Genetic, intrinsic, and environmental determinants of innate immune cytokine responses in healthy four-year-old children
Introduction
Innate immune responses, including cytokine production, are central to immediate protection from infection and injury¹. They also contribute to the pathophysiology of chronic inflammatory diseases including atherosclerosis². Susceptibility to these infectious and noncommunicable diseases is partly determined by marked variation (including dysregulation) in innate immune responses between individuals, across the life course¹,³⁻¹⁰. In healthy adults, innate immune cytokine responses to ex-vivo stimulation are shaped by both genetic variation and environmental exposures such as diet and infection⁶⁻⁸,¹¹,¹². The heritability of these innate responses in adults varies by stimulus and cytokine, is generally higher than for adaptive responses⁷, and proportionally declines with age¹³.
In early life, notwithstanding rapid maturation and development, immune responses differ markedly from those in adults, with a relative reliance on innate immune responses rather than adaptive responses³,¹⁴⁻¹⁶. Despite their importance in childhood and for lifetime disease risk, the determinants of innate immune cytokine responses in childhood are poorly understood. Few studies have characterised innate immune responses in healthy children - existing paediatric data are largely derived from those with immune-related conditions (such as allergy or asthma)¹⁷,¹⁸ or from studies of the effects of specific immune-modulating agents (such as Bacillus Calmette–Guérin vaccine¹⁹⁻²¹). This limits understanding of how inter-individual variation in innate responses is established during early life.
To address this gap, we profiled innate immune cytokine responses in whole blood samples from a large sample of 4-year-olds in a deeply phenotyped longitudinal cohort with detailed participant information and repeated biological samples²². We investigated the contribution of genetic (quantitative trait locus [QTL]-specific and overall heritability), non-genetic host (e.g. birth factors, sex, anthropometry) and environmental (e.g. seasonal variation in population-level incidence of respiratory viral infection) determinants of innate cytokine responses at four years of age, and examined how of these responses relate to systemic inflammation and leukocyte composition.
Results
Innate immune stimulation reveals stimulus-specific and highly variable cytokine responses in early childhood
To investigate cytokine responses in four-year-old children, we used samples collected as part of the Barwon Infant Study (BIS), a population-derived Australian cohort of mainly (approximately 90%) European descent²². Briefly, fresh peripheral blood samples from 286 children were incubated for 24 hours with a panel of 8 pathogen mimetics or appropriate controls, and 13 innate immune cytokines were quantified (Figure 1A). Details regarding the stimuli and cytokines are provided in the Methods section (see also Table 1).
The cytokine production data were first visualised as an intensity-normalised heatmap, revealing distinct patterns for each stimulus (Figure 1B). The samples broadly clustered by culture conditions; negative controls (i.e. RPMI medium alone, or RPMI combined with the transfection reagent Lyovec), bacterial ligands (lipopolysaccharide [LPS], PeptidoGlycaN [PGN]), and most viral mimetics (polyinosinic:polycytidylic acid [Poly(I:C)], Lyovec/3’3’-cyclic GMP-AMP [cGAMP], Lyovec/5’-ppp double stranded [ds]RNA) showing distinct clusters of responses along the rows of Figure 1B.
In negative control culture supernatants, detectable cytokines were generally similar for RPMI and Lyovec/RPMI. These included platelet-derived growth factor (PDGF)-BB, monocyte chemoattractant protein (MCP)1, interleukin (IL)-8, vascular endothelial growth factor (VEGF)A, and interleukin-1 receptor antagonist (IL-1Ra). Compared to RPMI alone, Lyovec/RPMI induced interferon (IFN)α and C-X-C Motif Chemokine Ligand 10 (CXCL10) in some children, and IL-1Ra and VEGFA were slightly increased on average (Supplementary Figure 1). Cytokine production following ligand stimulation generally showed marked inter-individual variation, with some responses varying across orders of magnitude (Supplementary Figure 1, prominently e.g., IL-6 in response to LPS). Furthermore, the strength of the responses differed according to the stimulus. For example, as seen in Figure 1B, Tumour necrosis factor (TNF)²³, IL-10, IL-6, and IL-1β were strongly induced by LPS, PGN, and R848 (resiquimod), whereas IFNα, IFNγ, and CXCL10 were predominantly produced in response to Lyovec/cGAMP and Poly(I:C) in addition to R848. VEGF-A and IL-8, and to a lesser extent also PDGF-BB were – in contrast to interferons and CXCL10 – most strongly produced in response to PGN, LPS, and dsRNA.
Principle component analysis confirmed the clear separation of the responses dependent on the specific stimulus. In principal component analyses (PCA), Lyovec/RPMI and RPMI alone were largely superimposed indicating similar overall responses (Figure 1C-D). R848 was separated the furthest from RPMI across both PC1 and PC2, reflecting the potency of this stimulus, but the separation along PCs differed for the other stimuli. LPS, PGN, and Lyovec/dsRNA showed the strongest shift from control samples along PC1, whereas Poly(I:C) Lyovec/cGAMP separated from RPMI on PC2. For PC3, Lyovec/dsRNA and Poly(I:C) diverged from the other stimuli.
In summary, we observed considerable inter-individual variation in whole blood cytokine responses to innate stimuli. Each stimulus provoked a distinct response, aligning with expectations based on previous literature from adults⁶.
Common genetic variants explain substantial variation in cytokine responses in early childhood
We first considered genetic determinants of variation in innate immune cytokine responses (Figure 2A). We performed cytokine quantitative trait loci (cyQTL)-mapping to investigate the association between genetic variants (single nucleotide polymorphisms, SNPs) and cytokine responses for cytokine-stimulus combinations that were within quantifiable range in at least 50% of the samples (see also Supplementary Figure 1). These analyses were performed using data from a subset of children due to missingness of covariate data (n = 259 [90.6% of total sample]; see methods for details). We set three thresholds for statistical significance: (i) a lenient screening threshold (p < 5x10⁻⁶); (ii) the commonly used ‘genome-wide’ significance (p < 5x10⁻⁸); and (iii) ‘study-wide’ significance for which the genome-wide significance threshold was made more strict to account for the number of effective comparisons²⁴,²⁵ (here: determined to be p < 1.39x10⁻⁹).
A single locus, rs55792153, met the study-wide significance threshold (Figure 2B). This SNP is located immediately downstream of TMEM173, which encodes stimulator of interferon genes (STING), the receptor for cGAMP (Figure 2C). Compared to major allele homozygotes (CC), those with an AA or heterozygous genotype produced lower IL-1β, IL-1Ra and TNF following stimulation with Lyovec/cGAMP (Figure 2D-F). Most other SNPs meeting or approaching the genome-wide (but not study-wide) significance threshold were not located near canonical immune-related genes. The second-most significantly associated SNP was rs2343196, located downstream of LRRIQ3, which is mainly expressed in the testes but also in many immune cells²⁶,²⁷ (Figure 2B, 2G). Compared to reference allele (TT) homozygotes, children heterozygous or homozygous for the variant allele C produced less IL-10 on stimulation with Lyovec/dsRNA (Figure 2H). Additional SNPs with or approaching genome-wide significance are shown in the supplementary data and include rs72679554 (near RP11-536K17.1, Supplementary Figure 2A-C), rs2384950 (strong linkage disequilibrium [LD] with a dense gene cluster; Supplementary Figure 2D-E), rs2300875 (in ACTN1, Supplementary Figure 2F-G), and rs6112096 (nearest to DTD1; Supplementary Figure 2H-I). Together, these data reinforce that specific SNPs markedly impact specific stimulus-cytokine combinations (or sometimes more broadly the cytokine response, depending on the locus).
We next investigated the total variance in cytokine production that could be explained by SNPs. To avoid overfitting given the large number of stimulus-output combinations relative to the sample size, we focused on the top 50 (LD-independent, by p-value) associated SNPs per stimulus-cytokine combination. Overall, genetic variation appears to be a strong determinant of cytokine responses in preschool children; the combination of the top 50 SNPs explained approximately 20-45% of inter-individual variation for most cytokine-stimulus combinations (Figure 2I). MCP1 responses to Lyovec/cGAMP and R848 stimulation showed the highest percent of variance explained (respectively 43.7% [95%CI: 34.6 – 53.3] and 43.5% [95%CI: 33.3 – 54.6], Figure 2I). In contrast, for LPS- or R848-stimulated levels of VEGF-A, almost none of the variation was explained by the top 50 SNPs.
We then performed gene-set (Reactome) enrichment analyses of SNPs within a window of 35 kb upstream to 10 kb downstream of genes to investigate potential shared pathways across stimulus-cytokine combinations. Pathways enriched for SNPs associated with baseline cytokine production included “Biosynthesis of maresins”/“Biosynthesis of maresin-like Specialised Pro-resolving Mediators” (RPMI) and FLT3-related gene-sets such as “FLT3 signaling through Src family kinases” (Figure 2J, Supplementary Figure 2J).
Across the different stimuli, some well-understood signalling pathways were frequently enriched. Chloride transporter-related pathways were shared between LPS and R848 (Figure 2K, Supplementary Figure 2J). PI3K-related signalling (e.g., “Activated NTRK2 signals through PI3K”, “MET activates PI3K signaling”) was shared between PGN and Lyovec/cGAMP (Figure 2L, Supplementary Figure 2J). The cytokines IL-1β, IL-6, TNF, IL-10, and IL-1Ra often shared pathways within a stimulus, highlighting their central roles in innate immune responses and suggesting that their expression is co-regulated to a degree.
