Skip to main content

Advertisement

SpringerLink
Log in

Circulating Plasma miRNA Homologs in Mice and Humans Reflect Familial Cerebral Cavernous Malformation Disease

  • Original Article
  • Published:
Translational Stroke Research Aims and scope Submit manuscript

Abstract

Patients with familial cerebral cavernous malformation (CCM) inherit germline loss of function mutations and are susceptible to progressive development of brain lesions and neurological sequelae during their lifetime. To date, no homologous circulating molecules have been identified that can reflect the presence of germ line pathogenetic CCM mutations, either in animal models or patients. We hypothesize that homologous differentially expressed (DE) plasma miRNAs can reflect the CCM germline mutation in preclinical murine models and patients. Herein, homologous DE plasma miRNAs with mechanistic putative gene targets within the transcriptome of preclinical and human CCM lesions were identified. Several of these gene targets were additionally found to be associated with CCM-enriched pathways identified using the Kyoto Encyclopedia of Genes and Genomes. DE miRNAs were also identified in familial-CCM patients who developed new brain lesions within the year following blood sample collection. The miRNome results were then validated in an independent cohort of human subjects with real-time-qPCR quantification, a technique facilitating plasma assays. Finally, a Bayesian-informed machine learning approach showed that a combination of plasma levels of miRNAs and circulating proteins improves the association with familial-CCM disease in human subjects to 95% accuracy. These findings act as an important proof of concept for the future development of translatable circulating biomarkers to be tested in preclinical studies and human trials aimed at monitoring and restoring gene function in CCM and other diseases.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code Availability

Not applicable.

References

  1. Awad IA, Polster SP. Cavernous angiomas: deconstructing a neurosurgical disease. J Neurosurg. 2019;131:1–13. https://doi.org/10.3171/2019.3.JNS181724.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Akers A, Al-Shahi Salman R, I AA, Dahlem K, Flemming K, Hart B, et al. Synopsis of guidelines for the clinical management of cerebral cavernous malformations: consensus recommendations based on systematic literature review by the Angioma Alliance scientific advisory board clinical experts panel. Neurosurgery. 2017;80:665–80. https://doi.org/10.1093/neuros/nyx091.

  3. Shenkar R, Shi C, Rebeiz T, Stockton RA, McDonald DA, Mikati AG, et al. Exceptional aggressiveness of cerebral cavernous malformation disease associated with PDCD10 mutations. Genet Med. 2015;17:188–96. https://doi.org/10.1038/gim.2014.97.

    Article  CAS  PubMed  Google Scholar 

  4. Polster SP, Cao Y, Carroll T, Flemming K, Girard R, Hanley D, et al. Trial readiness in cavernous angiomas with symptomatic hemorrhage (CASH). Neurosurgery. 2019;84:954–64. https://doi.org/10.1093/neuros/nyy108.

    Article  PubMed  Google Scholar 

  5. Snellings DA, Hong CC, Ren AA, Lopez-Ramirez MA, Girard R, Srinath A, et al. Cerebral cavernous malformation: from mechanism to therapy. Circ Res. 2021;129:195–215. https://doi.org/10.1161/CIRCRESAHA.121.318174.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Zhu W, Shen F, Mao L, Zhan L, Kang S, Sun Z, et al. Soluble FLT1 gene therapy alleviates brain arteriovenous malformation severity. Stroke. 2017;48:1420–3. https://doi.org/10.1161/STROKEAHA.116.015713.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Girard R, Li Y, Stadnik A, Shenkar R, Hobson N, Romanos S, et al. A roadmap for developing plasma diagnostic and prognostic biomarkers of cerebral cavernous angioma with symptomatic hemorrhage (CASH). Neurosurgery. 2021;88:686–97. https://doi.org/10.1093/neuros/nyaa478.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Mikati AG, Khanna O, Zhang L, Girard R, Shenkar R, Guo X, et al. Vascular permeability in cerebral cavernous malformations. J Cereb Blood Flow Metab. 2015;35:1632–9. https://doi.org/10.1038/jcbfm.2015.98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Zeineddine HA, Girard R, Cao Y, Hobson N, Fam MD, Stadnik A, et al. Quantitative susceptibility mapping as a monitoring biomarker in cerebral cavernous malformations with recent hemorrhage. J Magn Reson Imaging. 2018;47:1133–8. https://doi.org/10.1002/jmri.25831.

