Estimation of country-specific tuberculosis resistance antibiograms using pathogen genomics and machine learning.

BACKGROUND: Global tuberculosis (TB) drug resistance (DR) surveillance focuses on rifampicin. We examined the potential of public and surveillance Mycobacterium tuberculosis (Mtb) whole-genome sequencing (WGS) data, to generate expanded country-level resistance prevalence estimates (antibiograms) using in silico resistance prediction. METHODS: We curated and quality-controlled Mtb WGS data. We used a validated random forest model to predict phenotypic resistance to 12 drugs and bias-corrected for model performance, outbreak sampling and rifampicin resistance oversampling. Validation leveraged a national DR survey conducted in South Africa. RESULTS: Mtb isolates from 29 countries (n=19 149) met sequence quality criteria. Global marginal genotypic resistance among mono-resistant TB estimates overlapped with the South African DR survey, except for isoniazid, ethionamide and second-line injectables, which were underestimated (n=3134). Among multidrug resistant (MDR) TB (n=268), estimates overlapped for the fluoroquinolones but overestimated other drugs. Globally pooled mono-resistance to isoniazid was 10.9% (95% CI: 10.2-11.7%, n=14 012). Mono-levofloxacin resistance rates were highest in South Asia (Pakistan 3.4% (0.1-11%), n=111 and India 2.8% (0.08-9.4%), n=114). Given the recent interest in drugs enhancing ethionamide activity and their expected activity against isolates with resistance discordance between isoniazid and ethionamide, we measured this rate and found it to be high at 74.4% (IQR: 64.5-79.7%) of isoniazid-resistant isolates predicted to be ethionamide susceptible. The global susceptibility rate to pyrazinamide and levofloxacin among MDR was 15.1% (95% CI: 10.2-19.9%, n=3964). CONCLUSIONS: This is the first attempt at global Mtb antibiogram estimation. DR prevalence in Mtb can be reliably estimated using public WGS and phenotypic resistance prediction for key antibiotics, but public WGS data demonstrates oversampling of isolates with higher resistance levels than MDR. Nevertheless, our results raise concerns about the empiric use of short-course fluoroquinolone regimens for drug-susceptible TB in South Asia and indicate underutilisation of ethionamide in MDR treatment.

Phase variation as a major mechanism of adaptation in Mycobacterium tuberculosis complex.

Phase variation induced by insertions and deletions (INDELs) in genomic homopolymeric tracts (HT) can silence and regulate genes in pathogenic bacteria, but this process is not characterized in MTBC (Mycobacterium tuberculosis complex) adaptation. We leverage 31,428 diverse clinical isolates to identify genomic regions including phase-variants under positive selection. Of 87,651 INDEL events that emerge repeatedly across the phylogeny, 12.4% are phase-variants within HTs (0.02% of the genome by length). We estimated the in-vitro frameshift rate in a neutral HT at 100× the neutral substitution rate at [Formula: see text] frameshifts/HT/year. Using neutral evolution simulations, we identified 4,098 substitutions and 45 phase-variants to be putatively adaptive to MTBC (P < 0.002). We experimentally confirm that a putatively adaptive phase-variant alters the expression of espA, a critical mediator of ESX-1-dependent virulence. Our evidence supports the hypothesis that phase variation in the ESX-1 system of MTBC can act as a toggle between antigenicity and survival in the host.

Analysis of Genome-Wide Mutational Dependence in Naturally Evolving Mycobacterium tuberculosis Populations.

Pathogenic microorganisms are in a perpetual struggle for survival in changing host environments, where host pressures necessitate changes in pathogen virulence, antibiotic resistance, or transmissibility. The genetic basis of phenotypic adaptation by pathogens is difficult to study in vivo. In this work, we develop a phylogenetic method to detect genetic dependencies that promote pathogen adaptation using 31,428 in vivo sampled Mycobacterium tuberculosis genomes, a globally prevalent bacterial pathogen with increasing levels of antibiotic resistance. We find that dependencies between mutations are enriched in antigenic and antibiotic resistance functions and discover 23 mutations that potentiate the development of antibiotic resistance. Between 11% and 92% of resistant strains harbor a dependent mutation acquired after a resistance-conferring variant. We demonstrate the pervasiveness of genetic dependency in adaptation of naturally evolving populations and the utility of the proposed computational approach.

