Deep sequencing of HIV-infected cells: insights into nascent transcription and host-directed therapy.

ABSTRACT

UNLABELLED Polyadenylated mature mRNAs are the focus of standard transcriptome analyses. However, the profiling of nascent transcripts, which often include nonpolyadenylated RNAs, can unveil novel insights into transcriptional regulation. Here, we separately sequenced total RNAs (Total RNAseq) and mRNAs (mRNAseq) from the same HIV-1-infected human CD4(+) T cells. We found that many nonpolyadenylated RNAs were differentially expressed upon HIV-1 infection, and we identified 8 times more differentially expressed genes at 12 h postinfection by Total RNAseq than by mRNAseq. These expression changes were also evident by concurrent changes in introns and were recapitulated by later mRNA changes, revealing an unexpectedly significant delay between transcriptional initiation and mature mRNA production early after HIV-1 infection. We computationally derived and validated the underlying regulatory programs, and we predicted drugs capable of reversing these HIV-1-induced expression changes followed by experimental confirmation. Our results show that combined total and mRNA transcriptome analysis is essential for fully capturing the early host response to virus infection and provide a framework for identifying candidate drugs for host-directed therapy against HIV/AIDS. IMPORTANCE In this study, we used mass sequencing to identify genes differentially expressed in CD4(+) T cells during HIV-1 infection. To our surprise, we found many differentially expressed genes early after infection by analyzing both newly transcribed unprocessed pre-mRNAs and fully processed mRNAs, but not by analyzing mRNAs alone, indicating a significant delay between transcription initiation and mRNA production early after HIV-1 infection. These results also show that important findings could be missed by the standard practice of analyzing mRNAs alone. We then derived the regulatory mechanisms driving the observed expression changes using integrative computational analyses. Further, we predicted drugs that could reverse the observed expression changes induced by HIV-1 infection and showed that one of the predicted drugs indeed potently inhibited HIV-1 infection. This shows that it is possible to identify candidate drugs for host-directed therapy against HIV/AIDS using our genomics-based approach.