Biology subjects

Troyanskaya, O. G.

Publications and source records attributed to Troyanskaya, O. G..

At least 19 recordsLinked to original sources

bioRxiv2026

Spatiotemporal AI modeling of gene regulation and variant effects in the developing human brain

Understanding how genetic variation shapes gene regulation in the developing human brain remains a central challenge in neuroscience, particularly in transient cell types and developmental stages where data are limited. We present Seal, an interpretable AI transfer learning framework for genome-based modeling of gene expression and variant effects with spatiotemporal resolution across 26 brain regions, 30 cell types, and seven developmental stages. Seal predictions enable characterization of both germline and somatic brain variants, and provide mechanistic interpretation by linking sequence variants to transcriptional regulators in spatiotemporal context. Applying Seal to genome-wide association studies, we identify cell types and developmental windows relevant to neuropsychiatric disease risk, revealing shared and distinct regulatory architectures across six neuropsychiatric conditions that align with clinical trajectories. In the Simons Simplex Collection autism cohort whole genome data, Seal uncovers a significant burden of de novo regulatory variants in transient fetal excitatory neurons, revealing a cell-type and developmentally specific regulatory burden. By connecting genetic variation to spatiotemporal regulatory programs, Seal offers a general framework for uncovering mechanisms of human brain development and disease.

genomics↗

bioRxiv2026

A Temporal Interactome Atlas across RNA Viruses reveals Convergent Vulnerabilities for Antiviral Repurposing

Viruses remodel host protein interactions to facilitate their replication, yet these dynamic interactomes remain largely unmapped and difficult to compare across infections. Here, to facilitate comparative virology studies, we build the Interactome and Viral Infection Dynamics (InterVir) Atlas, performing thermal proximity coaggregation to profile temporal interactome remodeling across five major RNA viral pathogens. We develop PROTEA, an AI framework that quantifies interactome homology and identifies convergent regulatory hubs. Infection-specific signatures include neuronal SHC1 rewiring associated with altered measles virus replication and dengue-specific secretase interactomes linked to cell-junction remodeling. Comparative virology analyses uncover unexpected interactome convergence between measles and flaviviruses, unobserved in protein-abundance profiling. Mapping these shared regulatory programs to drug targets allowed prioritization of compound repurposing as antivirals. Experimental testing of ten compounds demonstrated antiviral efficacy across multiple infections. Together, InterVir and PROTEA establish an open-access resource for comparative virology, facilitating the identification of conserved host vulnerabilities and drug repurposing.

systems biology↗

bioRxiv2026

Multi-modal tissue-aware graph neural network for in silico genetic discovery

Understanding how perturbations influence gene function in a tissue-specific manner is key to uncovering novel drug targets. However, current computational approaches emphasize global network or sequence-derived features over context-driven dependencies. We introduce Mahi, a scalable and interpretable graph neural network framework that learns gene representations by integrating chromatin accessibility, transcription factor binding, histone modifications, and protein structure features in tissue-specific contexts. Through pretraining on tissue-specific network topologies followed by multi-modal feature integration, Mahi learns context-aware gene embeddings across 290 tissues and cell-types. Mahi outperforms sequence-based models in predicting gene essentiality across 1,183 cancer cell lines, demonstrating the advantage of integrating molecular context and functional connectivity. The learned embedding space reveals tissue-specific functional organization, with genes forming distinct clusters reflecting their context-dependent roles. In silico gene knockout perturbations demonstrate Mahis ability to model intricate perturbation responses, identifying disease-relevant pathways and therapeutic targets. Together, these results demonstrate Mahi as a foundation for modeling tissue-specific gene function and perturbation responses, enabling applications in precision medicine, therapeutic target discovery, and prediction of context-dependent genetic vulnerabilities. All embeddings and the framework are publicly available to facilitate use by the scientific community.

genomics↗

bioRxiv2026

Gene-centered representation of coding and regulatory variation enables outcome prediction

