Genomic Prediction of Antimicrobial Resistance: Ready or Not, Here It Comes!

Analytical and clinical validation of direct detection of antimicrobial resistance markers by plasma microbial cell-free DNA sequencing

Sequencing of plasma microbial cell-free DNA (mcfDNA) has gained increased acceptance as a valuable adjunct to standard-of-care testing for diagnosis of infections throughout the body. Here, we report the analytical and clinical validation of a novel application of mcfDNA sequencing, the non-invasive detection of seven common antimicrobial resistance (AMR) genetic markers in 18 important pathogens. The AMR markers include SCCmec, mecA, mecC, vanA, vanB, blaCTX-M, and blaKPC. The AMR markers were computationally linked to the pathogens detected. Analytical validation showed high reproducibility (100%), inclusivity (54 to 100%), and exclusivity (100%). Clinical accuracy was assessed with 114 unique plasma samples from patients at seven study sites with concordant culture results for target bacteria from a variety of specimen types and correlated with available phenotypic antimicrobial susceptibility test results and genotypic results. The positive percent agreement (PPA), negative percent agreement (NPA), and diagnostic yield (DY) were estimated for each AMR marker. DY was defined as the percentage of tests that yielded an actionable result of either detected or not detected. The results for the combination of SCCmec and mecA for staphylococci were PPA 19/20 (95.0%), NPA 21/22 (95.4%), DY 42/60 (70.0%); vanA for enterococci were PPA 3/3 (100%), NPA 2/2 (100%), DY 5/6 (83.3%); blaCTX-M for gram-negative bacilli were PPA 5/6 (83.3%), NPA 29/29 (100%), DY 35/49 (71.4%); and blaKPC for gram-negative bacilli were PPA 0/2 (0%), NPA: 23/23 (100%), DY 25/44 (56.8%). The addition of AMR capability to plasma mcfDNA sequencing should provide clinicians with an effective new culture-independent tool for optimization of therapy.

IMPORTANCE

This manuscript is ideally suited for the Innovative Diagnostic Methods sections as it reports the analytical and clinical validation of a novel application of plasma microbial cell-free DNA sequencing for direct detection of seven selected antimicrobial resistance markers in 18 target pathogens. Clearly, it has potential clinical utility in optimizing therapy and was incorporated into the Karius test workflow in September 2023. In addition, the workflow could readily be adapted to expand the number of target bacteria and antimicrobial resistance markers as needed.

Exploring diagnostic methods for drug-resistant tuberculosis: A comprehensive overview

Despite available global efforts and funding, Tuberculosis (TB) continues to affect a considerable number of patients worldwide. Policy makers and stakeholders set clear goals to reduce TB incidence and mortality, but the emergence of multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) complicate the reach of these goals. Drug-resistance TB needs to be diagnosed rapidly and accurately to effectively treat patients, prevent the transmission of MDR-TB, minimise mortality, reduce treatment costs and avoid unnecessary hospitalisations. In this narrative review, we provide a comprehensive overview of laboratory methods for detecting drug resistance in MTB, focusing on phenotypic, molecular and other drug susceptibility testing (DST) techniques. We found a large variety of methods used, with the BACTEC MGIT 960 being the most common phenotypic DST and the Xpert MTB/RIF being the most common molecular DST. We emphasise the importance of integrating phenotypic and molecular DST to address issues like resistance to new drugs, heteroresistance, mixed infections and low-level resistance mutations. Notably, most of the analysed studies adhered to the outdated definition of XDR-TB and did not consider the pre-XDR definition, thus posing challenges in aligning diagnostic methods with the current landscape of TB resistance.

Rapid inference of antibiotic resistance and susceptibility for Klebsiella pneumoniae by clinical shotgun metagenomic sequencing

OBJECTIVES

The study aimed to develop a genotypic antimicrobial resistance testing method for Klebsiella pneumoniae using metagenomic sequencing data.

METHODS

We utilized Lasso regression on assembled genomes to identify genetic resistance determinants for six antibiotics (Gentamicin, Tobramycin, Imipenem, Meropenem, Ceftazidime, Trimethoprim/Sulfamethoxazole). The genetic features were weighted, grouped into clusters to establish classifier models. Origin species of detected antibiotic resistant gene (ARG) was determined by novel strategy integrating "possible species," "gene copy number calculation" and "species-specific kmers." The performance of the method was evaluated on retrospective case studies.

