- Open Access
Host-virus interaction: a new role for microRNAs
© Scaria et al; licensee BioMed Central Ltd. 2006
- Received: 30 August 2006
- Accepted: 11 October 2006
- Published: 11 October 2006
MicroRNAs (miRNAs) are a new class of 18–23 nucleotide long non-coding RNAs that play critical roles in a wide spectrum of biological processes. Recent reports also throw light into the role of microRNAs as critical effectors in the intricate host-pathogen interaction networks. Evidence suggests that both virus and hosts encode microRNAs. The exclusive dependence of viruses on the host cellular machinery for their propagation and survival also make them highly susceptible to the vagaries of the cellular environment like small RNA mediated interference. It also gives the virus an opportunity to fight and/or modulate the host to suite its needs. Thus the range of interactions possible through miRNA-mRNA cross-talk at the host-pathogen interface is large. These interactions can be further fine-tuned in the host by changes in gene expression, mutations and polymorphisms. In the pathogen, the high rate of mutations adds to the complexity of the interaction network. Though evidence regarding microRNA mediated cross-talk in viral infections is just emerging, it offers an immense opportunity not only to understand the intricacies of host-pathogen interactions, and possible explanations to viral tropism, latency and oncogenesis, but also to develop novel biomarkers and therapeutics.
- Human Immunodeficiency Virus
- microRNA Target
- Latency Associate Transcript
- microRNA Level
- microRNA Target Prediction
MicroRNAs (miRNAs) are small RNA molecules which have recently gained widespread attention as critical regulators in complex gene regulatory networks in eukaryotes. These small RNA, processed from non-coding regions of the genome into 18–23 nucleotide long single stranded RNA, have been shown to regulate translation of messenger RNA (mRNA) by binding to it and effecting target cleavage or translational block depending on the extent of sequence complementarity with the target . Generally, in mammalian systems, microRNAs bind to targets with incomplete complementarity, in association with a host of cellular proteins – what is commonly termed as the RNA Induced Silencing Complex (RISC).
MicroRNA mediated regulation has been lately shown to encompass a wide spectrum of host biological processes ranging from growth and development to oncogenesis [2–5]. Recent genome-wide computational screens for microRNA targets in humans predict that 10%  to 30%  of all genes are regulated by microRNAs. The regulatory network of miRNA-mRNA interaction is rendered even more complex because of multiplicity and cooperativity of microRNA targeting.
MicroRNAs have recently been implicated in the intricate cross-talk between the host and the pathogen  in viral infections and is thought to play a major role in viral pathogenesis. Though studies into the entire spectrum of host-pathogen interactions at the microRNA level are still in its infancy, there has been a recent spurt in reports exploring the possibility in a number of major pathogenic viruses of humans.
MicroRNAs were initially discovered in 1993, in a genetic screen for mutants that disrupt the timing of post-embryonic development in the nematode Caenorhabditis elegans , and were thought to be an oddity in gene regulation, of nematodes till 2000. About 7 years later, when let7 was discovered , and was found to be highly conserved in eukaryotes, it led to a surge in discovery of new microRNAs in a number of organisms including humans.
MicroRNA gene location
MicroRNAs have been classically thought to be transcribed from intergenic regions, but recent large-scale genome-wide cloning experiments  have shown that microRNAs can be derived from introns as well. Intergenic microRNAs are sometimes found to occur as clusters which would be transcribed as polycistronic transcripts and are shown to share similar expression profiles . A significant proportion of microRNAs are encoded within the introns of protein-coding genes, presumably expressed in sync with them. A few microRNAs have been mapped to the exons of protein-coding genes. One example is hsa-mir-20a which is annotated by miRBase to arise from the exon 2 as well as introns 5 and 8 of alternative transcripts of C13orf25. The significance of these microRNAs and their roles in alternatively spliced transcripts are yet to be addressed.
