- Record: found
- Abstract: found
- Article: found
Effect of Serotype on Pneumococcal Competition in a Mouse Colonization Model
research-article
Author(s):
Krzysztof Trzciński
a
,
b
,
c
,
,
Yuan Li
a ,
Daniel M. Weinberger
a ,
Claudette M. Thompson
a ,
Derrick Cordy
a ,
Andrew Bessolo
a ,
Richard Malley
d ,
Marc Lipsitch
a
,
b
Publication date (Electronic):
15 September 2015
Journal:
mBio
Publisher:
American Society of Microbiology
Read this article at
There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
ABSTRACT
Competitive interactions between Streptococcus pneumoniae strains during host colonization could influence the serotype distribution in nasopharyngeal carriage and pneumococcal disease. We evaluated the competitive fitness of strains of serotypes 6B, 14, 19A, 19F, 23F, and 35B in a mouse model of multiserotype carriage. Isogenic variants were constructed using clinical strains as the capsule gene donors. Animals were intranasally inoculated with a mixture of up to six pneumococcal strains of different serotypes, with separate experiments involving either clinical isolates or isogenic capsule-switch variants of clinical strain TIGR4. Upper-respiratory-tract samples were repeatedly collected from animals in order to monitor changes in the serotype ratios using quantitative PCR. A reproducible hierarchy of capsular types developed in the airways of mice inoculated with multiple strains. Serotype ranks in this hierarchy were similar among pneumococcal strains of different genetic backgrounds in different strains of mice and were not altered when tested under a range of host conditions. This rank correlated with the measure of the metabolic cost of capsule synthesis and in vitro measure of pneumococcal cell surface charge, both parameters considered to be predictors of serotype-specific fitness in carriage. This study demonstrates the presence of a robust competitive hierarchy of pneumococcal serotypes in vivo that is driven mainly, but not exclusively, by the capsule itself.
IMPORTANCE
Streptococcus pneumoniae (pneumococcus) is the leading cause of death due to respiratory bacterial infections but also a commensal frequently carried in upper airways. Available vaccines induce immune responses against polysaccharides coating pneumococcal cells, but with over 90 different capsular types (serotypes) identified, they can only target strains of the selected few serotypes most prevalent in disease. Vaccines not only protect vaccinated individuals against disease but also protect by reducing carriage of vaccine-targeted strains to induce herd effects across whole populations. Unfortunately, reduction in the circulation of vaccine-type strains is offset by increase in carriage and disease from nonvaccine strains, indicating the importance of competitive interactions between pneumococci in shaping the population structure of this pathogen. Here, we showed that the competitive ability of pneumococcal strains to colonize the host strongly depends on the type of capsular polysaccharide expressed by pneumococci and only to a lesser degree on strain or host genetic backgrounds or on variation in host immune responses.
Related collections
Most cited references31
- Record: found
- Abstract: found
- Article: not found
Genetic Analysis of the Capsular Biosynthetic Locus from All 90 Pneumococcal Serotypes
Stephen Bentley, David Aanensen, Angeliki Mavroidi … (2006)
Introduction Streptococcus pneumoniae (the pneumococcus) is a major cause of morbidity and mortality worldwide, causing diseases that range in severity from meningitis, septicaemia, and pneumonia to sinusitis and acute otitis media [1,2]. Factor (typing) sera are used to divide pneumococci into serotypes and serogroups, which include immunologically related serotypes. These sera have been developed by a process of multiple cross-absorptions, which render them specific for the immunochemical differences between the pneumococcal capsular polysaccharides (CPSs) [3]. At present, 90 individual serotypes are recognised by their patterns of reactivity with the factor sera [4], and serotypes vary in the extent to which they are carried in the nasopharynx and the degree to which they are recovered from different disease states [5,6]. Expression of a capsule is important for survival in the blood and is strongly associated with the ability of pneumococci to cause invasive disease. The capsule is surface exposed, and antibodies against CPS provide protection against pneumococcal disease. Consequently, polyvalent polysaccharide vaccines have been developed in which CPS from the serotypes most commonly associated with invasive disease in children are linked to a protein carrier, and a seven-valent conjugated polysaccharide vaccine has been introduced and shown to be highly effective [7,8]. A 23-valent polysaccharide vaccine is also available for use in adults [9]. With the exception of types 3 and 37, which are synthesised by the synthase pathway [10–14], pneumococcal CPSs are generally synthesised by the Wzx/Wzy-dependent pathway (Figure 1). The genes for the latter pathway are located at the same chromosomal locus (cps), between dexB and aliA [15–17]. CPSs are synthesised by transfer of an initial monosaccharide phosphate from a nucleotide diphosphate sugar to a membrane-associated lipid carrier, followed by the sequential transfer of further monosaccharides to produce the lipid-linked repeat unit. This is transferred to the outer face of the cytoplasmic membrane by the repeat-unit transporter or flippase, polymerised to form the mature CPS, and then attached to the peptidoglycan [18]. The cps locus therefore typically encodes the enzymes to build the repeat unit, including an initial glycosyl phosphate transferase, and additional transferases responsible for the formation of the linkages, and to allow for the addition of sugars (or other moieties), or to otherwise modify the repeat unit, as well as a repeat-unit flippase and polymerase [15]. Figure 1 Representation of the Wzx/Wzy-Dependent Pathway for Biosynthesis of CPS 9A Pictured is a hypothetical model for capsule biosynthesis in S. pneumoniae based on a mixture of experimental evidence and speculation. For a recent review, see Yother [15]. (1) Non-housekeeping nucleotide sugar biosynthesis. (2) The initial transferase (WchA in this case) links the initial sugar as a sugar phosphate (Glc-P) to a membrane-associated lipid carrier (widely assumed to be undecaprenyl phosphate). (3) Glycosyl transferases sequentially link further sugars to generate repeat unit. (4) Wzx flippase transports the repeat unit across the cytoplasmic membrane. (5) Wzy polymerase links individual repeat units to form lipid-linked CPS. (6) Wzd/Wze complex translocates mature CPS to the cell surface and may be responsible for the attachment to peptidoglycan. The complex of WchA, Wzy, Wzx, Wzd, and Wze shown in the membrane is based on that in Figure 2 of Whitfield and Paiment [47] for the related Escherichia coli Type 1 capsule. Figure 2 Capsule Biosynthesis Genes and Repeat-Unit Polysaccharide Structures Shown are the cps gene clusters for cases discussed in the text, together with the polysaccharide structure of the encoded repeat unit where known [31] (the full set is shown in Figure S1). Genes are represented on the forward and reverse strands by boxes coloured according to the gene key, with gene designations indicated above each box. Grey blocks indicate regions of sequence similarity between gene clusters. Repeat-unit structures are displayed with the linkage to undecaprenyl pyrophosphate at the right-hand side (not necessarily the case for the published structures [31]), so residue numbers are counted from right to left. Monosaccharides are represented as shapes coloured according to the structure key. Housekeeping sugars are coloured grey. Non-housekeeping sugar colours correspond to the associated sugar biosynthesis gene colours. Glycerol, choline, and acetate are indicated as text. Also shown are the nature of linkages with the associated gene, and the linkages between repeat units created by the Wzy polymerase. Gene designations are in parentheses where their substrate specificity is unclear. The substantial diversity of pneumococcal CPSs is believed to have arisen as a consequence of selection for antigenic diversity imposed by the human immune system [6]. The evolutionary timescales and the genetic events by which novel serogroups and serotypes arise are unclear. Comparisons of the available cps loci indicate a variety of genetic mechanisms and show that the central genes responsible for the synthesis and polymerisation of the repeat unit are highly variable and often non-homologous between serotypes. These genes have a low percentage G+C content, and new serotypes may frequently have been generated by the introduction of novel cps genes into pneumococci by lateral gene transfer from other species. A much better understanding of the complex mechanisms by which antigenic diversity arises could be obtained by using the sequences of the complete set of pneumococcal cps loci. We therefore obtained sequences of the cps locus for all 90 serotypes and used these data, together with the available polysaccharide structures and the patterns of serological reactions with typing sera, to explore the genetics of capsular diversity in this major pathogen. Here we present highlights of our analysis to date, and a more exhaustive analysis will be reported elsewhere. Results General Features of the dexB–aliA Locus from 90 Serotypes PCR products were generated from genomic DNA using primers specific for the dexB and aliA genes and ranged in size from 10,337 bp (serotype 3) to 30,298 bp (serotype 38) with an average of 20,714 bp. The synthase gene (wchE) of serotype 3 is located within the cps locus, but the type 37 cps locus, which was very similar to that of serotype 33F, is defective and serotype is determined by the type 37 synthase gene (tts) located elsewhere on the chromosome [10]. Annotation and analysis of the cps sequences revealed the generality of several previously observed characteristics. Genes for the generation of CPSs are always orientated in the same direction as the dexB and aliA genes (Figures 2 and S1). The regulatory and processing genes wzg, wzh, wzd, and wze (also known as cpsABCD) are conserved with high sequence identity in all cases and are almost always in this gene order at the 5′ end of the cps locus. In most cps clusters, the fifth gene encodes the initial glucose phosphate transferase, WchA (also known as CpsE), responsible for linkage of an activated glucose phosphate to the lipid carrier (see below). The polysaccharide polymerase (wzy) and flippase (wzx) genes are always present downstream together with a varying set of genes for glycosyl transferases, acetyl transferases, nucleotide diphosphate sugar biosynthesis, and modifying enzymes. In every case, there is a region of low percentage G+C content within the cps locus. The first four genes and the non-housekeeping sugar biosynthesis genes have typical percentage G+C content for S. pneumoniae, while the “serotype-specific” genes, particularly wzy and wzx, tend to have more AT-rich sequences. In the regions between the cps genes and the flanking dexB and aliA genes, there is almost always evidence of mobile genetic elements. This is largely manifested as intact or disrupted genes for insertion-sequence (IS) transposases [19,20], although in four cases we identified group-II introns [21] (serotypes 19F, 25F, 25A, and 38). We could assign a functional designation to the products of all but 26 of the 1,999 predicted coding sequences in the 90 cps regions, with most of the remainder showing weak similarities to products of genes in bacterial polysaccharide gene clusters. Unsurprisingly, many coding sequences fall into the broad functional categories of glycosyl transferase (351), acetyl transferase (74), and sugar phosphate transferase (71). To make more specific assignments within such categories, we used the TribeMCL program to assemble all the annotated proteins into homology groups (HGs). With from two to 90 members in each, 91% of the proteins assembled into 175 HGs, with the remainder forming 74 single-member HGs (Table S1). The products of wzg, wzh, wzd, and wze each fall into a single HG covering every serotype. Ignoring IS element transposases, the next largest HG comprises 65 WchA initial transferases (HG5). At the other extreme, the serotype-specific gene products are diverse, with 87 HGs for non-initial sugar transferases and 40 and 13 groups of Wzy repeat-unit polymerases and Wzx flippases, respectively. Biosynthesis of Precursors for Sugars and Other CPS Components Of the 18 sugars and related compounds found in S. pneumoniae capsules, seven are available from housekeeping metabolic pathways and nine from known dedicated pathways encoded within the cps cluster (Figure S2). This includes 4-keto-N-acetyl-D-quinovosamine (UDP-KDQNAc), which is the intermediate in the two step reaction catalysed by FnlA [22]. We found a perfect correlation between the presence of a non-housekeeping sugar in the CPS and the presence of the appropriate biosynthetic genes in the cps locus. Two of the three remaining compounds are the sugar alcohol phosphates arabinitol-1-P and mannitol-6-P. The precursors for these have not been identified, but nucleotide-diphosphate-linked precursors can be easily derived from D-xylulose-5-phosphate or D-fructose-6-phosphate, respectively, by two-step pathways parallel to that for CDP-ribitol formation from ribulose-5-phosphate [23]. D-xylulose-5-phosphate and D-fructose-6-phosphate are central to major pathways, and there are appropriate genes for their conversion in the associated cps loci. The precursor for ribofuranose has also not been identified, but a proposed pathway for its biosynthesis by the product of a gene within CPS 19F (cps19R) [24] is supported by our observation that an orthologous gene (renamed rbsF) is present for all CPS that contain ribofuranose. Choline-1-phosphate, glycerol-1-phosphate, and glycerol-2-phosphate are also found in some of the structures. CDP-choline is known to be produced by S. pneumoniae as a precursor for teichoic acid biosynthesis [25]. For glycerol-1-phosphate, we find an intact gct gene for CDP-glycerol synthesis [26] in the cps where expected, and there are four genes associated with presence of glycerol-2-phosphate, three of which are thought to encode a CDP-2-glycerol pathway [27], while wchX encodes the glycerol phosphotransferase. The situation is illustrated in Figure 1 for cps9A, which has pathway genes for N-acetylmannosamine pyranose and glucuronic acid, but not for glucopyranose (Glcp) or galactopyranose (Galp) as these are available in S. pneumoniae from central metabolism. Initial Transferases and Polymerisation Initial transferase WchA adds glucose-1-phosphate to undecaprenol phosphate [28] to create Und-PP-Glc (Figure 1), and we assume it performs that function in all 65 serotypes where it is present. For the known structures, there is a perfect correlation between the presence/absence of wchA and the presence/absence of glucose in the repeating unit. Where wchA is absent, the products of the fifth cps gene fall into three HGs (WciI, WcjG, and WcjH) all with the same Pfam [29] domain and similar hydrophobicity profiles to the carboxy-terminal region of WchA. We suggest that they function as the initial sugar transferases, as it is known that for the Salmonella enterica wchA homologue, wbaP, the 3′ end of the gene is sufficient for transferase activity [30]. By correlation with CPS constituents, we predict the transferred initial sugars as N-acetylgalactosamine pyranose (GalpNAc) or N-acetylglucosamine pyranose (GlcpNAc) for WciI, Galp or galactofuranose for WcjG and Galp for WcjH. Serotype 1 is an exception as no gene product with similarity to an initial sugar transferase has yet been identified. The initial sugar of the repeat unit is also the donor sugar in the polymerisation of the repeat units (Figure 1), and the specificity of the Wzy polymerase determines the other component of this linkage, which in the case of CPS 9A is a beta (1–4) linkage to the terminal glucose of the next repeat unit. For the known structures [31], identification of the initial sugar allowed us to determine the polymerase linkage as both donor and acceptor sugar, and the linkages were defined once the initial sugar had been identified (see Figures 2 and S1). Where