Biology subjects

Ma'ayan, A.

Publications and source records attributed to Ma'ayan, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Perturb-Seqr: Signature Search Engine Integrating Connectivity Mapping Resources

High-throughput transcriptomics can be used to develop Connectivity Mapping resources for identifying potential reversers and mimickers of disease signatures for drug and target discovery. Recently, several new Connectivity Mapping datasets have emerged, but no efforts were made so far to evaluate and integrate them. Motivated by this opportunity, we have developed Perturb-Seqr, a comprehensive Connectivity Mapping search engine that integrates nine small molecule transcriptomics Connectivity Maps and seven single gene perturbation followed by expression Connectivity Maps. Specifically, Perturb-Seqr contains processed data from LINCS (L1000), Tahoe-100M, Novartis Institute for Biomedical Research (DRUG-seq), the original CMap (cDNA microarrays), Ginkgo Bioworks (DRUG-seq), SciPlex (scRNA-seq), DeepCover MoA (PISA proteomics), Perturb Atlas (RNA-seq), Replogle et al. (Perturb-seq), CREEDS (cDNA microarrays), CM4AI (Perturb-seq), and RummaGEO (RNA-seq). To serve these Connectivity Maps in one unifying search engine, we converted the expression signatures from these resources into 450,271 gene sets of up- and down-regulated genes measuring the effects of 9,856 unique drug and 17,643 unique gene perturbations, targeting 917 cell lines and cell types. We demonstrate how Perturb-Seqr, and the gene sets it serves, can be used to identify targets for drugs and drugs for targets, uncover new pathways, and identify potential side effects for preclinical compounds. Overall, Perturb-Seqr is a comprehensive up-to-date Connectivity Mapping search engine and database, enabling the discovery of new drugs and targets. Perturb-Seqr is accessible from: https://perturbseqr.maayanlab.cloud/.

bioinformatics↗

bioRxiv2026

Cerebellar microRNA-206 tunes Purkinje neuron firing dynamics to control sensorimotor gating

MicroRNAs are potent regulators of gene expression in the brain, yet the cellular mechanisms through which they shape neuronal activity and behavior remain poorly understood. MicroRNA-206 (miR-206) has been genetically and transcriptionally linked to schizophrenia and other neuropsychiatric disorders, but its functions in the nervous system are largely unknown. Here we show that miR-206 expression in the brain is restricted to postnatal cerebellar Purkinje cells (PCs). miR-206 was dispensable for PC cell fate specification, dendritic morphogenesis, and cerebellar-regulated motor coordination. Transcriptional profiling with single-nucleus and spatial resolution, integrated with Ago2-associated miRNA-target repression mapping (HITS-CLIP) and ribosome-associated RNA profiling (TRAP-seq), showed that miR-206 regulates translational programs in PCs controlling neuronal excitability. Accordingly, miR-206 deficiency increased the tonic firing of PCs, whereas elevating miR-206 expression shifted PCs from tonic to high-frequency burst firing. Constitutive or PC-specific deletion of miR-206 impaired prepulse inhibition (PPI) of the acoustic startle response, a conserved form of sensorimotor gating disrupted in schizophrenia and related disorders. Restoration of miR-206 expression in PCs rescued PPI deficits in miR-206-deficient mice, while elevating miR-206 expression in PCs impaired PPI in wild-type animals. Together, these findings reveal that a schizophrenia-linked microRNA tunes Purkinje neuron firing dynamics to control sensorimotor gating.

neuroscience↗

bioRxiv2026

BiomarkerKB: FAIR and Integrated Biomarker Knowledge Connecting Biomolecular and Clinical Data Types

