Pneumococcal genetic variability in age-dependent bacterial carriage

Streptococcus pneumoniae is a common commensal of the human upper respiratory tract and nasopharynx, but can also cause pneumonia and invasive diseases such as sepsis or meningitis (Bogaert et al., 2004). Invasive pneumococcal disease (IPD) has a high mortality, and the overall mortality rate from IPD is higher in extreme age ranges, such as infants and the elderly (Wahl et al., 2018; O Brien et al., 2009). In the Netherlands, pneumococcal carriage rates are higher in children than in adults, with a prevalence of up to 80% at 2 years of age (Wyllie et al., 2016).

Pneumococcal carriage manifests as multiple carriage episodes of different serotypes. From birth, mucosal immunity builds up against different serotypes due to exposure, while immunity from maternal antibodies wanes. Host age is known to affect carriage prevalence and carriage duration of different serotypes (Stearns et al., 2015; Turner et al., 2012b), which is suggested to be driven by differences in immunity.(Wyllie et al., 2020) Studies in mice and humans showed evidence for age-dependent host–pathogen interactions involving interleukin (IL)-1 response in reaction to the pore-forming pneumolysin (ply) toxin (Kuipers et al., 2018). IgA secretion is important in clearing S. pneumoniae from host upper respiratory tract mucosa, and this secretion is more effective in previously exposed individuals, the adults (Binsker et al., 2020). Bacterial genetics has been shown to explain over 60% of the variability in carriage duration, and specifically that the presence of a bacteriophage inserted in a mediator of genomic competence was associated with a decreased carriage duration (Lees et al., 2017b).

Pneumococci are highly genetically variable, displaying over 100 diverse capsular serotypes (Ganaie et al., 2020), which are a major antigen and the strongest predictor of carriage prevalence (Croucher et al., 2018). Pneumococcal conjugate vaccines (PCVs), targeting up to 13 capsule serotypes with high burden of invasive disease, decrease the rate of nasopharyngeal carriage and invasive disease (Whitney et al., 2003; Poehling et al., 2006). Besides a direct effect of vaccination with a PCV on the disease burden in the target population, that is, young children, it also reduces the disease burden caused by pneumococci with vaccine serotypes in the population not eligible for vaccination through indirect protection from colonization – reducing carriage rates in children reduces overall transmission of the most invasive serotypes (Croucher et al., 2018; Desai et al., 2015; von Gottberg et al., 2014). However, the introduction of PCV has resulted in the replacement of serotypes not covered by the vaccine (Croucher et al., 2013; Corander et al., 2017), which in some countries reaches levels of invasive disease return towards pre-vaccine levels (Ladhani et al., 2018; Koelman et al., 2020).

As not all serotypes can be included in a conjugate vaccine, three perspectives leading to improved pneumococcal vaccination have been proposed: whole-cell vaccines (Malley et al., 2001; Campo et al., 2018), protein vaccines (Moffitt and Malley, 2016), or changing components in the conjugate vaccine in response to the circulating population.(Colijn et al., 2020) Whole-cell vaccination trials are ongoing, but efficacy remains unproven in human populations (Morais et al., 2019). Protein vaccines contain antigens that elicit a strong mucosal immune response, with their targets chosen to be common or conserved in the target population, and ideally reducing onward transmission (Pichichero, 2017). In their current form, protein vaccines are not thought to be effective on their own, but if administered with serotype conjugates (possibly by replacing the carrier protein) they may help to reduce serotype replacement. Detailed modeling of the dynamics of pneumococcal population genetics has shown that targeting these vaccines towards serotypes prevalent in specific populations would likely be a superior strategy. This work further shows that providing age-specific vaccine design using complementary adult-administered vaccines (CAVs) is predicted to have the greatest effect on total IPD burden (Colijn et al., 2020). These authors also modeled including pilus in the vaccine, which is more frequently present in a few key invasive serotypes and in infant carriage, but found this to be inferior to conjugate vaccines.

