Urinary Levels of miR-491-5p and miR-592 as Potential Diagnostic Biomarkers in Female Aging Patients with OAB: A Pilot Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Patient Samples
2.2. Demographic and Clinical Differences
2.3. Sample Collection and Preparation
2.4. Isolation of miRNAs from Urine
2.5. Poly-Adenylation and cDNA Synthesis
2.6. qPCR
2.7. Statistics
3. Results
3.1. Subject Demographics and OAB Symptom Analysis
3.2. Quantitative Detection of miRNAs in Urine and Analysis of Data
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Haylen, B.; de Ridder, D.; Freeman, R.; Swift, S.; Berghmans, B.; Lee, J.; International Continence Society. An International Urogynecological Association (IUGA)/International Continence Society (ICS) joint report on the terminology for female pelvic floor dysfunction. Neurourol. Urodyn. 2010, 29, 4–20. [Google Scholar] [CrossRef] [PubMed]
- Yang, C.-F.; Huang, C.-Y.; Wang, S.-Y.; Chang, S.-R. Prevalence of and associated factors for overactive bladder subtypes in middle-aged women: A cross-sectional study. Medicina 2022, 58, 383. [Google Scholar] [CrossRef] [PubMed]
- Peyronnet, B.; Mironska, E.; Chapple, C.; Cardozo, L.; Oelke, M.; Dmochowski, R.; Amarenco, G.; Gamé, X.; Kirby, R.; Van Der Aa, F.; et al. A comprehensive review of overactive bladder pathophysiology: On the way to tailored treatment. Eur. Urol. 2019, 75, 988–1000. [Google Scholar] [CrossRef] [PubMed]
- Quaghebeur, J.; Petros, P.; Wyndaele, J.-J.; de Wachter, S. Pelvic-floor function, dysfunction, and treatment. Eur. J. Obstet. Gynecol. Reprod. Biol. 2021, 265, 143–149. [Google Scholar] [CrossRef]
- Conroy, J.N.; Coulson, E.J. High-affinity TrkA and p75 neurotrophin receptor complexes: A twisted affair. J. Biol. Chem. 2022, 298, 101568. [Google Scholar] [CrossRef] [PubMed]
- Nykjaer, A.; Lee, R.; Teng, K.K.; Jansen, P.; Madsen, P.; Nielsen, M.S.; Jacobsen, C.; Kliemannel, M.; Schwarz, E.; Willnow, T.E. Sortilin is essential for proNGF-induced neuronal cell death. Nature 2004, 427, 843. [Google Scholar] [CrossRef]
- Ioannou, M.S.; Fahnestock, M. ProNGF, but Not NGF, Switches from Neurotrophic to Apoptotic Activity in Response to Reductions in TrkA Receptor Levels. Int. J. Mol. Sci. 2017, 18, 599. [Google Scholar] [CrossRef]
- Choucry, A.M.; Al-Shorbagy, M.Y.; Attia, A.S.; El-Abhar, H.S. Pharmacological manipulation of Trk, p75NTR, and NGF balance restores memory deficit in global ischemia/reperfusion model in rats. J. Mol. Neurosci. 2019, 68, 78–90. [Google Scholar] [CrossRef]
- Chung, D.Y.; Song, K.; Choi, M.; Limanjaya, A.; Ghatak, K.; Ock, J.; Yin, G.N.; Hong, C.H.; Hong, S.; Suh, J.; et al. Neutralizing antibody to proNGF rescues erectile function by regulating the expression of neurotrophic and angiogenic factors in a mouse model of cavernous nerve injury. Andrology 2020, 9, 329–341. [Google Scholar] [CrossRef]
- Gamper, M.; Moser, R.; Viereck, V. Have we been led astray by the NGF biomarker data? Neurourol. Urodyn. 2015, 36, 203–204. [Google Scholar] [CrossRef] [Green Version]