there is ambiguity due to two residues of the initial sugar in the repeat unit, the polymerase linkage can be provisionally identified by considering the linkage catalysed by other members of the same Wzy HG. The predictions for initial sugars, and subsequent repeat-unit polymerisation linkage, correlate well with the polymerase HGs (Table S2). There are 32 polymerase HGs associated with WchA, five with WciI, four with WcjG and one with WcjH. These associations are mostly exclusive, with only five polymerase HGs associated with two initial transferases. In such cases, the linkages involve the same acceptor sugar anomerism (α or β isomer) and the same or a closely related donor sugar. This adds strong support to the inferences drawn for the specificity of the initial transferases. Relating cps Genes with CPS Structure and Serological Profile The availability of all of the annotated cps sequences allowed us to look for correlations between genes, known CPS structures, and serology (gene clusters, CPS structures, and antigenic formulae are summarised in Figure S1 and Table S3). In this way, we can attempt both to infer gene function and, by comparing related cps loci, to account for differences in CPS structure and serology. Variations between cps loci range from two base substitutions for 18B and 18C to wholesale differences in gene complement. Within this range, the variations likely to have a phenotypic effect include gene inactivation due to single base substitutions generating a premature stop codon, single base insertion/deletions leading to translational frameshifts, change of sequence leading to change of enzyme specificity, recombination or IS element insertion leading to gene truncation, and insertion/deletion/replacement of single and multiple genes. Within serogroups, the genetic differences were often subtle but were also sometimes surprisingly prominent. Comparisons also revealed some strong commonality between the cps of different serogroups and serotypes. Illustrative examples that demonstrate how structure, genetics, and serology were combined to analyse the cps loci are shown in Figure 2 and are discussed below. Serogroup 9 Previously described CPS structures [31] for all four serotypes of serogroup 9 show only subtle differences and provide an example of multiple serotypes arising by divergence from a single cps locus. Their cps genes fall into two pairs, with 9A highly similar to 9V [32], and 9L highly similar to 9N, but with the two pairs differing significantly in sequence ( Figure 2), suggesting an initial divergence to form two ancestral serotypes; this split correlates with a difference at residue 5 of the repeat unit, where 9L and 9N CPSs have GlcpNAc, whereas 9A and 9V have Glcp. Factor sera 9d reacts with 9A and 9V but not with 9L and 9N, suggesting that it is interacting with Glcp but not with GlcpNAc. Both are housekeeping sugars, and their differential incorporation is likely to be due to divergent forms of glycosyl transferase WcjC. Subsequently, one of these ancestral serotypes diverged to form 9L and 9N, the latter becoming unique in the group in having Glcp rather than Galp as residue 3 in the repeat unit. Their dexB–aliA loci have the same gene complement, and within the cps genes there are only 79 nucleotide differences. The highest number of amino acid substitutions (13) is within glycosyl transferase WcjA; ten are unique to 9N and presumably result in its altered specificity for Glcp rather than for Galp. The other ancestral serotype gave rise to 9V and 9A, which differ from each other only in their CPS acetylation; the former CPS has an O-acetylation pattern unique in the serogroup. This is likely due to the O-acetyl transferase–encoding wcjE gene, which is intact and apparently functional in 9V, disrupted by a frameshift mutation in 9A (deletion of guanine, nucleotide 726), and truncated in 9L and 9N by the insertion of an IS element. Interestingly, factor sera 9g reacts only with serotype 9V and may recognise an acetyl-based epitope determined by wcjE. Serogroup 9 cps loci also differ by the insertion, in 9A and 9V relative to 9L and 9N, of an O-acetyl transferase gene (wcjD) and an adjacent IS element. This correlates with recent nuclear magnetic resonance data (I. C. Skovsted, unpublished data), indicating that 9A CPS is partially acetylated. Serotypes 44 and 46 Are Related to Serogroup 12 The cps gene clusters of serogroup 12 and serotypes 44 and 46 are almost identical, differing only in IS transposase genes, and provide an example of common ancestry that is not apparent from serology. Structures have been determined for serotypes 12F and 12A only, although the individual constituents for serotype-46 CPS are known and all are present in 12F and 12A [31]. Although no factor serum cross-reacts with all five serotypes, serological reactions do indicate antigenic commonalities [4]; 44 cross-reacts with factor sera 12b and 12d, while 46 cross-reacts with 12c. Given the cps similarities, the significant differences between 12F and 12A CPS are perhaps surprising; 12A has a GalpNAc and 12F has a Galp side branch, and the first main-chain residue is GalpNAc in 12F and GlcpNAc in 12A. The nucleotide differences are concentrated within two glycosyl transferase genes (wciI and wcxB), and we predict that the initial transferases, WciI–12A and WciI–12F, with 38 amino acid differences, link GlcpNAc and GalpNAc, respectively, to the lipid carrier, while WcxB–12A and WcxB–12F, with 17 amino acid differences, account for the side-branch difference. Serotype 14 Is Closely Related to Serogroup 15 Serotype 14 shares no significant serological cross-reaction with serogroup 15, or with any other serotype, but the cps loci of these two serotypes are clearly related. All CPS structures for serotype 14 and serogroup 15 are known [31,33,34], and comparisons of structures and genes allow inferences about one to be made from the other. The four serogroup 15 pentasaccharide repeat units are identical, but polymerisation forms a linear polymer in 15A and 15F and a branched structure in 15B and 15C that correlates with the presence of wzy genes of different HGs (see Table S2). Serotypes 15B and 15C differ in the presence or absence of O-acetylation [35] and, as previously described [36], the difference is due to a variable-length TA tandem repeat region at the 5′ end of wciZ—in frame in 15B and out of frame in 15C strains. This gene is in frame in 15F (acetylated), but extensively degraded, rather than simply out of frame, in 15A (not acetylated). Genes for synthesis of glycerol-2-phosphate (gtp1, gtp2, and gtp3) are present in all serogroup 15 cps loci, but glycerol was reported to be present only in 15A, being replaced by choline-P in 15F, 15B, and 15C, with either residue being present on only a proportion of the repeat units [31]. In all cases, the transferase is presumed to be encoded by wchX, with the molecular basis of the structural polymorphism being contentious. However, recent nuclear magnetic resonance analysis indicates that 15B contains glycerol and not choline, suggesting that the same may also be true for 15F and 15C [34]. The 3′ end of 15F cps has four extra genes—rmlB, rmlD, glf, and a putative acetyl transferase gene wcjE—but they appear to have no effect on the structure as there is no rhamnose, galactofuranose, or extra acetylation in 15F CPS. Indeed, rmlA and rmlC would also be required for rhamnose biosynthesis. These four genes show synteny with the 3′ end of cps in several serotypes, particularly serotype 31, and their arrangement in 15F may indicate a recombination event. The serotype 14 [28,37,38] and basic 15 cps gene clusters clearly share common ancestry and differ only at the 3′ end, where the glycerol-2-phosphate–related genes in 15 are replaced in 14 by a gene (lrp) encoding a large (1,359 amino acid) repetitive protein, which correlates well with CPS structures [36]. The type-14 repeat unit most resembles the branched form of 15B and 15C, with the lack of O-acetylation due to the absence of wciZ. The lack of α-D-galactose is probably due to degradation of the relevant transferase gene, wchN. The large repetitive protein encoded by serotype-14 cps has a hydrophobic C-terminal region, suggesting that it may be anchored to the cell surface. This leads us to speculate that Lrp may serve as a dominant antigen that overwhelms the serological similarities to serogroup 15 that should be evident from their very similar repeat units. Discussion Several bacterial pathogens exist as a large number of antigenic variants because of differences in the polysaccharides presented at the cell surface. However, the sequencing and analysis of the cps loci of pneumococci described here are believed to provide the only such case where the whole gene repertoire is available, allowing genetics, chemistry, and immunology to be combined to predict the role of cps genes. This combined approach has allowed the confident prediction of most gene functions, but it has also highlighted the limitations where subtle sequence changes may alter enzyme substrate specificity. Analysis of the cps loci indicates that a number of different mechanisms have generated antigenic diversity in CPSs. Some of these involve the divergence of a single serotype into two related serotypes by the accumulation of point mutations (e.g., serogroup 6 [39]), or the insertion or deletion of a single gene, resulting in slightly different CPS structures (e.g., serogroup 18). In other cases, the cps loci of some serotypes within a serogroup seem to be virtually unrelated and probably reflect the sharing of a dominant epitope that led to them being placed within the same serogroup (e.g., serogroups 7, 17, 33, and 35). Similarly, some serotypes placed in different serogroups show more relatedness among their cps loci than those within the same serogroup (e.g., types 7B and 7C are more closely related to type 40 than to 7A and 7F). This is perhaps not surprising as serogroups were defined by common epitopes in the absence of any knowledge of the CPS structures or the cps sequences that code for their synthesis. Shared immunodominant epitopes will lead to inclusion in the same serogroup even if there are major differences in other parts of the structure and hence in the cps. A striking feature of the cps loci is the presence of many highly divergent forms of each of the key enzyme classes. Thus, there are 40 HGs for polysaccharide polymerases, 13 groups of flippases, and a great diversity of transferases. The presence of multiple non-homologous or highly divergent forms of these enzymes, together with the low percentage G+C content of the region in which these are encoded, supports the view that these genes have been imported into pneumococci (or their ancestors) on multiple occasions from different and unknown sources. The plethora of transferases in the pneumococcal cps loci provides an opportunity to continually generate new serotypes by gene shuffling, but there are no clear examples of serotypes arising as mosaics of two existing cps loci. One barrier to the frequent appearance of new serotypes by recombination is a lack of homology between the serotype-specific regions of cps loci of different serogroups. The appearance of new serotypes may also be limited by a need to change multiple cps genes; rare genetic events that create mosaics between existing cps loci probably typically fail to produce a capsule since new repeat units resulting from the capture of novel transferases are unlikely to be recognised as substrates by the resident repeat-unit polymerase. The cps sequences, and their associated polysaccharide structures and serological profiles, constitute an extensive dataset that, through further detailed analysis, will allow a clearer understanding of capsule biochemistry, genetics, and evolution and will precipitate advances in molecular serotyping of pneumococci [40,41]. Materials and Methods Strain selection, serotyping, and genomic DNA isolation. Representative strains of the 90 S. pneumoniae serotypes were selected from among the lyophilised strains in the strain collection of the World Health Organization Collaborating Centre for Reference and Research on Pneumococci, Statens Serum Institut (Copenhagen, Denmark) (Table S4). The strains were serotyped and cultured, and genomic DNA was extracted by standard methods [3,4,42]. PCR and DNA sequencing. PCR reactions were performed using the Expand Long Template PCR System (Roche, Basel, Switzerland), which contains proof-reading thermostable polymerases. Initial reactions used primers CPS1 (TTGCCAATGAAGAGCAAGACTTGACAGTAG) and CPS2 (CAATAATGTCACGCCCGCAAGGGCAAGT) [26]. Where these failed to produce an adequate product, further reactions were attempted using alternative dexB-specific primers (CPS1A [CGACCGTCGCTTCCTAGTTGTGGCTAAC] or PCPS3f [CACACAGAAAGCATCCCATGG]) and aliA-specific primers (CPS1B [GTCTTGAGCTTTGACTGCCGCGTATTCT] or PCPS3r [GAGACAGACCTGATAACCTCAACTATTTG]). The cps cluster for our serotype-5 strain was amplified using a primer based on the EMBL file (AY336008) specific for the wzg gene (CPS05F [CGTTCACAGAAAGTGAAGCG]) in combination with PCPS3r. PCR products spanning the cps locus were used directly to construct small-insert libraries [43], with 1- to 2-kb inserts in pUC18. Clones from each library were sequenced from each end using Big-Dye terminator chemistry (Applied Biosystems, Foster City, California, United States) on ABI3730 sequencing machines, to give an average of 8- to 10-fold coverage of each product. These reads were assembled with Phrap (CodonCode, Dedham, Massachusetts, United States), and any gaps or regions of poor coverage were re-sequenced using primer-directed sequencing directly from the original PCR product using Big-Dye primer chemistry (Applied Biosystems). This sequencing procedure should prevent any PCR errors from being represented in the final consensus sequence. Annotation and bioinformatic methods. Gene prediction and annotation were performed as previously described [44]. Predicted proteins were clustered into homology groups using TribeMCL (Centre for Mathematics and Computer Science and EMBL-EBI) [45] with a cut-off of 1e −50. The genes within the cps loci that encoded proteins within the same homology group were assigned the same name, the exceptions being the polymerases and flippases where we used the prior gene nomenclature, wzy and wzx, even though in both cases there were multiple homology groups. Alignment of gene clusters was performed using the Artemis Comparison Tool (Sanger Institute, Hinxton, United Kingdom). Nucleotide differences were identified using the EMBOSS program Diffseq (MRC Rosalind Franklin Centre for Genomics Research, Hinxton, United Kingdom) [46]. Supporting Information Figure S1 Capsule Biosynthesis Genes and Repeat-Unit Polysaccharide Structure for All 90 Serotypes (9.9 MB TIF) Click here for additional data file. Figure S2 Biosynthesis Pathways for Non-Housekeeping Sugars (50 KB PPT) Click here for additional data file. Table S1 Homology Groups including Numbers of Members and Product Description Proteins in different homology groups are so divergent that they are highly unlikely to have diverged from a common streptococcal ancestor. (306 KB DOC) Click here for additional data file. Table S2 Associations between Initial Transferases and Wzy Polymerase Groups Proposed Wzy groupings represent a sequential numbering of homology groups and are represented on structural diagrams. (104 KB DOC) Click here for additional data file. Table S3 Type Designations and Antigenic Formulae for the 90 Serotypes of S. pneumoniae The antigenic formulae represent arbitrary designations of cross-reactions as seen by the capsular reaction. (72 KB DOC) Click here for additional data file. Table S4 Type and Strain Designations for the 90 Strains of S. pneumoniae Analysed (68 KB DOC) Click here for additional data file. Accession Numbers The EMBL Nucleotide Sequence Database (http://www.ebi.ac.uk/embl), GenBank (http://www.ncbi.nlm.nih.gov/Genbank), and DNA Data Bank of Japan (http://www.ddbj.nig.ac.jp/Welcome-e-html) accession numbers for the sequences reported in this paper for the capsular biosynthetic genes of the 90 serotypes of S. pneumoniae are CR931632–CR931722. The EMBL Nucleotide Sequence Database (http://www.ebi.ac.uk/embl) accession number for the wzg gene is AY336008. The Pfam domain (http://www.sanger.ac.uk/cgi-bin/Pfam) for WchA, WciI, WcjG, and WcjH is PF02397.
