Multilocus sequence typing (MLST) involves analysing allelic sequences of housekeeping genes in order to track long-term/global epidemiology of microbes (Cooper & Feil 2004;
Aanensen & Spratt 2005). Multilocus sequence typing is used in many fields, such as epidemiology to track global disease outbreaks, evolutionary biology of microbial genomes and phylogenetic analysis to determine relationships between isolates (Sabat et al. 2013).
Actual sequence information is used to identify variation at the nucleotide level (Feil &
Enright 2004). The unique sequence of each allele is assigned a random integer number and the combination of alleles at each locus is termed an “allelic profile” and gives the sequence type (ST) (Enright & Spratt 1999; Larsen et al. 2012). A total of 7 housekeeping genes (Feil et al. 2000; Aanensen & Spratt 2005) are used to determine the MLST profile of E. coli (Wirth et al. 2006), with increased accuracy and sufficient discrimination of isolates (Larsen et al. 2012). Housekeeping genes evolve slowly and are under stabilizing, selective pressure and therefore ideal for MLST. In MLST analysis, loci that conform to housekeeping standards are used to identify clonal linkages whilst loci that are hypervariable can be used to analyze specific clones and to look for evidence of microevolution (Cooper & Feil 2004).
Sequence typing of ETEC is important as it will inform on what strains are present in the population and their epidemiology. Numerous typing methods exist such as pulsed-field gel electrophoresis, serotyping, randomly amplified polymorphic DNA, repetitive-element PCR, multilocus-enzyme electrophoresis, multilocus sequence typing and whole-genome sequencing to name a few (Sabat et al. 2013). Although serotyping is highly useful, its major shortfall is that is requires specific anti-sera, which are not always readily available and are expensive (Girardini et al. 2012). Molecular methods such as PCR and whole-genome sequencing are better in that the results are reproducible, quickly generated and highly efficient (Tang et al. 1997; Sabat et al. 2013).
Numerous MLST databases have been created for fungal and bacterial strains which can all be accessed from the centralized PubMLST server which is hosted by the University of Oxford (http://pubmlst.org) (Larsen et al. 2012). The MLST database for E. coli is hosted by
35 the University of Warwick found at (http://mlst.warwick.ac.uk/mlst/mlst/dbs/Ecoli/) (Wirth et al. 2006). This database contains information on seven housekeeping gene as well as protocols for the amplification of these alleles using PCR. An allelic profile is the combination of the seven alleles and is known as the isolate’s sequence type (ST). The ST can be obtained by amplifying an approximate 500 bp product of each housekeeping locus, sequencing, and seaching the seven query sequences against what is present on the database (Wirth et al. 2006). A dendogram showing the phylogenetic relationship can be constructed from the sequences using methods such as unweighted pair group method with arithmetic mean (UPGMA) and Neighbour-Joining (Feil & Enright 2004).
The seven housekepping genes used for E. coli MLST include icd (isocitrate dehydrogenase); mdh (malate deydrogenase); adk (adenylate kinase); fumC (fumarate hydratase); gyrB (DNA gyrase); recA (ATP/GTP binding motif) and purA (adenylosuccinate dehydrogenase) (Wirth et al. 2006). Traditionally MLST is performed by doing PCR to amplify the internal sequences of each loci followed by sequencing of the PCR products, this process is time consuming and costly (Larsen et al. 2012). Next-generation sequencing has drastically reduced the cost of sequencing whole bacterial genomes, which makes analysis more rapid and efficient. Software for analysis of WGS data is often freely available (Larsen et al. 2012). The Centre for Genomic Epidemiology database is a centralised server that allows for MLST analysis of a large number of microorganisms, found at (www.cbs.dtu.dk/services/MLST). The database uses whole-genome sequence data or short sequence reads in order to determine allele identity and sequence types. The database uses the BLAST algorithm to search the bacterial genome for alleles at each of the 7 MLST loci. The best matching allele is selected and the sequence type of the sample determined as the combination of matched alleles (Larsen et al. 2012). The database was curated in 2011 and is available as freeware for the analysis of whole-genome sequence data (Larsen et al. 2012).
The resulting output includes the nucleotide sequence of matching alleles, which can be used to construct a phylogenetic tree (Larsen et al. 2012).
Another software that is freely available for the analysis of MLST data is Sequence Type Analysis Recombinational Tests (START) which is available from PubMLST (updated to START2 (http://pubmlst.org/software/analysis/start2/) (Jolley et al. 2001). This software is able to perform data summary, lineage assignment, tests for recombination, test for selection
36 and to contruct phylogenetic trees (Jolley et al. 2001). The software can be downloaded to you computer and is extremely useful and user friendly.
Bacterial populations are thought to be highly clonal, meaning that bacterial isolates form a cluster of very closely-related genotypes, often termed clonal complexes (CCs). These CCs are slowly evolving and are dispersed over wide geographical regions (Feil & Enright 2004).
The founding genotype or clone of each CC is considered to be the genotype that differs from the highest number of other genotypes in that complex at one out of the seven loci (Feil &
Enright 2004). A previous study observed that vancomycin-resistant Enterococci isolated from pigs in Denmark and Switzerland were of the same clonal complex (Boerlin et al. 2001) indicating that all of the sequence types evolved from a single clone (Cooper & Feil 2004) regardless of geographical separation.
The few studies on E. coli in animals in South Africa reported that ETEC is prevalent in livestock including pigs. Enterotoxigenic E. coli was isolated from beef and pig at a frequency of 3.8 % in a study looking at the detection of E. coli, Staphylococcus aureus and Salmonella from pig and beef abattoirs (Tanih et al. 2015). Henton and Engelbrecht (1997) serotyped E.
coli causing colibacillosis collected from pig samples collected over a 20 year period.
Mohlatlole et al. (2013) analysed virulent E. coli strains from neonatal and weaning piglets and reported ETEC to be prevalent in 55.1 % of the samples tested. Chaora (2013) determined the breed susceptibility of South African pig populations (i.e. Large White, Landrace, Duroc, Indigenous breeds and crossbreeds) to ETEC infections, and Sikhosana (2015) determined the prevalence of and the antimicrobial resistance of the isolates. There is not much information available regarding ETEC sequence types and phylogeny and none that analysed the relationship of South African sequence types to international isolates. This study performed multilocus sequence typing using next-generation whole-genome sequencing tools to determine the relation within South African E. coli isolates and between South African and international E. coli. The aim of this study was to determine the sequence type, resulting clonal complexes and phylogenetic relationship of enterotoxigenic E. coli strains isolates from neonatal and weaning piglets sampled from two provinces of South Africa.
37