Differential expression of circular RNAs in the frontal cortex of rats under ischemia-reperfusion conditions

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Abstract

Circular RNAs (circRNAs) are covalently closed non-coding RNAs that have increased metabolic stability and are capable of regulating gene expression. CircRNAs are considered as potential biomarkers and therapeutic targets for various diseases, including ischemic stroke. The transient right middle cerebral artery occlusion (tMCAO) model is actively used in stroke transcriptomics. In this study, we used whole-genome RNA sequencing to study the circRNA expression profile in the frontal cortex of rat brain 24 h after tMCAO. We identified 64 differentially expressed circRNAs (Fold change >1.5; Padj <0.05), which predominantly increased their levels compared to sham-operated animals. According to MRI data, the studied frontal cortex region included the penumbra zone, cell survival in which is important for stroke recovery. Also, using our previously obtained data on differential mRNA expression in this brain region, we bioinformatically predicted mRNA–miRNA–circRNA regulatory networks. Functional analysis of these networks showed that genes whose expression may depend on circRNA activity during ischemia are responsible for synaptic signaling and inflammatory response. Our study shows a significant role of circRNA-mediated transcriptome regulation in the penumbra-associated brain region during ischemia and allows us to consider circRNAs as potential targets for new strategies for the treatment of stroke and post-stroke complications.

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About the authors

I. V. Mozgovoy

National Research Centre “Kurchatov Institute”

Email: filippenkov-ib.img@yandex.ru
Russian Federation, 123182 Moscow

Y. Y. Shpetko

National Research Centre “Kurchatov Institute”

Email: filippenkov-ib.img@yandex.ru
Russian Federation, 123182 Moscow

A. E. Denisova

Pirogov Russian National Research Medical University

Email: filippenkov-ib.img@yandex.ru
Russian Federation, 117997 Moscow

V. V. Stavchansky

National Research Centre “Kurchatov Institute”

Email: filippenkov-ib.img@yandex.ru
Russian Federation, 123182 Moscow

M. A. Vinogradina

National Research Centre “Kurchatov Institute”

Email: filippenkov-ib.img@yandex.ru
Russian Federation, 123182 Moscow

L. V. Gubsky

Pirogov Russian National Research Medical University; Federal Center for the Brain and Neurotechnology, Federal Medical Biological Agency

Email: filippenkov-ib.img@yandex.ru
Russian Federation, 117997 Moscow; 117513 Moscow

L. V. Dergunova

National Research Centre “Kurchatov Institute”

Email: filippenkov-ib.img@yandex.ru
Russian Federation, 123182 Moscow

S. A. Limborska

National Research Centre “Kurchatov Institute”

Email: filippenkov-ib.img@yandex.ru
Russian Federation, 123182 Moscow

I. B. Filippenkov

National Research Centre “Kurchatov Institute”

Author for correspondence.
Email: filippenkov-ib.img@yandex.ru
Russian Federation, 123182 Moscow

