Whole-genome Sequencing for Surveillance of Invasive Pneumococcal Diseases in Ontario, Canada: Rapid Prediction of Genotype, Antibiotic Resistance and Characterization of Emerging Serotype 22F

Phenotypic and molecular characterization of Streptococcus pneumoniae serotype 3 isolates from blood and respiratory samples in Canada: CANWARD 2007-21

BACKGROUND

Lower respiratory infections and invasive disease caused by Streptococcus pneumoniae serotype 3 remain major clinical challenges around the world, despite widespread availability of updated vaccines.

METHODS

As part of CANWARD, antimicrobial susceptibility testing and serotyping were performed on all S. pneumoniae isolates from 2007 to 2021. A subset of 226/264 (85.6%) serotype 3 isolates were selected for WGS to determine sequence type (ST)/clonal cluster (CC) and correspondence of antimicrobial resistance determinants (erm, mefAE, tetM, cat, folA, folP) with resistance phenotype.

RESULTS

Of the 3,039 S. pneumoniae isolates obtained from 2007 to 2021, 8.7% (n = 264) were serotype 3, with 64.0% of respiratory origin and 36.0% from blood. Of 226 sequenced serotype 3 isolates, 184 (81.4%) were ST180 (GPSC12). The proportion of ST8561 (single locus variant of ST180) increased from 7.2% to 16.6% during the study period. An increasing proportion of serotype 3 isolates had phenotypic resistance (P = 0.0007) and genetic resistance determinants (P = 0.004), comparing 2017-21 to 2007-11, largely due to a recently expanded ST180 clade with cat, tetM and mef determinants.

CONCLUSIONS

S. pneumoniae serotype 3 from GPSC12 continues to dominate throughout Canada, with an increase in the proportion of ST8561. The proportion of serotype 3 isolates that are phenotypically resistant and with genetic resistance determinants is increasing over time, reflecting a global increase in GPSC12 genotypes with known resistance determinants. Phylogenomic characterization of isolates collected over time and from around the world may facilitate improved treatment and enhanced prevention strategies, including new vaccines with activity against S. pneumoniae serotype 3.

Safety and Immunogenicity of V114 in Preterm Infants: A Pooled Analysis of Four Phase Three Studies

BACKGROUND

Risk of invasive pneumococcal disease is 3-fold higher in preterm versus full-term infants. V114 is a 15-valent pneumococcal conjugate vaccine (PCV) containing the 13 serotypes in PCV13 plus 2 unique serotypes, 22F and 33F. A pooled subgroup analysis was performed in preterm infants (<37 weeks gestational age) enrolled in 4 pediatric phase 3 studies evaluating the safety and immunogenicity of different 4-dose regimens of V114 or PCV13.

METHODS

Healthy preterm infants were randomized 1:1 to receive V114/PCV13 in the 4 studies. Safety was evaluated as the proportion of participants with adverse events (AEs) following receipt of PCV. Serotype-specific antipneumococcal immunoglobulin G (IgG) geometric mean concentrations, IgG response rates and opsonophagocytic activity geometric mean titers were measured at 30 days postdose 3, pretoddler dose and 30 days postdose 4.

RESULTS

V114 and PCV13 were administered to 174 and 180 participants, respectively. Mean gestational age was 35.4 weeks (range: 27 - <37 weeks). Proportions of participants with AEs were comparable between vaccination groups; most AEs experienced were of short duration (≤3 days) and mild-to-moderate intensity. V114-elicited IgG geometric mean concentrations, IgG response rates and opsonophagocytic activity geometric mean titers were generally comparable to PCV13 for the 13 shared serotypes and higher for serotypes 22F and 33F at 30 days postdose 3 and postdose 4.

CONCLUSIONS

In preterm infants, V114 was well tolerated and induced comparable immune responses to PCV13 for the 13 shared serotypes and higher immune responses to serotypes 22F and 33F. Results support the use of V114 in preterm infants.

Streptococcus pneumoniae genomic datasets from an Indian population describing pre-vaccine evolutionary epidemiology using a whole genome sequencing approach

Globally, India has a high burden of pneumococcal disease, and pneumococcal conjugate vaccine (PCV) has been rolled out in different phases across the country since May 2017 in the national infant immunization programme (NIP). To provide a baseline for assessing the impact of the vaccine on circulating pneumococci in India, genetic characterization of pneumococcal isolates detected prior to introduction of PCV would be helpful. Here we present a population genomic study of 480 Streptococcus pneumoniae isolates collected across India and from all age groups before vaccine introduction (2009–2017), including 294 isolates from pneumococcal disease and 186 collected through nasopharyngeal surveys. Population genetic structure, serotype and antimicrobial susceptibility profile were characterized and predicted from whole-genome sequencing data. Our findings revealed high levels of genetic diversity represented by 110 Global Pneumococcal Sequence Clusters (GPSCs) and 54 serotypes. Serotype 19F and GPSC1 (CC320) was the most common serotype and pneumococcal lineage, respectively. Coverage of PCV13 (Pfizer) and 10-valent Pneumosil (Serum Institute of India) serotypes in age groups of ≤2 and 3–5 years were 63–75 % and 60–69 %, respectively. Coverage of PPV23 (Merck) serotypes in age groups of ≥50 years was 62 % (98/158). Among the top five lineages causing disease, GPSC10 (CC230), which ranked second, is the only lineage that expressed both PCV13 (serotypes 3, 6A, 14, 19A and 19F) and non-PCV13 (7B, 13, 10A, 11A, 13, 15B/C, 22F, 24F) serotypes. It exhibited multidrug resistance and was the largest contributor (17 %, 18/103) of NVTs in the disease-causing population. Overall, 42 % (202/480) of isolates were penicillin-resistant (minimum inhibitory concentration ≥0.12 µg ml⁻¹) and 45 % (217/480) were multidrug-resistant. Nine GPSCs (GPSC1, 6, 9, 10, 13, 16, 43, 91, 376) were penicillin-resistant and among them six were multidrug-resistant. Pneumococci expressing PCV13 serotypes had a higher prevalence of antibiotic resistance. Sequencing of pneumococcal genomes has significantly improved our understanding of the biology of these bacteria. This study, describing the pneumococcal disease and carriage epidemiology pre-PCV introduction, demonstrates that 60–75 % of pneumococcal serotypes in children ≤5 years are covered by PCV13 and Pneumosil. Vaccination against pneumococci is very likely to reduce antibiotic resistance. A multidrug-resistant pneumococcal lineage, GPSC10 (CC230), is a high-risk clone that could mediate serotype replacement.

