Deciphering the distance to antibiotic resistance for the pneumococcus using genome sequencing data. - Related Documents




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462401.0000Deciphering the distance to antibiotic resistance for the pneumococcus using genome sequencing data. Advances in genome sequencing technologies and genome-wide association studies (GWAS) have provided unprecedented insights into the molecular basis of microbial phenotypes and enabled the identification of the underlying genetic variants in real populations. However, utilization of genome sequencing in clinical phenotyping of bacteria is challenging due to the lack of reliable and accurate approaches. Here, we report a method for predicting microbial resistance patterns using genome sequencing data. We analyzed whole genome sequences of 1,680 Streptococcus pneumoniae isolates from four independent populations using GWAS and identified probable hotspots of genetic variation which correlate with phenotypes of resistance to essential classes of antibiotics. With the premise that accumulation of putative resistance-conferring SNPs, potentially in combination with specific resistance genes, precedes full resistance, we retrogressively surveyed the hotspot loci and quantified the number of SNPs and/or genes, which if accumulated would confer full resistance to an otherwise susceptible strain. We name this approach the 'distance to resistance'. It can be used to identify the creep towards complete antibiotics resistance in bacteria using genome sequencing. This approach serves as a basis for the development of future sequencing-based methods for predicting resistance profiles of bacterial strains in hospital microbiology and public health settings.201728205635
511210.9999Genome-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. 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.201930381421
511120.9999Antimicrobial Resistance Prediction for Gram-Negative Bacteria via Game Theory-Based Feature Evaluation. The increasing prevalence of antimicrobial-resistant bacteria drives the need for advanced methods to identify antimicrobial-resistance (AMR) genes in bacterial pathogens. With the availability of whole genome sequences, best-hit methods can be used to identify AMR genes by differentiating unknown sequences with known AMR sequences in existing online repositories. Nevertheless, these methods may not perform well when identifying resistance genes with sequences having low sequence identity with known sequences. We present a machine learning approach that uses protein sequences, with sequence identity ranging between 10% and 90%, as an alternative to conventional DNA sequence alignment-based approaches to identify putative AMR genes in Gram-negative bacteria. By using game theory to choose which protein characteristics to use in our machine learning model, we can predict AMR protein sequences for Gram-negative bacteria with an accuracy ranging from 93% to 99%. In order to obtain similar classification results, identity thresholds as low as 53% were required when using BLASTp.201931597945
462330.9998Capturing the Resistome: a Targeted Capture Method To Reveal Antibiotic Resistance Determinants in Metagenomes. Identification of the nucleotide sequences encoding antibiotic resistance elements and determination of their association with antibiotic resistance are critical to improve surveillance and monitor trends in antibiotic resistance. Current methods to study antibiotic resistance in various environments rely on extensive deep sequencing or laborious culturing of fastidious organisms, both of which are heavily time-consuming operations. An accurate and sensitive method to identify both rare and common resistance elements in complex metagenomic samples is needed. Referencing the sequences in the Comprehensive Antibiotic Resistance Database, we designed a set of 37,826 probes to specifically target over 2,000 nucleotide sequences associated with antibiotic resistance in clinically relevant bacteria. Testing of this probe set on DNA libraries generated from multidrug-resistant bacteria to selectively capture resistance genes reproducibly produced higher numbers of reads on target at a greater length of coverage than shotgun sequencing. We also identified additional resistance gene sequences from human gut microbiome samples that sequencing alone was not able to detect. Our method to capture the resistome enables a sensitive means of gene detection in diverse environments where genes encoding antibiotic resistance represent less than 0.1% of the metagenome.201931611361
