Biology subjects

Vetsigian, K.

Publications and source records attributed to Vetsigian, K..

4 recordsLinked to original sources

bioRxiv2026

Pan-cell-type prediction of splicing patterns from sequence and splicing factor expression

AO_SCPLOWBSTRACTC_SCPLOWAlternative splicing is a core determinant of cell-type-specific gene expression in humans, and its dysregulation contributes to many diseases including neurodegeneration, autoimmunity, and cancer. However, current deep learning models for predicting RNA expression from sequence are limited in how they handle cellular context dependence. These models typically achieve cell-type specificity by training separate models or heads for each tissue or cell-type, which assumes discrete, predefined cell types. This design not only prevents learning from pathological or experimentally perturbed transcriptomes, but also prevents generalization to new cellular contexts. Here we introduce PanExonNet, a deep learning framework that integrates cis- and trans-regulation by conditioning sequence-based splicing predictions on an inferred "splicing state" derived from the expression of RNA-binding proteins (RBPs) and spliceosome components. PanExonNet is trained on diploid, individual-specific gene sequences containing indels to predict splicing profiles derived from short-read RNA-seq, and it outputs multiple tracks at single-nucleotide resolution as well as donor-acceptor junction usage. Compared to multi-headed baselines such as Borzoi and Pangolin, PanExonNet exhibits substantially higher context specificity--as quantified by a {Delta}PSI correlation metric designed to isolate context-specific variation--and, crucially, generalizes to unseen cell types. Adding perturbational knockdown followed by RNA sequencing (KD-RNA-seq) data to the training set further improves generalization. This performance is enabled in part by our introduction of contextualizable convolutions, a modular layer that may broadly benefit genomic sequence modeling. This framework provides a scalable foundation for future DNA-to-RNA models that could improve variant effect prediction, the design of oligonucleotide therapeutics, and biomarker discovery across diverse cellular contexts.

genomics↗

bioRxiv2023

Simple Causal Relationships in Gene Expression Discovered through Deep Learned Collective Variables

Developments in high-content phenotypic screening with single-cell read-out hold the promise of revealing interactions and functional relationships between genes at the genomic scale scale. However, the high-dimensionality and noisiness of gene expression makes this endeavor highly challenging when treated as a conventional problem in causal machine learning, both because of the statistical power required and because of the limits on computational tractability. Here we take different tack, and propose a deep-learning approach that finds low-dimensional representations of gene expression in which the response to genetic perturbation is highly predictable. We demonstrate that the interactions between genes that are cooperative in these representations are highly consistent with known ground-truth in terms of causal ordering, functional relatedness, and synergistic impact on cell growth and death. Our novel, statistical physics-inspired approach provides a tractable means through which to examine the response the living cell to perturbation, employing coarse graining that reduces data requirements and focuses on identifying simple relationships between groups of genes. Author summaryUnderstanding the causal relationships between genes and the functions of a cells molecular components has long been a challenge in biology and biomedicine. With recent advancements in technologies that manipulate and measure the activity of thousands of genes at once at the single-cell level, scientists are now afforded with the opportunity to interrogate such relationships at scale. However, extracting useful information from the vast readouts of these technologies is non-trivial, in part due to their many-dimensional and noisy nature. Here we develop a machine learning model that allows for the interpretation of complex genetic perturbations in terms of a simple set of causal relations. By analyzing cooperative groups of genes identified by our model, we demonstrate the model can group genes accurately based on their biological function, their relative ordering up- or downstream in the flow of causation, and how their activities combine to affect cell growth and death. Our approach complements existing machine learning methods in providing a simple way to interpret causal mechanism governing genetic interactions and functional states of cells.

genomics↗

bioRxiv2018

Emergence of evolutionary stable communities through eco-evolutionary tunneling

Ecological and evolutionary dynamics of communities are inexorably intertwined. The ecological state determines the fate of newly arising mutants, and mutations that increase in frequency can reshape the ecological dynamics. Evolutionary game theory and its extensions within adaptive dynamics (AD) have been the mathematical frameworks for understanding this interplay, leading to notions such as Evolutionary Stable States (ESS) in which no mutations are favored, and evolutionary branching points near which the population diversifies. A central assumption behind these theoretical treatments has been that mutations are rare so that the ecological dynamics has time to equilibrate after every mutation. A fundamental question is whether qualitatively new phenomena can arise when mutations are frequent. Here we describe an adaptive diversification process that robustly leads to complex ESS, despite the fact that such communities are unreachable through a step-by-step evolutionary process. Rather, the system as a whole tunnels between collective states over a short time scale. The tunneling rate is a sharply increasing function of the rate with which mutations arise in the population. This makes the emergence of ESS communities virtually impossible in small populations, but generic in large ones. Moreover, communities emerging through this process can spatially spread as single replication units that outcompete other communities. Overall, this work provides a qualitatively new mechanism for adaptive diversification and shows that complex structures can generically evolve even when no step-by-step evolutionary path exists.

evolutionary biology↗

bioRxiv2018

Stochastic exits from dormancy give rise to heavy-tailed distributions of descendants in bacterial populations

Variance in reproductive success is a major determinant of the degree of genetic drift in a population. While many plants and animals exhibit high variance in their number of progeny, far less is known about these distributions for microorganisms. Here, we used a strain barcoding approach to quantify variability in offspring number among replicate bacterial populations and developed a Bayesian method to infer the distribution of descendants from this variability. We applied our approach to measure the offspring distributions for 5 strains of bacteria from the genus Streptomyces after germination and growth in a homogenous laboratory environment. The distributions of descendants were heavy-tailed, with a few cells effectively \"winning the jackpot\" to become a disproportionately large fraction of the population. This extreme variability in reproductive success largely traced back to initial populations of spores stochastically exiting dormancy, which provided early-germinating spores with an exponential advantage. In simulations with multiple dormancy cycles, heavy-tailed distributions of descendants decreased the effective population size by many orders of magnitude and led to allele dynamics differing substantially from classical population genetics models with matching effective population size. Collectively, these results demonstrate that extreme variability in reproductive success can occur even in growth conditions that are far more homogeneous than the natural environment. Thus, extreme variability in reproductive success might be an important factor shaping microbial population dynamics with implications for predicting the fate of beneficial mutations, interpreting sequence variability within populations, and explaining variability in infection outcomes across patients.

bioinformatics↗