Genomic Analysis of Bacterial Outbreaks

Genomic Analysis of Bacterial Outbreaks Leonor Sánchez-Busó (1,3), Iñaki Comas (1,2, 4), Beatriz Beamud (1), Neris García-González (1), Marta Pla-Díaz (1) and Fernando González-Candelas (1,2) (1) Unidad Mixta “Infección y Salud Pública” FISABIO/CSISP – Universidad de Valencia/Instituto Cavanilles de Biodiversidad y Biología Evolutiva. Valencia, Spain. (2) CIBER en Epidemiología y Salud Pública. Valencia, Spain. (3) Pathogen Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridgeshire, United Kingdom. (4) Instituto de Biomedicina, CSCIC, Valencia, Spain. Author for Correspondence: Prof. Fernando González Candelas Evolución y Salud – Instituto Cavanilles Biodiversidad y Biología Evolutiva Universidad de Valencia c/Catedrático José Beltrán, 2. 46980 Paterna (Valencia). Spain. Email: fernando.gonzalez@uv.es Phone: + 34 961925961 , +34 963543653 FAX: +34 963543670 1 Introduction Outbreaks of infectious diseases often produce social alarms. These can be very local or reach every corner of every village and city on Earth. But all they share a need for a quick control and remediation that ensures the safety of the population. The identification and control of the source of an outbreak becomes a health priority and many efforts are devoted to these activities in the first days and weeks after the detection and/or declaration of an outbreak (Mortimer, 2003). Outbreaks come in many shapes and flavors. For epidemiologists, an outbreak is simply an unusual increase in the prevalence of a disease in time and space. Hence, some outbreaks may be declared and last for years while others are reduced to a few days or weeks; similarly, there might be an outbreak in a school or nursing home, but we talked a few years ago about an epidemic outbreak of “swine influenza” (Fraser et al, 2009;General Directorate of Epidemiology et al, 2009) and the WHO and other health organizations are currently worried about the spread of Zika virus. In some cases, the spread of the infectious pathogen occurs in a series of successive infections from one host to another thus producing transmission chains or networks, depending on the topology of the resulting connections among infected persons. One of the first tasks when an outbreak is suspected is to establish the basic parameters for controlling it. This can depend on the detection of a source, and the application of actions that prevent it from spreading the pathogen, or the characterization of the vector, so it can be controlled with chemical or biological agents, or the identification of the hereditary factors that allow the pathogen eluding previous, successful treatments and originate nosocomial outbreaks of multi-resistant strains. The advent of faster and cheaper gene sequencing techniques lead to the first systematic and general proposal of using a universal typing scheme that was reproducible, cheap, objective and easily exchangeable among laboratories, known as MultiLocus Sequence Typing or MLST (Maiden et al, 1998). In this method, the nucleotide sequence of 6-7 loci is determined and used to derive an array of allele profiles in these loci. A new combination of allele profiles corresponds to a new sequence type which is uploaded to a webserver for easy access. Typing schemes, with detailed laboratory protocols, proficiency tests, and full information on identified sequences types are available for tens of bacterial species in general and specific web-servers (see, for instance, http://www.PubMLST.org). For many pathogens, the availability of a MLST scheme represented a more than significant change in the analysis of outbreaks. This method quickly became the new “gold-standard” for typing pathogens and replaced previous methods. However, for a few but important pathogens no MLST scheme revealing enough genetic variation for effectively distinguishing among nonepidemiologically linked isolates could be designed. These pathogens include the causative agents of plague (Yersinia pestis), anthrax (Bacillus anthracis), tuberculosis (Mycobacterium tuberculosis) and leprosy (Mycobacterium leprae), among others, and are collectively known as “genetically monomorphic bacteria” (Achtman, 2012). Specific typing methods such as insertion sequence RFLP and MIRU-VNTR were applied to M. tuberculosis, the pathogenic bacteria with the highest incidence and causing more deaths every year in the history of humankind. In these and other cases, the solutions adopted relied on very fast evolving markers, which are usually prone to homoplastic changes, thus resulting in some false positive identifications of phenotypic identities as indicative of very recent ancestry. Although this is not a problem in most settings, it became evident that the same logic applied in using MLST could be extended to the complete genome sequences to attain “perfect” accuracy by using all the genetic information in the isolates and not only a small sample from it. 2 This approach was first used in an outbreak setting in the investigation of the letters covered with anthrax spores in the aftermath of the 9/11 attacks in the USA. Complete genome sequences were obtained from a B. anthracis isolate derived from one of the victims and one reference strain, providing 60 SNPs that could be used subsequently to probe the common origin of the strain used in the bioterrorist attacks (Read et al, 2002). This work clearly showed that using the complete genome sequence was a more effective method for comparing isolates even in almost completely monomorphic species. However, Sanger sequencing is rather slow and painstaking as a result of the need to cut or amplify the genome in small pieces that are subsequently sequenced and assembled into a complete genome sequence. This situation changed dramatically with the introduction of new sequencing methods, then known as “nextgeneration sequencing” technologies. They offered several advantages over the traditional Sanger method (Medini et al, 2008). At the same time, other problems arose, such as the difficulties in handling and analyzing very large volumes of data, a myriad of programs and methods to analyze them, and new conceptual challenges in the interpretation of the results. In this chapter we provide a brief overview of the different next-generation sequencing platforms and methods currently available for deriving complete genome sequences from bacteria, the main results in terms of the epidemiological and evolutionary advances that have resulted from their application to bacterial outbreaks and