References

  1. Абрамов А. Ю., Муравьева А. А., Михайлова Ю. В, Стерликов С. А. (2023) Заболеваемость цереброваскулярными болезнями в Российской Федерации в 2010-2022 годах, Казан. Мед. Журн., 104, 915-926, https://doi.org/10.17816/KMJ546013.
  2. Pu, L., Wang, L., Zhang, R., Zhao, T., Jiang, Y., and Han, L. (2023) Projected global trends in ischemic stroke incidence, deaths and disability-adjusted life years from 2020 to 2030, Stroke, 54, 1330-1339, https://doi.org/10.1161/STROKEAHA.122.040073.
  3. Shin, T. H., Lee, D. Y., Basith, S., Manavalan, B., Paik, M. J., Rybinnik, I., Mouradian, M. M., Ahn, J. H., and Lee, G. (2020) Metabolome changes in cerebral ischemia, Cells, 9, 1630, https://doi.org/10.3390/CELLS9071630.
  4. Qin, C., Yang, S., Chu, Y. H., Zhang, H., Pang, X. W., Chen, L., Zhou, L. Q., Chen, M., Tian, D. S., and Wang, W. (2022) Signaling pathways involved in ischemic stroke: molecular mechanisms and therapeutic interventions, Signal Transduct. Target. Ther., 7, 215, https://doi.org/10.1038/s41392-022-01064-1.
  5. Dergunova, L. V., Filippenkov, I. B., Stavchansky, V. V., Denisova, A. E., Yuzhakov, V. V., Mozerov, S. A., Gubsky, L. V., and Limborska, S. A. (2018) Genome-wide transcriptome analysis using RNA-Seq reveals a large number of differentially expressed genes in a transient MCAO rat model, BMC Genom., 19, 655, https://doi.org/10.1186/S12864-018-5039-5.
  6. Shi, J., Chen, X., Li, H., Wu, Y., Wang, S., Shi, W., Chen, J., and Ni, Y. (2017) Neuron-autonomous transcriptome changes upon ischemia/reperfusion injury, Sci. Rep., 7, 5800, https://doi.org/10.1038/s41598-017-05342-9.
  7. Salviano-Silva, A., Lobo-Alves, S. C., de Almeida, R. C., Malheiros, D., and Petzl-Erler, M. L. (2018) Besides pathology: long non-coding RNA in cell and tissue homeostasis, Noncoding RNA, 4, 3, https://doi.org/10.3390/NCRNA4010003.
  8. Lekka, E., and Hall, J. (2018) Noncoding RNAs in disease, FEBS Lett., 592, 2884, https://doi.org/10.1002/1873-3468.13182.
  9. Zhou, M., Li, S., and Huang, C. (2024) Physiological and pathological functions of circular RNAs in the nervous system, Neural. Regen. Res., 19, 342, https://doi.org/10.4103/1673-5374.379017.
  10. Filippenkov, I. B., Sudarkina, O. Y., Limborska, S. A., and Dergunova, L. V. (2018) Multi-step splicing of sphingomyelin synthase linear and circular RNAs, Gene, 654, 14-22, https://doi.org/10.1016/J.GENE.2018.02.030.
  11. Lasda, E., and Parker, R. (2014) Circular RNAs: diversity of form and function, RNA, 20, 1829-1842, https://doi.org/10.1261/RNA.047126.114.
  12. Kristensen, L. S., Andersen, M. S., Stagsted, L. V. W., Ebbesen, K. K., Hansen, T. B., and Kjems, J. (2019) The biogenesis, biology and characterization of circular RNAs, Nat. Rev. Genet., 20, 675-691, https://doi.org/10.1038/s41576-019-0158-7.
  13. Zhang, L., Liu, Y., Tao, H., Zhu, H., Pan, Y., Li, P., Liang, H., Zhang, B., and Song, J. (2021) Circular RNA CircUBE2J2 acts as the sponge of microRNA-370-5P to suppress hepatocellular carcinoma progression, Cell Death Dis., 12, 985, https://doi.org/10.1038/s41419-021-04269-4.
  14. Yifan, D., Jiaheng, Z., Yili, X., Junxia, D., and Chao T. (2025) CircRNA: a new target for ischemic stroke, Gene, 933, 148941, https://doi.org/10.1016/j.gene.2024.148941.
  15. Liu, C., Zhang, C., Yang, J., Geng, X., Du, H., Ji, X., and Zhao, H. (2017) Screening circular RNA expression patterns following focal cerebral ischemia in mice, Oncotarget, 8, 86535, https://doi.org/10.18632/ONCOTARGET.21238.
  16. Lin, S. P., Ye, S., Long, Y., Fan, Y., Mao, H. F., Chen, M. T., and Ma, Q. J. (2016) Circular RNA expression alterations are involved in OGD/R-induced neuron injury, Biochem. Biophys. Res. Commun., 471, 52-56, https://doi.org/10.1016/J.BBRC.2016.01.183.
