Comprehensive antibiotic-linked mutation assessment by resistance mutation sequencing (RM-seq)

Clinical Staphylococcus aureus inhibits human T-cell activity through interaction with the PD-1 receptor

IMPORTANCE

Therapies that target and aid the host immune defense to repel cancer cells or invading pathogens are rapidly emerging. Antibiotic resistance is among the largest threats to human health globally. Staphylococcus aureus (S. aureus) is the most common bacterial infection, and it poses a challenge to the healthcare system due to its significant ability to develop resistance toward current available therapies. In long-term infections, S. aureus further adapt to avoid clearance by the host immune defense. In this study, we discover a new interaction that allows S. aureus to avoid elimination by the immune system, which likely supports its persistence in the host. Moreover, we find that blocking the specific receptor (PD-1) using antibodies significantly relieves the S. aureus-imposed inhibition. Our findings suggest that therapeutically targeting PD-1 is a possible future strategy for treating certain antibiotic-resistant staphylococcal infections.

Ampicillin-controlled glucose metabolism manipulates the transition from tolerance to resistance in bacteria

The mechanism(s) of how bacteria acquire tolerance and then resistance to antibiotics remains poorly understood. Here, we show that glucose abundance decreases progressively as ampicillin-sensitive strains acquire resistance to ampicillin. The mechanism involves that ampicillin initiates this event via targeting pts promoter and pyruvate dehydrogenase (PDH) to promote glucose transport and inhibit glycolysis, respectively. Thus, glucose fluxes into pentose phosphate pathway to generate reactive oxygen species (ROS) causing genetic mutations. Meanwhile, PDH activity is gradually restored due to the competitive binding of accumulated pyruvate and ampicillin, which lowers glucose level, and activates cyclic adenosine monophosphate (cAMP)/cAMP receptor protein (CRP) complex. cAMP/CRP negatively regulates glucose transport and ROS but enhances DNA repair, leading to ampicillin resistance. Glucose and Mn²⁺ delay the acquisition, providing an effective approach to control the resistance. The same effect is also determined in the intracellular pathogen Edwardsiella tarda. Thus, glucose metabolism represents a promising target to stop/delay the transition of tolerance to resistance.

Multiplexed Target Enrichment Enables Efficient and In-Depth Analysis of Antimicrobial Resistome in Metagenomes

Antibiotic resistance genes (ARGs) pose a serious threat to public health and ecological security in the 21st century. However, the resistome only accounts for a tiny fraction of metagenomic content, which makes it difficult to investigate low-abundance ARGs in various environmental settings. Thus, a highly sensitive, accurate, and comprehensive method is needed to describe ARG profiles in complex metagenomic samples. In this study, we established a high-throughput sequencing method based on targeted amplification, which could simultaneously detect ARGs (n = 251), mobile genetic element genes (n = 8), and metal resistance genes (n = 19) in metagenomes. The performance of amplicon sequencing was compared with traditional metagenomic shotgun sequencing (MetaSeq). A total of 1421 primer pairs were designed, achieving extremely high coverage of target genes. The amplicon sequencing significantly improved the recovery of target ARGs (~9 × 104-fold), with higher sensitivity and diversity, less cost, and computation burden. Furthermore, targeted enrichment allows deep scanning of single nucleotide polymorphisms (SNPs), and elevated SNPs detection was shown in this study. We further performed this approach for 48 environmental samples (37 feces, 20 soils, and 7 sewage) and 16 clinical samples. All samples tested in this study showed high diversity and recovery of targeted genes. Our results demonstrated that the approach could be applied to various metagenomic samples and served as an efficient tool in the surveillance and evolution assessment of ARGs. Access to the resistome using the enrichment method validated in this study enabled the capture of low-abundance resistomes while being less costly and time-consuming, which can greatly advance our understanding of local and global resistome dynamics. IMPORTANCE ARGs, an increasing global threat to human health, can be transferred into health-related microorganisms in the environment by horizontal gene transfer, posing a serious threat to public health. Advancing profiling methods are needed for monitoring and predicting the potential risks of ARGs in metagenomes. Our study described a customized amplicon sequencing assay that could enable a high-throughput, targeted, in-depth analysis of ARGs and detect a low-abundance portion of resistomes. This method could serve as an efficient tool to assess the variation and evolution of specific ARGs in the clinical and natural environment.