434040.9998Predicting antimicrobial susceptibility from the bacterial genome: A new paradigm for one health resistance monitoring. The laboratory identification of antibacterial resistance is a cornerstone of infectious disease medicine. In vitro antimicrobial susceptibility testing has long been based on the growth response of organisms in pure culture to a defined concentration of antimicrobial agents. By comparing individual isolates to wild-type susceptibility patterns, strains with acquired resistance can be identified. Acquired resistance can also be detected genetically. After many decades of research, the inventory of genes underlying antimicrobial resistance is well known for several pathogenic genera including zoonotic enteric organisms such as Salmonella and Campylobacter and continues to grow substantially for others. With the decline in costs for large scale DNA sequencing, it is now practicable to characterize bacteria using whole genome sequencing, including the carriage of resistance genes in individual microorganisms and those present in complex biological samples. With genomics, we can generate comprehensive, detailed information on the bacterium, the mechanisms of antibiotic resistance, clues to its source, and the nature of mobile DNA elements by which resistance spreads. These developments point to a new paradigm for antimicrobial resistance detection and tracking for both clinical and public health purposes.202133010049
434450.9998Phenetic Comparison of Prokaryotic Genomes Using k-mers. Bacterial genomics studies are getting more extensive and complex, requiring new ways to envision analyses. Using the Ray Surveyor software, we demonstrate that comparison of genomes based on their k-mer content allows reconstruction of phenetic trees without the need of prior data curation, such as core genome alignment of a species. We validated the methodology using simulated genomes and previously published phylogenomic studies of Streptococcus pneumoniae and Pseudomonas aeruginosa. We also investigated the relationship of specific genetic determinants with bacterial population structures. By comparing clusters from the complete genomic content of a genome population with clusters from specific functional categories of genes, we can determine how the population structures are correlated. Indeed, the strain clustering based on a subset of k-mers allows determination of its similarity with the whole genome clusters. We also applied this methodology on 42 species of bacteria to determine the correlational significance of five important bacterial genomic characteristics. For example, intrinsic resistance is more important in P. aeruginosa than in S. pneumoniae, and the former has increased correlation of its population structure with antibiotic resistance genes. The global view of the pangenome of bacteria also demonstrated the taxa-dependent interaction of population structure with antibiotic resistance, bacteriophage, plasmid, and mobile element k-mer data sets.201728957508
511360.9998Identification of bacterial antibiotic resistance genes in next-generation sequencing data (review of literature). The spread of antibiotic-resistant human bacterial pathogens is a serious threat to modern medicine. Antibiotic susceptibility testing is essential for treatment regimens optimization and preventing dissemination of antibiotic resistance. Therefore, development of antibiotic susceptibility testing methods is a priority challenge of laboratory medicine. The aim of this review is to analyze the capabilities of the bioinformatics tools for bacterial whole genome sequence data processing. The PubMed database, Russian scientific electronic library eLIBRARY, information networks of World health organization and European Society of Clinical Microbiology and Infectious Diseases (ESCMID) were used during the analysis. In this review, the platforms for whole genome sequencing, which are suitable for detection of bacterial genetic resistance determinants, are described. The classic step of genetic resistance determinants searching is an alignment between the query nucleotide/protein sequence and the subject (database) nucleotide/protein sequence, which is performed using the nucleotide and protein sequence databases. The most commonly used databases are Resfinder, CARD, Bacterial Antimicrobial Resistance Reference Gene Database. The results of the resistance determinants searching in genome assemblies is more correct in comparison to results of the searching in contigs. The new resistance genes searching bioinformatics tools, such as neural networks and machine learning, are discussed in the review. After critical appraisal of the current antibiotic resistance databases we designed a protocol for predicting antibiotic resistance using whole genome sequence data. The designed protocol can be used as a basis of the algorithm for qualitative and quantitative antimicrobial susceptibility testing based on whole genome sequence data.202134882354
462870.9998Genomic Analysis of Molecular Bacterial Mechanisms of Resistance to Phage Infection. To optimize phage therapy, we need to understand how bacteria evolve against phage attacks. One of the main problems of phage therapy is the appearance of bacterial resistance variants. The use of genomics to track antimicrobial resistance is increasingly developed and used in clinical laboratories. For that reason, it is important to consider, in an emerging future with phage therapy, to detect and avoid phage-resistant strains that can be overcome by the analysis of metadata provided by whole-genome sequencing. Here, we identified genes associated with phage resistance in 18 Acinetobacter baumannii clinical strains belonging to the ST-2 clonal complex during a decade (Ab2000 vs. 2010): 9 from 2000 to 9 from 2010. The presence of genes putatively associated with phage resistance was detected. Genes detected were associated with an abortive infection system, restriction-modification system, genes predicted to be associated with defense systems but with unknown