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microRNAs with AAGUGC seed motif constitute an integral part of an oncogenic signaling network

microRNA (miRNA) dysregulation is a common feature of cancer cells, but the complex roles of miRNAs in cancer are not fully elucidated. Here, we used functional genomics to identify oncogenic miRNAs in non-small cell lung cancer and evaluate their impact on response to epidermal growth factor (EGFR)-targeting therapy. Our data demonstrate that miRNAs with an AAGUGC motif in their seed sequence increase both cancer cell proliferation and sensitivity to EGFR inhibitors. Global transcriptomics, proteomics and target prediction resulted in the identification of several tumor suppressors involved in the G1/S transition as AAGUGC-miRNA targets. The clinical implications of our findings were evaluated by analysis of AAGUGC-miRNA expression in multiple cancer types, supporting the link between this miRNA seed family, their tumor suppressor targets and cancer cell proliferation. In conclusion, we propose the AAGUGC seed motif as an oncomotif and that oncomotif-miRNAs promote cancer cell proliferation. These findings have potential therapeutic implications, especially in selecting patients for EGFR-targeting therapy.

INTRODUCTION
One of the most studied areas in cancer biology is related to cancer growth and oncogenic drivers of cancer cell proliferation. In addition to expanding the general knowledge of cancer biology, a major reason for this focus is that oncogenic drivers are tempting targets for anti-cancer therapy. The majority of targeted cancer therapies in use today or in clinical studies are inhibitors of proteins that increase cancer cell proliferation as exemplified by epidermal growth factor tyrosine kinase inhibitors (EGFR-TKIs) in non-small cell lung cancer (NSCLC) or ERBB2 inhibitors in breast cancer. Clinical studies have shown us that this type of targeted therapy is only efficient against tumors that rely on the targeted protein for proliferation. Furthermore, it is likely that several parallel pathways/networks need to be targeted simultaneously to achieve long-term efficacy and combat resistance development. To predict treatment response and select targeted therapies, it is therefore of great importance to fully understand the signaling networks that drive cancer cell proliferation.
In spite of massive research efforts, knowledge of specific oncogenic drivers is still lacking in a large part of cancer cases. [1][2][3] One of the reasons for this incomplete knowledge of oncogenic pro-growth signaling is the multiple levels of regulation used by the cancer cells, that is, epigenetic, transcriptional, translational and posttranslational regulation. To complete the current fragmentary picture, additional levels will be needed in the analysis, such as protein-level analysis by mass spectrometry (MS)-based proteomics. These methods are currently reaching sufficient analytical depth and throughput to be integrated in systems biology analysis as shown by us 4 and others, 5 and will certainly further improve our knowledge of cancer biology.
One important level of regulation used in cells is posttranscriptional regulation by microRNAs (miRNAs). miRNAs are small, non-coding RNAs that repress gene expression through base pairing between the miRNA seed sequence (5' nucleotides 1-8) and 3' untranslated regions (3'UTRs) of mRNAs, causing mRNA degradation, translation inhibition or both. 6 Each miRNA can target hundreds of different mRNAs, and it has been estimated that the 1000-1500 different miRNAs expressed in the human genome collectively have the capacity to repress more than 50% of all protein-coding genes. 7 A huge body of evidence supports the importance of miRNA deregulation in cancer, and both overexpression of cancer-promoting miRNAs (oncomiRs) and loss of cancer-inhibiting miRNAs (tumor suppressor (TS) miRs) are common. 8,9 However, the complex target spectrum and biology of miRNAs complicates the interpretation of data and consequently, even when measured, miRNA deregulation is often neglected when presenting the oncogenic drivers in cancer landscape publications.
In this study, we used a functional genomics approach to identify potentially oncogenic miRNAs in NSCLC. Our analysis indicated that expression of miRNAs with an AAGUGC motif in the seed sequence resulted in increased cellular proliferation, which, interestingly, was accompanied by increased sensitivity to EGFR-TKI inhibitors. Molecular profiling of the effects of AAGUGC-miRNA expression at the mRNA and protein level, as well as miRNA target prediction analysis resulted in a large number of potential AAGUGC-miRNA targets. Among these targets were several well-known TSs, explaining the proliferation promoting activities of AAGUGC-miRNAs. Expression of AAGUGC-miRNAs and targets were then evaluated in a number of different cancer types using public domain data. Collectively, our data have led us to suggest the AAGUGC seed sequence motif in miRNAs as an 'oncomotif' and, in addition, a model where oncomotif-miRNAs are an integral part of a signaling network that drives cancer cell proliferation. 1