Biomarkers are essential tools for disease detection, risk assessment, therapeutic monitoring, and precision medicine. However, biomarker data are dispersed across heterogeneous resources, inconsistently reported in the literature, and rarely standardized for computational use. This fragmentation limits reproducibility, cross-study integration, and the discovery of novel biomarker and disease relationships. We developed BiomarkerKB, a knowledgebase designed to harmonize and integrate biomarker information under a standardized data model. The model follows the FDA-NIH BEST biomarker definition and captures both core fields (biomarker entity, disease/condition, exposure agent) and contextual metadata (specimen, biomarker role, evidence, provenance). Biomarker data and related annotations were either curated from publications or collected from public resources (e.g., OpenTargets, GWAS Catalog, ClinVar, CIViC, OncoMX) and were also contributed by the Common Fund Data Coordinating Centers and the Early Detection Research Network (EDRN). Standardization was achieved using ontologies and reference resources such as Disease Ontology, UBERON, UniProtKB, and HUGO Gene Nomenclature Committee (HGNC) gene symbols. BiomarkerKB data were ingested into a Neo4j-based knowledge graph and integrated with the Common Fund Data Ecosystem (CFDE) Knowledge Graph. The initial release of BiomarkerKB contains over 200,000 biomarker-disease associations spanning genes, proteins, metabolites, glycans, and chemical elements. The knowledge graph comprises more than 300,000 nodes and 1.2 million edges, enabling structured exploration of biomarker relationships within CFDE data as demonstrated through the knowledge graph query-based use cases presented in this study. A publicly accessible web portal (https://biomarkerkb.org) provides keyword search, filtering, data downloads, and access to graph visualization to support both researchers and computational analyses. BiomarkerKB addresses a critical gap in biomarker informatics by providing an integrated, FAIR (Findable, Accessible, Interoperable, and Reusable), and unified framework for biomarker knowledge exploration and discovery.

bioinformatics↗

bioRxiv2025

The Data Distillery: A Graph Framework for Semantic Integration and Querying of Biomedical Data

The Data Distillery Knowledge Graph (DDKG) is a framework for semantic integration and querying of biomedical data across domains. Built for the NIH Common Fund Data Ecosystem, it supports translational research by linking clinical and experimental datasets in a unified graph model. Clinical standards such as ICD-10, SNOMED, and DrugBank are integrated through UMLS, while genomics and basic science data are structured using ontologies and standards such as HPO, GENCODE, Ensembl, STRING, and ClinVar. The DDKG uses a property graph architecture based on the UBKG infrastructure and supports ontology-based ingestion, identifier normalization, and graph-native querying. The system is modular and can be extended with new datasets or schema modules. We demonstrate its utility for informatics queries across eight use cases, including regulatory variant analysis, tissue-specific expression, biomarker discovery, and cross-species variant prioritization. The DDKG is accessible via a public interface, a programmatic API, and downloadable builds for local use.

bioinformatics↗

bioRxiv2025

ChEA-KG: Human Transcription Factor Regulatory Network with a Knowledge Graph Interactive User Interface

Gene expression is controlled by transcription factors (TFs) that selectively bind and unbind to DNA to regulate mRNA expression of all human genes. TFs control the expression of other TFs, forming a complex gene regulatory network (GRN) with switches, feedback loops, and other regulatory motifs. Many experimental and computational methods have been developed to reconstruct the human intracellular GRN. Here we present a different approach. By submitting thousands of "up" and "down" gene sets from the RummaGEO resource for TF enrichment analysis with ChEA3, we distill signed and directed edges that connect human TFs to construct a high quality human GRN. The GRN has 131,581 signed and directed edges connecting 701 source TF nodes to 1,559 target TF nodes. The GRN is accessible via the ChEA-KG web server application, which provides interactive network visualization and analysis tools. Users may query the GRN for single or pairs of TFs or submit gene sets to perform TF enrichment analysis with ChEA3, placing the enriched TFs within the GRN. To demonstrate the utility of ChEA-KG, several TF-centric atlases are also made available via the ChEA-KG website. These atlases host TF subnetworks that regulate 131 major normal human cell-types (Cell Type Atlas); 69 tumour subtypes from 10 cancers (Cancer Atlas); 30 consensus perturbation response signatures for common mechanisms of action (MoA Atlas); and 24 aging signatures from tissues profiled by GTEx. Overall, ChEA-KG is an interactive web-server application that presents to users a new method of exploring the human gene regulatory network through both network visualization and transcription factor enrichment analysis. The ChEA-KG application is available from: https://chea-kg.maayanlab.cloud/.

bioinformatics↗

bioRxiv2025

A Gene Set Foundation Model Pre-Trained on a Massive Collection of Diverse Gene Sets

