Department of Chemistry and Molecular Biology, University of Gothenburg, 405 30 Gothenburg, Sweden.
Centre for Antibiotic Resistance Research (CARe), University of Gothenburg, 413 45 Gothenburg, Sweden.
Anal Chem. 2024 Sep 3;96(35):14168-14177. doi: 10.1021/acs.analchem.4c02093. Epub 2024 Aug 20.
Antibiotic resistance can rapidly spread through bacterial populations via bacterial conjugation. The bacterial membrane has an important role in facilitating conjugation, thus investigating the effects on the bacterial membrane caused by conjugative plasmids, antibiotic resistance, and genes involved in conjugation is of interest. Analysis of bacterial membranes was conducted using gas cluster ion beam-secondary ion mass spectrometry (GCIB-SIMS). The complexity of the data means that data analysis is important for the identification of changes in the membrane composition. Preprocessing of data and several analytical methods for identification of changes in bacterial membranes have been investigated. GCIB-SIMS data from samples were subjected to principal components analysis (PCA), principal components-canonical variate analysis (PC-CVA), and Random Forests (RF) data analysis with the aim of extracting the maximum biological information. The influence of increasing replicate data was assessed, and the effect of diminishing biological variation was studied. Optimized / region-specific scaling provided improved clustering, with an increase in biologically significant peaks contributing to the loadings. PC-CVA improved clustering, provided clearer loadings, and benefited from larger data sets collected over several months. RF required larger sample numbers and while showing overlap with the PC-CVA, produced additional peaks of interest. The combination of PC-CVA and RF allowed very subtle differences between bacterial strains and growth conditions to be elucidated for the first time. Specifically, comparative analysis of an strain with and without the F-plasmid revealed changes in cyclopropanation of fatty acids, where the addition of the F-plasmid led to a reduction in cyclopropanation.
细菌通过细菌接合迅速传播抗生素耐药性。细菌膜在促进接合方面起着重要作用，因此研究接合质粒、抗生素耐药性和参与接合的基因对细菌膜的影响是很有意义的。使用气体团簇离子束-二次离子质谱（GCIB-SIMS）分析细菌膜。数据的复杂性意味着数据分析对于识别膜成分的变化很重要。研究了数据的预处理和几种用于识别细菌膜变化的分析方法。对来自 个样本的 GCIB-SIMS 数据进行了主成分分析（PCA）、主成分-典型变量分析（PC-CVA）和随机森林（RF）数据分析，旨在提取最大的生物学信息。评估了增加重复数据的影响，并研究了减少生物学变异性的影响。优化/区域特定缩放提供了更好的聚类，增加了生物显著峰的数量有助于加载。PC-CVA 提高了聚类效果，提供了更清晰的负载，并且受益于在几个月内收集的更大数据集。RF 需要更多的样本数量，虽然与 PC-CVA 重叠，但产生了其他感兴趣的峰。PC-CVA 和 RF 的组合首次允许阐明细菌菌株和生长条件之间非常细微的差异。具体来说，对带有和不带有 F 质粒的 菌株进行比较分析，揭示了脂肪酸环丙烷化的变化，其中添加 F 质粒导致环丙烷化减少。
