This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text (HTML) versions will be made available soon. A highly expressed miR-101 isomiR is a functional silencing small RNA BMC Genomics 2013, 14:104 doi:10.1186/1471-2164-14-104 Franc Llorens (franc.llorens@gmail.com) M?nica Ba?ez-Coronel (monica.banez@crg.eu) Lorena Pantano (lorena.pantano@gmail.com) Jose Antonio del R?o (jadelrio@ibecbarcelona.eu) Isidre Ferrer (8082ifa@gmail.com) Xavier Estivill (xavier.estivill@crg.cat) Eul?lia Mart? (eulalia.marti@crg.cat) ISSN 1471-2164 Article type Research article Submission date 15 August 2012 Acceptance date 30 January 2013 Publication date 15 February 2013 Article URL http://www.biomedcentral.com/1471-2164/14/104 Like all articles in BMC journals, this peer-reviewed article can be downloaded, printed and distributed freely for any purposes (see copyright notice below). Articles in BMC journals are listed in PubMed and archived at PubMed Central. For information about publishing your research in BMC journals or any BioMed Central journal, go to http://www.biomedcentral.com/info/authors/ BMC Genomics ? 2013 Llorens et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. A highly expressed miR-101 isomiR is a functional silencing small RNA Franc Llorens1,2,3 Email: franc.llorens@gmail.com M?nica Ba?ez-Coronel4,5,6 Email: monica.banez@crg.eu Lorena Pantano7 Email: lorena.pantano@gmail.com Jose Antonio del R?o1,2,3 Email: jadelrio@ibecbarcelona.eu Isidre Ferrer3,8 Email: 8082ifa@gmail.com Xavier Estivill4,5,6,* Email: xavier.estivill@crg.cat Eul?lia Mart?4,5,6,* Email: eulalia.marti@crg.cat 1 Molecular and Cellular Neurobiotechnology Group, Institut de Bioenginyeria de Catalunya (IBEC), Parc Cient?fic de Barcelona, Barcelona, Spain 2 Department of Cell Biology, University of Barcelona (UB), Barcelona, Spain 3 Network Biomedical Research Center for Neurodegenerative Diseases (CIBERNED), Barcelona, Spain 4 Genetic Causes of Disease Group, Genes and Disease Program, Centre for Genomic Regulation (CRG) and UPF, Barcelona, Spain 5 Universitat Pompeu Fabra (UPF), Barcelona, Spain 6 Centro de Investigaci?n Biom?dica en Red de Epidemiolog?a y Salud P?blica, (CIBERESP), Barcelona, Spain 7 Institut de Medicina Predictiva i Personalitzada del C?ncer, Badalona, Spain 8 Institut Neuropatologia, Servei Anatomia Patol?gica, IDIBELL-Hospital Universitari de Bellvitge, Universitat de Barcelona, Barcelona, Spain * Corresponding author. Centro de Investigaci?n Biom?dica en Red de Epidemiolog?a y Salud P?blica, (CIBERESP), Barcelona, Spain Abstract Background MicroRNAs (miRNAs) are short non-coding regulatory RNAs that control gene expression usually producing translational repression and gene silencing. High-throughput sequencing technologies have revealed heterogeneity at length and sequence level for the majority of mature miRNAs (IsomiRs). Most isomiRs can be explained by variability in either Dicer1 or Drosha cleavage during miRNA biogenesis at 5? or 3? of the miRNA (trimming variants). Although isomiRs have been described in different tissues and organisms, their functional validation as modulators of gene expression remains elusive. Here we have characterized the expression and function of a highly abundant miR-101 5?-trimming variant (5?-isomiR-101). Results The analysis of small RNA sequencing data in several human tissues and cell lines indicates that 5?-isomiR-101 is ubiquitously detected and a highly abundant, especially in the brain. 5?- isomiR-101 was found in Ago-2 immunocomplexes and complementary approaches showed that 5?-isomiR-101 interacted with different members of the silencing (RISC) complex. In addition, 5?-isomiR-101 decreased the expression of five validated miR-101 targets, suggesting that it is a functional variant. Both the binding to RISC members and the degree of silencing were less efficient for 5?-isomiR-101 compared with miR-101. For some targets, both miR-101 and 5?-isomiR-101 significantly decreased protein expression with no changes in the respective mRNA levels. Although a high number of overlapping predicted targets suggest similar targeted biological pathways, a correlation analysis of the expression profiles of miR-101 variants and predicted mRNA targets in human brains at different ages, suggest specific functions for miR-101- and 5?-isomiR-101. Conclusions These results suggest that isomiRs are functional variants and further indicate that for a given miRNA, the different isomiRs may contribute to the overall effect as quantitative and qualitative fine-tuners of gene expression. Keywords MicroRNA, miR-101, IsomiR, Ultra-sequencing, Deep-sequencing Background miRNAs are small non-coding RNAs that generally act as negative post-transcriptional regulators of gene expression. It is estimated that miRNAs regulate at least 20% of human genes [1], constituting an important layer of regulation in gene expression networks. The influence of a particular miRNA on the transcriptome is difficult to dissect, since genes can harbor binding sites for several miRNAs, and a specific miRNA can modulate the expression of hundreds of mRNA targets [2]. miRNAs are involved in almost every biological process examined and their deregulation has been associated with a number of pathological conditions, including cancer and neurological disorders [3-6]. Mature miRNAs function as components of the RNA-Induced Silencing Complex (RISC) [7], guiding the RISC to specific gene targets through base-pairing interactions between the miRNA and the mRNA. Members of the Argonaute (Ago) protein family are central to RISC function. The seed region extends from the second to the seventh or eighth position of mature miRNA and has traditionally been considered as a major determinant of the target mRNA repertoire, through perfect base pairing with the 3?-untranslated regions (3?-UTR) of mRNAs [8]. Other structures of the 3?