Transcriptional regulation of human SREBP-1c (sterol-regulatory-element-binding protein-1c): a key regulator of lipogenesis

2004 ◽  
Vol 32 (1) ◽  
pp. 107-109 ◽  
Author(s):  
E. Tarling ◽  
A. Salter ◽  
A. Bennett
Keyword(s):  
Transcription Factor ◽  
Sequence Analysis ◽  
Binding Sites ◽  
Binding Protein ◽  
Regulatory Element ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Factor Binding ◽  
Element Binding Protein ◽  
Sterol Regulatory Element

Sterol-regulatory-element-binding protein 1c (SREBP-1c) is one member of the family of transcription factors that stimulate sterol and fatty-acid biosynthesis in animal cells. Human SREBP-1c, mapped to chromosome 17p11.2, is expressed in liver, intestine, skeletal muscle and adipocytes. A section of genomic sequence from a chromosome 17 library, thought to contain the SREBP-1c promoter, was cloned. Putative transcription-factor-binding sites and a potential transcriptional start site were identified using the Genomatix Suite of sequence analysis tools (MatInspector®). Sequence analysis showed the human promoter to be 42% identical with the previously published mouse sequence. Two novel transcription-factor-binding sites were identified: those for PDX-1 (pancreatic–duodenal homoeobox-1) and HNF-4 (hepatic nuclear factor-4). Co-transfection experiments with overexpression plasmids for PDX-1 and HNF-4 suggested that both factors stimulate SREBP-1c gene expression, although further work is required to ascertain their mechanisms of action.

scholarly journals Prediction of Transcription Factor Binding Sites of SP1 on Human Chromosome1

2021 ◽  
Vol 11 (11) ◽  
pp. 5123
Author(s):  
Maiada M. Mahmoud ◽  
Nahla A. Belal ◽  
Aliaa Youssif
Keyword(s):  
Transcription Factor ◽  
Binding Sites ◽  
Messenger Rna ◽  
Area Under The Curve ◽  
Noisy Data ◽  
Transcription Factor Binding Sites ◽  
Classification Problem ◽  
Transcription Factor Binding ◽  
K Nearest Neighbors ◽  
Factor Binding

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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