Skip to main content
SpringerLink
Log in

Exploration of Positive and Negative Schizophrenia Symptom Heterogeneity and Establishment of Symptom-Related miRNA-mRNA Regulatory Network: Based on Transcriptome Sequencing Data

  • Published:
Molecular Neurobiology Aims and scope Submit manuscript

Abstract

Schizophrenia (SCZ) symptoms can be classified as positive and negative ones, each of which has distinct traits and possibly differences in gene expression and regulation. The co-expression networks linked to PANSS (Positive and Negative Syndrome Scale) scores were identified by weighted gene co-expression network analysis (WGCNA) using the expression profiles of miRNA and mRNA in the peripheral blood of first-episode SCZ patients. The heterogeneity between positive and negative symptoms was demonstrated using gene functional enrichment, gene-medication interaction, and immune cell composition analysis. Then, target gene prediction and correlation analysis of miRNA and mRNA constructed a symptom-related miRNA-mRNA regulatory network, screened regulatory pairs, and predicted binding sites. A total of six mRNA co-expression modules, two miRNA co-expression modules, and ten hub genes were screened to be significantly associated with positive symptoms; five mRNA co-expression modules and eight hub genes were correlated with negative symptoms. Positive symptom-related modules were significantly enriched in axon guidance, actin skeleton regulation, and sphingolipid signaling pathway, while negative symptom-related modules were significantly enriched in adaptive immune response, leukocyte migration, dopaminergic synapses, etc. The development of positive symptoms may have been influenced by potential regulatory pairings such as miR-98-5p-EIF3J, miR-98-5p-SOCS4, let-7b-5p-CLUH, miR-454-3p-GTF2H1, and let-7b-5p-SNX17. Additionally, immune cells were substantially connected with several hub genes for symptoms. Positive and negative symptoms in SCZ individuals were heterogeneous to some extent. miRNAs such as let-7b-5p and miR-98-5p might contribute to the incidence of positive symptoms by targeting mRNAs, while the immune system’s role in developing negative symptoms may be more nuanced.

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
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Jauhar S, Johnstone M, McKenna PJ (2022) Schizophrenia. Lancet (London, England) 399(10323):473–486. https://doi.org/10.1016/s0140-6736(21)01730-x

    Article  CAS  PubMed  Google Scholar 

  2. Gómez J, Jesús Marín-Méndez J, Molero P, Atakan Z, Ortuño F (2014) Time perception networks and cognition in schizophrenia: a review and a proposal. Psychiatry Res 220(3):737–744. https://doi.org/10.1016/j.psychres.2014.07.048

    Article  PubMed  Google Scholar 

  3. Oh J, Chun JW, Joon Jo H, Kim E, Park HJ, Lee B, Kim JJ (2015) The neural basis of a deficit in abstract thinking in patients with schizophrenia. Psychiatry Res 234(1):66–73. https://doi.org/10.1016/j.pscychresns.2015.08.007

    Article  PubMed  Google Scholar 

  4. Kring AM, Elis O (2013) Emotion deficits in people with schizophrenia. Annu Rev Clin Psychol 9:409–433. https://doi.org/10.1146/annurev-clinpsy-050212-185538

    Article  PubMed  Google Scholar 

  5. Insel TR (2010) Rethinking schizophrenia. Nature 468(7321):187–193. https://doi.org/10.1038/nature09552

    Article  CAS  PubMed  Google Scholar 

  6. Citrome L, Meyer JM (2023) Reviewing non-dopaminergic mechanisms for positive and negative schizophrenia symptom management. J Clin Psychiatry 84(4). https://doi.org/10.4088/JCP.sunscz3001sho

  7. Lähteenvuo M, Tiihonen J (2021) Antipsychotic polypharmacy for the management of schizophrenia: evidence and recommendations. Drugs 81(11):1273–1284. https://doi.org/10.1007/s40265-021-01556-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Phillips MR, Zhao ZA, Xiong XZ, Cheng XF, Sun GR, Wu NS (1991) Changes in the positive and negative symptoms of schizophrenic in-patients in China. Bri J Psychiat J Mental Sci 159:226–231. https://doi.org/10.1192/bjp.159.2.226