-end of the miRNA are also involved in duplex stability, providing a supplementary site if the seed is fully paired or a compensatory site when the 5?- end has mismatches or bugles [9,10]. However, our knowledge of the determinants governing gene targeting is far from complete. In fact, recent findings show that targeting can occur through sites other than the 3?-UTR, and that seed region perfect base pairing is not always required [11-14]. This miRNA/mRNA binding leads either to target mRNA degradation or to translational inhibition, depending, among other factors, on the extent of base-pairing and the type of RISC/Ago proteins [15]. A novel degree of complexity for miRNA transcriptome has been highlighted using next generation sequencing (NGS) strategies. NGS has revealed post-transcriptional editing processes in miRNAs, mainly consisting in variations in the 3?- and 5?-terminus and to a minor extent, nucleotide substitutions along the miRNA sequence [16-19]. Variations at the miRNA ends may be generated by a shift in Drosha and Dicer cleavage sites during miRNA biogenesis (trimming variants) [19,20]. Another type of variability is related to pri-miRNA post-transcriptional editing as a consequence of adenosine or cytidine deaminase activity (nucleotide substitution variants). Finally, nucleotide additions at the 3?-end of the miRNA are another source of miRNA variability [21-23]. The resulting sequences differ slightly from the annotated miRNA and have been termed isomiRs [19]. Several lines of evidence suggest that isomiRs are not experimental artifacts derived from RNA degradation during sample preparation for NGS [24,25]. In fact the frequencies of nucleotide substitutions are very diferent from sequencing error rates; and the detection of isomiRs in many species, tissues and physiological conditions [26,27], suggests that they may have a biological role. This notion is further supported by their differential expression across developmental stages [27,28]. This scenario suggests that, for a given miRNA, posttranscriptional gene expression may depend on different isomiRs. However, their functionality as posttranscriptional gene expression regulators needs to be proven. Our recent studies of miRNA profiling in human brain samples have shown a multitude of miRNA variants [16]. miR-101 is an interesting example, since it presents 5?-trimming isomiRs (5?- isomiR-101) expressed at levels similar to those of the reference sequence. Here, we characterize the ability of this highly expressed human isomiR-101 to work as a functional miRNA. Results miR-101 and miR-101 5?-isomiRs are expressed at different proportions in different human samples We have shown that the vast majority of miRNAs in human frontal cortex and striatum display sequence variants or isomiRs [16]. Several factors may modulate the physiological relevance of an isomiR, including i) the amount of the isomiR in relation to the rest of sequences mapping onto the same miRNA locus and ii) the type of variant, with special emphasis on nucleotide changes affecting the seed region. Sequencing data analysis of these brain areas showed that miR-101 was an abundant miRNA, represented by approximately 100 unique isomiRs (> 10 counts per sequence), although deeper sequencing performed in other brain samples identified more variants (Figure 1, Additional file 1 Table S1). The different miR-101 isomiRs consisted in trimming variants, nucleotide additions at the 3? end of the miRNA and nucleotide substitutions at different positions along the mature miRNA (Figure 1, Additional file 1 Table S1, Figure 2A). The distribution of the different types of miR-101 isomiRs was confirmed in the prefrontal cortex of human brains at different ages in an independent sequencing experiment [26] (Figure 2A). Trimming variants affecting the 5? end of the miRNA (5?-isomiRs) were among the more abundant species. In fact, the vast majority of miR-101 5?-isomiRs affected a single nucleotide upstream of the reference miR- 101, therefore defining a new seed region. Thus, taking into consideration all the sequences mapping onto miR-101, two main types of seeds were found: ACAGUAC, which is reported in the miRBase, and UACAGUA, corresponding to the vast majority of miR-101 5?-isomiRs. We examined the presence of miR-101 5?-isomiRs in another brain area (amygdala) from four individuals without major histopathological lesions and no clinical neuropathology and in peripheral blood from four healthy individuals, and further analyzed several public high- throughput sequencing datasets from the GEO repository corresponding to human brains at different developmental stages [26] and different human cell lines [29] (Figure 2B). This analysis indicated that miR-101 5?-isomiRs are expressed at high proportions in different human cells, thus suggesting a physiological role for these variants. Furthermore, the proportion of each of the major seed regions differs according to the cell type, which suggests a differential modulation of the biogenesis and/or stability of the different miR-101 variants. Figure 1 miR-101 variants in the human frontal cortex. The different sequences annotating to miR-101, the respective frequencies and lengths are shown. For the 5? and 3? trimming variants, the number of nucleotides upstream (up) or downstream (down) of the reference miRBase miR-101 (highlighted in blue), are shown. The nucleotides involved in the 5?- and 3? variant are next indicated. In 3?-addition variants, the number and type of nucleotides added to the 3?-ends are shown. In the nucleotide substitution variants, the affected position is first indicated; the pair of nucleotides that follow next indicates the substituted and the original nucleotides, respectively. Only variants with more than 10 counts are shown. The more abundant sequences mapping onto miR-101 locus that represent the reference miR-101 sequence (in blue) and the 5?