In the middle of the 20th century, enthusiasm about the success in the fight against infectious diseases caused by the introduction of antibiotics, improved hygienic measures, better nutrition, the availability of clean drinking water, and other factors led to the assumption that bacterial infections were no longer a serious health threat. First cracks appeared almost unperceived in the protective shield against infectious diseases. Currently, bacterial infections represent one of the most important public health issues, including emerging and reemerging bacterial diseases, food-borne and water-borne infections, hospital-acquired infections, and the problem of antibiotic resistance (Galloway et al., 2015). According to the World Health Organization, infectious diseases account for 18% of deaths per year globally (WHO, 2013; Dye, 2014).
Serious infectious disease agents in the pre-antibiotic era such as
Another unforeseen incidence was the appearance of the so-called emerging and reemerging infectious diseases, representing a serious challenge for future human as well as animal health, agricultural production, and public safety (Morens et al., 2004; Fauci et al., 2005; Becker et al., 2006; Jones et al., 2008; Cutler et al., 2010; Keesing et al., 2010; Lin et al.,2012; Johnson et al., 2015). More than 60% of emerging and reemerging human pathogens are zoonoses, that is, pathogens transmissible between different species (Jones et al., 2008; Cutler et al., 2010; Kilpatrick and Randolph, 2012; Van Doorn, 2014). The major zoonotic diseases from more than 800 are anthrax, animal influenza, bovine spongiform encephalopathy (BSE), food-borne zoonoses (campylobacteriosis,
These shifts in infectious diseases are caused by the adaptation of microorganisms to changes in human behavior, demographics, and life style (Cascio et al., 2011); changes in economic development and land use (Suhrcke et al., 2011); loss of biodiversity (Swaddle and Calos, 2008; Ostfeld, 2009); global travel (Hufnagel et al., 2004); immigration (Schmid et al., 2008); air conditioning; crowded intensive care units in large hospitals; global environmental and climate changes (Semenza et al., 2012); evolution of susceptible populations, exotic pets, exotic foods and pathogen adaptation (Casadevall et al., 2011; Price et al., 2012); as well as advances in detection techniques (Chan et al., 2010; Allerberger, 2012; van Doorn, 2014).
With industrialization of food processing, worldwide shipment of fresh and frozen food and an increased demand for fresh bagged produce (Allerberger et al., 2015) foodrelated outbreaks shifted from local, often family-based, outbreaks to multistate or multicountry outbreaks, often caused by a single source (Shane et al., 2002; Tauxe, 2002; Denny et al., 2007; Nygren et al., 2013; Schmid et al., 2014; Ruppitsch et al., 2015b; Inns et al., 2016).
Disease surveillance is an inevitable cornerstone for early identification of infectious disease outbreaks and for timely implementation of accurate measures to combat transmission and morbidity (Johns et al., 2011). Today, where pathogens easily cross national borders, the monitoring of diseases requires an efficient local, national, and international surveillance system. Since the first epidemiological outbreak investigation in 1854 by John Snow (Lilienfeld and Lilienfeld, 1984), our knowledge of microorganisms and epidemiology has largely improved. However, effective outbreak investigation still presents a challenge. The detection, notification, and verification of an outbreak can take 3 weeks and even longer, if the public health infrastructure fails or is weak and political pressure or fear of economic implications may prevent proper reporting (Chan et al., 2010). At the current stage of scientific knowledge, it is not possible to predict future outbreaks and pathogenic agents will certainly continue to surprise health authorities (Palm et al., 2012; Price et al., 2012).
A relatively new aspect in epidemiology is the recent expansion of Internet data sources for outbreak alert and response (Morse, 2014), such as ProMED-mail (Madoff et al., 2004), FrontlineSMS (Frontline SMS, 2010), Health-Map (Brownstein et al., 2008; Freifeld et al., 2008; 2010), MedISys (
The Austrian Agency for Health and Food Safety (AGES) acts as a national focal point for the European Centre for Disease Prevention and Control (ECDC) in Stockholm, Sweden. Data submitted to ECDC are considered to be the proprietary of the individual member states and cannot be shared with the above-mentioned open access media. It is up to the Health and Consumer Protection Directorate General (DG Sanco) of the European Commission and to the individual member state to share information with public media.
