>HSP90AB1|NM_007355|3'UTR
   1 GTTAGGAGTTCATAGTTGGAAAACTTGTGCCCTTGTATAGTGTCCCCATGGGCTCCCACTGCAGCCTCGAGTGCCCCTGT
  81 CCCACCTGGCTCCCCCTGCTGGTGTCTAGTGTTTTTTTCCCTCTCCTGTCCTTGTGTTGAAGGCAGTAAACTAAGGGTGT
 161 CAAGCCCCATTCCCTCTCTACTCTTGACAGCAGGATTGGATGTTGTGTATTGTGGTTTATTTTATTTTCTTCATTTTGTT
 241 CTGAAATTAAAGTATGCAAAATAAAGAATATGCCGTTTTTATACAGTTCT

ID	Duplex structure	Position	Score	MFE
1	miRNA 3' uuugagcgacUCGGGGu 5' |||||| Target 5' aagggtgtcaAGCCCCa 3'	153 - 169	120.00	-13.80
2	miRNA 3' uuuGAGCGACUCGGGGu 5' |||| | ||||| Target 5' agcCTCG-AGTGCCCCt 3'	63 - 78	117.00	-15.20
3	miRNA 3' uuugAGCG--ACUCGGGGu 5' || | ||:||:|| Target 5' agtgTCCCCATGGGCTCCc 3'	39 - 57	115.00	-11.50

Mutant ID	Mutant Position	Mutant Source
COSN30117971	28	COSMIC
COSN30574611	35	COSMIC
COSN26649924	36	COSMIC
COSN30145654	38	COSMIC
COSN30177415	50	COSMIC
COSN17133454	53	COSMIC
COSN30544685	108	COSMIC
COSM6682508	119	COSMIC
COSM8402933	119	COSMIC
COSN24383206	119	COSMIC
COSN31496762	124	COSMIC
COSN30166032	126	COSMIC
COSN32068086	131	COSMIC
COSN30154742	140	COSMIC
COSN508681	148	COSMIC
COSN30467067	173	COSMIC
COSN30534973	180	COSMIC
COSN32053590	214	COSMIC
COSN31547867	280	COSMIC

Mutant ID	Mutant Position	Mutant Source
rs35612006	2	dbSNP
rs1242816711	3	dbSNP
rs770404324	6	dbSNP
rs1185231526	7	dbSNP
rs776041241	12	dbSNP
rs367736156	13	dbSNP
rs774647753	15	dbSNP
rs1460255277	16	dbSNP
rs551377330	17	dbSNP
rs767617043	19	dbSNP
rs750456110	24	dbSNP
rs1432456665	25	dbSNP
rs776689694	25	dbSNP
rs779361652	25	dbSNP
rs183673959	31	dbSNP
rs766298258	33	dbSNP
rs909486634	35	dbSNP
rs1250586407	36	dbSNP
rs753632799	36	dbSNP
rs1453780093	37	dbSNP
rs1027102617	38	dbSNP
rs1484048386	39	dbSNP
rs1360419433	40	dbSNP
rs759906843	44	dbSNP
rs754815566	46	dbSNP
rs747863315	49	dbSNP
rs758072964	50	dbSNP
rs769984219	50	dbSNP
rs777476065	54	dbSNP
rs1230264421	56	dbSNP
rs746489407	57	dbSNP
rs1273842410	61	dbSNP
rs770493866	63	dbSNP
rs1205271978	65	dbSNP
rs952741828	67	dbSNP
rs1475001823	69	dbSNP
rs11538984	70	dbSNP
rs1161347492	71	dbSNP
rs1421623845	72	dbSNP
rs1202344628	77	dbSNP
rs1430917000	77	dbSNP
rs774542343	78	dbSNP
rs775657325	78	dbSNP
rs1175123533	79	dbSNP
rs1410533376	80	dbSNP
rs1156324663	82	dbSNP
rs762119482	83	dbSNP
rs773344374	84	dbSNP
rs1245440774	87	dbSNP
rs564816644	88	dbSNP
rs760727177	89	dbSNP
rs368791771	90	dbSNP
rs766384097	90	dbSNP
rs746575693	91	dbSNP
rs977441370	91	dbSNP
rs764469740	92	dbSNP
rs778731073	92	dbSNP
rs762064827	93	dbSNP
rs1289932363	94	dbSNP
rs753417674	96	dbSNP
rs1188316316	97	dbSNP
rs1221714002	100	dbSNP
rs1371038273	103	dbSNP
rs1260148973	104	dbSNP
rs778542075	107	dbSNP
rs781478132	107	dbSNP
rs897517124	108	dbSNP
rs752159202	109	dbSNP
rs759351114	109	dbSNP
rs1324459405	110	dbSNP
rs1322336967	112	dbSNP
rs552590291	112	dbSNP
rs765101866	112	dbSNP
rs752409983	113	dbSNP
rs199863567	114	dbSNP
rs1446186763	117	dbSNP
rs370670609	119	dbSNP
rs567895542	119	dbSNP
rs751172830	120	dbSNP
rs756841146	122	dbSNP
rs1442417188	128	dbSNP
rs1383726708	130	dbSNP
rs780506317	132	dbSNP
rs1190911088	133	dbSNP
rs534908596	138	dbSNP
rs747005176	140	dbSNP
rs15274	141	dbSNP
rs367726643	146	dbSNP
rs1156489083	147	dbSNP
rs779386388	148	dbSNP
rs758408951	149	dbSNP
rs772335941	153	dbSNP
rs968638137	156	dbSNP
rs998801833	157	dbSNP
rs371667983	158	dbSNP
rs771073911	162	dbSNP
rs956732365	166	dbSNP
rs776524131	167	dbSNP
rs765052339	168	dbSNP
rs15210	169	dbSNP
rs752606321	172	dbSNP
rs763924937	173	dbSNP
rs571860406	174	dbSNP
rs756933331	175	dbSNP
rs11538988	176	dbSNP
rs1228949123	176	dbSNP
rs992221316	177	dbSNP
rs770629980	179	dbSNP
rs780779136	179	dbSNP
rs1357106752	180	dbSNP
rs1292462062	182	dbSNP
rs1422289383	187	dbSNP
rs148217245	192	dbSNP
rs1269591578	193	dbSNP
rs1170860766	195	dbSNP
rs1450553964	196	dbSNP
rs748517962	197	dbSNP
rs781101907	198	dbSNP
rs779271158	199	dbSNP
rs745674957	201	dbSNP
rs748593515	203	dbSNP
rs1026961770	204	dbSNP
rs1410619984	207	dbSNP
rs770079113	208	dbSNP
rs888533951	208	dbSNP
rs1006944494	210	dbSNP
rs1433184371	216	dbSNP
rs550579126	216	dbSNP
rs749333030	220	dbSNP
rs969559486	220	dbSNP
rs772425850	221	dbSNP
rs778072022	222	dbSNP
rs1235267181	223	dbSNP
rs965968179	224	dbSNP
rs1326955083	225	dbSNP
rs186826280	226	dbSNP
rs1063511	230	dbSNP
rs1308301339	230	dbSNP
rs768830881	230	dbSNP
rs776811300	232	dbSNP
rs769829201	233	dbSNP
rs775583329	242	dbSNP
rs1396177533	244	dbSNP
rs1380288734	247	dbSNP
rs774691448	248	dbSNP
rs762917876	252	dbSNP
rs764016931	254	dbSNP
rs1361531629	256	dbSNP
rs774242370	258	dbSNP
rs1271689147	262	dbSNP
rs762438280	263	dbSNP
rs769398411	264	dbSNP
rs767048457	271	dbSNP
rs374676993	272	dbSNP
rs1324869204	274	dbSNP
rs750005410	275	dbSNP
rs1437492729	276	dbSNP
rs755590502	276	dbSNP
rs112093933	277	dbSNP
rs753230365	280	dbSNP
rs1270537808	281	dbSNP
rs745916072	283	dbSNP
rs918914936	284	dbSNP
rs1446987810	286	dbSNP

