Transforming clinical microbiology with bacterial genome sequencing

A multicenter study on accuracy and reproducibility of nanopore sequencing-based genotyping of bacterial pathogens

Nanopore sequencing has shown the potential to democratize genomic pathogen surveillance due to its ease of use and low entry cost. However, recent genotyping studies showed discrepant results compared to gold-standard short-read sequencing. Furthermore, although essential for widespread application, the reproducibility of nanopore-only genotyping remains largely unresolved. In our multicenter performance study involving five laboratories, four public health-relevant bacterial species were sequenced with the latest R10.4.1 flow cells and V14 chemistry. Core genome MLST analysis of over 500 data sets revealed highly strain-specific typing errors in all species in each laboratory. Investigation of the methylation-related errors revealed consistent DNA motifs at error-prone sites across participants at read level. Depending on the frequency of incorrect target reads, this either leads to correct or incorrect typing, whereby only minimal frequency deviations can randomly determine the final result. PCR preamplification, recent basecalling model updates and an optimized polishing strategy notably diminished the non-reproducible typing. Our study highlights the potential for new errors to appear with each newly sequenced strain and lays the foundation for computational approaches to reduce such typing errors. In conclusion, our multicenter study shows the necessity for a new validation concept for nanopore sequencing-based, standardized bacterial typing, where single nucleotide accuracy is critical.

Enhancing antimicrobial resistance detection with MetaGeneMiner: Targeted gene extraction from metagenomes

BACKGROUND

Accurately and efficiently extracting microbial genomic sequences from complex metagenomic data is crucial for advancing our understanding in fields such as clinical diagnostics, environmental microbiology, and biodiversity. As sequencing technologies evolve, this task becomes increasingly challenging due to the intricate nature of microbial communities and the vast amount of data generated. Especially in intensive care units (ICUs), infections caused by antibiotic-resistant bacteria are increasingly prevalent among critically ill patients, significantly impacting the effectiveness of treatments and patient prognoses. Therefore, obtaining timely and accurate information about infectious pathogens is of paramount importance for the treatment of patients with severe infections, which enables precisely targeted anti-infection therapies, and a tool that can extract microbial genomic sequences from metagenomic dataset would be of help.

METHODS

We developed MetaGeneMiner to help with retrieving specific microbial genomic sequences from metagenomes using a k-mer-based approach. It facilitates the rapid and accurate identification and analysis of pathogens. The tool is designed to be user-friendly and efficient on standard personal computers, allowing its use across a wide variety of settings. We validated MetaGeneMiner using eight metagenomic samples from ICU patients, which demonstrated its efficiency and accuracy.

RESULTS

The software extensively retrieved coding sequences of pathogens Acinetobacter baumannii and herpes simplex virus type 1 and detected a variety of resistance genes. All documentation and source codes for MetaGeneMiner are freely available at https://gitee.com/sculab/MetaGeneMiner .

CONCLUSIONS

It is foreseeable that MetaGeneMiner possesses the potential for applications across multiple domains, including clinical diagnostics, environmental microbiology, gut microbiome research, as well as biodiversity and conservation biology. Particularly in ICU settings, MetaGeneMiner introduces a novel, rapid, and precise method for diagnosing and treating infections in critically ill patients. This tool is capable of efficiently identifying infectious pathogens, guiding personalized and precise treatment strategies, and monitoring the development of antibiotic resistance, significantly impacting the diagnosis and treatment of severe infections.

Comparisons of genome assembly tools for characterization of Mycobacterium tuberculosis genomes using hybrid sequencing technologies

Background

Next-generation sequencing of Mycobacterium tuberculosis, the infectious agent causing tuberculosis, is improving the understanding of genomic diversity of circulating lineages and strain-types, and informing knowledge of drug resistance mutations. An increasingly popular approach to characterizing M. tuberculosis genomes (size: 4.4 Mbp) and variants (e.g., single nucleotide polymorphisms (SNPs)) involves the de novo assembly of sequence data.

Methods

We compared the performance of genome assembly tools (Unicycler, RagOut, and RagTag) on sequence data from nine drug resistant M. tuberculosis isolates (multi-drug (MDR) n = 1; pre-extensively-drug (pre-XDR) n = 8) generated using Illumina HiSeq, Oxford Nanopore Technology (ONT) PromethION, and PacBio platforms.

Results

Our investigation found that Unicycler-based assemblies had significantly higher genome completeness (~98.7%; p values = 0.01) compared to other assembler tools (RagOut = 98.6%, and RagTag = 98.6%). The genome assembly sizes (bp) across isolates and sequencers based on RagOut was significantly longer (p values < 0.001) (4,418,574 ± 8,824 bp) than Unicycler and RagTag assemblies (Unicycler = 4,377,642 ± 55,257 bp, and RagTag = 4,380,711 ± 51,164 bp). RagOut-based assemblies had the fewest contigs (~32) and the longest genome size (4,418,574 bp; vs. H37Rv reference size 4,411,532 bp) and therefore were chosen for downstream analysis. Pan-genome analysis of Illumina and PacBio hybrid assemblies revealed the greatest number of detected genes (4,639 genes; H37Rv reference contains 3,976 genes), while Illumina and ONT hybrid assemblies produced the highest number of SNPs. The number of genes from hybrid assemblies with ONT and PacBio long-reads (mean: 4,620 genes) was greater than short-read assembly alone (4,478 genes). All nine RagOut hybrid genome assemblies detected known mutations in genes associated with MDR-TB and pre-XDR-TB.

Conclusions

Unicycler software performed the best in terms of achieving contiguous genomes, whereas RagOut improved the quality of Unicycler's genome assemblies by providing a longer genome size. Overall, our approach has demonstrated that short-read and long-read hybrid assembly can provide a more complete genome assembly than short-read assembly alone by detecting pan-genomes and more genes, including IS6110, and SNPs.

Association of mutations in Mycobacterium tuberculosis complex (MTBC) respiration chain genes with hyper-transmission

BACKGROUND

The respiratory chain plays a key role in the growth of Mycobacterium tuberculosis complex (MTBC). However, the exact regulatory mechanisms of this system still need to be elucidated, and only a few studies have investigated the impact of genetic mutations within the respiratory chain on MTBC transmission. This study aims to explore the impact of respiratory chain gene mutations on the global spread of MTBC.

RESULTS

A total of 13,402 isolates of MTBC were included in this study. The majority of the isolates (n = 6,382, 47.62%) belonged to lineage 4, followed by lineage 2 (n = 5,123, 38.23%). Our findings revealed significant associations between Single Nucleotide Polymorphisms (SNPs) of specific genes and transmission clusters. These SNPs include Rv0087 (hycE, G178T), Rv1307 (atpH, C650T), Rv2195 (qcrA, G181C), Rv2196 (qcrB, G1250T), Rv3145 (nuoA, C35T), Rv3149 (nuoE, G121C), Rv3150 (nuoF, G700A), Rv3151 (nuoG, A1810G), Rv3152 (nuoH, G493A), and Rv3157 (nuoM, A1243G). Furthermore, our results showed that the SNPs of atpH C73G, atpA G271C, qcrA G181C, nuoJ G115A, nuoM G772A, and nuoN G1084T were positively correlated with cross-country transmission clades and cross-regional transmission clades.

CONCLUSIONS

Our study uncovered an association between mutations in respiratory chain genes and the transmission of MTBC. This important finding provides new insights for future research and will help to further explore new mechanisms of MTBC pathogenicity. By uncovering this association, we gain a more complete understanding of the processes by which MTBC increases virulence and spread, providing potential targets and strategies for preventing and treating tuberculosis.

