In cell mutational interference mapping experiment (in cell MIME) identifies the 5' polyadenylation signal as a dual regulator of HIV-1 genomic RNA production and packaging.

Non-coding RNA regulatory elements are important for viral replication, making them promising targets for therapeutic intervention. However, regulatory RNA is challenging to detect and characterise using classical structure-function assays. Here, we present in cell Mutational Interference Mapping Experiment (in cell MIME) as a way to define RNA regulatory landscapes at single nucleotide resolution under native conditions. In cell MIME is based on (i) random mutation of an RNA target, (ii) expression of mutated RNA in cells, (iii) physical separation of RNA into functional and non-functional populations, and (iv) high-throughput sequencing to identify mutations affecting function. We used in cell MIME to define RNA elements within the 5' region of the HIV-1 genomic RNA (gRNA) that are important for viral replication in cells. We identified three distinct RNA motifs controlling intracellular gRNA production, and two distinct motifs required for gRNA packaging into virions. Our analysis reveals the 73AAUAAA78 polyadenylation motif within the 5' PolyA domain as a dual regulator of gRNA production and gRNA packaging, and demonstrates that a functional polyadenylation signal is required for viral packaging even though it negatively affects gRNA production.

MIMEAnTo: profiling functional RNA in mutational interference mapping experiments.

The mutational interference mapping experiment (MIME) is a powerful method that, coupled to a bioinformatics analysis pipeline, allows the identification of domains and structures in RNA that are important for its function. In MIME, target RNAs are randomly mutated, selected by function, physically separated and sequenced using next-generation sequencing (NGS). Quantitative effects of each mutation at each position in the RNA can be recovered with statistical certainty using the herein developed user-friendly, cross-platform software MIMEAnTo (MIME Analysis Tool). AVAILABILITY AND IMPLEMENTATION: MIMEAnTo is implemented in C ++ using the boost library as well as Qt for the graphical user interface and is distributed under GPL (http://www.gnu.org/licences/gpl). The libraries are statically linked in a stand alone executable and are not required on the system. The plots are generated with gnuplot. Gnuplot-iostream (https://github.com/dstahlke/gnuplot-iostream) serves as gnuplot interface. Standalone executables including examples and source code can be downloaded from https://github.com/maureensmith/MIMEAnTo CONTACTS: msmith@zedat.fu-berlin.de or vkleist@zedat.fu-berlin.deSupplementary information: Supplementary data are available at Bioinformatics online.

Inferring HIV-1 Transmission Dynamics in Germany From Recently Transmitted Viruses.

BACKGROUND: Although HIV continues to spread globally, novel intervention strategies such as treatment as prevention (TasP) may bring the epidemic to a halt. However, their effective implementation requires a profound understanding of the underlying transmission dynamics. METHODS: We analyzed parameters of the German HIV epidemic based on phylogenetic clustering of viral sequences from recently infected seroconverters with known infection dates. Viral baseline and follow-up pol sequences (n = 1943) from 1159 drug-naïve individuals were selected from a nationwide long-term observational study initiated in 1997. Putative transmission clusters were computed based on a maximum likelihood phylogeny. Using individual follow-up sequences, we optimized our clustering threshold to maximize the likelihood of co-clustering individuals connected by direct transmission. RESULTS: The sizes of putative transmission clusters scaled inversely with their abundance and their distribution exhibited a heavy tail. Clusters based on the optimal clustering threshold were significantly more likely to contain members of the same or bordering German federal states. Interinfection times between co-clustered individuals were significantly shorter (26 weeks; interquartile range: 13-83) than in a null model. CONCLUSIONS: Viral intraindividual evolution may be used to select criteria that maximize co-clustering of transmission pairs in the absence of strong adaptive selection pressure. Interinfection times of co-clustered individuals may then be an indicator of the typical time to onward transmission. Our analysis suggests that onward transmission may have occurred early after infection, when individuals are typically unaware of their serological status. The latter argues that TasP should be combined with HIV testing campaigns to reduce the possibility of transmission before TasP initiation.