Using bacterial pan-genome-based feature selection approach to improve the prediction of minimum inhibitory concentration (MIC)

Ming-Ren Yang; Shun-Feng Su; Yu-Wei Wu

doi:10.3389/fgene.2023.1054032

Using bacterial pan-genome-based feature selection approach to improve the prediction of minimum inhibitory concentration (MIC)

Front Genet. 2023 May 30:14:1054032. doi: 10.3389/fgene.2023.1054032. eCollection 2023.

Authors

Ming-Ren Yang^{1

2}, Shun-Feng Su², Yu-Wei Wu^{1

3

4}

Affiliations

¹ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
² Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
³ Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan.
⁴ TMU Research Center for Digestive Medicine, Taipei Medical University, Taipei, Taiwan.

Abstract

Background: Predicting the resistance profiles of antimicrobial resistance (AMR) pathogens is becoming more and more important in treating infectious diseases. Various attempts have been made to build machine learning models to classify resistant or susceptible pathogens based on either known antimicrobial resistance genes or the entire gene set. However, the phenotypic annotations are translated from minimum inhibitory concentration (MIC), which is the lowest concentration of antibiotic drugs in inhibiting certain pathogenic strains. Since the MIC breakpoints that classify a strain to be resistant or susceptible to specific antibiotic drug may be revised by governing institutes, we refrained from translating these MIC values into the categories "susceptible" or "resistant" but instead attempted to predict the MIC values using machine learning approaches. Results: By applying a machine learning feature selection approach on a Salmonella enterica pan-genome, in which the protein sequences were clustered to identify highly similar gene families, we showed that the selected features (genes) performed better than known AMR genes, and that models built on the selected genes achieved very accurate MIC prediction. Functional analysis revealed that about half of the selected genes were annotated as hypothetical proteins (i.e., with unknown functional roles), and that only a small portion of known AMR genes were among the selected genes, indicating that applying feature selection on the entire gene set has the potential of uncovering novel genes that may be associated with and may contribute to pathogenic antimicrobial resistances. Conclusion: The application of the pan-genome-based machine learning approach was indeed capable of predicting MIC values with very high accuracy. The feature selection process may also identify novel AMR genes for inferring bacterial antimicrobial resistance phenotypes.

Keywords: MIC; Salmonella enterica; antimicrobial resistance (AMR); feature selection; minimum inhibitory concentration; pan-genome; regression.

Associated data

figshare/10.6084/m9.figshare.21913689.v1

Grants and funding

This work was supported by the Taiwan National Science and Technology Council (renamed from Ministry of Science and Technology) through grant MOST110-2221-E-038-019-MY3 and MOST111-2221-E-038-023-MY3. The publication costs are covered by grant MOST111-2221-E-038-023-MY3. The funding source has no roles in the design, execution, analysis, and interpretation of this study.