scholarly journals The CYP2B2 phenobarbital response unit contains binding sites for hepatocyte nuclear factor 4, PBX–PREP1, the thyroid hormone receptor β and the liver X receptor

2005 ◽  
Vol 388 (2) ◽  
pp. 407-418 ◽  
Author(s):  
Marie-Josée BEAUDET ◽  
Marc DESROCHERS ◽  
Antoine Amaury LACHAUD ◽  
Alan ANDERSON

A 163 bp enhancer in the CYP2B2 5′ flank confers PB (phenobarbital) inducibility and constitutes a PBRU (PB response unit). The PBRU contains several transcription factor binding sites, including NR1, NR2 and NR3, which are direct repeats separated by 4 bp of the nuclear receptor consensus half-site AGGTCA, as well as an ER (everted repeat) separated by 7 bp (ER-7). Constitutive androstane receptor (CAR)–RXR (retinoic X receptor) heterodimers are known to bind to NR1, NR2 and NR3. Electrophoretic mobility-shift analysis using nuclear extracts from livers of untreated or PB-treated rats revealed binding of several other proteins to different PBRU elements. Using supershift analysis and in vitro coupled transcription and translation, the proteins present in four retarded complexes were identified as TRβ (thyroid hormone receptor β), LXR (liver X receptor), HNF-4 (hepatocyte nuclear factor 4) and heterodimers of PBX–PREP1 (pre-B cell homoeobox–Pbx regulatory protein 1). LXR–RXR heterodimers bound to NR3 and TRβ bound to NR3, NR1 and ER-7, whereas the PBX–PREP1 site is contained within NR2. The HNF-4 site overlaps with NR1. A mutation described previously, GRE1m1, which decreases PB responsiveness, increased the affinity of this site for HNF-4. The PBRU also contains a site for nuclear factor 1. The PBRU thus contains a plethora of transcription factor binding sites. The profiles of transcription factor binding to NR1 and NR3 were quite similar, although strikingly different from, and more complex than, that of NR2. This parallels the functional differences in conferring PB responsiveness between NR1 and NR3 on the one hand, and NR2 on the other.

Diabetes ◽  
2002 ◽  
Vol 51 (4) ◽  
pp. 910-914 ◽  
Author(s):  
H. Iwahashi ◽  
K. Yamagata ◽  
I. Yoshiuchi ◽  
J. Terasaki ◽  
Q. Yang ◽  
...  

2021 ◽  
Vol 11 (11) ◽  
pp. 5123
Author(s):  
Maiada M. Mahmoud ◽  
Nahla A. Belal ◽  
Aliaa Youssif

Transcription factors (TFs) are proteins that control the transcription of a gene from DNA to messenger RNA (mRNA). TFs bind to a specific DNA sequence called a binding site. Transcription factor binding sites have not yet been completely identified, and this is considered to be a challenge that could be approached computationally. This challenge is considered to be a classification problem in machine learning. In this paper, the prediction of transcription factor binding sites of SP1 on human chromosome1 is presented using different classification techniques, and a model using voting is proposed. The highest Area Under the Curve (AUC) achieved is 0.97 using K-Nearest Neighbors (KNN), and 0.95 using the proposed voting technique. However, the proposed voting technique is more efficient with noisy data. This study highlights the applicability of the voting technique for the prediction of binding sites, and highlights the outperformance of KNN on this type of data. The study also highlights the significance of using voting.


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