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  Table of Contents  
SPECIAL ARTICLE
Year : 2019  |  Volume : 37  |  Issue : 2  |  Page : 147-162
 

Current strategy for local- to global-level molecular epidemiological characterisation of global antimicrobial resistance surveillance system pathogens


Department of Clinical Microbiology, Christian Medical College, Vellore, Tamil Nadu, India

Date of Submission18-Oct-2019
Date of Decision21-Oct-2019
Date of Acceptance01-Nov-2019
Date of Web Publication19-Nov-2019

Correspondence Address:
Dr. Veeraraghavan Balaji
Department of Clinical Microbiology, Christian Medical College, Vellore - 632 004, Tamil Nadu
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijmm.IJMM_19_396

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 ~ Abstract 


The prime goal of molecular epidemiology is to identify the origin and evolution of pathogens, which can potentially influence the public health worldwide. Traditional methods provide limited information which is not sufficient for outbreak investigation and studying transmission dynamics. The recent advancement of next-generation sequencing had a major impact on molecular epidemiological studies. Currently, whole-genome sequencing (WGS) has become the gold standard typing method, especially for clinically significant pathogens. Here, we aimed to describe the application of appropriate molecular typing methods for global antimicrobial resistance surveillance system pathogens based on the level of discrimination and epidemiological settings. This shows that sequence-based methods such as multi-locus sequence typing (MLST) are widely used due to cost-effectiveness and database accessibility. However, WGS is the only method of choice for studying Escherichia coli and Shigella spp. WGS is shown to have higher discrimination than other methods in typing Klebsiella pneumoniae, Acinetobacter baumannii and Salmonella spp. due to its changing accessory genome content. For Gram positives such as Streptococcus pneumoniae, WGS would be preferable to understand the evolution of the strains. Similarly, for Staphylococcus aureus, combination of MLST, staphylococcal protein A or SCCmec typing along with WGS could be the choice for epidemiological typing of hospital- and community-acquired strains. This review highlights that combinations of different typing methods should be used to get complete information since no one standalone method is sufficient to study the varying genome diversity.


Keywords: Antimicrobial resistance lineages, global antimicrobial resistance surveillance system, molecular epidemiology, outbreak investigation, whole genome sequencing


How to cite this article:
Muthuirulandi Sethuvel DP, Devanga Ragupathi NK, Bakthavatchalam YD, Vijayakumar S, Varghese R, Shankar C, Jacob JJ, Vasudevan K, Elangovan D, Balaji V. Current strategy for local- to global-level molecular epidemiological characterisation of global antimicrobial resistance surveillance system pathogens. Indian J Med Microbiol 2019;37:147-62

How to cite this URL:
Muthuirulandi Sethuvel DP, Devanga Ragupathi NK, Bakthavatchalam YD, Vijayakumar S, Varghese R, Shankar C, Jacob JJ, Vasudevan K, Elangovan D, Balaji V. Current strategy for local- to global-level molecular epidemiological characterisation of global antimicrobial resistance surveillance system pathogens. Indian J Med Microbiol [serial online] 2019 [cited 2019 Dec 14];37:147-62. Available from: http://www.ijmm.org/text.asp?2019/37/2/147/271181





 ~ Introduction Top


Identification of causative agents is a highly important issue in diagnostic microbiology laboratories, and this has now been a topic of interest in clinical research. Although conventional microbiological methods are inexpensive, they have certain limitations associated with it, especially for the identification and characterisation of fastidious, slow-growing, non-viable or non-cultivable organisms which can be easily detected by molecular methods. Currently, molecular methods are increasingly being used in the field of epidemiology due to its rapid turnaround time, sensitivity and specificity although they are expensive than the conventional methods.[1]

However, the currently available molecular methods are time-consuming and lack resolution and provide only limited information for outbreak investigation.[2] The recent advancement of next-generation sequencing (NGS) and whole-genome sequencing (WGS) methodologies had a major impact on molecular epidemiology.[3] Previous studies have shown that WGS had short turnaround time (48–96 h) and unprecedented level of resolution for outbreak investigations in the clinical setting.[4]

One of the prime goals of epidemiology is to identify the origin and evolution of a pathogen, which can potentially influence the public health worldwide. Although several methods such as multi-locus sequence typing (MLST), multiple locus variable number of tandem repeats (MLVA) and pulse field gel electrophoresis (PFGE) are available, currently WGS opens up many avenues of approach to analyse an isolate's genetic content.[5],[6] Generally, variability between bacterial genomes of the same species occurs for various reasons that include single nucleotide polymorphism (SNP), insertion or deletion of one or multiple base pairs (indels) or by recombination of large blocks of genetic sequences.[7],[8] Among which SNPs are the most common and simplest form of DNA variations and found to be the important driver of bacterial evolution and expansion. Therefore, SNPs can be used as a stable genetic marker for the dissemination of a particular strain.[9]

WGS has also been used to identify the international spread of antimicrobial-resistant pathogens.[2],[10] Antimicrobial resistance (AMR) is a growing problem in both clinical and socioeconomic setting. In 2015, the World Health Organization (WHO) launched the global AMR surveillance system (GLASS) to address this issue, which mainly focus on the human bacterial pathogens. Further, the accuracy, reliability and affordability of NGS would permit their use in clinical and public health laboratory for rapid identification of pathogen, outbreak investigation and infection control process.[11]

Primarily, all typing methods are eventually useful, but the challenge is to find what methods are appropriate for specific settings. Different epidemiological settings require different typing methods providing different level of discriminatory power. In this review, we aimed to provide information on the appropriateness of different molecular typing methods for GLASS pathogens for different levels of epidemiological characterisation. The global spread of AMR lineages of these pathogens was also discussed.


 ~ Typing System for Global Antimicrobial Resistance Surveillance System Pathogens: Appropriate Molecular Approaches Top


This could be broadly classified based on the need for local-, regional- and global-level discrimination of the strains [Figure 1]. The best approaches could be as follows.
Figure 1: Hierarchical molecular epidemiological typing methods to address outbreak at local, regional and global level. wg: Whole genome, SNP: Single-nucleotide polymorphism, MLST: Multi-locus sequence typing, SLV: Single locus variant, DLV: Double locus variant, Rmlst: Ribosomal MLST, cg: Core genome

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With WGS, MLST on genome-wide scale such as ribosomal MLST, core genome MLST and whole-genome MLST with increasing discriminatory power is achievable. The main characteristics of these methods were obtained from Enterobase database (https://enterobase.warwick.ac.uk/) that is given in [Table 1]. Typing methods can be chosen based on the targets and the level of discrimination required for analysis. For instance, in VRE outbreak analysis, PFGE is considered gold standard though the method is comparably laborious and time-consuming and requires experienced personal for analysis. Alternatively, MLST and MLVA are not reliable for outbreak analysis due to their less discriminatory power but can be used when combined with other data such as plasmid typing, virulence gene profiling, van A cluster typing. Methods such as plasmid typing and van A cluster typing are useful when used along with the basic methods such as MLST, MLVA and PFGE but not useful as a standalone method. However, WGS can provide all the information required for the analysis all at once.[12]
Table 1: Typing methods based on the targets and the level of discrimination required for analysis (Enterobase)

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 ~ Genome Features, Resistant Lineages and Appropriate Typing System for Individual Global Antimicrobial Resistance Surveillance System Pathogens – problems and Solutions Top


The information on the pathogen specific typing methods for local-, regional- and global-level analysis is detailed in [Table 2].
Table 2: Pathogen-specific typing methods for local-, regional- and global-level analysis

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 Escherichia More Details coli and Shigella

Escherichia coli is the part of normal gut microflora; however, pathogenic strains also exist and can cause infection in humans. Though being a commensal, the genetic flexibility of the species makes it an effective recipient and donor of AMR determinants through horizontal gene transfer mechanism.[13] Notably, Shigella spp., a human restricted pathogen, is considered as a human adapted E. coli and was viewed as the biotypes/pathotypes or clones of E. coli.[14] However, recent studies have indicated that Shigella spp. has emerged at least seven separate times from E. coli.[15]

Interestingly, multidrug resistance (MDR) E. coli, particularly strains that produce extended-spectrum β-lactamases (ESBLs), are a rising clinical and public health challenge. Similarly, Shigella clones with high virulence and MDR have spread globally. Moreover, differentiation of E. coli from Shigella spp. is still challenging in many microbiology laboratories because of their close genetic relatedness.[16] The recent development in WGS technologies provide accurate differentiation of these pathogens and enable us to understand the genome features and the epidemiological characteristics of these pathogens.

In general, E. coli genome comprised only 20% of core genes and 80% are accessory genes.[17] Similarly, Shigella spp. has varying accessory genes. Of the predicted E. coli pan-genome, the core genome consists of around 5000 genes. However, previous study on the phylogenomics of Shigella spp. showed that the predicted pan-genome has approximately 10,000 genes, which proves the fact that Shigella spp. does not have the same genomic profile as E. coli. Furthermore, Shigella contains a smaller genome than E. coli genomes.[18] This could be due to its distinctive characteristics of losing of genes as a part of an intracellular pathogenic lifestyle.[19],[20]

Further, increasing AMR in these pathogens are a significant public health concern. Remarkably, mobile genetic elements (MGEs) plays a major role in dissemination of resistance mainly through horizontal gene transfer especially among the enteric pathogens.[13] For instance, ST131 lineage carrying blaCTX-M-15 gene in E. coli has epidemic potential in spreading the resistance globally. In the same way, fluoroquinolone-resistant Shigella sonnei global lineage III has the ability to spread intercontinentally by acquiring mobile elements carrying AMR genes. Therefore, it is important to identify the circulating resistant lineage of these pathogens in the community and hospital settings to manage the spread of drug-resistant strains.

Molecular typing methods for  Escherichia coli Scientific Name Search ella spp.