Average Nucleotide Identity and Digital DNA-DNA Hybridization Analysis Following PromethION Nanopore-Based Whole Genome Sequencing Allows for Accurate Prokaryotic Typing

Whole-genome sequencing (WGS) is revolutionizing clinical bacteriology. However, bacterial typing remains investigated by reference techniques with inherent limitations. This stresses the need for alternative methods providing robust and accurate sequence type (ST) classification. This study optimized and evaluated a GridION nanopore sequencing protocol, adapted for the PromethION platform. Forty-eight Escherichia coli clinical isolates with diverse STs were sequenced to assess two alternative typing methods and resistance profiling applications. Multi-locus sequence typing (MLST) was used as the reference typing method. Genomic relatedness was assessed using Average Nucleotide Identity (ANI) and digital DNA-DNA Hybridization (DDH), and cut-offs for discriminative strain resolution were evaluated. WGS-based antibiotic resistance prediction was compared to reference Minimum Inhibitory Concentration (MIC) assays. We found ANI and DDH cut-offs of 99.3% and 94.1%, respectively, which correlated well with MLST classifications and demonstrated potentially higher discriminative resolution than MLST. WGS-based antibiotic resistance prediction showed categorical agreements of ≥ 93% with MIC assays for amoxicillin, ceftazidime, amikacin, tobramycin, and trimethoprim-sulfamethoxazole. Performance was suboptimal (68.8-81.3%) for amoxicillin-clavulanic acid, cefepime, aztreonam, and ciprofloxacin. A minimal sequencing coverage of 12× was required to maintain essential genomic features and typing accuracy. Our protocol allows the integration of PromethION technology in clinical laboratories, with ANI and DDH proving to be accurate and robust alternative typing methods, potentially offering superior resolution. WGS-based antibiotic resistance prediction holds promise for specific antibiotic classes.

Biomarkers in sepsis

Sepsis is a life-threatening condition characterized by dysregulated host response to infection leading to organ dysfunction. Despite advances in understanding its pathology, sepsis remains a global health concern and remains a major contributor to mortality. Timely identification is crucial for improving clinical outcomes, as delayed treatment significantly impacts survival. Accordingly, biomarkers play a pivotal role in diagnosis, risk stratification, and management. This review comprehensively discusses various biomarkers in sepsis and their potential application in antimicrobial stewardship and risk assessment. Biomarkers such as white blood cell count, neutrophil to lymphocyte ratio, erythrocyte sedimentation rate, C-reactive protein, interleukin-6, presepsin, and procalcitonin have been extensively studied for their diagnostic and prognostic value as well as in guiding antimicrobial therapy. Furthermore, this review explores the role of biomarkers in risk stratification, emphasizing the importance of identifying high-risk patients who may benefit from specific therapeutic interventions. Moreover, the review discusses the emerging field of transcriptional diagnostics and metagenomic sequencing. Advances in sequencing have enabled the identification of host response signatures and microbial genomes, offering insight into disease pathology and aiding species identification. In conclusion, this review provides a comprehensive overview of the current understanding and future directions of biomarker-based approaches in sepsis diagnosis, management, and personalized therapy.

Outbreak of Intermediate Species Leptospira venezuelensis Spread by Rodents to Cows and Humans in L. interrogans-Endemic Region, Venezuela

Leptospirosis is a common but underdiagnosed zoonosis. We conducted a 1-year prospective study in La Guaira State, Venezuela, analyzing 71 hospitalized patients who had possible leptospirosis and sampling local rodents and dairy cows. Leptospira rrs gene PCR test results were positive in blood or urine samples from 37/71 patients. Leptospira spp. were isolated from cultured blood or urine samples of 36/71 patients; 29 had L. interrogans, 3 L. noguchii, and 4 L. venezuelensis. Conjunctival suffusion was the most distinguishing clinical sign, many patients had liver involvement, and 8/30 patients with L. interrogans infections died. The Leptospira spp. found in humans were also isolated from local rodents; L. interrogans and L. venezuelensis were isolated from cows on a nearby, rodent-infested farm. Phylogenetic clustering of L. venezuelensis isolates suggested a recently expanded outbreak strain spread by rodents. Increased awareness of leptospirosis prevalence and rapid diagnostic tests are needed to improve patient outcomes.

16S rRNA amplicon sequencing and antimicrobial resistance profile of intensive care units environment in 41 Brazilian hospitals

Introduction

Infections acquired during healthcare setting stay pose significant public health threats. These infections are known as Healthcare-Associated Infections (HAI), mostly caused by pathogenic bacteria, which exhibit a wide range of antimicrobial resistance. Currently, there is no knowledge about the global cleaning process of hospitals and the bacterial diversity found in ICUs of Brazilian hospitals contributing to HAI.

Objective

Characterize the microbiome and common antimicrobial resistance genes present in high-touch Intensive Care Unit (ICU) surfaces, and to identify the potential contamination of the sanitizers/processes used to clean hospital surfaces.

Methods

In this national, multicenter, observational, and prospective cohort, bacterial profiles and several antimicrobial resistance genes from 41 hospitals across 16 Brazilian states were evaluated. Using high-throughput 16S rRNA amplicon sequencing and real-time PCR, the bacterial abundance and resistance genes presence were analyzed in both ICU environments and cleaning products.

Results

We identified a wide diversity of microbial populations with a recurring presence of HAI-related bacteria among most of the hospitals. The median bacterial positivity rate in surface samples was high (88.24%), varying from 21.62 to 100% in different hospitals. Hospitals with the highest bacterial load in samples were also the ones with highest HAI-related abundances. Streptococcus spp., Corynebacterium spp., Staphylococcus spp., Bacillus spp., Acinetobacter spp., and bacteria from the Flavobacteriaceae family were the microorganisms most found across all hospitals. Despite each hospital particularities in bacterial composition, clustering profiles were found for surfaces and locations in the ICU. Antimicrobial resistance genes mecA, bla KPC-like, bla NDM-like, and bla OXA-23-like were the most frequently detected in surface samples. A wide variety of sanitizers were collected, with 19 different active principles in-use, and 21% of the solutions collected showed viable bacterial growth with antimicrobial resistance genes detected.

Conclusion

This study demonstrated a diverse and spread pattern of bacteria and antimicrobial resistance genes covering a large part of the national territory in ICU surface samples and in sanitizers solutions. This data should contribute to the adoption of surveillance programs to improve HAI control strategies and demonstrate that large-scale epidemiology studies must be performed to further understand the implications of bacterial contamination in hospital surfaces and sanitizer solutions.

Machine learning based DNA melt curve profiling enables automated novel genotype detection

Surveillance for genetic variation of microbial pathogens, both within and among species, plays an important role in informing research, diagnostic, prevention, and treatment activities for disease control. However, large-scale systematic screening for novel genotypes remains challenging in part due to technological limitations. Towards addressing this challenge, we present an advancement in universal microbial high resolution melting (HRM) analysis that is capable of accomplishing both known genotype identification and novel genotype detection. Specifically, this novel surveillance functionality is achieved through time-series modeling of sequence-defined HRM curves, which is uniquely enabled by the large-scale melt curve datasets generated using our high-throughput digital HRM platform. Taking the detection of bacterial genotypes as a model application, we demonstrate that our algorithms accomplish an overall classification accuracy over 99.7% and perform novelty detection with a sensitivity of 0.96, specificity of 0.96 and Youden index of 0.92. Since HRM-based DNA profiling is an inexpensive and rapid technique, our results add support for the feasibility of its use in surveillance applications.

MSDeepAMR: antimicrobial resistance prediction based on deep neural networks and transfer learning

Introduction

Antimicrobial resistance (AMR) is a global health problem that requires early and effective treatments to prevent the indiscriminate use of antimicrobial drugs and the outcome of infections. Mass Spectrometry (MS), and more particularly MALDI-TOF, have been widely adopted by routine clinical microbiology laboratories to identify bacterial species and detect AMR. The analysis of AMR with deep learning is still recent, and most models depend on filters and preprocessing techniques manually applied on spectra.

Methods

This study propose a deep neural network, MSDeepAMR, to learn from raw mass spectra to predict AMR. MSDeepAMR model was implemented for Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus under different antibiotic resistance profiles. Additionally, a transfer learning test was performed to study the benefits of adapting the previously trained models to external data.

Results

MSDeepAMR models showed a good classification performance to detect antibiotic resistance. The AUROC of the model was above 0.83 in most cases studied, improving the results of previous investigations by over 10%. The adapted models improved the AUROC by up to 20% when compared to a model trained only with external data.

Discussion

This study demonstrate the potential of the MSDeepAMR model to predict antibiotic resistance and their use on external MS data. This allow the extrapolation of the MSDeepAMR model to de used in different laboratories that need to study AMR and do not have the capacity for an extensive sample collection.

Assessment and clinical utility of metagenomic next-generation sequencing for suspected lower respiratory tract infections

OBJECTIVES

This study aims to compare the diagnostic efficacy of metagenomic next-generation sequencing (mNGS) to traditional diagnostic methods in patients with lower respiratory tract infections (LRTIs), elucidate the etiological spectrum of these infections, and explore the impact of mNGS on guiding antimicrobial therapy.

