Skip to main content
Log in

Identification of potential microRNAs regulating metabolic plasticity and cellular phenotypes in glioblastoma

  • Original Article
  • Published:
Molecular Genetics and Genomics Aims and scope Submit manuscript

Abstract

MicroRNAs (miRNAs) play important role in regulating cellular metabolism, and are currently being explored in cancer. As metabolic reprogramming in cancer is a major mediator of phenotypic plasticity, understanding miRNA-regulated metabolism will provide opportunities to identify miRNA targets that can regulate oncogenic phenotypes by taking control of cellular metabolism. In the present work, we studied the effect of differentially expressed miRNAs on metabolism, and associated oncogenic phenotypes in glioblastoma (GBM) using patient-derived data. Networks of differentially expressed miRNAs and metabolic genes were created and analyzed to identify important miRNAs that regulate major metabolism in GBM. Graph network-based approaches like network diffusion, backbone extraction, and different centrality measures were used to analyze these networks for identification of potential miRNA targets. Important metabolic processes and cellular phenotypes were annotated to trace the functional responses associated with these miRNA-regulated metabolic genes and associated phenotype networks. miRNA-regulated metabolic gene subnetworks of cellular phenotypes were extracted, and important miRNAs regulating these phenotypes were identified. The most important outcome of the study is the target miRNA combinations predicted for five different oncogenic phenotypes that can be tested experimentally for miRNA-based therapeutic design in GBM. Strategies implemented in the study can be used to generate testable hypotheses in other cancer types as well, and design context-specific miRNA-based therapy for individual patient. Their usability can be further extended to other gene regulatory networks in cancer and other genetic diseases.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Alfardus H, de los Angeles Estevez-Cebrero M, Rowlinson J et al (2021) Intratumour heterogeneity in microRNAs expression regulates glioblastoma metabolism. Sci Rep 11:15908

    Article  Google Scholar 

  • Aloizou A-M, Pateraki G, Siokas V, Mentis A-FA, Liampas I, Lazopoulos G, Kovatsi L, Mitsias PD, Bogdanos DP, Paterakis K et al (2020) The role of MiRNA-21 in gliomas: hope for a novel therapeutic intervention? Toxicol Rep 7:1514–1530

    Article  Google Scholar 

  • Banelli B, Forlani A, Allemanni G, Morabito A, Pistillo MP, Romani M (2017) MicroRNA in glioblastoma: an overview. Int J Genomics 2017:7639084

    Article  Google Scholar 

  • Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M (2012) NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res 41:D991–D995

    Article  Google Scholar 

  • Bastian M, Heymann S, and Jacomy M (2009) Gephi: an open source software for exploring and manipulating networks. In: Proceedings of the international AAAI conference on web and social media, vol 3. AAAI ICWSM’09, San Jose, California, pp 361–362

  • Bendahou MA, Ibrahimi A, Boutarbouch M (2020) Bioinformatics analysis of differentially expressed genes and mirnas in low-grade gliomas. Cancer Inform 19:1176935120969692

    Article  Google Scholar 

  • Bhowmick R, Subramanian A, Sarkar RR (2015) Exploring the differences in metabolic behavior of astrocyte and glioblastoma: a flux balance analysis approach. Syst Synth Biol 9:159–177

    Article  Google Scholar 

  • Bradley BS, Loftus JC, Mielke CJ, Dinu V (2014) Differential expression of microRNAs as predictors of glioblastoma phenotypes. BMC Bioinform 15:21

    Article  Google Scholar 

  • Buruiană A, Florian I, Florian AI et al (2020) The roles of miRNA in glioblastoma tumor cell communication: diplomatic and aggressive negotiations. Int J Mol Sci 21:1950

    Article  Google Scholar 

  • Cai S, Shi CJ, Lu JX, Wang YP, Yuan T, Wang XP (2021) miR-124-3p inhibits the viability and motility of glioblastoma multiforme by targeting RhoG. Int J Mol Med 47:1–3

