Biology subjects

Moraes, T. J.

Publications and source records attributed to Moraes, T. J..

11 recordsLinked to original sources

bioRxiv2026

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↗

bioRxiv2025

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↗

bioRxiv2025

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↗

bioRxiv2025

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↗

bioRxiv2023

Early human fetal lung atlas reveals the temporal dynamics of epithelial cell plasticity.

While animal models have provided key insights into conserved mechanisms of how the lung forms during development, human-specific developmental mechanisms are not always captured. To fully appreciate how developmental defects and disease states alter the function of the lungs, studies in human lung models are important. Here, we sequenced >150,000 single single-cells from 19 healthy human fetal lung tissues from gestational weeks 10-19 and identified at least 58 unique cell types/states contributing to the developing lung. We captured novel dynamic developmental trajectories from various progenitor cells that give rise to club, ciliated, and pulmonary neuroendocrine cells. We also identified four CFTR-expressing progenitor cell types and pinpointed the temporal emergence of these cell types. These developmental dynamics reveal broader epithelial cell plasticity and novel lineage hierarchies that were not previously reported. Combined with spatial transcriptomics, we identified both cell autonomous and non-cell autonomous signalling pathways that may dictate the temporal and spatial emergence of cell lineages. Finally, we showed that human pluripotent stem cell-derived fetal lung models capture cell lineage trajectories specifically through CFTR-expressing progenitor cells, that were also observed in the native fetal tissue. Overall, this study provides a comprehensive single-cell atlas of the developing human lung, outlining the temporal and spatial complexities of cell lineage development. HighlightsO_LISingle-cell transcriptomics atlas from 19 human fetal lungs reveals cellular heterogeneity and previously unappreciated cellular plasticity in the epithelial compartment. C_LIO_LIIdentification of novel CFTR-expressing progenitor cells that gives rise to club, ciliated and PNEC. C_LIO_LINovel RNA velocity facilitated the identification of dynamic lineage trajectories in the epithelial compartment. C_LIO_LITemporally regulated cell signaling through promiscuous interactions between sender and receiving cells may dictate cell lineage fates. C_LIO_LIIntegration of human pluripotent stem cell (hPSC)-derived fetal lung cells and organoids with primary lung dataset show hPSC-differentiations captures key developmental trajectories of fetal epithelial cell states. C_LI

developmental biology↗

bioRxiv2023

Collagen Tubular Airway-on-Chip for Extended Epithelial Culture andInvestigation of Ventilation Dynamics

The lower respiratory tract is a hierarchical network of compliant tubular structures that are made from extracellular matrix proteins with a wall lined by an epithelium. While microfluidic airway-on-a-chip models incorporate the effects of shear and stretch on the epithelium, week-long air-liquid-interface (ALI) culture remains limited to static conditions. The circular cross-section and substrate compliance associated with intact airways have yet to be recapitulated to allow studies of epithelial injuries under physiological and ventilation conditions. To overcome these limitations, we present a collagen tube-based airway model. Sustaining a functional human bronchial epithelium during two-week perfusion is accomplished by continuously supplying warm, humid air at the apical side and culture medium at the basal side. The model faithfully recapitulates human airways in size, composition, and mechanical microenvironment, allowing for the first time dynamic studies of elastocapillary phenomena associated with regular breathing as well as mechanical ventilation, along with the impact on epithelial cells. Findings reveal the epithelium to become increasingly damaged when subjected to repetitive collapse and reopening as opposed to overdistension and suggest expiratory flow resistance to reduce atelectasis. We expect the model to find broad potential applications in organ-on-a-chip applications for various tubular tissues.

bioengineering↗

bioRxiv2023

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↗

bioRxiv2021

A new platform for high-throughput therapy testing on iPSC-derived, immature airway from Cystic Fibrosis Patients

Induced pluripotent, stem cell (iPSC)-derived models of airway tissue have successfully modeled the primary defect in regulated chloride conductance caused by the major Cystic Fibrosis causing mutation, F508del. However, it remains unclear if iPSC-derived airway cultures can be used in high-throughput therapy development for F508del and rarer mutations. There is an urgent need for airway tissue models that reflect the variability of patient-specific responses and are scalable for therapy development. In the current work, we describe a robust, high-throughput fluorescence assay of mutant CFTR function in iPSCs differentiated to immature airway epithelium. This assay measures reproducible functional responses to modulators targeting either the major CF mutant F508del or the nonsense mutant: W1282X-CFTR. We show that the ranking of patient-specific responses to interventions in this stem-cell based model recapitulates the ranking observed in primary nasal epithelial cultures obtained from the same individuals. In summary, these proof-of-concept studies show that this novel platform has the potential to support therapy development and precision medicine for Cystic Fibrosis patients. One Sentence SummaryWe describe a fluorescence-based platform that enables high-throughput Cystic Fibrosis therapy testing using iPSCs differentiated to immature lung.

