Literature DB >> 20628498

Prediction of human targets for viral-encoded microRNAs by thermodynamics and empirical constraints.

Alessandro Laganà1, Stefano Forte, Francesco Russo, Rosalba Giugno, Alfredo Pulvirenti, Alfredo Ferro.   

Abstract

MicroRNAs (miRNAs) are small RNA molecules that modulate gene expression through degradation of specific mRNAs and/or repression of their translation. miRNAs are involved in both physiological and pathological processes, such as apoptosis and cancer. Their presence has been demonstrated in several organisms as well as in viruses. Virus encoded miRNAs can act as viral gene expression regulators, but they may also interfere with the expression of host genes. Viral miRNAs may control host cell proliferation by targeting cell-cycle and apoptosis regulators. Therefore, they could be involved in cancer pathogenesis. Computational prediction of miRNA/target pairs is a fundamental step in these studies. Here, we describe the use of miRiam, a novel program based on both thermodynamics features and empirical constraints, to predict viral miRNAs/human targets interactions. miRiam exploits target mRNA secondary structure accessibility and interaction rules, inferred from validated miRNA/mRNA pairs. A set of genes involved in apoptosis and cell-cycle regulation was identified as target for our studies. This choice was supported by the knowledge that DNA tumor viruses interfere with the above processes in humans. miRNAs were selected from two cancer-related viruses, Epstein-Barr Virus (EBV) and Kaposi-Sarcoma-Associated Herpes Virus (KSHV). Results show that several transcripts possess potential binding sites for these miRNAs. This work has produced a set of plausible hypotheses of involvement of v-miRNAs and human apoptosis genes in cancer development. Our results suggest that during viral infection, besides the protein-based host regulation mechanism, a post-transcriptional level interference may exist. miRiam is freely available for downloading at http://ferrolab.dmi.unict.it/miriam.

Entities:  

Keywords:  EBV; KSHV; apoptosis; cancer; cell cycle; miRNA; virus

Year:  2010        PMID： 20628498      PMCID： PMC2902144     

Source DB:  PubMed          Journal:  J RNAi Gene Silencing        ISSN： 1747-0854


INTRODUCTION

MicroRNAs (miRNAs) are small non-coding RNAs involved in post-transcriptional gene silencing (PTGS) that is a highly-conserved mechanism for regulating gene expression. miRNAs inhibit protein synthesis by binding with the 3'UTR or, rarely, the coding sequence of specific mRNAs (Bartel, 2004). miRNAs are known to be involved in several physiological processes, such as apoptosis, development and cell differentiation, and their aberrant expression is observed in several diseases such as cancer, neurodegeneration and cardiac pathologies (Garofalo et al, 2008). miRNAs are also expressed by viruses (v-miRNAs), which may act as self-regulators. It has been shown that the EBV-encoded miRNA, miR-BART2, down-regulates the viral DNA polymerase BALF5, thus inhibiting transition from latent to lytic viral replication (Barth et al, 2008). v-miRNAs can also interference with the host cell transcripts (Cullen, 2006). The cellular protein p53, an up-regulated modulator of apoptosis (PUMA), is regulated by the EBV-encoded miR-BART5. The expression of miR-BART5 renders infected miR-BART5-expressing cells less sensitive to pro-apoptotic agents, but apoptosis can be triggered by depleting miR-BART5 or inducing the expression of PUMA. This shows the capability of v-miRNAs to facilitate the establishment of latent infection by promoting host cell survival (Choy et al, 2008). The silencing of some nodes of the apoptotic pathway would give to the virus an important advantage during its lysogenic phase, since the host cells would not die by cell death triggered from infection and the virus could complete its vital cycle. Many v-miRNAs are located in proximity or inside viral latency genes and, presumably, co-transcribed with them (Nair and Zavolan, 2006). On the other hand, in many cases of viral infection the activation of apoptosis is needed during the lytic cycle of the virus allowing the processing of capsides and viral particles dissemination (Mendez et al, 2004). The negative and positive modulation of the apoptotic process is a fundamental activity for beginning and concluding the vital cycle of the virus. The above mentioned cases and other recent findings show that, besides well-known protein mechanisms of apoptosis control by viruses, there are other strategies of apoptosis regulation based on translational repression by miRNAs (Klase et al, 2009). Apoptosis may also represent one of the most important links between viral infections and oncogenesis. All tumor related viruses seem to share the same approach to interfere with apoptosis and cell-cycle genes (Damania, 2007). Thus, they may directly promote neoplastic transformation of infected cells or facilitate cancer insurgence in favourable contexts. For example, the Kaposi Sarcoma Herpes Virus encodes a protein called LANA, which inhibits the tumor suppressor protein p53, thus promoting cell survival, virus latency and oncogenesis (Si and Robertson, 2006). In this article we present computational predictions of human targets for v-miRNAs encoded by two tumor related viruses, Epstein-Barr Virus (EBV) and Kaposi Sarcoma Herpes Virus (KSHV). The predictions are computed by a novel approach, which combines thermodynamics and empirical constraints in order to produce reliable miRNA/target duplexes. Our results demonstrate v-miRNA/human target pairs which possess good binding scores, some of which are also supported by publicly available data on gene expression profiles. These findings are consistent with previously reported regulation mechanisms and offer a set of candidate targets for further biological validation.