If proposing a future pneumococcal vaccination strategy based on host age, we should aim to better understand the differences between infant and adult carriage. Differences between other host niches have been found, some with a potential effect on onward transmission (Lees et al., 2017c; Lees et al., 2017a; Zafar et al., 2019). In particular, a previous study has suggested that the presence of pilus, which is found in a minority of pneumococcal isolates, has a selective advantage in infant carriage (Binsker et al., 2020).

We therefore wished to test three hypotheses. Firstly, carriage rates of individual strains or serotypes vary substantially between infants and adults in the same contact networks. Secondly, this variation is attributable at least in part due to pathogen genetic adaptation to either the infant or adult nasopharynx, which are immunologically different niches. Finally, this adaptation is due both to serotype and genetic background, and that some of the genetic effects are resolvable to individual genes, alleles, or regulatory variants that arise on multiple different genetic backgrounds due to a selective advantage. If we find clear associations, this would support proposals for age-specific vaccine design and may also suggest specific protein components that more broadly suppress carriage in the target age group than multivalent PCV alone.

For the first hypothesis, to test varying rates of carriage we can swab children and adults from the same population (and likely exposed to the same infection pressures) and quantify serotype and strain prevalence. For the second hypothesis, if we whole-genome sequence bacteria from these swabs, we can model the effects of all genomic variants on host age in a heritability analysis. This accounts for genetic variation that arose long ago and is fixed in particular lineage, but cannot map it to a particular region of the genome (Earle et al., 2016). To find individual genetic effects, which have necessarily occurred more recently and frequently, one study design would be to identify adult–infant transmission pairs and find variation that consistently occurs in localized regions of the genome, which would be particularly informative if also associated with a particular direction of transmission (Lees et al., 2017c). This removes genetic background as a confounder, giving a clean signal (Young et al., 2012). However, the identification of such pairs is very challenging for S. pneumoniae, and even when possible the small numbers limit power. We propose taking the more ‘opportunistic’ approach taken in genome-wide association studies (GWAS), where as many cases and controls (in this case, infant and adult samples) as possible are accumulated to boost statistical power, and genetic background is then controlled for in the association analysis. Where variation associated with age has arisen independently on multiple genetic backgrounds, GWAS has the ability to find these signals among the many lineage associations tested in the second step. In all cases, analysis can be improved by studying more than one population to determine whether findings are consistent among different host and pathogen populations.

To carry out these analyses, we used pneumococci isolated from nasopharyngeal swabs of 4320 infants and adults from the Netherlands (2009–2013) and Myanmar (October 2007–November 2008). Each cohort contains infant and adult samples from carriage, and there are significant differences between the host populations. This allows us to follow the above approach in each population and compare our findings between the populations. We present our findings with respect to each above hypothesis in turn and interpret them through the lens of using population genomics to determine optimal vaccination strategies.

We first analyzed the observed distribution of serotypes and strains in each of the two cohorts to assess overall trends of differences in carriage between adults and children exposed to similar forces of infection and look at the pathogen population’s genetic heterogeneity between the two cohorts. Although our cohorts were broadly matched in the primary phenotype, age, large differences between the pathogen population are expected due to different geographies, social backgrounds, and only children in one cohort being vaccinated. Nucleotide variation across the entire genome can be used to cluster genetically related isolates into consistently named strains, called global pneumococcal sequence clusters (GPSC) (Gladstone et al., 2019). We used this over older gene-by-gene approaches such as MLST as it has been shown to represent biologically discrete clusters in the population, uses the full resolution available from whole-genome sequencing, and has strong community support (Gladstone et al., 2020). For each sample, we enumerate the serotype (which is targeted by the vaccine) and GPSC membership, and count the number of each serotype observed in adult and child carriage.

