Biology subjects

Auerbach, B. J.

Publications and source records attributed to Auerbach, B. J..

3 recordsLinked to original sources

bioRxiv2026

Single-cell transcriptomics reveals cell-type-specific circadian rhythms and their disruption by acute misalignment in mouse aorta

The circadian molecular clock is a 24 hour cellular timekeeper that influences many features of cardiovascular function. Disruption of the circadian clock via misalignment with the light dark cycle is associated with a higher incidence of cardiovascular disease and raises cardiovascular risk factors in humans. Nonetheless, the cell type specific molecular basis for how misalignment affects the vasculature remains poorly understood. To address this, we performed single cell RNA sequencing from whole mouse aorta at ZT0, ZT6, ZT12, and ZT18 under aligned and acutely misaligned (6 hour phase advance) light dark cycles in both male and female mice. Leveraging Bayesian variational inference, we estimated posterior waveforms for 141,752 cells across four major cell types and identified hundreds of cycling genes in vascular smooth muscle cells (SMCs) and fibroblasts. Pathway and transcription factor enrichment analyses revealed coordinated circadian activity in cholesterol biosynthesis, smooth muscle contraction, and extracellular matrix organization. Notably, SMC genes implicated in phenotypic switching showed coordinated temporal patterns, with genes promoting switching peaking at dusk and genes restraining switching peaking at dawn. Comparing males and females, we found that female SMCs are broadly more rhythmic, with higher amplitudes and nearly twice as many cycling genes after controlling for cell counts and library sizes, a sex difference that was cell type specific and not observed in fibroblasts. After acute misalignment, cycling genes showed reduced amplitudes, and peak times showed limited adaptation to the new light dark cycle. Given that the central clock is known to adapt near completely during this timeframe, these observations suggest internal misalignment between central and peripheral rhythms. Moreover, altered relative timing of core clock genes within cells indicates that misalignment is created at the intracellular level as well. In SMCs, gene expression patterns were consistent with proteostatic stress, including broad downregulation of protein chaperones and stress response genes, which cells appear to cope with by upregulating protein degradation pathways. In parallel, in vivo vascular phenotyping showed increased vascular permeability in both sexes, reduced urinary nitrate in males, and increased microvascular thrombus formation in males following acute misalignment. This atlas provides a resource for understanding how circadian misalignment disrupts vascular homeostasis and may contribute to cardiovascular disease risk.

genomics↗

bioRxiv2022

Regularized sequence-context mutational trees capture variation in mutation rates across the human genome

Germline mutation is the mechanism by which genetic variation in a population is created. Inferences derived from mutation rate models are fundamental to many population genetics inference methods. Previous models have demonstrated that nucleotides flanking polymorphic sites - the local sequence context - explain variation in the probability that a site is polymorphic. However, limitations to these models exist as the size of the local sequence context window expands. These include a lack of robustness to data sparsity at typical sample sizes, lack of regularization to generate parsimonious models and lack of quantified uncertainty in estimated rates to facilitate comparison between models. To address these limitations, we developed Baymer, a regularized Bayesian hierarchical tree model that captures the heterogeneous effect of sequence contexts on polymorphism probabilities. Baymer implements an adaptive Metropolis-within-Gibbs Markov Chain Monte Carlo sampling scheme to estimate the posterior distributions of sequence-context based probabilities that a site is polymorphic. We show that Baymer accurately infers polymorphism probabilities and well-calibrated posterior distributions, robustly handles data sparsity, appropriately regularizes to return parsimonious models, and scales computationally at least up to 9-mer context windows. We demonstrate application of Baymer in three ways - first, identifying differences in polymorphism probabilities between continental populations in the 1000 Genomes Phase 3 dataset, second, in a sparse data setting to examine the use of polymorphism models as a proxy for de novo mutation probabilities as a function of variant age, sequence context window size, and demographic history, and third, comparing model concordance between different great ape species. We find a shared context-dependent mutation rate architecture underlying our models, enabling a transfer-learning inspired strategy for modeling germline mutations. In summary, Baymer is an accurate polymorphism probability estimation algorithm that automatically adapts to data sparsity at different sequence context levels, thereby making efficient use of the available data. AUTHOR SUMMARYMany biological questions rely on accurate estimates of where and how frequently mutations arise in populations. One factor that has been shown to predict the probability that a mutation occurs is the local DNA sequence surrounding a potential site for mutation. It has been shown that increasing the size of local DNA sequence immediately surrounding a site improves prediction of where, what type, and how frequently the site is mutated. However, current methods struggle to take full advantage of this trend as well as capturing how certain our estimates are, in practice. We have designed a model, implemented in software (named Baymer), that is able to use large windows of sequence context to accurately model mutation probabilities in a computationally efficient manner. We use Baymer to identify specific DNA sequences that have the biggest impacts on mutability and apply the model to find motifs that have potentially evolved mutability between different human populations. We also apply it to show that germline mutations observed as polymorphic sites in humans - those that have occurred in our recent evolutionary history - can model very young mutations (de novo mutations) as well as polymorphism observed in populations of closely related great ape species.

genomics↗

bioRxiv2022

Tempo: an unsupervised Bayesian algorithm for circadian phase inference in single-cell transcriptomics

The circadian clock is a 24-hour cellular timekeeping mechanism that temporally regulates human physiology. Answering several fundamental questions in circadian biology will require joint measures of single-cell circadian phases and transcriptomes. However, no widespread experimental approaches exist for this purpose. While computational approaches exist to infer cell phase directly from single-cell RNA-sequencing (scRNA-seq) data, existing methods yield poor circadian phase estimates, and do not quantify estimation uncertainty, which is essential for interpretation of results from highly sparse scRNA-seq data. To address these unmet needs, we developed Tempo, a Bayesian variational inference approach that incorporates domain knowledge of the clock and quantifies phase estimation uncertainty. Through simulations and analyses of real data, we demonstrate that Tempo yields more accurate estimates of circadian phase than existing methods and provides well-calibrated uncertainty quantifications. We further demonstrate that these properties generalize to the cell cycle. Tempo will facilitate large-scale studies of single-cell circadian transcription.

bioinformatics↗