    Article  CAS  Google Scholar 

  9. Kirschner M, Aleman A, Kaiser S (2017) Secondary negative symptoms - a review of mechanisms, assessment and treatment. Schizophr Res 186:29–38. https://doi.org/10.1016/j.schres.2016.05.003

    Article  PubMed  Google Scholar 

  10. Correll CU (2020) Current treatment options and emerging agents for schizophrenia. J Clin Psychiatry 81:(3). https://doi.org/10.4088/jcp.Ms19053br3c

    Article  Google Scholar 

  11. Zhu Y, Krause M, Huhn M, Rothe P, Schneider-Thoma J, Chaimani A, Li C, Davis JM et al (2017) Antipsychotic drugs for the acute treatment of patients with a first episode of schizophrenia: a systematic review with pairwise and network meta-analyses. Lancet Psychiatry 4(9):694–705. https://doi.org/10.1016/s2215-0366(17)30270-5

    Article  PubMed  Google Scholar 

  12. Baandrup L (2020) Polypharmacy in schizophrenia. Basic Clin Pharmacol Toxicol 126(3):183–192. https://doi.org/10.1111/bcpt.13384

    Article  CAS  PubMed  Google Scholar 

  13. Mueser KT, McGurk SR (2004) Schizophrenia. Lancet (London, England) 363(9426):2063–2072. https://doi.org/10.1016/s0140-6736(04)16458-1

    Article  PubMed  Google Scholar 

  14. Thoma N, Pilecki B, McKay D (2015) Contemporary cognitive behavior therapy: a review of theory, history, and evidence. Psychodynam Psychiat 43(3):423–461. https://doi.org/10.1521/pdps.2015.43.3.423

    Article  Google Scholar 

  15. Rabinowitz J, Levine SZ, Garibaldi G, Bugarski-Kirola D, Berardo CG, Kapur S (2012) Negative symptoms have greater impact on functioning than positive symptoms in schizophrenia: analysis of CATIE data. Schizophr Res 137(1-3):147–150. https://doi.org/10.1016/j.schres.2012.01.015

    Article  PubMed  Google Scholar 

  16. Du Y, Yu Y, Hu Y, Li XW, Wei ZX, Pan RY, Li XS, Zheng GE et al (2019) Genome-wide, integrative analysis implicates exosome-derived microrna dysregulation in schizophrenia. Schizophr Bull 45(6):1257–1266. https://doi.org/10.1093/schbul/sby191

    Article  PubMed  PubMed Central  Google Scholar 

  17. Liu S, Zhang F, Wang X, Shugart YY, Zhao Y, Li X, Liu Z, Sun N et al (2017) Diagnostic value of blood-derived microRNAs for schizophrenia: results of a meta-analysis and validation. Sci Rep 7(1):15328. https://doi.org/10.1038/s41598-017-15751-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Cairns MJ (2015) Circulating miRNA biomarkers for schizophrenia? Am J Psychiatry 172(11):1059–1061. https://doi.org/10.1176/appi.ajp.2015.15081060

    Article  PubMed  Google Scholar 

  19. Duchaine TF, Fabian MR (2019) Mechanistic insights into microRNA-Mediated gene silencing. Cold Spring Harb Perspect Biol 11:(3). https://doi.org/10.1101/cshperspect.a032771

    Article  CAS  PubMed Central  Google Scholar 

  20. Jin M, Zhu X, Sun Y, Li Z, Li X, Ai L, He Y, Liu Y et al (2022) Identification of peripheral blood miRNA biomarkers in first-episode drug-free schizophrenia patients using bioinformatics strategy. Mol Neurobiol 59(8):4730–4746. https://doi.org/10.1007/s12035-022-02878-4