RESULTS

Our study employed machine learning on 3928 K. pneumoniae isolates, yielding stable models with AUCs > 0.9 for various antibiotics. GenseqAMR, a read-based software, exhibited high accuracy (AUC 0.926-0.956) for short-read datasets. The integration of a species-specific kmer strategy significantly improved ARG-species attribution to an average accuracy of 96.67%. In a retrospective study of 191 K. pneumoniae-positive clinical specimens (0.68-93.39% genome coverage), GenseqAMR predicted 84.23% of AST results on average. It demonstrated 88.76-96.26% accuracy for resistance prediction, offering genotypic AST results with a shorter turnaround time (mean ± SD: 18.34 ± 0.87 hours) than traditional culture-based AST (60.15 ± 21.58 hours). Furthermore, a retrospective clinical case study involving 63 cases showed that GenseqAMR could lead to changes in clinical treatment for 24 (38.10%) cases, with 95.83% (23/24) of these changes deemed beneficial.

CONCLUSIONS

In conclusion, GenseqAMR is a promising tool for quick and accurate AMR prediction in Klebsiella pneumoniae, with the potential to improve patient outcomes through timely adjustments in antibiotic treatment.

Leukocytospermia and/or Bacteriospermia: Impact on Male Infertility

Infertility is a globally underestimated public health concern affecting almost 190 million people, i.e., about 17.5% of people during their lifetime, while the prevalence of male factor infertility is about 7%. Among numerous other causes, the prevalence of male genital tract infections reportedly ranges between 10% and 35%. Leukocytospermia is found in 30% of infertile men and up to 20% in fertile men. Bacterial infections cause an inflammatory response attracting leukocytes, which produce reactive oxygen species (ROS) and release cytokines, both of which can cause damage to sperm, rendering them dysfunctional. Although leukocytospermia and bacteriospermia are both clinical conditions that can negatively affect male fertility, there is still debate about their impact on assisted reproduction outcomes and management. According to World Health Organization (WHO) guidelines, leukocytes should be determined by means of the Endtz test or with monoclonal antibodies against CD15, CD68 or CD22. The cut-off value proposed by the WHO is 1 × 106 peroxidase-positive cells/mL. For bacteria, Gram staining and semen culture are regarded as the "gold standard", while modern techniques such as PCR and next-generation sequencing (NGS) are allowing clinicians to detect a wider range of pathogens. Whereas the WHO manual does not specify a specific value as a cut-off for bacterial contamination, several studies consider semen samples with more than 103 colony-forming units (cfu)/mL as bacteriospermic. The pathogenic mechanisms leading to sperm dysfunction include direct interaction of bacteria with the male germ cells, bacterial release of spermatotoxic substances, induction of pro-inflammatory cytokines and ROS, all of which lead to oxidative stress. Clinically, bacterial infections, including "silent" infections, are treatable, with antibiotics being the treatment of choice. Yet, non-steroidal antiphlogistics or antioxidants should also be considered to alleviate inflammatory lesions and improve semen quality. In an assisted reproduction set up, sperm separation techniques significantly reduce the bacterial load in the semen. Nonetheless, contamination of the semen sample with skin commensals should be prevented by applying relevant hygiene techniques. In patients where leukocytospermia is detected, the causes (e.g. infection, inflammation, varicocele, smoking, etc.) of the leukocyte infiltration have to be identified and addressed with antibiotics, anti-inflammatories or antioxidants in cases where high oxidative stress levels are detected. However, no specific strategy is available for the management of leukocytospermia. Therefore, the relationship between bacteriospermia and leukocytospermia as well as their specific impact on functional sperm parameters and reproductive outcome variables such as fertilization or clinical pregnancy must be further investigated. The aim of this narrative review is to provide an update on the current knowledge on leukocytospermia and bacteriospermia and their impact on male fertility.

Present and Future Non-Culture-Based Diagnostics: Stewardship Potentials and Considerations

The medical microbiologist plays a key role in the transition from culture-based to molecular test methods for diagnosis of infectious diseases. They must understand the scientific and technical bases underlying these tests along with their associated benefits and limitations and be able to educate administrators and patient providers on their proper use. Coordination of testing practices between clinical departments and the spectrum of public health and research laboratories is essential to optimize health care delivery.

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.

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.