For E. coli, PFGE is a reliable and highly discriminatory method and has been considered to be the “gold standard.” Recently, due to the establishment of Pulse Net, the use of PFGE has had a major impact on the bacterial sub-typing and outbreak investigation.[21],[22] However, analysis of multiple banding pattern leads to inter-laboratory variability and is not amenable to create public databases, thus not suitable for regional- or global-level analysis.[23] Further, fimH and fimC typing method has been developed and been previously used for epidemiological surveillance of global pandemic clones of E. coli. This has shown superior discrimination power than conventional MLST.[24]

Previous study revealed that the combination of MLST along with fim H and fum C typing showed good discrimination of E. coli strains.[24],[25] These sub-typing method has shown to be mainly applicable among the highly virulent ST131 clonal group as this group is the most predominant human pathogenic clone being reported.[26] This method is suitable for screening large collections of E. coli isolates, allowing for the rapid identification of sequence types (STs) or clonal complexes (CCs).[23] Similarly, single-locus or double-locus variants (SLV/DLV) approach which is the discrimination based on difference in one or two MLST housekeeping gene sequence from the founder genotype is termed SLV/DLV, used to differentiate closely related species.[16]

Moreover, there is no MLST database accessible currently for E. coli in pubMLST. However, MLST data can be derived only from whole-genome sequences through Enterobase database. Since WGS has higher discriminatory power than other typing methods, it can also be used to differentiate sub-clones of ST131. For example, the H41 sub-clone of ST131 is fluoroquinolone susceptible, while H30 is extended-spectrum cephalosporin-resistant and the prevalence of these sub-clones is different. This issue can be resolved with WGS data.[24]

Similarly, epidemiological typing of Shigella spp. has being done using the E. coli database till today from the evidence that the Shigella spp. is evolved from E. coli. Alike E. coli, conventional typing methods such as PFGE and MLVA can be used for the extent of local outbreak analysis. For local- and regional-level analysis, SLV/DLV approach can also be used. However, WGS method is the only method of choice to study the relatedness of the Shigella strains.

Antimicrobial resistance-specific lineages of Escherichia coli

There is a rising evidence that clonal lineages of E. coli ST 131 have more epidemic potential than other lineages within their species.[27] E. coli ST131 carrying blaCTX-M-15 is globally disseminated in community and healthcare settings. Most of the MDR E. coli carrying blaCTX-M-15 from different countries in Europe and North America was homogenously grouped into the E. coli O25:H4-ST131.[28],[29],[30] This particular lineage stands out among the other E. coli lineages due to its nature of resistance to fluoroquinolones and β-lactams, mediated by sequential accumulation of chromosomal mutations and plasmid containing blaCTX-M-15 ESBL, respectively.[31]

Particularly, for carbapenem resistance (CR), blaNDM was found to be carried by ST167 and ST405 in China. In addition, ST405 carried blaKPC for CR. Recently, ST410 was reported as a possible international high-risk clone with B2/H24R, B3/H24Rx and B4/H24RxC AMR clades. B3/H24Rx was reported to be evolved by acquisition of the blaCTX-M-15 and an IncFII plasmid. B4/H24RxC emerged by acquiring IncX3 plasmid with blaOXA-181 known for CR, which further acquired blaNDM-5, on a conserved IncFII plasmid. This newly emerging ST410 has been reported in countries such as Denmark, China, UK, France and India.[32]

In India, ST167 (18%) and ST405 (20%) were predominant type followed by ST131 (15%) and ST410 (12%). Among these STs, ST131 and ST405 clades carried blaCTX-M-15 for cephalosporin resistance, whereas ST167 and ST410 carried both blaCTX-M-15 and blaCMY genes. CR genes, blaNDM was found in ST405, 167 and 410 as observed elsewhere (unpublished data). This represents that the global scenario matches with the Indian data for prevalence of STs and associated AMR genes. The STs that are widespread and increasingly reported to be associated with antimicrobial resistance was mapped in [Figure 2]a.
Figure 2: Global distribution of predominant sequence types or lineages associated with antimicrobial resistance identified in Gram-negative pathogens is mapped. Sequence types or lineages are differentiated with different shapes and colours. (a) Sequence types associated with antimicrobial resistance reported in Escherichia coli. (b) Intercontinental dissemination of four distinct Shigella sonnei lineages. (c) Sequence types linked with antimicrobial resistance identified in Klebsiella pneumoniae. (d) Incidence of typhoidal and non-typhoidal  Salmonella More Details in global context

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Antimicrobial resistance-specific lineages of Shigella spp.

Studies combining epidemiological and pathogens genome data are increasingly being reported. This approach has unravelled the evolutionary history of Shigella species. Specifically, the phylogenomic studies shows the intercontinental dissemination of rapidly evolving highly clonal biotype g S. sonnei lineages.[33] There are four distinct S. sonnei lineages (lineage I, II, III and IV). The current pandemic involves globally distributed, MDR clones that belong to lineage III, which is also referred as global lineage III and with only low numbers of other lineages. The major lineage III has three sub-groups that carry a distinct class II integron (In2) variant, which is either plasmid-encoded (South America III) or integrated into the chromosome (Central Asia IIIa, Global III).[34]

Further, lineage III was found to carry transposon Tn7 and class II integron insertion within the chromosome encoding resistance to streptomycin, trimethoprim-sulphamethoxazole and tetracycline and mutations in chromosomal gyr A and par C, which encodes the target protein for fluoroquinolone. This ciprofloxacin-resistant S. sonnei has evolved as a single clone due to gradual accumulation of the triple mutations in gyr A and par C, most likely in South Asia, before spreading internationally to Southeast Asia and Europe.[34],[35] Recent report said that Asia is a primary hub for the recent international spread of ciprofloxacin-resistant S. sonnei. The distribution of antimicrobial resistance genes (ARGs) and mutations within the chromosomal genes suggests that selection for MDR played a key role in driving this global dissemination.

Besides, S. sonnei lineage III has faster mutation rate than other lineages and appears to have high capability of acquiring resistance to other antimicrobials including third-generation cephalosporins when introduced into new geographical locations.[34],[35] These characteristics make this particular lineage more predominant worldwide. Based on the available reports, the global distribution of S. sonnei lineage III and its subsequent international spread was mapped [Figure 2]b.

Klebsiella pneumoniae

Klebsiella pneumoniae is considered as an opportunistic pathogen and the most frequent cause of hospital-acquired infections for many decades. The WHO has recently recognised K. pneumoniae as a significant threat to global public health due to its high rates of hospital outbreaks and deaths associated with AMR clone.[36] In addition, there is an emergence of drug resistant hypervirulent K. pneumoniae strains capable of causing infection in the community.[37],[38] However, little is known about the population structure of K. pneumoniae; consequently, it is important to understand the emergence of clinically important resistant clones within this genetically diverse species. The genome structure, AMR-specific lineages and the available tools for epidemiological investigation of K. pneumoniae are discussed here.

The average genome size of K. pneumoniae is 5.5 Mbp and encodes ~5500 genes. The whole-genome comparison of 100 K. pneumoniae isolates indicates that the core genome comprises of only fewer than 2000 genes and the majority 3500 are accessory genes. This indicates that the species has access to a vast gene pool and has varied accessory genome content.[38] Besides, there is evidence that the exchange of mobile accessory genes within the species through plasmids and other conjugative elements may contribute to the survival of the species in different niches.[36],[39]

Furthermore, based on the developed molecular epidemiology and sequencing technologies, studies have demonstrated that K. pneumoniae can be divided into three distinct phylogroups (phylogroup KpI represents K. pneumoniae, KpII and KpIII belong to Klebsiella quasipneumoniae and Klebsiella variicola, respectively) with variation in their core and accessory genome.[38] In K. pneumoniae, AMR genes are commonly carried in diverse plasmid types. In particular, the pandemic lineage ST258 harbour IncFII plasmid carrying blaKPC gene, suggestive of successful spread of this clone. This evidenced that K. pneumoniae can accumulate a large accessory genome from a larger pool of available genes in the environment to colonise, infect and resist antibiotics in humans.[36],[39]

Molecular typing methods for Klebsiella pneumoniae

Investigating outbreaks of MDR K. pneumoniae in hospitals settings can be done by MLST or RAPD to identify the clonal relatedness of the isolates where WGS is not feasible. Smaller outbreaks in confined settings can also be identified using conventional techniques such as PFGE, RAPD and MLST. For regional- or national-level comparison of isolates, MLST can be used. Minim-typing or mini-MLST has also been described for K. pneumoniae in which high-resolution melting analysis is performed on fragments within MLST loci.[40] However, minim-typing in conjugation with AMR screening or WGS enable quicker surveillance in hospital settings.[40],[41]

However, core genome MLST (cgMLST) obtained through WGS has been used in several hospital outbreak investigations to decipher information on the relatedness of the isolates. It helps in identifying the resistance mechanisms, clone causing outbreak and also the relatedness of the strains such as single-nucleotide variants (SNVs).[42] On the other hand, WGS helps in identifying the transfer of MGEs carrying AMR genes between various species of Enterobacteriaceae. Integrons associated with blaIMP among Klebsiella spp., Enterobacter cloacae complex, Citrobacter spp. and E. coli were found to be similar from different regions of the world.[43]

SNP-based phylogeny has also aided in the determination of nosocomial spread of CR K. pneumoniae in about 32 countries in Europe.[44] This enables large-scale infection control practices. Average nucleotide identity and genetically nearest neighbour in the isolate collection were determined to establish the geographic spread.

Antimicrobial resistance-specific lineages of Klebsiella pneumoniae

K. pneumoniae ST258, is the lineage of concern, that carries plasmid-borne CR gene, blaKPC which confers resistance to all beta-lactams including carbapenems and cephalosporins.[45] blaKPC has also been observed in multiple plasmid contexts within ST258 such as IncFII and IncR. Since multiple resistance genes are carried on IncF plasmids, this suggests that this could benefit the successful spreading of epidemic clones.[31] [Figure 2]c shows the circulation of AMR K. pneumoniae clones across the world.

pKpQIL was the first KPC-encoding plasmid described for ST258. blaKPC is found on the transposon Tn4401, which usually spreads through highly conjugative plasmid between members of Enterobacteriaceae and found K. pneumoniae ST258 as a highly compatible host.[46] The factors contributing to the epidemiologic success of this clone still remain unclear; however, chromosomal or plasmid factors in addition to AMR may increase the strains fitness. A recent study has proposed that the dissemination of MDR clones such as ST258 K. pneumoniae and ST131 E. coli could be due to the intergenic mutations and transcriptional rewiring to overcome fitness cost. These clones also use anaerobic metabolism to colonise and disseminate in host.[47]

Besides, studies showed that antibiotic exposure leads to the development of resistance strains rather than in-hospital transmission.[48] Therefore, it is inevitable to do strain-typing for K. pneumoniae to identify the resistant clones that cause infections in nosocomial settings.

