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Biology subjects

Ambalavanan, A.

Publications and source records attributed to Ambalavanan, A..

3 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 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↗

Persistent DNA methylation changes associated with prenatal NO2 exposure in a Canadian prospective birth study

BackgroundAccumulating evidence suggests prenatal air pollution exposure alters DNA methylation (DNAm), which could go on to affect long-term health. However, it remains unclear whether prenatal DNAm alterations persist through early life. Identifying DNAm changes that persist from birth into childhood would provide greater insight into the molecular mechanisms that most likely contribute to the association of prenatal air pollution exposure with health outcomes such as atopic disease. ObjectivesThis study investigated the persistence of DNAm changes associated with prenatal NO2 exposure (a surrogate measure of traffic-related air pollution) at age one to begin characterizing which DNAm changes most likely to contribute to atopic disease. MethodsWe used an atopy-enriched subset of CHILD study participants (N=145) to identify individual and regional cord blood DNAm differences associated with prenatal NO2, followed by an investigation of persistence in age one peripheral blood. As we had repeated DNAm measures, we also isolated postnatal-specific DNAm changes and examined their association with NO2 exposure in the first year of life. MANOVA tests were used to examine the association between DNAm changes associated with NO2 and child wheeze and atopy. ResultsWe identified 24 regions of altered cord blood DNAm, with several annotated to HOX genes. Two regions annotated to MPDU1 and C5orf63 were significantly associated with age one wheeze. Further, we found the effect of prenatal NO2 exposure across CpGs within all altered regions remained similar at age one. A single region of postnatal-specific DNAm annotated to HOXB6 was associated with year one NO2 and age one atopy. DiscussionRegional cord blood DNAm changes associated with prenatal NO2 exposure persist through at least the first year of life, and some of these changes are associated with age one wheeze. The early-postnatal period remains a sensitive window to DNAm perturbations that may also influence child health.

genetics↗