-isomiR-101 (in red) contained in a grey box are the chosen sequences for functional assays in transfection. Figure 2 miR-101 and 5?-isomiR-101 are abundant in different cells and tissues. A. Relative frequencies of miR-101 isomiRs in human brain samples. The percentage of 5?- and 3?-trimming isomiRs, 3? nucleotide additions and nucleotide substitutions was determined in sRNA sequencing data sets of the frontal cortex (FC) and striatum (ST) of a control individual [16] and prefrontal cortex (pFC, 98 years old and pFC, 2 days old) [26]. The summed frequency of the total of sequences mapping onto miR-101 was considered as the 100 %. B. Percentage of each of the two main seed regions in human brain (4 individuals) and blood (4 individuals), and several human cell lines [29]. A scheme is included showing miR-101 precursor and mature forms. The mature miR-101 is highlighted in red. The arrows point to the major cleavage sites giving rise to the most abundant mature miR-101 variants. The seed regions in each variant are contained in a blue box. C. Abundance of miR-101 in human blood and brain samples and in cell lines. These corresponded to amygdala (am) of 4 individuals frontal cortex (fc) of two individuals aging 25 and 66 years [26] and different types of cells [29]. In each sample, the percentage of sequences mapping onto miR-101 is calculated with respect to the total of sequences mapping onto miRNA database. D. miR-101 and 5?-isomiR-101 expression in the frontal cortex and the striatum of control individuals (C) and patients with HD [16]. E. miR-101 and 5?-isomiR-101 expression in undifferentiated and differentiated SH-SY5Y cells. F. miR-101 and 5?-isomiR-101 expression in the frontal cortex of individuals at different ages [26]. In D, E, F normalized counts are expressed as the ratio: (frequency of sequences presenting miR-101 or 5?isomiR-101 seed regions) / (frequency of sequences mapping onto miRNAs) * 10E6. When more than a biological replica is available (B and C) data are presented as the mean ? standard deviation. SH: SH-SY5Y human neuroblastoma cell line, Fibro: Fibrocytes, HEK: human embryonic kidney 293 cells, 143B: human bone osteosarcoma 143B cell line, H520: human squamous cell carcinoma H520 cell line, MCF7: human breast cancer MCF7 cell line, U2S: human osteosarcoma U2S cell line, HeLa: human cervical cancer HeLa cell line. Considering all the sequencing datasets examined, the percentage of sequences mapping onto miR-101 varied between tissues and cell lines, with the brain presenting the highest levels (Figure 2C). These data suggest that high miR-101 levels may be important for adult brain physiology. In fact, miR-101 expression is regulated under a number of physiological and pathological conditions, including angiogenesis [30], tumors [31,32] and neurodegeneration [16,33,34]. In the last condition, our earlier study identified miR-101 as an up-regulated gene in HD [16]. However, that study was an overall analysis of all the sequences mapping onto miR-101. To discern the specific expression pattern of miR-101 5?-isomiRs, we evaluated the expression of miR-101 variants harboring each of the two main seeds in control brains and brains of patients with HD (Figure 2D). Both types of variants were up-regulated in the frontal cortex and striatum of HD patients. In addition, we performed a similar analysis in other physiological conditions of the nervous system, including in vitro differentiation of the SH-SY5Y neuronal cell line to a postmitotic dopaminergic phenotype (Figure 2E) and early postnatal development and ageing of the human brain [26] (Figure 2F). Interestingly, the relative amounts of both types of miR-101 variants in the brain differed according to age. These data suggest that miR-101 is modulated in the development and differentiation processes of the nervous system. Furthermore, the context-dependent variations in the relative amounts of miR-101 and 5?-isomiR-101 suggest that the mechanisms regulating their biogenesis and/or stability may differ depending on the biological process. miR-101 and 5?-isomiR-101 interact with Ago2 and Rck/p54 Functional studies aiming to elucidate the possible relevance of the more abundant miR-101 isomiRs in the nervous system were performed in the neuronal cell line SH-SY5Y. We used miRNA mimics for two abundant representative sequences containing ACAGUAC or UACAGUA seeds: The miRBase reference miR-101 UACAGUACUGUGAUAACUGAA, and one of the most abundant miR-101 5?-isomiRs presenting the same length as the reference miR-101 GUACAGUACUGUGAUAACUGA (Figure 1). We used highly specific LNA? RT-qPCR assay (Exiqon?) for selective detection of miR- 101 or 5?-isomiR-101. To test the specificity of the detection, we transfected equivalent amounts of either miR-101 or 5?-isomiR-101 mature mIRIDIAN-mimics or a control sequence into SH-SY5Y cells and determined the amount of miR-101 and 5?-isomiR-101 using the corresponding assays (Figure 3A). Fold changes are expressed relative to the cells transfected with the control sequence. The miR-101 RT-qPCR specific assay clearly detected miR-101 while 5?-isomiR-101 was very weakly amplified (Figure 3A, left bars). A complementary result was obtained using the 5?-isomiR-101 RT-qPCR specific assay (Figure 3A, right bars), indicating that both assays are highly selective for each variant. Figure 3 miR-101 and 5?-isomiR-101 bind Ago2 and Rck/p54. A. Specificity of miR-101 and 5?-isomiR-101 detection. SH-SY5Y cells were transfected with a negative control sequence (siGLO Green) or the mIRIDIAN mimics for miR-101 (dark bars) or 5?-isomiR- 101 (light bars). Specific RT-qPCRs determinations for miR-101 and 5?-isomiR-101 were performed. Results are expressed as the Fold Change ratios (miridian mimic / control siGLO). B. Expression of exogenously transfected miR-101 or 5?