Evaluation of multiplex nanopore sequencing for Salmonella serotype prediction and antimicrobial resistance gene and virulence gene detection

Exploring the Frontiers of Nanopore Sequencing in Food Safety and Food Microbiology

Foodborne illnesses are a significant global public health challenge, with an estimated 600 million cases annually. Conventional food microbiology methods tend to be laborious and time consuming, pose difficulties in real-time utilization, and can display subpar accuracy or typing capabilities. With the recent advancements in third-generation sequencing and microbial omics, nanopore sequencing technology and its long-read sequencing capabilities have emerged as a promising platform. In recent years, nanopore sequencing technology has been benchmarked for its amplicon sequencing, whole-genome and transcriptome analysis, meta-analysis, and other advanced omics approaches. This review comprehensively covers nanopore sequencing technology's current advances in food safety applications, including outbreak investigation, pathogen surveillance, and antimicrobial resistance profiling. Despite significant progress, ongoing research and development are crucial to overcoming challenges in sequencing chemistry, accuracy, bioinformatics, and real-time adaptive sampling to fully realize nanopore sequencing technology's potential in food safety and food microbiology.

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

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.

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

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 ( Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter cloacae ). Utilizing a dataset of 1,385 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. 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.

Impact Statement

This study provides a comprehensive comparison of short-read (Illumina) and long-read (Oxford Nanopore Technologies, ONT) sequencing technologies in the context of antimicrobial resistance (AMR) detection in ESKAPE pathogens. By analyzing a large dataset of 1,385 whole genome sequences, the research offers valuable insights into the strengths and limitations of each approach, as well as the benefits of the novel approach of hybrid assembly. These findings have broad utility across microbiology, genomics, and infectious disease research. In particular, they apply to the work of researchers and clinicians dealing with AMR surveillance, investigation into outbreaks, and bacterial genome analysis. Given the nuance with which technological differences in genomic completeness, pangenome structure, and AMR determinant detection have been explored in this study, it is a good basis for informed method selection for future research. While the output represents an incremental advance, its significance lies in its practical implications. It thus enables researchers to take more reasonable decisions in designing genomic studies of bacterial pathogens by showing the complementarity of various sequencing approaches and their specific strengths. This could lead to more accurate and comprehensive detection of AMR, which would contribute ultimately to improved antibiotic stewardship and public health strategies.

Data Summary

The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.

Repositories

All the sequences used for this study are publicly accessible from GenBank, and their individual IDs are disclosed in Supplementary Table 1.

Advances in genotypic antimicrobialresistance testing: a comprehensive review

Antimicrobial resistance (AMR) represents a substantial threat to global public health, complicating the treatment of common infections and leading to prolonged illness and escalated healthcare expenses. To effectively combat AMR, timely and accurate detection is crucial for AMR surveillance and individual-based therapy. Phenotypic antibiotic resistance testing (AST) has long been considered the gold standard in clinical applications, serving as the foundation for clinical AMR diagnosis and optimized therapy. It has significantly contributed to ensuring patients' health and the development of novel antimicrobials. Despite advancements in automated culture-based AST technologies, inherent limitations impede the widespread use of phenotypic AST in AMR surveillance. Genotypic AST technologies offer a promising alternative option, exhibiting advantages of rapidity, high sensitivity, and specificity. With the continuous advancement and expanding applications of genotypic AST technologies, such as microfluidics, mass spectrometry, and high-resolution melting curve analysis, new vigor has been injected into the development and clinical implementation of genotypic AST technologies. In this narrative review, we discuss the principles, applications, and advancements of emerging genotypic AST methods in clinical settings. The comprehensive review aims to highlight the significant scientific potential of emerging genotypic AST technologies in clinical AMR diagnosis, providing insights to enhance existing methods and explore novel approaches.

