Respiratory Infection- and Asthma-prone, Low Vaccine Responder Children Demonstrate Distinct Mononuclear Cell DNA Methylation Pathways

Background Infants with frequent viral and bacterial respiratory infections exhibit compromised immunity to routine immunisations. They are also more likely to develop chronic respiratory diseases in later childhood. This study investigated the feasibility of epigenetic profiling to reveal endotype-specific molecular pathways with potential for early identification and immuno-modulation. Peripharal immune cells from respiratory infection allergy/asthma prone (IAP) infants were retrospectively selected for genome-wide DNA methylation and single nucleotide polymorphism analysis. The IAP infants were enriched for the low vaccine responsiveness (LVR) phenotype (Fishers Exact p-value = 0.01). Results An endotype signature of 813 differentially methylated regions (DMRs) comprising 238 lead CpG associations (FDR < 0.05) emerged, implicating pathways related to asthma, mucin production, antigen presentation and inflammasome activation. Allelic variation explained only a minor portion of this signature. Stimulation of mononuclear cells with monophosphoryl lipid A (MPLA), a TLR agonist, partially reversing this signature at a subset of CpGs, suggesting the potential for epigenetic remodelling. Conclusions This proof-of-concept study establishes a foundation for precision endotyping of IAP children and highlights the potential for immune modulation strategies using adjuvants for furture investigation.


Background
Respiratory infections in early life are among the leading causes of childhood morbidity and mortality and the most common cause of respiratory admissions to hospitals (1).While vaccines can help prevent some respiratory diseases, some children exhibit a phenotype of low responsiveness to their routine schedule of vaccines (2), and are vulnerable to upper respiratory tract infections, particularly acute otitis media (3,4).A 10-year study of acute otitis media (AOM) children conducted in Rochester, New York, revealed that children prone to AOM, termed otitis prone (OP), exhibit sub-protective IgG antibody levels to most of their scheduled paediatric vaccine antigens (5).Clinically, they also experience substantially higher rates of subsequent allergies and asthma (6).Otitis prone children are more susceptible to respiratory infections due to associated nasopharyngeal (NP) and systemic immune de cits (7,8).Speci cally, a reduced capacity to respond to infections is associated with reduced activation of Toll-like receptor (TLR) signalling pathways and cytokine production (8), leading to attenuated stimulation of CD4 + T cell responses, speci cally Th17 responses to extracellular pathogens (9).This disparity in immune development in children who suffer from high rates of otitis-media, allergy and asthma constitutes a unique endotype of 'respiratory infection, allergy/asthma-prone children' (IAP) that merits focused investigation for the development of precision interventions (2,(9)(10)(11).
Endotyping approaches to classify diseases based on molecular and biological pathways, rather than clinical symptoms, have the potential to lead to new options for early preventative treatments (12,13).In this study we undertook a precision endotyping approach focused on DNA methylation biomarkers (CpG methylation) which are regulatory base modi cations to DNA that in uence cellular immune responses across the life course (14).Such modi cations vary according to host genotypic variation and environmental in uences, and therefore contain information about both genome and environment (15).Their potential utility for endotyping is supported by studies that demonstrate respiratory infections modify host CpG methylation in both nasal tissue and blood leukocytes (16,17).
Post-infectious epigenetic modi cations to leukocyte DNA can be retained at immune-response genes, a concept known as 'epigenetic scars', and these modi cations can induce cellular non-responsiveness ('tolerance'), increasing host susceptibility to severe infections and sepsis (18).We postulated that DNA methylation pro ling of PBMCs might reveal similar markers in IAP children that could be harnessed to develop endotype signatures.One particularly attractive feature of the 'epigenetic scars' concept is the potential to restore functionality using adjuvants that reverse speci c nucleic acid base modi cations (19,20).As part of the mission of the National Institutes of Health (NIH)/National Insitute of Allergy & Infectious Diseases (NIAID) Immune Development in Early Life (IDEAL) program, we sought to identify modi able pathways of immune development with therapeutic potential by investigating the impact of a common adjuvant on endotype-speci c molecular signatures.This approach may identify molecules with the potential to redirect the course of immune development in vulnerable children away from endotypes associated with disease and towards those associated with health (21).
In this proof-of-concept study, we retrospectively selected children from a longitudinal child cohort enrolled and followed prospectively in Rochester from 6 to 60 months of age.PBMCs from 14 IAP infants collected in the rst year of life were matched to 16 non-respiratory infection allergy/asthma-prone (NIAP) controls using extremes of phenotype design.Sample classi cations for vaccine responsiveness determined from antibody responses to primary vaccinations were previously available on this cohort (22).PBMCs collected from these children, were cultured with vehicle control or with monophosphoryl lipid A (MPL) which is a TLR4 agonist.At culture endpoint, cells were harvested for methylome-and genome-wide association analysis using In nium microarray technology.Using differential analysis, we identi ed an endotype signature of epigenetic differences in unstimulated cells that was partially modi able using MPL adjuvant following in vitro stimulation

