Catechol-Escherichia coli UM146 interaction revealed through multi-omics

• Author / Creator

  Islam, Md Shiful

• Microorganisms are intricately linked with life on earth. The substantial enhancement of environment and human health is influenced largely due to their biotransformation potency. The comprehensive identification of bacterial byproducts can be accomplished by employing a systematic approach involving untargeted metabolomics techniques, with the integration of liquid chromatography high-resolution mass spectrometry (LC-HRMS), nuclear magnetic resonance (NMR), and gas chromatography-mass spectrometry (GC-MS). Untargeted metabolomics is an analytical approach to characterize the global metabolites without any prior knowledge. This study attempted to combine untargeted metabolomics and RNA sequencing (RNA-Seq) to determine unknown bacterial byproducts and genes to highlight the metabolic pathway in a particular condition.
  Studying the Escherichia coli UM146 strain grown on catechol-containing media in aerobic and anaerobic conditions, we identified novel genetic and metabolic changes through transcriptomic and untargeted metabolomics analysis. As highlighted in Chapter 2, many inconsistencies were noted between the NMR and MS metabolomics results, most specifically, false positive results were observed in the MS analyses. We concluded that these results were highly dependent on the available software being used and the analytical workflow employed.
  Benchmarking experiments were performed to evaluate false positive and false negative rates in the identification of a set of 28 compounds in a synthetic mixture. XCMS performed better than Metaboanalyst and MZmine2 in determining minimum features with coverage of more compounds. Sensitivity and specificity were tested based on three distinct approaches, including a) all features, b) putative ID, and c) putative ID & true positive (PID28). In negative mode, all three software packages provided similar sensitivity (70-75%). Variability was observed for PID28 in positive mode, XCMS (75%) has outperformed both Metaboanalyst (58.33%) and MZmine2 (66.67%). The specificity of Metaboanalyst in the criteria of all features is 44.44%, which is quite inferior compared to XCMS (96.09%) and MZmine2 (100%). As inconsistent results were observed with the synthetic mixture, pure concentrated lysine from Sigma-Aldrich (purity ≥98%) was used to determine the contaminants through resin-based column chromatography and 1H-NMR. Among the three software packages in LC-HRMS analysis, entirely Metaboanalyst was able to determine the unknown contaminant (2-piperdinone) in the lysine solution.
  Bacterial by-product identification using LC-HRMS analysis is challenging due to false positive results. Our analysis demonstrated that a combination of software packages is required to screen the actual features and reduce the false positive results in MS analysis.

• Subjects / Keywords

  • Catechol
  • Escherichia coli
  • Untargeted metabolomics
  • transcriptomics
  • NMR
  • LC-MS
  • GC-MS

• Graduation date

  Fall 2024

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-shxs-h385

• License

  This thesis is made available by the University of Alberta Library with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.