人们对将恶臭假单胞菌KT2440作为微生物底盘用于转化可再生和基于废物的原料越来越感兴趣。恶臭假单胞菌的代谢工程依赖于对基因之间功能关系的理解。在这项工作中,将独立成分分析(ICA)应用于在179个独特实验条件下生长的P.putidaKT2440的随机条形码转座子插入测序(RB-TnSeq)的现有适应性数据汇编.ICA鉴定出84个独立的基因群,我们称之为fModules(“功能模块”),基因成员在特定的细胞过程中表现出共同的功能影响。这种基于机器学习的方法既成功地概括了先前表征的功能关系,又建立了迄今为止未知的基因之间的关联。从fModules中选择的用于羟基肉桂酸代谢和胁迫抗性的基因成员,乙酰辅酶A同化,用恶臭假单胞菌的工程突变体验证了氮代谢。此外,将来自RB-TnSeq数据集的ICA的功能基因簇与来自RNAseq数据集的先前ICA的调节基因簇进行比较,以在基因调控和功能之间建立联系。因为ICA同时描述了几个不同基因网络的功能作用,相对于手动管理RB-TnSeq数据集,它可以减少注释基因功能所需的时间。重要性这项研究表明,阐明复杂遗传网络中功能模块的自动化方法。虽然恶臭假单胞菌随机条形码转座子插入测序数据被用作概念证明,这种方法适用于具有现有功能基因组学数据集的任何生物体,并且可以作为许多有价值的应用的有用工具,例如指导其他微生物的代谢工程工作或了解致病微生物中毒力相关基因之间的功能关系。此外,这项工作表明,比较从转录组学和基因适应性数据集的独立成分分析获得的数据可以阐明基因之间的调节功能关系,它可以在各种应用中使用,比如代谢建模,应变工程,或识别抗菌药物靶标。
There is growing interest in engineering Pseudomonas putida KT2440 as a microbial chassis for the conversion of renewable and waste-based feedstocks, and metabolic engineering of P. putida relies on the understanding of the functional relationships between genes. In this work, independent component analysis (ICA) was applied to a compendium of existing fitness data from randomly barcoded transposon insertion sequencing (RB-TnSeq) of P. putida KT2440 grown in 179 unique experimental conditions. ICA identified 84 independent groups of genes, which we call fModules (\"functional modules\"), where gene members displayed shared functional influence in a specific cellular process. This machine learning-based approach both successfully recapitulated previously characterized functional relationships and established hitherto unknown associations between genes. Selected gene members from fModules for hydroxycinnamate metabolism and stress resistance, acetyl coenzyme A assimilation, and nitrogen metabolism were validated with engineered mutants of P. putida. Additionally, functional gene clusters from ICA of RB-TnSeq data sets were compared with regulatory gene clusters from prior ICA of RNAseq data sets to draw connections between gene regulation and function. Because ICA profiles the functional role of several distinct gene networks simultaneously, it can reduce the time required to annotate gene function relative to manual curation of RB-TnSeq data sets.
OBJECTIVE: This study demonstrates a rapid, automated approach for elucidating functional modules within complex genetic networks. While Pseudomonas putida randomly barcoded transposon insertion sequencing data were used as a proof of concept, this approach is applicable to any organism with existing functional genomics data sets and may serve as a useful tool for many valuable applications, such as guiding metabolic engineering efforts in other microbes or understanding functional relationships between virulence-associated genes in pathogenic microbes. Furthermore, this work demonstrates that comparison of data obtained from independent component analysis of transcriptomics and gene fitness datasets can elucidate regulatory-functional relationships between genes, which may have utility in a variety of applications, such as metabolic modeling, strain engineering, or identification of antimicrobial drug targets.