MGS2AMR: a gene-centric mining of metagenomic sequencing data for pathogens and their antimicrobial resistance profile

BACKGROUND

Identification of pathogenic bacteria from clinical specimens and evaluating their antimicrobial resistance (AMR) are laborious tasks that involve in vitro cultivation, isolation, and susceptibility testing. Recently, a number of methods have been developed that use machine learning algorithms applied to the whole-genome sequencing data of isolates to approach this problem. However, making AMR assessments from more easily available metagenomic sequencing data remains a big challenge.

RESULTS

We present the Metagenomic Sequencing to Antimicrobial Resistance (MGS2AMR) pipeline, which detects antibiotic resistance genes (ARG) and their possible organism of origin within a sequenced metagenomics sample. This in silico method allows for the evaluation of bacterial AMR directly from clinical specimens, such as stool samples. We have developed two new algorithms to optimize and annotate the genomic assembly paths within the raw Graphical Fragment Assembly (GFA): the GFA Linear Optimal Path through seed segments (GLOPS) algorithm and the Adapted Dijkstra Algorithm for GFA (ADAG). These novel algorithms improve the sensitivity of ARG detection and aid in species annotation. Tests based on 1200 microbiome samples show a high ARG recall rate and correct assignment of the ARG origin. The MGS2AMR output can further be used in many downstream applications, such as evaluating AMR to specific antibiotics in samples from emerging intestinal infections. We demonstrate that the MGS2AMR-derived data is as informative for the entailing prediction models as the whole-genome sequencing (WGS) data. The performance of these models is on par with our previously published method (WGS2AMR), which is based on the sequencing data of bacterial isolates.

CONCLUSIONS

MGS2AMR can provide researchers with valuable insights into the AMR content of microbiome environments and may potentially improve patient care by providing faster quantification of resistance against specific antibiotics, thereby reducing the use of broad-spectrum antibiotics. The presented pipeline also has potential applications in other metagenome analyses focused on the defined sets of genes. Video Abstract.

Joining Forces against Antibiotic Resistance: The One Health Solution

Antibiotic resistance is a significant global health concern that affects both human and animal populations. The One Health approach acknowledges the interconnectedness of human health, animal health, and the environment. It emphasizes the importance of collaboration and coordination across these sectors to tackle complex health challenges such as antibiotic resistance. In the context of One Health, antibiotic resistance refers to the ability of bacteria to withstand the efficacy of antibiotics, rendering them less effective or completely ineffective in treating infections. The emergence and spread of antibiotic-resistant bacteria pose a threat to human and animal health, as well as to the effectiveness of medical treatments and veterinary interventions. In particular, One Health recognizes that antibiotic use in human medicine, animal agriculture, and the environment are interconnected factors contributing to the development and spread of antibiotic resistance. For example, the misuse and overuse of antibiotics in human healthcare, including inappropriate prescribing and patient non-compliance, can contribute to the selection and spread of resistant bacteria. Similarly, the use of antibiotics in livestock production for growth promotion and disease prevention can contribute to the development of antibiotic resistance in animals and subsequent transmission to humans through the food chain. Addressing antibiotic resistance requires a collaborative One Health approach that involves multiple participants, including healthcare professionals, veterinarians, researchers, and policymakers.

Genus-wide genomic characterization of Macrococcus: insights into evolution, population structure, and functional potential

Introduction

Macrococcus species have been isolated from a range of mammals and mammal-derived food products. While they are largely considered to be animal commensals, Macrococcus spp. can be opportunistic pathogens in both veterinary and human clinical settings. This study aimed to provide insight into the evolution, population structure, and functional potential of the Macrococcus genus, with an emphasis on antimicrobial resistance (AMR) and virulence potential.

Methods

All high-quality, publicly available Macrococcus genomes (n = 104, accessed 27 August 2022), plus six South African genomes sequenced here (two strains from bovine clinical mastitis cases and four strains from beef products), underwent taxonomic assignment (using four different approaches), AMR determinant detection (via AMRFinderPlus), and virulence factor detection (using DIAMOND and the core Virulence Factor Database).