    Article  CAS  PubMed  Google Scholar 

  21. Li Z, Li X, Jin M, Liu Y, He Y, Jia N, Cui X, Liu Y et al (2022) Identification of potential blood biomarkers for early diagnosis of schizophrenia through RNA sequencing analysis. J Psychiatr Res 147:39–49. https://doi.org/10.1016/j.jpsychires.2022.01.003

    Article  PubMed  Google Scholar 

  22. Liu L, Yuan G, Cheng Z, Zhang G, Liu X, Zhang H (2013) Identification of the mRNA expression status of the dopamine D2 receptor and dopamine transporter in peripheral blood lymphocytes of schizophrenia patients. PLoS One 8(9):e75259. https://doi.org/10.1371/journal.pone.0075259

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Pinacho R, Villalmanzo N, Roca M, Iniesta R, Monje A, Haro JM, Meana JJ, Ferrer I et al (2013) Analysis of Sp transcription factors in the postmortem brain of chronic schizophrenia: a pilot study of relationship to negative symptoms. J Psychiatr Res 47(7):926–934. https://doi.org/10.1016/j.jpsychires.2013.03.004

    Article  PubMed  Google Scholar 

  24. Wirgenes KV, Sønderby IE, Haukvik UK, Mattingsdal M, Tesli M, Athanasiu L, Sundet K, Røssberg JI et al (2012) TCF4 sequence variants and mRNA levels are associated with neurodevelopmental characteristics in psychotic disorders. Transl Psychiatry 2(5):e112. https://doi.org/10.1038/tp.2012.39

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Luoni A, Riva MA (2016) MicroRNAs and psychiatric disorders: from aetiology to treatment. Pharmacol Ther 167:13–27. https://doi.org/10.1016/j.pharmthera.2016.07.006

    Article  CAS  PubMed  Google Scholar 

  26. Seo MS, Scarr E, Lai CY, Dean B (2014) Potential molecular and cellular mechanism of psychotropic drugs. Clin Psychopharmacol Neurosci 12(2):94–110. https://doi.org/10.9758/cpn.2014.12.2.94

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Agarwal V, Bell GW, Nam JW, Bartel DP (2015) Predicting effective microRNA target sites in mammalian mRNAs. eLife 4. doi:https://doi.org/10.7554/eLife.05005

  28. Chen Y, Wang X (2020) miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res 48(D1):D127–d131. https://doi.org/10.1093/nar/gkz757

    Article  CAS  PubMed  Google Scholar 

  29. Huang HY, Lin YC, Li J, Huang KY, Shrestha S, Hong HC, Tang Y, Chen YG et al (2020) miRTarBase 2020: updates to the experimentally validated microRNA-target interaction database. Nucleic Acids Res 48(D1):D148–d154. https://doi.org/10.1093/nar/gkz896

    Article  CAS  PubMed  Google Scholar 

  30. Davis AP, Wiegers TC, Johnson RJ, Sciaky D, Wiegers J, Mattingly CJ (2022) Comparative Toxicogenomics Database (CTD): update 2023. Nucleic Acids Res. https://doi.org/10.1093/nar/gkac833

  31. Yoo M, Shin J, Kim J, Ryall KA, Lee K, Lee S, Jeon M, Kang J et al (2015) DSigDB: drug signatures database for gene set analysis. Bioinformatics (Oxford, England) 31(18):3069–3071. https://doi.org/10.1093/bioinformatics/btv313

    Article  CAS  PubMed  Google Scholar 

  32. Chang CC, Wu HS, Hong CJ, Liu CY, Chen CW, Yang CY (2023) Exploring the effectiveness of group cognitive stimulation training in people with schizophrenia: a randomized controlled trial. J Nurs Res: JNR 31(5):e291. https://doi.org/10.1097/jnr.0000000000000576

