Identification of virulence factors and antibiotic

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Identification of virulence factors and antibiotic resistance markers using bacterial genomics

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Sofiane Bakour1, Senthil Alias Sankar1, Jaishriram Rathored1, Philippe Biagini2, Didier Raoult1 & Pierre-Edouard Fournier*,1

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In recent years, the number of multidrug-resistant bacteria has increased rapidly and several epidemics were signaled in different regions of the world. Faced with this situation that presents a major global public health concern, the development and the use of new and rapid technologies is more than urgent. The use of the next-generation sequencing platforms by microbiologists and infectious disease specialists has allowed great progress in the medical field. Here, we review the usefulness of whole-genome sequencing for the detection of virulence and antibiotic resistance associated genes.

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First draft submitted: 14 August 2015; Accepted for publication: 7 December 2015; Published online: 4 March 2016

Keywords

• antibiotic resistance • bacteria • virulence • whole-genome

sequencing

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Since the sequencing of the first bacterial virus by Sanger et al. in 1977 [1] , scientists have sought to improve sequencing technologies and developed new methods and sequencers enabling highthroughput sequencing. The first bacterial genome, from Haemophilus influenzae [2] , was sequenced in 1995, soon followed by that of Mycoplasma genitalium [3] . Since then, more than 60,000 sequencing projects have been made public (Genomes OnLine Database [4]). These include more than 49,000 bacterial genome projects, the sequences of 61.2% of which are available in public databases. Thanks to the various next-generation sequencing (NGS) technologies and platforms that are commercially available, including the Miseq and Hiseq (Illumina), GS (Roche-454), Ion Torrent PGM (Life Technologies), Minion (Oxford Nanopore Technologies) and PacBio (Pacific Biosciences) (Figure 1) , genome sequencing had a significant impact on clinical microbiology by enabling the development of various sequence-based tools [5] , notably molecular detection, serological and genotyping assays [6,7] . In addition, by providing access to the complete gene repertoire of a strain, NGS also provides a unique way to decipher the virulence potential and predict the antibiotic resistance pattern of clinical isolates (Figure 1) . Identification and characterization of virulence factors, notably toxins, and antibiotic resistance markers of pathogens are crucial in understanding bacterial pathogenesis and their interactions with the host, and in the development of novel drugs, vaccines and molecular diagnostic tools. In addition, detecting such virulence or resistance markers may help improving outbreak monitoring and therapeutic management. For example, Shiga toxin production in Escherichia coli is induced by β-lactams, sulfonamides and fluoroquinolones [8] and Panton–Valentine leukocidin is induced in Staphylococcus aureus by oxacillin [9] .

Unité de recherche sur les maladies infectieuses et tropicales émergentes (URMITE), UM 63, CNRS 7278, IRD 198, INSERM 1095, IHU Méditerranée Infection, Faculté de Médecine et de Pharmacie, Aix-Marseille-Université, Marseille, France 2 UMR CNRS 7268 Equipe “Emergence et coévolution virale,” Etablissement Français du Sang Alpes-Méditerranée et Aix-Marseille Université, 27 Boulevard Jean Moulin, 13005 Marseille *Author for correspondence: Tel.: +33 491 385 517; Fax: +33 491 387 772; [email protected] 1

10.2217/fmb.15.149 © 2016 Future Medicine Ltd

Future Microbiol. (Epub ahead of print)

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ISSN 1746-0913

Review Bakour, Sankar, Rathored, Biagini, Raoult & Fournier

Miseq and Hiseq (Illumina)

GS (Roche-454)

WGS

Minion (Oxford Nanopore Technologies)

PacBio (Pacific Biosciences)

Culture

Ion Torrent PGM (Life technologies)

Comparison of virulent/or resistant and susceptible strains

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Identification of MALDI-TOF

Identification of LGT-acquired genes

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Search for homology with known virulence/ or resistence genes

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DNA extraction

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Figure 1. Laboratory workflow for the identification of virulence and antibiotic resistance factors of bacterial pathogens. LGT: Lateral gene transfer; MALDI-TOF: Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry; WGS: Wholegenome sequencing.