Trained with large datasets, foundation models can capture complex patterns within these datasets to create embeddings that can be used for a variety of useful applications. Here we created a gene set foundation model that was trained on a massive collection of unlabeled gene sets from two databases: Rummagene and RummaGEO. Rummagene automatically extracts gene sets from supplemental tables of publications; and RummaGEO has gene sets automatically computed from comparing groups of samples from RNA-seq studies deposited into the gene expression omnibus. Several foundation model architectures and data sources for training were benchmarked in the task of predicting gene function. Such predictions were also compared to other state-of-the-art gene function prediction methods and models. One of the GSFM architectures achieves superior performance compared to all other methods and models. This model was used to systematically predict gene functions for all human gene. These predictions are served on gene pages that are accessible from https://gsfm.maayanlab.cloud.

bioinformatics↗

bioRxiv2025

The CFDE Workbench: Integrating Metadata and Processed Data from Common Fund Programs

The NIH Common Fund Data Ecosystem (CFDE) program was established to facilitate data accessibility and interoperability across multiple Common Fund (CF) programs and promote collaborations and new discoveries by combining data from different CF programs. The CFDE Data Resource Center (DRC) was tasked with developing two web-based portals: an Information Portal to serve information about the CFDE, and a Data Portal to host harmonized metadata and processed data contributed by participating CF Data Coordination Centers (DCCs) and other sources. To achieve these goals, the CFDE DRC developed the CFDE Workbench, a web-based platform that hosts highly processed data, metadata, tools, use cases, and analyses developed by the CFDE. The Crosscut Metadata Model (C2M2) and several other processed data formats are hosted by the CFDE Workbench, including set libraries (XMTs), Knowledge Graph (KG) assertions, and attribute tables. These processed data formats make information derived from CF programs more findable, accessible, interoperable, and reusable (FAIR) and artificial intelligence (AI)-ready for cross-DCC knowledge discovery. In addition to these processed data formats, several other assets are served by the Workbench, including Extract Transform Load (ETL) pipelines, OpenAPI-documented and SmartAPI-registered Representational State Transfer (REST) Application Programming Interfaces (APIs), entity pages such as gene, drug, disease, or tissue landing pages, Playbook Workflow Builder (PWB) metanodes which are codified workflow steps involving DCC APIs, and chatbot specifications which are prompts helping chatbots to use DCC APIs to answer a wide range of user queries. Besides serving data, metadata, and code assets, the CFDE Workbench also has several tools that use these data, metadata, and code assets to enable cross-CF-program knowledge discovery use cases. Overall, the CFDE Workbench is a platform that consolidates efforts toward making CF programs funded resources harmonized, FAIR, and AI-ready. The CFDE Workbench website is available from: https://cfde.info/.

bioinformatics↗

bioRxiv2024

Transient Notch Activation Converts Pluripotent Stem Cell-Derived Cardiomyocytes towards a Purkinje Fiber Fate

Cardiac Purkinje fibers form the most distal part of the ventricular conduction system. They coordinate contraction and play a key role in ventricular arrhythmias. While many cardiac cell types can be generated from human pluripotent stem cells, methods to generate Purkinje fiber cells remain limited, hampering our understanding of Purkinje fiber biology and conduction system defects. To identify signaling pathways involved in Purkinje fiber formation, we analyzed single cell data from murine embryonic hearts and compared Purkinje fiber cells to trabecular cardiomyocytes. This identified several genes, processes, and signaling pathways putatively involved in cardiac conduction, including Notch signaling. We next tested whether Notch activation could convert human pluripotent stem cell-derived cardiomyocytes to Purkinje fiber cells. Following Notch activation, cardiomyocytes adopted an elongated morphology and displayed altered electrophysiological properties including increases in conduction velocity, spike slope, and action potential duration, all characteristic features of Purkinje fiber cells. RNA-sequencing demonstrated that Notch-activated cardiomyocytes undergo a sequential transcriptome shift, which included upregulation of key Purkinje fiber marker genes involved in fast conduction such as SCN5A, HCN4 and ID2, and downregulation of genes involved in contractile maturation. Correspondingly, we demonstrate that Notch-induced cardiomyocytes have decreased contractile force in bioengineered tissues compared to control cardiomyocytes. We next modified existing in silico models of human pluripotent stem cell-derived cardiomyocytes using our transcriptomic data and modeled the effect of several anti-arrhythmogenic drugs on action potential and calcium transient waveforms. Our models predicted that Purkinje fiber cells respond more strongly to dofetilide and amiodarone, while cardiomyocytes are more sensitive to treatment with nifedipine. We validated these findings in vitro, demonstrating that our new cell-specific in vitro model can be utilized to better understand human Purkinje fiber physiology and its relevance to disease.