  17. Duan, X., Li, L., Gan, J., Peng, C., Wang, X., Chen, W., and Peng, D. (2019) Identification and functional analysis of circular RNAs induced in rats by middle cerebral artery occlusion, Gene, 701, 139-145, https://doi.org/10.1016/ J.GENE.2019.03.053.
  18. Chen, W., Wang, H., Feng, J., and Chen, L. (2020) Overexpression of CircRNA CircUCK2 attenuates cell apoptosis in cerebral ischemia-reperfusion injury via MiR-125b-5p/GDF11 signaling, Mol. Ther. Nucleic. Acids, 22, 673-683, https://doi.org/10.1016/j.omtn.2020.09.032.
  19. Wu, F., Han, B., Wu, S., Yang, L., Leng, S., Li, M., Liao, J., Wang, G., Ye, Q., Zhang, Y., Chen, H., Chen, X., Zhong, M., Xu, Y., Liu, Q., Zhang, J. H., and Yao, H. (2019) Circular RNA TLK1 aggravates neuronal injury and neurological deficits after ischemic stroke via MiR-335-3p/TIPARP, J. Neurosci., 39, 7369-7393, https://doi.org/10.1523/ JNEUROSCI.0299-19.2019.
  20. Bai, Y., Zhang, Y., Han, B., Yang, L., Chen, X., Huang, R., Wu, F., Chao, J., Liu, P., and Hu, G. (2018) Circular RNA DLGAP4 Ameliorates Ischemic Stroke Outcomes by Targeting MiR-143 to regulate endothelial-mesenchymal transition associated with blood-brain barrier integrity, J. Neurosci., 38, 32-50, https://doi.org/10.1523/ JNEUROSCI.1348-17.2017.
  21. Dai, Q., Ma, Y., Xu, Z., Zhang, L., Yang, H., Liu, Q., and Wang, J. (2021) Downregulation of circular RNA HECTD1 induces neuroprotection against ischemic stroke through the microRNA-133b/TRAF3 pathway, Life Sci., 264, 118626, https://doi.org/10.1016/j.lfs.2020.118626.
  22. Han, B., Zhang, Y., Zhang, Y., Bai, Y., Chen, X., Huang, R., Wu, F., Leng, S., Chao, J., and Zhang, J. H. (2018) Novel insight into circular RNA HECTD1 in astrocyte activation via autophagy by targeting MIR142-TIPARP: implications for cerebral ischemic Stroke, Autophagy, 14, 1164-1184, https://doi.org/10.1080/15548627. 2018.1458173.
  23. Filippenkov, I. B., Stavchansky, V. V., Denisova, A. E., Valieva, L. V., Remizova, J. A., Mozgovoy, I. V., Zaytceva, E. I., Gubsky, L. V., Limborska, S. A., and Dergunova, L. V. (2021) Genome-wide RNA-sequencing reveals massive circular RNA expression changes of the neurotransmission genes in the rat brain after ischemia-reperfusion, Genes (Basel), 12, 1870, https://doi.org/10.3390/GENES12121870.
  24. Filippenkov, I. B., Shpetko, Y. Yu., Stavchansky, V. V., Denisova, A. E., Gubsky, L. V., Andreeva, L. A., Myasoedov, N. F., Limborska, S. A., and Dergunova, L. V. (2024) ACTH-like peptides compensate rat brain gene expression profile disrupted by ischemia a day after experimental stroke, Biomedicines, 12, 2830, https://doi.org/10.3390/BIOMEDICINES12122830.
  25. Koizumi, J., Yoshida, Y., Nakazawa, T., and Ooneda, G. (1986) Experimental studies of ischemic brain edema. A new experimental model of cerebral embolism in rats in which recirculation can be introduced in the ischemic area, Japan. J. Stroke, 8, 1-8, https://doi.org/10.3995/JSTROKE.8.1.
  26. Filippenkov, I. B., Remizova, J. A., Denisova, A. E., Stavchansky, V. V., Golovina, K. D., Gubsky, L. V., Limborska, S. A., and Dergunova, L. V. (2022) Comparative use of contralateral and sham operated controls reveals traces of a bilateral genetic response in the rat brain after focal stroke, Int. J. Mol. Sci., 23, 7308, https://doi.org/10.3390/IJMS23137308/S1.
  27. Langmead, B., Wilks, C., Antonescu, V., Charles, R. (2019) Scaling read aligners to hundreds of threads on general-purpose processors, Bioinformatics, 35, 421-432, https://doi.org/10.1093/bioinformatics/bty648.
  28. Izuogu, O. G., Alhasan, A. A., Alafghani, H. M., Santibanez-Koref, M., Elliott, D. J., and Jackson, M. S. (2016) PTESFinder: a computational method to identify post-transcriptional exon shuffling (PTES) events, BMC Bioinform., 17, 31, https://doi.org/10.1186/s12859-016-0881-4.