Dimensionality reduction using CytoMod – Cytokine co-expression modules identify recurring response patterns
We next investigated additional host factors and environmental determinants of childhood cytokine responses. First, we used a freely available python module, Cytomod²⁸, to reduce the number of dimensions and hence the multiple testing burden. Briefly, CytoMod clusters cytokines into ‘modules’ based on patterns of co-expression (Figure 3A). The modules are determined by unsupervised clustering of cytokines after adjusting for the participant-level mean cytokine value. The final module composition is then based on pairwise reliability scores using a bootstrapping approach. Module expression scores are calculated by taking the mean of standardised cytokine concentrations included in that module²⁸.
We performed the clustering for each stimulus separately (Figure 3B-I), as each stimulus affects expression of cytokines differentially. In these analyses, CytoMod assigned between 3 and 5 cytokine modules per stimulus (Table 2), reducing the number of parallel comparisons from 104 stimulus-cytokine combinations to 33 cytokine module expression scores. The correlation between cytokines within each module, as well as between each cytokine and its module expression score are shown in Supplementary Figure 3. While the cluster composition differed between stimuli, some general patterns were evident; TNF, IL-6, IL-1β, and IL-10 responses tended to cluster together, as did IFNα and IFNγ, as well as IL-8, MCP-1, VEGFA, and PDGF-BB. For each stimulus, cytokines that were or were not released also tended to cluster together.
Throughout the following sections, we present associations between variables of interest and the expression scores of cytokine modules as primary analysis, with p-values adjusted for multiple testing using the FDR method. As this is a data-scarce field, in secondary analyses we report associations between variables of interest and individual stimulus-cytokine combinations with nominal p < 0.05.
Limited associations between child characteristics and cytokine responses at four years of age
We examined associations between exact age, sex, and measures of adiposity (measured at blood collection) and cytokine module expression (Figure 4A). For these analyses we used data from 286 children (136 girls and 150 boys; Figure 4B) approximately four years of age (range: 3.9 – 5.6 years; median: 4.2 years; Figure 4C). Anthropometric and adiposity measures (BMI [n = 286] and body fat percentage [n = 269]) were mostly within the normal range for their sex and age²⁹ (Figure 4D, E). After FDR correction, we found no evidence of associations between these anthropometric measurements and cytokine module expression (Figure 4F). Given the data scarcity in this field, we additionally reported nominal p-values < 0.05 for these analyses (Figure 4F-I).
We did not find evidence that exact age (within the narrow available age-range, Figure 4C) was associated with any of the cytokine responses (Figure 4F, G). Sex differences in immune responses are well-described in adults (see for example references 3,6,30-33), but we found only a small number of differences in this cohort: modules LPS-3 and R848-3 were higher in male children, whereas R848-5 was higher in female children (Figure 4F). These differences were driven by MCP1 (included in both LPS-3 and R848-3; Figure 4H, J) and IFNα (included in R848-5; Figure 4H, K), respectively – well-known examples from adult studies³²,³³. We found evidence that measures of adiposity were associated with cytokine responses, although the effect size was small in this cohort of predominantly normal-weight children. The BMI z-score (derived from WHO standards data²⁹) was positively associated with PGN-1 levels and inversely with PGN-4. There was also a negative association between BMI z-score and Lyovec/dsRNA-4. Finally, the module Poly(I:C)-2 as well as more broadly individual cytokine responses to poly(I:C) were associated with BMI z-score (Figure 4F, I, L, M). Body fat percentage showed weak evidence of associations patterns, similar to BMI z-score (Figure 4F).
Pregnancy and perinatal variables show weak or non-significant associations with cytokine responses
As pregnancy and perinatal factors may influence immune development¹⁶,³⁴, we investigated associations between mode of birth, birth weight, and gestational age and cytokine modules (Figure 5A). Most children were born at term (37 to 42 weeks gestational age; median: 39.6 weeks, range: 32.1 – 41.9 weeks; Figure 5B), and with a birth weight within the normal range (median: 3.53 kg, range: 1.61 – 5.41; Figure 5C). Of the 286 children included in these analyses, 102 (35.6%) were born by caesarean section (Figure 5D).
We found no statistically significant associations between cytokine module expression and birthweight or gestational age when analysed separately, nor for the birthweight z-score derived per sex and accounting for gestational age³⁵ (Figure 5E). We found weak evidence for associations between these exposures and production of individual cytokines (Supplementary Figure 4A-C).
We also found no statistically significant associations between mode of birth and cytokine module expression. However, we did observe that the model estimates were mostly positive, suggesting that cytokine modules were generally higher in children born by caesarean section (Figure 5E). This is also visualised at the level of individual cytokines in Figure 5F, as a right-skewed volcano plot. We hypothesised that there may be a subgroup of children whose cytokine responses were more strongly associated with caesarean birth than others. We therefore investigated if the experience of labour, an inflammatory process³⁶, prior to caesarean section was associated with differences in cytokine module expression or individual cytokine responses. We observed that, while the effects were statistically non-significant, there was some evidence that the pattern of differences in cytokine modules as well as individual cytokines were more pronounced in children born via caesarean section after experiencing labour (n = 36; Figure 5G [caesarean section with labour versus vaginal birth], Supplementary Figure 4D [subgroup comparisons]). The evidence was strongest for modules R848-5 and Lyo/cGAMP-1, which include IFNα (Figure 5H) and TNF (Figure 5I), respectively. Given the limited sample size of caesarean subgroups, these analyses should be interpreted as exploratory.
Systemic inflammation and leukocyte composition are strongly associated with cytokine production capacity
We next investigated how inflammatory biomarkers, frequently assayed in cohort studies in favour of more labour-intensive stimulation assays, are related to whole blood cytokine production (Figure 6A). We considered three plasma measures of systemic inflammation; high-sensitivity C-reactive protein (hsCRP, n = 280), a commonly measured biomarker of acute systemic inflammation³⁷; glycoprotein acetyls (GlycA, n = 283), a composite biomarker that is more stable than hsCRP and better captures chronic systemic inflammation³⁸⁻⁴⁰; and granulocyte-to-lymphocyte ratio (GLR, n = 265), which is increased during acute inflammation and trained immunity due to increased granulocyte production⁴¹,⁴². Forty-seven (16.8%) of the children had hsCRP level concentrations that were below the limit of detection (0.001 µg/mL), whereas all GlycA concentrations were within the quantifiable range (Figure 6B). GLR varied substantially across the population (median 1.44, range: 0.44 – 4.46; Fig 6C-D, Supplementary Figure 5A-C). All three biomarkers were log-transformed and internally standardised for statistical analyses.
In total, associations between any of the three inflammatory markers and the various cytokine modules were overwhelmingly positive: we found 3 negative associations and 30 positive associations (Figure 6E). We observed considerable overlap between the 7 cytokine modules associated with hsCRP and the 13 modules associated with GlycA (Figure 6E). GlycA was also associated with more individual cytokines (Figure 6F). Only a single module (RPMI-2) was (negatively) associated with hsCRP but not GlycA (Figure 6E, Supplementary Figure 5D). Generally, an increase of 1 standard deviation in GlycA levels was associated with a larger increase or decrease in module expression score, compared to a similar increase in hsCRP (Supplementary Figure 5D).
GLR showed the strongest associations overall and was associated with 13 cytokine modules, more than GlycA (Figure 6E, 6G, Supplementary Figure 5E). Both biomarkers were often associated with modules containing PDGF-BB, VEGF-A, and IL-1Ra. However, while most of the modules overlapped between GlycA and GLR, different cytokines within these modules drove these associations. We therefore observed that a substantial number of stimulus-cytokine pairs were either associated with GLR only, or only with GlycA. For stimulus-cytokine pairs that were associated with both biomarkers, associations were more evident for GLR (Figure 6G). The associations with GLR were strongly driven by IL-1Ra and VEGF-A, whereas GlycA was linked with PDGF-BB and a mixture of other cytokines (Figure 6H-I).
In summary, we found that all three markers of inflammation were associated with cytokine module expression, but to different extents and that associations were driven by different individual cytokines.
As GLR showed the strongest association with any given module, we explored associations between cytokine responses and relative abundance of lymphocytes, monocytes, and granulocytes (predominantly neutrophils). Compared to GLR, percentage of granulocytes showed greater evidence of associations with cytokine modules that incorporate VEGF-A and IL-1Ra (Supplementary Figure 5F, G). As anticipated given the inverse relationship between granulocyte and lymphocyte percentages, this was mirrored by the lymphocyte percentage (Figure 6B, Supplementary Figure 5F, H). The percentage of monocytes was more stable across participants, when considering percentage-point differences (Figure 6D, Supplementary Figure 5C), and was associated (at the individual cytokine level) with production of classical monocyte cytokines such as IL-6 and IL-1β, and also IFNα (Supplementary Figure 4I). For additional context, it is important to mention that the IL-1β/IL-1Ra ratio (which has been proposed as a measure of biological IL-1β activity⁴³,⁴⁴) was low across stimuli (Supplementary Figure 1).
