The advantage of intergenic regions as genomic features for machine-learning-based host attribution of Salmonella Typhimurium from the USA.

Salmonella enterica is a taxonomically diverse pathogen with over 2600 serovars associated with a wide variety of animal hosts including humans, other mammals, birds and reptiles. Some serovars are host-specific or host-restricted and cause disease in distinct host species, while others, such as serovar S. Typhimurium (STm), are generalists and have the potential to colonize a wide variety of species. However, even within generalist serovars such as STm it is becoming clear that pathovariants exist that differ in tropism and virulence. Identifying the genetic factors underlying host specificity is complex, but the availability of thousands of genome sequences and advances in machine learning have made it possible to build specific host prediction models to aid outbreak control and predict the human pathogenic potential of isolates from animals and other reservoirs. We have advanced this area by building host-association prediction models trained on a wide range of genomic features and compared them with predictions based on nearest-neighbour phylogeny. SNPs, protein variants (PVs), antimicrobial resistance (AMR) profiles and intergenic regions (IGRs) were extracted from 3883 high-quality STm assemblies collected from humans, swine, bovine and poultry in the USA, and used to construct Random Forest (RF) machine learning models. An additional 244 recent STm assemblies from farm animals were used as a test set for further validation. The models based on PVs and IGRs had the best performance in terms of predicting the host of origin of isolates and outperformed nearest-neighbour phylogenetic host prediction as well as models based on SNPs or AMR data. However, the models did not yield reliable predictions when tested with isolates that were phylogenetically distinct from the training set. The IGR and PV models were often able to differentiate human isolates in clusters where the majority of isolates were from a single animal source. Notably, IGRs were the feature with the best performance across multiple models which may be due to IGRs acting as both a representation of their flanking genes, equivalent to PVs, while also capturing genomic regulatory variation, such as altered promoter regions. The IGR and PV models predict that ~45 % of the human infections with STm in the USA originate from bovine, ~40 % from poultry and ~14.5 % from swine, although sequences of isolates from other sources were not used for training. In summary, the research demonstrates a significant gain in accuracy for models with IGRs and PVs as features compared to SNP-based and core genome phylogeny predictions when applied within the existing population structure. This article contains data hosted by Microreact.

Acquisition and loss of CTX-M plasmids in Shigella species associated with MSM transmission in the UK.

Shigellosis in men who have sex with men (MSM) is caused by multidrug resistant Shigellae, exhibiting resistance to antimicrobials including azithromycin, ciprofloxacin and more recently the third-generation cephalosporins. We sequenced four blaCTX-M-27-positive MSM Shigella isolates (2018-20) using Oxford Nanopore Technologies; three S. sonnei (identified as two MSM clade 2, one MSM clade 5) and one S. flexneri 3a, to explore AMR context. All S. sonnei isolates harboured Tn7/Int2 chromosomal integrons, whereas S. flexneri 3a contained the Shigella Resistance Locus. All strains harboured IncFII pKSR100-like plasmids (67-83kbp); where present blaCTX-M-27 was located on these plasmids flanked by IS26 and IS903B, however blaCTX-M-27 was lost in S. flexneri 3a during storage between Illumina and Nanopore sequencing. IncFII AMR regions were mosaic and likely reorganised by IS26; three of the four plasmids contained azithromycin-resistance genes erm(B) and mph(A) and one harboured the pKSR100 integron. Additionally, all S. sonnei isolates possessed a large IncB/O/K/Z plasmid, two of which carried aph(3')-Ib/aph(6)-Id/sul2 and tet(A). Monitoring the transmission of mobile genetic elements with co-located AMR determinants is necessary to inform empirical treatment guidance and clinical management of MSM-associated shigellosis.

Patchy promiscuity: machine learning applied to predict the host specificity of Salmonella enterica and Escherichia coli.