Results

Overall, the 110 Macrococcus genomes were of animal commensal, veterinary clinical, food-associated (including food spoilage), and environmental origins; five genomes (4.5%) originated from human clinical cases. Notably, none of the taxonomic assignment methods produced identical results, highlighting the potential for Macrococcus species misidentifications. The most common predicted antimicrobial classes associated with AMR determinants identified across Macrococcus included macrolides, beta-lactams, and aminoglycosides (n = 81, 61, and 44 of 110 genomes; 73.6, 55.5, and 40.0%, respectively). Genes showing homology to Staphylococcus aureus exoenzyme aureolysin were detected across multiple species (using 90% coverage, n = 40 and 77 genomes harboring aureolysin-like genes at 60 and 40% amino acid [AA] identity, respectively). S. aureus Panton-Valentine leucocidin toxin-associated lukF-PV and lukS-PV homologs were identified in eight M. canis genomes (≥40% AA identity, >85% coverage). Using a method that delineates populations using recent gene flow (PopCOGenT), two species (M. caseolyticus and M. armenti) were composed of multiple within-species populations. Notably, M. armenti was partitioned into two populations, which differed in functional potential (e.g., one harbored beta-lactamase family, type II toxin-antitoxin system, and stress response proteins, while the other possessed a Type VII secretion system; PopCOGenT p < 0.05).

Discussion

Overall, this study leverages all publicly available Macrococcus genomes in addition to newly sequenced genomes from South Africa to identify genomic elements associated with AMR or virulence potential, which can be queried in future experiments.

Toward a Universal Unit for Quantification of Antibiotic Resistance Genes in Environmental Samples

Surveillance of antibiotic resistance genes (ARGs) has been increasingly conducted in environmental sectors to complement the surveys in human and animal sectors under the "One-Health" framework. However, there are substantial challenges in comparing and synthesizing the results of multiple studies that employ different test methods and approaches in bioinformatic analysis. In this article, we consider the commonly used quantification units (ARG copy per cell, ARG copy per genome, ARG density, ARG copy per 16S rRNA gene, RPKM, coverage, PPM, etc.) for profiling ARGs and suggest a universal unit (ARG copy per cell) for reporting such biological measurements of samples and improving the comparability of different surveillance efforts.

More than mcr: canonical plasmid- and transposon-encoded mobilized colistin resistance genes represent a subset of phosphoethanolamine transferases

Mobilized colistin resistance genes (mcr) may confer resistance to the last-resort antimicrobial colistin and can often be transmitted horizontally. mcr encode phosphoethanolamine transferases (PET), which are closely related to chromosomally encoded, intrinsic lipid modification PET (i-PET; e.g., EptA, EptB, CptA). To gain insight into the evolution of mcr within the context of i-PET, we identified 69,814 MCR-like proteins present across 256 bacterial genera (obtained by querying known MCR family representatives against the National Center for Biotechnology Information [NCBI] non-redundant protein database via protein BLAST). We subsequently identified 125 putative novel mcr-like genes, which were located on the same contig as (i) ≥1 plasmid replicon and (ii) ≥1 additional antimicrobial resistance gene (obtained by querying the PlasmidFinder database and NCBI's National Database of Antibiotic Resistant Organisms, respectively, via nucleotide BLAST). At 80% amino acid identity, these putative novel MCR-like proteins formed 13 clusters, five of which represented putative novel MCR families. Sequence similarity and a maximum likelihood phylogeny of mcr, putative novel mcr-like, and ipet genes indicated that sequence similarity was insufficient to discriminate mcr from ipet genes. A mixed-effect model of evolution (MEME) indicated that site- and branch-specific positive selection played a role in the evolution of alleles within the mcr-2 and mcr-9 families. MEME suggested that positive selection played a role in the diversification of several residues in structurally important regions, including (i) a bridging region that connects the membrane-bound and catalytic periplasmic domains, and (ii) a periplasmic loop juxtaposing the substrate entry tunnel. Moreover, eptA and mcr were localized within different genomic contexts. Canonical eptA genes were typically chromosomally encoded in an operon with a two-component regulatory system or adjacent to a TetR-type regulator. Conversely, mcr were represented by single-gene operons or adjacent to pap2 and dgkA, which encode a PAP2 family lipid A phosphatase and diacylglycerol kinase, respectively. Our data suggest that eptA can give rise to "colistin resistance genes" through various mechanisms, including mobilization, selection, and diversification of genomic context and regulatory pathways. These mechanisms likely altered gene expression levels and enzyme activity, allowing bona fide eptA to evolve to function in colistin resistance.