Mechanism-Based Approach to New Antibiotic Producers Screening among Actinomycetes in the Course of the Citizen Science Project

Since the discovery of streptomycin, actinomycetes have been a useful source for new antibiotics, but there have been diminishing rates of new finds since the 1960s. The decreasing probability of identifying new active agents led to reduced interest in soil bacteria as a source for new antibiotics. At the same time, actinomycetes remain a promising reservoir for new active molecules. In this work, we present several reporter plasmids encoding visible fluorescent protein genes. These plasmids provide primary information about the action mechanism of antimicrobial agents at an early stage of screening. The reporters and the pipeline described have been optimized and designed to employ citizen scientists without specialized skills or equipment with the aim of essentially crowdsourcing the search for new antibiotic producers in the vast natural reservoir of soil bacteria. The combination of mechanism-based approaches and citizen science has proved its effectiveness in practice, revealing a significant increase in the screening rate. As a proof of concept, two new strains, Streptomyces sp. KB-1 and BV113, were found to produce the antibiotics pikromycin and chartreusin, respectively, demonstrating the efficiency of the pipeline.

Niche-specific genome degradation and convergent evolution shaping Staphylococcus aureus adaptation during severe infections

During severe infections, Staphylococcus aureus moves from its colonising sites to blood and tissues and is exposed to new selective pressures, thus, potentially driving adaptive evolution. Previous studies have shown the key role of the agr locus in S. aureus pathoadaptation; however, a more comprehensive characterisation of genetic signatures of bacterial adaptation may enable prediction of clinical outcomes and reveal new targets for treatment and prevention of these infections. Here, we measured adaptation using within-host evolution analysis of 2590 S. aureus genomes from 396 independent episodes of infection. By capturing a comprehensive repertoire of single nucleotide and structural genome variations, we found evidence of a distinctive evolutionary pattern within the infecting populations compared to colonising bacteria. These invasive strains had up to 20-fold enrichments for genome degradation signatures and displayed significantly convergent mutations in a distinctive set of genes, linked to antibiotic response and pathogenesis. In addition to agr-mediated adaptation, we identified non-canonical, genome-wide significant loci including sucA-sucB and stp1. The prevalence of adaptive changes increased with infection extent, emphasising the clinical significance of these signatures. These findings provide a high-resolution picture of the molecular changes when S. aureus transitions from colonisation to severe infection and may inform correlation of infection outcomes with adaptation signatures.

The bacterium Staphylococcus aureus lives harmlessly on our skin and noses. However, occasionally, it gets into our blood and internal organs, such as our bones and joints, where it causes severe, long-lasting infections that are difficult to treat.

Over time, S. aureus acquire characteristics that help them to adapt to different locations, such as transitioning from the nose to the blood, and avoid being killed by antibiotics. Previous studies have identified changes, or ‘mutations’, in genes that are likely to play an important role in this evolutionary process. One of these genes, called accessory gene regulator (or agr for short), has been shown to control the mechanisms S. aureus use to infect cells and disseminate in the body. However, it is unclear if there are changes in other genes that also help S. aureus adapt to life inside the human body.

To help resolve this mystery, Giulieri et al. collected 2,500 samples of S. aureus from almost 400 people. This included bacteria harmlessly living on the skin or in the nose, as well as strains that caused an infection. Gene sequencing revealed a small number of genes, referred to as ‘adaptive genes’, that often acquire mutations during infection. Of these, agr was the most commonly altered. However, mutations in less well-known genes were also identified: some of these genes are related to resistance to antibiotics, while others are involved in chemical processes that help the bacteria to process nutrients.