    Article  Google Scholar 

  • Carlin DE, Demchak B, Pratt D, Sage E, Ideker T (2017) Network propagation in the cytoscape cyberinfrastructure. PLoS Comput Biol 13:e1005598

    Article  Google Scholar 

  • Chen X, Dong D, Pan C, Xu C, Sun Y, Geng Y, Kong L, Xiao X, Zhao Z, Zhou W et al (2018) Identification of grade-associated MicroRNAs in brainstem gliomas based on microarray data. J Cancer 9:4463–4476

    Article  Google Scholar 

  • Chen Y, Wang X (2019) miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res 48:D127–D131

    Article  Google Scholar 

  • Chen M, Medarova Z, Moore A (2021) Role of microRNAs in glioblastoma. Oncotarget 12:1707–1723

    Article  Google Scholar 

  • Cheng CP, Huang LC, Chang YL, Hsieh CH, Huang SM, Hueng DY (2016) The mechanisms of malic enzyme 2 in the tumorigenesis of human gliomas. Oncotarget 7:41460–41472

    Article  Google Scholar 

  • Csardi G, Nepusz T (2006) The igraph software package for complex network research. Int J Complex Syst 1695:1–9

    Google Scholar 

  • Saldanha Gama Fischer J, Costa Carvalho P, da Fonseca CO et al (2011) Chemo-resistant protein expression pattern of glioblastoma cells (A172) to perillyl alcohol. J Proteome Res 10:153–160

    Article  Google Scholar 

  • Domagalski R, Neal ZP, Sagan B (2021) Backbone: An R package for extracting the backbone of bipartite projections. PLoS ONE 16:e0244363

    Article  Google Scholar 

  • Feng L, Ma J, Ji H, Liu Y, Hu W (2017) miR-330-5p suppresses glioblastoma cell proliferation and invasiveness through targeting ITGA5. Biosci Rep. https://doi.org/10.1042/BSR20170019

  • Gabriely G, Yi M, Narayan RS, Niers JM, Wurdinger T, Imitola J, Ligon KL, Kesari S, Esau C, Stephens RM et al (2011) Human glioma growth is controlled by microRNA-10b. Cancer Res 71:3563–3572

    Article  Google Scholar 

  • Gao P, Tchernyshyov I, Chang TC, Lee YS, Kita K, Ochi T, Zeller KI, De Marzo AM, Van Eyk JE, Mendell JT et al (2009) c-Myc suppression of miR-23a/b enhances mitochondrial glutaminase expression and glutamine metabolism. Nature 458:762–765

    Article  Google Scholar 

  • Gao ZG, Yang P, Huang J, Ding YQ (2021) CircFBXW7 alleviates glioma progression through regulating miR-23a-3p/PTEN axis. Anat Rec (hoboken) 304:279–290

    Article  Google Scholar 

  • García-Moreno A, López-Domínguez R, Ramirez-Mena A, Pascual-Montano A, Aparicio-Puerta E, Hackenberg M, Carmona-Saez P (2021) GeneCodis 4: expanding the modular enrichment analysis to regulatory elements. bioRxiv. https://doi.org/10.1101/2021.04.15.439962

    Article  Google Scholar 

  • Gillespie M, Jassal B, Stephan R, Milacic M, Rothfels K, Senff-Ribeiro A, Griss J, Sevilla C, Matthews L, Gong C et al (2021) The reactome pathway knowledgebase 2022. Nucleic Acids Res 50:D687–D692

    Article  Google Scholar 

  • Gulluoglu S, Tuysuz EC, Sahin M, Kuskucu A, KaanYaltirik C, Ture U, Kucukkaraduman B, Akbar MW, Gure AO, Bayrak OF et al (2018) Simultaneous miRNA and mRNA transcriptome profiling of glioblastoma samples reveals a novel set of OncomiR candidates and their target genes. Brain Res 1700:199–210

    Article  Google Scholar 

  • Gustavsen JA, Pai S, Isserlin R, Demchak B, Pico AR (2019) RCy3: Network biology using cytoscape from within R. F1000Res 8:1774

