Integrated analysis of directly captured microRNA targets reveals the impact of microRNAs on mammalian transcriptome
Open accessPublished version of a preprint
Abstract
MicroRNA (miRNA)-mediated regulation is widespread, relatively mild but functionally important. It remains challenging to unequivocally identify miRNA targeted RNAs at a genomic scale and determine how changes in miRNA levels affect the transcriptome. Here, we captured individual miRNAs and their targeted RNA sites in wild-type, miR-200 family knockout and induced epithelial cells. We detected 1797 miRNAs interacting with 13,830 transcripts at 616,127 sites by sequencing 1,230,019 unique miRNA:RNA chimeras. Although mRNA sites that are bound by miRNAs and contain matches to seed sequences confer the strongest regulation, ∼40%–60% of miRNA bound regions do not contain seed matches. Different miRNAs have different preferences to seed matches and 3′ end base-pairing. For individual miRNAs, the effectiveness of mRNA regulation is highly correlated with the number of captured miRNA:mRNA chimeras. Notably, elevated miR-200 expression robustly represses existing targets with little impact on newly recognized targets. Global analysis of directly captured mRNA targets reveals pathways that are involved in cancer and cell adhesion and signaling pathways that are highly regulated by many different miRNAs in epithelial cells. Comparison between experimentally captured and TargetScan predicted targets indicates that our approach is more effective in identifying bona fide targets by reducing false positive and negative predictions. This study reveals the global binding landscape and impact of miRNAs on the mammalian transcriptome.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Chimeric CLIP-seq approaches incorporate a phosphorylation step and an additional ligation step to increase the frequency of chimeric fragments to as much as 5.3%, although it often remains less than 2%.
“chimeric CLIP-seq approaches (including CLASH, CLEAR-CLIP, and modified iPAR-CLIP) have modified this approach by incorporating both a phosphorylation step (using 3' phosphatase minus T4 Polynucleotide Kinase) as well as an additional ligation step (without adapters) to encourage proximity-based ligation and increase the frequency of chimeric fragments to as much as 5.3%, although it often remains”
From Scalable and deep profiling of mRNA targets for individual microRNAs with chimeric eCLIP (2022) · cites this paper as Bjerke and Yi, 2020
The Introduction (page unavailable) says CLEAR-CLIP adds a ligation step to improve the low frequency of chimeric reads in the original CLASH study. The passages do not substantiate the claimed phosphorylation step, the 5.3% or less-than-2% frequencies, or the inclusion of modified iPAR-CLIP. (LLM, conf=0.85)
Experimental — accuracy not yet measuredWidespread adoption of chimeric CLIP methods remains limited because poor recovery of AGO-crosslinked RNA and low fraction of chimeric reads make it difficult to deeply profile the interactome of a miRNA of interest at a reasonable cost.
“However, despite the obvious power of direct experimental identification of miRNA targets and recent efforts to improve the efficiency of these approaches in capturing additional chimeric molecules (Bjerke and Yi, 2020;Gay et al., 2018), widespread adoption of these methods remains limited, as both the poor recovery of AGO-crosslinked RNA as well as the low fraction of chimeric reads make it”
From Scalable and deep profiling of mRNA targets for individual microRNAs with chimeric eCLIP (2022) · cites this paper as Bjerke and Yi, 2020
Chimeric CLIP can both refine computationally predicted targets and reveal new targets not captured by prediction alone.
“matching previous findings that chimeric CLIP can both refine computationally predicted targets as well as reveal new targets not captured by prediction alone (Bjerke and Yi, 2020).”