MATERIALS AND METHODS

Computational prediction of v-miRNA/target duplexes

v-miRNAs/human target interactions were predicted by miRiam, a novel tool capable of screening large databases of miRNAs in order to identify the most probable binding sites on a given mRNA, in a reasonable time. miRiam is based on a combined approach which makes use of thermodynamics and pairing constraints inferred from known miRNA/target pairs. The importance of target secondary structure in miRNA-based silencing has been previously demonstrated (Robins et al, 2005). Target binding sites must be accessible and interactions with miRNAs have to be energetically favourable (Kertesz et al, 2007). Moreover, to predict duplexes reliably several features inferred from known miRNA/mRNA pairs need to be taken into account. For example, miRNA 5'end contains the seed region, which is important in their binding with the target using near perfect complementarity. Nevertheless, the seed region may sometimes contain G:U wobbles, and the number of G:U wobbles in this region appears to be inversely related to silencing efficacy of the miRNA. Almost all known miRNA binding sites are located in the 3' UTRs of target mRNAs, and multiple binding sites have been observed in several cases, potentially increasing the efficacy of silencing (Grimson et al, 2007). Given a target mRNA sequence and a database of miRNAs, which can be retrieved from miRBase, miRiam performs the following steps: Target accessibility evaluation: Base pairing probabilities of the target are computed by Vienna RNA package functions (Hofacker et al, 1994) and used to identify accessible regions, i.e. less paired sub-sequences. Alignment of miRNA/binding sites: The complementary base-pair alignment of accessible sub-sequences with miRNAs is then computed. A variant of the classical Needleman-Wunsch pairwise sequence alignment in which only matches of complementary base pairs are allowed is used. Post alignment filtering: All duplexes which do not satisfy previously described empirical sequence constraints are discarded. The remaining pairs are presented to the user ordered by their binding energies.

miRNAs and targets selection

EBV and KSHV miRNAs were obtained from miRBase (Griffiths-Jones et al, 2008). Target mRNA transcripts were downloaded from GenBank, chosen based on their Gene Ontology annotations. Genes annotated with the following terms were selected: Pro-apoptosis, cell cycle arrest, cell cycle checkpoint, negative regulation of cell cycle, negative regulation of cyclin-dependent kinase activity, and negative regulation of mitotic cell cycle.

Expression data

The expression profiles of the predicted targets were retrieved from the NCBI GEO Profiles database. Among the available datasets, NCBI GDS940 and GDS989 were selected as they were obtained from suitable biological specimens (infected cells and corresponding uninfected controls). For each predicted target, experiments performed on EBV and KSHV infected cells were selected and the ratios between the mean gene expression values in the infected and in the control samples were computed. The evaluation of fold change significance using formal statistical methods was not possible due to the small number of available samples. For this reason the observed ratios should be considered as qualitative descriptors supporting or confuting the proposed hypotheses. The data on the v-miRNA expressing tissues were obtained from the literature (Pfeffer et al, 2004; Pfeffer et al, 2005; Cai and Cullen, 2006; Cai et al, 2006; Kim et al, 2007; Marshall et al, 2007; Zhu et al, 2009) and from the miR-Z database (Hausser et al, 2009).

RESULTS

Representative results of our predictions are shown in Tables 1 and 2. Table 1 contains the EBV miRNAs predicted targets while Table 2 shows the predictions for KSHV miRNAs. For each v-miRNA, the tissues where it is expressed, the predicted human targets and. Only the transcripts with perfect matches with the seed of v-miRNAs in structurally-accessible regions were selected (maximum one G:U wobble allowed in the seed region). These results indicate the putative capability of EBV and KSHV to interfere with the expression of host genes involved in apoptosis and cell cycle regulation. Moreover, some of the predictions are supported by gene expression profiles in EBV and KSHV infected cells.
Table 1.

Predicted targets for EBV v-miRNAs. For each miRNA, the list of tissues where it is expressed, the predicted targets, the number of site on each target transcript and the associated ontological term are given. Tissues legend: AML: Acute myeloid leukemia, BCLL: B-Chronic lymphocytic leukemia, BL: Burkitt's lymphoma, ESC: Embryonic stem cells, GC: Gastric carcinoma, HD: Hodgkin's disease, L: Lymphoblasts, LAC: Lung's adenocarcinoma, LBCL: Large B-cell lymphoma, LU: Lung, MCL: Mantle cell lymphoma, M: Myeloblasts, NC: Nasopharyngeal carcinoma, PEL: Primary-effusion lymphoma, USSC: Unrestricted somatic stem cells.

v-miRNATissuesTargetSitesTarget's GO terms
miR-BART3-3pBCLL, BL, HG, L, LBCL, MCLCASP101Pro-Apoptosis
miR-BART6-3pBCLL, BL, HG, L, LBCL, MCL, NCBAD1Pro-Apoptosis
miR-BART7AML, BCLL, BL, ESC, GC, HG, LBCL, MCL, USSCRB11Negative regulation of cell cycle
miR-BART9BCLL, BL, HG, MCLRAD12Cell cycle checkpoint
RB11Negative regulation of cell cycle
miR-BART10BCLL, BL, GC, HG, MCLZAK1Cell cycle arrest
miR-BART11-5pBCLL, BL, HG, L, NCCASP31Pro-Apoptosis
miR-BART11-3pBCLL, BL, HG, L, MCL, NCTP531Regulation of cell cycle
miR-BART12BCLL, BL, GC, NC, PELTP531Regulation of cell cycle
miR-BART13BCLL, BL, HG, L, MCLCASP31Pro-Apoptosis
APC2Negative Regulation of cyclin-dependent kinase activity
miR-BART15BCLL, BL, HG, MCLCASP31Pro-Apoptosis
miR-BART16BCLL, BL, HG, L, MCLBID1Pro-Apoptosis
miR-BART19BL, HG, MCLAPBB22Cell cycle arrest
ZAK1Cell cycle arrest
STK42Pro-Apoptosis
miR-BART20-3pBCLL, BL, LBAX1Pro-Apoptosis
miR-BHRF1-1BCLL, BL, HG, L, MCLCUL42Cell cycle arrest
miR-BHRF1-2AML, BCLL, BL, ESC, HG, L, LBCL, M, MCL, LAC, LU, USSCRAD11Cell cycle checkpoint
miR-BHRF1-2*-CCNG21Cell cycle checkpoint
GAS12Cell cycle arrest
RB12Negative regulation of cell cycle
miR-BHRF1-3BCLL, BL, HG, L, LBCL, MCLBID1Pro-Apoptosis
Table 2.