Identification of a Specific Biomarker of Acinetobacter baumannii Global Clone 1 by Machine Learning and PCR Related to Metabolic Fitness of ESKAPE Pathogens

Since the emergence of high-risk clones worldwide, constant investigations have been undertaken to comprehend the molecular basis that led to their prevalent dissemination in nosocomial settings over time. So far, the complex and multifactorial genetic traits of this type of epidemic clones have allowed only the identification of biomarkers with low specificity. A machine learning algorithm was able to recognize unequivocally a biomarker for early and accurate detection of Acinetobacter baumannii global clone 1 (GC1), one of the most disseminated high-risk clones. A support vector machine model identified the U1 sequence with a length of 367 nucleotides that matched a fragment of the moaCB gene, which encodes the molybdenum cofactor biosynthesis C and B proteins. U1 differentiates specifically between A. baumannii GC1 and non-GC1 strains, becoming a suitable biomarker capable of being translated into clinical settings as a molecular typing method for early diagnosis based on PCR as shown here. Since the metabolic pathways of Mo enzymes have been recognized as putative therapeutic targets for ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) pathogens, our findings highlight that machine learning can also be useful in knowledge gaps of high-risk clones and provides noteworthy support to the literature to identify relevant nosocomial biomarkers for other multidrug-resistant high-risk clones. IMPORTANCE A. baumannii GC1 is an important high-risk clone that rapidly develops extreme drug resistance in the nosocomial niche. Furthermore, several strains have been identified worldwide in environmental samples, exacerbating the risk of human interactions. Early diagnosis is mandatory to limit its dissemination and to outline appropriate antibiotic stewardship schedules. A region with a length of 367 bp (U1) within the moaCB gene that is not subjected to lateral genetic transfer or to antibiotic pressures was successfully found by a support vector machine model that predicts A. baumannii GC1 strains. At the same time, research on the group of Mo enzymes proposed this metabolic pathway related to the superbug's metabolism as a potential future drug target site for ESKAPE pathogens due to its central role in bacterial fitness during infection. These findings confirm that machine learning used for the identification of biomarkers of high-risk lineages can also serve to identify putative novel therapeutic target sites.

Global environmental resistome: Distinction and connectivity across diverse habitats benchmarked by metagenomic analyses

The widely distributed antibiotic resistance genes (ARGs) were unevenly proliferated in various habitats. Great endeavors are needed to resolve the resistome features that can differentiate or connect different habitats. This study retrieved a broad spectrum of resistome profiles from 1723 metagenomes categorized into 13 habitats, encompassing industrial, urban, agricultural, and natural environments, and spanning most continents and oceans. The resistome features (ARG types, subtypes, indicator ARGs, and emerging mobilizable ARGs: mcr and tet(X)) in these habitats were benchmarked via a standardized workflow. We found that wastewater and wastewater treatment works were characterized to be reservoirs of more diverse genotypes of ARGs than any other habitats including human and livestock fecal samples, while fecal samples were with higher ARG abundance. Bacterial taxonomy composition was significantly correlated with resistome composition across most habitats. Moreover, the source-sink connectivities were disentangled by developing the resistome-based microbial attribution prediction model. Environmental surveys with standardized bioinformatic workflow proposed in this study will help comprehensively understand the transfer of ARGs in the environment, thus prioritizing the critical environments with high risks for intervention to tackle the problem of ARGs.

Rapid diagnostics for skin and soft tissue infections: the current landscape and future potential

PURPOSE OF REVIEW

Managing antimicrobial therapy in patients with complicated skin and soft tissue infections (SSTI) constitutes a growing challenge due to the wide spectrum of potential pathogens and resistance phenotypes. Today, microbiological documentation relies on cultural methods. This review summarizes the available evidence regarding the clinical input of rapid microbiological diagnostic tools (RMDT) and their impact on the management of antimicrobial therapy in SSTI.

RECENT FINDINGS

Accurate tools are already available for the early detection of methicillin-resistant Staphylococcus aureus (MRSA) in SSTI samples and may help avoiding or shortening empirical anti-MRSA coverage. Further research is necessary to develop and evaluate RMDT detecting group A streptococci (e.g., antigenic test) and Gram-negative pathogens (e.g., multiplex PCR assays), including through point-of-care utilization. Next-generation sequencing (NGS) methods could provide pivotal information for the stewardship of antimicrobial therapy, especially in case of polymicrobial or fungal SSTI and in the immunocompromised host; however, a shortening in the turnaround time and prospective data regarding their therapeutic input are needed to better appraise the clinical positioning of these promising approaches.