A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis.

Long diagnostic wait times hinder international efforts to address antibiotic resistance in M. tuberculosis. Pathogen whole genome sequencing, coupled with statistical and machine learning models, offers a promising solution. However, generalizability and clinical adoption have been limited by a lack of interpretability, especially in deep learning methods. Here, we present two deep convolutional neural networks that predict antibiotic resistance phenotypes of M. tuberculosis isolates: a multi-drug CNN (MD-CNN), that predicts resistance to 13 antibiotics based on 18 genomic loci, with AUCs 82.6-99.5% and higher sensitivity than state-of-the-art methods; and a set of 13 single-drug CNNs (SD-CNN) with AUCs 80.1-97.1% and higher specificity than the previous state-of-the-art. Using saliency methods to evaluate the contribution of input sequence features to the SD-CNN predictions, we identify 18 sites in the genome not previously associated with resistance. The CNN models permit functional variant discovery, biologically meaningful interpretation, and clinical applicability.

Draft Genome Sequences of Two Clostridium botulinum Group II Strains Carrying Phage-Like Plasmids.

Clostridium botulinum is responsible for botulism, a potentially lethal foodborne intoxication. Here, we report the draft genome sequences of C. botulinum group II strains 202F (serotype F) and Hazen (serotype E). The genomes share many similarities, including multiple mobile genetic elements.

Population structure, biogeography and transmissibility of Mycobacterium tuberculosis.

Mycobacterium tuberculosis is a clonal pathogen proposed to have co-evolved with its human host for millennia, yet our understanding of its genomic diversity and biogeography remains incomplete. Here we use a combination of phylogenetics and dimensionality reduction to reevaluate the population structure of M. tuberculosis, providing an in-depth analysis of the ancient Indo-Oceanic Lineage 1 and the modern Central Asian Lineage 3, and expanding our understanding of Lineages 2 and 4. We assess sub-lineages using genomic sequences from 4939 pan-susceptible strains, and find 30 new genetically distinct clades that we validate in a dataset of 4645 independent isolates. We find a consistent geographically restricted or unrestricted pattern for 20 groups, including three groups of Lineage 1. The distribution of terminal branch lengths across the M. tuberculosis phylogeny supports the hypothesis of a higher transmissibility of Lineages 2 and 4, in comparison with Lineages 3 and 1, on a global scale. We define an expanded barcode of 95 single nucleotide substitutions that allows rapid identification of 69 M. tuberculosis sub-lineages and 26 additional internal groups. Our results paint a higher resolution picture of the M. tuberculosis phylogeny and biogeography.

Role of Epistasis in Amikacin, Kanamycin, Bedaquiline, and Clofazimine Resistance in Mycobacterium tuberculosis Complex.

Antibiotic resistance among bacterial pathogens poses a major global health threat. Mycobacterium tuberculosis complex (MTBC) is estimated to have the highest resistance rates of any pathogen globally. Given the low growth rate and the need for a biosafety level 3 laboratory, the only realistic avenue to scale up drug susceptibility testing (DST) for this pathogen is to rely on genotypic techniques. This raises the fundamental question of whether a mutation is a reliable surrogate for phenotypic resistance or whether the presence of a second mutation can completely counteract its effect, resulting in major diagnostic errors (i.e., systematic false resistance results). To date, such epistatic interactions have only been reported for streptomycin that is now rarely used. By analyzing more than 31,000 MTBC genomes, we demonstrated that the eis C-14T promoter mutation, which is interrogated by several genotypic DST assays endorsed by the World Health Organization, cannot confer resistance to amikacin and kanamycin if it coincides with loss-of-function (LoF) mutations in the coding region of eis. To our knowledge, this represents the first definitive example of antibiotic reversion in MTBC. Moreover, we raise the possibility that mmpR (Rv0678) mutations are not valid markers of resistance to bedaquiline and clofazimine if these coincide with an LoF mutation in the efflux pump encoded by mmpS5 (Rv0677c) and mmpL5 (Rv0676c).

GenTB: A user-friendly genome-based predictor for tuberculosis resistance powered by machine learning.

