>PIGO|NM_032634|3'UTR
   1 CCTAGTCTGTGATTACTGGCACTTGGCTACAGAGAGTGCTTGAGAACAGTGTAGCCTGGCCTGTACAGGTACTGGATGAT
  81 CTGCAAGACAGGCTCAGCCATACTCTTACTATCATGCAGCCAGGGGCCGCTGACATCTAGGACTTCATTATTCTATAATT
 161 CAGGACCACAGTGGAGTATGATCCCTAACTCCTGATTTGGATGCATCTGAGGGACAAGGGGGGCGGTCTCCGAAGTGGAA
 241 TAAAATAGGCCGGGCGTGGTGACTTGCACCTATAATCCCAGCACTTTGGGAGGCAGAGGTGGGAGGATTGCTTGGTCCCA
 321 GGAGTTCAAGACCAGCCTGTGGAACATAACAAGACCCCGTCTCTACTATTTAAAAAAAAGTGTAATAAAATGATAATATA
 401 ATTAAAAAAAAAAAAAAAAAA

ID	Duplex structure	Position	Score	MFE
1	miRNA 3' uggUUUCC-GUCGGGACCGUUGa 5' | ||| |:| | |||::|| Target 5' aaaATAGGCCGGGCGTGGTGACt 3'	242 - 264	127.00	-16.00
2	miRNA 3' ugguuuCCGUCGGGACCGUUGa 5' |||| |:|||| || Target 5' attactGGCA--CTTGGCTACa 3'	12 - 31	124.00	-12.40
3	miRNA 3' ugGUUUCCGUCGGG-ACCGUUGa 5' |||: |||||: ||| ||| Target 5' ttCAAGACCAGCCTGTGG-AACa 3'	325 - 346	116.00	-15.30

Mutant ID	Mutant Position	Mutant Source
420520	12	ClinVar
366751	41	ClinVar
366750	61	ClinVar
976623	219	ClinVar
914786	220	ClinVar
914785	221	ClinVar
914784	251	ClinVar
366749	267	ClinVar
366748	372	ClinVar
914783	382	ClinVar
COSN26988999	16	COSMIC
COSN26988997	21	COSMIC
COSN30492374	33	COSMIC
COSN26989000	63	COSMIC
COSN20124296	72	COSMIC
COSN31560429	83	COSMIC
COSN31587627	126	COSMIC
COSN14391077	132	COSMIC
COSN21869874	224	COSMIC
COSN216737	396	COSMIC

Mutant ID	Mutant Position	Mutant Source
rs1247395316	2	dbSNP
rs756919705	10	dbSNP
rs540040672	12	dbSNP
rs1260819942	16	dbSNP
rs1220174027	18	dbSNP
rs1406865214	23	dbSNP
rs980833480	25	dbSNP
rs1319625810	27	dbSNP
rs370278095	29	dbSNP
rs1248689344	31	dbSNP
rs1384019174	32	dbSNP
rs777180949	34	dbSNP
rs556766	41	dbSNP
rs752056058	48	dbSNP
rs1280804859	54	dbSNP
rs563964781	55	dbSNP
rs886063903	61	dbSNP
rs1240110322	62	dbSNP
rs1316316128	63	dbSNP
rs1362128057	67	dbSNP
rs957013734	73	dbSNP
rs534476409	84	dbSNP
rs1031247420	85	dbSNP
rs1433779836	87	dbSNP
rs1289543081	92	dbSNP
rs1438633492	93	dbSNP
rs976081162	95	dbSNP
rs997876792	105	dbSNP
rs1236478769	109	dbSNP
rs1439141418	111	dbSNP
rs1178005379	114	dbSNP
rs541010481	119	dbSNP
rs900857696	125	dbSNP
rs1385781255	126	dbSNP
rs957499876	128	dbSNP
rs144078170	129	dbSNP
rs1006649423	131	dbSNP
rs562339787	132	dbSNP
rs1033158972	143	dbSNP
rs977613542	146	dbSNP
rs542458590	147	dbSNP
rs1445380223	155	dbSNP
rs1315061687	156	dbSNP
rs1385641486	161	dbSNP
rs576670043	167	dbSNP
rs1284007945	169	dbSNP
rs1024512842	172	dbSNP
rs1193102061	185	dbSNP
rs1448074769	191	dbSNP
rs556623359	198	dbSNP
rs1048256868	199	dbSNP
rs894140972	207	dbSNP
rs1209148105	212	dbSNP
rs1034242704	216	dbSNP
rs546029434	219	dbSNP
rs773911038	220	dbSNP
rs1056995769	221	dbSNP
rs1464730123	223	dbSNP
rs1051621463	224	dbSNP
rs1403673671	224	dbSNP
rs938569934	225	dbSNP
rs1337547240	231	dbSNP
rs927974729	232	dbSNP
rs183228101	235	dbSNP
rs1039748905	242	dbSNP
rs936223799	247	dbSNP
rs981140184	251	dbSNP
rs977582480	252	dbSNP
rs969538484	255	dbSNP
rs868398793	256	dbSNP
rs1413913525	260	dbSNP
rs79276933	267	dbSNP
rs1447429820	272	dbSNP
rs1195599732	287	dbSNP
rs957075790	289	dbSNP
rs1477310976	298	dbSNP
rs533914060	299	dbSNP
rs1176672053	301	dbSNP
rs1314341955	303	dbSNP
rs998451776	308	dbSNP
rs965586767	310	dbSNP
rs1464093674	313	dbSNP
rs1017957904	314	dbSNP
rs1386320954	315	dbSNP
rs993001169	316	dbSNP
rs1296759542	327	dbSNP
rs954086260	331	dbSNP
rs958375134	336	dbSNP
rs1340248352	337	dbSNP
rs1418250023	339	dbSNP
rs1006958875	344	dbSNP
rs1303647067	344	dbSNP
rs1329473002	353	dbSNP
rs1232612338	358	dbSNP
rs888231080	359	dbSNP
rs1048599606	366	dbSNP
rs1351346025	368	dbSNP
rs1218655617	371	dbSNP
rs886063902	372	dbSNP
rs1487827660	390	dbSNP
rs1476054864	394	dbSNP
rs748784361	396	dbSNP
rs1426369808	397	dbSNP
rs1186376342	403	dbSNP
rs1431028171	404	dbSNP
rs1056857253	405	dbSNP

