Siying, J. T. | bioRxiv Science

bioRxiv2024

Identification of Cyclin L1 as a host factor regulating Hepatitis B Virus replication

Background & AimsUnderstanding the regulatory interactions between Hepatitis B virus (HBV) and human host factors is key to the development of next-generation host-directed antiviral therapies and achieving a functional HBV cure. In this study, we aimed to investigate HBV-induced alterations in host gene expression in primary human hepatocytes (PHH) to identify host-specific factors that are regulated and exploited by the virus for replication and survival. MethodsWe performed whole transcriptome sequencing (WTS) of HBV-infected PHH to identify host pathways that could potentially influence the HBV life cycle. RNA-interference-based validation of putative targets to evaluate the function of dysregulated candidate genes resulted in the identification of Cyclin L1 (CCNL1) as a key host factor. ResultsRNAi-knockdown of CCNL1 revealed that it is essential for HBV gene expression, including HBV-surface antigen (HBsAg). Mechanistically, we found that CCNL1 can phosphorylate the C-terminal domain (CTD) of RNA Polymerase II (RNAPII) at serine 2 (S2), likely to regulate HBV transcription. Furthermore, the knockdown of CCNL1 inhibited the binding of total and phospho- (Ser2-Ser5) RNAPII, pan-acetylated H3ac, and H3K27ac to HBV cccDNA, implicating its function in the regulation of cccDNA-dependent viral transcription. Finally, enhanced CCNL1 expression in chronic hepatitis B patients, as compared to those with resolved infection, underscores a functional link between this host factor and CHB. ConclusionOur data demonstrates that CCNL1 regulates HBV RNA transcription and replication by modulating RNAPII phosphorylation and activity, making it a potential host susceptibility factor for HBV. LAY SUMMARYHepatitis B requires human host cell factors and biological processes to establish an efficient infection. Identifying host factors that support and/or restrict HBV infection is essential for understanding the molecular basis of chronic HBV infection and for developing host-targeting anti-HBV drugs. Here, we report that CCNL1 can serve as a potential host susceptibility factor for HBV, as reduced CCNL1 function results in reduced viral replication and gene expression. Graphical SummaryGraphical summary, highlighting the approach and validation experiments. From whole transcriptomics analysis, we identified known HBV-host factors such as SRPK1, CDK1, NXF1 among others as well as new factors such as CCNL1. Employing various RNAi approaches and different cell models including primary human hepatocytes, we validated the role of CCNL1 during HBV infection cycle. Pull-down experiments followed by ChIP-PCR further showed a reduction in cccDNA-based transcription upon knockdown of CCNL1. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/619969v2_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@1ee9cd9org.highwire.dtl.DTLVardef@7bac2dorg.highwire.dtl.DTLVardef@164c3bborg.highwire.dtl.DTLVardef@6e497a_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2020

Prediction of novel components of bacterial ribosomes by crowd-sourced analysis of RNA sequencing data

Bacterial resistance to antibiotics is a growing problem that is projected to cause more deaths than cancer in 2050. Consequently, novel antibiotics are urgently needed. Since more than half of the available antibiotics target the bacterial ribosomes, proteins that are involved in protein synthesis are thus prime targets for the development of novel antibiotics. However, experimental identification of these potential antibiotic target proteins can be labor-intensive and challenging, as these proteins are likely to be poorly characterized and specific to few bacteria. In order to identify these novel proteins, we established a Large-Scale Transcriptomic Analysis Pipeline in Crowd (LSTrAP-Crowd), where 285 individuals processed 26 terabytes of RNA-sequencing data of the 17 most notorious bacterial pathogens. In total, the crowd processed 26,269 RNA-seq experiments and used the data to construct gene co-expression networks, which were used to identify more than a hundred uncharacterized genes that were transcriptionally associated with protein synthesis. We provide the identity of these genes together with the processed gene expression data. The data can be used to identify other vulnerabilities or bacteria, while our approach demonstrates how the processing of gene expression data can be easily crowdsourced.

bioinformatics↗