RESULTS
miRNAs with an AAGUGC motif in their seed sequence increase proliferation and EGFR-TKI sensitivity in NSCLC cells To identify potentially oncogenic miRNAs in NSCLC and to investigate their impact on EGFR-TKI response, a functional genomics screen was performed (Figure 1a). In brief, a library of miRNA expression vectors (n =~450) 10 was individually transduced into the NSCLC cell line U1810, and after selection of stable miRNA expressing cells (~80% of library vectors yielded stable clones), cells were pooled and split into nine cultures. Twenty-four hours after seeding, triplicate untreated cultures were harvested for use as a baseline reference. Triplicate cultures were then treated with an EGFR-TKI (gefitinib) or left untreated for a period of 30 days. At the end of the experiment, altered relative abundances of miRNA-expressing populations between the three conditions (Ctrl0, Ctrl30 and Gef30) were evaluated by deep sequencing (Supplementary Table 1).
The main focus of our analysis was on miRNA-expressing clones that were enriched after 30 days of culture in normal medium (indicating increased proliferation, Figure 1b), and miRNA clones that were depleted after EGFR-TKI treatment (indicating increased EGFR-TKI sensitivity, Figure 1c). We arbitrarily considered only miR-Vecs that had been sequenced at least 300 times in all three replicates of one condition, resulting in 140 unique miR-Vec miRNAs remaining for the enrichment/depletion analysis. Interestingly, several (11) of the miR-Vec miRNAs that increased proliferation also increased EGFR-TKI sensitivity. In addition, a group of these miR-Vecs contain miRNAs (hsa-mir-372, hsa-mir-373, hsa-mir-519c and hsa-mir-520c) with strong seed sequence homology, as they all contain an AAGUGC motif in the 5' end (nt 1-8) of the mature miRNA ( Figure 1d). As previous studies have shown oncogenic potential for miRNAs with an AAGUGC motif in their seed sequence, we decided to investigate this group of miRNAs further.
To validate the impact of AAGUGC-miRNA expression on cell proliferation and EGFR-TKI sensitivity, four different lung cancer cell lines (U1810, A549, NCI-H1703 and SK-MES-1) were stably transduced with a hsa-mir-372 (hereafter refered to as miR-372) expression vector or a control vector. Stably transduced cells were  fluorescence-activated cell-sorted based on green fluorescent protein (GFP) expression from the miR-372 and control vectors.  qPCR analysis of miR-372-3p expression in all parental, control  and miR-372 transduced cells demonstrated the absence of  miR-372-3p in all parental and control cells and clear expression in  all four miR-372 transduced cell lines (Figure 2a). As a reference, we also measured miR-372-3p expression level in a testicular germ cell tumor cell line (833KE) with previously shown endogenous expression of miR-372-3p, 10 indicating that the miR-372 expression in our NSCLC models was physiologically relevant. Flow cytometry analysis showed that miR-372 expression resulted in altered cell cycle distribution in all four miR-372 models, with a general trend of a decrease in the G0/G1 population, and an increase of the S and G2/M populations (Figure 2b). In concordance with the results from the functional genomics screen, miR-372 expression resulted in an increased proliferation index, defined as the ratio between S/G2/M cells and G0/G1 cells in three of the miR-372 models (A549, NCI-H1703 and SK-MES-1, Figure 2c). The impact of miR-372 expression on EGFR-TKI sensitivity was evaluated using clonogenic survival assay in U1810, A549 and NCI-H1703 cell line models (Figure 2d). Strikingly, in all three cell line models, EGFR inhibition using gefitinib resulted in a complete loss of colonies formed by miR-372 expressing cells, while only causing a modest decrease in colony size in parental and control cells. The results from the clonogenic assay therefore fully supported the results from the functional genomics screen. In summary, our functional genomics screen indicated that expression of miR-372 and other AAGUGC-containing miRNAs in lung cancer cells results in increased proliferation and increased sensitivity to EGFR inhibitors. Further, these results were confirmed for miR-372 in several NSCLC models stably expressing miR-372.
Global target analysis of the AAGUGC-miRNA miR-372-3p Our next objective was to investigate the molecular consequences of AAGUGC-miRNA expression in relation to the detected phenotype. As miRNAs exert their cellular functions through targeting mRNAs, resulting in mRNA degradation, translation inhibition or both, we performed global mRNA-and protein-level analysis of cells treated with synthetic miR-372-3p mimics to identify potential AAGUGC-miRNA targets. Briefly, triplicate cultures of U1810 cells were transfected with miR-372-3p mimics or non-targeting siRNA and harvested 30 h after transfection (Figure 3a). The choice of time point was based on a previously published global analysis of miRNA effects at both protein and mRNA level. 11 For the mRNAlevel analysis of miR-372-3p effects, RNA sequencing was performed resulting in the identification and quantification of transcripts corresponding to 12 657 genes (Supplementary Table 2). The protein-level analysis was performed by high-resolution isoelectric focusing-liquid chromatography-mass spectrometry (HiRIEF-LC-MS) with relative quantification between samples using isobaric tags, and led to the identification and quantification of proteins corresponding to 9514 genes (Supplementary Table 3). Potential miR-372-3p target genes were defined as genes being downregulated at mRNA (1741) or protein (649) level in miR-372-3p mimic-treated cells compared with non-targeting siRNA treated cells (P-value cutoff of 0.05, Figures 3b and c). mRNA targets of specific miRNAs can be predicted based on complementarity between the miRNA seed sequence and the mRNA 3'UTR sequence. To predict miR-372-3p targets, we used nine different miRNA target prediction algorithms through the miRWalk database. 12 In total, 7508 different miR-372-3p targets were predicted, with the number of predictions per algorithm ranging from 451 (miRDB) to 6146 (PICTAR5) (Supplementary   Figure 1b). The mRNAs and proteins quantified in response to miR-372-3p mimics were then stratified into four groups according to the number of algorithms predicting them as targets of miR-372-3p (no predictions, 1-2 predictions, 3-4 predictions and more than 4 predictions).
The cumulative distribution of mRNA or protein fold changes upon treatment with miR-372-3p mimics across the four groups showed no general downregulation of mRNAs or proteins predicted by one to two algorithms, whereas a clear enrichment of downregulated mRNAs and proteins was evident in the group predicted by more than four algorithms (Figures 4a  and b). Interestingly, the distribution of the number of algorithms predicting the same target mRNA was bimodal with a peak in targets predicted by five algorithms suggesting a threshold for higher algorithm consistency (Supplementary Figure 1b). For further analysis, we therefore focused on targets predicted by five or more algorithms.
Combining the global mRNA-and protein-level analysis of miR-372-3p targets with the target prediction analysis resulted in a list of 525 putative miR-372-3p targets that were downregulated at mRNA and/or protein level and also predicted as targets by more than four different algorithms (Figure 4c, Supplementary Table 4).
miRNAs with the AAGUGC-oncomotif target a wide range of tumor suppressors with growth inhibiting functions According to the miRNA database miRBase, the human genome contains a total of 28 different miRNAs with an AAGUGC motif in their seed sequence (Supplementary Figure 2). These 28 miRNAs are expressed from seven different loci, and in six of these loci, several different AAGUGC-miRNAs are clustered together.
In two cases, these miRNA clusters are located within an intron of a so-called miRNA host gene (MIR17HG for miR-17~92 and MCM7 for miR-106b~25), and in remaining cases, they are located in intergenic regions. Although it is likely that these different miRNAs have their own unique target mRNAs, our and others' data indicate that there is a common oncogenic phenotype for many of these miRNAs, suggesting that there is also a common set of target mRNAs. The performed analysis indicates that the AAGUGC-core motif present in the seed sequence of all these miRNAs is important for their oncogenic properties. For this reason, we will refer to this core motif as the oncomotif and to the group of 28 miRNAs as oncomotif-miRNAs.
To identify mRNA targets involved in the regulation of cell proliferation, we performed a literature search of previously described and validated targets of individual oncomotif-miRNAs. Our primary focus was directed towards TSs because inhibition of such targets would promote oncogenic growth. The literature search resulted in the identification of seven well-established TS targets previously described to be regulated by one or several different oncomotif-miRNAs (TGFBR2, 13 CDKN1A, 14 LATS2, 10 RBL2, 15 ZBTB7A, 16 PTEN 17 and RB1; 18 for additional references and details, see Supplementary Table 5). All of these TS targets except RB1 were among the 525 putative miR-372-3p targets here identified, suggesting that they are common oncomotif-miRNA targets (Figures 5a and b). Conversely, several oncomotif-miRNAs have also been shown to target E2F1, resulting in translational inhibition but no impact on mRNA stability, indicating that they may be involved in negative regulation of proliferation. 19 Our experimental analysis of miRNA targets showed concordance between mRNA-and protein-level analysis in some cases (for example, TGFBR2, RBL2 and ZBTB7A), whereas in other cases, the regulation was only seen at one level (for example, LATS2 at mRNA level and CDKN1A at protein level). The overlap between the analyses was also not complete as illustrated by E2F1, which Protein level analysis of miR-372-3p effects was not identified by mass spectrometry. To further evaluate our findings, we selected three oncomotif-miRNA targets (TGFBR2, CDKN1A and E2F1) for western blot validation in four different cell lines (U1810, A549, NCI-H1703 and SK-MES-1) using miR-372-3p mimics. The western blot data showed robust downregulation of the targets at the protein level in all cell lines ( Figure 5c).
Oncomotif-miRNA expression in lung adenocarcinoma is linked to E2F-driven cancer cell proliferation, TP53 mutation and MYC amplification To investigate the clinical relevance of oncomotif-miRNAs in lung adenocarcinoma (LUAD), miRNA and mRNA expression data generated in The Cancer Genome Atlas (TCGA) project were downloaded from the UCSC Cancer Browser. The expression of oncomotif-miRNAs in 445 LUAD cases indicated dramatically different expression levels across the patient cohort, with hsa-mir-93 showing the highest expression followed by hsa-mir-17 and hsa-mir-106b (Supplementary Figure 3a). Even though hsa-mir-93 was the dominant oncomotif-miRNA in most tumors, sporadic cases of high expression was seen for most miRNAs as exemplified for the 10 tumors with the highest sum expression of oncomotif-miRNAs (Supplementary Figure 3b). The five miRNAs of the miR-302 family (hsa-mir-302 a-e) were not expressed in LUAD, which is in agreement with previous reports showing stem cell-specific expression of these miRNAs. 20 Because of the overlapping target spectrum of oncomotif-miRNAs, we considered the sum of their expression for the continued analysis of the TCGA data. To investigate differences in signaling between tumors with high or low oncomotif-miRNA expression, we performed differential mRNA expression analysis between the top and bottom 5th percentiles of the 445 TCGA LUAD samples (Supplementary Figure 4a). Gene set enrichment analysis 21,22 indicated a strong association between oncomotif-miRNA expression and cell proliferation as both KEGG and Reactome cell cycle gene sets were enriched among the genes with higher expression in oncomotif-miRNA high tumors (Supplementary Figure 4b).
Next, Ingenuity pathway analysis (www.ingenuity.com) was used to identify pathways or networks associated with oncomotif-miRNA expression by analyzing the top 275 genes separating the high and low expressing tumors (log2 abs(fold) 41, false discovery rateo 1.  the three cases with the highest oncomotif-miRNA expression (indicated with an asterisk) but different dominating miRNAs (C19MC cluster miRNAs, miR-371~373 cluster miRNAs and miR-93, respectively, Supplementary Figure 3b). When analyzing oncomotif-miRNA expression in relation to common genetic alterations in LUAD, the top two findings were significantly higher expression of oncomotif-miRNAs in tumors harboring TP53 mutation and MYC amplifications ( Supplementary Figures 5a and b). In addition, but with lower significance, oncomotif-miRNA expression was higher in tumors with mutated KEAP1 or RB1, and in tumors with TERT amplification. The association of the miRNA signature with clinical parameters was also investigated showing significantly lower relapse-free survival in patients with tumors belonging to the high oncomotif-miRNA cluster (Figure 6b). To test whether these results were independent of genetic alterations associated with oncomotif-miRNA expression, we performed univariate and multivariate survival analysis including also TP53, KEAP1 and RB1 mutation as well as MYC or TERT amplification. The only significant finding in this analysis was that patients with tumors belonging to the high oncomotif-miRNA cluster has shorter relapse-free survival ( Supplementary Figures 5c and d). In summary, our analyses show that oncomotif-miRNA expression in LUAD is strongly associated with E2F-driven tumor cell proliferation, TP53 mutation, MYC amplification and shorter relapse-free survival.
Oncomotif-miRNAs are part of an oncogenic signaling network in LUAD As at least part of the impact of miRNAs on their targets is excerted through mRNA degradation, the correlation between miRNA expression and mRNA levels in clinical samples can provide information about targets that are clinically relevant. Out of the 525 potential targets defined here, 194 were found to inversely correlate with oncomotif-miRNA expression in the TCGA LUAD cohort (pearson correlation o − 0.15, Supplementary Table 7). Importantly, all seven TS targets discussed here were found to inversely correlate with oncomotif-miRNA expression in LUAD (Figure 7a). Among these 194 candidate targets were also several other previously reported targets of individual oncomotif-miRNAs, supporting a common target spectrum.   Furthermore, a positive correlation was seen between miRNA expression and known oncomotif-miRNA transcription factors (E2F1-3 and MYC) and host genes (MCM7 and MIR17HG).
In addition, a strong positive correlation was observed between oncomotif-miRNA expression and cell cycle-related genes as well as the proliferation marker MKI67.
On the basis of our findings, we suggest that oncomotif-miRNAs are an integral part of a signaling network that regulates cell proliferation, and that overexpression of these miRNAs has the potential to promote oncogenic growth through the inhibition of a series of TS targets (Figure 7b).
Pan-cancer oncomotif-miRNA analysis To further investigate the oncomotif-miRNA expression pattern in the context of cancer, we used miRNA-profiling data generated in the TCGA project. Oncomotif-miRNA expression analysis in 32 different cancer types showed large differences in overall   Figure 4 for details). Heatmap colors represent mean centered log2 fpkm values. Inserts, from top to bottom, indicate; cancer (yellow), normal (green), oncomotif-miRNA high cancer (top 5%, red) and oncomotif-miRNA low cancer (bottom 5%, blue); TP53-mutated (blue) or TP53 wt (gray); MYC gain (light red), MYC high-level gain (red) or no gain (gray). Asterisks indicate the three cases with the highest oncomotif-miRNA expression. Indicated on the right are a few genes with increased expression (red, oncomotif-miRNA transcription factor, oncomotif-miRNA host gene and cell cycle related genes) or decreased expression (green, oncomotif-miRNA target) in the oncomotif-miRNA high compared with low clusters. (b) Kaplan-Meier plot of relapse-free survival (truncated at 6 years) for the oncomotif-miRNA high cluster patients (red, n = 261) and oncomotif-miRNA low cluster patients (green, n = 184). Figure 6). The highest expression was seen in testicular germ cell cancer (TGCT, median number of oncomotif-miRNA reads per million miRNA reads mapped equaled 55 023) followed by acute myeloid leukemia (30 344) and thymoma (THYM, 26 629). Cancer types with low oncomotif-miRNA expression included pancreatic cancer (PAAD, 3493) and kidney cancer (KIRC, 3875 and KICH, 2587). The relative contribution of different oncomotif-miRNAs to the summed expression showed a general pattern with the highest expression of hsa-mir-93 followed by hsa-mir-17 in the majority of cancer types (Supplementary Figure 7a). A striking exception from this general pattern was noticed in TGCT where the miR-371~373 cluster was dominating together with the miR-302~367 and C19MC clusters. Also, THYM showed a specific oncomotif-miRNA expression pattern with relatively high expression of the C19MC cluster. Similar to the results in LUAD, sporadic high expression of oncomotif-miRNAs from the miR-371~373 and C19MC clusters was noticed in several other cancer types (Supplementary Figure 7b).