- Antunes-Lopes, T.; Cruz, F. Urinary Biomarkers in Overactive Bladder: Revisiting the Evidence in 2019. Eur. Urol. Focus 2019, 5, 329–336. [Google Scholar] [CrossRef] [PubMed]
- Mossa, A.H.; Cammisotto, P.G.; Shamout, S.; Campeau, L. Imbalance of nerve growth factor metabolism in aging women with overactive bladder syndrome. World J. Urol. 2021, 39, 2055–2063. [Google Scholar] [CrossRef] [PubMed]
- Mall, C.; Rocke, D.M.; Durbin-Johnson, B.; Weiss, R.H. Stability of miRNA in human urine supports its biomarker potential. Biomark. Med. 2013, 7, 623–631. [Google Scholar] [CrossRef]
- Paolini, A.; Baldassarre, A.; Bruno, S.P.; Felli, C.; Muzi, C.; Badi, S.A.; Siadat, S.D.; Sarshar, M.; Masotti, A. Improving the diagnostic potential of extracellular miRNAs coupled to multiomics data by exploiting the power of artificial intelligence. Front. Microbiol. 2022, 13, 888414. [Google Scholar] [CrossRef] [PubMed]
- Mossa, A.H.; Shamout, S.; Cammisotto, P.; Campeau, L. Urinary metabolomics predict the severity of overactive bladder syndrome in an aging female population. Int. Urogynecology J. 2019, 31, 1023–1031. [Google Scholar] [CrossRef]
- Wishart, D.S.; Knox, C.; Guo, A.C.; Eisner, R.; Young, N.; Gautam, B.; Hau, D.D.; Psychogios, N.; Dong, E.; Bouatra, S. HMDB: A knowledgebase for the human metabolome. Nucleic Acids Res. 2009, 37 (Suppl. 1), D603–D610. [Google Scholar] [CrossRef]
- Bouatra, S.; Aziat, F.; Mandal, R.; Guo, A.C.; Wilson, M.R.; Knox, C.; Bjorndahl, T.C.; Krishnamurthy, R.; Saleem, F.; Liu, P.; et al. The Human Urine Metabolome. PLoS ONE 2013, 8, e73076. [Google Scholar] [CrossRef]
- Cheng, L.; Sun, X.; Scicluna, B.J.; Coleman, B.M.; Hill, A. Characterization and deep sequencing analysis of exosomal and non-exosomal miRNA in human urine. Kidney Int. 2014, 86, 433–444. [Google Scholar] [CrossRef]
- Braicu, C.; Buiga, R.; Cojocneanu, R.; Buse, M.; Raduly, L.; Pop, L.A.; Chira, S.; Budisan, L.; Jurj, A.; Ciocan, C.; et al. Connecting the dots between different networks: miRNAs associated with bladder cancer risk and progression. J. Exp. Clin. Cancer Res. 2019, 38, 433. [Google Scholar] [CrossRef]
- Li, S.; Wang, X.; Gu, Y.; Chen, C.; Wang, Y.; Liu, J.; Hu, W.; Yu, B.; Wang, Y.; Ding, F. Let-7 microRNAs regenerate peripheral nerve regeneration by targeting nerve growth factor. Mol. Ther. 2015, 23, 423–433. [Google Scholar] [CrossRef] [Green Version]
- Bruno, M.A.; Cuello, A.C. Activity-dependent release of precursor nerve growth factor, conversion to mature nerve growth factor, and its degradation by a protease cascade. Proc. Natl. Acad. Sci. USA 2006, 103, 6735–6740. [Google Scholar] [CrossRef] [PubMed]
- Yan, W.; Zhang, W.; Sun, L.; Liu, Y.; You, G.; Wang, Y.; Kang, C.; You, Y.; Jiang, T. Identification of MMP-9 specific microRNA expression profile as potential targets of anti-invasion therapy in glioblastoma multiforme. Brain Res. 2011, 1411, 108–115. [Google Scholar] [CrossRef] [PubMed]