transmission networks, and provide a more detailed analysis of two cases, the analysis of Legionella pneumophila outbreaks and of M. tuberculosis transmission networks. High throughput sequencing technologies in outbreak investigations Several high throughput sequencing platforms have been applied to the genomic study of both bacterial and virus pathogens. Encouraged by the increasing need of sequencing human genomes, three technologies were almost simultaneously released from different companies: 454 (Roche, introduced in 2005 and discontinued in 2016), Solexa (Illumina, introduced in 2006), and SOLiD (Life Technologies, introduced in 2006). These platforms share a general workflow, based on the idea of performing billions of sequencing reactions simultaneously. These are produced through molecular amplification of DNA fragments that are previously attached to a solid surface. These have been enhanced in their subsequent updates to increase both sequencing quality and throughput (Figure 1). Although 454 was the first released platform, its use has mainly been relegated to metagenomic studies (Schlüter et al, 2008b;Schlüter et al, 2008a;Ghai et al, 2010) because of its long reads and relatively high error rates, which complicates the study of transmission chains or related cases during outbreak investigations. However, it has been used as the main technology in several studies (Lewis et al, 2010;Kennemann et al, 2011;Loman and Constantinidou, 2013) and also following mixed strategies involving the usage of 454 reads as scaffolds and posterior error correction using Illumina (McAdam et al, 2012;Hasan et al, 2012). SOLiD has been the least used for outbreak investigations due to shorter and lower quality reads. As an example, it has been punctually applied in the investigation of L. pneumophila outbreaks in an endemic locality in Spain (Sánchez-Busó et al, 2014), Mycobacterium abscessus subsp. bolletii in Brazil and UK outbreaks (Davidson et al, 2013) or Coccidioides immitis producing coccidioidomycosis in transplanted patients in Los Angeles (Engelthaler et al, 2011). By far, Illumina has been the most 3 widely used platform because of its high quality and sensible sized reads, which allow more accurate mapping and SNP calling. A thorough summary of the application of different sequencing technologies to analyze different mainly bacterial outbreaks is shown in Table 1. In 2010, the Ion Torrent (Life Technologies) platform, a new benchtop device with a different sequencing strategy was commercialized. This technology is based on monitoring pH changes in multi-well plates. A single reaction occurs per well so that when a hydrogen atom is released after the incorporation of each nucleotide during amplification, the pH in the media changes in a nucleotide-specific manner, so that the system is able to translate chemical into digital information. Reads produced by the Ion Torrent were of relatively good quality and was punctually applied to the study of Escherichia coli outbreaks (Mellmann et al, 2011;Holmes et al, 2015) and Pseudomonas aeruginosa (Snyder et al, 2013;Witney et al, 2014). In early 2011, the PacBio RS system was also released, being the first platform performing Single Molecule Real Time (SMRT) sequencing, which is being increasingly applied to complete microbial genomes because of the long read lengths (Mutreja et al, 2011). But the definite current revolution in sequencing technologies with an impact in public health has been the release of the Oxford Nanopore MinION platform, currently in test mode, and scalable in the form of the GridION platform. These contain a membrane with millions of embedded nanopores coupled with a polymerase. Changes in the electrical conductivity in the membrane as the different four bases pass through the nanopore are measured, allowing sequencing in real time. Specifically, the MinION platform is an USB-like device which can be connected directly to a computer and provide the sequences from extracted DNA in real time after a very simple library preparation. The portable MinION platform has been shown to be useful in real-time outbreak investigations, such as the 2015 Ebola virus disease epidemic in West Africa (Quick et al, 2016). The different platforms differ in their sequencing strategy, which yields different throughputs and sequence qualities. Currently, the highest throughput can be achieved with the HiSeq X Ten Illumina platform, which can yield up to 3 billion of paired-end 150 bp sequences. This high level throughput is mainly directed to population-scale human genome sequencing projects. In the case of microorganism sequencing, because their genomes are much smaller, sequencing throughput must depend on the depth of coverage required for each specific study. However, large-scale microbial sequencing projects can benefit from these high throughput platforms by multiplexing different strains in the same run. Coverage depths of 50X-100X are usually sought for base call error correction, minimizing the rate of false positive SNPs. Currently, the technologies with the lowest error rates are Illumina platforms, and the highest error rate from raw data is provided by Oxford Nanopore and PacBio platforms. However, bioinformatics pipelines for error correction during the post-processing of reads improve these rates, especially in the second case, in which the current final error rate can get as low as 1E-05. Multiple reviews on the characteristics of the different sequencing technologies, applications, advantages and drawbacks have been published in the literature up to now (Metzker, 2010;Casey et al, 2013;Ekblom and Wolf, 2014). Choosing the most appropriate sequencing technology depends on the scope of the study. High throughput technologies can be applied in different steps during an outbreak investigation (Köser et al, 2012); from the detection and identification of the pathogen in direct uncultured samples (i.e. blood, sputum, etc.), epidemiological typing and detection of mutations associated to drug susceptibility to the study of transmission chains and potential super-spreaders. 