Whole-Genome Sequencing Reveals Temporal Trends in Antibiotic Resistance Genes in Escherichia coli Causing Pediatric Urinary Tract Infections in Central Vietnam.

(1) Background: Pediatric urinary tract infections (UTIs) pose significant challenges due to drug-resistant Escherichia coli (E. coli) strains. This study utilizes whole-genome sequencing to analyze temporal trends in antibiotic resistance genes (ARGs) in clinical E. coli isolates from pediatric UTI cases in central Vietnam. (2) Methods: We conducted whole-genome sequencing on 71 E. coli isolates collected from pediatric UTI patients between 2018 and 2020. ARGs were identified, and their prevalence over time was analyzed. Statistical tests were used to correlate ARG presence with antibiotic resistance. (3) Results: Of the 47 E. coli isolates with complete data, 40 distinct ARGs were identified, with a median of 10 resistance genes per isolate. A significant increase in the total number of ARGs per isolate was observed over time, from an average of 8.88 before June 2019 to 11.63 after. Notably, the prevalence of the aadA2 gene (aminoglycoside resistance) rose from 0% to 26.7%, and that of the blaNDM-5 gene (beta-lactam and carbapenem resistance) increased from 0% to 23.3%. Key correlations include blaEC with cephalosporin resistance, blaNDM-5 with carbapenem resistance, and sul2 with sulfamethoxazole/trimethoprim resistance. (4) Conclusions: Whole-genome sequencing reveals complex and evolving antibiotic resistance patterns in pediatric E. coli UTIs in central Vietnam, with a marked increase in ARG prevalence over time. Continuous surveillance and targeted treatments are essential to address these trends. Understanding genetic foundations is crucial for effective intervention strategies.

PanKA: Leveraging population pangenome to predict antibiotic resistance.

Machine learning has the potential to be a powerful tool in the fight against antimicrobial resistance (AMR), a critical global health issue. Machine learning can identify resistance mechanisms from DNA sequence data without prior knowledge. The first step in building a machine learning model is a feature extraction from sequencing data. Traditional methods like single nucleotide polymorphism (SNP) calling and k-mer counting yield numerous, often redundant features, complicating prediction and analysis. In this paper, we propose PanKA, a method using the pangenome to extract a concise set of relevant features for predicting AMR. PanKA not only enables fast model training and prediction but also improves accuracy. Applied to the Escherichia coli and Klebsiella pneumoniae bacterial species, our model is more accurate than conventional and state-of-the-art methods in predicting AMR.

Efficient inference of large prokaryotic pangenomes with PanTA.

Pangenome inference is an indispensable step in bacterial genomics, yet its scalability poses a challenge due to the rapid growth of genomic collections. This paper presents PanTA, a software package designed for constructing pangenomes of large bacterial datasets, showing unprecedented efficiency levels multiple times higher than existing tools. PanTA introduces a novel mechanism to construct the pangenome progressively without rebuilding the accumulated collection from scratch. The progressive mode is shown to consume orders of magnitude less computational resources than existing solutions in managing growing datasets. The software is open source and is publicly available at https://github.com/amromics/panta and at 10.6084/m9.figshare.23724705 .

AMRomics: a scalable workflow to analyze large microbial genome collections.

Whole genome analysis for microbial genomics is critical to studying and monitoring antimicrobial resistance strains. The exponential growth of microbial sequencing data necessitates a fast and scalable computational pipeline to generate the desired outputs in a timely and cost-effective manner. Recent methods have been implemented to integrate individual genomes into large collections of specific bacterial populations and are widely employed for systematic genomic surveillance. However, they do not scale well when the population expands and turnaround time remains the main issue for this type of analysis. Here, we introduce AMRomics, an optimized microbial genomics pipeline that can work efficiently with big datasets. We use different bacterial data collections to compare AMRomics against competitive tools and show that our pipeline can generate similar results of interest but with better performance. The software is open source and is publicly available at https://github.com/amromics/amromics under an MIT license.

AMRViz enables seamless genomics analysis and visualization of antimicrobial resistance.

We have developed AMRViz, a toolkit for analyzing, visualizing, and managing bacterial genomics samples. The toolkit is bundled with the current best practice analysis pipeline allowing researchers to perform comprehensive analysis of a collection of samples directly from raw sequencing data with a single command line. The analysis results in a report showing the genome structure, genome annotations, antibiotic resistance and virulence profile for each sample. The pan-genome of all samples of the collection is analyzed to identify core- and accessory-genes. Phylogenies of the whole genome as well as all gene clusters are also generated. The toolkit provides a web-based visualization dashboard allowing researchers to interactively examine various aspects of the analysis results. Availability: AMRViz is implemented in Python and NodeJS, and is publicly available under open source MIT license at https://github.com/amromics/amrviz .

Pasa: leveraging population pangenome graph to scaffold prokaryote genome assemblies.

Whole genome sequencing has increasingly become the essential method for studying the genetic mechanisms of antimicrobial resistance and for surveillance of drug-resistant bacterial pathogens. The majority of bacterial genomes sequenced to date have been sequenced with Illumina sequencing technology, owing to its high-throughput, excellent sequence accuracy, and low cost. However, because of the short-read nature of the technology, these assemblies are fragmented into large numbers of contigs, hindering the obtaining of full information of the genome. We develop Pasa, a graph-based algorithm that utilizes the pangenome graph and the assembly graph information to improve scaffolding quality. By leveraging the population information of the bacteria species, Pasa is able to utilize the linkage information of the gene families of the species to resolve the contig graph of the assembly. We show that our method outperforms the current state of the arts in terms of accuracy, and at the same time, is computationally efficient to be applied to a large number of existing draft assemblies.