    Article  PubMed  Google Scholar 

  33. Johnstone EC (1989) The assessment of negative and positive features in schizophrenia. Br J Psychiatry Suppl 7:41–44

    Article  Google Scholar 

  34. Owen MJ, Sawa A, Mortensen PB (2016) Schizophrenia. Lancet (London, England) 388(10039):86–97. https://doi.org/10.1016/s0140-6736(15)01121-6

    Article  PubMed  Google Scholar 

  35. Grace AA, Bunney BS, Moore H, Todd CL (1997) Dopamine-cell depolarization block as a model for the therapeutic actions of antipsychotic drugs. Trends Neurosci 20(1):31–37. https://doi.org/10.1016/s0166-2236(96)10064-3

    Article  CAS  PubMed  Google Scholar 

  36. Maia TV, Frank MJ (2017) An integrative perspective on the role of dopamine in schizophrenia. Biol Psychiatry 81(1):52–66. https://doi.org/10.1016/j.biopsych.2016.05.021

    Article  CAS  PubMed  Google Scholar 

  37. Tandon R, Greden JF (1989) Cholinergic hyperactivity and negative schizophrenic symptoms. A model of cholinergic/dopaminergic interactions in schizophrenia. Arch Gen Psychiatry 46(8):745–753. https://doi.org/10.1001/archpsyc.1989.01810080075010

    Article  CAS  PubMed  Google Scholar 

  38. Missale C, Nash SR, Robinson SW, Jaber M, Caron MG (1998) Dopamine receptors: from structure to function. Physiol Rev 78(1):189–225. https://doi.org/10.1152/physrev.1998.78.1.189

    Article  CAS  PubMed  Google Scholar 

  39. Tamminga CA (2006) The neurobiology of cognition in schizophrenia. J Clin Psychiatry 67(Suppl 9):9–13 discussion 36-42

    CAS  PubMed  Google Scholar 

  40. Li Q, Zhang H, Kou Y, Dong Z, Zhang H, Si Y (2016) Meta-analysis of N-methyl-D-aspartic receptor antagonists for treatment of schizophrenia. China J Modern Med 26(18):104–109. https://doi.org/10.3969/j.issn.1005-8982.2016.18.021

    Article  Google Scholar 

  41. Safa A, Badrlou E, Arsang-Jang S, Sayad A, Taheri M, Ghafouri-Fard S (2020) Expression of NF-κB associated lncRNAs in schizophrenia. Sci Rep 10(1):18105. https://doi.org/10.1038/s41598-020-75333-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Ramos-Miguel A, Barakauskas V, Alamri J, Miyauchi M, Barr AM, Beasley CL, Rosoklija G et al (2019) The SNAP25 interactome in ventromedial caudate in schizophrenia includes the mitochondrial protein ARF1. Neuroscience 420:97–111. https://doi.org/10.1016/j.neuroscience.2018.12.045

    Article  CAS  PubMed  Google Scholar 

  43. Duncan EJ, Bollini AM, Lewison B, Keyes M, Jovanovic T, Gaytan O, Egan G, Szilagyi S et al (2006) Medication status affects the relationship of symptoms to prepulse inhibition of acoustic startle in schizophrenia. Psychiatry Res 145(2-3):137–145. https://doi.org/10.1016/j.psychres.2006.04.006

    Article  PubMed  Google Scholar 

  44. Wang ZR, Tan YL, Yang FD, Zhang WF, Zou YZ, Tan SP, Song CS, Li YL et al (2013) Impaired prepulse inhibition of acoustic startle in Chinese patients with first-episode, medication-naïve schizophrenia. Chin Med J 126(3):526–531

    Article  PubMed  Google Scholar 

  45. Openshaw RL, Thomson DM, Thompson R, Penninger JM, Pratt JA, Morris BJ, Dawson N (2020) Map2k7 haploinsufficiency induces brain imaging endophenotypes and behavioral phenotypes relevant to schizophrenia. Schizophr Bull 46(1):211–223. https://doi.org/10.1093/schbul/sbz044