MicroRNAs are transcribed by RNA Polymerase II as primary microRNAs (pri-miRNAs) which range from hundreds to thousands of nucleotides in length and resemble protein-coding transcripts in that they are poly-adenylated and capped . These pri-miRNAs are then processed by nuclear localized enzymes Drosha or Pasha (DGCR8 in humans) to produce thermodynamically stable hairpin structures known as pre-microRNAs (pre-miRNAs), of ~70 bases. These pre-microRNAs are then exported to the cytoplasm by Exportin-5 and are then further processed by the RNAase III enzyme Dicer, to form duplexes of 18–23 bases. This duplex is unwound by a yet to be discovered helicase. In steps shared with the siRNA (small interfering RNA) pathway the strand with lower stability in the 5' end (guide strand) is preferentially selected  from the double stranded molecule (which composes of miRNA and its complementary strand miRNA*) to be associated with the RNA Induced Silencing Complex (RISC).
Mechanisms of action
The mechanism of action of microRNAs is considered to be by two modes – translational repression and target degradation. The former is common in mammalian systems while the latter is found predominantly in plants. The basic difference in the two mechanisms is thought to be primarily governed by the levels of complementarity between microRNAs and their target transcripts. Perfect or near perfect complementarity as is common in plant microRNAs and in a small class of eukaryotic microRNAs causes target cleavage and degradation , analogous to the action of siRNAs which have perfect complementarity to the target regions. Evidence suggests that microRNA bound transcripts are sequestrated into P bodies [15, 16] where they are maintained in a silenced state either by associating with proteins that prevent translation or possibly by removal of the cap structure .
Non-classical mechanisms of action
Recent evidence suggests that microRNAs can also regulate protein expression through non-classical ways. Genome-wide expression analysis of microRNA targets have shown to decrease the transcript levels . But whether this is a direct or indirect effect is yet to be explored. A couple of recent reports also suggest that microRNAs can modulate de-adenylation of transcripts [19, 20]. MicroRNAs are classically thought to be negative regulators, but with exceptions as in the case of a reported human microRNA which can cause abundance of Hepatitis RNA  (vide infra). A recent report by Bhattacharyya et al suggests that microRNA mediated mechanism of post-transcriptional repression of gene expression is indeed reversible , suggesting that the proteins associated with microRNA-mRNA complex modulate the expression of the transcript in a switch-like mechanism, responding to particular stimuli. This mechanism, if proved to be a generalized phenomenon, would have large implications in the regulation of gene expression in relation to particular stimuli. In summary microRNAs can act as a trans-acting element for reversible and dynamic regulation of spatial and temporal protein expression.
Computational predictions have been the mainstay for discovery of microRNAs and their targets. The algorithms for microRNA prediction range from custom-made programs to search for hairpin loops and energetic stability to advanced algorithms employing machine learning approaches [23–25]. Even though the algorithms for microRNA prediction have improved over time, accurate de novo prediction of microRNA still remains a challenging task. This is especially important in the case of viruses as viral microRNAs do not share close homology even in the same class. We hope the prediction algorithms will improve with a better understanding of the sequence and structural components of precursor hairpins involved in microRNA biogenesis . We have recently developed a de novo method (unpublished results) for microRNA prediction in viruses based on Support Vector Machines, rationalizing that virus encoded microRNA precursor hairpins would share the sequence and structure features with that of host as they share the same microRNA processing machinery. The algorithms for microRNA target prediction also have been significantly improved from first-generation algorithms which rely on sequence complementarity rules, thermodynamic stability and conservation [6, 27, 28] by incorporation of features like target RNA structure .