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Herein, we reviewed the methods and output of whole-genome sequencing (WGS) for identifying virulence factors and antibiotic resistance markers. Deciphering the virulence repertoire of a bacterial strain

●●Bacterial virulence factors

Bacteria associate and co-evolve with other living organisms, exerting beneficial and/or harmful effects on their hosts that are dependent on various factors. Bacterial virulence factors enable bacteria to survive the selection pressure and adapt to new pathogenic niches. These genes encode a wide array of molecules interacting directly with eukaryotic cells, ranging from the membrane to secretory proteins (toxins, exoenzymes, type I to VI secretion systems), biofilm-forming proteins, siderophores

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as well as polysaccharides that compose the capsules and exhibit anti-phagocytic properties [10] . Surface-exposed proteins have roles in adhesion, colonization, invasion and/or resistance to antimicrobials [11] . In addition, Gram-negative bacteria have lipopolysaccharide (LPS), an outer cell membrane component also named endotoxin. These equipment enable bacterial pathogens to establish infection by invading cells, overcoming host defense mechanism and proliferate [12] . Bacterial virulence factors are often acquired by horizontal transfer and are thus localized on specific genomic loci, referred to as pathogenicity islands [13,14] . ●●Identification of virulence factors

Prior to genome sequencing, identification of virulence factors successively relied on biochemical

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Genomic prediction of virulence & antibiotic resistance.

Homology search with known virulence genes

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Screening specialized databases is a convenient way of identifying virulence genes in a genome. Several databases are dedicated to virulence factors (Table 1) : the virulence factor database (VFDB) is an integrated and comprehensive database of virulence factors for bacterial pathogens that contains 2505 virulence factors on 28 July 2015 [20] . Using VFDB, the genome of Acinetobacter baumannii strain LCA-4 had 615 hits in VFDB, including a heme utilization cluster [21] ; the Victors database [22] contains 5173 virulence factors from 193 different pathogens including pathogenic bacteria such as Mycobacterium tuberculosis, Brucella spp., Yersinia pestis and Streptococcus spp., fungi and viruses; VirulentPred and Virulent-GO are used to predict bacterial virulent protein sequences using five kinds of features and gene ontology, respectively [23,24] ; PathogenFinder differentiates virulent from nonvirulent strains using both protein and genome sequences [25] ; Virulence Searcher is based on the PRINTS database that contains fingerprints from well characterized proteins [26] . Virulence Searcher enabled the detection of 91 virulence factors in S. aureus MW2 [27] ; Virulence finder contains virulence genes from E. coli, Enterococcus sp. and S. aureus [17] . Other databases are limited to specific virulence factors such as toxins or adhesins. The T3db database includes >15,000 genes for 3673 toxins [28] ; BTXpred, the bacterial toxin

prediction server, is based on a set of 77 exotoxin and 73 endotoxin amino acid sequences [29] ; and DBETH, the database of bacterial exotoxins for human, contains 229 exotoxins from 26 bacterial genera [30] . SPAAN combines 105 compositional properties to identify adhesions [31] , whereas Virmugen DB includes virulent genes used to develop live attenuated vaccines [32] . In addition, several multi-criteria genome analysis tools enable detecting virulence factors. The MvirDB combines several databases including PRINTS and VFDB to identify virulence factors [44] ; the Pathosystems resource integration center (PATRIC) combines the VFDB, Victors and PATRIC VF databases to detect virulence factors and host–pathogen protein–protein interactions [45] ; and the PHI-base (pathogen–host interaction) database [49] can identify virulence factors and host–pathogen protein–protein interactions also enables detecting 2875 virulence genes and 4102 host–pathogen interactions [33] . Finally, in addition to the above-cited tools, several bioinformatic tools and websites are commonly used by scientists attempting to predict virulence or antibiotic resistance markers in a genome, including the RAST server (Rapid Annotation using Subsystems Technology) [46] , the Clusters of Orthologous Groups database [47] and the Basic Local Alignment Search Tool (BLAST) of the National Center for Biotechnology Information [48] .

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approaches, or systematic molecular screening of a panel of genes demonstrated to play a role in pathogenesis using molecular cloning and/or mutagenesis [15,16] . In biochemical approaches, bacterial toxins or other virulence factors are purified and then their pathogenic effects are studied in vivo or in vitro. In molecular approaches, virulence genes are analyzed by mutagenesis and/or cloning and expression in nonpathogenic, often E. coli strains. Over the past two decades, thanks to genomics combined to functional analyses (transcriptomics and proteomics), the rate of virulence factor discovery has increased dramatically. Bacterial virulence factors in genomes may be identified by homology search with known virulence genes [17] , by comparing strains with various levels of virulence [18] , or by analysis of horizontally acquired genes [19] .

