Rapid Countermeasure Discovery against Francisella tularensis Based on a Metabolic Network Reconstruction

In the future, we may be faced with the need to provide treatment for an emergent biological threat against which existing vaccines and drugs have limited efficacy or availability. To prepare for this eventuality, our objective was to use a metabolic network-based approach to rapidly identify potential drug targets and prospectively screen and validate novel small-molecule antimicrobials. Our target organism was the fully virulent Francisella tularensis subspecies tularensis Schu S4 strain, a highly infectious intracellular pathogen that is the causative agent of tularemia and is classified as a category A biological agent by the Centers for Disease Control and Prevention. We proceeded with a staggered computational and experimental workflow that used a strain-specific metabolic network model, homology modeling and X-ray crystallography of protein targets, and ligand- and structure-based drug design. Selected compounds were subsequently filtered based on physiological-based pharmacokinetic modeling, and we selected a final set of 40 compounds for experimental validation of antimicrobial activity. We began screening these compounds in whole bacterial cell-based assays in biosafety level 3 facilities in the 20th week of the study and completed the screens within 12 weeks. Six compounds showed significant growth inhibition of F. tularensis, and we determined their respective minimum inhibitory concentrations and mammalian cell cytotoxicities. The most promising compound had a low molecular weight, was non-toxic, and abolished bacterial growth at 13 µM, with putative activity against pantetheine-phosphate adenylyltransferase, an enzyme involved in the biosynthesis of coenzyme A, encoded by gene coaD. The novel antimicrobial compounds identified in this study serve as starting points for lead optimization, animal testing, and drug development against tularemia. Our integrated in silico/in vitro approach had an overall 15% success rate in terms of active versus tested compounds over an elapsed time period of 32 weeks, from pathogen strain identification to selection and validation of novel antimicrobial compounds.未来，我们可能需要针对突发生物威胁开展救治——现有疫苗与药物对该类威胁的疗效有限或可及性不足。为应对此类突发状况，本研究旨在采用基于代谢网络的方法，快速筛选潜在药物靶点，并前瞻性地筛选、验证新型小分子抗菌剂。本研究的目标病原菌为强毒力型土拉弗朗西斯菌土拉亚种Schu S4菌株（Francisella tularensis subspecies tularensis Schu S4）：该菌为高传染性胞内致病菌，是土拉菌病的病原菌，且被美国疾病控制与预防中心（Centers for Disease Control and Prevention, CDC）列为A类生物战剂。本研究采用分阶段计算与实验结合的工作流程，包括菌株特异性代谢网络模型、蛋白质靶点同源建模与X射线晶体学分析，以及基于配体与结构的药物设计。随后基于生理药代动力学模型对候选化合物进行筛选，最终选取40种化合物开展抗菌活性实验验证。本研究于研究第20周起在生物安全三级（BSL-3）实验室中开展全细菌细胞水平的化合物筛选实验，并于12周内完成全部筛选工作。其中6种化合物对土拉弗朗西斯菌展现出显著的生长抑制活性，本研究测定了它们的最低抑菌浓度（minimum inhibitory concentration, MIC）以及对哺乳动物细胞的细胞毒性。最具开发潜力的化合物分子量较低且无细胞毒性，在13 µM浓度下即可完全抑制细菌生长，其推定靶点为泛酰巯基乙胺磷酸腺苷酰转移酶——该酶由coaD基因编码，参与辅酶A（coenzyme A）的生物合成过程。本研究发现的新型抗菌剂可作为土拉菌病治疗药物的先导优化、动物实验及药物开发的起点。本研究采用的计算机模拟（in silico）-体外（in vitro）整合实验策略，从病原菌菌株鉴定到新型抗菌剂筛选验证的32周历时周期内，活性化合物相较于测试化合物的整体成功率达15%。