4 Achievements and limitations of NGS in outbreak investigations Initial results. Although NGS techniques and devices became available around 2005 (Loman and Pallen, 2015), it took a few more years until the new technologies were firstly applied to analyze an outbreak. This corresponded to an outbreak of methicillin-resistant Staphylococcus aureus (MRSA) (Harris et al, 2010). They analyzed a set of 63 isolates from two origins, a global collection of 43 samples collected between 1982 and 2003, and 20 isolates from a Thai hospital sampled in a very short time period (months), suspected to correspond to a transmission chain. Their results provided evidence for the international spread of the resistant clone of S. aureus and the single origin of the samples from the hospital. But they also showed that bacteria can and do evolve rapidly. They estimated that in the core genome, the set of shared positions among all the studied isolates, the rate of divergence was about 1 SNP every 6 weeks. This explained the lack of identity among most hospital isolates, which differed in a few SNPs from each other, but it also revealed differences from the patterns of evolution revealed by other markers, such as spa and PFGE. Of note, the analysis of complete genomes showed that over a quarter of the homoplasies found among the isolates were directly related to the evolution of resistance to antibiotics. At about the same time, Lewis et al. (2010) used complete genome sequences to establish relationships among otherwise indistinguishable strains of Acinetobacter baumannii which had cause a small outbreak at a British hospital. The SNPs found by WGS allowed the investigators to discriminate among alternative epidemiological hypotheses. These pioneering studies have been followed many studies (Table 1) which have dealt with outbreaks and transmission networks of over 30 different bacteria species infecting humans. An even larger number of works have been published about viral infections (not included in this review) and a few have dealt with fungal infections. Two particular bacteria, M. tuberculosis and L. pneumophila, the main etiological agents of tuberculosis and legionellosis, respectively, are analyzed in more detail below, but some general patterns and conclusions have started to emerge from the analysis of more than 30 pathogenic bacteria, that we briefly review next. From retrospective to real time analysis of outbreaks. We have previously commented that the molecular analysis of outbreaks and transmission networks is necessarily a complement to the epidemiological investigations leading to the identification and control of the source(s), vectors or routes so to put a fast stop to ongoing processes. Hence, it is very important that the information obtained from the molecular analyses can be shared with the epidemiology team for a better evaluation of the total evidence available thus far and more appropriate and accurate decisions can be adopted. The initial methodologies available for WGS were very labor intensive and the shortest time since a sample was obtained until its complete sequence could be determined was in the order of weeks. Too long for a pressing demand of action. However, the advent of new technologies, such as Ion Torrent PGM and, more recently, MinION have changed this situation. Both methods can deliver sequence information within a few hours of gaining access to the sample, thus allowing a very rapid communication of results to field workers. The first case in which these new technologies were applied during the investigation of the source of an outbreak was that an enteroaggregative Escherichia coli O104:H4 strain that affected several European countries in the spring of 2011 (Mellmann et al, 2011). Complete genome sequences were obtained from a representative isolate of the outbreak and a reference strain which produced similar clinical features in just 62 hours. The comparison revealed key differences in plasmid and gene contents between the strains, indicating that the outbreak was 5 due to a new and not a previously circulating strain of the bacterium. It also allowed the design of a test to be applied for quick diagnostic in any lab. Loss of identity as hallmark of relatedness. One consequence of using complete genome sequences for the analysis of outbreaks and transmission chains is the necessary dismissal of complete identity as the proof of charge in considering two or more isolates as linked to the same transmission event or episode. This was usually the case for most previous markers which explored only a minor fraction of the nucleotides in the genome of the pathogenic bacteria. Except for a few rapidly evolving markers, usually associated to tandem repeats, the number of differences expected between two isolates depends on three factors: the mutation rate per site, the number of sites being compared and the time since they diverged from their last common ancestor. When the number of generations since divergence is relatively small, as in outbreaks and most transmission networks, and the number of sites being sampled is also small, the probabilities of finding a SNP (or a different allele in the case of MLST) are also very small. However, using complete genome sequences, and assuming that the previous assumptions remaining identical, will increase those probabilities in a three-fold factor or more, because the number of sites interrogated is now in the order of millions instead of tens or hundreds. Within-host evolution. In addition, the exploration of complete genome sequences of long- or chronically-infecting bacteria has shown that evolution does occur within hosts at relevant rates for being reflected in some nucleotide changes (Didelot et al, 2016). Even for pathogens that produce acute infections, a low per site mutation rate is compensated by the large number of nucleotides present in a genome and the different random and directional processes that occur in an infected individual, thus leading to some new mutations arising in many newly replicated genomes (Kennemann et al, 2011;Mathers et al, 2015). If the infection last longer or becomes chronic, the chances that changes occur in the pathogen are very high and additional evolutionary processes such as compartmentalization may contribute to within patient differentiation of bacterial sub-populations. These processes have important consequences at different levels. On the one hand, a variable population can adapt more rapidly to new environmental conditions which might include new treatments or an adaptive immune response by the host (Mwangi et al, 2007). On the other hand, a variable population will result in different initial compositions in successive transmission events, which will be reflected in differences among the populations established in the new hosts. The analysis of transmission networks becomes more complicated because using a single genome sequence per host cannot reveal the whole range of variation