Biology subjects

Dumas Ang, S.

Publications and source records attributed to Dumas Ang, S..

3 recordsLinked to original sources

bioRxiv2026

Differential Methylation by Early Life Adversity in the Future of Families Child Wellbeing Study

Early life adversity (ELA) has a well-established link to mental health disorders later in life, yet the molecular mechanisms behind this relationship are incompletely understood. The Future of Families Child Wellbeing Study (FFCWS) provides an opportunity to examine experience encoding in the genome through functional changes in DNA methylation (DNAm) in a cohort enriched in subjects exposed to ELA. We investigated epigenome-wide differences in DNAm across thirteen early-life exposures in salivary samples from FFCWS participants. We calculated differential methylation associations with disease-linked genetic variants, evaluated tissue-specific gene expression, and assessed the persistence of DNAm changes from ages 9 to 15 years. Using data from the mSTARR-seq assay, we characterized methylation-dependent regulatory activity. Differential methylation in the FFCWS validated prior results and identified new genomic regions associated with child adversity. Differential methylation occurs in genomic regions likely to impact gene expression, and affected genes are expressed in disease-relevant tissues. We also identified association of genetic variants associated with downstream disorders near differential methylation, including depression, alcohol and substance use, and anxiety disorders. Overall, cumulative ELA is associated with specific DNAm changes, functional regulation, and persistence over time. Our findings indicate that ELA-associated differential methylation in the FFCWS does not simply occur at random, but in genomic regions that are functional. Our results support the conclusion that altered DNAm represents a biological link between early life experience and later health outcomes.

genomics↗

bioRxiv2020

A Comprehensive Study of Synthetic DNA Preservation for DNA Data Storage

Synthetic DNA has recently risen as a viable alternative for long-term digital data storage. To ensure that information is safely recovered after storage, it is essential to appropriately preserve the physical DNA molecules encoding the data. While preservation of biological DNA has been studied previously, synthetic DNA differs in that it is typically much shorter in length, it has different sequence profiles with fewer, if any, repeats (or homopolymers), and it has different contaminants. In this paper we evaluate nine different methods used to preserve data files encoded in synthetic DNA by accelerated aging of nearly 29,000 DNA sequences. In addition to a molecular count comparison, we also sequence and analyze the DNA after aging. Our findings show that errors and erasures are stochastic and show no practical distribution difference between preservation methods. Finally, we compare the physical density of these methods and provide a stability versus density trade-offs discussion.

synthetic biology↗

bioRxiv2019

Experimental Assessment of PCR Specificity and Copy Number for Reliable Data Retrieval in DNA Storage

Synthetic DNA has been gaining momentum as a potential storage medium for archival data storage1-9. Digital information is translated into sequences of nucleotides and the resulting synthetic DNA strands are then stored for later individual file retrieval via PCR7-9 (Fig. 1a). Using a previously presented encoding scheme9 and new experiments, we demonstrate reliable file recovery when as few as 10 copies per sequence are stored, on average. This results in density of about 17 exabytes/g, nearly two orders of magnitude greater than prior work has shown6. Further, no prior work has experimentally demonstrated access to specific files in a pool more complex than approximately 106 unique DNA sequences9, leaving the issue of accurate file retrieval at high data density and complexity unexamined. Here, we demonstrate successful PCR random access using three files of varying sizes in a complex pool of over 1010 unique sequences, with no evidence that we have begun to approach complexity limits. We further investigate the role of file size on successful data recovery, the effect of increasing sequencing coverage to aid file recovery, and whether DNA strands drop out of solution in a systematic manner. These findings substantiate the robustness of PCR as a random access mechanism in complex settings, and that the number of copies needed for data retrieval does not compromise density significantly.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC=\"FIGDIR/small/565150_fig1.gif\" ALT=\"Figure 1\">\nView larger version (29K):\norg.highwire.dtl.DTLVardef@4b79a7org.highwire.dtl.DTLVardef@11fca45org.highwire.dtl.DTLVardef@18b830org.highwire.dtl.DTLVardef@e47101_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO (a) A high-level representation of the DNA data storage pipeline. (b) (Left) The bar chart depicts contents of the initial, undiluted pool. (Right) The illustration shows the serial nature of subsequent dilutions. Mean copy number refers to the mean number of copies of each files unique sequences as determined by qPCR (Supplemental Section 1). One serial dilution used water as the diluent in each step; the other used a solution of 150Nmers to dilute the pool to much greater complexity. (c) Details of how the samples were diluted. Note that the dilution steps were identical regardless of diluent. The smallest percent of pool accessed is calculated by dividing the size of the smallest file by the number of unique sequences in the 1 {micro}L of solution used for PCR random access. This percentage refers to the 150N diluent pool since the small file in the water diluent pool is a constant 0.13%.\n\nC_FIG

synthetic biology↗