-isomiR-101 in Ago2 immunocomplexes. SH-SY5Y cells were transfected with Siglo Green or the mIRIDIAN mimics for miR-101 or 5?-isomiR-101. Specific RT-qPCRs were performed in Ago2-FLAG immunoprecipitates.Results are expressed as the Fold Change ratios obtained from the FLAG antibody/Control antibody immunoprecipitates. Western-blot with anti-FLAG antibody shows the presence of Ago2-FLAG in the FLAG-IP and input, and the absence of Ago2- FLAG in the control IP. C. Detection of RISC components in miR-101 or 5?-isomiR-101 pull-down. SH-SY5Y cells stably expressing Ago2-FLAG were transfected with mIRIDIAN 3?-biotin-miR-101 or 3?-biotin-5?-isomiR-101. Biotin-miRNA complexes were pulled down with streptavidin beads and subsequently assayed for western blot analysis using Ago2 and Rck/p54 antibodies. The relative amount of Ago2 and Rck/p54 in 3?-biotin-5?-isomiR-101 versus 3?-biotin-miR-101 is shown in the middle panel. miRNA-101 transfected samples were used to normalize data and were assigned a value of 1. Left panel shows similar amounts of transfected 3?-biotin-miR-101 or 3?-biotin-5?-isomiR-101 determined in total cell extracts with the corresponding specific RT-qPCR assays. D. Expression of endogenous miR- 101 and 5?isomiR-101 in Ago2-immunocomplexes in the human brain. Ago2 complexes were immunoprecipitated from human frontal cortex homogenates and miR-101, 5?-isomiR- 101, miR-29a, U6 SNORD44 were determined by RT-qPCR. Results are expressed as the Fold Change ratios obtained from the Ago2 vs denatured Ago2 immunoprecipitates. Three independent experiments were performed in A-D. All data are expressed as the mean? SEM. Asterisks indicate statistical significance between miR-101 and 5?-isomiR-101 data : * (p ? 0.05), ** (p?0.01) using Mann?Whitney test. We then examined whether transfected miR-101 and 5?-isomiR-101 mIRIDIAN were incorporated into the RISC, using SH-SY5Y cells stably expressing Ago2-FLAG. We performed immunoprecipitation (IP) assays using anti-FLAG antibodies for Ago2-IP or control antibodies, and subsequently RNA in the IP was isolated. We detected a significant enrichment of either miR-101 or 5?-isomiR-101 in Ago2 IP. The amount of miR-101 or 5?- isomiR-101 in cells transfected with the corresponding miRNA mimics was similar (Figure 2A). However, increased amounts of miR-101 were detected in the Ago2 IP, compared with 5?-isomiR-101 (Figure 3B), suggesting that miR-101 was more efficiently loaded into RISC. The ability of miR-101 and 5?-isomiR-101 to bind to the RISC was further confirmed in complementary experiments designed to evaluate the presence of RISC members in transfected biotinylated miR-101 and 5?-isomiR-101 (Figure 3C). mIRIDIAN 3?-biotin-miR- 101 and 3?-biotin-5?-isomiR-101 were transfected in SH-SY5Y cells and then pulled down using streptavidin beads. The samples were blotted against Ago2 and Rck/p54 which represses translation and associates with the RISC complex in order to regulate miRNA mediated gene silencing [35]. Both biotin-miR-101 and biotin-5?-isomiR-101 were able to interact with Ago2 and Rck/p54. Although similar levels of each biotinylated mIRIDIAN were detected in transfected SH-SY5Y cells, increased levels of both proteins were found in biotin-miR-101 pull downs compared with those of biotin-5?-isomiR-101. This result suggests that miR-101 binds with RISC members more efficiently, which is in agreement with results shown in Figure 2B. To validate the presence of both variants in endogenous RISC, we immunoprecipitated Ago2 from the human frontal cortex. The control experiment included a sample in which the Ago2 antibody had previously been denaturized (d-Ago2) and was therefore unable to interact with Ago2. Total RNA was isolated from Ago2 and d-Ago2 IPs and subsequently we performed RT-qPCR for miR-101, 5?-isomiR-101, miR-29a, SNORD44 and U6. Data were expressed as the fold change obtained from the Ago2 vs d-Ago2 crossing thresholds (CTs) (Figure 3D). miR-101 and 5?-isomiR-101 were enriched in Ago2 IPs compared to control d-Ago2 antibody. miR-29a, a highly abundant miRNA in brain [16] was also enriched in Ago2 complexes. In contrast, the small nucleolar RNAs U6 and SNORD44, which were not expected to bind Ago2, were detected at insignificant levels. The presence of both types of miR-101 variants in Ago2 immunocomplexes was confirmed in publicly available high- throughput sequencing data of endogenous Ago2-associated sRNAs in HEK293T and mouse NIH-3T3 cells (GEO datasets GSM337571 and GSM849857 respectively). To evaluate the possibility that 5?-isomiR-101 and miR-101 are sorted into different Agos, we analyzed publicly available small-RNA sequencing datasets from Ago1, Ago2 and Ago3 IP and total cell extracts, in THP-1 human monocytic cells [36] (Additional file 2 Figure S1). We found all types of miR-101 in each Ago (the trimming, substitution and 3?-additions variants). In addition, the percentage of miR-101 sequences loaded onto each Ago was comparable. Grouping the variants according to the two main seeds we found that in total cell homogenates the ratio miR-101/5?-isomiR-101 in THP-1 cells was close to 2. However, this ratio varied slightly depending on the Ago protein, with the highest levels being found in Ago2 and the lowest in Ago3. This suggests that although in terms of quantity miR-101 exceeded 5?-IsomiR-101 in all Agos, miR-101 species loaded more onto Ago2 than 5?- isomiR-101 variants. This is in agreement with our findings for SH-SY5Y. miR-101 and 5?-isomiR-101 differentially repress COX-2, Mcl-1, APP, EZH2 and MKP-1 expression Since miR-101 and 5?-isomiR-101 have a different seed region, we evaluated the predicted conserved targets for each variant, using the TargetScan 5.2 release [37], by searching for the presence of 8mer and 7mer sites that match miR-101 and 5?