present within it (Worby et al, 2014). Under these circumstances, the use of evolutionary methods to reveal the common ancestry of isolates derived from patients presumably included in the same network becomes an absolute necessity. Mutation patterns and processes. Apart from revealing larger amounts of variation than anticipated from previous studies with just a few gene sequences, whole genome sequences have also informed about the types and distribution of mutational changes occurring at different time-scales. A few years ago, the contribution of homologous recombination and horizontal gene transfer to genetic variation in bacterial genomes was found to be considerably more important than previously thought (Doolittle, 1998). But this was thought to be the result of millions of generations in which a generally rare process might have been acting. In shorter timescales, months or years, the impact of processes generating variation other than point mutation was thought to be negligible except for loci including repeat units, such as in MIRU-VNTRs in M. tuberculosis, in which slippage-and-mispairing during replication often lead to new alleles. 6 Recent analyses at the complete genome level have shown that this view is incorrect, at least for some bacteria such as Neisseria gonorrhoeae, Salmonella enterica or L. pneumophila (Didelot and Maiden, 2010;Sánchez-Busó et al, 2014). In fact, a comparison of the relative effects of recombination and point mutation in almost 50 bacterial species revealed variation of three orders of magnitude (Vos and Didelot, 2009). Although there are not quantitative estimates yet, horizontal gene transfer, with or without final stabilization in the receiving genome, is also known to play a significant role in the short term evolution of many bacteria, as unfortunately shown by the ease of spread of many antibiotic resistance genes across species. The additional variation introduced by these processes has to be considered when analyzing large transmission networks or long-lasting outbreaks, because the incorporation of these new variants may confound inferences of recent ancestry based on overall similarity or on a few loci. Rates of evolution. The increased availability of complete genome sequences from bacteria with a more or less direct epidemiological link has also provided an opportunity for a more detailed study of evolutionary processes at the population genomic level. Apart from the different types of variants introduced in these populations, the access to asynchronically sampled isolates allows the application of Bayesian methods to estimate evolutionary rates (Drummond et al, 2006). These methods can accommodate strict and relaxed clock models, different demographic regimes, as well as variation in rates among lineages, thus allowing the estimation of relevant evolutionary parameters from organisms with different natural and evolutionary histories. Most often they are applied to rapidly evolving organisms, collectively known as measurably evolving populations (Drummond et al, 2003;Biek et al, 2015), which mainly include viruses along with some bacteria. But the methods are also valid for more slowly evolving organisms with sampling dates different enough as to provide estimates of the evolutionary rate. Recently, this approach has been used with bacterial genomes obtained from ancient samples (Schuenemann et al, 2013;Bos et al, 2014;Mendum et al, 2014;Rasmussen et al, 2015;Bos et al, 2016;Maixner et al, 2016). One apparent feature of the estimates of bacterial evolutionary rates is the negative correlation between the time to the most recent common ancestor of the sample studied and the inferred evolutionary rate (Figure 1). Higher evolutionary rates at short times can be explained by the relative inefficiency of natural selection and/or genetic drift in the removal of neutral or quasineutral polymorphisms which are continuously arising in bacterial populations. Hence, transitional polymorphisms contribute significantly to the apparent acceleration of evolutionary rates in short time-scales. At the same time, they also provide a wealth of variation what might have an adaptive value if the circumstances are appropriate. On the long run, many of these transient variants will have disappeared and evolutionary rates are reduced correspondingly. This negative correlation has to be taken into account when comparing rates across studies, even for the same species, and in the inference of other evolutionary parameters (Biek et al, 2015). The analysis of (almost) complete genome data. One of the main advantages of MLST or SBT over alternative methods for the analysis of pathogenic bacteria in the context of outbreaks and transmission chains is the objectivity and simplicity in the specification of the variants found in any isolate. The nucleotide sequences obtained for each locus are compared to a predetermined database in which previous homologous sequences have been deposited. If there is a perfect match, the newly determined variant received the same identifier as the pre-existing one. If that is not the case, curators of the database will assign a new code to the variant. The combination of allele codes in the loci included in the typing scheme is summarized in a sequence type (ST) 7 with a different number of each combination of variants. This procedure is easily communicated because it requires the identification of nucleotide variants, usually through Sanger sequencing, in just 6 or 7 loci. However, the advent of NGS and the determination of complete genome sequences makes this procedure of denoting the variants impractical. Several alternative have already been proposed for the identification of complete genome sequences for epidemiological analysis. One method consists in extending the MLST naming scheme to more loci, eventually all the loci in the genome of the corresponding species, thus leading to “whole genome MLST” (wgMLST) schemes (Cody et al, 2013). The first proposal of wgMSLT was done for Campylobacter isolates and the initial MLST scheme based on 7 loci was extended to 1667 loci, although this number was reduced to 1026 when only those present in all the isolates analyzed were considered. This represents the “core genome” of the species, which is complemented by the “auxiliary genome”, the set of loci which are present in some but not all the isolates of a species. In light of the very large genome plasticity of many bacterial species, fixed compositions of the core and auxiliary genomes are almost impossible, which