expression between cancer types (Supplementary
To evaluate a general role of oncomotif-miRNAs in oncogenic signaling, correlation networks for an additional 15 cancer types were produced through correlating the oncomotif-miRNA expression with mRNA expression levels from TCGA as was performed for LUAD ( Figure 8). This analysis revealed that the correlations found in the LUAD analysis were present also for other cancer types. The strongest average negative correlation between oncomotif-miRNA expression and the seven TS targets was seen in TGCT, the cancer type with the highest expression of oncomotif-miRNAs. Conversely, the weakest negative correlation was seen in KIRC, the cancer type with the lowest expression of oncomotif-miRNAs of the cancer types analyzed. Despite these results, we did not see a direct connection between oncomotif-miRNA expression level and TS target correlation. As an example, the second and third strongest negative To further validate the results from the LUAD analysis, the breast cancer dataset (BRCA) was selected because it showed a generally high correlation between oncomotif-miRNAs and TS targets. Hierarchical clustering of 762 samples from BRCA patients and 87 normal samples based on the expression of the 275 oncomotif-miRNA signature genes defined in LUAD generated results that very closely mimicked the results in LUAD (Supplementary Figure 8a). Two main clusters were produced, one containing cancers with high oncomotif-miRNA expression showing higher expression of genes related to cell cycle activity and proliferation, and one cluster with low oncomotif-miRNA-expressing tumors and normal samples. Also, in BRCA, the expression of oncomotif-miRNAs was higher in tumors harboring TP53 mutation and MYC copy number gain (Supplementary Figure 8b). In addition, although without reaching statistical significance, there was a trend for shorter relapse-free survival in BRCA patients in the oncomotif-miRNA-high cluster compared with the oncomotif-miRNA-low cluster, similarly to what was seen in lung cancer (Supplementary Figure 8c).
In summary, the analysis across multiple cancer types strongly supports the results of the LUAD analysis, indicating that oncomotif-miRNAs are in fact an integrated part of the oncogenic signaling network that drives cancer cell proliferation.

DISCUSSION
Our miRNA target analyses revealed broad oncomotif-miRNA targeting of bona fide TSs responsible for inhibition of cell proliferation. RB1 (pRb) and RBL2 (p130) are directly involved in inhibition of activating E2F transcription factors (E2F1-3) that transactivate genes crucial for G1/S progression. RB1 binds to and inhibits E2F1-3 directly, and RBL2 forms a repressor complex together with inhibitory E2F transcription factors (E2F4-5) resulting in repression of E2F1-3 target genes. 23 CDKN1A (p21) is a cyclindependent kinase (CDK) inhibitor that binds to and inhibits the activity of cyclin/CDK complexes resulting in RB1-dependent inhibition of activating E2F transcription factors. TGFBR2 also has a negative impact on cell proliferation as TGFBR2 signaling results in the upregulation of several different CDK inhibitors including CDKN1A. 24 PTEN is an inhibitor of the PI3K/AKT signaling pathway that promotes both proliferation and survival. The LATS2 TS is suggested to function through inhibition of MDM2 resulting in stabilization of the TS TP53 (p53), 25 as well as directly inhibiting the activity of the cyclinE/CDK2 complex. 26 ZBTB7A has been shown to be a TS in prostate cancer as ZBTB7A activity results in increased RB1 stability. 16 Our analysis of oncomotif-miRNA targets included miRNA target prediction algorithms that predict targets based on complementarity between the seed sequence of the miRNA and the 3' UTR of mRNAs as well as evolutionary conservation in the 3' UTR sequence. It should be acknowledged that other parts of the miRNA, not considered by the prediction algorithms, also affect the binding to mRNA, resulting in both false-negative and false-positive predictions. 27 Importantly, all of the TS targets here discussed have previously been described and validated as targets of different oncomotif-miRNAs. Our analysis here shows that expression of a single oncomotif-miRNA (miR-372-3p) is sufficient for targeting an entire panel of these tumor supressors. Our data also support previous studies showing that different oncomotif-miRNAs share common TS targets. Consequently, whenever two or more oncomotif-miRNAs are expressed in the same cell, they will jointly contribute to the silencing of those targets. In addition, our data demonstrate that high expression of oncomotif-miRNAs in cancer cells promotes cell cycle progression and increases proliferation.
In normal cells, expression of oncomotif-miRNAs through activating E2F transcription factors 14 and MYC 19 in response to, for example, growth factor signaling, could be beneficial to rapidly induce proliferation when needed. In analogy, it has been shown that a set of miRNAs that target immediate early genes responsible for cell cycle entry are rapidly downregulated in response to growth factor stimulation. 28 Such signaling, however, needs to be carefully regulated not to become oncogenic and this is usually accomplished by efficient negative feedback loops. It has previously been suggested that several oncomotif-miRNAs themselves are part of such a negative feedback loop via targeting of activating E2F transcription factors. 19 Even though we also detect E2F1 targeting, the overall analysis supports a powerful feed-forward loop through oncomotif-miRNA targeting of several TSs known to cause inhibition of E2F activity and G1/S progression. The oncomotif-miRNA-dependent increase in E2F activity would subsequently result in additional oncomotif-miRNA transcription, creating a self-propagating loop. This model is further supported by the fact that ectopic expression of oncomotif-miRNAs results in increased proliferation. Our data thus suggest that even if oncomotif-miRNAs target E2F transcription factors, the net effect when taking into account the TS targets of oncomotif-miRNAs is increased proliferation.
Oncogenic growth signaling, for example, through EGFR or KRAS, results in cyclinD/cdk4-dependent hyperphosphorylation of retinoblastoma protein (RB1), release of activating E2F transcription factors (E2F1-3) from RB1 inhibition and E2F-dependent transcription of genes involved in cell cycle progression. Our data suggest that E2F-dependent transcription of oncomotif-miRNAs (hsa-mir-93 and hsa-mir-106b) 14 contributes to cell cycle progression by targeting TSs that would otherwise inhibit cell cycle entry. Likewise, oncogenic MYC signaling would result in inhibition of the same TSs through transcription of other oncomotif-miRNAs (hsa-mir-17 and hsa-mir-20a). 19 The fact that hsa-mir-93 and hsa-mir-17 are the dominant oncomotif-miRNAs in most cancer cases suggests that oncogene-dependent transcription through MYC and E2Fs is an important contributor to oncomotif-miRNA expression in cancer, but the elucidation of specific mechanisms of oncomotif-miRNA expression in different cancer types warrants further investigation. Further, our analysis shows that in sporadic cases, oncomotif-miRNA expression from other genomic loci becomes dominant. Interestingly, three of the alternative loci (including miR-106a~363, miR-371~373 and miR-302~367 clusters) have been shown to be expressed specifically in embryonic stem cells during early embryogenesis 29 and are important for proliferation. 30 Sporadic re-expression of stem cell-specific miRNAs could potentially be a result of a genetic translocation, placing the miRNA locus under the control of an active promoter as previously shown for oncomotif-miRNAs of the C19MC and miR-371~373 clusters. 15,31 Additional mechanisms for altered regulation could include genetic imprinting, epigenetic regulation and antisensemediated regulation as described for the miR-371~373 cluster. 32 Importantly, cancer cases with high oncomotif-miRNA expression cluster together irrespective of the relative contribution of individual miRNAs, supporting an overlapping core set of target mRNAs and redundant oncogenic properties. This finding motivates the analytical approach used here, where different miRNAs with strong seed sequence homology are analyzed as a group. The grouped analysis also allows for identification of low frequency events that would otherwise have been difficult to find.
An important cellular defense mechanism against oncogene activation is through induction of permanent cell cycle arrest, a process termed oncogene-induced senescence. 33 Both RB1 and TP53 are key regulators of the senescence program that includes and relies on the activation of several different CDK inhibitors including CDKN1A. 34 It has previously been shown that several different oncomotif-miRNAs have the potential to rescue cells from oncogene-induced senescence by targeting CDKN1A 35 or LATS2. 10,35 In addition, oncomotif-miRNA-dependent inhibition of TGFBR2 would result in further hampering of the senescence program by decreasing the level of additional CDK inhibitors activated by TGF-beta signaling.
Without a functional negative feedback regulatory system and with the oncogene-induced senescence system impaired, a final defense mechanism for the organism against oncogenic signaling is to activate the programmed cell death machinery. It is well known that increased E2F1 signaling as well as oncogenic MYC signaling can result in induction of apoptosis. 36,37 TP53 is generally described as the master regulator of apoptosis and it has been shown that the induction of oncogene-driven apoptosis is dependent, at least partially, on functional TP53. Consequently, a fully operational apoptosis system should have the capacity to efficiently protect against the oncogenic signaling described here. Unfortunately, the apoptosis system is commonly disabled in cancer, for example, through mutation of TP53, which would allow the oncogenic signaling to continue. We have shown here that the expression of oncomotif-miRNAs in both LUAD and breast cancer is significantly higher in tumors harboring TP53 mutations, indicating that the oncogenic signaling including oncomotif-miRNAs is tolerated in cells with the apoptotic machinery inactivated.
Previous studies have described connections between all individual oncomotif-miRNA containing miRNA clusters and cancer as exemplified by miR-17~92 cluster in lymphoma, 38 miR-106a~363 cluster in T-cell leukemia, 39 miR-106b~25 cluster in gastric cancer, 14 C19MC cluster in CNS-PNET, 40 miR-371~373 cluster in testicular cancer 10 and miR-302~367 cluster in germ cell tumors. 41 Hence, there is a wellestablished association between individual oncomotif-miRNA containing miRNA clusters and cancer. Importantly, oncomotif-miRNAs are also members of a suggested oncogenic miRNA superfamily defined by a central GUGC core motif that was shown associated with targeting of TSs. 42 Our analysis supports these studies, and further suggests that multiple different oncomotif-miRNAs will cooperate in driving proliferation through targeting of the same set of TSs. Our analysis also shows that the expression level of oncomotif-miRNAs varies dramatically across and within a large number of different cancer types, indicating that the importance of oncomotif-miRNA expression for cancer cell growth varies between cancer types and cancer cases. Furthermore, in lung adenocarcinoma (LUAD) and breast cancer (BRCA), our analysis indicates that high expression of oncomotif-miRNAs is connected with shorter relapse-free survival. Collectively, these findings suggest that oncomotif-miRNA expression analysis could contribute important information for prognostication and therapy prediction in certain cancer types and for certain drugs, as discussed below.
Our data demonstrate that expression of oncomotif-miRNAs not only results in increased proliferation, but also in increased sensitivity to EGFR inhibitors. A possible connection between these two findings could be that cells become addicted to the oncogenic signaling potentiated by oncomotif-miRNA expression. The addicted cells would then be more sensitive to treatments such as EGFR inhibitors that target upstream signaling. These findings have several potential clinical implications. The first and most obvious is that patients with certain cancer types driven by EGFR signaling, which also have a high expression of oncomotif-miRNAs, may benefit from treatment with therapies targeting EGFR. This hypothesis needs to be further investigated, and for such an investigation, the performance of oncomotif-miRNAs as biomarkers in this setting should also be assayed. Interestingly, the clinical relevance of circulating cell-free miRNAs as biomarkers in cancer is currently being investigated intensively. 43 In fact, many different oncomotif-miRNAs have already been suggested as diagnostic and prognostic blood-based biomarkers for different cancer types. Whether circulating cell-free oncomotif-miRNAs can be used as predictive biomarkers for EGFR-TKI-based therapy remains to be tested.
Second, our analysis suggests that elevated oncomotif-miRNA expression in patients results in loss of the G1/S checkpoint leading to uncontrolled proliferation of cancer cells. Large ongoing efforts are made to investigate pharmacological strategies to restore the G1/S checkpoint to halt cancer cell proliferation, for example, through inhibition of CDK4/6 (palbociclib 44 ). Clinical trials have demonstrated promising results of palbociclib in treatment of breast cancer 45,46 and active research is now focusing on identifying predictive markers for improved selection of patients for palbociclib treatment. Our study suggests that evaluation of tumor expression of oncomotif-miRNAs could contribute important information in this setting.
Finally, the discovery of oncomiRs and TS miRNAs during the last 15 years spawned the idea of targeting miRNAs for cancer treatment. In principle, such treatment would involve either inhibition of oncogenic miRNAs or administration/derepression of TS miRNAs as reviewed by Ling et al. 47 Highly interesting in relation to oncomotif-miRNA expression in cancer, is the development of seed-targeting tiny locked nucleic acids. 48 These 8-mer oligonucleotides can be designed to inhibit entire seed sequence families, which would make it possible to target all oncomotif-miRNAs simultaneously. Also, it was shown that, when systematically administered, unconjugated tiny locked nucleic acids showed uptake in breast tumors in mice, coinciding with long-term miRNA inhibition. In fact, it was subsequently shown that tiny locked nucleic acids targeting miRNAs with an AAAGUGCU-seed motif (for example, miR-17-5p and miR-93-5p) were able to reduce cancer cell proliferation and prolong survival in mouse models of medulloblastoma. 49 It is tempting to speculate that targeting the entire oncomotif-miRNA group by tiny locked nucleic acids could be an efficient way of re-establishing the G1/S checkpoint in cancers with high expression of oncomotif-miRNAs. For such an approach, careful consideration would have to be taken regarding the effects on normal cells because these miRNAs have various functions also in non-cancer cells.
In conclusion, our data suggest that oncomotif-miRNAs are an integral part of an oncogenic signaling network, and that oncomotif-miRNAs form a feed-forward loop promoting cell proliferation. In cancer, especially when pushed by MYC amplification and without the inhibitory effects of a functional TP53 system, this feed-forward loop becomes a self-propagating, continuous oncogenic driver of uncontrolled cell growth. In addition, we show that knowledge of oncomotif-miRNA expression in cancer patients may contribute valuable information in terms of prognostication, and even more importantly for selection of therapy.
For miR-Lib virus production, EcoPack II cells were cultured in Dulbecco's modified Eagle's medium (41966, Invitrogen, Grand Island, NY, USA) supplemented with 10% fetal calf serum and antibiotics (complete medium). Retrovirus was made by polyethyleneimine transfection of EcoPack II cells. The pMSCV-miR constructs were made as described previously. 10 All miRNA transfection and virus collection steps were carried out on a Hamilton ML STAR (Hamilton Bonaduz, Bonaduz, Switzerland). Protocols were developed at the Netherlands Cancer Institute using Hamilton STAR Software 3.2. The methods were completely automated.
For phenotype validation (flow cytometry and clonogenic assay) of miR-372 effects, stable hsa-mir-372-expressing cell lines and control cell lines were generated as follows. GFP-containing plasmids were purchased from SBI (System Biosciences, Mountain View, CA, USA, pre-miR-372 expression plasmid product number: PMIRH372PA-1; control plasmid product number: PMIRH000VA-1). The plasmids were transformed into E. coli separately and plasmid DNA was extracted using GeneJET plasmid midiprep kit (Thermo Fisher Scientific, K0482) according to product protocol. Lentiviruses were produced by transfecting plasmids into the virus packing cell line HEK293T together with Gag-pol/Rev/Envelope VsV-g pantropic plasmid (a gift from Professor Thomas Helleday Lab at Karolinska Institutet). Viruses were harvested every 12 h by collecting virus-containing culture media and used to infect target cell lines at the time of harvest. In total, target cell lines were infected three times. Virus-infected cells were sorted with BD Influx Cell Sorter (BD Biosciences, San Diego, CA, USA) using 140 micron nozzle tip. High GFP expression cell population (more than 1000 times GFP expression than basal cells) were sorted, collected and expanded for further experiments. Validation of GFP expression was performed by seeding 10 000 cells from each cell line in 12-well plates; GFP photos were taken with Bio-Rad ZOE Fluorescent Cell Imager (Bio-Rad, Hercules, CA, USA) to analyze expression of the constructs. miRNA dropout screen Polyclonal pool of U1810 cells stably carrying the murine ecotropic receptor were generated to allow infections with ecotropic retroviral supernatants as described previously. 51 U1810 cells were individually transduced with approximately 450 miRNA vectors (miR-Vecs) from a miRNA expression library (miR-Lib; for details see Voorhoeve et al. 10 and Huang et al. 52 ) using retroviral infection and drug selection (Blasticidin 5 ug/ml) to obtain resistant growing cells, each containing a unique integrated miR-Vec. Around 80% of vectors yielded stable clones. Cells were then pooled and plated at 1 000 000 cells per 10 cm dish for the three different conditions (Ctrl0, Ctrl30 and Gef30) in biological triplicates. Twenty-four hours after seeding, triplicate cultures were harvested for use as the baseline reference (Ctrl0). Ctrl30 and Gef30 cultures were cultured in separate biological triplicates in the absence or presence of 10 uM gefitinib and the medium was refreshed twice per week for 30 days and cells were split when needed to avoid confluency. After harvesting of cells, genomic DNA was isolated using QiaAmp kit (Qiagen) according to the manufacturer's protocol. miR-Vec inserts were recovered from 250 ng of genomic DNA by Phusion PCR amplification (Thermo Fisher Scientific) according to the manufacturer's protocol using primers specific for the pMSCV vector. Indexes and adaptors for deep sequencing (Illumina) were incorporated into PCR primers in two consecutive PCR reactions. PCR products were retrieved by electrophoresis followed by cutting out bands and purification using QIAEX II gel extraction kit (Qiagen). Deep sequencing was performed using the Illumina platform (Illumina, San Diego, CA, USA) at the NKI Central genomics facility. Sequencing indexes and miR-Vec sequences were segregated and deconvoluted from each sequencing read. Samples were normalized to total reads. We arbitrarily considered only miR-Vecs that had been sequenced at least 300 times in all three biological replicates of one condition, resulting in 140 unique miR-Vec miRNAs remaining for the enrichment/depletion analysis. P-values for enrichment/ depletion analysis were calculated using two-sided t-test without correction for multiple testing.