    Article  PubMed  Google Scholar 

  10. Girard R, Fam MD, Zeineddine HA, Tan H, Mikati AG, Shi C, et al. Vascular permeability and iron deposition biomarkers in longitudinal follow-up of cerebral cavernous malformations. J Neurosurg. 2017;127:102–10. https://doi.org/10.3171/2016.5.JNS16687.

    Article  PubMed  Google Scholar 

  11. Huang Z, Ma L, Huang C, Li Q, Nice EC. Proteomic profiling of human plasma for cancer biomarker discovery. Proteomics. 2017;17. https://doi.org/10.1002/pmic.201600240.

  12. Loke SY, Lee ASG. The future of blood-based biomarkers for the early detection of breast cancer. Eur J Cancer. 2018;92:54–68. https://doi.org/10.1016/j.ejca.2017.12.025.

    Article  CAS  PubMed  Google Scholar 

  13. Koskimaki J, Girard R, Li Y, Saadat L, Zeineddine HA, Lightle R, et al. Comprehensive transcriptome analysis of cerebral cavernous malformation across multiple species and genotypes. JCI Insight. 2019;4. https://doi.org/10.1172/jci.insight.126167.

  14. Koskimaki J, Polster SP, Li Y, Romanos S, Srinath A, Zhang D, et al. Common transcriptome, plasma molecules, and imaging signatures in the aging brain and a Mendelian neurovascular disease, cerebral cavernous malformation. Geroscience. 2020;42:1351–63. https://doi.org/10.1007/s11357-020-00201-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lopez-Ramirez MA, Pham A, Girard R, Wyseure T, Hale P, Yamashita A, et al. Cerebral cavernous malformations form an anticoagulant vascular domain in humans and mice. Blood. 2019;133:193–204. https://doi.org/10.1182/blood-2018-06-856062.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Lopez-Ramirez MA, Fonseca G, Zeineddine HA, Girard R, Moore T, Pham A, et al. Thrombospondin1 (TSP1) replacement prevents cerebral cavernous malformations. J Exp Med. 2017;214:3331–46. https://doi.org/10.1084/jem.20171178.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Jenny Zhou H, Qin L, Zhang H, Tang W, Ji W, He Y, et al. Endothelial exocytosis of angiopoietin-2 resulting from CCM3 deficiency contributes to cerebral cavernous malformation. Nat Med. 2016;22:1033–42. https://doi.org/10.1038/nm.4169.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Wustehube J, Bartol A, Liebler SS, Brutsch R, Zhu Y, Felbor U, et al. Cerebral cavernous malformation protein CCM1 inhibits sprouting angiogenesis by activating DELTA-NOTCH signaling. Proc Natl Acad Sci U S A. 2010;107:12640–5. https://doi.org/10.1073/pnas.1000132107.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Stockton RA, Shenkar R, Awad IA, Ginsberg MH. Cerebral cavernous malformations proteins inhibit Rho kinase to stabilize vascular integrity. J Exp Med. 2010;207:881–96. https://doi.org/10.1084/jem.20091258.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Wetzel-Strong SE, Weinsheimer S, Nelson J, Pawlikowska L, Clark D, Starr MD, et al. Pilot investigation of circulating angiogenic and inflammatory biomarkers associated with vascular malformations. Orphanet J Rare Dis. 2021;16:372. https://doi.org/10.1186/s13023-021-02009-7.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Koskimaki J, Zhang D, Li Y, Saadat L, Moore T, Lightle R, et al. Transcriptome clarifies mechanisms of lesion genesis versus progression in models of Ccm3 cerebral cavernous malformations. Acta Neuropathol Commun. 2019;7:132. https://doi.org/10.1186/s40478-019-0789-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Correia de Sousa M, Gjorgjieva M, Dolicka D, Sobolewski C, Foti M. Deciphering miRNAs’ action through miRNA editing. Int J Mol Sci. 2019;20. https://doi.org/10.3390/ijms20246249.

  23. Backes C, Meese E, Keller A. Specific miRNA disease biomarkers in blood, serum and plasma: challenges and prospects. Mol Diagn Ther. 2016;20:509–18. https://doi.org/10.1007/s40291-016-0221-4.