    Article  Google Scholar 

  • Hermansen SK, Sørensen MD, Hansen A, Knudsen S, Alvarado AG, Lathia JD, Kristensen BW (2017) A 4-miRNA signature to predict survival in glioblastomas. PLoS ONE 12:e0188090

    Article  Google Scholar 

  • Hintze A, Adami C (2010) Modularity and anti-modularity in networks with arbitrary degree distribution. Biol Direct 5:32

    Article  Google Scholar 

  • Huo L, Wang B, Zheng M, Zhang Y, Xu J, Yang G, Guan Q (2019) miR-128-3p inhibits glioma cell proliferation and differentiation by targeting NPTX1 through IRS-1/PI3K/AKT signaling pathway. Exp Ther Med 17:2921–2930

    Google Scholar 

  • Ivo D’Urso P, Fernando D’Urso O, Damiano Gianfreda C, Mezzolla V, Storelli C, Marsigliante S (2015) miR-15b and miR-21 as circulating biomarkers for diagnosis of glioma. Curr Genomics 16:304–311

    Article  Google Scholar 

  • Jalili M, Salehzadeh-Yazdi A, Asgari Y, Arab SS, Yaghmaie M, Ghavamzadeh A, Alimoghaddam K (2015) CentiServer: a comprehensive resource, web-based application and r package for centrality analysis. PLoS ONE 10:e0143111

    Article  Google Scholar 

  • Jassal B, Matthews L, Viteri G, Gong C, Lorente P, Fabregat A, Sidiropoulos K, Cook J, Gillespie M, Haw R et al (2020) The reactome pathway knowledgebase. Nucleic Acids Res 48:D498-d503

    Google Scholar 

  • Jia X, Wang X, Guo X, Ji J, Lou G, Zhao J, Zhou W, Guo M, Zhang M, Li C et al (2019) MicroRNA-124: an emerging therapeutic target in cancer. Cancer Med 8:5638–5650

    Article  Google Scholar 

  • Kanehisa M, Sato Y (2020) KEGG Mapper for inferring cellular functions from protein sequences. Protein Sci 29:28–35

    Article  Google Scholar 

  • Kanehisa M, Furumichi M, Sato Y, Ishiguro-Watanabe M, Tanabe M (2021) KEGG: integrating viruses and cellular organisms. Nucleic Acids Res 49:D545-d551

    Article  Google Scholar 

  • Kant S, Kesarwani P, Prabhu A, Graham SF, Buelow KL, Nakano I, Chinnaiyan P (2020) Enhanced fatty acid oxidation provides glioblastoma cells metabolic plasticity to accommodate to its dynamic nutrient microenvironment. Cell Death Dis 11:253

    Article  Google Scholar 

  • Karsy M, Arslan E, Moy F (2012) Current progress on understanding micrornas in glioblastoma multiforme. Genes Cancer 3:3–15

    Article  Google Scholar 

  • Katz L (1953) A new status index derived from sociometric analysis. Psychometrika 18:39–43

    Article  Google Scholar 

  • Khwairakpam AD, Shyamananda MS, Sailo BL, Rathnakaram SR, Padmavathi G, Kotoky J, Kunnumakkara AB (2015) ATP citrate lyase (ACLY): a promising target for cancer prevention and treatment. Curr Drug Targets 16:156–163

    Article  Google Scholar 

  • Kouhkan F, Mobarra N, Soufi-Zomorrod M, Keramati F, Hosseini Rad SM, Fathi-Roudsari M, Tavakoli R, Hajarizadeh A, Ziaei S, Lahmi R et al (2016) MicroRNA-129-1 acts as tumour suppressor and induces cell cycle arrest of GBM cancer cells through targeting IGF2BP3 and MAPK1. J Med Genet 53:24–33

