Deciphering HIV-1 Transcription Initiation and Elongation from Single-Molecule Imaging Data

Area of Science:

• Molecular Biology
• Computational Biology
• Virology

Background:

• HIV-1 gene expression relies on transcription initiation and elongation.
• Single-molecule imaging reveals HIV-1 transcription occurs over multiple timescales, impacting latency.
• Current understanding of HIV-1 transcription mechanisms is limited by a lack of unified modeling and advanced computational analysis.

Purpose of the Study:

• To develop a general stochastic model for HIV-1 transcription dynamics.
• To create a statistical inference method integrating initiation-time and nascent RNA data.
• To elucidate the molecular mechanisms of HIV-1 transcription and the role of Tat.

Main Methods:

• Developed a general stochastic model to characterize HIV-1 transcription dynamics.
• Computed distributions of initiation times and nascent RNA counts.
• Created a statistical inference method integrating initiation-time and nascent RNA data.

Main Results:

• Coordination between initiation and elongation modulates HIV-1 transcription.
• Leveraging initiation-time data significantly enhances model identification.
• The developed inference method accurately estimates initiation rate and elongation time, independent of model choice.
• Tat protein plays a dual role in HIV-1 transcriptional regulation.
• Tat alters promoter silent states, inducing viral reactivation and latency exit.

Conclusions:

• The new modeling and inference framework accurately characterizes HIV-1 transcription dynamics.
• Initiation-time data is crucial for precise parameter estimation in HIV-1 transcription models.
• Tat's regulation of promoter states is key to HIV-1 reactivation and latency.
• This approach offers potential improvements for HIV-1 cure strategies.