Salmonella spp.

Salmonella spp. is one of the best understood food-borne pathogens and one of the leading causes of gastroenteritis in humans.[49] More than 2500 serotypes have been described for Salmonella till date and less than 100 serotypes account for most human infections.[50] Accurate identification and characterisation of this pathogen is one of the key tasks of public health laboratory surveillance for outbreak investigation and long-term epidemiology.[50]

The pan-genome of Salmonella enterica is considered complete since the number of new genes carried by each strain is not relatively large over the average genome size. S. enterica and E. coli are close relatives. The recent common ancestor of S. enterica and E. coli existed about 100 million years ago, as estimated using the protein clock model.[51] Although Salmonella is closely related to E. coli, they have an additional number of virulence genes. Some of which are reported to be located in the genomic island (GI) of the bacteria, usually near tRNA genes, which seems to facilitate the integration of the GIs into the chromosome due to their high conservation. Many Salmonella-specific GIs play a major role in virulence and found to have influence on host specificity as well as on the degree of invasiveness.[52]

Studies have shown concordant relationship within core and accessory genome of S. enterica, indicating that the acquisition of genomic material within accessory genome is not just a random event, but the selection within specific niches establishes regulatory elements that enable the survival of the bacteria. In this regard, the rapid emergence of highly clonal MDR H58 lineages is found to harbour MDR elements within the transmissible IncHI1 plasmid. This indicates that certain lineages may be characterised by the acquisition of specific accessory genetic markers, which can be used to improve identification of the source in current epidemics.

Recent studies have shown that combining core genome analysis with accessory genes, such as pan-genome-wide association studies (pan-GWASs), has enhanced understanding on evolutionary and phylogeographic patterns of this pathogen.[53] Therefore, it is important to study the pan-genome of the bacteria to understand why particular clones are more prevalent or possess particular phenotype.[54]

Molecular typing methods for Salmonella spp.

Classification of Salmonella isolates in outbreak investigation is generally based on the standard serotyping method, but now it has been replaced by a range of molecular genotyping methods. Different typing methods have been employed for the identification of the source or origin of outbreak and relatedness of the isolates, includes ribotyping, AFLP, MLST, REP-PCR, ERIC-PCR, plasmid profiling, MLVA and PFGE.[55],[56],[57] Among these methods, PFGE is the most commonly used tool and still remains as a standard method for Salmonella spp. Sub-typing for routine surveillance in microbiology laboratories, worldwide. However, the diversity of PFGE pattern limits its use in outbreak investigations.[55]

Recently, MLVA has been proposed as a supplementary method to PFGE for sub-typing of Salmonella in many countries. MLVA can able to differentiate the strains that has similar PFGE pattern. In European countries and Unites States, a standardised protocol is being used with five or seven VNTR loci for identification and outbreak investigations.[50] Another study suggests that MLVA appears a promising alternative to PFGE sub-typing Salmonella typhimurium.[58] These suggests that MLVA can be used for local outbreak analysis due to its high discrimination and PFGE along with MLVA can be used for a national-level data analysis. Banding pattern-based molecular sub-typing methods such as PFGE cannot accurately sub-type recently evolved Salmonella serovars. Further, for long-term studies of bacterial population structures, conventional sequence-based method such as MLST is an appropriate method but cannot be useful when a species having high rate of genetic recombination is studied.[58]

A latest study revealed that whole-genome MLST provides superior discriminatory power and accurate phylogenetic inferences with high epidemiological correlation, as well as provides valuable information on virulence determinants, drug resistance and genome evolution. WGS has displaced PFGE as a typing method in both ongoing surveillance and outbreak investigations.[59],[60] However, the method relies on detailed bioinformatics analysis and not feasible to be a routine typing method in a low-resource setting but can be used for global-level comparison of the isolates.[50],[58]

Antimicrobial resistance-specific lineages of Salmonella spp.

The recent rapid emergence of highly clonal multidrug-resistant H58 typhoid lineage is a worldwide concern due to its association with antimicrobial resistance. The H58 isolates can harbour a complex MDR element residing either on transmissible IncHI1 plasmids or within multiple chromosomal integration sites, which may be the primary factor driving its current expansion. SNP-based studies suggest that this clone has followed a specific course of evolution. First, the strain received an MDR plasmid (IncHI1 plasmid) and expanded under pressure of first-line antimicrobials. Second, chromosomal mutation under fluoroquinolone pressure extended the clonal expansion.[61] This MDR-associated haplotype (H58) has been broadly reported in Asia and locally in Africa.

Although nontyphoid Salmonella (NTS) predominantly causes self-limiting gastrointestinal infections, close to 5% of the isolates cause invasive bacteraemia.[62] Global monitoring of Salmonella serovar distribution suggests that S. typhimurium and S. enteritidis are the most commonly isolated serovars. Both S. typhimurium and S. enteritidis have a broad host range and can cause invasive extra-intestinal infections, leading to bacteraemia in humans.[63] Similar to the host adaptation of Salmonella typhi, recent evolution of S. typhimurium pathovars to adapt to human hosts found to be an ongoing process.[64] Phage type DT2, ST ST313 and other monophasic/non-motile variants (DT 104) are some of the patho-variants of S. typhimurium. Interestingly, patho-variant ST313 of serovar typhimurium was considered as the major etiological agent of fatal bacteraemia in Sub-Saharan Africa (sSA).[65] Unlike gastroenteritis causing ST19, ST34 and ST36 genotypes, ST313 isolates from sSA region are found to have higher survival rate inside macrophages and cause bacteraemia. MDR also has contributed to the expansion of S. typhimurium ST313.

Recently, the ST313 clade has acquired resistance to ceftriaxone, used as a first-line antibiotic for MDR bacterial infections. Further, genomic comparison studies showed that classical ST19 and ST313 shares >4000 genes, and their core genome differs by ~1000 SNPs.[66] Subsequent genome-based studies revealed two distinct phylogenetic lineages of ST313. Both the lineages are associated with AMR-mediated by differing Tn21-like integrons on the virulence plasmid. The clonal lineage 1 was replaced by lineage 2 by acquisition of chloramphenicol resistance.[67] Other than genotype ST313, S. typhimurium phage types such as DT9, DT204, DT104, and the current S. 4,[5],12:i: − (monophasic typhimurium variant) DT193/DT120 are considered as the dominant MDR pandemic clones.[68] These monophasic phage types harbours multidrug resistance with antimicrobial susceptibility pattern of ampicillin, chloramphenicol, streptomycin, sulphonamides and tetracycline). Phage types such as DT2, DT99 (both variant copenhagen), DT56, DT56 var, DT40 and DT160 are some of the other identified S. typhimurium pathovars distributed to particular zoonotic reservoirs but adapted to human hosts later on.[68] The global prevalence of typhoidal and non-typhoidal clones are illustrated in [Figure 2]d.

Acinetobacter baumannii

Acinetobacter baumannii is an opportunistic pathogen responsible for 2%–10% of all Gram-negative hospital infections. A. baumannii has reported to acquire resistance to majority of the antimicrobials in recent decades. The ability of the pathogen to acquire resistance often through horizontal gene transfer (HGT) is a contributing factor for the success of A. baumannii as a nosocomial pathogen.[69] The genome evolution, resistance acquisition and molecular typing methods for the identification of MDR clonal lineages in outbreak settings are described as follows.

The open pan-genome of A. baumannii is remarkably large and found to have around 8800 CDS, of which ~1400 are core genes while ~7300 are acquired genes. This highlights the significance of gene acquisition and loss events in the evolution of this human pathogen. The highest contribution to the species pan-genome is provided by the accessory genes that are not shared by all strains, of which 25%–45% of genes are unique to each strain.[69],[70] This suggests that the success of this species as a human pathogen appears to be associated with the acquisition of resistance genes through horizontal gene transfer.

Furthermore, A. baumannii has an ability to adapt to different habitats; due to a reduction in its genetic diversity along with the antibiotic pressure, the species led to the evolution and spread of a highly homogenous clones which are particularly adapted to the nosocomial environment.[71] The apparent predominance of a few successful MDR lineages/clones such as international clone 2 (IC2) worldwide emphasises the significance of knowledge on the spread and epidemiology of such A. baumannii strains at national and global level.[72]

Molecular typing methods for Acinetobacter baumannii

Several typing methods have been used to recognise distinct clonal lineages of A. baumannii during hospital outbreaks such as PFGE, RFLP, MLST and rep-PCR.[73] In recent years, MLST has emerged as the gold standard for classifying A. baumannii isolates and characterising their continental or global spread of clonal lineages at national and global level. MLST returns SNPs in conserved housekeeping genes and therefore more accurately represents population phylogeny.[74],[75]

Currently, two MLST schemes are available for A. baumannii, the Oxford scheme devised by Bartual et al. and the other is the Institute Pasteur scheme.[76],[77] Each scheme uses seven housekeeping genes, three of which are shared. Different levels of resolution have been observed with both the schemes.[78],[79] For example, ST2 under Pasteur scheme covers more than 15 STs in Oxford scheme. Although less resolution with Pasteur scheme over Oxford scheme has been observed, thus it is considered significant.[78] Studies have reported that the gpi gene under Oxford scheme forms a part of the capsule gene cluster and undergo frequent recombination.[79],[80] Another novel problem with the in silico determination of Oxford profiles is due to the presence of gdhB paralog, gdhB2.[79] Studies have suggested that the alleles identified based on the gdhB2 should be removed from the database and the genomes should be re-analysed to identify the correct gdhB allele and the ST.[79] As a whole, complete agreement has not been achieved with both the schemes and studies report that to uncover the true relationships among the isolates, more robust genotyping methods are required especially with highly dynamic bacterial pathogen like A. baumannii.[78],[79]

Furthermore, studies have reported poor agreement between PFGE and MLST for A. baumannii and have revealed that isolates from a single ST type yield markedly different PFGE patterns. These dissimilar results shows the sensitivity of PFGE to the changes of accessory genome content and this suggest that PFGE should not be used to determine relationships between distantly related bacterial isolates.[74]