NanoCore: core-genome-based bacterial genomic surveillance and outbreak detection in healthcare facilities from Nanopore and Illumina data

Genomic surveillance enables the early detection of pathogen transmission in healthcare facilities and contributes to the reduction of substantial patient harm. Fast turnaround times, flexible multiplexing, and low capital requirements make Nanopore sequencing well suited for genomic surveillance purposes; the analysis of Nanopore data, however, can be challenging. We present NanoCore, a user-friendly method for Nanopore-based genomic surveillance in healthcare facilities, enabling the calculation and visualization of cgMLST-like (core-genome multilocus sequence typing) sample distances directly from unassembled Nanopore reads. NanoCore implements a mapping, variant calling, and multilevel filtering strategy and also supports the analysis of Illumina data. We validated NanoCore on two 24-isolate data sets of methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus faecium (VRE). In the Nanopore-only mode, NanoCore-based pairwise distances between closely related isolates were near-identical to Illumina-based SeqSphere+ distances, a gold standard commercial method (average differences of 0.75 and 0.81 alleles for MRSA and VRE; sd = 0.98 and 1.00), and gave an identical clustering into closely related and non-closely related isolates. In the "hybrid" mode, in which only Nanopore data are used for some isolates and only Illumina data for others, increased average pairwise isolate distance differences were observed (average differences of 3.44 and 1.95 for MRSA and VRE, respectively; sd = 2.76 and 1.34), while clustering results remained identical. NanoCore is computationally efficient (<15 hours of wall time for the analysis of a 24-isolate data set on a workstation), available as free software, and supports installation via conda. In conclusion, NanoCore enables the effective use of the Nanopore technology for bacterial pathogen surveillance in healthcare facilities.

IMPORTANCE

Genomic surveillance involves sequencing the genomes and measuring the relatedness of bacteria from different patients or locations in the same healthcare facility, enabling an improved understanding of pathogen transmission pathways and the detection of "silent" outbreaks that would otherwise go undetected. It has become an indispensable tool for the detection and prevention of healthcare-associated infections and is routinely applied by many healthcare institutions. The earlier an outbreak or transmission chain is detected, the better; in this context, the Oxford Nanopore sequencing technology has important potential advantages over traditionally used short-read sequencing technologies, because it supports "real-time" data generation and the cost-effective "on demand" sequencing of small numbers of bacterial isolates. The analysis of Nanopore sequencing data, however, can be challenging. We present NanoCore, a user-friendly software for genomic surveillance that works directly based on Nanopore sequencing reads in FASTQ format, and demonstrate that its accuracy is equivalent to traditional gold standard short read-based analyses.

Bridging Classical Methodologies in Salmonella Investigation with Modern Technologies: A Comprehensive Review

Advancements in genomics and machine learning have significantly enhanced the study of Salmonella epidemiology. Whole-genome sequencing has revolutionized bacterial genomics, allowing for detailed analysis of genetic variation and aiding in outbreak investigations and source tracking. Short-read sequencing technologies, such as those provided by Illumina, have been instrumental in generating draft genomes that facilitate serotyping and the detection of antimicrobial resistance. Long-read sequencing technologies, including those from Pacific Biosciences and Oxford Nanopore Technologies, offer the potential for more complete genome assemblies and better insights into genetic diversity. In addition to these sequencing approaches, machine learning techniques like decision trees and random forests provide powerful tools for pattern recognition and predictive modeling. Importantly, the study of bacteriophages, which interact with Salmonella, offers additional layers of understanding. Phages can impact Salmonella population dynamics and evolution, and their integration into Salmonella genomics research holds promise for novel insights into pathogen control and epidemiology. This review revisits the history of Salmonella and its pathogenesis and highlights the integration of these modern methodologies in advancing our understanding of Salmonella.