Serotypes and strains are variably carried between age groups and between cohorts

The Dutch cohort was made up of 1329 S. pneumoniae isolates comprising 41 unique serotypes (Supplementary file 1). Of these isolates, 689 (52%) comprised seven serotypes: 19A (225; 17%), 11A (111; 8%), 6C (97; 7%), 23B (84; 6%), 10A (67; 5%), 16F (54; 4%), and 21 (51; 4%). In this cohort of which the children were vaccinated, a minority of isolates (101; 8%) belonged to one of the vaccine serotypes (Supplementary file 2). A total of 3085 pneumococcal isolates of the Maela (unvaccinated) cohort comprised 64 unique serotype groups (Supplementary file 3). Of these isolates, 1631 (53%) comprised five serotypes: non-typable (511; 17%), 19F (402, 13%), 23F (307, 10%), 6B (236; 8%), and 14 (175; 6%). In the Dutch cohort, there were 59 unique sequence clusters of which the four largest sequence clusters were GPSC 4 (171; 13%), GPSC 3 (156; 12%), GPSC 7 (131; 10%), and GPSC 11 (119; 9%) (Supplementary file 4). There were 127 unique sequence clusters found in the Maela cohort (Supplementary file 5). The four largest sequence clusters were GPSC 1 (352; 13%), GPSC 28 (190; 7%), GPSC 20 (168; 6%), and GPSC 42 (123; 5%). We also looked at a subset of the Maela cohort, which included only the earliest obtained samples from unique individuals (mothers and children). This subset consisted of 762 isolates, including 380 from mother–child pairs. Isolates in this subset had the same serotypes among the most common serotypes as in the full dataset (Supplementary file 6 and Supplementary file 7). Restricting this subset to mother–child paired samples only included the same serotypes and sequence clusters among the most prevalent (Supplementary file 6 and Supplementary file 7).

Some serotypes exhibited a large difference in colonization frequency between the two age groups (Figure 1). In the Dutch cohort, serotype 6C (chi-squared test, p=0.02, not corrected for multiple testing) and serotype 15B (p=0.02) were overrepresented in children relative to adults, serotype 3 was overrepresented in adults relative to children (p=2.5 × 10^–5), while in the Maela cohort, serotype groups overrepresented in children were serotype 23F (chi-squared test, p=0.04) and serotype 6B (chi-squared test, p=0.04); while non-typeable serogroup was overrepresented in adults (chi-squared test, p=3.0 × 10^–4) (Table 1). None of the 20 largest groups of sequence clusters overlapped between the cohorts. In the Dutch cohort, only GPSC 11 was significantly associated with carriage in children (chi-squared test, p=0.03, not corrected for multiple testing), while GPSC 12 (chi-squared test, p=1.2 × 10^–4) and GPSC 38 (chi-squared test, p=2.1 × 10^–4) were overrepresented in adults. In the Maela cohort, only sequence cluster GPSC 128 was overrepresented in children compared to adults (chi-squared test, p=0.04), while GPSC 20 (chi-squared test, p=7.0 × 10^–3) and GPSC 74 (chi-squared test, p=0.04) were overrepresented in adults (Table 2).

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Table 1

Serotype	Dutch cohort		Maela cohort
	χ2 p-value	Age group	χ2 p-value	Age group
Non-typeable	0.188	Adults	3.0 × 10–4	Adults
19A	0.089	Children	0.690	Children
11A	0.591	Children	0285	Adults
19F	1	Adults	0.131	Children
6C	0.022	Children	1	Adults
6B	0.099	Children	0.040	Children
35F	0.279	Children	0.100	Children
3	2.5 × 10–5	Adults	0.129	Adults
6A	0.709	Children	1	Children
23A	1	Adults	-	-
15B	0.023	Children	-	-
17F	0.943	Children	-	-
23B	0.727	Children	-	-
10A	0.155	Adults	-	-
15C	1.000	Adults	-	-
35B	0.775	Adults	-	-
22F	1	Adults	-	-
33F	0.132	Adults	-	-
23F	-	-	0.040	Children
14	-	-	0.949	Children
35C	-	-	0.961	Children
34	-	-	0.690	Children
13	-	-	0.756	Adults
10B	-	-	0.756	Adults
4	-	-	0.966	Children
5	-	-	0.710	Adults
33B	-	-	1	Children
28F	-	-	0.652	Children
19B	-	-	0.710	Adults
7F	-	-	0.971	Children
20	-	-	0.971	Children
18C	-	-	1	Adults

1. χ², chi-square; -, not applicable.