developmental biology↗

bioRxiv2024

Playbook Workflow Builder: Interactive Construction of Bioinformatics Workflows from a Network of Microservices

Many biomedical research projects produce large-scale datasets that may serve as resources for the research community for hypothesis generation, facilitating diverse use cases. Towards the goal of developing infrastructure to support the findability, accessibility, interoperability, and reusability (FAIR) of biomedical digital objects and maximally extracting knowledge from data, complex queries that span across data and tools from multiple resources are currently not easily possible. By utilizing existing FAIR application programming interfaces (APIs) that serve knowledge from many repositories and bioinformatics tools, different types of complex queries and workflows can be created by using these APIs together. The Playbook Workflow Builder (PWB) is a web-based platform that facilitates interactive construction of workflows by enabling users to utilize an ever-growing network of input datasets, semantically annotated API endpoints, and data visualization tools contributed by an ecosystem. Via a user-friendly web-based user interface (UI), workflows can be constructed from contributed building-blocks without technical expertise. The output of each step of the workflows are provided in reports containing textual descriptions, as well as interactive and downloadable figures and tables. To demonstrate the ability of the PWB to generate meaningful hypotheses that draw knowledge from across multiple resources, we present several use cases. For example, one of these use cases sieves novel targets for individual cancer patients using data from the GTEx, LINCS, Metabolomics, GlyGen, and the ExRNA Communication Consortium (ERCC) Common Fund (CF) Data Coordination Centers (DCCs). The workflows created with the PWB can be published and repurposed to tackle similar use cases using different inputs. The PWB platform is available from: https://playbook-workflow-builder.cloud/.

bioinformatics↗

bioRxiv2024

RummaGEO: Automatic Mining of Human and Mouse Gene Sets from GEO

The Gene Expression Omnibus (GEO) is a major open biomedical research repository for transcriptomics and other omics datasets. It currently contains millions of gene expression samples from tens of thousands of studies collected by many biomedical research laboratories from around the world. While users of the GEO repository can search the metadata describing studies for locating relevant datasets, there are currently no methods or resources that facilitate global search of GEO at the data level. To address this shortcoming, we developed RummaGEO, a webserver application that enables gene expression signature search of a large collection of human and mouse RNA-seq studies deposited into GEO. To develop the search engine, we performed offline automatic identification of sample conditions from the uniformly aligned GEO studies available from ARCHS4. We then computed differential expression signatures to extract gene sets from these studies. In total, RummaGEO currently contains 135,264 human and 158,062 mouse gene sets extracted from 23,395 GEO studies. Next, we analyzed the contents of the RummaGEO database to identify statistical patterns and perform various global analyses. The contents of the RummaGEO database are provided as a web-server search engine with signature search, PubMed search, and metadata search functionalities. Overall, RummaGEO provides an unprecedented resource for the biomedical research community enabling hypothesis generation for many future studies. The RummaGEO search engine is available from: https://rummageo.com/.