  29. Love, M. I., Huber, W., and Anders, S. (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2, Genome Biol., 15, 550, https://doi.org/10.1186/s13059-014-0550-8.
  30. Pfaffl, M. W., Horgan, G. W., and Dempfle, L. (2002) Relative expression software tool (REST) for group-wise comparison and statistical analysis of relative expression results in real-time PCR, Nucleic Acids Res., 30, e36, https://doi.org/10.1093/NAR/30.9.E36.
  31. Enright, A. J., John, B., Gaul, U., Tuschl, T., Sander, C., and Marks, D. S. (2003) MicroRNA targets in Drosophila, Genome Biol., 5, R1, https://doi.org/10.1186/GB-2003-5-1-R1.
  32. Krüger, J., and Rehmsmeier, M. (2016) RNAhybrid: microRNA target prediction easy, fast and flexible, Nucleic Acids Res., 34, W451-4, https://doi.org/10.1093/NAR/GKL243.
  33. Lewis, B. P., Burge, C. B., and Bartel, D. P. (2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets, Cell, 120, 15-20, https://doi.org/10.1016/j.cell. 2004.12.035.
  34. Kozomara, A., Birgaoanu, M., and Griffiths-Jones, S. (2019) miRBase: from microRNA sequences to function, Nucleic Acids Res., 47, D155-D162, https://doi.org/10.1093/nar/gky1141.
  35. Marín, R. M., and Vaníek, J. (2011) Efficient use of accessibility in microRNA target prediction, Nucleic Acids Res., 39, 19, https://doi.org/10.1093/NAR/GKQ768.
  36. Sherman, B. T., Hao, M., Qiu, J., Jiao, X., Baseler, M. W., Lane, H. C., Imamichi, T., and Chang, W. (2022) DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update), Nucleic Acids Res., 50, W216-W221, https://doi.org/10.1093/NAR/GKAC194.
  37. Bardutzky, J., Shen, Q., Henninger, N., Schwab, S., Duong, T. Q., and Fisher, M. (2007) Characterizing tissue fate after transient cerebral ischemia of varying duration using quantitative diffusion and perfusion imaging, Stroke, 38, 1336-1344, https://doi.org/10.1161/01.STR.0000259636.26950.3b.
  38. Ebinger, M., De Silva, D. A., Christensen, S., Parsons, M. W., Markus, R., Donnan, G. A., and Davis, S. M. (2009) Imaging the penumbra – strategies to detect tissue at risk after ischemic stroke, J. Clin. Neurosci., 16, 178-187, https://doi.org/10.1016/j.jocn.2008.04.002.
  39. Fisher, M., Feuerstein, G., Howells, D. W., Hurn, P. D., Kent, T. A., Savitz, S. I., and Lo, E. H. (2009) Update of the stroke therapy academic industry roundtable preclinical recommendations, Stroke, 40, 2244, https://doi.org/10.1161/STROKEAHA.108.541128.
  40. Friedrich, J., Lindauer, U., and Höllig, A. (2022) Procedural and methodological quality in preclinical stroke research-a cohort analysis of the rat MCAO model comparing periods before and after the publication of STAIR/ARRIVE, Front. Neurol., 13, 834003, https://doi.org/10.3389/FNEUR.2022.834003.
  41. Fisher, M. (1999) Recommendations for standards regarding preclinical neuroprotective and restorative drug development, Stroke, 30, 2752-2758, https://doi.org/10.1161/01.STR.30.12.2752.
  42. Zhang, Y., Liu, J., Su, M., Wang, X., and Xie, C. (2021) Exosomal microRNA-22-3p alleviates cerebral ischemic injury by modulating KDM6B/BMP2/BMF axis, Stem Cell Res. Ther., 12, 111, https://doi.org/10.1186/S13287-020-02091-X/.
  43. Filippenkov, I. B., Kolomin, T. A., Limborska, S. A., and Dergunova, L. V. (2018) Developmental stage-specific expression of genes for sphingomyelin synthase in rat brain, Cell Tissue. Res., 372, 33-40, https://doi.org/ 10.1007/s00441-017-2762-1.
  44. Filippenkov, I. B., Sudarkina, O. Y., Limborska, S. A., and Dergunova, L. V. (2015) Circular RNA of the human sphingomyelin synthase 1 gene: multiple splice variants, evolutionary conservatism and expression in different tissues, RNA Biol., 12, 1030-1042, https://doi.org/10.1080/15476286.2015.1076611.
