Biology subjects

Yauk, C.

Publications and source records attributed to Yauk, C..

4 recordsLinked to original sources

bioRxiv2026

A robust mutation spectrum analysis framework for high-depth error-corrected sequencing data

Comparing mutational spectra across experimental groups requires inference at the level of the biological replicate. Error-corrected sequencing (ECS) generates hundreds to thousands of mutations per replicate, yet classical pooled G2 tests treat individual mutations as independent observations and ignore replicate-level variability, inflating statistical significance (pseudo-replication). We present a replicate-aware Ridge-Penalized Dirichlet-Multinomial (RP-DMM) framework that models biological replicates directly and accounts for overdispersion. An L2 (ridge) penalty, with strength chosen by leave-one-out cross-validation, stabilizes spectrum estimates by shrinking weakly supported mutation categories toward a common baseline, and inference uses a parametric bootstrap. We also provide a complementary non-parametric rank-permutation test for sparse datasets. In simulations, the pooled G2 test became increasingly anti-conservative as mutation depth increased, with false-positive rates approaching 100% under the null hypothesis. RP-DMM maintained Type I error near the nominal 5% level and had substantially greater power than the rank-permutation test. Regularization was most consequential for high-dimensional spectra. False positive rates approached the nominal 5% level once the lower-mutation group contained approximately 300-500 mutations, depending on the number of biological replicates. A practical target of approximately 400 mutations in the lower-mutation group provided robust Type I error control across all scenarios examined; below this, the rank-permutation test performed robustly and is preferred. In Duplex Sequencing data from benzo[a]pyrene-exposed mice, RP-DMM distinguished exposure-associated mutational signatures from comparisons with higher mutation counts but null effect size, whereas the pooled G2 test declared all comparisons significant. The cross-validated penalty decreased as mutation depth increased, indicating that regularization strength should be tuned to the data. The framework is implemented in the MutSeqR R package.

bioinformatics↗

bioRxiv2023

Error-corrected Duplex Sequencing enables direct detection and quantification of mutations in human TK6 cells with remarkable inter-laboratory consistency

Error-corrected Duplex Sequencing (DuplexSeq) enables direct quantification of low-frequency mutations and offers tremendous potential for chemical mutagenicity assessment. We investigated the utility of DuplexSeq to quantify induced mutation frequency (MF) and spectrum in human lymphoblastoid TK6 cells exposed to a prototypical DNA alkylating agent, N-ethyl-N-nitrosourea (ENU). Furthermore, we explored appropriate experimental parameters for this application, and assessed inter-laboratory reproducibility. In two independent experiments in two laboratories, TK6 cells were exposed to ENU (25-200 {micro}M) and DNA was sequenced 48, 72, and 96 h post-exposure. A DuplexSeq mutagenicity panel targeting twenty 2.4-kb regions distributed across the genome was used to sample diverse, genome-representative sequence contexts. A robust increase in MF that was unaffected by time was observed in both laboratories. Concentration-response in the MF from the two laboratories was strongly positively correlated (R2=0.95). C:G>T:A, T:A>C:G, T:A>A:T, and T:A>G:C mutations increased in consistent, concentration-dependent manners in both laboratories, with high proportions of C:G>T:A at all time points. The target sites responded similarly between the two laboratories and revealed a higher average MF in intergenic regions. These results, demonstrating remarkable reproducibility across time and laboratory for both MF and spectrum, support the high value of DuplexSeq for characterizing chemical mutagenicity in both research and regulatory evaluation.

genomics↗

bioRxiv2020

High-throughput transcriptomic evaluation of per- and polyfluoroalkyl substances (PFAS) in primary human liver spheroids to inform read-across

Per- and poly-fluoroalkyl substances (PFAS) are widely found in the environment because of their extensive use and persistence. Although several PFAS are well studied, most lack toxicity data to inform human health hazard and risk assessment. This study focussed on four model PFAS: perfluorooctanoic acid (PFOA; 8 carbon), perfluorobutane sulfonate (PFBS; 4 carbon), perfluorooctane sulfonate (PFOS; 8 carbon), and perfluorodecane sulfonate (PFDS; 10 carbon). Human primary liver cell spheroids (pooled from 10 donors) were exposed to 10 concentrations of each PFAS and analyzed at four time-points. The approach aimed to: (1) identify gene expression changes mediated by the PFAS; (2) identify similarities in biological responses; (3) compare PFAS potency through benchmark concentration analysis; and (4) derive bioactivity exposure ratios (ratio of the concentration at which biological responses occur, relative to daily human exposure). All PFAS induced transcriptional changes in cholesterol biosynthesis and lipid metabolism pathways, and predicted PPAR activation. PFOS exhibited the most transcriptional activity and had a highly similar gene expression profile to PFDS. PFBS induced the least transcriptional changes and the highest benchmark concentration (i.e., was the least potent). The data indicate that these PFAS may have common molecular targets and toxicities, but that PFOS and PFDS are the most similar. The transcriptomic bioactivity exposure ratios derived here for PFOA and PFOS were comparable to those derived using rodent apical endpoints in risk assessments. These data provide a baseline level of toxicity for comparison with other known PFAS using this testing strategy.

genomics↗

bioRxiv2020

Chronic low-dose dioxin exposure accelerates high fat diet-induced hyperglycemia in female mice

ObjectiveHuman studies consistently show an association between exposure to persistent organic pollutants, including 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD, aka "dioxin"), and increased diabetes risk, but rarely consider potential sex differences. We previously showed that a single high-dose TCDD exposure (20 {micro}g/kg) decreased plasma insulin levels in both male and female mice in vivo, but effects on glucose homeostasis were sex-dependent. The purpose of the current study was to determine whether prolonged exposure to a physiologically relevant low-dose of TCDD impacts glucose homeostasis and/or the islet phenotype in a sex-dependent manner in either chow-fed or high fat diet (HFD)-fed mice. MethodsMale and female mice were exposed to 20 ng/kg/d TCDD 2x/week for 12 weeks and simultaneously fed standard chow or a 45% HFD. Glucose homeostasis was assessed by glucose and insulin tolerance tests, and glucose-induced plasma insulin levels were measured in vivo. Histological analysis was performed on pancreas from male and female mice, and islets were isolated from females at 12 weeks for Tempo-Seq(R) analysis. ResultsLow-dose TCDD exposure did not lead to adverse metabolic consequences in chow-fed male or female mice, or in HFD-fed males. However, TCDD accelerated the onset of HFD-induced hyperglycemia and impaired glucose-induced plasma insulin levels in female mice. TCDD caused a modest increase in islet area in males irrespective of diet, but reduced % beta cell area within islets in females. RNAseq analysis of female islets also revealed abnormal changes to endocrine and metabolic pathways in TCDD-exposed HFD-fed females compared chow-fed females. ConclusionsOur data suggest that prolonged low-dose TCDD exposure has minimal effects on glucose homeostasis and islet morphology in chow-fed male and female mice, but promotes maladaptive metabolic responses in HFD-fed females.

pharmacology and toxicology↗