METHODS

We retrospectively analyzed data from 128 patients admitted to the Respiratory Department of Anqing 116 Hospital between July 2022 and July 2023. All patients had undergone both mNGS and conventional microbiological techniques (CMT) for LRTI diagnosis. We assessed the diagnostic performance of these methods and examined the influence of mNGS on antimicrobial decision-making.

RESULTS

Overall, mNGS demonstrated superior sensitivity (96.8%) and accuracy (96.8%) compared to CMT. For Mycobacterium tuberculosis detection, the accuracy and sensitivity of mNGS was 88.8% and 77.6%, which was lower than the 94.7% sensitivity of the T-spot test and the 79.6% sensitivity of CMT. In fungal pathogen detection, mNGS showed excellent sensitivity (90.5%), specificity (86.7%), and accuracy (88.0%). Bacteria were the predominant pathogens detected (75.34%), with Mycobacterium tuberculosis (41.74%), Streptococcus pneumoniae (21.74%), and Haemophilus influenzae (16.52%) being most prevalent. Bacterial infections were most common (62.10%), followed by fungal and mixed infections (17.74%). Of the 118 patients whose treatment regimens were adjusted based on mNGS results, 102 (86.5%) improved, 7 (5.9%) did not respond favorably, and follow-up was lost for 9 patients (7.6%).

CONCLUSIONS

mNGS offers rapid and precise pathogen detection for patients with suspected LRTIs and shows considerable promise in diagnosing Mycobacterium tuberculosis and fungal infections. By broadening the pathogen spectrum and identifying polymicrobial infections, mNGS can significantly inform and refine antibiotic therapy.

Uncovering the spread of drug-resistant bacteria through next-generation sequencing based surveillance: transmission of extended-spectrum β-lactamase-producing Enterobacterales by a contaminated duodenoscope

Contamination of duodenoscopes is a significant concern due to the transmission of multidrug-resistant organisms (MDROs) among patients who undergo endoscopic retrograde cholangiopancreatography (ERCP), resulting in outbreaks worldwide. In July 2020, it was determined that three different patients, all had undergone ERCP with the same duodenoscope, were infected. Two patients were infected with blaCTX-M-15 encoding Citrobacter freundii, one experiencing a bloodstream infection and the other a urinary tract infection, while another patient had a bloodstream infection caused by blaSHV-12 encoding Klebsiella pneumoniae. Molecular characterization of isolates was available as every ESBL-producing isolate undergoes Next-Generation Sequencing (NGS) for comprehensive genomic analysis in our center. After withdrawing the suspected duodenoscope, we initiated comprehensive epidemiological research, encompassing case investigations, along with a thorough duodenoscope investigation. Screening of patients who had undergone ERCP with the implicated duodenoscope, as well as a selection of hospitalized patients who had ERCP with a different duodenoscope during the outbreak period, led to the discovery of three additional cases of colonization in addition to the three infections initially detected. No microorganisms were detected in eight routine culture samples retrieved from the suspected duodenoscope. Only after destructive dismantling of the duodenoscope, the forceps elevator was found to be positive for blaSHV-12 encoding K. pneumoniae which was identical to the isolates detected in three patients. This study highlights the importance of using NGS to monitor the transmission of MDROs and demonstrates that standard cultures may fail to detect contaminated medical equipment such as duodenoscopes.

Qualitative and quantitative rapid detection of VOCs differentially released by VAP-associated bacteria using PTR-MS and FGC-PTR-MS

Ventilator-associated pneumonia (VAP) is a prevalent disease caused by microbial infection, resulting in significant morbidity and mortality within the intensive care unit (ICU). The rapid and accurate identification of pathogenic bacteria causing VAP can assist clinicians in formulating timely treatment plans. In this study, we attempted to differentiate bacterial species in VAP by utilizing the volatile organic compounds (VOCs) released by pathogens. We cultured 6 common bacteria in VAP in vitro, including Acinetobacter baumannii, Enterobacter cloacae, Escherichia coli, Pseudomonas aeruginosa, Stenotrophomonas maltophilia, and Staphylococcus aureus, which covered most cases of VAP infection in clinic. After the VOCs released by bacteria were collected in sampling bags, they were quantitatively detected by a proton transfer reaction-mass spectrometry (PTR-MS), and the characteristic ions were qualitatively analyzed through a fast gas chromatography-proton transfer reaction-mass spectrometry (FGC-PTR-MS). After conducting principal component analysis (PCA) and analysis of similarities (ANOSIM), we discovered that the VOCs released by 6 bacteria exhibited differentiation following 3 h of quantitative cultivation in vitro. Additionally, we further investigated the variations in the types and concentrations of bacterial VOCs. The results showed that by utilizing the differences in types of VOCs, 6 bacteria could be classified into 5 sets, except for A. baumannii and E. cloacae which were indistinguishable. Furthermore, we observed significant variations in the concentration ratio of acetaldehyde and methyl mercaptan released by A. baumannii and E. cloacae. In conclusion, the VOCs released by bacteria could effectively differentiate the 6 pathogens commonly associated with VAP, which was expected to assist doctors in formulating treatment plans in time and improve the survival rate of patients.

Whole Genome Sequencing: Applications in Clinical Bacteriology

The success in determining the whole genome sequence of a bacterial pathogen was first achieved in 1995 by determining the complete nucleotide sequence of Haemophilus influenzae Rd using the chain-termination method established by Sanger et al. in 1977 and automated by Hood et al. in 1987. However, this technology was laborious, costly, and time-consuming. Since 2004, high-throughput next-generation sequencing technologies have been developed, which are highly efficient, require less time, and are cost-effective for whole genome sequencing (WGS) of all organisms, including bacterial pathogens. In recent years, the data obtained using WGS technologies coupled with bioinformatics analyses of the sequenced genomes have been projected to revolutionize clinical bacteriology. WGS technologies have been used in the identification of bacterial species, strains, and genotypes from cultured organisms and directly from clinical specimens. WGS has also helped in determining resistance to antibiotics by the detection of antimicrobial resistance genes and point mutations. Furthermore, WGS data have helped in the epidemiological tracking and surveillance of pathogenic bacteria in healthcare settings as well as in communities. This review focuses on the applications of WGS in clinical bacteriology.

Optimized bacterial community characterization through full-length 16S rRNA gene sequencing utilizing MinION nanopore technology

BACKGROUND

Accurate identification of bacterial communities is crucial for research applications, diagnostics, and clinical interventions. Although 16S ribosomal RNA (rRNA) gene sequencing is a widely employed technique for bacterial taxonomic classification, it often results in misclassified or unclassified bacterial taxa. This study sought to refine the full-length 16S rRNA gene sequencing protocol using the MinION sequencer, focusing on the V1-V9 regions. Our methodological enquiry examined several factors, including the number of PCR amplification cycles, choice of primers and Taq polymerase, and specific sequence databases and workflows employed. We used a microbial standard comprising eight bacterial strains (five gram-positive and three gram-negative) in known proportions as a validation control.

RESULTS

Based on the MinION protocol, we employed the microbial standard as the DNA template for the 16S rRNA gene amplicon sequencing procedure. Our analysis showed that an elevated number of PCR amplification cycles introduced PCR bias, and the selection of Taq polymerase and primer sets significantly affected the subsequent analysis. Bacterial identification at genus level demonstrated Pearson correlation coefficients ranging from 0.73 to 0.79 when assessed using BugSeq, Kraken-Silva and EPI2ME-16S workflows. Notably, the EPI2ME-16S workflow exhibited the highest Pearson correlation with the microbial standard, minimised misclassification, and increased alignment accuracy. At the species taxonomic level, the BugSeq workflow was superior, with a Pearson correlation coefficient of 0.92.

CONCLUSIONS

These findings emphasise the importance of careful selection of PCR settings and a well-structured analytical framework for 16S rRNA full-length gene sequencing. The results showed a robust correlation between the predicted and observed bacterial abundances at both the genus and species taxonomic levels, making these findings applicable across diverse research contexts and with clinical utility for reliable pathogen identification.

Active surveillance of carbapenemase-producing Enterobacterales using genomic sequencing for hospital-based infection control interventions

BACKGROUND

Whole-genome sequencing (WGS) is increasingly used to characterize hospital outbreaks of carbapenemase-producing Enterobacterales (CPE). However, access to WGS is variable and testing is often centralized, leading to delays in reporting of results.