Review


Identifying virulence genes among horizontally transferred genes

Horizontal gene transfer (HGT) is the most important mechanism of gene transfer and prokaryotic evolution [50] . HGT may occur between closely or distantly related species, by various mechanisms including natural transformation, transduction and conjugation. In genomes, genes acquired by HGT are often associated to genetic markers of the mobile genetic elements (MGEs) with which they were transferred [13,14] . Genomic fragments that contain HGT-acquired material are often referred to as genomic islands (GEIs). Those that contain virulence genes are named pathogenicity islands (PAIs) [51–53] . Detecting PAIs in a bacterial genome enables detecting all virulence genes that it contains, including those that are not expressed by the bacterium. PAIs often differ in base composition and codon usage when compared with the core genome. This

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Review Bakour, Sankar, Rathored, Biagini, Raoult & Fournier Table 1. Genomic prediction of virulence and antibiotic resistance. Usefulness of the database/numbers and types of genes included

Website

Ref.

29,746 virulence factor-related genes on 18 November 2015; also contains antivirulence genes Contains 5173 virulence factors from 193 different pathogens Predicts bacterial virulent protein sequences using five kinds of features Predicts bacterial virulent protein sequences using gene ontology Differentiates virulent from nonvirulent strains using both protein and genome sequences Predicts potential virulence factors in unannotated genomes; it is based on the PRINTS database that contains fingerprints from well-characterized proteins. This database enables detection of toxins, proteins associated with resistance to antibiotics, membrane-associated proteins, ranging from adhesins and invasins to translocaters, proteases, siderophores, kinases and miscellaneous virulence factors Contains virulence genes from Escherichia coli, Enterococcus sp. and Staphylococcus aureus Contains >15,000 genes for 3673 toxins including pollutants, pesticides, drugs and food toxins Predicts bacterial toxins and their functions from primary amino acid sequences, based on a set of 77 exotoxin and 73 endotoxin amino acid sequences Database of sequences, structures, interaction networks and analytical results for 229 exotoxins, from 26 different human pathogenic bacterial genus Combines 105 compositional properties to identify adhesions and adhesin-like proteins in species belonging to a wide phylogenetic spectrum Database of virulent genes used for development of live attenuated vaccines, it includes 225 Virmugens on 10 November 2015 Identifies virulence factors and host–pathogen protein–protein interactions and also enables detecting 2875 virulence genes and 4102 host–pathogen interactions

www.mgc.ac.cn/VFs/main.htm

Virulence gene detection Virulence factor database (VFDB) Victors database VirulentPred Virulent-GO PathogenFinder

http://203.92.44.117/virulent/ submit.html

[23] [24]

http://cge.cbs.dtu.dk/services/ PathogenFinder/ http://www.hpa-bioinfotools. org.uk/pise/virfactfind_small. html

[25] [27]

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Virulence finder Toxin and Toxin Target Database (T3DB) Bacterial toxin prediction server (BTXpred)

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Database of bacterial exotoxins for human (DBETH)

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SPAAN

Virmugen DB

www.phidias.us/victors/

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Virulence Searcher

[20]

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Pathogen-host interaction database (PHI-base)

http://cge.cbs.dtu.dk/services/ VirulenceFinder/ www.t3db.ca/

[17] [28]

www.imtech.res.in/raghava/ btxpred

[29]

www.hpppi.iicb.res.in/btox/

[30]

https://sourceforge.net/ projects/adhesin/files/SPAAN/

[31]

www.violinet.org/virmugendb/

[32]

www.phi-base.org/

[33]

Antibiotic resistance detection

Antibiotic Resistance Genes Online (ARGO) Antibiotic Resistance DataBase (ARDB) Repository of Antibiotic resistance Cassettes (RAC) Resistance Gene Finder (ResFinder) Comprehensive Antibiotic Resistance Database (CARD)

