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Baseline levels of miR-223-3p correlate with the effectiveness of electroconvulsive therapy in patients with major depression

Kaurani, Lalit, Besse, Matthias, Methfessel, Isabel, Methi, Aditi, Zhou, Jiayin, Pradhan, Ranjit, Burkhardt, Susanne, Kranaster, Laura, Sartorius, Alexander, Habel, Ute, Grözinger, Michael, Fischer, Andre, Wiltfang, Jens, Zilles-Wegner, David·2023·Translational Psychiatry
DOI 10.1038/s41398-023-02582-4PMC PMC10497550
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Abstract

There is a strong medical need to develop suitable biomarkers to improve the diagnosis and treatment of depression, particularly in predicting response to certain therapeutic approaches such as electroconvulsive therapy (ECT). MicroRNAs are small non-coding RNAs that have the ability to influence the transcriptome as well as proteostasis at the systems level. Here, we investigate the role of circulating microRNAs in depression and response prediction towards ECT. Of the 64 patients with treatment-resistant major depression (MDD) who received ECT treatment, 62.5% showed a response, defined as a reduction of ≥50% in the MADRS total score from baseline. We performed smallRNA sequencing in blood samples that were taken before the first ECT, after the first and the last ECT. The microRNAome was compared between responders and non-responders. Co-expression network analysis identified three significant microRNA modules with reverse correlation between ECT- responders and non-responders, that were amongst other biological processes linked to inflammation. A candidate microRNA, namely miR-223-3p was down-regulated in ECT responders when compared to non-responders at baseline. In line with data suggesting a role of miR-223-3p in inflammatory processes we observed higher expression levels of proinflammatory factors Il-6, Il-1b, Nlrp3 and Tnf-α in ECT responders at baseline when compared to non-responders. ROC analysis of confirmed the diagnostic power of miR-223-3p demarcating ECT-responders from non-responder subjects (AUC = 0.76, p = 0.0031). Our data suggest that miR-223-3p expression and related cytokine levels could serve as predictors of response to ECT in individuals with treatment-resistant depressive disorders.

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Fig. 1 Co-expressing deregulated miR modules during the course of ECT. A This figure depicts study design. Blood was taken from each participant (64 subjects) at three different visits. Visit 1 represents baseline. Each participant was given electroconvulsive therapy (ECT) at visit two and three. After the treatment smallRNA sequencing was performed. After initial quality control of the sequencing dataset microRNAome was analyzed for 166 smallRNAseq dataset from 60 subjects. B Module eigengene values were plotted for black, yellow and red modules. The horizontal line in the box plot represents the median, the box spans 25 and 75% quantile, and the whiskers represent the smallest and largest values in the 1.5x interquartile range. C Heatmap shows the expression of co-expressed miRs in black, yellow and red modules among ECT-responder and non- responders for each visit. The intensity of the color represents the expression status. Red represents higher expression and blue represents reduced expression at each visit. (*P < 0.05; **P < 0.01, ***P < 0.001; ****P < 0.0001). Two-tailed unpaired t-test was performed to calculate significance.
Fig. 1 Co-expressing deregulated miR modules during the course of ECT. A This figure depicts study design. Blood was taken from each participant (64 subjects) at three different visits. Visit 1 represents baseline. Each participant was given electroconvulsive therapy (ECT) at visit two and three. After the treatment smallRNA sequencing was performed. After initial quality control of the sequencing dataset microRNAome was analyzed for 166 smallRNAseq dataset from 60 subjects. B Module eigengene values were plotted for black, yellow and red modules. The horizontal line in the box plot represents the median, the box spans 25 and 75% quantile, and the whiskers represent the smallest and largest values in the 1.5x interquartile range. C Heatmap shows the expression of co-expressed miRs in black, yellow and red modules among ECT-responder and non- responders for each visit. The intensity of the color represents the expression status. Red represents higher expression and blue represents reduced expression at each visit. (*P < 0.05; **P < 0.01, ***P < 0.001; ****P < 0.0001). Two-tailed unpaired t-test was performed to calculate significance.CC BY · PubMed Central
Fig. 2 Changes in inflammatory response and synaptic plasticity are linked with ECT. A This figure depicts the selected deregulated biological processes targeted by co-expressed miRs of black, yellow and red modules. Selected deregulated biological processes are enriched for inflammation, VEGF signaling, synaptic assembly and axon guidance. B The overlap among target genes of co-expressed miR modules. C Hypergeometric overlap of the co-expressed miR module target genes with genes related with depression based on human genome-wide association studies and transcriptional-based study. Fisher’s exact test was performed, and the data are presented with a Benjamini Hochberg (BH) corrected p-value. A color map illustrates the significance and overlap strength. The PsyGeNEt database was utilized to identify genes associated with depression and TRD.
