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LncRNAs in neuropsychiatric disorders and computational insights for their prediction

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Abstract

Long non-coding RNAs (lncRNAs) are 200 nucleotide extended transcripts that do not encode proteins or possess limited coding ability. LncRNAs epigenetically control several biological functions such as gene regulation, transcription, mRNA splicing, protein interaction, and genomic imprinting. Over the years, drastic progress in understanding the role of lncRNAs in diverse biological processes has been made. LncRNAs are reported to show tissue-specific expression patterns suggesting their potential as novel candidate biomarkers for diseases. Among all other non-coding RNAs, lncRNAs are highly expressed within the brain-enriched or brain-specific regions of the neural tissues. They are abundantly expressed in the neocortex and pre-mature frontal regions of the brain. LncRNAs are co-expressed with the protein-coding genes and have a significant role in the evolution of functions of the brain. Any deregulation in the lncRNAs contributes to disruptions in normal brain functions resulting in multiple neurological disorders. Neuropsychiatric disorders such as schizophrenia, bipolar disease, autism spectrum disorders, and anxiety are associated with the abnormal expression and regulation of lncRNAs. This review aims to highlight the understanding of lncRNAs concerning normal brain functions and their deregulation associated with neuropsychiatric disorders. We have also provided a survey on the available computational tools for the prediction of lncRNAs, their protein coding potentials, and sub-cellular locations, along with a section on existing online databases with known lncRNAs, and their interactions with other molecules.

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References

  1. Jarroux J, Morillon A, Pinskaya M (2017) History, discovery, and classification of lncRNAs. Adv Exp Med Biol 1008:1–46. DOI: https://doi.org/10.1007/978-981-10-5203-3_1

    Article  CAS  PubMed  Google Scholar 

  2. Palazzo AF, Lee ES (2015) Non-coding RNA: what is functional and what is junk? Front Genet. DOI: https://doi.org/10.3389/fgene.2015.00002

    Article  PubMed  PubMed Central  Google Scholar 

  3. Cuevas-Diaz Duran R, Wei H, Kim DH, Wu JQ (2019) Long non-coding RNAs: important regulators in the development, function, and disorders of the central nervous system. Neuropathol Appl Neurobiol 45(6):538. DOI: https://doi.org/10.1111/NAN.12541

    Article  CAS  PubMed  Google Scholar 

  4. de Almeida RA, Fraczek MG, Parker S, Delneri D, O’Keefe RT (2016) Non-coding RNAs and disease: the classical ncRNAs make a comeback. Biochem Soc Trans 44(4):1073–1078. DOI: https://doi.org/10.1042/BST20160089

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Mattick JS, Makunin IV (2006) Non-coding RNA. Hum Mol Genet 15:R17–R29. DOI: https://doi.org/10.1093/hmg/ddl046

    Article  CAS  PubMed  Google Scholar 

  6. Policarpo R, Sierksma A, De Strooper B, d’Ydewalle C (2021) From Junk to Function: LncRNAs in CNS Health and Disease. Front Mol Neurosci 14:151. DOI: https://doi.org/10.3389/FNMOL.2021.714768/BIBTEX

    Article  Google Scholar 

  7. Bhattacharyya N, Pandey V, Bhattacharyya M, Dey A (2021) Regulatory role of long non coding RNAs (lncRNAs) in neurological disorders: From novel biomarkers to promising therapeutic strategies. Asian J Pharm Sci 16(5):533–550. DOI: https://doi.org/10.1016/J.AJPS.2021.02.006

    Article  PubMed  PubMed Central  Google Scholar 

  8. Zimmer-Bensch G (2019) Emerging Roles of Long Non-Coding RNAs as Drivers of Brain Evolution. Cells 8(11):1399. DOI: https://doi.org/10.3390/cells8111399

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Statello L, Guo CJ, Chen LL, Huarte M (2020) Gene regulation by long non-coding RNAs and its biological functions. Nat Rev Mol Cell Biol 22(2):96–118. DOI: https://doi.org/10.1038/s41580-020-00315-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Andersen RE, Lim DA (2018) Forging our understanding of lncRNAs in the brain. Cell Tissue Res 371(1):55–71. DOI: https://doi.org/10.1007/s00441-017-2711-z

    Article  CAS  PubMed  Google Scholar 

  11. Rusconi F, Battaglioli E, Venturin M (2020) Psychiatric Disorders and lncRNAs: A Synaptic Match. Int J Mol Sci 21(9):3030. DOI: https://doi.org/10.3390/ijms21093030

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Zhang X-Q, Wang Z-L, Poon M-W, Yang J-H (2017) Spatial-temporal transcriptional dynamics of long non-coding RNAs in human brain. Hum Mol Genet. DOI: https://doi.org/10.1093/hmg/ddx203

