scholarly journals Genetic Diversity in HIV-1 Subtype C LTR from Brazil and Mozambique Generates New Transcription Factor-Binding Sites

Viruses ◽  
2014 ◽  
Vol 6 (6) ◽  
pp. 2495-2504 ◽  
Author(s):  
José Boullosa ◽  
Mahesh Bachu ◽  
Dulce Bila ◽  
Udaykumar Ranga ◽  
Theodoro Süffert ◽  
...  
2016 ◽  
Vol 90 (16) ◽  
pp. 7046-7065 ◽  
Author(s):  
Anjali Verma ◽  
Pavithra Rajagopalan ◽  
Rishikesh Lotke ◽  
Rebu Varghese ◽  
Deepak Selvam ◽  
...  

ABSTRACTOf the various genetic subtypes of human immunodeficiency virus types 1 and 2 (HIV-1 and HIV-2) and simian immunodeficiency virus (SIV), only in subtype C of HIV-1 is a genetically variant NF-κB binding site found at the core of the viral promoter in association with a subtype-specific Sp1III motif. How the subtype-associated variations in the core transcription factor binding sites (TFBS) influence gene expression from the viral promoter has not been examined previously. Using panels of infectious viral molecular clones, we demonstrate that subtype-specific NF-κB and Sp1III motifs have evolved for optimal gene expression, and neither of the motifs can be replaced by a corresponding TFBS variant. The variant NF-κB motif binds NF-κB with an affinity 2-fold higher than that of the generic NF-κB site. Importantly, in the context of an infectious virus, the subtype-specific Sp1III motif demonstrates a profound loss of function in association with the generic NF-κB motif. An additional substitution of the Sp1III motif fully restores viral replication, suggesting that the subtype C-specific Sp1III has evolved to function with the variant, but not generic, NF-κB motif. A change of only two base pairs in the central NF-κB motif completely suppresses viral transcription from the provirus and converts the promoter into heterochromatin refractory to tumor necrosis factor alpha (TNF-α) induction. The present work represents the first demonstration of functional incompatibility between an otherwise functional NF-κB motif and a unique Sp1 site in the context of an HIV-1 promoter. Our work provides important leads as to the evolution of the HIV-1 subtype C viral promoter with relevance for gene expression regulation and viral latency.IMPORTANCESubtype-specific genetic variations provide a powerful tool to examine how these variations offer a replication advantage to specific viral subtypes, if any. Only in subtype C of HIV-1 are two genetically distinct transcription factor binding sites positioned at the most critical location of the viral promoter. Since a single promoter regulates viral gene expression, the promoter variations can play a critical role in determining the replication fitness of the viral strains. Our work for the first time provides a scientific explanation for the presence of a unique NF-κB binding motif in subtype C, a major HIV-1 genetic family responsible for half of the global HIV-1 infections. The results offer compelling evidence that the subtype C viral promoter not only is stronger but also is endowed with a qualitative gain-of-function advantage. The genetically variant NF-κB and the Sp1III motifs may be respond differently to specific cell signal pathways, and these mechanisms must be examined.


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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