Hepatitis C virus
Jopling et al  reported an interesting case wherein the tissue specificity of microRNA expression was exploited by a virus to establish tissue selectivity. A liver specific microRNA mir-122 was shown to cause accumulation of viral RNA by binding to the 5' non-coding region of the viral genome. The authors have also verified the findings by mutational analysis as well as sequestration of the microRNA. It is possible that this novel mechanism of microRNAs targeting the 5'UTR of the transcript may be mediated through a translation controlling switch at the 5'end of the transcript through changes in RNA secondary structure. This, by and large, remains the only report of a microRNA targeting the 5'UTR in a mammalian system and causing RNA accumulation.
Interestingly viruses have also evolved to evade RNAi by a variety of strategies. The suppressors of antiviral RNAi is better understood in plant viruses. To counteract the small RNA mediated interference, viruses express suppressors that interfere with microRNA as well as siRNA pathways . The virus encoded antiviral RNAi suppressors include small RNAs to proteins, and are thought to effect through various mechanisms like sequestration and/or inhibition of siRNA formation [38–40]. The entire spectrum of suppressors of RNAi encoded by animal viruses is yet to be unraveled.
List of virus-encoded microRNAs
Number of microRNAs
Epstein Barr virus
Kaposi sarcoma-associated herpesvirus
Simian virus 40
Human Immunodeficiency Virus
Interestingly the viral microRNAs, unlike their vertebrate counterparts do not share a high level of homology, even within members of the same family or with that of the host. This has been attributed to the higher rate of mutations and the faster evolution in viruses as compared to eukaryotes. Though this would mean an evolutionary advantage to rapidly adapt to the host and environmental conditions, it offers a challenge to computational biologists as most of the algorithms for microRNA prediction relies heavily on conservation and would prove inadequate in case of viruses. This would be just the tip of the iceberg as de novo prediction of microRNA candidates is still an unmet challenge for computational biologists. With the advent of high throughput validation methods like microarrays being employed, and with better and efficient computational algorithms for prediction of microRNAs, the count is all set to rise. Similarly there is a severe gap in the understanding the targets of these microRNAs and necessitates the use of better technology clubbed with efficient computational algorithms.
Regulation of cellular processes by virus encoded microRNA
Bennasser et al  reported a computational screen for HIV-1 encoded microRNAs and further went about predicting their cellular targets and found five pre-miRNA candidates which has potential to encode 10 microRNAs and through them regulate ~1000 host transcripts. In a similar computational screen for targets to potential HIV encoded microRNA, Couturier et al  showed that the HIV pro-viral genome had multiple matches of complementarity in important cellular proteins/cytokines well known to play crucial roles in HIV pathogenesis like CD28, CD40L, IL-2, IL-3, TNF-β, IL-12 and CD4. The current understanding is that multiple gapped stretches of complementarity can result in translational repression, along with the discovery that the HIV-1 pro-viral genome has such levels of complementarity especially with human protein coding genes, points to the possibility that HIV transcripts may encode for microRNAs, or small regulatory RNAs. This along with the evidence that a large number of these cellular genes including CD28 are well documented to be down regulated in HIV pathogenesis, points to the possible cross-talk between the virus and host at the microRNA level.
Of late, Cui et al discovered novel virus encoded microRNAs from HSV genome . Subsequently Gupta et al, discovered that Herpes simplex-1 (HSV-1) latency associated transcript (LAT) encodes for a microRNA which target critical genes of the apoptosis pathway  including those involved in the TGF-β signaling pathway, including TGF-β1 and SMAD3, thereby protecting the cells from apoptosis. This is perhaps the first experimental evidence of a virus encoded microRNA targeting cellular transcripts.
Such a mechanism could operate in other related herpes viruses like Epstein-Barr virus, which not only causes latent infection, but also associated with a wide spectrum of neoplasms in humans including Burkitts lymphoma and nasopharyngeal carcinoma. It is all the more probable that oncogenic herpes viruses like EBV and KSHV encoded microRNAs can target critical genes involved in oncogenesis. This is especially so in the case of EBV encoded microRNAs as they have been shown to be differentially expressed in different phases of the viral life . Our analysis of cellular targets of 32 EBV encoded microRNAs using robust computational approaches using consensus of microRNA target prediction software revealed that the target genes are involved in apoptosis and tumor suppressor pathways, suggesting that EBV encoded microRNAs play crucial roles in oncogenic transformation induced by the virus (unpublished results). The discovery of virus encoded microRNAs playing crucial roles in pathogenesis of diseases caused by viruses not only throws light on a new level of host-pathogen interactions, but also would help in designing novel preventive and therapeutic strategies.
