High-throughput Identification of DNA-Encoded IgG Ligands that Distinguish Active and Latent Mycobacterium tuberculosis Infections.

ACS chemical biology(2017)

引用 51|浏览21
暂无评分
摘要
The circulating antibody repertoire encodes a patient's health status and pathogen exposure history, but identifying antibodies with diagnostic potential usually requires knowledge of the antigen(s). We previously circumvented this problem by screening libraries of bead-displayed small molecules against case and control serum samples to discover "epitope surrogates" (ligands of IgGs enriched in the case sample). Here, we describe an improved version of this technology that employs DNA-encoded libraries and high-throughput FACS-based screening to discover epitope surrogates that differentiate noninfectious/latent (LTB) patients from infectious/active TB (ATB) patients, which is imperative for proper treatment selection and antibiotic stewardship. Normal control/LTB (10 patients each, NCL) and ATB (10 patients) serum pools were screened against a library (5 × 10 beads, 448 000 unique compounds) using fluorescent antihuman IgG to label hit compound beads for FACS. Deep sequencing decoded all hit structures and each hit's occurrence frequencies. ATB hits were pruned of NCL hits and prioritized for resynthesis based on occurrence and homology. Several structurally homologous families were identified and 16/21 resynthesized representative hits validated as selective ligands of ATB serum IgGs (p < 0.005). The native secreted TB protein Ag85B (though not the E. coli recombinant form) competed with one of the validated ligands for binding to antibodies, suggesting that it mimics a native Ag85B epitope. The use of DNA-encoded libraries and FACS-based screening in epitope surrogate discovery reveals thousands of potential hit structures. Distilling this list down to several consensus chemical structures yielded a diagnostic panel for ATB composed of thermally stable and economically produced small molecule ligands in place of protein antigens.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要