Rajneesh Srivastava1, Radmila Micanovic2, Tarek M El-Achkar3, Sarath Chandra Janga4. 1. Department of Biohealth Informatics, School of Informatics and Computing, Indiana University Purdue University Indianapolis, Indianapolis, Indiana. 2. Division of Nephrology, Department of Medicine, School of Medicine, Indiana University and Roudebush Veterans Affairs Medical Center, Indianapolis, Indiana; Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University and Roudebush Veterans Affairs Medical Center, Indianapolis, Indiana. 3. Division of Nephrology, Department of Medicine, School of Medicine, Indiana University and Roudebush Veterans Affairs Medical Center, Indianapolis, Indiana; Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University and Roudebush Veterans Affairs Medical Center, Indianapolis, Indiana. Electronic address: telachka@iu.edu. 4. Department of Biohealth Informatics, School of Informatics and Computing, Indiana University Purdue University Indianapolis, Indianapolis, Indiana; Department of Medical and Molecular Genetics, School of Medicine, Indiana University and Roudebush Veterans Affairs Medical Center, Indianapolis, Indiana; Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University and Roudebush Veterans Affairs Medical Center, Indianapolis, Indiana. Electronic address: scjanga@iupui.edu.
Abstract
PURPOSE: Uromodulin is a kidney specific glycoprotein whose expression can modulate kidney homeostasis. However, the set of sequence specific transcription factors that regulate the uromodulin gene UMOD and their upstream binding locations are not well characterized. We built a high resolution map of its transcriptional regulation. MATERIALS AND METHODS: We applied in silico phylogenetic footprinting on the upstream regulatory regions of a diverse set of human UMOD orthologs to identify conserved binding motifs and corresponding position specific weight matrices. We further analyzed the predicted binding motifs by motif comparison, which identified transcription factors likely to bind these discovered motifs. Predicted transcription factors were then integrated with experimentally known protein-protein interactions available from public databases and tissue specific expression resources to delineate important regulators controlling UMOD expression. RESULTS: Analysis allowed the identification of a reliable set of binding motifs in the upstream regulatory regions of UMOD to build a high confidence compendium of transcription factors that could bind these motifs, such as GATA3, HNF1B, SP1, SMAD3, RUNX2 and KLF4. ENCODE deoxyribonuclease I hypersensitivity sites in the UMOD upstream region of the mouse kidney confirmed that some of these binding motifs were open to binding by predicted transcription factors. The transcription factor-transcription factor network revealed several highly connected transcription factors, such as SP1, SP3, TP53, POU2F1, RARB, RARA and RXRA, as well as the likely protein complexes formed between them. Expression levels of these transcription factors in the kidney suggest their central role in controlling UMOD expression. CONCLUSIONS: Our findings will form a map for understanding the regulation of uromodulin expression in health and disease.
PURPOSE:Uromodulin is a kidney specific glycoprotein whose expression can modulate kidney homeostasis. However, the set of sequence specific transcription factors that regulate the uromodulin gene UMOD and their upstream binding locations are not well characterized. We built a high resolution map of its transcriptional regulation. MATERIALS AND METHODS: We applied in silico phylogenetic footprinting on the upstream regulatory regions of a diverse set of humanUMOD orthologs to identify conserved binding motifs and corresponding position specific weight matrices. We further analyzed the predicted binding motifs by motif comparison, which identified transcription factors likely to bind these discovered motifs. Predicted transcription factors were then integrated with experimentally known protein-protein interactions available from public databases and tissue specific expression resources to delineate important regulators controlling UMOD expression. RESULTS: Analysis allowed the identification of a reliable set of binding motifs in the upstream regulatory regions of UMOD to build a high confidence compendium of transcription factors that could bind these motifs, such as GATA3, HNF1B, SP1, SMAD3, RUNX2 and KLF4. ENCODE deoxyribonuclease I hypersensitivity sites in the UMOD upstream region of the mouse kidney confirmed that some of these binding motifs were open to binding by predicted transcription factors. The transcription factor-transcription factor network revealed several highly connected transcription factors, such as SP1, SP3, TP53, POU2F1, RARB, RARA and RXRA, as well as the likely protein complexes formed between them. Expression levels of these transcription factors in the kidney suggest their central role in controlling UMOD expression. CONCLUSIONS: Our findings will form a map for understanding the regulation of uromodulin expression in health and disease.