From Scalable and deep profiling of mRNA targets for individual microRNAs with chimeric eCLIP (2022) · cites this paper as Bjerke and Yi, 2020
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
MicroRNAs: Target Recognition and Regulatory Functions
Bartel, D. P. (2009) · Cell · doi:10.1016/j.cell.2009.01.002 · Cited by 20,068 · see citing works
Heinz, S., Benner, C., Spann, N., Bertolino, E., Lin, Y. C., Laslo, P., Cheng, J. X., Murre, C., Singh, H., & Glass, C. K. (2010) · Molecular Cell · doi:10.1016/j.molcel.2010.05.004 · Cited by 13,382 · see citing works
Enrichr: a comprehensive gene set enrichment analysis web server 2016 update
Kuleshov, M. V., Jones, M. R., Rouillard, A. D., Fernandez, N. F., Duan, Q., Wang, Z., Koplev, S., Jenkins, S. L., Jagodnik, K. M., Lachmann, A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw377 · Cited by 10,799 · see citing works
The functions of animal microRNAs
Ambros, V. (2004) · Nature · doi:10.1038/nature02871 · Cited by 10,728 · see citing works
MicroRNA expression profiles classify human cancers
Lü, J., Getz, G. A., Miska, E. A., Alvarez-Saavedra, E., Lamb, J., Peck, D., Sweet-Cordero, A., Ebert, B. L., Mak, R. H., Ferrando, A. A., et al. (2005) · Nature · doi:10.1038/nature03702 · Cited by 9,497 · see citing works
MicroRNA therapeutics: towards a new era for the management of cancer and other diseases
Rupaimoole, R., & Slack, F. J. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.246 · Cited by 4,741 · see citing works
Mammalian microRNAs predominantly act to decrease target mRNA levels
Guo, H., Ingolia, N. T., Weissman, J. S., & Bartel, D. P. (2010) · Nature · doi:10.1038/nature09267 · Cited by 3,886 · see citing works
Bartel, D. P. (2018) · Cell · doi:10.1016/j.cell.2018.03.006 · Cited by 3,751 · see citing works
The impact of microRNAs on protein output
Baek, D., Villén, J., Shin, C., Camargo, F. D., Gygi, S. P., & Bartel, D. P. (2008) · Nature · doi:10.1038/nature07242 · Cited by 3,592 · see citing works
Transcriptome-wide Identification of RNA-Binding Protein and MicroRNA Target Sites by PAR-CLIP
Hafner, M., Landthaler, M., Burger, L., Khorshid, M., Hausser, J., Berninger, P., Rothballer, A., Ascano, M., Jungkamp, A.-C., Munschauer, M., et al. (2010) · Cell · doi:10.1016/j.cell.2010.03.009 · Cited by 2,873 · see citing works
Fast and effective prediction of microRNA/target duplexes
REHMSMEIER, M., STEFFEN, P., HÖCHSMANN, M., & GIEGERICH, R. (2004) · RNA · doi:10.1261/rna.5248604 · Cited by 2,560 · see citing works
MicroRNA biogenesis pathways in cancer
Lin, S., & Gregory, R. I. (2015) · Nature reviews. Cancer · doi:10.1038/nrc3932 · Cited by 2,117 · see citing works
Towards a molecular understanding of microRNA-mediated gene silencing
Jonas, S., & Izaurralde, E. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3965 · Cited by 1,872 · see citing works
Argonaute HITS-CLIP decodes microRNA–mRNA interaction maps
Chi, S. W., Zang, J. B., Mele, A., & Darnell, R. B. (2009) · Nature · doi:10.1038/nature08170 · Cited by 1,791 · see citing works
MicroRNAs in Stress Signaling and Human Disease
Mendell, J. T., & Olson, E. N. (2012) · Cell · doi:10.1016/j.cell.2012.02.005 · Cited by 1,673 · see citing works
Mapping the Human miRNA Interactome by CLASH Reveals Frequent Noncanonical Binding
Helwak, A., Kudla, G., Dudnakova, T., & Tollervey, D. (2013) · Cell · doi:10.1016/j.cell.2013.03.043 · Cited by 1,269 · see citing works
Diversifying microRNA sequence and function
Ameres, S. L., & Zamore, P. D. (2013) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3611 · Cited by 1,175 · see citing works
Jansson, M., & Lund, A. H. (2012) · Molecular Oncology · doi:10.1016/j.molonc.2012.09.006 · Cited by 1,120 · see citing works