Study Participant Characteristics
The characteristics of the participants are presented in Table 1.The IAP group consisted entirely of Caucasian ancestry, whereas the NIAP group comprised 81% Caucasians and 19% other ethnicities.Principal component analysis (PCA) of genotypes con rmed that the study cohort was predominantly of Caucasian ancestry, with three individuals clustering with middle clines re ecting Latino and African American ancestry components (Figure S1).The IAP group were enriched with the low vaccine responder (LVR) phenotype (Fishers Exact p-value = 0.01) and had a higher burden of stringently de ned otitis media (Fisher's Exact p-value = 3.3x10 − 5 ).
To assess the role of genetic variation in shaping this endotype-associated methylation signature, methylation quantitative trait loci (mQTL) analysis was performed using individual regression models for SNP-CpG pairs within a +/-500 kb window of endotype-associated CpGs (1,079,685 SNP markers).This analysis revealed 52 unique SNP associations with methylation levels at 5 CpG dinucleotides (FDR < 0.05) (Fig. 1B).
To decipher the cellular speci city this endotype signature, immune cell subset deconvolution of peripheral blood methylation pro les was performed using differentially methylated cell type (DMCT) analysis (39).This analysis did not reveal differentially methylated cell types, suggesting a generalized signature across immune cell subsets (Figure S2).

In vitro MPL stimulation Alters DNA Methylation in Endotype-Associated Genes
To explore how innate immune activation might affect disease-linked methylation patterns, PBMC from the same subjects were stimulated with MPL for 24 hours and analyzed via EWAS.Compared to resting cells, MPL stimulation induced modest methylation changes (average effect size ~ +/-5%) in 113 regions (FDR < 0.05), encompassing both hypermethylation (60 regions) and hypomethylation (53 regions).
Immune cell type distributions remained unchanged after stimulation (Figure S2).
Gene ontology analysis revealed enrichment of similar pathways as the endotype signature (eg, asthma, type 1 diabetes) and additional pathways linked to in ammasome signaling (NF-κB signaling, Th17 cell differentiation) suggesting the potential of MPL to target endotype-associated methylation (Fig. 2A).A modest but signi cant overlap (P = 0.002 hypergeometic test) between endotype-and stimulationassociated regions was detected (Fig. 2B).Interestingly, 183 of 268 signi cant CpGs differentiating NIAP and IAP groups lost signi cance after MPL treatment (FDR P ≥ 0.05), indicating speci c methylation pattern modulation by MPL.