Protein extraction, digestion and Isobaric labeling
For extraction of proteins, pellets (biological triplicates) containing five million cells were lysed with sodium deoxycholate buffer (5% sodium deoxycholate, 1 mM dithiothreitol (Sigma-Aldrich, Product No. 43819), 25 mM HEPES), vortexed and kept on ice for 10 min, and then boiled in 95°C for 10 min followed by sonication (2 cycles of 30 s, 80% energy). Vials were centrifuged at 11 000 r.p.m. for 15 min. Supernatants containing proteins were collected into new vials. Protein concentration was determined by Bio-Rad Quick Start Bradford protein assay.
Protein extracts were digested using trypsin (Thermo Fisher Scientific, 90058) using a filter-aided sample preparation protocol. 53 The peptides generated from each sample were subsequently individually labeled with tandem mass tag (TMT)-10plex isobaric label reagents (Thermo Fisher Scientific, Cat. No. 90110). After pooling, the labeled peptides were cleaned by strong cation exchange solid-phase extraction (Phenomenex Strata-X-C, Phenomenex, Torrance, CA, USA) and aliquoted to 200 μg peptides mixture for isoelectric focusing (IEF).

Peptide-level isoelectric focusing (HiRIEF)
Each labeled peptide pool (200 μg) was dissolved in 250 μl of rehydration solution (8 M urea, 1% pharmalyte for pH range 3-10 from GE Healthcare, Little Chalfont, UK), which was then used to re-swell an immobilized pH gradient gel-strip (GE Healthcare) pH 3-10. For the IEF in the 3.7-4.9 pH range, 200 μg of labeled peptide pool were dissolved in 150 μl of 8 M urea, and this solution was used to rehydrate a sample gel bridge (pH 3.7) overnight. The 3.7-4.9 immobilized pH gradient strip was rehydrated in 8 M urea, 1% pharmalyte for pH range 2.5-5 (GE Healthcare). All IEFs were run on an Ettan IPGphor (GE Healthcare) until at least 150 kVh for the 3-10 range and until at least 250 kVh for the 3.7-4.9 range (~1 day running time in either case). After focusing was complete, a well-former with 72 wells was applied onto each strip, and liquid-handling robotics (GE Healthcare prototype) added MilliQ water and, after 3 × 30 min incubation/transfer cycles, transferred the 72 fractions into a microtiter plate (96 wells, V-bottom, Corning, Lowell, MA, USA, Cat. No. 3894), which was then dried in a SpeedVac.

