Bacterial populations often consist of multiple co-circulating lineages. Determining how such population structures arise requires understanding what drives bacterial diversification. Using 616 systematically sampled genomes, we show that Streptococcus pneumoniae lineages are typically characterized by combinations of infrequently transferred stable genomic islands: those moving primarily through transformation, along with integrative and conjugative elements and phage-related chromosomal islands. The only lineage containing extensive unique sequence corresponds to a set of atypical unencapsulated isolates that may represent a distinct species. However, prophage content is highly variable even within lineages, suggesting frequent horizontal transmission that would necessitate rapidly diversifying anti-phage mechanisms to prevent these viruses sweeping through populations. Correspondingly, two loci encoding Type I restriction-modification systems able to change their specificity over short timescales through intragenomic recombination are ubiquitous across the collection. Hence short-term pneumococcal variation is characterized by movement of phage and intragenomic rearrangements, with the slower transfer of stable loci distinguishing lineages.

Surveillance of drug-resistant bacteria is essential for healthcare providers to deliver effective empirical antibiotic therapy. However, traditional molecular epidemiology does not typically occur on a timescale that could affect patient treatment and outcomes. Here, we present a method called 'genomic neighbour typing' for inferring the phenotype of a bacterial sample by identifying its closest relatives in a database of genomes with metadata. We show that this technique can infer antibiotic susceptibility and resistance for both Streptococcus pneumoniae and Neisseria gonorrhoeae. We implemented this with rapid k-mer matching, which, when used on Oxford Nanopore MinION data, can run in real time. This resulted in the determination of resistance within 10 min (91% sensitivity and 100% specificity for S. pneumoniae and 81% sensitivity and 100% specificity for N. gonorrhoeae from isolates with a representative database) of starting sequencing, and within 4 h of sample collection (75% sensitivity and 100% specificity for S. pneumoniae) for clinical metagenomic sputum samples. This flexible approach has wide application for pathogen surveillance and may be used to greatly accelerate appropriate empirical antibiotic treatment.

Conjugate vaccination against seven pneumococcal serotypes (PCV7) reduced disease prevalence due to antibiotic-resistant strains throughout the 2000s. However, diseases caused by resistant nonvaccine type (NVT) strains increased. Some of these emerging strains were derived from vaccine types (VT) that had changed their capsule by recombination. The introduction of a vaccine targeting 13 serotypes (PCV13) in 2010 has led to concern that this scenario will repeat itself. We generated high-quality draft genomes from 265 isolates of NVT pneumococci not susceptible to penicillin (PNSP) in 2009 and compared them with the genomes of 581 isolates from 2012 to 2013 collected by the Active Bacterial Core surveillance (ABCs) of the Centers for Disease Control and Prevention (CDC). Of the seven sequence clusters (SCs) identified, three SCs fell into a single lineage associated with serogroup 23, which had an origin in 1908 as dated by coalescent analysis and included isolates with a divergent 23B capsule locus. Three other SCs represented relatively deep-branching lineages associated with serotypes 35B, 15A, and 15BC. In all cases, the resistant clones originated prior to 2010, indicating that PNSP are at present dominated by descendants of NVT clones present before vaccination. With one exception (15BC/ST3280), these SCs were related to clones identified by the Pneumococcal Molecular Epidemiology Network (PMEN). We conclude that postvaccine diversity in NVT PNSP between 2009 and 2013 was driven mainly by the persistence of preexisting strains rather than through adaptation, with few cases of serotype switching. Future surveillance is essential for documenting the long-term dynamics and resistance of NVT PNSP.