function, and CRISPR-Cas system. Between 118 and 171 genes were found in the 18 clinical strains. On average, 26% of these genes were detected inside genomic islands in the 2000 strains and 32% in the 2010 strains. Furthermore, 38 potential CRISPR arrays in 17 of 18 of the strains were found, as well as 705 proteins associated with CRISPR-Cas systems. A moderately higher presence of these genes in the strains of 2010 in comparison with those of 2000 was found, especially those related to the restriction-modification system and CRISPR-Cas system. The presence of these genes in genomic islands at a higher rate in the strains of 2010 compared with those of 2000 was also detected. Whole-genome sequencing and bioinformatics could be powerful tools to avoid drawbacks when a personalized therapy is applied. In this study, it allows us to take care of the phage resistance in A. baumannii clinical strains to prevent a failure in possible phage therapy.202135250902
940580.9998Functional Metagenomic Screening for Antimicrobial Resistance in the Oral Microbiome. A large proportion of bacteria, from a multitude of environments, are not yet able to be grown in the laboratory, and therefore microbiological and molecular biological investigations of these bacteria are challenging. A way to circumvent this challenge is to analyze the metagenome, the entire collection of DNA molecules that can be isolated from a particular environment or sample. This collection of DNA molecules can be sequenced and assembled to determine what is present and infer functional potential, or used as a PCR template to detect known target DNA and potentially unknown regions of DNA nearby those targets; however assigning functions to new or conserved hypothetical, functionally cryptic, genes is difficult. Functional metagenomics allows researchers to determine which genes are responsible for selectable phenotypes, such as resistance to antimicrobials and metabolic capabilities, without the prerequisite needs to grow the bacteria containing those genes or to already know which genes are of interest. It is estimated that a third of the resident species of the human oral cavity is not yet cultivable and, together with the ease of sample acquisition, makes this metagenome particularly suited to functional metagenomic studies. Here we describe the methodology related to the collection of saliva samples, extraction of metagenomic DNA, construction of metagenomic libraries, as well as the description of functional assays that have previously led to the identification of new genes conferring antimicrobial resistance.202134410638
462990.9998Screening and in silico characterization of prophages in Helicobacter pylori clinical strains. The increase of antibiotic resistance calls for alternatives to control Helicobacter pylori, a Gram-negative bacterium associated with various gastric diseases. Bacteriophages (phages) can be highly effective in the treatment of pathogenic bacteria. Here, we developed a method to identify prophages in H. pylori genomes aiming at their future use in therapy. A polymerase chain reaction (PCR)-based technique tested five primer pairs on 74 clinical H. pylori strains. After the PCR screening, 14 strains most likely to carry prophages were fully sequenced. After that, a more holistic approach was taken by studying the complete genome of the strains. This study allowed us to identify 12 intact prophage sequences, which were then characterized concerning their morphology, virulence, and antibiotic-resistance genes. To understand the variability of prophages, a phylogenetic analysis using the sequences of all H. pylori phages reported to date was performed. Overall, we increased the efficiency of identifying complete prophages to 54.1 %. Genes with homology to potential virulence factors were identified in some new prophages. Phylogenetic analysis revealed a close relationship among H. pylori-phages, although there are phages with different geographical origins. This study provides a deeper understanding of H. pylori-phages, providing valuable insights into their potential use in therapy.202539368610
5114100.9998Datasets for benchmarking antimicrobial resistance genes in bacterial metagenomic and whole genome sequencing. Whole genome sequencing (WGS) is a key tool in identifying and characterising disease-associated bacteria across clinical, agricultural, and environmental contexts. One increasingly common use of genomic and metagenomic sequencing is in identifying the type and range of antimicrobial resistance (AMR) genes present in bacterial isolates in order to make predictions regarding their AMR phenotype. However, there are a large number of alternative bioinformatics software and pipelines available, which can lead to dissimilar results. It is, therefore, vital that researchers carefully evaluate their genomic and metagenomic AMR analysis methods using a common dataset. To this end, as part of the Microbial Bioinformatics Hackathon and Workshop 2021, a 'gold standard' reference genomic and simulated metagenomic dataset was generated containing raw sequence reads mapped against their corresponding reference genome from a range of 174 potentially pathogenic bacteria. These datasets and their accompanying metadata are freely available for use in benchmarking studies of bacteria and their antimicrobial resistance genes and will help improve tool development for the identification of AMR genes in complex samples.202235705638