SUMMARY

The clinical input of RMDT in SSTI is currently limited due to the scarcity of available dedicated assays and the polymicrobial feature of certain cases. NGS appears as a relevant tool but requires further developments before its implementation in routine clinical practice.

Epidemiological cut-off values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing of M. tuberculosis

Drug susceptibility testing of M. tuberculosis is rooted in a binary susceptible/resistant paradigm. While there are considerable advantages in measuring the minimum inhibitory concentrations (MICs) of a panel of drugs for an isolate, it is necessary to measure the epidemiological cut-off values (ECOFF/ECVs) to permit comparison with qualitative data. Here we present ECOFF/ECVs for 13 anti-tuberculosis compounds, including bedaquiline and delamanid, derived from 20 637 clinical isolates collected by 14 laboratories based in 11 countries on five continents. Each isolate was incubated for 14 days on a dry 96-well broth microdilution plate and then read. Resistance to most of the drugs due to prior exposure is expected and the MIC distributions for many of the compounds are complex, and therefore a phenotypically wild-type population could not be defined. Since a majority of samples also underwent genetic sequencing, we defined a genotypically wild-type population and measured the MIC of the 99th percentile by direct measurement and via fitting a Gaussian using interval regression. The proposed ECOFF/ECVs were then validated by comparing with the MIC distributions of high-confidence genetic variants that confer resistance and with qualitative drug susceptibility tests obtained via the Mycobacterial Growth Indicator Tube (MGIT) system or Microscopic-Observation Drug Susceptibility (MODS) assay. These ECOFF/ECVs will inform and encourage the more widespread adoption of broth microdilution: this is a cheap culture-based method that tests the susceptibility of 12-14 antibiotics on a single 96-well plate and so could help personalise the treatment of tuberculosis.

Next Generation and Other Sequencing Technologies in Diagnostic Microbiology and Infectious Diseases

Next-generation sequencing (NGS) technologies have become increasingly available for use in the clinical microbiology diagnostic environment. There are three main applications of these technologies in the clinical microbiology laboratory: whole genome sequencing (WGS), targeted metagenomics sequencing and shotgun metagenomics sequencing. These applications are being utilized for initial identification of pathogenic organisms, the detection of antimicrobial resistance mechanisms and for epidemiologic tracking of organisms within and outside hospital systems. In this review, we analyze these three applications and provide a comprehensive summary of how these applications are currently being used in public health, basic research, and clinical microbiology laboratory environments. In the public health arena, WGS is being used to identify and epidemiologically track food borne outbreaks and disease surveillance. In clinical hospital systems, WGS is used to identify multi-drug-resistant nosocomial infections and track the transmission of these organisms. In addition, we examine how metagenomics sequencing approaches (targeted and shotgun) are being used to circumvent the traditional and biased microbiology culture methods to identify potential pathogens directly from specimens. We also expand on the important factors to consider when implementing these technologies, and what is possible for these technologies in infectious disease diagnosis in the next 5 years.

Recent Developments in Phenotypic and Molecular Diagnostic Methods for Antimicrobial Resistance Detection in Staphylococcus aureus: A Narrative Review

Staphylococcus aureus is an opportunistic pathogen responsible for a wide range of infections in humans, such as skin and soft tissue infections, pneumonia, food poisoning or sepsis. Historically, S. aureus was able to rapidly adapt to anti-staphylococcal antibiotics and become resistant to several classes of antibiotics. Today, methicillin-resistant S. aureus (MRSA) is a multidrug-resistant pathogen and is one of the most common bacteria responsible for hospital-acquired infections and outbreaks, in community settings as well. The rapid and accurate diagnosis of antimicrobial resistance in S. aureus is crucial to the early initiation of directed antibiotic therapy and to improve clinical outcomes for patients. In this narrative review, I provide an overview of recent phenotypic and molecular diagnostic methods for antimicrobial resistance detection in S. aureus, with a particular focus on MRSA detection. I consider methods for resistance detection in both clinical samples and isolated S. aureus cultures, along with a brief discussion of the advantages and the challenges of implementing such methods in routine diagnostics.