Table 2

GPSC	Dutch cohort		Maela cohort
	χ2 p-value	Age group	χ2 p-value	Age group
60	0.568	Adults	0.727	Adults
4	0.298	Children	-	-
3	0.392	Adults	-	-
7	0.858	Children	-	-
11	0.03	Children	-	-
35 and 36	0.617	Adults	-	-
29	0.049	Children	-	-
46	0.563	Children	-	-
75	0.666	Adults	-	-
19	0.978	Children	-	-
12	1.2 × 10–4	Adults	-	-
44	1	Adults	-	-
24	0.094	Children	-	-
49	1	Children	-	-
109	0.817	Adults	-	-
16	0.249	Adults	-	-
38	2.1 × 10–4	Adults	-	-
146	0.489	Children	-	-
99	1	Children	-	-
15	0.22	Adults	-	-
42	-	-	0.134	Children
1	-	-	0.276	Adults
28	-	-	0.110	Children
73	-	-	0.253	Children
10	-	-	0.777	Adults
9	-	-	1	Children
30	-	-	0.993	Children
20	-	-	7.0 × 10–3	Adults
128	-	-	0.042	Children
66	-	-	1	Children
87	-	-	0.450	Adults
63	-	-	1	Adults
45	-	-	0.129	Adults
130	-	-	1	Adults
74	-	-	0.040	Adults
149	-	-	0.686	Adults
8	-	-	0.364	Children
25	-	-	1	Adults
187	-	-	0.371	Adults
154	-	-	1	Adults
118	-	-	0995	Children
110	-	-	0.995	Children
106	-	-	0.073	Adults

1. χ², chi-square; -, not applicable; GPSC, global pneumococcal sequence clusters.

A phylogenetic tree of pooled sequences from both cohorts, with serotype, sequence cluster, age group, and cohort for each sequence, revealed clonal discrimination between cohorts (Figure 2). Combined with the effects shown in Figure 1, this highlighted a key feature of our analysis of these datasets, which was the genetic heterogeneity between the two cohorts. Individually, each dataset clearly has strains and serotypes with strong signals of host age differences, but the overall makeup of each dataset is very different (nine common serotypes are shared, but only a single common GPSC), and where there are shared serotypes these can have different effect directions between the two cohorts.

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Host age is heritable and mostly explained by strain and serotype

To quantify the amount of variability in carriage age explained by variability in the genome, we calculated a heritability estimate (h²) for each cohort. For isolates in the Dutch cohort, we did not find strong evidence that genetic variability in bacteria was related to variance in host age (h² = 0.10, 95% CI 0.00–0.69). In the Maela cohort, we found significant evidence that affinity with host age was heritable (h² = 0.56, 95% CI 0.23–0.87) and thus genetic variation in this cohort explained variation in carriage age to a greater degree. In both cohorts, pan-genomic variation could be used to predict host age to some degree of accuracy (area under the receiver-operating characteristic [ROC] curve 0.82 [Dutch cohort]; 0.91 [Maela cohort]), suggestive of some level of heritability and association of host age with strain (Figure 3). Prediction between cohorts using a simple linear model failed as the genetic variants chosen as predictors were not found in the other cohort – again highlighting the high level of genetic heterogeneity between cohorts.

Figure 3

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To further investigate the association of serotype and sequence cluster to carriage age, we determined the proportion of variation in carriage age explained by serotype and sequence cluster alone. Here, we estimated h²_serotype = 0.07 (95% CI 0.04–0.14) and h²_GPSC = 0.06 (95% CI 0.03–0.13) for the Dutch cohort and h²_serotype = 0.11 (95% CI 0.05–0.21) and h²_GPSC = 0.20 (95% CI 0.12–0.31) for the Maela cohort, confirming a larger contribution of serotype and sequence cluster to carriage age heritability in sequences from the Dutch cohort. We also performed a genome-wide association analysis, but without controlling for population structure. This reveals genetic variants specific to serotype as determinants for carriage age (p-values<5.0 × 10^–8) in both cohorts (Supplementary file 8, Dutch cohort, and Supplementary file 9, Maela cohort). Among the genetic variants with the lowest p-values were variants in capsule locus genes (Cps) in both cohorts. This further supports a role of strain and serotype in association with host age, but does not distinguish between the two.