    Article  PubMed  PubMed Central  Google Scholar 

  13. Nie J-H, Li T-X, Zhang X-Q, Liu J (2019) Roles of Non-Coding RNAs in Normal Human Brain Development, Brain Tumor, and Neuropsychiatric Disorders. Non-Coding RNA 5(2):36. DOI: https://doi.org/10.3390/ncrna5020036

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Quinn JJ, Chang HY (2015) Unique features of long non-coding RNA biogenesis and function. Nat Rev Genet 17(1):47–62. DOI: https://doi.org/10.1038/nrg.2015.10

    Article  CAS  Google Scholar 

  15. Yoshino Y, Dwivedi Y (2020) Non-Coding RNAs in Psychiatric Disorders and Suicidal Behavior. Front Psychiatry 11:890. DOI: https://doi.org/10.3389/FPSYT.2020.543893/BIBTEX

    Article  Google Scholar 

  16. García-Fonseca Á, Martin-Jimenez C, Barreto GE, Pachón AFA, González J (2021) The Emerging Role of Long Non-Coding RNAs and MicroRNAs in Neurodegenerative Diseases: A Perspective of Machine Learning. Biomolecules 11(8):1132. DOI: https://doi.org/10.3390/BIOM11081132

    Article  PubMed  PubMed Central  Google Scholar 

  17. Mishra P, Kumar S (2021) Association of lncRNA with regulatory molecular factors in brain and their role in the pathophysiology of schizophrenia. Metab Brain Dis 36(5):849–858. DOI: https://doi.org/10.1007/s11011-021-00692-w

    Article  CAS  PubMed  Google Scholar 

  18. Bhatti GK, Khullar N, Sidhu IS, Navik US, Reddy AP, Reddy PH, Bhatti JS (2021) Emerging role of non-coding RNA in health and disease. Metab Brain Dis 36(6):1119–1134. DOI: https://doi.org/10.1007/S11011-021-00739-Y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Laird FM (2005) BACE1, a Major Determinant of Selective Vulnerability of the Brain to Amyloid- Amyloidogenesis, is Essential for Cognitive, Emotional, and Synaptic Functions. J Neurosci 25(50):11693–11709. DOI: https://doi.org/10.1523/JNEUROSCI.2766-05.2005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Faghihi MA, Modarresi F, Khalil AM, Wood DE, Sahagan BG, Morgan TE, Finch CE, St. Laurent G III, Kenny PJ, Wahlestedt C (2008) Expression of a noncoding RNA is elevated in Alzheimer’s disease and drives rapid feed-forward regulation of β-secretase. Nat Med 14(7):723–730. DOI: https://doi.org/10.1038/nm1784

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Bekinschtein P, Cammarota M, Medina JH (2014) BDNF and memory processing. Neuropharmacology 76:677–683. DOI: https://doi.org/10.1016/j.neuropharm.2013.04.024

    Article  CAS  PubMed  Google Scholar 

  22. Bambah-Mukku D, Travaglia A, Chen DY, Pollonini G, Alberini CM (2014) A Positive Autoregulatory BDNF Feedback Loop via C/EBP Mediates Hippocampal Memory Consolidation. J Neurosci 34(37):12547–12559. DOI: https://doi.org/10.1523/JNEUROSCI.0324-14.2014

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Poitras L, YuM, Lesage-Pelletier C, MacDonald RB, Gagné J-P, Hatch G, Kelly I, Hamilton SP, Rubenstein JLR, Poirier GG, Ekker M (2010) An SNP in an ultraconserved regulatory element affects Dlx5/Dlx6 regulation in the forebrain. Development137(18):3089–3097. DOI: https://doi.org/10.1242/dev.051052

  24. Patel K, Cherian J, Gohil K, Atkinson D (2014) Schizophrenia: overview and treatment options. Pharm Theurapeutics 39(9):638–645

    Google Scholar 

  25. Merelo V, Durand D, Lescallette AR, Vrana KE, Hong LE, Faghihi MA, Bellon A (2015) Associating schizophrenia, long non-coding RNAs and neurostructural dynamics. Front Mol Neurosci. DOI: https://doi.org/10.3389/fnmol.2015.00057

    Article  PubMed  PubMed Central  Google Scholar 

  26. Miyoshi K, Honda A, Baba K, Taniguchi M, Oono K, Fujita T, Kuroda S, Katayama T, Tohyama M (2003) Disrupted-In-Schizophrenia 1, a candidate gene for schizophrenia, participates in neurite outgrowth. Mol Psychiatry 8(7):685–694. DOI: https://doi.org/10.1038/sj.mp.4001352