bioinformatics↗

bioRxiv2023

Rummagene: Mining Gene Sets from Supporting Materials of PMC Publications

Every week thousands of biomedical research papers are published with a portion of them containing supporting tables with data about genes, transcripts, variants, and proteins. For example, supporting tables may contain differentially expressed genes and proteins from transcriptomics and proteomics assays, targets of transcription factors from ChIP-seq experiments, hits from genome-wide CRISPR screens, or genes identified to harbor mutations from GWAS studies. Because these gene sets are commonly buried in the supplemental tables of research publications, they are not widely available for search and reuse. Rummagene, available from https://rummagene.com, is a web server application that provides access to hundreds of thousands human and mouse gene sets extracted from supporting materials of publications listed on PubMed Central (PMC). To create Rummagene, we first developed a softbot that extracts human and mouse gene sets from supporting tables of PMC publications. So far, the softbot has scanned 5,448,589 PMC articles to find 121,237 articles that contain 642,389 gene sets. These gene sets are served for enrichment analysis, free text, and table title search. Users of Rummagene can submit their own gene sets to find matching gene sets ranked by their overlap with the input gene set. In addition to providing the extracted gene sets for search, we investigated the massive corpus of these gene sets for statistical patterns. We show that the number of gene sets reported in publications is rapidly increasing, containing both short sets that are highly enriched in highly studied genes, and long sets from omics profiling. We also demonstrate that the gene sets in Rummagene can be used for transcription factor and kinase enrichment analyses, and for gene function predictions. By combining gene set similarity with abstract similarity, Rummagene can be used to find surprising relationships between unexpected biological processes, concepts, and named entities. Finally, by overlaying the Rummagene gene set space with the Enrichr gene set space we can discover areas of biological and biomedical knowledge unique to each resource.

bioinformatics↗

bioRxiv2022

ReproTox-KG: Toxicology Knowledge Graph for Structural Birth Defects

Birth defects are functional and structural abnormalities that impact 1 in 33 births in the United States. Birth defects have been attributed to genetic as well as other factors, but for most birth defects there are no known causes. Small molecule drugs, cosmetics, foods, and environmental pollutants may cause birth defects when the mother is exposed to them during pregnancy. These molecules may interfere with the process of normal fetal development. To characterize associations between small molecule compounds and their potential to induce specific birth abnormalities, we gathered knowledge from multiple sources to construct a reproductive toxicity Knowledge Graph (ReproTox-KG) with an initial focus on associations between birth defects, drugs, and genes. Specifically, to construct ReproTox-KG we gathered data from drug/birth-defect associations from co-mentions in published abstracts, gene/birth-defect associations from genetic studies, drug- and preclinical-compound-induced gene expression data, known drug targets, genetic burden scores for all human genes, and placental crossing scores for all small molecules in ReproTox-KG. Using the data stored within ReproTox-KG, we scored 30,000 preclinical small molecules for their potential to induce birth defects. Querying the ReproTox-KG, we identified over 500 birth-defect/gene/drug cliques that can be used to explain molecular mechanisms for drug-induced birth defects. The ReproTox-KG is provided as curated tables and via a web-based user interface that can enable users to explore the associations between birth defects, approved and preclinical drugs, and human genes.

bioinformatics↗

bioRxiv2021

CPTAC Pancancer Phosphoproteomics Kinase Enrichment Analysis with ProKAP Provides Insights into Immunogenic Signaling Pathways

The National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium (CPTAC) initiative has generated extensive phosphoproteomics and proteomics data for tumor and tumor-adjacent normal tissue across multiple cancer types. This dataset provides an unprecedented opportunity to systematically characterize pan-cancer kinase activities, which is essential for coupling tumor subtypes with kinase inhibitors as potential treatment. In this work, we performed Kinase Enrichment Analysis (KEA) using a CPTAC phosphoproteomics dataset to identify putative differences in kinase state between tumor and normal tissues within and across five types of cancer. We then implemented an interactive web-portal, the ProTrack Kinase Activity Portal (ProKAP), for querying, visualizing, and downloading the derived pan-cancer kinase activity scores together with the corresponding sample metadata, and protein and phosphoprotein expression profiles. To illustrate the usage of this digital resource, we analyzed the association between kinase activity scores and immune subtypes of clear cell renal cell carcinoma (ccRCC) derived from the CPTAC ccRCC study. We found multiple kinases, whose inhibition has been suggested to have therapeutic effect in other tumor types, are highly active in CD8+-enriched ccRCC tumors. The ProTrack Kinase Activity Portal (ProKAP) is available at: https://pancan-kea3.cptac-data-view.org.

bioinformatics↗

bioRxiv2021

Making Common Fund data more Findable: Catalyzing a Data Ecosystem

The Common Fund Data Ecosystem (CFDE) has created a flexible system of data federation that enables users to discover datasets from across the U.S. National Institutes of Health Common Fund without requiring that data owners move, reformat, or rehost those data. The CFDEs federation system is centered on a catalog that ingests metadata from individual Common Fund Programs Data Coordination Centers (DCCs) into a uniform metadata model that can then be indexed and searched from a centralized portal. This uniform Crosscut Metadata Model (C2M2) supports the wide variety of data types and metadata terms used by the individual DCCs and is designed to enable easy expansion to accommodate new data types. We describe its use to ingest and index data from ten DCCs.