LC-MS/MS analysis
For each LC-MS run of a HiRIEF fraction, the auto sampler (Ultimate 3000 RSLC system, Thermo Fisher Scientific) dispensed 15 μl of mobile phase A (95% water, 5% dimethylsulfoxide, 0.1% formic acid) into the corresponding well of the microtiter plate, mixed by aspirating/dispensing 10 μl ten times, and finally injected 7 μl into a C18 guard desalting column (Acclaim pepmap 100, 75 μm × 2cm, nanoViper, Thermo Fisher Scientific). After 5 min of flow at 5 μl/min with the loading pump, the 10-port valve switched to analysis mode in which the NC pump provided a flow of 250 nl/min through the guard column. The curved gradient (curve 6 in the Chromeleon software) then proceeded from 3% mobile phase B (90% acetonitrile, 5% dimethylsulfoxide, 5% water, 0.1% formic acid) to 45% B in 50 min followed by wash at 99% B and re-equilibration. Total LC-MS run time was 74 min. We used a nano EASY-Spray column (pepmap RSLC, C18, 2 μm bead size, 100 Å, 75 μm internal diameter, 50 cm long, Thermo Fisher Scientific) on the nano electrospray ionization EASY-Spray source (Thermo Fisher Scientific) at 60°C. Online LC-MS was performed using a hybrid Q-Exactive mass spectrometer (Thermo Fisher Scientific). FTMS master scans with 70 000 resolution (and mass range 300-1700 m/z) were followed by data-dependent MS/MS (35 000 resolution) on the top five ions using higher energy collision dissociation at 30% normalized collision energy. Precursors were isolated with a 2 m/z window. Automatic gain control targets were 1e6 for MS1 and 1e5 for MS2. Maximum injection times were 100 ms for MS1 and 150 ms for MS2. The entire duty cycle lasted~1.5 s. Dynamic exclusion was used with 60 s duration. Precursors with unassigned charge state or charge state 1 were excluded. An underfill ratio of 1% was used.

Proteomics database search pipeline
Raw MS/MS files were converted to mzML format using msconvert from the ProteoWizard tool suite. 54 Spectra were then searched using MSGF+ 55 (v10072) and Percolator 56 (v2.08), where eight subsequent search results were grouped for Percolator target/decoy analysis. The reference database used was the human protein subset of ENSEMBL 79. MSGF+ settings included precursor mass tolerance of 10 p.p.m., fully tryptic peptides, maximum peptide length of 50 amino acids and a maximum charge of 6. Fixed modifications were TMT-10plex on lysines and N-termini, and carbamidomethylation on cysteine residues, a variable modification was used for oxidation on methionine residues. Quantification of TMT-10plex reporter ions was carried out using OpenMS project's IsobaricAnalyzer 57 (v2.0). Peptide spectrum matches found at 1% false discovery rate were used to infer gene identities, which were quantified using the medians of peptide spectrum matches quantification ratios. P-values for protein-level regulation in response to miR-372-3p mimics were calculated using two-sided t-test without correction for multiple testing. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD004163.

RNA sequencing and mapping
Total RNA was prepared in biological triplicates from U1810 cells (transfected and cultured as described above) using RNeasy Plus Mini Kit . Cell cycle phases were automatically determined by NovoExpress software using the cell cycle analyze function and the read out contains G0/G1, S, G2/M phase cells percentile. Proliferation index, defined as the ratio between S, G2/M cells and G0/G1 cells, was calculated as previously described. 60,61 P-values were calculated using two-sided t-test without correction for multiple testing.

Clonogenic assay
Clonogenic assay was performed as previously described. 62 Briefly, 1000 cells from each cell line (A549, U1810 and NCI-H1703) and type (parental cells (PC), GFP-control cells (CC) and hsa-mir-372-transduced cells (MC)) were seeded in Corning Costar 6-well flat bottom cell culture plates (Sigma-Aldrich, CLS3516). Cells were cultured for 12 days in RPMI-1640 AQ media with or without 10 μM gefitinib, new media were replaced every 3 days. Each condition was analyzed in biological triplicates. Plates were rinsed with phosphate-buffered saline and incubated with 2 ml of 6% glutaraldehyde and 0.5% crystal violet for at least 30 min in room temperature. All the staining solutions were removed and plates were rinsed with water. Plates were then left to dry at room temperature in normal air before pictures were taken. Representative pictures from one of the replicates of each analysis is shown.

Western blot
For western blotting, 0.8 million cells were transfected as described above and harvested by trypsinization 48 h after transfection. Cells were lysed with CHAPS buffer (1% CHAPS, 0.1% Triton X-100, 20 mM HEPES pH 7.5, 150 mM NaCl) and protein concentration was determined by Bio-Rad Quick Start Bradford protein assay. Samples were diluted in 4 × Laemmli Sample buffer (Bio-Rad, 1610747) with 1:10 β-mercaptoethanol. Samples were boiled at 95°C for 5 min and run on a 10% TGX-gel (Bio-Rad, 4561036) at 150 V for 1 h. Proteins were transferred onto a mini nitrocellose membrane (Bio-Rad, 1704158) using Bio-Rad's Trans-Blot Turbo system at 1.3 A for 7 min. Membranes were blocked with 5% milk in TBS Tween buffer (28360, Thermo Fisher Scientific), for 1 h at room temperature and incubated with primary antibodies against TGFBR2 (Santa Cruz Biotechnology, Dallas, TX, USA, sc-33929), CDKN1A (Cell Signaling, Boston, MA, USA, 2947), E2F1 (Cell Signaling, 3742) and GAPDH (Sigma-Aldrich, G8795) overnight at 4°C. Membranes were washed 3 × 10 min with TBS, incubated with secondary antibody (goat anti-mouse or goat anti-rabbit) conjugated with HRP at room temperature for 1 h. Membranes were developed with ECL (GE Healthcare, RPN2108) according to the manufacturer's instructions, and images were generated using ChemiDoc MP system (Bio-Rad). All western blot experiments were performed in biological triplicates. Representative images from one replicate experiment is shown.

TCGA data analysis
Data from TCGA were accessed via the UCSC Cancer Browser. miRNA expression data were retrieved for all 32 cancer types and gene expression data for 16 cancer types. Copy number data, gene-level mutation data as well as clinical information were further retrieved for LUAD and breast invasive carcinoma (BRCA). Differential gene expression analysis was conducted to identify genes differentially expressed between tumors with high or low oncomotif-miRNA expression. A two-sided t-test was performed between the top and bottom 5 percentiles of the TCGA LUAD samples (n = 445). P-values were adjusted for multiple testing using the false discovery rate with the procedure outlined by Benjamini and Hochberg. 63 All analyses were performed in R (version 3.2.2) using the t.test and p.adjust functions from the R stats package. The oncomotif-miRNA signature was defined by selecting all genes with a false discovery rateo1.0 E-05 and an absolute log2 fold-change 41. The signature comprises 275 genes, including 240 upregulated and 35 downregulated genes.
Unsupervised cluster analysis of the LUAD and BRCA gene expression profiles was performed using the 275 genes of the oncomotif-miRNA signature. All patients and genes were clustered using hierarchical clustering with complete linkage and Spearman's correlation. Heatmaps were produced using the heatmap.2 function from the gplots package in R.
Differences in oncomotif-miRNA expression in the LUAD and BRCA data sets between tumors harboring TP53 mutations and MYC amplifications were examined using the Wilcoxon rank-sum test from the R stats package.
Univariate and multivariate survival analyses, including Cox proportional hazard regression and Kaplan-Meier survival curves, were carried out in R using the stats package. Patients were split into two groups using the two main clusters defined by the hierarchical clustering based on the oncomotif-miRNA signature. Cluster membership, mutation status and copy number status were all modeled as categorical variables. For the Kaplan-Meier analyses, the differences in survival time between two groups were assessed using the P-value of the survdiff function. A significance level of 5% was considered as statistically significant. miRNA target prediction, gene set enrichment analysis and Ingenuity pathway analysis All miRNA target predictions were performed on 5 November 2014 using the miRWalk 12 portal and standard settings (3´UTR sequences, minimum seed length:7, P-value:0.05). At that time, the available algorithms in addition to the miRWalk algorithm were: DIANAmT (version 3.0); miRanda (August 2010 release); miRDB (April 2009 release); PICTAR5 (March 2007 release); PITA (August 2008 release); RNA22 (May 2008 release); RNAhybrid (version 2.1) and TargetScan (version 5.1). For gene set enrichment analysis, 21,22 a ranked list of log2 ratios between the oncomotif-miRNA high and low groups (see results section) was loaded into the gene set enrichment analysis software. For gene set enrichment analysis, the C2:canonical pathways gene sets were used, and the analysis was performed using standard settings. Ingenuity pathway analysis (www.ingenuity.com) was performed by loading the 275 genes in the oncomotif-miRNA signature gene list with corresponding log2 ratios between the oncomotif-miRNA high and low groups into the ingenuity software. As background, the Ingenuity standard background gene list was used, and the analysis was performed using standard settings.


Application of Nucleic Acid Aptamers to Viral Detection and Inhibition

Nucleic acid aptamers are small oligonucleotides that specifically bind to other molecules through noncovalent interactions that rely on complex tridimensional structural arrange‐ ments. Aptamers are generated through the iterative in vitro selection method called SE‐ LEX, resulting in specific binding against a wide variety of molecular targets including viruses. Because aptamers are obtained in vitro and can be synthetically produced, they have been envisioned as future diagnostic and therapeutic tools for human diseases in‐ cluding virus-borne pathologies. Aptamers have been isolated against a number of virus‐ es including pandemic influenza virus, human papillomavirus and hepatitis C virus. Although aptamers have proven themselves as extremely sensitive detection tools trig‐ gering the development of affordable and highly diagnostic methods, their use as thera‐ peutic moieties has been hampered by biostability, delivery and pharmacodynamical issues. Nevertheless, a new generation of chemically modified aptamers shows promise for the coming of age of protein-targeted noncatalytic oligonucleotides for the therapy of viral disease. The present review focuses on the most successful antiviral aptamers re‐ ported and includes a description of some of the novel methods developed for their use as diagnostic and therapeutic tools