- Irmady, K.; Jackman, K.A.; Padow, V.A.; Shahani, N.; Martin, L.A.; Cerchietti, L.; Unsicker, K.; Iadecola, C.; Hempstead, B.L. Mir-592 regulates the induction and cell death-promoting activity of p75NTR in neuronal ischemic injury. J. Neurosci. 2014, 34, 3419–3428. [Google Scholar] [CrossRef] [PubMed]
- Othumpangat, S.; Walton, C.; Piedimonte, G. MicroRNA-221 Modulates RSV Replication in Human Bronchial Epithelium by Targeting NGF Expression. PLoS ONE 2012, 7, e30030. [Google Scholar] [CrossRef]
- Liao, W.; Zhang, H.; Feng, C.; Wang, T.; Zhang, Y.; Tang, S. Downregulation of TrkA protein expression by miRNA-92a promotes the proliferation and migration of human neuroblastoma cells. Mol. Med. Rep. 2014, 10, 778–784. [Google Scholar] [CrossRef]
- Montalban, E.; Mattugini, N.; Ciarapica, R.; Provenzano, C.; Savino, M.; Scagnoli, F.; Prosperini, G.; Carissimi, C.; Fulci, V.; Matrone, C.; et al. MiR-21 is an Ngf-modulated microRNA that supports Ngf signaling and regulates neuronal degeneration in PC12 cells. Neuro Mol. Med. 2014, 16, 415–430. [Google Scholar] [CrossRef]
- el Fatimy, R.; Boulaassafre, S.; Bouchmaa, N.; el Khayari, A.; Vergely, C.; Malka, G.; Rochette, L. The emerging role of miRNA-132/212 cluster in neurologic and cardiovascular diseases: Neuroprotective role in cells with prolonged longevity. Mech. Ageing Dev. 2021, 199, 111566. [Google Scholar] [CrossRef]
- Cuello, A.C.; Pentz, R.; Hall, H. The Brain NGF Metabolic Pathway in Health and in Alzheimer’s Pathology. Front. Neurosci. 2019, 13, 62. [Google Scholar] [CrossRef]
- Paczek, L.; Michalska, W.; Bartlomiejczyk, I. Trypsin, elastase, plasmin and MMP-9 activity in the serum during the human ageing process. Age Ageing 2008, 37, 318–323. [Google Scholar] [CrossRef]
- Costantini, C.; Scrable, H.; Puglielli, L. An aging pathway controls the TrkA to p75NTR receptor switch and amyloid β-peptide generation. EMBO J. 2006, 25, 1997–2006. [Google Scholar] [CrossRef] [Green Version]
- Dhahbi, J.M.; Spindler, S.R.; Atamna, H.; Yamakawa, A.; Guerrero, N.; Boffelli, D.; Mote, P.; Martin, D.I. Deep sequencing identifies circulating mouse miRNAs that are functionally implicated in manifestations of aging and responsive to calorie restriction. Aging 2013, 5, 130–141. [Google Scholar] [CrossRef] [PubMed]
- Abdolahi, S.; Zare-Chahoki, A.; Noorbakhsh, F.; Gorji, A. A review of molecular interplay between neurotrophins and miRNAs in neuropsychological disorders. Mol. Neurobiol. 2022, 1–21. [Google Scholar] [CrossRef] [PubMed]
- Qian, Y.; Song, J.; Ouyang, Y.; Han, Q.; Chen, W.; Zhao, X.; Xie, Y.; Chen, Y.; Yuan, W.; Fan, C. Advances in roles of miR-132 in the nervous system. Front. Pharmacol. 2017, 8, 770. [Google Scholar] [CrossRef] [PubMed]
- Kamal, N.N.S.B.N.M.; Shahidan, W.N.S. Non-exosomal and exosomal circulatory micrornas: Which are more valid as biomarkers? Front. Pharmacol. 2020, 10, 1500. [Google Scholar] [CrossRef]