bioinformatics↗

bioRxiv2021

A LINCS microenvironment perturbation resource for integrative assessment of ligand-mediated molecular and phenotypic responses

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods (synapse.org/LINCS_MCF10A). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes.

systems biology↗

bioRxiv2021

PPARdelta signaling activation improves metabolic and contractile maturation of human pluripotent stem cell-derived cardiomyocytes.

Pluripotent stem cell-derived cardiomyocytes (PSC-CMs) provide an unprecedented opportunity to study human heart development and disease. A major caveat however is that they remain functionally and structurally immature in culture, limiting their potential for disease modeling and regenerative approaches. Here, we address the question of how different metabolic pathways can be modulated in order to induce efficient hPSC-CM maturation. We show that PPAR signaling acts in an isoform-specific manner to balance glycolysis and fatty acid oxidation (FAO). PPARD activation or inhibition results in efficient respective up- or down-regulation of the gene regulatory networks underlying FAO in hPSC-CMs. PPARD induction further increases mitochondrial and peroxisome content, enhances mitochondrial cristae formation and augments FAO flux. Lastly PPARD activation results in enhanced myofibril organization and improved contractility. Transient lactate exposure, commonly used in hPSC-CM purification protocols, induces an independent program of cardiac maturation, but when combined with PPARD activation equally results in a metabolic switch to FAO. In summary, we identify multiple axes of metabolic modifications of hPSC-CMs and a role for PPARD signaling in inducing the metabolic switch to FAO in hPSC-CMs. Our findings provide new and easily implemented opportunities to generate mature hPSC-CMs for disease modeling and regenerative therapy.

cell biology↗

bioRxiv2021

PrismExp: Predicting Human Gene Function by Partitioning Massive RNA-seq Co-expression Data

Gene co-expression correlations from mRNA-sequencing (RNA-seq) can be used to predict gene function based on the covariance structure that exists within such data. In the past, we showed that RNA-seq co-expression data is highly predictive of gene function and protein-protein interactions. We demonstrated that the performance of such predictions is dependent on the source of the gene expression data. Furthermore, since genes function in different cellular contexts, predictions derived from tissue-specific gene co-expression data outperform predictions derived from cross-tissue gene co-expression data. However, the identification of the optimal tissue type to maximize gene function predictions for all mammalian genes is not trivial. Here we introduce and validate an approach we term Partitioning RNA-seq data Into Segments for Massive co-EXpression-based gene function Predictions (PrismExp), for improved gene function prediction based on RNA-seq co-expression data. With coexpression data from ARCHS4, we apply PrismExp to predict a wide variety of gene functions, including pathway membership, phenotypic associations, and protein-protein interactions. PrismExp outperforms the cross-tissue co-expression correlation matrix approach on all tested domains. Hence, PrismExp can enhance machine learning methods that utilize RNA-seq coexpression correlations to impute knowledge about understudied genes and proteins.

bioinformatics↗

bioRxiv2020

Modulating the transcriptional landscape of SARS-CoV-2 as an effective method for developing antiviral compounds

To interfere with the biology of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, we focused on restoring the transcriptional response induced by infection. Utilizing expression patterns of SARS-CoV-2-infected cells, we identified a region in gene expression space that was unique to virus infection and inversely proportional to the transcriptional footprint of known compounds characterized in the Library of Integrated Network-based Cellular Signatures. Here we demonstrate the successful identification of compounds that display efficacy in blocking SARS-CoV-2 replication based on their ability to counteract the virus-induced transcriptional landscape. These compounds were found to potently reduce viral load despite having no impact on viral entry or modulation of the host antiviral response in the absence of virus. RNA-Seq profiling implicated the induction of the cholesterol biosynthesis pathway as the underlying mechanism of inhibition and suggested that targeting this aspect of host biology may significantly reduce SARS-CoV-2 viral load.

microbiology↗