Poster Presentation 27th Annual Lorne Proteomics Symposium 2022

An investigation into how proteomic informatics can currently address the needs of the clinic in characterising the antimicrobial resistance capabilities of a bacterial infection (#179)

Stephanie E L Town 1 , Matt P Padula 1
  1. School of Life Sciences, University of Technology Sydney, Broadway, NSW, Australia

Introduction:

To best address antimicrobial resistance (AMR), clinicians must be provided with tools that accurately characterise the AMR profiles of bacterial infections [1]. Proteome informatics process data from experiments by analysing how the proteome varies in complexity and application [2]. Though important for the clinic, bioinformatics remains a barrier to entry for the clinician wanting to engage proteomics as a solution [3].

Aim:

This study sought to construct a workflow that maximally characterised a bacterial isolate's AMR capabilities and identify deficiencies in the field to improve proteomic informatics overall.

Method:

Escherichia coli strain 2009-52 was utilised for its "complete" genome [4], and previously-obtained AMR proteome profile, obtained via antimicrobial susceptibility testing and a bottom-up LC-MS/MS-based workflow examining AMR in response to a sub-lethal dose of Ampicillin. PATRIC BRC [5] was utilised to provide genome annotation and translation to canonical proteins, as well as information on AMR. Other informatics used were PEAKS Studio X Pro [6], MaxQuant [7], LFQ Analyst [8], ResFinder [9], VirulenceFinder [10], and STRING DB [11] (which uses UniProt KB [12] and KEGG [13]).

Results:

Many databases had little experimentally determined annotations on the protein level, even for well-characterised bacterial strains. There were many unresolvable contradictions between proposed biological meanings or associations, and it was not immediately apparent which result was most reliable. Contradictions could be overcome with the manual, time-consuming assessment of entries by the user.

Discussion:

There is a distinct lack of functional knowledge about non-model organisms in databases. Active participation in uploading findings must be prioritised so that databases better represent proteomic, not genomic, evidence. It is essential to standardise annotations and records management in the field to bring much-needed benefit to clinical and research spaces alike. Until this is resolved, the clinic cannot easily address its needs.

  1. Vasala, A., V.P. Hytönen, and O.H. Laitinen, Modern Tools for Rapid Diagnostics of Antimicrobial Resistance. Frontiers in cellular and infection microbiology, 2020. 10: p. 308-308. doi: 10.3389/fcimb.2020.00308
  2. Keerthikumar, S., An Introduction to Proteome Bioinformatics. Methods Mol Biol, 2017. 1549: p. 1-3. doi: 10.1007/978-1-4939-6740-7_1
  3. Chen, C.-y., et al., Detection of Antimicrobial Resistance Using Proteomics and the Comprehensive Antibiotic Resistance Database: A Case Study. PROTEOMICS – Clinical Applications, 2020. 14(4): p. 1800182. doi: https://doi.org/10.1002/prca.201800182
  4. McKinnon, J., P. Roy Chowdhury, and S.P. Djordjevic, Genomic analysis of multidrug-resistant Escherichia coli ST58 causing urosepsis. International Journal of Antimicrobial Agents, 2018. 52(3): p. 430-435. doi: https://doi.org/10.1016/j.ijantimicag.2018.06.017
  5. Davis, J.J., et al., The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities. Nucleic Acids Res, 2020. 48(D1): p. D606-d612. doi: 10.1093/nar/gkz943
  6. Peaks Studio Xpro. 2021, Bioinformatics Solutions Inc.: Ontario, Canada. https://www.bioinfor.com/peaks-xpro/
  7. Cox, J. and M. Mann, MaxQuant enables high peptide identification rates, individualised p.p.b.-range mass accuracies and proteome-wide protein quantification. Nature Biotechnology, 2008. 26(12): p. 1367-1372. doi: 10.1038/nbt.1511
  8. Shah, A.D., et al., LFQ-Analyst: An Easy-To-Use Interactive Web Platform To Analyse and Visualise Label-Free Proteomics Data Preprocessed with MaxQuant. Journal of Proteome Research, 2020. 19(1): p. 204-211. doi: 10.1021/acs.jproteome.9b00496
  9. Bortolaia, V., et al., ResFinder 4.0 for predictions of phenotypes from genotypes. J Antimicrob Chemother, 2020. 75(12): p. 3491-3500. doi: 10.1093/jac/dkaa345
  10. Tetzschner, A.M.M., et al., In Silico Genotyping of Escherichia coli Isolates for Extraintestinal Virulence Genes by Use of Whole-Genome Sequencing Data. Journal of Clinical Microbiology, 2020. 58(10): p. e01269-20. doi:10.1128/JCM.01269-20
  11. Szklarczyk, D., et al., STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res, 2015. 43(Database issue): p. D447-52. doi: 10.1093/nar/gku1003
  12. Consortium, T.U., UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Research, 2020. 49(D1): p. D480-D489. doi: 10.1093/nar/gkaa1100
  13. Kanehisa, M. and S. Goto, KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research, 2000. 28(1): p. 27-30. doi: 10.1093/nar/28.1.27