Predicting Salmonella MIC and Deciphering Genomic Determinants of Antibiotic Resistance and Susceptibility

Abstract

Salmonella spp., a leading cause of foodborne illness, is a formidable global menace due to escalating antimicrobial resistance (AMR). The evaluation of minimum inhibitory concentration (MIC) for antimicrobials is critical for characterizing AMR. The current whole genome sequencing (WGS)-based approaches for predicting MIC are hindered by both computational and feature identification constraints. We propose an innovative methodology called the “Genome Feature Extractor Pipeline” that integrates traditional machine learning (random forest, RF) with deep learning models (multilayer perceptron (MLP) and DeepLift) for WGS-based MIC prediction. We used a dataset from the National Antimicrobial Resistance Monitoring System (NARMS), comprising 4500 assembled genomes of nontyphoidal Salmonella, each annotated with MIC metadata for 15 antibiotics. Our pipeline involves the batch downloading of annotated genomes, the determination of feature importance using RF, Gini-index-based selection of crucial 10-mers, and their expansion to 20-mers. This is followed by an MLP network, with four hidden layers of 1024 neurons each, to predict MIC values. Using DeepLift, key 20-mers and associated genes influencing MIC are identified. The 10 most significant 20-mers for each antibiotic are listed, showcasing our ability to discern genomic features affecting Salmonella MIC prediction with enhanced precision. The methodology replaces binary indicators with k-mer counts, offering a more nuanced analysis. The combination of RF and MLP addresses the limitations of the existing WGS approach, providing a robust and efficient method for predicting MIC values in Salmonella that could potentially be applied to other pathogens.

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

“…Integrating traditional machine learning with deep learning models was used to predict minimum inhibitory concentrations of 15 antimicrobials against Salmonella [56]. Another study predicted minimum inhibitory concentrations of 15 antimicrobials for nontyphoidal Salmonella [39].…”

Antibiotic Resistance Profile of Salmonella sp. Isolates from Commercial Laying Hen Farms in Central-Western Brazil

“…MDR Salmonella is characterized by its resistance to conventional first-line antibiotics such as ampicillin, chloramphenicol, and trimethoprim-sulfamethoxazole [4][5][6]. The global rise in the isolation of Salmonella serotypes resistant to one or more antibiotics [7] is largely attributed to the inappropriate use, overuse, and easy accessibility of antibiotics across various countries [2].…”