Contains 555 β -lactamases and 115 vancomycin resistance genes Contains 23,137 resistance genes from 1737 bacterial species Contains 389 resistance cassettes

http://bioinformatics.org/argo/ beta/antibioticresistance.php [34] http://ardb.cbcb.umd.edu/ http://rac.aihi.mq.edu.au/rac/ [35]

Contains more than 1800 different resistance genes from 12 different antimicrobial classes Contains 4221 genes responsible for resistance to many antibiotic classes such as β-lactams, aminoglycosides, tetracyclines, rifampin, macrolides, fluoroquinolones and sulfonamides, and with antibiotic efflux

www.genomicepidemiology. org http://arpcard.mcmaster.ca

[36] [37]

PAI: Pathogenicity island; PATRIC VF: Pathosystems resource integration center virulence factor database; REI: Resistance island; VFDB: Virulence factor database.

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Review

Table 1. Genomic prediction of virulence and antibiotic resistance (cont.). Usefulness of the database/numbers and types of genes included Antibiotic Resistance Gene Annotation Contains 1689 genes causing resistance to different (ARG-ANNOT) antibiotic classes (β-lactams, aminoglycosides, fosfomycin, fluoroquinolones, glycopeptides, macrolide-lincosamidestreptogramin, phenicols, rifampin, sulfonamides, tetracyclines and trimethoprim)

Website

Ref.

http://en.mediterraneeinfection.com/article. php?laref=283&titre=argannot-

[38]

www.paidb.re.kr

[39]

http://genomes.urv.es/HGTDB/

[40]

Genomic island detection Contains 88 types of REIs (108 accession numbers) and 223 types of PAIs (1331 accession numbers) predicted from 2673 prokaryotic genomes The Horizontal Gene Transfer DataBase The database includes the analysis of 479 organism (HGT-DB) genomes; it includes statistical parameters such as G+C content, codon and amino-acid usage as well as information about which genes deviate in these parameters for prokaryotic complete genome IslandViewer database Screen any genome against a database of 18,919 virulence genes from 1277 genomes from bacterial pathogens PredictBias database Enables searching virulence factors from 213 protein families Contains 3927 islands in 1302 genomes

Multi criteria searching tools MvirDB

General purpose tools

Contains toxins, virulence factors and antibiotic resistance http://mvirdb.llnl.gov/ genes Combines the VFDB, Victors and PATRIC VF databases to www.patricbrc.org/portal/ detect virulence factors and host–pathogen protein–protein portal/patric/HPITool interactions

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Pathosystems resource integration center (PATRIC)

For annotating complete or nearly complete bacterial and archaeal genomes The database enables to have an expanded microbial genome coverage and improved protein family annotation Finds regions of local similarity between sequences. The tool compares nucleotide or protein sequences to sequence databases and calculates the statistical significance of matches

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Rapid Annotation using Subsystems Technology (RAST) Clusters of Orthologous Groups of proteins (COGs) Basic Local Alignment Search Tool (BLAST)

http://pathogenomics.sfu.ca/ islandviewer www.davvbiotech.res.in/ PredictBias http://bioinformatics.sandia. gov/islander/

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Islander database

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Pathogenicity Island Database (PAIDB)

[41] [42] [43]

[44] [45]

http://rast.nmpdr.org/

[46]

www.ncbi.nlm.nih.gov/COG/

[47]

http://blast.ncbi.nlm.nih.gov/ Blast.cgi

[48]

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PAI: Pathogenicity island; PATRIC VF: Pathosystems resource integration center virulence factor database; REI: Resistance island; VFDB: Virulence factor database.

specificity enables their detection using various methods such as phylogenetic analyses (incongruence between gene trees and species tree) or study of the G+C content and codon or amino acid usage variation along the chromosome. Several databases have been developed that may be used to identify putative PAIs. The Pathogenicity Island Database (PAIDB) explores and analyses PAIs and REIs [39] . In 2007, Yoon et al. published the first version of PAIDB that contained 112 types of Pathogenicity Islands (PAIs) and 889 GenBank accession numbers virulence loci described in 497 pathogenic bacterial strains [54] . Currently,


PAIDB contains sequences from 223 bacterial PAIs [39] . The Horizontal Gene Transfer DataBase (HGT-DB) details the HGT events from 479 organisms genomes [40] ; the IslandViewer database proposes a set of bioinformatic tools to screen any genome against a database of 18,919 virulence genes from 1277 genomes from bacterial pathogens [41] ; the PredictBias database enables searching virulence factors from 213 protein families [42] ; and the Islander database contains 3927 islands in 1302 genomes that were detected by identifying their integration into tRNA/tmRNA genes [43] .