creates an additional problem for the stability of the scheme. Nevertheless, this approach has gained some popularity and cgMLST (“core genome MLST”, a reduced version of wgMLST as described above) schemes are now available for several pathogens including S. aureus, Listeria monocytogenes, Enterococcus faecium (de Been et al, 2015), and S. enterica (Taylor et al, 2015), among others. To prevent the proliferation of STs which inevitably accompanies wgMLST or cgMLST, a first level classification of STs into clusters or clonal groups is usually performed (Cody et al, 2013;Qin et al, 2016). These can be based on an extension of the BURST method (Feil et al, 2001;Feil et al, 2004), which considers as variants of the same clonal group to those that differ in one single locus of the original MLST scheme, or use more sophisticated approaches based on the population genetic analysis of the actual SNPs detected in the loci included in the wgMLST or cgMLST (Qin et al, 2016) with different molecular population methods such as BAPS (Corander and Tang, 2007) or STRUCTURE (Rosenberg et al, 2002). These methods share the advantage of portability thus allowing comparisons among different laboratories and needs. However, they also discard important information, eventually crucial, contained in the auxiliary genome. Hence, although standard typing schemes are useful, whole genome sequence information should not be reduced to a ST number or complex under a wgMLST and the complete data should still be available for future use by the scientific community. Outbreak investigation in Mycobacterium tuberculosis: the genome as an epidemiological marker Mycobacterium tuberculosis is the main causative agent of human tuberculosis in the world. Every year more than 1.5 million persons die of tuberculosis, more than of any other infectious disease (WHO, 2014). The epidemiology of the disease has to take into account the natural history of the bacteria. It is an obligate human pathogen with very effective airborne transmission and that typically infects the lungs. It is estimated that one third of the human population is infected by the bacilli and this explains why every year around 9 million new cases are declared. In most cases the initial infection derives in an asymptomatic state called latency in which the bacteria have not been eliminated but are controlled by the immune system. In 58 10% of the latent cases the disease progresses to an active state in which the bacteria actively replicate and cause pulmonary disease. Only an active tuberculosis case can transmit the disease and thus in tuberculosis, disease and transmission are linked. The typical window of progress to active disease after infection is two years but the bacteria may remain latent for years or even decades. Mycobacterium tuberculosis has been traditionally regarded as a monomorphic organism due to the low genetic diversity found among representative strains datasets (Achtman, 2008). Thus epidemiological tools were developed based on fast evolving genetic elements (Barnes and Cave, 2013). Typing of the insertion sequence IS6110 by RFLP and of minisatellites, called MIRUVNTR, are the two gold standards in tuberculosis molecular epidemiology and, together with spoligotyping, based on the CRISPR region of the bacteria, have allowed to define successful M. tuberculosis clones. Among these clones, the identification of an hypervirulent clade, called Beijing family, has attracted much attention (Parwati et al., 2010). Strains from the Beijing family are more common in East Asia but can be identified across the globe. Experimental and epidemiological research have identified Beijing strains as hypervirulent in the mice model of infection and with frequent association to drug resistance in humans. In South Africa Beijing strains have been on the rise for the last 40 years (Cowley et al., 2008). Beijing strains belong to one of the seven lineages of human tuberculosis strains (Comas et al., 2013). The most common is lineage 4, which is highly frequent in Africa, Europe and America. There is a strong association between lineages and their geographic origin, being the most extreme cases the two lineages of Mycobacterium africanum, that can only be found in West Africa (De Jong et al, 2010), and Lineage 7 recently described in Ethiopia (Comas et al., 2013). Regardless the lineage, drug resistance to first and second line treatments have been identified (Farhat et al., 2013). The mutations responsible for drug resistance are always chromosomal mutations because there is no ongoing horizontal gene transfer in M. tuberculosis. Although ecological theory predicts that drug resistance mutations have a fitness cost, experimental evolution and molecular epidemiology have shown that different drug resistance mutations have different fitness costs (Comas et al., 2012). As a consequence, multidrug-resistance cases (MDR-TB) among people never treated before, and therefore due to transmission, are on the rise and in some particular areas represent more than 50% of the tuberculosis burden of the region. Although not part of this review whole genome sequencing is allowing to define the set of mutations associated to resistance to the different antibiotics but also the genotype of highly successful MDR-TB strains. The first study that showed the potential of the genome as an epidemiological marker dates back to 2009 (Niemann et al, 2009). In this study, three strains which looked almost identical using traditional molecular epidemiology markers such as restriction fragment length polymorphisms (RFLP) and minisatellite (MIRU-VNTR) were shown to differ in more than 100 SNPs. Later on, Jennifer Gardy and collaborators (2011) used genome comparison techniques to solve an on-going outbreak in British Columbia suspected to have started in the early 1990s. By combining genomic, epidemiological and social contact data the authors showed that it can be gained get a better resolution of the transmission events within transmission clusters. Such events are very difficult to identify with traditional molecular epidemiology markers. This work already defined index cases associated to multiple secondary cases, also denoted as superspreaders. Super-spreaders are becoming a common topic when analyzing large transmission clusters (Walker et al, 2013b) instead of the traditional view of a stepwise "chain" of transmission. 