  45. Jia, X., Sun, Y., Wang, T., Zhong, L., Deng, J., and Zhu, X. (2023) Mechanism of circular RNA-mediated regulation of L-DOPA to improve wet age-related macular degeneration, Gene, 861, 147247, https://doi.org/10.1016/ J.GENE.2023.147247.
  46. Ma, X., Wang, H., Ye, G., Zheng, X., and Wang, Y. (2024) Hsa_circ_0018401 and MiR-127-5p expressions are diagnostic and prognostic markers for traumatic brain injury (TBI) in trauma patients, Neuroscience, 545, 59-68, https://doi.org/10.1016/J.NEUROSCIENCE.2024.03.010.
  47. Zhang, X., Connelly, J., Levitan, E. S., Sun, D., and Wang, J. Q. (2021) Calcium/calmodulin-dependent protein kinase II in cerebrovascular diseases, Transl. Stroke Res., 12, 513-529, https://doi.org/10.1007/S12975-021-00901-9.
  48. McCullough, L. D., Tarabishy, S., Liu, L., Benashski, S., Xu, Y., Ribar, T., Means, A., and Li, J. (2013) Inhibition of calcium/calmodulin-dependent protein kinase kinase β and calcium/calmodulin-dependent protein kinase IV is detrimental in cerebral ischemia, Stroke, 44, 2559-2566, https://doi.org/10.1161/STROKEAHA. 113.001030.
  49. Ayuso, M. I., Martínez-Alonso, E., Regidor, I., and Alcázar, A. (2016) Stress granule induction after brain ischemia is independent of eukaryotic translation initiation factor (EIF) 2α phosphorylation and is correlated with a decrease in EIF4B and EIF4E proteins, J. Biol. Chem., 291, 27252-27264, https://doi.org/10.1074/JBC. M116.738989.
  50. Hao, M. Q., Xie, L. J., Leng, W., and Xue, R. W. (2019) Trim47 is a critical regulator of cerebral ischemia-reperfusion injury through regulating apoptosis and inflammation, Biochem. Biophys. Res. Commun., 515, 651-657, https://doi.org/10.1016/J.BBRC.2019.05.065.
  51. Pisignano, G., Michael, D. C., Visal, T. H., Pirlog, R., Ladomery, M., and Calin, G. A. (2023) Going circular: history, present, and future of circRNAs in cancer, Oncogene, 42, 2783-2800, https://doi.org/10.1038/s41388-023-02780-w.
  52. Liu, W., and Wang, X. (2019) Prediction of functional microRNA targets by integrative modeling of microRNA Binding and target expression data, Genome Biol., 20, 18, https://doi.org/10.1186/S13059-019-1629-Z.
  53. Lee, D., and Shin, C. (2012) MicroRNA-target interactions: new insights from genome-wide approaches, Ann. N. Y. Acad. Sci., 1271, 118, https://doi.org/10.1111/J.1749-6632.2012.06745.X.
  54. Selbach, M., Schwanhäusser, B., Thierfelder, N., Fang, Z., Khanin, R., and Rajewsky, N. (2008) Widespread changes in protein synthesis induced by microRNAs, Nature, 455, 58-63, https://doi.org/10.1038/NATURE07228.
  55. Friedman, R. C., Farh, K. K. H., Burge, C. B., and Bartel, D. P. (2009) Most mammalian mRNAs are conserved targets of microRNAs, Genome Res., 19, 92, https://doi.org/10.1101/GR.082701.108.
  56. Ma, Q., Li, G., Tao, Z., Wang, J., Wang, R., Liu, P., Luo, Y., and Zhao, H. (2019) Blood microRNA-93 as an indicator for diagnosis and prediction of functional recovery of acute stroke patients, J. Clin. Neurosci., 62, 121-127, https://doi.org/10.1016/j.jocn.2018.12.003.
  57. Icli, B., Wu, W., Ozdemir, D., Li, H., Cheng, H. S., Haemmig, S., Liu, X., Giatsidis, G., Avci, S. N., and Lee, N. (2019) MicroRNA-615-5p regulates angiogenesis and tissue repair by targeting Akt/ENOS (protein kinase B/endothelial nitric oxide synthase) signaling in endothelial cells, Arterioscler. Thromb. Vasc. Biol., 39, 1458-1474, https://doi.org/10.1161/ATVBAHA.119.312726.
  58. Theofilatos, K., Korfiati, A., Mavroudi, S., Cowperthwaite, M. C., and Shpak, M. (2019) Discovery of stroke-related blood biomarkers from gene expression network models, BMC Med. Genomics, 12, 118, https://doi.org/10.1186/S12920-019-0566-8.