Predicted targets for KSHV v-miRNAs. For each miRNA, the list of tissues where it is expressed, the predicted targets, the number of site on each target's transcript and the GO term associated to the targets are shown. Tissues legend: BCBL: Body cavity-based lymphoma, BL: Burkitt's lymphoma, PEL: Primary-effusion lymphoma.

v-miRNATissuesTargetSitesTarget's GO terms
miR-K12-1BCBL, BL, PELRBL11Negative regulation of cell cycle
RAD11Cell cycle checkpoint
miR-K12-2BCBL, BL, PELCASP101Pro-Apoptosis
APC2Negative Regulation of cyclin-dependent kinase activity
RAD11Cell cycle checkpoint
miR-K12-3BCBL, BL, PELAPC1Negative Regulation of cyclin-dependent kinase activity
STK43Pro-Apoptosis
miR-K12-4-5pBCBL, BL, PELRBL11Negative regulation of cell cycle
ZAK1Cell cycle arrest
miR-K12-6-3pBCBL, BL, PELBID1Pro-Apoptosis
BTG31Negative Regulation of mitotic cell cycle
miR-K12-9*BCBL, BL, PELCASP81Pro-Apoptosis
TP531Regulation of cell cycle
miR-K12-10aBCBL, BL, PELCASP101Pro-Apoptosis
miR-K12-11BLRB11Negative regulation of cell cycle
RBL11Negative regulation of cell cycle
APC3Negative Regulation of cyclin-dependent kinase activity
miR-K12-12PELCASP101Pro-Apoptosis
Predicted targets for EBV v-miRNAs. For each miRNA, the list of tissues where it is expressed, the predicted targets, the number of site on each target transcript and the associated ontological term are given. Tissues legend: AML: Acute myeloid leukemia, BCLL: B-Chronic lymphocytic leukemia, BL: Burkitt's lymphoma, ESC: Embryonic stem cells, GC: Gastric carcinoma, HD: Hodgkin's disease, L: Lymphoblasts, LAC: Lung's adenocarcinoma, LBCL: Large B-cell lymphoma, LU: Lung, MCL: Mantle cell lymphoma, M: Myeloblasts, NC: Nasopharyngeal carcinoma, PEL: Primary-effusion lymphoma, USSC: Unrestricted somatic stem cells. Predicted targets for KSHV v-miRNAs. For each miRNA, the list of tissues where it is expressed, the predicted targets, the number of site on each target's transcript and the GO term associated to the targets are shown. Tissues legend: BCBL: Body cavity-based lymphoma, BL: Burkitt's lymphoma, PEL: Primary-effusion lymphoma. Table 3 shows, for each predicted target, the ratios between the mean expression values in the infected and control cells. A value less than 1 indicates that the gene is under-expressed in the infected samples, compared to the control cells. Only these values are reported, since they are the significant ones concerning our analysis. The complete data are given in supplementary tables (Tables S1-S3).
Table 3.

Expression profiles of the predicted targets in infected cells. The profiles were retrieved from the NCBI GEO Profiles database. For each target, the GEO record ID, the virus, the mean value in the control sample (not infected), the mean value in the infected cell and the infected/control ratio are shown. Only values less than 1 are reported, since they indicate that the gene is under-expressed in the infected sample, compared to the control cell.

GeneGEO RecordVirusMean (Control)Mean (Infected)Ratio (I/C)
ABL1GDS989 - 1636_g_atEBV5928.753531.800.59
GDS989 - 39730_atEBV5839.953504.200.60
GDS989 - 1635_atEBV2786.601428.300.51
GDS940 - 202123_s_atKSHV406.35246.350,60
APCGDS940 - 203525_s_atKSHV110.6063.900.57
BIDGDS940 - 204493_atKSHV263.25140.400.53
BTG3GDS940 - 213134_x_atKSHV471.05233.250.49
CCNG2GDS989 - 1913_atEBV2690.401679.800.62
GDS940 - 211559_s_atKSHV109.6598.550.89
RAD1GDS989 - 36857_atEBV2365.451954.60.82
GDS940 - 204461_x_atKSHV176.5077.550.43
GDS940 - 204460_s_atKSHV136.7573.850.54
GDS940 - 210216_x_atKSHV158.55102.850.64
RB1GDS989 - 2044_s_atEBV1307.351149.600.87
GDS940 - 203132_atKSHV157.80140.900.89
SMAD3GDS940 - 205398_s_atKSHV314.75157.800.50
STK4GDS940 - 211085_s_atKSHV19.6512.100.61
TP53GDS989 - 1974_s_atEBV1712.401561.700.91
GDS989 - 1974_s_atKSHV1712.401530.250.89
GDS940 - 201746_s_atKSHV405.70370.000.91
ZAKGDS940 - 218833_atKSHV42.5030.250.71
Expression profiles of the predicted targets in infected cells. The profiles were retrieved from the NCBI GEO Profiles database. For each target, the GEO record ID, the virus, the mean value in the control sample (not infected), the mean value in the infected cell and the infected/control ratio are shown. Only values less than 1 are reported, since they indicate that the gene is under-expressed in the infected sample, compared to the control cell. Finally, in order to assess the reliability of the proposed method, we performed a comparison between miRiam and some of the most popular available tools for miRNA target prediction: RNA Hybrid, miRanda, RNA22 and PITA (Rehmsmeier et al, 2004; John et al, 2005; Huynh et al, 2006). For this purpose, we chose a set of validated v-miRNA/human targets for which binding sites details were available. Results are reported on Table 4. They show the capability of miRiam to identify all the validated binding sites, in most cases as top ranking. We evaluated the statistical significance of our results by the Friedman rank test. While miRiam is comparable to PITA, in terms of number of correct sites identified, it outperforms the other tools in terms of the number of correct binding sites and their ranking.
Table 4.