9 From 2010, NGS has been successfully applied to deeply resolve tuberculosis outbreaks. Considerably attention has been paid to understand those outbreaks that have been on-going over years. For example, a large outbreak in Hamburg, Germany, was identified by classical genotyping data in 1996 (Roetzer et al, 2013). However, clustering data not always correlated with epidemiological and geographical information leading to the suspicion that the outbreak was more complex than previously anticipated. By whole genome sequencing of 86 strains from the outbreak (1996-2011), Roetzer et al. (2013) were able to identify an independent transmission network, thus confirming the non-clonality of the outbreak. Two clusters were determined, one starting in 1997 and the other starting in 2010, much more in agreement with epidemiological investigations. Therefore, one important application of whole genome sequencing to investigate tuberculosis outbreaks is to ability to assign with higher confidence cases to the outbreak and exclude those that, albeit genetically close, correspond to a different chain of events. Similarly, in Bern, Switzerland, a genotype detected by RFLP profiling caused a large number of tuberculosis cases during the 1990’s (Stucki et al, 2015). The cases were associated to the typical risk factors in local populations found in European cities such as HIV infection or alcoholism. Stucki et al. (2015) sequenced the complete genome of strains belonging to the original outbreak along with local control strains. By comparing outbreak and control strains they designed a realtime SNP typing assay based on the detection of genome position with a polymorphism specific to the outbreak strains. Next, they typed a retrospective collection of isolates of the Canton of Bern from 1993 to 2011. They were able to identify 68 additional cases of the outbreak based on the presence of the mentioned SNP including cases from 2011. Therefore, the combination of whole genome sequencing and SNP typing allowed them to identify cases associated to the outbreak and find that the outbreak that started in early nineties was still on-going at the time of investigation. In addition, they obtained the whole genome sequence of all the isolates assigned to the outbreak. With this information, they were able to resolve the individual transmission patterns for 75% of the strains. Importantly, 66 out of the 68 strains had exactly the same RFLP pattern. Furthermore, the analysis of the transmission network together with the epidemiological information revealed two different sub-outbreaks initiated by two different "super-spreaders". Therefore, next generation sequencing of the Hamburg (Roetzer et al, 2013), the Bern outbreak (Stucki et al, 2015) and others (Török and Peacock, 2012;Smit et al, 2015;Lee et al, 2015) have revealed the complexity of tuberculosis outbreaks. Given that tuberculosis is not an acute disease and that a tuberculosis case can be latent, asymptomatic for years, the true extent of tuberculosis outbreaks can only be revealed by a sustained genotyping efforts over years. Furthermore, as in the case of the Bern outbreak, whole genome sequence data can be used to design new diagnostics and/or surveillance tools. A similar approach has been used to prospectively identify new outbreak-associated cases in sputum samples (Pérez-Lago et al, 2015). Apart from specific outbreaks, genomic epidemiology has been used in a population-based scale to evaluate its utility for surveillance and diagnostics. In a series of publications starting in 2012, Public Health England has applied next generation sequencing to incorporate whole genome sequencing as the default typing method of Mycobacterium tuberculosis in the United Kingdom (Walker and Beatson, 2012;Walker et al, 2014). They have shown that the genome data allow to delineate outbreaks better than MIRU-VNTR analyses. Furthermore, in an attempt to derive a rule of thumb to identify a transmission event between two cases they also sequence several 10 isolates from the same patient and known household contacts. They were able to identify a threshold of five SNPs when the cases had a confirmed epidemiological link and they proposed a threshold of up to 12 SNPs for casual transmission in the community (Walker and Beatson, 2012). Other studies have found a similar distribution of SNPs when analyzing transmission events in populations (Bryant et al, 2013a;Casali et al, 2014). However, we are still blinded about how these thresholds apply to different clinical settings than the low-burden countries of Europe. In high-burden countries delineating transmission clusters should be more difficult if public health interventions cannot stop transmission events (Yates et al, 2016). Thus, the circulating strains may be participating at the same time in several clusters. The only population-based study published in a high-burden country shows that the threshold described in (Pérez-Lago et al, 2015) may be useful, although more work will be needed to generalize the results to, for example, large urban areas. There are several factors that may distort the proposed threshold values. One of these factors is mixed infections. The true extent of co-infections in high-burden countries is not clear and there is hope that whole genome data can distinguish between relapses and re-infections (Bryant et al, 2013a;Guerra-Assunção et al, 2015). This issue is critical to delineate transmission in high burden countries but also for clinical trials investigations because relapse is one of the end points of those investigations. However, it is the diversity that can be found during infection from a single strain what is attracting more research and attention. From drug susceptibility clinical data, it has been clear for decades that several populations may co-exist in the same patient. These subpopulations were flagged due to inconsistent results in drug resistance susceptibility tests between isolates of the same patient (Rinder et al, 2001). Whole genome sequencing has shown that, in fact, this is the case and what is recovered from a sputum sample is often a mix of different sub-populations (Sun et al, 2012). These sub-populations can be revealed by looking at positions in which a mutant and a wild-type allele can be identified at the same time. In the context of drug resistance, it has been shown that several drug resistant subpopulations may co-exist and compete and that their frequencies may change over time (Liu et al, 2015). A similar phenomenon has been shown outside the context of drug resistance. The issue of within patient diversity not only has clinical and diagnostic implications. If several