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Simons, E.

Publications and source records attributed to Simons, E..

6 recordsLinked to original sources

Interactions between human milk components and infant polygenic risk predict childhood atopy

BackgroundAlthough human milk (HM) confers important health benefits, how bioactive milk components (e.g., microbiota, oligosaccharides, and fatty acids) interact with infant genetics to influence childhood atopy remains poorly understood. ObjectiveWe investigated interactions between infant genomic susceptibility and exposure to maternal human milk components (HMCs) and assessed whether integrating these genetic and milk features improves prediction of childhood atopy. MethodsLeveraging infant genomic and maternal HMC data from the CHILD Cohort Study, we conducted gene-milk interaction analysis using linear regression models that integrated polygenic risk scores (PRS) of nursing infants with multiple HMC types. Gradient-boosting machines (GBMs) were used to evaluate predictive performance of HMCs and infant PRS for childhood atopy. ResultsChildhood atopy was associated with interactions between infant genomics (e.g., PRS associated with atopy) and exposure to specific human milk microbes (e.g., Abiotrophia, PBonf=0.005, {beta}=0.29), as well as networks of co-occurring HMCs (e.g., a module containing Bifidobacterium longum, 2-fucosyllactose, and eicosapentaenoic acid, P=0.009, {beta}=-12.3). A GBM integrating HMCs and infant PRS achieved the highest predictive performance for childhood atopy with an area under the curve (AUC) of 0.78, outperforming models based on individual HMC types or PRS alone (AUC range: 0.54-0.63). ConclusionIntegration of maternal HMC exposures with infant genomics reveals interaction effects that contribute to prediction of childhood atopy. Understanding how early-life exposures such as HMCs impact the health of children differently depending on their genomic profiles may facilitate the development of personalized intervention strategies to reduce the burden of these health outcomes during childhood. Key messagesO_LIInteractions between infant polygenic risk and exposure to human milk components are associated with childhood atopy. C_LIO_LINetworks of co-occurring human milk microbiota, oligosaccharides, and fatty acids may influence childhood atopy, with effects varying by infant genomic susceptibility. C_LIO_LIIntegration of human milk components with infant genomics improves prediction of childhood atopy compared with individual milk components or genomics alone. C_LI Capsule SummaryThis study demonstrates that interactions between infant polygenic risk and maternal milk components improve prediction of childhood atopy, highlighting opportunities for personalized early-life prevention strategies.

genomics↗

Human breast milk extracellular vesicles from mothers with asthma differentially modulate the release of inflammatory cytokines by primary human airway smooth muscle cells in a recipient-cell specific manner

Breastfeeding provides health benefits in childhood, reducing the frequency of gastrointestinal and respiratory infections. Breastmilk (BM) is a rich source of bioactive molecules including extracellular vesicles (EVs), which exert immunomodulatory signalling in recipient cells, with cargo that is affected by maternal characteristics. Here we investigated the biophysical characteristics of BM-EVs from mothers with (asthmatic BM-EVs) or without asthma (control BM-EVs) and their effect on the release of cytokines from primary human hTERT-immortalized airway smooth muscle cells (hASMs) from asthmatic or non-asthmatic (control) donors. BM-EVs were isolated using size exclusion chromatography (N=5/group), characterized biophysically and by EV-specific protein markers. In addition, BM-EV were co-cultured (48h) with primary hASM cells from both non-asthmatic (control) and asthmatic donors to determine the effect on cytokine release. All participants were Caucasian and the BM was collected 12-15 weeks postpartum. BM-EVs showed the presence of intact and small-EVs ([~]100 nm). Asthmatic BM-EVs appeared to have a smaller average EV size (135.6 nm) vs. controls (148.3 nm, p=0.0613), but [~]5-fold higher concentration of both total (p=0.0014) and small EVs (p=0.0016). The expression of EV subtype protein expression was reduced in asthmatic BM-EVs vs. control BM-EVs: CD63 by 86% (p=0.0224), flotillin-1 by 40% (p=0.0196), CD9 by 24% (p=0.0646) and HSP70 by 69% (p=0.0873). Asthmatic BM-EVs co-cultured with hASMs from control donors decreased pro-inflammatory cytokine release: MCP-1 by 55% (p=0.0286), IL-6 by 45% (p=0.0801) and IL-2 by 32% (p=0.0970) vs. control-BM-EVs. Conversely, asthmatic BM-EVs co-cultured with hASMs from asthmatic donors increased secretion of anti-inflammatory cytokine IL-10 by 32% (p=0.0660), and IL-1Ra by 75% (p=0.0875), and pro-inflammatory IL-2 by 57% (p=0.0688) vs. control-BM-EVs. Internalization of control and asthmatic BM-EVs was confirmed by labelled EV uptake experiments. No detrimental effects on cell viability with BM-EV treatment were observed. In summary, asthmatic BM-EVs are smaller and enriched in BM, and exert differential effects on cytokine release in a BM-donor and recipient-cell specific manner. Given that BM can enter infant airways, the immunomodulatory effects of BM-EVs on hASMs warrants further investigation to delineate the under underlying mechanisms.