Introduction
Nucleic acid aptamers are small single-stranded oligonucleotides capable of adopting complex tertiary structures that allow noncovalent interactions with other molecules. Because aptamers closely interact with their targets, their structural features are essential for highly specific binding. The term aptamer was coined in 1990 from the Latin "aptus" meaning "fitting" and the Greek "meros" meaning "particle" [1]. Aptamers are generated through the iterative in vitro selection method called SELEX (systematic evolution of ligands by exponential enrichment) and are raised against a wide variety of molecular targets ranging from ions and macromolecules to whole organisms, including viruses, bacteria, yeast and mammalian cells [2]. Although many modifications to the SELEX method have been established to include new technologies and improve selection [3], the basic steps of SELEX remain immutable.
The SELEX method involves three well-defined steps [4]: the start point is the production of a synthetic oligonucleotide combinatorial library or oligonucleotide pool containing a central randomized region (15-70 nt) flanked by anchor sequences to allow polymerase chain reaction (PCR) amplification. The aleatory nature of the central region results in the production of an enormous pool of diverse oligonucleotides with diverse structures, thus providing the conformational variability necessary to produce moieties with binding capabilities for a desired target. The oligonucleotide pool can be directly used for SELEX to generate singlestranded DNA (ssDNA) aptamers, or as in vitro transcription template to produce an RNA pool to isolate RNA aptamers. Next, a selection procedure is performed based on the interaction properties of the library with the intended target. Only a very small fraction of the oligonucleotide pool tends to interact with the target, satisfying the selection criteria. Oligonucleotides that bind the target (aptamers) are recovered while the nonbound are removed through different strategies according to the nature of the aptamer-ligand complex (size, affinity, electric charge, hydrophobicity, etc.). In the final step, the recovered aptamers are amplified by PCR in order to regenerate a library with less variability but more affinity to the target that will be used in the next selection cycle. RNA pools are amplified by reverse transcription-coupled PCR (RT-PCR) and subsequent in vitro transcription before starting the next cycle.
The iterative selection cycles produce aptamers with high binding affinity to the target. Usually, a few cycles are required to isolate aptamers (4-20 cycles), but the precise number of cycles necessary for the isolation of highly specific aptamers depends on the selection criteria, the nature of the target and the type of library used. After the last selection cycle, aptamers are cloned and sequenced to obtain information on the individual oligonucleotides, which can be further characterized based on its ability to bind the target. It is common to observe conserved sequences or structures among the selected aptamers; these are indicative of efficient selection and may represent domains required for interaction.
Aptamer specificity is based on three-dimensional arrangements of a small number of contact points between the aptamer and its target, so the aptamer can achieve high selectivity to discriminate between two highly related molecules (i.e. enantiomers), or minimal structural differences such as the presence or absence of methyl or hydroxyl groups. The molecular recognition specificity and affinity level achieved by aptamers is comparable or even better than those of antibodies. These features place aptamers as an emerging class of molecules on their own with a huge range of diagnostic and therapeutic applications plus several advantages over antibodies including: • Isolation by an in vitro process not dependent on animal cells or in vivo conditions. Therefore, the properties of aptamers can change on demand, and isolation can be manipulated to obtain aptamers with desirable properties for diagnosis. In addition, it allows aptamer isolation against toxins or poorly immunogenic molecules.
• Production by chemical synthesis with accuracy and reproducibility, thus insuring mass production with high quality control standards.
• Aptamers can be reversibly denatured allowing conditional binding through simple temperature control.

Aptamers against viruses
Aptamer isolation to inhibit viral infection can be performed by using purified molecules from the viral surface through canonical SELEX approaches, or by modified SELEX methods with the use of attenuated whole viral particles. The advantage of this last variant is the isolation of aptamers through binding to the native viral conformation. Moreover, a deep knowledge of the viral infection mechanisms or potential surface target molecules is not required to obtain antiviral or neutralizing aptamers that tightly bind infectious particles. On the other hand, this method does not disclose the sites that directly interact with the aptamers, so further studies are required to determine specific interactions useful for potential aptamer improvement.
Many efforts have been focused on the isolation of aptamers to detect and treat viral diseases relevant to public health such as AIDS, hepatitis, influenza and some cancers. Here, we summarize the successful application of aptamers selected against HIV-1, HPV, HCV, and influenza.

Aptamers against Rous Sarcoma Virus (RSV)
The first approach using whole viruses to isolate RNA aptamers without previous knowledge of the virion structural features was performed against Rous sarcoma virus (RSV), an avian retrovirus. Nineteen RNA aptamers were isolated from a canonical SELEX procedure and five of them were able to neutralize the virus infection [17]. These results immediately revealed the potential of aptamers isolated against viral surface epitopes leading to the development of nucleic acid aptamers as novel diagnostic or therapeutic tools, especially on human viral diseases requiring fast diagnostics (i.e. pandemic influenza or Ebola) or asymptomatic chronic viral-induced conditions such as acquired immunodeficiency syndrome (AIDS), hepatitis C or cervical cancer [18].

Aptamers against Human Immunodeficiency Virus type I (HIV-1)
HIV-1 is the etiologic agent of AIDS [19,20]. Most anti-HIV-1 aptamers are directed to HIV-1 reverse transcriptase (RT), RNaseH, integrase, Tat, Gag, nucleocapsid, gp120 and the TARelement RNA ( The HIV-1 integrase incorporates the viral DNA in the host genome [22]. RNA aptamers were isolated targeting HIV-1 integrase and classified into three groups according to their K D (10 nM, 80 nM and 800 nM) [23]. Furthermore, DNA aptamers were also isolated targeting HIV-1 integrase by two different research groups, all with G-quadruplex structures and showing in vitro inhibitory activity [24,25]. The characteristic structure of aptamers binding the HIV-1 integrase interacts within a channel of the tetrameric protein blocking catalytic amino acid residues essential for integrase function in vitro [26].
HIV-1 Tat protein regulates viral gene expression by interaction with the trans-activation responsive (TAR) elements within the long-terminal repeats (LTRs) [27]. Unlike the natural target of Tat (TAR-1 RNA), the isolated RNA aptamer (RNA Tat ) was highly specific to Tat and did not interact with other cellular factors. Moreover, RNA Tat binds Tat protein over 100-fold higher than TAR-1 RNA and inhibited Tat function in vitro and in vivo [28,29]. Based on these results, RNA Tat was used in a preliminary study to develop a molecular beacon by flanking the 5′ and 3′ ends of the native aptamer stem-loop structure with a fluorophore and a quencher.
In the absence of Tat, the quencher and fluorophore remain close to each other by the formation of the stem producing no signal. When the loop interacts with Tat, the complexed structure becomes more stable resulting in strand separation, thus holding apart the fluorophore and quencher allowing fluorescence [30].
Two other biosensors have been developed using RNA aptamers specific to HIV-1 Tat. These biosensors were created by immobilizing a biotinylated aptamer on a streptavidin layer over quartz crystals included in surface plasmon resonance (SPR) chips [31]. Another approach used a diamond field-effect transistor (FET) technique to detect Tat protein by RNA aptamers. Aptamer-FET is based on a gate potential shift generated by the presence of HIV-1 Tat bound to the RNA aptamer on a solid diamond surface. Efficient detection showed a potential use for aptamer-FET in clinical applications [32].
HIV-1 Rev is essential to regulate the splicing and shuttle the viral mRNA through their nuclear and export localization signals. Also, Rev interacts with viral mRNA through a cis-acting Revbinding element (RBE) within a Rev-responsive element (RRE). RNA aptamers against Rev have been isolated using random libraries or by randomizing the RRE minimal binding sequence [33][34][35]. Randomized RRE produced aptamers with up to 16-fold tighter binding than the minimal wild-type RBE (wtRBE). The RBE was then substituted by these RNA aptamers and tested in vivo using a reporter system [35]. The aptamer substitutions showed a better response than wtRBE [36]. Another technique used to isolate RNA aptamers against Rev protein was the cross-linking SELEX consisting of a 5′-iodo uracil (5-IU)-modified RNA library, which is reactive under long-wavelength UV irradiation producing cross-links between 5-IU oligonucleotides and the protein target. This method resulted in the selection of highly specific aptamers capable of forming covalent bonds with HIV-1 Rev [37]. Some other efforts have focused on the inhibition of HIV-1 replication in human T cells using RNA aptamers as decoys to sequester Rev [38]. Rev decoys and ribozymes have been combined to increase the anti-HIV effect relative to independent ribozyme or decoy effects [39][40][41].
HIV-1 gp120 is a surface glycoprotein involved in the early stages of HIV-1 infection. The gp120 protein interacts with the human surface receptor CD4 producing conformational changes and further receptor interactions to allow HIV-1 entry into the host cell. Due to its importance on the onset of the viral infection, gp120 represents a potential target for the isolation of aptamers to block HIV-1 entry. Several RNA aptamers have been isolated to block gp120 and CD4 interaction neutralizing diverse subtypes of the virus [42]. Characterization of aptamer B40 showed high specificity to HIV-1 R5 strain and neutralization in human peripheral blood mononuclear cells [43,44]. Additional analyses produced a shorter synthetic B40 derivative (UCLA1) able to inhibit entry of HIV-1 at the nanomolar range. Moreover, the aptamer showed synergistic effects with a gp41 fusion inhibitor (T20) and anti-CD4 binding site monoclonal antibody (IgG1b12), suggesting a potential use as adjuvant [45].
2′-Fluoride (2′-F) modified RNA aptamers selected to bind HIV-1 Bal gp120 and specifically internalized by cells expressing HIV-1 Bal gp120 were used to deliver anti-HIV siRNA into HIV-1-infected cells [46]. Two aptamer-siRNA chimeras were used: one covalent chimera presented a 2′-F-modified gp120 aptamer covalently attached to the sense strand of tat/rev siRNA and reduced the plasma viral load in a RAG-hu mouse model by suppressing HIV-1 replication and preventing CD4+ T cell decline. This effect was extended by several weeks beyond the last dose [47]. The second chimera consisted of a single aptamer with three different siRNAs targeting viral and cellular transcripts. The siRNA was linked to the aptamer by a bridge sequence of 16 nt that allowed complementary base pairing of one of the two siRNA strands to the aptamer. The aptamer-siRNA chimera showed a potent suppression of HIV-1 and protection from viral CD4+ T-cell depletion in vivo. In addition, the inhibitory effects were also extended several weeks after the last injection, providing an attractive therapeutic approach to HIV-1 therapy [48].