subpopulations co-exist and accumulate a different number of SNPs then chances are that the epidemiological investigation of outbreaks may be distorted by the isolate chosen for the analysis (Walker et al, 2013a;Walker et al, 2013b). An analysis of cases in which higher than expected diversity was expected confirmed that, although the thresholds proposed to delineate a transmission event are in general valid, there are epidemiologically cases in which a larger than expected number of SNPs can be found (Pérez-Lago et al, 2014). How frequent are those "outliers" is a matter of on-going investigation. High throughput investigation of Legionella pneumophila outbreaks High throughput sequencing can also be used to study organisms with higher level of polymorphism and strictly environmental, contrary to Mycobacterium tuberculosis. This is the case of L. pneumophila, causative agent of Legionellosis, and for which there is only one report of a possible person-to-person transmission (Correia et al, 2016) up to date. This opportunistic pathogen can produce pneumonia after inhalation of aerosols with enough bacterial load, with 11 the highest burden in warm water-related environments. The first reported outbreak dates from 1976 when more than hundred legionnaires were infected in a convention in Philadelphia (Fraser et al, 1977). A legionellosis outbreak is defined as a cluster of more than three cases occurring at the same place and time and the epidemiological investigation is crucial to find the environmental sources. The investigation of legionellosis outbreaks has traditionally been conducted by using biochemical or molecular methods that allows comparing the clinical isolates with the strains obtained from the environment (Fields et al, 2002). Broad techniques such as serogrouping benefited from genetic methods that provided improved resolution in the so-called SequenceBased Typing (SBT) (Gaia et al, 2003;Gaia et al, 2005), based on Multi-Locus Sequence Typing (MLST) approach (Urwin and Maiden, 2003) but incorporating virulence genes in the scheme to increase the discrimination power among strains. However, although SBT provided researchers with a tool that allowed the classification of strains into groups (Sequence Types, STs), the introduction of high-throughput sequencing techniques for microbial analysis and outbreak investigations in other species derived in its application to legionellosis outbreaks because of its increased discrimination power. The first published work was indeed a pilot study to test the potential of whole-genome sequencing (WGS) on the discrimination between isolates from an outbreak produced in the UK in 2003 and non-outbreak related strains (Reuter et al, 2013b). From this point, a number of other outbreaks have been analyzed using WGS, as for example an outbreak of ST62 associated to a cooling tower in Quebec City in 2012 (Lévesque et al, 2014) or a massive outbreak that occurred in Edinburgh (UK, 2012) related to multiple STs and including mixed infections (McAdam et al, 2014). WGS has also been used to investigate the persistent infection history of ST23 in a hotel in Spain in 2012 (SánchezBusó et al, 2016) and the eradication of L. pneumophila associated to a hospital in Australia that have been responsible of nosocomial cases (Bartley et al, 2016). The environmental source of legionellosis cases has been historically difficult to trace, and because of the high social and economic impact of this kind of outbreaks on the affected populations, public health interventions are obliged to be rapid and accurate. WGS has shown further variability within many STs (Underwood et al, 2013a;Sánchez-Busó et al, 2014), showing evidence that at least some of them are not clonal. This observation complicates the study of legionellosis outbreaks and was the leading aim in the study by Sánchez-Busó et al. (2014). In this work, 69 isolates including strains associated to 13 different outbreaks and sporadic cases occurred in a single locality (Alcoy, Spain) during more than 10 years (1999-2010) were analyzed by high throughput sequencing. Different STs were included, with special interest on ST578 cases, which had been recurrently reported as the causing ST of most of those outbreaks (Coscollá et al, 2010). The analysis showed two main lineages within the endemic ST578, more than 1,000 SNPs apart from each other. Not all the strains from the same outbreak clustered together, revealing the non-clonality of the isolates, as these were phylogenetically grouped independently of their source (clinical or environmental), sampling date or outbreak. Because ST578 is known to be endemic in the area of Alcoy, these results suggest that it is indeed very complicated to find an infectious source using just molecular data in endemic areas. These should be used together with the epidemiological investigation to be able to draw the accurate conclusions that public health interventions require. 12 Other interesting fact that this work shows is that the genomic data can reflect public health actions along time. As an example, using Bayesian inference, an estimate of the ST578 population dynamics revealed a decreased population size between 2006 and 2008, which correlated with a moment in which public health measures were taken in the city by removing high-risk installation from the city center. In the case of organisms where person-to-person transmission is very rare or even inexistent, whole genome sequencing can provide the most discriminant tool to link clinical cases with environmental sources, providing the accuracy that public health interventions require in these cases. But, moreover, it can help understand how outbreaks occur, which is the starting line to be able to predict and even prevent their occurrence. Conclusion Complete genome analysis of bacterial pathogens is still far from being the usual method for analyzing outbreaks and transmission networks, although it will not take long before it does so. The increasing speed, ease and reliability as well as the reduced costs associated to new highthroughput sequencing technologies point to that direction. But gaining information is only a part of the process. More data also mean an increased need for interpretative tools at all levels, from the mere analysis of reads to the inference of the evolutionary and genealogical relationships among the isolates. Progress is still pending at all levels, from the technology to obtain, fast and cheap, complete genome sequence data of a specific pathogen from an infected individual or a potential vector o source to analytical tools capable of extracting the relevant information from the deluge of data generated by high-throughput sequencers and for the integration of this information with the clinical, epidemiological and evolutionary information which are needed when they have to be interpreted in the appropriate context. Acknowledgements We thank Dr. Pierre Pontarotti for his kind invitation to write this chapter. This work has been funded by project BFU2014-58656-R from MINECO (Spanish Government) to FGC. IC is supported by Ramón y Cajal Spanish research grant RYC-2012-10627, MINECO research grant SAF2013-43521-R, and the European Research Council (ERC) (638553-TB-ACCELERATE). BB has been recipient of a Beca de Colaboración from the Spanish Ministerio de Educación y Cultura. 