Comprehensive Single-Cell Multi-Omics Study on HIV Latency Reversal Reveals Novel Regulators of Viral Reactivation

This paper, titled “Integrated single-cell multiomic analysis of HIV latency reversal reveals novel regulators of viral reactivation,” was jointly completed by Manickam Ashokkumar, Wenwen Mei, and several other researchers from institutions including the University of North Carolina at Chapel Hill and Texas A&M University. It was published ahead of print in the journal “Genomics, Proteomics & Bioinformatics” on June 20, 2024.

Research Background

Despite the tremendous success of antiretroviral therapy (ART) in controlling human immunodeficiency virus (HIV) infection, HIV remains incurable, primarily due to the presence of a small reservoir of latently infected cells in the body. These reservoirs escape immune system attacks during treatment, allowing the virus to rapidly rebound when treatment is stopped. Therefore, understanding the mechanisms of HIV latency is crucial for developing curative HIV therapies.

To reverse HIV latency, researchers typically use latency reversing agents (LRAs) to induce viral gene expression, thereby activating latent HIV for clearance by the immune system. However, existing LRA methods are inefficient, reversing only about 10% of replication-competent viruses. This is mainly because HIV gene expression is influenced by multiple layers of suppression mechanisms, as well as cellular environment heterogeneity and proviral integration sites. Therefore, a comprehensive understanding of these suppression mechanisms is needed to develop broader LRA strategies.

Research Objectives and Methods

In this study, the research team employed integrated single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq) methods to reveal the mechanisms of HIV gene expression regulation and latency reversal by simultaneously analyzing genomic and chromatin accessibility.

The study mainly included the following steps:

1. Experimental Design and Data Collection:
   • The study used HIV-gfp infected Primary CD4+ T cells and Jurkat cell line 2D10, stimulated with three LRAs: Vorinostat, iBET151, and Prostratin.
   • Flow cytometry and single-cell multi-omics techniques were used to analyze cells after each treatment.
2. Data Processing and Analysis:
   • Performed homologous analysis of HIV-gfp infected Primary CD4+ T cells and Jurkat cell line 2D10 through scRNA-seq and scATAC-seq, clustering the resulting multi-omics data.
   • Calculated transcription factor bias scores to identify transcription factors exhibiting differential epigenetic characteristics after LRA treatment.
3. Transcription Factor and Viral Transcription Association Analysis:
   • Used machine learning methods to train models predicting which transcription factors are associated with HIV gene expression.
4. Functional Validation:
   • Utilized shRNA and CRISPR technologies to verify the regulatory roles of key transcription factors in HIV gene expression.

Research Results

Data Analysis and Preliminary Findings

Integrating scRNA-seq and scATAC-seq analyses, the study obtained high-quality chromatin accessibility and RNA data from approximately 125,000 cells:

1. Under different conditions, dimensionality reduction methods revealed significant separation of primary CD4+ T cells and 2D10 cells after different LRA treatments, indicating that each LRA affects the cellular transcriptome differently.
2. Detection of HIV transcription gene expression showed that LRAs Vorinostat and Prostratin significantly upregulated viral RNA abundance, while iBET151 had minimal effect. This indicates that these LRAs have varying effectiveness in reactivating HIV genes.

Identification of Key Regulatory Factors

Through analysis, the research team identified multiple cellular transcription factors and chromatin features associated with HIV transcription:

1. In the Jurkat cell line 2D10, the most significantly correlated transcription factors were GATA3 (positively correlated) and CTCF (negatively correlated).
2. In primary CD4+ T cells, significantly correlated genes such as CENPF (intermediate filament protein), GAPDH (associated with active cell metabolism and cell division), and BACH2 (transcription factor) were discovered. Additionally, the accessibility of proviral chromatin was found to be associated with viral gene expression, and several transcription factors with significant regulatory roles, such as AP-1 and NF-κB family members, were identified.
3. Using machine learning methods to analyze the obtained data, results showed that the machine learning models were significantly effective in predicting HIV expression in cells.

Functional Validation

1. Role of GATA3 in HIV reactivation:
   • Using shRNA technology to inhibit GATA3 gene expression, results showed a significant decrease in viral gene expression after Prostratin stimulation.
2. Role of FOXP1 in HIV reactivation:
   • Inhibition of FOXP1 gene expression led to activation of viral gene expression. Conversely, overexpression of FOXP1 could suppress viral gene expression.
3. In primary CD4+ T cells, GATA3 and FOXP1 genes were knocked out using CRISPR/Cas9 technology, followed by Prostratin stimulation, which significantly affected HIV expression, validating the important regulatory roles of GATA3 and FOXP1 in HIV latency reversal.

Conclusions and Outlook

Research Conclusions

Through the integration of single-cell multi-omics technologies, the research team identified some potentially significant new regulatory factors for HIV reactivation, particularly the transcription factors GATA3 and FOXP1. These findings not only enrich the theoretical understanding of HIV latency and its reversal mechanisms but also provide new potential targets for HIV cure strategies.

Application Value

These data demonstrate that multi-omics approaches based on single-cell resolution can effectively reveal the complex network of HIV gene expression regulation, thereby providing important scientific basis for developing more effective LRA strategies and completely eliminating HIV latent reservoirs.

Research Highlights

• Innovative Multi-Omics Analysis Method: Comprehensive use of scRNA-seq and scATAC-seq simultaneously captures gene expression and chromatin accessibility at the single-cell level, providing a comprehensive multi-omics perspective.
• Machine Learning-Assisted Analysis: Utilizing machine learning techniques to extract important features from high-dimensional data, improving the prediction accuracy of HIV latency reversal mechanisms.
• Successful Validation of Key Transcription Factors: Further confirmation of the crucial roles of GATA3 and FOXP1 in HIV latency reversal through functional validation.

Research Significance

This study provides a new research paradigm for exploring HIV latency mechanisms, while also taking an important step towards curing HIV by developing more broadly effective LRA strategies. In the future, further detailed analysis and functional validation of more HIV reactivation regulatory factors may provide more possibilities for ending HIV.