Aptamers against HPV
HPVs are small DNA viruses that infect squamous epithelia inducing proliferative lesions ranging from benign warts to cancer. High-grade papillomavirus, especially types 16 and 18 (HPV-16 and HPV-18) are associated with cervical carcinoma, the second most common cancer affecting women worldwide. HPVs have a circular double-stranded DNA genome of approximately 8 kb that is organized into three regions: the upstream regulatory region (URR), the early region (E) and the late region (L). The URR contains several transcription factor binding sites to control gene expression, the early region encodes six genes (E1, E2, E4, E5, E6 and E7) involved in viral replication, transcription and cell transformation and the late region encodes the L1 and L2 capsid proteins which self-assemble to produce the virion [49].
Because preventive vaccines for HPV infection are only protective for naive individuals [50], several research groups have been developing nucleic acid-based aptamers targeting HPV proteins in order to inhibit the oncoproteins activity, block viral infection or identify the absence/presence of viral proteins as biomarkers to determine cell transformation or cancer progression (   Table 2. Aptamers isolated against HPV proteins.
The oncoproteins E6 and E7 are involved in cell immortalization and malignant transformation. E6 promotes the degradation of the tumor suppressor p53 [51], and E7 binds and destabilizes the cell cycle control protein pRb [52]. E6 and E7 have an important role in cancer progression, situating these oncoproteins as the principal potential targets to bind aptamers to block their oncogenic activity and cancer progression.
Several RNA aptamers were isolated against the PDZ-binding motif of the HPV-16 E6 oncoprotein, two of them were able to inhibit the interaction between E6 and proteins with PDZ domain (Magi 1) resulting in apoptosis. The aptamer interaction with PDZ domain was very specific and the interaction between E6 and p53 was not affected [53]. The same research group also isolated RNA aptamers against E7 oncoprotein that were able to disturb the E7-pRb interaction by targeting E7 for degradation and showed that one of them (A2) was able to inhibit cellular proliferation by inducing apoptosis in SiHa cervical carcinoma cells [54]. This effect was specific to HPV-16 transformed cells because it was not observed in HPV-free or HPV-18 cell lines [55]. Specific apoptosis induction of RNA aptamers targeting E6 and E7 oncoproteins suggests that these aptamers could have further applications in the future as therapeutic moieties.
A deeply characterized RNA aptamer targeting HPV-16 E7 oncoprotein named G5α3N.4 interacts with E7 through two stem-loop motifs in a clamp-like manner, suggesting a change in aptamer structure due to protein contact. The complex formation was observed exclusively in HPV-positive cervical carcinoma cells, suggesting that G5α3N.4 could be used to detect HPV infection and cervical cancer [56,57].
The L1 protein is the main component of the HPV capsid. It is arranged in 72 capsomers, each consisting of five 55-kDa L1 monomers and a single 74-kDa L2 unit (theoretical 5:1 ratio). The L1 protein can self-assemble, forming virus-like particles (VLPs) that are structurally and immunologically similar to the infectious virions. HPV-16 L1 VLPs have been broadly used in HPV virology research, as delivery agents for epitopes or genes and to successfully produce prophylactic vaccines against HPV infection. The first RNA aptamer, targeting the L1 protein (Sc5-c3), was obtained using HPV-16 VLPs as targets [58]. Sc5-c3 structure consists of a hairpin structure with a 16-nt loop that directly binds VLPs with very low K D (0.05 pM). This aptamer was able to specifically bind VLPs in complex protein mixtures (murine cervical washes), suggesting that Sc5-c3 may provide a potential diagnostic tool for active HPV infections and, with further refinement, could be used as a potential tool to inhibit viral infection [58].
Nucleic acid-based aptamers have been also isolated against whole HPV-infected cells. A cellbased SELEX protocol (cell-SELEX), was used to isolate RNA aptamers able to internalize into HPV-16 E6/E7 transformed human tonsillar epithelial cells (HTEC). This was the first report of aptamers that specifically internalize into HPV-16-transformed cells, providing a plausible mechanism to specifically deliver therapeutic agents into HPV-16-associated tumors [59]. Moreover, DNA aptamers have been isolated by a cell-SELEX modification for use with adherent cells (AC-SELEX). These aptamers recognize cell surface differences between HPVtransformed and nontumorigenic cell lines and one of them (Aptamer 14) was able to enter the cells independent of cell surface protein binding. These selected aptamers have potential to elucidate biomarkers for cellular changes associated to nontumorigenic phenotype in HPVinfected cells [60].

Aptamers against influenza virus
Influenza viruses are associated with most flu pandemics. They are enveloped RNA viruses of 80 to 120 nm diameter that infect the upper respiratory tract. The disease severity depends on the virus type: A, B or C. Influenza A virus infects birds and mammals, influenza B targets mainly humans and influenza C is less common than A or B but it also causes disease. Although the three virus types infect different hosts, it has been reported that all of them can infect humans and thus they have been the subject of several SELEX protocols.

Influenza A Virus (IAV)
The IAV genome comprises eight segments of linear RNA and two surface glycoproteins: hemagglutinin (HA) and neuraminidase (NA). These proteins are used to classify the IAV subtypes. Seventeen HA (H1-H17) and nine NA (N1-N9) variants have been identified and implicated on viral attachment, membrane fusion and viral entry to the host cell. Many aptamers have been isolated to bind HA and NA in order to inhibit and detect the viral infection, mainly H5N1, H9N2, H1N1 and H3N2 subtypes (Table 3).   Table 3. Aptamers against human IAV proteins.
Two DNA aptamers, A21 and A22, were isolated against an HA peptide containing amino acid positions 91-261. A22 was the most efficient aptamer to inhibit viral infection in vivo and in vitro by blocking the cellular receptor from binding HA. Moreover, A22 showed high binding activity against different IAV strains (H3N2 and H2N2) and reduced virus burden by 90%-99% in mice [61]. Further studies using the whole virus demonstrated the ability of RNA aptamers to distinguish between related strains within the H3N2 subtype of influenza type A viruses [62]. The selected aptamer P30-10-16 was able to discriminate between A/Panama/ 2007/1999 and A/Aichi/2/1968 H3N2 subtypes and its binding affinity to HA was even 15-fold higher compared with a monoclonal antibody specific to HA. A consensus aptamer sequence (5′-GUCGNCNU(N) 23 GUA-3′) was selected by surface plasmon resonance (SPR) using an RNA pool based on randomized P30-10-16 (doped RNA pool). The GNCNU sequence was identified as the minimal element required to bind HA [63], suggesting a potential use as tools for influenza virus genotyping.
An aptamer selected against H5N1 HA (A10), showed inhibition of receptor binding producing in vitro inhibition of viral infection [64]. To increase the specificity, some aptamers were isolated using recombinant HA in the initial selection cycles and then the whole inactivated H5N1 virus for further selection cycles. The selected aptamers were able to discriminate among H5N2, H5N3, H5N9, H9N2 and H7N2, showing better specificity than anti-H5N1 monoclonal antibodies [65]. These aptamers were used on quartz crystal microbalance (QCM) biosensors coated with hydrogel. The hydrogel consisted of cross-linked hybridized ssDNA and aptamer.
In the presence of H5N1 the hybridization is disturbed producing hydrogel swelling which is detected by a QCM sensor [66].
Although some aptamers have been isolated to bind a specific IVA subtype, some others identify more than one virus subtype. A 113-nt-long RNA aptamer (8-3) was isolated against HAs from H5-N1 and H7N7. The full 8-3 and shortened version called 8-3S aptamer were able to bind HA with high affinity and interfere with the cell surface HA-glycan interaction, suggesting a potential application in diagnosis and interference of virus-host interactions [67]. Furthermore, DNA aptamers were selected against recombinant hemagglutinin (rHa) to detect different subtypes of IVA such as H5N1, H1N1 and H3N2. The selected DNA aptamers: RHA0006, RH0385 and RHA1635 were able to successfully bind the three mentioned IVA subtypes. RHA0006 and RH0385 were also used in a sandwich enzyme-linked aptamer assay (ELAA), developing a novel, rapid and cost-effective diagnostic tool to identify various IVA subtypes [68].

Influenza B Virus (IVB)
Some aptamers have been isolated against whole virus or purified proteins in order to discriminate IVB from IVA (Table 4). An RNA aptamer against HA B/Johannesburg/05/1999 virus was able to discriminate between the HA from different strains and prevented viral infection by membrane fusion inhibition [62]. Two aptamers have been selected against intact HA of influenza strains B/Tokyo/S3/99 and Jilin/20/2003. The sensitivity of Tokyo aptamer was approximately 250-fold higher than a commercial antibody, demonstrating its potential to detect influenza viruses [69].  Table 4. Aptamers against IBV.