Comparison of miRiam to other target prediction tools. We chose a set of validated v-miRNA/human targets for which binding sites details were available. For each tested tool, the ranking of the site in the output is reported. A “-“ symbol indicates that the tool wasn't able to identify the site. P-values are computed by using the Friedman Rank Test, to assess whether or not miRiam significantly tends to perform better than the other tools across experimentally validated targets. Results show that miRiam performs better than RNA Hybrid, miRanda and RNA22, while is comparable to PITA.

VirusmIRNATargetTranscriptSitemiRiamRNAHybridmiRandaRNA22PITA
KSHVmiR-K12-11BACH-1NM_001186.23156-316222--3
KSHVmiR-K12-11BACH-1NM_001186.24504-45113---2
KSHVmiR-K12-11BACH-1NM_001186.24565-45711---1
KSHVmiR-K12-11BACH-1NM_001186.24714-47204---7
KSHVmiR-K12-11BACH-1NM_206866.13284-329023--3
KSHVmiR-K12-11BACH-1NM_206866.14632-46393---2
KSHVmiR-K12-11BACH-1NM_206866.14693-46991---1
KSHVmiR-K12-11BACH-1NM_206866.14842-48484---7
HCMVmiR-UL112-1MICBNM_005931.31319-132316*12--
EBVmiR-BART5BBC3NM_001127240.11366-1372111-1
EBVmiR-BART5BBC3NM_001127241.11147-11531*11-1
EBVmiR-BART5BBC3NM_001127242.1956-9621*11-1
EBVmiR-BART5BBC3NM_014417.31450-1456111-1
KSHVmiR-K5BCLAF1NM_014739.25301-5308110-23
KSHVmiR-K5BCLAF1NM_001077440.15148-515519-23
KSHVmiR-K5BCLAF1NM_001077441.14782-4789110-23
p-values-0.050.03< 0.00010.25

*miRiam was able to identify these sites by relaxing the structural accessibility filter.

Comparison of miRiam to other target prediction tools. We chose a set of validated v-miRNA/human targets for which binding sites details were available. For each tested tool, the ranking of the site in the output is reported. A “-“ symbol indicates that the tool wasn't able to identify the site. P-values are computed by using the Friedman Rank Test, to assess whether or not miRiam significantly tends to perform better than the other tools across experimentally validated targets. Results show that miRiam performs better than RNA Hybrid, miRanda and RNA22, while is comparable to PITA. *miRiam was able to identify these sites by relaxing the structural accessibility filter.

DISCUSSION

v-miRNAs vs human targets

Epstein Barr herpesvirus (EBV) is associated with several tumor types, such as Burkitt's lymphoma, nasopharyngeal and gastric carcinoma (Damania, 2007). EBV expresses two families of miRNAs, which are located in the BART and BHRF1 genes (Pfeffer et al, 2004; Cai et al, 2006). The BHRF1 miRNAs are expressed in infected primary lymphoma, Burkitt's lymphoma and lymphoblast cell lines during late latency and lytic stages, while BART members are always expressed in lymphoblasts in all latent stages. miR-BART12 has also been found in infected nasopharyngeal and gastric carcinoma cell lines (Kim et al, 2007). Kaposi's sarcoma-associated herpesvirus (KSHV) is associated with several forms of cancer like Kaposi's sarcoma and primary effusion lymphomas (Damania, 2007). KSHV expresses an array of 13 miRNAs in infected primary effusion lymphoma and endothelial cell lines (Pfeffer et al, 2005; Marshall et al, 2007). miR-BHRF1-3, miR-BART16 in EBV and miR-K12-6-3p in KSHV show good matches to BID transcript. Moreover, BID mRNA levels decrease after viral infection. The BID silencing could cause the partial block of the connection between the extrinsic apoptotic pathway and the mitochondrial one. BID is a target of proteolytic activity of CASP8, which is activated by cell surface death receptors. Truncated BID translocates in the mitochondria engaging BAX to trigger its pro-apoptotic activity, starting the release of the cytochrome c. This is a key event in the formation of the apoptosoma complex (cytochrome c, APAF1 and CASP9) for the beginning of the intrinsic death pathway. Consequently, BID inhibition may lower cell sensitivity to external signals and promote resistance to apoptosis. Moreover, it has been shown that BHRF, the host gene of these miRNAs, encodes a protein which acts downstream of BID cleavage and upstream of mitochondrial damage, resulting in inhibition of TRAIL-induced apoptosis (Kawanishi et al, 2002). This makes plausible a function of these miRNAs as repressors of apoptosis. TP53 is a predicted target for EBV miR-BART11-3p, miR-BART12 and KSHV miR-K12-9*. In particular two different favorable binding sites for both EBV-miRBART12 and KSHV-miR-K12-9* have been identified in TP53 transcript. We also observed a significant decrease in TP53 transcript levels following EBV and KSHV infections (see Table 3). The presence of multiple binding sites for the same miRNA has been observed in several experimentally validated pairs. This potentially increases the degree of translational suppression. TP53 is an important regulator of cell cycle arrest, apoptosis, and cellular senescence and it may be activated by various forms of cellular stress, as viral infection. Several reports provide evidence of how TP53 is involved in antiviral activity and how viruses in their infection strategy lead to protein inactivation of TP53 (Si and Robertson, 2006; Pampin et al, 2006). That being so, a further repression device of TP53 based on RNA Interference from viruses looks plausible. Moreover, some data suggest that functional TP53 can promote the lytic cycle of some viruses. Consequently a repression strategy controlled by viral microRNAs could be an efficient and inexpensive switch on / off system of TP53 activities. Our data suggest that Cyclin G2 (CCNG2) could be target of miRNAs from both EBV and KSHV. This pro-apoptotic protein has been found to be downregulated in many different tumours (Choi et al, 2009), and a decrease in its activity has been associated with improved proliferation. Its key role in cell-cycle and cell plasticity in cancer is underlined by the observation that the therapeutic effect of rapamicyn is also exerted through the enhancement of its transcription rate (Kasukabe et al, 2008). The ability to interfere with tp53 pathway through the post transcriptional regulation of some of its fundamental components (tp53, BID, cyclin g2) may constitute a mechanism to tightly and efficiently control the host survival rate improving replication and diffusion of viral progeny. Similarly, other genes involved in apoptosis and in the regulation of cell cycle, BAD, BTG3, BAX, RAD1, ZAK and RB1, among others, are predicted to be targets of EBV and KSHV miRNAs. The repression of these genes may help the virus to gain control of the host cells, thus favoring the accomplishment of the viral life cycle.