- Fırat, E.; Aybek, Z.; Akgün, Ş.; Küçüker, K.; Akça, H.; Aybek, H. Exploring biomarkers in the overactive bladder: Alterations in miRNA levels of a panel of genes in patients with OAB. Neurourol. Urodyn. 2019, 38, 1571–1578. [Google Scholar] [CrossRef]
- Martins, T.S.; Vaz, M.; Henriques, A.G. A review on comparative studies addressing exosome isolation methods from body fluids. Anal. Bioanal. Chem. 2022, 1–25. [Google Scholar] [CrossRef]
CTR | OAB Group | p Value | |
---|---|---|---|
Demographic and serum analysis: | |||
Age (years) | 56.25 (5.22) | 68.9 (11.38) | <0.001 |
BMI (kg/m2) | 29.75 (7.65) | 28.82 (5.45) | ns |
eGFR (mL/min/1.73 m2) | 98.5 (14.52) | 76 (19.78) | <0.001 |
HOMA-IR | 2.13 (1.03) | 3.11 (1.18) | 0.020 |
Total Cholesterol/HDL | 3.50 (1.18) | 3.23 (0.81) | ns |
Questionnaires’ scores: | |||
OABSS (0–28) | 7.3 (3.56) | 17.45 (4.45) | <0.001 |
ICIQ-SF (0–22) | 3.26 (3.98) | 8.05 (3.83) | <0.001 |
IIQ-7 (0–100) | 2.4 (5.2) | 28.9 (23.2) | <0.001 |
Voiding diary parameters: | |||
24 h frequency | 9.15 (2.28) | 11.4 (3.03) | 0.012 |
Daytime frequency | 8.5 (2.04) | 9.5 (2.09) | ns |
Night frequency | 0.65 (0.81) | 1.9 (1.71) | 0.005 |
24 h voiding volume (mL) | 2705 (2346.02) | 1859.6 (865.37) | ns |
Night voiding volume (mL) | 495.25 (253.88) | 449.75 (270.77) | ns |
Mean voided volume (mL) | 322.25 (311.1) | 167.36 (75.2) | 0.037 |
Maximum voided volume (mL) | 480.75 (193.44) | 327.25 (126.7) | 0.005 |
CTR | OAB Group | p Value | |
---|---|---|---|
miR-98-5p | 0.196 (0.012, 1.508) | 0.318 (0.0703, 2.366) | 0.343 |
let-7b-5p | 0.608 (0.176, 1.109) | 0.633 (0.304, 1.874) | 0.584 |
let-7d-5p | 0.922 (0.344, 1.541) | 0.379 (0.208, 1.194) | 0.130 |
miR-491-5p | 0.534 (0.254, 1.686) | 0.118 (0.013, 0.365) | 0.005 |
miR-885-5p | 0.0633 (0.009, 0.419) | 0.124 (0.031, 0.394) | 0.327 |
miR-221-5p | 0.626 (0.297, 1.231) | 0.859 (0.385, 2.424) | 0.299 |
miR-92a-3p | 0.287 (0.089, 0.847) | 0.268 (0.032, 2.195) | 0.715 |
miR-592 | 0.777 (0.375, 1.622) | 0.460 (0.234, 0.612) | 0.010 |
miR-21-5p | 0.398 (0.178, 0.942) | 0.854 (0.318, 1.392) | 0.224 |
miR-132 | 0.713 (0.327, 1.615) | 0.593 (0.242, 1.287) | 0.384 |
miR-212-5p | 0.089 (0.024, 0.561) | 0.113 (0.058, 0.482) | 0.756 |
Confounders | CTR | OAB Group | p Value | |
---|---|---|---|---|
miR-98-5p | Age | 0.704 (0.02–1.387) | 1.275 (0.592–1.959) | 0.283 |
HOMA-IR | 0.768 (−0.008–1.545) | 1.560 (0.753–2.367) | 0.179 | |
eGFR | 0.985 (0.332–1.637) | 0.994 (0.342–1.647) | 0.985 | |
let-7b-5p | Age | 0.653 (0.1–1.205) | 1.302 (0.731–1.873) | 0.142 |
HOMA-IR | 0.755 (0.209–1.301) | 1.262 (0.669–1.855) | 0.237 | |
eGFR | 0.779 (0.232–1.325) | 1.169 (0.605–1.733) | 0.360 | |
let-7d-5p | Age | 0.965 (0.593–1.338) | 0.726 (0.354–1.099) | 0.409 |
HOMA-IR | 1.161 (0.748–1.575) | 0.712 (0.284–1.140) | 0.151 | |
eGFR | 0.978 (0.612–1.344) | 0.713 (0.347–1.079) | 0.346 | |
miR-491-5p | Age | 0.999 (0.619–1.378) | 0.225 (−0.167–0.616) | 0.013 |