- Record: found
- Abstract: found
- Article: found
Serotype-Specific Changes in Invasive Pneumococcal Disease after Pneumococcal Conjugate Vaccine Introduction: A Pooled Analysis of Multiple Surveillance Sites
Daniel Feikin, Eunice W Kagucia, Jennifer D. Loo … (2013)
Introduction In 2008, Streptococcus pneumoniae was estimated to have caused 540,000 deaths among children less than 5 years old worldwide [1]. Seven-valent pneumococcal conjugate vaccine (PCV7) was licensed and introduced in 2000 into the routine infant immunization schedule in the United States. Significant reductions in the incidence of invasive pneumococcal disease (IPD) were observed not only among children, but also among adults, reflecting reduced transmission and herd protection [2]. Several high- and middle-income countries introduced PCV7 in the several years after 2000. While IPD caused by vaccine serotypes (VTs) declined in virtually all settings, reported increases in IPD rates due to non-vaccine serotypes (NVTs) were negligible in some [3] and substantial in others [4]. Increases in NVT IPD following routine introduction of PCV7 were presumed to represent serotype replacement of VT by NVT, a phenomenon well-documented in pneumococcal nasopharyngeal colonization from randomized controlled trials [5] and observational studies [6],[7]. Direct comparison between settings, however, is complicated by variability in vaccine schedule and coverage and surveillance system characteristics. Understanding serotype replacement is even more critical in low-income countries where most pneumococcal deaths occur [1],[8], a more diverse distribution of serotypes causes disease, and nasopharyngeal colonization occurs earlier in infancy [9]. At the request of its Strategic Advisory Group of Experts (SAGE) on Immunizations, the World Health Organization (WHO) convened an expert consultation on serotype replacement in July 2010. A key recommendation of the consultation was that a comprehensive analysis be undertaken to provide an estimate of the magnitude and variability of pneumococcal serotype replacement following PCV7 use to inform the expected experience of low-income countries currently introducing PCVs [10]. The key findings of that analysis are described here. Methods Search Strategy We identified datasets from IPD surveillance systems that report rates through two approaches. First, we identified datasets gathered from a comprehensive systematic literature review on PCV dosing schedules [11]. In that systematic literature review, a search for English language publications on the immunogenicity, and direct and indirect effects of various PCV schedules on nasopharyngeal (NP) carriage, IPD, and pneumonia among children was performed using 14 databases (i.e., African Index Medicus; BioAbst/Reports, Reviews, Meetings; Biological Abstracts; Cochrane Library; EMBASE; Global Health; Index Medicus for Eastern Med. Region; Index Medicus for South-East Asia Region; IndiaMed; Latin America and Caribbean Health Sciences Information; Pan-American Health Organization; Pascal Biomed; PubMed; and Western Region Index Medicus) as well as meeting abstracts of the International Symposium on Pneumococci and Pneumococcal Disease (ISPPD) and the Interscience Conference on Antimicrobial Agents and Chemotherapy (ICAAC). The search included studies published between 1994 and 2010. The complete list of database-specific and Medical Subject Headings (MeSH) search terms used in the literature search is detailed by the authors. We reviewed those publications with IPD as an outcome; these publications needed to include at least one “narrow vaccine” search term as well as an IPD related search term, i.e., (“Invasive disease” [all fields]), (“invasive pneumococcal disease” [all fields]), and/or (“invasive bacterial disease” [all fields]). Second, we solicited potential datasets from experts in pneumococcal disease, WHO headquarters and regional offices, and by reviewing references from publications. Data Collection We solicited datasets from investigators using a standardized format, requesting IPD case counts for up to 5 years before and 10 years after PCV7 introduction, stratified by age groups (0–1, 2–4, 5–17, 18–49, 50–64, and ≥65 years old), clinical syndrome (overall IPD and meningitis specifically), hospitalization status, and serotype (Text S1). Meningitis was defined as isolation of pneumococcus from cerebrospinal fluid by culture. We requested age- and year-specific catchment population denominators to estimate rates, and we solicited descriptions of the PCV7 vaccination program, IPD surveillance system, changes to surveillance methodology or clinical practices, and potential IPD outbreaks. Data Quality Review Two coordinators conducted a quality check of datasets included in the analysis using a checklist (Box 1). Any requests for data clarification were emailed to the contributing investigator and the data were updated as applicable. 10.1371/journal.pmed.1001517.t008 Box 1. Dataset quality checks performed Review of Case Counts by Year and Age Group Checklist Item Follow-up Action A. Are there dramatic changes in overall case counts from year to year that might not be explained by vaccine introduction? If yes: Clarify with co-investigator. Exclude if indicates changes in surveillance or bias that would affect the analysis. B. What is happening with counts of VT cases? If counts are stable or increase: Clarify with co-investigator. Exclude stratum from further analysis if indicates changes in surveillance or bias that would affect the analysis. C. Verify that VT plus NVTs plus unknowns equals the total number of cases provided. If no: Clarify case numbers with co-investigator. D. Are there dramatic changes from year to year in serotypes 8 or 12F, suggesting a potential outbreak? If yes: Exclude those cases and re-analyze the data without them. E. Calculate the percentage of all cases for which serotype is known. Exclude strata with 5 years) in the same region (e.g., North America, Europe, rest of world). G. Review the numbers of cases for each syndrome. Are they plausible, i.e., are the meningitis cases uniformly fewer than the other cases? Are the hospitalized cases <5 fewer than all cases <5? If no: Clarify case numbers with co-investigator. H. Look at the variables related to year. Is year zero the correct year? Pay special attention to sites that had multi-stage introductions. Use the survey to define these variables. If no: Clarify with co-investigator Review of Denominators Checklist Item Follow-up Action A. Do the denominators in each age group change over time? If no: Clarify with co-investigator. If annual population denominator not available rates may be an underestimate. B. Do the denominators in each age group make sense relative to each other? For example, are the denominators for the adult groups substantially larger than for the child age groups? If no: Clarify with co-investigator. Data Analysis The inclusion criteria of the datasets for collection and analysis are given in Figure 1. 10.1371/journal.pmed.1001517.g001 Figure 1 Flow diagram of datasets included in the analysis. In datasets where serotypes 6A and 6C were not differentiated, we distributed these serotypes according to the known distribution of 6A and 6C in the same geographic region or globally in the pre- and post-PCV7 periods [12]. First, we calculated the percentage of 6A isolates out of all 6A and 6C isolates, using datasets where 6A and 6C isolates were distinguished. The percentage of true 6A isolates was calculated for all datasets, as well as by region for datasets from Europe and North America. Estimates of the percentage of true 6A isolates were weighted by the size of the site and calculated in four different time periods: pre-PCV introduction; 1–2 years post-; 3–4 years post-; and 5+ years post-PCV introduction. In sites that did not differentiate 6A and 6C serotypes, 6A/6C isolates were then redistributed according to the estimated regional (for North America and Europe datasets) or using all datasets’ (for datasets from sites outside of Europe or North America) distribution of differentiated 6A and 6C isolates. After redistributing serotype 6A with VT (serotypes 4, 6B, 9V, 14, 18C, 19F, and 23F) and 6C with NVT (all other serotypes), remaining isolates with unknown serotype were redistributed. Specifically, isolates with known serotype were classified into four groups: VT serotypes (PCV7 serotypes and 6A); serotypes 1 and 5; serotypes 3, 7F, and 19A; and all other NVT serotypes. Serotypes 1 and 5 were grouped together to allow for modeling expected rates absent the potential influence of outbreaks of these two serotypes. The remaining additional serotypes included in higher valency PCVs–3, 7F, and 19A—were grouped together for analyses of changes over time as they, along with serotypes 1 and 5, are likely to be prevented by introduction of the higher valency PCVs. Non-typeable isolates were added to the category of all other NVT serotypes. We calculated the percentage of each of the four groups of known serotypes out of all known serotypes. Unknown isolates that were not serotyped were then redistributed into the four serotype groups per the calculated proportional distribution. Redistribution was performed by site, age group, year, and syndrome. To minimize the effect of temporal and geographic differences in blood culturing practice among children in the outpatient setting, we restricted our analysis to hospitalized cases for children <5 years [13],[14]. Since IPD among adults is almost always a severe illness among inpatients, we assumed all cases among older persons were hospitalized. This assumption was confirmed in the few sites that did capture data on hospitalization status among adults with IPD [59],[60]. We excluded persons aged 5–17 years because case counts were too small for meaningful analysis. Because IPD rates were changing before PCV7 introduction in some sites, we used the pre-PCV7 IPD trends (excluding the year of introduction) to predict future years’ IPD rates, absent PCV7 use [15],[16]. We used Poisson regression to model expected rates of VT, NVT, and overall IPD absent PCV7 introduction. We assumed that overall IPD was a more stable indicator of pre-PCV7 trends than either VT or NVT, which could be affected by outbreaks of a single serotype [16]. Therefore, we used the regression intercept and slope of the pre-PCV7 annual rates of overall IPD to estimate future rates, absent PCV7, for overall, VT, and NVT IPD. Because serotypes 1 and 5 rates can fluctuate annually owing to outbreaks, we excluded them from the regression estimation of pre-PCV7 trends, but included them in the actual rate estimates on the basis of the trends. Separately, we calculated the pre-PCV7 average proportions of IPD caused by VT and NVT and applied each to the expected overall IPD rate to generate the expected VT and NVT IPD rates. The annual surveillance population denominator was included as an offset variable, and the slope of the modeled expected rates was assigned a value of zero from 4 years post-PCV7 onwards, assuming stabilization of any pre-PCV7 IPD surveillance trends by then. For children aged <5 years, expected rates for 11 of 19 sites (58%) were modeled. Among the 15 sites included in the IPD analysis for adults aged 18–49 years, 50–64 years, and ≥65 years, expected rates were generated using modeling for 10 (67%), 5 (33%), and 7(47%) sites, respectively. For age strata with an annual pre-PCV7 average of <20 IPD cases or <3 years of pre-PCV7 data, we felt that pre-PCV7 rates were unreliable to define surveillance trends because of small sample size or too few years. For these strata, expected IPD rates absent PCV7 introduction were estimated by averaging annual IPD rates before PCV7 introduction. We estimated the change in IPD rates following PCV7 introduction by calculating rate ratios (RRs), dividing the observed IPD rate by the expected IPD rate for each post-PCV7 year. We calculated 95% confidence intervals around RRs through simulation of observed and expected case counts and the delta method [17]. The delta method can be used to approximate the variance of a ratio and has previously been applied to estimate the variance of the log RR [17],[18]. To estimate the variance of the log RR, we simulated 200 observed and expected case counts using the Poisson distribution with the actual observed and calculated expected number of cases as the mean. We converted these simulated observed and expected case counts to rates. From these simulated rates we calculated the variance of the observed and expected rate, as well as the covariance between these rates using STATA Version 12.1 (StataCorp.). Using the delta method formula below, we combined the variance of the observed and expected rate to estimate the variance of the log RR. Where σ2 is the variance; Y is the observed rate; X is the expected rate; and COV (X,Y) is the covariance between the observed and expected rate. We included the covariance in the calculation of the variance of the log RR because for a few strata the covariance was greater than zero and so we were unable to assume independence between the observed and expected rates. The square-root of the variance of the log RR was used to estimate the standard error of the log RR. The standard error of the log RR was calculated separately for each site, age group, serotype combination, and post-PCV7 year. A value of 0·5 cases was assigned as a continuity correction to each stratum (i.e., site-age group-serotype group) with zero cases reported [19] so as to avoid undefined