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Review Bakour, Sankar, Rathored, Biagini, Raoult & Fournier Comparing the genomes from virulent and avirulent strains

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Comparative genomic analysis of strains from the same species or of closely related species exhibiting different levels of pathogenicity between virulent and nonvirulent strains may enable to identify, among differentially present genes, those that are involved in virulence. The comparison of a pathogenic strain of E. coli O157:H7 and a nonpathogenic strain of E. coli K-12 permitted the identification of 1387 new genes that were specific of the former strain, several of which encoded known or putative virulence-associated proteins [18] . In a genomic comparison of Enterococcus faecium strains of diverse virulence, Van Schaik et al. identified the esp gene, involved in urinary tract infections and biofilm formation, on a 64–104 kb PAI exclusively present on pathogenic strains [55] .

Neisseria meningitidis, single-sequence repeat tracts and site-specific recombinations control capsular type, LPS structure, pilin diversity and outer membrane protein expression [59] . The above-mentioned limitations demonstrate that functional analyses (transcriptomics and proteomics) should be coupled to genomic analyses in order to identify formally the role of virulence factors; are all virulence mechanisms elucidated? Contingent to virulence factors, some bacterial pathogens possess antivirulence genes that modulate virulence gene expression or interfere with environmental factors [60] . This phenomenon, initially discovered in Shigella species, has also been confirmed in Francisella tularensis, Salmonella species and Yersinia pestis [61–63] . Therefore, virulence may not only be explained by gene acquisition but also gene loss. Genome reduction has been suspected as playing a role in pathogen evolution by causing increased virulence, especially in bacterial species with small genomes. As an example, the genomic comparison of Rickettsia species demonstrated that the most virulent species, R. prowazekii, the agent of epidemic typhus, had the smallest genome, without any specific gene [64] . This result suggested that virulence in Rickettsia species was mostly associated with gene loss, notably regulation genes. Similar conclusions were drawn by Georgiades and Raoult regarding other of the most dangerous epidemic bacteria [65] . Bliven and Maurelli suggested that the increased virulence associated to genome reduction was, at least partially, explained by the loss of antivirulence genes [60] . To date, more than a dozen genes, involved in metabolism, biofilm synthesis, LPS modification or host vasoconstriction, have been demonstrated to play a role in antivirulence [60] . Looking for these genes in a genome and, when present, studying their sequence to determine whether they are degraded, might also explain its increased pathogenicity.

●●Limitations of in silico prediction of

virulence prediction

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Although predicting virulence factors in a genome is a valuable strategy, it suffers from several drawbacks: virulence factors may also be found in nonpathogenic strains. The expression of virulence genes may depend on several environmental factors, and so-called ‘virulence factors’ may not confer virulence. By screening the genomes from 51 bacterial pathogens and 278 nonpathogens against the VFDB database, Niu et al. observed that 68.8% of 1988 virulence genes were present in both pathogens and nonpathogens [56] ; the ability of a pathogenic bacterium to cause disease in a susceptible host may be determined by multiple virulence factors acting individually or in combination, that may vary at different stages of infection; a genome sequence may not systematically predict the proteome of a bacterial strain in a given condition or strain. Cassat et al. demonstrated that S. aureus strain UAMS-1 expressed more surface proteins than the avirulent strain RN6390 [57] . Dubern et al. observed that the virulence of Pseudomonas aeruginosa varied depending on the host (Drosophila melanobacter, Caenorhabditis elegans, human cell lines, mice) [58] . Therefore, the presence of a virulence factor in the genome of a pathogenic strain does not obligately imply that this gene plays a role in this phenotype; virulence may be influenced by dynamic changes in the genome during infection (hypermutable sequenced). In

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Detection of antibiotic resistance markers ●●Emergence of antibiotic resistance

Which came first, antibiotics or resistance? After the discovery of penicillin by Fleming in 1928, several years before the introduction of penicillin as a therapeutic, the members of the penicillin discovery team identified a bacterial penicillinase [66] . This finding and the detection of resistance determinants from ancient