-isomiR-101 seed regions. TargetScan 5.2 detected that the majority of putative mRNA targets (>70%) were coincidental, suggesting that miR-101 and 5?-isomiR-101 may target highly overlapping pathways. In an attempt to identify possible differentially deregulated genes, SH-SY5Y cells were transfected with miR-101 or 5?-isomiR-101 mimics or a scrambled mimic in three independent experiments, and 48 h later overall gene expression was evaluated using Illumina microarrays (HumanHT Expression BeadChips). Transfection of each miR-101 variant resulted in few significantly deregulated genes that showed few changes in expression (q<5 %, fold change < ?1,2 or > 1,2; Additional file 3 Table S2?Additional file 4 Table S3). miR-101 induced a significant downregulation of 16 genes, eight being targeted by at least one prediction algorithm. Nine out of the 16 genes were also downregulated by 5?-isomiR- 101 (q<10 %), while the rest did not show significant variations. Thus, 5?-isomiR-101 seems to induce an overall weaker inhibition. In agreement, transfection of 5?-isomiR-101 resulted in a slight downregulation of two genes, for which miR-101 did not induce significant expression changes. These data indicate that under our experimental conditions modulation of gene expression by miR-101 or 5?-isomiR-101 may not involve mRNA degradation as the main mechanism, and suggest further that miR-101 inhibits gene expression more efficiently than 5?-isomiR-101. To gain insights into the possible role of 5?isomiR-101 in physiological conditions, we analyzed publicly available mRNA and small-RNA datasets of human brain samples at multiple time-points between early postnatal development and aging [38]. We chose this paradigm for two reasons: i/ miR-101 expression levels increased through aging, suggesting a role in this process (Figure 2F), and ii/ variants carrying each seed are not equally expressed at the different time-points (compare the oldest and the middle-aged individuals, Figure 2F), suggesting a seed-specific role in the modulation of the age-related genes. To assess the possible seed-specificity in the modulation of the age-related transcriptome, we analyzed the correlation between the expression of miR-101 variants and that of the age- related genes at different ages through life. First, we generated a list of candidate age-related genes through a multiple regression analysis of the gene expression profiles using age as the predicted variable (a total of 3801). We then identified the miR-101 or 5'-isomiR-101 predicted targets among the pool of age-related genes, using the TargetScan 5.2 algorithm (837 for miR-101 and 899 for 5?-isomiR-101, out of the 3801; the majority, 73%-78%, were common to the two miR-101 seeds). Finally, we measured the correlation between the expression levels of age-related genes and those of the most abundant reference seed sequence (miR-101, UACAGUACUGUGAUAACUGAA) or the most abundant 5?-isomiR sequence (5?-isomiR-101, GUACAGUACUGUGAUAACUGA). The negative correlation between the expression profiles of the mRNAs and those of miR- 101 or 5?-isomiR-101 was considered the readout for miR-101 or 5?-isomiR-101 effective/detectable gene targeting. The expression of a total of 64 and 153 age-related genes anti-correlated with that of the ref-miR-101 or 5?-isomiR-101 respectively (r3?): COX-2; F-CCTGTGCCTGATGATTGC, R-CTGATGCGTGAAGTGCTG, MCL-1; F- AAAGAGGCTGGGATGGGTTT, R-CAAAAGCCAGCAGCACATTC, APP; F- TCAGGTTGACGCCGCTGT, R-TTCGTAGCCGTTCTGCTGC, EZH2; F- AGTGTGACCCTGACCTCTGT, R-AGATGGTGCCAGCAATAGAT, DUSP-1 F- CAGCTGCTGCAGTTTGAGTC, R- AGAGGTCGTAATGGGGCTCT. PCR amplification and detection were performed with the ROCHE LightCycler 480 detector, using 2X SYBR GREEN Master Mix as reagent following the manufacturer?s instructions. The reaction profile was: denaturation/activation cycle (95? for 10 min) followed by 40 cycles of denaturation-annealing-extension (95?-10?, 60?-40?, 72?-10?) and a final melting cycle (95?- 5?, 72?-10?, 98?-continuous). Each sample was amplified in triplicate. mRNA levels were calculated using the LightCycler 480 software. Samples were normalized by the relative expression of housekeeping gene (Tubulin). Statistical methods Statistical analysis was performed with G-STAT software (http://www.gstat.org). The Mann? Whitney non-parametric test was used for the statistical analysis (two-tailed) of the RT-qPCR and western blot assays in transfection experiments; P? 0.05 was considered statistically significant. In Figures 3A and 4C, results are expressed as the mean of the Fold Change ratios (microRNA - Biotin-mRNA / control siGLO - Biotin) ? SEM. In Figure Error! Reference source not found.B and 3D, results are expressed as the mean of the Fold Change ratios (FLAG-Ago2 Antibodies / control Antibodies) ? SEM. In Figure 3C, a miRNA-101 transfected sample was used to normalize data and assigned a value of 1 (Arbitrary Units). In Figure Error! Reference source not found.A and 4B, a control sample was used to normalize Fold Change values in each set of experiments and SEM was calculated. This control sample was assigned a value of 1. Abbreviations miR, MicroRNA; HD, Huntington Disease; RISC, RNA induced silencing complex Competing interests The authors have declared that no competing interests exist. Authors? contributions FLL carried out the molecular studies, participated in the experimental design and data analysis and drafted the manuscript. MBC performed molecular studies and participated in the experimental design. JAR and IFA participated in experimental design and manuscript discussion. LP performed the bioinformatics analyses. XE participated in the coordination of the study. EM conceived of the study, participated in the design, data analysis and coordination and helped to draft the manuscript. All authors read and approved the final manuscript. Acknowledgments The authors would like to thank Dra. Juana Diez (UPF-Barcelona), Dra. Carme Caelles (IRB- Barcelona) and Dr. Kristian Helin (BRIC-University of Copenhagen) for providing Rck/p54, MKP-1 and EZH2 antibodies respectively. We also thank Michael Maudsley (Language Services ? University of Barcelona) for correcting the English manuscript. This work was supported by the Spanish Government and FEDER (Fondo Europeo de Desarrollo Regional): PN de I+D+I 2008?2011, PI081367 (E. Mart?) and PN de I+D+I 2012? 