Fig. 2 Changes in inflammatory response and synaptic plasticity are linked with ECT. A This figure depicts the selected deregulated biological processes targeted by co-expressed miRs of black, yellow and red modules. Selected deregulated biological processes are enriched for inflammation, VEGF signaling, synaptic assembly and axon guidance. B The overlap among target genes of co-expressed miR modules. C Hypergeometric overlap of the co-expressed miR module target genes with genes related with depression based on human genome-wide association studies and transcriptional-based study. Fisher’s exact test was performed, and the data are presented with a Benjamini Hochberg (BH) corrected p-value. A color map illustrates the significance and overlap strength. The PsyGeNEt database was utilized to identify genes associated with depression and TRD.CC BY · PubMed Central
Fig. 3 Peripheral blood miR change after completion of ECT treatment. This figure represents deregulated miRs in ECT-responder after completion of the course of ECT. A Significantly downregulated miRs in ECT responders. Y-axes represent normalized counts. Each dot in box plot represents normalized expression of miRs in each sample at visit 3. B Pathway analysis is showing deregulated biological processes (BP). Selected deregulated BP are enriched for neuron projection, neuron and glia development, cell adhesion, synapse and dendrite development and histone modifications. C Scatter plot of MADRS score and normalized counts at baseline (visit 1) and after completion of course of ECT. The correlation coefficient and its p-value were indicated by r and p, respectively. (*P < 0.05; **P < 0.01, ***P < 0.001; ****P < 0.0001). Two-tailed unpaired t-test was performed to calculate significance.
Fig. 3 Peripheral blood miR change after completion of ECT treatment. This figure represents deregulated miRs in ECT-responder after completion of the course of ECT. A Significantly downregulated miRs in ECT responders. Y-axes represent normalized counts. Each dot in box plot represents normalized expression of miRs in each sample at visit 3. B Pathway analysis is showing deregulated biological processes (BP). Selected deregulated BP are enriched for neuron projection, neuron and glia development, cell adhesion, synapse and dendrite development and histone modifications. C Scatter plot of MADRS score and normalized counts at baseline (visit 1) and after completion of course of ECT. The correlation coefficient and its p-value were indicated by r and p, respectively. (*P < 0.05; **P < 0.01, ***P < 0.001; ****P < 0.0001). Two-tailed unpaired t-test was performed to calculate significance.CC BY · PubMed Central
Fig. 4 The immune system influence and contribute to the efficacy of ECT. A miR-223-3p expression in ECT-responder and non-responder subjects. Normalized counts of ECT- responders were compared with non-responders for each visit. Two-tailed unpaired t-test applied to evaluate the significance of expression difference between groups. The data are presented as the mean ± SEM in panel A. B The Volcano plot illustrates the relationship between the fold change and statistical significance at baseline for ECT responders and non-responders (visit 1). The negative log-fold change in the graph reflects miRs with downregulation, whereas the positive log-fold change represents miRs with up regulation. The only miR shown to be significantly dysregulated was miR-223- 3p (basemean ≥ 100, fold change ± 0.30, adjusted p-value < 0.1). C miRTarBase was used to detect validated targets of miR-223-3p. Pathways with adjusted p-value < 0.05 were selected for further analysis. miR-223-3p targeted genes are enriched for inflammation-related pathways in ECT responders. D The increase in NLRP3, IL-1b, IL-6 and TNF-α levels at baseline in responders compares to non-responders were observed. The baseline levels of IL-6 and TNF-α decrease towards the end of course of ECT among responders. (*P < 0.05 at the first and the last session; t-test unpaired; 2-tailed). The horizontal line in the box plot represents the median, the box spans 25 and 75% quantile, and the whiskers represent the smallest and largest values in the 1.5x interquartile range. E Receiver-operating characteristic (ROC) analysis of miR-223-3p in ECT-responder (n = 38) versus non-responder subjects (n = 16). Using z-score transformation of normalized counts, miR-223-3p had significant diagnostic power. (*P < 0.05; **P < 0.01, ***P < 0.001; ****P < 0.0001).
