Biology subjects

Tadych, A.

Publications and source records attributed to Tadych, A..

7 recordsLinked to original sources

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↗

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

CROTONdb: genome-wide variant effect prediction database for CRISPR/Cas9 editing outcomes

CRISPR/Cas9 is a genome editing tool widely used in biological research and clinical therapeutics. Natural human genetic variations, through altering the sequence context of CRISPR/Cas9 target regions, can significantly affect its DNA repair outcomes and ultimately lead to different editing efficiencies. However, these effects have not been systematically studied, even as CRISPR/Cas9 is broadly applied to primary cells and patient samples that harbor such genetic diversity. Here, we present comprehensive investigations of natural genetic variations on CRISPR/Cas9 outcomes across the human genome. The utility of our analysis is illustrated in two case studies, on both preclinical discoveries of CD33 knockout in Chimeric Antigen Receptor (CAR)-T cell therapy, and clinical applications of TTR inactivation for treating ATTR amyloidosis. We further expand our analysis to genome scale, population stratified common variants that may lead to gene editing disparity. Our analyses demonstrate pitfalls of failing to account for the widespread genetic variations in Cas9 target selection, and how they can be effectively examined and avoided using our method. To facilitate broad access to our analysis, a web platform CROTONdb is developed, which provides predictions for all possible CRISPR/Cas9 target sites in the coding region, spanning over 5.38 million gRNA targets and 90.82 million estimated variant effects. We anticipate CROTONdb having broad clinical utilities in gene and cellular therapies.

bioengineering↗

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↗

bioRxiv2020

Spatial Transcriptional Mapping of the Human Nephrogenic Program

Congenital abnormalities of the kidney and urinary tract are amongst the most common birth defects affecting 3% of newborns. The human kidney develops over a 30-week period in which a nephron progenitor pool gives rise to around a million nephrons. To establish a framework for human nephrogenesis, we spatially resolved a stereotypical process by which equipotent nephron progenitors generate a nephron anlagen, then applied data-driven approaches to construct three-dimensional protein maps on anatomical models of the nephrogenic program. Single cell RNA sequencing identified novel progenitor states which were spatially mapped to the nephron anatomy enabling the generation of functional gene-networks predicting interactions within and between nephron cell-types. Network mining identified known developmental disease genes and predicts new targets of interest. The spatially resolved nephrogenic program made available through the Human Nephrogenesis Atlas (https://sckidney.flatironinstitute.org/) will facilitate an understanding of kidney development and disease, and enhance efforts to generate new kidney structures.

cell biology↗

bioRxiv2020

DeepArk: modeling cis-regulatory codes of model species with deep learning

To enable large-scale analyses of regulatory logic in model species, we developed DeepArk (https://DeepArk.princeton.edu), a set of deep learning models of the cis-regulatory codes of four widely-studied species: Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, and Mus musculus. DeepArk accurately predicts the presence of thousands of different context-specific regulatory features, including chromatin states, histone marks, and transcription factors. In vivo studies show that DeepArk can predict the regulatory impact of any genomic variant (including rare or not previously observed), and enables the regulatory annotation of understudied model species.

bioinformatics↗

bioRxiv2017

Transcriptome Analysis of Adult C. elegans Cells Reveals Tissue-specific Gene and Isoform Expression

The biology and behavior of adults differ substantially from those of developing animals, and cell-specific information is critical for deciphering the biology of multicellular animals. Thus, adult tissue-specific transcriptomic data are critical for understanding molecular mechanisms that control their phenotypes. We used adult cell-specific isolation to identify the transcriptomes of C. elegans four major tissues (or \"tissue-ome\"), identifying ubiquitously expressed and tissue-specific \"super-enriched\" genes. These data newly reveal the hypodermis metabolic character, suggest potential worm-human tissue orthologies, and identify tissue-specific changes in the Insulin/IGF-1 signaling pathway. Tissue-specific alternative splicing analysis identified a large set of collagen isoforms and a neuron-specific CREB isoform. Finally, we developed a machine learning-based prediction tool for 70 sub-tissue cell types, which we used to predict cellular expression differences in IIS/FOXO signaling, stage-specific TGF-b activity, and basal vs. memory-induced CREB transcription. Together, these data provide a rich resource for understanding the biology governing multicellular adult animals

genomics↗