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Mol Endocrinol. 2011 Apr;25(4):694-706. doi: 10.1210/me.2010-0454. Epub 2011 Feb 3.

Species-specific strategies underlying conserved functions of metabolic transcription factors.

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Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania 19104-6149, USA.


The winged helix protein FOXA2 and the nuclear receptor peroxisome proliferator-activated receptor-γ (PPARγ) are highly conserved, regionally expressed transcription factors (TFs) that regulate networks of genes controlling complex metabolic functions. Cistrome analysis for Foxa2 in mouse liver and PPARγ in mouse adipocytes has previously produced consensus-binding sites that are nearly identical to those used by the corresponding TFs in human cells. We report here that, despite the conservation of the canonical binding motif, the great majority of binding regions for FOXA2 in human liver and for PPARγ in human adipocytes are not in the orthologous locations corresponding to the mouse genome, and vice versa. Of note, TF binding can be absent in one species despite sequence conservation, including motifs that do support binding in the other species, demonstrating a major limitation of in silico binding site prediction. Whereas only approximately 10% of binding sites are conserved, gene-centric analysis reveals that about 50% of genes with nearby TF occupancy are shared across species for both hepatic FOXA2 and adipocyte PPARγ. Remarkably, for both TFs, many of the shared genes function in tissue-specific metabolic pathways, whereas species-unique genes fail to show enrichment for these pathways. Nonetheless, the species-unique genes, like the shared genes, showed the expected transcriptional regulation by the TFs in loss-of-function experiments. Thus, species-specific strategies underlie the biological functions of metabolic TFs that are highly conserved across mammalian species. Analysis of factor binding in multiple species may be necessary to distinguish apparent species-unique noise and reveal functionally relevant information.

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