Fig. 4 The immune system influence and contribute to the efficacy of ECT. A miR-223-3p expression in ECT-responder and non-responder subjects. Normalized counts of ECT- responders were compared with non-responders for each visit. Two-tailed unpaired t-test applied to evaluate the significance of expression difference between groups. The data are presented as the mean ± SEM in panel A. B The Volcano plot illustrates the relationship between the fold change and statistical significance at baseline for ECT responders and non-responders (visit 1). The negative log-fold change in the graph reflects miRs with downregulation, whereas the positive log-fold change represents miRs with up regulation. The only miR shown to be significantly dysregulated was miR-223- 3p (basemean ≥ 100, fold change ± 0.30, adjusted p-value < 0.1). C miRTarBase was used to detect validated targets of miR-223-3p. Pathways with adjusted p-value < 0.05 were selected for further analysis. miR-223-3p targeted genes are enriched for inflammation-related pathways in ECT responders. D The increase in NLRP3, IL-1b, IL-6 and TNF-α levels at baseline in responders compares to non-responders were observed. The baseline levels of IL-6 and TNF-α decrease towards the end of course of ECT among responders. (*P < 0.05 at the first and the last session; t-test unpaired; 2-tailed). The horizontal line in the box plot represents the median, the box spans 25 and 75% quantile, and the whiskers represent the smallest and largest values in the 1.5x interquartile range. E Receiver-operating characteristic (ROC) analysis of miR-223-3p in ECT-responder (n = 38) versus non-responder subjects (n = 16). Using z-score transformation of normalized counts, miR-223-3p had significant diagnostic power. (*P < 0.05; **P < 0.01, ***P < 0.001; ****P < 0.0001).CC BY · PubMed Central

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Research works printed but not linked: 66

  1. Kolshus E, Ryan KM, Blackshields G, Smyth P, Sheils O, McLoughlin DM. Peripheral blood microRNA and VEGFA mRNA changes following electroconvulsive therapy: implications for psychotic depression. Acta Psychiatr Scand. 2017;136:594–606. · DOI
  2. Philip NS, Carpenter LL, Tyrka AR, Price LH. Pharmacologic approaches to treatment resistant depression: a re-examination for the modern era. Expert Opin Pharmacother. 2010;11:709–22. · DOI
  3. Fries GR, Zhang W, Benevenuto D, Quevedo J. MicroRNAs in major depressive disorder. Adv Exp Med Biol. 2019;1118:175–90. · DOI
  4. Lin C-Y, Hung S-Y, Chen H-T, Tsou H-K, Fong Y-C, Wang S-W, et al. Brain-derived neurotrophic factor increases vascular endothelial growth factor expression and enhances angiogenesis in human chondrosarcoma cells. Biochem Pharmacol. 2014;91:522–33. · DOI
  5. Miranda M, Morici JF, Zanoni MB, Bekinschtein P. Brain-derived neurotrophic factor: a key molecule for memory in the healthy and the pathological brain. Front Cell Neurosci. 2019;13:363. · DOI · Paper page
  6. Faust K, Vajkoczy P, Xi B, Harnack D. The effects of deep brain stimulation of the subthalamic nucleus on vascular endothelial growth factor, brain-derived neurotrophic factor, and glial cell line-derived neurotrophic factor in a rat model of Parkinson’s disease. Stereotact Funct Neurosurg. 2021;99:256–66. · DOI
  7. Nøhr AK, Lindow M, Forsingdal A, Demharter S, Nielsen T, Buller R, et al. A large-scale genome-wide gene expression analysis in peripheral blood identifies very few differentially expressed genes related to antidepressant treatment and response in patients with major depressive disorder. Neuropsychopharmacology. 2021;46:1324–32. · DOI
  8. Ma Y, Rosenheck R, Ye B, Fan N, He H. Effectiveness of electroconvulsive therapy in patients with “less treatment‐resistant” depression by the Maudsley Staging Model. Brain Behav. 2020;10:e01654. · DOI