    Article  CAS  PubMed  Google Scholar 

  24. Lugli G, Cohen AM, Bennett DA, Shah RC, Fields CJ, Hernandez AG, et al. Plasma exosomal miRNAs in persons with and without Alzheimer disease: altered expression and prospects for biomarkers. PLoS ONE. 2015;10:e0139233. https://doi.org/10.1371/journal.pone.0139233.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kar S, Bali KK, Baisantry A, Geffers R, Samii A, Bertalanffy H. Genome-wide sequencing reveals microRNAs downregulated in cerebral cavernous malformations. J Mol Neurosci. 2017;61:178–88. https://doi.org/10.1007/s12031-017-0880-6.

    Article  CAS  PubMed  Google Scholar 

  26. Lyne SB, Girard R, Koskimaki J, Zeineddine HA, Zhang D, Cao Y, et al. Biomarkers of cavernous angioma with symptomatic hemorrhage. JCI Insight. 2019;4. https://doi.org/10.1172/jci.insight.128577.

  27. Wendler A, Wehling M. The translatability of animal models for clinical development: biomarkers and disease models. Curr Opin Pharmacol. 2010;10:601–6. https://doi.org/10.1016/j.coph.2010.05.009.

    Article  CAS  PubMed  Google Scholar 

  28. Tang AT, Choi JP, Kotzin JJ, Yang Y, Hong CC, Hobson N, et al. Endothelial TLR4 and the microbiome drive cerebral cavernous malformations. Nature. 2017;545:305–10. https://doi.org/10.1038/nature22075.

    Article  CAS  PubMed Central  Google Scholar 

  29. Morrison L, Akers A. Cerebral cavernous malformation, familial. In: Adam MP, Ardinger HH, Pagon RA, Wallace SE, Bean LJH, Gripp KW, et al., editors. GeneReviews((R)). Seattle (WA)1993.

  30. de Champfleur NM, Langlois C, Ankenbrandt WJ, Le Bars E, Leroy MA, Duffau H, et al. Magnetic resonance imaging evaluation of cerebral cavernous malformations with susceptibility-weighted imaging. Neurosurgery. 2011;68:641–7; discussion 7–8. https://doi.org/10.1227/NEU.0b013e31820773cf.

  31. Blondal T, Brunetto MR, Cavallone D, Mikkelsen M, Thorsen M, Mang Y, et al. Genome-wide comparison of next-generation sequencing and qPCR platforms for microRNA profiling in serum. Methods Mol Biol. 2017;1580:21–44. https://doi.org/10.1007/978-1-4939-6866-4_3.

    Article  CAS  PubMed  Google Scholar 

  32. Potla P, Ali SA, Kapoor M. A bioinformatics approach to microRNA-sequencing analysis. Osteoarthritis and Cartilage Open. 2021;3:100131. https://doi.org/10.1016/j.ocarto.2020.100131.

  33. Decker JT, Hall MS, Blaisdell RB, Schwark K, Jeruss JS, Shea LD. Dynamic microRNA activity identifies therapeutic targets in trastuzumab-resistant HER2(+) breast cancer. Biotechnol Bioeng. 2018;115:2613–23. https://doi.org/10.1002/bit.26791.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Kim SH, MacIntyre DA, Binkhamis R, Cook J, Sykes L, Bennett PR, et al. Maternal plasma miRNAs as potential biomarkers for detecting risk of small-for-gestational-age births. EBioMedicine. 2020;62:103145. https://doi.org/10.1016/j.ebiom.2020.103145.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Johnson JJ, Loeffert AC, Stokes J, Olympia RP, Bramley H, Hicks SD. Association of salivary microRNA changes with prolonged concussion symptoms. JAMA Pediatr. 2018;172:65–73. https://doi.org/10.1001/jamapediatrics.2017.3884.

    Article  PubMed  Google Scholar 

  36. Rohart F, Gautier B, Singh A, Le Cao KA. mixOmics: an R package for ‘omics feature selection and multiple data integration. PLoS Comput Biol. 2017;13:e1005752. https://doi.org/10.1371/journal.pcbi.1005752.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Dweep H, Gretz N, Sticht C. miRWalk database for miRNA-target interactions. Methods Mol Biol. 2014;1182:289–305. https://doi.org/10.1007/978-1-4939-1062-5_25.