Most mutations were caused by random errors being introduced in to the bacteria’s genetic code which stopped genes from working. However, in some cases, genes were turned off by small fragments of DNA moving around and inserting themselves into different parts of the genome.

This study highlights a group of genes that help S. aureus to thrive inside the body and cause severe and prolonged infections. If these results can be confirmed, it may help to guide which antibiotics are used to treat different infections. Furthermore, understanding which genes are important for infection could lead to new strategies for eliminating this dangerous bacterium.

Machine Learning Prediction of Resistance to Subinhibitory Antimicrobial Concentrations from Escherichia coli Genomes

Escherichia coli is an important cause of bacterial infections worldwide, with multidrug-resistant strains incurring substantial costs on human lives. Besides therapeutic concentrations of antimicrobials in health care settings, the presence of subinhibitory antimicrobial residues in the environment and in clinics selects for antimicrobial resistance (AMR), but the underlying genetic repertoire is less well understood. Here, we used machine learning to predict the population doubling time and cell growth yield of 1,407 genetically diverse E. coli strains expanding under exposure to three subinhibitory concentrations of six classes of antimicrobials from single-nucleotide genetic variants, accessory gene variation, and the presence of known AMR genes. We predicted cell growth yields in the held-out test data with an average correlation (Spearman's ρ) of 0.63 (0.36 to 0.81 across concentrations) and cell doubling times with an average correlation of 0.59 (0.32 to 0.92 across concentrations), with moderate increases in sample size unlikely to improve predictions further. This finding points to the remaining missing heritability of growth under antimicrobial exposure being explained by effects that are too rare or weak to be captured unless sample size is dramatically increased, or by effects other than those conferred by the presence of individual single-nucleotide polymorphisms (SNPs) and genes. Predictions based on whole-genome information were generally superior to those based only on known AMR genes and were accurate for AMR resistance at therapeutic concentrations. We pinpointed genes and SNPs determining the predicted growth and thereby recapitulated many known AMR determinants. Finally, we estimated the effect sizes of resistance genes across the entire collection of strains, disclosing the growth effects for known resistance genes in each individual strain. Our results underscore the potential of predictive modeling of growth patterns from genomic data under subinhibitory concentrations of antimicrobials, although the remaining missing heritability poses a challenge for achieving the accuracy and precision required for clinical use. IMPORTANCE Predicting bacterial growth from genome sequences is important for a rapid characterization of strains in clinical diagnostics and to disclose candidate novel targets for anti-infective drugs. Previous studies have dissected the relationship between bacterial growth and genotype in mutant libraries for laboratory strains, yet no study so far has examined the predictive power of genome sequence in natural strains. In this study, we used a high-throughput phenotypic assay to measure the growth of a systematic collection of natural Escherichia coli strains and then employed machine learning models to predict bacterial growth from genomic data under nontherapeutic subinhibitory concentrations of antimicrobials that are common in nonclinical settings. We found a moderate to strong correlation between predicted and actual values for the different collected data sets. Moreover, we observed that the known resistance genes are still effective at sublethal concentrations, pointing to clinical implications of these concentrations.

The future of antibiotics begins with discovering new combinations

Antibiotic resistance is a worldwide and growing clinical problem. With limited drug development in the antibacterial space, combination therapy has emerged as a promising strategy to combat multidrug‐resistant bacteria. Antibacterial combinations can improve antibiotic efficacy and suppress antibacterial resistance through independent, synergistic, or even antagonistic activities. Combination therapies are famously used to treat viral and mycobacterial infections and cancer. However, antibacterial combinations are only now emerging as a common treatment strategy for other bacterial infections owing to challenges in their discovery, development, regulatory approval, and commercial/clinical deployment. Here, we focus on discovery—where the sheer scale of combinatorial chemical spaces represents a significant challenge—and discuss how combination therapy can impact the treatment of bacterial infections. Despite these challenges, recent advancements, including new in silico methods, theoretical frameworks, and microfluidic platforms, are poised to identify the new and efficacious antibacterial combinations needed to revitalize the antibacterial drug pipeline.

