Comparative genomics and proteomics analysis of phages infecting multi-drug resistant Escherichia coli O177 isolated from cattle faeces.

The increasing prevalence of antimicrobial-resistant (AMR) pathogens has become a major global health concern. To address this challenge, innovative strategies such as bacteriophage therapy must be optimised. Genomic characterisation is a crucial step in identifying suitable phage candidates for combating AMR pathogens. The aim of this study was to characterise seven phages that infect the Escherichia coli O177 strain using a whole genome sequencing. The analysis of genome sequences revealed that these phages had linear dsDNA, with genome sizes spanning from 136, 483 to 166,791 bp and GC content varying from 35.39 to 43.63%. Taxonomically, the phages were classified under three different subfamilies (Stephanstirmvirinae, Tevenvirinae, and Vequintavirinae) and three genera (Phapecoctavirus, Tequatrovirus, and Vequintavirus) within the class Caudoviricetes. In silico PhageAI analysis predicted that all the phages were virulent, with confidence levels between 96.07 and 97.26%. The phage genomes contained between 66 and 82 ORFs, which encode hypothetical and putative functional proteins. In addition, the phage genomes contained core genes associated with molecular processes such as DNA replication, transcription modulation, nucleotide metabolism, phage structure (capsid and tail), and lysis. None of the genomes carried genes associated with undesirable traits such as integrase, antimicrobial resistance, virulence, and toxins. The study revealed high genome and proteome homology among E. coli O177 phages and other known Escherichia phages. The results suggest that the seven phages are new members of the genera Phapecoctavirus, Tequatrovirus, and Vequintavirus under the subfamilies Stephanstirmvirinae, Tevenvirinae, and Vequintavirinae, respectively.

Mycobacteriosis in slaughter pigs from South Africa from 1991 to 2002: Mycobacterium spp. diversity and Mycobacterium avium complex genotypes.

Introduction: Mycobacterium avium complex (MAC) bacteria are the most prominent etiological agents of lymphadenitis in pigs. M. avium subspecies hominissuis (MAH) is a member of MAC and has been reported in many parts of the world to be the most prevalent non-tuberculous mycobacteria (NTM) to cause mycobacteriosis in humans, mainly in children. Thus, the economic and zoonotic impact of MAC species are increasingly being recognized. In South Africa, little is known about the distribution of NTM and the molecular epidemiology of M. avium in pigs. Materials and methods: In this study, lymph nodes including mandibular, mesenteric, submandibular, and retropharyngeal, with tuberculosis-like lesions were collected during routine meat inspection of slaughter pigs with no disease symptoms (n = 132), between 1991 and 2002. These pigs were slaughtered at 44 abattoirs distributed across seven of the nine South African provinces. Mycobacterial culture, polymerase chain reaction (PCR), and sequencing of the Mycobacterium specific 577 bp 16S rRNA gene fragment were performed for species and subspecies identification. Results: The majority of the isolates (each per sample); 114 (86.4%) were identified as MAH, 8 (6%) as MAA/M. avium subsp. silvaticum, 4 (3%) were Mycobacterium tuberculosis, 2 (1.5%) as Mycobacterium intracellulare, and 1 (0.75%) as Mycobacterium bovis. The other isolates were identified as Mycobacterium lentiflavum (0.75%), Mycobacterium novocastrense (0.75%), and a Micrococcus spp. (0.75%). Using an eight-marker MLVA typing tool, we deciphered at least nine MIRU VNTR INMV types of MAH and MAA. Discussion: Identification of known zoonotic mycobacteria, including MAH, MAA, M. intracellulare, M. bovis, and M. tuberculosis, from slaughter pigs has a potential public health impact and also strengthens recognition of the potential economic impact of MAC. This study has also for the first time in South Africa, revealed MAC MIRU VNTR INMV genotypes which will aid in the future epidemiological investigation of MAC in South Africa.

Genomic Characterization of Listeria innocua Isolates Recovered from Cattle Farms, Beef Abattoirs, and Retail Outlets in Gauteng Province, South Africa.

Whole-genome sequencing (WGS) was used for the genomic characterization of one hundred and ten strains of Listeria innocua (L. innocua) isolated from twenty-three cattle farms, eight beef abattoirs, and forty-eight retail outlets in Gauteng province, South Africa. In silico multilocus sequence typing (MLST) was used to identify the isolates' sequence types (STs). BLAST-based analyses were used to identify antimicrobial and virulence genes. The study also linked the detection of the genes to the origin (industries and types of samples) of the L. innocua isolates. The study detected 14 STs, 13 resistance genes, and 23 virulence genes. Of the 14 STs detected, ST637 (26.4%), ST448 (20%), 537 (13.6%), and 1085 (12.7%) were predominant, and the frequency varied significantly (p < 0.05). All 110 isolates of L. innocua were carriers of one or more antimicrobial resistance genes, with resistance genes lin (100%), fosX (100%), and tet(M) (30%) being the most frequently detected (p < 0.05). Of the 23 virulence genes recognized, 13 (clpC, clpE, clpP, hbp1, svpA, hbp2, iap/cwhA, lap, lpeA, lplA1, lspA, oatA, pdgA, and prsA2) were found in all 110 isolates of L. innocua. Overall, diversity and significant differences were detected in the frequencies of STs, resistance, and virulence genes according to the origins (source and sample type) of the L. innocua isolates. This, being the first genomic characterization of L. innocua recovered from the three levels/industries (farm, abattoir, and retail) of the beef production system in South Africa, provides data on the organism's distribution and potential food safety implications.