The basis for efficient surveillance is rapid identification of dangerous microbial strains and data sharing (Morse, 2014; Varan et al., 2015). Recent advances in diagnostics facilitate identification and characterization of microorganisms by more accurate and effective tools such as polymerase chain reaction (PCR), real-time PCR, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS), Sanger sequencing, and whole genome sequence (WGS) analysis (Figure 1) (Baker et al., 2010).
Typing discriminates isolates or strains below species or subspecies level. This is achieved through the application of a multitude of methods comprising DNA-fragment-based, DNA-sequence-based, and PCR-based typing methods. The potential of MALDI-TOF MS for typing has still to be confirmed (Giebel et al., 2010; Kok et al., 2013). Typing results can identify the source of an outbreak, allow study of the microbial population dynamics, and are helpful in epidemiological surveillance of bacterial infections. Besides the ability of a typing method to clearly identify isolates that are involved in an outbreak, the method must accurately differentiate outbreak strains from non-outbreak isolates. However, despite progress and exciting innovations in molecular microbiology, the process of typing in diagnostic laboratories is still laborious and time consuming (Palm et al., 2012). For example, although WGS allowed determination of the genome sequence of the Shiga toxin-producing
Nevertheless, technological advances in whole genome research will not only raise analysis of pathogen genomes to a new dimension but will also allow analysis of human genomic variations that affect immunity (Casanova and Abel, 2007; Rowell et al., 2012). Currently, research interests in infectious diseases are still underrepresented in human genome epidemiology (i.e., susceptibility to infections reliant on ethnic group and gender), although infectious diseases are a leading cause of morbidity worldwide (Rowell et al., 2012; Palm et al., 2014).
The aim of this outline is to review the value of strain typing in public health and in the food industry. Selected examples are used to give an overview on current molecular typing tools, discussing the potential as well as the shortcomings of diverse techniques in reference to our own work and to present an outlook on upcoming technologies based on WGS.
Identification denotes the assignment of a microorganism into a classification scheme based on diverse criteria to species or subspecies level (Figure 1). In principle, this is a well-established process in microbiological laboratories. In clinical medicine, rapid and accurate pathogen identification is the basis for correct diagnosis and proper treatment.
In the microbiology laboratory, technological evolution is changing the identification process from conventional phenotypic techniques to PCR- and sequence-based methods. However, in contrast to viral diagnostics, culture-based methods are still dominating in clinical microbiology (Mancini et al., 2010). Over the past years, sequence analysis of the 16S rRNA gene has allowed identification of several new bacterial species or bacterial communities (Woo et al., 2008; Loncaric et al., 2011) and represents the “gold standard” for bacteria classification because of quality and accuracy of a sequence-based method and the availability of comprehensive databases such as the Ribosomal Database Project (RDP) (Cole et al., 2014) containing 3,356,809 16S rRNAs by November 2016 (rdp.cme. msu.edu) or Greengenes (DeSantis et al., 2006) (
For selected pathogens, specific PCR detection assays, including real-time PCR methods, have been developed: they are very powerful, simple, and effective tools for fast detection and identification, even directly from clinical or environmental specimens (Maheux et al., 2013). To bypass the main disadvantage of PCR-based identification technologies, that is, the one primer pair for each pathogen principle, diverse broad range and multiplex PCR protocols have been published to allow detection of the 35 main important pathogens in a single and closed-tube reaction format, considerably shortening the time to result and improving the outcome of patients (Dark et al., 2009; Mancini et al., 2010; Lucignano et al., 2011).
In our laboratory, a high-resolution melting (HRM) PCR assay was developed for rapid and accurate differentiation of highly pathogenic
Typing is the differentiation of isolates or strains (well-characterized isolates) below species or subspecies level (Figure 1). In past years, molecular biological methods have replaced conventional methods in infectious disease epidemiology, yielding the discipline of “molecular epidemiology” (Foxman and Riley, 2001). Molecular bacterial typing generates isolate-specific genetic fingerprints suitable for assessing epidemiological relatedness.