    Article  PubMed  Google Scholar 

  46. Winchester CL, Ohzeki H, Vouyiouklis DA, Thompson R, Penninger JM, Yamagami K, Norrie JD, Hunter R et al (2012) Converging evidence that sequence variations in the novel candidate gene MAP2K7 (MKK7) are functionally associated with schizophrenia. Hum Mol Genet 21(22):4910–4921. https://doi.org/10.1093/hmg/dds331

    Article  CAS  PubMed  Google Scholar 

  47. Openshaw RL, Thomson DM, Penninger JM, Pratt JA, Morris BJ (2017) Mice haploinsufficient for Map2k7, a gene involved in neurodevelopment and risk for schizophrenia, show impaired attention, a vigilance decrement deficit and unstable cognitive processing in an attentional task: impact of minocycline. Psychopharmacology 234(2):293–305. https://doi.org/10.1007/s00213-016-4463-y

    Article  CAS  PubMed  Google Scholar 

  48. Sun L, Min L, Li M, Shao F, Wang W (2018) Transcriptomic analysis reveals oxidative phosphorylation activation in an adolescent social isolation rat model. Brain Res Bull 142:304–312. https://doi.org/10.1016/j.brainresbull.2018.08.013

    Article  CAS  PubMed  Google Scholar 

  49. Tovilla-Zárate CA, Vargas I, Hernández S, Fresán A, Aguilar A, Escamilla R, Saracco R, Palacios J et al (2014) Association study between the MDR1 gene and clinical characteristics in schizophrenia. Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999) 36(3):227–232. https://doi.org/10.1590/1516-4446-2013-1270

    Article  PubMed  Google Scholar 

  50. Martin AK, Robinson G, Reutens D, Mowry B (2015) Clinical and parental age characteristics of rare copy number variant burden in patients with schizophrenia. American journal of medical genetics Part B. Neuropsychiat Gen official Public Int Soc Psychiat Gen 168b(5):374–382. https://doi.org/10.1002/ajmg.b.32321

    Article  CAS  Google Scholar 

  51. Martin AK, Robinson G, Reutens D, Mowry B (2015) Common genetic risk variants are associated with positive symptoms and decision-making ability in patients with schizophrenia. Psychiatry Res 229(1-2):606–608. https://doi.org/10.1016/j.psychres.2015.04.045

    Article  CAS  PubMed  Google Scholar 

  52. Kranz TM, Berns A, Shields J, Rothman K, Walsh-Messinger J, Goetz RR, Chao MV, Malaspina D (2016) Phenotypically distinct subtypes of psychosis accompany novel or rare variants in four different signaling genes. EBioMedicine 6:206–214. https://doi.org/10.1016/j.ebiom.2016.03.008

    Article  PubMed  PubMed Central  Google Scholar 

  53. Park HJ, Kim JW, Lee SK, Kim SK, Park JK, Cho AR, Chung JH, Song JY (2011) Association between the SLC6A12 gene and negative symptoms of schizophrenia in a Korean population. Psychiatry Res 189(3):478–479. https://doi.org/10.1016/j.psychres.2011.01.023

    Article  CAS  PubMed  Google Scholar 

  54. Li W, Yang Y, Lin J, Wang S, Zhao J, Yang G, Wang X, Ding M et al (2013) Association of serotonin transporter gene (SLC6A4) polymorphisms with schizophrenia susceptibility and symptoms in a Chinese-Han population. Prog Neuro-Psychopharmacol Biol Psychiatry 44:290–295. https://doi.org/10.1016/j.pnpbp.2013.04.003

    Article  CAS  Google Scholar 

  55. Bartel DP (2009) MicroRNAs: target recognition and regulatory functions. Cell 136(2):215–233. https://doi.org/10.1016/j.cell.2009.01.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Roumans S, Sundquist K, Memon AA, Hedelius A, Sundquist J, Wang X (2021) Association of circulating let-7b-5p with major depressive disorder: a nested case-control study. BMC Psychiatry 21(1):616. https://doi.org/10.1186/s12888-021-03621-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Zhang J, Hou L, Klann E, Nelson DL (2009) Altered hippocampal synaptic plasticity in the FMR1 gene family knockout mouse models. J Neurophysiol 101(5):2572–2580. https://doi.org/10.1152/jn.90558.2008

