MINTyper: an outbreak-detection method for accurate and rapid SNP typing of clonal clusters with noisy long reads

Closing the gap: Oxford Nanopore Technologies R10 sequencing allows comparable results to Illumina sequencing for SNP-based outbreak investigation of bacterial pathogens

Whole-genome sequencing has become the method of choice for bacterial outbreak investigation, with most clinical and public health laboratories currently routinely using short-read Illumina sequencing. Recently, long-read Oxford Nanopore Technologies (ONT) sequencing has gained prominence and may offer advantages over short-read sequencing, particularly with the recent introduction of the R10 chemistry, which promises much lower error rates than the R9 chemistry. However, limited information is available on its performance for bacterial single-nucleotide polymorphism (SNP)-based outbreak investigation. We present an open-source workflow, Prokaryotic Awesome variant Calling Utility (PACU) (https://github.com/BioinformaticsPlatformWIV-ISP/PACU), for constructing SNP phylogenies using Illumina and/or ONT R9/R10 sequencing data. The workflow was evaluated using outbreak data sets of Shiga toxin-producing Escherichia coli and Listeria monocytogenes by comparing ONT R9 and R10 with Illumina data. The performance of each sequencing technology was evaluated not only separately but also by integrating samples sequenced by different technologies/chemistries into the same phylogenomic analysis. Additionally, the minimum sequencing time required to obtain accurate phylogenetic results using nanopore sequencing was evaluated. PACU allowed accurate identification of outbreak clusters for both species using all technologies/chemistries, but ONT R9 results deviated slightly more from the Illumina results. ONT R10 results showed trends very similar to Illumina, and we found that integrating data sets sequenced by either Illumina or ONT R10 for different isolates into the same analysis produced stable and highly accurate phylogenomic results. The resulting phylogenies for these two outbreaks stabilized after ~20 hours of sequencing for ONT R9 and ~8 hours for ONT R10. This study provides a proof of concept for using ONT R10, either in isolation or in combination with Illumina, for rapid and accurate bacterial SNP-based outbreak investigation.

Phenotypic and Molecular Characterization of a Hospital Outbreak Clonal Lineage of Salmonella enterica Subspecies enterica serovar Mikawasima Containing blaTEM-1B and blaSHV-2 That Emerged on a Neonatal Ward, During the COVID-19 Pandemic

Nontyphoid salmonella can cause severe infections in newborns and is therefore declared a pathogen of major health significance at this age. The aim of the study was molecular and antimicrobial characterization of β-lactamase-producing Salmonella Mikawasima outbreak clone on a Neonatal ward, University Hospital of Split (UHS), Croatia during the COVID-19 pandemic. From April 2020, until April 2023, 75 nonrepetitive strains of Salmonella Mikawasima were isolated from stool specimens and tested for antimicrobial resistance. All 75 isolates were resistant to ampicillin and gentamicin, while 98% of isolates were resistant to amoxicillin/clavulanic acid. A high level of resistance was observed to third-generation cephalosporins (36% to ceftriaxone and 47% to ceftazidime). Extended-spectrum β-lactamase production was phenotypically detected by double-disk synergy test in 40% of isolates. Moderate resistance to quinolones was detected; 7% of isolates were resistant to pefloxacin and ciprofloxacin. All isolates were susceptible to carbapenems, chloramphenicol, and co-trimoxazole. Fourteen representative isolates, from 2020, 2021, 2022, and 2023, were analyzed with PFGE and all of them belong to the same clone. Whole-genome sequencing (WGS) analysis of three outbreak-related strains (SM1 and SM2 from 2020 and SM3 from 2023) confirmed that these strains share the same serotype (Mikawasima), multilocus sequence typing profile (ST2030), resistance genes [blaTEM-1B, aac(6')-Iaa, aac(6')-Im, and aph(2'')-Ib)] and carry incompatibility group C (IncC) plasmid. Furthermore, the gene blaSHV-2 was detected in SM1 and SM2. In summary, WGS analysis of three representative strains clearly demonstrates the persistence of β-lactamase-producing Salmonella Mikawasima in UHS during the 4-year period.

