Influence of Sequencing Technology on Pangenome-Level Analysis and Detection of Antimicrobial Resistance Genes in ESKAPE Pathogens

Abstract

As sequencing costs decrease, short-read and long-read technologies are indispensable tools for uncovering the genetic drivers behind bacterial pathogen resistance. This study explores the differences between the use of short-read (Illumina) and long-read (Oxford Nanopore Technologies [ONT]) sequencing in detecting antimicrobial resistance (AMR) genes in ESKAPE pathogens (ie, Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter cloacae). Utilizing a dataset of 1385 whole genome sequences and applying commonly used bioinformatic methods in bacterial genomics, we assessed the differences in genomic completeness, pangenome structure, and AMR gene and point mutation identification. Illumina presented higher genome completeness, while ONT identified a broader pangenome. Hybrid assembly outperformed both Illumina and ONT at identifying key AMR genetic determinants, presented results closer to Illumina's completeness, and revealed ONT-like pangenomic content. Notably, Illumina consistently detected more AMR-related point mutations than its counterparts. This highlights the importance of method selection based on research goals, particularly when using publicly available data ranging a wide timespan. Differences were also observed for specific gene classes and bacterial species, underscoring the need for a nuanced understanding of technology limitations. Overall, this study reveals the strengths and limitations of each approach, advocating for the use of Illumina for common AMR analysis, ONT for studying complex genomes and novel species, and hybrid assembly for a more comprehensive characterization, leveraging the benefits of both technologies.

Keywords: ESKAPE pathogens; Illumina; Oxford Nanopore Technologies; antimicrobial resistance; bacterial genomics.

Figure 1.

Complete workflow for performing antimicrobial resistance gene identification in ESKAPE pathogens using a pangenome approach, as well as statistical analysis to identify the potential differences associated with the sequencing technology (ie, Illumina and Oxford Nanopore Technologies) and hybrid method. Abbreviations: AMR, antimicrobial resistance; BUSCO, benchmarking universal single-copy orthologs; QC, quality control.

Figure 2.

Comparative analysis of antimicrobial resistance (AMR) detection in Enterococcus faecium using different sequencing technologies. A, Differences in the identification of AMR genes and point mutations conferring resistance by Illumina (green), Oxford Nanopore Technologies (ONT; orange), and hybrid assembly (blue). The horizontal bars represent the total number and its corresponding percentage of genes detected within each antimicrobial class, followed by the percentage of genes that were significantly differently detected by each pair of sequencers (0% = no differences in detection). B, Relationship between the total number of sequenced bases by each sequencer model, from Illumina (HiSeq, MiniSeq, MiSeq, NextSeq, and NovaSeq) and ONT (GridION, MinION, and PromethION). C, Overall differences in the number of AMR and point mutation genes detected by Illumina and ONT sequencing platforms, where the y-axis represents the total number of genes and point mutations identified. Abbreviations: AMR, antimicrobial resistance; MLS, macrolide-lincosamide-streptogramin; ONT, Oxford Nanopore Technologies; PhLOPSA, Phenicols, Lincosamides, Oxazolidinones, Pleuromutilins, and Streptogramin A.

Figure 3.

Comparative analysis of antimicrobial resistance (AMR) detection in Staphylococcus aureus using different sequencing technologies. A, Differences in the identification of AMR genes and point mutations conferring resistance by Illumina (green), Oxford Nanopore Technologies (ONT; orange), and hybrid assembly (blue). The horizontal bars represent the total number and its corresponding percentage of genes detected within each antimicrobial class, followed by the percentage of genes that were significantly differently detected by each pair of sequencers (0% = no differences in detection). B, Relationship between the total number of sequenced bases by each sequencer model, from Illumina (HiSeq, MiniSeq, MiSeq, NextSeq, and NovaSeq) and ONT (GridION, MinION, and PromethION). C, Overall differences in the number of AMR and point mutation genes detected by Illumina and ONT sequencing platforms, where the y-axis represents the total number of genes and point mutations identified. Abbreviations: AMR, antimicrobial resistance; MLS, macrolide-lincosamide-streptogramin; ONT, Oxford Nanopore Technologies.