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Sotelo P, Farfán P, Benitez ML, Bu G, Marzolo MP (2014) Sorting nexin 17 regulates ApoER2 recycling and reelin signaling. PLoS One 9(4):e93672. https://doi.org/10.1371/journal.pone.0093672

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Tu YT, Barrientos A (2015) The human mitochondrial DEAD-box protein DDX28 resides in RNA granules and functions in mitoribosome assembly. Cell Rep 10(6):854–864. https://doi.org/10.1016/j.celrep.2015.01.033

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Moehlman AT, Youle RJ (2020) Mitochondrial quality control and restraining innate immunity. Annu Rev Cell Dev Biol 36:265–289. https://doi.org/10.1146/annurev-cellbio-021820-101354

    Article  CAS  PubMed  Google Scholar 

  61. Chenghuan S, Jiyun S, Jianrong X, Lanxue Z, Yongfang Z, Wanying H, Yu Q, Rui Z et al (2021) Post-transcriptional regulation of α7 nAChR expression by miR-98-5p modulates cognition and neuroinflammation in an animal model of Alzheimer's disease. FASEB J 35(6):e21658. https://doi.org/10.1096/fj.202100257R

  62. Qiushi L, Xidong L, Li W, Yanhui Z, Long C (2016) miR-98-5p Acts as a Target for Alzheimer’s Disease by Regulating Aβ Production Through Modulating SNX6 Expression. J Mol Neurosci 60(4):413–420.https://doi.org/10.1007/s12031-016-0815-7

  63. Fraser CS, Berry KE, Hershey JW, Doudna JA (2007) eIF3j is located in the decoding center of the human 40S ribosomal subunit. Mol Cell 26(6):811–819. https://doi.org/10.1016/j.molcel.2007.05.019

    Article  CAS  PubMed  Google Scholar 

  64. English JA, Fan Y, Föcking M, Lopez LM, Hryniewiecka M, Wynne K, Dicker P, Matigian N et al (2015) Reduced protein synthesis in schizophrenia patient-derived olfactory cells. Transl Psychiatry 5(10):e663. https://doi.org/10.1038/tp.2015.119

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Sekine Y, Lin-Moore A, Chenette DM, Wang X, Jiang Z, Cafferty WB, Hammarlund M, Strittmatter SM (2018) Functional genome-wide screen identifies pathways restricting central nervous system axonal regeneration. Cell Rep 23(2):415–428. https://doi.org/10.1016/j.celrep.2018.03.058

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Sun Y, Sukumaran P, Schaar A, Singh BB (2015) TRPM7 and its role in neurodegenerative diseases. Channels (Austin) 9(5):253–261. https://doi.org/10.1080/19336950.2015.1075675

    Article  PubMed  Google Scholar 

  67. Abumaria N, Li W, Clarkson AN (2019) Role of the chanzyme TRPM7 in the nervous system in health and disease. Cellul Mole Life Sci: CMLS 76(17):3301–3310. https://doi.org/10.1007/s00018-019-03124-2

    Article  CAS  Google Scholar 

  68. Oury J, Zhang W, Leloup N, Koide A, Corrado AD, Ketavarapu G, Hattori T, Koide S et al (2021) Mechanism of disease and therapeutic rescue of Dok7 congenital myasthenia. Nature 595(7867):404–408. https://doi.org/10.1038/s41586-021-03672-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Rué L, López-Soop G, Gelpi E, Martínez-Vicente M, Alberch J, Pérez-Navarro E (2013) Brain region- and age-dependent dysregulation of p62 and NBR1 in a mouse model of Huntington's disease. Neurobiol Dis 52:219–228. https://doi.org/10.1016/j.nbd.2012.12.008