3829110.9998Associations among Antibiotic and Phage Resistance Phenotypes in Natural and Clinical Escherichia coli Isolates. The spread of antibiotic resistance is driving interest in new approaches to control bacterial pathogens. This includes applying multiple antibiotics strategically, using bacteriophages against antibiotic-resistant bacteria, and combining both types of antibacterial agents. All these approaches rely on or are impacted by associations among resistance phenotypes (where bacteria resistant to one antibacterial agent are also relatively susceptible or resistant to others). Experiments with laboratory strains have shown strong associations between some resistance phenotypes, but we lack a quantitative understanding of associations among antibiotic and phage resistance phenotypes in natural and clinical populations. To address this, we measured resistance to various antibiotics and bacteriophages for 94 natural and clinical Escherichia coli isolates. We found several positive associations between resistance phenotypes across isolates. Associations were on average stronger for antibacterial agents of the same type (antibiotic-antibiotic or phage-phage) than different types (antibiotic-phage). Plasmid profiles and genetic knockouts suggested that such associations can result from both colocalization of resistance genes and pleiotropic effects of individual resistance mechanisms, including one case of antibiotic-phage cross-resistance. Antibiotic resistance was predicted by core genome phylogeny and plasmid profile, but phage resistance was predicted only by core genome phylogeny. Finally, we used observed associations to predict genes involved in a previously uncharacterized phage resistance mechanism, which we verified using experimental evolution. Our data suggest that susceptibility to phages and antibiotics are evolving largely independently, and unlike in experiments with lab strains, negative associations between antibiotic resistance phenotypes in nature are rare. This is relevant for treatment scenarios where bacteria encounter multiple antibacterial agents.IMPORTANCE Rising antibiotic resistance is making it harder to treat bacterial infections. Whether resistance to a given antibiotic spreads or declines is influenced by whether it is associated with altered susceptibility to other antibiotics or other stressors that bacteria encounter in nature, such as bacteriophages (viruses that infect bacteria). We used natural and clinical isolates of Escherichia coli, an abundant species and key pathogen, to characterize associations among resistance phenotypes to various antibiotics and bacteriophages. We found associations between some resistance phenotypes, and in contrast to past work with laboratory strains, they were exclusively positive. Analysis of bacterial genome sequences and horizontally transferred genetic elements (plasmids) helped to explain this, as well as our finding that there was no overall association between antibiotic resistance and bacteriophage resistance profiles across isolates. This improves our understanding of resistance evolution in nature, potentially informing new rational therapies that combine different antibacterials, including bacteriophages.201729089428
4380120.9998Comparative genome analysis of ciprofloxacin-resistant Pseudomonas aeruginosa reveals genes within newly identified high variability regions associated with drug resistance development. The alarming rise of ciprofloxacin-resistant Pseudomonas aeruginosa has been reported in several clinical studies. Though the mutation of resistance genes and their role in drug resistance has been researched, the process by which the bacterium acquires high-level resistance is still not well understood. How does the genomic evolution of P. aeruginosa affect resistance development? Could the exposure of antibiotics to the bacteria enrich genomic variants that lead to the development of resistance, and if so, how are these variants distributed through the genome? To answer these questions, we performed 454 pyrosequencing and a whole genome analysis both before and after exposure to ciprofloxacin. The comparative sequence data revealed 93 unique resistance strain variation sites, which included a mutation in the DNA gyrase subunit A gene. We generated variation-distribution maps comparing the wild and resistant types, and isolated 19 candidates from three discrete resistance-associated high variability regions that had available transposon mutants, to perform a ciprofloxacin exposure assay. Of these region candidates with transposon disruptions, 79% (15/19) showed a reduction in the ability to gain high-level resistance, suggesting that genes within these high variability regions might enrich for certain functions associated with resistance development.201323808957