The molecular basis for typing is the genomic difference between genomes, particularly single nucleotide polymorphisms (SNPs), and insertions and deletions. All genetic variations can be detected by several methods, that is, PCR based, DNA fragment based, sequence based or WGS based. However, the typing resolution differs between the different methods.
For rapid typing of isolates in routine diagnostics, PCR-based methods targeting a single genetic region are currently the most powerful techniques in terms of cost, simplicity, and turnaround time with potential for standardization (Figure 2). All other molecular-based techniques, either DNA fragment based such as pulsed-field gel electrophoresis (PFGE) (Schwartz et al., 1983), amplified fragment length polymorphism (AFLP) (Vos et al., 1995), restriction fragment length polymorphism (RFLP), and random amplified polymorphic DNA (RAPD) or sequence based such as multi-locus sequence typing (MLST) (Maiden et al., 1998), multi-virulence-locus sequence typing (MVLST) (Zhang et al., 2004), and WGS (Köser et al., 2012) are more time consuming and more cost-intensive approaches (van Belkum et al., 2007; Köser et al., 2012). A disadvantage of the PCR-based method is the fact that for each target, you need a specific pair of primers. Multiplex PCR assays may circumvent this shortcoming via parallel analyses of several loci.
For particular pathogens (
Detection of diverse toxin and virulence genes by PCR. VPI, vibrio pathogenic island; (+) target is present; (-) target is absent. The absence and presence of targets yield isolate-specific binary codes.
Tabelle 1. Nachweis von Toxin- und Virulenzgenen mittels PCR. VPI, Vibrionen pathogenitätsinsel, (+) Target ist vorhanden, (-) Target fehlt. Die Anwesenheit und das Fehlen vonTargets ergibt Isolat spezifische Binärcodes.
Strain ID | VPI | Serogroup | CTX | toxR | Toxine genes | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
tcpI | tcpA (class.) | tcpA * | vpi R | ssrA | vpi L | Vc-O139 | Vc-O1 | ctxA | zot | ace | st | hlyA (class.) | hlyA * | ompU | ||
CHT 18/01 | + | + | - | + | + | + | - | + | - | + | + | + | - | - | - | + |
CHT 57/03 | + | + | - | + | + | + | - | + | - | + | + | + | - | - | - | + |
CHT 17/01 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
CHT 59/04 | + | - | + | + | + | + | - | + | - | + | + | + | - | + | + | + |
CHT 60/04 | + | - | + | - | + | + | - | + | + | + | + | + | - | + | + | + |
W 15/EU 158 | - | - | - | - | + | - | - | - | - | - | - | + | - | + | + | - |
W 16/EU 156 | - | - | - | - | + | - | - | - | - | - | - | + | - | + | + | + |
W 27/CHT 71 | - | - | - | - | - | - | - | - | - | - | - | + | - | + | + | - |
CHT 14/00 | - | - | - | - | - | - | - | - | - | - | - | - | - | + | - | - |
CHT 15/00 | - | - | - | - | - | - | - | - | - | - | - | + | - | - | + | - |
CHT 16/01 | - | - | - | - | - | - | - | - | - | - | - | + | - | - | + | - |
CHT 19/01 | - | - | - | - | - | - | - | - | - | - | - | + | - | - | + | - |
CHT 20/01 | - | - | - | - | - | - | - | - | - | - | - | + | - | - | - | - |
CHT 54/02 | - | - | - | - | + | - | - | - | - | - | - | + | - | + | + | + |
CHT 55/02 | - | - | - | - | - | - | - | - | - | - | - | - | - | + | - | - |
CHT 61/04 | - | - | - | - | + | - | - | - | - | - | - | + | - | + | + | + |
CHT 62/04 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
CHT 63/05 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
CHT 64/05 | - | - | - | - | + | - | - | - | - | - | - | + | - | + | + | - |
CHT 67/05 | - | - | - | - | + | - | - | - | - | - | - | + | - | + | + | - |
CHT 68/05 | - | - | - | - | - | - | - | - | - | - | - | - | - | + | - | - |
Toxigenic
In addition, our laboratory performs toxin gene profiling in cases of
The development of high-resolution analysis of DNA melting curves represents a considerable improvement in PCR analysis. This new, simple, rapid, and precise PCR technique allows detection of mutations throughout the entire amplification product (Wittwer et al., 2003; Reed et al., 2007; Ruppitsch et al.,2008; Pietzka et al., 2009; 2010; 2011; Antolinos et al., 2012; Mayerhofer et al., 2012; Zeinzinger et al., 2012), yielding considerably more information than conventional PCR or probe-dependent classical real-time PCR genotyping methods. The specificity of single nucleotide polymorphism (SNP) detection using HRM curve technology is comparable to DNA sequencing. The potential of HRM curve technology to detect diverse mutations within a DNA fragment in a single and simple PCR step makes the method very powerful for SNP-based high-throughput typing applications (Figure 3).