- Nature methods, 2011

Cross-linking and immunoprecipitation (CLIP) is increasingly used to map transcriptome-wide binding sites of RNA-binding proteins. We developed a method for CLIP data analysis, and applied it to compare CLIP with photoactivatable ribonucleoside-enhanced CLIP (PAR-CLIP) and to uncover how differences in cross-linking and ribonuclease digestion affect the identified sites. We found only small differences in accuracies of these methods in identifying binding sites of HuR, which binds low-complexity sequences, and Argonaute 2, which has a complex binding specificity. We found that cross-link-induced mutations led to single-nucleotide resolution for both PAR-CLIP and CLIP. Our results confirm the expectation from original CLIP publications that RNA-binding proteins do not protect their binding sites sufficiently under the denaturing conditions used during the CLIP procedure, and we show that extensive digestion with sequence-specific RNases strongly biases the recovered binding sites. This bias can be substantially reduced by milder nuclease digestion conditions.

- Nature, 2013

Circular RNAs (circRNAs) in animals are an enigmatic class of RNA with unknown function. To explore circRNAs systematically, we sequenced and computationally analysed human, mouse and nematode RNA. We detected thousands of well-expressed, stable circRNAs, often showing tissue/developmental-stage-specific expression. Sequence analysis indicated important regulatory functions for circRNAs. We found that a human circRNA, antisense to the cerebellar degeneration-related protein 1 transcript (CDR1as), is densely bound by microRNA (miRNA) effector complexes and harbours 63 conserved binding sites for the ancient miRNA miR-7. Further analyses indicated that CDR1as functions to bind miR-7 in neuronal tissues. Human CDR1as expression in zebrafish impaired midbrain development, similar to knocking down miR-7, suggesting that CDR1as is a miRNA antagonist with a miRNA-binding capacity ten times higher than any other known transcript. Together, our data provide evidence that circRNAs form a large class of post-transcriptional regulators. Numerous circRNAs form by head-to-tail splicing of exons, suggesting previously unrecognized regulatory potential of coding sequences.

- mBio, 2013

UNLABELLED: The question of how HIV-1 interfaces with cellular microRNA (miRNA) biogenesis and effector mechanisms has been highly controversial. Here, we first used deep sequencing of small RNAs present in two different infected cell lines (TZM-bl and C8166) and two types of primary human cells (CD4(+) peripheral blood mononuclear cells [PBMCs] and macrophages) to unequivocally demonstrate that HIV-1 does not encode any viral miRNAs. Perhaps surprisingly, we also observed that infection of T cells by HIV-1 has only a modest effect on the expression of cellular miRNAs at early times after infection. Comprehensive analysis of miRNA binding to the HIV-1 genome using the photoactivatable ribonucleoside-induced cross-linking and immunoprecipitation (PAR-CLIP) technique revealed several binding sites for cellular miRNAs, a subset of which were shown to be capable of mediating miRNA-mediated repression of gene expression. However, the main finding from this analysis is that HIV-1 transcripts are largely refractory to miRNA binding, most probably due to extensive viral RNA secondary structure. Together, these data demonstrate that HIV-1 neither encodes viral miRNAs nor strongly influences cellular miRNA expression, at least early after infection, and imply that HIV-1 transcripts have evolved to avoid inhibition by preexisting cellular miRNAs by adopting extensive RNA secondary structures that occlude most potential miRNA binding sites. IMPORTANCE: MicroRNAs (miRNAs) are a ubiquitous class of small regulatory RNAs that serve as posttranscriptional regulators of gene expression. Previous work has suggested that HIV-1 might subvert the function of the cellular miRNA machinery by expressing viral miRNAs or by dramatically altering the level of cellular miRNA expression. Using very sensitive approaches, we now demonstrate that neither of these ideas is in fact correct. Moreover, HIV-1 transcripts appear to largely avoid regulation by cellular miRNAs by adopting an extensive RNA secondary structure that occludes the ability of cellular miRNAs to interact with viral mRNAs. Together, these data suggest that HIV-1, rather than seeking to control miRNA function in infected cells, has instead evolved a mechanism to become largely invisible to cellular miRNA effector mechanisms.