Phenotypic and genotypic antimicrobial resistance patterns of Escherichia coli and Klebsiella isolated from dairy farm milk, farm slurry and water in Punjab, India

Antibiotic resistance is a mushrooming pandemic at national and international levels which if not controlled at this very moment, can lead to global problems. Main reason for emerging bacterial resistance is repeated exposure of bacteria to antimicrobial agents and access of bacteria to increasingly large pools of antimicrobial resistance genes in mixed bacterial populations. A total of 51 villages were sampled in the current study contributing to a total of 153 farms. A total of 612 samples comprising 153 each of raw pooled milk samples, slurry, animal drinking water and human drinking water were gathered from small, medium and large farms located in all seven tehsils of Ludhiana district of Punjab. In addition to that, 37 samples of village pond water were also collected from the targeted villages. Out of total 153 slurry, raw pooled milk samples, animal drinking water and human drinking water samples (each), the prevalence of 24.8%, 60%, 26.7% and 16.3% was found for E. coli respectively. On the other hand, for Klebsiella, the overall prevalence of 19.6%, 51%, 20.2% and 5.8% was found from slurry, raw pooled milk samples, animal drinking water and human drinking water respectively. In all matrices, the comparative frequency of resistance genes in positive isolates of E. coli and K. pneumoniae was: tetA > tetB > tetC, qnrS > qnrB > qnrA, sulII > sulI > sulIII. The highest proportion of resistance genes was found in slurry (193 genes) followed by milk (71 genes). The overall pattern of resistant genes was tetA > sulII > qnrS. In conclusion, data from the present study suggested that commensal E. coli and Klebsiella may act as reservoirs of antimicrobial drug resistance genes which may be mobilised into human populations and untreated animal waste may be considered an important source of resistant bacteria leading to environmental pollution.

Mechanisms and detection methods of Mycobacterium tuberculosis rifampicin resistance: The phenomenon of drug resistance is complex

Tuberculosis (TB) is an infectious disease that poses a serious threat to human health. Rifampin (RIF) is an important first-line anti-TB drug, and rifampin resistance (RIF-R) is a key factor in formulating treatment regimen and evaluating the prognosis of TB. Compared with other drugs resistance, the RIF-R mechanism of Mycobacterium tuberculosis (M. tuberculosis) is one of the clearest, which is mainly caused by RIF resistance-related mutations in the rpoB gene. This provides a convenient condition for developing rapid detection methods, and also an ideal object for studying the general drug resistance mechanisms of M. tuberculosis. This review focuses on the mechanisms that influence the RIF resistance of M. tuberculosis and related detection methods. Besides the mutations in rpoB, M. tuberculosis can decrease the amount of drugs entering the cells, enhance the drugs efflux, and be heterogeneous RIF susceptibility to resist drug pressure. Based on the results of current researches, many genes participate in influencing the susceptibility to RIF, which indicates the phenomenon of M. tuberculosis drug resistance is very complex.

Computational resources in the management of antibiotic resistance: Speeding up drug discovery

This article reviews more than 50 computational resources developed in past two decades for forecasting of antibiotic resistance (AR)-associated mutations, genes and genomes. More than 30 databases have been developed for AR-associated information, but only a fraction of them are updated regularly. A large number of methods have been developed to find AR genes, mutations and genomes, with most of them based on similarity-search tools such as BLAST and HMMER. In addition, methods have been developed to predict the inhibition potential of antibiotics against a bacterial strain from the whole-genome data of bacteria. This review also discuss computational resources that can be used to manage the treatment of AR-associated diseases.