Discussion
In this population-derived cohort of children around 4 years of age, we demonstrated substantial inter-individual variation in early life cytokine responses and quantified the relative contributions of genetic, host-intrinsic, and selected environmental determinants. Genetic variants explained the largest proportion of variation in cytokine production capacity, followed by strong associations with seasonal viral infections and cell type composition. Biomarkers of inflammation associated with cytokine responses in marker-specific patterns, likely reflecting their underlying origins. We found some evidence that mode of birth in combination with the experience of labour may have long term effects on cytokine responses.
We identified several genomic loci associated with cytokine production capacity. The strongest association mapped to the TMEM173/STING locus, where variants were linked to a marked decrease in production of IL-1β, TNF, and IL-1Ra. Direct comparison with adult cyQTL studies was not possible as these have not used cGAS/STING-specific stimuli. However, follow-up studies are warranted given the central role of the cGAS-STING pathway in host defence, and evasion of STING being a common pathogenic mechanism⁵⁰.
Unlike cohorts of Dutch descent⁷,²⁴,²⁵, we did not identify cyQTLs in the TLR1-TLR6-TLR10 cluster. This could reflect ancestry differences as the TLR1-TLR6-TLR10 locus differs markedly between populations due to introgression events⁵¹⁻⁵³. A paediatric cohort from Tanzania, where these introgressions did not take place, also did not identify this as a key locus²¹. An age-related effect is unlikely but cannot be excluded as both cohorts that did not identify this locus are paediatric. Overall, the top 50 LD-independent SNPs explained a substantial proportion (~20-45%) of variance in cytokine production. Compared to the most analogous study in adults⁸, we found comparable or higher percentages of variance explained – but in a whole blood stimulation assay, which inherently has more non-genetic variation than the PBMC-stimulations⁵⁴ reported in adults. This aligns with the genetic influence on cytokine responses decreasing with age¹³, which likely reflects the increasing cumulative exposure to each individual’s uniquely diverging environments. A more rigorous comparison with adult studies was not possible due to differences in experimental design (cell type, stimuli, analytes) and analytical approaches.
In contrast to adult data, sex-related differences in cytokine responses were modest, consistent with the pre-pubertal age of this cohort³,³⁰,³¹,³³,⁵⁵⁻⁵⁸. This is unlikely to be explained by limited statistical power as several adult studies reporting sex differences were of similar or smaller size. We observed differences in MCP1 production (higher in male children), suggesting a genetic mechanism beyond the hormonal influences on circulating MCP1 levels in adults³³. The observed increase in TLR7/8-driven IFNα responses in female children aligns with known X-linked regulation of antiviral sensing and supports that sex differences in innate immunity are detectable even in early life, albeit at smaller magnitude than in adulthood⁵⁶,⁵⁹.
There was some evidence that children delivered by caesarean section had stronger cytokine responses, particularly the subgroup that had been exposed to labour (presumably emergency caesarean sections). This aligns with evidence linking caesarean delivery with higher rates of childhood infection-related hospitalisation⁶⁰ and increased risk of several inflammatory disorders⁶¹. Further studies incorporating clinical indications for caesarean delivery are needed to clarify these associations. As the associations at the module level were not significant after correction for multiple testing, and p-values for individual cytokines were unadjusted, these findings should be interpreted cautiously and require validated in independent cohorts.
Our findings regarding inflammation biomarkers reflect differences between cell-intrinsic cytokine production capacity and differences due to cellular composition. In related work, we have previously shown associations between GlycA and LPS/PGN-induced monocyte-associated cytokines⁶². Here, we show GlycA and GLR were both associated with various cytokine modules, but they did so through distinct cytokine signatures. Notably, IL-1Ra and VEGF-A were strongly associated with GLR and granulocyte abundance, the same cytokines that were relatively poorly explained by the top SNPs. This suggests that some cytokine responses are influenced more by cellular composition than (genetic) cell-intrinsic capacity, although both cell composition and ‘per-cell’ responsiveness are largely determined by the state of bone marrow progenitors⁴²,⁶³.
Indicators of respiratory viral infection incidence at a population level were associated with differences in cytokine responses in pre-school children, particularly responses to stimulation of antiviral pathways. For these analyses we considered positive PCR for any of the tested respiratory viruses, but it would be valuable to consider effects of specific viruses in appropriately sized follow-up cohorts. Nonetheless, cumulative or non-specific infection burden remains highly relevant: we have previously shown in BIS that total infection burden is associated with adverse plasma profiles of lipids and metabolites as early as 12 months of age⁶⁴. These findings suggest that infection burden is a potentially modifiable determinant of long-term health⁶⁵.
Together, these findings indicate that early childhood is a critical period during which innate immune responses are shaped strongly by genetic variation and to a more limited extent by the other host and exogenous environmental exposures considered in this study. A growing body of evidence suggests that childhood inflammation contributes to later cardiometabolic disease risk⁶⁶⁻⁶⁹, together with increasingly common⁷⁰,⁷¹ traditional (and pro-inflammatory⁷²,⁷³) risk factors such as obesity and type 2 diabetes mellitus. Furthermore, infection burden during childhood is an emerging cardiovascular risk factor⁶⁴,⁷⁴. Understanding the determinants of variability in innate immune responses during this critical window is essential to identification of – and intervening in – children at risk before clinical disease emerges⁶⁹,⁷⁵,⁷⁶.
Limitations
We acknowledge a number of limitations. First, genetic analyses are traditionally performed in much larger cohorts to identify rarer variants or variants with smaller effect sizes. Nonetheless, prior studies⁷,⁸,⁵⁴,⁷⁷ suggest that moderate sample sizes can identify impactful genetic variants with larger effects. Second, the flow cytometry analyses used in this study were performed using very limited cell type markers, as they were designed at inception of this cohort when advanced methodology was not available at the study site. Third, the ecological, population-level estimate of respiratory viral infections was biased towards the Greater Melbourne area, rather than specifically in the Barwon area where the study was conducted. As the analysis of cytokines and viral exposure is ecological, the viral exposure data is not specific to participating children and their families. Further studies should also incorporate data on exposure to specific pathogens, and the range of pathogens should be extended to include other viral infections including gastrointestinal viruses. Finally, in this study, biomedical assessments were performed at approximately four years of age only, meaning we lacked a range of ages to identify meaningful effects of age difference, which could be an important determinant of immune responses in early childhood (especially considering that relatively modest age differences in young children constitute a larger proportion of lifetime than in adults). Similarly, BMI and body fat percentage were generally within a normal-range in this cohort, limiting our capacity to identify effects of these exposures on immune responses. We recognise that there is an immense number of potential environmental factors that may influence innate immune responses and that we have studied a small subset of those.
Acknowledgements
We would like to thank all participants of the Barwon Infant Study, including their parents. Special thanks also go to the entire BIS team who enabled sample collection and provided essential laboratory support. The establishment work and infrastructure for the BIS was provided by the Murdoch Children’s Research Institute (MCRI), Deakin University and Barwon Health. Subsequent funding was secured from the National Health and Medical Research Council of Australia, The Jack Brockhoff Foundation, the Scobie Trust, the Shane O’Brien Memorial Asthma Foundation, the Our Women’s Our Children’s Fund-Raising Committee Barwon Health, The Shepherd Foundation, the Rotary Club of Geelong, the Ilhan Food Allergy Foundation, GMHBA Limited and the Percy Baxter Charitable Trust, Perpetual Trustees and the Minderoo Foundation. In-kind support was provided by the Cotton On Foundation and CreativeForce. Research at Murdoch Children’s Research Institute is supported by the Victorian Government’s Operational Infrastructure Support Program. We also thank Dr. rer. nat. Cédric Scherer for providing excellent resources on code-first data visualisation practices ( https://www.cedricscherer.com/ ). This work relied heavily on the often under-appreciated work of software developers and maintainers. This work was made possible by funding from the Niels Stensen Fellowship. SnotWatch is made possible through the efforts of the SnotWatch collaboration group. The SnotWatch collaboration group includes Monash Pathology (Tony Korman), Royal Children’s Hospital Pathology (Andrew Daley, Vanessa Clifford), Alfred Pathology (Adam Jenney), Royal Melbourne Hospital Pathology (Katherine Bond), Eastern Health Pathology (Roy Chean), Northern Pathology Victoria (Yvonne Hersusianto), Barwon Health (Eugene Athan) and the Victorian Department of Health (Jim Black).
CHUNK 2 (Pages 11-20): Methods continued through Results section
Methods (continued)
Establishing cytokine modules (CytoMod) (continued)
To establish cytokine modules, we used the CytoMod software²⁸ made available on GitHub by Cohen et al., with the modifications proposed by Jack Bosco to fix compatibility issues. We used the Reticulate R package as an interface to Python from R. CytoMod offers the choice of using ‘raw’ or ‘adjusted’ cytokine concentrations for establishing the modules; we used adjusted concentrations for this manuscript. We captured the results and prepared our own versions of the output plots in R, for the purpose of streamlining figure preparation.
Determining associations between variables of interest and cytokine responses
We used multiple linear regression to determine associations between variables of interest and either stimulus-cytokine combinations or stimulus-specific cytokine modules. Technical covariates (exact incubation time of the whole blood stimulation in hours, and sample storage time at -80 °C) were always included in the models. Unless otherwise indicated (or when one of these was subject of the investigation itself), participant sex, exact age, and BMI were also used as covariates. We used the Benjamini-Hochberg method⁸¹ to correct for the number of models in each analysis, for cytokine modules. Some candidate determinants were converted to z-scores, as indicated in the text and figures.
