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Biology subjects

Scheibe, D.

Publications and source records attributed to Scheibe, D..

2 recordsLinked to original sources

Identification of small molecule agonists of fetal hemoglobin expression for the treatment of sickle cell disease

Induction of fetal hemoglobin (HbF) has been shown to be a viable therapeutic approach to treating sickle cell disease and potentially other {beta}-hemoglobinopathies. To identify targets and target-modulating small molecules that enhance HbF expression, we engineered a human umbilical-derived erythroid progenitor reporter cell line (HUDEP2_HBG1_HiBiT) by genetically tagging a HiBiT peptide to the carboxyl (C)-terminus of the endogenous HBG1 gene locus, which codes for {gamma}-globin protein, a component of HbF. Employing this reporter cell line, we performed a chemogenomic screen of approximately 5000 compounds annotated with known targets or mechanisms that have achieved clinical stage or approval by the US Food and Drug Administration (FDA). Among them, 10 compounds were confirmed for their ability to induce HbF in the HUDEP2 cell line. These include several known HbF inducers, such as pomalidomide, lenalidomide, decitabine, idoxuridine, and azacytidine, which validate the translational nature of this screening platform. We identified avadomide, autophinib, triciribine, and R574 as novel HbF inducers from these screens. We orthogonally confirmed HbF induction activities of the top hits in both parental HUDEP2 cells as well as in human primary CD34+ hematopoietic stem and progenitor cells (HSPCs). Further, we demonstrated that pomalidomide and avadomide, but not idoxuridine, induced HbF expression through downregulation of several transcriptional repressors such as BCL11A, ZBTB7A, and IKZF1. These studies demonstrate a robust phenotypic screening workflow that can be applied to large-scale small molecule profiling campaigns for the discovery of targets and pathways, as well as novel therapeutics of sickle cell disease and other {beta}-hemoglobinopathies. Key PointsO_LIEstablished a robust HbF luciferase reporter cell line to monitor endogenous {gamma}-globin expression for a chemogenomic screen of compounds for the treatment of sickle cell disease. C_LIO_LILead hit compounds were mechanistically confirmed for their ability to decrease expression of several transcriptional repressors such as BCL11A, ZBTB7A, and IKZF1. C_LI Visual Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=194 SRC="FIGDIR/small/601536v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@7f86f6org.highwire.dtl.DTLVardef@75b613org.highwire.dtl.DTLVardef@49fc9org.highwire.dtl.DTLVardef@196472e_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

Integrating inflammatory biomarker analysis and artificial intelligence-enabled image-based profiling to identify drug targets for intestinal fibrosis

Intestinal fibrosis is a common complication of several enteropathies with inflammatory bowel disease being the major cause. The progression of intestinal fibrosis may lead to intestinal stenosis and obstruction. Even with an increased understanding of tissue fibrogenesis, there are no approved treatments for intestinal fibrosis. Historically, drug discovery for diseases like intestinal fibrosis has been impeded by a lack of screenable cellular phenotypes. Here we applied Cell Painting, a scalable image-based morphology assay, augmented with machine learning algorithms to identify small molecules that were able to morphologically reverse the activated fibrotic phenotype of intestinal myofibroblasts under pro-fibrotic TNF stimulus. In combination with measuring CXCL10, a common pro-inflammatory cytokine in intestinal fibrosis, we carried out a high-throughput small molecule chemogenomics screen of approximately 5000 compounds with known targets or mechanisms, which have achieved clinical stage or approval by the FDA. Through the use of two divergent analytical methods, we identified several compounds and target classes that are potentially able to treat intestinal fibrosis. The phenotypic screening platform described here represents significant improvements in identifying a wide range of drug targets over conventional methods by integrating morphological analyses and artificial intelligence using pathologically-relevant cells and disease-relevant stimuli.

pharmacology and toxicology↗