Genomic Insights Into Last-Line Antimicrobial Resistance in Multidrug-Resistant Staphylococcus and Vancomycin-Resistant Enterococcus

Multidrug-resistant Staphylococcus and vancomycin-resistant Enterococcus (VRE) are important human pathogens that are resistant to most clinical antibiotics. Treatment options are limited and often require the use of 'last-line' antimicrobials such as linezolid, daptomycin, and in the case of Staphylococcus, also vancomycin. The emergence of resistance to these last-line antimicrobial agents is therefore of considerable clinical concern. This mini-review provides an overview of resistance to last-line antimicrobial agents in Staphylococcus and VRE, with a particular focus on how genomics has provided critical insights into the emergence of resistant clones, the molecular mechanisms of resistance, and the importance of mobile genetic elements in the global spread of resistance to linezolid.

Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches

Rifampicin resistance is a major therapeutic challenge, particularly in tuberculosis, leprosy, P. aeruginosa and S. aureus infections, where it develops via missense mutations in gene rpoB. Previously we have highlighted that these mutations reduce protein affinities within the RNA polymerase complex, subsequently reducing nucleic acid affinity. Here, we have used these insights to develop a computational rifampicin resistance predictor capable of identifying resistant mutations even outside the well-defined rifampicin resistance determining region (RRDR), using clinical M. tuberculosis sequencing information. Our tool successfully identified up to 90.9% of M. tuberculosis rpoB variants correctly, with sensitivity of 92.2%, specificity of 83.6% and MCC of 0.69, outperforming the current gold-standard GeneXpert-MTB/RIF. We show our model can be translated to other clinically relevant organisms: M. leprae, P. aeruginosa and S. aureus, despite weak sequence identity. Our method was implemented as an interactive tool, SUSPECT-RIF (StrUctural Susceptibility PrEdiCTion for RIFampicin), freely available at https://biosig.unimelb.edu.au/suspect_rif/ .

Staphylococcus aureus induces cell-surface expression of immune stimulatory NKG2D ligands on human monocytes

Staphylococcus aureus is among the leading causes of bacterial infections worldwide. The pathogenicity and establishment of S. aureus infections are tightly linked to its ability to modulate host immunity. Persistent infections are often associated with mutant staphylococcal strains that have decreased susceptibility to antibiotics; however, little is known about how these mutations influence bacterial interaction with the host immune system. Here, we discovered that clinical S. aureus isolates activate human monocytes, leading to cell-surface expression of immune stimulatory natural killer group 2D (NKG2D) ligands on the monocytes. We found that expression of the NKG2D ligand ULBP2 (UL16-binding protein 2) is associated with bacterial degradability and phagolysosomal activity. Moreover, S. aureus-induced ULBP2 expression was linked to altered host cell metabolism, including increased cytoplasmic (iso)citrate levels, reduced glycolytic flux, and functional mitochondrial activity. Interestingly, we found that the ability of S. aureus to induce ULBP2 and proinflammatory cytokines in human monocytes depends on a functional ClpP protease in S. aureus These findings indicate that S. aureus activates ULBP2 in human monocytes through immunometabolic mechanisms and reveal that clpP inactivation may function as a potential immune evasion mechanism. Our results provide critical insight into the interplay between the host immune system and S. aureus that has evolved under the dual selective pressure of host immune responses and antibiotic treatment. Our discovery of an immune stimulatory pathway consisting of human monocyte-based defense against S. aureus suggests that targeting the NKG2D pathway holds potential for managing persistent staphylococcal infections.

Innovative and rapid antimicrobial susceptibility testing systems

Antimicrobial resistance (AMR) is a major threat to human health worldwide, and the rapid detection and quantification of resistance, combined with antimicrobial stewardship, are key interventions to combat the spread and emergence of AMR. Antimicrobial susceptibility testing (AST) systems are the collective set of diagnostic processes that facilitate the phenotypic and genotypic assessment of AMR and antibiotic susceptibility. Over the past 30 years, only a few high-throughput AST methods have been developed and widely implemented. By contrast, several studies have established proof of principle for various innovative AST methods, including both molecular-based and genome-based methods, which await clinical trials and regulatory review. In this Review, we discuss the current state of AST systems in the broadest technical, translational and implementation-related scope.

