>KRT8|NM_002273|3'UTR
   1 ACAGCTGCGGCAGCCCCTCCCAGCCTACCCCTCCTGCGCTGCCCCAGAGCCTGGGAAGGAGGCCGCTATGCAGGGTAGCA
  81 CTGGGAACAGGAGACCCACCTGAGGCTCAGCCCTAGCCCTCAGCCCACCTGGGGAGTTTACTACCTGGGGACCCCCCTTG
 161 CCCATGCCTCCAGCTACAAAACAATTCAATTGCTTTTTTTTTTTGGTCCAAAATAAAACCTCAGCTAGCTCTGCCAATGT
 241 CAAAAAA

ID	Duplex structure	Position	Score	MFE
1	miRNA 3' cuuguauugugccuaUUGGAGa 5' |||||| Target 5' tttggtccaaaataaAACCTCa 3'	202 - 223	120.00	-6.10
2	miRNA 3' cuUGUAUUGUGCCU----AUU-GGAGa 5' || |:| :| | ||: |||| Target 5' ccACCTGAGGCTCAGCCCTAGCCCTCa 3'	96 - 122	98.00	-7.20
3	miRNA 3' cuuGUAUUGUGCCUAUUG-GAga 5' :|| :|| ||:||:| || Target 5' cgcTAT-GCA-GGGTAGCACTgg 3'	64 - 84	89.00	-8.59

Mutant ID	Mutant Position	Mutant Source
66520	8	ClinVar
66518	31	ClinVar
66519	38	ClinVar
COSN8407329	8	COSMIC
COSN30492742	9	COSMIC
COSN30515031	15	COSMIC
COSN30504677	20	COSMIC
COSN13403339	34	COSMIC
COSN30473347	45	COSMIC
COSN508960	69	COSMIC
COSN26981956	70	COSMIC
COSN30474326	93	COSMIC
COSN31547261	123	COSMIC
COSN20109594	194	COSMIC
COSN31602654	194	COSMIC

Mutant ID	Mutant Position	Mutant Source
rs777483081	2	dbSNP
rs1404558251	3	dbSNP
rs1390087131	7	dbSNP
rs201942002	8	dbSNP
rs779087897	9	dbSNP
rs749368577	11	dbSNP
rs545020896	16	dbSNP
rs1414000985	17	dbSNP
rs1250486743	19	dbSNP
rs753950633	20	dbSNP
rs780116654	24	dbSNP
rs1233136015	28	dbSNP
rs756592751	29	dbSNP
rs267607662	31	dbSNP
rs537130699	32	dbSNP
rs201337004	34	dbSNP
rs1335514586	35	dbSNP
rs1036842953	36	dbSNP
rs377432334	37	dbSNP
rs267607663	38	dbSNP
rs1050380499	43	dbSNP
rs779614156	43	dbSNP
rs537786190	45	dbSNP
rs776291517	46	dbSNP
rs1450511546	50	dbSNP
rs770456933	54	dbSNP
rs1347972648	57	dbSNP
rs760593694	64	dbSNP
rs773173782	65	dbSNP
rs1411182027	68	dbSNP
rs1233086421	71	dbSNP
rs771815578	72	dbSNP
rs747961993	74	dbSNP
rs778642491	75	dbSNP
rs1304987729	77	dbSNP
rs768871342	78	dbSNP
rs749456634	82	dbSNP
rs780056653	85	dbSNP
rs35144325	86	dbSNP
rs756215976	87	dbSNP
rs750941020	88	dbSNP
rs1268084927	93	dbSNP
rs1065710	98	dbSNP
rs1206840179	99	dbSNP
rs1318874611	101	dbSNP
rs567445786	102	dbSNP
rs757797239	112	dbSNP
rs1365203188	115	dbSNP
rs751973854	116	dbSNP
rs913432950	117	dbSNP
rs764493203	123	dbSNP
rs1387733943	124	dbSNP
rs754731231	133	dbSNP
rs1437040873	134	dbSNP
rs753520220	141	dbSNP
rs765850955	142	dbSNP
rs760214632	143	dbSNP
rs772552429	145	dbSNP
rs1426852858	148	dbSNP
rs767526007	150	dbSNP
rs1049512669	152	dbSNP
rs761673515	154	dbSNP
rs555540459	155	dbSNP
rs768428985	157	dbSNP
rs1486872473	158	dbSNP
rs749579549	163	dbSNP
rs979708912	164	dbSNP
rs775758235	165	dbSNP
rs1465610972	170	dbSNP
rs769858185	172	dbSNP
rs1234675418	175	dbSNP
rs1364264591	177	dbSNP
rs1295303211	182	dbSNP
rs1026840953	183	dbSNP
rs1383890246	189	dbSNP
rs994128525	200	dbSNP
rs745990748	204	dbSNP
rs1468698787	205	dbSNP
rs371986184	205	dbSNP
rs550199200	205	dbSNP
rs750038190	205	dbSNP
rs1393575050	208	dbSNP
rs1158647725	212	dbSNP
rs113980111	213	dbSNP
rs757848546	222	dbSNP
rs1323724799	226	dbSNP
rs1376824374	235	dbSNP
rs1372968730	236	dbSNP
rs73296024	237	dbSNP
rs1435725829	238	dbSNP

- 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.