OBJECTIVE

We describe the utility of a local sequencing service to promptly respond to facility needs over an 8-year period.

METHODS

The study was conducted at Royal Prince Alfred Hospital in Sydney, Australia. All CPE isolated from patient (screening and clinical) and environmental samples from 2015 onward underwent prospective WGS. Results were notified to the infection control unit in real time. When outbreaks were identified, WGS reports were also provided to senior clinicians and the hospital executive administration. Enhanced infection control interventions were refined based on the genomic data.

RESULTS

In total, 141 CPE isolates were detected from 123 patients and 5 environmental samples. We identified 9 outbreaks, 4 of which occurred in high-risk wards (intensive care unit and/or solid-organ transplant ward). The largest outbreak involved Enterobacterales containing an NDM gene. WGS detected unexpected links among patients, which led to further investigation of epidemiological data that uncovered the outpatient setting and contaminated equipment as reservoirs for ongoing transmission. Targeted interventions as part of outbreak management halted further transmission.

CONCLUSIONS

WGS has transitioned from an emerging technology to an integral part of local CPE control strategies. Our results show the value of embedding this technology in routine surveillance, with timely reports generated in clinically relevant timeframes to inform and optimize local control measures for greatest impact.

Hound: a novel tool for automated mapping of genotype to phenotype in bacterial genomes assembled de novo

Increasing evidence suggests that microbial species have a strong within species genetic heterogeneity. This can be problematic for the analysis of prokaryote genomes, which commonly relies on a reference genome to guide the assembly process. Differences between reference and sample genomes will therefore introduce errors in final assembly, jeopardizing the detection from structural variations to point mutations-critical for genomic surveillance of antibiotic resistance. Here we present Hound, a pipeline that integrates publicly available tools to assemble prokaryote genomes de novo, detect user-given genes by similarity to report mutations found in the coding sequence, promoter, as well as relative gene copy number within the assembly. Importantly, Hound can use the query sequence as a guide to merge contigs, and reconstruct genes that were fragmented by the assembler. To showcase Hound, we screened through 5032 bacterial whole-genome sequences isolated from farmed animals and human infections, using the amino acid sequence encoded by blaTEM-1, to detect and predict resistance to amoxicillin/clavulanate which is driven by over-expression of this gene. We believe this tool can facilitate the analysis of prokaryote species that currently lack a reference genome, and can be scaled either up to build automated systems for genomic surveillance or down to integrate into antibiotic susceptibility point-of-care diagnostics.

Chemical Nose Strategy with Metabolic Labeling and "Antibiotic-Responsive Spectrum" Enables Accurate and Rapid Pathogen Identification

The worldwide antimicrobial resistance (AMR) dilemma urgently requires rapid and accurate pathogen phenotype discrimination and antibiotic resistance identification. The conventional protocols are either time-consuming or depend on expensive instrumentations. Herein, we demonstrate a metabolic-labeling-assisted chemical nose strategy for phenotyping classification and antibiotic resistance identification of pathogens based on the "antibiotic-responsive spectrum" of different pathogens. d-Amino acids with click handles were metabolically incorporated into the cell wall of pathogens for further clicking with dibenzocyclooctyne-functionalized upconversion nanoparticles (DBCO-UCNPs) in the presence/absence of six types of antibiotics, which generates seven-channel sensing responses. With the assistance of machine learning algorithms, eight types of pathogens, including three types of antibiotic-resistant bacteria, can be well classified and discriminated in terms of microbial taxonomies, Gram phenotypes, and antibiotic resistance. The present metabolic-labeling-assisted strategy exhibits good anti-interference capability and improved discrimination ability rooted in the unique sensing mechanism. Sensitive identification of pathogens with 100% accuracy from artificial urinary tract infection samples at a concentration as low as 105 CFU/mL was achieved. Pathogens outside of the training set can also be discriminated well. This clearly demonstrated the potential of the present strategy in the identification of unknown pathogens in clinical samples.

Inference of Infectious Disease Transmission through a Relaxed Bottleneck Using Multiple Genomes Per Host

In recent times, pathogen genome sequencing has become increasingly used to investigate infectious disease outbreaks. When genomic data is sampled densely enough amongst infected individuals, it can help resolve who infected whom. However, transmission analysis cannot rely solely on a phylogeny of the genomes but must account for the within-host evolution of the pathogen, which blurs the relationship between phylogenetic and transmission trees. When only a single genome is sampled for each host, the uncertainty about who infected whom can be quite high. Consequently, transmission analysis based on multiple genomes of the same pathogen per host has a clear potential for delivering more precise results, even though it is more laborious to achieve. Here, we present a new methodology that can use any number of genomes sampled from a set of individuals to reconstruct their transmission network. Furthermore, we remove the need for the assumption of a complete transmission bottleneck. We use simulated data to show that our method becomes more accurate as more genomes per host are provided, and that it can infer key infectious disease parameters such as the size of the transmission bottleneck, within-host growth rate, basic reproduction number, and sampling fraction. We demonstrate the usefulness of our method in applications to real datasets from an outbreak of Pseudomonas aeruginosa amongst cystic fibrosis patients and a nosocomial outbreak of Klebsiella pneumoniae.

The zoonotic pathogen Wohlfahrtiimonas chitiniclastica - current findings from a clinical and genomic perspective

The zoonotic pathogen Wohlfahrtiimonas chitiniclastica can cause several diseases in humans, including sepsis and bacteremia. Although the pathogenesis is not fully understood, the bacterium is thought to enter traumatic skin lesions via fly larvae, resulting in severe myiasis and/or wound contamination. Infections are typically associated with, but not limited to, infestation of an open wound by fly larvae, poor sanitary conditions, cardiovascular disease, substance abuse, and osteomyelitis. W. chitiniclastica is generally sensitive to a broad spectrum of antibiotics with the exception of fosfomycin. However, increasing drug resistance has been observed and its development should be monitored with caution. In this review, we summarize the currently available knowledge and evaluate it from both a clinical and a genomic perspective.

Spectrum of bacterial pathogens in inflammatory and noninflammatory cutaneous ulcers of American tegumentary leishmaniasis

Background

Cutaneous leishmaniasis (CL) ulcers exhibiting an inflammatory phenotype, characterized by purulent exudate, erythema, pain, and/or lymphatic involvement, are empirically treated with antibiotics.

Objective

The spectrum of bacteria present in localized versus inflammatory phenotypes of CL is elucidated herein.

Methods

Filter paper lesion impressions (FPLIs) from 39 patients with CL (19 inflammatory and 20 noninflammatory ulcers) were evaluated via real-time polymerase chain reaction (qPCR) and end-point PCR targeting: Staphylococcus aureus, Enterobacter cloacae, Streptococcus pyogenes, Enterococcus spp., Citrobacter freundii, Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, and 16S rDNA. Whole genome sequencing (WGS) was performed on six specimens.

Results

In total, 30/39 (77%) patients' ulcers had ⩾1 bacterium detected, which included the following species: S. aureus (n = 16, 41%), C. freundii (n = 13, 33%), P. aeruginosa (n = 12, 31%), E. cloacae (n = 12, 31%), K. pneumoniae (n = 11, 28%), Enterococcus spp. (n = 7, 18%), E. coli (n = 6, 15%), and S. pyogenes (n = 4, 10). Prevalence of bacterial species did not differ by CL phenotype (p = 0.63). However, patients with inflammatory phenotypes were, on average, over a decade older than patients with noninflammatory phenotypes (42 years vs 27 years) (p = 0.01). The inflammatory phenotype was more prevalent among ulcers of Leishmania Viannia braziliensis (58%) and L. V. panamensis (83%) compared to those of L. V. guyanensis (20%) (p = 0.0369).

Conclusion

The distribution of flora did not differ between inflammatory and noninflammatory CL phenotypes. Further prospective analysis, including additional WGS studies of all CL ulcers for nonbacterial organisms, is necessary to determine the role of empiric antibiotic therapy in inflammatory and purulent CL.

