Enhanced nasopharyngeal infection and shedding associated with an epidemic lineage of emm3 group A Streptococcus.

BACKGROUND: A group A Streptococcus (GAS) lineage of genotype emm3, sequence type 15 (ST15) was associated with a 6 month upsurge in invasive GAS disease in the UK. The epidemic lineage (Lineage C) had lost 2 typical emm3 prophages, Φ315.1 and Φ315.2 associated with the superantigen ssa, but gained a different prophage (ΦUK-M3.1) associated with a different superantigen, speC and a DNAse spd1. METHODS AND RESULTS: The presence of speC and spd1 in Lineage C ST15 strains enhanced both in vitro mitogenic and DNase activities over non-Lineage C ST15 strains. Invasive disease models in Galleria mellonella and SPEC-sensitive transgenic mice, revealed no difference in overall invasiveness of Lineage C ST15 strains compared with non-Lineage C ST15 strains, consistent with clinical and epidemiological analysis. Lineage C strains did however markedly prolong murine nasal infection with enhanced nasal and airborne shedding compared with non-Lineage C strains. Deletion of speC or spd1 in 2 Lineage C strains identified a possible role for spd1 in airborne shedding from the murine nasopharynx. CONCLUSIONS: Nasopharyngeal infection and shedding of Lineage C strains was enhanced compared with non-Lineage C strains and this was, in part, mediated by the gain of the DNase spd1 through prophage acquisition.

Genome analysis following a national increase in Scarlet Fever in England 2014.

BACKGROUND: During a substantial elevation in scarlet fever (SF) notifications in 2014 a national genomic study was undertaken of Streptococcus pyogenes (Group A Streptococci, GAS) isolates from patients with SF with comparison to isolates from patients with invasive disease (iGAS) to test the hypotheses that the increase in SF was due to either the introduction of one or more new/emerging strains in the population in England or the transmission of a known genetic element through the population of GAS by horizontal gene transfer (HGT) resulting in infections with an increased likelihood of causing SF. Isolates were collected to provide geographical representation, for approximately 5% SF isolates from each region from 1st April 2014 to 18th June 2014. Contemporaneous iGAS isolates for which genomic data were available were included for comparison. Data were analysed in order to determine emm gene sequence type, phylogenetic lineage and genomic clade representation, the presence of known prophage elements and the presence of genes known to confer pathogenicity and resistance to antibiotics. RESULTS: 555 isolates were analysed, 303 from patients with SF and 252 from patients with iGAS. Isolates from patients with SF were of multiple distinct emm sequence types and phylogenetic lineages. Prior to data normalisation, emm3 was the predominant type (accounting for 42.9% of SF isolates, 130/303 95%CI 37.5-48.5; 14.7% higher than the percentage of emm3 isolates found in the iGAS isolates). Post-normalisation emm types, 4 and 12, were found to be over-represented in patients with SF versus iGAS (p < 0.001). A single gene, ssa, was over-represented in isolates from patients with SF. No single phage was found to be over represented in SF vs iGAS. However, a "meta-ssa" phage defined by the presence of :315.2, SPsP6, MGAS10750.3 or HK360ssa, was found to be over represented. The HKU360.vir phage was not detected yet the HKU360.ssa phage was present in 43/63 emm12 isolates but not found to be over-represented in isolates from patients with SF. CONCLUSIONS: There is no evidence that the increased number of SF cases was a strain-specific or known mobile element specific phenomenon, as the increase in SF cases was associated with multiple lineages of GAS.

Whole genome sequencing of Streptococcus pneumoniae: development, evaluation and verification of targets for serogroup and serotype prediction using an automated pipeline.

