>NRBP1|NM_013392|3'UTR
   1 AGCTCACTCGGGCCAGGCCCTGATCTGCGCTGTGGCTGTCCCTGGACGTGCTGCAGCCCTCCTGTCCCTTCCCCCCAGTC
  81 AGTATTACCCTGTGAAGCCCCTTCCCTCCTTTATTATTCAGGAGGGCTGGGGGGGCTCCCTGGTTCTGAGCATCATCCTT
 161 TCCCCTCCCCTCTCTTCCTCCCCTCTGCACTTTGTTTACTTGTTTTGCACAGACGTGGGCCTGGGCCTTCTCAGCAGCCG
 241 CCTTCTAGTTGGGGGCTAGTCGCTGATCTGCCGGCTCCCGCCCAGCCTGTGTGGAAAGGAGGCCCACGGGCACTAGGGGA
 321 GCCGAATTCTACAATCCCGCTGGGGCGGCCGGGGCGGGAGAGAAAGGTGGTGCTGCAGTGGTGGCCCTGGGGGGCCATTC
 401 GATTCGCCTCAGTTGCTGCTGTAATAAAAGTCTACTTTTTGCTAAAAAAAAAAAAAAA

ID	Duplex structure	Position	Score	MFE
1	miRNA 3' ggGGUGGA-AAAGAAGGGGg 5' || ||| | ||||||| Target 5' gcCCTCCTGTCCCTTCCCCc 3'	56 - 75	153.00	-26.10
2	miRNA 3' ggGGUGGAAAAGAAGGGGg 5' |: || | ||||||:| Target 5' ccCTCCCCTCTCTTCCTCc 3'	163 - 181	141.00	-22.30
3	miRNA 3' ggGGUGGAAAA-------GAAGGGGg 5' ::||| | | ||||||: Target 5' taTTACCCTGTGAAGCCCCTTCCCTc 3'	83 - 108	125.00	-17.30

Mutant ID	Mutant Position	Mutant Source
COSN20050730	13	COSMIC
COSN30161511	27	COSMIC
COSN31541066	29	COSMIC
COSN26986981	30	COSMIC
COSN30143850	41	COSMIC
COSN30531112	51	COSMIC
COSN19624911	57	COSMIC
COSN30145630	62	COSMIC
COSN30103776	69	COSMIC
COSN26986982	94	COSMIC
COSN31509494	95	COSMIC
COSN28850318	136	COSMIC
COSN1237187	415	COSMIC