Genome-wide association analysis does not find genetic variants independent of strain

Following these observations that serotype and strain do not explain the full heritability, specifically in the Maela cohort, we performed a pathogen genome-wide association analysis to investigate whether we can detect genetic variants irrespective of the genetic background that are associated with carriage in children or adults. Though the cohorts have little genetic overlap in terms of genetic background, we would be well-powered to detect genetic variation independent of background (‘locus’ associations) (Earle et al., 2016; Lees et al., 2016). In the Dutch cohort, none of the unitigs, SNPs, COGs, or rare variants surpassed the threshold for multiple testing correction (Figure 4—figure supplement 1). The burden (sum) of rare variants in a gene for tryptophan synthase, trpB, approaches the multiple testing threshold, but was not significant. In the Maela cohort, unitigs in the ugpA gene surpassed the threshold for statistical significance (Figure 4—figure supplement 2), but these did not hold after meta-analysis. After meta-analysis, there were two hits that surpassed the threshold for statistical significance (Figure 4).

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The first is a nucleotide sequence marked by multiple unitigs of which the lowest has a p-value of 1.2 × 10^–9 (Supplementary file 10). This sequence does not map to the S. pneumoniae D39V reference sequence (Altschul et al., 1990) For this reason, it is not visualized on the Manhattan plot, for which unitigs were mapped to the S. pneumoniae D39V reference sequence (Figure 4). Upon inspection of the individual sequences these unitigs are called from, we find them to map in the intergenic region between open-reading frames encoding the accessory Sec-dependent serine-rich glycoprotein adhesin and a MarR-like regulator, respectively. This region contains sequences resembling transposable elements and an open-reading frame encoding a transposase. The unitigs map upstream of the start codon of the accessory Sec-dependent serine-rich glycoprotein adhesin. The sequence is present in 169 out of 1282 (13%) sequences in the Dutch cohort and in 241 out of 3085 (8%) in the Maela cohort. The sequence is present in isolates dispersed over the phylogenetic tree and associated with carriage in children (Figure 2—figure supplement 1). This protein is involved in adhesion to epithelial cells and biofilm formation (Chan et al., 2020; Middleton et al., 2021; Weiser et al., 2018). Given that this sequence lies just upstream of the start codon, it is plausible that variation of this sequence alters the expression of the Sec-dependent adhesin protein, and therefore affects carriage.

The second hit is a burden of rare variants in a gene for tryptophan synthase, trpB, that surpass the threshold for statistical significance at a p-value of 5.0 × 10^–5. The variants are two frameshift variants of very low frequency. These result in a predicted dysfunctional trpB gene in 9 out of 1282 (1%) sequences in the Dutch cohort and in 12 out of 3073 (0.4%) sequences in the Maela cohort. This association of the trpB gene is likely to be an artifact of low allele frequency as we estimate we are only powered to detect variation in at least 5% of isolates.

The age of the host is known to have an important effect on pneumococcal colonization (Croucher et al., 2018). Observational studies have demonstrated variation in serotype prevalence and carriage duration between infants and adults. Mechanistic studies in mice and humans have shown examples of differing immune responses depending both on host factors and pathogen factors. Findings from these studies include the observation that capsular polysaccharides (determinants of serotype) inhibit phagocytic clearance in animal models of upper respiratory tract colonization (Nelson et al., 2007). A pneumolysin-induced IL-1 response determined colonization persistence in an age-dependent manner Kuipers et al., 2018; and pilus-expressing strains were found to preferentially colonize children because of immune exclusion via secretory IgA in non-naïve hosts (Binsker et al., 2020).