- Genes & development, 2013

When adapting to environmental stress, cells attenuate and reprogram their translational output. In part, these altered translation profiles are established through changes in the interactions between RNA-binding proteins and mRNAs. The Argonaute 2 (Ago2)/microRNA (miRNA) machinery has been shown to participate in stress-induced translational up-regulation of a particular mRNA, CAT-1; however, a detailed, transcriptome-wide understanding of the involvement of Ago2 in the process has been lacking. Here, we profiled the overall changes in Ago2-mRNA interactions upon arsenite stress by cross-linking immunoprecipitation (CLIP) followed by high-throughput sequencing (CLIP-seq). Ago2 displayed a significant remodeling of its transcript occupancy, with the majority of 3' untranslated region (UTR) and coding sequence (CDS) sites exhibiting stronger interaction. Interestingly, target sites that were destined for release from Ago2 upon stress were depleted in miRNA complementarity signatures, suggesting an alternative mode of interaction. To compare the changes in Ago2-binding patterns across transcripts with changes in their translational states, we measured mRNA profiles on ribosome/polysome gradients by RNA sequencing (RNA-seq). Increased Ago2 occupancy correlated with stronger repression of translation for those mRNAs, as evidenced by a shift toward lighter gradient fractions upon stress, while release of Ago2 was associated with the limited number of transcripts that remained translated. Taken together, these data point to a role for Ago2 and the mammalian miRNAs in mediating the translational component of the stress response.

- Genome biology, 2014

BACKGROUND: Various microRNAs (miRNAs) are up- or downregulated in tumors. However, the repression of cognate miRNA targets responsible for the phenotypic effects of this dysregulation in patients remains largely unexplored. To define miRNA targets and associated pathways, together with their relationship to outcome in breast cancer, we integrated patient-paired miRNA-mRNA expression data with a set of validated miRNA targets and pathway inference. RESULTS: To generate a biochemically-validated set of miRNA-binding sites, we performed argonaute-2 photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation (AGO2-PAR-CLIP) in MCF7 cells. We then defined putative miRNA-target interactions using a computational model, which ranked and selected additional TargetScan-predicted interactions based on features of our AGO2-PAR-CLIP binding-site data. We subselected modeled interactions according to the abundance of their constituent miRNA and mRNA transcripts in tumors, and we took advantage of the variability of miRNA expression within molecular subtypes to detect miRNA repression. Interestingly, our data suggest that miRNA families control subtype-specific pathways; for example, miR-17, miR-19a, miR-25, and miR-200b show high miRNA regulatory activity in the triple-negative, basal-like subtype, whereas miR-22 and miR-24 do so in the HER2 subtype. An independent dataset validated our findings for miR-17 and miR-25, and showed a correlation between the expression levels of miR-182 targets and overall patient survival. Pathway analysis associated miR-17, miR-19a, and miR-200b with leukocyte transendothelial migration. CONCLUSIONS: We combined PAR-CLIP data with patient expression data to predict regulatory miRNAs, revealing potential therapeutic targets and prognostic markers in breast cancer.

- Neoplasia (New York, N.Y.), 2016

MicroRNA (miRNA) deregulation in prostate cancer (PCa) contributes to PCa initiation and metastatic progression. To comprehensively define the cancer-associated changes in miRNA targeting and function in commonly studied models of PCa, we performed photoactivatable ribonucleoside-enhanced cross-linking immunoprecipitation of the Argonaute protein in a panel of PCa cell lines modeling different stages of PCa progression. Using this comprehensive catalogue of miRNA targets, we analyzed miRNA targeting on known drivers of PCa and examined tissue-specific and stage-specific pathway targeting by miRNAs. We found that androgen receptor is the most frequently targeted PCa oncogene and that miR-148a targets the largest number of known PCa drivers. Globally, tissue-specific and stage-specific changes in miRNA targeting are driven by homeostatic response to active oncogenic pathways. Our findings indicate that, even in advanced PCa, the miRNA pool adapts to regulate continuing alterations in the cancer genome to balance oncogenic molecular changes. These findings are important because they are the first to globally characterize miRNA changes in PCa and demonstrate how the miRNA target spectrum responds to staged tumorigenesis.