HOMA-IR | 0.909 (0.531–1.287) | 0.266 (−0.141–0.672) | 0.030 | |
eGFR | 1.1 (0.735–1.465) | 0.118 (−0.258–0.494) | 0.001 | |
miR-885-5p | Age | 0.255 (−0.028–0.538) | 0.389 (0.106–0.671) | 0.548 |
HOMA-IR | 0.272 (−0.034–0.578) | 0.453 (0.147–0.759) | 0.413 | |
eGFR | 0.328 (0.059–0.596) | 0.316 (0.047–0.585) | 0.954 | |
miR-221-5p | Age | 0.744 (0.233–1.256) | 1.425 (0.929–1.921) | 0.086 |
HOMA-IR | 0.768 (0.276–1.259) | 1.216 (0.725–1.708) | 0.216 | |
eGFR | 0.749 (0.249–1.249) | 1.421 (0.935–1.906) | 0.079 | |
miR-92a-3p | Age | 0.276 (−0.589–1.142) | 1.668(0.885–2.4510 | 0.036 |
HOMA-IR | 0.604 (−0.332–1.540) | 1.446 (0.581–2.311) | 0.206 | |
eGFR | 0.318 (−0.525–1.161) | 1.633 (0.868–2.398) | 0.039 | |
miR-592 | Age | 0.981 (0.705–1.257) | 0.445 (0.151–0.739) | 0.019 |
HOMA-IR | 1.096 (0.796–1.396) | 0.405 (0.082–0.728) | 0.005 | |
eGFR | 1.002 (0.728–1.2750 | 0.421 (0.130–0.713) | 0.011 | |
miR-21-5p | Age | 0.595 (0.229–0.96) | 0.912 (0.558–1.266) | 0.251 |
HOMA-IR | 0.613 (0.222–1.0040 | 1.029 (0.638–1.420) | 0.152 | |
eGFR | 0.655 (0.290–1.021) | 0.855 (0.501–1.208) | 0.468 | |
miR-132 | Age | 0.997 (0.620–1.375) | 0.818 (0.429–1.208) | 0.549 |
HOMA-IR | 1.134 (0.727–1.541) | 0.815 (0.377–1.253) | 0.309 | |
eGFR | 1.024 (0.656–1.392) | 0.790 (0.410–1.169) | 0.413 | |
miR-212-5p | Age | 0.415 (0.097–0.733) | 0.285 (−0.058–0.627) | 0.616 |
HOMA-IR | 0.353 (0.082–0.625) | 0.262 (−0.032–0.556) | 0.648 | |
eGFR | 0.414 (0.110–0.718) | 0.286 (−0.040–0.612) | 0.593 |
miRNA | Variables | Correlation Coefficient (r) | p Value |
---|---|---|---|
miR-592 | OABSS | −0.325 | 0.047 |
ICIQ-SF | −0.392 | 0.016 | |
IIQ-7 | −0.422 | 0.008 | |
miR-491-5p | OABSS | −0.405 | 0.011 |
ICIQ-SF | -0.379 | 0.019 | |
IIQ-7 | −0.494 | 0.001 | |
miR-21-5p | 24 h frequency | −0.522 | 0.001 |
Daytime frequency | 0.45 | 0.005 | |
miR-212-5p | IIQ-7 | 0.356 | 0.026 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Cammisotto, P.G.; Mossa, A.H.; Shamout, S.; Campeau, L. Urinary Levels of miR-491-5p and miR-592 as Potential Diagnostic Biomarkers in Female Aging Patients with OAB: A Pilot Study. Metabolites 2022, 12, 820. https://doi.org/10.3390/metabo12090820
Cammisotto PG, Mossa AH, Shamout S, Campeau L. Urinary Levels of miR-491-5p and miR-592 as Potential Diagnostic Biomarkers in Female Aging Patients with OAB: A Pilot Study. Metabolites. 2022; 12(9):820. https://doi.org/10.3390/metabo12090820
Chicago/Turabian StyleCammisotto, Philippe G., Abubakr H. Mossa, Samer Shamout, and Lysanne Campeau. 2022. "Urinary Levels of miR-491-5p and miR-592 as Potential Diagnostic Biomarkers in Female Aging Patients with OAB: A Pilot Study" Metabolites 12, no. 9: 820. https://doi.org/10.3390/metabo12090820
APA StyleCammisotto, P. G., Mossa, A. H., Shamout, S., & Campeau, L. (2022). Urinary Levels of miR-491-5p and miR-592 as Potential Diagnostic Biomarkers in Female Aging Patients with OAB: A Pilot Study. Metabolites, 12(9), 820. https://doi.org/10.3390/metabo12090820