Further, the performance of WGS was investigated by various researchers in outbreak settings and found that the WGS provided greater resolution than conventional methods in typing Acinetobacter at the levels of species identification, population structure and intra-hospital outbreak analysis due to its enhanced discriminatory power.[74] Another study investigated the epidemiology of CR-A. baumannii outbreaks have shown that cgMLST can be used for strain sub-typing. The study also highlights that cgMLST provides additional information on genetic diversity of the species and can probably become gold standard for epidemiological typing in epidemiological investigations.[71]

Antimicrobial resistance-specific lineages of Acinetobacter baumannii

Recently, WHO listed Acinetobacter baumannii as the top priority pathogen in research and for development of new antibiotics, particularly due to its threat to intensive care unit setting.[81] There are three main epidemic lineages of A. baumannii with MDR phenotype that are spread worldwide and are responsible for the majority of hospital outbreaks, referred as IC1, IC2 and IC3.[74] The dominant clone circulating worldwide is CC92 belongs to IC2, which is associated with class D OXAs along with transposons and/or insertion sequences. In particular, blaOXA-23 is the predominant class D carbapenemase, together with tranposons such as Tn2006 and Tn2008, and has spread in successful clonal lineages of A. baumannii.[82] Among the STs of CC92, ST848, ST451 and ST195 were predominantly reported worldwide which suggests its global transmission.[83],[84] The geographical distribution of international clones of A. baumannii is shown in [Figure 3].
Figure 3: Global spread of international clones of Acinetobacter baumannii with specific to blaOXA-51 variant. Different international clones are shown in different shapes and colours

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Staphylococcus aureus

Staphylococcus aureus is one of the major causes of hospital-associated infections worldwide. Majority of these infections are attributed to methicillin-resistant S. aureus with the development of AMR. The occurrence of AMR in S. aureus is well documented and poses significant challenge to infection control.[85] The genomic features presented here emphasise the acquisition of resistance driving the emergence and spread of MDR MRSA clones that are capable of causing infection in the community.

S. aureus genome consists of a single circular chromosome which can range from 2.7 to 3.1 million base pairs.[8],[86] The core genome, which comprises approximately 75% of the S. aureus genome, is the conserved region across all strains but not always identical.[8] Genes involved in essential functions, such as metabolism and survival, comprise the majority of the genes in the core genome. The accessory genome of S. aureus contains non-essential genes that are involved in virulence, resistance and other metabolic functions.[8],[87] The accessory genome can be highly variable between strains, due to the HGT of MGEs such as plasmids, transposons, staphylococcal cassette chromosomes (SCCs) or lysogenic phages.[88] This exchange is one of the mechanisms by which S. aureus evolves.

There is strong evidence that MGEs contribute to the emergence of highly virulent and multidrug-resistant clones; for example, a clone of the ST772 lineage shows resistance to six different antibiotics, mostly acquired through HGT of MGEs.[89] Successful HGT of MGEs is attributed to be the reason for the evolutionary success and dominance of certain MRSA clones.[90] Furthermore, recombination plays an important role in the diversification of MGEs. Large chromosomal replacements via homologous recombination are likely to influence the long-term evolution of MRSA.[91],[92] For example, ST239 is a descendent of ST8 which acquired a large (635 kb) fragment from ST30[86] and exhibits a mosaic genome profile.

Molecular typing methods for Staphylococcus aureus

Molecular typing of S. aureus and outbreak investigation is expansive. Nucleic acid-based typing approaches including PFGE, MLST or DNA arrays are commonly used to assess the epidemiological relatedness of S. aureus.[93] Staphylococcal protein A (spa) typing of S. aureus is also an important sequence-based tool in the study of clonal relatedness and epidemiology of S. aureus outbreak.[94] Differentiation of strains, critically at the genome level, could not achieved using classical DNA-based methods.[95]

The investigation of S. aureus are conducted at two levels: (i) global (extended time frame) and (ii) local (short time frame).[96] A combination of MLST and SCC mec typing has been widely used to describe the regional grouping of MRSA strains.[97] However, these methods do not possess discriminatory power for the investigation of outbreaks at the local level. For local investigation, PFGE or MLST along with spa typing is recommended.[98] A high-resolution spa typing was shown to be equally discriminatory to PFGE and can be used as an alternative method for studying the local MRSA outbreak.[99] This combinations are appropriate for use in macro-epidemiology and evolutionary studies.[100],[101]

More recently, WGS provide access to the complete genome, providing information's on AMR, virulence, the emergence and spread of lineages and the population structure of S. aureus.[102] This approach provides optimal resolution to infer phylogenetic relatedness, allows accurate characterisation of transmission events and outbreaks by studying SNVs.[103] SNVs are considered as the molecular clock to measure the length of time that takes for the isolates to share a common ancestor.[103] A study has reported that the global comparison of ST239 genome revealed the presence of ≤14 SNVs in this lineage accounts for the population diversity.[104] cgMLST has reported with higher resolution than MLST and spa typing for understanding the regional and global S. aureus epidemiology.[105] Thus, genome analysis would help to link the genetically related strains having different phenotypes.

Antimicrobial resistance-specific lineages of Staphylococcus aureus

In recent years, methicillin resistant S. aureus (MRSA) epidemiology has changed with the emergence of community-associated MRSA (CA-MRSA) strains. There is an increasing emergence of CA-MRSA clones in Africa, Asia and the Indian sub-continent.[89] One of such clone, MDR ST772-MRSA-V which is also called Bengal Bay clone that was originally isolated from India and Bangladesh has now become globally disseminated. The clone continued to be reported in community- and healthcare-associated settings in India, where it has become one of the dominant epidemic lineages of CA-MRSA.[85],[106]

There are approximately 11 major S. aureus CCs and most MRSA isolates in healthcare locations belonging to CC5, CC8, CC22, CC30 and CC45.[8] In these five CCs, containing both MRSA and methicillin susceptible S. aureus (MSSA), there are at least 11 major STs;[107] for example, ST247 is globally widespread but is commonly known as the Iberian clone,[108] and the highly transmissible and MDR clone ST239 is known as the Brazilian clone. There are a number of other major epidemic (E) MRSA sequence types that are found across the world; ST5, ST22, ST30 and ST45. Furthermore, MSSA clones ST8 also contains EMRSA clones that have acquired SCCmec types II and IV.[107] However, although some strains appear to dominate certain geographic locations, there is still great diversity seen at one particular location. [Figure 4]a shows the global distribution of predominant STs associated with hospital-associated MRSA and CA-MRSA.
Figure 4: Global distribution of predominant sequence types or lineages identified in Gram-positive pathogens is mapped. (a) Sequence types associated with hospital-acquired and community-acquired Staphylococcus aureus infections. (b) Predominance of international pneumococcal lineages, Global Pneumococcal Sequence Clusters types of Streptococcus pneumoniae and multidrug resistance percentage reported

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Streptococcus pneumoniae

Streptococcus pneumoniae remains a significant risk to human health and is the leading cause of community-acquired pneumonia and meningitis globally. MDR pneumococci are predominantly related with vaccine serotypes or serotypes associated with carriage. However, resistant non-vaccine-serotype clones continue to emerge and expand.[109] S. pneumoniae has received a great attention from a genomics perspective, with more than 4000 genomes sequenced till date.[110] Although primary mechanism of resistance in S. pneumoniae has been identified, genomic studies continue to provide new insights into evolution and spread of vaccine and non-vaccine serotypes of pneumococcal clones.

S. pneumoniae (the pneumococcus) is a highly recombinogenic bacterium. The rate of variation through recombination is much higher than the rate at which the organism acquires variation through spontaneous mutations.[111] Homologous recombination, which involves DNA exchange from closely related loci, occurs in the core genome and can also occur between MGEs such as plasmids, transposons, bacteriophage, insertion sequence and integrons. Random mutations and homologous recombinations are responsible for the variation in the pneumococcal core genome.[111]

Pneumococci possess a 2.1 megabases (Mb) pair circular genome that consists of over 2000 predicted protein coding regions and approximately 5% insertion elements. On average, the core genome of S. pneumoniae consists of 1647 predicted coding sequences (including paralogs). The remaining coding sequences that are not conserved in all members of the species are collectively referred as the 'accessory' genome, which usually contain dispensable genes that encode proteins that are not essential to the species; however, it plays a major role in pathogen evolution.[111] This is largely due to the acquisition of MGEs that harbour antibiotic resistance determinants and virulence factors. Being highly recombinant only WGS can provide high resolution on pneumococcal evolution. Considering the advantage of pneumococcal WGS, CDC has initiated Global Pneumococcal Sequencing (GPS) project, performing WGS free of cost for the S. pneumoniae isolates from all over the world. Using the global genome dataset, GPS project has introduced a new scheme for assigning pneumococcal genomes to Global Pneumococcal Sequence Clusters (GPSCs) with a reference database https://www.pneumogen.net/gps/assigningGPSCs. html.[112]

Molecular typing methods for Streptococcus pneumoniae

Recently, it is observed that 74% of the pneumococcal genome is altered by recombination.[113] High genetic variation demands continuous genotypic or molecular methods for epidemiologic typing compared to serotyping. The molecular typing methods can be either DNA finger printing or sequence based. The selection of appropriate method is depending on whether the interest is short-term or long-term epidemiology.[114] Short-term studies such as outbreak investigations use molecular methods based on genes that are highly variable and thereby provide higher discrimination. Currently, PFGE, MLVA and penicillin-binding protein (PBP) typing are the most commonly used methods for higher discrimination.[115]

Conventional MLST is less discriminatory due to high recombination events observed in S. pneumoniae but still used to understand the evolving pneumococcal population over a long time period. MLVA is based on VNTR loci that are greatly influenced by environmental factors and highly polymorphic. However, MLVA can able to further differentiate the isolates having the same sequence type by MLST.[115] Moreover, MLVA database consists profiles with unamplified loci making it less preferable. PFGE is highly discriminative and most widely used. Since it is gel-based method, data cannot be comparable with other laboratories. Sequence-based methods such as PBP typing have the advantage of reproducible data than other methods that can be compared between laboratories. The regional MLST data can be compared globally, with the availability of large database (www.pubmlst.org). In addition, PBP typing or PFGE when combined with MLST gives higher discrimination.[116]