Streptococcus pneumoniae typically express one of 92 serologically distinct capsule polysaccharide (cps) types (serotypes). Some of these serotypes are closely related to each other; using the commercially available typing antisera, these are assigned to common serogroups containing types that show cross-reactivity. In this serotyping scheme, factor antisera are used to allocate serotypes within a serogroup, based on patterns of reactions. This serotyping method is technically demanding, requires considerable experience and the reading of the results can be subjective. This study describes the analysis of the S. pneumoniae capsular operon genetic sequence to determine serotype distinguishing features and the development, evaluation and verification of an automated whole genome sequence (WGS)-based serotyping bioinformatics tool, PneumoCaT (Pneumococcal Capsule Typing). Initially, WGS data from 871 S. pneumoniae isolates were mapped to reference cps locus sequences for the 92 serotypes. Thirty-two of 92 serotypes could be unambiguously identified based on sequence similarities within the cps operon. The remaining 60 were allocated to one of 20 'genogroups' that broadly correspond to the immunologically defined serogroups. By comparing the cps reference sequences for each genogroup, unique molecular differences were determined for serotypes within 18 of the 20 genogroups and verified using the set of 871 isolates. This information was used to design a decision-tree style algorithm within the PneumoCaT bioinformatics tool to predict to serotype level for 89/94 (92 + 2 molecular types/subtypes) from WGS data and to serogroup level for serogroups 24 and 32, which currently comprise 2.1% of UK referred, invasive isolates submitted to the National Reference Laboratory (NRL), Public Health England (June 2014-July 2015). PneumoCaT was evaluated with an internal validation set of 2065 UK isolates covering 72/92 serotypes, including 19 non-typeable isolates and an external validation set of 2964 isolates from Thailand (n = 2,531), USA (n = 181) and Iceland (n = 252). PneumoCaT was able to predict serotype in 99.1% of the typeable UK isolates and in 99.0% of the non-UK isolates. Concordance was evaluated in UK isolates where further investigation was possible; in 91.5% of the cases the predicted capsular type was concordant with the serologically derived serotype. Following retesting, concordance increased to 99.3% and in most resolved cases (97.8%; 135/138) discordance was shown to be caused by errors in original serotyping. Replicate testing demonstrated that PneumoCaT gave 100% reproducibility of the predicted serotype result. In summary, we have developed a WGS-based serotyping method that can predict capsular type to serotype level for 89/94 serotypes and to serogroup level for the remaining four. This approach could be integrated into routine typing workflows in reference laboratories, reducing the need for phenotypic immunological testing.

Integration of Genomic and Other Epidemiologic Data to Investigate and Control a Cross-Institutional Outbreak of Streptococcus pyogenes.

Single-strain outbreaks of Streptococcus pyogenes infections are common and often go undetected. In 2013, two clusters of invasive group A Streptococcus (iGAS) infection were identified in independent but closely located care homes in Oxfordshire, United Kingdom. Investigation included visits to each home, chart review, staff survey, microbiologic sampling, and genome sequencing. S. pyogenes emm type 1.0, the most common circulating type nationally, was identified from all cases yielding GAS isolates. A tailored whole-genome reference population comprising epidemiologically relevant contemporaneous isolates and published isolates was assembled. Data were analyzed independently using whole-genome multilocus sequencing and single-nucleotide polymorphism analyses. Six isolates from staff and residents of the homes formed a single cluster that was separated from the reference population by both analytical approaches. No further cases occurred after mass chemoprophylaxis and enhanced infection control. Our findings demonstrate the ability of 2 independent analytical approaches to enable robust conclusions from nonstandardized whole-genome analysis to support public health practice.

An outbreak of high-level azithromycin resistant Neisseria gonorrhoeae in England.

OBJECTIVES: To investigate a potential outbreak of high-level azithromycin resistant (HL-AziR) gonococcal infections diagnosed in eight patients attending a sexual health clinic in Leeds, North England, between November 2014 and March 2015. METHODS: Eight cases of infection with gonococci exhibiting azithromycin minimum inhibitory concentrations (MICs) ≥256 mg/L were identified from patients in Leeds as part of the routine service provided by the Sexually Transmitted Bacteria Reference Unit. All patient records were reviewed to collate epidemiological and clinical information including evaluation of patient management. Whole-genome sequencing (WGS) was performed on seven gonococcal isolates to determine Neisseria gonorrhoeae multiantigen sequence type (NG-MAST), WGS comparison and mutations in the 23S rRNA genes. RESULTS: All patients were heterosexual (five male, three female) from a range of ethnic backgrounds and from the Leeds area. Three patients were linked by partner notification. All patients were infected at genital sites and two women had pharyngeal infection also. Six patients received the recommended first-line therapy for uncomplicated gonorrhoea, one was treated for pelvic inflammatory disease and one received spectinomycin followed later by ciprofloxacin. Test of cure was achieved in seven patients and confirmed successful eradication. All seven isolates sequenced were identical by NG-MAST and WGS comparison, and contained an A2143G mutation in all four 23S rRNA alleles. CONCLUSIONS: Epidemiological and microbiological investigations confirm that an outbreak of a gonococcal strain showing HL-AziR is ongoing in the North of England. Every effort should be made to identify and curtail dissemination of this strain as it presents a significant threat to the current recommended front-line dual therapy.

Emergence of a novel lineage containing a prophage in emm/M3 group A Streptococcus associated with upsurge in invasive disease in the UK.

