Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States.
Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States.
ACS Synth Biol. 2022 Jun 17;11(6):2015-2021. doi: 10.1021/acssynbio.2c00119. Epub 2022 Jun 3.
Randomly barcoded transposon insertion sequencing (RB-TnSeq) is an efficient, multiplexed method to determine microbial gene function during growth under a selection condition of interest. This technique applies to growth, tolerance, and persistence studies in a variety of hosts, but the wealth of data generated can complicate the identification of the most critical gene targets. Experimental and analytical methods for improving the resolution of RB-TnSeq are proposed, using KT2440 as an example organism. Several key parameters, such as baseline media selection, substantially influence the determination of gene fitness. We also present options to increase statistical confidence in gene fitness, including increasing the number of biological replicates and passaging the baseline culture in parallel with selection conditions. These considerations provide practitioners with several options to identify genes of importance in TnSeq data sets, thereby streamlining metabolic characterization.
随机条形码转座子插入测序(RB-TnSeq)是一种高效、多重的方法,可在感兴趣的选择条件下确定微生物生长过程中的基因功能。该技术适用于多种宿主的生长、耐受和持久性研究,但生成的大量数据可能会使确定最关键的基因靶标变得复杂。使用 KT2440 作为示例生物体,提出了用于提高 RB-TnSeq 分辨率的实验和分析方法。几个关键参数,如基础培养基的选择,极大地影响了基因适应性的确定。我们还提供了几种方法来提高基因适应性的统计置信度,包括增加生物学重复的数量以及在选择条件下与基线培养并行传代。这些考虑因素为从业人员提供了几种在 TnSeq 数据集识别重要基因的选择,从而简化代谢特征分析。