Chemical Engineering Division, CSIR-National Chemical Laboratory, Pune, Maharashtra, India.
PLoS One. 2019 Jan 4;14(1):e0210008. doi: 10.1371/journal.pone.0210008. eCollection 2019.
In the post genomic era, high throughput data augment stoichiometric flux balance models to compute accurate metabolic flux states, growth and energy phenotypes. Investigating altered metabolism in the context of evolved resistant genotypes potentially provide simple strategies to overcome drug resistance and induce susceptibility to existing antibiotics. A genome-scale metabolic model (GSMM) for Chromobacterium violaceum, an opportunistic human pathogen, was reconstructed using legacy data. Experimental constraints were used to represent antibiotic susceptible and resistant populations. Model predictions were validated using growth and respiration data successfully. Differential flux distribution and metabolic reprogramming were identified as a response to antibiotics, chloramphenicol and streptomycin. Streptomycin resistant populations (StrpR) redirected tricarboxylic acid (TCA) cycle flux through the glyoxylate shunt. Chloramphenicol resistant populations (ChlR) resorted to overflow metabolism producing acetate and formate. This switch to fermentative metabolism is potentially through excess reducing equivalents and increased NADH/NAD ratios. Reduced proton gradients and changed Proton Motive Force (PMF) induced by antibiotics were also predicted and verified experimentally using flow cytometry based membrane potential measurements. Pareto analysis of NADH and ATP maintenance showed the decoupling of electron transfer and ATP synthesis in StrpR. Redox homeostasis and NAD+ cycling through rewiring metabolic flux was implicated in re-sensitizing antibiotic resistant C. violaceum. These approaches can be used to probe metabolic vulnerabilities of resistant pathogens. On the verge of a post-antibiotic era, we foresee a critical need for systems level understanding of pathogens and host interaction to extend shelf life of antibiotics and strategize novel therapies.
在后基因组时代,高通量数据扩充了代谢通量平衡模型,以计算准确的代谢通量状态、生长和能量表型。研究进化抗性基因型中的代谢改变,可能为克服耐药性和诱导对现有抗生素的敏感性提供简单策略。使用遗留数据为一种机会性病原体紫罗兰色单胞菌(Chromobacterium violaceum)重建了基因组规模代谢模型(GSMM)。使用抗生素敏感和耐药群体的实验约束来表示模型。通过成功使用生长和呼吸数据验证了模型预测。将差异通量分布和代谢重编程鉴定为对抗生素、氯霉素和链霉素的反应。链霉素耐药群体(StrpR)通过乙醛酸支路重新引导三羧酸(TCA)循环通量。氯霉素耐药群体(ChlR)通过产生乙酸盐和甲酸盐的溢出代谢来恢复。这种向发酵代谢的转变可能是通过过多的还原当量和增加的 NADH/NAD 比值。抗生素预测的质子梯度降低和质子动力势(PMF)改变,并通过基于流式细胞术的膜电位测量实验验证。NADH 和 ATP 维持的帕累托分析表明 StrpR 中电子传递和 ATP 合成的解耦。通过重新布线代谢通量来维持氧化还原平衡和 NAD+循环,与重新敏感化抗生素耐药的 C. violaceum 有关。这些方法可用于探测耐药病原体的代谢脆弱性。在后抗生素时代的边缘,我们预见到迫切需要系统水平地了解病原体和宿主相互作用,以延长抗生素的保质期并制定新的治疗策略。