  59. Zheng, T., Yang, J., Zhang, J., Yang, C., Fan, Z., Li, Q., Zhai, Y., Liu, H., and Yang, J. (2021) Downregulated microRNA-327 attenuates oxidative stress-mediated myocardial ischemia reperfusion injury through regulating the FGF10/Akt/Nrf2 signaling pathway, Front. Pharmacol., 12, 669146, https://doi.org/10.3389/FPHAR. 2021.669146.
  60. Alsbrook, D. L., Di Napoli, M., Bhatia, K., Biller, J., Andalib, S., Hinduja, A., Rodrigues, R., Rodriguez, M., Sabbagh, S. Y., and Selim, M. (2023) Neuroinflammation in acute ischemic and hemorrhagic stroke, Curr. Neurol. Neurosci. Rep., 23, 407-431, https://doi.org/10.1007/S11910-023-01282-2.
  61. Gugliandolo, A., Silvestro, S., Sindona, C., Bramanti, P., and Mazzon, E. (2021) MiRNA: involvement of the MAPK pathway in ischemic stroke. A promising therapeutic target, Medicina, 57, 1053, https://doi.org/10.3390/ MEDICINA57101053.
  62. Ye, J., Shan, Y., Zhou, X., Tian, T., and Gao, W. (2023) Identification of novel circular RNA targets in key penumbra region of rats after cerebral ischemia-reperfusion injury, J. Mol. Neurosci., 73, 751, https://doi.org/10.1007/S12031-023-02153-8.
  63. Nikbakhtzadeh, M., Bordbar, S., Seyedi, S., Ranjbaran, M., Ashabi, G., and Kheradmand, A. (2024) Significance of neurotransmitters in cerebral ischemia: understanding the role of serotonin, dopamine, glutamate, and GABA in stroke recovery and treatment, Cent. Nerv. Syst. Agents Med. Chem., 24, https://doi.org/10.2174/ 0118715249302594240801171612.
  64. Shen, Z., Xiang, M., Chen, C., Ding, F., Wang, Y., Shang, C., Xin, L., Zhang, Y., and Cui, X. (2022) Glutamate excitotoxicity: potential therapeutic target for ischemic stroke, Biomed. Pharmacother., 151, 113125, https://doi.org/10.1016/J.BIOPHA.2022.113125.
  65. Lai, T. W., Zhang, S., and Wang, Y. T. (2014) Excitotoxicity and stroke: identifying novel targets for neuroprotection, Prog. Neurobiol., 115, 157-188, https://doi.org/10.1016/J.PNEUROBIO.2013.11.006.
  66. Wang, F., Xie, X., Xing, X., and Sun, X. (2022) Excitatory synaptic transmission in ischemic stroke: a new outlet for classical neuroprotective strategies, Int. J. Mol. Sci., 23, 9381, https://doi.org/10.3390/IJMS23169381.
  67. Lv, W., Zhang, Q., Li, Y., Liu, D., Wu, X., He, X., Han, Y., Fei, X., Zhang, L., and Fei, Z. (2024) Homer1 ameliorates ischemic stroke by inhibiting necroptosis-induced neuronal damage and neuroinflammation, Inflamm. Res., 73, 131-144, https://doi.org/10.1007/S00011-023-01824-X.
  68. Gray, M., Nash, K. R., and Yao, Y. (2024) Adenylyl cyclase 2 expression and function in neurological diseases, CNS Neurosci. Ther., 30, e14880, https://doi.org/10.1111/CNS.14880.
  69. Dohovics, R., Janáky, R., Varga, V., Hermann, A., Saransaari, P., and Oja, S. S. (2003) Regulation of glutamatergic neurotransmission in the striatum by presynaptic adenylyl cyclase-dependent processes, Neurochem. Int., 42, 1-7, https://doi.org/10.1016/S0197-0186(02)00066-9.
  70. Allegra, A., Cicero, N., Tonacci, A., Musolino, C., and Gangemi, S. (2022) Circular RNA as a novel biomarker for diagnosis and prognosis and potential therapeutic targets in multiple myeloma, Cancers (Basel), 14, 1700, https://doi.org/10.3390/CANCERS14071700.
  71. Chen, L., and Shan, G. (2021) CircRNA in cancer: fundamental mechanism and clinical potential, Cancer Lett., 505, 49-57, https://doi.org/10.1016/J.CANLET.2021.02.004.
  72. Xu, Z., Yan, Y., Zeng, S., Dai, S., Chen, X., Wei, J., and Gong, Z. (2017) Circular RNAs: clinical relevance in cancer, Oncotarget, 9, 1444, https://doi.org/10.18632/ONCOTARGET.22846.
  73. Rybina, O. Y., and Pasyukova, E. G. (2004) Aging biomarkers in assessing the efficacy of geroprotective therapy: problems and prospects, Nanobiotechnol. Rep., 19, 318-328, https://doi.org/10.1134/S2635167624601104.

