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Group A streptococcus expresses a trio of surface proteins containing protective epitopes.

ABSTRACT: Group A streptococci (GAS) (Streptococcus pyogenes) are common causes of infections in humans for which there is no licensed vaccine. Decades of work has focused on the role of the surface M protein in eliciting type-specific protective immunity. Recent studies have identified additional surface proteins of GAS that contain opsonic epitopes. In the present study, we describe a serotype M65 GAS originally isolated during an epidemiologic study in Bamako, Mali, which simultaneously expressed M, M-related protein (Mrp), and streptococcal protective antigen (Spa) on the bacterial surface. The emm, mrp, and spa genes were sequenced from PCR amplicons derived from the M65 chromosome. Rabbit antisera raised against synthetic peptides copying the N-terminal regions of M, Mrp, and Spa were highly specific for each peptide, reacted with the surface of M65 GAS, and promoted bactericidal activity against the organism. A mixture of antisera against all three peptides was most effective in the bactericidal assays. Immunofluorescence microscopy revealed that the M, Mrp, and Spa antisera bound to the bacterial surface in the presence of human plasma proteins and resulted in the deposition of complement. Five additional spa genes were identified in the Mrp-positive GAS serotypes, and their sequences were determined. Our results indicate that there are multiple antigens on the surface of GAS that evoke antibodies that promote bacterial killing. A more complete understanding of the relative contributions of M, Mrp, and Spa in eliciting protective immunity may aid in the development of GAS vaccines with enhanced coverage and efficacy.

SUBMITTER: Niedermeyer SE

PROVIDER: S-EPMC4266352 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Project description:The core Mga (multiple gene activator) regulon of group A Streptococcus (GAS) contains genes encoding proteins involved in adhesion and immune evasion. While all GAS genomes contain genes for Mga and C5a peptidase, the intervening genes encoding M and M-like proteins vary between strains. The genetic make-up of the Mga regulon of GAS was characterized by utilizing a collection of 1,688 GAS genomes that are representative of the global GAS population. Sequence variations were examined with multiple alignments, and the expression of all core Mga regulon genes was examined by quantitative reverse transcription-PCR in a representative strain collection. In 85.2% of the sampled genomes, the Mga locus contained genes encoding Mga, Mrp, M, Enn, and C5a peptidase proteins. These isolates account for 53% of global infections. Only 9.1% of genomes did not contain either an mrp or an enn gene. The pairwise identity within Enn (68.6%) and Mrp (83.2%) protein sequences was higher than within M proteins (44.7%). Gene expression varied between strains tested, but high expression was recorded for all genes in at least one strain. Previous nomenclature issues were clarified with molecular gene definitions. Our findings support a shift in focus in the GAS research field to further consider the role of Mrp and Enn in virulence and vaccine development.IMPORTANCE While the GAS M protein has been the leading vaccine target for decades, the bacteria encode many other virulence factors of interest for vaccine development. In this work, we show that emm-like genes are encoded in a remarkable majority of GAS genomes and expressed at a level similar to that for the emm gene. In collaboration with the U.S. Centers for Disease Control, we developed molecular definitions of the different emm and emm-like gene families. This clarification should abrogate mistyping of strains, especially in the area of whole-genome typing. We have also updated the emm-typing collection by removing emm-like gene sequences and provided in-depth analysis of Mrp and Enn protein sequence structure and diversity.

Project description:BACKGROUND:The application of peptide based diagnostics and therapeutics mimicking part of protein antigen is experiencing renewed interest. So far selection and design rationale for such peptides is usually driven by T-cell epitope prediction, available experimental and modelled 3D structure, B-cell epitope predictions such as hydrophilicity plots or experience. If no structure is available the rational selection of peptides for the production of functionally altering or neutralizing antibodies is practically impossible. Specifically if many alternative antigens are available the reduction of required synthesized peptides until one successful candidate is found is of central technical interest. We have investigated the integration of B-cell epitope prediction with the variability of antigen and the conservation of patterns for post-translational modification (PTM) prediction to improve over state of the art in the field. In particular the application of machine-learning methods shows promising results. RESULTS:We find that protein regions leading to the production of functionally altering antibodies are often characterized by a distinct increase in the cumulative sum of three presented parameters. Furthermore the concept to maximize antigenicity, minimize variability and minimize the likelihood of post-translational modification for the identification of relevant sites leads to biologically interesting observations. Primarily, for about 50% of antigen the approach works well with individual area under the ROC curve (AROC) values of at least 0.65. On the other hand a significant portion reveals equivalently low AROC values of < or = 0.35 indicating an overall non-Gaussian distribution. While about a third of 57 antigens are seemingly intangible by our approach our results suggest the existence of at least two distinct classes of bioinformatically detectable epitopes which should be predicted separately. As a side effect of our study we present a hand curated dataset for the validation of protectivity classification. Based on this dataset machine-learning methods further improve predictive power to a class separation in an equilibrated dataset of up to 83%. CONCLUSION:We present a computational method to automatically select and rank peptides for the stimulation of potentially protective or otherwise functionally altering antibodies. It can be shown that integration of variability, post-translational modification pattern conservation and B-cell antigenicity improve rational selection over random guessing. Probably more important, we find that for about 50% of antigen the approach works substantially better than for the overall dataset of 57 proteins. Essentially as a side effect our method optimizes for presumably best applicable peptides as they tend to be likely unmodified and as invariable as possible which is answering needs in diagnosis and treatment of pathogen infection. In addition we show the potential for further improvement by the application of machine-learning methods, in particular Random Forests.

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Group A streptococcus expresses a trio of surface proteins containing protective epitopes.

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