Building upon these observations, we sought to investigate and quantify the contribution of pathogen genetic variation to carriage in infant versus adult hosts using a top-down approach to systematically test the effect of pathogen genome variation on niche specificity (with age as the niche). We used whole-genome sequencing and applied statistical genetic methods to two large S. pneumoniae carriage cohorts. We aimed to quantify the differing patterns of serotype and strain prevalence between the two age classes during carriage and search for other genetic factors associated with host age. We show evidence that bacterial genetic variability indeed influences predilection for host age, though the effect size appears to be highly variable between populations.

Strain, or genetic background, explains 60% of the total heritability in the Dutch cohort, but only a minority in the Maela cohort. We found sequences in one region that map closely to the start codon of the accessory Sec-dependent serine-rich glycoprotein adhesin to be associated with carriage age independent of genetic background, in a meta-analysis of the two cohorts.

In previous bacterial GWAS of antimicrobial resistance (such as a single gene that causes antibiotic resistance), large monogenic effects have typically been found to have high heritabilities close to one, and the GWAS identify the causal variant precisely.(Earle et al., 2016; Chewapreecha et al., 2014b; Lees et al., 2020). When applied to virulence and carriage duration phenotypes, heritable effects have also been found, but these only explained some of the variation in the phenotype. These appeared to be caused by many weaker effects, known as a polygenic trait, not all of which could be detected using the relatively small cohorts available (Lees et al., 2017b; Lees et al., 2019a). Polygenic effects were also seen in another study on the contribution of genetic variation to disease severity of IPD (Cremers et al., 2019). We found similar results for host age heritability in our two cohorts.

We could not distinguish between genetic background or serotype being the primary effect due to their correlation. We did note a difference in effect size of serotype between the two cohorts, which may make it unlikely to be the single largest effect on host age. This difference in cohorts could be explained by strain/GPSC being the main and consistent effect on host age. As strains are different between cohorts and each serotype appears in multiple strains, combining them in different amounts would create different directions of effect for serotype.

Our results are therefore suggestive of the following genetic architecture for association with host age. Primarily, whether a particular infant is colonized with a pneumococcus, when compared to an adult from the same population, is not due to the pathogen’s genetics. This may be due to technical factors such as detectability related to pathogen load (which varies between adults and children, as well as between serotypes), different local forces of exposure, or other environmental or host factors such as diet (which may affect survival to trpB-defective pneumococci). However, some of the variation of these patterns can be explained by the pathogen’s genetics. This appears largely to be driven by the fact that some strains and serotypes are more likely to be found in an infant or adult nasopharynx. In addition, there are likely to be many variants that each contribute a small amount to host age preference, but no single universally important gene or variant (a polygenic trait).

From this study, we cannot say specifically which regions of the genome contain these small effects, but it is useful to rule out recently adaptive variants with individually strong effects. We did not replicate the association of piliated genomes in infant hosts in our newly sequenced cohort, further demonstrating important differences between populations. An important corollary of our work is on future pneumococcal vaccine optimization efforts. A promising approach for future vaccination strategies is to target the different age groups (Colijn et al., 2020). Whether these should consist of the dominant disease-causing serotypes overrepresented in carriage by each age group or whether there are age-specific pathogen proteins that should be included is an open question. Our study therefore suggests that targeting these age groups using serotype makeup alone would be sufficient and supports previous observational and modeling studies that advise targeting the serotype makeup in the vaccine at specific populations to maximize their effect.

Three reasons can contribute to not finding individual effects: a high proportion of the heritability being caused by lineage effects; rare locus effects that could not be detected with the current sample size; and by sampling from a cohort with vaccinated children and unvaccinated adults and comparing with a cohort of unvaccinated children and adults, we had lower power due to the reduced overlap within and between cohorts in pan-genome content. Although differences in vaccination status between cohorts is one plausible explanation for interpreting our findings, we were unable to rule out other factors, for example, a population-specific host effect, geographical differences (Li et al., 2019), or the broad effects of different socioeconomic status between these cohorts. Pneumococcal factors such as differences in detectability due to carriage density could also influence results.

