Cooper Helena B, Vezina Ben, Hawkey Jane, Passet Virginie, López-Fernández Sebastián, Monk Jonathan M, Brisse Sylvain, Holt Kathryn E, Wyres Kelly L
Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Victoria 3004, Australia.
Centre to Impact AMR, Monash University, Clayton, Victoria 3800, Australia.
Microb Genom. 2024 Feb;10(2). doi: 10.1099/mgen.0.001206.
The species complex (KpSC) is a major source of nosocomial infections globally with high rates of resistance to antimicrobials. Consequently, there is growing interest in understanding virulence factors and their association with cellular metabolic processes for developing novel anti-KpSC therapeutics. Phenotypic assays have revealed metabolic diversity within the KpSC, but metabolism research has been neglected due to experiments being difficult and cost-intensive. Genome-scale metabolic models (GSMMs) represent a rapid and scalable approach for exploring metabolic diversity, which compile genomic and biochemical data to reconstruct the metabolic network of an organism. Here we use a diverse collection of 507 KpSC isolates, including representatives of globally distributed clinically relevant lineages, to construct the most comprehensive KpSC pan-metabolic model to date, KpSC pan v2. Candidate metabolic reactions were identified using gene orthology to known metabolic genes, prior to manual curation via extensive literature and database searches. The final model comprised a total of 3550 reactions, 2403 genes and can simulate growth on 360 unique substrates. We used KpSC pan v2 as a reference to derive strain-specific GSMMs for all 507 KpSC isolates, and compared these to GSMMs generated using a prior KpSC pan-reference (KpSC pan v1) and two single-strain references. We show that KpSC pan v2 includes a greater proportion of accessory reactions (8.8 %) than KpSC pan v1 (2.5 %). GSMMs derived from KpSC pan v2 also generate more accurate growth predictions, with high median accuracies of 95.4 % (aerobic, =37 isolates) and 78.8 % (anaerobic, =36 isolates) for 124 matched carbon substrates. KpSC pan v2 is freely available at https://github.com/kelwyres/KpSC-pan-metabolic-model, representing a valuable resource for the scientific community, both as a source of curated metabolic information and as a reference to derive accurate strain-specific GSMMs. The latter can be used to investigate the relationship between KpSC metabolism and traits of interest, such as reservoirs, epidemiology, drug resistance or virulence, and ultimately to inform novel KpSC control strategies.
肺炎克雷伯菌复合种（KpSC）是全球医院感染的主要来源，对抗菌药物具有很高的耐药率。因此，人们越来越关注了解其毒力因子及其与细胞代谢过程的关联，以开发新型抗KpSC治疗方法。表型分析揭示了KpSC内的代谢多样性，但由于实验难度大且成本高，代谢研究一直被忽视。基因组规模代谢模型（GSMMs）是探索代谢多样性的一种快速且可扩展的方法，它整合基因组和生化数据来重建生物体的代谢网络。在此，我们使用了507株KpSC分离株的多样化集合，包括全球分布的临床相关谱系的代表，构建了迄今为止最全面的KpSC泛代谢模型KpSC pan v2。在通过广泛的文献和数据库搜索进行人工整理之前，利用与已知代谢基因的基因同源性来识别候选代谢反应基团。最终模型总共包含3550个反应、2403个基因，并且可以模拟在360种独特底物上的生长情况。我们以KpSC pan v2作为参考，为所有507株KpSC分离株推导菌株特异性的GSMMs，并将其与使用先前的KpSC泛参考（KpSC pan v1）和两个单菌株参考生成的GSMMs进行比较。我们发现，KpSC pan v2包含的辅助反应比例（8.8%）高于KpSC pan v1（2.5%）。源自KpSC pan v2的GSMMs也能产生更准确的生长预测，对于124种匹配的碳底物，需氧条件下（n = 37株）的中位准确率高达95.4%，厌氧条件下（n = 36株）为78.8%。KpSC pan v2可在https://github.com/kelwyres/KpSC-pan-metabolic-model上免费获取，这对科学界来说是一项宝贵的资源，既是经过整理的代谢信息来源，也是推导准确的菌株特异性GSMMs的参考。后者可用于研究KpSC代谢与感兴趣的特征之间的关系，如宿主、流行病学、耐药性或毒力，最终为新型KpSC控制策略提供依据。
