>PFN1|NM_005022|3'UTR
   1 CCTCGTCTGTCCCTTCCCCTTCACCGCTCCCCACAGCTTTGCACCCCTTTCCTCCCCATACACACACAAACCATTTTATT
  81 TTTTGGGCCATTACCCCATACCCCTTATTGCTGCCAAAACCACATGGGCTGGGGGCCAGGGCTGGATGGACAGACACCTC
 161 CCCCTACCCATATCCCTCCCGTGTGTGGTTGGAAAACTTTTGTTTTTTGGGGTTTTTTTTTTCTGAATAAAAAAGATTCT
 241 ACTAACAAGG

ID	Duplex structure	Position	Score	MFE
1	miRNA 3' cuuuagagaugucuucGGGGUAu 5' |||||| Target 5' tttgcacccctttcctCCCCATa 3'	38 - 60	120.00	-10.80
2	miRNA 3' cuuuagagaugucuucGGGGUAu 5' |||||| Target 5' attttttgggccattaCCCCATa 3'	78 - 100	120.00	-11.30
3	miRNA 3' cuuUAGAGAUGUCUUCGGGGUAu 5' ||: ||:| || || ||| Target 5' cttATTGCTGCCAAAACCACATg 3'	104 - 126	104.00	-8.40

Mutant ID	Mutant Position	Mutant Source
COSN31552215	5	COSMIC
COSN31493356	18	COSMIC
COSN26988707	26	COSMIC
COSN31502031	26	COSMIC
COSN30523369	31	COSMIC
COSN31493004	55	COSMIC
COSN30505287	105	COSMIC
COSN24471461	108	COSMIC
COSN31530244	239	COSMIC

Mutant ID	Mutant Position	Mutant Source
rs200415929	4	dbSNP
rs778615999	5	dbSNP
rs1345779550	6	dbSNP
rs369839911	7	dbSNP
rs1233461521	8	dbSNP
rs1374058259	10	dbSNP
rs11538689	13	dbSNP
rs749215794	16	dbSNP
rs781369153	18	dbSNP
rs754987286	19	dbSNP
rs751605549	22	dbSNP
rs780168473	23	dbSNP
rs758578095	24	dbSNP
rs750830676	25	dbSNP
rs1804220	26	dbSNP
rs754367238	27	dbSNP
rs371081907	29	dbSNP
rs763710875	32	dbSNP
rs760127827	34	dbSNP
rs367572626	39	dbSNP
rs1190266656	41	dbSNP
rs1484819934	44	dbSNP
rs1256811796	45	dbSNP
rs11538687	47	dbSNP
rs1216668199	48	dbSNP
rs775028140	49	dbSNP
rs565692021	51	dbSNP
rs1170629373	52	dbSNP
rs556065443	53	dbSNP
rs1034131780	58	dbSNP
rs894209727	58	dbSNP
rs985968607	63	dbSNP
rs1000012325	64	dbSNP
rs1359125112	65	dbSNP
rs1041588121	67	dbSNP
rs561252974	68	dbSNP
rs1470503988	69	dbSNP
rs954548657	69	dbSNP
rs923063598	72	dbSNP
rs1217886011	75	dbSNP
rs976877029	77	dbSNP
rs945915055	84	dbSNP
rs1174519345	101	dbSNP
rs1480174613	102	dbSNP
rs1233593321	104	dbSNP
rs536157904	105	dbSNP
rs1488241167	106	dbSNP
rs570221126	107	dbSNP
rs113941832	108	dbSNP
rs2233658	120	dbSNP
rs932033689	124	dbSNP
rs570839359	126	dbSNP
rs1417970074	132	dbSNP
rs989472510	133	dbSNP
rs547907008	138	dbSNP
rs1461595714	146	dbSNP
rs1037771054	147	dbSNP
rs527646073	150	dbSNP
rs1348795431	152	dbSNP
rs11538691	156	dbSNP
rs1264676038	157	dbSNP
rs1033933701	158	dbSNP
rs561769619	161	dbSNP
rs906712275	164	dbSNP
rs1201339987	165	dbSNP
rs1399131490	167	dbSNP
rs942137449	169	dbSNP
rs1311122613	171	dbSNP
rs750015194	176	dbSNP
rs751294548	180	dbSNP
rs548819256	181	dbSNP
rs529161805	187	dbSNP
rs1297489382	189	dbSNP
rs1453899540	191	dbSNP
rs764046450	192	dbSNP
rs1294185700	193	dbSNP
rs918210049	194	dbSNP
rs1385398484	200	dbSNP
rs897558698	201	dbSNP
rs1469440120	202	dbSNP
rs992891665	202	dbSNP
rs958422464	204	dbSNP
rs1164974617	205	dbSNP
rs1365785783	206	dbSNP
rs1355201383	209	dbSNP
rs1418784455	209	dbSNP
rs1296046385	211	dbSNP
rs563647031	212	dbSNP
rs139399100	213	dbSNP
rs543229238	213	dbSNP
rs1408276372	215	dbSNP
rs941700039	216	dbSNP
rs888745254	221	dbSNP
rs77350166	222	dbSNP
rs1010110796	223	dbSNP
rs1216166927	223	dbSNP
rs775818152	223	dbSNP
rs77750855	223	dbSNP
rs879109845	223	dbSNP
rs890359160	223	dbSNP
rs1237381114	227	dbSNP
rs1051713119	234	dbSNP
rs1184899496	235	dbSNP
rs757650091	235	dbSNP
rs1445063819	236	dbSNP
rs764597778	238	dbSNP
rs1181379613	239	dbSNP
rs1409842777	242	dbSNP
rs1401047394	245	dbSNP
rs1482685188	245	dbSNP
rs140057352	247	dbSNP

- Genes & development, 2011

MicroRNAs are important regulators in many cellular processes, including stem cell self-renewal. Recent studies demonstrated their function as pluripotency factors with the capacity for somatic cell reprogramming. However, their role in human embryonic stem (ES) cells (hESCs) remains poorly understood, partially due to the lack of genome-wide strategies to identify their targets. Here, we performed comprehensive microRNA profiling in hESCs and in purified neural and mesenchymal derivatives. Using a combination of AGO cross-linking and microRNA perturbation experiments, together with computational prediction, we identified the targets of the miR-302/367 cluster, the most abundant microRNAs in hESCs. Functional studies identified novel roles of miR-302/367 in maintaining pluripotency and regulating hESC differentiation. We show that in addition to its role in TGF-beta signaling, miR-302/367 promotes bone morphogenetic protein (BMP) signaling by targeting BMP inhibitors TOB2, DAZAP2, and SLAIN1. This study broadens our understanding of microRNA function in hESCs and is a valuable resource for future studies in this area.

- 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.

- 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.