Loop-Mediated Isothermal Amplification of DNA (LAMP) as an Alternative Method for Determining Bacteria in Wound Infections

The increasing number of patients with chronic wounds requires the development of quick and accurate diagnostics methods. One of the key and challenging aspects of treating ulcers is to control wound infection. Early detection of infection is essential for the application of suitable treatment methods, such as systemic antibiotics or other antimicrobial agents. Clinically, the most frequently used method for detecting microorganisms in wounds is through a swab and culture on appropriate media. This test has major limitations, such as the long bacterial growth time and the selectivity of bacterial growth. This article presents an overview of molecular methods for detecting bacteria in wounds, including real-time polymerase chain reaction (rtPCR), quantitative polymerase chain reaction (qPCR), genotyping, next-generation sequencing (NGS), and loop-mediated isothermal amplification (LAMP). We focus on the LAMP method, which has not yet been widely used to detect bacteria in wounds, but it is an interesting alternative to conventional detection methods. LAMP does not require additional complicated equipment and provides the fastest detection time for microorganisms (approx. 30 min reaction). It also allows the use of many pairs of primers in one reaction and determination of up to 15 organisms in one sample. Isothermal amplification of DNA is currently the easiest and most economical method for microbial detection in wound infection. Direct visualization of the reaction with dyes, along with omitting DNA isolation, has increased the potential use of this method.

Mobilome-driven partitions of the resistome in Salmonella

IMPORTANCE

Antimicrobial resistance (AMR) has become a significant global challenge, with an estimated 10 million deaths annually by 2050. The emergence of AMR is mainly attributed to mobile genetic elements (MGEs or mobilomes), which accelerate wide dissemination among pathogens. The interaction between mobilomes and AMR genes (or resistomes) in Salmonella, a primary cause of diarrheal diseases that results in over 90 million cases annually, remains poorly understood. The available fragmented or incomplete genomes remain a significant limitation in investigating the relationship between AMR and MGEs. Here, we collected the most extensive closed Salmonella genomes (n = 1,817) from various sources across 58 countries. Notably, our results demonstrate that resistome transmission between Salmonella lineages follows a specific pattern of MGEs and is influenced by external drivers, including certain socioeconomic factors. Therefore, targeted interventions are urgently needed to mitigate the catastrophic consequences of Salmonella AMR.

Virulence attributes of successful methicillin-resistant Staphylococcus aureus lineages

Methicillin-resistant Staphylococcus aureus (MRSA) is a leading cause of severe and often fatal infections. MRSA epidemics have occurred in waves, whereby a previously successful lineage has been replaced by a more fit and better adapted lineage. Selection pressures in both hospital and community settings are not uniform across the globe, which has resulted in geographically distinct epidemiology. This review focuses on the mechanisms that trigger the establishment and maintenance of current, dominant MRSA lineages across the globe. While the important role of antibiotic resistance will be mentioned throughout, factors which influence the capacity of S. aureus to colonize and cause disease within a host will be the primary focus of this review. We show that while MRSA possesses a diverse arsenal of toxins including alpha-toxin, the success of a lineage involves more than just producing toxins that damage the host. Success is often attributed to the acquisition or loss of genetic elements involved in colonization and niche adaptation such as the arginine catabolic mobile element, as well as the activity of regulatory systems, and shift metabolism accordingly (e.g., the accessory genome regulator, agr). Understanding exactly how specific MRSA clones cause prolonged epidemics may reveal targets for therapies, whereby both core (e.g., the alpha toxin) and acquired virulence factors (e.g., the Panton-Valentine leukocidin) may be nullified using anti-virulence strategies.

Escherichia coli O157:H7 tir 255 T > A allele strains differ in chromosomal and plasmid composition

Shiga toxin-producing Escherichia coli (STEC) O157:H7 strains with the T allele in the translocated intimin receptor polymorphism (tir) 255 A > T gene associate with human disease more than strains with an A allele; however, the allele is not thought to be the direct cause of this difference. We sequenced a diverse set of STEC O157:H7 strains (26% A allele, 74% T allele) to identify linked differences that might underlie disease association. The average chromosome and pO157 plasmid size and gene content were significantly greater within the tir 255 A allele strains. Eighteen coding sequences were unique to tir 255 A allele chromosomes, and three were unique to tir 255 T allele chromosomes. There also were non-pO157 plasmids that were unique to each tir 255 allele variant. The overall average number of prophages did not differ between tir 255 allele strains; however, there were different types between the strains. Genomic and mobile element variation linked to the tir 255 polymorphism may account for the increased frequency of the T allele isolates in human disease.

Ultrafast detection of β-lactamase resistance in Klebsiella pneumoniae from blood culture by nanopore sequencing

Aim: This study aimed to assess the ultra-fast method using MinION™ sequencing for rapid identification of β-lactamase-producing Klebsiella pneumoniae clinical isolates from positive blood cultures. Methods: Spiked-blood positive blood cultures were extracted using the ultra-fast method and automated DNA extraction for MinION sequencing. Raw reads were analyzed for β-lactamase resistance genes. Multilocus sequence typing and β-lactamase variant characterization were performed after assembly. Results: The ultra-fast method identified clinically relevant β-lactamase resistance genes in less than 1 h. Multilocus sequence typing and β-lactamase variant characterization required 3-6 h. Sequencing quality showed no direct correlation with pore number or DNA concentration. Conclusion: Nanopore sequencing, specifically the ultra-fast method, is promising for the rapid diagnosis of bloodstream infections, facilitating timely identification of multidrug-resistant bacteria in clinical samples.

Joint deep autoencoder and subgraph augmentation for inferring microbial responses to drugs

Exploring microbial stress responses to drugs is crucial for the advancement of new therapeutic methods. While current artificial intelligence methodologies have expedited our understanding of potential microbial responses to drugs, the models are constrained by the imprecise representation of microbes and drugs. To this end, we combine deep autoencoder and subgraph augmentation technology for the first time to propose a model called JDASA-MRD, which can identify the potential indistinguishable responses of microbes to drugs. In the JDASA-MRD model, we begin by feeding the established similarity matrices of microbe and drug into the deep autoencoder, enabling to extract robust initial features of both microbes and drugs. Subsequently, we employ the MinHash and HyperLogLog algorithms to account intersections and cardinality data between microbe and drug subgraphs, thus deeply extracting the multi-hop neighborhood information of nodes. Finally, by integrating the initial node features with subgraph topological information, we leverage graph neural network technology to predict the microbes' responses to drugs, offering a more effective solution to the 'over-smoothing' challenge. Comparative analyses on multiple public datasets confirm that the JDASA-MRD model's performance surpasses that of current state-of-the-art models. This research aims to offer a more profound insight into the adaptability of microbes to drugs and to furnish pivotal guidance for drug treatment strategies. Our data and code are publicly available at: https://github.com/ZZCrazy00/JDASA-MRD.

Economic evaluations of whole-genome sequencing for pathogen identification in public health surveillance and health-care-associated infections: a systematic review

Whole-genome sequencing (WGS) has resulted in improvements to pathogen characterisation for the rapid investigation and management of disease outbreaks and surveillance. We conducted a systematic review to synthesise the economic evidence of WGS implementation for pathogen identification and surveillance. Of the 2285 unique publications identified through online database searches, 19 studies met the inclusion criteria. The economic evidence to support the broader application of WGS as a front-line pathogen characterisation and surveillance tool is insufficient and of low quality. WGS has been evaluated in various clinical settings, but these evaluations are predominantly investigations of a single pathogen. There are also considerable variations in the evaluation approach. Economic evaluations of costs, effectiveness, and cost-effectiveness are needed to support the implementation of WGS in public health settings.

Carotenoids in Health as Studied by Omics-Related Endpoints

Carotenoids have been associated with risk reduction for several chronic diseases, including the association of their dietary intake/circulating levels with reduced incidence of obesity, type 2 diabetes, certain types of cancer, and even lower total mortality. In addition to some carotenoids constituting vitamin A precursors, they are implicated in potential antioxidant effects and pathways related to inflammation and oxidative stress, including transcription factors such as nuclear factor κB and nuclear factor erythroid 2-related factor 2. Carotenoids and metabolites may also interact with nuclear receptors, mainly retinoic acid receptor/retinoid X receptor and peroxisome proliferator-activated receptors, which play a role in the immune system and cellular differentiation. Therefore, a large number of downstream targets are likely influenced by carotenoids, including but not limited to genes and proteins implicated in oxidative stress and inflammation, antioxidation, and cellular differentiation processes. Furthermore, recent studies also propose an association between carotenoid intake and gut microbiota. While all these endpoints could be individually assessed, a more complete/integrative way to determine a multitude of health-related aspects of carotenoids includes (multi)omics-related techniques, especially transcriptomics, proteomics, lipidomics, and metabolomics, as well as metagenomics, measured in a variety of biospecimens including plasma, urine, stool, white blood cells, or other tissue cellular extracts. In this review, we highlight the use of omics technologies to assess health-related effects of carotenoids in mammalian organisms and models.