The usefulness of this technique was demonstrated during a multinational outbreak of listeriosis in 2009 and 2010 affecting Austria, Germany, and the Czech Republic, where a newly developed internalin B gene scanning assay was used successfully for screening more than 100 food isolates for source identification (Fretz et al., 2010a; 2010b). In this outbreak, HRM curve profiling allowed unambiguous differentiation between two different clones, as well as immediate identification of new cases as either outbreak related or unrelated (Fretz et al., 2010a).HRM was used further for rapid identification of multid-rug-resistant (MDR)
AFLP is a typing technique based on the amplification of genomic restriction fragments via PCR (Figure 4) (Vos et al., 1995). AFLP represents a relatively simple, low cost, rapid, and highly discriminatory method that covers a larger portion of the genome than other typing techniques (Vos et al., 1995; Szaluś-Jordanow et al., 2013). No prior knowledge of the DNA sequence of the respective organism is necessary (Pietzka et al., 2008a; Bhowmick et al., 2009). In principle, the method scans the genome for sequence polymorphisms producing DNA fragments, mainly between 50 and 700 basepairs in size. The presence and absence of fragments produces a band pattern or AFLP profile comparable to bar codes used for product identification in commerce. Here it determines a genetic fingerprint. For subsequent data analysis, the resulting AFLP profile is finally converted into a binary presence or absence (+/− or 1/0) code, a process known as “scoring” (Kück et al., 2012). The bin code obtained is specific for a species and represents the basis for determining the relatedness of strains (Figure 4). AFLP has been very useful for taxonomic studies because it clearly classifies bacteria belonging to the same genomic species and is very discriminative in differentiating highly related bacterial strains belonging to the same species (Lin et al., 1996; Keim et al., 1997). AFLP has been successfully applied to a variety of bacteria, for example,
Our laboratory applied this method for typing of
PFGE was initially described in 1983 (Schwartz et al., 1983) and still represents the “gold standard” in molecular typing of most bacterial species. PFGE is a RFLP-based gel electrophoresis method in which genomic DNA is restricted with rare cutting restriction endonucleases (macrorestriction) yielding a limited and separable number of high molecular weight DNA fragments. The fragments are finally separated in an electric field where the orientation is changed periodically (pulsed-field) (Figure 5). The development of this method overcame the limitations of conventional electrophoresis by separating large DNA fragments. A common problem of gel-based fragment analysis method is that, in principle, only patterns generated on the same gel can be compared directly. To compare data obtained from different runs or— even worse—from different laboratories, strict protocols must be applied (Swaminathan et al., 2001). Owing to its high discriminatory power, PFGE is the method of choice in outbreak investigation, although faster and simpler methods are highly appreciated (Ruppitsch et al., 2007a, 2007c; Fretz et al., 2010a, 2010b; Huhulescu et al., 2011; Chung et al., 2012). In our laboratory, PFGE analysis was applied for epidemiological investigation and elucidation of a multinational listeriosis outbreak in Austria, Germany, and the Czech Republic in the years 2009– 2010 (Fretz et al., 2010a, 2010b) and an ongoing outbreak in Germany (Ruppitsch et al., 2015b). Furthermore, PFGE was used to identify the source of infection in a fatal case of