13 References Literature Cited Achtman M. (2008) Evolution, Population structure, and phylogeography of genetically monomorphic bacterial pathogens. Annu Rev Microbiol 62:53–70. Achtman M (2012). Insights from genomic comparisons of genetically monomorphic bacterial pathogens. Phil Trans R Soc B 367:860-867. 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Analysis of a Streptococcus pyogenes puerperal sepsis cluster by use of whole-genome sequencing. J Clin Microbiol 50:2224-2228. Zhou Y, Gao H, Mihindukulasuriya K, Rosa P, Wylie K, Vishnivetskaya T, Podar M, Warner B, Tarr P, Nelson D, Fortenberry JD, Holland M, Burr S, Shannon W, Sodergren E, Weinstock G (2013). Biogeography of the ecosystems of the healthy human body. Genome Biology 14:R1. 28 Table 1. A summary of published works analyzing complete genome sequences of bacterial pathogens for the study of outbreaks and transmission chains. 29 Pathogen Acinetobacter baumannii Genome size (Mb) 4.11 Bacillus anthracis Campylobacter jejuni Chlamydia trachomatis 5.2 1.64 ≈1.0 Clostridium difficile 4.0 Enterobacter cloacae Enterococcus faecium 5.31 2.9 Escherichia coli ≈5.2 Helicobacter pylori Klebsiella pneumoniae 1.5-1.7 5.6 Sequencing strategy Illumina HiSeq 2000 2x100 bp Illumina MiSeq 2x150 bp, 2x250 bp Roche 454 GS FLX PacBio Sanger Illumina HiSeq 2000 76 bp Illumina GA Illumina GAII PE 2x37 bp Illumina GAII/GAIIx 2x51/100108bp Illumina HiSeq2000 2x100bp; 2x54/108/76bp Illumina MiSeq Illumina MiSeq References (Lewis et al, 2010;Hornsey et al, 2011;Kanamori et al, 2016) (Fitzpatrick et al, 2016) Roche 454 GS Junior Roche 454 Titanium Illumina MiSeq Illumina Solexa Illumina HiSeq2000 2x101 Ilumina GAIIx Ion Torrent PGM Roche 454 Illumina Hi Seq 2000 Illumina MiSeq platform Roche 454 Titanium XLR (Mellmann et al, 2011;Brzuszkiewicz et al, 2011;Ahmed et al, 2012;Ju et al, 2012;Grad et al, 2012;Grad et al, 2013;Underwood et al, 2013b;Shah et al, 2014;Holmes et al, 2015) (Read et al, 2002) (Cody et al, 2013) (Harris et al, 2012;Seth-Smith et al, 2013) (Didelot et al, 2012a;Eyre et al, 2012;Eyre et al, 2013a;Eyre et al, 2013b;He et al, 2013;Knetsch et al, 2014) (Reuter et al, 2013a) (Reuter et al, 2013a;Pinholt et al, 2015) (Kennemann et al, 2011) (Snitkin et al, 2012;Espedido et al, 2013;Onori et al, 2015) 30 Legionella pneumophila Listeria monocytogenes Mycobacterium abscessus M. abscessus subsp. bolletii Mycobacterium canettii 3.5 3 5-5.2 ≈5 ≈ 4.5 4.4 Mycobacterium tuberculosis Illumina HiSeq 2x100 bp Illumina MiSeq 2x250 bp, 2x150bp SOLiD 5500XL SE 75bp Roche 454 GS-FLX Illumina HiSeq 2x75 bp Life Technologies SOLiD HiSeq2000 MiSeq Illumina Illumina GAII PE 2x36bp; 2x50 Illumina GAIIx PE 2x76; 2x108 Illumina HiSeq PE 2x75 bp Illumina MiSeq 150 bp Roche 454 GS FLX 36bp (Reuter et al, 2013a;Reuter et al, 2013b;Sánchez-Busó et al, 2014;Bartley et al, 2016) (Gilmour et al, 2010;Schmid et al, 2014;Kwong et al, 2016) (Bryant et al, 2013b) (Davidson et al, 2013) (Blouin et al, 2014) (Ioerger et al, 2010;Schürch et al, 2010;Gardy et al, 2011;Sandegren et al, 2011;Casali et al, 2012;Kato-Maeda et al, 2013;Bryant et al, 2013a;Roetzer et al, 2013;Köser et al, 2013;Török et al, 2013;Walker et al, 2013a;Walker et al, 2013b;Pérez-Lago et al, 2014;Coscollá et al, 2015) (Grad et al, 2014) Neisseria gonorrhoeae 2.1 Illumina HiSeq Neisseria meningitidis 2.2 Illumina GAIIx PE 2x76 Illumina MiSeq (Jolley et al, 2012;Reuter et al, 2013a;Bennett et al, 2012) Pseudomonas aeruginosa Salmonella enterica 6.26 4.76 (Witney et al, 2014;Snyder et al, 2013) (Holt et al, 2008;Lienau et al, 2011;Quick et al, 2015;Allard et al, 2013;Cao et al, 2013;Allard et al, 2012;Taylor et al, 2015;Bekal et al, 2016) Salmonella Typhimurium Shigella sonnei 4.7 5.06 Ion Torrent Illumina MiSeq Illumina HiSeq 2500 MinION Roche 454 Illumina GA II system Illumina GAII PE 2x54 bp Illumina MiSeq Illumina HiSeq2000 (Okoro et al, 2012) (Holt et al, 2012;Holt et al, 2013;McDonnell et al, 2013) 31 Staphylococcus aureus Streptococcus pneumoniae 2.8-3 1.98 - 2.19 Streptococcus pyogenes 1.85 Streptococcus suis 2.15 Vibrio cholerae ≈4 Yersinia pestis 5.46 Illumina MiSeq PE 2x150 bp Illumina GAIIx PE Illumina GAII SE 150 bp Illumina HiSeq2000 Roche 454 GS FLX Illumina HiSeq 2000 2x75 bp Illumina PE 2x54bp Roche 454 Illumina HiSeq 2000 Illumina GA1s Roche 454 Roche 454 / GS 20 Solexa Illumina HiSeq Illumina GAI Illumina GAIIx PacBio-RS Roche 454 GS FLX Illumina (Harris et al, 2010;Eyre et al, 2012;McAdam et al, 2012;Young et al, 2012;Köser et al, 2012;Holden et al, 2013;Nübel et al, 2013;Harris et al, 2013;Price et al, 2014;Azarian et al, 2015;Paterson et al, 2015;Senn et al, 2016;Kinnevey et al, 2016;Reuter et al, 2016) (Croucher et al, 2011;Loman et al, 2013;Croucher et al, 2013;Chewapreecha et al, 2014) (Zakour et al, 2012;Fittipaldi et al, 2013) (Holden et al, 2009) (Mutreja et al, 2011;Hendriksen et al, 2011;Chin et al, 2011;Hasan et al, 2012;Shah et al, 2014;Schmid et al, 2014;Devault et al, 2014;Wagner et al, 2014;Knetsch et al, 2014) (Cui et al, 2013;Wagner et al, 2014) 32 Figure 1. General workflow followed during high throughput sequencing (Metzker, 2010). 33 Figure 2. Estimates of evolutionary rate for different bacterial species and its relationship to the time elapsed since the most recent common ancestor of the isolates used to determine the rate. Sources of data: H. pylori (Kennemann et al, 2011), C. difficile (Didelot et al, 2012b), Sh. sonnei (Holt et al, 2012), Y. pestis rasmussen2015a (Rasmussen et al, 2015), S. aureus (Harris et al, 2010;Ward et al, 2014), S. pneumoniae (Croucher et al, 2011), L. pneumophila (Sánchez-Busó et al, 2014), M. tuberculosis (Comas et al, 2013), M. leprae (Schuenemann et al, 2013), S. enterica (Zhou et al, 2013). 34