Integrating coding and regulatory variation into unified, interpretable representations remains a challenge in functional genomics. Current approaches either focus on common variants or analyze individual variants in isolation, missing the cumulative, cell-type-specific impact of both coding and noncoding variants on each gene. We present Volaria, a computational framework that integrates coding and regulatory genetic variation into unified, gene-centered representations for disease outcome prediction from whole-genome sequencing. Volaria leverages deep learning models to capture variant effects on cell-type-specific gene expression and integrates them with AI-predicted exonic variant pathogenicity to produce representations that capture the cumulative effect of genome-wide rare and common variation. Applied to whole genomes of individuals with rare glomerular diseases, Volaria predicts individual outcomes directly from germline sequence, demonstrating that structured, cell-type-aware representations capture predictive signals beyond population-based polygenic risk scores and unstructured representations. Importantly, the framework identifies context-specific biological mechanisms, providing interpretability that can be aligned with clinical measurements. By encoding genome-wide variation into compact and biologically grounded representations, Volaria provides a scalable foundation for genome interpretation and individualized outcome modeling from germline sequence, complementing phenotypic and clinical information in the future integrative frameworks.

genomics↗

bioRxiv2025

An Evidence-Grounded Research Assistant for Functional Genomics and Drug Target Assessment

The growing availability of biological data resources has transformed research, yet their effective use remains challenging: selecting appropriate sources requires domain knowledge, data are fragmented across databases, and synthesizing results into reliable conclusions is labor-intensive. Although large language models promise to address these barriers, their impact in biomedicine has been limited by unsupported statements, incorrect claims, and lack of provenance. We introduce Alvessa, an evidence-grounded agentic research assistant designed around verifiability. Alvessa integrates entity recognition, orchestration of pre-validated biological tools, and data-constrained answer generation with statement-level verification against retrieved records, explicitly flagging unsupported claims and guiding revision when reliability criteria are not met. We evaluate Alvessa on dbQA from LAB-Bench and GenomeArena, a benchmark of 720 questions spanning gene and variant annotation, pathways, molecular interactions, miRNA targets, drug-target evidence, protein structure, and gene-phenotype associations. Alvessa substantially improves accuracy relative to general-purpose language models and performs comparably to coding-centric agents while producing fully traceable outputs. Using adversarial perturbations, we show that detection of fabricated statements depends critically on access to retrieved evidence. We further demonstrate application to drug discovery, where evidence-grounded synthesis enables identification of candidate targets missed or misattributed by literature-centered reasoning alone. Alvessa and GenomeArena are released to the community to support reproducible, verifiable AI-assisted biological research.

bioinformatics↗

bioRxiv2025

Variant-resolved prediction of context-specific isoform variation with a graph-based attention model

In eukaryotes, most genes produce multiple transcript isoforms that diversify the transcriptome and proteome, serving as a key mechanism of functional regulation. Genetic variation can disrupt the RNA processing signals that shape isoform structure and abundance, yet modeling these effects at full-length isoform resolution remains challenging due to the complexity of transcript regulation. Here, we introduce Otari, an attention-based graph neural network framework trained on the human genomic sequence and long-read transcriptomes across 30 tissue types and brain regions. Otari predicts tissue-specific differential isoform abundance by integrating sequence-derived epigenetic and post-transcriptional signals, enabling isoform-resolved variant effect interpretation. Applied to large-scale variant datasets including an autism cohort, Otari uncovers patterns of isoform dysregulation undetectable at the gene level, such as variant-driven perturbations in isoform abundance and microexon usage implicated in autism pathophysiology. We provide Otari as a resource for powering isoform-level analyses across tissues at scale.

bioinformatics↗

bioRxiv2024

Astrocyte-derived extracellular matrix proteins regulate synapse remodeling in stress-induced depression

Major depressive disorder (MDD) is a common mood condition affecting multiple brain regions and cell types. Changes in astrocyte function contribute to depressive-like behaviors. However, while neuronal mechanisms driving MDD have been studied in some detail, molecular mechanisms by which astrocytes promote depression have not been extensively explored. To uncover astrocyte contributions to MDD, we subjected male mice to chronic social defeat stress precipitated by encounters with a dominant male. Animals exposed to this treatment exhibit symptoms indicative of MDD, including reduced social interactions, anxiety, despair, and anhedonia. We then measured astrocyte translating mRNA expression changes in mice that underwent chronic social defeat and control animals using ribosome affinity purification. Bioinformatic analyses reveal significant alterations in the prefrontal cortex (PFC), consistent with previous studies implicating this brain region in MDD. Expression of genes encoding extracellular matrix (ECM) proteins, cell-cell interaction proteins, and proteins controlling glutamatergic synaptic function are significantly altered. These changes correlate with perturbation of glutamatergic transmission, measured by electrophysiology, and increased synaptic cleft size. Among ECM genes, increased expression of mRNA encoding the synaptic remodeling protein secreted protein acidic and rich in cysteine (Sparc) correlates the most with the depressive phenotype. Furthermore, presence of SPARC and other ECM proteins in synaptosomes is also increased and overexpressing Sparc in PFC partially alleviates stress symptoms. Our results raise the possibility that increased expression of Sparc may be a natural protective mechanism against stress-induced synaptic dysfunction in depression.