v-miRNAs involvement in cancer

Viruses are often involved in cancer development. They may directly promote cell malignancy or facilitate the progression of already established tumors (Damania, 2007). While protein based mechanisms of interaction between virus and hosts have been widely described, a direct involvement of v-miRNAs in cancer is not yet demonstrated. However, since experimental evidences show the capability of v-miRNAs to perturb host genes expression (Barth et al, 2008; Klase et al, 2009), their contribution to tumor insurgence is plausible. Moreover, a miRNA based host regulation system may represent a faster way for viruses to counteract cellular response to infection promoting their latency. According to Pfeffer and Voinnet (Pfeffer and Voinnet, 2006), v-miRNAs could act as direct oncogenes or indirectly promote cancer development and diffusion, by supporting external etiologic agents. The predictions presented in this study are consistent with these scenarios. In particular the inhibition of central nodes of the apoptotic pathways may lead to loss of cell cycle control by the cell itself.

CONCLUSIONS

The aim of this work was to produce a set of plausible hypotheses of involvement of v-miRNAs and human apoptosis genes in cancer development. In the study, we have presented computational predictions of v-miRNAs and human targets interactions, and show miRNA/target duplexes which were energetically favourable and consistent with other experimentally validated pairs. Moreover, the target binding sites were characterized by high structural-accessibility for interactions with miRNAs. Future works will focus on providing experimental validations of such estimated interactions. Extension of predictions to other reasonable human transcripts, such as additional nodes of apoptosis pathway, cell-cycle regulators and genes involved in immune response, will be performed.
v-miRNATissuesTargetSitesTarget's GO terms
miR-BART1-5pBCLL, BL, HG, MCLAPC1Negative Regulation of cyclin-dependent kinase activity
CUL21Cell cycle arrest
TBRG11Cell cycle arrest
miR-BART2BCLL, BL, HG, MCLCDKN2A1Negative Regulation of cyclin-dependent kinase activity
CDKN2B1Cell cycle arrest
CUL31Pro-Apoptosis
miR-BART3-3pBCLL, BL, HG, L, LBCL, MCLCASP101Pro-Apoptosis
miR-BART5BCLL, BL, HG, MCLAIFM21Pro-Apoptosis
DAPK11Pro-Apoptosis
miR-BART6-5pBCLL, BL, HG, LBCL, MCLGAS71Cell cycle arrest
miR-BART6-3pBCLL, BL, HG, L, LBCL, MCL, NCBAD1Pro-Apoptosis
miR-BART7AML, BCLL, BL, ESC, GC, HG, LBCL, MCL, USSCCASP61Pro-Apoptosis
CUL31Pro-Apoptosis
EIF4G21Cell cycle arrest
FOXO41Cell cycle arrest
RB11Negative regulation of cell cycle
TRAF31Pro-Apoptosis
miR-BART8-3pAML, BCLL, BL, HG, LBCL, MCLAPBB21Cell cycle arrest
EIF4G21Cell cycle arrest
FEM1B1Pro-Apoptosis
GAS11Cell cycle arrest
SIPA11Negative regulation of cell cycle
TP53BP21Negative regulation of cell cycle
miR-BART9BCLL, BL, HG, MCLRAD12Cell cycle checkpoint
RB11Negative regulation of cell cycle
APBB21Cell cycle arrest
STK31Pro-Apoptosis
miR-BART10BCLL, BL, GC, HG, MCLCDKN2C1Cell cycle arrest
CUL51Cell cycle arrest
ZAK1Cell cycle arrest
miR-BART11-5pBCLL, BL, HG, L, NCCASP31Pro-Apoptosis
CUL21Cell cycle arrest
miR-BART11-3pBCLL, BL, HG, L, MCL, NCTP531Regulation of cell cycle
miR-BART12BCLL, BL, GC, NC, PELTP531Regulation of cell cycle
APBB21Cell cycle arrest
miR-BART13BCLL, BL, HG, L, MCLAPC2Negative Regulation of cyclin-dependent kinase activity
CASP31Pro-Apoptosis
CDKN2B1Cell cycle arrest
MAPK11Pro-Apoptosis
miR-BART14-3pBCLL, BL, HG, MCLABL11Pro-Apoptosis
BCL2111Pro-Apoptosis
CDKN2D1Cell cycle arrest
DIABLO11Pro-Apoptosis
PAWR1Pro-Apoptosis
STK41Pro-Apoptosis
miR-BART15BCLL, BL, HG, MCLCASP31Pro-Apoptosis
CUL31Pro-Apoptosis
CUL51Cell cycle arrest
DAPK11Pro-Apoptosis
miR-BART16BCLL, BL, HG, L, MCLBID1Pro-Apoptosis
CIDEB1Pro-Apoptosis
DHCR241Cell cycle arrest
miR-BART17-3pBCLL, BL, MCLBOK1Pro-Apoptosis
TRAF31Pro-Apoptosis
miR-BART18BCLL, BL, HG, MCLAPC1Negative Regulation of cyclin-dependent kinase activity
LTA1Pro-Apoptosis
miR-BART19BL, HG, MCLAPBB22Cell cycle arrest
APC1Negative Regulation of cyclin-dependent kinase activity
BTG41Cell cycle arrest
CDKN2C1Cell cycle arrest
IFNW11Cell cycle arrest
MAPK11Pro-Apoptosis
MPHOSPH11Cell cycle arrest
NLRP11Pro-Apoptosis
STK42Pro-Apoptosis
TNFRSF251Pro-Apoptosis
ZAK1Cell cycle arrest
miR-BART20-3pBCLL, BL, LAPC1Negative Regulation of cyclin-dependent kinase activity
BAX1Pro-Apoptosis
CDKN1B1Pro-Apoptosis
DHCR241Cell cycle arrest
ERN11Cell cycle arrest