Using genomics to understand meticillin- and vancomycin-resistant Staphylococcus aureus infections

Resistance to meticillin and vancomycin in Staphylococcus aureus significantly complicates the management of severe infections like bacteraemia, endocarditis or osteomyelitis. Here, we review the molecular mechanisms and genomic epidemiology of resistance to these agents, with a focus on how genomics has provided insights into the emergence and evolution of major meticillin-resistant S. aureus clones. We also provide insights on the use of bacterial whole-genome sequencing to inform management of S. aureus infections and for control of transmission at the hospital and in the community.

Unstable chromosome rearrangements in Staphylococcus aureus cause phenotype switching associated with persistent infections

Staphylococcus aureus is a major human pathogen known to exhibit subpopulations of small-colony variants (SCVs) that cause persistent or recurrent infections. The underlying mechanisms promoting the SCV phenotypic switching and adaptation to persistent infection are poorly understood. Moreover, the instability of this frequently reverting phenotype hampers diagnosis and study. Here we show that SCVs with reduced virulence but increased immune evasion and persistence properties can arise from reversible chromosomal instability. This mechanism of SCV generation implies an asymmetric chromosome inversion and the activation of prophage-encoding genes used for immune evasion. Assessment of major S. aureus lineages indicates this genomic plasticity is a common but previously unrecognized mechanism used by S. aureus to cause persistent and relapsing infections.

Staphylococcus aureus small-colony variants (SCVs) are associated with unusually chronic and persistent infections despite active antibiotic treatment. The molecular basis for this clinically important phenomenon is poorly understood, hampered by the instability of the SCV phenotype. Here we investigated the genetic basis for an unstable S. aureus SCV that arose spontaneously while studying rifampicin resistance. This SCV showed no nucleotide differences across its genome compared with a normal-colony variant (NCV) revertant, yet the SCV presented the hallmarks of S. aureus linked to persistent infection: down-regulation of virulence genes and reduced hemolysis and neutrophil chemotaxis, while exhibiting increased survival in blood and ability to invade host cells. Further genome analysis revealed chromosome structural variation uniquely associated with the SCV. These variations included an asymmetric inversion across half of the S. aureus chromosome via recombination between type I restriction modification system (T1RMS) genes, and the activation of a conserved prophage harboring the immune evasion cluster (IEC). Phenotypic reversion to the wild-type–like NCV state correlated with reversal of the chromosomal inversion (CI) and with prophage stabilization. Further analysis of 29 complete S. aureus genomes showed strong signatures of recombination between hsdMS genes, suggesting that analogous CI has repeatedly occurred during S. aureus evolution. Using qPCR and long-read amplicon deep sequencing, we detected subpopulations with T1RMS rearrangements causing CIs and prophage activation across major S. aureus lineages. Here, we have discovered a previously unrecognized and widespread mechanism of reversible genomic instability in S. aureus associated with SCV generation and persistent infections.

The Natural Product Elegaphenone Potentiates Antibiotic Effects against Pseudomonas aeruginosa

Natural products represent a rich source of antibiotics that address versatile cellular targets. The deconvolution of their targets via chemical proteomics is often challenged by the introduction of large photocrosslinkers. Here we applied elegaphenone, a largely uncharacterized natural product antibiotic bearing a native benzophenone core scaffold, for affinity-based protein profiling (AfBPP) in Gram-positive and Gram-negative bacteria. This study utilizes the alkynylated natural product scaffold as a probe to uncover intriguing biological interactions with the transcriptional regulator AlgP. Furthermore, proteome profiling of a Pseudomonas aeruginosa AlgP transposon mutant provided unique insights into the mode of action. Elegaphenone enhanced the elimination of intracellular P. aeruginosa in macrophages exposed to sub-inhibitory concentrations of the fluoroquinolone antibiotic norfloxacin.