    Article  CAS  PubMed  Google Scholar 

  27. Kamiya A, Kubo K, Tomoda T, Takaki M, Youn R, Ozeki Y, Sawamura N, Park U, Kudo C, Okawa M, Ross CA, Hatten ME, Nakajima K, Sawa A (2005) A schizophrenia-associated mutation of DISC1 perturbs cerebral cortex development. Nat Cell Biol 7(12):1167–1178. DOI: https://doi.org/10.1038/ncb1328

    Article  CAS  PubMed  Google Scholar 

  28. Chubb JE, Bradshaw NJ, Soares DC, Porteous DJ, Millar JK (2008) The DISC locus in psychiatric illness. Mol Psychiatry 13(1):36–64. DOI: https://doi.org/10.1038/sj.mp.4002106

    Article  CAS  PubMed  Google Scholar 

  29. Tsuiji H, Yoshimoto R, Hasegawa Y, Furuno M, Yoshida M, Nakagawa S (2011) Competition between a noncoding exon and introns: Gomafu contains tandem UACUAAC repeats and associates with splicing factor-1. Genes Cells 16(5):479–490. DOI: https://doi.org/10.1111/j.1365-2443.2011.01502.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Barry G, Briggs JA, Vanichkina DP, Poth EM, Beveridge NJ, Ratnu VS, Nayler SP, Nones K, Hu J, Bredy TW, Nakagawa S, Rigo F, Taft RJ, Cairns MJ, Blackshaw S, Wolvetang EJ, Mattick JS (2014) The long non-coding RNA Gomafu is acutely regulated in response to neuronal activation and involved in schizophrenia-associated alternative splicing. Mol Psychiatry 19(4):486–494. DOI: https://doi.org/10.1038/mp.2013.45

    Article  CAS  PubMed  Google Scholar 

  31. Magistri M, Faghihi MA, St Laurent G, Wahlestedt C (2012) Regulation of chromatin structure by long noncoding RNAs: focus on natural antisense transcripts. Trends Genet 28(8):389–396. DOI: https://doi.org/10.1016/j.tig.2012.03.013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Pruunsild P, Kazantseva A, Aid T, Palm K, Timmusk T (2007) Dissecting the human BDNF locus: Bidirectional transcription, complex splicing, and multiple promoters. Genomics 90(3):397–406. DOI: https://doi.org/10.1016/j.ygeno.2007.05.004

    Article  CAS  PubMed  Google Scholar 

  33. Favalli G, Li J, Belmonte-de-Abreu P, Wong AHC, Daskalakis ZJ (2012) The role of BDNF in the pathophysiology and treatment of schizophrenia. J Psychiatr Res 46(1):1–11. DOI: https://doi.org/10.1016/j.jpsychires.2011.09.022

    Article  PubMed  Google Scholar 

  34. Issler O, van der Zee YY, Ramakrishnan A, Wang J, Tan C, Loh Y-HE, Purushothaman I, Walker DM, Lorsch ZS, Hamilton PJ, Peña CJ, Flaherty E, Hartley BJ, Torres-Berrío A, Parise EM, Kronman H, Duffy JE, Estill MS, Calipari ES et al (2020) Sex-Specific Role for the Long Non-coding RNA LINC00473 in Depression. Neuron 106(6):912–926e5. DOI: https://doi.org/10.1016/j.neuron.2020.03.023

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Yu H, Chen Z (2011) The role of BDNF in depression on the basis of its location in the neural circuitry. Acta Pharmacol Sin 32(1):3–11. DOI: https://doi.org/10.1038/aps.2010.184

    Article  CAS  PubMed  Google Scholar 

  36. Ni X, Liao Y, Li L, Zhang X, Wu Z (2018) Therapeutic role of long non-coding RNA TCONS_00019174 in depressive disorders is dependent on Wnt/β-catenin signaling pathway. J Integr Neurosci 17(2). DOI: https://doi.org/10.31083/JIN-170052

  37. Ye N, Rao S, Du T, Hu H, Liu Z, Shen Y, Xu Q (2017) Intergenic variants may predispose to major depression disorder through regulation of long non-coding RNA expression. Gene 601:21–26. DOI: https://doi.org/10.1016/j.gene.2016.11.041

    Article  CAS  PubMed  Google Scholar 

  38. Zhou Y, Lutz P-E, Wang YC, Ragoussis J, Turecki G (2018) Global long non-coding RNA expression in the rostral anterior cingulate cortex of depressed suicides. Translational Psychiatry 8(1):224. DOI: https://doi.org/10.1038/s41398-018-0267-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Tang J, Yu Y, Yang W (2017) Long noncoding RNA and its contribution to autism spectrum disorders. CNS Neurosci Ther 23(8):645–656. DOI: https://doi.org/10.1111/cns.12710