  9. Cano M, Martínez-Zalacaín I, Bernabéu-Sanz Á, Contreras-Rodríguez O, Hernández-Ribas R, Via E, et al. Brain volumetric and metabolic correlates of electroconvulsive therapy for treatment-resistant depression: a longitudinal neuroimaging study. Transl Psychiatry. 2017;7:e1023. · DOI
  10. Leaver AM, Espinoza R, Wade B, Narr KL. Parsing the network mechanisms of electroconvulsive therapy. Biol Psychiatry. 2022;92:193–203. · DOI
  11. Broderick JA, Zamore PD. MicroRNA therapeutics. Gene Ther. 2011;18:1104–10. · DOI
  12. Mullany LE, Herrick JS, Wolff RK, Stevens JR, Samowitz W, Slattery ML. MicroRNA-transcription factor interactions and their combined effect on target gene expression in colon cancer cases. Genes Chromosomes Cancer. 2018;57:192–202. · DOI
  13. Treiber T, Treiber N, Meister G. Regulation of microRNA biogenesis and its crosstalk with other cellular pathways. Nat Rev Mol Cell Biol. 2019;20:5–20. · DOI
  14. Dwivedi Y. MicroRNAs in depression and suicide: recent insights and future perspectives. J Affect Disord. 2018;240:146–54. · DOI
  15. Zhou L, Zhu Y, Chen W, Tang Y. Emerging role of microRNAs in major depressive disorder and its implication on diagnosis and therapeutic response. J Affect Disord. 2021;286:80–6. · DOI
  16. Bayraktar R, Van Roosbroeck K, Calin GA. Cell‐to‐cell communication: microRNAs as hormones. Mol Oncol. 2017;11:1673–86. · DOI · Paper page
  17. Jose AM. Movement of regulatory RNA between animal cells. Genesis. 2015;53:395–416. · DOI
  18. Mustapic M, Eitan E, Werner JK, Berkowitz ST, Lazaropoulos MP, Tran J, et al. Plasma extracellular vesicles enriched for neuronal origin: a potential window into brain pathologic processes. Front Neurosci. 2017;11:278. · DOI · Paper page
  19. Goldberg M, Islam MR, Kerimoglu C, Lancelin C, Gisa V, Burkhardt S, et al. Exercise as a model to identify microRNAs linked to human cognition: a role for microRNA-409 and microRNA-501. Transl Psychiatry. 2021;11:1–13. · DOI · Paper page
  20. O’Connor RM, Dinan TG, Cryan JF. Little things on which happiness depends: microRNAs as novel therapeutic targets for the treatment of anxiety and depression. Mol Psychiatry. 2012;17:359–76. · DOI
  21. Gururajan A, Naughton ME, Scott KA, O’Connor RM, Moloney G, Clarke G, et al. MicroRNAs as biomarkers for major depression: a role for let-7b and let-7c. Transl Psychiatry. 2016;6:e862. · DOI
  22. Roy B, Yoshino Y, Allen L, Prall K, Schell G, Dwivedi Y. Exploiting circulating micrornas as biomarkers in psychiatric disorders. Mol Diagn Ther. 2020;24:279–98. · DOI
  23. Borza T, Engedal K, Bergh S, Benth JŠ, Selbæk G. The course of depression in late life as measured by the Montgomery and Asberg Depression Rating Scale in an observational study of hospitalized patients. BMC Psychiatry. 2015;15:191. · DOI
  24. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:R25.
  25. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinforma. 2008;9:559. · DOI · Paper page
  26. Risso D, Ngai J, Speed TP, Dudoit S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat Biotechnol. 2014;32:896–902. · DOI · Paper page
  27. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.
  28. Islam MR, Kaurani L, Berulava T, Heilbronner U, Budde M, Centeno TP, et al. A microRNA signature that correlates with cognition and is a target against cognitive decline. EMBO Mol Med. 2021;13:e13659. · DOI · Paper page
  29. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504. · DOI · Paper page
  30. Gutiérrez-Sacristán A, Grosdidier S, Valverde O, Torrens M, Bravo À, Piñero J, et al. PsyGeNET: a knowledge platform on psychiatric disorders and their genes. Bioinformatics. 2015;31:3075–7. · DOI
  31. Cuomo-Haymour N, Kaiser S, Hartmann-Riemer M, Guetter K, Klaus F, Cathomas F, et al. Differential expression of serum extracellular vesicle microRNAs and analysis of target-gene pathways in major depressive disorder. Biomark Neuropsychiatry. 2022;6:100049. · DOI