RRs (when zero cases of IPD were expected in a year) or undefined variances (as Poisson simulation would generate missing values for zero cells). Because the impact of PCV7 was expected to be heterogeneous across sites, we used random effects meta-analysis to pool the site-specific RRs [20]. Meta-analysis was performed for each age and serotype group for each of the 7 years after PCV7 introduction, generating a summary RR with 95% confidence intervals. Meta-analysis of RRs was performed both including all datasets available for each year post-PCV7, as well as including only those datasets with at least 7 years of post-PCV7 data, which was the last year with enough datasets for robust meta-analysis (i.e., five datasets). The same analysis comparing observed and expected rates was performed limited to meningitis cases. We performed several sensitivity analyses for IPD. First, we used a continuity correction of 0·1. Second, we performed an analysis completely excluding serotypes 1 and 5 from both pre- and post-PCV7 IPD rates. Third, we performed the analyses with the expected IPD rate as the observed average pre-PCV7 introduction IPD rate for all site-age group-serotype group strata (i.e., no modeling of expected IPD rates). Additionally, we performed an analysis comparing observed and expected IPD rates for two separate NVT serotype groups: NVT serotypes in the higher valency pneumococcal conjugate vaccines that are not in PCV7 (i.e., serotypes 1, 3, 5, 7F, and 19A) and NVT serotypes not in the higher valency vaccines. The RR of the observed over the expected rates in the years after PCV7 introduction and 95% CI were calculated for each site, age, and year stratum for both of these categories of NVT. A summary RR for both NVT categories was obtained for each age group in each post-PCV7 year using random-effects meta-analysis. To compare the contribution of these two NVT categories to the overall IPD incidence post-PCV7 introduction, we performed a separate analysis restricted to the post-PCV7 period where we defined the RR as the observed rate of IPD due to the NVT included in the higher valency vaccines over the observed rate of all other NVT not included in those vaccines. The 95% CI for this RR was also calculated using the delta method for each site, age, and post-PCV7 year. A summary RR for each age group and post-PCV7 year was calculated using random-effects meta-analysis. The analysis dataset was generated using SAS Version 9·2 (SAS Institute Inc.). Meta-analyses were conducted using STATA Version 12·1 (StataCorp). Results Description of Sites We identified 72 potentially eligible datasets and requested information from the investigators (Figure 1). Of 32 datasets received, 21 from four geographic regions (six North America, 11 Europe, three Australasia, one South America) met the inclusion criteria for analysis (Figure 1). For children, 19 datasets were included in the IPD and meningitis analyses, although two sites were included only for IPD and two different sites were included only for meningitis. For adults, 15 and 11 datasets were included in the analyses of IPD and meningitis, respectively. At least 19 datasets included in the analysis have previously published IPD surveillance data, though not necessarily including the same data used for this analysis (i.e., age group, case population, syndrome, and years of surveillance) [3],[4],[16],[21]–[36]. Specific reasons for exclusion from analysis for 11 datasets received were the following: no denominator provided (one); serogroup 19 not serotyped (one), <70% coverage of the primary PCV7 series by 12 months of age (four) [37]; <2 years of pre-PCV7 data (three); inability to define a proper denominator population (one); and substantial changes over time in case ascertainment of the surveillance system (one). The average annual number of IPD cases pre-PCV7 introduction for the 11 datasets excluded (six Europe, three North America, one Africa, and one Western Pacific) ranged from 8–1,490. Furthermore, among datasets included, two and six site-age group strata were excluded from the IPD and meningitis analyses, respectively, because <50% of isolates in those strata were serotyped (Table 1). In one site, adult strata were excluded from the analysis due to an increase in VT cases in the post-PCV7 introduction period, indicating changes in surveillance or bias that would affect the analysis (Table 1). No sites were excluded due to implausible distributions of serotype 6A/6C isolates. 10.1371/journal.pmed.1001517.t001 Table 1 Datasets included. Site IPD Analysis Meningitis Analysis <5 y 18–49 y 50–64 y ≥65 y <5 y 18–49 y 50–64 y ≥65 y Active Bacterial Core Surveillance (USA) INCL INCL INCL INCL INCL INCL INCL INCL Alaska (USA) INCL INCL INCL INCL INCL INCL INCL EXCLa Australia Indigenous (Northern Territories) INCL INCL INCL INCL INCL No VT cases INCL No cases Australia Non-Indigenous INCL INCL INCL INCL INCL EXCLa INCL INCL Calgary (Canada) INCL INCL INCL INCL INCL INCL INCL INCL Switzerland INCL INCL INCL INCL INCL INCL INCL INCL Czech Republic INCL INCL INCL INCL Data not provided Denmark INCL INCL INCL INCL INCL INCL INCL INCL England and Wales INCL INCL INCL INCL INCL INCL INCL INCL France EXCLa Data not provided INCL Data not provided Greece (Crete) INCL INCL INCL INCL INCL No NVT cases INCL No VT cases Ireland EXCLa INCL EXCLa EXCLa INCL Israel INCL Data did not include all cases INCL Data did not include all cases Navajo (USA) INCL INCL INCL INCL INCL INCL No VT cases No VT cases Kaiser Permanente Northern California (USA) INCL Data not provided INCL Data not provided The Netherlands INCL INCL INCL INCL INCL INCL INCL INCL Norway INCL INCL INCL INCL EXCLa INCL INCL EXCLa New Zealand INCL INCL INCL INCL INCL INCL INCL INCL Scotland INCL INCL INCL INCL INCL INCL INCL INCL Uruguay INCL EXCLb INCL EXCLb Utah (USA) INCLc Data not provided INCL Data not provided a <50% serotyped in some years. b Major changes or biases in surveillance that could affect estimates of serotype-specific rate and could not be adjusted for in the analysis. c Included only in year +1; <50% serotyped in year 2. The PCV7 schedules used included two primary doses plus a booster (nine sites), three primary doses without a booster (one site), and three primary doses with a booster (11 sites); 16 sites had catch-up campaigns (Table 2). All sites achieved ≥70% immunization coverage during the surveillance period and the range of average immunization coverage estimates for all post-PCV7 years was 55%–97% (Table S1). 10.1371/journal.pmed.1001517.t002 Table 2 Characteristics of surveillance sites included in meta-analysis (n = 21). Country Population Vaccine Schedulea Catch-up Percent PCV7 Coverageb Type of Surveillancec n Surveillance Yearsd Average Annual n IPD Isolates Pre-PCV7 Percent Meningitis Cases pre-PCV7 Year 1 Maximum Pre-PCV7 Post-PCV7 <5 y ≥18 y <5 y ≥18 y Australia Indigenous (NT) 3+PPVa Y 73 86 P 5 8 20 31 9 0 Australia Non-indigenous 3+0 Y 89 92 P 3 5 415 831 3 0 Canada Calgary 3+1 Y 89 94 A 4 7 14 77 19 3 Czech Republic National 3+1 N 80 80 P 2 1 35 207 N/Ae N/Ae Denmark National 2+1 Y 89 90 P 5 3 91 984 22 6 England and Wales National 2+1 Y 84 93 P 5f 3 690 4,929 13 2 France Metropolitan 2+1 N N/Pb 80 A 2 6 N/Ag N/Ag 23 N/Ae Greece Crete 3+1 Y 60 92 P 5 4 2 3 25 0 Ireland National 2+1 Y N/Pb 88 P 4 2 N/Ag N/Ag 4 0 Israel National 2+1 Y 85 85 A 5 1 238 N/Ag 11 N/Ae The Netherlands National 3+1 N 94 94 P 5f 3 49 596 34 8 New Zealand National 3+1 Y 88 90 P 5 2 159 341 7 2 Norway National 2+1 N 94 95 P 4f 4 92 969 N/Ae 5 Scotland National 2+1 Y N/Pb 97 P 3 4 86 568 8 1 Switzerland National 2+1 N 30 80 P 3 3 73 783 8 2 Uruguay National 2+1 Y 91 91 P 5 2 103 N/Ag 10 N/Ae USA Seven sites (ABCs) 3+1 Y 7 93 A 2 9 358 2,796 13 3 USA Alaska 3+1 Y 20 87 A 5 7 19 76 14 5 USA Navajo 3+1 Y 80 90 A 5f 10 20 91 4 1 USA KPNC 3+1 Y 33 81 P 4 4 22 N/Ag 9 N/Ae USA Utah 3+1 Y N/Pb 90 A 3 10 20 N/Ag 21 N/Ae Australia non-Indigenous does not include data from the State of New South Wales. a Vaccine schedule = Primary + booster. b Proportion of children receiving the full infant dose by 12 months. N/P (not provided), meaning that immunization coverage not provided for year 1 and/or last year of surveillance data provided, although all included datasets were from sites that indicated they reached ≥70% coverage in the post-PCV period. c Active (A), proactive effort to identify all cases in an area; passive (P), reporting of cases by clinicians or laboratories without a systematic approach to capture cases not reported. d Number of surveillance years included in the IPD analysis for children <5 y. Number of surveillance years the same for adult age groups unless otherwise indicated. e Not applicable (N/A), age group not included in meningitis analysis. For some sites, some ≥18 y age categories excluded from meningitis analysis (Table 1; Table S1). f Site, adult age group (n surveillance years). England and Wales: 18–49 y (4); 50–64 y, and ≥65 y (2). The Netherlands: ≥18 y (2). Norway: ≥18 y (2). USA-Navajo: 50–64 y (4). g Not applicable (NA), age group not included in IPD analysis. France and Ireland only included in the meningitis only analysis. ABCs, Active Bacterial Core Surveillance; KPNC, Kaiser Permanente Northern California; NT, Northern Territory; PPV, pneumococcal polysaccharide vaccine. Children <5 Years Old The annual number of IPD isolates at baseline for children <5 years ranged from 2 to 690 and the median baseline rate was 31·4 cases per 100,000 (range 4·7–280·3) (Figure 2; Table 2). Our meta-analysis showed that the rate of overall IPD decreased significantly by 1 year after introduction (summary RR 0·55, 95% CI 0·46–0·65), which was then maintained out to 7 years post-introduction (RR 0·49, 95% CI 0·35–0·68) (Figure 3; Table 3). Although there was heterogeneity in the effect across sites, as expressed by the I2 statistic, the point estimates tended in the same direction with all 19 sites showing a decrease (in 15, these reductions were statistically significant) compared to baseline in overall IPD in at least one post-introduction year (Figure 4). The rate of VT IPD declined significantly by 1 year after introduction (summary RR 0·34, 95% CI 0·28–0·41) and continued to decrease through 7 years (summary RR 0·03, 95% CI 0·01–0·10) (Figures 3 and 5; Table 3). The rate of NVT IPD increased significantly by 2 years after introduction (summary RR 1·34, 95% CI 1·02–1·77) and increased through 5 years, with little change thereafter through year 7 (summary RR 2·81, 95% CI 2·12–3·71) (Figure 3; Table 3). Most sites (seven statistically significant) showed an increase in NVT IPD rate in at least one post-introduction year (Figure 6). To account for the possible confounder of varying numbers of datasets included by year after PCV introduction, we repeated the meta-analysis including only the five sites with 7 years of post-PCV7 data. For VT, NVT, and all serotypes, the summary RRs were similar to those when all sites were included (Tables 3 and S2). The results were also similar when using a continuity correction of 0·1 instead of 0·5 (Table S3) and when excluding serotypes 1 and 5 (Table S4). In the analysis in which all expected rates used the average pre-PCV7 rates (i.e., no modeling of expected rates), the trends of post-PCV7 IPD changes were similar to those from the modeling approach, although NVT summary RRs tended to be slightly higher, as would be expected with no adjustment for increasing surveillance sensitivity over time (Table S5). 10.1371/journal.pmed.1001517.g002 Figure 2 Pre-PCV7 introduction average annual invasive pneumococcal disease rates and percent vaccine serotype isolates. (A) IPD rates as cases per 100,000. (B) Percent VT isolates as a proportion of all pre-PCV7 introduction isolates. *Only children aged <5 years included. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; CZE, Czech Republic; DEN, Denmark; E&W, England and Wales; GRC, Greece; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. 10.1371/journal.pmed.1001517.g003 Figure 3 Post-PCV7 introduction invasive pneumococcal disease summary rate ratios. Summary RRs from random effects meta-analysis. Summary RRs estimated by dividing observed over expected rates and calculated for each age-serotype group. 95% confidence interval indicated by error bars. Y-Axis on log scale. 10.1371/journal.pmed.1001517.t003 Table 3 Invasive pneumococcal disease summary rate ratios from random effects meta-analysis, comparing observed over expected rates, by age, serotype group, and post-PCV7 introduction year for all sites. Year Post-PCV7 Introduction RR (95% CI) 1 2 3 4 5 6 7 Children <5 y Number of sites 19 16 14 10 6 5 5 VT 0·34 (0·28–0·41) 0·14 (0·10–0·20) 0·09 (0·06–0·14) 0·07 (0·04–0·12) 0·05 (0·03–0·08) 0·06 (0·01–0·29) 0·03 (0·01–0·10) NVT 1·18 (0·99–1·41) 1·34 (1·02–1·77) 1·62 (1·16–2·24) 1·30 (0·71–2·41) 2·81 (2·06–3·85) 2·27 (1·48–3·48) 2·81 (2·12–3·71) All serotypes 0·55 (0·46–0·65) 0·43 (0·34–0·54) 0·44 (0·35–0·55) 0·33 (0·23–0·46) 0·48 (0·37–0·61) 0·41 (0·35–0·50) 0·49 (0·35–0·68) Persons 18–49 y Number of sites 15 14 13 9 6 5 5 VT 0·77 (0·67–0·89) 0·56 (0·46–0·69) 0·39 (0·30–0·50) 0·21 (0·15–0·28) 0·19 (0·14–0·26) 0·18 (0·11–0·28) 0·10 (0·08–0·13) NVT 1·04 (0·86–1·26) 1·10 (0·88–1·37) 1·17 (0·93–1·48) 1·32 (0·82–2·11) 1·41 (0·68–2·93) 1·00 (0·51–1·95) 0·85 (0·39–1·86) All serotypes 0·90 (0·78–1·04) 0·84 (0·72–0·98) 0·80 (0·67–0·96) 0·74 (0·54–1·02) 0·75 (0·51–1·11) 0·63 (0·38–1·03) 0·52 (0·29–0·91) Persons 50–64 y Number of sites 15 14 13 9 6 5 5 VT 0·90 (0·79–1·02) 0·60 (0·50–0·73) 0·45 (0·35–0·59) 0·30 (0·23–0·39) 0·25 (0·17–0·35) 0·20 (0·13–0·30) 0·15 (0·12–0·19) NVT 1·08 (0·94–1·24) 1·38 (1·22–1·55) 1·59 (1·34–1·87) 1·61 (1·29–2·01) 2·05 (1·38–3·05) 1·68 (1·34–2·11) 1·72 (1·52–1·96) All serotypes 0·98 (0·87–1·11) 0·98 (0·86–1·12) 1·03 (0·87–1·20) 0·90 (0·76–1·06) 1·06 (0·83–1·36) 0·92 (0·75–1·13) 0·84 (0·77–0·93) Persons ≥65 y Number of sites 15 14 13 9 6 5 5 VT 0·88 (0·76–1·01) 0·66 (0·57–0·77) 0·42 (0·35–0·50) 0·34 (0·25–0·48) 0·17 (0·13–0·22) 0·13 (0·11–0·15) 0·12 (0·09–0·17) NVT 1·17 (1·03–1·32) 1·34 (1·15–1·55) 1·55 (1·32–1·82) 1·76 (1·23–2·51) 2·04 (1·32–3·16) 1·62 (1·20–2·18) 1·45 (1·00–2·11) All serotypes 1·01 (0·91–1·12) 0·96 (0·85–1·09) 0·94 (0·83–1·08) 0·99 (0·70–1·39) 0·91 (0·70–1·17) 0·89 (0·63–1·26) 0·74 (0·58–0·95) 10.1371/journal.pmed.1001517.g004 Figure 4 All serotype invasive pneumococcal disease summary rate ratio forest plots by post-introduction year from random effects meta-analysis for children aged <5 years. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; CZE, Czech Republic; DEN, Denmark; E&W, England and Wales; GRC, Greece; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. 10.1371/journal.pmed.1001517.g005 Figure 5 Vaccine serotype invasive pneumococcal disease summary rate ratio forest plots by post-introduction year from random effects meta-analysis for children aged <5 years. Site abbreviations: ABCs (USA Active Bacterial Core Surveillance); AIP (USA Alaska); Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; CZE, Czech Republic; DEN, Denmark; E&W, England and Wales; GRC, Greece; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. 10.1371/journal.pmed.1001517.g006 Figure 6 Non-vaccine serotype invasive pneumococcal disease summary rate ratio forest plots by post-introduction year from random effects meta-analysis for children aged <5 years. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; CZE, Czech Republic; DEN, Denmark; E&W, England and Wales; GRC, Greece; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. In the pre-PCV7 period, the percentage of IPD due to meningitis ranged from 3%–34% by site (Table 2). The meta-analysis results for meningitis were similar to overall IPD, with sustained reductions in meningitis due to all serotypes through 7 years post-PCV7 introduction (RR 0·40, 95% CI 0·25–0·64) (Figure 7; Tables 4 and S6). Due to smaller numbers of meningitis cases, there was more variability by year and wider confidence intervals for the RR point estimates (Figures and 7; Tables 3 and 4). 10.1371/journal.pmed.1001517.g007 Figure 7 Post-PCV7 introduction pneumococcal meningitis summary rate ratios. Summary RRs from random effects meta-analysis. Summary RRs esimated by dividing observed by expected rates and calculated for each age-serotype group. 95% confidence interval indicated by error bars. Y-Axis on log scale. 10.1371/journal.pmed.1001517.t004 Table 4 Meningitis summary rate ratios from random effects meta-analysis, comparing observed over expected rates, by age, serotype group and post-PCV7 introduction year for all sites. Year Post-PCV7 Introduction RR (95% CI) 1 2 3 4 5 6 7 Children <5 y Number of sites 19 18 13 8 6 6 5 VT 0·59 (0·49–0·71) 0·24 (0·15–0·39) 0·19 (0·13–0·28) 0·24 (0·13–0·45) 0·10 (0·06–0·18) 0·05 (0·02–0·11) 0·12 (0·04–0·38) NVT 1·52 (1·19–1·95) 1·61 (1·24–2·10) 1·96 (1·49–2·58) 2·14 (1·49–3·06) 2·47 (1·69–3·63) 2·67 (1·84–3·88) 2·15 (1·05–4·40) All serotypes 0·81 (0·69–0·94) 0·59 (0·50–0·70) 0·54 (0·42–0·69) 0·48 (0·29–0·81) 0·54 (0·33–0·89) 0·49 (0·27–0·90) 0·40 (0·25–0·64) Persons 18–49 y Number of sites 11 11 10 6 4 4 4 VT 0·87 (0·61–1·24) 0·68 (0·49–0·96) 0·44 (0·28–0·69) 0·38 (0·12–1·16) 0·36 (0·09–1·42) 0·23 (0·11–0·49) 0·15 (0·04–0·52) NVT 1·26 (0·90–1·75) 1·21 (0·88–1·66) 1·36 (1·00–1·87) 1·39 (0·83–2·33) 1·54 (0·93–2·55) 1·76 (1·08–2·88) 1·32 (0·74–2·37) All serotypes 1·05 (0·84–1·32) 0·95 (0·75–1·21) 0·86 (0·67–1·10) 0·71 (0·49–1·03) 0·74 (0·51–1·07) 0·87 (0·59–1·30) 0·61 (0·40–0·95) Persons 50–64 y Number of sites 13 12 11 7 5 4 4 VT 1·35 (0·95–1·92) 0·88 (0·59–1·32) 0·88 (0·57–1·37) 0·84 (0·46–1·52) 0·69 (0·35–1·36) 0·27 (0·10–0·73) 0·19 (0·06–0·65) NVT 1·07 (0·75–1·53) 2·07 (1·47–2·92) 1·81 (1·26–2·61) 1·62 (0·89–2·96) 2·55 (1·32–4·92) 1·91 (0·98–3·73) 2·83 (1·46–5·47) All serotypes 1·24 (0·96–1·61) 1·59 (1·23–2·06) 1·36 (1·03–1·78) 1·22 (0·76–1·94) 1·47 (0·93–2·33) 0·93 (0·57–1·52) 1·27 (0·82–1·97) Persons ≥65 y Number of sites 10 10 8 4 3 2 2 VT 1·06 (0·72–1·55) 0·71 (0·47–1·08) 0·51 (0·31–0·82) 0·40 (0·16–1·02) 0·26 (0·09–0·76) 0·19 (0·05–0·75) 0·12 (0·02–0·72) NVT 1·07 (0·77–1·50) 1·11 (0·68–1·83) 1·05 (0·59–1·89) 0·61 (0·28–1·33) 0·83 (0·19–3·69) 1·05 (0·51–2·17) 0·85 (0·40–1·81) All serotypes 1·05 (0·77–1·43) 0·91 (0·60–1·39) 0·80 (0·46–1·39) 0·54 (0·28–1·04) 0·61 (0·17–2·15) 0·69 (0·39–1·22) 0·53 (0·28–1·00) Adults For adults, the annual number of IPD isolates at baseline ranged from 3 to 4,929 with a median IPD baseline rate of 14·2 cases per 100,000 (range 0·6–101·7) (Figure 2; Table 2). The summary RR point estimates from the meta-analysis showed reductions in overall IPD for most years, though not statistically significant in years 1–6 post-introduction (Figures 3, 8, 11, and 14; Table 3). Among the five sites with data 7 years post-introduction, statistically significant reductions were seen in year 7 for persons 18–49 years (summary RR 0·52, 95% CI 0·29–0·91), for persons 50–64 years old (summary RR 0·84, 95% CI 0·77–0·93), and for persons ≥65 years old (summary RR 0·74, 95% CI 0·58–0·95) (Figures 8, 11, and 14; Table S2). VT IPD decreased significantly for all adult age groups by the second year after PCV7 introduction (Figures 3, 9, 12, and 15; Table 3). In contrast to children, this decrease in VT IPD rates occurred more gradually; not until the fourth year after PCV7 introduction did adults have decreases in VT IPD similar in magnitude to those seen among children in the first post-PCV7 year (Figure 3; Table 3). In adults aged 18–49 years old, there was no significant increase in NVT IPD rates compared to baseline for any year, while for adults aged 50–64 years and ≥65 years, significant increases in NVT IPD were observed from year 2 and year 1 post-introduction, respectively (Figures 3, 10, 13, and 16; Table 3). There was substantial variability in the magnitude of NVT IPD increase by site (Figures 10, 13, and 16). For adults, the meta-analyses using a 0·1 continuity correction, excluding serotypes 1 and 5, limited to the five sites with 7 years of data, and using only averaged pre-PCV7 rates showed similar findings (Tables S2–S5). 10.1371/journal.pmed.1001517.g008 Figure 8 All serotype invasive pneumococcal disease summary rate ratio forest plots by post-introduction year from random effects meta-analysis for adults aged 18–49 years. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; CZE, Czech Republic; DEN, Denmark; E&W, England and Wales; GRC, Greece; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. 10.1371/journal.pmed.1001517.g009 Figure 9 Vaccine serotype invasive pneumococcal disease summary rate ratio forest plots by post-introduction year from random effects meta-analysis for adults aged 18–49 years. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; CZE, Czech Republic; DEN, Denmark; E&W, England and Wales; GRC, Greece; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. 10.1371/journal.pmed.1001517.g010 Figure 10 Non-vaccine serotype invasive pneumococcal disease summary rate ratio forest plots by post-introduction year from random effects meta-analysis for adults aged 18–49 years. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; CZE, Czech Republic; DEN, Denmark; E&W, England and Wales; GRC, Greece; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. 10.1371/journal.pmed.1001517.g011 Figure 11 All serotype invasive pneumococcal disease summary rate ratio forest plots by post-introduction year from random effects meta-analysis for adults aged 50–64 years. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; CZE, Czech Republic; DEN, Denmark; E&W, England and Wales; GRC, Greece; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. 10.1371/journal.pmed.1001517.g012 Figure 12 Vaccine serotype invasive pneumococcal disease summary rate ratio forest plots by post-introduction year from random effects meta-analysis for adults aged 50–64 years. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; CZE, Czech Republic; DEN, Denmark; E&W, England and Wales; GRC, Greece; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. 10.1371/journal.pmed.1001517.g013 Figure 13 Non-vaccine serotype invasive pneumococcal disease summary rate ratio forest plots by post-introduction year from random effects meta-analysis for adults aged 50–64 years. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; CZE, Czech Republic; DEN, Denmark; E&W, England and Wales; GRC, Greece; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. 10.1371/journal.pmed.1001517.g014 Figure 14 All serotype invasive pneumococcal disease summary rate ratio forest plots by post-introduction year from random effects meta-analysis for adults aged ≥65 years. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; CZE, Czech Republic; DEN, Denmark; E&W, England and Wales; GRC, Greece; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. 10.1371/journal.pmed.1001517.g015 Figure 15 Vaccine serotype invasive pneumococcal disease summary rate ratio forest plots by post-introduction year from random effects meta-analysis for adults aged ≥65 years. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; CZE, Czech Republic; DEN, Denmark; E&W, England and Wales; GRC, Greece; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. 10.1371/journal.pmed.1001517.g016 Figure 16 Non-vaccine serotype invasive pneumococcal disease summary rate ratio forest plots by post-introduction year from random effects meta-analysis for adults aged ≥65 years. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; CZE, Czech Republic; DEN, Denmark; E&W, England and Wales; GRC, Greece; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. Among all adults in the pre-PCV7 period, the percentage of IPD due to meningitis ranged from 0%–8% by site (Table 2). The findings for meningitis were similar to overall IPD for 18–49 year olds, with statistically significant reductions at 7 years post-PCV7 introduction (RR 0·61, 95% CI 0·40–0·95) (Figure 7; Tables 3 and 4). For persons 50–64 years old, in most years the increase in NVT meningitis tended to be higher than for NVT IPD, resulting in some early years (i.e., years 2 and 3) when there was an increase in overall pneumococcal (i.e., any serotype) meningitis, although this significant increase was not sustained in subsequent years (Figure 7; Tables 3 and 4). In contrast to 50–64 year olds, among persons ≥65 years there was less of an increase in NVT meningitis than NVT IPD in most years, resulting in relatively greater reductions in overall meningitis due to all serotypes, although never reaching a statistically significant decrease (Figure 7; Tables 3 and 4). NVT Serotypes Included in Higher Valency Conjugate Vaccines The magnitude of increases in IPD rates due to the subset of NVT included in higher valency conjugate vaccines but not PCV7 (i.e., serotypes 1, 3, 5, 7F, 19A) was similar to the increases among all the other NVT not in the higher valency vaccines (Table 5). However, the rates due to IPD caused by the five NVT included in higher valency vaccines were higher than rates of the NVT not in the higher valency vaccines in most post-PCV7 years for children (Table 6). In contrast, among adults aged 50–64 years and ≥65 years old, IPD rates of NVT not in the higher valency vaccines were higher than rates caused by the NVT in the higher valency vaccines for most years (Table 6). 