    Article  Google Scholar 

  • Li H, Li Y, Tian D, Zhang J, Duan S (2021) miR-940 is a new biomarker with tumor diagnostic and prognostic value. Mol Ther Nucleic Acids 25:53–66

    Article  Google Scholar 

  • Linnebank M, Semmler A, Moskau S, Smulders Y, Blom H, Simon M (2008) The methylenetetrahydrofolate reductase (MTHFR) variant c.677C>T (A222V) influences overall survival of patients with glioblastoma multiforme. Neuro Oncol 10:548–552

    Article  Google Scholar 

  • Liu Z, Su D, Qi X, Ma J (2018) MiR-500a-5p promotes glioblastoma cell proliferation, migration and invasion by targeting chromodomain helicase DNA binding protein 5. Mol Med Rep 18:2689–2696

    Google Scholar 

  • Lou W, Ding B, Xu L, Fan W (2019) Construction of potential glioblastoma multiforme-related miRNA-mRNA regulatory network. Front Mol Neurosci 12:66

    Article  Google Scholar 

  • Macfarlane LA, Murphy PR (2010) MicroRNA: biogenesis, function and role in cancer. Curr Genomics 11:537–561

    Article  Google Scholar 

  • McCarthy DJ, Chen Y, Smyth GK (2012) Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res 40:4288–4297

    Article  Google Scholar 

  • Medarova Z, Pantazopoulos P, Yoo B (2020) Screening of potential miRNA therapeutics for the prevention of multi-drug resistance in cancer cells. Sci Rep 10:1970

    Article  Google Scholar 

  • Menyhárt O, Győrffy B (2021) Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis. Comput Struct Biotechnol J 19:949–960

    Article  Google Scholar 

  • Pavlopoulos GA, Kontou PI, Pavlopoulou A, Bouyioukos C, Markou E, Bagos PG (2018) Bipartite graphs in systems biology and medicine: a survey of methods and applications. GigaScience 7:1–31

    Article  Google Scholar 

  • Pedroza-Torres A, Romero-Córdoba SL, Justo-Garrido M, Salido-Guadarrama I, Rodríguez-Bautista R, Montaño S, Muñiz-Mendoza R, Arriaga-Canon C, Fragoso-Ontiveros V, Álvarez-Gómez RM et al (2019) MicroRNAs in tumor cell metabolism: roles and therapeutic opportunities. Front Oncol 9:1404

    Article  Google Scholar 

  • Piwecka M, Rolle K, Belter A, Barciszewska AM, Żywicki M, Michalak M, Nowak S, Naskręt-Barciszewska MZ, Barciszewski J (2015) Comprehensive analysis of microRNA expression profile in malignant glioma tissues. Mol Oncol 9:1324–1340

    Article  Google Scholar 

  • Potapov AP, Goemann B, Wingender E (2008) The pairwise disconnectivity index as a new metric for the topological analysis of regulatory networks. BMC Bioinformatics 9:227

    Article  Google Scholar 

  • Qin CZ, Lv QL, Yang YT, Zhang JM, Zhang XJ, Zhou HH (2017) Downregulation of MicroRNA-320d predicts poor overall survival and promotes the growth and invasive abilities in glioma. Chem Biol Drug Des 89:806–814

    Article  Google Scholar 

  • Qiu S, Huang D, Yin D, Li F, Li X, Kung HF, Peng Y (2013) Suppression of tumorigenicity by microRNA-138 through inhibition of EZH2-CDK4/6-pRb-E2F1 signal loop in glioblastoma multiforme. Biochem Biophys Acta 1832:1697–1707

    Google Scholar 

  • Rasper M, Schäfer A, Piontek G, Teufel J, Brockhoff G, Ringel F, Heindl S, Zimmer C, Schlegel J (2010) Aldehyde dehydrogenase 1 positive glioblastoma cells show brain tumor stem cell capacity. Neuro Oncol 12:1024–1033

    Article  Google Scholar 

  • Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47–e47

    Article  Google Scholar 

  • Rupaimoole R, Slack FJ (2017) MicroRNA therapeutics: towards a new era for the management of cancer and other diseases. Nat Rev Drug Discovery 16:203–222

