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      Distinct Effects on Diversifying Selection by Two Mechanisms of Immunity against Streptococcus pneumoniae

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          Abstract

          Antigenic variation to evade host immunity has long been assumed to be a driving force of diversifying selection in pathogens. Colonization by Streptococcus pneumoniae, which is central to the organism's transmission and therefore evolution, is limited by two arms of the immune system: antibody- and T cell- mediated immunity. In particular, the effector activity of CD4 + T H17 cell mediated immunity has been shown to act in trans, clearing co-colonizing pneumococci that do not bear the relevant antigen. It is thus unclear whether T H17 cell immunity allows benefit of antigenic variation and contributes to diversifying selection. Here we show that antigen-specific CD4 + T H17 cell immunity almost equally reduces colonization by both an antigen-positive strain and a co-colonized, antigen-negative strain in a mouse model of pneumococcal carriage, thus potentially minimizing the advantage of escape from this type of immunity. Using a proteomic screening approach, we identified a list of candidate human CD4 + T H17 cell antigens. Using this list and a previously published list of pneumococcal Antibody antigens, we bioinformatically assessed the signals of diversifying selection among the identified antigens compared to non-antigens. We found that Antibody antigen genes were significantly more likely to be under diversifying selection than the T H17 cell antigen genes, which were indistinguishable from non-antigens. Within the Antibody antigens, epitopes recognized by human antibodies showed stronger evidence of diversifying selection. Taken together, the data suggest that T H17 cell-mediated immunity, one form of T cell immunity that is important to limit carriage of antigen-positive pneumococcus, favors little diversifying selection in the targeted antigen. The results could provide new insight into pneumococcal vaccine design.

          Author Summary

          Streptococcus pneumoniae, or pneumococcus, is a leading cause of morbidity and mortality in young children and elderly persons worldwide. Current pneumococcus vaccines target a limited number of clinically important serotypes, while strains with serotypes not targeted by current vaccines are increasing in importance in both carriage and invasive disease. As a result, there has been a substantial interest to develop novel, cost-effective vaccines based on protein antigens from pneumococcus. To this end, it is critical to understand how the human immune system exerts selection pressures on the targeted antigens. Two immune mechanisms targeting pneumococcal protein antigens have been documented, mediated by antibody and T cells, respectively. In this study, we screened for pneumococcal antigens that are commonly recognized by human CD4 + T H17 cells. Using a mouse model of pneumococcal colonization, we demonstrate that T H17 cell-based immunity almost equally reduces colonization by both an antigen-positive strain and a co-colonizing, antigen-negative strain. Furthermore, we demonstrate that the DNA sequences of T H17 cell antigens demonstrate no detectable signs of being under selective pressure, unlike pneumococcal antigens known to be strong antibody targets. Thus, one form of the T cell-mediated immunity that is important to limit carriage of antigen-positive pneumococcus favors little diversifying selection in the targeted antigen. These results suggest evolution of escape from T H17 -based vaccines may be slower than from antibody-based vaccines.

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            An algorithm for progressive multiple alignment of sequences with insertions.

            Dynamic programming algorithms guarantee to find the optimal alignment between two sequences. For more than a few sequences, exact algorithms become computationally impractical, and progressive algorithms iterating pairwise alignments are widely used. These heuristic methods have a serious drawback because pairwise algorithms do not differentiate insertions from deletions and end up penalizing single insertion events multiple times. Such an unrealistically high penalty for insertions typically results in overmatching of sequences and an underestimation of the number of insertion events. We describe a modification of the traditional alignment algorithm that can distinguish insertion from deletion and avoid repeated penalization of insertions and illustrate this method with a pair hidden Markov model that uses an evolutionary scoring function. In comparison with a traditional progressive alignment method, our algorithm infers a greater number of insertion events and creates gaps that are phylogenetically consistent but spatially less concentrated. Our results suggest that some insertion/deletion "hot spots" may actually be artifacts of traditional alignment algorithms.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Pathog
                PLoS Pathog
                plos
                plospath
                PLoS Pathogens
                Public Library of Science (San Francisco, USA )
                1553-7366
                1553-7374
                November 2012
                November 2012
                8 November 2012
                : 8
                : 11
                : e1002989
                Affiliations
                [1 ]Department of Epidemiology and Department of Immunology & Infectious Diseases, Harvard School of Public Health, Boston, Massachusetts, United States of America
                [2 ]Genocea Biosciences, Inc., Cambridge, Massachusetts, United States of America
                [3 ]Department of Pediatric Immunology and Infectious Diseases, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht, The Netherlands
                [4 ]Division of Infectious Diseases, Department of Medicine, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
                Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Malawi
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: YL TG KT CMT NC JBF RM ML. Performed the experiments: YL TG PG CMT. Analyzed the data: YL TG NC ML. Contributed reagents/materials/analysis tools: KT CBF JBF NC RM. Wrote the paper: YL TG ML.

                Article
                PPATHOGENS-D-12-00854
                10.1371/journal.ppat.1002989
                3493470
                23144610
                659ea6f4-a08f-4956-be70-2b08e178506c
                Copyright @ 2012

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 5 April 2012
                : 29 August 2012
                Page count
                Pages: 11
                Funding
                The authors gratefully acknowledge PATH for supporting these studies. R.M. gratefully acknowledges support from the Translational Research Program at Children’s Hospital Boston. We thank Oliver Hofmann for suggestions in the orthology analysis; Sarah Cobey for discussions on the evolutionary models. We are grateful to Daniel Weinberger, Andrew Bessolo, Taijiao Jiang, and Bill Hanage for their assistance and discussions about this project. J.B.F. and T.G. thank Darren Higgins, George Siber, and Robert Kohberger for helpful discussions on T cell antigen screening and data analysis. The study was supported in part by NIH grants R01 AI048935 to M.L. and R01 AI066013 to R.M. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Evolutionary Biology
                Comparative Genomics
                Evolutionary Immunology
                Evolutionary Processes
                Genomic Evolution
                Immunology
                Genetics of the Immune System
                Immune Response
                Immunity

                Infectious disease & Microbiology
                Infectious disease & Microbiology

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