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Li L, Zhuang Y, Zhao X, Li X (2019) Long Non-coding RNA in Neuronal Development and Neurological Disorders. Front Genet. DOI: https://doi.org/10.3389/fgene.2018.00744

    Article  PubMed  PubMed Central  Google Scholar 

  41. Ziats MN, Rennert OM (2013) Aberrant Expression of Long Noncoding RNAs in Autistic Brain. J Mol Neurosci 49(3):589–593. DOI: https://doi.org/10.1007/s12031-012-9880-8

    Article  CAS  PubMed  Google Scholar 

  42. Parikshak NN, Swarup V, Belgard TG, Irimia M, Ramaswami G, Gandal MJ, Hartl C, Leppa V, Ubieta L, de la T, Huang J, Lowe JK, Blencowe BJ, Horvath S, Geschwind DH (2016) Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature 540(7633):423–427. DOI: https://doi.org/10.1038/nature20612

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Vieta E, Salagre E, Grande I, Carvalho AF, Fernandes BS, Berk M, Birmaher B, Tohen M, Suppes T (2018) Early Intervention in Bipolar Disorder. Am J Psychiatry 175(5):411–426. DOI: https://doi.org/10.1176/appi.ajp.2017.17090972

    Article  PubMed  Google Scholar 

  44. Sayad A, Taheri M, Omrani MD, Fallah H, Kholghi Oskooei V, Ghafouri-Fard S (2019) Peripheral expression of long non-coding RNAs in bipolar patients. J Affect Disord 249:169–174. DOI: https://doi.org/10.1016/j.jad.2019.02.034

    Article  CAS  PubMed  Google Scholar 

  45. DeMartini J, Patel G, Fancher TL (2019) Generalized Anxiety Disorder. Ann Intern Med 170(7):ITC49. DOI: https://doi.org/10.7326/AITC201904020

    Article  PubMed  Google Scholar 

  46. Xu X, Liu S, Yang Z, Zhao X, Deng Y, Zhang G, Pang J, Zhao C, Zhang W (2021) A systematic review of computational methods for predicting long noncoding RNAs. Brief Funct Genomics 20(3):162–173. DOI: https://doi.org/10.1093/BFGP/ELAB016

    Article  CAS  PubMed  Google Scholar 

  47. Ito EA, Katahira I, Vicente FF da, Pereira R, Lopes LFP FM (2018) BASiNET—BiologicAl Sequences NETwork: a case study on coding and non-coding RNAs identification. Nucleic Acids Res 46(16):e96–e96. DOI: https://doi.org/10.1093/NAR/GKY462

    Article  PubMed  PubMed Central  Google Scholar 

  48. Cao L, Wang Y, Bi C, Ye Q, Yin T, Ye N (2020) PreLnc: An Accurate Tool for Predicting lncRNAs Based on Multiple Features. Genes 11(9):981. DOI: https://doi.org/10.3390/GENES11090981

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Zhao J, Song X, Wang K (2016) lncScore: alignment-free identification of long noncoding RNA from assembled novel transcripts. Sci Rep 6(1):1–12. DOI: https://doi.org/10.1038/srep34838

    Article  CAS  Google Scholar 

  50. Baek J, Lee B, Kwon S, Yoon S (2018) LncRNAnet: long non-coding RNA identification using deep learning. Bioinformatics 34(22):3889–3897. DOI: https://doi.org/10.1093/BIOINFORMATICS/BTY418

    Article  CAS  PubMed  Google Scholar 

  51. Liu S, Zhao X, Zhang G, Li W, Liu F, Liu S, Zhang W (2019) PredLnc-GFStack: A Global Sequence Feature Based on a Stacked Ensemble Learning Method for Predicting lncRNAs from Transcripts. Genes 10(9). DOI: https://doi.org/10.3390/GENES10090672

  52. Zheng H, Talukder A, Li X, Hu H (2021) A systematic evaluation of the computational tools for lncRNA identification. Brief Bioinform 22(6):1–18. DOI: https://doi.org/10.1093/BIB/BBAB285

    Article  Google Scholar 

  53. Wang L, Park HJ, Dasari S, Wang S, Kocher JP, Li W (2013) CPAT: Coding-Potential Assessment Tool using an alignment-free logistic regression model. Nucleic Acids Res 41(6):e74–e74. DOI: https://doi.org/10.1093/NAR/GKT006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Lin MF, Jungreis I, Kellis M (2011) PhyloCSF: a comparative genomics method to distinguish protein coding and non-coding regions. Bioinformatics 27(13):i275–i282. DOI: https://doi.org/10.1093/BIOINFORMATICS/BTR209

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Kong L, Zhang Y, Ye ZQ, Liu XQ, Zhao SQ, Wei L, Gao G (2007) CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine. Nucleic Acids Res 35(suppl2):W345–W349. DOI: https://doi.org/10.1093/NAR/GKM391