A sudden increase in invasive Group A Streptococcus (iGAS) infections associated with emm/M3 isolates during the winter of 2008/09 prompted the initiation of enhanced surveillance in England. In order to characterise the population of emm/M3 GAS within the UK and determine bacterial factors that might be responsible for this upsurge, 442 emm/M3 isolates from cases of invasive and non-invasive infections during the period 2001-2013 were subjected to whole genome sequencing. MLST analysis differentiated emm/M3 isolates into three sequence types (STs): ST15, ST315 and ST406. Analysis of the whole genome SNP-based phylogeny showed that the majority of isolates from the 2008-2009 upsurge period belonged to a distinct lineage characterized by the presence of a prophage carrying the speC exotoxin and spd1 DNAase genes but loss of two other prophages considered typical of the emm/M3 lineage. This lineage was significantly associated with the upsurge in iGAS cases and we postulate that the upsurge could be attributed in part to expansion of this novel prophage-containing lineage within the population. The study underlines the importance of prompt genomic analysis of changes in the GAS population, providing an advanced public health warning system for newly emergent, pathogenic strains.

Corrigendum: Emergence of a novel lineage containing a prophage in emm/M3 group A Streptococcus associated with upsurge in invasive disease in the UK.

[This corrects the article DOI: 10.1099/mgen.0.000059.].

High-Resolution Analysis by Whole-Genome Sequencing of an International Lineage (Sequence Type 111) of Pseudomonas aeruginosa Associated with Metallo-Carbapenemases in the United Kingdom.

Whole-genome sequencing (WGS) was carried out on 87 isolates of sequence type 111 (ST-111) of Pseudomonas aeruginosa collected between 2005 and 2014 from 65 patients and 12 environmental isolates from 24 hospital laboratories across the United Kingdom on an Illumina HiSeq instrument. Most isolates (73) carried VIM-2, but others carried IMP-1 or IMP-13 (5) or NDM-1 (1); one isolate had VIM-2 and IMP-18, and 7 carried no metallo-beta-lactamase (MBL) gene. Single nucleotide polymorphism analysis divided the isolates into distinct clusters; the NDM-1 isolate was an outlier, and the IMP isolates and 6/7 MBL-negative isolates clustered separately from the main set of 73 VIM-2 isolates. Within the VIM-2 set, there were at least 3 distinct clusters, including a tightly clustered set of isolates from 3 hospital laboratories consistent with an outbreak from a single introduction that was quickly brought under control and a much broader set dominated by isolates from a long-running outbreak in a London hospital likely seeded from an environmental source, requiring different control measures; isolates from 7 other hospital laboratories in London and southeast England were also included. Bayesian evolutionary analysis indicated that all the isolates shared a common ancestor dating back ∼50 years (1960s), with the main VIM-2 set separating approximately 20 to 30 years ago. Accessory gene profiling revealed blocks of genes associated with particular clusters, with some having high similarity (≥95%) to bacteriophage genes. WGS of widely found international lineages such as ST-111 provides the necessary resolution to inform epidemiological investigations and intervention policies.

snp-search: simple processing, manipulation and searching of SNPs from high-throughput sequencing.

BACKGROUND: A typical bacterial pathogen genome mapping project can identify thousands of single nucleotide polymorphisms (SNP). Interpreting SNP data is complex and it is difficult to conceptualise the data contained within the large flat files that are the typical output from most SNP calling algorithms. One solution to this problem is to construct a database that can be queried using simple commands so that SNP interrogation and output is both easy and comprehensible. RESULTS: Here we present snp-search, a tool that manages SNP data and allows for manipulation and searching of SNP data. After creation of a SNP database from a VCF file, snp-search can be used to convert the selected SNP data into FASTA sequences, construct phylogenies, look for unique SNPs, and output contextual information about each SNP. The FASTA output from snp-search is particularly useful for the generation of robust phylogenetic trees that are based on SNP differences across the conserved positions in whole genomes. Queries can be designed to answer critical genomic questions such as the association of SNPs with particular phenotypes. CONCLUSIONS: snp-search is a tool that manages SNP data and outputs useful information which can be used to test important biological hypotheses.

Tracing the transition of methicillin resistance in sub-populations of Staphylococcus aureus, using SELDI-TOF Mass Spectrometry and Artificial Neural Network Analysis.

Strains (n=99) of Staphylococcus aureus isolated from a large number of clinical sources and tested for methicillin sensitivity were analysed by MALDI-TOF-MS using the Weak Cation Exchange (CM10) ProteinChip Array (designated SELDI-TOF-MS). The profile data generated was analysed using Artificial Neural Network (ANN) Analysis modelling techniques. Seven key ions identified by the ANNs that were predictive of MRSA and MSSA were validated by incorporation into a model. This model exhibited an area under the ROC curve value of 0.9147 indicating the potential application of this approach for rapidly characterising MRSA and MSSA isolates. Nearly all strains (n=97) were correctly assigned to the correct group, with only two aberrant MSSA strains being misclassified. However, approximately 21% of the strains appeared to be in a process of transition as resistance to methicillin was being acquired.