Current Uses and Future Perspectives of Genomic Technologies in Clinical Microbiology

Recent advancements in sequencing technology and data analytics have led to a transformative era in pathogen detection and typing. These developments not only expedite the process, but also render it more cost-effective. Genomic analyses of infectious diseases are swiftly becoming the standard for pathogen analysis and control. Additionally, national surveillance systems can derive substantial benefits from genomic data, as they offer profound insights into pathogen epidemiology and the emergence of antimicrobial-resistant strains. Antimicrobial resistance (AMR) is a pressing global public health issue. While clinical laboratories have traditionally relied on culture-based antimicrobial susceptibility testing, the integration of genomic data into AMR analysis holds immense promise. Genomic-based AMR data can furnish swift, consistent, and highly accurate predictions of resistance phenotypes for specific strains or populations, all while contributing invaluable insights for surveillance. Moreover, genome sequencing assumes a pivotal role in the investigation of hospital outbreaks. It aids in the identification of infection sources, unveils genetic connections among isolates, and informs strategies for infection control. The One Health initiative, with its focus on the intricate interconnectedness of humans, animals, and the environment, seeks to develop comprehensive approaches for disease surveillance, control, and prevention. When integrated with epidemiological data from surveillance systems, genomic data can forecast the expansion of bacterial populations and species transmissions. Consequently, this provides profound insights into the evolution and genetic relationships of AMR in pathogens, hosts, and the environment.

Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology

Full Laboratory Automation is revolutionizing work habits in an increasing number of clinical microbiology facilities worldwide, generating huge streams of digital images for interpretation. Contextually, deep learning architectures are leading to paradigm shifts in the way computers can assist with difficult visual interpretation tasks in several domains. At the crossroads of these epochal trends, we present a system able to tackle a core task in clinical microbiology, namely the global interpretation of diagnostic bacterial culture plates, including presumptive pathogen identification. This is achieved by decomposing the problem into a hierarchy of complex subtasks and addressing them with a multi-network architecture we call DeepColony. Working on a large stream of clinical data and a complete set of 32 pathogens, the proposed system is capable of effectively assist plate interpretation with a surprising degree of accuracy in the widespread and demanding framework of Urinary Tract Infections. Moreover, thanks to the rich species-related generated information, DeepColony can be used for developing trustworthy clinical decision support services in laboratory automation ecosystems from local to global scale.

Current and Future Technologies for the Detection of Antibiotic-Resistant Bacteria

Antibiotic resistance is a global public health concern, posing a significant threat to the effectiveness of antibiotics in treating bacterial infections. The accurate and timely detection of antibiotic-resistant bacteria is crucial for implementing appropriate treatment strategies and preventing the spread of resistant strains. This manuscript provides an overview of the current and emerging technologies used for the detection of antibiotic-resistant bacteria. We discuss traditional culture-based methods, molecular techniques, and innovative approaches, highlighting their advantages, limitations, and potential future applications. By understanding the strengths and limitations of these technologies, researchers and healthcare professionals can make informed decisions in combating antibiotic resistance and improving patient outcomes.

CRISPR-Based Precision Molecular Diagnostics for Disease Detection and Surveillance

Sensitive, rapid, and portable molecular diagnostics is the future of disease surveillance, containment, and therapy. The recent SARS-CoV-2 pandemic has reminded us of the vulnerability of lives from ever-evolving pathogens. At the same time, it has provided opportunities to bridge the gap by translating basic molecular biology into therapeutic tools. One such molecular biology technique is CRISPR (clustered regularly interspaced short palindromic repeat) which has revolutionized the field of molecular diagnostics at the need of the hour. The use of CRISPR-Cas systems has been widespread in biology research due to the ease of performing genetic manipulations. In 2012, CRISPR-Cas systems were, for the first time, shown to be reprogrammable, i.e., capable of performing sequence-specific gene editing. This discovery catapulted the field of CRISPR-Cas research and opened many unexplored avenues in the field of gene editing, from basic research to therapeutics. One such field that benefitted greatly from this discovery was molecular diagnostics, as using CRISPR-Cas technologies enabled existing diagnostic methods to become more sensitive, accurate, and portable, a necessity in disease control. This Review aims to capture some of the trajectories and advances made in this arena and provides a comprehensive understanding of the methods and their potential use as point-of-care diagnostics.

Application of metagenomics for diagnosis of broilers displaying neurological symptoms

BACKGROUND

Thirty-two-day-old broiler chickens at a farm located in northwestern South Korea displayed adverse neurological symptoms including limping, lying down, and head shaking. Approximately 2.1% of chickens died or were culled due to severe symptoms. Five carcasses were submitted to the Avian Disease Division of the Animal and Plant Quarantine Agency (APQA) for disease diagnosis.

RESULTS

Broilers displayed severe pericarditis and perihepatitis associated with gross lesions. Broilers also displayed microscopic lesions in the cerebrum and in the granular layer of the cerebellum, which were associated with multifocal perivascular cuffing and purulent necrosis in the cerebrum, and severe meningitis with heterophil and lymphocyte infiltration. Staphylococcus spp. were identified in the liver and heart using bacteriological culture. PCR/RT-PCR assays revealed that broilers were negative for avian Clostridium botulinum, Newcastle disease virus, and avian encephalomyelitis virus. Bacterial and viral metagenomic analysis of brain sample further revealed the presence of Pseudomonas spp. and Marek's disease virus, which are known etiological agents of chicken meningoencephalitis.

CONCLUSIONS

This study reports a diagnostic analysis of gross and histopathological lesions from 32-day-old broilers displaying unique neurological symptoms that revealed the presence of the several neurological diseases including meningoencephalitis. The causative agents associated with meningoencephalitis of broilers that had not been identified by routine diagnostic methods could be diagnosed by metagenomics, which proves the usefulness of metagenomics as a diagnostic tool for unknown neurological diseases in broilers.

Characterizing the Genetic Diversity of Salmonella Isolated from U.S. Raw Inshell Pistachios Using Whole Genome Sequencing

The genetic diversity of 169 Salmonella isolates from pistachios collected from California storage silos during the 2010, 2011, and 2012 harvests (silo survey isolates) was determined by analyzing the whole genome sequence data using the CFSAN SNP pipeline developed by the U.S. Food and Drug Administration's Center for Food Safety and Applied Nutrition. Salmonella isolates clustered by serovars Agona, Enteritidis, Montevideo, Sandiego, Senftenberg, Liverpool, Tennessee, and Worthington in the phylogenetic tree. Within each serovar, isolates grouped into one or two clusters (≤14 SNPs). Two distinct clusters (>14 SNPs; A and B) were identified for Salmonella Enteritidis, Montevideo, and Liverpool for a total of 11 unique strains. Sequences of representative silo survey isolates clustered with sequences of Salmonella strains isolated from U.S. pistachio-associated samples collected between 2008 and 2018 available on the National Center for Biotechnology Information database, and, in all but two cases, not with sequences of Salmonella strains recovered from raw California almonds from 2001 through 2013. The genomic evidence suggests that strains of Salmonella Agona, Liverpool Cluster A, Montevideo Clusters A and B, Senftenberg, and Worthington have persisted in the California pistachio environment for ≥3 years and some of these strains have been reported exclusively in association with pistachios.

A Comprehensive Bioinformatics Resource Guide for Genome-Based Antimicrobial Resistance Studies

The use of high-throughput sequencing technologies and bioinformatic tools has greatly transformed microbial genome research. With the help of sophisticated computational tools, it has become easier to perform whole genome assembly, identify and compare different species based on their genomes, and predict the presence of genes responsible for proteins, antimicrobial resistance, and toxins. These bioinformatics resources are likely to continuously improve in quality, become more user-friendly to analyze the multiple genomic data, efficient in generating information and translating it into meaningful knowledge, and enhance our understanding of the genetic mechanism of AMR. In this manuscript, we provide an essential guide for selecting the popular resources for microbial research, such as genome assembly and annotation, antibiotic resistance gene profiling, identification of virulence factors, and drug interaction studies. In addition, we discuss the best practices in computer-oriented microbial genome research, emerging trends in microbial genomic data analysis, integration of multi-omics data, the appropriate use of machine-learning algorithms, and open-source bioinformatics resources for genome data analytics.