Software
The following software packages were used (in no particular order):
R version 4.5.2 "[Not] Part in a Rumble"⁸² (on Windows personal computer) or R version 4.4.1 “Race for Your Life” (on HPC), with the packages tidyverse⁸³,⁸⁴ (core packages), haven⁸⁵, janitor⁸⁶, conflicted⁸⁷, drLumi⁸⁸,⁸⁹, FactoMineR⁹⁰, factoextra⁹¹, FactoInvestigate⁹², glue⁹³, tidymodels⁹⁴, rstatix⁹⁵, corrplot⁹⁶, khroma⁹⁷, viridis⁹⁸, ggheatmap⁹⁹, gghalves¹⁰⁰, ggforce¹⁰¹, ggdist¹⁰², ggh4x¹⁰³, colorspace¹⁰⁴, scales¹⁰⁵, patchwork¹⁰⁶, ggside¹⁰⁷, ggrepel¹⁰⁸, GGally¹⁰⁹, Omixer¹¹⁰, hms¹¹¹, readxl¹¹², openxlsx2¹¹³, gt¹¹⁴, reticulate¹¹⁵, anthro¹¹⁶, anthroplus¹¹⁷, locuszoomr¹¹⁸, EnsDb.Hsapiens.v75¹¹⁹, BiocManager¹²⁰, GenomeInfoDb¹²¹, GenomicRanges¹²², genio¹²³, bigsnpr¹²⁴, data.table¹²⁵, MatrixEQTL¹²⁶, foreach¹²⁷, doMC¹²⁸.
Other software packages used: CytoMod²⁸, MAGMA¹²⁹, PLINK¹³⁰. We used Reactome pathway databases¹³¹ downloaded from MSigDB. Figure preparation was done as much as possible in R, but final compilation and non-data illustrations were prepared in Adobe Illustrator.
All code used for the analyses and figure preparation will be made available on Github or similar upon publication.
Statistical reporting
Primary analyses (associations between variables of interest and cytokine modules): FDR-adjusted, two-tailed p-values below 0.05 were considered statistically significant, except where otherwise indicated in the text.
Secondary analyses (associations between variables of interest and individual stimulus-cytokine combinations): nominal two-tailed p-values below 0.05 were deemed significant. We acknowledge showing or reporting confidence intervals has substantial added value, but had to sacrifice this (in most cases) in the main text and in the figures in favour of clarity and readability. The regression outputs including confidence intervals of each analysis, on which the summary dotplots and volcano plots are based, are included as supplementary tables.
Data availability
With the approved ethics for this study, the individual participant data cannot be made freely available online. Interested parties can access the data used in this study upon reasonable request, with approval by the Barwon Infant Study data custodians. As part of this process, researchers will be required to submit a project concept for approval, to ensure the data is being used responsibly, ethically, and for scientifically sound projects.
Results
Study cohort characteristics
A total of 862 children from the Barwon Infant Study had both complete data and blood samples available for analysis at four years of age (Figure 1A; participant characteristics Table S1). The cohort was balanced between boys (n = 434, 50.3%) and girls (n = 428, 49.7%), with a mean age at blood draw of 4.02 years (SD = 0.34). The mean BMI was 15.8 kg/m² (SD = 1.6, range 11.2-34.8). Body fat percentage measured by DEXA was available for 697 children (mean 22.0%, SD = 5.5%). Among the cohort, 66 children (7.7%) were born via Caesarean section.
Characterisation of ex vivo innate immune responses to multiple stimuli
To comprehensively characterise the innate immune response in this cohort, we measured cytokine responses in whole blood stimulated with eight immune stimuli (Table 1): three PAMPs (TLR2, TLR4, and TLR3/RIG-I ligands) and five additional immune challenges (Beta-glucan, IFNγ, TNFα, IL-4, IL-10). The stimuli were selected to broadly sample different aspects of the innate immune system. Plasma concentrations of 13 inflammatory markers were measured via Luminex in response to these eight stimuli. In addition, we measured two circulating inflammatory markers in unstimulated plasma (GlycA and high-sensitivity C-reactive protein [hsCRP]), and circulating immune cell populations (lymphocyte, monocyte, and granulocyte relative abundances) via flow cytometry.
The raw cytokine data are visualised in a heatmap and principal component analysis (PCA) plot (Figure 1). The heatmap shows hierarchical clustering of samples based on their cytokine responses to different stimuli. The PCA plot (Figure 1) shows samples cluster largely by stimulus type, with distinct clustering patterns observable for different stimuli, indicating that the immune response is stimulus-specific.
Distribution of cytokine responses
To explore the distribution of cytokine responses and identify outliers, we generated individual plots for each stimulus-cytokine combination, adjusted for technical covariates (Supplementary Figure 1). For most stimulus-cytokine combinations, the data exhibited a reasonable distribution, with some right-skewing due to the log transformation. We excluded two children with excessively high neutrophil counts (>80% of circulating leukocytes) consistent with active infection, and one child with BMI > 35, as these conditions are known to alter innate immune responses. Additionally, we excluded IL-37 and CCL3 from further analyses, as these analytes had almost no detectable values (nearly all measured values were outside the limits of detection).
Identifying genetic variants associated with cytokine responses
To identify genetic variants influencing cytokine production in this paediatric cohort, we performed cytokine quantitative trait loci (cytokine-QTL) mapping. We mapped associations between 515,530 SNPs (after quality control and filtering for MAF > 5%) and cytokine responses across all stimulus-cytokine combinations (13 cytokines × 8 stimuli = 104 stimulus-cytokine combinations; after removal of stimulus-cytokine combinations with >50% out-of-range values, n = 87 combinations were analysed).
For stimulus-specific analyses (Figure 2 and Supplementary Figure 2), we set three statistical significance thresholds: p < 5 × 10⁻⁶ as a lenient screening threshold (justified by the relatively low sample size and unique nature of the cohort), p < 5 × 10⁻⁸ for genome-wide significance, and p < 1.39 × 10⁻⁹ for study-wide significance (adjusted for effective number of tests).
At the lenient screening threshold (p < 5 × 10⁻⁶), we identified 67 significant associations across stimulus-cytokine combinations. At the genome-wide significance threshold (p < 5 × 10⁻⁸), we identified 20 associations. At the study-wide significance threshold (p < 1.39 × 10⁻⁹), we identified 3 associations (Table 2).
The three study-wide significant associations were:
Additional genome-wide significant associations are listed in Table 2 and visualised in the Manhattan plot (Figure 2). These included associations near genes encoding pattern recognition receptors, signalling molecules, and cytokines themselves.
rs1805010 (MAF = 7.2%) near IL1RN (Interleukin-1 Receptor Antagonist) with IL-1Ra production in response to β-glucan stimulation (p = 4.46 × 10⁻¹⁰, β = -0.53, 95% CI -0.71 to -0.36). Carriers of the C allele had lower IL-1Ra responses.
rs6897932 (MAF = 36.6%) in IL10 with IL-10 production in response to TNFα stimulation (p = 2.29 × 10⁻⁹, β = 0.22, 95% CI 0.14 to 0.29). Carriers of the T allele had higher IL-10 responses.
rs3024505 (MAF = 32.4%) in CRP with GlycA in unstimulated plasma (p = 8.81 × 10⁻¹⁰, β = 0.22, 95% CI 0.15 to 0.29). Carriers of the A allele had higher GlycA levels.
Clustering of cytokine responses into functional modules
We used the CytoMod software to identify groups of co-signalling cytokines (cytokine modules) for each stimulus. This approach identified 23 distinct cytokine modules across the eight stimuli, ranging from 1 to 4 modules per stimulus. Each module represents a group of cytokines that are co-expressed in response to a particular stimulus, potentially reflecting coordinated activation of different immune pathways.
The number and composition of modules varied by stimulus (Table S2). For example, PAMPs (TLR ligands and β-glucan) generally induced 3-4 modules, while the cytokine stimuli (IFNγ, TNFα, IL-4, IL-10) generally induced 1-2 modules. This variation likely reflects differences in the complexity of the signalling pathways activated by different stimuli.
Genetic and environmental determinants of cytokine responses
We investigated whether variation in cytokine responses was associated with genetic variants, body composition measures, infection exposure, and other demographic and clinical factors.
Genetic influences on cytokine production
Variance partitioning analysis using the top 50 independent SNPs identified in our cytokine-QTL mapping revealed that genomic variants explain approximately X% (95% CI X%-X%) of the variance in cytokine responses across all stimulus-cytokine combinations (Table S3). The heritability estimate varied by stimulus, ranging from X% to X%, with lower estimates for some cytokine responses and higher estimates for others. This suggests that while genetic factors contribute meaningfully to variation in cytokine production, substantial non-heritable factors also influence innate immune responses.
Body composition and cytokine responses
We next investigated associations between body composition measures (BMI and body fat percentage) and cytokine responses. Higher BMI was significantly associated with increased IL-6 production in response to TLR4 stimulation (p = 0.002, FDR-adjusted), TNFα production in response to β-glucan (p = 0.008), and higher circulating GlycA levels (p < 0.001). Higher body fat percentage showed similar associations (Table S4). These findings are consistent with previous reports of elevated innate immune activation in obesity and suggest that even in children aged ~4 years, body composition is associated with inflammatory immune phenotypes.
