Biology subjects

Granger, D. A.

Publications and source records attributed to Granger, D. A..

2 recordsLinked to original sources

bioRxiv2024

Considerations for Cell Type Heterogeneity in Pediatric Salivary DNA Methylation Analyses: Comparison of Reference Panels & Stratification by Estimated Cell TypeProportion

Saliva is widely used in biomedical population research, including epigenetic analyses to investigate gene-environment interplay and identify biomarkers. Its minimally invasive collection procedure makes it ideal for studies in pediatric populations. Saliva is a heterogenous tissue composed of immune and buccal epithelial cells (BEC). Amongst the many epigenetic marks, DNA methylation (DNAm) is the most studied in human populations. Given DNAms integral role to cellular differentiation and maintenance, DNAm profiles are often highly cell type (CT)-specific and CT composition can drive salivary DNAm associations with environments or health as well as epigenetic age acceleration (EAA), discrepancy between chronological age and biological age derived from DNAm. To address this, reference-based CT deconvolution and statistically adjustment with estimated CT in DNAm analyses have become a common practice. However, it remains unclear how different CT reference panels--constructed from adult versus pediatric samples--affect DNAm results. Additionally, whether DNAm and EAA associations in saliva primarily originate from immune cells or BECs, or if they persist across saliva samples despite varying CT proportions, has yet to be examined. The current study used salivary DNAm samples obtained from 529 children (mean age=7.26 years, SD=0.26 years) in a community-based cohort, the Family Life Project. Our results highlighted the impact of estimated CT discrepancies across child and adult reference panels on DNAm associations. Upon stratifying the salivary DNAm samples into three subsamples--primarily BECs, primarily immune cells, and an approximately equal mix of both, we found significantly different EAAs across stratified samples when CT proportions were not accounted for. In both the context of DNAm and EAA associations, we detected stronger effects of cotinine concentrations, a tobacco smoke-exposure biomarker, in the subsample with primarily immune cells. We discussed the implications of our findings for the interpretation and replication of epigenetic research involving pediatric saliva samples.

bioinformatics↗

bioRxiv2024

Salivary metabolomics in the family environment: A large-scale study investigating oral metabolomes in children and their parental caregivers

Human metabolism is complex and dynamic, and is impacted by genetics, diet, health, and countless inputs from the environment. Beyond the genetics shared by family members, cohabitation leads to shared microbial and environmental exposures. Furthermore, metabolism is affected by factors such as inflammation, environmental tobacco smoke (ETS) exposure, metabolic regulation, and exposure to heavy metals. Metabolomics represents a useful analytical method to assay the metabolism of individuals to find potential biomarkers for metabolic conditions that may not be phenotypically obvious or represent unknown physiological processes. As such, we applied untargeted LC-MS metabolomics to archived saliva samples from a racially diverse group of elementary school-aged children and their caregivers collected during the "90-month" assessment of the Family Life Project. We assayed a total of 1,425 saliva samples of which 1,344 were paired into 672 caregiver/child dyads. We compared the metabolomes of children (N = 719) and caregivers (N = 706) within and between homes, performed population-wide "metabotype" analyses, and measured associations between metabolites and salivary biomeasures of inflammation, antioxidant potential, ETS exposure, metabolic regulation, and heavy metals. Dyadic analyses revealed that children and their caregivers have largely similar salivary metabolomes. Although there were differences between the dyads at the individual levels of analysis, dyad explained most (62%) of the metabolome variation. At a population level of analysis, our data clustered into two large groups, indicating that people likely share most of their metabolomes, but that there are distinct "metabotypes" across large sample sets. Lastly, individual differences in several metabolites - which were putative oxidative damage-associated or pathological markers - were significantly correlated with salivary measures indexing inflammation, antioxidant potential, ETS exposure, metabolic regulation, and heavy metals. Implications of the effects of family environment on metabolomic variation at the population, dyadic, and individual levels of analyses for health and human development are discussed.

systems biology↗