    Article  CAS  PubMed  Google Scholar 

  38. Sticht C, De La Torre C, Parveen A, Gretz N. miRWalk: an online resource for prediction of microRNA binding sites. PLoS ONE. 2018;13:e0206239. https://doi.org/10.1371/journal.pone.0206239.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Dweep H, Sticht C, Pandey P, Gretz N. miRWalk–database: prediction of possible miRNA binding sites by “walking” the genes of three genomes. J Biomed Inform. 2011;44:839–47. https://doi.org/10.1016/j.jbi.2011.05.002.

    Article  CAS  PubMed  Google Scholar 

  40. Sulakhe D, Balasubramanian S, Xie B, Feng B, Taylor A, Wang S, et al. Lynx: a database and knowledge extraction engine for integrative medicine. Nucleic Acids Res. 2014;42:D1007–12. https://doi.org/10.1093/nar/gkt1166.

    Article  CAS  PubMed  Google Scholar 

  41. Amur S, LaVange L, Zineh I, Buckman-Garner S, Woodcock J. Biomarker qualification: toward a multiple stakeholder framework for biomarker development, egulatory acceptance, and utilization. Clin Pharmacol Ther. 2015;98:34–46. https://doi.org/10.1002/cpt.136.

    Article  CAS  PubMed  Google Scholar 

  42. Johnston S, Gallaher Z, Czaja K. Exogenous reference gene normalization for real-time reverse transcription-polymerase chain reaction analysis under dynamic endogenous transcription. Neural Regen Res. 2012;7:1064–72. https://doi.org/10.3969/j.issn.1673-5374.2012.14.004.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Dakterzada F, Targa A, Benitez ID, Romero-ElKhayat L, de Gonzalo-Calvo D, Torres G, et al. Identification and validation of endogenous control miRNAs in plasma samples for normalization of qPCR data for Alzheimer’s disease. Alzheimers Res Ther. 2020;12:163. https://doi.org/10.1186/s13195-020-00735-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Andersen CL, Jensen JL, Orntoft TF. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 2004;64:5245–50. https://doi.org/10.1158/0008-5472.CAN-04-0496.

    Article  CAS  PubMed  Google Scholar 

  45. Pfaffl MW. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 2001;29:e45. https://doi.org/10.1093/nar/29.9.e45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Ruiz-Villalba A, Ruijter JM, van den Hoff MJB. Use and misuse of Cq in qPCR data analysis and reporting. Life (Basel). 2021;11. https://doi.org/10.3390/life11060496.

  47. Selst MV, Jolicoeur P. A solution to the effect of sample size on outlier elimination. Q J Exp Psychol. 1994;47:631–50.

    Article  Google Scholar 

  48. Davies L, Gather U. The identification of multiple outliers. J Am Stat Assoc. 1993;88:782–92.

    Article  Google Scholar 

  49. Yuan JS, Reed A, Chen F, Stewart CN Jr. Statistical analysis of real-time PCR data. BMC Bioinformatics. 2006;7:85. https://doi.org/10.1186/1471-2105-7-85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Leti F, DiStefano JK. miRNA quantification method using quantitative polymerase chain reaction in conjunction with C q method. Methods Mol Biol. 2018;1706:257–65. https://doi.org/10.1007/978-1-4939-7471-9_14.

    Article  CAS  PubMed  Google Scholar 

  51. Peng F, He Ja, Loo JFC, Kong SK, Li B, Gu D. Identification of serum MicroRNAs as diagnostic biomarkers for influenza H7N9 infection. Virology Reports. 2017;7:1–8. https:/doi.org/https://doi.org/10.1016/j.virep.2016.11.001.

  52. Dridi N, Giremus A, Giovannelli JF, Truntzer C, Hadzagic M, Charrier JP, et al. Bayesian inference for biomarker discovery in proteomics: an analytic solution. EURASIP J Bioinform Syst Biol. 2017;2017:9. https://doi.org/10.1186/s13637-017-0062-4.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Girard R, Zeineddine HA, Koskimaki J, Fam MD, Cao Y, Shi C, et al. Plasma biomarkers of inflammation and angiogenesis predict cerebral cavernous malformation symptomatic hemorrhage or lesional growth. Circ Res. 2018;122:1716–21. https://doi.org/10.1161/CIRCRESAHA.118.312680.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Girard R, Zeineddine HA, Fam MD, Mayampurath A, Cao Y, Shi C, et al. Plasma biomarkers of inflammation reflect seizures and hemorrhagic activity of cerebral cavernous malformations. Transl Stroke Res. 2018;9:34–43. https://doi.org/10.1007/s12975-017-0561-3.

