Persistence of group B Streptococcus vaginal colonization and prevalence of hypervirulent CC-17 clone correlate with the country of birth: a prospective 3-month follow-up cohort study

Group B Streptococci recto-vaginal colonization, antimicrobial susceptibility pattern, and associated factors among pregnant women at selected health facilities of Wolaita Sodo Town, Southern Ethiopia

Background

Streptococcus agalactiae or Group B Streptococcal colonization of the gastrointestinal and genital tracts of pregnant women usually remains asymptomatic, even though it is the critical determinant of infection in neonates and young infants. It causes early and late onset of invasive Group B Streptococcus (GBS) disease manifesting as septicemia, meningitis, and pneumonia. Now it is recognized as an important cause of maternal and neonatal morbidity and mortality in many parts of the world including Ethiopia, where the magnitude of the problem has been little studied. The aim of this study was to assess the prevalence of GBS colonization and to identify associated risk factors and antimicrobial susceptibility patterns among pregnant women at selected health facilities of Wolaita Sodo Town, Southern Ethiopia.

Methodology

A health-facility-based cross-sectional study design was conducted at WSUCSH & Wolaita Sodo Health Center from June to August, 2022. A total of 279 pregnant women who were in ANC follow-up at 35-37 weeks of gestation were included. For GBS isolation, recto-vaginal swabs were inoculated in 1 mL Todd-Hewitt broth medium supplemented with 10 μg/mL colistin and 15 μg/mL nalidixic acid, followed by identification of isolates based on colonial morphology, gram stains, catalase reaction, and CAMP tests. Antimicrobial susceptibility testing was performed using a modified Kirby-Bauer disc diffusion method. All collected data were entered in Epi info 4.6.0.2, then transferred and tabulated using SPSS version 20. Logistic regression analysis was used to see the association between variables. Finally, a p-value <0.05 was considered statistically significant.

Results

In the present study, 279 pregnant mothers, aged between 15 to 38 years with a mean of 26.5 ± 4.5 years, were included. Of all participants, the highest proportion (120) (43.01%) were housewives. The overall carriage rate of GBS was 67 (24.0%). GBS colonization showed a statistically significant association with college and above levels of maternal education [AOR = 6.610, 95% CI (1.724-25.349), p = 0.01]. High susceptibility of GBS isolate was seen with Penicillin G & Chloramphenicol (92.5%), Ampicillin, Ceftriaxone (89.6%), Vancomycin (74.62%), and Erythromycin (77%). Relatively, GBS showed high resistance to Tetracycline (88%).

Conclusion and recommendation

In this study, the overall prevalence of GBS colonization was 24.0%. College and above educational level was statistically significant with GBS colonization. This study aimed to draw attention to the management of Group B Streptococci in pregnant women by making GBS culture one of the routine diagnoses during ANC follow-up and to prevent infection with early detection.

Inferring longitudinal patterns of group B Streptococcus colonization during pregnancy

Maternal colonization by Group B Streptococcus (GBS) can lead to severe infection in neonates and has also been associated with prematurity and stillbirth. Better quantitative understanding of the trajectories of GBS carriage during pregnancy is essential for the design of informative epidemiological studies. Here, we describe analyses of published longitudinal data using Bayesian hidden Markov models, which involve the estimation of parameters related to the succession of latent states (infection status) and observations (culture positivity). In addition to quantifying infection acquisition and clearance probabilities, the statistical approach also suggests that for some longitudinal patterns of culture results, pregnant women were likely to have been GBS-colonized despite a negative diagnostic result. We believe this method, if used in future longitudinal studies of maternal GBS colonization, would improve our understanding of the pathologies linked to this bacterium and could also inform maternal GBS vaccine trial design.

A Facile machine learning multi-classification model for Streptococcus agalactiae clonal complexes

Background

The clinical significance of group B streptococcus (GBS) was different among different clonal complexes (CCs), accurate strain typing of GBS would facilitate clinical prognostic evaluation, epidemiological investigation and infection control. The aim of this study was to construct a practical and facile CCs prediction model for S. agalactiae.

Methods

A total of 325 non-duplicated GBS strains were collected from clinical samples in Xinhua Hospital, Shanghai, China. Multilocus sequence typing (MLST) method was used for molecular classification, the results were analyzed to derive CCs by Bionumeric 8.0 software. Antibiotic susceptibility test was performed using Vitek-2 Compact system combined with K-B method. Multiplex PCR method was used for serotype identification. A total of 45 virulence genes associated with adhesion, invasion, immune evasion were detected by PCR method and electrophoresis. Three types of features, including antibiotic susceptibility (A), serotypes (S) and virulence genes (V) tests, and XGBoost algorithm was established to develop multi-class CCs identification models. The performance of proposed models was evaluated by the receiver operating characteristic curve (ROC).

Results

The 325 GBS were divided into 47 STs, and then calculated into 7 major CCs, including CC1, CC10, CC12, CC17, CC19, CC23, CC24. A total of 18 features in three kinds of tests (A, S, V) were significantly different from each CC. The model based on all the features (S&A&V) performed best with AUC 0.9536. The model based on serotype and antibiotic resistance (S&A) only enrolled 5 weighed features, performed well in predicting CCs with mean AUC 0.9212, and had no statistical difference in predicting CC10, CC12, CC17, CC19, CC23 and CC24 when compared with S&A&V model (all p > 0.05).

Conclusions

The S&A model requires least parameters while maintaining a high accuracy and predictive power of CCs prediction. The established model could be used as a promising tool to classify the GBS molecular types, and suggests a substantive improvement in clinical application and epidemiology surveillance in GBS phenotyping.

Genomic epidemiology of group B streptococci spanning 10 years in an Irish maternity hospital, 2008-2017

OBJECTIVES

The genomic epidemiology of group b streptococcal (GBS) isolates from the Rotunda maternity hospital, Dublin, 2008-2017, was investigated.

METHODS

Whole genome sequences of isolates (invasive, n = 114; non-invasive, n = 76) from infants and women were analysed using the PubMLST database (https://pubmlst.org/sagalactiae/).

RESULTS

Serotypes III (36%), Ia (18%), V (17%), II (11%) and Ib, (9%) and sequence types (ST) 17 (23%), ST-23 (14%), ST-1 (12%) and ST-19 (7%) were most common. Core genome MLST (cgMLST) differentiated isolates of the same ST, grouped STs into five lineages congruent with known clonal complexes and identified known mother-baby pairs and suspected linked infant cases. Clonal complex (CC) 17 accounted for 40% and 22% of infant and maternal invasive cases, respectively and 21% of non-invasive isolates. CC23 and CC19 were associated with maternal disease (30%) and carriage (24%), respectively. Erythromycin (26%) and clindamycin (18%) resistance increased over the study period and was associated with presence of the erm(B) gene (55%), CC1 (33%) and CC19 (24%). A multi-resistant integrative conjugative element incorporated in the PI-1 locus was detected in CC17, an ST-12 and ST-23 isolate confirming the global dissemination of this element. All isolates possessed one or more pilus islands. Genes encoding other potential protective proteins including Sip, C5a peptidase and Srr1 were present in 100%, 99.5% and 65.8% of isolates, respectively. The srr2 gene was unique to CC17.

CONCLUSIONS

The PubMLST.org website provides a valuable framework for genomic GBS surveillance to inform on local and global GBS epidemiology, preventive and control measures.