The 13-valent pneumococcal conjugate vaccine (PCV-13) was introduced in the United States in 2010. Using a large paediatric carriage sample collected from shortly after the introduction of PCV-7 to several years after the introduction of PCV-13, we investigate alterations in the composition of the pneumococcal population following the introduction of PCV-13, evaluating the extent to which the post-vaccination non-vaccine type (NVT) population mirrors that from prior to vaccine introduction and the effect of PCV-13 on vaccine type lineages. Draft genome assemblies from 736 newly sequenced and 616 previously published pneumococcal carriage isolates from children in Massachusetts between 2001 and 2014 were analysed. Isolates were classified into one of 22 sequence clusters (SCs) on the basis of their core genome sequence. We calculated the SC diversity for each sampling period as the probability that any two randomly drawn isolates from that period belong to different SCs. The sampling period immediately after the introduction of PCV-13 (2011) was found to have higher diversity than preceding (2007) or subsequent (2014) sampling periods {Simpson’s D 2007: 0.915 [95 % confidence interval (CI) 0.901, 0.929]; 2011:  0.935 [0.927, 0.942]; 2014 :  0.912 [0.901, 0.923]}. Amongst NVT isolates, we found the distribution of SCs in 2011 to be significantly different from that in 2007 or 2014 (Fisher’s exact test P=0.018, 0.0078), but did not find a difference comparing 2007 to 2014 (Fisher’s exact test P=0.24), indicating greater similarity between samples separated by a longer time period than between samples from closer time periods. We also found changes in the accessory gene content of the NVT population between 2007 and 2011 to have been reduced by 2014. Amongst the new serotypes targeted by PCV-13, four were present in our sample. The proportion of our sample composed of PCV-13-only vaccine serotypes 19A, 6C and 7F decreased between 2007 and 2014, but no such reduction was seen for serotype 3. We did, however, observe differences in the genetic composition of the pre- and post-PCV-13 serotype 3 population. Our isolates were collected during discrete sampling periods from a small geographical area, which may limit the generalizability of our findings. Pneumococcal diversity increased immediately following the introduction of PCV-13, but subsequently returned to pre-vaccination levels. This is reflected in the distribution of NVT lineages, and, to a lesser extent, their accessory gene frequencies. As such, there may be a period during which the population is particularly disrupted by vaccination before returning to a more stable distribution. The persistence and shifting genetic composition of serotype 3 is a concern and warrants further investigation.

23Surveillance of drug-resistant bacteria is essential for healthcare providers to deliver effective 24 empiric antibiotic therapy. However, traditional molecular epidemiology does not typically occur 25 on a timescale that could impact patient treatment and outcomes. Here we present a method 26 called 'genomic neighbor typing' for inferring the phenotype of a bacterial sample by identifying 27 its closest relatives in a database of genomes with metadata. We show that this technique can 28 infer antibiotic susceptibility and resistance for both S. pneumoniae and N. gonorrhoeae. We 29 implemented this with rapid k-mer matching, which, when used on Oxford Nanopore MinION 30 data, can run in real time. This resulted in determination of resistance within ten minutes 31 (sens/spec 91%/100% for S. pneumoniae and 81%/100% N. gonorrhoeae from isolates with a 32 representative database) of sequencing starting, and for clinical metagenomic sputum samples 33 (75%/100% for S. pneumoniae), within four hours of sample collection. This flexible approach has 34 wide application to pathogen surveillance and may be used to greatly accelerate appropriate 35 empirical antibiotic treatment. 36 45The molecular epidemiology of infectious disease allows us to identify high-risk pathogens and 46 determine their patterns of spread, on the basis of their genetics or (increasingly) genomics. 47Conventionally such studies, including outbreak investigations and characterization of novel 48 resistant strains, have been conducted in retrospect, but this has been changing with the 49 availability of new and increasingly inexpensive sequencing technologies 2,3 . The wealth of data 50 generated by genomics is promising but introduces a new challenge: while many features of a 51 sequence are correlated with the phenotype of interest, few are causative. 52 53 Prescription, however, has long been informed by correlative features when causative ones are 54 difficult to measure, for example whether the same syndrome or pathogen occurring in other 55 patients from the same clinical environment have responded to a particular antibiotic. This has 56 also been observed at the genetic level as well, as a result of genetic linkage between resistance 57 elements and the rest of the genome. An example is given by the pneumococcus (Streptococcus 58 pneumoniae). The Centers for Disease Control have rated the threat level of drug-resistant 59 pneumococcus as 'serious' 4 . While resistance arises in pneumococci through a variety of 60 mechanisms, approximately 90% of the variance in the minimal inhibitory concentration (MIC) 61 for antibiotics of different classes can be explained by the loci determining the strain type 5 , even 62 though none of these loci themselves causes resistance. Thus, in the overwhelming majority of 63 cases, resistance and susceptibility can be inferred from coarse strain typing based on population Results 81 82 Resistance is associated with clones in S. pneumoniae and N. gonorrhoeae 83 84 To quantify the association of clones with antibi...

BackgroundRemarkably high carriage prevalence of a community-associated meticillin-resistant Staphylococcus aureus (MRSA) strain of sequence type (ST) 22 in the Gaza strip was reported in 2012. This strain is linked to the pandemic hospital-associated EMRSA-15. The origin and evolutionary history of ST22 in Gaza communities and the genomic elements contributing to its widespread predominance are unknown. Methods: We generated high-quality draft genomes of 61 ST22 isolates from Gaza communities and, along with 175 ST22 genomes from global sources, reconstructed the ST22 phylogeny and examined genotypes unique to the Gaza isolates. Results: The Gaza isolates do not exhibit a close relationship with hospital-associated ST22 isolates, but rather with a basal population from which EMRSA-15 emerged. There were two separate resistance acquisitions by the same MSSA lineage, followed by diversification of other genetic determinants. Nearly all isolates in the two distinct clades, one characterised by staphylococcal cassette chromosome mec (SCCmec) IVa and the other by SCCmec V and MSSA isolates, contain the toxic shock syndrome toxin-1 gene. Discussion: The genomic diversity of Gaza ST22 isolates is not consistent with recent emergence in the region. The results indicate that two divergent Gaza clones evolved separately from susceptible isolates. Researchers should not assume that isolates identified as ST22 in the community are examples of EMRSA-15 that have escaped their healthcare roots. Future surveillance of MRSA is essential to the understanding of ST22 evolutionary dynamics and to aid efforts to slow the further spread of this lineage.