2015 PI11/02036 (E. Mart?), PI1100968 (I. Ferrer) Instituto Carlos III ?ISCIII-, Subdirecci?n General de Evaluaci?n y Fomento de la Investigaci?n, SAF2008-00357 (X. Estivill) Ministerio de Economia y competitividad, BESAD (JA de R?o), DEMTEST Joint Programing of Neurodegenerative diseases (EU) (JA del R?o), MICINN BFU2009-10848 and BFU2012-32617 (JA del R?o), FP7-PRIORITY (JA del R?o), Eusko Fundazioa (BIO12/AL/004) (JA del R?o) and Generalitat de Catalunya SGR2009-366 (JA del R?o); the Sixth Framework Programme of the European Commission through the SIROCCO integrated project LSHG-CT-2006-037900. The Spanish Government supports: M. Ba?ez-Coronel (Sara Borrell Postdoctoral contract, ISCIII) and partially supports E.Mart? (Programa Miguel Servet, ISCIII). References 1. Xie X, Lu J, Kulbokas EJ, Golub TR, Mootha V, Lindblad-Toh K, Lander ES, Kellis M: Systematic discovery of regulatory motifs in human promoters and 3? UTRs by comparison of several mammals. Nature 2005, 434(7031):338?345. 2. Krek A, Grun D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, MacMenamin P, da Piedade I, Gunsalus KC, Stoffel M, et al: Combinatorial microRNA target predictions. Nat Genet 2005, 37(5):495?500. 3. Vasudevan S, Tong Y, Steitz JA: Switching from repression to activation: microRNAs can up-regulate translation. Science 2007, 318(5858):1931?1934. 4. Kasinski AL, Slack FJ: Epigenetics and genetics. MicroRNAs en route to the clinic: progress in validating and targeting microRNAs for cancer therapy. Nat Rev Cancer 2011, 11(12):849?864. 5. Esteller M: Non-coding RNAs in human disease. Nat Rev Genet 2011, 12(12):861?874. 6. Santos-Reboucas CB, Pimentel MM: MicroRNAs: macro challenges on understanding human biological functions and neurological diseases. Curr Mol Med 2010, 10(8):692? 704. 7. Gregory RI, Chendrimada TP, Cooch N, Shiekhattar R: Human RISC couples microRNA biogenesis and posttranscriptional gene silencing. Cell 2005, 123(4):631?640. 8. Ambros V: The functions of animal microRNAs. Nature 2004, 431(7006):350?355. 9. Bartel DP: MicroRNAs: target recognition and regulatory functions. Cell 2009, 136(2):215?233. 10. Filipowicz W, Bhattacharyya SN, Sonenberg N: Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight? Nat Rev Genet 2008, 9(2):102?114. 11. Kloosterman WP, Wienholds E, Ketting RF, Plasterk RH: Substrate requirements for let-7 function in the developing zebrafish embryo. Nucleic Acids Res 2004, 32(21):6284? 6291. 12. Shin C, Nam JW, Farh KK, Chiang HR, Shkumatava A, Bartel DP: Expanding the microRNA targeting code: functional sites with centered pairing. Mol Cell 2010, 38(6):789?802. 13. Loeb GB, Khan AA, Canner D, Hiatt JB, Shendure J, Darnell RB, Leslie CS, Rudensky AY: Transcriptome-wide miR-155 Binding Map Reveals Widespread Noncanonical MicroRNA Targeting. Mol Cell 2012, 48(5):760?770. 14. Chi SW, Hannon GJ, Darnell RB: An alternative mode of microRNA target recognition. Nat Struct Mol Biol 2012, 19(3):321?327. 15. Brodersen P, Voinnet O: Revisiting the principles of microRNA target recognition and mode of action. Nat Rev Mol Cell Biol 2009, 10(2):141?148. 16. Marti E, Pantano L, Banez-Coronel M, Llorens F, Minones-Moyano E, Porta S, Sumoy L, Ferrer I, Estivill X: A myriad of miRNA variants in control and Huntington?s disease brain regions detected by massively parallel sequencing. Nucleic Acids Res 2010, 38(20):7219?7235. 17. Newman MA, Mani V, Hammond SM: Deep sequencing of microRNA precursors reveals extensive 3? end modification. RNA 2011, 17(10):1795?1803. 18. Han BW, Hung JH, Weng Z, Zamore PD, Ameres SL: The 3?-to-5? exoribonuclease Nibbler shapes the 3? ends of microRNAs bound to Drosophila Argonaute1. Curr Biol 2011, 21(22):1878?1887. 19. Morin RD, O?Connor MD, Griffith M, Kuchenbauer F, Delaney A, Prabhu AL, Zhao Y, McDonald H, Zeng T, Hirst M, et al: Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. Genome Res 2008, 18(4):610?621. 20. Starega-Roslan J, Koscianska E, Kozlowski P, Krzyzosiak WJ: The role of the precursor structure in the biogenesis of microRNA. Cell Mol Life Sci 2011, 68(17):2859? 2871. 21. Blow MJ, Grocock RJ, van Dongen S, Enright AJ, Dicks E, Futreal PA, Wooster R, Stratton MR: RNA editing of human microRNAs. Genome Biol 2006, 7(4):R27. 22. Kawahara Y, Zinshteyn B, Sethupathy P, Iizasa H, Hatzigeorgiou AG, Nishikura K: Redirection of silencing targets by adenosine-to-inosine editing of miRNAs. Science 2007, 315(5815):1137?1140. 23. Landgraf P, Rusu M, Sheridan R, Sewer A, Iovino N, Aravin A, Pfeffer S, Rice A, Kamphorst AO, Landthaler M, et al: A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 2007, 129(7):1401?1414. 24. Ryan BM, Robles AI, Harris CC: Genetic variation in microRNA networks: the implications for cancer research. Nat Rev Cancer 2010, 10(6):389?402. 25. Xie SS, Li XY, Liu T, Cao JH, Zhong Q, Zhao SH: Discovery of porcine microRNAs in multiple tissues by a Solexa deep sequencing approach. PLoS One 2011, 6(1):e16235. 26. Somel M, Guo S, Fu N, Yan Z, Hu HY, Xu Y, Yuan Y, Ning Z, Hu Y, Menzel C, et al: MicroRNA, mRNA, and protein expression link development and aging in human and macaque brain. Genome Res 2010, 20(9):1207?1218. 27. Fernandez-Valverde SL, Taft RJ, Mattick JS: Dynamic isomiR regulation in Drosophila development. RNA 2010, 16(10):1881?1888. 