    Article  PubMed  PubMed Central  Google Scholar 

  56. Kang YJ, Yang DC, Kong L, Hou M, Meng YQ, Wei L, Gao G (2017) CPC2: a fast and accurate coding potential calculator based on sequence intrinsic features. Nucleic Acids Res 45(W1):W12–W16. DOI: https://doi.org/10.1093/NAR/GKX428

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Pircher A, Gebetsberger J, Polacek N (2014) Ribosome-associated ncRNAs: An emerging class of translation regulators. RNA Biol 11(11):1335–1339. DOI: https://doi.org/10.1080/15476286.2014.996459

    Article  PubMed  Google Scholar 

  58. Zeng C, Fukunaga T, Hamada M (2018) Identification and analysis of ribosome-associated lncRNAs using ribosome profiling data. BMC Genomics 19(1):414. DOI: https://doi.org/10.1186/s12864-018-4765-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Zeng C, Hamada M (2021) Detection and Characterization of Ribosome-Associated Long Noncoding RNAs. In: Haiming Cao (ed) Functional analysis of long non coding RNAs, Humana press, pp 179–194. DOI: https://doi.org/10.1007/978-1-0716-1158-6_11

  60. Zeng C, Hamada M (2018) Identifying sequence features that drive ribosomal association for lncRNA. BMC Genomics 19(10):906. DOI: https://doi.org/10.1186/s12864-018-5275-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Djebali S, Davis CA, Merkel A, Dobin A, Lassmann T, Mortazavi A, Tanzer A, Lagarde J, Lin W, Schlesinger F, Xue C, Marinov GK, Khatun J, Williams BA, Zaleski C, Rozowsky J, Röder M, Kokocinski F, Abdelhamid RF et al (2012) Landscape of transcription in human cells. Nature 489(7414):101–108. DOI: https://doi.org/10.1038/nature11233

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Braidotti G, Baubec T, Pauler F, Seidl C, Smrzka O, Stricker S, Yotova I, Barlow DP (2004) The Air Noncoding RNA: An Imprinted cis-silencing Transcript. Cold Spring Harb Symp Quant Biol 69:55–66. DOI: https://doi.org/10.1101/sqb.2004.69.55

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Liu B, Sun L, Liu Q, Gong C, Yao Y, Lv X, Lin L, Yao H, Su F, Li D, Zeng M, Song E (2015) A Cytoplasmic NF-κB Interacting Long Noncoding RNA Blocks IκB Phosphorylation and Suppresses Breast Cancer Metastasis. Cancer Cell 27(3):370–381. DOI: https://doi.org/10.1016/j.ccell.2015.02.004

    Article  CAS  PubMed  Google Scholar 

  64. Mas-Ponte D, Carlevaro-Fita J, Palumbo E, Hermoso Pulido T, Guigo R, Johnson R (2017) LncATLAS database for subcellular localization of long noncoding RNAs. RNA 23(7):1080–1087. DOI: https://doi.org/10.1261/rna.060814.117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Cao Z, Pan X, Yang Y, Huang Y, Shen H-B (2018) The lncLocator: a subcellular localization predictor for long non-coding RNAs based on a stacked ensemble classifier. Bioinformatics 34(13):2185–2194. DOI: https://doi.org/10.1093/bioinformatics/bty085

    Article  CAS  PubMed  Google Scholar 

  66. Wang Y, Zhu X, Yang L, Hu X, He K, Yu C, Jiao S, Chen J, Guo R, Yang S (2022) IDDLncLoc: Subcellular Localization of LncRNAs Based on a Framework for Imbalanced Data Distributions. Interdisciplinary Sciences: Computational Life Sciences 14(2):409–420. DOI: https://doi.org/10.1007/s12539-021-00497-6

    Article  CAS  PubMed  Google Scholar 

  67. Bao Z, Yang Z, Huang Z, Zhou Y, Cui Q, Dong D (2019) LncRNADisease 2.0: an updated database of long non-coding RNA-associated diseases. Nucleic Acids Res 47(D1):D1034–D1037. DOI: https://doi.org/10.1093/nar/gky905

    Article  CAS  PubMed  Google Scholar 

  68. Miao Y-R, Liu W, Zhang Q, Guo A-Y (2018) lncRNASNP2: an updated database of functional SNPs and mutations in human and mouse lncRNAs. Nucleic Acids Res 46(D1):D276–D280. DOI: https://doi.org/10.1093/nar/gkx1004

