26 Recently, PBMs have been used to define the DNA-binding specificity of large classes of TFs27, 28 and have been shown to correlate well with gel shift results.29 Whereas as others have pioneered the technology using the DNA-binding domain (DBD) of TFs purified from bacteria, here we adapt the PBM technology to more closely approximate physiological conditions. Because HNF4α has a very strong dimerization domain outside of the DBD and a very low affinity for DNA when expressed in bacteria,14, 30, 31 we ectopically

expressed full-length, native HNF4α in COS-7 cells and prepared minimally processed nuclear extracts (Fig. 1B) that we then applied Selleck Tyrosine Kinase Inhibitor Library directly to a PBM specifically designed for HNF4α (Fig. 1C,D). The PBM was developed with a highly specific antibody to the C-terminus of HNF4α (Supporting Fig. 1), allowing us to examine a completely native TF. The full-length HNF4α protein selleck screening library in the crude extracts yielded an excellent signal with a range of intensities, whereas extracts from mock-transfected cells yielded no reproducible signals (Fig. 1E). We compared two species (rat and human) and two isoforms of HNF4α (HNF4α2 and HNF4α8), as well as antibodies that recognized different regions of HNF4α (Fig. 2A). There was an excellent correlation between replicate arrays in the first-generation

PBM (PBM1) using crude nuclear extracts, regardless of antibody used (R2 = 0.78), and results with affinity-purified protein were very similar to those with crude extracts (R2 = 0.68) (Fig. 2B). In a second generation of the PBM (PBM2), different HNF4α isoforms (HNF4α2 versus HNF4α8) and species (human versus rat) also produced excellent correlations (R2 > 0.9), indicating that these isoform and species differences do not influence the binding of HNF4α to DNA. This is not surprising considering that the DBD is identical in these constructs (Fig. 2A). PBM1 identified ∼500 new HNF4α binding sequences with the DR1-derived sequences exhibiting the best binding affinities relative to negative controls 5-FU mouse (P

< 8.274 × 10−12) (Fig. 3A ). Sequences derived from ChIP-chip analysis bound roughly as well as the DR1 variants. In PBM2, an additional ∼1000 novel sequences that strongly bind HNF4α were identified, including sequences identified by SVM1. The signal-to-noise ratio (literature-derived versus random sites) was also significantly improved in PBM2 due to optimization of the binding conditions (P < 2.6 × 10−11 versus P < 2.6 × 10−16, respectively, using the Student t test) (Fig. 3B). The PBM2 results also correlated very well with gel shift results (Fig. 3C). Additionally, SVM2 derived from PBM2 predicted binding sequences with a high degree of accuracy (R2 = 0.76) (Fig. 3D). Even though position weight matrices (PWMs) do not capture the interdependence between the positions in a motif as do PBMs and SVMs, they are useful for describing motifs.