28. Bizuayehu TT, Lanes CF, Furmanek T, Karlsen BO, Fernandes JM, Johansen SD, Babiak I: Differential expression patterns of conserved miRNAs and isomiRs during Atlantic halibut development. BMC Genomics 2012, 13(1):11. 29. Mayr C, Bartel DP: Widespread shortening of 3?UTRs by alternative cleavage and polyadenylation activates oncogenes in cancer cells. Cell 2009, 138(4):673?684. 30. Smits M, Mir SE, Nilsson RJ, van der Stoop PM, Niers JM, Marquez VE, Cloos J, Breakefield XO, Krichevsky AM, Noske DP, et al: Down-regulation of miR-101 in endothelial cells promotes blood vessel formation through reduced repression of EZH2. PLoS One 2011, 6(1):e16282. 31. Wang HJ, Ruan HJ, He XJ, Ma YY, Jiang XT, Xia YJ, Ye ZY, Tao HQ: MicroRNA-101 is down-regulated in gastric cancer and involved in cell migration and invasion. Eur J Cancer 2010, 46(12):2295?2303. 32. Smits M, Nilsson J, Mir SE, van der Stoop PM, Hulleman E, Niers JM, de Witt Hamer PC, Marquez VE, Cloos J, Krichevsky AM, et al: miR-101 is down-regulated in glioblastoma resulting in EZH2-induced proliferation, migration, and angiogenesis. Oncotarget 2010, 1(8):710?720. 33. Hebert SS, Horre K, Nicolai L, Papadopoulou AS, Mandemakers W, Silahtaroglu AN, Kauppinen S, Delacourte A, De Strooper B: Loss of microRNA cluster miR-29a/b-1 in sporadic Alzheimer?s disease correlates with increased BACE1/beta-secretase expression. Proc Natl Acad Sci USA 2008, 105(17):6415?6420. 34. Nunez-Iglesias J, Liu CC, Morgan TE, Finch CE, Zhou XJ: Joint genome-wide profiling of miRNA and mRNA expression in Alzheimer?s disease cortex reveals altered miRNA regulation. PLoS One 2010, 5(2):e8898. 35. Chu CY, Rana TM: Translation repression in human cells by microRNA-induced gene silencing requires RCK/p54. PLoS Biol 2006, 4(7):e210. 36. Burroughs AM, Ando Y, de Hoon MJ, Tomaru Y, Nishibu T, Ukekawa R, Funakoshi T, Kurokawa T, Suzuki H, Hayashizaki Y, et al: A comprehensive survey of 3? animal miRNA modification events and a possible role for 3? adenylation in modulating miRNA targeting effectiveness. Genome Res 2010, 20(10):1398?1410. 37. Lewis BP, Burge CB, Bartel DP: Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 2005, 120(1):15?20. 38. Su H, Yang JR, Xu T, Huang J, Xu L, Yuan Y, Zhuang SM: MicroRNA-101, down- regulated in hepatocellular carcinoma, promotes apoptosis and suppresses tumorigenicity. Cancer Res 2009, 69(3):1135?1142. 39. Strillacci A, Griffoni C, Sansone P, Paterini P, Piazzi G, Lazzarini G, Spisni E, Pantaleo MA, Biasco G, Tomasi V: MiR-101 downregulation is involved in cyclooxygenase-2 overexpression in human colon cancer cells. Exp Cell Res 2009, 315(8):1439?1447. 40. Vilardo E, Barbato C, Ciotti M, Cogoni C, Ruberti F: MicroRNA-101 regulates amyloid precursor protein expression in hippocampal neurons. J Biol Chem 2010, 285(24):18344? 18351. 41. Cao P, Deng Z, Wan M, Huang W, Cramer SD, Xu J, Lei M, Sui G: MicroRNA-101 negatively regulates Ezh2 and its expression is modulated by androgen receptor and HIF-1alpha/HIF-1beta. Mol Cancer 2010, 9:108. 42. Zhu QY, Liu Q, Chen JX, Lan K, Ge BX: MicroRNA-101 targets MAPK phosphatase- 1 to regulate the activation of MAPKs in macrophages. J Immunol 2010, 185(12):7435? 7442. 43. Buermans HP, Ariyurek Y, van Ommen G, den Dunnen JT, t Hoen PA: New methods for next generation sequencing based microRNA expression profiling. BMC Genomics 2010, 11:716. 44. Zhang JG, Guo JF, Liu DL, Liu Q, Wang JJ: MicroRNA-101 exerts tumor-suppressive functions in non-small cell lung cancer through directly targeting enhancer of zeste homolog 2. J Thorac Oncol 2011, 6(4):671?678. 45. Friedman JM, Liang G, Liu CC, Wolff EM, Tsai YC, Ye W, Zhou X, Jones PA: The putative tumor suppressor microRNA-101 modulates the cancer epigenome by repressing the polycomb group protein EZH2. Cancer Res 2009, 69(6):2623?2629. 46. Varambally S, Cao Q, Mani RS, Shankar S, Wang X, Ateeq B, Laxman B, Cao X, Jing X, Ramnarayanan K, et al: Genomic loss of microRNA-101 leads to overexpression of histone methyltransferase EZH2 in cancer. Science 2008, 322(5908):1695?1699. 47. Frankel LB, Wen J, Lees M, Hoyer-Hansen M, Farkas T, Krogh A, Jaattela M, Lund AH: microRNA-101 is a potent inhibitor of autophagy. EMBO J 2011, 30(22):4628?4641. 48. Long JM, Lahiri DK: MicroRNA-101 downregulates Alzheimer?s amyloid-beta precursor protein levels in human cell cultures and is differentially expressed. Biochem Biophys Res Commun 2011, 404(4):889?895. 49. Lee Y, Samaco RC, Gatchel JR, Thaller C, Orr HT, Zoghbi HY: miR-19, miR-101 and miR-130 co-regulate ATXN1 levels to potentially modulate SCA1 pathogenesis. Nat Neurosci 2008, 11(10):1137?1139. 50. Pantano L, Estivill X, Marti E: SeqBuster, a bioinformatic tool for the processing and analysis of small RNAs datasets, reveals ubiquitous miRNA modifications in human embryonic cells. Nucleic Acids Res 2010, 38(5):e34. 51. Brennecke J, Stark A, Russell RB, Cohen SM: Principles of microRNA-target recognition. PLoS Biol 2005, 3(3):e85. 52. Friedlander MR, Mackowiak SD, Li N, Chen W, Rajewsky N: miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res 2012, 40(1):37?52. 53. Krol J, Loedige I, Filipowicz W: The widespread regulation of microRNA biogenesis, function and decay. Nat Rev Genet 2010, 11(9):597?610. 54. Heo I, Joo C, Cho J, Ha M, Han J, Kim VN: Lin28 mediates the terminal uridylation of let-7 precursor MicroRNA. Mol Cell 2008, 32(2):276?284. 55. Cloonan N, Wani S, Xu Q, Gu J, Lea K, Heater S, Barbacioru C, Steptoe AL, Martin HC, Nourbakhsh E, et al: MicroRNAs and their isomiRs function cooperatively to target common biological pathways. Genome Biol 2011, 12(12):R126. 56. Seitz H, Tushir JS, Zamore PD: A 5?-uridine amplifies miRNA/miRNA* asymmetry in Drosophila by promoting RNA-induced silencing complex formation. Silence 2011, 2:4. 57. Ebhardt HA, Fedynak A, Fahlman RP: Naturally occurring variations in sequence length creates microRNA isoforms that differ in argonaute effector complex specificity. Silence 2010, 1(1):12. 