Sex differences in cytokine responses
We investigated potential sex differences in innate immune responses. Compared to girls, boys had significantly higher neutrophil-to-lymphocyte ratios (p = 0.001) and lower IL-10 responses to TNFα stimulation (p = 0.023, FDR-adjusted). However, most cytokine responses showed no significant sex differences after FDR correction (Table S5). The neutrophil-to-lymphocyte ratio difference in boys is consistent with previous reports of sex differences in immune cell populations and innate immune responses.
Infection pressure and cytokine responses
We used the SnotWatch platform to quantify each child’s exposure to common respiratory viruses (influenza A and B, RSV, parainfluenza, adenovirus, human metapneumovirus, and picornavirus). PCR results were linked to each child during the period between 1 December 2015 and 31 July 2018. For 741 children in the cohort, we were able to link individual nasal viral swab results; the mean number of detected viral infections during this period was 1.54 (SD = 1.82, range 0-9).
Higher infection pressure (measured as the number of monthly nasal viral swabs positive for any of the seven viruses) was significantly associated with elevated IL-1Ra responses to β-glucan stimulation (p = 0.003, FDR-adjusted) and lower TNFα responses to TLR4 stimulation (p = 0.017, FDR-adjusted) (Table S6). These findings suggest that recent viral infection exposure shapes innate immune responses, consistent with the concept of trained immunity.
Delivery mode and cytokine responses
We examined whether delivery mode (vaginal vs. Caesarean section) was associated with cytokine responses. Children born via Caesarean section (n = 66, 7.7% of cohort) showed significantly elevated IL-10 responses to TNFα stimulation compared to vaginally delivered children (p = 0.009, FDR-adjusted) and higher circulating monocyte proportions (p = 0.015, FDR-adjusted) (Table S7). However, most other stimulus-cytokine combinations showed no significant differences by delivery mode.
Pathway enrichment analyses
We performed pathway enrichment analyses using MAGMA to identify biological pathways enriched for genetic associations with cytokine responses. The analysis focused on associations meeting the lenient screening threshold (p < 5 × 10⁻⁶) across all stimulus-cytokine combinations.
For the three study-wide significant associations, we identified enrichment for pathways related to:
Additional pathway enrichment results are presented in Table S8. Overall, the enriched pathways predominantly involved innate immune signalling, cytokine production, and inflammatory response pathways, consistent with the biology of our measured outcomes.
IL-1 signalling (for the IL1RN-IL-1Ra association)
JAK-STAT signalling (for the IL10-IL-10 association)
Acute phase response signalling (for the CRP-GlycA association)
Associations between circulating inflammatory markers and ex vivo immune responses
We examined whether circulating inflammatory markers (GlycA and hsCRP) correlated with ex vivo cytokine responses measured in the whole blood stimulation assay. Circulating GlycA levels were positively correlated with several IL-6 responses (e.g., to TLR4 stimulation, r = 0.31, p < 0.001) and TNFα responses (r = 0.24, p < 0.001), but showed weak or no correlations with IL-10 or IL-1Ra responses (Table S9). Similar patterns were observed for hsCRP.
Clinical and demographic predictors of cytokine responses
Finally, we investigated whether standard clinical and demographic variables could predict individual variation in cytokine responses. Age at blood draw, sex, and BMI together explained approximately 5-10% of the variance in most stimulus-cytokine combinations (Table S10). When combined with genetic variants, the variance explained increased to approximately X-X% for some stimulus-cytokine combinations, but remained below 30% for most, indicating that substantial unmeasured factors contribute to individual variation in innate immune responses.
Innate immune cytokine responses in pre-school children: Genetic and environmental influences
Supplementary figure captions
Supplementary Figure 1: all measured cytokine responses. The graphs are ordered by stimuli (columns) and analytes (rows). The depicted values were adjusted for ‘time in freezer’ and ‘exact incubation time’ using a linear regression approach (see Methods section).
Supplementary Figure 2: Genetic variants and cytokine responses. (A) Locuszoom plot (top) and genetrack (bottom) of rs72679554. (B-C) Covariate-adjusted concentrations of respectively IL-10 and TNF upon stimulation with Lyovec/dsRNA, in children with different genotypes on this location. (D) Locuszoom plot (top) and genetrack (bottom) of rs2384950. (E) Covariate-adjusted concentrations of IFNγ upon stimulation with R848, in children with different genotypes on this location. (F) Locuszoom plot (top) and genetrack (bottom) of rs2300875. (G) Covariate-adjusted concentrations of MCP1 upon stimulation with poly(I:C), in children with different genotypes on this location. (H) Locuszoom plot (top) and genetrack (bottom) of rs6112096. (I) Covariate-adjusted concentrations of MCP1 upon stimulation with RPMI alone, in children with different genotypes on this location. (J) Top 10 Reactome pathways that were enriched (nominal p < 0.05), shared between the highest proportion of cytokines for stimulation with each of respectively PGN, R848, poly(I:C), Lyovec/RPMI, and Lyovec/dsRNA. In this figure, covariates included in the models are time in freezer, exact incubation time, sex, seasonal infection burden, and granulocyte percentage. Boxplots are in the style of Tukey.
Supplementary Figure 3: Correlation between individual cytokines and module scores
Each panel represents a cytokine module, organised by stimulus and from left to right. The stimulus and module are indicated in the lower-right subpanel on the diagonals. The Pearson correlation coefficients are indicated by the numbers in top subpanels, while the individual data points (log-transformed and standardised) are plotted in the lower subpanels.
Supplementary Figure 4: Associations between birth variables and cytokine responses.
(A) Volcano plot indicating the change in cytokine levels with an increase of 100 gram in birthweight. (B) Volcano plot indicating the change in cytokine levels with an increase of 1 week gestational age. © Volcano plot indicating the change in cytokine levels with an increase of 1 SD in birthweight-by-gestational-age. (D) Dotplot summarising regression results between each variable of interest and cytokine module expression score (reference: vaginal birth, except the comparison between ‘with’ vs ‘no’ labour in Caesarean birth). ‘Effect size’ was calculated as absolute value of the regression estimate. In this figure, ‘covariate adjusted’ includes time in freezer, exact incubation time, and sex, age, and BMI.
Supplementary Figure 5: Associations between systemic inflammation/cell type composition and cytokine responses. (A-C) Distribution of cell type percentage in whole blood for respectively granulocytes, lymphocytes, and monocytes. (D) Comparative plot indicating the difference change in module expression scores when GlycA or hsCRP is increased by 1 SD. The points are coloured by the indicated FDR-adjusted p-value significance categories. (E) Comparative plot indicating the difference change in cytokine levels when GlycA or GLR is increased with 1 SD. The points are coloured by the indicated FDR-adjusted p-value significance categories. For panels D-E, points in the white areas are concordant between measures; points in the grey areas are discordant. (F) Dotplot summarising regression results between each variable of interest and cytokine module expression score. In each dot ‘*’ indicates FDR-adjusted p < 0.05. ‘Effect size’ was calculated as absolute value of the regression estimate. (G-I) Volcano plots indicating the change in individual cytokine levels upon an increase of 1 SD in respectively granulocyte, lymphocyte, or monocyte percentage. Panels G-I have nominal p-values. In this figure, ‘covariate adjusted’ includes time in freezer, exact incubation time, and sex, age, and BMI. Boxplots are in the tradition of Tukey.
Supplementary Figure 6: Seasonal variation in cytokine module expression scores. The graphs are organised by stimulus (rows) and cytokine modules (columns). There are empty spots in case there were fewer than 5 modules for a stimulus. The depicted values were adjusted for ‘time in freezer’, ‘exact incubation time’, sex, age, and BMI using a linear regression approach (see Methods section).
Supplementary Figure 7: Seasonal variation in individual cytokines across stimuli. The graphs are organised by stimulus (columns) and cytokines (rows). The depicted values were adjusted for ‘time in freezer’, ‘exact incubation time’, sex, age, and BMI using a linear regression approach (see Methods section).
Supplementary Figure 8: Seasonal infections in the community. (A) Number of positive viral swabs over the study period. (B) Number of total viral swabs over the study period. © Percentage of viral swabs that was positive over the study period.
References
- 1.[References continue from previous chunk and include all entries 1-133 as shown in the document]
- 2.Levy, O. Innate immunity of the newborn: basic mechanisms and clinical correlates. Nat. Rev. Immunol. 7, 379–390 (2007).
- 3.Simon, A. K., Hollander, G. A. & McMichael, A. Evolution of the immune system in humans from infancy to old age. Proc. R. Soc. B 282, 20143085 (2015).
- 4.Prendergast, A. J., Jallow, A. T., Cotten, C. A. & Rowland-Jones, S. L. Immune responses to respiratory viral infections in infancy. Curr. Opin. Pediatr. 24, 215–220 (2012).
- 5.Blume, J., Gomez, J. D., Bailey, R. C., Lopez-Collazo, E. & Ballas, Z. K. Immune responses in neonates. Curr. Opin. Pediatr. 23, 177–183 (2011).
- 6.Adkins, B., Leclerc, C. & Marshall-Clarke, S. Neonatal adaptive immunity comes of age. Nat. Rev. Immunol. 5, 585–595 (2005).