BACKGROUND: Multidrug-resistant Mycobacterium tuberculosis (Mtb) is a significant global public health threat. Genotypic resistance prediction from Mtb DNA sequences offers an alternative to laboratory-based drug-susceptibility testing. User-friendly and accurate resistance prediction tools are needed to enable public health and clinical practitioners to rapidly diagnose resistance and inform treatment regimens. RESULTS: We present Translational Genomics platform for Tuberculosis (GenTB), a free and open web-based application to predict antibiotic resistance from next-generation sequence data. The user can choose between two potential predictors, a Random Forest (RF) classifier and a Wide and Deep Neural Network (WDNN) to predict phenotypic resistance to 13 and 10 anti-tuberculosis drugs, respectively. We benchmark GenTB's predictive performance along with leading TB resistance prediction tools (Mykrobe and TB-Profiler) using a ground truth dataset of 20,408 isolates with laboratory-based drug susceptibility data. All four tools reliably predicted resistance to first-line tuberculosis drugs but had varying performance for second-line drugs. The mean sensitivities for GenTB-RF and GenTB-WDNN across the nine shared drugs were 77.6% (95% CI 76.6-78.5%) and 75.4% (95% CI 74.5-76.4%), respectively, and marginally higher than the sensitivities of TB-Profiler at 74.4% (95% CI 73.4-75.3%) and Mykrobe at 71.9% (95% CI 70.9-72.9%). The higher sensitivities were at an expense of ≤ 1.5% lower specificity: Mykrobe 97.6% (95% CI 97.5-97.7%), TB-Profiler 96.9% (95% CI 96.7 to 97.0%), GenTB-WDNN 96.2% (95% CI 96.0 to 96.4%), and GenTB-RF 96.1% (95% CI 96.0 to 96.3%). Averaged across the four tools, genotypic resistance sensitivity was 11% and 9% lower for isoniazid and rifampicin respectively, on isolates sequenced at low depth (< 10× across 95% of the genome) emphasizing the need to quality control input sequence data before prediction. We discuss differences between tools in reporting results to the user including variants underlying the resistance calls and any novel or indeterminate variants CONCLUSIONS: GenTB is an easy-to-use online tool to rapidly and accurately predict resistance to anti-tuberculosis drugs. GenTB can be accessed online at https://gentb.hms.harvard.edu , and the source code is available at https://github.com/farhat-lab/gentb-site .

Globally diverse Mycobacterium tuberculosis resistance acquisition: a retrospective geographical and temporal analysis of whole genome sequences.

BACKGROUND: Mycobacterium tuberculosis whole genome sequencing (WGS) data can provide insights into temporal and geographical trends in resistance acquisition and inform public health interventions. We aimed to use a large clinical collection of M tuberculosis WGS and resistance phenotype data to study how, when, and where resistance was acquired on a global scale. METHODS: We did a retrospective analysis of WGS data. We curated a set of clinical M tuberculosis isolates with high-quality sequencing and culture-based drug susceptibility data (spanning four lineages and 52 countries in Africa, Asia, the Americas, and Europe) using public databases and literature curation. For inclusion, sequence quality criteria and country of origin data were required. We constructed geographical and lineage specific M tuberculosis phylogenies and used Bayesian molecular dating with BEAST, version 1.10.4, to infer the most recent common susceptible ancestor age for 4869 instances of resistance to ten drugs. FINDINGS: Between Jan 1, 1987, and Sept 12, 2014, of 10 299 M tuberculosis clinical isolates, 8550 were curated, of which 6099 (71%) from 15 countries met criteria for molecular dating. The number of independent resistance acquisition events was lower than the number of resistant isolates across all countries, suggesting ongoing transmission of drug resistance. Ancestral age distributions supported the presence of old resistance, 20 years or more before, in most countries. A consistent order of resistance acquisition was observed globally starting with resistance to isoniazid, but resistance ancestral age varied by country. We found a direct correlation between gross domestic product per capita and resistance age (r 2=0·47; p=0·014). Amplification of fluoroquinolone and second-line injectable resistance among multidrug-resistant isolates is estimated to have occurred very recently (median ancestral age 4·7 years [IQR 1·9-9·8] before sample collection). We found the sensitivity of commercial molecular diagnostics for second-line resistance to vary significantly by country (p<0·0003). INTERPRETATION: Our results highlight that both resistance transmission and amplification are contributing to disease burden globally but vary by country. The observation that wealthier nations are more likely to have old resistance (most recent common susceptible ancestor >20 years before isolation) suggests that programmatic improvements can reduce resistance amplification, but that fit resistant strains can circulate for decades subsequently implies the need for continued surveillance.