Variable number of tandem repeats (VNTR) or simple sequence repeats (SSR) are reiterations of at least two nucleotides in the genome (Britten and Kohne 1968). The terminology depends on the number of nucleotides within a repeat unit. Microsatellites (SSR or tandem repeats) contain 2–7 nucleotides, minisatellites harbor 10–100 nucleotides, and repeat units with more than 100 nucleotides are termed macrosatellites. The analysis of several loci is named multi-locus VNTR analysis (MLVA). The variations in repeat unit numbers, sizes, sequences, and the possibility to include several loci into the analysis scheme render MLVA a very powerful and highly discriminatory typing technique. MLVA typing schemes have been developed for nearly all pathogenic bacteria (Figure 6) (van Belkum, 2007). The final result is the number of repeat units that is calculated from the fragment size. In our laboratory, SSR analysis was used for typing of
In the case of tuberculosis surveillance, MLVA has become the gold standard of typing. This MLVA application is generally called mycobacterial interspersed repetitive units (MIRU)—VNTR analysis—and was applied in our laboratory for typing of 1,294
Sequence-based typing methods are the most advanced and accurate techniques currently available for characterization of isolates or strains. Depending on the target and questions that have to be answered in an outbreak or epidemiological investigation, single or multiple genes can or must be analyzed for strain characterization.
Single-locus sequence typing (SLST) applications is the sequence analysis of a single gene with sufficient sequence variations for typing bacterial isolates (Beall et al., 1996;Frenay et al., 1996; Olvera et al., 2006; Bennett et al., 2007). Typing the polymorphic X region of the staphylococcal protein A gene (
In November 2016, 718 different repeat units and 16,430
In our laboratory,
MLST uses sequence variations in up to seven housekeeping genes to characterize bacterial isolates via the Internet (Aanensen and Spratt, 2005) and is generally accepted for the characterization of currently 120 microorganisms (
The progress in technology from automated Sanger sequencing (first-generation sequencing) to next-generation sequencing (NGS) alias WGS has revolutionized the field of genomics, genetics, as well as microbiology. NGS allows fast, high-throughput, and, nowadays, with introduction of the third or fourth generation of NGS instruments, also inexpensive analysis of several hundreds of genes or entire (bacterial) genomes within a single day (for approximately 50 €) (Almeida and Araujo, 2012).
The major improvement of NGS/WGS over other sequencing technologies or sequence-based methods is the immense amount of data. The ability to interpret this data represents the current bottleneck of NGS preventing the broad usage in diagnostics (Stone et al., 2012). NGS/WGS platforms exist from Illumina, Thermo Fisher, Oxford Nanopore, and Pacific Biosciences. Although the biochemistry of the diverse platforms might be different, the workflow is similar (Shendure and Hanlee, 2008) including template preparation, sequencing and imaging, genome alignment, and assembly (Metzker, 2010). The advantages as well as disadvantages of diverse platforms (Metzker,2010; Quail et al., 2012; Doi et al., 2014; Hinrichs et al., 2015; Hahn et al., 2016) have been reviewed.