    Article  CAS  PubMed  Google Scholar 

  70. Li QS, Cai D (2021) Integrated miRNA-Seq and mRNA-Seq study to identify miRNAs associated with Alzheimer’s disease using post-mortem brain tissue samples. Front Neurosci 15:620899. https://doi.org/10.3389/fnins.2021.620899

    Article  PubMed  PubMed Central  Google Scholar 

  71. Guo R, Fan G, Zhang J, Wu C, Du Y, Ye H, Li Z, Wang L et al (2017) A 9-microRNA signature in serum serves as a noninvasive biomarker in early diagnosis of Alzheimer’s disease. J Alzheim Dis : JAD 60(4):1365–1377. https://doi.org/10.3233/jad-170343

    Article  CAS  Google Scholar 

  72. Gunasekaran S, Jacob RS, Omkumar RV (2022) Differential expression of miR-148b, miR-129-2 and miR-296 in animal models of schizophrenia-Relevance to NMDA receptor hypofunction. Neuropharmacology 210:109024. https://doi.org/10.1016/j.neuropharm.2022.109024

    Article  CAS  PubMed  Google Scholar 

  73. Deguchi M, Hata Y, Takeuchi M, Ide N, Hirao K, Yao I, Irie M, Toyoda A et al (1998) BEGAIN (brain-enriched guanylate kinase-associated protein), a novel neuronal PSD-95/SAP90-binding protein. J Biol Chem 273(41):26269–26272. https://doi.org/10.1074/jbc.273.41.26269

    Article  CAS  PubMed  Google Scholar 

  74. Yao I, Iida J, Nishimura W, Hata Y (2002) Synaptic and nuclear localization of brain-enriched guanylate kinase-associated protein. J Neurosci 22(13):5354–5364. https://doi.org/10.1523/jneurosci.22-13-05354.2002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Katano T, Fukuda M, Furue H, Yamazaki M, Abe M, Watanabe M, Nishida K, Yao I et al (2016) Involvement of Brain-enriched guanylate kinase-associated protein (BEGAIN) in chronic pain after peripheral nerve injury. eNeuro 3(5). https://doi.org/10.1523/eneuro.0110-16.2016

  76. Guan Y, Chen X, Zhao B, Shi Y, Han F (2022) What happened in the hippocampal axon in a rat model of posttraumatic stress disorder. Cell Mol Neurobiol 42(3):723–737. https://doi.org/10.1007/s10571-020-00960-w

    Article  CAS  PubMed  Google Scholar 

  77. Pearce MM, Wormer DB, Wilkens S, Wojcikiewicz RJ (2009) An endoplasmic reticulum (ER) membrane complex composed of SPFH1 and SPFH2 mediates the ER-associated degradation of inositol 1,4,5-trisphosphate receptors. J Biol Chem 284(16):10433–10445. https://doi.org/10.1074/jbc.M809801200

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Prole DL, Taylor CW (2016) Inositol 1,4,5-trisphosphate receptors and their protein partners as signalling hubs. J Physiol 594(11):2849–2866. https://doi.org/10.1113/jp271139

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Brini M, Calì T, Ottolini D, Carafoli E (2014) Neuronal calcium signaling: function and dysfunction. Cellul Mole Life Sci: CMLS 71(15):2787–2814. https://doi.org/10.1007/s00018-013-1550-7

    Article  CAS  Google Scholar 

  80. Wang S, Li B, Solomon V, Fonteh A, Rapoport SI, Bennett DA, Arvanitakis Z, Chui HC et al (2022) Calcium-dependent cytosolic phospholipase A(2) activation is implicated in neuroinflammation and oxidative stress associated with ApoE4. Mol Neurodegener 17(1):42. https://doi.org/10.1186/s13024-022-00549-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Mizrachi T, Vaknin-Dembinsky A, Brenner T, Treinin M (2021) Neuroinflammation modulation via α7 nicotinic acetylcholine receptor and its chaperone, RIC-3. Molecules (Basel, Switzerland) 26(20). https://doi.org/10.3390/molecules26206139

