Biology subjects

Wang, A. L. E.

Publications and source records attributed to Wang, A. L. E..

4 recordsLinked to original sources

bioRxiv2024

pyVIPER: A fast and scalable Python package for rank-based enrichment analysis of single-cell RNASeq data

SummarySingle-cell sequencing has revolutionized biomedical research by offering insights into cellular heterogeneity at unprecedented resolution. Yet, the low signal-to-noise ratio, characteristic of single-cell RNA sequencing (scRNASeq), challenges quantitative analyses. We have shown that gene regulatory network (GRN) analysis can help overcome this obstacle and support mechanistic elucidation of cellular state determinants, for example by using the VIPER algorithm to identify Master Regulator (MR) proteins from gene expression data. A key challenge, as the size and complexity of scRNASeq datasets grow, is the need for highly scalable tools supporting the analysis of large-scale datasets with up to hundreds of thousands of cells. To address it, we introduce pyVIPER, a fast, memory-efficient, and highly scalable Python toolkit for assessing protein activity in large-scale scRNASeq datasets. pyVIPER supports multiple enrichment analysis algorithms, data transformation/postprocessing modules, a novel data structure for GRNs manipulation, and seamless integration with AnnData, Scanpy and several widely adopted machine learning libraries. Compared to VIPER, benchmarking reveals orders of magnitude runtime reduction for large datasets--i.e., from hours to minutes-- thus supporting VIPER-based analysis of virtually any large-scale single-cell dataset, as well as integration with other Python-based tools. Availability and ImplementationpyVIPER is available on GitHub (https://github.com/alevax/pyviper) and PyPI (https://pypi.org/project/viper-in-python/). Contactav2729@cumc.columbia.edu Supplementary informationSupplementary data are available at Bioinformatics online. Accompanying data for the tutorials are available on Zenodo (https://zenodo.org/records/10059791).

bioinformatics↗

bioRxiv2024

A forward genetic screen identifies Sirtuin1 as a driver of neuroendocrine prostate cancer

Although localized prostate cancer is relatively indolent, advanced prostate cancer manifests with aggressive and often lethal variants, including neuroendocrine prostate cancer (NEPC). To identify drivers of aggressive prostate cancer, we leveraged Sleeping Beauty (SB) transposon mutagenesis in a mouse model based on prostate-specific loss-of-function of Pten and Tp53. Compared with control mice, SB mice developed more aggressive prostate tumors, with increased incidence of metastasis. Notably, a significant percentage of the SB prostate tumors display NEPC phenotypes, and the transcriptomic features of these SB mouse tumors recapitulated those of human NEPC. We identified common SB transposon insertion sites (CIS) and prioritized associated CIS-genes differentially expressed in NEPC versus non-NEPC SB tumors. Integrated analysis of CIS-genes encoding for proteins representing upstream, post-translational modulators of master regulators controlling the transcriptional state of SB-mouse and human NEPC tumors identified sirtuin 1 (Sirt1) as a candidate mechanistic determinant of NEPC. Gain-of-function studies in human prostate cancer cell lines confirmed that SIRT1 promotes NEPC, while its loss-of-function or pharmacological inhibition abrogates NEPC. This integrative analysis is generalizable and can be used to identify novel cancer drivers for other malignancies. SummaryUsing an unbiased forward mutagenesis screen in an autochthonous mouse model, we have investigated mechanistic determinants of aggressive prostate cancer. SIRT1 emerged as a key regulator of neuroendocrine prostate cancer differentiation and a potential target for therapeutic intervention.

cancer biology↗

bioRxiv2023

Elucidating Compound Mechanism of Action and Polypharmacology with a Large-scale Perturbational Profile Compendium

Drug Mechanism of Action (MoA) is often represented as a small repertoire of tissue-independent, high-affinity binding targets. Yet drug activity and polypharmacology are also manifested through a broad range of off-target and tissue-specific effector proteins. To address this dichotomy, we generated a compendium of drug perturbation profiles for >700 oncology drugs in cell lines representing high-fidelity models of 23 aggressive tumor subtypes, as well as an integrative framework for the proteome-wide assessment of drug-mediated, tissue-specific differential protein activity. Extending MoA and polypharmacology assessment via proteome-wide, multi-tissue analyses revealed novel polypharmacology, including for MAPK, PI3K, and folate pathways inhibitors--as supported by Western blots, thermal shift assays, and structural analysis--as well as functional inhibitors of challenging oncoprotein targets, including MYC, CTNNB1, CENPF and UHRF1. The dataset and associated computational framework might be extensible to additional compound libraries, supporting global, quantitative approaches to defining the mechanisms of action of many drugs.

systems biology↗

bioRxiv2020

Pancreatic Ductal Adenocarcinoma Comprises Coexisting Regulatory States with both Common and Distinct Dependencies

Despite extensive efforts, reproducible assessment of pancreatic ductal adenocarcinoma (PDA) heterogeneity and plasticity at the single cell level remains elusive. Systematic, network-based analysis of regulatory protein activity in single cells identified three PDA Developmental Lineages (PDLs), coexisting in virtually all tumors, whose transcriptional states are mechanistically driven by aberrant activation of Master Regulator (MR) proteins associated with gastrointestinal lineages (GLS state), morphogen and EMT pathways (MOS state), and acinar-to-ductal metaplasia (ALS state), respectively. Each PDL is further subdivided into sub-states characterized by low vs. high MAPK pathway activity. This taxonomy was remarkably conserved across multiple cohorts, cell lines, and PDX models, and harmonized with bulk profile analyses. Cross-state plasticity and MR essentiality was confirmed by barcode-based lineage tracing and CRISPR/Cas9 assays, respectively, while MR ectopic expression induced PDL transdifferentiation. Together these data provide a mechanistic foundation for PDA heterogeneity and a roadmap for targeting PDA cellular subtypes.

cancer biology↗