Within-host diversity improves phylogenetic and transmission reconstruction of SARS-CoV-2 outbreaks

Accurate inference of who infected whom in an infectious disease outbreak is critical for the delivery of effective infection prevention and control. The increased resolution of pathogen whole-genome sequencing has significantly improved our ability to infer transmission events. Despite this, transmission inference often remains limited by the lack of genomic variation between the source case and infected contacts. Although within-host genetic diversity is common among a wide variety of pathogens, conventional whole-genome sequencing phylogenetic approaches exclusively use consensus sequences, which consider only the most prevalent nucleotide at each position and therefore fail to capture low-frequency variation within samples. We hypothesized that including within-sample variation in a phylogenetic model would help to identify who infected whom in instances in which this was previously impossible. Using whole-genome sequences from SARS-CoV-2 multi-institutional outbreaks as an example, we show how within-sample diversity is partially maintained among repeated serial samples from the same host, it can transmitted between those cases with known epidemiological links, and how this improves phylogenetic inference and our understanding of who infected whom. Our technique is applicable to other infectious diseases and has immediate clinical utility in infection prevention and control.

Pangenome graphs in infectious disease: a comprehensive genetic variation analysis of Neisseria meningitidis leveraging Oxford Nanopore long reads

Whole genome sequencing has revolutionized infectious disease surveillance for tracking and monitoring the spread and evolution of pathogens. However, using a linear reference genome for genomic analyses may introduce biases, especially when studies are conducted on highly variable bacterial genomes of the same species. Pangenome graphs provide an efficient model for representing and analyzing multiple genomes and their variants as a graph structure that includes all types of variations. In this study, we present a practical bioinformatics pipeline that employs the PanGenome Graph Builder and the Variation Graph toolkit to build pangenomes from assembled genomes, align whole genome sequencing data and call variants against a graph reference. The pangenome graph enables the identification of structural variants, rearrangements, and small variants (e.g., single nucleotide polymorphisms and insertions/deletions) simultaneously. We demonstrate that using a pangenome graph, instead of a single linear reference genome, improves mapping rates and variant calling for both simulated and real datasets of the pathogen Neisseria meningitidis. Overall, pangenome graphs offer a promising approach for comparative genomics and comprehensive genetic variation analysis in infectious disease. Moreover, this innovative pipeline, leveraging pangenome graphs, can bridge variant analysis, genome assembly, population genetics, and evolutionary biology, expanding the reach of genomic understanding and applications.

Results of the 2020 Genomic Proficiency Test for the network of European Union Reference Laboratory for Antimicrobial Resistance assessing whole-genome-sequencing capacities

The global surveillance and outbreak investigation of antimicrobial resistance (AMR) is amidst a paradigm shift from traditional biology to bioinformatics. This is due to developments in whole-genome-sequencing (WGS) technologies, bioinformatics tools, and reduced costs. The increased use of WGS is accompanied by challenges such as standardization, quality control (QC), and data sharing. Thus, there is global need for inter-laboratory WGS proficiency test (PT) schemes to evaluate laboratories' capacity to produce reliable genomic data. Here, we present the results of the first iteration of the Genomic PT (GPT) organized by the Global Capacity Building Group at the Technical University of Denmark in 2020. Participating laboratories sequenced two isolates and corresponding DNA of Salmonella enterica, Escherichia coli and Campylobacter coli, using WGS methodologies routinely employed at their laboratories. The participants' ability to obtain consistently good-quality WGS data was assessed based on several QC WGS metrics. A total of 21 laboratories from 21 European countries submitted WGS and meta-data. Most delivered high-quality sequence data with only two laboratories identified as overall underperforming. The QC metrics, N50 and number of contigs, were identified as good indicators for high-sequencing quality. We propose QC thresholds for N50 greater than 20 000 and 25 000 for Campylobacter coli and Escherichia coli, respectively, and number of contigs >200 bp greater than 225, 265 and 100 for Salmonella enterica, Escherichia coli and Campylobacter coli, respectively. The GPT2020 results confirm the importance of systematic QC procedures, ensuring the submission of reliable WGS data for surveillance and outbreak investigation to meet the requirements of the paradigm shift in methodology.

Intestinal stem cells and gut microbiota therapeutics: hype or hope?

The vital role of the intestines as the main site for the digestion and absorption of nutrients for the body continues subconsciously throughout one's lifetime, but underneath all the complex processes lie the intestinal stem cells and the gut microbiota that work together to maintain the intestinal epithelium. Intestinal stem cells (ISC) are multipotent stem cells from which all intestinal epithelial cells originate, and the gut microbiota refers to the abundant collection of various microorganisms that reside in the gastrointestinal tract. Both reside in the intestines and have many mechanisms and pathways in place with the ultimate goal of co-managing human gastrointestinal tract homeostasis. Based on the abundance of research that is focused on either of these two topics, this suggests that there are many methods by which both players affect one another. Therefore, this review aims to address the relationship between ISC and the gut microbiota in the context of regenerative medicine. Understanding the principles behind both aspects is therefore essential in further studies in the field of regenerative medicine by making use of the underlying designed mechanisms.

Genomic analysis of Enterococcus faecium strain RAOG174 associated with acute chorioamnionitis carried antibiotic resistance gene: is it time for precise microbiological identification for appropriate antibiotic use?

BACKGROUND

Preterm labor syndrome is associated with high perinatal morbidity and mortality, and intra-amniotic infection is a cause of preterm labor. The standard identification of causative microorganisms is based on the use of biochemical phenotypes, together with broth dilution-based antibiotic susceptibility from organisms grown in culture. However, such methods could not provide an accurate epidemiological aspect and a genetic basis of antimicrobial resistance leading to an inappropriate antibiotic administration. Hybrid genome assembly is a combination of short- and long-read sequencing, which provides better genomic resolution and completeness for genotypic identification and characterization. Herein, we performed a hybrid whole genome assembly sequencing of a pathogen associated with acute histologic chorioamnionitis in women presenting with PPROM.

RESULTS

We identified Enterococcus faecium, namely E. faecium strain RAOG174, with several antibiotic resistance genes, including vancomycin and aminoglycoside. Virulence-associated genes and potential bacteriophage were also identified in this genome.

CONCLUSION

We report herein the first study demonstrating the use of hybrid genome assembly and genomic analysis to identify E. faecium ST17 as a pathogen associated with acute histologic chorioamnionitis. The analysis provided several antibiotic resistance-associated genes/mutations and mobile genetic elements. The occurrence of E. faecium ST17 raised the awareness of the colonization of clinically relevant E. faecium and the carrying of antibiotic resistance. This finding has brought the advantages of genomic approach in the identification of the bacterial species and antibiotic resistance gene for E. faecium for appropriate antibiotic use to improve maternal and neonatal care.

Estimating the fitness cost and benefit of antimicrobial resistance from pathogen genomic data

Increasing levels of antibiotic resistance in many bacterial pathogen populations are a major threat to public health. Resistance to an antibiotic provides a fitness benefit when the bacteria are exposed to this antibiotic, but resistance also often comes at a cost to the resistant pathogen relative to susceptible counterparts. We lack a good understanding of these benefits and costs of resistance for many bacterial pathogens and antibiotics, but estimating them could lead to better use of antibiotics in a way that reduces or prevents the spread of resistance. Here, we propose a new model for the joint epidemiology of susceptible and resistant variants, which includes explicit parameters for the cost and benefit of resistance. We show how Bayesian inference can be performed under this model using phylogenetic data from susceptible and resistant lineages and that by combining data from both we are able to disentangle and estimate the resistance cost and benefit parameters separately. We applied our inferential methodology to several simulated datasets to demonstrate good scalability and accuracy. We analysed a dataset of Neisseria gonorrhoeae genomes collected between 2000 and 2013 in the USA. We found that two unrelated lineages resistant to fluoroquinolones shared similar epidemic dynamics and resistance parameters. Fluoroquinolones were abandoned for the treatment of gonorrhoea due to increasing levels of resistance, but our results suggest that they could be used to treat a minority of around 10% of cases without causing resistance to grow again.