Transmission, adaptation and geographical spread of the Pseudomonas aeruginosa Liverpool epidemic strain.

The Liverpool epidemic strain (LES) is an important transmissible clonal lineage of Pseudomonas aeruginosa that chronically infects the lungs of people with cystic fibrosis (CF). Previous studies have focused on the genomics of the LES in a limited number of isolates, mostly from one CF centre in the UK, and from studies highlighting identification of the LES in Canada. Here we significantly extend the current LES genome database by genome sequencing 91 isolates from multiple CF centres across the UK, and we describe the comparative genomics of this large collection of LES isolates from the UK and Canada. Phylogenetic analysis revealed that the 145 LES genomes analysed formed a distinct clonal lineage when compared with the wider P. aeruginosa population. Notably, the isolates formed two clades: one associated with isolates from Canada, and the other associated with UK isolates. Further analysis of the UK LES isolates revealed clustering by clinic geography. Where isolates clustered closely together, the association was often supported by clinical data linking isolates or patients. When compared with the earliest known isolate, LESB58 (from 1988), many UK LES isolates shared common loss-of-function mutations, such as in genes gltR and fleR. Other loss-of-function mutations identified in previous studies as common adaptations during CF chronic lung infections were also identified in multiple LES isolates. Analysis of the LES accessory genome (including genomic islands and prophages) revealed variations in the carriage of large genomic regions, with some evidence for shared genomic island/prophage complement according to clinic location. Our study reveals divergence and adaptation during the spread of the LES, within the UK and between continents.

In-host population dynamics of Mycobacterium tuberculosis complex during active disease.

Tuberculosis (TB) is a leading cause of death globally. Understanding the population dynamics of TB's causative agent Mycobacterium tuberculosis complex (Mtbc) in-host is vital for understanding the efficacy of antibiotic treatment. We use longitudinally collected clinical Mtbc isolates that underwent Whole-Genome Sequencing from the sputa of 200 patients to investigate Mtbc diversity during the course of active TB disease after excluding 107 cases suspected of reinfection, mixed infection or contamination. Of the 178/200 patients with persistent clonal infection >2 months, 27 developed new resistance mutations between sampling with 20/27 occurring in patients with pre-existing resistance. Low abundance resistance variants at a purity of ≥19% in the first isolate predict fixation in the subsequent sample. We identify significant in-host variation in 27 genes, including antibiotic resistance genes, metabolic genes and genes known to modulate host innate immunity and confirm several to be under positive selection by assessing phylogenetic convergence across a genetically diverse sample of 20,352 isolates.

The Salmonella enterica Plasmidome as a Reservoir of Antibiotic Resistance.

The emergence of multidrug-resistant bacterial strains worldwide has become a serious problem for public health over recent decades. The increase in antimicrobial resistance has been expanding via plasmids as mobile genetic elements encoding antimicrobial resistance (AMR) genes that are transferred vertically and horizontally. This study focuses on Salmonella enterica, one of the leading foodborne pathogens in industrialized countries. S. enterica is known to carry several plasmids involved not only in virulence but also in AMR. In the current paper, we present an integrated strategy to detect plasmid scaffolds in whole genome sequencing (WGS) assemblies. We developed a two-step procedure to predict plasmids based on i) the presence of essential elements for plasmid replication and mobility, as well as ii) sequence similarity to a reference plasmid. Next, to confirm the accuracy of the prediction in 1750 S. enterica short-read sequencing data, we combined Oxford Nanopore MinION long-read sequencing with Illumina MiSeq short-read sequencing in hybrid assemblies for 84 isolates to evaluate the proportion of plasmid that has been detected. At least one scaffold with an origin of replication (ORI) was predicted in 61.3% of the Salmonella isolates tested. The results indicated that IncFII and IncI1 ORIs were distributed in many S. enterica serotypes and were the most prevalent AMR genes carrier, whereas IncHI2A/IncHI2 and IncA/C2 were more serotype restricted but bore several AMR genes. Comparison between hybrid and short-read assemblies revealed that 81.1% of plasmids were found in the short-read sequencing using our pipeline. Through this process, we established that plasmids are prevalent in S. enterica and we also substantially expand the AMR genes in the resistome of this species.

