Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells
Abstract
MicroRNAs (miRNAs) are emerging as important, albeit poorly characterized, regulators of biological processes. Key to further elucidation of their roles is the generation of more complete lists of their numbers and expression changes in different cell states. Here, we report a new method for surveying the expression of small RNAs, including microRNAs, using Illumina sequencing technology. We also present a set of methods for annotating sequences deriving from known miRNAs, identifying variability in mature miRNA sequences, and identifying sequences belonging to previously unidentified miRNA genes. Application of this approach to RNA from human embryonic stem cells obtained before and after their differentiation into embryoid bodies revealed the sequences and expression levels of 334 known plus 104 novel miRNA genes. One hundred seventy-one known and 23 novel microRNA sequences exhibited significant expression differences between these two developmental states. Owing to the increased number of sequence reads, these libraries represent the deepest miRNA sampling to date, spanning nearly six orders of magnitude of expression. The predicted targets of those miRNAs enriched in either sample shared common features. Included among the high-ranked predicted gene targets are those implicated in differentiation, cell cycle control, programmed cell death, and transcriptional regulation.
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This difference can have an important effect on PCR- and hybridization-based strategies that may target a secondarily abundant microRNA in the isomiR family, altering the reported expression level of a microRNA.
“This difference, which has previously been reported on a smaller scale (Morin et al. 2008), can have an important effect on PCR- and hybridization-based strategies that may target a secondarily abundant microRNA in the isomiR family, altering the reported expression level of a microRNA.”
From Toward the human cellular microRNAome (2017) · cites this paper as Morin et al. 2008
Due to inexact cutting of DICER1 and DROSHA and nucleotide additions/modifications, a collection of mostly similar sequences (with most diversity on the 3′ end) make up the isomiR family of a microRNA.
“Due to the inexact cutting of DICER1 and DROSHA and nucleotide additions/modifications, a collection of mostly similar sequences (with most diversity on the 3′ end) make up the isomiR family of a microRNA (Morin et al. 2008; Neilsen et al. 2012).”
From Toward the human cellular microRNAome (2017) · cites this paper as Morin et al. 2008
Cites
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206 · see citing works
A Mammalian microRNA Expression Atlas Based on Small RNA Library Sequencing
Landgraf, P., Rusu, M., Sheridan, R., Sewer, A., Iovino, N., Aravin, A., Pfeffer, S., Rice, A., Kamphorst, A. O., Landthaler, M., et al. (2007) · Cell · doi:10.1016/j.cell.2007.04.040 · Cited by 3,626 · see citing works
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A high-coverage genome sequence from an archaic denisovan individual
Meyer, M., Kircher, M., Gansauge, M.-T., Li, H., Racimo, F., Mallick, S., Schraiber, J. G., Jay, F., Prüfer, K., de Filippo, C., et al. (2012) · Science · doi:10.1126/science.1224344 · Cited by 1,967 · see citing works
MicroRNAs and their isomiRs function cooperatively to target common biological pathways
Cloonan, N., Wani, S., Xu, Q., Gu, J., Lea, K., Heater, S., Barbacioru, C., Steptoe, A. L., Martin, H. C., Nourbakhsh, E., et al. (2011) · Genome Biology · doi:10.1186/gb-2011-12-12-r126 · Cited by 345 · see citing works
Deconstructing a Syndrome: Genomic Insights Into PCOS Causal Mechanisms and Classification
Dapas, M., & Dunaif, A. (2022) · Endocrine Reviews · doi:10.1210/endrev/bnac001 · Cited by 300 · see citing works
Fejes, A. P., Robertson, G., Bilenky, M., Varhol, R., Bainbridge, M., & Jones, S. J. M. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn305 · Cited by 272 · see citing works
Rosa, A., & Brivanlou, A. H. (2011) · The EMBO Journal · doi:10.1038/emboj.2010.319 · Cited by 198 · see citing works
What would you do if you could sequence everything?
Kahvejian, A., Quackenbush, J., & Thompson, J. F. (2008) · Nature Biotechnology · doi:10.1038/nbt1494 · Cited by 189 · see citing works
Toward the human cellular microRNAome
McCall, M. N., Kim, M.-S., Adil, M., Patil, A. H., Lu, Y., Mitchell, C. J., Leal-Rojas, P., Xu, J., Kumar, M., Dawson, V. L., et al. (2017) · Genome Research · doi:10.1101/gr.222067.117 · Cited by 151 · see citing works
Kok, M. G. M., de Ronde, M. W. J., Moerland, P. D., Ruijter, J. M., Creemers, E. E., & Pinto-Sietsma, S. J. (2018) · Biomolecular Detection and Quantification · doi:10.1016/j.bdq.2017.11.002 · Cited by 105 · see citing works
Lopez-Valenzuela, M., Ramirez, O., Rosas, A., Garcia-Vargas, S., de la Rasilla, M., Lalueza-Fox, C., & Espinosa-Parrilla, Y. (2012) · Molecular Biology and Evolution · doi:10.1093/molbev/mss023 · Cited by 31 · see citing works
Wei, W., Hou, J., Alder, O., Ye, X., Lee, S., Cullum, R., Chu, A., Zhao, Y., Warner, S. M., Knight, D. A., et al. (2013) · Hepatology · doi:10.1002/hep.26252 · Cited by 19 · see citing works
Self-citationDeciphering synergistic regulatory networks of microRNAs in hESCs and fibroblasts
Sahu, M., & Mallick, B. (2018) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2018.03.061 · Cited by 16 · see citing works
- Authors
- Morin, R. D., O’Connor, M. D., Griffith, M., Kuchenbauer, F., Delaney, A., Prabhu, A.-L., Zhao, Y., McDonald, H., Zeng, T., Hirst, M., Eaves, C. J., & Marra, M. A.
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2008-02-19
- Volume
- 18
- Issue
- 4
- Pages
- 610-621
- ISSN
- 1088-9051
- Cited by
- 972
- References
- 2
- Language
- en
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