BackgroundThe 13-valent pneumococcal conjugate vaccine (PCV-13) was introduced in the United States in 2010. Using a large pediatric carriage sample collected from shortly after the introduction of PCV-7 to several years after the introduction of PCV-13, we investigate alterations in the composition of the pneumococcal population following the introduction of PCV-13, evaluating the extent to which the post-vaccination non-vaccine type (NVT) population mirrors that from prior to vaccine introduction and the effect of PCV-13 on vaccine type lineages.Methods and FindingsDraft genome assemblies from 736 newly sequenced and 616 previously published pneumococcal carriages isolates from children in Massachusetts between 2001 and 2014 were analyzed. Isolates were classified into one of 22 sequence clusters (SCs) on the basis of their core genome sequence. We calculated the SC diversity for each sampling period as the probability that any two randomly drawn isolates from that period belong to different SCs. The sampling period immediately after the introduction of PCV-13 (2011) was found to have higher diversity than preceding (2007) or subsequent (2014) sampling periods (Simpson’s D 2007: 0.915 95% CI [0.901, 0.929]; 2011: 0.935 [0.927, 0.942]; 2014: 0.912 [0.901, 0.923]). Amongst NVT isolates, we found the distribution of SCs in 2011 to be significantly different from that in 2007 or 2014 (Fisher’s Exact Test p=0.018, 0.0078), but did not find a difference comparing 2007 to 2014 (Fisher’s Exact Test p=0.24), indicating greater similarity between samples separated by a longer time period than between samples from closer time periods. We also found changes in the accessory gene content of the NVT population between 2007 and 2011 to have been reduced by 2014. Amongst the new serotypes targeted by PCV-13, four were present in our sample. The proportion of our sample composed of PCV-13-only vaccine serotypes 19A, 6C, and 7F decreased between 2007 and 2014, but no such reduction was seen for serotype 3. We did, however, observe differences in the genetic composition of the pre- and post-PCV-13 serotype 3 population. Our isolates were collected during discrete sampling periods from a small geographic area, which may limit the generalizability our findings.ConclusionPneumococcal diversity increased immediately following the introduction of PCV-13, but subsequently returned to pre-vaccination levels. This is reflected in the distribution of NVT lineages, and, to a lesser extent, their accessory gene frequencies. As such, there may be a period during which the population is particularly disrupted by vaccination before returning to a more stable distribution. The persistence and shifting genetic composition of serotype 3 is a concern and warrants further investigation.

Background: Remarkably high carriage prevalence of a community-associated meticillin-resistant Staphylococcus aureus (MRSA) strain of sequence type (ST) 22 in the Gaza strip was reported in 2012. This strain is linked to the pandemic hospital-associated EMRSA-15. The origin and evolutionary history of ST22 in Gaza communities and the genomic elements contributing to its widespread predominance are unknown. Methods: We generated high-quality draft genomes of 61 ST22 isolates from Gaza communities and, along with 175 ST22 genomes from global sources, reconstructed the ST22 phylogeny and examined genotypes unique to the Gaza isolates. Results: The Gaza isolates do not exhibit a close relationship with hospital-associated ST22 isolates, but rather with a basal population from which EMRSA-15 emerged. There were two separate resistance acquisitions by the same MSSA lineage, followed by diversification of other genetic determinants. Nearly all isolates in the two distinct clades, one characterised by staphylococcal cassette chromosome mec (SCCmec) IVa and the other by SCCmec V and MSSA isolates, contain the toxic shock syndrome toxin-1 gene. Discussion: The genomic diversity of Gaza ST22 isolates is not consistent with recent emergence in the region. The results indicate that two divergent Gaza clones evolved separately from susceptible isolates. Researchers should not assume that isolates identified as ST22 in the community are examples of EMRSA-15 that have escaped their healthcare roots. Future surveillance of MRSA is essential to the understanding of ST22 evolutionary dynamics and to aid efforts to slow the further spread of this lineage.