  82. Maes M (2011) Depression is an inflammatory disease, but cell-mediated immune activation is the key component of depression. Prog Neuro-Psychopharmacol Biol Psychiatry 35(3):664–675. https://doi.org/10.1016/j.pnpbp.2010.06.014

    Article  CAS  Google Scholar 

  83. Pereira AC, Oliveira J, Silva S, Madeira N, Pereira CMF, Cruz MT (2021) Inflammation in bipolar disorder (BD): identification of new therapeutic targets. Pharmacol Res 163:105325. https://doi.org/10.1016/j.phrs.2020.105325

    Article  CAS  PubMed  Google Scholar 

  84. Calsolaro V, Edison P (2016) Neuroinflammation in Alzheimer’s disease: Current evidence and future directions. Alzheimers Dement 12(6):719–732. https://doi.org/10.1016/j.jalz.2016.02.010

    Article  PubMed  Google Scholar 

  85. Zalcman S, Green-Johnson JM, Murray L, Nance DM, Dyck D, Anisman H, Greenberg AH (1994) Cytokine-specific central monoamine alterations induced by interleukin-1, -2 and -6. Brain Res 643(1-2):40–49. https://doi.org/10.1016/0006-8993(94)90006-x

    Article  CAS  PubMed  Google Scholar 

  86. Frydecka D, Misiak B, Pawlak-Adamska E, Karabon L, Tomkiewicz A, Sedlaczek P, Kiejna A, Beszłej JA (2015) Interleukin-6: the missing element of the neurocognitive deterioration in schizophrenia? The focus on genetic underpinnings, cognitive impairment and clinical manifestation. Eur Arch Psychiatry Clin Neurosci 265(6):449–459. https://doi.org/10.1007/s00406-014-0533-5

    Article  PubMed  Google Scholar 

  87. Peter L 2015） WGCNA for RNA-Seq Sample Set, Outliers, and Spearman correlation. Bioconductor. https://support.bioconductor.org/p/65124/

Download references

Acknowledgements

We would like to thank the professional help from psychiatrists and all the participants who volunteered to take part in this study.

Funding

This work was supported by the National Natural Science Foundation of China (81673253) and the Jilin Provincial Ministry of Education S&T Project (JJKH20190091KJ).

Author information

Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, 1163 Xinmin Street, Changchun, 130021, China

    Mengdi Jin, Mengtong Xie, Lin Dong, Fengyu Xue, Weizhen Li, Lintong Jiang, Junnan Li, Min Zhang, Haideng Song, Qingxing Lu & Qiong Yu

Authors
  1. Mengdi Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mengtong Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lin Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fengyu Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Weizhen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lintong Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Junnan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Min Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Haideng Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Qingxing Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Qiong Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

QY and MJ conceived and designed the study. Bioinformatics analysis was performed by MJ and MX. HS and QL collected the sample. FX, WL, and MZ standardized the high-throughput sequencing data. MJ and MX wrote the first draft of the manuscript. The graphs were revised by LJ and JL. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Qiong Yu.

Ethics declarations

Ethics Approval

This study was performed in line with the principles of the Declaration of Helsinki. The study was approved by the Ethics Committee of the School of Public Health of Jilin University (2016-03-013).

Consent to Participate

Informed consent was obtained from all individual participants or their guardians included in the study.

Consent for Publication

Not applicable.

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.

Supplementary Information

ESM 1

(PDF 391 kb)

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

Jin, M., Xie, M., Dong, L. et al. Exploration of Positive and Negative Schizophrenia Symptom Heterogeneity and Establishment of Symptom-Related miRNA-mRNA Regulatory Network: Based on Transcriptome Sequencing Data. Mol Neurobiol (2024). https://doi.org/10.1007/s12035-024-03942-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12035-024-03942-x

Keywords

Search

Navigation