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the determination of antibiotic resistance in bacteria mostly relied on various methods to infer the minimum inhibitory concentrations (MICs) of antimicrobial drugs. In recent years, other methods have been developed, including matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry [81,82] and rapid biochemical enzyme detection (mainly carbapenemases) methods based on antibiotic hydrolysis such as the Carba NP [83,84] , CarbaAcineto NP [85] and Blue Carba tests [86] . In contrast, the use of WGS coupled to the development of several bioinformatic tools provided a unique access to the full catalogue of antimicrobial resistance genes mechanisms. In addition, WGS can be useful: for fastidious micro-organisms for which phenotypic assays are not easily applicable, in particular those which develop resistance under prolonged antibiotic therapy, such as Coxiella burnetii, Tropheryma whipplei and M. tuberculosis [87] ; and for resistance mechanisms that are undetected using phenotypic methods such as mecC in S. aureus or doxycycline resistance in C. burnetii [74,88] . As detailed below, the strategy for detecting resistance markers in a genome is very similar to that aimed at predicting virulence, and is based on either searching for homology with known resistance genes, identifying resistance markers among LGT-acquired genes or comparing the genomes of susceptible and resistant strains.

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or environmental sources reported in recent studies demonstrate that antibiotic resistanceencoding genes pre-existed the introduction of antibiotics [6,67–69] . However, the high selection pressure exerted by the frequent and inadequate use of antibiotics has greatly contributed to the worldwide antibiotic resistance, showing the strong power of adaptation of bacteria [70] . Over the past years, the emergence of multidrug-resistant (MDR) bacteria has become a major public health concern worldwide. In Europe, it was estimated that 400,000 infections and 25,000 deaths are directly caused by MDR bacteria, whereas in the USA, they are responsible for 20 billion USDs in excess care costs and 35 billion USDs in societal costs [71] . In addition, during the same period, the number of new antibiotic molecules has decreased. Currently, some pathogenic MDR bacteria are resistant to all antibiotics in use in therapy, including last-resort antibiotics such as imipenem and colistin [7,72,73] . Bacterial infections are caused by several pathogenic MDR bacteria, notably methicillin-resistant S. aureus (MRSA) [74] , M. tuberculosis [75] , Enterobacteriaceae, Acinetobacter and Pseudomonas species [76] . Intra-hospital outbreaks caused by MDR strains are regularly reported [66,72] . Therefore, deciphering the resistome of a bacterium became mandatory in order to understand resistance mechanisms, predict its resistance phenotype, enable effective infection control, optimize the antibiotic therapy and patient management, design PCR assays for detecting resistance causing genes or mutations, identify targets for novel drugs and prior to using bacteria that may be used as probiotics [7,77] . Several mechanisms have been described through which bacteria become resistant to antibiotics: production of natural or acquired enzymes that metabolize the antibiotic; producing antibiotic-modifying enzymes; modifications of the antibiotic target that prevent its binding; membrane impermeability and overexpression of efflux systems [72,78–80] . The acquired mechanisms of resistance are generally spread by MGEs, notably plasmids, transposons and integrons, and are identifiable by genome sequencing.

Review

●●Predicting antibiotic resistance from a

genomic sequence

Since the first phenotypic tests for antimicrobial susceptibility were developed by Fleming [36] ,


Search for homology with known resistance genes

Several databases dedicated to antibiotic resistance markers are currently available, with many bioinformatics tools created and available online, including the Antibiotic Resistance Genes Online (ARGO) [89] , Antibiotic Resistance DataBase (ARDB) [34] , Repository of Antibiotic resistance Cassettes (RAC) [35] , Resistance Gene Finder (ResFinder) [36] , Comprehensive Antibiotic Resistance Database (CARD) [37] and Antibiotic Resistance Gene Annotation (ARG-ANNOT) [38] (Table 1) . In 2005, Scaria et al. developed ARGO, an online database that provides information on β-lactam and vancomycin-causing resistance genes [89] . This database contains 555 β-lactamase and 115 vancomycin resistancecoding gene sequences [89] . Three years later, Liu and Pop created ARDB, a manually curated


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Review Bakour, Sankar, Rathored, Biagini, Raoult & Fournier Searching horizontally acquired genes