    Article  Google Scholar 

  • Sana J, Busek P, Fadrus P, Besse A, Radova L, Vecera M, Reguli S, StollinovaSromova L, Hilser M, Lipina R et al (2018) Identification of microRNAs differentially expressed in glioblastoma stem-like cells and their association with patient survival. Sci Rep 8:2836

    Article  Google Scholar 

  • Sasayama T, Tanaka K, and Kohmura E (2016) The roles of microRNAs in glioblastoma biology and biomarker. In: Agrawal A (ed) Neurooncology, 1st edn. Newer Developments, p 27–66

  • Saurty-Seerunghen MS, Bellenger L, El-Habr EA, Delaunay V, Garnier D, Chneiweiss H, Antoniewski C, Morvan-Dubois G, Junier MP (2019) Capture at the single cell level of metabolic modules distinguishing aggressive and indolent glioblastoma cells. Acta Neuropathol Commun 7:155

    Article  Google Scholar 

  • Semonche A, Shah AH, Ivan ME, Komotar RJ (2019) Towards a microRNA-based gene therapy for glioblastoma. Neurosurgery 85:E210-e211

    Article  Google Scholar 

  • Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504

    Article  Google Scholar 

  • Sharkey KJ (2017) A control analysis perspective on Katz centrality. Sci Rep 7:17247

    Article  Google Scholar 

  • Shea A, Harish V, Afzal Z, Chijioke J, Kedir H, Dusmatova S, Roy A, Ramalinga M, Harris B, Blancato J et al (2016) MicroRNAs in glioblastoma multiforme pathogenesis and therapeutics. Cancer Med 5:1917–1946

    Article  Google Scholar 

  • Silber J, Lim DA, Petritsch C, Persson AI, Maunakea AK, Yu M, Vandenberg SR, Ginzinger DG, James CD, Costello JF et al (2008) miR-124 and miR-137 inhibit proliferation of glioblastoma multiforme cells and induce differentiation of brain tumor stem cells. BMC Med 6:14

    Article  Google Scholar 

  • Szczepanek J, Skorupa M, Tretyn A (2022) MicroRNA as a potential therapeutic molecule in cancer. Cells 11:1008

    Article  Google Scholar 

  • Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, Doncheva NT, Legeay M, Fang T, Bork P et al (2020) The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 49:D605–D612

    Article  Google Scholar 

  • Veillon L, Fakih C, Abou-El-Hassan H, Kobeissy F, Mechref Y (2018) Glycosylation Changes in brain cancer. ACS Chem Neurosci 9:51–72

    Article  Google Scholar 

  • Wang N, Tan HY, Feng YG, Zhang C, Chen F, Feng Y (2018a) microRNA-23a in human cancer: its roles, mechanisms and therapeutic relevance. Cancers 11:7

    Article  Google Scholar 

  • Wang R, Zuo X, Wang K, Han Q, Zuo J, Ni H, Liu W, Bao H, Tu Y, Xie P (2018b) MicroRNA-485-5p attenuates cell proliferation in glioma by directly targeting paired box 3. Am J Cancer Res 8:2507–2517

    Google Scholar 

  • Wang X, Dalkic E, Wu M, Chan C (2008) Gene module level analysis: identification to networks and dynamics. Curr Opin Biotechnol 19:482–491

    Article  Google Scholar 

  • Wang WY, Lu WC (2020) Reduced expression of hsa-miR-338-3p contributes to the development of glioma cells by targeting mitochondrial 3-Oxoacyl-ACP Synthase (OXSM) in glioblastoma (GBM). Onco Targets Ther 13:9513–9523

    Article  Google Scholar 

  • Wang Y, Chen R, Zhou X, Guo R, Yin J, Li Y, Ma G (2020) miR-137: a novel therapeutic target for human glioma. Mol Ther Nucleic Acids 21:614–622