Currently, WGS is the preferred method for understanding genome evolution of S. pneumoniae. Since the WGS provides all the whole genome data in a single shot, the sequence can be analysed in various ways and can be compared with the global isolates.[117],[118] The increase in global whole genome data raised the need for a genome-based scheme that can be used to compare lineages across the countries to assess the geographical spread of AMR, non-vaccine types and PCV impact. The GPS group characterised the global pneumococcal genome data in to GPSCs, which is the international definition of pneumococcal lineages.[112] While CCs consist of related sequence types grouped by MLST, GPSC consists of cluster of related CCs which is further divided into sub-clusters based on mean pairwise SNP distances. The whole pneumococcal genome GPSCs were further used to assess lineages associated with serotype replacement and antibiotic resistance post-PCV13 introduction.[119]

Antimicrobial resistance-specific lineages of Streptococcus pneumoniae

S. pneumoniae CC320 (ST320) was identified as an MDR clone responsible for the global emergence of non-vaccine serotype 19A in the years following the introduction of PCV7. The clone is originated from the MDR Taiwan19F-14 Pneumococcal Molecular Epidemiology Network clone, which is a double-locus variant of Taiwan19F-14– ST236).[120] The predicted founder of the complex, ST320, was a serotype 19A clone carrying dual macrolide resistance determinants (Mega and erm (B)) was the prevalent clone in Asian countries. The clone represented a 19F-19A capsule switch and horizontal acquisition of multiple antibiotic resistance mechanisms, suggesting that vaccine and antibiotic pressures influenced its emergence. This highlights the serotype-specific clonal evolution of MDR pathogen. Studies suggested that use of vaccine is not the only factor involved in serotype and ST changes in a population. It could be due to its highly resistant nature that contributes to its continued existence in the post-vaccine era.[121] In addition, recent findings have suggested that the ST320 clone was a better coloniser of the nasopharynx than its progenitor ST236.[110] Based on the new GPSC scheme, ST320/CC320 belongs to GPSC1 group consists of other CCs, 12229, 2834, 3791 and 420. Considering GPSC association with resistance, GPSCs and country within GPSCs are found to be predictors for resistance. Recently, GPSC55 (Israel), GPSC59 (USA), 138 (USA) and GPSC168 (South Africa) were found to be associated with 100% penicillin resistance in the PCV13 era.[119] The predominant GPSCs associated with resistance and MDR percentages reported in various countries (SENTRY Surveillance) have been mapped in [Figure 4]b.


 ~ Metagenomics: A new Gateway for Transmission Dynamics and One Health Top


Resistance has traditionally been viewed as a clinical problem, but a more holistic approach is needed. The emergence of AMR is a complex process often involving the interplay between human, animal and environment. Recently, non-clinical environments have been highlighted as an important factor in the dissemination of ARGs.[122] The emerging field of metagenomics, the culture-independent sequencing, has the potential application in AMR surveillance. Indeed, it has proven to be useful in investigating novel species, strains and outbreaks. The success of metagenomics is largely based on the quality and quantity of the DNA extracted from the sample due its unique matrix, concentration of the pathogens and resident microflora.[123]

Metagenomics could also help to track the transmission of AMR genes and its associated mobile elements.[124] These sequence data can then be used to predict microbiome, resistome, virulome, and mobilome.[125] The integration of genomic data along with epidemiological data could enhance outbreak investigation and provide potential information on transmission events [Figure 5].
Figure 5: Schematic representation of metagenomics approach for transmission event analysis of antimicrobial resistant pathogens in a One Health perspective. PAIs: Pathogenicity islands, AMR: Antimicrobial resistance

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 ~ Challenges of Implementing Next-Generation Sequencing in Clinical Laboratories Top


Although WGS has an excellent discriminatory power, it has some drawbacks compared to other methods especially for bioinformatics analysis. Besides, free bioinformatics tools are widely available, but it is not always easy to find the appropriate ones. Furthermore, free access software's requires bioinformatics expertise and are more laborious and time-consuming. The method also requires high computational power to process and analyse more number of genomes.[58],[126] Furthermore, the ability to translate the sequence data into relevant information supporting microbiologist, clinicians and public health epidemiologist to implement control measures in real-time is challenging. Further constant update and curation of public database are required for the prediction of virulence and antibiotic resistance from the bacterial genome. In spite of these above-mentioned drawbacks, the technology still shows increased resolution and provides wealth of information which no other method can accomplish at the same level.


 ~ Conclusion Top


Over the last decade, there are multiple reports of dissemination of drug-resistant clones throughout the world. The successful survival of these bacterial pathogens is probably determined by a complex of interplay between pathogenicity, epidemicity and AMR. Earlier studies imply that AMR may be a significant factor in maintaining existing lineages within specific locales. In addition, horizontally transferred elements could help establish the emerging resistant clones. Therefore, more extensive studies on these lineages can help to understand the evolution of new clones and spread of the existing clones. WGS is currently considered as the gold standard for molecular typing. However, combinations of different typing methods are widely used for microbial typing and no one method will be sufficient.

Here, we provide the physicians and microbiologist, an information on the appropriate use of the different typing methods to study the molecular epidemiology of outbreaks caused by GLASS pathogens. Important recommendation are summarised as follows:

  1. WGS is now recommended to be the gold standard method and prioritised as the method of choice for all outbreak investigations. The traditional typing methods should be chosen based on the settings and characteristics of the outbreak
  2. For E. coli and Shigella, WGS is the only appropriate method for outbreak investigation due to its constantly changing accessory genome
  3. Among the conventional methods, PFGE is still remains as a standard method for Salmonella spp. Sub-typing for routine surveillance. However, currently, MLVA found to be promising and considered as an alternative method to PFGE, if WGS is not available
  4. PFGE and MLST, in general, are the method of choice for local outbreak analysis caused by K. pneumoniae and A. baumannii. MLST can be the early typing method for a long-term epidemiological analysis, when a wider view of a pathogen is needed. Otherwise, WGS provides greater resolution than conventional typing methods
  5. In addition to PFGE or MLST, spa typing is a recognised methodology for outbreak investigation of MRSA strains. Combination of MLST and SCCmec typing can be used to define the regional grouping of MRSA strains. cgMLST has reported with higher resolution than MLST and spa typing for phylogenetic relatedness
  6. Although MLST is less discriminatory due to high recombination events observed in S. pneumoniae, it still can be used for long-term epidemiological studies. Targeted sequencing of PBP when combined with MLST gives higher discrimination. Recently, WGS is the preferred method for understanding the evolution of S. pneumoniae.


Acknowledgement

We gratefully acknowledge the Institutional Review Board of the Christian Medical College, Vellore, and the Indian Council of Medical Research, New Delhi.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
 ~ References Top

1.
Malhotra S, Sharma S, Bhatia NJ, Kumar P, Hans C. Molecular methods in microbiology and their clinical application. J Mol Genet Med 2014;8:142.  Back to cited text no. 1
    
2.
Deurenberg RH, Bathoorn E, Chlebowicz MA, Couto N, Ferdous M, García-Cobos S, et al. Application of next generation sequencing in clinical microbiology and infection prevention. J Biotechnol 2017;243:16-24.  Back to cited text no. 2
    
3.
Hall N. Advanced sequencing technologies and their wider impact in microbiology. J Exp Biol 2007;210:1518-25.  Back to cited text no. 3
    
4.
McGann P, Bunin JL, Snesrud E, Singh S, Maybank R, Ong AC, et al. Real time application of whole genome sequencing for outbreak investigation – What is an achievable turnaround time? Diagn Microbiol Infect Dis 2016;85:277-82.  Back to cited text no. 4
    
5.
Peterson LR, Brossette SE. Hunting health care-associated infections from the clinical microbiology laboratory: Passive, active, and virtual surveillance. J Clin Microbiol 2002;40:1-4.  Back to cited text no. 5
    
6.
Harris SR, Cartwright EJ, Török ME, Holden MT, Brown NM, Ogilvy-Stuart AL, et al. Whole-genome sequencing for analysis of an outbreak of meticillin-resistant Staphylococcus aureus: A descriptive study. Lancet Infect Dis 2013;13:130-6.  Back to cited text no. 6
    
7.
Castillo-Ramírez S, Corander J, Marttinen P, Aldeljawi M, Hanage WP, Westh H, et al. Phylogeographic variation in recombination rates within a global clone of methicillin-resistant Staphylococcus aureus. Genome Biol 2012;13:R126.  Back to cited text no. 7
    
8.
Lindsay JA, Holden MT. Understanding the rise of the superbug: Investigation of the evolution and genomic variation of Staphylococcus aureus. Funct Integr Genomics 2006;6:186-201.  Back to cited text no. 8
    
9.
Schürch AC, Arredondo-Alonso S, Willems RJ, Goering RV. Whole genome sequencing options for bacterial strain typing and epidemiologic analysis based on single nucleotide polymorphism versus gene-by-gene-based approaches. Clin Microbiol Infect 2018;24:350-4.  Back to cited text no. 9
    
10.
Johnson AP. Surveillance of antibiotic resistance. Philos Trans R Soc Lond B Biol Sci 2015;370:20140080.  Back to cited text no. 10
    
11.
Besser J, Carleton HA, Gerner-Smidt P, Lindsey RL, Trees E. Next-generation sequencing technologies and their application to the study and control of bacterial infections. Clin Microbiol Infect 2018;24:335-41.  Back to cited text no. 11
    
12.
Werner G. Molecular typing of enterococci/VRE. J Bacteriol Parasitol 2013;10:2155-9597.  Back to cited text no. 12
    
13.
Devanga Ragupathi NK, Muthuirulandi Sethuvel DP, Gajendran R, Anandan S, Walia K, Veeraraghavan B. Horizontal transfer of antimicrobial resistance determinants among enteric pathogens through bacterial conjugation. Curr Microbiol 2019;76:666-72.  Back to cited text no. 13
    
14.
Ud-Din A, Wahid S. Relationship among Shigella spp. And enteroinvasive Escherichia coli (EIEC) and their differentiation. Braz J Microbiol 2014;45:1131-8.  Back to cited text no. 14
    