- Cell, 2010

RNA transcripts are subject to posttranscriptional gene regulation involving hundreds of RNA-binding proteins (RBPs) and microRNA-containing ribonucleoprotein complexes (miRNPs) expressed in a cell-type dependent fashion. We developed a cell-based crosslinking approach to determine at high resolution and transcriptome-wide the binding sites of cellular RBPs and miRNPs. The crosslinked sites are revealed by thymidine to cytidine transitions in the cDNAs prepared from immunopurified RNPs of 4-thiouridine-treated cells. We determined the binding sites and regulatory consequences for several intensely studied RBPs and miRNPs, including PUM2, QKI, IGF2BP1-3, AGO/EIF2C1-4 and TNRC6A-C. Our study revealed that these factors bind thousands of sites containing defined sequence motifs and have distinct preferences for exonic versus intronic or coding versus untranslated transcript regions. The precise mapping of binding sites across the transcriptome will be critical to the interpretation of the rapidly emerging data on genetic variation between individuals and how these variations contribute to complex genetic diseases.

- 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.

- 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.

- Genome research, 2016

DNA damage activates TP53-regulated surveillance mechanisms that are crucial in suppressing tumorigenesis. TP53 orchestrates these responses directly by transcriptionally modulating genes, including microRNAs (miRNAs), and by regulating miRNA biogenesis through interacting with the DROSHA complex. However, whether the association between miRNAs and AGO2 is regulated following DNA damage is not yet known. Here, we show that, following DNA damage, TP53 interacts with AGO2 to induce or reduce AGO2's association of a subset of miRNAs, including multiple let-7 family members. Furthermore, we show that specific mutations in TP53 decrease rather than increase the association of let-7 family miRNAs, reducing their activity without preventing TP53 from interacting with AGO2. This is consistent with the oncogenic properties of these mutants. Using AGO2 RIP-seq and PAR-CLIP-seq, we show that the DNA damage-induced increase in binding of let-7 family members to the RISC complex is functional. We unambiguously determine the global miRNA-mRNA interaction networks involved in the DNA damage response, validating them through the identification of miRNA-target chimeras formed by endogenous ligation reactions. We find that the target complementary region of the let-7 seed tends to have highly fixed positions and more variable ones. Additionally, we observe that miRNAs, whose cellular abundance or differential association with AGO2 is regulated by TP53, are involved in an intricate network of regulatory feedback and feedforward circuits. TP53-mediated regulation of AGO2-miRNA interaction represents a new mechanism of miRNA regulation in carcinogenesis.