One important difference between our study cohorts was that children from the Dutch cohort were vaccinated, while children from the Maela cohort were not. While our findings demonstrate that vaccinated versus unvaccinated children were colonized with different bacterial serotypes and different sequence clusters, we observed differences in prevalence beyond just the serotypes included in the vaccine. Another difference between the cohorts was that adults from the Dutch cohort were males and females, while adults from the Maela cohort were females only.

In summary, we found an effect of pneumococcal genetics on carriage in children versus adult hosts, which varies between cohorts, and is likely primarily driven by serotype or strain (lineage) effects rather than large population-wide effects of individual genes.

Fastq sequences of bacterial isolates from the Dutch cohort were deposited in the European Nucleotide Archive (ENA, study and accession numbers in Supplementary file 12). Sequences of bacterial isolates in the Maela cohort are available at ENA under study numbers ERP000435, ERP000483, ERP000485, ERP000487, ERP000598 and ERP000599 (Supplementary file 13). Summary statistics for the results from the genome wide association studies can be found at https://figshare.com/articles/dataset/S_pneumoniae_carriage_GWAS/14431313.

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Author details

1. Philip HC Kremer

   Department of Neurology, Amsterdam UMC, University of Amsterdam, Meibergdreef, Netherlands

   Contribution

   Conceptualization, Formal analysis, Methodology, Writing - original draft, Project administration, Writing – review and editing

   For correspondence

   philip_kremer@hotmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0483-841X

2. Bart Ferwerda

   1. Department of Neurology, Amsterdam UMC, University of Amsterdam, Meibergdreef, Netherlands
   2. Department of Clinical Epidemiology, Biostatistics and Bioinformatics, University of Amsterdam, Amsterdam, Netherlands

   Contribution

   Formal analysis, Supervision, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Hester J Bootsma

   Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, Netherlands

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

4. Nienke Y Rots

   Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, Netherlands

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Alienke J Wijmenga-Monsuur

   Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, Netherlands

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5663-860X

6. Elisabeth AM Sanders

   1. Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, Netherlands
   2. Department of Pediatric Immunology and Infectious D, Wilhelmina Children's Hospital, Utrecht, Netherlands

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

7. Krzysztof Trzciński

   Department of Pediatric Immunology and Infectious D, Wilhelmina Children's Hospital, Utrecht, Netherlands

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

8. Anne L Wyllie

   1. Department of Pediatric Immunology and Infectious D, Wilhelmina Children's Hospital, Utrecht, Netherlands
   2. Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, United States

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6015-0279

9. Paul Turner

   1. Cambodia Oxford Medical Research Unit, Angkor Hospital for Children, Siem Reap, Cambodia
   2. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1013-7815

10. Arie van der Ende

    1. Department of Medical Microbiology and Infection Prevention, Amsterdam UMC, Amsterdam, Netherlands
    2. The Netherlands Reference Laboratory for Bacterial Meningitis, Amsterdam, Netherlands

    Contribution

    Supervision, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

11. Matthijs C Brouwer

    Department of Neurology, Amsterdam UMC, University of Amsterdam, Meibergdreef, Netherlands

    Contribution

    Supervision, Writing – review and editing

    Competing interests

    No competing interests declared

12. Stephen D Bentley

    Parasites and Microbes, Wellcome Sanger Institute, Cambridge, United Kingdom

    Contribution

    Supervision, Funding acquisition, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

13. Diederik van de Beek

    Department of Neurology, Amsterdam UMC, University of Amsterdam, Meibergdreef, Netherlands

    Contribution

    Supervision, Funding acquisition, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

14. John A Lees

    1. European Molecular Biology Laboratory–European Bioinformatics Institute, Cambridge, United Kingdom
    2. MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom

    Contribution

    Conceptualization, Formal analysis, Supervision, Methodology, Writing – review and editing

    For correspondence

    jlees@ebi.ac.uk

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-5360-1254

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