    Article  CAS  PubMed  Google Scholar 

  69. Zhou B, Ji B, Liu K, Hu G, Wang F, Chen Q, Yu R, Huang P, Ren J, Guo C, Zhao H, Zhang H, Zhao D, Li Z, Zeng Q, Yu J, Bian Y, Cao Z, Xu S et al (2021) EVLncRNAs 2.0: an updated database of manually curated functional long non-coding RNAs validated by low-throughput experiments. Nucleic Acids Res 49(D1):D86–D91. DOI: https://doi.org/10.1093/NAR/GKAA1076

    Article  CAS  PubMed  Google Scholar 

  70. Fukunaga T, Iwakiri J, Ono Y, Hamada M (2019) LncRRIsearch: A Web Server for lncRNA-RNA Interaction Prediction Integrated With Tissue-Specific Expression and Subcellular Localization Data. Front Genet DOI. https://doi.org/10.3389/fgene.2019.00462

    Article  Google Scholar 

  71. John B, Enright AJ, Aravin A, TuschlT, Sander C, Marks DS (2004) Human MicroRNA targets. PLoS Biol 2(11). DOI: https://doi.org/10.1371/JOURNAL.PBIO.0020363

  72. Agarwal V, Bell GW, Nam JW, Bartel DP (2015) Predicting effective microRNA target sites in mammalian mRNAs. ELife. DOI: https://doi.org/10.7554/ELIFE.05005

  73. Kruger J, Rehmsmeier M (2006) RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res 34(Web Server):W451–W454. DOI: https://doi.org/10.1093/nar/gkl243

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Pinzón N, Li B, Martinez L, Sergeeva A, Presumey J, Apparailly F, Seitz H (2017) microRNA target prediction programs predict many false positives. Genome Res 27(2):234–245. DOI: https://doi.org/10.1101/gr.205146.116

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Huang Y-A, Huang Z-A, You Z-H, Zhu Z, Huang W-Z, Guo J-X, Yu C-Q (2019) Predicting lncRNA-miRNA Interaction via Graph Convolution Auto-Encoder. Front Genet 10. DOI: https://doi.org/10.3389/fgene.2019.00758

  76. Hao L, Fu J, Tian Y, Wu J (2017) Systematic analysis of lncRNAs, miRNAs and mRNAs for the identification of biomarkers for osteoporosis in the mandible of ovariectomized mice. Int J Mol Med 40(3):689–702. DOI: https://doi.org/10.3892/ijmm.2017.3062

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Meng X, Li A, Yu B, Li S (2021) Interplay between miRNAs and lncRNAs: Mode of action and biological roles in plant development and stress adaptation. Comput Struct Biotechnol J 19:2567–2574. DOI: https://doi.org/10.1016/j.csbj.2021.04.062

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Bonnet E, He Y, Billiau K, Van de Peer Y (2010) TAPIR, a web server for the prediction of plant microRNA targets, including target mimics. Bioinformatics 26(12):1566–1568. DOI: https://doi.org/10.1093/bioinformatics/btq233

    Article  CAS  PubMed  Google Scholar 

  79. Chen Y, Wang X (2020) miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res 48(D1):D127. DOI: https://doi.org/10.1093/NAR/GKZ757

    Article  CAS  PubMed  Google Scholar 

  80. Huang H-Y, Lin Y-C-D, Cui S, Huang Y, Tang Y, Xu J, Bao J, Li Y, Wen J, Zuo H, Wang W, Li J, Ni J, Ruan Y, Li L, Chen Y, Xie Y, Zhu Z, Cai X et al (2022) miRTarBase update 2022: an informative resource for experimentally validated miRNA–target interactions. Nucleic Acids Res 50(D1):D222–D230. DOI: https://doi.org/10.1093/NAR/GKAB1079

    Article  CAS  PubMed  Google Scholar 

  81. Paz I, Kosti I, Ares M, Cline M, Mandel-Gutfreund Y (2014) RBPmap: a web server for mapping binding sites of RNA-binding proteins. Nucleic Acids Res 42(W1):W361–W367. DOI: https://doi.org/10.1093/nar/gku406

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Rao S, Tian L, Cao H, Baranova A, Zhang F (2022) Involvement of the long intergenic non-coding RNA LINC00461 in schizophrenia. BMC Psychiatry 22(1):59. DOI: https://doi.org/10.1186/s12888-022-03718-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Ghafouri-Fard S, Eghtedarian R, Seyedi M, Pouresmaeili F, Arsang-Jang S, Taheri M (2022) Upregulation of VDR-associated lncRNAs in Schizophrenia. J Mol Neurosci 72(2):239–245. DOI: https://doi.org/10.1007/s12031-021-01901-y

    Article  CAS  PubMed  Google Scholar 

  84. Huang Z, Zhao J, Wang W, Zhou J, Zhang J (2020) Depletion of LncRNA NEAT1 Rescues Mitochondrial Dysfunction Through NEDD4L-Dependent PINK1 Degradation in Animal Models of Alzheimer’s Disease. Frontiers in Cellular Neuroscience. DOI: https://doi.org/10.3389/fncel.2020.00028