Detection of Minority Variants and Mixed Infections in Mycobacterium tuberculosis by Direct Whole-Genome Sequencing on Noncultured Specimens Using a Specific-DNA Capture Strategy

Detection of mixed Mycobacterium tuberculosis (MTB) infections is essential, particularly when resistance mutations are present in minority bacterial populations that may affect patients' disease evolution and treatment. Whole-genome sequencing (WGS) has extended the amount of key information available for the diagnosis of MTB infection, including the identification of mixed infections. Having genomic information at diagnosis for early intervention requires carrying out WGS directly on the clinical samples. However, few studies have been successful with this approach due to the low representation of MTB DNA in sputa. In this study, we evaluated the ability of a strategy based on specific MTB DNA enrichment by using a newly designed capture platform (MycoCap) to detect minority variants and mixed infections by WGS on controlled mixtures of MTB DNAs in a simulated sputum genetic background. A pilot study was carried out with 12 samples containing 98% of a DNA pool from sputa of patients without MTB infection and 2% of MTB DNA mixtures at different proportions. Our strategy allowed us to generate sequences with a quality equivalent to those obtained from culture: 62.5× depth coverage and 95% breadth coverage (for at least 20× reads). Assessment of minority variant detection was carried out by manual analysis and allowed us to identify heterozygous positions up to a 95:5 ratio. The strategy also automatically distinguished mixed infections up to a 90:10 proportion. Our strategy efficiently captures MTB DNA in a nonspecific genetic background, allows detection of minority variants and mixed infections, and is a promising tool for performing WGS directly on clinical samples. IMPORTANCE We present a new strategy to identify mixed infections and minority variants in Mycobacterium tuberculosis by whole-genome sequencing. The objective of the strategy is the direct detection in patient sputum; in this way, minority populations of resistant strains can be identified at the time of diagnosis, facilitating identification of the most appropriate treatment for the patient from the first moment. For this, a platform for capturing M. tuberculosis-specific DNA was designed to enrich the clinical sample and obtain quality sequences.

A Novel Multidrug Resistant, Non-Tn4401 Genetic Element-Bearing, Strain of Klebsiella pneumoniae Isolated From an Urban Lake With Drinking and Recreational Water Reuse

Antimicrobial resistance (AMR) is an increasing and urgent issue for human health worldwide, as it leads to the reduction of available antibiotics to treat bacterial infections, in turn increasing hospital stays and lethality. Therefore, the study and genomic surveillance of bacterial carriers of resistance in and outside of clinical settings is of utter importance. A colony of multidrug resistant (MDR) bacteria identified as Klebsiella spp., by 16S rDNA amplicon sequencing, has been isolated from an urban lake in Brazil, during a drug-degrading bacterial prospection. Genomic analyses revealed the bacteria as Klebsiella pneumoniae species. Furthermore, the in silico Multilocus Sequence Typing (MLST) identified the genome as a new sequence type, ST5236. The search for antimicrobial resistance genes (ARGs) detected the presence of genes against beta-lactams, fosfomycin, acriflavine and efflux pumps, as well as genes for heavy metal resistance. Of particular note, an extended-spectrum beta-lactamase gene (blaCTX-M-15) has been detected in close proximity to siphoviridae genes, while a carbapenemase gene (KPC-2) has been found in an extrachromosomal contig, within a novel non-Tn4401 genetic element (NTEKPC). An extrachromosomal contig found in the V3 isolate is identical to a contig of a K. pneumoniae isolate from a nearby hospital, which indicates a putative gene flow from the hospital network into Paranoá lake. The discovery of a MDR isolate in this lake is worrisome, as the region has recently undergone periods of water scarcity causing the lake, which receives treated wastewater effluent, and is already used for recreational purposes, to be used as an environmental buffer for drinking water reuse. Altogether, our results indicate an underrepresentation of environmental K. pneumoniae among available genomes, which may hamper the understanding of the population dynamics of the species in the environment and its consequences in the spread of ARGs and virulence genes.

Comparison of Culture With Antibiogram to Next-Generation Sequencing Using Bacterial Isolates and Formalin-Fixed, Paraffin-Embedded Gastric Biopsies

BACKGROUND & AIMS

The decline in Helicobacter pylori cure rates emphasizes the need for readily available methods to determine antimicrobial susceptibility. Our aim was to compare targeted next-generation sequencing (NGS) and culture-based H pylori susceptibility testing using clinical isolates and paired formalin-fixed, paraffin-embedded (FFPE) gastric biopsies.