Invasive pneumococcal disease due to 22F and 33F in England: A tail of two serotypes

BACKGROUND

A 15-valent pneumococcal conjugate vaccine (PCV15) aims to protect against serotype 22F and 33F in addition to the serotypes within the 13-valent PCV (PCV13) which was introduced to the UK childhood immunisation programme in April 2010. Little is known about the specific epidemiology, clinical features or outcomes of invasive pneumococcal disease (IPD) due to these two serotypes.

METHODS

Public Health England (PHE) conducts enhanced IPD surveillance in England. Hospital laboratories routinely submit invasive pneumococcal isolates to PHE for serotyping and enhanced clinical information is collected through questionnaires sent to general practitioners. IPD due to serotypes 22F and 33F diagnosed during 2014/15-2018/19 were compared with IPD due to PCV13 serotypes and remaining serotypes.

RESULTS

In total, 25,415 isolates (93.4%) were serotyped and questionnaires were completed for 22,097 (86.9%) cases. Serotype 22F was responsible for 1,788 (7.0%) and serotype 33F for 893 (3.5%) cases compared to 19.9% (n = 5,047) for PCV13 and 69.6% (n = 17,687) for the remaining serotypes. IPD incidence increased for both serotypes since 2005/06, especially in older adults, but plateaued after PCV13 introduction. Comorbidity prevalence was 68.7% (n = 1,037) for serotype 22F and 67.2% (n = 505) for serotype 33F, with invasive pneumonia being the most common clinical presentation 1,067/1,482; 72.0%, and 514/755; 68.1%, respectively. There were 3,617 deaths within 30 days of disease onset, including 236 (CFR, 15.4%) among 22F, 128 (CFR, 16.5%) among 33F and 21.3% (925/4,350) among PCV13-type IPD cases. When compared with PCV13-type IPD, serotype 22F (aOR 0.58, 95%CI 0.49-0.68, p < 0.001) and 33F (aOR 0.73, 95%CI 0.59-0.91, p = 0.004) were independently associated with lower odds of death. The major circulating sequence types (STs) in 22F (ST 433, ST698) and 33F (ST717, ST100, ST673) were not associated with an increased risk of death compared to the other STs.

CONCLUSIONS

Serotype 22F and 33F-type IPD are associated with a lower risk of death compared to PCV13-type, with those presenting with septicaemia more likely to have a fatal outcome compared to pneumonia. PCV15 has the potential to prevent up to an additional 10% of IPD cases in England.

Interpreting k-mer-based signatures for antibiotic resistance prediction

BACKGROUND

Recent years have witnessed the development of several k-mer-based approaches aiming to predict phenotypic traits of bacteria on the basis of their whole-genome sequences. While often convincing in terms of predictive performance, the underlying models are in general not straightforward to interpret, the interplay between the actual genetic determinant and its translation as k-mers being generally hard to decipher.

RESULTS

We propose a simple and computationally efficient strategy allowing one to cope with the high correlation inherent to k-mer-based representations in supervised machine learning models, leading to concise and easily interpretable signatures. We demonstrate the benefit of this approach on the task of predicting the antibiotic resistance profile of a Klebsiella pneumoniae strain from its genome, where our method leads to signatures defined as weighted linear combinations of genetic elements that can easily be identified as genuine antibiotic resistance determinants, with state-of-the-art predictive performance.

CONCLUSIONS

By enhancing the interpretability of genomic k-mer-based antibiotic resistance prediction models, our approach improves their clinical utility and hence will facilitate their adoption in routine diagnostics by clinicians and microbiologists. While antibiotic resistance was the motivating application, the method is generic and can be transposed to any other bacterial trait. An R package implementing our method is available at https://gitlab.com/biomerieux-data-science/clustlasso.

Genome-Based Prediction of Bacterial Antibiotic Resistance

Clinical microbiology has long relied on growing bacteria in culture to determine antimicrobial susceptibility profiles, but the use of whole-genome sequencing for antibiotic susceptibility testing (WGS-AST) is now a powerful alternative. This review discusses the technologies that made this possible and presents results from recent studies to predict resistance based on genome sequences. We examine differences between calling antibiotic resistance profiles by the simple presence or absence of previously known genes and single-nucleotide polymorphisms (SNPs) against approaches that deploy machine learning and statistical models. Often, the limitations to genome-based prediction arise from limitations of accuracy of culture-based AST in addition to an incomplete knowledge of the genetic basis of resistance. However, we need to maintain phenotypic testing even as genome-based prediction becomes more widespread to ensure that the results do not diverge over time. We argue that standardization of WGS-AST by challenge with consistently phenotyped strain sets of defined genetic diversity is necessary to compare the efficacy of methods of prediction of antibiotic resistance based on genome sequences.