Mutant ID	Mutant Position	Mutant Source
rs535411023	2	dbSNP
rs759847694	4	dbSNP
rs1216903220	9	dbSNP
rs375675632	10	dbSNP
rs760680335	11	dbSNP
rs1227367288	12	dbSNP
rs1266701845	13	dbSNP
rs976056468	16	dbSNP
rs764155385	18	dbSNP
rs768296717	22	dbSNP
rs753974931	27	dbSNP
rs776158770	28	dbSNP
rs375201231	29	dbSNP
rs377203491	30	dbSNP
rs368455683	33	dbSNP
rs761404745	34	dbSNP
rs1409555240	36	dbSNP
rs1458562659	41	dbSNP
rs546092844	48	dbSNP
rs200302973	49	dbSNP
rs573033458	52	dbSNP
rs955712651	56	dbSNP
rs1360718698	58	dbSNP
rs1408718899	59	dbSNP
rs752849843	65	dbSNP
rs1176623141	68	dbSNP
rs988459210	71	dbSNP
rs540472637	72	dbSNP
rs559793463	75	dbSNP
rs1021238151	76	dbSNP
rs530162657	77	dbSNP
rs1175151167	85	dbSNP
rs1484403008	89	dbSNP
rs1377760990	90	dbSNP
rs1203571420	92	dbSNP
rs968695550	93	dbSNP
rs1270061032	104	dbSNP
rs1229142493	105	dbSNP
rs780111	106	dbSNP
rs1305088848	111	dbSNP
rs563499343	124	dbSNP
rs1046056285	126	dbSNP
rs1344639585	128	dbSNP
rs1337218135	129	dbSNP
rs910710967	129	dbSNP
rs1451784332	130	dbSNP
rs530832914	130	dbSNP
rs976150273	131	dbSNP
rs552404679	133	dbSNP
rs796661055	135	dbSNP
rs923334140	136	dbSNP
rs929231332	137	dbSNP
rs1047009482	143	dbSNP
rs1460355332	155	dbSNP
rs887126792	158	dbSNP
rs111824981	163	dbSNP
rs1005562998	164	dbSNP
rs1038482104	166	dbSNP
rs1209942250	168	dbSNP
rs1048185629	169	dbSNP
rs1259056420	170	dbSNP
rs887740787	175	dbSNP
rs746396991	181	dbSNP
rs1322594026	186	dbSNP
rs1238646375	191	dbSNP
rs941944794	192	dbSNP
rs1439072766	197	dbSNP
rs1231009713	209	dbSNP
rs1380342218	210	dbSNP
rs1299645731	212	dbSNP
rs570815784	215	dbSNP
rs1383600210	220	dbSNP
rs1196067274	221	dbSNP
rs1286679195	224	dbSNP
rs1459095347	227	dbSNP
rs1365863870	231	dbSNP
rs1245598842	234	dbSNP
rs1162563403	238	dbSNP
rs144467172	239	dbSNP
rs905707246	240	dbSNP
rs1392427112	242	dbSNP
rs1189713638	243	dbSNP
rs1428507059	246	dbSNP
rs1259742019	249	dbSNP
rs528661328	250	dbSNP
rs1291257179	253	dbSNP
rs1220538835	254	dbSNP
rs1431975672	256	dbSNP
rs757107431	257	dbSNP
rs1464337632	262	dbSNP
rs1276579029	268	dbSNP
rs781151669	269	dbSNP
rs1228057680	272	dbSNP
rs1325827629	278	dbSNP
rs1292796377	280	dbSNP
rs757863510	287	dbSNP
rs997064024	288	dbSNP
rs1302074224	300	dbSNP
rs1422380375	301	dbSNP
rs1372787262	303	dbSNP
rs1030320106	304	dbSNP
rs1401603040	308	dbSNP
rs1461898967	309	dbSNP
rs1412112994	310	dbSNP
rs891539933	311	dbSNP
rs1321714190	314	dbSNP
rs955575336	315	dbSNP
rs1330594209	318	dbSNP
rs1197520324	320	dbSNP
rs999112455	322	dbSNP
rs1021705356	330	dbSNP
rs546908558	332	dbSNP
rs1338533980	334	dbSNP
rs980098469	339	dbSNP
rs945302408	342	dbSNP
rs1323553900	344	dbSNP
rs1285371519	345	dbSNP
rs1282156137	346	dbSNP
rs1402718145	350	dbSNP
rs1351996001	351	dbSNP
rs1031319456	352	dbSNP
rs976202354	356	dbSNP
rs1174764385	358	dbSNP
rs1470621641	365	dbSNP
rs957040827	366	dbSNP
rs1428675924	379	dbSNP
rs779484201	384	dbSNP
rs746652723	385	dbSNP
rs1364553482	386	dbSNP
rs1341444077	388	dbSNP
rs1248170247	389	dbSNP
rs983517487	389	dbSNP
rs568134656	395	dbSNP
rs1288609234	402	dbSNP
rs368081851	407	dbSNP
rs1261935174	422	dbSNP
rs556270039	426	dbSNP
rs1260506250	430	dbSNP
rs928472538	431	dbSNP
rs1330641611	433	dbSNP
rs1044726097	443	dbSNP
rs927179643	444	dbSNP

- 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.

- 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.