RNA polymerase II at initiation

cccDNA Surrogate MC-HBV-Based Screen Identifies Cohesin Complex as a Novel HBV Restriction Factor

BACKGROUND & AIMS

Covalently closed circular DNA (cccDNA) of hepatitis B virus (HBV), existing as a stable minichromosome in the hepatocyte, is responsible for persistent HBV infection. Maintenance and sustained replication of cccDNA require its interaction with both viral and host proteins. However, the cccDNA-interacting host factors that limit HBV replication remain elusive.

METHODS

Minicircle HBV (MC-HBV), a recombinant cccDNA, was constructed based on chimeric intron and minicircle DNA technology. By mass spectrometry based on pull-down with biotinylated MC-HBV, the cccDNA-hepatocyte interaction profile was mapped. HBV replication was assessed in different cell models that support cccDNA formation.

RESULTS

MC-HBV supports persistent HBV replication and mimics the cccDNA minichromosome. The MC-HBV-based screen identified cohesin complex as a cccDNA binding host factor, leading to reduced HBV replication. Mechanistically, with the help of CCCTC-binding factor (CTCF), which has specific binding sites on cccDNA, cohesin loads on cccDNA and reshapes cccDNA confirmation to prevent RNA polymerase II enrichment. Interestingly, HBV X protein transcriptionally reduces structural maintenance of chromosomes complex expression to partially relieve the inhibitory role of the cohesin complex on HBV replication.

CONCLUSIONS

Our data not only provide a feasible approach to explore cccDNA-binding factors, but also identify cohesin/CTCF complex as a critical host restriction factor for cccDNA-driven HBV replication. These findings provide a novel insight into cccDNA-host interaction and targeted therapeutic intervention for HBV infection.

Protein network study of human AF4 reveals its central role in RNA Pol II-mediated transcription and in phosphorylation-dependent regulatory mechanisms

AF4 belongs to a family of proteins implicated in childhood lymphoblastic leukaemia, FRAXE (Fragile X E site) mental retardation and ataxia. AF4 is a transcriptional activator that is involved in transcriptional elongation. Although AF4 has been implicated in MLL (mixed-lineage leukaemia)-related leukaemogenesis, AF4-dependent physiological mechanisms have not been clearly defined. Proteins that interact with AF4 may also play important roles in mediating oncogenesis, and are potential targets for novel therapies. Using a functional proteomic approach involving tandem MS and bioinformatics, we identified 51 AF4-interacting proteins of various Gene Ontology categories. Approximately 60% participate in transcription regulatory mechanisms, including the Mediator complex in eukaryotic cells. In the present paper we report one of the first extensive proteomic studies aimed at elucidating AF4 protein cross-talk. Moreover, we found that the AF4 residues Thr²²⁰ and Ser²¹² are phosphorylated, which suggests that AF4 function depends on phosphorylation mechanisms. We also mapped the AF4-interaction site with CDK9 (cyclin-dependent kinase 9), which is a direct interactor crucial for the function and regulation of the protein. The findings of the present study significantly expand the number of putative members of the multiprotein complex formed by AF4, which is instrumental in promoting the transcription/elongation of specific genes in human cells.

Phenotype analysis using network motifs derived from changes in regulatory network dynamics

The intrinsic dynamic response of a transcriptional regulatory network depends directly on molecular interactions in the cellular transcription, translation, and degradation machineries. These interactions can be incorporated into dynamic mathematical models of the biochemical system using the biophysical relationship with the model parameters. Modifications of such interactions bring changes to the biological behavior of the cells, and therefore, many normal and pathological cellular states depend on them. It is important for analysis, prediction, diagnosis, and treatment of cellular function to have an experimentally derived model with parameters that adequately represent the molecular interactions of interest. Finding the model and parameters of a transcriptional regulatory network is a difficult task that has been approached at different levels and with different techniques. We develop here a new analysis method (based on previous work on network inference, modeling, and parameter identification) that finds the most changed parameters from yeast oligonucleotide microarray expression patterns in cases where a phenotype difference exists between two samples. We then relate and examine the changed parameters with their associated genes, corresponding genetic functional categories, and particular subnetworks and connectivities. The biophysical bases for these changes are also identified by studying the relationship of the changed parameters with the transcription, translation, and degradation mechanisms. The method is improved to cases where there are two or more transcription factors influencing transcription, and a statistical analysis is performed to give a measurement of the uniqueness and robustness of the parameter fit.