neuroscience↗

bioRxiv2024

Innate immune epigenomic landscape following controlled human influenza virus infection

Viral infections can induce changes in innate immunity that persist after virus clearance. Here, we used blood samples from a human influenza H3N2 challenge study to perform comprehensive multi-omic analyses. We detected remodeling of immune programs in innate immune cells after resolution of the infection that was proportional in magnitude to the level of prior viral load. We found changes associated with suppressed inflammation including decreased cytokine and AP-1 gene expression as well as decreased accessibility at AP-1 targets and interleukin-related gene promoter regions. We also found decreased histone deacetylase gene expression, increased MAP kinase gene expression, and increased accessibility at interferon-related gene promoter regions. Genes involved in inflammation and epigenetic-remodeling showed modulation of gene-chromatin site regulatory circuit activity. These results reveal a coordinated rewiring of the epigenetic landscape in innate immune cells induced by mild influenza virus infection.

immunology↗

bioRxiv2023

Automated single-cell omics end-to-end framework with data-driven batch inference

To facilitate single-cell multi-omics analysis and improve reproducibility, we present SPEEDI (Single-cell Pipeline for End to End Data Integration), a fully automated end-to-end framework for batch inference, data integration, and cell type labeling. SPEEDI introduces data-driven batch inference and transforms the often heterogeneous data matrices obtained from different samples into a uniformly annotated and integrated dataset. Without requiring user input, it automatically selects parameters and executes pre-processing, sample integration, and cell type mapping. It can also perform downstream analyses of differential signals between treatment conditions and gene functional modules. SPEEDIs data-driven batch inference method works with widely used integration and cell-typing tools. By developing data-driven batch inference, providing full end-to-end automation, and eliminating parameter selection, SPEEDI improves reproducibility and lowers the barrier to obtaining biological insight from these valuable single-cell datasets. The SPEEDI interactive web application can be accessed at https://speedi.princeton.edu/.

bioinformatics↗

bioRxiv2023

Integrated single-cell multiome analysis reveals muscle fiber-type gene regulatory circuitry modulated by endurance exercise

Endurance exercise is an important health modifier. We studied cell-type specific adaptations of human skeletal muscle to acute endurance exercise using single-nucleus (sn) multiome sequencing in human vastus lateralis samples collected before and 3.5 hours after 40 min exercise at 70% VO2max in four subjects, as well as in matched time of day samples from two supine resting circadian controls. High quality same-cell RNA-seq and ATAC-seq data were obtained from 37,154 nuclei comprising 14 cell types. Among muscle fiber types, both shared and fiber-type specific regulatory programs were identified. Single-cell circuit analysis identified distinct adaptations in fast, slow and intermediate fibers as well as LUM-expressing FAP cells, involving a total of 328 transcription factors (TFs) acting at altered accessibility sites regulating 2,025 genes. These data and circuit mapping provide single-cell insight into the processes underlying tissue and metabolic remodeling responses to exercise.

genomics↗

bioRxiv2023

Peak-agnostic high-resolution cis-regulatory circuitry mapping using single cell multiome data