  85. Wang Q, Ge X, Zhang J, Chen L (2020) Effect of lncRNA WT1-AS regulating WT1 on oxidative stress injury and apoptosis of neurons in Alzheimer’s disease via inhibition of the miR-375/SIX4 axis. Aging 12(23):23974–23995. DOI: https://doi.org/10.18632/aging.104079

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Hayes CS, Labuzan SA, Menke JA, Haddock AN, Waddell DS (2019) Ttc39c is upregulated during skeletal muscle atrophy and modulates ERK1/2 MAP kinase and hedgehog signaling. J Cell Physiol 234(12):23807–23824. DOI: https://doi.org/10.1002/jcp.28950

    Article  CAS  PubMed  Google Scholar 

  87. Cheng J, Duan Y, Zhang F, Shi J, Li H, Wang F, Li H (2021) The Role of lncRNA TUG1 in the Parkinson Disease and Its Effect on Microglial Inflammatory Response. Neuromol Med 23(2):327–334. DOI: https://doi.org/10.1007/s12017-020-08626-y

    Article  CAS  Google Scholar 

  88. Lin Q, Hou S, Dai Y, Jiang N, Lin Y (2019) LncRNA HOTAIR targets miR-126-5p to promote the progression of Parkinson’s disease through RAB3IP. Biol Chem 400(9):1217–1228. DOI: https://doi.org/10.1515/hsz-2018-0431

    Article  CAS  PubMed  Google Scholar 

  89. Ströhle A, Gensichen J, Domschke K (2018) The Diagnosis and Treatment of Anxiety Disorders. Deutsches Ärzteblatt International. DOI: https://doi.org/10.3238/arztebl.2018.0611

  90. Ghafouri-Fard S, Badrlou E, Taheri M, Dürsteler KM, Beatrix Brühl A, Sadeghi-Bahmani D, Brand S (2021) A Comprehensive Review on the Role of Non-Coding RNAs in the Pathophysiology of Bipolar Disorder. Int J Mol Sci 22(10):5156. DOI: https://doi.org/10.3390/ijms22105156

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Wang L, Zhang M, Zhu H, Sun L, Yu B, Cui X (2021) Combined identification of lncRNA NONHSAG004550 and NONHSAT125420 as a potential diagnostic biomarker of perinatal depression. J Clin Lab Anal 35(8). DOI: https://doi.org/10.1002/jcla.23890

  92. Ghafouri-Fard S, Namvar A, Arsang-Jang S, Komaki A, Taheri M (2020) Expression Analysis of BDNF, BACE1, and Their Natural Occurring Antisenses in Autistic Patients. J Mol Neurosci 70(2):194–200. DOI: https://doi.org/10.1007/s12031-019-01432-7

    Article  CAS  PubMed  Google Scholar 

  93. Ang C, Ma Q, Wapinski O, Fan S, Flynn R, Lee Q, Coe B, Onoguchi M, Olmos V, Do B, Dukes-Rimsky L, Xu J, Tanabe J, Wang L, Elling U, Penninger J, Zhao Y, Qu K, Eichler E et al (2019) The novel lncRNA lnc-NR2F1 is pro-neurogenic and mutated in human neurodevelopmental disorders. ELife. DOI: https://doi.org/10.7554/eLife.41770.001

  94. Kukharsky MS, Ninkina NN, An H, Telezhkin V, Wei W, de Meritens CR, Cooper-Knock J, Nakagawa S, Hirose T, Buchman VL, Shelkovnikova TA (2020) Long non-coding RNA Neat1 regulates adaptive behavioural response to stress in mice. Translational Psychiatry 10(1):171. DOI: https://doi.org/10.1038/s41398-020-0854-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Rodrigues DC, Mufteev M, Weatheritt RJ, Djuric U, Ha KCH, Ross PJ, Wei W, Piekna A, Sartori MA, Byres L, Mok RSF, Zaslavsky K, Pasceri P, Diamandis P, Morris Q, Blencowe, BJ, EllisJ (2020) Shifts in Ribosome Engagement Impact Key Gene Sets in Neurodevelopment and Ubiquitination in Rett Syndrome. Cell Rep 30(12):4179–4196e11. DOI: https://doi.org/10.1016/j.celrep.2020.02.107

    Article  CAS  PubMed  Google Scholar 

  96. Fu G, Chen W, Li H, Wang Y, Liu L, Qian Q (2021) A potential association of RNF219 - AS1 with ADHD: Evidence from categorical analysis of clinical phenotypes and from quantitative exploration of executive function and white matter microstructure endophenotypes. CNS Neurosci Ther 27(5):603–616. DOI: https://doi.org/10.1111/cns.13629