Aptamers against HCV
HCV is one of the causes of chronic liver disease associated with end-stage cirrhosis and hepatocellular carcinoma. HCV are small enveloped viruses with a linear single-stranded RNA + genome containing a single ORF encoding a polyprotein flanked by untranslated regions (UTR) and processed into three structural proteins (C, E1 and E2) and seven nonstructural proteins (p7, NS2, NS3, NS4A, NS4B, NS5A and NS5B). The 5′-UTR contains an internal ribosomal entry site (IRES) important for mediated translation by association with the host cell small ribosomal unit (40S). Due to its importance in viral infection, replication and proliferation, HCV aptamers have been mainly isolated against NS3, NS5 proteins and some IRES domains (Table 5).  Table 5. Aptamers isolated against HCV proteins.
NS3 has a trypsin-like serine protease and NTPase/helicase activity [70,71]. NS3 is required for proteolytic processing of nonstructural proteins [72]. The HCV protease domain disrupts the interferon (IFN) and toll-like receptor-3 (TLR3) signaling pathways by cleaving the caspase recruitment domain of mitochondrial antiviral signaling protein (MAVS) and the TIR domain containing an adapter-inducing interferon-β sequence (TRIF) [73]. As NS3 activity is crucial for viral replication, many aptamers have been isolated against NS3.
The 10G-1 RNA aptamer was selected against NS3 protease domain using a 12-18 nt randomized library and can reduce protease activity by 20% compared with serine protease inhibitors [74]. However, a new SELEX protocol was used to select anti-NS3 aptamers with improved binding and inhibition activities increasing the structural pool complexity by using larger randomized domains (120 nt) and competition against 10G-1. As a result, two new RNA aptamers were selected (G6- 16 and G6-19) showing efficient NS3 binding. The G6-16 concentration needed to inhibit 50% of the NS3 activity was 3 µM, and although both aptamers inhibited the protease and helicase activity, they showed lower efficacy compared with known serine protease inhibitors [75]. To further improve aptamer efficacy and inhibit the NS3 RNA binding helicase, a truncated form (∆NS3) only including the protease domain was used as a target, and the random sequence of the RNA pool was reduced to 30 nt to ease the SELEX process. Three highly specific aptamers (G9-I, G9-II and G9-III) were obtained against ∆NS3 containing the conserved sequence GA(A/U)UGGGAC that was present inside an identical loop in all aptamer structures. These aptamers showed K D values of 11.6 nM, 6.3 nM and 8.9 nm, respectively. The G9 aptamers produced 90% of NS3 protease activity inhibition alone and 70% in the presence of NS4A used to simulate physiological conditions [76]. Structure analyses suggested that interaction of stem I and stem-loop II is essential to G9-I aptamer NS3 binding. To achieve in vivo applications, the G9-II aptamer was conjugated with cis-acting genomic human hepatitis delta virus (HDV) ribozymes. The aptamer was inserted into the nonfunctional stem IV region of the HDV ribozyme promoting in vivo stable structure that lasted up to 4 days after transfection. The HDV ribozyme-G9-II aptamer (HA) was attached to nuclear export signal CTEM45 (HAC) and ligated in tandem to increase the aptamers dosage in cells. These new constructs showed efficient NS3 protease inhibition in vivo and in vitro [77].
To also inhibit NS3 helicase activity, a poly U tail (14U) was added to the minimum functional sequence of the G9-I aptamer (∆NEOIII) to mask and inhibit the helicase substrate-binding region [78]. NEOIII-14U displayed dual functions by inhibiting NS3 protease activity in vivo and in vitro and inhibiting the NS3 unwinding helicase reaction (IC 50 1 µM) [79].
More RNA aptamers were selected against NS3 helicase domain, including the conserved sequence GGA(U/C)GGAGCC at stem-loop regions. Further deletion and mutagenesis analyses demonstrated that the whole structure of the conserved stem-loop is needed for helicase inhibition. Aptamer #5 presented the best inhibition of helicase in vitro activity with an IC 50 of 50 nM [77]. Bifunctional aptamers constructed conjugating RNA aptamers ∆NEOIII and G9-II with aptamer #5 through an oligo U spacer. The spacer length was optimized by protease and helicase inhibition assays [80]. The resulting advanced dual-functional (ADD) aptamers (NEO-34-s41 and G925-s50) showed superior inhibitory activities of NS3 [81].
NS5B is an RNA-dependent RNA polymerase that synthesizes the HCV-negative strand RNA using genomic positive RNA strand as a template. NS5B has an essential role in the HCV's life cycle and its variability has been associated with worse disease prognosis [82]. The highly specific B.2 RNA aptamer selected against a truncated NS5B target (NS5B∆C55) presented a conserved sequence that was folded on stem loop structure associated with a tight interaction to NS5B (K D = 1.5 ± 0.2 nM). Also, B.2 demonstrated inhibition of NS5B activity by a noncompetitive mechanism [9].
Two DNA aptamers selected against NS5B (27v and 127v) showed inhibition of polymerase activity in vitro. Although both aptamers were isolated from the same SELEX procedure and presented an 11-nt conserved sequence, they displayed different mechanisms to inhibit NS5B. The 27v aptamer competed with RNA template and inhibited both initiation and elongation of RNA synthesis, while 127v competed poorly and just inhibited initiation. Also, 27v was able to inhibit RNA synthesis and HCV particles production on Huh7 cells [83,84]. The RNA aptamers r10/43 and r10/47 were isolated against NSB5 of HVC subtype 3a and resulted in the inhibition of polymerase activity with an estimated K D = 1.4 and 6.0 nM, respectively [85].
In a different approach, chemically modified RNA aptamers (2′-hydroxyl or 2′-fluoropyrimidine) were isolated against NS5B. The 2′-hydroxyl aptamer inhibited HCV replication on human liver cells without producing off-target effects or generation of escape mutants. The 2′fluoropyrimidine aptamer showed increased affinity to NS5B and efficient inhibition of HCV replication in cultured cells. This last aptamer was further conjugated with cholesterol or galactose-polyethylene glycol ligand to increase its availability and specificity for the liver inhibiting replication of HCV genotype 1b and 2a [86].
Two other RNA aptamers targeting NS5A (NS5A-4 and NS5A-5) reduced the levels of intracellular infectious virions and viral RNAs by 3-fold and 1-fold, respectively, affecting virus assembly and release through prevention of the NS5A-core protein interaction. These NS5A aptamers were specific to HCV without affecting HBV replication and produced cytotoxicity in human hepatocytes [87].
NS2 contains a transmembrane segment in the N-terminal and a cytoplasmic region in the C-terminal domain. Although NS2 is essential for HCV RNA replication, its role in HCV's life cycle is still unknown. Aptamers NS2-2 and NS2-3 were isolated against NS2 and demonstrated reduced infectious virus production without in vitro cytotoxicity. These aptamers were specific to HCV and did not trigger innate immunity responses. NS2-2 aptamer produces its antiviral effects through binding the NS2 N-terminus thus disrupting NS2-NS5 interaction [88].
E2 is an enveloped glycoprotein implicated on initial steps of viral infection by the direct interaction with CD81. Through cell surface SELEX (CS-SELEX), specific DNA aptamers were isolated against E2 expressed on CT26 cells. Aptamer ZE2 showed the highest affinity and specificity to E2 and was able to detect HCV particles and block HCV infection on human cultured hepatocytes by CD81 binding inhibition [89]. A similar inhibition mechanism was observed on the DNA aptamer E1E2-6, which inhibited viral infection by blocking host cell binding [90]. A new system developed to quantify immobilized infectious HCV particles in microplates (so-called enzyme linked apto-sorbent assay or ELASA) used aptamers against E2 instead of antibodies and resulted in an effective and easy-to-use tool to quantify infectious units of HCV and to monitor anti-HCV drug efficacies [91].

Aptamer structures
Nucleic acid aptamers have a diverse range of secondary structures such as stems, loops, symmetric or asymmetric internal loops, bulge, single-base bulges and junctions. Aptamer internal loops and bulges generally present different conformations in solution and adopt defined secondary and tertiary structures on ligand-aptamer complex [92]. This effect was observed on aptamer Sc5-c3 selected against HPV-16 VLPs. Sc5-c3 showed a hairpin structure with an internal loop, where the main loop (ML) presented two different structures in the absence of a target (Table 2). Sc5-c3 transition structure was demonstrated by ribonuclease mapping. Further experiments using Sc5-c3 mutants generated both stable stem and stable loop conformations, demonstrating that the loop structure binds better to the VLPs [58]. Thus, as observed in several aptamers, the binding region remains as a flexible single strand as bulges or loops stabilize conformation arrangements in the presence of a target, producing a very specific binding.
Although bulges and loops are quite common target-binding motifs in aptamer RNAs, they are not the only structures present in aptamer-target complexes. Pseudoknots and G-quadruplexes have also been reported as functional components of aptamers [93]. For example, some of the aptamers isolated against HIV integrase (93 del and 112 del) presented a G-rich nucleic acid sequence that was stabilized in the presence of K+ as G-tetrad, increasing their inhibitory effect [25]. Later reports showed that 93 del adopts an unusually stable dimeric quadruplex structure [94].
The binding properties of an aptamer are dictated by its sequence and subsequent folding into secondary and tertiary structures. Recently, functional RNA structures were classified as critical, connecting, neutral and forbidden structures regarding their particular roles within a structure [95]. This classification is also applicable to nucleic acid aptamers and is an important clue to design novel and functional variants for viral detection or therapy.

Challenges for aptamer technology
According to their molecular characteristics, RNA or DNA aptamers have some limitations in their use in animal models and humans. They have limited stability in biological fluids and are readily degraded by nucleases, unmodified aptamers in the bloodstream possess a halflife time of less than two minutes. However, many post-SELEX modifications have been developed to avoid nuclease attack and improve stability in biological fluids. Some modification examples include nucleotide substitutions by 2′-modified variants such as 2′-fluoro (2′-F), 2′-amino (2′-NH2) or 2′-O-alkyl. Because the most abundant nucleases in biological fluids are specific to pyrimidines, substitutions in pyrimidine positions appear to be sufficient to prevent degradation. Another method to stabilize RNA aptamers is the substitution of Dribose by L-ribose. As a first step, the aptamers bind the mirror image of the target molecule to obtain a D-aptamer, then the selected aptamer sequence is synthesized in L-conformation. As a result of molecular symmetry, the L-ribose-containing aptamer can bind to the target molecule avoiding degradation by D-ribose-specific nucleases. Moreover, to efficiently overcome binding issues produced by the introduction of modified nucleotides on the aptamer sequence, the SELEX procedure can be carried out in the presence of modified libraries.
Therapeutic aptamers selected against intracellular or nuclear proteins represent bigger challenges as they need to go across physiological barriers (i.e. cell membrane) before they reach their targets. DNA and RNA aptamers are characterized by rapid renal clearance leading to short half-lives in the bloodstream. To address this issue, aptamers can be conjugated to synthetic polymers such as polyethylene glycol (PEG) to increase their in vivo half-life and pharmacodynamics [96]. Additionally, PEG-conjugated aptamers show higher cellular uptake than the unconjugated form [96]. Alternatively, delivery systems such as viral and nonviral vectors may have improved aptamer cell uptake and nuclear distribution [97]. Vectors or aptamers alone can be delivered either ex vivo or in vivo. In vivo approaches include intravenous injection or local implantation and ex vivo refer to the removal of cells followed by in vitro genetic manipulation and the reintroduction of modified cells. These therapies are still under evaluation and further studies are necessary to demonstrate their clinical safety. Aptamers against extracellular or surface viral targets have obvious advantages over aptamers targeting viral proteins intracellularly expressed, as they can reach exposed areas of infection, such as the respiratory tract or reproductive organs. This availability makes it possible to develop new antiviral drugs administrated by noninvasive methods, such as aerosols in case of respiratory tract infection or topical creams/lotions in case of reproductive organ infections. Many aptamers are undergoing clinical trials, some of them administrated by noninvasive methods but, so far, no antiviral aptamer has been approved for human use [18,98].

Conclusion
In the last few years, aptamers have become successful tools for specific viral diagnosis and genotyping, resulting in the development of many methods based on aptamer-target detection with very high sensibility and accuracy. On the other hand, aptamer's role as an antiviral drug has demonstrated the inhibition of viral infection through in vitro assays and in vivo experiments using cell lines or animal models. Nevertheless, most aptamers failed to produce results in clinical trials mostly due to nuclease-associated degradation. Therefore, further development of aptamer's stability in biofluids and improved pharmacodynamics and delivery methods are required to overcome clinical issues that would allow its successful therapeutic application.

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