Expansion of LINEs and species-specific DNA repeats drives genome expansion in Asian Gypsy Moths.

Two subspecies of Asian gypsy moth (AGM), Lymantria dispar asiatica and L. dispar japonica, pose a serious alien invasive threat to North American forests. Despite decades of research on the ecology and biology of this pest, limited AGM-specific genomic resources are currently available. Here, we report on the genome sequences and functional content of these AGM subspecies. The genomes of L.d. asiatica and L.d. japonica are the largest lepidopteran genomes sequenced to date, totaling 921 and 999 megabases, respectively. Large genome size in these subspecies is driven by the accumulation of specific classes of repeats. Genome-wide metabolic pathway reconstructions suggest strong genomic signatures of energy-related pathways in both subspecies, dominated by metabolic functions related to thermogenesis. The genome sequences reported here will provide tools for probing the molecular mechanisms underlying phenotypic traits that are thought to enhance AGM invasiveness.

Reservoirs of resistance: polymyxin resistance in veterinary-associated companion animal isolates of Pseudomonas aeruginosa.

BACKGROUND: Pseudomonas aeruginosa is an opportunistic pathogen and a major cause of infections. Widespread resistance in human infections are increasing the use of last resort antimicrobials such as polymyxins. However, these have been used for decades in veterinary medicine. Companion animals are an understudied source of antimicrobial resistant P. aeruginosa isolates. This study evaluated the susceptibility of P. aeruginosa veterinary isolates to polymyxins to determine whether the veterinary niche represents a potential reservoir of resistance genes for pathogenic bacteria in both animals and humans. METHODS AND RESULTS: Clinical P. aeruginosa isolates (n=24) from UK companion animals were compared for antimicrobial susceptibility to a panel of human-associated isolates (n=37). Minimum inhibitory concentration (MIC) values for polymyxin B and colistin in the companion animals was significantly higher than in human isolates (P=0.033 and P=0.013, respectively). Genotyping revealed that the veterinary isolates were spread throughout the P. aeruginosa population, with shared array types from human infections such as keratitis and respiratory infections, suggesting the potential for zoonotic transmission. Whole genome sequencing revealed mutations in genes associated with polymyxin resistance and other antimicrobial resistance-related genes. CONCLUSION: The high levels of resistance to polymyxin shown here, along with genetic similarities between some human and animal isolates, together suggest a need for sustained surveillance of this veterinary niche as a potential reservoir for resistant, clinically relevant bacteria in both animals and humans.

GWAS for quantitative resistance phenotypes in Mycobacterium tuberculosis reveals resistance genes and regulatory regions.

Drug resistance diagnostics that rely on the detection of resistance-related mutations could expedite patient care and TB eradication. We perform minimum inhibitory concentration testing for 12 anti-TB drugs together with Illumina whole-genome sequencing on 1452 clinical Mycobacterium tuberculosis (MTB) isolates. We evaluate genome-wide associations between mutations in MTB genes or non-coding regions and resistance, followed by validation in an independent data set of 792 patient isolates. We confirm associations at 13 non-canonical loci, with two involving non-coding regions. Promoter mutations are measured to have smaller average effects on resistance than gene body mutations. We estimate the heritability of the resistance phenotype to 11 anti-TB drugs and identify a lower than expected contribution from known resistance genes. This study highlights the complexity of the genomic mechanisms associated with the MTB resistance phenotype, including the relatively large number of potentially causal loci, and emphasizes the contribution of the non-coding portion of the genome.

Beyond multidrug resistance: Leveraging rare variants with machine and statistical learning models in Mycobacterium tuberculosis resistance prediction.