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2. Fig. 1. RNA-Seq analysis of differential expression of circRNAs in the rat frontal cortex 24 h after tMCAO. a – The total number of annotated circRNAs is shown in the gray circle; the numbers of DECs that increased and decreased their levels in the IR vs. LO comparison (fold change > 1.5; Padj < 0.05) are shown in white and black sectors of the circle, respectively. b – Volcano plot of RNA-Seq results. c – The first 10 DECs that showed the greatest change in expression in the IR vs. LO comparison (5 that showed the greatest increase in expression and 5 that showed the greatest decrease in expression). d – PCR verification of RNA-Seq results. Each PCR data comparison group includes 5 animals; each RNA-seq data comparison group included 3 animals; symbols “*” and “#” mark statistically significant results according to RNA-Seq and PCR data, respectively. NRs – normalized number of RNA-Seq reads; ΔCt = Ct(tar) – Ct(ref), where Ct(tar) is the average Ct value for the studied circRNAs and Ct(ref) is the average Ct value for the mRNA of the comparison gene (Gapdh); group “IR” – rats were decapitated 24 h after tMCAO; group “LO” – rats were decapitated 24 h after sham operation

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3. Fig. 2. Analysis of the structure of circRNA in the frontal cortex of rats 24 h after tMCAO. a – Exon-intron structure of the Psd2 gene. Exons are shown as numbered rectangles. Exons 13 and 8, connected by an arc, are involved in the formation of circRNA circPsd2-13.8. Primers (F-forward, R-reverse) and their direction are shown by arrows. b – Electrophoresis of the PCR product of the reaction carried out with these primers; on the left – the position of the GeneRuler 100 bp DNA Ladder marker (Thermo Fisher Scientific). c – A fragment of the PCR product sequence, including the backsplicing site between exons 13 and 8, according to the results of Sanger sequencing. g – The structure of the circPsd2-13.8 circRNA. The exons of the Psd2 gene included in the circRNA are shown as white numbers on the segments of the ring; the number inside the ring is the length of the entire circRNA; the black arc indicates the resulting PCR product