    Article  CAS  PubMed  Google Scholar 

  55. Arlot S, Celisse A. A survey of cross-validation procedures for model selection. Statistics Surveys. 2010;4(40–79):40.

    Google Scholar 

  56. Xia J, Broadhurst DI, Wilson M, Wishart DS. Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics. 2013;9:280–99. https://doi.org/10.1007/s11306-012-0482-9.

    Article  CAS  PubMed  Google Scholar 

  57. Lien EC, Dibble CC, Toker A. PI3K signaling in cancer: beyond AKT. Curr Opin Cell Biol. 2017;45:62–71. https://doi.org/10.1016/j.ceb.2017.02.007.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Alzahrani AS. PI3K/Akt/mTOR inhibitors in cancer: at the bench and bedside. Semin Cancer Biol. 2019;59:125–32. https://doi.org/10.1016/j.semcancer.2019.07.009.

    Article  CAS  PubMed  Google Scholar 

  59. Chen H, Zhou L, Wu X, Li R, Wen J, Sha J, et al. The PI3K/AKT pathway in the pathogenesis of prostate cancer. Front Biosci (Landmark Ed). 2016;21:1084–91. https://doi.org/10.2741/4443.

    Article  CAS  PubMed  Google Scholar 

  60. Kar S, Samii A, Bertalanffy H. PTEN/PI3K/Akt/VEGF signaling and the cross talk to KRIT1, CCM2, and PDCD10 proteins in cerebral cavernous malformations. Neurosurg Rev. 2015;38:229–36; discussion 36–7. https://doi.org/10.1007/s10143-014-0597-8.

  61. Martinez-Lopez A, Salvador-Rodriguez L, Montero-Vilchez T, Molina-Leyva A, Tercedor-Sanchez J, Arias-Santiago S. Vascular malformations syndromes: an update. Curr Opin Pediatr. 2019;31:747–53. https://doi.org/10.1097/MOP.0000000000000812.

    Article  PubMed  Google Scholar 

  62. Ren AA, Snellings DA, Su YS, Hong CC, Castro M, Tang AT, et al. PIK3CA and CCM mutations fuel cavernomas through a cancer-like mechanism. Nature. 2021https://doi.org/10.1038/s41586-021-03562-8

  63. Hong T, Xiao X, Ren J, Cui B, Zong Y, Zou J, et al. Somatic MAP3K3 and PIK3CA mutations in sporadic cerebral and spinal cord cavernous malformations. Brain. 2021https://doi.org/10.1093/brain/awab117

  64. Weng J, Yang Y, Song D, Huo R, Li H, Chen Y, et al. Somatic MAP3K3 mutation defines a subclass of cerebral cavernous malformation. Am J Hum Genet. 2021;108:942–50. https://doi.org/10.1016/j.ajhg.2021.04.005.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Lisowska J, Rodel CJ, Manet S, Miroshnikova YA, Boyault C, Planus E, et al. The CCM1-CCM2 complex controls complementary functions of ROCK1 and ROCK2 that are required for endothelial integrity. J Cell Sci. 2018;131. https://doi.org/10.1242/jcs.216093.

  66. McKerracher L, Shenkar R, Abbinanti M, Cao Y, Peiper A, Liao JK, et al. A brain-targeted orally available ROCK2 inhibitor benefits mild and aggressive cavernous angioma disease. Transl Stroke Res. 2020;11:365–76. https://doi.org/10.1007/s12975-019-00725-8.

    Article  CAS  PubMed  Google Scholar 

  67. Wei S, Li Y, Polster SP, Weber CR, Awad IA, Shen L. Cerebral cavernous malformation proteins in barrier maintenance and regulation. Int J Mol Sci. 2020;21. https://doi.org/10.3390/ijms21020675.