Figure 4.

Comparative analysis of antimicrobial resistance (AMR) detection in Klebsiella pneumoniae using different sequencing technologies. A, Differences in the identification of AMR genes and point mutations conferring resistance by Illumina (green), Oxford Nanopore Technologies (ONT; orange), and hybrid assembly (blue). The horizontal bars represent the total number and its corresponding percentage of genes detected within each antimicrobial class, followed by the percentage of genes that were significantly differently detected by each pair of sequencers (0% = no differences in detection). B, Relationship between the total number of sequenced bases by each sequencer model, from Illumina (HiSeq, MiniSeq, MiSeq, NextSeq, and NovaSeq) and ONT (GridION, MinION, and PromethION). C, Overall differences in the number of AMR and point mutation genes detected by Illumina and ONT sequencing platforms, where the y-axis represents the total number of genes and point mutations identified. Abbreviations: AMR, antimicrobial resistance; MLS, macrolide-lincosamide-streptogramin; ONT, Oxford Nanopore Technologies.

Figure 5.

Comparative analysis of antimicrobial resistance (AMR) detection in Acinetobacter baumannii using different sequencing technologies. A, Differences in the identification of AMR genes and point mutations conferring resistance by Illumina (green), Oxford Nanopore Technologies (ONT; orange), and hybrid assembly (blue). The horizontal bars represent the total number and its corresponding percentage of genes detected within each antimicrobial class, followed by the percentage of genes that were significantly differently detected by each pair of sequencers (0% = no differences in detection). B, Relationship between the total number of sequenced bases by each sequencer model, from Illumina (HiSeq, MiniSeq, MiSeq, NextSeq, and NovaSeq) and ONT (GridION, MinION, and PromethION). C, Overall differences in the number of AMR and point mutation genes detected by Illumina and ONT sequencing platforms, where the y-axis represents the total number of genes and point mutations identified. Abbreviations: AMR, antimicrobial resistance; ONT, Oxford Nanopore Technologies.

Figure 6.

Comparative analysis of antimicrobial resistance (AMR) detection in Pseudomonas aeruginosa using different sequencing technologies. A, Differences in the identification of AMR genes and point mutations conferring resistance by Illumina (green), Oxford Nanopore Technologies (ONT; orange), and hybrid assembly (blue). The horizontal bars represent the total number and its corresponding percentage of genes detected within each antimicrobial class, followed by the percentage of genes that were significantly differently detected by each pair of sequencers (0% = no differences in detection). B, Relationship between the total number of sequenced bases by each sequencer model, from Illumina (HiSeq, MiniSeq, MiSeq, NextSeq, and NovaSeq) and ONT (GridION, MinION, and PromethION). C, Overall differences in the number of AMR and point mutation genes detected by Illumina and ONT sequencing platforms, where the y-axis represents the total number of genes and point mutations identified. Abbreviations: AMR, antimicrobial resistance; ONT, Oxford Nanopore Technologies.

Figure 7.

Comparative analysis of antimicrobial resistance (AMR) detection in Enterobacter cloacae using different sequencing technologies. A, Differences in the identification of AMR genes and point mutations conferring resistance by Illumina (green), Oxford Nanopore Technologies (ONT; orange), and hybrid assembly (blue). The horizontal bars represent the total number and its corresponding percentage of genes detected within each antimicrobial class, followed by the percentage of genes that were significantly differently detected by each pair of sequencers (0% = no differences in detection). B, Relationship between the total number of sequenced bases by each sequencer model, from Illumina (HiSeq, MiniSeq, MiSeq, NextSeq, and NovaSeq) and ONT (GridION, MinION, and PromethION). C, Overall differences in the number of AMR and point mutation genes detected by Illumina and ONT sequencing platforms, where the y-axis represents the total number of genes and point mutations identified. Abbreviations: AMR, antimicrobial resistance; ONT, Oxford Nanopore Technologies.