Viral gene regulation by virus-encoded microRNAs
Omoto et al, using a combinatorial approach incorporating both computational prediction and experimental validation demonstrated the possibility that a virus-encoded microRNA could auto-regulate itself. A nef derived microRNA could down regulate nef expression in vitro suggesting that it could be a mechanism of maintaining low viremia in Long term non-progressor (LTNP) states . This finding was later expanded by the same group with an additional discovery that nef derived microRNA also suppress transcription  by reducing HIV-1 promoter activity through the negative responsive element in the 5'-LTR, thus contributing to an additional layer of auto regulation.
In yet another instance, a virus encoded microRNA, is effectively used by the virus to tune down a set of genes and thereby evade cytotoxic T cell response . Simian Virus 40 (SV40) was shown to produce a microRNA which was primarily expressed in the late stages of infection. Curiously, the expression of the viral microRNAs did not have any untoward effect on the viral replication. Further analysis revealed that the microRNAs had near perfect complementary matches in the early expressed genes of the virus which would target them for interference mediated degradation. The genes include the T antigen which is a determinant for invasion of T cells, thus providing an advantage in camouflaging the virus infected cells from the cellular immune system.
Recently microRNA expression profiles have been analyzed for viral infections like HIV . MicroRNA expression has been shown to be specific to various stages of infection in Herpesviruses  and have been proposed to be associated with latency in HIV infection [31, 50], promising an early biomarker for cancers caused by oncogenic viruses. MicroRNA profiles have also been explored in a number of patho-physiological conditions . Recent reports suggest that microRNA profiles can be used not only to classify different classes of cancers [52–54], but could also be used as biomarkers for diagnosis and prognosis of disease states .
MicroRNAs and anti-microRNA oligonucleotides (AMOs) have been proposed as novel therapeutics [55, 56]. Recent advances in nucleotide chemistry like Locked Nucleic Acids (LNA) , and other backbone modifications have made it possible to design small RNA oligonucleotides which are highly stable in biological systems circumventing one major hurdle in using microRNAs as future therapeutics. Oligonucleotide modifications have already made their way to the microRNA experimental biologists workbench . The success of siRNA based strategies in targeting specific genes could be extended to microRNAs also. This would include delivery strategies . This would be even more important as siRNA based therapeutics for viral pathogens in different stages of clinical trials and are showing promising results.
Artificial microRNAs (amiRNAs) and microRNA engineering
MicroRNAs are promising candidates for developing novel bio-therapeutics against viruses, as it requires only partial complementarity unlike siRNAs and thus can tackle the high rate of mutations in viruses better than siRNAs. Initial experiments creating microRNAs based on siRNA design rules have shown promising results . Our group has recently developed an algorithm for design of highly specific microRNAs on against sequences to be targeted (unpublished results). This would allow design of microRNAs against highly conserved sequences in viral genome. Artificial microRNAs also offer the advantage that they can be optimized to create less off-target events in the host thus substantially reducing untoward side effects. MicroRNAs or microRNA target sequences could also be engineered into transforming viruses and could enable tissue specific and environment sensitive expression of genes.
The authors thank Dr. Elayanambi Sundaramoorthy for reviewing the manuscript and providing valuable suggestions. Authors also acknowledge the Council of Scientific and Industrial Research (CSIR), India for funding through Task Force Project CMM0017. VS acknowledges the Senior Research Fellowship from CSIR.
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