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Wang G, Yin H, Li B, Yu C, Wang F, Xu X, Cao J, Bao Y, Wang L, Abbasi AA, Bajic VB, Ma L, Zhang Z (2019) Characterization and identification of long non-coding RNAs based on feature relationship. Bioinf (Oxford England) 35(17):2949–2956. DOI: https://doi.org/10.1093/BIOINFORMATICS/BTZ008

    Article  CAS  Google Scholar 

  98. Han S, Liang Y, Ma Q, Xu Y, Zhang Y, Du W, Wang C, Li Y (2019) LncFinder: an integrated platform for long non-coding RNA identification utilizing sequence intrinsic composition, structural information and physicochemical property. Brief Bioinform 20(6):2009–2027. DOI: https://doi.org/10.1093/BIB/BBY065

    Article  CAS  PubMed  Google Scholar 

  99. Schneider HW, Raiol T, Brigido MM, Walter MEMT, Stadler PF (2017) A Support Vector Machine based method to distinguish long non-coding RNAs from protein coding transcripts. BMC Genomics 18(1):1–14. DOI: https://doi.org/10.1186/S12864-017-4178-4/TABLES/11

    Article  Google Scholar 

  100. Wucher V, Legeai F, Hédan B, Rizk G, Lagoutte L, Leeb T, Jagannathan V, Cadieu E, David A, Lohi H, Cirera S, Fredholm M, Botherel N, Leegwater PAJ, Le Béguec C, Fieten H, Johnson J, Alföldi J, André C et al (2017) FEELnc: a tool for long non-coding RNA annotation and its application to the dog transcriptome. Nucleic Acids Res 45(8). DOI: https://doi.org/10.1093/NAR/GKW1306

  101. Hu L, Xu Z, Hu B, Lu ZJ (2017) COME: a robust coding potential calculation tool for lncRNA identification and characterization based on multiple features. Nucleic Acids Res 45(1):e2–e2. DOI: https://doi.org/10.1093/NAR/GKW798

    Article  PubMed  Google Scholar 

  102. Hou M, Tang X, Tian F, Shi F, Liu F, Gao G (2016) AnnoLnc: A web server for systematically annotating novel human lncRNAs. BMC Genomics 17(1):1–10. DOI: https://doi.org/10.1186/S12864-016-3287-9/TABLES/2

    Article  Google Scholar 

  103. Sun L, Liu H, Zhang L, Meng J (2015) lncRScan-SVM: A Tool for Predicting Long Non-Coding RNAs Using Support Vector Machine. PLoS ONE 10(10):e0139654. DOI: https://doi.org/10.1371/JOURNAL.PONE.0139654

    Article  PubMed  PubMed Central  Google Scholar 

  104. Li A, Zhang J, Zhou Z (2014) PLEK: A tool for predicting long non-coding RNAs and messenger RNAs based on an improved k-mer scheme. BMC Bioinformatics 15(1):1–10. DOI: https://doi.org/10.1186/1471-2105-15-311/FIGURES/3

    Article  Google Scholar 

  105. Sun K, Chen X, Jiang P, Song X, Wang H, Sun H (2013) iSeeRNA: Identification of long intergenic non-coding RNA transcripts from transcriptome sequencing data. BMC Genomics 14(2):1–10. DOI: https://doi.org/10.1186/1471-2164-14-S2-S7/FIGURES/5

    Article  CAS  Google Scholar 

  106. Sun L, Luo H, Bu D, Zhao G, Yu K, Zhang C, Liu Y, Chen R, Zhao Y (2013) Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts. Nucleic Acids Res 41(17):e166–e166. DOI: https://doi.org/10.1093/NAR/GKT646

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

Dr. Pankaj Barah would like to acknowledge Department of Biotechnology, Ministry of Science and Technology, Government of India for providing the Ramalingaswami Re-entry Fellowship grant.

Funding

This work was supported by Ramalingaswami Re-entry Fellowship grant. Author P.B. has received the research support from Department of Biotechnology, Ministry of Science and Technology, Government of India.

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All authors contributed to the review article. Pankaj Barah conceptualized the article content. Cinmoyee Baruah prepared the first draft of the manuscript and all authors commented on the previous versions of the manuscript. Cinmoyee Baruah and Prangan Nath contributed to the computational part of the article. All authors read and approved the final manuscript.

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Correspondence to Pankaj Barah.

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Baruah, C., Nath, P. & Barah, P. LncRNAs in neuropsychiatric disorders and computational insights for their prediction. Mol Biol Rep 49, 11515–11534 (2022). https://doi.org/10.1007/s11033-022-07819-x

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