cell biology↗

Human milk components interact with infant genomics to modulate gut microbiota, childhood asthma and atopy

The benefits of breastfeeding are well established; however, the mechanisms by which human milk components (HMCs) impact childrens long-term health remain poorly understood. We leveraged datasets from the CHILD Cohort Study to explore how exposure to variable HMCs-- including oligosaccharides (HMOs), fatty acids (HMFAs), and microbiota (HMM)--may influence infants gut microbiota and risk of childhood asthma and atopy. We identified HMCs (e.g., HMO lacto-N-fucopentaose III and HMFA linoleic acid) associated with gut microbes and microbial networks implicated in atopic diseases. Additionally, we determined that HMCs (e.g., HMM Pseudomonas oryzihabitans) interact with infants polygenic risk scores (PRSs) to influence these gut microbial features. Integration of HMCs, gut microbiota, and disease-associated PRSs into an unsupervised machine-learning model that clustered two groups of infants with differing disease prevalence. Our findings suggest that HMCs influence childhood asthma and atopy through modifications to the gut microbiota and modulated by interactions with infant genomics.

genomics↗

RAMEN: Dissecting individual, additive and interactive gene-environment contributions to DNA methylome variability in cord blood

DNA methylation (DNAme) is the most commonly studied epigenetic mark in human populations. DNAme has gained attention in the Developmental Origins of Health and Disease field due to its gene expression regulation and potential long-term stability. Genetic variation and environmental exposures are amongst the main factors influencing inter-individual DNAme variability. However, the proportion and genomic distribution of their individual, additive and interactive effects on the DNA methylome remains unclear. Here, we introduce RAMEN, a Findable, Accessible, Interoperable, and Reusable (FAIR) framework tailored for DNAme microarrays. Using machine learning and statistical techniques, RAMEN models and dissects gene-environment contributions to genome-wide Variably Methylated Regions (VMRs), while controlling for spurious associations. To comprehensively test the power of RAMEN, we analyzed and characterized VMRs from cord blood samples from two independent cohorts (CHILD and PREDO; overall n=1,662). We identified genetics as a consistent key contributor to DNAme variability, usually in additive and interactive combinations with the environment, with genetic terms explaining the largest proportion of DNAme variance, compared to environmental and interaction terms. Operationalizing RAMEN as an R package to conduct scalable genome-exposome contribution analyses, our results highlighted the importance of genetic variation in sculpting DNAme patterns in early life.

bioinformatics↗

Advancing Pediatric and Longitudinal DNA Methylation Studies with CellsPickMe, an Integrated Blood Cell Deconvolution Method

Prospective birth cohorts offer the potential to interrogate the relation between early life environment and embedded biological processes such as DNA methylation (DNAme). These association studies are frequently conducted in the context of blood, a heterogeneous tissue composed of diverse cell types. Accounting for this cellular heterogeneity across samples is essential, as it is a main contributor to inter-individual DNAme variation. Integrated blood cell deconvolution of pediatric and longitudinal birth cohorts poses a major challenge, as existing methods fail to account for the distinct cell population shift between birth and adolescence. In this paper, we critically evaluated the reference-based deconvolution procedure and optimized its prediction accuracy for longitudinal birth cohorts using DNAme data from the Canadian Healthy Infant Longitudinal Development (CHILD) cohort. The optimized algorithm, CellsPickMe, integrates cord and adult references and picks DNAme features for each population of cells with machine learning algorithms. It demonstrated improved deconvolution accuracy in cord, pediatric, and adult blood samples compared to existing benchmark methods. CellsPickMe supports blood cell deconvolution across early developmental periods under a single framework, enabling cross-time-point integration of longitudinal DNAme studies. Given the increased resolution of cell populations predicted by CellsPickMe, this R package empowers researchers to explore immune system dynamics using DNAme data in population studies across the life course.

bioinformatics↗

Transcriptomic diversity of amygdalar subdivisions across humans and nonhuman primates

The amygdaloid complex mediates learning, memory, and emotions. Understanding the cellular and anatomical features that are specialized in the amygdala of primates versus other vertebrates requires a systematic, anatomically-resolved molecular analysis of constituent cell populations. We analyzed five nuclear subdivisions of the primate amygdala with single-nucleus RNA sequencing in macaques, baboons, and humans to examine gene expression profiles for excitatory and inhibitory neurons and confirmed our results with single-molecule FISH analysis. We identified distinct subtypes of FOXP2+ interneurons in the intercalated cell masses and protein-kinase C-{delta} interneurons in the central nucleus. We also establish that glutamatergic, pyramidal-like neurons are transcriptionally specialized within the basal, lateral, or accessory basal nuclei. Understanding the molecular heterogeneity of anatomically-resolved amygdalar neuron types provides a cellular framework for improving existing models of how amygdalar neural circuits contribute to cognition and mental health in humans by using nonhuman primates as a translational bridge.

neuroscience↗