4631130.9998Genome Analysis of an Enterococcal Prophage, Entfac.MY. BACKGROUND: Bacteriophages are bacterial parasites. Unlike lytic bacteriophages, lysogenic bacteriophages do not multiply immediately after entering the host cells and may integrate their genomes into the bacterial genomes as prophages. Prophages can include various phenotypic and genotypic effects on the host bacteria. Enterococcus spp. are Gram-positive bacteria that cause infections in humans and animals. In recent decades, these bacteria have become resistant to various antimicrobials, including vancomycin. The aim of this study was to analyze genome of an enterococcal prophage. METHODS: In this study, Enterococcus faecium EntfacYE was isolated from biological samples and its genome was analyzed using next-generation sequencing method. RESULTS: Overall, 254 prophage genes were identified in the bacterial genome. The prophage included 39 housekeeping, 41 replication and regulation, 80 structural and packaging, and 48 lysis genes. Moreover, 46 genes with unknown functions were identified. All genes were annotated in DNA Data Bank of Japan. CONCLUSION: In general, most prophage genes were linked to packaging and structure (31.5%) gene group. However, genes with unknown functions included a high proportion (18.11%), which indicated necessity of further analyses. Genomic analysis of the prophages can be effective in better understanding of their roles in development of bacterial resistance to antibiotics. Moreover, identification and study of prophages can help researchers develop genetic engineering tools and novel infection therapies.202236061127
4390140.9998Integrated Co-functional Network Analysis on the Resistance and Virulence Features in Acinetobacter baumannii. Acinetobacter baumannii is one of the most troublesome bacterial pathogens that pose major public health threats due to its rapidly increasing drug resistance property. It is not only derived from clinic setting but also emerges from aquaculture as a fish pathogen, which could pass the resistant genes in the food chain. Understanding the mechanism of antibiotic resistance development and pathogenesis will aid our battle with the infections caused by A. baumannii. In this study, we constructed a co-functional network by integrating multiple sources of data from A. baumannii and then used the k-shell decomposition to analyze the co-functional network. We found that genes involving in basic cellular physiological function, including genes for antibiotic resistance, tended to have high k-shell values and locate in the internal layer of our network. In contrast, the non-essential genes, such as genes associated with virulence, tended to have lower k-shell values and locate in the external layer. This finding allows us to fish out the potential antibiotic resistance factors and virulence factors. In addition, we constructed an online platform ABviresDB (https://acba.shinyapps.io/ABviresDB/) for visualization of the network and features of each gene in A. baumannii. The network analysis in this study will not only aid the study on A. baumannii but also could be referenced for the research of antibiotic resistance and pathogenesis in other bacteria.202033224132
9920150.9998Designing antibiotic cycling strategies by determining and understanding local adaptive landscapes. The evolution of antibiotic resistance among bacteria threatens our continued ability to treat infectious diseases. The need for sustainable strategies to cure bacterial infections has never been greater. So far, all attempts to restore susceptibility after resistance has arisen have been unsuccessful, including restrictions on prescribing [1] and antibiotic cycling [2], [3]. Part of the problem may be that those efforts have implemented different classes of unrelated antibiotics, and relied on removal of resistance by random loss of resistance genes from bacterial populations (drift). Here, we show that alternating structurally similar antibiotics can restore susceptibility to antibiotics after resistance has evolved. We found that the resistance phenotypes conferred by variant alleles of the resistance gene encoding the TEM β-lactamase (bla(TEM)) varied greatly among 15 different β-lactam antibiotics. We captured those differences by characterizing complete adaptive landscapes for the resistance alleles bla(TEM-50) and bla(TEM-85), each of which differs from its ancestor bla(TEM-1) by four mutations. We identified pathways through those landscapes where selection for increased resistance moved in a repeating cycle among a limited set of alleles as antibiotics were alternated. Our results showed that susceptibility to antibiotics can be sustainably renewed by cycling structurally similar antibiotics. We anticipate that these results may provide a conceptual framework for managing antibiotic resistance. This approach may also guide sustainable cycling of the drugs used to treat malaria and HIV.201323418506
