DNMDP and related compounds, or velcrins, induce complex formation between the phosphodiesterase PDE3A and the SLFN12 protein, leading to a cytotoxic response in cancer cells that express elevated levels of both proteins. The mechanisms by which velcrins induce complex formation, and how the PDE3A-SLFN12 complex causes cancer cell death, are not fully understood. Here, we show that PDE3A and SLFN12 form a heterotetramer stabilized by binding of DNMDP. Interactions between the C-terminal alpha helix of SLFN12 and residues near the active site of PDE3A are required for complex formation, and are further stabilized by interactions between SLFN12 and DNMDP. Moreover, we demonstrate that SLFN12 is an RNase, that PDE3A binding increases SLFN12 RNase activity, and that SLFN12 RNase activity is required for DNMDP response. This new mechanistic understanding will facilitate development of velcrin compounds into new cancer therapies.

Highlights d Trisomy 21 disrupts nuclear architecture and transcriptome of neural progenitors d Trisomy 21 harboring neural progenitors display signatures of cellular senescence d Senolytic drugs ameliorate trisomy-21-associated molecular and cellular dysfunctions

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. 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. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis.

Transcription and metabolism both influence cell function, but dedicated transcriptional control of metabolic pathways that regulate cell fate has rarely been defined. We discovered, using a chemical suppressor screen, that inhibition of the pyrimidine biosynthesis enzyme dihydroorotate dehydrogenase (DHODH) rescues erythroid differentiation in bloodless zebrafish moonshine (mon) mutant embryos defective for transcriptional intermediary factor 1 gamma (tif1γ). This rescue depends on the functional link of DHODH to mitochondrial respiration. The transcription elongation factor TIF1γ directly controls coenzyme Q (CoQ) synthesis gene expression. Upon tif1γ loss, CoQ levels are reduced, and a high succinate/α-ketoglutarate ratio leads to increased histone methylation. A CoQ analog rescues mon’s bloodless phenotype. These results demonstrate that mitochondrial metabolism is a key output of a lineage transcription factor that drives cell fate decisions in the early blood lineage.

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.