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4. Fig. 3. Comparative analysis of differential expression of circRNA and mRNA in the frontal cortex 24 h after tMCAO. a – Venn diagram showing the intersection of the genes encoding DECs obtained in this study with the DEGs identified earlier. b – The first 10 DECs with the highest fold change in expression and the corresponding DEGs from the intersection in the Venn diagram (5 showing the highest increase in expression and 5 showing the highest decrease in expression). c – DECs whose genes were not reliably identified as DEGs. NRs – normalized read counts; group “IR” – rats were decapitated 24 h after tMCAO; group “LO” – rats were decapitated 24 h after sham operation

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5. Fig. 4. Bioinformatics approach used to identify the DEC–miRNA–DEG network. a – Schematic representation of the sequence of actions during network identification. b – Venn diagram showing the number of common and unique miRNA–DEC pairs, according to the three programs. c – Venn diagram showing the number of common and unique miRNA–DEG pairs, according to the three programs

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6. Fig. 5. KEGG signaling pathways associated with genes in the DEC–miRNA–DEG network. a – Top 10 pathways with the highest association significance with a set of 2258 DEGs. For each pathway, the number of up- and down-regulated DEGs associated with the pathway and the pathway significance level (Padj, adjusted with the Benjamini–Hochberg correction) are shown. b – Number of up- and down-regulated circRNAs (DECs) competing with DEGs from the pathway

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7. Fig. 6. Element of the KEGG-microRNA-DEG ​​network associated with the glutamatergic synapse signaling pathway. DEGs (mRNAs) are shown as rectangles (inner circle); KEGs are shown as ovals (outer circle). MicroRNAs are shown as lines connecting the nodes of the network. Thus, KEGGs and DEGs connected by a line compete with each other for binding to one or more microRNAs.

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8. Appendix 1. Experimental flow chart
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9. Appendix 2. Method of establishing the tMCAO model and MRI analysis of ischemic brain injury in rats
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10. Appendix 3. Processing RNA-Seq results using DESeq2 (v. 1.32.0).
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11. Appendix 4. Genes encoding DEC in the rat frontal cortex 24 h after tMCAO
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12. Appendix 5. DEC-miRNA-DEG ​​interaction network in rat frontal cortex 24 hours after tMCAO
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13. Appendix 6. Analysis of signaling pathways associated with DEGs involved in the DEC-miRNA-DEG ​​network in the rat frontal cortex 24 hours after tMCAO (based on David 2021)
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