Metagenomic Sequencing of Positive Blood Culture Fluid for Accurate Bacterial and Fungal Species Identification: A Pilot Study

With blood stream infections (BSIs) representing a major cause of mortality and morbidity worldwide, blood cultures play a crucial role in diagnosis, but their clinical application is dampened by the long turn-around time and the detection of only culturable pathogens. In this study, we developed and validated a shotgun metagenomics next-generation sequencing (mNGS) test directly from positive blood culture fluid, allowing for the identification of fastidious or slow growing microorganisms more rapidly. The test was built based on previously validated next-generation sequencing tests, which rely on several key marker genes for bacterial and fungal identification. The new test utilizes an open-source metagenomics CZ-ID platform for the initial analysis to generate the most likely candidate species, which is then used as a reference genome for downstream, confirmatory analysis. This approach is innovative because it takes advantage of an open-source software's agnostic taxonomic calling capability while still relying on the more established and previously validated marker gene-based identification scheme, increasing the confidence in the final results. The test showed high accuracy (100%, 30/30) for both bacterial and fungal microorganisms. We further demonstrated its clinical utility especially for anaerobes and mycobacteria that are either fastidious, slow growing, or unusual. Although applicable in only limited settings, the Positive Blood Culture mNGS test provides an incremental improvement in solving the unmet clinical needs for the diagnosis of challenging BSIs.

Influence of bacterial and alveolar cell co-culture on microbial VOC production using HS-GC/MS

Volatile organic compounds (VOCs) found in exhaled breath continue to garner interest as an alternative diagnostic tool in pulmonary infections yet, their clinical integration remains a challenge with difficulties in translating identified biomarkers. Alterations in bacterial metabolism secondary to host nutritional availability may explain this but is often inadequately modelled in vitro. The influence of more clinically relevant nutrients on VOC production for two common respiratory pathogens was investigated. VOCs from Staphylococcus aureus (S.aureus) and Pseudomonas aeruginosa (P.aeruginosa) cultured with and without human alveolar A549 epithelial cells were analyzed using headspace extraction coupled with gas chromatography-mass spectrometry. Untargeted and targeted analyses were performed, volatile molecules identified from published data, and the differences in VOC production evaluated. Principal component analysis (PCA) could differentiate alveolar cells from either S. aureus or P. aeruginosa when cultured in isolation based on PC1 (p = 0.0017 and 0.0498, respectively). However, this separation was lost for S. aureus (p = 0.31) but not for P. aeruginosa (p = 0.028) when they were cultured with alveolar cells. S. aureus cultured with alveolar cells led to higher concentrations of two candidate biomarkers, 3-methyl-1-butanol (p = 0.001) and 3-methylbutanal (p = 0.002) when compared to S. aureus, alone. P. aeruginosa metabolism resulted in less generation of pathogen-associated VOCs when co-cultured with alveolar cells compared to culturing in isolation. VOC biomarkers previously considered indicative of bacterial presence are influenced by the local nutritional environment and this should be considered when evaluating their biochemical origin.

Insight into the dynamics of drinking water resistome in China

Antibiotic resistance (AR) is a serious environmental hazard of the current age. Antibiotic resistance genes (ARGs) are the fundamental entities that spread AR in the environment. ARGs are likely to be transferred from the non-pathogenic to pathogenic microbes that might ultimately be responsible for the AR in humans and other organisms. Drinking water (DW) is the primary interaction route between ARGs and humans. Being the highest producer and consumer of antibiotics China poses a potential threat to developing superbugs and ARGs dissemination. Herein, we comprehensively seek to review the ARGs from dominant DW sources in China. Furthermore, the origin and influencing factors of the ARGs to the DW in China have been evaluated. Commonly used methods, both classical and modern, are being compiled. In addition, the risk posed and mitigation strategies of DW ARGs in China have been outlined. Overall, we believe this review would contribute to the assessment of ARGs in DW of China and their dissemination to humans and other animals and ultimately help the policymakers and scientists in the field to counteract this problem on an emergency basis.

Whole-Genome Sequencing-Based Resistome Analysis of Nosocomial Multidrug-Resistant Non-Fermenting Gram-Negative Pathogens from the Balkans

Non-fermenting Gram-negative bacilli (NFGNB), such as Pseudomonas aeruginosa and Acinetobacter baumannii, are among the major opportunistic pathogens involved in the global antibiotic resistance epidemic. They are designated as urgent/serious threats by the Centers for Disease Control and Prevention and are part of the World Health Organization’s list of critical priority pathogens. Also, Stenotrophomonas maltophilia is increasingly recognized as an emerging cause for healthcare-associated infections in intensive care units, life-threatening diseases in immunocompromised patients, and severe pulmonary infections in cystic fibrosis and COVID-19 individuals. The last annual report of the ECDC showed drastic differences in the proportions of NFGNB with resistance towards key antibiotics in different European Union/European Economic Area countries. The data for the Balkans are of particular concern, indicating more than 80% and 30% of invasive Acinetobacter spp. and P. aeruginosa isolates, respectively, to be carbapenem-resistant. Moreover, multidrug-resistant and extensively drug-resistant S. maltophilia from the region have been recently reported. The current situation in the Balkans includes a migrant crisis and reshaping of the Schengen Area border. This results in collision of diverse human populations subjected to different protocols for antimicrobial stewardship and infection control. The present review article summarizes the findings of whole-genome sequencing-based resistome analyses of nosocomial multidrug-resistant NFGNBs in the Balkan countries.

Full-Length 16S rRNA Gene Analysis Using Long-Read Nanopore Sequencing for Rapid Identification of Bacteria from Clinical Specimens

Amplicon sequencing of the 16S ribosomal RNA (rRNA) gene is a practical and reliable measure for taxonomic profiling of bacterial communities. This chapter describes the detailed workflow for full-length 16S rRNA gene amplicon analysis using nanopore sequencing and bioinformatics pipelines to analyze nanopore sequencing data for taxonomic assignment. This approach offers a higher taxonomic resolution for bacterial identification from clinical specimens with a markedly reduced timeframe and improved versatility.

Clinical Implementation of Routine Whole-genome Sequencing for Hospital Infection Control of Multi-drug Resistant Pathogens

BACKGROUND

Prospective whole-genome sequencing (WGS)-based surveillance may be the optimal approach to rapidly identify transmission of multi-drug resistant (MDR) bacteria in the healthcare setting.

METHODS

We prospectively collected methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci (VRE), carbapenem-resistant Acinetobacter baumannii (CRAB), extended-spectrum beta-lactamase (ESBL-E), and carbapenemase-producing Enterobacterales (CPE) isolated from blood cultures, sterile sites, or screening specimens across three large tertiary referral hospitals (2 adult, 1 paediatric) in Brisbane, Australia. WGS was used to determine in silico multi-locus sequence typing (MLST) and resistance gene profiling via a bespoke genomic analysis pipeline. Putative transmission events were identified by comparison of core genome single nucleotide polymorphisms (SNPs). Relevant clinical meta-data were combined with genomic analyses via customised automation, collated into hospital-specific reports regularly distributed to infection control teams.

RESULTS

Over 4 years (April 2017 to July 2021) 2660 isolates were sequenced. This included MDR gram-negative bacilli (n = 293 CPE, n = 1309 ESBL), MRSA (n = 620), and VRE (n = 433). A total of 379 clinical reports were issued. Core genome SNP data identified that 33% of isolates formed 76 distinct clusters. Of the 76 clusters, 43 were contained to the 3 target hospitals, suggesting ongoing transmission within the clinical environment. The remaining 33 clusters represented possible inter-hospital transmission events or strains circulating in the community. In 1 hospital, proven negligible transmission of non-multi-resistant MRSA enabled changes to infection control policy.

CONCLUSIONS

Implementation of routine WGS for MDR pathogens in clinical laboratories is feasible and can enable targeted infection prevention and control interventions.

Phylogenetic Analysis of Bacterial Pathogen Genomes

The development of high-throughput sequencing technology has led to a significant reduction in the time and cost of sequencing whole genomes of bacterial pathogens. Studies can sequence and compare hundreds or even thousands of genomes within a given bacterial population. A phylogenetic tree is the most frequently used method of depicting the relationships between these bacterial pathogen genomes. However, the presence of homologous recombination in most bacterial pathogen species can invalidate the application of standard phylogenetic tools. Here we describe a method to produce phylogenetic analyses that accounts for the disruptive effect of recombination. This allows users to investigate the recombination events that have occurred, as well as to produce more meaningful phylogenetic analyses which recover the clonal genealogy representing the clonal relationships between genomes.