NDUFA131Pro-Apoptosis
RUNX31Negative regulation of cell cycle
miR-BHRF1-1BCLL, BL, HG, L, MCLBCL2111Pro-Apoptosis
BIK1Pro-Apoptosis
CUL4A2Cell cycle arrest
EI241Pro-Apoptosis
LATS21Negative Regulation of cyclin-dependent kinase activity
MAPK12Pro-Apoptosis
TRAF31Pro-Apoptosis
miR-BHRF1-2AML, BCLL, BL, ESC, HG, L, LBCL, M, MCL, LAC, LU, USSCCUL11Cell cycle arrest
RAD11Cell cycle checkpoint
miR-BHRF1-2*-APC1Negative Regulation of cyclin-dependent kinase activity
BNIP31Pro-Apoptosis
CCNG21Cell cycle checkpoint
CUL11Cell cycle arrest
CUL31Pro-Apoptosis
DAXX1Pro-Apoptosis
DEDD1Pro-Apoptosis
DIABLO1Pro-Apoptosis
ERN11Cell cycle arrest
FOXO3A1Pro-Apoptosis
GAS12Cell cycle arrest
ING41Cell cycle arrest
RB12Negative regulation of cell cycle
STK41Pro-Apoptosis
TSPYL21Negative regulation of cell cycle
miR-BHRF1-3BCLL, BL, HG, L, LBCL, MCLBID1Pro-Apoptosis
v-miRNATissuesTargetSitesTarget's GO terms
miR-K12-1BCBL, BL, PELFOXO3A1Pro-Apoptosis
HBP11Cell cycle arrest
PDCD41Negative regulation of cell cycle
PCDC61Negative regulation of cell cycle
RBL11Negative regulation of cell cycle
RAD11Cell cycle checkpoint
TNFRSF10A2Pro-Apoptosis
miR-K12-2BCBL, BL, PELAPC2Negative Regulation of cyclin-dependent kinase activity
CASP10a1Pro-Apoptosis
CDKN2C1Cell cycle arrest
CUL11Cell cycle arrest
CUL31Pro-Apoptosis
CUL51Cell cycle arrest
LATS21Negative Regulation of cyclin-dependent kinase activity
MAPK11Pro-Apoptosis
RAD11Cell cycle checkpoint
RBBP81Cell cycle checkpoint
STK41Pro-Apoptosis
miR-K12-3BCBL, BL, PELAPBB11Cell cycle arrest
APC1Negative Regulation of cyclin-dependent kinase activity
CDKN2B1Cell cycle arrest
CUL52Cell cycle arrest
FAF11Pro-Apoptosis
RASSF11Cell cycle arrest
RBBP82Cell cycle checkpoint
SMAD32Cell cycle arrest
STK31Pro-Apoptosis
STK43Pro-Apoptosis
miR-K12-4-5pBCBL, BL, PELCCNG21Cell cycle checkpoint
ING11Negative regulation of cell cycle
RBL11Negative regulation of cell cycle
ZAK1Cell cycle arrest
miR-K12-6-3pBCBL, BL, PELBID1Pro-Apoptosis
BTG31Negative Regulation of mitotic cell cycle
TBRG11Cell cycle arrest
miR-K12-7PELABL11Pro-Apoptosis
miR-K12-9PELFOXO41Cell cycle arrest
TBRG11Cell cycle arrest
miR-K12-9*BCBL, BL, PELCASP8a1Pro-Apoptosis
TP531Regulation of cell cycle
miR-K12-10aBCBL, BL, PELCASP10a1Pro-Apoptosis
CASP10d1Pro-Apoptosis
CUL22Cell cycle arrest
CUL4a1Cell cycle arrest
EI241Pro-Apoptosis
FOXO41Cell cycle arrest
SMAD31Cell cycle arrest
miR-K12-10bPELGAS71Cell cycle arrest
miR-K12-11BLBCL101Pro-Apoptosis
RB11Negative regulation of cell cycle
RBL11Negative regulation of cell cycle
APC3Negative Regulation of cyclin-dependent kinase activity
MPHOSPH11Cell cycle arrest
miR-K12-12PELCASP10a1Pro-Apoptosis
GeneGEO RecordVirusMean (Control)Mean (Infected)Ratio (I/C)
ABL1GDS989 - 1636_g_atEBV5928.753531.800.59
GDS989 - 39730_atEBV5839.953504.200.60
GDS989 - 1635_atEBV2786.601428.300.51
GDS940 - 202123_s_atKSHV406.35246.350,60
APCGDS940 - 203525_s_atKSHV110.6063.900.57
BCL10GDS940 - 205263_atKSHV637.50439.550.68
BIDGDS940 - 204493_atKSHV263.25140.400.53
BTG3GDS940 - 213134_x_atKSHV471.05233.250.49
CCNG2GDS989 - 1913_atEBV2690.401679.800.62
GDS940 - 211559_s_atKSHV109.6598.550.89
CDKN2CGDS940 - 204159_atKSHV116.1068.950.59
CUL1GDS940 - 207614_s_atKSHV266.50214.250.80
CUL2GDS989 - 37894_atEBV817.95454.500.55
GDS940 - 203079_s_atKSHV254.00164.400.64
CUL3GDS940 - 201371_s_atKSHV766.05533.750.69
CUL4AGDS940 - 201423_s_atKSHV270.50208.750.77
CUL5GDS940 - 203531_atKSHV323.30256.550.79
EI24GDS940 - 216396_s_atKSHV349.60202.050.57
FAF1GDS940 - 218080_x_atKSHV205.95123.650.60
FOXO3AGDS940 - 204132_s_at /KSHV62.1555.600.89
HBP1GDS940 - 209102_s_atKSHV281.35274.650.97
ING1GDS940 - 209808_x_atKSHV73.9061.050.82
MAPK1GDS989 - 976_s_atEBV1619.10898.900.55
GDS940 - 212271_atKSHV750.10399.500.53
MPHOSPH1GDS940 - 205235_s_atKSHV120.1554.950.45
RAD1GDS989 - 36857_atEBV2365.451954.60.82
GDS940 - 204461_x_atKSHV176.5077.550.43
GDS940 - 204460_s_atKSHV136.7573.850.54
GDS940 - 210216_x_atKSHV158.55102.850.64
RASSF1GDS940 - 204346_s_atKSHV116.6573.900.63
RB1GDS989 - 2044_s_atEBV1307.351149.600.87
GDS940 - 203132_atKSHV157.80140.900.89
GDS940 - 211540_s_atKSHV50.6044.700.88
RBBP8GDS940 - 203344_s_atKSHV200.60112.400.56
SMAD3GDS940 - 205398_s_atKSHV314.75157.800.50
STK3GDS940 / 208855_s_atKSHV933.70642.400.68
STK4GDS940 - 211085_s_atKSHV19.6512.100.61
TP53GDS989 - 1974_s_atEBV1712.401561.700.91
GDS989 - 1974_s_atKSHV1712.401530.250.89
GDS940 - 201746_s_atKSHV405.70370.000.91
ZAKGDS940 - 218833_atKSHV42.5030.250.71
  29 in total