Single same cell RNAseq/ATACseq multiome data provide unparalleled potential to develop high resolution maps of the cell-type specific transcriptional regulatory circuitry underlying gene expression. We present CREMA, a framework that recovers the full cis-regulatory circuitry by modeling gene expression and chromatin activity in individual cells without peak-calling or cell type labeling constraints. We demonstrate that CREMA overcomes the limitations of existing methods that fail to identify about half of functional regulatory elements which are outside the called chromatin "peaks". These circuit sites outside called peaks are shown to be important cell type specific functional regulatory loci, sufficient to distinguish individual cell types. Analysis of mouse pituitary data identifies a Gata2-circuit for the gonadotrope-enriched disease-associated Pcsk1 gene, which is experimentally validated by reduced gonadotrope expression in a gonadotrope conditional Gata2-knockout model. We present a web accessible human immune cell regulatory circuit resource, and provide CREMA as an R package.

bioinformatics↗

bioRxiv2022

Identifying genes and pathways linking astrocyte regional specificity to Alzheimer's disease susceptibility

Astrocytes have been shown to play a central role in Alzheimers Disease (AD). However, the genes and biological pathways underlying disease manifestation are unknown, and it is unclear whether regional molecular differences among astrocytes contribute to regional specificity of disease. Here, we began to address these challenges with integrated experimental and computational approaches. We constructed a human astrocyte-specific functional gene network using Bayesian integration of a large compendium of human functional genomics data, as well as regional astrocyte gene expression profiles we generated in the mouse. This network identifies likely region-specific astrocyte pathways that operate in healthy brains. We leveraged our findings to compile genome-wide astrocyte-associated disease-gene predictions, employing a novel network-guided differential expression analysis (NetDIFF). We also used this data to predict a list of astrocyte-expressed genes mediating region-specific human disease, using a network-guided shortest path method (NetPATH). Both the network and our results are publicly available using an interactive web interface at http://astrocyte.princeton.edu. Our experimental and computational studies propose a strategy for disease gene and pathway prediction that may be applied to a host of human neurological disorders.

systems biology↗

bioRxiv2022

The proto-oncogene DEK regulates neuronal excitability and tau accumulation in Alzheimer's disease vulnerable neurons

Neurons from layer II of the entorhinal cortex (ECII) are the first to accumulate tau protein aggregates and degenerate during prodromal Alzheimers disease. Here, we use a data-driven functional genomics approach to model ECII neurons in silico and identify the proto-oncogene DEK as a potential driver of tau pathology. By modulating DEK levels in EC neurons in vitro and in vivo, we first validate the accuracy and cell-type specificity of our network predictions. We then show that Dek silencing changes the inducibility of immediate early genes and alters neuron excitability, leading to dysregulation of neuronal plasticity genes. We further find that loss of function of DEK leads to tau accumulation in the soma of ECII neurons, reactivity of surrounding microglia, and eventually microglia-mediated neuron loss. This study validates a pathological gene discovery tool that opens new therapeutic avenues and sheds light on a novel pathway driving tau pathology in vulnerable neurons.

neuroscience↗

bioRxiv2021

A sequence-based global map of regulatory activity for deciphering human genetics

Sequence is at the basis of how the genome shapes chromatin organization, regulates gene expression, and impacts traits and diseases. Epigenomic profiling efforts have enabled large-scale identification of regulatory elements, yet we still lack a sequence-based map to systematically identify regulatory activities from any sequence, which is necessary for predicting the effects of any variant on these activities. We address this challenge with Sei, a new framework for integrating human genetics data with sequence information to discover the regulatory basis of traits and diseases. Our framework systematically learns a vocabulary for the regulatory activities of sequences, which we call sequence classes, using a new deep learning model that predicts a compendium of 21,907 chromatin profiles across >1,300 cell lines and tissues, the most comprehensive to-date. Sequence classes allow for a global view of sequence and variant effects by quantifying diverse regulatory activities, such as loss or gain of cell-type-specific enhancer function. We show that sequence class predictions are supported by experimental data, including tissue-specific gene expression, expression QTLs, and evolutionary constraints based on population allele frequencies. Finally, we applied our framework to human genetics data. Sequence classes uniquely provide a non-overlapping partitioning of GWAS heritability by tissue-specific regulatory activity categories, which we use to characterize the regulatory architecture of 47 traits and diseases from UK Biobank. Furthermore, the predicted loss or gain of sequence class activities suggest specific mechanistic hypotheses for individual regulatory pathogenic mutations. We provide this framework as a resource to further elucidate the sequence basis of human health and disease.