BACKGROUND: The diagnosis of multidrug resistant and extensively drug resistant tuberculosis is a global health priority. Whole genome sequencing of clinical Mycobacterium tuberculosis isolates promises to circumvent the long wait times and limited scope of conventional phenotypic antimicrobial susceptibility, but gaps remain for predicting phenotype accurately from genotypic data especially for certain drugs. Our primary aim was to perform an exploration of statistical learning algorithms and genetic predictor sets using a rich dataset to build a high performing and fast predicting model to detect anti-tuberculosis drug resistance. METHODS: We collected targeted or whole genome sequencing and conventional drug resistance phenotyping data from 3601 Mycobacterium tuberculosis strains enriched for resistance to first- and second-line drugs, with 1228 multidrug resistant strains. We investigated the utility of (1) rare variants and variants known to be determinants of resistance for at least one drug and (2) machine and statistical learning architectures in predicting phenotypic drug resistance to 10 anti-tuberculosis drugs. Specifically, we investigated multitask and single task wide and deep neural networks, a multilayer perceptron, regularized logistic regression, and random forest classifiers. FINDINGS: The highest performing machine and statistical learning methods included both rare variants and those known to be causal of resistance for at least one drug. Both simpler L2 penalized regression and complex machine learning models had high predictive performance. The average AUCs for our highest performing model was 0.979 for first-line drugs and 0.936 for second-line drugs during repeated cross-validation. On an independent validation set, the highest performing model showed average AUCs, sensitivities, and specificities, respectively, of 0.937, 87.9%, and 92.7% for first-line drugs and 0.891, 82.0% and 90.1% for second-line drugs. Our method outperforms existing approaches based on direct association, with increased sum of sensitivity and specificity of 11.7% on first line drugs and 3.2% on second line drugs. Our method has higher predictive performance compared to previously reported machine learning models during cross-validation, with higher AUCs for 8 of 10 drugs. INTERPRETATION: Statistical models, especially those that are trained using both frequent and less frequent variants, significantly improve the accuracy of resistance prediction and hold promise in bringing sequencing technologies closer to the bedside.

Whole genome sequencing identifies bacterial factors affecting transmission of multidrug-resistant tuberculosis in a high-prevalence setting.

Whole genome sequencing (WGS) can elucidate Mycobacterium tuberculosis (Mtb) transmission patterns but more data is needed to guide its use in high-burden settings. In a household-based TB transmissibility study in Peru, we identified a large MIRU-VNTR Mtb cluster (148 isolates) with a range of resistance phenotypes, and studied host and bacterial factors contributing to its spread. WGS was performed on 61 of the 148 isolates. We compared transmission link inference using epidemiological or genomic data and estimated the dates of emergence of the cluster and antimicrobial drug resistance (DR) acquisition events by generating a time-calibrated phylogeny. Using a set of 12,032 public Mtb genomes, we determined bacterial factors characterizing this cluster and under positive selection in other Mtb lineages. Four of the 61 isolates were distantly related and the remaining 57 isolates diverged ca. 1968 (95%HPD: 1945-1985). Isoniazid resistance arose once and rifampin resistance emerged subsequently at least three times. Emergence of other DR types occurred as recently as within the last year of sampling. We identified five cluster-defining SNPs potentially contributing to transmissibility. In conclusion, clusters (as defined by MIRU-VNTR typing) may be circulating for decades in a high-burden setting. WGS allows for an enhanced understanding of transmission, drug resistance, and bacterial fitness factors.

Major Release of 161 Whole-Genome Sequences from the International Pseudomonas Consortium Database.

Pseudomonas aeruginosa is an environmental bacterium and opportunistic pathogen. Here, we present draft genome sequences for 161 isolates from diverse clinical and environmental sources. This set of genome sequences complements other major public data releases from the International Pseudomonas Consortium Database.

Genomics of antibiotic-resistance prediction in Pseudomonas aeruginosa.

Antibiotic resistance is a worldwide health issue spreading quickly among human and animal pathogens, as well as environmental bacteria. Misuse of antibiotics has an impact on the selection of resistant bacteria, thus contributing to an increase in the occurrence of resistant genotypes that emerge via spontaneous mutation or are acquired by horizontal gene transfer. There is a specific and urgent need not only to detect antimicrobial resistance but also to predict antibiotic resistance in silico. We now have the capability to sequence hundreds of bacterial genomes per week, including assembly and annotation. Novel and forthcoming bioinformatics tools can predict the resistome and the mobilome with a level of sophistication not previously possible. Coupled with bacterial strain collections and databases containing strain metadata, prediction of antibiotic resistance and the potential for virulence are moving rapidly toward a novel approach in molecular epidemiology. Here, we present a model system in antibiotic-resistance prediction, along with its promises and limitations. As it is commonly multidrug resistant, Pseudomonas aeruginosa causes infections that are often difficult to eradicate. We review novel approaches for genotype prediction of antibiotic resistance. We discuss the generation of microbial sequence data for real-time patient management and the prediction of antimicrobial resistance.