4393160.9998Mechanisms of Staphylococcus aureus Antibiotics Resistance Revealed by Adaptive Laboratory Evolution. Infection caused by drug-resistant Staphylococcus aureus is a serious public health and veterinary concern. Lack of a comprehensive understanding of the mechanisms underlying the emergence of drug-resistant strains, it makes S. aureus one of the most intractable pathogenic bacteria. To identify mutations that confer resistance to anti-S. aureus drugs, we established a laboratory-based adaptive evolution system and performed 10 rounds of evolution experiments against 15 clinically used antibiotics. We discovered a panel of known and novel resistance-associated sites after performing whole-genome sequencing. Furthermore, we found that the resistance evolved at distinct rates. For example, streptomycin, rifampicin, fusidic acid and novobiocin all developed significant resistance quickly in the second round of evolution. Intriguingly, the cross-resistance experiment reveals that nearly all drug-resistant strains have varying degrees of increased sensitivity to fusidic acid, pointing to a novel approach to battle AMR. In addition, the in silico docking analysis shows that the evolved mutants affect the interaction of rifampcin-rpoB, as well as the novobiocin-gyrB. Moreover, for the genes we got in the laboratory evolution, mutant genes of clinical isolates of human had significant differences from the environmental isolates and animal isolates. We believe that the strategy and data set in this research will be helpful for battling AMR issue of S. aureus, and adaptable to other pathogenic microbes.202539762552
5110170.9998Surveillance of carbapenem-resistant organisms using next-generation sequencing. The genomic data generated from next-generation sequencing (NGS) provides nucleotide-level resolution of bacterial genomes which is critical for disease surveillance and the implementation of prevention strategies to interrupt the spread of antimicrobial resistance (AMR) bacteria. Infection with AMR bacteria, including Gram-negative Carbapenem-Resistant Organisms (CRO), may be acute and recurrent-once they have colonized a patient, they are notoriously difficult to eradicate. Through phylogenetic tools that assess the single nucleotide polymorphisms (SNPs) within a pathogen genome dataset, public health scientists can estimate the genetic identity between isolates. This information is used as an epidemiologic proxy of a putative outbreak. Pathogens with minimal to no differences in SNPs are likely to be the same strain attributable to a common source or transmission between cases. These genomic comparisons enhance public health response by prompting targeted intervention and infection control measures. This methodology overview demonstrates the utility of phenotypic and molecular assays, antimicrobial susceptibility testing (AST), NGS, publicly available genomics databases, and open-source bioinformatics pipelines for a tiered workflow to detect resistance genes and potential clusters of illness. These methods, when used in combination, facilitate a genomic surveillance workflow for detecting potential AMR bacterial outbreaks to inform epidemiologic investigations. Use of this workflow helps to target and focus epidemiologic resources to the cases with the highest likelihood of being related.202337255756
4627180.9998Antibiotic resistance mechanisms of Myroides sp. Bacteria of the genus Myroides (Myroides spp.) are rare opportunistic pathogens. Myroides sp. infections have been reported mainly in China. Myroides sp. is highly resistant to most available antibiotics, but the resistance mechanisms are not fully elucidated. Current strain identification methods based on biochemical traits are unable to identify strains accurately at the species level. While 16S ribosomal RNA (rRNA) gene sequencing can accurately achieve this, it fails to give information on the status and mechanisms of antibiotic resistance, because the 16S rRNA sequence contains no information on resistance genes, resistance islands or enzymes. We hypothesized that obtaining the whole genome sequence of Myroides sp., using next generation sequencing methods, would help to clarify the mechanisms of pathogenesis and antibiotic resistance, and guide antibiotic selection to treat Myroides sp. infections. As Myroides sp. can survive in hospitals and the environment, there is a risk of nosocomial infections and pandemics. For better management of Myroides sp. infections, it is imperative to apply next generation sequencing technologies to clarify the antibiotic resistance mechanisms in these bacteria.201626984839
4265190.9998Bacteriophages as vehicles of the resistome in cystic fibrosis. Environmental microbial communities and human microbiota represent a huge reservoir of mobilizable genes, the 'mobilome', including a pool of genes encoding antimicrobial resistance, the 'resistome'. Whole-genome sequencing of bacterial genomes from cystic fibrosis (CF) patients has demonstrated that bacteriophages contribute significantly to bacterial genome alterations, and metagenomic analysis of respiratory tract DNA viral communities has revealed the presence of genes encoding antimicrobial resistance in bacteriophages of CF patients. CF airways should now be considered as the site of complex microbiota, where bacteriophages are vehicles for the adaptation of bacteria to this specific environment and for the emergence and selection of multidrug-resistant bacteria with chimeric repertoires. As phages are already known to be mobilized during chronic infection of the lungs of patients with CF, it seems particularly important to improve the understanding of the mechanisms of phage induction to prevent the spread of virulence and/or antimicrobial resistance determinants within the CF population as well as in the community. Such a modern point of view may be a seminal reflection for clinical practice in the future since current antimicrobial therapy guidelines in the context of CF may lead to the emergence of genes encoding antimicrobial resistance.201121816766