  68. Polster SP, Stadnik A, Akers AL, Cao Y, Christoforidis GA, Fam MD, et al. Atorvastatin treatment of cavernous angiomas with symptomatic hemorrhage exploratory proof of concept (AT CASH EPOC) Trial. Neurosurgery. 2019;85:843–53. https://doi.org/10.1093/neuros/nyy539.

    Article  PubMed  Google Scholar 

  69. Zhou Z, Tang AT, Wong WY, Bamezai S, Goddard LM, Shenkar R, et al. Cerebral cavernous malformations arise from endothelial gain of MEKK3-KLF2/4 signalling. Nature. 2016;532:122–6. https://doi.org/10.1038/nature17178.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Lopez-Ramirez MA, Lai CC, Soliman SI, Hale P, Pham A, Estrada EJ, et al. Astrocytes propel neurovascular dysfunction during cerebral cavernous malformation lesion formation. J Clin Invest. 2021;131. https://doi.org/10.1172/JCI139570.

  71. Park JH, Lee JY, Shin DH, Jang KS, Kim HJ, Kong G. Loss of Mel-18 induces tumor angiogenesis through enhancing the activity and expression of HIF-1alpha mediated by the PTEN/PI3K/Akt pathway. Oncogene. 2011;30:4578–89. https://doi.org/10.1038/onc.2011.174.

    Article  CAS  PubMed  Google Scholar 

  72. Hong CC, Tang AT, Detter MR, Choi JP, Wang R, Yang X, et al. Cerebral cavernous malformations are driven by ADAMTS5 proteolysis of versican. J Exp Med. 2020;217. https://doi.org/10.1084/jem.20200140.

  73. Liu M, Banerjee R, Rossa C Jr, D’Silva NJ. RAP1-RAC1 signaling has an important role in adhesion and migration in HNSCC. J Dent Res. 2020;99:959–68. https://doi.org/10.1177/0022034520917058.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Rho SS, Ando K, Fukuhara S. Dynamic regulation of vascular permeability by vascular endothelial cadherin-mediated endothelial cell-cell junctions. J Nippon Med Sch. 2017;84:148–59. https://doi.org/10.1272/jnms.84.148.

    Article  CAS  PubMed  Google Scholar 

  75. Li X, Zhang R, Draheim KM, Liu W, Calderwood DA, Boggon TJ. Structural basis for small G protein effector interaction of Ras-related protein 1 (Rap1) and adaptor protein Krev interaction trapped 1 (KRIT1). J Biol Chem. 2012;287:22317–27. https://doi.org/10.1074/jbc.M112.361295.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Wu M, Wang G, Tian W, Deng Y, Xu Y. MiRNA-based therapeutics for lung cancer. Curr Pharm Des. 2018;23:5989–96. https://doi.org/10.2174/1381612823666170714151715.

    Article  CAS  PubMed  Google Scholar 

  77. Rupaimoole R, Slack FJ. MicroRNA therapeutics: towards a new era for the management of cancer and other diseases. Nat Rev Drug Discov. 2017;16:203–22. https://doi.org/10.1038/nrd.2016.246.

    Article  CAS  PubMed  Google Scholar 

  78. Burns TC, Li MD, Mehta S, Awad AJ, Morgan AA. Mouse models rarely mimic the transcriptome of human neurodegenerative diseases: a systematic bioinformatics-based critique of preclinical models. Eur J Pharmacol. 2015;759:101–17. https://doi.org/10.1016/j.ejphar.2015.03.021.

    Article  CAS  PubMed  Google Scholar 

  79. Mullane K, Williams M. Preclinical models of Alzheimer’s disease: relevance and translational validity. Curr Protoc Pharmacol. 2019;84:e57. https://doi.org/10.1002/cpph.57.

    Article  PubMed  Google Scholar 

  80. Detter MR, Shenkar R, Benavides CR, Neilson CA, Moore T, Lightle R, et al. Novel murine models of cerebral cavernous malformations. Angiogenesis. 2020;23:651–66. https://doi.org/10.1007/s10456-020-09736-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Zeineddine HA, Girard R, Saadat L, Shen L, Lightle R, Moore T, et al. Phenotypic characterization of murine models of cerebral cavernous malformations. Lab Invest. 2019;99:319–30. https://doi.org/10.1038/s41374-018-0030-y.