    Article  Google Scholar 

  • Wickham H, Averick M, Bryan J, Chang W, McGowan LDA, François R, Grolemund G, Hayes A, Henry L, Hester J (2019) Welcome to the tidyverse. J Open Source Softw 4:1686

    Article  Google Scholar 

  • Xiong W, Ran J, Jiang R, Guo P, Shi X, Li H, Lv X, Li J, Chen D (2018) miRNA-320a inhibits glioma cell invasion and migration by directly targeting aquaporin 4. Oncol Rep 39:1939–1947

    Google Scholar 

  • Xu W, Liu M, Peng X, Zhou P, Zhou J, Xu K, Xu H, Jiang S (2013) miR-24-3p and miR-27a-3p promote cell proliferation in glioma cells via cooperative regulation of MXI1. Int J Oncol 42:757–766

    Article  Google Scholar 

  • Yan C, Kong X, Gong S, Liu F, Zhao Y (2020) Recent advances of the regulation roles of MicroRNA in glioblastoma. Int J Clin Oncol 25:1215–1222

    Article  Google Scholar 

  • Yeh M, Wang YY, Yoo JY, Oh C, Otani Y, Kang JM, Park ES, Kim E, Chung S, Jeon YJ et al (2021) MicroRNA-138 suppresses glioblastoma proliferation through downregulation of CD44. Sci Rep 11:9219

    Article  Google Scholar 

  • Yin CY, Kong W, Jiang J, Xu H, Zhao W (2019) miR-7-5p inhibits cell migration and invasion in glioblastoma through targeting SATB1. Oncol Lett 17:1819–1825

    Google Scholar 

  • Yu J, Wu SW, Wu WP (2017) A tumor-suppressive microRNA, miRNA-485-5p, inhibits glioma cell proliferation and invasion by down-regulating TPD52L2. Am J Transl Res 9:3336–3344

    Google Scholar 

  • Zhan J, Gurung S, Parsa SPK (2017) Identification of top-K nodes in large networks using Katz centrality. J Big Data 4:16

    Article  Google Scholar 

  • Zhang JF, Zhang JS, Zhao ZH, Yang PB, Ji SF, Li N, Shi QD, Tan J, Xu X, Xu CB et al (2018) MicroRNA-770 affects proliferation and cell cycle transition by directly targeting CDK8 in glioma. Cancer Cell Int 18:195

    Article  Google Scholar 

  • Zhao C, Guo R, Guan F, Ma S, Li M, Wu J, Liu X, Li H, Yang B (2020) MicroRNA-128-3p enhances the chemosensitivity of temozolomide in glioblastoma by targeting c-Met and EMT. Sci Rep 10:9471

    Article  Google Scholar 

  • Zhu Z, Leung GKK (2020) More than a metabolic enzyme: MTHFD2 as a novel target for anticancer therapy? Front Oncol 10:658

    Article  Google Scholar 

Download references

Acknowledgements

RB acknowledges Council of Scientific & Industrial Research (CSIR) for the Senior Research Fellowship (CSIR File No.: 31/011(1047)/2018-EMR-I dated 26-04-2018). RRS acknowledges CSIR-National Chemical Laboratory for funding support (MLP038826) to carry out this work.

Author information

Authors and Affiliations

Authors

Contributions

RRS conceived and conceptualized the work. RB designed the methodologies, wrote and executed the computer programs and performed the simulations and analyses. RB wrote the original manuscript. RRS supervised the work, reviewed and edited the manuscript.

Corresponding author

Correspondence to Ram Rup Sarkar.

Ethics declarations

Conflict of interest

The authors declare no conflict of interests.

Additional information

Communicated by Shuhua Xu.

Publisher's Note

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

Supplementary Information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bhowmick, R., Sarkar, R.R. Identification of potential microRNAs regulating metabolic plasticity and cellular phenotypes in glioblastoma. Mol Genet Genomics 298, 161–181 (2023). https://doi.org/10.1007/s00438-022-01966-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00438-022-01966-3

Keywords

Navigation