging and novel bacterial infections is fundamental to guide decisions on therapeutic treatments. Here, we engineered a novel rational strategy to design peptide microarray platforms, which combines structural and genomic analyses to predict the binding interfaces between diverse protein antigens and antibodies against Burkholderia cepacia complex infections present in the sera of Cystic Fibrosis (CF) patients. The predicted binding interfaces on the antigens are synthesized in the form of isolated peptides and chemically optimized for controlled orientation on the surface. Our platform displays multiple Burkholderia-related epitopes and is shown to diagnose infected individuals even in presence of superinfections caused by other prevalent CF pathogens, with limited cost and time requirements. Moreover, our data point out that the specific patterns determined by combined probe responses might provide a characterization of Burkholderia infections even at the subtype level (genomovars). The method is general and immediately applicable to other bacteria.
快速诊断新发和新型细菌感染对于指导治疗决策至关重要。在这里，我们设计了一种新的合理策略来设计肽微阵列平台，该平台结合结构和基因组分析，预测不同蛋白抗原与囊性纤维化（CF）患者血清中存在的抗伯克霍尔德菌复合感染的抗体之间的结合界面。抗原上预测的结合界面以分离肽的形式合成，并经过化学优化以实现表面的受控取向。我们的平台显示了多个与伯克霍尔德菌相关的表位，即使在存在其他常见 CF 病原体引起的合并感染的情况下，也能够诊断感染个体，而且成本和时间要求有限。此外，我们的数据指出，通过组合探针反应确定的特定模式甚至可能在亚型水平（基因组变种）上提供对伯克霍尔德菌感染的特征描述。该方法具有通用性，可立即应用于其他细菌。
