Rhodes, O. E. | Search bioRxiv

bioRxiv2021

The aging DNA methylome reveals environment-by-aging interactions in a model teleost

The rate at which individuals age underlies variation in life history and attendant health and disease trajectories. Age specific patterning of the DNA methylome ("epigenetic aging") is strongly correlated with chronological age in humans and can be modeled to produce epigenetic age predictors. However, epigenetic age estimates vary among individuals of the same age, and this mismatch is correlated to the onset of age-related disease and all-cause mortality. Yet, the origins of epigenetic-to-chronological age discordance are not resolved. In an effort to develop a tractable model in which environmental drivers of epigenetic aging can be assessed, we investigate the relationship between aging and DNA methylation in a small teleost, medaka (Oryzias latipes). We find that age-associated DNA methylation patterning occurs broadly across the genome, with the majority of age-related changes occurring during early life. By modeling the stereotypical nature of age-associated DNA methylation dynamics, we built an epigenetic clock, which predicts chronological age with a mean error of 29.1 days (~4% of average lifespan). Characterization of clock loci suggests that aspects of epigenetic aging are functionally similar across vertebrates. To understand how environmental factors interact with epigenetic aging, we exposed medaka to four doses of ionizing radiation for seven weeks, hypothesizing that exposure to such an environmental stressor would accelerate epigenetic aging. While the epigenetic clock was not significantly affected, radiation exposure accelerated and decelerated patterns of normal epigenetic aging, with radiation-induced epigenetic alterations enriched at loci that become hypermethylated with age. Together, our findings advance ongoing research attempting to elucidate the functional role of DNA methylation in integrating environmental factors into the rate of biological aging.

genomics↗

bioRxiv2020

Improved microbial community characterization of 16S rRNA via metagenome hybridization capture enrichment

Environmental microbial diversity is often investigated from a molecular perspective using 16S ribosomal RNA (rRNA) gene amplicons and shotgun metagenomics. While amplicon methods are fast, low-cost, and have curated reference databases, they can suffer from amplification bias and are limited in genomic scope. In contrast, shotgun metagenomic methods sample more genomic regions with fewer sequence acquisition biases. However, shotgun metagenomic sequencing is much more expensive (even with moderate sequencing depth) and computationally challenging. Here, we develop a set of 16S rRNA sequence capture baits that offer a potential middle ground with the advantages from both approaches for investigating microbial communities. These baits cover the diversity of all 16S rRNA sequences available in the Greengenes (v. 13.5) database, with no sequence having < 80% sequence similarity to at least one bait for all segments of 16S. The use of our baits provide comparable results to 16S amplicon libraries and shotgun metagenomic libraries when assigning taxonomic units from 16S sequences within the metagenomic reads. We demonstrate that 16S rRNA capture baits can be used on a range of microbial samples (i.e., mock communities and rodent fecal samples) to increase the proportion of 16S rRNA sequences (average >400-fold) and decrease analysis time to obtain consistent community assessments. Furthermore, our study reveals that bioinformatic methods used to analyze sequencing data may have a greater influence on estimates of community composition than library preparation method used, likely in part to the extent and curation of the reference databases considered.

microbiology↗