1.  Identification of virus-encoded microRNAs.

Authors:  Sébastien Pfeffer; Mihaela Zavolan; Friedrich A Grässer; Minchen Chien; James J Russo; Jingyue Ju; Bino John; Anton J Enright; Debora Marks; Chris Sander; Thomas Tuschl
Journal:  Science       Date:  2004-04-30       Impact factor: 47.728

Review 2.  DNA tumor viruses and human cancer.

Authors:  Blossom Damania
Journal:  Trends Microbiol       Date:  2006-11-20       Impact factor: 17.079

3.  The role of site accessibility in microRNA target recognition.

Authors:  Michael Kertesz; Nicola Iovino; Ulrich Unnerstall; Ulrike Gaul; Eran Segal
Journal:  Nat Genet       Date:  2007-09-23       Impact factor: 38.330

4.  MicroRNA targeting specificity in mammals: determinants beyond seed pairing.

Authors:  Andrew Grimson; Kyle Kai-How Farh; Wendy K Johnston; Philip Garrett-Engele; Lee P Lim; David P Bartel
Journal:  Mol Cell       Date:  2007-07-06       Impact factor: 17.970

5.  Identification of microRNAs of the herpesvirus family.

Authors:  Sébastien Pfeffer; Alain Sewer; Mariana Lagos-Quintana; Robert Sheridan; Chris Sander; Friedrich A Grässer; Linda F van Dyk; C Kiong Ho; Stewart Shuman; Minchen Chien; James J Russo; Jingyue Ju; Glenn Randall; Brett D Lindenbach; Charles M Rice; Viviana Simon; David D Ho; Mihaela Zavolan; Thomas Tuschl
Journal:  Nat Methods       Date:  2005-02-16       Impact factor: 28.547

Review 6.  Viruses, microRNAs and cancer.