The most immediate field where NGS have been introduced into daily routine diagnostics in microbiology is surveillance and outbreak investigation. Several studies on a variety of bacterial species have already shown that WGS-based typing, based either on single nucleotide variants (SNVs) (Turabelidze et al., 2013; Pightling et al., 2015) or on gene-by-gene allelic profiling of core genome genes, frequently named core genome MLST (cgMLST) or MLST+ (Laing et al., 2010; Mellmann et al., 2011; Köser et al., 2012; Maiden et al., 2013; Antwerpen et al., 2015; de Been et al., 2015; Moran-Gilad et al., 2015; Chaudhari et al., 2016; Moura et al., 2016), currently represents the ultimate diagnostic typing tool that have been successfully applied for outbreak investigations (Figure 9) (den Bakker et al., 2014; Schmid et al., 2014;Ruppitsch et al., 2015b; Burckhardt et al., 2016; Chen et al., 2016; Jackson et al., 2016). Backward compatibility of NGS to former methods can be maintained by the extraction of the respective information from NGS data (Hyden et al., 2016). The undeniable advantage of NGS is the optimal resolution that can be achieved by a method for strain typing. With NGS, even small genetic variations occurring in an outbreak strain over the course of an outbreak can be monitored. By tracking the presence or absence of these mutations in all pathogenic genomes from a given outbreak, it is possible to identify where particular variants arose and trace person-to-person transmission events (Köser et al., 2012).
With the development of BIGSdb (Jolley and Maiden, 2010), GenomeTrakR (Allard et al., 2016), and cgMLST. org
Although conventional typing techniques such as phage typing or serotyping have largely been replaced by molecular methods because of cost, speed, accuracy, data interpretation, sensitivity, specificity, and simplicity, there is still no general typing method that meets all desirable criteria available. Thus, the method chosen for typing largely depends on the microbiological question (van Belkum et al., 2001). Molecular epidemiology is based on the principle of comparing genotypic characteristics of pathogens. Molecular epidemiology combines data obtained from molecular analysis of strains and isolates with epidemiological data obtained from the detection, investigation, and analysis of infectious diseases in human, animal, or plant populations. As a consequence, the pure molecular analysis of isolates or strains without a linkage to epidemiological data may not be regarded as “molecular epidemiology.”
Attribution of a particular pathogenic subtype to an outbreak or to contamination of foods and feeds is often possible, simply by relying on phylogenetic markers. However, indistinguishable typing patterns must not be misunderstood as absolute proof of causative relation, because alternate hypotheses will exist without exception (Keim et al., 2011). Molecular techniques usually provide highest sensitivity and specificity and, therefore, deliver more precise data than phenotypic methods. In the field of epidemiology application of molecular techniques enables detection of new pathogens, disease surveillance, surveillance of outbreaks, identification of transmission patterns, study of host–pathogen interactions, selective effect of genetic polymorphisms in humans by infectious pathogens, association of cancer or other infectious diseases to infective agents, and detection of unculturable microorganisms (Foxman and Riley, 2001).
The key part for accurate diagnosis, for effective and timely treatment of a disease, and subsequently, for the discovery of the source of infection or outbreak is the proper identification of the infective agent (Vlek et al., 2012). The process of identification is well established for known pathogens in microbiological laboratories, and therefore, nearly all clinically important microorganisms can be identified without difficulty. The disadvantage of this system is, however, that we recognize only what we already know. The elucidation of the cause of new and emerging infectious diseases is more challenging and often even impossible when using only conventional methods. The shift from phenotypic to molecular methods has largely improved the identification process and facilitated the detection of yet unknown microorganisms (Relman et al., 1992; Roads et al., 2012).
The final product of this ongoing evolution in diagnostics is the implementation of mass spectrometry for fast and accurate identification of microorganisms. The value of MALDI-TOF MS for typing has to be elucidated (Giebel et al., 2010; Kok et al., 2013). To classify newly discovered microorganisms, 16S rDNA sequencing—or more probably in the very near future NGS—will still remain an essential identification tool.
The introduction of these modern technologies in the microbiological laboratories is still slow, because of the traditionally well-established conventional identification methods. However, many traditional methods require days to properly identify certain pathogens, whereas modern molecular methods, in addition to being much faster, display also an improved sensitivity and specificity (te Witt et al., 2010). For the fast and specific detection of pathogens directly from sample material, a specific PCR assay is the state-of-the-art method. The advantage of PCR technology is the specific and fast detection of pathogens directly from specimen without the need for cultivation. The drawback of PCR technology—one specific primer for each pathogen—was at least partially eliminated through the development of multiplex PCR assay that allows detection of up to 35 different pathogens in a single reaction (Mancini et al., 2010).