Advancing animal tuberculosis surveillance using culture-independent long-read whole-genome sequencing

Animal tuberculosis is a significant infectious disease affecting both livestock and wildlife populations worldwide. Effective disease surveillance and characterization of Mycobacterium bovis (M. bovis) strains are essential for understanding transmission dynamics and implementing control measures. Currently, sequencing of genomic information has relied on culture-based methods, which are time-consuming, resource-demanding, and concerning in terms of biosafety. This study explores the use of culture-independent long-read whole-genome sequencing (WGS) for a better understanding of M. bovis epidemiology in African buffaloes (Syncerus caffer). By comparing two sequencing approaches, we evaluated the efficacy of Illumina WGS performed on culture extracts and culture-independent Oxford Nanopore adaptive sampling (NAS). Our objective was to assess the potential of NAS to detect genomic variants without sample culture. In addition, culture-independent amplicon sequencing, targeting mycobacterial-specific housekeeping and full-length 16S rRNA genes, was applied to investigate the presence of microorganisms, including nontuberculous mycobacteria. The sequencing quality obtained from DNA extracted directly from tissues using NAS is comparable to the sequencing quality of reads generated from culture-derived DNA using both NAS and Illumina technologies. We present a new approach that provides complete and accurate genome sequence reconstruction, culture independently, and using an economically affordable technique.

Scaling neighbor joining to one million taxa with dynamic and heuristic neighbor joining

MOTIVATION

The neighbor-joining (NJ) algorithm is a widely used method to perform iterative clustering and forms the basis for phylogenetic reconstruction in several bioinformatic pipelines. Although NJ is considered to be a computationally efficient algorithm, it does not scale well for datasets exceeding several thousand taxa (>100 000). Optimizations to the canonical NJ algorithm have been proposed; these optimizations are, however, achieved through approximations or extensive memory usage, which is not feasible for large datasets.

RESULTS

In this article, two new algorithms, dynamic neighbor joining (DNJ) and heuristic neighbor joining (HNJ), are presented, which optimize the canonical NJ method to scale to millions of taxa without increasing the memory requirements. Both DNJ and HNJ outperform the current gold standard methods to construct NJ trees, while DNJ is guaranteed to produce exact NJ trees.

AVAILABILITY AND IMPLEMENTATION

https://bitbucket.org/genomicepidemiology/ccphylo.git.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

PlasmidHostFinder: Prediction of Plasmid Hosts Using Random Forest

Plasmids play a major role facilitating the spread of antimicrobial resistance between bacteria. Understanding the host range and dissemination trajectories of plasmids is critical for surveillance and prevention of antimicrobial resistance. Identification of plasmid host ranges could be improved using automated pattern detection methods compared to homology-based methods due to the diversity and genetic plasticity of plasmids. In this study, we developed a method for predicting the host range of plasmids using machine learning-specifically, random forests. We trained the models with 8,519 plasmids from 359 different bacterial species per taxonomic level; the models achieved Matthews correlation coefficients of 0.662 and 0.867 at the species and order levels, respectively. Our results suggest that despite the diverse nature and genetic plasticity of plasmids, our random forest model can accurately distinguish between plasmid hosts. This tool is available online through the Center for Genomic Epidemiology (https://cge.cbs.dtu.dk/services/PlasmidHostFinder/). IMPORTANCE Antimicrobial resistance is a global health threat to humans and animals, causing high mortality and morbidity while effectively ending decades of success in fighting against bacterial infections. Plasmids confer extra genetic capabilities to the host organisms through accessory genes that can encode antimicrobial resistance and virulence. In addition to lateral inheritance, plasmids can be transferred horizontally between bacterial taxa. Therefore, detection of the host range of plasmids is crucial for understanding and predicting the dissemination trajectories of extrachromosomal genes and bacterial evolution as well as taking effective countermeasures against antimicrobial resistance.