15.
Sahl JW, Morris CR, Emberger J, Fraser CM, Ochieng JB, Juma J, et al. Defining the phylogenomics of Shigella species: A pathway to diagnostics. J Clin Microbiol 2015;53:951-60.  Back to cited text no. 15
    
16.
Devanga Ragupathi NK, Muthuirulandi Sethuvel DP, Inbanathan FY, Veeraraghavan B. Accurate differentiation of Escherichia coli and Shigella serogroups: Challenges and strategies. New Microbes New Infect 2018;21:58-62.  Back to cited text no. 16
    
17.
Lukjancenko O, Wassenaar TM, Ussery DW. Comparison of 61 sequenced Escherichia coli genomes. Microb Ecol 2010;60:708-20.  Back to cited text no. 17
    
18.
Hershberg R, Tang H, Petrov DA. Reduced selection leads to accelerated gene loss in Shigella. Genome Biol 2007;8:R164.  Back to cited text no. 18
    
19.
Moran NA. Microbial minimalism: Genome reduction in bacterial pathogens. Cell 2002;108:583-6.  Back to cited text no. 19
    
20.
Lan R, Reeves PR. Escherichia coli in disguise: Molecular origins of Shigella. Microbes Infect 2002;4:1125-32.  Back to cited text no. 20
    
21.
Ribot EM, Fair MA, Gautom R, Cameron DN, Hunter SB, Swaminathan B, et al. Standardization of pulsed-field gel electrophoresis protocols for the subtyping of Escherichia coli O157:H7, Salmonella, and Shigella for PulseNet. Foodborne Pathog Dis 2006;3:59-67.  Back to cited text no. 21
    
22.
Fratamico PM, DebRoy C, Liu Y, Needleman DS, Baranzoni GM, Feng P. Advances in molecular serotyping and subtyping of Escherichia coli. Front Microbiol 2016;7:644.  Back to cited text no. 22
    
23.
Dias RC, Moreira BM, Riley LW. Use of FimH single-nucleotide polymorphisms for strain typing of clinical isolates of Escherichia coli for epidemiologic investigation. J Clin Microbiol 2010;48:483-8.  Back to cited text no. 23
    
24.
Ren Y, Palusiak A, Wang W, Wang Y, Li X, Wei H, et al. A high-resolution typing assay for uropathogenic Escherichia coli based on fimbrial diversity. Front Microbiol 2016;7:623.  Back to cited text no. 24
    
25.
Yun KW, Kim DS, Kim W, Lim IS. Molecular typing of uropathogenic Escherichia coli isolated from Korean children with urinary tract infection. Korean J Pediatr 2015;58:20-7.  Back to cited text no. 25
    
26.
Roer L, Tchesnokova V, Allesøe R, Muradova M, Chattopadhyay S, Ahrenfeldt J, et al. Development of a web tool for Escherichia coli subtyping based on FimH alleles. J Clin Microbiol 2017;55:2538-43.  Back to cited text no. 26
    
27.
Dautzenberg MJ, Haverkate MR, Bonten MJ, Bootsma MC. Epidemic potential of Escherichia coli ST131 and Klebsiella pneumoniae ST258: A systematic review and meta-analysis. BMJ Open 2016;6:e009971.  Back to cited text no. 27
    
28.
Nicolas-Chanoine MH, Blanco J, Leflon-Guibout V, Demarty R, Alonso MP, Caniça MM. Intercontinental emergence of Escherichia coli clone O25:H4-ST131 producing CTX-M-15. J Antimicrob Chemother 2008;61:273-81.  Back to cited text no. 28
    
29.
Ewers C, Grobbel M, Stamm I, Kopp PA, Diehl I, Semmler T, et al. Emergence of human pandemic O25:H4-ST131 CTX-M-15 extended-spectrum-beta-lactamase-producing Escherichia coli among companion animals. J Antimicrob Chemother 2010;65:651-60.  Back to cited text no. 29
    
30.
Brolund A, Edquist PJ, Mäkitalo B, Olsson-Liljequist B, Söderblom T, Wisell KT, et al. Epidemiology of extended-spectrum β-lactamase-producing Escherichia coli in Sweden 2007-2011. Clin Microbiol Infect 2014;20:O344-52.  Back to cited text no. 30
    
31.
Hawken SE, Snitkin ES. Genomic epidemiology of multidrug-resistant gram-negative organisms. Ann N Y Acad Sci 2019;1435:39-56.  Back to cited text no. 31
    
32.
Roer L, Overballe-Petersen S, Hansen F, Schønning K, Wang M, Røder BL, et al. Escherichia coli sequence type 410 is causing new international high-risk clones. mSphere 2018;3. pii: E00337-18.  Back to cited text no. 32
    
33.
Baker S, The HC. Recent insights into Shigella. Curr Opin Infect Dis 2018;31:449-54.  Back to cited text no. 33
    
34.
Holt KE, Thieu Nga TV, Thanh DP, Vinh H, Kim DW, Vu Tra MP, et al. Tracking the establishment of local endemic populations of an emergent enteric pathogen. Proc Natl Acad Sci U S A 2013;110:17522-7.  Back to cited text no. 34
    
35.
Chung The H, Rabaa MA, Pham Thanh D, De Lappe N, Cormican M, Valcanis M, et al. South Asia as a reservoir for the global spread of ciprofloxacin-resistant Shigella sonnei: A cross-sectional study. PLoS Med 2016;13:e1002055.  Back to cited text no. 35
    
36.
Wyres KL, Holt KE. Klebsiella pneumoniae population genomics and antimicrobial-resistant clones. Trends Microbiol 2016;24:944-56.  Back to cited text no. 36
    
37.
Shankar C, Nabarro L, Ravi R, Anandan S, Balaji V. Emergence of carbapenem-resistant hypervirulent Klebsiella pneumoniae: A global threat?. InOpen Forum Infect Dis 2016;3:1058.  Back to cited text no. 37
    
38.
Holt KE, Wertheim H, Zadoks RN, Baker S, Whitehouse CA, Dance D, et al. Genomic analysis of diversity, population structure, virulence, and antimicrobial resistance in Klebsiella pneumoniae, an urgent threat to public health. Proc Natl Acad Sci U S A 2015;112:E3574-81.  Back to cited text no. 38
    
39.
Martin RM, Bachman MA. Colonization, infection, and the accessory genome of Klebsiella pneumoniae. Front Cell Infect Microbiol 2018;8:4.  Back to cited text no. 39
    
40.
Andersson P, Tong SY, Bell JM, Turnidge JD, Giffard PM. Minim typing – A rapid and low cost MLST based typing tool for Klebsiella pneumoniae. PLoS One 2012;7:e33530.  Back to cited text no. 40
    
41.
Bezdicek M, Nykrynova M, Plevova K, Brhelova E, Kocmanova I, Sedlar K, et al. Application of mini-MLST and whole genome sequencing in low diversity hospital extended-spectrum beta-lactamase producing Klebsiella pneumoniae population. PLoS One 2019;14:e0221187.  Back to cited text no. 41
    
42.
Snitkin ES, Zelazny AM, Thomas PJ, Stock F; NISC Comparative Sequencing Program Group, Henderson DK, et al. Tracking a hospital outbreak of carbapenem-resistant Klebsiella pneumoniae with whole-genome sequencing. Sci Transl Med 2012;4:148ra116.  Back to cited text no. 42
    
43.
Matsumura Y, Peirano G, Bradford PA, Motyl MR, DeVinney R, Pitout JD. Genomic characterization of IMP and VIM carbapenemase-encoding transferable plasmids of Enterobacteriaceae. J Antimicrob Chemother 2018;73:3034-8.  Back to cited text no. 43
    
44.
David S, Reuter S, Harris SR, Glasner C, Feltwell T, Argimon S, et al. Epidemic of carbapenem-resistant Kleriaceae pneumoniae in Europe is driven by nosocomial spread. Nat Microbiol 2019;4:1919-29.  Back to cited text no. 44
    
45.
Becker L, Kaase M, Pfeifer Y, Fuchs S, Reuss A, von Laer A, et al. Genome-based analysis of carbapenemase-producing Klebsiella pneumoniae isolates from German hospital patients, 2008-2014. Antimicrob Resist Infect Control 2018;7:62.  Back to cited text no. 45
    
46.
Chen L, Mathema B, Pitout JD, DeLeo FR, Kreiswirth BN. Epidemic Klebsiella pneumoniae ST258 is a hybrid strain. MBio 2014;5:e01355-14.  Back to cited text no. 46
    
47.
Dunn SJ, Connor C, McNally A. The evolution and transmission of multi-drug resistant Escherichia coli and Klebsiella pneumoniae: The complexity of clones and plasmids. Curr Opin Microbiol 2019;51:51-6.  Back to cited text no. 47
    
48.
Macesic N, Nelson B, Mcconville TH, Giddins MJ, Green DA, Stump S, et al. Emergence of polymyxin resistance in clinical Klebsiella pneumoniae through diverse genetic adaptations: A genomic, retrospective cohort study. Clin Infect Dis 2019. pii: Ciz623.  Back to cited text no. 48
    
49.
Fu S, Octavia S, Tanaka MM, Sintchenko V, Lan R. Defining the core genome of Salmonella enterica serovar typhimurium for genomic surveillance and epidemiological typing. J Clin Microbiol 2015;53:2530-8.  Back to cited text no. 49
    
50.
Liu Y, Shi X, Li Y, Chen Q, Jiang M, Li W, et al. The evaluation and application of multilocus variable number tandem repeat analysis (MLVA) for the molecular epidemiological study of Salmonella enterica subsp. enterica serovar enteritidis infection. Ann Clin Microbiol Antimicrob 2016;15:4.  Back to cited text no. 50
    
51.
Gordienko EN, Kazanov MD, Gelfand MS. Evolution of pan-genomes of Escherichia coli, Shigella spp. and Salmonella enterica. J Bacteriol 2013;195:2786-92.  Back to cited text no. 51
    
52.
Jacobsen A, Hendriksen RS, Aaresturp FM, Ussery DW, Friis C. The Salmonella enterica pan-genome. Microb Ecol 2011;62:487-504.  Back to cited text no. 52
    
