Figure 1
Figure 1. Genome-wide RNAi screen identifies a large number of putative hepcidin activators enriched for signal transducers, transcription regulators, and inflammatory genes. (A) Schematic representation of the high-throughput RNAi screening strategy. (B) Normalized luciferase intensities obtained upon RNAi of target genes were calculated as z-scores and indicate suppression (low-negative z-scores) or activation (high-positive z-scores) of the hepcidin promoter. (B) Distribution of z-scores after knockdown of the positive control genes SMAD4, STAT3, and SMAD7 is well separated from the negative scrambled siRNA control. (C) Distribution of the mean z-scores for 19 599 screened target genes. Indicated are the z-scores obtained for the siRNA-mediated knockdown of STAT3 (for the identification of 1651 putative hepcidin activators), the z-score of 1.75 (for the identification of 508 putative hepcidin suppressors), and the z-scores obtained for the known hepcidin regulators included in the siRNA library. (D) Functional enrichment analysis using DAVID software identifies genes involved in signal transduction, transcription regulation, and defense/inflammatory response to be overrepresented among putative hepcidin activators. (E) Enriched categories that are marked in green in (D) are the focus of the STRING interaction networks of the positive putative hepcidin regulators. The interaction network is visualized using the software tool Cytoscape. *The category “adapter protein” was by itself not enriched among the putative activators but was visualized as an interesting functional subgroup that also contains overrepresented SH2 domain proteins.

Genome-wide RNAi screen identifies a large number of putative hepcidin activators enriched for signal transducers, transcription regulators, and inflammatory genes. (A) Schematic representation of the high-throughput RNAi screening strategy. (B) Normalized luciferase intensities obtained upon RNAi of target genes were calculated as z-scores and indicate suppression (low-negative z-scores) or activation (high-positive z-scores) of the hepcidin promoter. (B) Distribution of z-scores after knockdown of the positive control genes SMAD4, STAT3, and SMAD7 is well separated from the negative scrambled siRNA control. (C) Distribution of the mean z-scores for 19 599 screened target genes. Indicated are the z-scores obtained for the siRNA-mediated knockdown of STAT3 (for the identification of 1651 putative hepcidin activators), the z-score of 1.75 (for the identification of 508 putative hepcidin suppressors), and the z-scores obtained for the known hepcidin regulators included in the siRNA library. (D) Functional enrichment analysis using DAVID software identifies genes involved in signal transduction, transcription regulation, and defense/inflammatory response to be overrepresented among putative hepcidin activators. (E) Enriched categories that are marked in green in (D) are the focus of the STRING interaction networks of the positive putative hepcidin regulators. The interaction network is visualized using the software tool Cytoscape. *The category “adapter protein” was by itself not enriched among the putative activators but was visualized as an interesting functional subgroup that also contains overrepresented SH2 domain proteins.

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