Authors:  S Pfeffer; O Voinnet
Journal:  Oncogene       Date:  2006-10-09       Impact factor: 9.867

7.  Transcriptional origin of Kaposi's sarcoma-associated herpesvirus microRNAs.

Authors:  Xuezhong Cai; Bryan R Cullen
Journal:  J Virol       Date:  2006-03       Impact factor: 5.103

8.  Cotylenin A, a new differentiation inducer, and rapamycin cooperatively inhibit growth of cancer cells through induction of cyclin G2.

Authors:  Takashi Kasukabe; Junko Okabe-Kado; Yoshio Honma
Journal:  Cancer Sci       Date:  2008-08       Impact factor: 6.716

9.  HIV-1 TAR miRNA protects against apoptosis by altering cellular gene expression.

Authors:  Zachary Klase; Rafael Winograd; Jeremiah Davis; Lawrence Carpio; Richard Hildreth; Mohammad Heydarian; Sidney Fu; Timothy McCaffrey; Eti Meiri; Mila Ayash-Rashkovsky; Shlomit Gilad; Zwi Bentwich; Fatah Kashanchi
Journal:  Retrovirology       Date:  2009-02-16       Impact factor: 4.602

10.  Epstein-Barr virus-encoded microRNA miR-BART2 down-regulates the viral DNA polymerase BALF5.

Authors:  Stephanie Barth; Thorsten Pfuhl; Alfredo Mamiani; Claudia Ehses; Klaus Roemer; Elisabeth Kremmer; Christoph Jäker; Julia Höck; Gunter Meister; Friedrich A Grässer
Journal:  Nucleic Acids Res       Date:  2007-12-10       Impact factor: 16.971
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  17 in total

1.  Neuropilin-2 Is upregulated in lung cancer cells during TGF-β1-induced epithelial-mesenchymal transition.

Authors:  Patrick Nasarre; Robert M Gemmill; Vincent A Potiron; Joëlle Roche; Xian Lu; Anna E Barón; Christopher Korch; Elizabeth Garrett-Mayer; Alessandro Lagana; Philip H Howe; Harry A Drabkin
Journal:  Cancer Res       Date:  2013-10-11       Impact factor: 12.701

2.  miR-EdiTar: a database of predicted A-to-I edited miRNA target sites.

Authors:  Alessandro Laganà; Alessio Paone; Dario Veneziano; Luciano Cascione; Pierluigi Gasparini; Stefania Carasi; Francesco Russo; Giovanni Nigita; Valentina Macca; Rosalba Giugno; Alfredo Pulvirenti; Dennis Shasha; Alfredo Ferro; Carlo M Croce
Journal:  Bioinformatics       Date:  2012-10-07       Impact factor: 6.937

3.  Selective targeting of point-mutated KRAS through artificial microRNAs.

Authors:  Mario Acunzo; Giulia Romano; Giovanni Nigita; Dario Veneziano; Luigi Fattore; Alessandro Laganà; Nicola Zanesi; Paolo Fadda; Matteo Fassan; Lara Rizzotto; Raleigh Kladney; Vincenzo Coppola; Carlo M Croce
Journal:  Proc Natl Acad Sci U S A       Date:  2017-05-08       Impact factor: 11.205

4.  Cytomegalovirus microRNA expression is tissue specific and is associated with persistence.

Authors:  Christine Meyer; Finn Grey; Craig N Kreklywich; Takeshi F Andoh; Rebecca S Tirabassi; Susan L Orloff; Daniel N Streblow
Journal:  J Virol       Date:  2010-10-27       Impact factor: 5.103

5.  Tissue and exosomal miRNA editing in Non-Small Cell Lung Cancer.

Authors:  Giovanni Nigita; Rosario Distefano; Dario Veneziano; Giulia Romano; Mohammad Rahman; Kai Wang; Harvey Pass; Carlo M Croce; Mario Acunzo; Patrick Nana-Sinkam
Journal:  Sci Rep       Date:  2018-07-05       Impact factor: 4.379

6.  microRNA editing in seed region aligns with cellular changes in hypoxic conditions.

Authors:  Giovanni Nigita; Mario Acunzo; Giulia Romano; Dario Veneziano; Alessandro Laganà; Marika Vitiello; Dorothee Wernicke; Alfredo Ferro; Carlo M Croce
Journal:  Nucleic Acids Res       Date:  2016-06-13       Impact factor: 16.971

7.  Extracellular circulating viral microRNAs: current knowledge and perspectives.

Authors:  Alessandro Laganà; Francesco Russo; Dario Veneziano; Sebastiano Di Bella; Rosalba Giugno; Alfredo Pulvirenti; Carlo M Croce; Alfredo Ferro
Journal:  Front Genet       Date:  2013-06-24       Impact factor: 4.599

8.  RepTar: a database of predicted cellular targets of host and viral miRNAs.

Authors:  Naama Elefant; Amnon Berger; Harel Shein; Matan Hofree; Hanah Margalit; Yael Altuvia
Journal:  Nucleic Acids Res       Date:  2010-12-10       Impact factor: 16.971

9.  Epstein-Barr virus microRNAs and lung cancer.

Authors:  J Koshiol; M L Gulley; Y Zhao; M Rubagotti; F M Marincola; M Rotunno; W Tang; A W Bergen; P A Bertazzi; D Roy; A C Pesatori; I Linnoila; D Dittmer; A M Goldstein; N E Caporaso; L M McShane; E Wang; M T Landi
Journal:  Br J Cancer       Date:  2011-06-07       Impact factor: 7.640

10.  KSHV-Encoded MicroRNAs: Lessons for Viral Cancer Pathogenesis and Emerging Concepts.

Authors:  Zhiqiang Qin; Andrew Jakymiw; Victoria Findlay; Chris Parsons
Journal:  Int J Cell Biol       Date:  2012-02-19
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