Genomic islands that contain mostly antimicrobial resistance genes are named resistance islands (REIs). Detecting REIs in a bacterial genome enables detecting all resistance genes that it contains, including those that are not expressed by the bacterium. As REIs confer simultaneous resistance to several antibiotics, they promote the emergence of MDR pathogens such as S. typhimurium in which the Salmonella genomic island 1 is associated to an MDR form [90] . REIs have also been described in other species, including Shigella flexneri, Vibrio cholerae and S. aureus [91] . By comparing the genome sequences of A. baumannii strains ACICU and ATCC 17978, and that of Acinetobacter baylyi strain ADP1, Iacono et al. identified an REI named AbaR2 in strain ACICU. In addition, 36 putative alien islands (pAs) were found, 15 of which encoded drug resistance genes [92] . The PAIDB database contains both PAIs and REIs [39] . The latter GIs were added to PAIDB in 2014 [39] . This new version contains 88 types of REIs (108 accession numbers) and 223 types of PAIs (1331 accession numbers) predicted from 2673 prokaryotic genomes.

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database aimed at centralizing all information on antibiotic resistance in a compendium, at identifying and characterizing new genes and at facilitating the consistent annotation of resistance information in newly sequenced organisms [34] . In addition, ARDB may enable the identification of mutational resistance for several antibiotics [34] . The ARDB database contains resistance information for 23,137 genes from 1737 bacterial species. Among 1221 genes retrieved from the ARDB database and responsible for resistance against anti-M. tuberculosis drugs, Joshi et al. identified 53 genes that were involved in resistance to both first- and second-line antibiotics [75] . The RAC database, developed in 2011 [35] , allows researchers to screen genomes for antibiotic resistance cassettes using the Attacca annotation software. It contains 389 resistance cassettes [35] . In 2012, Zankari et al. developed ResFinder, a web-based application that uses BLAST for identification of acquired antimicrobial resistance genes in whole-genome data [36] . Briefly, this application can detect the presence of more than 1800 different resistance genes from 12 different antimicrobial classes from short sequence reads, pre-assembled, partial or complete genomes [36] . In 2013, McArthur et al. developed CARD [37] , a tool combining a large antibiotic resistance gene database of 4221 genes, the Antibiotic Resistance Ontology tool and the Resistance Gene Identifier (RGI) that can identify antibiotic resistance genes from partial or WGS data [37] . The analysis of the whole genome of A. baumannii strain TCDC-AB0715 using CARD enabled the identification of 64 open reading frames in relationship with resistance to many antibiotic classes [37] . In 2013 also, Gupta et al. created ARG-ANNOT, a new, rapid and simple bioinformatic tool for the detection of known and putative new antibiotic resistance genes in bacterial genomes [38] . This tool enables analyzing sequences using a local BLAST program. The ARG-ANNOT database includes 1689 genes causing resistance to different antibiotic. The analysis of 178 A. baumannii and 20 S. aureus genomes using ARG-ANNOT enabled the detection of 2011 antibiotic resistance genes and point mutations in target genes known to be responsible for antibiotic resistance [38] . In addition to the above-mentioned tools, the MvirDB, in addition to virulence factors, also contains sequences from all publicly available antibiotic resistance genes [44] .

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Comparison of resistant & susceptible strains

In 2006, Fournier et al. compared the genomes of the MDR and epidemic A. baumannii strain AYE and the antibiotic susceptible, body louse-associated strain SDF [93,94] . The results showed that the AYE genome encoded at least 52 antimicrobial resistance genes, of which 45 clustered in a 8619 Kb REI named AbaR1. These genes were associated with resistance to several antibiotic classes. In 2014, Zhao et al. used WGS to explore the molecular mechanisms associated to resistance to sulphonamides or fluoroquinolones in Stenotrophomonas maltophilia strain WJ66 isolated from a patient in China. By comparing this genome to that of the susceptible strain K279a, the authors showed that both strains had highly similar genomes, except that strainWJ66 contained additional antibiotic resistance markers, including an amino acid substitution (Q83L) in fluoroquinolone target GyrA and an aadA2 gene in the resistance gene cassette [95] . ●●Genomics for the surveillance of MDR

bacteria outbreaks

In the past years, several studies showed the importance of WGS in understanding the



