PREDICTING ANTIMICROBIAL MICS FOR NONTY-PHOIDAL SALMONELLA USING MULTITASK REPRE-SENTATIONS LEARNING OF TRANSFORMER

Abstract

The antimicrobial resistance (AMR) pathogens have become an increasingly worldwide issue, posing a significant threat to global public health. To obtain an optimized therapeutic effect, the antibiotic sensitivity is usually evaluated in a clinical setting, whereas traditional culture-dependent antimicrobial sensitivity tests are labor-intensive and relatively slow. Rapid methods can greatly optimize antimicrobial therapeutic strategies and improve patient outcomes by reducing the time it takes to test antibiotic sensitivity. The booming development of sequencing technology and machine learning techniques provide promising alternative approaches for antimicrobial resistance prediction based on sequencing. In this study, we used a lightweight Multitask Learning Transformer to predict the MIC of 14 antibiotics for Salmonella strains based on the genomic information, including point mutations, pan-genome structure, and the profile of antibiotic resistance genes from 5,278 publicly available whole genomes of nontyphoidal Salmonella. And we got better prediction results (improved more than 10% for raw accuracy and 3% for accuracy within ±1 2-fold dilution step) and provided better interpretability than the other ML models. Besides the potential clinical application, our models would cast light on mechanistic understanding of key genetic regions influencing AMR.

1. INTRODUCTION

Antibiotics are chemical compounds that are used for killing or inhibiting the growth of bacteria, playing a pivotal role in the control of infectious diseases. However, the ever-increasing antimicrobial resistance (AMR) threatens the clinical effectiveness of antibiotic treatments. The antibiotic resistance of pathogens could result in treatment failure, including high morbidity or mortality, and increase the health care cost substantially. Over 70 percent of the bacteria which promote hospitalacquired infections are resistant to at least one common antibiotic used for treatment (Stone et al., 2009) . In clinical settings, testing the antimicrobial resistance of pathogens is critical for the appropriate choice of antibiotics in the treatment. Antimicrobial susceptibility/ sensitivity testing (AST) is an approach to determine whether antibiotics can inhibit the bacteria/fungi growth, thus measure the susceptibility, or reflect the resistance of bacteria/fungi to specific the antibiotics. Several AST methods are widely used, including broth microdilution, antimicrobial gradient, disk diffusion test, and rapid automated instrument methods (Barth et al., 2009) . Minimum inhibitory concentration (MIC) is one of the most frequently used AST methods, quantifying the lowest concentration of antibiotics preventing the growth of a microorganism. Qualitative descriptions (resistant/sensitive, etc.) of the antimicrobial sensitivity provide no accurate quantification of antimicrobial sensitivity and limit its power in certain scientific and clinical applications. In contrast, MIC measures provide a competent resolution while antimicrobial susceptibility of strains varies in a population, and this is useful for many epidemiological and clinical objectives. Since traditional antimicrobial sensitivity testing relies on culture-dependent methods, it is laborintensive and relatively slow. In the conventional microbiological laboratory diagnosis, the total time for the bacteria growth, isolation, taxonomic identification, and antibacterial MIC determination for

