Glass Emma M, Dillard Lillian R, Kolling Glynis L, Warren Andrew S, Papin Jason A
Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.
Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America.
PLoS Biol. 2024 Nov 18;22(11):e3002907. doi: 10.1371/journal.pbio.3002907. eCollection 2024 Nov.
Bacterial pathogens pose a major risk to human health, leading to tens of millions of deaths annually and significant global economic losses. While bacterial infections are typically treated with antibiotic regimens, there has been a rapid emergence of antimicrobial resistant (AMR) bacterial strains due to antibiotic overuse. Because of this, treatment of infections with traditional antimicrobials has become increasingly difficult, necessitating the development of innovative approaches for deeply understanding pathogen function. To combat issues presented by broad- spectrum antibiotics, the idea of narrow-spectrum antibiotics has been previously proposed and explored. Rather than interrupting universal bacterial cellular processes, narrow-spectrum antibiotics work by targeting specific functions or essential genes in certain species or subgroups of bacteria. Here, we generate a collection of genome-scale metabolic network reconstructions (GENREs) of pathogens through an automated computational pipeline. We used these GENREs to identify subgroups of pathogens that share unique metabolic phenotypes and determined that pathogen physiological niche plays a role in the development of unique metabolic function. For example, we identified several unique metabolic phenotypes specific to stomach pathogens. We identified essential genes unique to stomach pathogens in silico and a corresponding inhibitory compound for a uniquely essential gene. We then validated our in silico predictions with an in vitro microbial growth assay. We demonstrated that the inhibition of a uniquely essential gene, thyX, inhibited growth of stomach-specific pathogens exclusively, indicating possible physiological location-specific targeting. This pioneering computational approach could lead to the identification of unique metabolic signatures to inform future targeted, physiological location-specific, antimicrobial therapies, reducing the need for broad-spectrum antibiotics.
细菌病原体对人类健康构成重大风险,每年导致数千万人死亡,并造成巨大的全球经济损失。虽然细菌感染通常用抗生素疗法治疗,但由于抗生素的过度使用,抗菌耐药(AMR)细菌菌株迅速出现。因此,用传统抗菌药物治疗感染变得越来越困难,这就需要开发创新方法来深入了解病原体的功能。为了解决广谱抗生素带来的问题,此前已经提出并探索了窄谱抗生素的概念。窄谱抗生素不是干扰普遍的细菌细胞过程,而是通过靶向某些细菌物种或亚群中的特定功能或必需基因来发挥作用。在这里,我们通过自动化计算流程生成了病原体的基因组规模代谢网络重建(GENREs)集合。我们使用这些GENREs来识别具有独特代谢表型的病原体亚群,并确定病原体的生理生态位在独特代谢功能的发展中起作用。例如,我们确定了几种特定于胃部病原体的独特代谢表型。我们在计算机上识别了胃部病原体特有的必需基因以及一种针对独特必需基因的相应抑制化合物。然后,我们通过体外微生物生长试验验证了我们的计算机预测。我们证明,抑制一个独特的必需基因thyX仅能抑制胃部特异性病原体的生长,这表明可能存在针对生理位置的特异性靶向作用。这种开创性的计算方法可能会导致识别独特的代谢特征,为未来有针对性的、针对生理位置的抗菌治疗提供信息,从而减少对广谱抗生素的需求。