Nanopore ultra-long read sequencing technology for antimicrobial resistance detection in Mannheimia haemolytica

Disruptive innovations in long-range, cost-effective direct template nucleic acid sequencing are transforming clinical and diagnostic medicine. A multidrug resistant strain and a pan-susceptible strain of Mannheimia haemolytica, isolated from pneumonic bovine lung samples, were sequenced at 146× and 111× coverage, respectively with Oxford Nanopore Technologies MinION. De novo assembly produced a complete genome for the non-resistant strain and a nearly complete assembly for the drug resistant strain. Functional annotation using RAST (Rapid Annotations using Subsystems Technology), CARD (Comprehensive Antibiotic Resistance Database) and ResFinder databases identified genes conferring resistance to different classes of antibiotics including β-lactams, tetracyclines, lincosamides, phenicols, aminoglycosides, sulfonamides and macrolides. Resistance phenotypes of the M. haemolytica strains were determined by minimum inhibitory concentration (MIC) of the antibiotics. Sequencing with a highly portable MinION device corresponded to MIC assays with most of the antimicrobial resistant determinants being identified with as few as 5437 reads, except for the genes responsible for resistance to Fluoroquinolones. The resulting quality assemblies and AMR gene annotation highlight the efficiency of ultra-long read, whole-genome sequencing (WGS) as a valuable tool in diagnostic veterinary medicine.

Genome-Based Prediction of Bacterial Antibiotic Resistance

Clinical microbiology has long relied on growing bacteria in culture to determine antimicrobial susceptibility profiles, but the use of whole-genome sequencing for antibiotic susceptibility testing (WGS-AST) is now a powerful alternative. This review discusses the technologies that made this possible and presents results from recent studies to predict resistance based on genome sequences.

Clinical microbiology has long relied on growing bacteria in culture to determine antimicrobial susceptibility profiles, but the use of whole-genome sequencing for antibiotic susceptibility testing (WGS-AST) is now a powerful alternative. This review discusses the technologies that made this possible and presents results from recent studies to predict resistance based on genome sequences. We examine differences between calling antibiotic resistance profiles by the simple presence or absence of previously known genes and single-nucleotide polymorphisms (SNPs) against approaches that deploy machine learning and statistical models. Often, the limitations to genome-based prediction arise from limitations of accuracy of culture-based AST in addition to an incomplete knowledge of the genetic basis of resistance. However, we need to maintain phenotypic testing even as genome-based prediction becomes more widespread to ensure that the results do not diverge over time. We argue that standardization of WGS-AST by challenge with consistently phenotyped strain sets of defined genetic diversity is necessary to compare the efficacy of methods of prediction of antibiotic resistance based on genome sequences.

Indexed variation graphs for efficient and accurate resistome profiling

Motivation

Antimicrobial resistance (AMR) remains a major threat to global health. Profiling the collective AMR genes within a metagenome (the 'resistome') facilitates greater understanding of AMR gene diversity and dynamics. In turn, this can allow for gene surveillance, individualized treatment of bacterial infections and more sustainable use of antimicrobials. However, resistome profiling can be complicated by high similarity between reference genes, as well as the sheer volume of sequencing data and the complexity of analysis workflows. We have developed an efficient and accurate method for resistome profiling that addresses these complications and improves upon currently available tools.

Results

Our method combines a variation graph representation of gene sets with a locality-sensitive hashing Forest indexing scheme to allow for fast classification of metagenomic sequence reads using similarity-search queries. Subsequent hierarchical local alignment of classified reads against graph traversals enables accurate reconstruction of full-length gene sequences using a scoring scheme. We provide our implementation, graphing Resistance Out Of meTagenomes (GROOT), and show it to be both faster and more accurate than a current reference-dependent tool for resistome profiling. GROOT runs on a laptop and can process a typical 2 gigabyte metagenome in 2 min using a single CPU. Our method is not restricted to resistome profiling and has the potential to improve current metagenomic workflows.

Availability and implementation

GROOT is written in Go and is available at https://github.com/will-rowe/groot (MIT license).

Supplementary information

Supplementary data are available at Bioinformatics online.