Rapid and sensitive detection of Salmonella in agro-Food and environmental samples: A review of advances in rapid tests and biosensors

Salmonella is as an intracellular bacterium, causing many human fatalities when the host-specific serotypes reach the host gastrointestinal tract. Nontyphoidal Salmonella are responsible for numerous foodborne outbreaks and product recalls worldwide whereas typhoidal Salmonella are responsible for Typhoid fever cases in developing countries. Yet, Salmonella-related foodborne disease outbreaks through its food and water contaminations have urged the advancement of rapid and sensitive Salmonella-detecting methods for public health protection. While conventional detection methods are time-consuming and ineffective for monitoring foodstuffs with short shelf lives, advances in microbiology, molecular biology and biosensor methods have hastened the detection. Here, the review discusses Salmonella pathogenic mechanisms and its detection technology advancements (fundamental concepts, features, implementations, efficiency, benefits, limitations and prospects). The time-efficiency of each rapid test method is discussed in relation to their limit of detections (LODs) and time required from sample enrichment to final data analysis. Importantly, the matrix effects (LODs and sample enrichments) were compared within the methods to potentially speculate Salmonella detection from environmental, clinical or food matrices using certain techniques. Although biotechnological advancements have led to various time-efficient Salmonella-detecting techniques, one should consider the usage of sophisticated equipment to run the analysis by moderately to highly trained personnel. Ultimately, a fast, accurate Salmonella screening that is readily executed by untrained personnels from various matrices, is desired for public health procurement.

Whole genome sequencing in the palm of your hand: how to implement a MinION Galaxy-based workflow in a food safety laboratory for rapid Salmonella spp. serotyping, virulence, and antimicrobial resistance gene identification

Introduction

Whole Genome Sequencing (WGS) implementation in food safety laboratories is a significant advancement in food pathogen control and outbreak tracking. However, the initial investment for acquiring next-generation sequencing platforms and the need for bioinformatic skills represented an obstacle for the widespread use of WGS. Long-reading technologies, such as the one developed by Oxford Nanopore Technologies, can be easily implemented with a minor initial investment and with simple protocols that can be performed with basic laboratory equipment.

Methods

Herein, we report a simple MinION Galaxy-based workflow with analysis parameters that allow its implementation in food safety laboratories with limited computer resources and without previous knowledge in bioinformatics for rapid Salmonella serotyping, virulence, and identification of antimicrobial resistance genes. For that purpose, the single use Flongle flow cells, along with the MinION Mk1B for WGS, and the community-driven web-based analysis platform Galaxy for bioinformatic analysis was used. Three strains belonging to three different serotypes, monophasic S. Typhimurium, S. Grancanaria, and S. Senftenberg, were sequenced.

Results

After 24 h of sequencing, enough coverage was achieved in order to perform de novo assembly in all three strains. After evaluating different tools, Flye de novo assemblies with medaka polishing were shown to be optimal for in silico Salmonella spp. serotyping with SISRT tool followed by antimicrobial and virulence gene identification with ABRicate.

Discussion

The implementation of the present workflow in food safety laboratories with limited computer resources allows a rapid characterization of Salmonella spp. isolates.

Recent trends in Helicobacter pylori management: harnessing the power of AI and other advanced approaches

Background

Helicobacter pylori (H. pylori) is a bacterial infection that is prevalent and affects more than half of the world's population, causing stomach disorders such as gastritis, peptic ulcer disease, and gastric cancer.

Main body

The diagnosis of H. pylori infection relies on invasive and non-invasive techniques emerging artificial intelligence, and antibiotic therapy is available, but antibiotic resistance is a growing concern. The development of a vaccine is crucial in preventing H. pylori-associated diseases, but it faces challenges due to the bacterium's variability and immune escape mechanisms. Despite the challenges, ongoing research into H. pylori's virulence factors and immune escape mechanisms, as well as the development of potential vaccine targets, provides hope for more effective management and prevention of H. pylori-associated diseases. Recent research on H. pylori's immune escape mechanisms and novel immune checkpoint inhibitors could also lead to biomarkers for early cancer detection. Therefore, experts have suggested a combination of traditional and herbal medicine with artificial intelligence to potentially eradicate H. pylori.

Short conclusion

H. pylori infection remains a significant global health problem, but ongoing research into its properties and advanced technologies in addition to the combination of traditional and herbal medicine with artificial intelligence may also lead to the eradication of H. pylori-associated diseases.

Graphical abstract