Ventromedial hypothalamic neurons control a defensive emotion state
Kunwar, P. S., Zelikowsky, M., Remedios, R., Cai, H., Balban, M. Y., Meister, M., & Anderson, D. J. (2015) · eLife · doi:10.7554/elife.06633 · Cited by 1,112 · see citing works
Quorum sensing triggers the stochastic escape of individual cells from Pseudomonas putida biofilms
Cárcamo‐Oyarce, G., Lumjiaktase, P., Kümmerli, R., & Eberl, L. (2015) · Nature Communications · doi:10.1038/ncomms6945 · Cited by 959 · see citing works
Structural basis for microRNA targeting
Schirle, N. T., Sheu‐Gruttadauria, J., & MacRae, I. J. (2014) · Science · doi:10.1126/science.1258040 · Cited by 563 · see citing works
Morphogenesis in skin is governed by discrete sets of differentially expressed microRNAs
Yi, R., O'Carroll, D., Pasolli, H. A., Zhang, Z., Dietrich, F. S., Tarakhovsky, A., & Fuchs, E. (2006) · Nature Genetics · doi:10.1038/ng1744 · Cited by 555 · see citing works
Self-citationNONCODEV5: a comprehensive annotation database for long non-coding RNAs
Fang, S., Zhang, L., Guo, J., Niu, Y., Wu, Y., Li, H., Zhao, L., Li, X., Teng, X., Sun, X., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1107 · Cited by 548 · see citing works
Experimental strategies for microRNA target identification
Thomson, D. W., Bracken, C. P., & Goodall, G. J. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr330 · Cited by 544 · see citing works
Pairing beyond the Seed Supports MicroRNA Targeting Specificity
Broughton, J. P., Lovci, M. T., Huang, J. L., Yeo, G. W., & Pasquinelli, A. E. (2016) · Molecular Cell · doi:10.1016/j.molcel.2016.09.004 · Cited by 487 · see citing works
The Role of miRNAs in Regulating Gene Expression Networks
Gurtan, A. M., & Sharp, P. A. (2013) · Journal of Molecular Biology · doi:10.1016/j.jmb.2013.03.007 · Cited by 441 · see citing works
Andl, T., Murchison, E. P., Liu, F., Zhang, Y., Yunta-Gonzalez, M., Tobias, J. W., Andl, C. D., Seykora, J. T., Hannon, G. J., & Millar, S. E. (2006) · Current Biology · doi:10.1016/j.cub.2006.04.005 · Cited by 359 · see citing works
MicroRNA-mediated regulation of T helper cell differentiation and plasticity
Baumjohann, D., & Ansel, K. M. (2013) · Nature reviews. Immunology · doi:10.1038/nri3494 · Cited by 354 · see citing works
Moore, M. J., Scheel, T. K. H., Luna, J. M., Park, C. Y., Fak, J. J., Nishiuchi, E., Rice, C. M., & Darnell, R. B. (2015) · Nature Communications · doi:10.1038/ncomms9864 · Cited by 326 · see citing works
Cross-linking, ligation, and sequencing of hybrids reveals RNA–RNA interactions in yeast
Kudla, G., Granneman, S., Hahn, D., Beggs, J. D., & Tollervey, D. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1017386108 · Cited by 308 · see citing works
microRNA target prediction programs predict many false positives
Pinzón, N., Li, B., Martinez, L., Sergeeva, A., Presumey, J., Apparailly, F., & Seitz, H. (2017) · Genome Research · doi:10.1101/gr.205146.116 · Cited by 268 · see citing works
irCLIP platform for efficient characterization of protein–RNA interactions
Zarnegar, B. J., Flynn, R. A., Shen, Y., Do, B. T., Chang, H. Y., & Khavari, P. A. (2016) · Nature Methods · doi:10.1038/nmeth.3840 · Cited by 264 · see citing works
An Argonaute phosphorylation cycle promotes microRNA-mediated silencing
Golden, R. J., Chen, B., Li, T., Braun, J., Manjunath, H., Chen, X., Wu, J., Schmid, V., Chang, T.-C., Kopp, F. B., et al. (2017) · Nature · doi:10.1038/nature21025 · Cited by 259 · see citing works
Quantitative functions of Argonaute proteins in mammalian developmentPreprint
Wang, D., Zhang, Z., O’Loughlin, E., Lee, T., Houel, S., O’Carroll, D., Tarakhovsky, A., Ahn, N. G., & Yi, R. (2012) · Genes & Development · doi:10.1101/gad.182758.111 · Cited by 179 · see citing works