  32. Inta D, Lima-Ojeda JM, Lau T, Tang W, Dormann C, Sprengel R, et al. Electroconvulsive therapy induces neurogenesis in frontal rat brain areas. PLoS ONE. 2013;8:e69869. · DOI
  33. Järventausta K, Sorri A, Kampman O, Björkqvist M, Tuohimaa K, Hämäläinen M, et al. Changes in interleukin-6 levels during electroconvulsive therapy may reflect the therapeutic response in major depression. Acta Psychiatr Scand. 2017;135:87–92. · DOI
  34. Muhammad RN, Ahmed LA, Abdul Salam RM, Ahmed KA, Attia AS. Crosstalk among NLRP3 inflammasome, ETBR signaling, and miRNAs in stress-induced depression-like behavior: a modulatory role for SGLT2 inhibitors. Neurotherapeutics. 2021;18:2664–81. · DOI
  35. Borrione L, Bellini H, Razza LB, Avila AG, Baeken C, Brem A-K, et al. Precision non-implantable neuromodulation therapies: a perspective for the depressed brain. Braz J Psychiatry. 2020;42:403–19. · DOI
  36. van Diermen L, van den Ameele S, Kamperman AM, Sabbe BCG, Vermeulen T, Schrijvers D, et al. Prediction of electroconvulsive therapy response and remission in major depression: meta-analysis. Br J Psychiatry. 2018;212:71–80. · DOI
  37. van Diermen L, Poljac E, Van der Mast R, Plasmans K, Van den Ameele S, Heijnen W, et al. Toward targeted ECT: the interdependence of predictors of treatment response in depression further explained. J Clin Psychiatry. 2020;82:20m13287. · DOI
  38. Belge J-B, van Diermen L, Sabbe B, Parizel P, Morrens M, Coppens V, et al. Inflammation, hippocampal volume, and therapeutic outcome following electroconvulsive therapy in depressive patients: a pilot study. Neuropsychobiology. 2020;79:222–32. · DOI
  39. Blanco I, Conant K. Extracellular matrix remodeling with stress and depression: studies in human, rodent and zebrafish models. Eur J Neurosci. 2021;53:3879–88. · DOI
  40. Madsen TM, Treschow A, Bengzon J, Bolwig TG, Lindvall O, Tingström A. Increased neurogenesis in a model of electroconvulsive therapy. Biol Psychiatry. 2000;47:1043–9. · DOI
  41. Ousdal OT, Brancati GE, Kessler U, Erchinger V, Dale AM, Abbott C, et al. The neurobiological effects of electroconvulsive therapy studied through magnetic resonance: what have we learned, and where do we go? Biol Psychiatry. 2022;91:540–9. · DOI
  42. Argyelan M, Oltedal L, Deng Z-D, Wade B, Bikson M, Joanlanne A, et al. Electric field causes volumetric changes in the human brain. eLife. 2019;8:e49115. · DOI
  43. Gorinski N, Bijata M, Prasad S, Wirth A, Abdel Galil D, Zeug A, et al. Attenuated palmitoylation of serotonin receptor 5-HT1A affects receptor function and contributes to depression-like behaviors. Nat Commun. 2019;10:3924. · DOI
  44. Keller MB. Issues in treatment-resistant depression. J Clin Psychiatry. 2005;66:5–12. · DOI
  45. Ali S, Khan MS, Wani JA, Faiz S, Rehman MU, Majid S, et al. Chapter 13 - miRNAs: the genetic regulators of immunity. In: Rehman MU, Arafah A, Ali MDN, Ali S, editors. A Molecular Approach to Immunogenetics, Academic Press; 2022. p. 299–325. https://doi.org/10.1016/B978-0-323-90053-9.00003-8, https://www.sciencedirect.com/science/article/pii/B9780323900539000038. · DOI
  46. Bauernfeind F, Rieger A, Schildberg FA, Knolle PA, Schmid-Burgk JL, Hornung V. NLRP3 inflammasome activity is negatively controlled by miR-223. J Immunol. 2012;189:4175–81. · DOI · Paper page
  47. Neudecker V, Brodsky KS, Clambey ET, Schmidt EP, Packard TA, Davenport B, et al. Neutrophil transfer of miR-223 to lung epithelial cells dampens acute lung injury in mice. Sci Transl Med. 2017;9:eaah5360. · DOI
  48. Yuan S, Wu Q, Wang Z, Che Y, Zheng S, Chen Y, et al. miR-223: an immune regulator in infectious disorders. Front Immunol. 2021;12:781815. · DOI
  49. Qu H, Liu R, Chen J, Zheng L, Chen R. Aerobic exercise inhibits CUMS-depressed mice hippocampal inflammatory response via activating hippocampal miR-223/TLR4/MyD88-NF-κB pathway. Int J Environ Res Public Health. 2020;17:2676. · DOI
  50. Swanson KV, Deng M, Ting JP-Y. The NLRP3 inflammasome: molecular activation and regulation to therapeutics. Nat Rev Immunol. 2019;19:477–89. · DOI · Paper page