Discussion
This study introduces a novel approach to identifying children vulnerable to respiratory infections and low vaccine responsiveness through precision epigenetic endotyping.This approach holds potential to revolutionize early detection and guide targeted interventions, ultimately improving immune resilience and preventing chronic disease in this at-risk group (5).Aligning with the NIH/NIAID's IDEAL consortium mission, our work aims to personalize immunizations and prevent infectious diseases in early life by combining disease-and adjuvant-associated molecular signatures to identify molecules capable of reversing disease pathways.These results pave the way for larger-scale validation studies with the hope of personalized immunotherapies, transforming healthcare for children at high risk of severe respiratory illnesses.
Children within this long-term Rochester cohort experiencing a high burden of recurrent infections in their rst ve years of life (5) exhibited a distinct molecular signature in mononuclear immune cells collected during infancy.The endotype-signature was characterized by increased methylation in genes governing pro-in ammatory immune responses including in ammasome formation, antigen processing, and glycan biosynthesis.This suggests a potential role in heightened susceptibility to respiratory infections (23,40).While our analysis indicated minimal in uence from genetic risk variants, implying the disease signature re ects more of a developmental programming element, further studies with larger sample sizes are needed to solidify this conclusion.
Notably, this study demonstrates the ability of our methylation pro ling approach to identify clinically relevant signatures that corroborate existing clinical and immunological ndings in IAP children (2,4,7,8,10,(41)(42)(43)(44).For example, we observed increased methylation at several MHC genes, consistent with the previous reports of reduced surface expression of MHC II proteins in this population (2).Additionally, our ndings regarding increased methylation in MyD88, a key component of PRR-mediated signalling, align with previous observations of reduced IRF7 mRNA expression, and IFN-α production (2,(9)(10)(11).Similarly, increased methylation of in ammasome-related genes (NLRP1, IL18RAP, PCSK6, FLT4) in IAP children is consistent with reduced IL-1β mediated T-helper 17 immunity reported in children prone to AOM (9).Future large-scale investigations incorporating functional assays are curcial to validate these observations and explore their potential for personalized interventions in vulnerable children.
While increased methylation at identi ed genes suggests potential dysregulation in relevant pathways, the functional consequences require further investigation.Hypermethylation often impedes DNA accessibility, potentially reducing gene expression and contributing to the IAP phenotype (45).However, the context-dependent nature of methylation necessitates additional functional follow-up studies.Importantly, it is critical to recognize that the observed changes may not re ect causal drivers but could also arise from gene-environment interactions or disease manifestations.
While our ndings reveal minimal overall overlap between endotype-and stimulation-associated methylation patterns, the selective modulation of 8 CpGs by MPL suggests its potential to ne-tune methylation landscapes and potentially in uence downstream transcriptional and functional pathways.
Further investigations into these speci c CpGs are warranted to elucidate the precise mechanisms underlying MPL's functional impact.The results reported here encourage further development of this approach, leveraging a platform of in vitro modelling combined with single-cell epigenetic analysis to relate adjuvant compounds to their target genes.Developing a computational epigenetic signatures database could provide a valuable resource for precision immunisation approaches in vulnerable populations and a starting point for pre-clinical studies.This study establishes the foundation for a precision epigenetic endotyping approach, potentially improving health outcomes in children at high risk for respiratory illnesses.

Sample Selection
Participants in a prospective trial conducted in Rochester NY, USA were children recruited from community-based primary care pediatric practices, as previously described (5).Infection prone status was determined from a data-driven analysis of illness visits over the rst 5 years of life, described in (6).For the current study, a subset of 30 participants was selected, 14 of which were IAP cases and 16 NIAP controls.Participant selection was intentionally guided to emphasize the extremes of clinical phenotypes, that is, respiratory infection allergy/asthma-prone children (IAP) enriched for low vaccine responder (LVR) phenotype vs. non-respiratory infection allergy/asthma prone (NIAP) (Table 1) (22).The study ethics and protocol were approved by The Rochester Regional Health Human Subjects Review Board under the approval number CIC 1141-B-09-1 (46).

PBMC stimulation in vitro
Cryopreserved PBMCs were thawed by dropwise addition of cold RPMI 1640 medium (Gibco) supplemented with penicillin/streptomycin (Gibco), 2 mM l-glutamine (Gibco), and 10% bovine serum (Hyclone).Stimuli included synthetic Monophosphoryl Hexa-acyl Lipid A, 3-Deacyl (3D(6A)PHAD, Avanti Polar Lipids), abbreviated to MPL.MPL was used at a concentration of 1 µg/mL.MPL was prepared at 10X the desired concentration and 20 µL was added to at-bottom sterile, pyrogen-free 96-well culture dish wells (Corning).PBMCs were washed with RPMI, counted, and viable cells determined by Trypan Blue staining (Gibco) were resuspended in RPMI supplemented with penicillin/streptomycin, 2 mM lglutamine, and 10% autologous plasma at a concentration of 2.78 million cells/mL.180 µL of cell suspension (500.000PBMCs) were plated on top of stimuli.Plates were incubated for 24 hours in a humidi ed incubator with 5% CO2.After 24 hours, culture supernatants were stored at − 80 C and cells were centrifuged for 3 minutes at 500 rpm.Cell pellets were resuspended in 500 µL RNAlater (Sigma-Aldrich) for downstream DNA methylation pro ling and frozen at -80 degrees.