Self-citationHomeostatic control of Argonaute stability by microRNA availability
Smibert, P., Yang, J.-S., Azzam, G., Liu, J., & Lai, E. C. (2013) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.2606 · Cited by 146 · see citing works
MicroRNA-205 controls neonatal expansion of skin stem cells by modulating the PI(3)K pathway
Wang, D., Zhang, Z., O’Loughlin, E., Wang, L., Fan, X., Lai, E. C., & Yi, R. (2013) · Nature Cell Biology · doi:10.1038/ncb2827 · Cited by 139 · see citing works
Self-citationA challenge for miRNA: multiple isomiRs in miRNAomics
Guo, L., & Chen, F. (2014) · Gene · doi:10.1016/j.gene.2014.04.039 · Cited by 117 · see citing works
Bias in microRNA functional enrichment analysis
Bleazard, T., Lamb, J. A., & Griffiths‐Jones, S. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv023 · Cited by 114 · see citing works
The microRNA-200 family coordinately regulates cell adhesion and proliferation in hair morphogenesis
Hoefert, J. E., Bjerke, G. A., Wang, D., & Yi, R. (2018) · The Journal of Cell Biology · doi:10.1083/jcb.201708173 · Cited by 68 · see citing works
Self-citationHigh-efficiency RNA cloning enables accurate quantification of miRNA expression by deep sequencing
Zhang, Z., Lee, J. E., Riemondy, K. A., Anderson, E. M., & Yi, R. (2013) · Genome biology · doi:10.1186/gb-2013-14-10-r109 · Cited by 61 · see citing works
Self-citationSaito, T., & Sætrom, P. (2012) · Silence · doi:10.1186/1758-907x-3-3 · Cited by 36 · see citing works
MicroRNA-203 represses selection and expansion of oncogenic Hras transformed tumor initiating cells
Riemondy, K. A., Wang, X., Torchia, E. C., Roop, D. R., & Yi, R. (2015) · eLife · doi:10.7554/elife.07004 · Cited by 19 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Epithelial miR-141 regulates IL-13–induced airway mucus production
Siddiqui, S., Johansson, K., Joo, A., Bonser, L. R., Koh, K. D., Tonquèze, O. L., Bolourchi, S., Bautista, R. A., Zlock, L. T., Roth, T. L., et al. (2021) · JCI Insight · doi:10.1172/jci.insight.139019 · Cited by 88 · see citing works
Scalable and deep profiling of mRNA targets for individual microRNAs with chimeric eCLIP
Manakov, S. A., Shishkin, A. A., Yee, B. A., Shen, K. A., Cox, D. C., Park, S. S., Foster, H. M., Chapman, K. B., Yeo, G. W., & Van Nostrand, E. L. (2022) · doi:10.1101/2022.02.13.480296 · Cited by 43 · see citing works
RNA regulatory mechanisms controlling TGF-β signaling and EMT in cancer
Bracken, C. P., Goodall, G. J., & Gregory, P. A. (2024) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2024.06.001 · Cited by 40 · see citing works
A nuclear function for an oncogenic microRNA as a modulator of snRNA and splicing
Fatimy, R. E., Zhang, Y., Deforzh, E., Ramadas, M., Saravanan, H., Wei, Z., Rabinovsky, R., Teplyuk, N. M., Uhlmann, E. J., & Krichevsky, A. M. (2022) · Molecular Cancer · doi:10.1186/s12943-022-01494-z · Cited by 31 · see citing works
Cezar‐dos‐Santos, F., Assolini, J. P., Okuyama, N. C. M., Viana, K. F., de Oliveira, K. B., & Itano, E. N. (2020) · Infection Genetics and Evolution · doi:10.1016/j.meegid.2020.104586 · Cited by 24 · see citing works
Regulation of airway immunity by epithelial miRNAs
Johansson, K., Woodruff, P. G., & Ansel, K. M. (2021) · Immunological Reviews · doi:10.1111/imr.13028 · Cited by 15 · see citing works
Wang, J., Fu, Y., Huang, W., Biswas, R., Banerjee, A., Broussard, J. A., Zhao, Z., Wang, D., Bjerke, G. A., Raghavan, S., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2220635120 · Cited by 16 · see citing works
Self-citationDifferential expression and role of miR-200 family in multiple tumors.
