Revisiting molecular serotyping of Streptococcus pneumoniae

Abstract

Background: Ninety-two Streptococcus pneumoniae serotypes have been described so far, but the pneumococcal conjugate vaccine introduced in the Brazilian basic vaccination schedule in 2010 covers only the ten most prevalent in the country. Pneumococcal serotype-shifting after massive immunization is a major concern and monitoring this phenomenon requires efficient and accessible serotyping methods. Pneumococcal serotyping based on antisera produced in animals is laborious and restricted to a few reference laboratories. Alternatively, molecular serotyping methods assess polymorphisms in the cps gene cluster, which encodes key enzymes for capsular polysaccharides synthesis in pneumococci. In one such approach, cps-RFLP, the PCR amplified cps loci are digested with an endonuclease, generating serotype-specific fingerprints on agarose gel electrophoresis.

Methods: In this work, in silico and in vitro approaches were combined to demonstrate that XhoII is the most discriminating endonuclease for cps-RFLP, and to build a database of serotype-specific fingerprints that accommodates the genetic diversity within the cps locus of 92 known pneumococci serotypes.

Results: The expected specificity of cps-RFLP using XhoII was 76% for serotyping and 100% for serogrouping. The database of cps-RFLP fingerprints was integrated to Molecular Serotyping Tool (MST), a previously published web-based software for molecular serotyping. In addition, 43 isolates representing 29 serotypes prevalent in the state of Minas Gerais, Brazil, from 2007 to 2013, were examined in vitro; 11 serotypes (nine serogroups) matched the respective in silico patterns calculated for reference strains. The remaining experimental patterns, despite their resemblance to their expected in silico patterns, did not reach the threshold of similarity score to be considered a match and were then added to the database.

Conclusion: The cps-RFLP method with XhoII outperformed the antisera-based and other molecular serotyping methods in regard of the expected specificity. In order to accommodate the genetic variability of the pneumococci cps loci, the database of cps-RFLP patterns will be progressively expanded to include new variant in vitro patterns. The cps-RFLP method with endonuclease XhoII coupled with MST for computer-assisted interpretation of results may represent a relevant contribution to the real time detection of changes in regional pneumococci population diversity in response to mass immunization programs.

Figure 1

Clustering the in silico cps-RFLP patterns calculated for the reference strains. (A) Dendrogram showing the results of clustering the 107 cps-RFLP patterns generated by in silico restriction with XhoII endonuclease. The dashed line represents the distance threshold under which patterns are indistinguishable by MST software; (B) Schematic representation of the cps-RFLP patterns; and (C) their respective cps amplicons. Fragment sizes are in base pairs.

Figure 2

cps-RFLP experimental patterns of serotypes 29, 9N, 19F, 6C, 19A, 29, 6A, 19F, 18B, 6B. Experimental cps-RFLP patterns in agarose gel (1.5%). Lanes: M = molecular weight marker; 1 = 79/11-HEM (serotype 29); 2 = 103/11-LCR (serotype 6A); 3 = 149/11-LCR (serotype 9N); 4 = 24/12-LCR (serotype 19F); 5 = 387/11-LCR (serotype 6C); 6 = 124/11-LCR (serotype 19A); 7 = 79/11-HEM (serotype 29); 8 = 103/11-LCR (serotype 6A); 9 = ATCC49619 (serotype 19F); 10 = 240/11-LCR (serotype 18B); 11 = 143/11-LCR (serotype 6B); 12 = 387/11-LCR (serotype 6C); 13 = 149/11-LCR (serotype 9N). Fragment sizes are in base pairs.

Figure 3

Correlation between experimentally generated and in silico predicted cps-RFLP patterns of serotype 35F. (A) Experimental cps-RFLP pattern of a strain of serotype 35F in agarose gel (1.5%). Lanes: M = molecular weight marker; 1 = cps-RFLP pattern of clinical isolate 169/11-HEM 35F serotype. (B) Fragments sizing using the GelAnalyzer software. (C) Output of MST showing the schematic representation of the cps-RFLP pattern obtained in vitro aligned with the closest reference pattern in the database. Fragment sizes are in base pairs.

Figure 4

Clustering the experimental and in silico cps-RFLP patterns. (A) Dendrogram showing the results of clustering 31 experimental cps-RFLP patterns (E_*) and the in silico patterns (Sp_*) of the corresponding serotypes. The dashed line represents the distance threshold under which patterns are indistinguishable by MST software; (B) Schematic representation of the cps-RFLP patterns; and (C) their respective cps amplicons. Fragment sizes are in base pairs.