10.1371/journal.pmed.1001517.t005 Table 5 Summary rate ratios from random effects meta-analysis, comparing observed over expected rates for non-vaccine serotypes, divided into those in higher valency vaccines and those not, by age, serotype group, and post-PCV7 introduction year for all sites. Year Post-PCV7 Introduction RR (95% CI) 1 2 3 4 5 6 7 Number of sites 19 16 14 10 6 5 5 Children <5 y Types 1, 3, 5, 7F, and 19A a 1·22 (0·97–1·54) 1·39 (0·98–1·97) 1·46 (0·99–2·15) 1·46 (0·72–2·99) 3·65 (2·50–5·34) 2·57 (1·21–5·44) 2·09 (0·81–5·37) Other NVT a 1·23 (1·04–1·44) 1·23 (0·91–1·66) 1·64 (1·25–2·17) 1·10 (0·65–1·86) 2·07 (1·51–2·84) 1·57 (1·06–2·32) 2·03 (1·41–2·92) Number of sites 15 14 13 9 6 5 5 Persons 18–49 y Types 1, 3, 5, 7F, and 19A 1·10 (0·82–1·48) 1·12 (0·83–1·51) 1·08 (0·79–1·48) 1·27 (0·66–2·44) 1·36 (0·44–4·19) 0·94 (0·34–2·61) 0·81 (0·25–2·60) Other NVT 0·93 (0·85–1·02) 1·03 (0·85–1·26) 1·26 (0·94–1·67) 1·27 (0·86–1·88) 1·28 (0·80–2·05) 1·04 (0·60–1·79) 0·87 (0·44–1·69) Number of sites 15 14 13 9 6 5 5 Persons 50–64 y Types 1, 3, 5, 7F, and 19A 1·07 (0·89–1·30) 1·35 (1·10–1·65) 1·46 (1·18–1·80) 1·55 (1·20–1·99) 2·01 (1·15–3·50) 1·69 (1·17–2·46) 1·82 (1·50–2·21) Other NVT 1·09 (0·97–1·24) 1·39 (1·27–1·52) 1·65 (1·44–1·89) 1·62 (1·29–2·02) 2·00 (1·55–2·59) 1·69 (1·44–1·99) 1·67 (1·44–1·94) Number of sites 15 14 13 9 6 5 5 Persons ≥65 y Types 1, 3, 5, 7F, and 19A 1·18 (0·99–1·40) 1·30 (1·11–1·52) 1·42 (1·15–1·75) 1·62 (1·05–2·48) 1·86 (1·30–2·66) 1·48 (1·22–1·80) 1·23 (0·60–2·51) Other NVT 1·11 (1·00–1·23) 1·36 (1·15–1·60) 1·59 (1·37–1·84) 1·85 (1·30–2·65) 2·05 (1·25–3·38) 1·60 (1·24–2·07) 1·45 (1·26–1·67) a Serotypes included in higher valency PCVs. 10.1371/journal.pmed.1001517.t006 Table 6 Summary rate ratios comparing the rate of serotypes 1, 3, 5, 7F, and 19A over the rate of all other non-vaccine types in each year post-PCV7 introduction, from random effects meta-analysis. Year Post-PCV7 Introduction RR (95% CI) Children <5 y Persons 18–49 y Persons 50–64 y Persons ≥65 y 1 1·59 (1·27–1·98) 1·18 (0·80–1·74) 0·87 (0·72–1·06) 0·83 (0·69–1·00) n 19 15 15 15 2 1·66 (1·28–2·16) 1·10 (0·80–1·51) 0·83 (0·68–1·02) 0·74 (0·66–0·84) n 16 14 14 14 3 1·25 (0·97–1·62) 0·86 (0·59–1·27) 0·75 (0·61–0·93) 0·70 (0·62–0·79) n 14 13 13 13 4 1·53 (1·01–2·31) 0·91 (0·55–1·49) 0·76 (0·60–0·98) 0·64 (0·53–0·77) n 10 9 9 9 5 1·76 (1·18–2·63) 0·76 (0·38–1·51 0·79 (0·45–1·39) 0·65 (0·58–0·74) n 6 6 6 6 6 1·75 (0·93–3·30) 0·71 (0·54–0·93) 0·68 (0·53–0·88) 0·54 (0·47–0·62) n 5 5 5 5 7 1·01 (0·36–2·87) 0·73 (0·51–1·04) 0·69 (0·59–0·80) 0·53 (0·38–0·75) n 5 5 5 5 Five Non-vaccine serotypes included in higher valency PCVs. Serotype 6A is not included as it was grouped with vaccine serotypes. Discussion This study was unique in being able to collect, restrict, adjust, and analyze multiple IPD surveillance datasets in a standardized and systematic way, allowing summary estimates and cross-site comparisons of PCV7 impact on IPD rates that are not possible from individual site-specific publications [4],[14],[38]. The most important public health implication of our analysis was that decreases in overall IPD rates in children–the group targeted for PCV7 vaccination–occurred quickly and were sustained after vaccine introduction despite increases in NVT rates. The summary reduction in the rate of overall IPD in children was 50%–60% compared with pre-introduction rates through 7 years after PCV7 introduction. We found similar overall rate reductions for pneumococcal meningitis as for overall IPD; meningitis might be less susceptible to changes over time in clinical practice and reporting compared to bacteremia. Over a half million children still die annually from pneumococcal disease, mostly in low-income countries [1], and WHO’s SAGE urges all countries to implement routine immunization with PCVs [39], a recommendation supported by this study’s finding that PCV introduction has resulted in sustained, widespread reduction in overall IPD rates in children despite the occurrence of some serotype replacement. The relative stability in overall IPD reductions from years one to seven after PCV7 introduction belies changes in both VT and NVT IPD incidence that occurred over the years. Point estimates of VT disease continued to decrease out to seven years when VT IPD became uncommon in most sites. Point estimates of NVT, on the other hand, increased out to at least 5 years after vaccine introduction, albeit with variable magnitude across sites. This increase in NVT IPD across sites is consistent with serotype replacement, but the magnitude of those increases was smaller than the reductions in VT disease, thereby resulting in a reduction of overall IPD rates. The temporal association of the rise in NVT IPD following PCV7 introduction suggests a causal relationship. In our analysis, increases in NVT among children under 5 years were seen within 2–3 years of PCV7 introduction in all sites. The lag between the decrease in VT IPD and rise in NVT IPD, as shown here, has been pointed out previously [14]. Our data suggest that much of the NVT IPD occurring after PCV7 introduction will likely be prevented by the current use of higher valency conjugate vaccine formulations [40]–[42]. The NVT pneumococci most frequently observed to increase in carriage in areas using PCV7 are generally less likely to result in invasive disease in children than those serotypes included in PCV7 [43]–[46]. Nonetheless, our data show that serotypes other than those in PCV13 also can cause serotype replacement. Whether the higher valency vaccines will ultimately lead to further sustained reductions in overall IPD than those observed after PCV7 introduction is not yet clear and should be carefully monitored in the years ahead. Our findings among adults showed a similar trend as in children, with some notable differences. There was a lag of at least 2 years before significant decreases in VT IPD rates were observed, an expected finding as the level of herd protection will depend on the accumulated size of the vaccinated group [47]. Moreover, the relative reduction in VT IPD, although substantial, was not of the same magnitude as in children. The variability of the changes in NVT IPD rate was greater in adults, with some sites having increases and others having decreases. Moreover, some differences in adult age groups were noticeable, with 50–64 year olds having the most modest decrease in overall IPD and meningitis, which has been shown before; this perhaps reflects the greater contribution of underlying illness to IPD in this age group [48],[49]. With increased susceptibility, this population might be more likely to show increases in IPD from less invasive replacing NVTs. These differences in VT and NVT IPD rate changes post-PCV7 among adults resulted in the finding that although overall IPD decreased in adults, there was more variability in the magnitude of the decrease by site and age group. Though the majority of sites showed a decrease in overall IPD among adults, there were a few sites in which adults had an increase in overall IPD in some post-PCV7 years, emphasizing the need for ongoing, methodologically sound and consistent surveillance among not just children but adults to document the full population impact of PCVs. Despite the evidence from both IPD and carriage studies that PCV7 leads to some serotype replacement, other factors can also contribute to the observed increases in NVT disease rates. First, secular trends in serotype prevalence occur over time, absent vaccine, as has been shown in Spain, Denmark, Chile, and the US [38],[50]–[53]. One cause of short-term fluctuations in IPD is outbreaks, particularly due to serotypes 1, 5, 8, and 12F [54]. Removal of serotypes 1 and 5 from our analyses did not alter the overall findings, suggesting outbreaks of these two serotypes did not account for the increases in NVT incidence. Second, rapid temporal changes in antibiotic use could lead to competitive advantage of serotypes commonly resistant to antibiotics. This mechanism, particularly increased macrolide use in some countries, has been postulated as contributing to the rapid rise of serotype 19A [38],[55],[56]. Third, certain characteristics of surveillance systems can significantly influence whether changes in NVT IPD rates are identified. For example, if serotyping is performed only on the most severe cases, or if the selection of isolates for serotyping changes over time, then the observed distribution of serotypes in any given year may not reflect the true distribution in the population. Additionally, if sensitivity of case ascertainment changes over time, then findings are likely biased. For example, if clinical investigation of suspected cases, or reporting of known pneumococcal cases increases because of publicity surrounding a national vaccination program or if identification of cases decreases because of changing clinical practices (e.g., blood culturing frequency), then identification of NVT IPD cases over time will increase or decrease, respectively. Lastly, if the susceptibility of the population to pneumococcal diseases changes, for example by increased use of antiretroviral therapy in persons with HIV infection, then the rates of IPD in the population can change over time. Similarly, if the prevalence of underlying or immunocompromising illness increases over time, the population might become more susceptible to IPD from less invasive NVT serotypes, leading to an apparent increase in serotype replacement. Although these non-vaccine factors might have played a part in the observed IPD rates post-vaccination, we attempted to eliminate or adjust for them in multiple ways, leading us to believe that their overall contribution to the observed serotype-specific IPD changes, including serotype replacement, were secondary. This analysis had certain limitations. First, as mentioned, this review includes only data from programs using PCV7. PCV7 is no longer produced and so it will be important to be cautious when extrapolating to programs using the newer PCV10 and PCV13 formulations. Nonetheless, if PCV10 and PCV13 affect nasopharyngeal colonization in a manner similar to that of PCV7, IPD serotype replacement will likely occur to some degree following immunization with the higher valency formulations; the epidemiology and the policy implications of serotype replacement learned from PCV7 will continue to be relevant. Second, we may not have fully identified or controlled for temporal trends in IPD surveillance or possible outbreaks of serotypes besides 1 and 5 in some datasets. Third, these data represent the experience in high-income countries. Findings from the two indigenous populations (i.e., Navajo and Australian Indigenous), known to be at high risk of IPD and to share pneumococcal epidemiologic characteristics with lower-income settings, did not diverge substantially from the findings of the overall analysis. Nonetheless, the results of this analysis might differ in developing countries, where there are differences in the pressure of pneumococcal carriage, serotype distributions, prevalence of risk factors, and co-morbidities. To assess the impact of pneumococcal conjugate vaccines in such populations, longitudinal surveillance of serotype-specific disease will be important. Fourth, only five sites had data out to 6 and 7 years post-introduction, which might have limited the representativeness of the findings for those years, although these five sites showed similar results to all sites in years 1–5 post-introduction (Table S8). Fifth, we could not control for inherent differences in clinical practice across sites, such as the criteria for hospitalization and performing lumbar punctures and blood cultures, which might, in part, explain heterogeneity of findings across sites. The focus of our analysis was to describe post-PCV IPD epidemiology across many sites, rather than identify site-specific variables that might predict serotype replacement. Finally, these conclusions apply only to IPD and may not be fully representative of serotype replacement in non-bacteremic pneumococcal pneumonia, the most important cause of pneumococcal morbidity and mortality worldwide [8],[57]. Based on our experience in reviewing many datasets for this evaluation, we have several recommendations for the collection and interpretation of IPD surveillance data (Table 7). In settings where these recommendations cannot be implemented, introduction of PCV should still occur as quickly as possible. However, attempts to identify and characterize serotype replacement using surveillance systems that do not meet these criteria could lead to erroneous conclusions. With so many countries having introduced or about to introduce PCV, and with the need for multiple years of stable and complete pre- and post-IPD rate data, it may be too late to establish many new surveillance sites to monitor serotype replacement. Many countries have existing systems, however, which can be assessed and enhanced to meet the rigorous, high-quality IPD surveillance requirements to monitor the impact of PCVs. Optimizing surveillance data that allows for valid interpretations of the vaccine effect on disease is essential for sound policy decisions [58]. 10.1371/journal.pmed.1001517.t007 Table 7 Recommendations for maximizing the interpretability of surveillance data on invasive pneumococcal disease rates in the context of serotype replacement. Topic Recommendations Purpose Type of surveillance • Active or passive case detection acceptable • Minimizes serotype-specific IPD trends introduced by changes in surveillance methodology • Regularly collect data that can assess system for sensitivity and consistency • Allows for adjustment of disease rates for system changes in sensitivity Numerators • Do not attempt to analyze serotype replacement in settings where small changes in numerators substantially alter estimates of rates • Prevent erroneous interpretation of replacement based on unstable rates due to small sample size • Collect information on hospitalization status and syndrome • Assists in interpretation of changes in healthcare seeking or clinical care practices Denominators • Rates should be calculable • IPD rates more reliable than case counts due to temporal changes in catchment population and healthcare-utilization • Obtain population denominators from the most reliable source available • Inaccurate denominators can lead to IPD trends independent of PCV Duration • ≥2 years of data pre-PCV • Prevent erroneous interpretation of replacement based from a single atypical or inaccurate baseline year or insufficient time after PCV introduction • ≥3 years of data post-PCV Serotyping • Serotype isolates from representative sample of ≥50% of cases • Reduce bias associated with serotyping a subset of systematically selected cases (e.g., most severe) • Apply serotype distribution of cases with known serotypes to that of cases with unknown serotype for each year and age group • Avoid differential underestimation of serotype-specific rates by year of surveillance • Distinguish between serotypes 6A and 6C • Reduce misclassification of serotypes that have different post-PCV epidemiology Case definition • Hospitalized cases with pneumococcus isolated from normally sterile sites (e.g., blood, CSF) • Maximize comparability of rates between sites, countries, and regions with different clinical practices Minimum variables to collect • Age • Serotype distribution varies substantially across age, clinical presentation, and comorbidities, so want to stratify or adjust for these when possible • Clinical syndrome • Comorbidities, especially HIV Vaccine coverage • Collect vaccine coverage over time in the surveillance population • Prevent erroneous identification of serotype replacement when PCV coverage is low • When coverage is <70%, interpret increases in non-PCV serotypes with caution Supporting evidence • Evaluate other data sources (e.g., nasopharyngeal colonization studies, observational studies of vaccine effectiveness, evaluation of trends in pneumonia hospitalizations) • Other sources of data can provide corroborating or contradictory evidence of serotype replacement. Collaboration • Collaborate with investigators experienced in the development and interpretation of IPD surveillance systems • Avoid potential biases in case ascertainment • Consider alternative and potentially important modifications to the analysis or interpretation CSF, cerebrospinal fluid. Supporting Information Figure S1 Vaccine serotype meningitis summary rate ratios from random effects meta-analysis for children aged <5 years. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; DEN, Denmark; E&W, England and Wales; FRA, France; GRC, Greece; IRL, Ireland; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. (PDF) Click here for additional data file. Figure S2 Vaccine serotype meningitis summary rate ratios from random effects meta-analysis for adults aged 18–49 years. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; DEN, Denmark; E&W, England and Wales; FRA, France; GRC, Greece; IRL, Ireland; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. (PDF) Click here for additional data file. Figure S3 Vaccine serotype meningitis summary rate ratios from random effects meta-analysis for adults aged 50–64 years. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; DEN, Denmark; E&W, England and Wales; FRA, France; GRC, Greece; IRL, Ireland; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. (PDF) Click here for additional data file. Figure S4 Vaccine serotype meningitis summary rate ratios from random effects meta-analysis for adults aged ≥65 years. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; DEN, Denmark; E&W, England and Wales; FRA, France; GRC, Greece; IRL, Ireland; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. (PDF) Click here for additional data file. Figure S5 Non-vaccine serotype summary rate ratios from random effects meta-analysis for children aged <5 years. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; DEN, Denmark; E&W, England and Wales; FRA, France; GRC, Greece; IRL, Ireland; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. (PDF) Click here for additional data file. Figure S6 Non-vaccine serotype meningitis summary rate ratios from random effects meta-analysis for adults aged 18–49 years. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; DEN, Denmark; E&W, England and Wales; FRA, France; GRC, Greece; IRL, Ireland; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. (PDF) Click here for additional data file. Figure S7 Non-vaccine serotype meningitis summary rate ratios from random effects meta-analysis for adults aged 50–64 years. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; DEN, Denmark; E&W, England and Wales; FRA, France; GRC, Greece; IRL, Ireland; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. (PDF) Click here for additional data file. Figure S8 Non-vaccine serotype meningitis summary rate ratios from random effects meta-analysis for adults aged ≥65 years. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; DEN, Denmark; E&W, England and Wales; FRA, France; GRC, Greece; IRL, Ireland; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. (PDF) Click here for additional data file. Figure S9 All serotype meningitis summary rate ratios from random effects meta-analysis for children aged <5 years. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; DEN, Denmark; E&W, England and Wales; FRA, France; GRC, Greece; IRL, Ireland; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. (PDF) Click here for additional data file. Figure S10 All serotype meningitis summary rate ratios from random effects meta-analysis for adults aged 18–49 years. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; DEN, Denmark; E&W, England and Wales; FRA, France; GRC, Greece; IRL, Ireland; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. (PDF) Click here for additional data file. Figure S11 All serotype meningitis summary rate ratios from random effects meta-analysis for adults aged 50–64 years. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; DEN, Denmark; E&W, England and Wales; FRA, France; GRC, Greece; IRL, Ireland; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. (PDF) Click here for additional data file. Figure S12 All serotype meningitis summary rate ratios from random effects meta-analysis for adults aged ≥65 years. Site abbreviations: ABCs, USA Active Bacterial Core Surveillance; AIP, USA Alaska; AUSI, Australian Indigenous Northern Territory; AUSN, Australian Non-Indigenous; CAL, Canada Calgary; CHE, Switzerland; DEN, Denmark; E&W, England and Wales; FRA, France; GRC, Greece; IRL, Ireland; ISR, Israel; NAV, USA Navajo; NCK, USA Kaiser Permanente Northern California; NLD, The Netherlands; NOR, Norway; NZL, New Zealand; SCT, Scotland; URY, Uruguay; UTA, USA Utah. (PDF) Click here for additional data file. Table S1 PCV7 immunization coverage estimates for sites. (DOCX) Click here for additional data file. Table S2 Invasive pneumococcal disease summary rate ratios from random effects meta-analysis, comparing observed over expected rates, by age, serotype group, and post-PCV7 introduction year for sites with 7 years of data post-PCV7 introduction. Analysis conducted using the 0.5 continuity correction. (DOCX) Click here for additional data file. Table S3 Invasive pneumococcal disease summary rate ratios from random effects meta-analysis, comparing observed over expected rates, by age, serotype group, and post-PCV7 introduction year for all sites. Analysis conducted using the 0.1 continuity correction. (DOCX) Click here for additional data file. Table S4 Invasive pneumococcal disease summary rate ratios from random effects meta-analysis, comparing observed over expected rates, by age, serotype group, and year post-PCV7 introduction for all sites. Analysis conducted using the 0.5 continuity correction and excluding serotypes 1 and 5. (DOCX) Click here for additional data file. Table S5 Invasive pneumococcal disease summary rate ratios from random effects meta-analysis, comparing observed over expected rates, by age, serotype group, and year post-PCV7 introduction for all sites. Analysis conducted using the 0.5 continuity correction and with expected rates for all strata calculated using the average pre-PCV7 IPD rate. (DOCX) Click here for additional data file. Table S6 Meningitis summary rate ratios from random effects meta-analysis, comparing observed over expected rates, by age, serotype group, and post-PCV7 introduction year for sites with 7 years of data post-PCV7 introduction. Analysis conducted using the 0.5 continuity correction. (DOCX) Click here for additional data file. Table S7 Meningitis summary rate ratios from random-effects meta-analysis, excluding strata with zero cases in the pre-PCV introduction period. (DOCX) Click here for additional data file. Table S8 Invasive pneumococcal disease summary rate ratios from random effects meta-analysis, excluding sites with 7 y of post-PCV7 data. (DOCX) Click here for additional data file. Text S1 Serotype-specific data requested from co-investigators. (DOCX) Click here for additional data file. Text S2 MOOSE checklist. (DOCX) Click here for additional data file.
- Record: found
- Abstract: found
- Article: not found
Interleukin-17A Mediates Acquired Immunity to Pneumococcal Colonization
Ying-Jie Lu, Jane E. Gross, Debby Bogaert … (2008)
Introduction Streptococcus pneumoniae (pneumococcus) is an “extracellular” pathogen, generally considered to be killed by phagocytic ingestion, which is facilitated by opsonic antibodies. The success of anti-pneumococcal serum therapy using passive transfer of serotype-specific antibodies [1] and of vaccinations based on purified or conjugated capsular antigens [2],[3] clearly shows that anticapsular antibodies protect humans against pneumococcal colonization and disease. There is good epidemiologic evidence for the importance of such immunity in certain common serotypes [4],[5]. However, we and others have found that factors other than anticapsular antibodies may play a role in the natural development of protection against pneumococcal colonization and disease. First, the reduction in pneumococcal disease incidence after the first year of life occurs simultaneously for both rare and common serotypes, suggesting the acquisition of one rather than many individual immune responses [6]. Similarly, the duration of carriage of many serotypes declines steeply between the first and second birthdays for many serotypes [7]. Since experience with conjugate vaccines has suggested that anticapsular antibodies reduce incidence of carriage but leave duration unaffected [8], this observation also suggests a mechanism of acquired immunity other than anticapsular antibodies. Moreover, the declines in carriage duration and invasive disease incidence precede by several years the detection of naturally-acquired anticapsular antibody in most children [6],[7]. Experimental [9],[10] and observational [4],[11] studies in adults have found little or no evidence that higher anticapsular antibody concentrations are associated with greater protection from colonization. Pneumococci also express non-capsular antigens common among serotypes, and certain of these have been found to elicit antibodies with protective potential in animal models. The role of such antibodies in human immunity has been evaluated [12],[13],[14],[15],[16],[17]. Surprisingly however, recent studies have shown that immunity in mice to pneumococcal colonization acquired from prior exposure to live bacteria [18] or a killed, whole-cell vaccine [WCV, consisting of killed pneumococcal whole cell antigen (WCA) with cholera toxin (CT) as an adjuvant] [19] is independent of antibodies of any specificity, and clearance of longstanding carriage in previously unexposed animals can likewise be antibody-independent [20]. Immunity had been shown to be dependent on the presence of CD4+ T cells at the time of challenge [18],[19], but the co-participation of specific immune factors other than antibody was not ruled out. Here we show that intranasal immunization with the WCV confers protection against experimental pneumococcal colonization via the chemoattractant and neutrophil activating cytokine IL-17A, in a neutrophil-dependent fashion. Methods were devised to assay expression of IL-17A in vitro using peripheral blood samples. IL-17A expression by peripheral blood of WCV-immunized mice is highly correlated with subsequent protection against colonization, and expression by human cells, including those from adults and children, can be shown as well. Finally, we developed a surface phagocytosis assay with which we show that IL-17A enhances pneumococcal killing by human polymorphonuclear cells in the absence as well as presence of opsonins. The data indicate the possibility that IL-17A responses play a role in naturally-acquired immunity to pneumococcus in humans and that assay of this cytokine in vitro may assist in the evaluation of certain candidate pneumococcal vaccines that target mucosal colonization. Results Prior exposure of mice to killed or live pneumococci reduces the duration of experimental pneumococcal carriage The duration of carriage was followed after intranasal challenge with serotype 6B pneumococci 4 weeks post-exposure to WCV. Both WCV-vaccinated and control mice immunized with CT alone were colonized one day after challenge. In mice immunized with WCV however, carriage became significantly reduced after 4 days compared to controls given cholera toxin (CT) adjuvant alone (median density of colonization on day 4 in WCV- vs. CT-immunized mice 251 vs. 3720 cfu/nasal wash, P = 0.029 by Mann-Whitney U test) and was undetectable by day 6 (0/4 WCV-immunized mice had detectable colonies on day 6 vs. 4/4 mice that received CT, P = 0.029 by Fisher's Exact test, Figure 1A). A similar differential was observed in mice that had been repeatedly exposed to live pneumococci vs. saline controls: the density of colonization became significantly different by day 4 after inoculation (Figure 1A). By day 6, similar to what we observed in WCV-immunized mice, 0/4 mice exposed to live pneumococci had detectable colonies compared to 4/4 saline controls (P = 0.029 by Fisher's exact test). When data from all time points were compared, mice immunized with WCV or exposed to live pneumococci had a significantly shorter time to clearance compared to their respective CT or saline controls (P = 0.0001 for comparison of WCV vs. CT and P = 0.004 for comparison of live exposure vs. saline). Thus the protection by prior pneumococcal exposure involves not immediate blockage of colonization but rather an accelerated clearance over days. Subsequent studies compared WCV-vaccinated with control animals 7 days after the intranasal challenge. 