Wen, B., Zhu, R., Jin, H., & Zhao, K. (2021) · Analytical Biochemistry · doi:10.1016/j.ab.2021.114243 · Cited by 13 · see citing works
Integrated bioinformatics analysis of validated and circulating miRNAs in ovarian cancer
Doğan, B., Gümüşoğlu-Acar, E., Ülgen, E., Sezerman, O. U., & Günel, T. (2022) · Genomics & Informatics · doi:10.5808/gi.21067 · Cited by 12 · see citing works
Li, X., Jin, D. S., Eadara, S., Caterina, M. J., & Meffert, M. K. (2023) · Neurobiology of Pain · doi:10.1016/j.ynpai.2023.100119 · Cited by 10 · see citing works
Ressel, S., Kumar, S., Bermúdez‐Barrientos, J. R., Gordon, K., Lane, J., Wu, J. C., Abreu‐Goodger, C., Schwarze, J., & Buck, A. H. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae116 · Cited by 11 · see citing works
Fan, M., He, P., Lin, X., Yang, C., Li, C., & Xing, L. (2020) · The Kaohsiung Journal of Medical Sciences · doi:10.1002/kjm2.12277 · Cited by 10 · see citing works
SCRAP: a bioinformatic pipeline for the analysis of small chimeric RNA-seq data
Mills, W. T., Eadara, S., Jaffe, A. E., & Meffert, M. K. (2022) · RNA · doi:10.1261/rna.079240.122 · Cited by 8 · see citing works
MicroRNA 3′ ends shorten during adolescent brain maturation
Thomas, K. T., Vermare, A., Egleston, S. O., Wang, Y., Mishra, A., Lin, T., Peng, J., & Zakharenko, S. S. (2023) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2023.1168695 · Cited by 8 · see citing works
Transgenic overexpression of the miR-200b/200a/429 cluster inhibits mammary tumor initiation
Watson, K. L. M., Yi, R., & Moorehead, R. A. (2021) · Translational Oncology · doi:10.1016/j.tranon.2021.101228 · Cited by 5 · see citing works
Self-citationLi, X., Mills, W. T., Jin, D. S., & Meffert, M. K. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100836 · Cited by 3 · see citing works
Computational Analysis Tutorial for Chimeric Small Noncoding RNA: Target RNA Sequencing Libraries
Eadara, S., Li, X., Eiss, E. A., & Meffert, M. K. (2023) · Journal of Visualized Experiments · doi:10.3791/65779 · Cited by 2 · see citing works
Desmosomes compartmentalize mRNA and translation in the skin
D’Alessandro, A., Badu-Nkansah, K. A., Link, S., Hlavaty, D., Bjerke, G., Nicchitta, C. V., Yi, R., & Lechler, T. (2026) · Developmental Cell · doi:10.1016/j.devcel.2026.02.013 · Cited by 2 · see citing works
Self-citationHong, Y., Min, S. Y., Kim, D., Kim, S., Seo, D., Lee, K. H., & Han, S. H. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23052769 · Cited by 1 · see citing works
PACeR: a bioinformatic pipeline for the analysis of chimeric RNA-seq dataPreprint
Mills, W. T., Jaffe, A. E., & Meffert, M. K. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.25.493487 · Cited by 1 · see citing works
Compartmentalization of mRNA and Translation by DesmosomesPreprint
D’Alessandro, A., Badu-Nkansah, K. A., Link, S., Hlavaty, D., Bjerke, G. A., Nicchitta, C. V., Yi, R., & Lechler, T. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.17.649424 · Cited by 1 · see citing works
Self-citationDas, A., Fu, Y. C., Li, H., Wong, M. A., Che, A., Singh, A., Han, J., Bjerke, G. A., Wang, D., & Yi, R. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.09.698672 · Cited by 1 · see citing works
Self-citation
- Authors
- Bjerke, G. A., & Yi, R.
- Affiliations
- University of Colorado Boulder (US)
- Corresponding author
- Yi, Rui
- Venue
- RNA
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2020-01-03
- Volume
- 26
- Issue
- 3
- Pages
- 306-323
- ISSN
- 1355-8382 (ISSN-L)
- 1469-9001
- PMC
- PMC7025510
- OpenAlex
- W2998484034
- Cited by
- 22
- References
- 42
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 3 · 2023: 4 · 2022: 6 · 2021: 4 · 2020: 2
- FWCI
- 1.37
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- RNA (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health (R01AR059697, R01AR066703)
- National Institutes of Health
- American Cancer Society postdoctoral fellowship (129540-PF-16-059-01-RMC)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › MicroRNA in disease regulation
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Plant Molecular Biology Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer-related molecular mechanisms research
- Keywords
- microRNA, miRNA target identification, miR-200 family, seed sequence, mRNA regulation, transcriptome, epithelial cells, cell adhesion, cancer pathways, TargetScan, false positive reduction
- MeSH terms
- Animals, Cell Adhesion, Humans, Mammals, Neoplasms, RNA, Messenger, Signal Transduction, Gene Expression Regulation, Neoplastic, MicroRNAs, Gene Regulatory Networks, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
