One of the main objectives of our laboratory is to study the establishment of HIV latency coupled with stochastic HIV transcription and the host functional genome. 

 

Using quantitative gnomic and machine-learning-based approaches tailored from the laboratory, we aim to deepen our grasp on how variations, with resolution of single viruses, in HIV genome integrity and transcription influence the configuration of HIV reservoirs. Two research lines aligning with this objective are conducted.

 

1. Role of HIV antisense transcripts (AST) in the establishment of latency

We propose that fluctuations in HIV transcription can appear at, at least two levels: (1) the chromosomal landscape and (2) the in situ HIV integration site (Figure 1). In the former case, proviruses integrating in different genomic locations demonstrate a variety of transcriptional bursting and can be classified in noise space constructed by parameters associated with coefficient of variation or using a mathematical model fitting a curve of exponential decay. In the latter case, stochastic HIV gene expression tends to be a pure epigenetic phenomenon: the identical provirus demonstrates the turnover of HIV infection with elevated frequency. We observed inheritance of such stochastic fluctuations and identified the provirus integration site, which is in the vicinity of the genomic repetitive regions associated with intense signals of H3K27ac and H3K4me3. In both cases, we observed similar consecutive expression patterns and a positive correlation between sense and antisense RNA transcripts over time, suggesting that the independent yields of both transcripts determine the stochastic phenotype of provirus transcription, rather than their ratios. Notably, both HIV LTRs are responsive to drug stimulation and could differently react to drugs as transcriptional bursting is epigenetics-driven. Overall, our data suggest that antisense transcripts are most likely involved in the stochastic nature of HIV transcription.

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2. Distinguishable topology of the task-evoked functional genome networks in HIV reservoirs

Our len to view HIV reservoir is that it can be represented as the topological (task-evoked) property of a network consisting of different communities of the genes targeted by HIV. Such communities are so-called immunologic signatures. HIV integration frequency within a network might be used as a proxy to define specific immune cell types and proinflammatory soluble factors, facilitating fine-tuning of the microenvironment of reservoirs. To deepen our understanding of such heterogeneous HIV reservoirs and their functional implications, we pioneer the integration of a convergence approach to characterize the composition of HIV reservoirs.

 

Based on graph-theoretical tools, we observe noticeable topological properties in networks, featuring immunologic signatures enriched by genes harboring intact and defective proviruses, when comparing antiretroviral therapy (ART)-treated HIV-1-infected individuals and elite controllers. The key variable, the rich factor, plays a pivotal role in classifying distinct topological properties in networks. The host gene expression strengthens the accuracy of classification between elite controllers and ART-treated patients. Markov chain Monte Carlo modeling for the simulation of different graph networks demonstrated the presence of an intrinsic barrier between elite controllers and non-elite controllers. Notably, our work provides a prime example of leveraging genomic approaches alongside mathematical tools to unravel the complexities of HIV reservoirs.

 

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