DNA methylation Pro ling
PBMC pellets were thawed and washed in phosphate buffered saline solution (PBS) prior to genomic DNA extraction using the Chemagic DNA 400 kit H96 (cat # CMG-1491).DNA samples were quanti ed using the Qant-iT HS kit (cat# Q33120) and randomized by endotype label prior to being sent to the Australian Genome Research Facility in Melbourne, Western Australia, for bisul te conversion and genotyping using Illumina In nium MethylationEPIC Beadchip v1 arrays.Bisul te-converted genomic DNA was analyzed using Illumina's In nium Human Methylation EPIC BeadChips, which enable methylation measures at over 850,000 CpG sites.Raw.iDAT les were pre-processed using the Min (47) package from the Bioconductor project (http://www.bioconductor.org) in the R statistical environment (http://cran.r-project.org/version 4.2.2).Sample quality was assessed using control probes on the array, and no samples were removed.Array normalization employed the strati ed quantile method to correct for type 1 and type 2 probe biases.Probes exhibiting a P-detection call rate of > 0.01 in one or more samples were removed (44,881 probes) prior to analysis.Probes containing SNPs at the single base extension site or at the CpG assay site were removed, as were probes measuring non-CpG loci (30,015 probes).Probes reported to have off-target effects by McCartney et al. (48) were also removed (39,489 probes).After ltering, the nal dataset consisted of 120 samples and 751,474 probes.Methylation ratios were derived as β values ((methylated alleles)/((unmethylated + methylated) × 100)) with log 2 transformation to M values for statistical analysis.

Genotyping and imputation
Aliquots of genomic DNA samples were genotyped by the Australian Genome Research Facility using the Illumina Global Screening Array v3 with a multi-disease drop-in.Genotype calling was performed using the gencall algorithm in GenomeStudio (Illumina).Quality control was performed using the plinkQC package (v 0.3.4)(49) to remove samples with > 5% missing data, with high relatedness (PI_HAT > 0.2) in identity-by-descent analysis for all pairs of samples, or with mismatched ancestry estimates based on principal component analysis of merged data with the 1000 genomes phase 3 data set.We also excluded SNPs characterized by > 5% missing values, a Hardy-Weinberg equilibrium p-value < 0.001, and a minor allele frequency of < 5%.Quality controlled data were then imputed with the Haplotype Reference Consortium hg19 r1.1 reference panels using Beagle 5.4 on the Michigan imputation server.Imputed genotypes were ltered to remove SNPs with a minor allele frequency of < 5% and Hardy-Weinberg equilibrium p-value < 0.001, with an r2 value > 0.3.

Statistical analysis
Linear regression modelling employed the R statistical environment using the limma package (50) to test the association between DNA methylation, IAP status, and stimulation conditions.A factorial model with main effects on disease status and stimulation, including covariate adjustment for cell-type proportions, the 1st ve principal components of the methylation data set, 1st two principal components of the SNP data set, sex, and age, was tted to the methylation ratios (M-values).We used the cor t function to adjust the variance estimates for the repeated-measures samples.To determine endotype-speci c signatures, we compared unstimulated samples for the main effect of disease status adjusted for covariates.Summary statistics from the model t were used to identify differentially methylated regions (DMR) using the R package DMRcate (51).DMRs were de ned using a lambda of 1000, min.cpgs of 4, and adjusted p-cutoff of 0.05.To derive stimulus-speci c signatures, we compared each stimulation with the unstimulated control samples, adjusting for disease status and covariates.Enrichment analysis of all differentially methylated regions was conducted using the missMethyl R package (52).Cell-type proportions were estimated using the EpiDish (Epigenetics Dissection of Intra-Sample Heterogeneity) package in R (39).The cis-meQTL test for association of nearby SNPs with DNA methylation measurements was carried out using the MatrixEQTL R package (v2.3)(53), with covariate adjustment for sex, and the ve principal components of methylation array control probes and genotyping array ancestry estimates.
Data management and deposition.Data management for this project utilized a centralized cloud computing based management approach as previously described (54,55).Using quality control (QC) procedures for clinical, immunologic and epigenetic data, we implemented rigorous checks to ensure the reliability and accuracy of the datasets.Quality assurance (QA) involved systematic processes to validate the integrity and consistency of the generated data.This included verifying data completeness across datasets, conducting checks for

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Table 1 -
Demographics of study cohort