    Article  CAS  PubMed  Google Scholar 

  82. Shenkar R, Venkatasubramanian PN, Wyrwicz AM, Zhao JC, Shi C, Akers A, et al. Advanced magnetic resonance imaging of cerebral cavernous malformations: part II. Imaging of lesions in murine models. Neurosurgery. 2008;63:790–7; discussion 7–8. https://doi.org/10.1227/01.NEU.0000315862.24920.49.

  83. Nagy ZB, Bartak BK, Kalmar A, Galamb O, Wichmann B, Dank M, et al. Comparison of circulating miRNAs expression alterations in matched tissue and plasma samples during colorectal cancer progression. Pathol Oncol Res. 2019;25:97–105. https://doi.org/10.1007/s12253-017-0308-1.

    Article  CAS  PubMed  Google Scholar 

  84. Malczewska A, Kidd M, Matar S, Kos-Kudla B, Modlin IM. A comprehensive assessment of the role of miRNAs as biomarkers in gastroenteropancreatic neuroendocrine tumors. Neuroendocrinology. 2018;107:73–90. https://doi.org/10.1159/000487326.

    Article  CAS  PubMed  Google Scholar 

Download references

Funding

This work was supported by grants from the NIH (R21NS087328, 5U01NS104157- 02, 1R01NS107887-01, 2 P01 NS092521-06), K01 HL133530 to MLR, William and Judith Davis Fund in Neurovascular Research to IAA, by the Be Brave for Life Foundation to RG, and the Safadi Program at the University of Chicago Translational Fellowship to RG.

Author information

Author notes

    Authors and Affiliations

    1. Department of Neurological Surgery, Neurovascular Surgery Program, University of Chicago Medicine and Biological Sciences, 5841 S. Maryland, MC3026/Neurosurgery J341, Chicago, IL, 60637, USA

      Sharbel G. Romanos, Abhinav Srinath, Ying Li, Chang Chen, Yan Li, Thomas Moore, Dehua Bi, Je Yeong Sone, Rhonda Lightle, Nick Hobson, Dongdong Zhang, Janne Koskimäki, Agnieszka Stadnik, Kristina Piedad, Julián Carrión-Penagos, Abdallah Shkoukani, Robert Shenkar, Yuan Ji, Romuald Girard & Issam A. Awad

    2. Department of Neurosurgery, First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, China

      Ying Li

    3. Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA

      Bingqing Xie

    4. Bioinformatics Core, Biological Sciences Division, University of Chicago, Chicago, IL, USA

      Chang Chen, Yan Li & Dinanath Sulakhe

    5. Department of Public Health Sciences, University of Chicago, Chicago, IL, USA

      Dehua Bi & Yuan Ji

    6. Department of Surgery, The University of Chicago, Chicago, IL, USA

      Le Shen

    7. Department of Medicine, University of California San Diego, La Jolla, San Diego, CA, USA

      Sara McCurdy, Catherine Chinhchu Lai, Miguel A. Lopez-Ramirez & Mark H. Ginsberg

    8. Molecular Genetics and Microbiology Department, Duke University Medical Center, Durham, NC, USA

      Daniel Snellings & Douglas A. Marchuk

    9. Department of Pharmacology, University of California San Diego, La Jolla, San Diego, CA, USA

      Miguel A. Lopez-Ramirez

    10. Department of Medicine and Cardiovascular Institute, University of Pennsylvania, Philadelphia, PA, USA

      Mark L. Kahn

    Authors
    1. Sharbel G. Romanos
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Abhinav Srinath
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Ying Li
      View author publications