The ability to track bacterial outbreak strains accurately is essential for control and prevention of disease. The Antibiotic Resistance, Prevention and Control (ARPAC) project of the European Commission highly recommended the use of molecular typing techniques by national health authorities for alert organisms (MacKenzie et al., 2005). In cases of outbreak, for surveillance purposes, epidemiological analysis, clinical identification of particular strains, vaccine development and monitoring, and understanding the evolution of pathogenic organisms, the step following accurate identification of a microorganism is the more detailed characterization, that is, typing or subtyping the microorganism. The “gold standard” typing method allows typing of all isolates, a high degree of reproducibility, appropriate stability of the targets under investigation, and excellent resolving power (van Belkum et al., 2001). It is also important that typing methods are not too expensive, too complicated, and easily available (van Belkum et al., 2001). The discriminatory power of a typing method can be expressed as a number enabling comparison of diverse typing methods and facilitating choice of the most appropriate method in question of. Nowadays, typing is dominated by molecular methods that have almost completely displaced phenotypic methods. The introduction of molecular techniques in the field of epidemiology has created the discipline of molecular epidemiology. In the field of public health, molecular epidemiology currently mainly rely on PCR-based typing, DNA-fragment-based typing, and DNA-sequence-based typing as depicted in Figure 1. A methodical exception to the shift from conventional to molecular typing techniques is the classical serotyping of
The most widely used molecular typing technique is PFGE. Like AFLP, RFLP, and RAPD, PFGE is a fragment-based technique. The main advantage of fragment-based methods is that no sequence information is needed when performing the analysis. The disadvantage of all fragment-based methods is that band patterns are difficult to compare between laboratories and that reproducibility is low for some of these methods. Nevertheless, PFGE is highly discriminative and shows a good reproducibility (van Belkum et al., 2007) but is limited by interlaboratory comparability, problems of band interpretation, low throughput, sophisticated sample preparation, and relatively high costs. Hence, there is demand for more accurate methods (Cookson et al., 1996). PFGE is also not the optimal method for long-term epidemiological investigations, because it is often too overdiscriminating. Thus, sequence-based methods have an explicit advantage in speed, unambiguous data interpretation, simplicity in the creation of databases, and simplicity of standardization between different laboratories worldwide (MacKenzie et al., 2005). Multilocus sequence typing is a highly discriminatory method, widely used for strain typing. The major disadvantage of this technique is that sequence analysis of seven or more genes is still a time-consuming and costly approach. Furthermore, the submission of data is not automated and, therefore, also time consuming. In conclusion, the length of analysis time impedes the value of PFGE or MLST typing in an outbreak situation because of tremendous political and media pressure and demonstrates beyond all questions the merit of rapid typing methods such as HRM curve profiling (Pietzka et al., 2011).
A clear advancement in sequence-based typing was the introduction of automated
Besides the ability of a typing method to clearly identify isolates that are involved in an outbreak, the method must accurately differentiate outbreak strains from nonoutbreak isolates. Typing methods can be used for a variety of purposes to understand phylogeny (evolution) and bacterial population genetics, to identify specific strains spreading globally in specific populations and/or in core groups, to identify temporal and geographic changes in strain types as well as the emergence and transmission of individual strains, to establish strain identity/difference in contact tracing or test of cure, to confirm/disprove treatment failures, to resolve medicolegal issues such as sexual abuse, and to confirm presumed epidemiological connections or discriminate isolates of suspected clusters and outbreaks. Finally, such information can be applied to design different public health preventive measures and interventions (Unemo and Dillon, 2011).
WGS has become the ultimate method for the characterization of bacterial isolates as it provides the highest possible resolution in strain typing (i.e., the DNA sequence level) and represents a new paradigm for outbreak investigation and contamination-source tracking. An additional benefit of NGS is the opportunity to extract specific information, such as classical MLST profiles for backward data comparability, the determination of virulence and antibiotic resistance status, as well as the assignment to serogroups as a first-level information, respectively, which is a clear additional benefit of this new technology.