resistance prediction

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Although WGS may play an important role in predicting resistance to antibiotics, it suffers several drawbacks that limit its usefulness: in unfinished genomes, some MGEs, such as integrons and transposons that convey the antimicrobial resistance genes, may be located in contig gaps and thus could be missed; when the resistance depends on a combination of mechanisms, WGS may not reveal the real determinant of this resistance. As an example, in Enterobacteriaceae, carbapenem resistance may be caused by a combination of porin loss and ESBL enzymes but not by these mechanisms taken separately [98] . Furthermore, Ming-Feng Lin et al. showed recently that many important efflux pump systems such as AdeABC, AdeIJK and MacAB-TolC found in A. baumannii strains contribute to the resistance to certain antibiotics, especially tigecyclin [99] ; finally, the detection of resistance gene by WGS may not systematically predict the resistance phenotype as the incriminated gene(s) may not be expressed, or variably expressed depending on the strain. Recently, Gordon et al. demonstrated that the blaZ gene was not systematically expressed in S. aureus strains. Briefly, the authors sequenced and assembled the genomes of 501 unrelated S. aureus isolates, and compared their genomic resistome to the in vitro susceptibility testing against 12 antimicrobial agents. They observed that, despite the presence of the blaZ gene, more than 20 strains were susceptible to methicillin. The authors subsequently identified a novel blaZ frameshift mutation that was associated with a susceptible phenotype [100] .

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●●Limitations of WGS for antibiotic

important phyla, genomics has become an essential tool to decipher the virulence and resistance mechanisms of human pathogens. NGS has notably proven its value in providing a unique access to the full genetic repertoire of a strain, but may also be used to investigate outbreaks caused by pathogenic and MDR bacteria. However, several challenges remain, as a genome sequence may not systematically predict the phenotype of a strain, making functional studies indissociable from genomics; the choice of bioinformatic tools is crucial; improving and homogenizing genome annotation and enriching genome, virulence factor and antimicrobial resistance databases is mandatory; virulence and resistance often result from a combination of genomic factors as well as postgenomic modifications and detecting a gene in a genome may not systematically predict its expression. With a constantly decreasing cost and increasing rapidity, there is little doubt that in the coming years WGS will become even more widely used than today, and that databases will be enriched with thousands of genomes from the most virulent and resistant pathogens. It is also likely that the sequencing of isolates of unusual pathogenicity will be included routinely in the management of outbreaks or of patients suffering from severe infections. However, the presence of virulence factor associated genes in the genomes from nonpathogenic bacteria has raised concerns that predictions from in silico analyses might be, at least partially, misleading. Therefore, other parameters such as organism history, genome properties and organization of virulence factors will also have to be systematically taken into account. The development of high-throughput transcriptomics and proteomics will also contribute to understanding the virulence and resistance phenotypes of pathogens.

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transmission of pathogens during outbreaks and in the surveillance of antibiotic resistance spread. Indeed, in several studies, WGS was used to survey the spread of antibiotic-resistant strains. Koser et al. used NGS to distinguish between MRSA isolates associated and nonassociated with a putative outbreak in a neonatal intensive care unit [96] . Dettman demonstrated the intercontinental spread of MDR P. aeruginosa strain Liverpool in cystic fibrosis patients [97] .

Review

Conclusion & future perspective With almost 50,000 bacterial genome sequences available to date, covering the most


Financial & competing interests disclosure The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript.


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Review Bakour, Sankar, Rathored, Biagini, Raoult & Fournier Executive summary Next-generation sequencing technologies ●●

T hanks to next-generation sequencing technologies and the availability of high-throughput sequencing platforms, more than 60,000 sequencing projects have been made public including more than 45,000 bacterial genome projects.

Whole genome sequencing in routine microbiology ●●

enome sequencing has entered the microbiology laboratory by providing new diagnostic and genotypic tools as G well as a unique access to the full gene repertoire of bacterial pathogens.

Detection of virulence factors ●●

T he whole-genome sequencing strategy may be used to decipher the virulence repertoire of a bacterial strain by searching homology with known virulence genes in specialized databases, identifying virulence genes among horizontally transferred genes and comparing the genomes from virulent and avirulent strains.

Detection of antibiotic resistance markers

of

enome sequencing also constitutes a valuable technology to predict antibiotic resistance from a genomic sequence G using several bioinformatic tools and similar strategies to those employed for virulence markers.

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