Role of AmpC-Inducing Genes in Modulating Other Serine Beta-Lactamases in Escherichia coli

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

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.

Prevalence of Extended-Spectrum β-Lactamase-Resistant Genes in Escherichia coli Isolates from Central China during 2016-2019

The emergence and dissemination of Escherichia coli (E. coli) strains that produce extended-spectrum beta-lactamases (ESBLs) represents a major public health threat. The present study was designed to evaluate the prevalence and characteristics of ESBL-producing Escherichia coli isolates from chickens in central China during 2016-2019. A total of 407 E. coli strains isolated from 581 chicken swabs were identified conventionally and analyzed for various cephalosporin susceptibility by disk-diffusion assay. ESBL-producing strains were screened using the double=disk synergy test and ESBL-encoding genes were carried out by PCR/sequencing. A total of 402 E. coli isolates exhibited strong resistance to first- to fourth-generation cephalosporins and monobactam antibiotics, especially cefazolin (60.69%), cefuroxime (54.05%), cefepime (35.14%), ceftriaxone (54.30%), and aztreonam (40.29%). Piperacillin/tazobactam (1.72%) was the most effective drug against the strains, but the resistance rates increased each year. Among the isolates, 262 were identified as ESBL producers and the isolation rates for the ESBL producers increased from 63.37% to 67.35% over the four years. CTX-M (97.33%) was the most prevalent type, followed by TEM (76.72%) and SHV (3.05%). The most common ESBL genotype combination was bla_TEM + bla_CTX-M (74.46%), in which the frequency of carriers increased steadily, followed by bla_CTX-M + bla_SHV (3.05%). In addition, the most predominant specific CTX-M subtypes were CTX-M-55 (48.47%) and CTX-M-1 (17.94%), followed by CTX-M-14 (11.01%), CTX-M-15 (8.02%), CTX-M-9 (6.11%), CTX-M-65 (4.58%), and CTX-M-3 (1.15%). Moreover, a novel multiplex qPCR assay was developed to detect bla_CTX-M, bla_TEM, and bla_SHV, with limits of detection of 2.06 × 10¹ copies/μL, 1.10 × 10¹ copies/μL, and 1.86 × 10¹ copies/μL, respectively, and no cross-reactivity with other ESBL genes and avian pathogens. The assays exhibited 100% sensitivity and specificities of 85%, 100%, and 100% for bla_CTX-M, bla_TEM, and bla_SHV, respectively. In conclusion, our findings indicated that ESBL-producing E.coli strains isolated from chickens in central China were highly resistant to cephalosporins and frequently harbored diversity in ESBL-encoding genes. These isolates can pose a significant public health risk. The novel multiplex qPCR method developed in this study may be a useful tool for molecular epidemiology and surveillance studies of ESBL genes.