53.
Palma F, Manfreda G, Silva M, Parisi A, Barker DO, Taboada EN, et al. Genome-wide identification of geographical segregated genetic markers in Salmonella enterica serovar typhimurium variant 4,[5],12:i:. Sci Rep 2018;8:15251.  Back to cited text no. 53
    
54.
Laing CR, Whiteside MD, Gannon VP. Pan-genome analyses of the species Salmonella enterica, and identification of genomic markers predictive for species, subspecies, and serovar. Front Microbiol 2017;8:1345.  Back to cited text no. 54
    
55.
Lin T, Lin L, Zhang F. Review on molecular typing methods of pathogens. Open J Med Microbiol 2014;4:147.  Back to cited text no. 55
    
56.
Ross IL, Heuzenroeder MW. A comparison of three molecular typing methods for the discrimination of Salmonella enterica serovar infantis. FEMS Immunol Med Microbiol 2008;53:375-84.  Back to cited text no. 56
    
57.
Smith SI, Fowora MA, Goodluck HA, Nwaokorie FO, Aboaba OO, Opere B. Molecular typing of Salmonella spp isolated from food handlers and animals in Nigeria. Int J Mol Epidemiol Genet 2011;2:73-7.  Back to cited text no. 57
    
58.
Ferrari RG, Panzenhagen PH, Conte-Junior CA. Phenotypic and genotypic eligible methods for Salmonella typhimurium source tracking. Front Microbiol 2017;8:2587.  Back to cited text no. 58
    
59.
Deng X, den Bakker HC, Hendriksen RS. Genomic epidemiology: Whole-genome-sequencing-powered surveillance and outbreak investigation of foodborne bacterial pathogens. Annu Rev Food Sci Technol 2016;7:353-74.  Back to cited text no. 59
    
60.
Franz E, Gras LM, Dallman T. Significance of whole genome sequencing for surveillance, source attribution and microbial risk assessment of foodborne pathogens. Curr Opin Food Sci 2016;8:74–9.  Back to cited text no. 60
    
61.
Wong VK, Baker S, Pickard DJ, Parkhill J, Page AJ, Feasey NA, et al. Phylogeographical analysis of the dominant multidrug-resistant H58 clade of Salmonella typhi identifies inter- and intracontinental transmission events. Nat Genet 2015;47:632-9.  Back to cited text no. 61
    
62.
Acheson D, Hohmann EL. Nontyphoidal salmonellosis. Clin Infect Dis 2001;32:263-9.  Back to cited text no. 62
    
63.
Preziosi MJ, Kandel SM, Guiney DG, Browne SH. Microbiological analysis of nontyphoidal Salmonella strains causing distinct syndromes of bacteremia or enteritis in HIV/AIDS patients in San Diego, California. J Clin Microbiol 2012;50:3598-603.  Back to cited text no. 63
    
64.
Tanner JR, Kingsley RA. Evolution of Salmonella within hosts. Trends Microbiol 2018;26:986-98.  Back to cited text no. 64
    
65.
Kingsley RA, Msefula CL, Thomson NR, Kariuki S, Holt KE, Gordon MA, et al. Epidemic multiple drug resistant Salmonella typhimurium causing invasive disease in Sub-Saharan Africa have a distinct genotype. Genome Res 2009;19:2279-87.  Back to cited text no. 65
    
66.
Hammarlöf DL, Kröger C, Owen SV, Canals R, Lacharme-Lora L, Wenner N, et al. Role of a single noncoding nucleotide in the evolution of an epidemic African clade of Salmonella. Proc Natl Acad Sci U S A 2018;115:E2614-23.  Back to cited text no. 66
    
67.
Ashton PM, Owen SV, Kaindama L, Rowe WP, Lane CR, Larkin L, et al. Public health surveillance in the UK revolutionises our understanding of the invasive Salmonella typhimurium epidemic in Africa. Genome Med 2017;9:92.  Back to cited text no. 67
    
68.
Branchu P, Bawn M, Kingsley RA. Genome variation and molecular epidemiology of Salmonella enterica serovar typhimurium pathovariants. Infect Immun 2018;86. pii: E00079-18.  Back to cited text no. 68
    
69.
Antunes LC, Visca P, Towner KJ. Acinetobacter baumannii: Evolution of a global pathogen. Pathog Dis 2014;71:292-301.  Back to cited text no. 69
    
70.
Imperi F, Antunes LC, Blom J, Villa L, Iacono M, Visca P, et al. The genomics of Acinetobacter baumannii: Insights into genome plasticity, antimicrobial resistance and pathogenicity. IUBMB Life 2011;63:1068-74.  Back to cited text no. 70
    
71.
Venditti C, Vulcano A, D'Arezzo S, Gruber CE, Selleri M, Antonini M, et al. Epidemiological investigation of an Acinetobacter baumannii outbreak using core genome multilocus sequence typing. J Glob Antimicrob Resist 2019;17:245-9.  Back to cited text no. 71
    
72.
Zarrilli R, Pournaras S, Giannouli M, Tsakris A. Global evolution of multidrug-resistant Acinetobacter baumannii clonal lineages. Int J Antimicrob Agents 2013;41:11-9.  Back to cited text no. 72
    
73.
Ravi NS, Anandan S, Vijayakumar S, Gopi R, Lopes BS, Veeraraghavan B. The potential of different molecular biology methods in tracking clones of Acinetobacter baumannii in an ICU setting. J Med Microbiol 2018;67:1340-7.  Back to cited text no. 73
    
74.
Fitzpatrick MA, Ozer EA, Hauser AR. Utility of whole-genome sequencing in characterizing Acinetobacter epidemiology and analyzing hospital outbreaks. J Clin Microbiol 2016;54:593-612.  Back to cited text no. 74
    
75.
Di Popolo A, Giannouli M, Triassi M, Brisse S, Zarrilli R. Molecular epidemiological investigation of multidrug-resistant Acinetobacter baumannii strains in four Mediterranean countries with a multilocus sequence typing scheme. Clin Microbiol Infect 2011;17:197-201.  Back to cited text no. 75
    
76.
Bartual SG, Seifert H, Hippler C, Luzon MA, Wisplinghoff H, Rodríguez-Valera F. Development of a multilocus sequence typing scheme for characterization of clinical isolates of Acinetobacter baumannii. J Clin Microbiol 2005;43:4382-90.  Back to cited text no. 76
    
77.
Diancourt L, Passet V, Nemec A, Dijkshoorn L, Brisse S. The population structure of Acinetobacter baumannii: Expanding multiresistant clones from an ancestral susceptible genetic pool. PLoS One 2010;5:e10034.  Back to cited text no. 77
    
78.
Castillo-Ramírez S, Graña-Miraglia L. Inaccurate multilocus sequence typing of Acinetobacter baumannii. Emerg Infect Dis 2019;25:186-7.  Back to cited text no. 78
    
79.
Gaiarsa S, Batisti Biffignandi G, Esposito EP, Castelli M, Jolley KA, Brisse S, et al. Comparative analysis of the two Acinetobacter baumannii multilocus sequence typing (MLST) schemes. Front Microbiol 2019;10:930.  Back to cited text no. 79
    
80.
Hamidian M, Nigro SJ, Hall RM. Problems with the Oxford multilocus sequence typing scheme for Acinetobacter baumannii: Do sequence type 92 (ST92) and ST109 exist? J Clin Microbiol 2017;55:2287-9.  Back to cited text no. 80
    
81.
Saffari F, Monsen T, Karmostaji A, Azimabad FB, Widerström M. Significant spread of extensively drug-resistant Acinetobacter baumannii genotypes of clonal complex 92 among intensive care unit patients in a university hospital in Southern Iran. J Med Microbiol 2017;66:1656-62.  Back to cited text no. 81
    
82.
Evans BA, Amyes SG. OXA β-lactamases. Clin Microbiol Rev 2014;27:241-63.  Back to cited text no. 82
    
83.
Kim DH, Choi JY, Kim HW, Kim SH, Chung DR, Peck KR, et al. Spread of carbapenem-resistant Acinetobacter baumannii global clone 2 in Asia and AbaR-type resistance islands. Antimicrob Agents Chemother 2013;57:5239-46.  Back to cited text no. 83
    
84.
Vijayakumar S, Mathur P, Kapil A, Das BK, Ray P, Gautam V, et al. Molecular characterization & epidemiology of carbapenem-resistant Acinetobacter baumannii collected across India. Indian J Med Res 2019;149:240-6.  Back to cited text no. 84
[PUBMED]  [Full text]  
85.
Steinig EJ, Duchene S, Robinson DA, Monecke S, Yokoyama M, Laabei M, et al. Global phylogenomics of multidrug-resistant Staphylococcus aureus sequence type 772: The Bengal Bay clone. bioRxiv 2017. doi:org/10.1101/ 233395.  Back to cited text no. 85
    
86.
Holden MT, Lindsay JA, Corton C, Quail MA, Cockfield JD, Pathak S, et al. Genome sequence of a recently emerged, highly transmissible, multi-antibiotic- and antiseptic-resistant variant of methicillin-resistant Staphylococcus aureus, sequence type 239 (TW). J Bacteriol 2010;192:888-92.  Back to cited text no. 86
    
87.
Shittu AO, Udo EE, Lin J. Insights on virulence and antibiotic resistance: A review of the accessory genome of Staphylococcus aureus. Wounds 2007;19:237-44.  Back to cited text no. 87
    
88.
Frost LS, Leplae R, Summers AO, Toussaint A. Mobile genetic elements: The agents of open source evolution. Nat Rev Microbiol 2005;3:722-32.  Back to cited text no. 88
    
89.
Steinig EJ, Andersson P, Harris SR, Sarovich DS, Manoharan A, Coupland P, et al. Single-molecule sequencing reveals the molecular basis of multidrug-resistance in ST772 methicillin-resistant Staphylococcus aureus. BMC Genomics 2015;16:388.  Back to cited text no. 89
    
90.
Lindsay JA, Knight GM, Budd EL, McCarthy AJ. Shuffling of mobile genetic elements (MGEs) in successful healthcare-associated MRSA (HA-MRSA). Mob Genet Elements 2012;2:239-43.  Back to cited text no. 90
    
91.
Feil EJ, Cooper JE, Grundmann H, Robinson DA, Enright MC, Berendt T, et al. How clonal is Staphylococcus aureus? J Bacteriol 2003;185:3307-16.  Back to cited text no. 91
    