Identification of Listeria species and Multilocus Variable-Number Tandem Repeat Analysis (MLVA) Typing of Listeria innocua and Listeria monocytogenes Isolates from Cattle Farms and Beef and Beef-Based Products from Retail Outlets in Mpumalanga and North West Provinces, South Africa.

In this study, Listeria isolates (214) were characterized as follows: L. innocua (77.10%), L. monocytogenes (11.21%), L. welshimeri (5.61%), L. grayi (1.40%), L. seeligeri (0.93%), and L. species (3.73%) that were not identified at the species level, from beef and beef based products from retail and farms in Mpumalanga and North West provinces of South Africa. MLVA was further used to type Listeria innocua isolates (165) and Listeria monocytogenes isolates (24). The L. monocytogenes isolates were also serogrouped using PCR. The MLVA protocol for L. monocytogenes typing included six tandem repeat primer sets, and the MLVA protocol for L. innocua included the use of three tandem repeats primer sets. The L. monocytogenes serogroups were determined as follows: 4b-4d-4e (IVb) (37.50%), 1/2a-3a (IIa) (29.16%), 1/2b-3b (IIb) (12.50%), 1/2c-3c (IIc) (8.33%), and IVb-1 (4.16%). MLVA could cluster isolates belonging to each specie, L. monocytogenes, and L. innocua isolates, into MLVA-related strains. There were 34 and 10 MLVA types obtained from the MLVA typing of L. innocua and L. monocytogenes, respectively. MLVA clustered the L. monocytogenes isolates irrespective of sample category, serogroups, and geographical origin. Similarly, the L. innocua isolates clustered irrespective of meat category and geographical origin. MLVA was able to cluster isolates based on MLVA relatedness. The clustering of isolates from farms and retailers indicates transmission of Listeria spp. MLVA is an affordable, simple, and discriminatory method that can be used routinely to type L. monocytogenes and L. innocua isolates.

Molecular Characterization of Salmonella Detected along the Broiler Production Chain in Trinidad and Tobago.

This cross-sectional study determined the serovars, antimicrobial resistance genes, and virulence factors of Salmonella isolated from hatcheries, broiler farms, processing plants, and retail outlets in Trinidad and Tobago. Salmonella in silico serotyping detected 23 different serovars where Kentucky 20.5% (30/146), Javiana 19.2% (28/146), Infantis 13.7% (20/146), and Albany 8.9% (13/146) were the predominant serovars. There was a 76.0% (111/146) agreement between serotyping results using traditional conventional methods and whole-genome sequencing (WGS) in in silico analysis. In silico identification of antimicrobial resistance genes conferring resistance to aminoglycosides, cephalosporins, peptides, sulfonamides, and antiseptics were detected. Multidrug resistance (MDR) was detected in 6.8% (10/146) of the isolates of which 100% originated from broiler farms. Overall, virulence factors associated with secretion systems and fimbrial adherence determinants accounted for 69.3% (3091/4463), and 29.2% (1302/4463) counts, respectively. Ten of 20 isolates of serovar Infantis (50.0%) showed MDR and contained the blaCTX-M-65 gene. This is the first molecular characterization of Salmonella isolates detected along the entire broiler production continuum in the Caribbean region using WGS. The availability of these genomes will help future source tracking during epidemiological investigations associated with Salmonella foodborne outbreaks in the region and worldwide.

EuGI: a novel resource for studying genomic islands to facilitate horizontal gene transfer detection in eukaryotes.

BACKGROUND: Genomic islands (GIs) are inserts of foreign DNA that have potentially arisen through horizontal gene transfer (HGT). There are evidences that GIs can contribute significantly to the evolution of prokaryotes. The acquisition of GIs through HGT in eukaryotes has, however, been largely unexplored. In this study, the previously developed GI prediction tool, SeqWord Gene Island Sniffer (SWGIS), is modified to predict GIs in eukaryotic chromosomes. Artificial simulations are used to estimate ratios of predicting false positive and false negative GIs by inserting GIs into different test chromosomes and performing the SWGIS v2.0 algorithm. Using SWGIS v2.0, GIs are then identified in 36 fungal, 22 protozoan and 8 invertebrate genomes. RESULTS: SWGIS v2.0 predicts GIs in large eukaryotic chromosomes based on the atypical nucleotide composition of these regions. Averages for predicting false negative and false positive GIs were 20.1% and 11.01% respectively. A total of 10,550 GIs were identified in 66 eukaryotic species with 5299 of these GIs coding for at least one functional protein. The EuGI web-resource, freely accessible at http://eugi.bi.up.ac.za , was developed that allows browsing the database created from identified GIs and genes within GIs through an interactive and visual interface. CONCLUSIONS: SWGIS v2.0 along with the EuGI database, which houses GIs identified in 66 different eukaryotic species, and the EuGI web-resource, provide the first comprehensive resource for studying HGT in eukaryotes.