genomics↗

bioRxiv2021

Single nucleus pituitary transcriptomic and epigenetic landscape reveals human stem cell heterogeneity with diverse regulatory mechanisms

Despite their importance in tissue homeostasis and renewal, human pituitary stem cells (PSCs) are incompletely characterized. We describe a human single nucleus (sn) RNAseq and ATACseq resource from pediatric, adult, and aged pituitaries (snpituitaryatlas.princeton.edu) and characterize cell type-specific gene expression and chromatin accessibility programs for all major pituitary cell lineages. We identify uncommitted PSCs, committing progenitor cells, and sex differences. Pseudotime trajectory analysis indicates that early life PSCs are distinct from the other age groups. Linear modeling of same-cell multiome data identifies regulatory domain accessibility sites and transcription factors (TFs) that are significantly associated with gene expression in PSCs compared to other cell types and within PSCs. Modeling the heterogeneous expression of two markers for committing cell lineages among PSCs shows significant correlation with regulatory domain accessibility for GATA3, but with TF expression for POMC. These findings characterize human stem cell lineages and reveal diverse mechanisms regulating key PSC genes.

cell biology↗

bioRxiv2021

Double-stranded RNA drives SARS-CoV-2 nucleocapsid protein to undergo phase separation at specific temperatures

Betacoronavirus SARS-CoV-2 infections caused the global Covid-19 pandemic. The nucleocapsid protein (N-protein) is required for multiple steps in the betacoronavirus replication cycle. SARS-CoV-2-N-protein is known to undergo liquid-liquid phase separation (LLPS) with specific RNAs at particular temperatures to form condensates. We show that N-protein recognizes at least two separate and distinct RNA motifs, both of which require double-stranded RNA (dsRNA) for LLPS. These motifs are separately recognized by N-proteins two RNA binding domains (RBDs). Addition of dsRNA accelerates and modifies N-protein LLPS in vitro and in cells and controls the temperature condensates form. The abundance of dsRNA tunes N-protein-mediated translational repression and may confer a switch from translation to genome packaging. Thus, N-proteins two RBDs interact with separate dsRNA motifs, and these interactions impart distinct droplet properties that can support multiple viral functions. These experiments demonstrate a paradigm of how RNA structure can control the properties of biomolecular condensates.

molecular biology↗

bioRxiv2021

AMBIENT: Accelerated Convolutional Neural Network Architecture Search for Regulatory Genomics

Convolutional neural networks (CNN) have become a standard approach for modeling genomic sequences. CNNs can be effectively built by Neural Architecture Search (NAS) by trading computing power for accurate neural architectures. Yet, the consumption of immense computing power is a major practical, financial, and environmental issue for deep learning. Here, we present a novel NAS framework, AMBIENT, that generates highly accurate CNN architectures for biological sequences of diverse functions, while substantially reducing the computing cost of conventional NAS.

bioinformatics↗

bioRxiv2021

mRNA-1273 efficacy in a severe COVID-19 model: attenuated activation of pulmonary immune cells after challenge

The mRNA-1273 vaccine was recently determined to be effective against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from interim Phase 3 results. Human studies, however, cannot provide the controlled response to infection and complex immunological insight that are only possible with preclinical studies. Hamsters are the only model that reliably exhibit more severe SARS-CoV-2 disease similar to hospitalized patients, making them pertinent for vaccine evaluation. We demonstrate that prime or prime-boost administration of mRNA-1273 in hamsters elicited robust neutralizing antibodies, ameliorated weight loss, suppressed SARS-CoV-2 replication in the airways, and better protected against disease at the highest prime-boost dose. Unlike in mice and non-human primates, mRNA-1273- mediated immunity was non-sterilizing and coincided with an anamnestic response. Single-cell RNA sequencing of lung tissue permitted high resolution analysis which is not possible in vaccinated humans. mRNA-1273 prevented inflammatory cell infiltration and the reduction of lymphocyte proportions, but enabled antiviral responses conducive to lung homeostasis. Surprisingly, infection triggered transcriptome programs in some types of immune cells from vaccinated hamsters that were shared, albeit attenuated, with mock-vaccinated hamsters. Our results support the use of mRNA-1273 in a two-dose schedule and provides insight into the potential responses within the lungs of vaccinated humans who are exposed to SARS-CoV-2.

immunology↗