Grafton, F. | bioRxiv Science

bioRxiv2024

Transcriptomics-informed pharmacology identifies epigenetic and cell cycle regulators as enhancers of AAV production

Recombinant adeno-associated virus (rAAV) is a widely used viral vector for gene therapy. Despite its clinical efficacy, the manufacturing of rAAV faces challenges in productivity and quality, leading to limited availability. To address the growing demand, next-generation process development should be informed by a mechanistic understanding of the cellular response to rAAV. In this study, we performed transcriptomic analysis of 5 cell lines with variable capacities for rAAV production. Using an intersectional approach, we assessed the transcriptional response to rAAV production and compared transcriptional profiles between high and baseline producers to identify possible targets for enhancing production. Modulation of cell cycle and nucleosome components suggested a reduction of proliferative capacity and a shift toward DNA replication to support rAAV production. During rAAV production, we observed upregulation of several core functions including transcription, stress response, and Golgi and endoplasmic reticulum organization. Conversely, inhibitors of DNA-binding proteins and mitochondrial components were consistently downregulated during rAAV production. We next performed a drug connectivity analysis of these results and identified 5 classes of drugs predicted to enhance rAAV production. Validation studies confirmed the efficacy of HDAC and microtubule inhibitors. Our data uncover novel and previously identified pathways that may enhance rAAV productivity, potentially enabling a path to engineer improved processes and cell lines for higher yields and better quality rAAV production. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=158 SRC="FIGDIR/small/599118v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1f2e93org.highwire.dtl.DTLVardef@12ab5fborg.highwire.dtl.DTLVardef@1119951org.highwire.dtl.DTLVardef@68acfb_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2021

Deep Learning Predicts Patterns of Cardiotoxicity in a High-Content Screen Using Induced Pluripotent Stem Cell-Derived Cardiomyocytes

Drug-induced cardiotoxicity and hepatotoxicity are major causes of drug attrition. To decrease late-stage drug attrition, pharmaceutical and biotechnology industries need to establish biologically relevant models that use phenotypic screening to predict drug-induced toxicity. In this study, we sought to rapidly detect patterns of cardiotoxicity using high-content image analysis with deep learning and induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). We screened a library of 1280 bioactive compounds and identified those predicted to have cardiotoxic liabilities using a single-parameter score based on deep learning. Compounds with major predicted cardiotoxicity included DNA intercalators, ion channel blockers, epidermal growth factor receptor, cyclin-dependent kinase, and multi-kinase inhibitors. We also screened a diverse library of molecules with unknown targets and identified chemical frameworks with predicted cardiotoxic liabilities. By using this screening approach during target discovery and lead optimization, we can de-risk early-stage drug discovery. We show that the broad applicability of combining deep learning with iPSC technology is an effective way to interrogate cellular phenotypes and identify drugs that protect against diseased phenotypes and deleterious mutations. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=161 HEIGHT=200 SRC="FIGDIR/small/436666v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@695707org.highwire.dtl.DTLVardef@1d0980forg.highwire.dtl.DTLVardef@1af30e7org.highwire.dtl.DTLVardef@b3a312_HPS_FORMAT_FIGEXP M_FIG C_FIG CONTRIBUTION TO THE FIELDIn this article, Grafton and colleagues use induced pluripotent stem cell technology and deep learning to train a neural network capable of detecting patterns of cardiotoxicity. To identify bioactive and chemical classes that lead to cardiotoxicity, they combine the neural network with high-content screening of 2560 compounds. The methods described in this study can be used to de-risk early-stage drug development, triage hits, and identify drugs that protect against disease. This screening paradigm will serve as a useful resource for drug discovery and phenotypic interrogation of stem cells and stem cell-derived cell types.

cell biology↗