PMC

Most strategies for determining the function and relevant targets of virus-encoded miRNAs can be grouped into two different approaches. First, in the “Bottom-up” approach, lists of putative targets can be generated by computational methods or by changes cDNA expression or RISC association studies, performed in the presence of increased or decreased activity of the miRNA. The challenge from these approaches is to glean the function of these miRNAs in the context of infection. To accomplish this, one must weed out the “bystander” targets from the functionally relevant targets. The second approach, the “Top-down” approach”, involves first determining whether a virus-encoded miRNA affects a virologically relevant function, and then to use this information to help identify the relevant target(s). The challenge with such an approach is developing relevant functional screens that can accommodate a reasonable amount of throughput.

With a few rare, important exceptions, most virus miRNAs are not predicted to tap into existing host miRNA-target regulatory networks (Top panel). Some virus-encoded miRNAs have been shown to regulate a fairly large number of mRNA transcripts involved in a particular process, despite the fact that these miRNAs share no seed sequence identity with host miRNAs. This implies that sometimes, virus-encoded miRNAs are able to tap into viral-specific, miRNA-mRNA transcript networks (Middle panel). Finally, we propose the hypothesis that the major function of many virus-encoded miRNAs will be to regulate a small number of key (host or viral) transcripts (Bottom panel). Despite the fact that exogenous expression of such miRNAs will result in changes in the steady state levels of numerous (~hundreds) of transcripts, only a small minority of these need be of functional relevance and the rest may simply be bystanders. Parsing out the relevant targets is the major challenge of “Bottom-up” approaches (described in ).

Mutants of KSHV that delete 10 of the 12 pre-miRNA have an increased potential to undergo spontaneous lytic replication, thus implicating the KSHV miRNAs as playing a role in regulating lytic replication (; ). Three different miRNAs encoded by KSHV likely play a particularly important role in this process. Two miRNAs target the viral-encoded RTA, the master lytic switch transactivating protein that is both necessary and sufficient to trigger lytic replication in KSHV-infected cells. A third miRNA targets the host-encoded IkappaBalpha, an inhibitor of NF Kappa B signaling. Lower NF Kappa B signaling is associated with increased lytic activation for several gamma herpesviruses (). Thus, KSHV miRNA-mediated inhibition of an inhibitor of NF Kappa B promotes latency. We note a few KSHV miRNAs also play a “anti-lytic induction” role in some contexts (; ), (not depicted). miRNA-RISC complexes and mRNAs are depicted as described in the legend for . In the cartoon for latently-infected cells, the viral episome is depicted as circle attached to the chromosome.

Depicted are cartoon diagrams of virions (not exactly to scale) from virus families that have been reported to encode miRNAs. Indicated is the number of distinct pre-miRNAs reported for each family. The Herpesviridae encode the most distinct pre-miRNAs, with greater than two hundred from at least 15 different viruses. All herpesviruses that are known to encode miRNAs, express multiple pre-miRNAs (from a ~7 to greater than 25). Several members of the Polyomaviridae are known to encode a single pre-miRNA. Some members of the adenoviridae encode two non-canonical pre-miRNA-like molecules, called Virus Associated (VA) RNAs. miRNA biogenesis from VA RNAs does not utilize Drosha. These atypical pre-miRNAs are pol III transcripts that are inefficiently processed into miRNAs (less than 1% processing efficiency), but nonetheless make up the most abundant miRNAs present in the cell due to the sheer abundance of the VA RNA precursor (10 ^7 – 10 ^8 copies per cell). Currently, two insect viruses, an ascovirus and a baculovirus, are known to encode a pre-miRNA. There have been several controversial reports purporting that HIV, a member of the retroviridae, encodes pre-miRNAs. These reports have not been independently verified and their existence is the subject of some debate (). See also .

A, B: Statistical analysis of seed sharing. We analyzed the sharing of 6mer (nt. 2–7 of the mature miRNA sequence, top panel) or 7mer (nt. 2–7, bottom panel) seeds between human and human herpes- or polyomavirus miRNAs (left columns in each panel), or for comparison between human and mouse miRNAs (right columns in each panel). All sequences were curated from the September 2010 release 16 of miRBase. We first identified the total number of unique seeds in each entity, then calculated the number of shared seeds that would be expected to occur by mere chance. The red and light green bars show the relative enrichment of the number of actually observed number of shared seeds over the calculated expect value. As a control, we performed two tests using random and shuffled seed sequences (dark and light blue columns, respectively; see text for further details). P-values for the hypothesis that differences between the observed enrichment and either test set are statistical significant (one-tailed hypergeometric test) are indicated by asterisks (*: p < 0.5, ** p < 0.1, ***: p < 0.01). C: Alignment of the KSHV encoded miR-K11 (top), human and murine miR-155 (center) and miR-M4 encoded by MDV1 (bottom). Nucleotides that are conserved between both cellular mir-155 orthologues as well as matching nucleotides in viral miRNAs are shown in white on black background. Nucleotides in viral miRNAs that match residues that are not conserved between the cellular mir-155 orthologues are shown black on gray background. The seed region (nts. 2–8) and the region which is considered to be of special importance for auxiliary base pairing (nts. 13–16) () are marked underneath the alignment.

mRNAs are depicted as white circles (the 5’ cap) extending to the 3’ poly A tails (AAA…), miRNAs are depicted as being associated with the RNA induced silencing complex (RISC) with their important target binding determinant region (the “seed” region, nucleotides 2–8) depicted in color. The respective miRNA/RISC docking site is shown as a color-matched bar in 3’ UTR of the mRNA transcripts. This hypothetical example demonstrates several points thought to be common in many miRNA-regulated networks. mRNAs 1–4 play a role in biological process 1, mRNA 5–7 play a role in biological process 2. Note that several different miRNAs can regulate a single mRNA and conversely several different mRNAs are regulated by one miRNA. In addition, the same mRNA target can contain multiple miRNA docking sites for the same miRNA, as shown for mRNA 7. Combined, these features allow for fine-tuning regulation of gene expression. Typically, the more miRNAs regulating a transcript, the greater the degree of repression. A single miRNA can regulate hundreds of mRNA targets and can play a role in regulating different biological functions. This role can serve to regulate the amount of or the timing of gene expression. mRNA 5 can trigger a negative feedback loop of its own expression as it induces increased autoregulatory miRNA levels. Positive feed-forward loops (not depicted) also occur. Note, the potential for extensive cross talk between miRNAs and targets (and this is likely a gross oversimplification as there are greater than 900 human miRNAs known with ~hundreds of different ones being expressed in any single cell type at a given time). Furthermore, conservative estimates suggest 30%, but perhaps as many as 70% of transcripts (or more) are regulated by miRNAs. The overall picture emerging is that of complex, interlaced, regulatory networks that can serve as rheostats to finely regulate gene expression. Virus-encoded miRNA regulation offers several advantages to the virus including: the ability to regulate hundreds of transcripts, non-immunogenicity (since no proteins are required to be made by the virus), and a relatively small amount of genomic space.