METHODS

H pylori isolates and FFPE tissues were tested for susceptibility to amoxicillin, clarithromycin, metronidazole, levofloxacin, tetracycline, and rifabutin using agar dilution and NGS targeted to 23S rRNA, gyrA, 16S rRNA, pbp1, rpoB and rdxA. Agreement was quantified using κ statistics.

RESULTS

Paired comparisons included 170 isolates and FFPE tissue for amoxicillin, clarithromycin, metronidazole, and rifabutin and 57 isolates and FFPE tissue for levofloxacin and tetracycline. Agreement between agar dilution and NGS from culture isolates was very good for clarithromycin (κ = 0.90012), good for levofloxacin (κ = 0.78161) and fair for metronidazole (κ = 0.55880), and amoxicillin (κ = 0.21400). Only 1 isolate was resistant to tetracycline (culture) and 1 to rifabutin (NGS). Comparison of NGS from tissue blocks and agar dilution from isolates from the same stomachs demonstrated good accuracy to predict resistance for clarithromycin (94.1%), amoxicillin (95.9%), metronidazole (77%), levofloxacin (87.7%), and tetracycline (98.2%). Lack of resistance precluded comparisons for tetracycline and rifabutin.

CONCLUSIONS

Compared with agar dilution, NGS reliably determined resistance to clarithromycin, levofloxacin, rifabutin, and tetracycline from clinical isolates and formalin-fixed gastric tissue. Consistency was fair for metronidazole and amoxicillin. Culture-based testing can predict treatment outcomes with clarithromycin and levofloxacin. Studies are needed to compare the relative ability of both methods to predict treatment outcomes for other antibiotics.

Metagenomics next-generation sequencing tests take the stage in the diagnosis of lower respiratory tract infections

•

The applications of mNGS for LRIs span a wide range of areas including LRI diagnosis, airway microbiome analyses, human host response analyses, and prediction of drug resistance.

•

The workflow of mNGS used in clinical practice involves the wet-lab pipeline and dry-lab pipeline, the complex workflow poses challenges for its extensive use.

•

mNGS will become an important tool in the field of infectious disease diagnosis in the next decade.

Metagenomic next-generation sequencing (mNGS) has changed the diagnosis landscape of lower respiratory tract infections (LRIs). With the development of newer sequencing assays, it is now possible to assess all microorganisms in a sample using a single mNGS analysis. The applications of mNGS for LRIs span a wide range of areas including LRI diagnosis, airway microbiome analyses, human host response analyses, and prediction of drug resistance. mNGS is currently in an exciting transitional period; however, before implementation in a clinical setting, there are several barriers to overcome, such as the depletion of human nucleic acid, discrimination between colonization and infection, high costs, and so on.

Aim of Review: In this review, we summarize the potential applications and challenges of mNGS in the diagnosis of LRIs to promote the integration of mNGS into the management of patients with respiratory tract infections in a clinical setting.

Key Scientific Concepts of Review: Once its analytical validation, clinical validation and clinical utility been demonstrated, mNGS will become an important tool in the field of infectious disease diagnosis.

Antibiotic resistance: Time of synthesis in a post-genomic age

Antibiotic resistance has been highlighted by international organizations, including World Health Organization, World Bank and United Nations, as one of the most relevant global health problems. Classical approaches to study this problem have focused in infected humans, mainly at hospitals. Nevertheless, antibiotic resistance can expand through different ecosystems and geographical allocations, hence constituting a One-Health, Global-Health problem, requiring specific integrative analytic tools. Antibiotic resistance evolution and transmission are multilayer, hierarchically organized processes with several elements (from genes to the whole microbiome) involved. However, their study has been traditionally gene-centric, each element independently studied. The development of robust-economically affordable whole genome sequencing approaches, as well as other -omic techniques as transcriptomics and proteomics, is changing this panorama. These technologies allow the description of a system, either a cell or a microbiome as a whole, overcoming the problems associated with gene-centric approaches. We are currently at the time of combining the information derived from -omic studies to have a more holistic view of the evolution and spread of antibiotic resistance. This synthesis process requires the accurate integration of -omic information into computational models that serve to analyse the causes and the consequences of acquiring AR, fed by curated databases capable of identifying the elements involved in the acquisition of resistance. In this review, we analyse the capacities and drawbacks of the tools that are currently in use for the global analysis of AR, aiming to identify the more useful targets for effective corrective interventions.