  51. Alcocer-Gómez E, de Miguel M, Casas-Barquero N, Núñez-Vasco J, Sánchez-Alcazar JA, Fernández-Rodríguez A, et al. NLRP3 inflammasome is activated in mononuclear blood cells from patients with major depressive disorder. Brain Behav Immun. 2014;36:111–7. · DOI · Paper page
  52. Tian H, Li G, Xu G, Liu J, Wan X, Zhang J, et al. Inflammatory cytokines derived from peripheral blood contribute to the modified electroconvulsive therapy-induced cognitive deficits in major depressive disorder. Eur Arch Psychiatry Clin Neurosci. 2021;271:475–85. · DOI
  53. Roohi E, Jaafari N, Hashemian F. On inflammatory hypothesis of depression: what is the role of IL-6 in the middle of the chaos? J Neuroinflammation. 2021;18:45. · DOI
  54. Beurel E, Toups M, Nemeroff CB. The bidirectional relationship of depression and inflammation: double trouble. Neuron. 2020;107:234–56. · DOI · Paper page
  55. Lee C-H, Giuliani F. The role of inflammation in depression and fatigue. Front Immunol. 2019;10:1696. · DOI
  56. Gay F, Romeo B, Martelli C, Benyamina A, Hamdani N. Cytokines changes associated with electroconvulsive therapy in patients with treatment-resistant depression: a meta-analysis. Psychiatry Res. 2021;297:113735. · DOI
  57. Dahl J, Ormstad H, Aass HCD, Sandvik L, Malt UF, Andreassen OA. Recovery from major depressive disorder episode after non-pharmacological treatment is associated with normalized cytokine levels. Acta Psychiatr Scand. 2016;134:40–47. · DOI
  58. Yrondi A, Sporer M, Péran P, Schmitt L, Arbus C, Sauvaget A. Electroconvulsive therapy, depression, the immune system and inflammation: a systematic review. Brain Stimul. 2018;11:29–51. · DOI
  59. Hestad KA, Tønseth S, Støen CD, Ueland T, Aukrust P. Raised plasma levels of tumor necrosis factor alpha in patients with depression: normalization during electroconvulsive therapy. J ECT. 2003;19:183–8. · DOI · Paper page
  60. Das S, Halushka MK. Extracellular vesicle microRNA transfer in cardiovascular disease. Cardiovasc Pathol. 2015;24:199–206. · DOI
  61. Men Y, Yelick J, Jin S, Tian Y, Chiang MSR, Higashimori H, et al. Exosome reporter mice reveal the involvement of exosomes in mediating neuron to astroglia communication in the CNS. Nat Commun. 2019;10:4136.
  62. Nogueras-Ortiz CJ, Mahairaki V, Delgado-Peraza F, Das D, Avgerinos K, Eren E, et al. Astrocyte- and neuron-derived extracellular vesicles from Alzheimer’s disease patients effect complement-mediated neurotoxicity. Cells. 2020;9:1618. · DOI
  63. Badhwar A, Haqqani AS. Biomarker potential of brain‐secreted extracellular vesicles in blood in Alzheimer’s disease. Alzheimers Dement. 2020;12:e12001.
  64. Brymer KJ, Romay-Tallon R, Allen J, Caruncho HJ, Kalynchuk LE. Exploring the potential antidepressant mechanisms of TNFα antagonists. Front Neurosci. 2019;13:98. · DOI
  65. Raison CL, Rutherford RE, Woolwine BJ, Shuo C, Schettler P, Drake DF, et al. A randomized controlled trial of the tumor necrosis factor-alpha antagonist infliximab in treatment resistant depression: role of baseline inflammatory biomarkers. JAMA Psychiatry. 2013;70:31–41. · DOI · Paper page
  66. Ye L, Xiao X, Xu Y, Zheng C, Chen S, Luo T, et al. Prelimbic cortex miR-34a contributes to (2R,6R)-hydroxynorketamine-mediated antidepressant-relevant actions. Neuropharmacology. 2022;208:108984. · DOI

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Authors
Kaurani, L., Besse, M., Methfessel, I., Methi, A., Zhou, J., Pradhan, R., Burkhardt, S., Kranaster, L., Sartorius, A., Habel, U., Grözinger, M., Fischer, A., Wiltfang, J., & Zilles-Wegner, D.
ORCID
Venue
Translational Psychiatry
Publisher
Springer Science and Business Media LLC
Published
2023-09-13
Volume
13
Issue
1
ISSN
2158-3188
DOI
10.1038/s41398-023-02582-4
PMC
PMC10497550
Cited by
36
References
13
Language
en
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