      You can also search for this author in PubMed Google Scholar

    4. Bingqing Xie
      View author publications

      You can also search for this author in PubMed Google Scholar

    5. Chang Chen
      View author publications

      You can also search for this author in PubMed Google Scholar

    6. Yan Li
      View author publications

      You can also search for this author in PubMed Google Scholar

    7. Thomas Moore
      View author publications

      You can also search for this author in PubMed Google Scholar

    8. Dehua Bi
      View author publications

      You can also search for this author in PubMed Google Scholar

    9. Je Yeong Sone
      View author publications

      You can also search for this author in PubMed Google Scholar

    10. Rhonda Lightle
      View author publications

      You can also search for this author in PubMed Google Scholar

    11. Nick Hobson
      View author publications

      You can also search for this author in PubMed Google Scholar

    12. Dongdong Zhang
      View author publications

      You can also search for this author in PubMed Google Scholar

    13. Janne Koskimäki
      View author publications

      You can also search for this author in PubMed Google Scholar

    14. Le Shen
      View author publications

      You can also search for this author in PubMed Google Scholar

    15. Sara McCurdy
      View author publications

      You can also search for this author in PubMed Google Scholar

    16. Catherine Chinhchu Lai
      View author publications

      You can also search for this author in PubMed Google Scholar

    17. Agnieszka Stadnik
      View author publications

      You can also search for this author in PubMed Google Scholar

    18. Kristina Piedad
      View author publications

      You can also search for this author in PubMed Google Scholar

    19. Julián Carrión-Penagos
      View author publications

      You can also search for this author in PubMed Google Scholar

    20. Abdallah Shkoukani
      View author publications

      You can also search for this author in PubMed Google Scholar

    21. Daniel Snellings
      View author publications

      You can also search for this author in PubMed Google Scholar

    22. Robert Shenkar
      View author publications

      You can also search for this author in PubMed Google Scholar

    23. Dinanath Sulakhe
      View author publications

      You can also search for this author in PubMed Google Scholar

    24. Yuan Ji
      View author publications

      You can also search for this author in PubMed Google Scholar

    25. Miguel A. Lopez-Ramirez
      View author publications

      You can also search for this author in PubMed Google Scholar

    26. Mark L. Kahn
      View author publications

      You can also search for this author in PubMed Google Scholar

    27. Douglas A. Marchuk
      View author publications

      You can also search for this author in PubMed Google Scholar

    28. Mark H. Ginsberg
      View author publications

      You can also search for this author in PubMed Google Scholar

    29. Romuald Girard
      View author publications

      You can also search for this author in PubMed Google Scholar

    30. Issam A. Awad
      View author publications

      You can also search for this author in PubMed Google Scholar

    Contributions

    IAA and RG designed and conceptualized the study, oversaw data analyses, and edited the final manuscript. SR, AbS, and RG helped optimize the study design. SR, AbS, YiL, TM, JYS, RL, NH, DZ, JK, LS, SM, AgS, KP, JCP, AbSh, DS, RS, MLR, CCL, MK, DM, and MG acquired the data. SR, AbS, YiL, BX, CC, DB, YaL, DS, YJ, RG, and IAA analyzed and interpreted the data. The manuscript was written by SR, AbS, RG, IAA, and all authors edited and gave final approval of the published work.

    Corresponding author

    Correspondence to Issam A. Awad.

    Ethics declarations

    Ethics Approval

    All animal experiments adhered to the NIH Guide for the Care and Use of Laboratory Animals and were approved by the respective Institutional Animal Care and Use Committees at the University of Chicago and University of California San Diego. All human studies were approved by the University of Chicago Institutional Review Board, which is guided by ethical principles consistent with the Belmont Report, and comply with the rules and regulations of the US Department of Health and Human Services Federal Policy for the Protection of Human Subjects (56 FR 28003).

    Consent to Participate

    All human subjects gave written informed consent in compliance with the Declaration of Helsinki.

    Consent for Publication

    Human research participants provided informed consent for publication of any images within the manuscript.

    Conflict of Interest

    The authors declare no competing interests.

    Additional information

    Publisher's Note

    Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

    Abhinav Srinath, Ying Li, Romuald Girard and Issam A. Awad are authors with equal contributions.

    Supplementary Information

    Rights and permissions

    Reprints and permissions

    About this article

    Check for updates. Verify currency and authenticity via CrossMark

    Cite this article

    Romanos, S.G., Srinath, A., Li, Y. et al. Circulating Plasma miRNA Homologs in Mice and Humans Reflect Familial Cerebral Cavernous Malformation Disease. Transl. Stroke Res. 14, 513–529 (2023). https://doi.org/10.1007/s12975-022-01050-3

    Download citation

    • Received:

    • Revised:

    • Accepted:

    • Published:

    • Issue Date:

    • DOI: https://doi.org/10.1007/s12975-022-01050-3

    Keywords

    Search

    Navigation