92.
Grundmann H, Aires-de-Sousa M, Boyce J, Tiemersma E. Emergence and resurgence of meticillin-resistant Staphylococcus aureus as a public-health threat. Lancet 2006;368:874-85.  Back to cited text no. 92
    
93.
Michael Dunne W Jr., Pouseele H, Monecke S, Ehricht R, van Belkum A. Epidemiology of transmissible diseases: Array hybridization and next generation sequencing as universal nucleic acid-mediated typing tools. Infect Genet Evol 2018;63:332-45.  Back to cited text no. 93
    
94.
Ruppitsch W, Indra A, Stöger A, Mayer B, Stadlbauer S, Wewalka G, et al. Classifying spa types in complexes improves interpretation of typing results for methicillin-resistant Staphylococcus aureus. J Clin Microbiol 2006;44:2442-8.  Back to cited text no. 94
    
95.
Sabat AJ, Budimir A, Nashev D, Sá-Leão R, van Dijl Jm, Laurent F, et al. Overview of molecular typing methods for outbreak detection and epidemiological surveillance. Euro Surveill 2013;18:20380.  Back to cited text no. 95
    
96.
Cunningham SA, Chia N, Jeraldo PR, Quest DJ, Johnson JA, Boxrud DJ, et al. Comparison of whole-genome sequencing methods for analysis of three methicillin-resistant Staphylococcus aureus outbreaks. J Clin Microbiol 2017;55:1946-53.  Back to cited text no. 96
    
97.
Cookson BD, Robinson DA, Monk AB, Murchan S, Deplano A, De Ryck R, et al. Evaluation of molecular typing methods in characterizing a European collection of epidemic methicillin-resistant Staphylococcus aureus strains: The HARMONY collection. J Clin Microbiol 2007;45:1830-7.  Back to cited text no. 97
    
98.
Petersson AC, Olsson-Liljequist B, Miörner H, Haeggman S. Evaluating the usefulness of spa typing, in comparison with pulsed-field gel electrophoresis, for epidemiological typing of methicillin-resistant Staphylococcus aureus in a low-prevalence region in Sweden 2000-2004. Clin Microbiol Infect 2010;16:456-62.  Back to cited text no. 98
    
99.
Strommenger B, Braulke C, Heuck D, Schmidt C, Pasemann B, Nübel U, et al. Spa typing of Staphylococcus aureus as a frontline tool in epidemiological typing. J Clin Microbiol 2008;46:574-81.  Back to cited text no. 99
    
100.
O'Hara FP, Suaya JA, Ray GT, Baxter R, Brown ML, Mera RM, et al. Spa typing and multilocus sequence typing show comparable performance in a macroepidemiologic study of Staphylococcus aureus in the United States. Microb Drug Resist 2016;22:88-96.  Back to cited text no. 100
    
101.
Kong H, Yu F, Zhang W, Li X, Wang H. Molecular epidemiology and antibiotic resistance profiles of methicillin-resistant Staphylococcus aureus strains in a tertiary hospital in China. Front Microbiol 2017;8:838.  Back to cited text no. 101
    
102.
Price JR, Cole K, Bexley A, Kostiou V, Eyre DW, Golubchik T, et al. Transmission of Staphylococcus aureus between health-care workers, the environment, and patients in an intensive care unit: A longitudinal cohort study based on whole-genome sequencing. Lancet Infect Dis 2017;17:207-14.  Back to cited text no. 102
    
103.
Price JR, Didelot X, Crook DW, Llewelyn MJ, Paul J. Whole genome sequencing in the prevention and control of Staphylococcus aureus infection. J Hosp Infect 2013;83:14-21.  Back to cited text no. 103
    
104.
Harris SR, Feil EJ, Holden MT, Quail MA, Nickerson EK, Chantratita N, et al. Evolution of MRSA during hospital transmission and intercontinental spread. Science 2010;327:469-74.  Back to cited text no. 104
    
105.
Park KH, Greenwood-Quaintance KE, Uhl JR, Cunningham SA, Chia N, Jeraldo PR, et al. Molecular epidemiology of Staphylococcus aureus bacteremia in a single large Minnesota medical center in 2015 as assessed using MLST, core genome MLST and spa typing. PLoS One 2017;12:e0179003.  Back to cited text no. 105
    
106.
Yamuna DB, Francis YI, Doss GP, Balaji V. Molecular characterization of panton-valentine leukocidin (PVL) toxin-encoding phages from South India. New Microbes New Infect 2017;20:34-8.  Back to cited text no. 106
    
107.
Enright MC, Robinson DA, Randle G, Feil EJ, Grundmann H, Spratt BG. The evolutionary history of methicillin-resistant Staphylococcus aureus (MRSA). Proc Natl Acad Sci U S A 2002;99:7687-92.  Back to cited text no. 107
    
108.
Sanches IS, Aires de Sousa M, Sobral L, Calheiros I, Felicio L, Pedra I, et al. Multidrug-resistant iberian epidemic clone of methicillin-resistant Staphylococcus aureus endemic in a hospital in Northern Portugal. Microb Drug Resist 1995;1:299-306.  Back to cited text no. 108
    
109.
Kim L, McGee L, Tomczyk S, Beall B. Biological and epidemiological features of antibiotic-resistant Streptococcus pneumoniae in pre – And post-conjugate vaccine eras: A United States perspective. Clin Microbiol Rev 2016;29:525-52.  Back to cited text no. 109
    
110.
Tettelin H, Chancey S, Mitchell T, Denapaite D, Schahle Y, Rieger M, et al. Genomics, genetic variation, and regions of differences. In: Streptococcus pneumoniae Molecular Mechanisms of Host-Pathogen Interaction. London, United Kingdom: Academic Press; 2015. p. 81-107.  Back to cited text no. 110
    
111.
Chaguza C, Cornick JE, Everett DB. Mechanisms and impact of genetic recombination in the evolution of Streptococcus pneumoniae. Comput Struct Biotechnol J 2015;13:241-7.  Back to cited text no. 111
    
112.
Gladstone RA, Lo SW, Lees JA, Croucher NJ, van Tonder AJ, Corander J, et al. International genomic definition of pneumococcal lineages, to contextualise disease, antibiotic resistance and vaccine impact. EBioMedicine 2019;43:338-46.  Back to cited text no. 112
    
113.
Croucher NJ, Harris SR, Fraser C, Quail MA, Burton J, van der Linden M, et al. Rapid pneumococcal evolution in response to clinical interventions. Science 2011;331:430-4.  Back to cited text no. 113
    
114.
Urwin R, Maiden MC. Multi-locus sequence typing: A tool for global epidemiology. Trends Microbiol 2003;11:479-87.  Back to cited text no. 114
    
115.
Pichon B, Moyce L, Sheppard C, Slack M, Turbitt D, Pebody R, et al. Molecular typing of pneumococci for investigation of linked cases of invasive pneumococcal disease. J Clin Microbiol 2010;48:1926-8.  Back to cited text no. 115
    
116.
Pillai DR, Shahinas D, Buzina A, Pollock RA, Lau R, Khairnar K, et al. Genome-wide dissection of globally emergent multi-drug resistant serotype 19A Streptococcus pneumoniae. BMC Genomics 2009;10:642.  Back to cited text no. 116
    
117.
Chewapreecha C, Harris SR, Croucher NJ, Turner C, Marttinen P, Cheng L, et al. Dense genomic sampling identifies highways of pneumococcal recombination. Nat Genet 2014;46:305-9.  Back to cited text no. 117
    
118.
Croucher NJ, Finkelstein JA, Pelton SI, Parkhill J, Bentley SD, Lipsitch M, et al. Population genomic datasets describing the post-vaccine evolutionary epidemiology of Streptococcus pneumoniae. Sci Data 2015;2:150058.  Back to cited text no. 118
    
119.
Lo SW, Gladstone RA, van Tonder AJ, Lees JA, du Plessis M, Benisty R, et al. Pneumococcal lineages associated with serotype replacement and antibiotic resistance in childhood invasive pneumococcal disease in the post-PCV13 era: An international whole-genome sequencing study. Lancet Infect Dis 2019;19:759-69.  Back to cited text no. 119
    
120.
Oodate M, Kimura K, Banno H, Yokoyama S, Jin W, Wachino JI, et al. Predominance of serogroup 19 CC320/271 among penicillin-nonsusceptible Streptococcus pneumoniae isolates after introduction of the PCV7 vaccine in several regions of Japan. Jpn J Infect Dis 2018;71:14-20.  Back to cited text no. 120
    
121.
Setchanova L, Alexandrova A, Pencheva D, Sirakov I, Mihova K, Kaneva R, et al. Rise of multidrug-resistant Streptococcus pneumoniae clones expressing non-vaccine serotypes among children following introduction of the 10-valent pneumococcal conjugate vaccine in Bulgaria. J Glob Antimicrob Resist 2018;15:6-11.  Back to cited text no. 121
    
122.
Berglund B. Environmental dissemination of antibiotic resistance genes and correlation to anthropogenic contamination with antibiotics. Infect Ecol Epidemiol 2015;5:28564.  Back to cited text no. 122
    
123.
Bachmann NL, Rockett RJ, Timms VJ, Sintchenko V. Advances in clinical sample preparation for identification and characterization of bacterial pathogens using metagenomics. Front Public Health 2018;6:363.  Back to cited text no. 123
    
124.
Oniciuc EA, Likotrafiti E, Alvarez-Molina A, Prieto M, Santos JA, Alvarez-Ordóñez A, et al. The present and future of whole genome sequencing (WGS) and whole metagenome sequencing (WMS) for surveillance of antimicrobial resistant microorganisms and antimicrobial resistance genes across the food chain. Genes (Basel) 2018;9. pii: E268.  Back to cited text no. 124
    
125.
Miller RR, Montoya V, Gardy JL, Patrick DM, Tang P. Metagenomics for pathogen detection in public health. Genome Med 2013;5:81.  Back to cited text no. 125
    
126.
Kwong JC, McCallum N, Sintchenko V, Howden BP. Whole genome sequencing in clinical and public health microbiology. Pathology 2015;47:199-210.  Back to cited text no. 126
    


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2004 - Indian Journal of Medical Microbiology
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