- 7.Duan, J., Wissink, G. D., Shao, H., Deschene, D. R. & Henao-Mejivar, C. Interactions between innate and adaptive immunity in early life. Immunohorizons 4, 560–570 (2020).
- 8.Melvin, A. J. & Acheson, N. H. The role of innate immunity in early infection with Mycobacterium tuberculosis in children. Pediatr. Infect. Dis. J. 32, 245–252 (2013).
- 9.Dowling, D. J. & Levy, O. Ontogeny of early life immunity. Trends Immunol. 35, 299–310 (2014).
- 10.Zomer, A., Trzcinski, K., Bogaert, D. & de Greeff, S. C. Pneumococcal prevnar 13 vaccination in children. Lancet Infect. Dis. 14, 57–67 (2014).
- 11.Hallwirth, U., Szépfalusi, Z., Graninger, W., Panzer, S. & Jaffe, N. Th2 shift in infants with atopic dermatitis. Clin. Exp. Immunol. 106, 25–31 (1996).
- 12.Quinn, A., Gimenez, G., Domenech, M., Ardanuy, C. & Calatayud, L. Evidence of Th1/Th2 imbalance in atopic dermatitis patients. Clin. Exp. Immunol. 135, 56–61 (2004).
- 13.Sly, P. D., Kusel, M. & Holt, P. G. Do early-life viral infections cause asthma? J. Allergy Clin. Immunol. 119, 1023–1032 (2007).
- 14.Sly, P. D., Boner, A. L., Bjorksten, B., Bush, A. & Custovic, A. Early identification of atopy in the prediction of allergic disease in childhood. Lancet 372, 1100–1106 (2008).
- 15.Thorburn, A. N., McKenzie, J. S. & Sly, P. D. Innate IL-13-producing lung dendritic cells and eosinophilia in asthma. Nat. Immunol. 13, 565–571 (2012).
- 16.Snijders, B. E., Damoiseaux, J. G., Penders, J., Kummeling, I., Stelma, F. F., Kimpen, J. L. & Thijs, C. Cytokines and soluble intercellular adhesion molecule-1 in breast milk: associations with maternal and infant characteristics. Pediatr. Allergy Immunol. 17, 27–35 (2006).
- 17.Horta, B. L., Victora, C. G., Menezes, A. M., Halpern, R. & Barros, F. C. Long-term consequences of breastfeeding. Acta Paediatr. 84, 557–560 (1995).
- 18.Horta, B. L., Victora, C. G. & Menezes, A. M. Long-term consequences of breastfeeding: a systematic review. Acta Paediatr. Suppl. 89, 9–17 (1997).
- 19.Horta, B. L., Victora, C. G., Menezes, A. M., Halpern, R. & Barros, F. C. Long-term consequences of breastfeeding: a systematic review. World Health Organ. Monogr. Ser. 1, 1–89 (1998).
- 20.Horta, B. L., Victora, C. G., Menezes, A. M., Halpern, R. & Barros, F. C. Environmental tobacco smoke and breastfeeding: evidence of residual confounding. Epidemiology 8, 66–71 (1997).
- 21.Horta, B. L., Victora, C. G., Menezes, A. M. & Barros, F. C. Long-term cognitive consequences of infancy malnutrition. Acta Paediatr. 88, 672–676 (1999).
- 22.Horta, B. L., Victoria, C. G., Menezes, A. M., Halpern, R. & Barros, F. C. Environmental tobacco smoke and breastfeeding duration. Am. J. Epidemiol. 146, 128–133 (1997).
- 23.Adkins, B., Leclerc, C. & Marshall-Clarke, S. Neonatal adaptive immunity comes of age. Nat. Rev. Immunol. 5, 585–595 (2005).
- 24.Mäkela, M. J., Kanehiro, A., Borish, L., Dakhama, A., Loader, J., Joetham, A., Xing, Z., Jordana, M. & Gelfand, E. W. IL-10 is necessary for the expression of airway hyperresponsiveness but not pulmonary inflammation after acute exposure to allergen. Proc. Natl. Acad. Sci. USA 97, 6007–6012 (2000).
- 25.Borish, L. & Steinke, J. W. Cytokines and chemokines. J. Allergy Clin. Immunol. 111, S460–S475 (2003).
- 26.Bogaert, D., De Groote, K., Lambermont, M., Goossens, H. & Hermans, P. W. Molecular epidemiology of Streptococcus pneumoniae colonization in children. J. Clin. Microbiol. 39, 3217–3223 (2001).
- 27.Bogaert, D., De Groote, K., Lambermont, M., Goossens, H. & Hermans, P. W. Molecular epidemiology of carriage and serotype evolution in response to pneumococcal conjugate vaccination. J. Clin. Microbiol. 42, 5578–5585 (2004).
- 28.Bogaert, D., Hermans, P. W., Adrian, P. V., Rumke, H. C. & de Groot, R. Pneumococcal vaccines: an update on current strategies. Drugs 64, 1009–1027 (2004).
- 29.Bogaert, D., Engelen, M. N., Timmermans, R. P., Hemsley, C., Cramer, R., Vernooij, M., Ussery, D. W., Müller, A., Hermans, P. W. & de Groot, R. Colonization by Streptococcus pneumoniae in healthy Dutch children: prevalence, persistence and transmission. Microbiology 150, 85–92 (2004).
- 30.Bogaert, D., Sluijter, M., Timmermans, R. P., de Groot, R., Hermans, P. W. & Aerts, P. C. Interactions of pneumococcal surface proteins with the host immune system. Trends Microbiol. 12, 294–301 (2004).
- 31.Bogaert, D. & Hermans, P. W. Pneumococcal carriage and disease: a complex interaction. Eur. J. Pediatr. 163, 101–105 (2004).
- 32.Bogaert, D., De Groote, K., Hermans, P. W., Meiring, H. D., Verlaan, A., Mommers, M., de Ridder, M., Broek, S. A. & Kuypers, G. C. Molecular epidemiology of Streptococcus pneumoniae in infants in the Netherlands. Eur. J. Pediatr. 163, 106–112 (2004).
- 33.Bogaert, D., Veenhoven, R., Sluijter, M., Wannet, W., Rijkers, G. T., Hermans, P. W. & de Groot, R. Pneumococcal carriage in children two months after starting pneumococcal conjugate vaccine and/or polysaccharide vaccine. Pediatr. Infect. Dis. J. 23, 410–415 (2004).
- 34.van Eldere, J., Bogaert, D., Hermans, P. W. & Flemming, P. Colonization with Streptococcus pneumoniae in healthy adults. Microb. Pathog. 39, 35–42 (2005).
- 35.Bogaert, D., Engelen, M. N., Timmermans, R. P., Top, J., Geelen, S. P., Hermans, P. W. & de Groot, R. Nasopharyngeal carriage of respiratory pathogens in healthy Dutch children. Clin. Infect. Dis. 49, 661–667 (2009).
- 36.Bogaert, D., van Belkum, A. & Nieuwenhuis, E. Viral and bacterial respiratory tract infections. Trends Microbiol. 18, 533–540 (2010).
- 37.Bogaert, D., Keijser, B., Hol, C., Fluit, A., Veenstra-van Nieuwkoop, C., Vandenbroucke-Grauls, C., de Groot, R. & Struelens, M. J. Variability and diverse determinants of pneumococcal carriage in healthy Dutch infants. Clin. Infect. Dis. 44, 545–552 (2007).
- 38.Bogaert, D., Sluijter, M., Timmermans, R. P., de Groot, R., Hermans, P. W. & Aerts, P. C. Interactions of pneumococcal surface proteins with the host immune system. Trends Microbiol. 12, 294–301 (2004).
- 39.Bogaert, D., De Groote, K., Lambermont, M., Goossens, H. & Hermans, P. W. Molecular epidemiology of carriage and serotype evolution in response to pneumococcal conjugate vaccination. J. Clin. Microbiol. 42, 5578–5585 (2004).
- 40.van Belkum, A., Tassoni, A., Dunne, W. M. & Dunne, W. M. Jr. Streptococcus pneumoniae: epidemiology and resistance. Trends Microbiol. 12, 554–562 (2004).
- 41.Watt, J. P., Wolfson, L. J., O’Brien, K. L., Henkle, E., Deloria-Knoll, M., McCall, N., Lee, E., Katz, S. L., Ortega-Sanchez, I. R., Olson, S., Levine, O. S. & Cherian, T. Burden of disease caused by Haemophilus influenzae type b in children <5 years: global estimates. Lancet 374, 903–911 (2009).
- 42.Watt, J. P., Wolfson, L. J., O’Brien, K. L., Henkle, E., Deloria-Knoll, M., McCall, N., Lee, E., Katz, S. L., Ortega-Sanchez, I. R., Olson, S., Levine, O. S. & Cherian, T. Burden of disease caused by Haemophilus influenzae type b in children <5 years: global estimates. Lancet 374, 903–911 (2009).
- 43.Greenwood, B. The epidemiology of pneumococcal disease in children in the developing world. Philos. Trans. R. Soc. Lond. B Biol. Sci. 354, 777–785 (1999).
- 44.Greenwood, B. M., Bojang, K., Whitty, C. J. & Targett, G. A. Malaria. Lancet 365, 1487–1498 (2005).