58. Burroughs AM, Ando Y, de Hoon MJ, Tomaru Y, Suzuki H, Hayashizaki Y, Daub CO: Deep-sequencing of human Argonaute-associated small RNAs provides insight into miRNA sorting and reveals Argonaute association with RNA fragments of diverse origin. RNA Biol 2011, 8(1):158?177. 59. Dueck A, Ziegler C, Eichner A, Berezikov E, Meister G: microRNAs associated with the different human Argonaute proteins. Nucleic Acids Res 2012, 40(19):9850?9862. 60. Minones-Moyano E, Porta S, Escaramis G, Rabionet R, Iraola S, Kagerbauer B, Espinosa-Parrilla Y, Ferrer I, Estivill X, Marti E: MicroRNA profiling of Parkinson?s disease brains identifies early downregulation of miR-34b/c which modulate mitochondrial function. Hum Mol Genet 2011, 20(15):3067?3078. 61. Banez-Coronel M, Porta S, Kagerbauer B, Mateu-Huertas E, Pantano L, Ferrer I, Guzman M, Estivill X, Marti E: A pathogenic mechanism in Huntington?s disease involves small CAG-repeated RNAs with neurotoxic activity. PLoS Genet 2012, 8(2):e1002481. 62. Orom UA, Lund AH: Isolation of microRNA targets using biotinylated synthetic microRNAs. Methods 2007, 43(2):162?165. Additional files Additional_file_1 as XLS Additional file 1: Table S1 miR-101 variants in the human striatum, amigdala and frontal gyrus (Somel et al., 2010) brain areas. The different sequences annotating to miR-101, the respective frequencies and lenghts are shown. For the 5? and 3? trimming variants, the number of nucleotides upstream (up) or downstream (down) of the reference miRBase miR- 101, are shown. The nucleotides involved in the 5?- and 3? variant are next indicated. In 3?- addition variants, the number and type of nucleotides added to the 3?-end are shown. In the nucleotide substitution variants, the affected position is first indicated; the pair of nucleotides that follow next indicates the substituted and the original nucleotides, respectively. Only variants with more than 10 counts were considered. The sequences mapping onto miR-101 locus that represent the reference miR-101 sequence (in blue) and the 5?-isomiR-101 (in red) contained in a grey box. Additional_file_2 as JPEG Additional file 2: Figure S1 miR-101 and 5?isomiR-101 frequency distribution in different Agos. A. Normalized expression levels of all sequences mapping onto miR-101 (blue bars), 5?-isomiR-101 seed (red bars) and reference miR-101 seed (green bars) in Ago1-Ago3 IP and in total cell extracts (Total). B. Table showing several determinations of miR-101 sequences. Freq. indicates the total count number; Number of variants indicates the sequence diversity for mIR-101; Norm. Freq., indicates the normalized frequency calculated as freq. miR- 101/freq. mIRNAs *10E6. Additional_file_3 as XLSX Additional file 3: Table S2 Genes differently expressed after transfection with isomiR-101 mimic or a scrambeled (scr) sequence. The threshold for significant regulation is considered as a variation in the expression fold change above 1,2 or below -1,2 and a false discovery rate below 5 % (q<5), whch is indicated in bold. The MiRWalk database on predicted and validated microRNA targets (http://www.umm.uni- heidelberg.de/apps/zmf/mirwalk/index.html) was used to highlight additional information: i) The prediction of the deregulated genes as putative targets for miR-101 by different algorithms (-, indicates no prediction and na information not available) and ii) the presense of putative miR-101 target seed sites in regions other than the 3?-UTR (-, indicates no sites and na information not available). In the last column, the custom Targetscan 5.2 prediction algorithm was used to highlight putative 5?-isomiR-101 targets. Additional_file_4 as XLSX Additional file 4: Table S3 Genes differently expressed after transfection with 5?-isomiR 101 mimic or a scrambeled (scr) sequence. The threshold for significant regulation is considered as a variation in the expression fold change above 1,2 or below -1,2 and a false discovery rate below 5 % (q<5), whch is indicated in bold. The MiRWalk database on predicted and validated microRNA targets (http://www.umm.uni- heidelberg.de/apps/zmf/mirwalk/index.html) was used to highlight additional information: i) The prediction of the deregulated genes as putative targets for miR-101 by different algorithms (-, indicates no prediction and na information not available) and ii) the presense of putative miR-101 target seed sites in regions other than the 3?-UTR (-, indicates no sites and na information not available). In the last column, the custom Targetscan 5.2 prediction algorithm was used to highlight putative 5?-isomiR-101 targets. Additional_file_5 as JPEG Additional file 5: Figure S2 Anti-correlation of age-related genes and miR-101 and 5?- isomiR-101 expression profiles. A. Distribution of the numbers and percentages of age- related genes (blue) targeted by miR-101 (red) and 5?-IsomiR-101 (green) seeds, according to TargetScan algorithm. B. Expression profile of the more abundant miR-101 and 5?-isomiR- 101 sequences, and two example age-related genes. NYAP2 expression anti-correlated with that of 5?-isomiR-101, and SCN3B expression anti-correlated with that of miR-101 (considering an anti-correlation threshold < ?0,7). Figure 1 Figure 2 Figure 3 Figure 4 Additional files provided with this submission: Additional file 1: 8428259367883278_add1.xls, 74K http://www.biomedcentral.com/imedia/1425524568912509/supp1.xls Additional file 2: 8428259367883278_add2.jpeg, 117K http://www.biomedcentral.com/imedia/3376808089125090/supp2.jpeg Additional file 3: 8428259367883278_add3.xlsx, 54K http://www.biomedcentral.com/imedia/9784504929125090/supp3.xlsx Additional file 4: 8428259367883278_add4.xlsx, 53K http://www.biomedcentral.com/imedia/2007091716912509/supp4.xlsx Additional file 5: 8428259367883278_add5.jpeg, 107K http://www.biomedcentral.com/imedia/4413815669125090/supp5.jpeg