Coherent pipeline for biomarker discovery using mass spectrometry and bioinformatics.

BACKGROUND: Robust biomarkers are needed to improve microbial identification and diagnostics. Proteomics methods based on mass spectrometry can be used for the discovery of novel biomarkers through their high sensitivity and specificity. However, there has been a lack of a coherent pipeline connecting biomarker discovery with established approaches for evaluation and validation. We propose such a pipeline that uses in silico methods for refined biomarker discovery and confirmation. RESULTS: The pipeline has four main stages: Sample preparation, mass spectrometry analysis, database searching and biomarker validation. Using the pathogen Clostridium botulinum as a model, we show that the robustness of candidate biomarkers increases with each stage of the pipeline. This is enhanced by the concordance shown between various database search algorithms for peptide identification. Further validation was done by focusing on the peptides that are unique to C. botulinum strains and absent in phylogenetically related Clostridium species. From a list of 143 peptides, 8 candidate biomarkers were reliably identified as conserved across C. botulinum strains. To avoid discarding other unique peptides, a confidence scale has been implemented in the pipeline giving priority to unique peptides that are identified by a union of algorithms. CONCLUSIONS: This study demonstrates that implementing a coherent pipeline which includes intensive bioinformatics validation steps is vital for discovery of robust biomarkers. It also emphasises the importance of proteomics based methods in biomarker discovery.

Predicting protein function by machine learning on amino acid sequences--a critical evaluation.

BACKGROUND: Predicting the function of newly discovered proteins by simply inspecting their amino acid sequence is one of the major challenges of post-genomic computational biology, especially when done without recourse to experimentation or homology information. Machine learning classifiers are able to discriminate between proteins belonging to different functional classes. Until now, however, it has been unclear if this ability would be transferable to proteins of unknown function, which may show distinct biases compared to experimentally more tractable proteins. RESULTS: Here we show that proteins with known and unknown function do indeed differ significantly. We then show that proteins from different bacterial species also differ to an even larger and very surprising extent, but that functional classifiers nonetheless generalize successfully across species boundaries. We also show that in the case of highly specialized proteomes classifiers from a different, but more conventional, species may in fact outperform the endogenous species-specific classifier. CONCLUSION: We conclude that there is very good prospect of successfully predicting the function of yet uncharacterized proteins using machine learning classifiers trained on proteins of known function.

Feature selection and the class imbalance problem in predicting protein function from sequence.

When the standard approach to predict protein function by sequence homology fails, other alternative methods can be used that require only the amino acid sequence for predicting function. One such approach uses machine learning to predict protein function directly from amino acid sequence features. However, there are two issues to consider before successful functional prediction can take place: identifying discriminatory features, and overcoming the challenge of a large imbalance in the training data. We show that by applying feature subset selection followed by undersampling of the majority class, significantly better support vector machine (SVM) classifiers are generated compared with standard machine learning approaches. As well as revealing that the features selected could have the potential to advance our understanding of the relationship between sequence and function, we also show that undersampling to produce fully balanced data significantly improves performance. The best discriminating ability is achieved using SVMs together with feature selection and full undersampling; this approach strongly outperforms other competitive learning algorithms. We conclude that this combined approach can generate powerful machine learning classifiers for predicting protein function directly from sequence.

FrankSum: new feature selection method for protein function prediction.

In the study of in silico functional genomics, improving the performance of protein function prediction is the ultimate goal for identifying proteins associated with defined cellular functions. The classical prediction approach is to employ pairwise sequence alignments. However this method often faces difficulties when no statistically significant homologous sequences are identified. An alternative way is to predict protein function from sequence-derived features using machine learning. In this case the choice of possible features which can be derived from the sequence is of vital importance to ensure adequate discrimination to predict function. In this paper we have successfully selected biologically significant features for protein function prediction. This was performed using a new feature selection method (FrankSum) that avoids data distribution assumptions, uses a data independent measurement (p-value) within the feature, identifies redundancy between features and uses an appropriate ranking criterion for feature selection. We have shown that classifiers generated from features selected by FrankSum outperforms classifiers generated from full feature sets, randomly selected features and features selected from the Wrapper method. We have also shown the features are concordant across all species and top ranking features are biologically informative. We conclude that feature selection is vital for successful protein function prediction and FrankSum is one of the feature selection methods that can be applied successfully to such a domain.