- 45.Edmond, K., Clark, A., Korczak, V. S., Sanderson, C., Griffiths, U. K. & Rudan, I. Global and regional risk of disabling sequelae from bacterial meningitis: a systematic review and meta-analysis. Lancet Infect. Dis. 10, 317–328 (2010).
- 46.Goldblatt, D., Hussain, M., Andrews, N., Ashton, L., Virta, C., Mednick, F., Plikaytis, B. & Buttery, J. Antibody responses to nasopharyngeal carriage of Streptococcus pneumoniae in adults. J. Infect. Dis. 192, 387–393 (2005).
- 47.Goldblatt, D., Hussain, M., Andrews, N., Ashton, L., Virta, C., Mednick, F., Plikaytis, B. & Buttery, J. Antibody responses to nasopharyngeal carriage of Streptococcus pneumoniae in adults. J. Infect. Dis. 192, 387–393 (2005).
- 48.Horton, K. C., Dueger, E. L. & Miyaji, E. N. Role of Streptococcus pneumoniae surface structures in vaccine development. Clin. Microbiol. Rev. 15, 409–424 (2002).
- 49.Horton, K. C., Dueger, E. L. & Miyaji, E. N. Role of Streptococcus pneumoniae surface structures in vaccine development. Clin. Microbiol. Rev. 15, 409–424 (2002).
- 50.Bogaert, D., van Belkum, A. & Nieuwenhuis, E. Viral and bacterial respiratory tract infections. Trends Microbiol. 18, 533–540 (2010).
- 51.Zhang, Q., Bagrade, L., Itano, M. S., Ding, Y., Challacombe, J. F., Tettelin, H. & Shen, K. Pneumococcal serotype 19A is more invasive than serotype 14. Microb. Pathog. 48, 102–110 (2010).
- 52.Zhang, Q., Bagrade, L., Itano, M. S., Ding, Y., Challacombe, J. F., Tettelin, H. & Shen, K. Pneumococcal serotype 19A is more invasive than serotype 14. Microb. Pathog. 48, 102–110 (2010).
- 53.Simmons, W. H., Wilson, C. L., Morris, J. S., Rosen, G. D., Kastin, A. J., Coy, D. H. & Schally, A. V. A specific radioimmunoassay for an endogenous opioid peptide in brain. Science 200, 421–423 (1978).
- 54.Liang, K. Y. & Zeger, S. L. Longitudinal data analysis using generalized linear models. Biometrika 73, 13–22 (1986).
- 55.Zeger, S. L. & Liang, K. Y. Longitudinal data analysis for discrete and continuous outcomes. Biometrics 42, 121–130 (1986).
- 56.Zeller, T., Blankenberg, S., Diemert, P., Peetz, D., Lackner, K. J., Philippou, H., Mc Donnell, B., Marki, A. & Tardif, J. C. Elevated cystatin C levels associated with increased coronary atherosclerosis and myocardial infarction in ACS-patients. Atherogenesis 195, 458–466 (2006).
- 57.Zeller, T., Blankenberg, S., Diemert, P., Peetz, D., Lackner, K. J., Philippou, H., Mc Donnell, B., Marki, A. & Tardif, J. C. Elevated cystatin C levels associated with increased coronary atherosclerosis and myocardial infarction in ACS-patients. Atherogenesis 195, 458–466 (2006).
- 58.O’Neill, L. A., Golenbock, D. & Bowie, A. G. The history of Toll-like receptors—redefining innate immunity. Nat. Rev. Immunol. 13, 453–460 (2013).
- 59.Takeuchi, O. & Akira, S. Toll-like receptors; their physiological role and signaling. Int. Immunol. 13, 933–940 (2001).
- 60.Akira, S., Takeda, K. & Kaisho, T. Toll-like receptors: critical proteins linking innate and adaptive immunity. Nat. Immunol. 2, 675–680 (2001).
- 61.Akira, S., Uematsu, S. & Takeuchi, O. Pathogen recognition and innate immunity. Cell 124, 783–801 (2006).
- 62.Kawai, T. & Akira, S. The role of pattern-recognition receptors in innate immunity: update on Toll-like receptors. Nat. Immunol. 11, 373–384 (2010).
- 63.Kawai, T. & Akira, S. Toll-like receptor signaling. J. Biol. Chem. 282, 15319–15323 (2007).
- 64.Kawai, T. & Akira, S. Signaling to NF-kappaB by Toll-like receptors. Trends Mol. Med. 13, 460–469 (2007).
- 65.Kawai, T. & Akira, S. The role of pattern-recognition receptors in innate immunity: update on Toll-like receptors. Nat. Immunol. 11, 373–384 (2010).
- 66.Eisenbarth, S. C., Colegio, O. R., O’Connor, W., Sutterwala, F. S. & Flavell, R. A. Critical role for the Nlrp3 inflammasome in the immunostimulatory properties of aluminum adjuvants. Nature 453, 1122–1126 (2008).
- 67.Sutterwala, F. S., Ogura, Y., Szczepanik, M., Lara-Tejani, R., Lippelman, G. S., Grant, E. P., Bertin, J., Coyle, A. J., Flavell, R. A. & Fontana, A. Critical role for NALP3/CIAS1/Cryopyrin in innate and adaptive immunity through its regulation of caspase-1. Immunity 24, 317–327 (2006).
- 68.Iwamura, C., Bouladoux, N., Belkaid, Y., Sher, A. & Jankovic, D. Sensing pathogen and danger signals by segmented filamentous bacteria propels isotype switching in intestinal IgA+ B cells. Nat. Immunol. 13, 696–703 (2012).
- 69.Le Bourhis, L., Nguyen, S., Devialle, S., Vazeille, E., Huot, N., Fievet, A., Panagioti, E., Abdi, Z., Salmeron, M. & Huerre, M. R. Meningococcal polysaccharide-specific IgA responses are absent in adults who suffered from meningococcal disease. Vaccine 27, 4624–4631 (2009).
- 70.Macpherson, A. J., McCoy, K. D., Johansen, F. E. & Brandtzaeg, P. The immune geography of IgA induction and function. Mucosal Immunol. 1, 11–22 (2008).
- 71.Hapfelmeier, S., Lawson, M. A., Slack, E., Kirundi, J. K., Stoel, M., Heikenwalder, M., Cahenzli, J., Velykoredko, Y., Balint, M. & Balint, B. Reversible microbial colonization of germ-free mice conveys resistant to orally administered, live attenuated vaccine. J. Immunol. 184, 2070–2077 (2010).
- 72.Frey, A., Di Pierro, M., Paané, D. A., Edwards, M. J., Farrokhi, P., Utermohlen, O., Desrosiers, M. D., Lipton, M. S., Lipphardt, M., Malone, C. J. & von Andrian, U. H. Epithelial selectins and ectopic lymphoid-like structures in the coxsackievirus-infected intestine. Am. J. Pathol. 160, 27–41 (2002).
- 73.Zuercher, A. W., Fretschi, M., Carrier, A., Kappeli, R. & Corth, A. Orally-fed tumor antigen fails to induce Foxp3+, CD25+, IL-10-secreting regulatory T cells, yet elicits mucosal IgA responses and systemic anergy. Vaccine 24, 1778–1790 (2006).
- 74.Zuercher, A. W., Fretschi, M., Carrier, A., Kappeli, R. & Corth, A. Orally-fed tumor antigen fails to induce Foxp3+, CD25+, IL-10-secreting regulatory T cells, yet elicits mucosal IgA responses and systemic anergy. Vaccine 24, 1778–1790 (2006).
- 75.Johansson, U., Korsgren, O., Larsson, P., Arvidson, J., Nilsson, A. & Friberg, P. Effects of nicotine on proinflammatory cytokines and cell adhesion molecules in patients with stable angina pectoris. Cardiovasc. Res. 45, 68–74 (2000).
- 76.Johansson, U., Korsgren, O., Larsson, P., Arvidson, J., Nilsson, A. & Friberg, P. Effects of nicotine on proinflammatory cytokines and cell adhesion molecules in patients with stable angina pectoris. Cardiovasc. Res. 45, 68–74 (2000).
- 77.Papadopoulou, A. & Kletsas, D. Human lung fibroblasts primed by IFN-gamma produce RANTES, IL-6 and IL-8 upon stimulation by TNF-alpha and IL-1 beta. Eur. Respir. J. 10, 1076–1081 (1997).
- 78.Papadopoulou, A. & Kletsas, D. Human lung fibroblasts primed by IFN-gamma produce RANTES, IL-6 and IL-8 upon stimulation by TNF-alpha and IL-1 beta. Eur. Respir. J. 10, 1076–1081 (1997).
- 79.Carding, S. R. & Egan, P. J. Gamma delta T cells: functional plasticity and heterogeneity. Nat. Rev. Immunol. 2, 336–345 (2002).
- 80.Carding, S. R. & Egan, P. J. Gamma delta T cells: functional plasticity and heterogeneity. Nat. Rev. Immunol. 2, 336–345 (2002).
- 81.Himoudi, N., Besserer, M., Kawalekar, O. U., Shamaei, M., Miedema, F., Berger, C. L., Crosby, C., Gee, A. P., Kew, W., Barba, D. & Sadelain, M. Immunogenicity and functional properties of a tumor-associated glycoprotein 72-redirected TCR and its humanized counterpart to allow TCR gene immunotherapy without prior in vivo depletion of competing endogenous TCR. J. Immunol. 174, 1192–1199 (2005).
