Soldatou Sylvia, Eldjárn Grímur Hjörleifsson, Ramsay Andrew, van der Hooft Justin J J, Hughes Alison H, Rogers Simon, Duncan Katherine R
Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow G4 0RE, UK.
School of Computing Science, University of Glasgow, Glasgow G12 8RZ, UK.
Mar Drugs. 2021 Feb 10;19(2):103. doi: 10.3390/md19020103.
Biosynthetic and chemical datasets are the two major pillars for microbial drug discovery in the era. Despite the advancement of analysis tools and platforms for multi-strain metabolomics and genomics, linking these information sources remains a considerable bottleneck in strain prioritisation and natural product discovery. In this study, molecular networking of the 100 metabolite extracts derived from applying the OSMAC approach to 25 Polar bacterial strains, showed growth media specificity and potential chemical novelty was suggested. Moreover, the metabolite extracts were screened for antibacterial activity and promising selective bioactivity against drug-persistent pathogens such as and was observed. Genome sequencing data were combined with metabolomics experiments in the recently developed computational approach, NPLinker, which was used to link BGC and molecular features to prioritise strains for further investigation based on biosynthetic and chemical information. Herein, we putatively identified the known metabolites ectoine and chrloramphenicol which, through NPLinker, were linked to their associated BGCs. The metabologenomics approach followed in this study can potentially be applied to any large microbial datasets for accelerating the discovery of new (bioactive) specialised metabolites.
生物合成和化学数据集是这个时代微生物药物发现的两大支柱。尽管多菌株代谢组学和基因组学的分析工具和平台有所进步,但在菌株优先级排序和天然产物发现中,将这些信息源联系起来仍然是一个相当大的瓶颈。在本研究中,对应用OSMAC方法从25株极地细菌菌株中获得的100种代谢物提取物进行分子网络分析,显示出生长培养基特异性,并表明存在潜在的化学新颖性。此外,对代谢物提取物进行了抗菌活性筛选,并观察到对诸如[此处原文缺失具体病原体名称]等耐药病原体具有有前景的选择性生物活性。基因组测序数据与代谢组学实验在最近开发的计算方法NPLinker中相结合,该方法用于将生物合成基因簇(BGC)与分子特征联系起来,以便根据生物合成和化学信息对菌株进行优先级排序以进行进一步研究。在此,我们推定鉴定出已知代谢物依克多因和氯霉素,通过NPLinker将它们与其相关的BGC联系起来。本研究中采用的代谢基因组学方法有可能应用于任何大型微生物数据集,以加速新的(生物活性)特殊代谢物的发现。