Salmonella enterica and Escherichia coli are bacterial species that colonize different animal hosts with sub-types that can cause life-threatening infections in humans. Source attribution of zoonoses is an important goal for infection control as is identification of isolates in reservoir hosts that represent a threat to human health. In this study, host specificity and zoonotic potential were predicted using machine learning in which Support Vector Machine (SVM) classifiers were built based on predicted proteins from whole genome sequences. Analysis of over 1000 S.enterica genomes allowed the correct prediction (67 -90 % accuracy) of the source host for S. Typhimurium isolates and the same classifier could then differentiate the source host for alternative serovars such as S. Dublin. A key finding from both phylogeny and SVM methods was that the majority of isolates were assigned to host-specific sub-clusters and had high host-specific SVM scores. Moreover, only a minor subset of isolates had high probability scores for multiple hosts, indicating generalists with genetic content that may facilitate transition between hosts. The same approach correctly identified human versus bovine E. coli isolates (83 % accuracy) and the potential of the classifier to predict a zoonotic threat was demonstrated using E. coli O157. This research indicates marked host restriction for both S. enterica and E. coli, with only limited isolate subsets exhibiting host promiscuity by gene content. Machine learning can be successfully applied to interrogate source attribution of bacterial isolates and has the capacity to predict zoonotic potential.

Analysis of whole genome sequencing for the Escherichia coli O157:H7 typing phages.

BACKGROUND: Shiga toxin producing Escherichia coli O157 can cause severe bloody diarrhea and haemolytic uraemic syndrome. Phage typing of E. coli O157 facilitates public health surveillance and outbreak investigations, certain phage types are more likely to occupy specific niches and are associated with specific age groups and disease severity. The aim of this study was to analyse the genome sequences of 16 (fourteen T4 and two T7) E. coli O157 typing phages and to determine the genes responsible for the subtle differences in phage type profiles. RESULTS: The typing phages were sequenced using paired-end Illumina sequencing at The Genome Analysis Centre and the Animal Health and Veterinary Laboratories Agency and bioinformatics programs including Velvet, Brig and Easyfig were used to analyse them. A two-way Euclidian cluster analysis highlighted the associations between groups of phage types and typing phages. The analysis showed that the T7 typing phages (9 and 10) differed by only three genes and that the T4 typing phages formed three distinct groups of similar genomic sequences: Group 1 (1, 8, 11, 12 and 15, 16), Group 2 (3, 6, 7 and 13) and Group 3 (2, 4, 5 and 14). The E. coli O157 phage typing scheme exhibited a significantly modular network linked to the genetic similarity of each group showing that these groups are specialised to infect a subset of phage types. CONCLUSION: Sequencing the typing phage has enabled us to identify the variable genes within each group and to determine how this corresponds to changes in phage type.

Insight into Shiga toxin genes encoded by Escherichia coli O157 from whole genome sequencing.

The ability of Shiga toxin-producing Escherichia coli (STEC) to cause severe illness in humans is determined by multiple host factors and bacterial characteristics, including Shiga toxin (Stx) subtype. Given the link between Stx2a subtype and disease severity, we sought to identify the stx subtypes present in whole genome sequences (WGS) of 444 isolates of STEC O157. Difficulties in assembling the stx genes in some strains were overcome by using two complementary bioinformatics methods: mapping and de novo assembly. We compared the WGS analysis with the results obtained using a PCR approach and investigated the diversity within and between the subtypes. All strains of STEC O157 in this study had stx1a, stx2a or stx2c or a combination of these three genes. There was over 99% (442/444) concordance between PCR and WGS. When common source strains were excluded, 236/349 strains of STEC O157 had multiple copies of different Stx subtypes and 54 had multiple copies of the same Stx subtype. Of those strains harbouring multiple copies of the same Stx subtype, 33 had variants between the alleles while 21 had identical copies. Strains harbouring Stx2a only were most commonly found to have multiple alleles of the same subtype (42%). Both the PCR and WGS approach to stx subtyping provided a good level of sensitivity and specificity. In addition, the WGS data also showed there were a significant proportion of strains harbouring multiple alleles of the same Stx subtype associated with clinical disease in England.