genomics↗

bioRxiv2020

Repeatability and reproducibility assessment in a large-scale population-based microbiota study: case study on human milk microbiota

BackgroundQuality control including assessment of batch variabilities and confirmation of repeatability and reproducibility are integral component of high throughput omics studies including microbiome research. Batch effects can mask true biological results and/or result in irreproducible conclusions and interpretations. Low biomass samples in microbiome research are prone to reagent contamination; yet, quality control procedures for low biomass samples in large-scale microbiome studies are not well established. ResultsIn this study we have proposed a framework for an in-depth step-by-step approach to address this gap. The framework consists of three independent stages: 1) verification of sequencing accuracy by assessing technical repeatability and reproducibility of the results using mock communities and biological controls; 2) contaminant removal and batch variability correction by applying a two-tier strategy using statistical algorithms (e.g. decontam) followed by comparison of the data structure between batches; and 3) corroborating the repeatability and reproducibility of microbiome composition and downstream statistical analysis. Using this approach on the milk microbiota data from the CHILD Cohort generated in two batches (extracted and sequenced in 2016 and 2019), we were able to identify potential reagent contaminants that were missed with standard algorithms, and substantially reduce contaminant-induced batch variability. Additionally, we confirmed the repeatability and reproducibility of our reslults in each batch before merging them for downstream analysis. ConclusionThis study provides important insight to advance quality control efforts in low biomass microbiome research. Within-study quality control that takes advantage of the data structure (i.e. differential prevalence of contaminants between batches) would enhance the overall reliability and reproducibility of research in this field.

microbiology↗

bioRxiv2020

Consumption of artificially sweetened beverages during pregnancy impacts infant gut microbiota and body mass index

Artificial sweetener consumption by pregnant women has been associated with an increased risk of infant obesity, but the underlying mechanisms are unknown. We aimed to determine if maternal consumption of artificially sweetened beverages (ASB) during pregnancy is associated with modifications of infant gut bacterial community composition during the first year of life, and whether these alterations are linked with infant body mass index (BMI) at one year of age. This research included 100 infants from the prospective Canadian CHILD Cohort Study, selected based on maternal ASB consumption during pregnancy (50 non-consumers and 50 daily consumers). We identified four microbiome clusters, of which two recapitulated the maturation trajectory of the infant gut bacterial communities from immature to mature and two deviated from this trajectory. Maternal ASB consumption was associated with the depletion of several Bacteroides sp. and higher infant BMI. As we face an unprecedented rise in childhood obesity, future studies should evaluate the causal role of gut microbiota in the association between maternal ASB consumption, infant development and metabolism, and body composition.

microbiology↗

bioRxiv2019

Non-nutritive sweetener consumption during pregnancy affects adiposity in mouse and human offspring

Overweight and obesity affect over 20% of children worldwide. Emerging evidence shows that nonnutritive sweeteners (NNS) could adversely influence weight gain and metabolic health, particularly during critical periods of development. Thus, we aimed to investigate the impact of prenatal NNS exposure on postnatal growth and adiposity. Among 2298 families participating in the CHILD cohort study, children born to mothers who regularly consumed NNS during pregnancy had elevated body mass index and adiposity at 3 years of age. In a complementary study designed to eliminate confounding by human lifestyle factors and investigate causal mechanisms, we exposed pregnant mice and cultured adipocytes to NNS (aspartame or sucralose) at doses relevant to human consumption. In mice, maternal NNS exposure caused elevated body weight, adiposity and insulin resistance in offspring, especially in males. Further, in 3T3-L1 pre-adipocyte cells, sucralose exposure during early stages of differentiation caused increased lipid accumulation and expression of adipocyte differentiation genes (e.g. C/EBP-, FABP4, FAS). The same genes were upregulated in the adipose tissue of male mouse offspring born to sucralose-fed dams. Together, these clinical and experimental findings provide evidence suggesting that maternal NNS consumption induces obesity risk in the offspring through effects on adiposity and adipocyte differentiation.\n\nOne Sentence SummaryMaternal consumption of non-nutritive sweeteners during pregnancy stimulates adipocyte differentiation, insulin resistance, weight gain, and adiposity in mouse and human offspring.

developmental biology↗