10.1371/journal.ppat.1000159.g001 Figure 1 Duration of carriage and effect of adoptive transfer following immunization with killed or live pneumococci. A. Effect of intranasal immunization with WCV or live exposure upon density of pneumococcal colonization in C57BL/6 mice. Density of colonization in mice immunized with WCV vs. CT alone or repeatedly exposed to live pneumococcal strain 0603 vs. saline alone at various time points (n = 4 per time point) following challenge. By day 4, both the incidence and density of carriage were significantly lower in mice immunized with WCV or exposed to live pneumococcus compared to mice immunized with CT or saline, respectively. * P 0.5 vs. CT controls for % colonized mice or density of colonization, Figure 2A). It is noteworthy that IL-17AR-knockout mice in the CT control group had, on average, a ten-fold greater density of colonization than the corresponding IFN-γ or IL-4 deficient mice, suggesting that IL-17A may also be involved in resistance to colonization in naïve mice. 10.1371/journal.ppat.1000159.g002 Figure 2 Role of T-helper-subset-associated cytokines in protection from nasopharyngeal colonization. A. Mice defective in IFN-γ, IL-4 or IL-17A receptor were immunized as described, then challenged with pneumococcal strain 0603. Mice with IFN-γ or IL-4 deficiency were significantly protected by WCV (P 0.5 vs. CT). Dashed line represents the lower limit of detection of bacterial colonization. B. Expression of IL-17A from splenocytes of WCV-immunized mice. Cultured splenocytes from mice immunized with WCV (black columns) or CT alone (white columns) were stimulated for 72 hours with medium alone, Concanavalin A (5 µg/ml), WCA (10 µg dry weight) after which IL-17A production was measured by ELISA. Significantly more IL-17A was expressed following WCA stimulation of WCV-immunized vs. CT-immunized mice, although response to concanavalin A was similar. C. Effect of CD4+ T cell depletion upon IL-17A expression from splenocytes. Splenocytes (without or with CD4+ T cell depletion) from mice immunized with WCV were stimulated for 72 hours with medium alone or WCA after which IL-17A was measured by ELISA. IL-17A expression in splenocytes following WCA stimulation was significantly higher in the presence of CD4+ T cells compared to stimulation with medium alone or when CD4+ T cells were depleted. Repletion of CD4+ T cells restored the response. ** P 90% depletion of neutrophils in most mice, although variability was observed. Because of this variability, peripheral neutrophil counts were determined at the time of euthanasia and correlated with the number of recovered pneumococci from that animal. Measurement of IL-17A secretion by splenocytes Cellular suspensions of splenocytes were obtained by passing spleens from immunized or control mice through a 70-µm cell strainer (BD Biosciences, Bedford, MA). After washing and removal of red blood cells by hemolysis, cells were plated into 24-well tissue culture plates at a concentration of 5×106 cells/well in 500 µl of DMEM/F12 with L-glutamine supplemented with 10% fetal calf-serum, 50 µM 2-mercaptoethanol (Sigma), and 10 µg/ml ciprofloxacin. Following 72-hour stimulation with concanavalin A (5 µg/ml, Sigma) or WCA (equivalent to 106 cfu/ml), supernatants were collected following centrifugation and stored at −80°C until analyzed by ELISA for IL-17A concentration (R&D Systems, Minneapolis, MN). Supernatants were analyzed in duplicate and read against a standard, following directions provided by the manufacturer. For CD4+ T cell depletion, splenocytes were harvested as described above. CD4+ T cells were depleted from half of each spleen by magnetic bead selection (Miltenyi Biotec, Auburn, CA) following instructions by the manufacturer. Flow cytometry confirmed removal of >95% CD4+ T cells (data not shown). Cells were seeded at the same concentration as described above (5×106 cells/well). In some cases, we repleted CD4+ T cells from depleted splenocytes, by adding 106 CD4+ T cells in the relevant wells. Intracellular staining for IL-17A Splenocytes were harvested, seeded, and stimulated with medium or WCA (10 µg/ml) as described above. Twenty-four later, monensin (BD GolgiStop, BD Biosciences) was added as per the manufacturer's instructions and cells were harvested 12 hours later. Cells were washed, stained with anti-CD4+ antibody (antiCD4+-PE, BD Biosciences) in the presence of Fc block, permeabilized with Perm/Wash buffer (BD Biosciences), and incubated with antimouse IL17A Alexa Fluor-647 (eBioscience) for 30 minutes. Intracellular cytokine staining for IL-17A was compared in CD4- or CD4+ cells in medium alone or following stimulation with WCA. Samples were analyzed on a Cytomation MoFlo (Beckman Coulter, Fullerton, CA), and results analyzed with Summit Version 4.3 (Dako, Fort Collins, CO). Measurement of IL-17A secretion by NALT NALT was harvested from immunized and control mice as described [51]. Mice were euthanized humanely, bled via intracardiac puncture to avoid blood contamination, and placed on a dissection board. The mouth was opened wide to expose the palate, which was cut carefully, so that the strips of NALT could be easily peeled off. These strips of cells were collected in medium (DMEM/F12 with L-glutamine supplemented with 10% fetal calf-serum, 50 µM 2-mercaptoethanol (Sigma), and 10 µg/ml ciprofloxacin) on ice. Cells were passed through a 70 µm strainer as described above and plated at 3×105 cells/well in a 96-well tissue culture plate in a total volume of 100 µl. Cells were stimulated with medium with or without added WCA (10 µg/ml) for a total of 3 days, after which supernatants were collected and assayed for IL-17A concentration by ELISA as above. Measurement of IL-17A secretion by whole blood For whole blood assays, blood of mice or humans at a final concentration of 10% was incubated in DMEM/F12 with L-glutamine supplemented with 10% fetal calf-serum, 50 µM 2-mercaptoethanol (Sigma), and 10 µg/ml ciprofloxacin in the absence or presence of killed pneumococcal antigen (corresponding to 107 cfu/ml for mice and 106 cfu/ml for human samples). Supernatants were collected after 6 days and the concentration of IL-17A measured as above for mice and, for human samples, by IL-17A ELISA (eBioscience Inc, San Diego, CA). Human subjects and samples For peripheral blood, samples were obtained at Children's Hospital Boston (for healthy adult volunteers) or from Cambridge Health Alliance, Cambridge, MA (for parturient women or umbilical cord) after written informed consent had been obtained. The studies were approved by the Children's Hospital Boston and Cambridge Health Alliance research ethics committees. For tonsillar specimens, tonsils were obtained from children who were 2 to 12 years old (median age, 5 years), were undergoing tonsillectomy for hypertrophy, and were otherwise healthy at Bristol Royal Hospital for Children, Bristol, United Kingdom. Patients who were immunized against pneumococcus previously, who had received antibiotics within 2 weeks of the operation or steroids, or who had an immunodeficiency or serious infection were excluded. The study was approved by the South Bristol local research ethics committee and written informed consent was obtained in all cases. Agar surface phagocytic killing without opsonins This assay approximates the “surface phagocytosis” described by Smith and Wood [52]. Neutrophils were isolated from heparinized blood by density gradient centrifugation (Histopaque, Sigma) following manufacturer's instructions. Neutrophils were washed extensively then resuspended in Hanks' Balanced Solution (+ Ca2+ and Mg2+) with 0.2% bovine serum albumin (Sigma), then co-incubated for 30 minutes at 37°C with recombinant human IL-17A (R&D Biosystems) at different concentrations. In some experiments, the cells were harvested by centrifugation and the supernatant collected, to examine whether the potentiating effect of IL-17A could be detected with the supernatant alone. Between 8–10 replicates of 10 µl of a bacterial suspension containing on average 100 cfu of strain 0603 were plated onto blood agar and the fluid allowed to adsorb into the agar for 15 min; 15 µl of the neutrophil suspension was overlaid and allowed to adsorb; the plates were incubated at 37°C with 5% C02 overnight after which colonies were counted. Phagocytic killing in suspension with suboptimal opsonization Neutrophils were isolated from whole blood as described above, washed twice with cold Hanks Balanced Salt Solution (HBSS-) (Mediatech, Herndon, VA), and resuspended to a final concentration of 6×106 cells/ml in cold HBSS containing calcium and magnesium (HBSS+) (Cellgro Mediatech, Herndon, VA) then held on ice until used. Cell counts were determined on a standard hemocytometer by counting viable cells (as determined by an absence of blue staining in the presence of Trypan Blue (Cellgro Mediatech, Herndon, VA)). S. pneumoniae (strain 0603 [49]) was diluted in HBSS+ to a final concentration of 5×104 bacteria/ml and incubated with antibodies to pneumococcal polysaccharide (Bacterial Polysaccharide Immune Globulin, BPIG-8, a kind gift of Dr. George Siber, consisting of concentrated IgG obtained from serum of adult volunteers immunized with pneumococcal, Haemophilus and meningococcal polysaccharide vaccines [27]) diluted in HBSS+. The reaction was incubated at 37°C for 15 minutes rotating at 200 RPM to promote bacterial opsonization. After bacterial opsonization, the opsonophagocytic killing reaction was initiated with the addition of baby rabbit complement (Pelfreez Biologicals, Rogers, AR) and neutrophils (ratio of 1∶200 bacteria∶cells) with or without recombinant human IL-17A (R&D Systems, Minneapolis, MN) at 0.01, 0.1 or 1 µg/ml. A 1∶1600 dilution of BPIG was chosen to give sub-optimal bacterial killing (<50% killing when compared to the same conditions without BPIG) in the presence of complement and neutrophils. The opsonophagocytic killing assay was performed in a 96-well round-bottom plate (Thermo Fisher Scientific, Waltham, MA) at 37°C for 90 minutes rotating at 200 RPM. After incubation, the opsonophagocytic reaction was diluted two fold and aliquots of each reaction were plated on blood agar plates then incubated at 37°C with 5% CO2 overnight. Isolation and culture of tonsillar mononuclear cells Mononuclear cells were isolated by using methods described previously [53],[54]. Tonsillar MNC were washed in sterile phosphate-buffered saline (PBS) and resuspended at a concentration of 4×106 cells/ml in RPMI medium containing HEPES, 2 mM glutamine, 100 U/ml penicillin, 100 µg/ml streptomycin, and 10% fetal bovine serum (Sigma, Dorset, United Kingdom). Cells were cultured in 96-well culture plates (Corning Inc, Corning, NY), and cell culture supernatants were collected at predetermined times and stored at −70°C until assays for human IL-17A were performed by sandwich ELISA (R&D Biosystems). Statistical analysis Incidence of carriage was compared by Fisher's exact test and colonization density in challenged mice was compared by the Mann-Whitney U test. Statistical significance of the difference between time-to-clearance curves was assessed as follows. For each group i (i = WCV, CT, live, or naïve), the proportion of mice cleared at each time point t, pi (t), was calculated. Using the max-min formula for isotonic regression [55], these proportions were smoothed to assure they were nondecreasing in t, yielding smoothed proportions qi (t). Then, a test statistic was calculated to quantify the distance between the smoothed curves for two groups (e.g., WCV vs. CT): . The significance level of this test statistic was estimated by permuting the group identifiers of the cleared mice at each time point, fixing the total number of mice in each group and the total number cleared at each time point. 100,000 replicates of the permuted data were obtained, and T was calculated for each. The p value was calculated as the fraction of these 100,000 permutations having a test statistic strictly less than that calculated for the data. The correlation between neutrophil count or IL-17A concentration and colonization density was determined by Spearman rank correlation. The effect of increasing IL-17A concentrations on enhancing killing of pneumococcus was assessed by Wilcoxon matched pairs test. For all comparisons, P<0.05 was considered to represent a significant difference.
Author and article information
Journal
Journal ID (nlm-ta): mBio
Journal ID (iso-abbrev): MBio
Journal ID (hwp): mbio
Journal ID (pmc): mbio
Journal ID (publisher-id): mBio
Title:
mBio
Publisher:
American Society of Microbiology
(1752 N St., N.W., Washington, DC
)
ISSN
(Electronic):
2150-7511
Publication date
(Electronic):
15
September
2015
Publication date Collection: Sep-Oct 2015
Volume: 6
Issue: 5
Electronic Location Identifier: e00902-15
Affiliations
[a
]Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts,
USA
[b
]Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public
Health, Boston, Massachusetts, USA
Author notes
Address correspondence to Krzysztof Trzciński,
krzytrzy@
123456gmail.com
.
Article
Publisher ID: mBio00902-15DOI: 10.1128/mBio.00902-15
PMC ID: 4600102
PubMed ID: 26374118
SO-VID: 6d69c657-7884-4404-88ea-3e7285dbdfd5
Copyright © Copyright © 2015 Trzciński et al.
License:
This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-ShareAlike 3.0 Unported license, which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original author and source are credited.
History
Date
received
: 30
May
2015
Date
accepted
: 17
August
2015
Page count
supplementary-material: 5,
Figures: 7,
Tables: 0,
Equations: 0,
References: 49,
Pages: 9,
Words: 7913
Categories
Subject:
Research Article
Custom metadata
cover-date September/October 2015
ScienceOpen disciplines: Life sciences
Data availability:
ScienceOpen disciplines: Life sciences