Biology subjects

Young, L.

Publications and source records attributed to Young, L..

3 recordsLinked to original sources

bioRxiv2020

Next-generation cytosine base editors with minimized unguided DNA and RNA off-target events and high on-target activity

Abstract/introductory paragraphCytosine base editors (CBEs) are molecular machines which enable efficient and programmable reversion of T*A to C*G point mutations in the human genome without induction of DNA double strand breaks1, 2. Recently, the foundational cytosine base editor (CBE) BE3, containing rAPOBEC1, was reported to induce unguided, genomic DNA3, 4 and cellular RNA5 cytosine deamination when expressed in living cells. To mitigate spurious off-target events, we developed a sensitive, high-throughput cellular assay to select next-generation CBEs that display reduced spurious deamination profiles relative to rAPOBEC1-based CBEs, whilst maintaining equivalent or superior on-target editing frequencies. We screened 153 CBEs containing cytidine deaminase enzymes with diverse sequences and identified four novel CBEs with the most promising on/off target ratios. These spurious-deamination-minimized CBEs (BE4 with either RrA3F, AmAPOBEC1, SsAPOBEC3B, or PpAPOBEC1) were further optimized for superior on- and off-target DNA editing profiles through structure-guided mutagenesis of the deaminase domain. These next-generation CBEs display comparable overall DNA on-target editing frequencies, whilst eliciting a 10- to 49-fold reduction in C-to-U edits in the transcriptome of treated cells, and up to a 33-fold overall reduction in unguided off-target DNA deamination relative to BE4 containing rAPOBEC1. Taken together, these next-generation CBEs represent a new collection of base editing tools for applications in which minimization of spurious deamination is desirable and high on-target activity is required.

genomics↗

bioRxiv2019

Heritability and relationship of oxytocin receptor gene variants with social behavior and central oxytocin in colony-reared adult female rhesus macaques

The genetic contributions to sociality are an important research focus for understanding individual variation in social function and risk of social deficits in neurodevelopmental disorders (e.g. autism). The neuropeptide oxytocin (OXT) and its receptor, OXTR, influence social behavior across species. In humans and animals, common variants within the OXTR gene (OXTR) have been associated with varying socio-behavioral traits. However, the reported magnitude of influence of individual variants on complex behavior has been inconsistent. Compared to human studies, non-human primate (NHP) studies in controlled environments have the potential to result in robust effects detectable in relatively small samples. Here we estimate heritability of social behavior and central OXT concentrations in 214 socially-housed adult female rhesus macaques, a species sharing high similarity with humans in genetics, physiology, brain and social complexity. We present a bioinformatically-informed approach for identifying single nucleotide polymorphisms (SNPs) with likely biological relevance. We tested 13 common SNPs in regulatory and coding regions of OXTR for associations with behavior (pro-social, anxiety-like, and aggressive) and OXT concentration in cerebrospinal fluid (CSF). We found moderate rates of heritability for both social behavior and CSF OXT concentrations. No tested SNPs showed significant associations with behaviors or CSF in this sample. Associations between OXT CSF and social behavior were not significant either. SNP effect sizes were generally comparable to those reported in human studies of complex traits. While environmental control and a socio-biological similarity with humans is an advantage of rhesus models for detecting smaller genetic effects, it is insufficient to obviate large sample sizes necessary for appropriate statistical power.

neuroscience↗

bioRxiv2018

Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA

BackgroundBlood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer.\n\nMethodsWhole-genome sequencing was performed on cfDNA extracted from plasma samples (N=546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validation to assess generalization performance.\n\nResultsIn a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91-0.93) with a mean sensitivity of 85% (95% CI 83-86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance.\n\nConclusionsA machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies. Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway.

cancer biology↗