ARC Centre of Excellence in Synthetic Biology, School of Natural Sciences, Macquarie University, Sydney, Australia.
Faculty of Medicine, University of Würzburg, Würzburg, Germany.
mSystems. 2024 Apr 16;9(4):e0066523. doi: 10.1128/msystems.00665-23. Epub 2024 Mar 12.
Functional genomics techniques, such as transposon insertion sequencing and RNA-sequencing, are key to studying relative differences in bacterial mutant fitness or gene expression under selective conditions. However, certain stress conditions, mutations, or antibiotics can directly interfere with DNA synthesis, resulting in systematic changes in local DNA copy numbers along the chromosome. This can lead to artifacts in sequencing-based functional genomics data when comparing antibiotic treatment to an unstressed control. Further, relative differences in gene-wise read counts may result from alterations in chromosomal replication dynamics, rather than selection or direct gene regulation. We term this artifact "chromosomal location bias" and implement a principled statistical approach to correct it by calculating local normalization factors along the chromosome. These normalization factors are then directly incorporated into statistical analyses using standard RNA-sequencing analysis methods without modifying the read counts themselves, preserving important information about the mean-variance relationship in the data. We illustrate the utility of this approach by generating and analyzing a ciprofloxacin-treated transposon insertion sequencing data set in as a case study. We show that ciprofloxacin treatment generates chromosomal location bias in the resulting data, and we further demonstrate that failing to correct for this bias leads to false predictions of mutant drug sensitivity as measured by minimum inhibitory concentrations. We have developed an R package and user-friendly graphical Shiny application, ChromoCorrect, that detects and corrects for chromosomal bias in read count data, enabling the application of functional genomics technologies to the study of antibiotic stress.IMPORTANCEAltered gene dosage due to changes in DNA replication has been observed under a variety of stresses with a variety of experimental techniques. However, the implications of changes in gene dosage for sequencing-based functional genomics assays are rarely considered. We present a statistically principled approach to correcting for the effect of changes in gene dosage, enabling testing for differences in the fitness effects or regulation of individual genes in the presence of confounding differences in DNA copy number. We show that failing to correct for these effects can lead to incorrect predictions of resistance phenotype when applying functional genomics assays to investigate antibiotic stress, and we provide a user-friendly application to detect and correct for changes in DNA copy number.
功能基因组学技术，如转座子插入测序和 RNA 测序，是研究细菌突变体在选择性条件下相对适应性或基因表达差异的关键。然而，某些应激条件、突变或抗生素会直接干扰 DNA 合成，导致染色体上局部 DNA 拷贝数的系统变化。这可能会导致在比较抗生素处理与未受应激对照时，基于测序的功能基因组学数据出现假象。此外，基因水平上读数值的相对差异可能源于染色体复制动力学的改变，而不是选择或直接基因调控。我们将这种假象称为“染色体位置偏差”，并通过计算染色体上的局部归一化因子来实施一种有原则的统计方法来纠正它。然后，这些归一化因子直接纳入使用标准 RNA 测序分析方法的统计分析中，而不修改读数值本身，保留了数据中均值-方差关系的重要信息。我们通过生成和分析一个环丙沙星处理的转座子插入测序数据集作为案例研究来说明这种方法的实用性。我们表明，环丙沙星处理会在产生的数据中产生染色体位置偏差，我们进一步证明，如果不纠正这种偏差，就会导致最小抑菌浓度测量的突变体药物敏感性的错误预测。我们已经开发了一个 R 包和用户友好的图形 Shiny 应用程序 ChromoCorrect，它可以检测和纠正读数值数据中的染色体偏差，使功能基因组学技术能够应用于抗生素应激研究。
在各种实验技术下，各种应激条件下都观察到了由于 DNA 复制变化引起的基因剂量改变。然而，测序的功能基因组学分析中很少考虑基因剂量变化的影响。我们提出了一种纠正基因剂量变化影响的统计方法，能够在 DNA 拷贝数存在混杂差异的情况下，测试单个基因的适应性效应或调控差异。我们表明，如果不纠正这些影响，在应用功能基因组学分析来研究抗生素应激时，可能会导致耐药表型的错误预测，并提供了一个用户友好的应用程序来检测和纠正 DNA 拷贝数的变化。
