The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
MicroRNA 144 Impairs Insulin Signaling by Inhibiting the Expression of Insulin Receptor Substrate 1 in Type 2 Diabetes Mellitus
Open accessAbstract
BACKGROUND: Dysregulation of microRNA (miRNA) expression in various tissues and body fluids has been demonstrated to be associated with several diseases, including Type 2 Diabetes mellitus (T2D). Here, we compare miRNA expression profiles in different tissues (pancreas, liver, adipose and skeletal muscle) as well as in blood samples from T2D rat model and highlight the potential of circulating miRNAs as biomarkers of T2D. In parallel, we have examined the expression profiles of miRNAs in blood samples from Impaired Fasting Glucose (IFG) and T2D male patients. METHODOLOGY/PRINCIPAL FINDINGS: Employing miRNA microarray and stem-loop real-time RT-PCR, we identify four novel miRNAs, miR-144, miR-146a, miR-150 and miR-182 in addition to four previously reported diabetes-related miRNAs, miR-192, miR-29a, miR-30d and miR-320a, as potential signature miRNAs that distinguished IFG and T2D. Of these microRNAs, miR-144 that promotes erythropoiesis has been found to be highly up-regulated. Increased circulating level of miR-144 has been found to correlate with down-regulation of its predicted target, insulin receptor substrate 1 (IRS1) at both mRNA and protein levels. We could also experimentally demonstrate that IRS1 is indeed the target of miR-144. CONCLUSION: We demonstrate that peripheral blood microRNAs can be developed as unique biomarkers that are reflective and predictive of metabolic health and disorder. We have also identified signature miRNAs which could possibly explain the pathogenesis of T2D and the significance of miR-144 in insulin signaling.
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1 retracted
RETRACTED: Human chromosome 21-derived miRNAs are overexpressed in down syndrome brains and hearts
Kuhn, D. E., Nuovo, G. J., Martin, M. M., Malana, G. E., Pleister, A. P., Jiang, J., Schmittgen, T. D., Terry, A. V., Gardiner, K., Head, E., et al. (2008) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2008.03.120 · Cited by 153
RetractedThe C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14
Lee, R. C., Feinbaum, R. L., & Ambros, V. (1993) · Cell · doi:10.1016/0092-8674(93)90529-y · Cited by 12,948
(2004) · The Lancet · doi:10.1016/s0140-6736(03)15268-3 · Cited by 12,717
Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus
The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus* (2002) · Diabetes Care · doi:10.2337/diacare.25.2007.s5 · Cited by 10,338
Circulating microRNAs as stable blood-based markers for cancer detection
Mitchell, P. S., Parkin, R. K., Kroh, E. M., Fritz, B. R., Wyman, S. K., Pogosova-Agadjanyan, E. L., Peterson, A., Noteboom, J., O'Briant, K. C., Allen, A., et al. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0804549105 · Cited by 7,969
Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus
The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus (1997) · Diabetes Care · doi:10.2337/diacare.20.7.1183 · Cited by 6,220
Combinatorial microRNA target predictions
Krek, A., Grün, D., Poy, M. N., Wolf, R., Rosenberg, L., Epstein, E. J., MacMenamin, P., da Piedade, I., Gunsalus, K. C., Stoffel, M., et al. (2005) · Nature Genetics · doi:10.1038/ng1536 · Cited by 4,603
Chen, X., Ba, Y., Ma, L., Cai, X., Yin, Y., Wang, K., Guo, J., Zhang, Y., Chen, J., Guo, X., et al. (2008) · Cell Research · doi:10.1038/cr.2008.282 · Cited by 4,560
Switching from Repression to Activation: MicroRNAs Can Up-Regulate Translation
Vasudevan, S., Tong, Y., & Steitz, J. A. (2007) · Science · doi:10.1126/science.1149460 · Cited by 2,822
Exosomes: Extracellular organelles important in intercellular communication
Mathivanan, S., Ji, H., & Simpson, R. J. (2010) · Journal of Proteomics · doi:10.1016/j.jprot.2010.06.006 · Cited by 2,537
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Application of microRNAs in diabetes mellitus
Chen, H., Lan, H.-Y., Roukos, D. H., & Cho, W. C. (2014) · Journal of Endocrinology · doi:10.1530/joe-13-0544 · Cited by 124
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Metadata
- Authors
- Karolina, D. S., Armugam, A., Tavintharan, S., Wong, M. T. K., Lim, S. C., Sum, C. F., & Jeyaseelan, K.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2011-08-01
- Volume
- 6
- Issue
- 8
- Pages
- e22839
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2168856472
- Cited by
- 434
- References
- 68
- FWCI
- 8.64
- Type
- journal article
- Language
- en
- Open access
- gold
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- CoChat. (2026). Citation check: MicroRNA 144 Impairs Insulin Signaling by Inhibiting the Expression of Insulin Receptor Substrate 1 in Type 2 Diabetes Mellitus. https://app.cochat.ai/papers/10.1371/journal.pone.0022839
- The paper
- Karolina, D. S., Armugam, A., Tavintharan, S., Wong, M. T. K., Lim, S. C., Sum, C. F., & Jeyaseelan, K. (2011). MicroRNA 144 Impairs Insulin Signaling by Inhibiting the Expression of Insulin Receptor Substrate 1 in Type 2 Diabetes Mellitus. PLoS ONE. https://doi.org/10.1371/journal.pone.0022839
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