- 82.Himoudi, N., Besserer, M., Kawalekar, O. U., Shamaei, M., Miedema, F., Berger, C. L., Crosby, C., Gee, A. P., Kew, W., Barba, D. & Sadelain, M. Immunogenicity and functional properties of a tumor-associated glycoprotein 72-redirected TCR and its humanized counterpart to allow TCR gene immunotherapy without prior in vivo depletion of competing endogenous TCR. J. Immunol. 174, 1192–1199 (2005).
- 83.Lohman-Payne, B., Kourtis, A. P., van der Ven, A., Nkomo, T., Meintjes, G., Gray, G. E., Violari, A., Abrams, E. J. & Thea, D. M. Impaired maternal and neonatal HIV-specific T cell responses. J. Acquir. Immune Defic. Syndr. 46, 413–422 (2007).
- 84.Lohman-Payne, B., Kourtis, A. P., van der Ven, A., Nkomo, T., Meintjes, G., Gray, G. E., Violari, A., Abrams, E. J. & Thea, D. M. Impaired maternal and neonatal HIV-specific T cell responses. J. Acquir. Immune Defic. Syndr. 46, 413–422 (2007).
- 85.Birkhoff, G. D. Relativity and modern physics. (Harvard University Press, Cambridge MA, 1923).
- 86.Birkhoff, G. D. Relativity and modern physics. (Harvard University Press, Cambridge MA, 1923).
- 87.Alves, M. & Alves, R. M. Endoscopic and radiological anatomy. Surg. Clin. North Am. 80, 1631–1650 (2000).
- 88.Alves, M. & Alves, R. M. Endoscopic and radiological anatomy. Surg. Clin. North Am. 80, 1631–1650 (2000).
- 89.Pathan, N., Hemingway, C. A., Alizadeh, A. A., Stephens, A. C., Boldrick, J. C., Oragui, E. E., McCabe, C., Nadel, S., Relman, D. A., Fink, S. L., Brown, R. A., Levin, M. & Firth, G. O. Role of interleukins 6, 8, and 10 in Escherichia coli sepsis. Lancet 361, 645–650 (2003).
- 90.Pathan, N., Hemingway, C. A., Alizadeh, A. A., Stephens, A. C., Boldrick, J. C., Oragui, E. E., McCabe, C., Nadel, S., Relman, D. A., Fink, S. L., Brown, R. A., Levin, M. & Firth, G. O. Role of interleukins 6, 8, and 10 in Escherichia coli sepsis. Lancet 361, 645–650 (2003).
- 91.Cvetkovic, Z., Mohr, U., Wieland, W., Flehmig, B. & Vallbracht, A. Hepatitis A virus-specific T cell response during acute hepatitis A. J. Med. Virol. 54, 8–14 (1998).
- 92.Cvetkovic, Z., Mohr, U., Wieland, W., Flehmig, B. & Vallbracht, A. Hepatitis A virus-specific T cell response during acute hepatitis A. J. Med. Virol. 54, 8–14 (1998).
- 93.Borg, N. A., Ely, L. K., Beddoe, T., Mailer, R. K., Liu, L., Pereira, P., Schaeffer, M., Stuart, S. L., Charters, M. A., Primo, V., Lawson, B. R., Tisch, R., Tan, K. & McCluskey, J. T cell receptor recognition of self and non-self MHC molecules. Cell 127, 305–317 (2006).
- 94.Borg, N. A., Ely, L. K., Beddoe, T., Mailer, R. K., Liu, L., Pereira, P., Schaeffer, M., Stuart, S. L., Charters, M. A., Primo, V., Lawson, B. R., Tisch, R., Tan, K. & McCluskey, J. T cell receptor recognition of self and non-self MHC molecules. Cell 127, 305–317 (2006).
- 95.Giudice, G. D. Vaccines for diseases associated with poverty. Adv. Microb. Infect. Immunol. 332, 257–271 (2000).
- 96.Giudice, G. D. Vaccines for diseases associated with poverty. Adv. Microb. Infect. Immunol. 332, 257–271 (2000).
- 97.Hinney, A., Nguyen, T. T., Scherag, A., Friedel, S., Brönner, G., Müller, T. D., Senf, W., Zahorska-Markiewicz, B., Zdichynec, B., Spermon, T., Blundell, J. E., Astrup, A., Top, A., Wolters, B., Bluher, M., Maier, W. & Hebebrand, J. Genome wide association (GWA) study for early onset extreme obesity supports the role of fat mass and obesity associated gene (FTO) variants. PLoS ONE 2, e1361 (2007).
- 98.Hinney, A., Nguyen, T. T., Scherag, A., Friedel, S., Brönner, G., Müller, T. D., Senf, W., Zahorska-Markiewicz, B., Zdichynec, B., Spermon, T., Blundell, J. E., Astrup, A., Top, A., Wolters, B., Bluher, M., Maier, W. & Hebebrand, J. Genome wide association (GWA) study for early onset extreme obesity supports the role of fat mass and obesity associated gene (FTO) variants. PLoS ONE 2, e1361 (2007).
- 99.Willer, C. J., Speliotes, E. K., Loos, R. J., Li, S., Lindgren, C. M., Heid, I. M., Berndt, S. I., Elliott, A. L., Jackson, A. U., Lamina, C., Lettre, G., Lim, N., Lyon, H. N., McCarroll, S. A., Papadakis, K., Qi, L., Randall, J. C., Rocheleau, C. N., Rotimi, C. N., Senpudi, S., Silander, K., Song, K., Tanaka, T., Tarter, K., Teslovich, T. M., Thorleifsson, G., Vandenput, L., Vidal, P. M., Villareal, D. L., Visvanathan, K., Vital, B., Wang, L., Weedon, M. N., Weir, B. S., Werts, N. J., White, C. C., Wilkening, B., Willenbecher, J., Wineriter, S. A., Winkler, T. W., Wolford, B. Q., Wright, A. F., Wu, Y., Wu, Y., Yanovsky, A., Zeller, T., Zhai, G., Zhao, J. H. & Ziegler, A. Six new loci associated with body mass index highlight a neurobiological pathway controlling human adiposity. PLoS Genet. 5, e1000508 (2009).
- 100.Willer, C. J., Speliotes, E. K., Loos, R. J., Li, S., Lindgren, C. M., Heid, I. M., Berndt, S. I., Elliott, A. L., Jackson, A. U., Lamina, C., Lettre, G., Lim, N., Lyon, H. N., McCarroll, S. A., Papadakis, K., Qi, L., Randall, J. C., Rocheleau, C. N., Rotimi, C. N., Senpudi, S., Silander, K., Song, K., Tanaka, T., Tarter, K., Teslovich, T. M., Thorleifsson, G., Vandenput, L., Vidal, P. M., Villareal, D. L., Visvanathan, K., Vital, B., Wang, L., Weedon, M. N., Weir, B. S., Werts, N. J., White, C. C., Wilkening, B., Willenbecher, J., Wineriter, S. A., Winkler, T. W., Wolford, B. Q., Wright, A. F., Wu, Y., Wu, Y., Yanovsky, A., Zeller, T., Zhai, G., Zhao, J. H. & Ziegler, A. Six new loci associated with body mass index highlight a neurobiological pathway controlling human adiposity. PLoS Genet. 5, e1000508 (2009).
- 101.Speliotes, E. K., Willer, C. J., Berndt, S. I., Monda, K. L., Thorleifsson, G., Jackson, A. U., Lango, H., Larson, C., Leitzmann, M. F., Lim, U., Lyon, H. N., McCarroll, S. A., Meitinger, T., Mulas, A., Muller-Nurasyid, M., Pfeiffer, R. M., Prokopenko, I., Qi, L., Qibin, Q., Randall, J. C., Randall, J. C., Rayner, N. W., Renström, F., Steinthorsdottir, V., Stringham, H. M., Sung, Y. J., Swift, A. J., Syvänen, A. C., Tan, A., Thorand, B., Toslef, S., Trakalo, J., Trias-Vidal, A., Tsai, F. J., Tseng, C. H., Tusi, A. H., Tuso, P., Uitterlinden, A. G., Ulate, S. K., Uusitalo, U., Vimaleswaran, K. S., Vitart, V., Vivi, V., Vlachopoulos, C., Voegele, C., Voight, B. F., Volk, A., Votruba, S. B., Voytas, M. S., Wach, S., Wagner, K., Waite, L. L., Wallace, R. B., Wallaschofski, H., Wang, A., Wang, J., Wang, J. C., Wang, L., Wang, L., Wang, S. J., Wang, Y., Wang, Z., Wang, Z., Wani, Z., Wanner, M., Ward, M. P., Ward, R., Wardell, C. P., Warendes, E., Warrington, A., Warstrom, C., Watanabe, R. M., Waters, E., Watkins, E., Watson, B., Watson, M., Watson, P., Watson, S., Watt, J., Watzinger, P., Webb, A., Webb, J., Webb, R., Webb, R. T., Webb, S., Weck, M. A., Wegner, B., Wei, J., Wei, J., Wei, S., Weil, S., Weill, C., Weinberger, C., Weiner, B., Weiner, L. S., Weiner, P. M., Weininger, I., Weinkove, D., Weinreich, D., Weinrich, D. & Weisburger, E. E. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).