Extensive/Multidrug-Resistant Pneumococci Detected in Clinical Respiratory Tract Samples in Southern Sweden Are Closely Related to International Multidrug-Resistant Lineages

Background/Objective

The frequencies of non-susceptibility against common antibiotics among pneumococci vary greatly across the globe. When compared to other European countries antibiotic resistance against penicillin and macrolides has been uncommon in Sweden in recent years. Multidrug resistance (MDR) is, however, of high importance since relevant treatment options are scarce. The purpose of this study was to characterize the molecular epidemiology, presence of resistance genes and selected virulence genes of extensively drug-resistant (XDR) (n=15) and MDR (n=10) Streptococcus pneumoniae detected in clinical respiratory tract samples isolated from patients in a southern Swedish county 2016-2018. With the aim of relating them to global MDR pneumococci.

Methods

Whole genome sequencing (WGS) was performed to determine molecular epidemiology, resistance genes and presence of selected virulence factors. Antimicrobial susceptibility profiles were determined using broth microdilution testing. Further analyses were performed on isolates from the study and from the European nucleotide archive belonging to global pneumococcal sequence cluster (GPSC) 1 (n=86), GPSC9 (n=55) and GPSC10 (n=57). Bacteria were analyzed regarding selected virulence determinants (pilus islet 1, pilus islet 2 and Zinc metalloproteinase C) and resistance genes.

Results

Nineteen of 25 isolates were related to dominant global MDR lineages. Seventeen belonged to GPSC1, GPSC9 or GPSC10 with MDR non-PCV serotypes in GPSC9 (serotype 15A and 15C) as well as GPSC10 (serotype 7B, 15B and serogroup 24). Pilus islet-1 and pilus islet-2 were present in most sequence types belonging to GPSC1 and in two isolates within GPSC9 but were not detected in isolates belonging to GPSC10. Zinc metalloproteinase C was well conserved within all analyzed isolates belonging to GPSC9 but were not found in isolates from GPSC1 or GPSC10.

Conclusions

Although MDR S. pneumoniae is relatively uncommon in Sweden compared to other countries, virulent non-PCV serotypes that are MDR may become an increasing problem, particularly from clusters GPSC9 and GPSC10. Since the incidence of certain serotypes (3, 15A, and 19A) found among our MDR Swedish study isolates are persistent or increasing in invasive pneumococcal disease further surveillance is warranted.

Predicting Antimicrobial Resistance Using Partial Genome Alignments

Antimicrobial resistance (AMR) is an important global health threat that impacts millions of people worldwide each year. Developing methods that can detect and predict AMR phenotypes can help to mitigate the spread of AMR by informing clinical decision making and appropriate mitigation strategies. Many bioinformatic methods have been developed for predicting AMR phenotypes from whole-genome sequences and AMR genes, but recent studies have indicated that predictions can be made from incomplete genome sequence data. In order to more systematically understand this, we built random forest-based machine learning classifiers for predicting susceptible and resistant phenotypes for Klebsiella pneumoniae (1,640 strains), Mycobacterium tuberculosis (2,497 strains), and Salmonella enterica (1,981 strains). We started by building models from alignments that were based on a reference chromosome for each species. We then subsampled each chromosomal alignment and built models for the resulting subalignments, finding that very small regions, representing approximately 0.1 to 0.2% of the chromosome, are predictive. In K. pneumoniae, M. tuberculosis, and S. enterica, the subalignments are able to predict multiple AMR phenotypes with at least 70% accuracy, even though most do not encode an AMR-related function. We used these models to identify regions of the chromosome with high and low predictive signals. Finally, subalignments that retain high accuracy across larger phylogenetic distances were examined in greater detail, revealing genes and intergenic regions with potential links to AMR, virulence, transport, and survival under stress conditions.

IMPORTANCE Antimicrobial resistance causes thousands of deaths annually worldwide. Understanding the regions of the genome that are involved in antimicrobial resistance is important for developing mitigation strategies and preventing transmission. Machine learning models are capable of predicting antimicrobial resistance phenotypes from bacterial genome sequence data by identifying resistance genes, mutations, and other correlated features. They are also capable of implicating regions of the genome that have not been previously characterized as being involved in resistance. In this study, we generated global chromosomal alignments for Klebsiella pneumoniae, Mycobacterium tuberculosis, and Salmonella enterica and systematically searched them for small conserved regions of the genome that enable the prediction of antimicrobial resistance phenotypes. In addition to known antimicrobial resistance genes, this analysis identified genes involved in virulence and transport functions, as well as many genes with no previous implication in antimicrobial resistance.