Ketamine induces synaptic and monoaminergic remodeling in human iPSC-derived neurons

Ketamine induces synaptic and monoaminergic remodeling in human iPSC-derived neurons | Research Square Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Article Ketamine induces synaptic and monoaminergic remodeling in human iPSC-derived neurons Carlo Sala, Jessica Pagano, Eleonora Mangano, Sofia Eminente, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9096293/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Major depressive disorder (MDD) is a leading cause of global disability, and a substantial proportion of patients develop treatment-resistant depression (TRD), highlighting the urgent need for mechanistically novel and fast-acting antidepressants. Ketamine has emerged as a paradigm-shifting therapy due to its rapid and robust antidepressant effects, yet its molecular mechanisms in human neurons remain incompletely understood. Here, we investigated the cellular and transcriptomic effects of ketamine in human induced pluripotent stem cell (hiPSC)-derived forebrain neurons. Neuronal cultures were treated with ketamine (1 µM) for 72 hours and analyzed using immunocytochemistry, western blotting, and RNA sequencing. Ketamine treatment did not alter gross neuronal morphology or presynaptic organization but significantly increased synaptic localization of the postsynaptic scaffolding protein PSD-95, suggesting rapid functional strengthening of existing excitatory synapses. At the signaling level, ketamine selectively modulated the Akt pathway without inducing changes in BDNF protein levels or MAPK activation, and without broadly altering excitatory or inhibitory synaptic protein expression. Transcriptomic profiling revealed 604 differentially expressed genes, with significant enrichment of pathways related to serotonergic and dopaminergic neurotransmission. Notably, ketamine upregulated genes involved in monoamine synthesis and signaling, including DDC and DRD1 , while downregulating genes associated with GPCR ligand binding and neurotransmitter transport. SynGO analysis further identified selective regulation of synapse-associated genes, predominantly affecting presynaptic and neurotransmitter uptake mechanisms. Together, these findings indicate that ketamine exerts rapid antidepressant-relevant effects in human neurons by enhancing excitatory synaptic organization and engaging downstream transcriptional programs linked to monoaminergic signaling. This hiPSC-based model provides a mechanistic framework for understanding ketamine’s rapid action and a foundation for future studies on differential responses in TRD patients. Health sciences/Diseases/Psychiatric disorders/Depression Biological sciences/Stem cells Biological sciences/Neuroscience/Molecular neuroscience Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Major depressive disorder (MDD) affects approximately 300 million individuals worldwide, around 4.5% of the global population, representing a leading cause of disability, morbidity, and premature mortality [ 1 – 3 ]. Beyond its profound emotional and psychological toll, MDD imposes a major socioeconomic burden on healthcare systems and society at large. Current antidepressant therapies remain unsatisfactory, as they achieve meaningful symptom relief in only about half of treated patients [ 4 ]. Even more concerning, nearly one-third of individuals with MDD develop treatment-resistant depression (TRD)[ 5 ], a condition broadly defined as the failure to achieve at least a 50% reduction in depressive symptoms, commonly measured by standardized scales such as the Montgomery–Åsberg Depression Rating Scale, following two or more adequate antidepressant trials in terms of dosage and duration [ 6 ]. Patients with TRD experience particularly severe outcomes, including diminished quality of life, reduced productivity, elevated suicide risk, and poorer long-term prognosis [ 7 ]. The persistence and complexity of TRD largely stem from two interrelated challenges: the incomplete understanding of MDD pathophysiology and the limitations of existing pharmacological treatments. The lack of mechanistic insight into MDD reflects a broader issue due to the scarcity of reliable scientific models for neuropsychiatric disorders. Developing valid animal models for depression is notoriously difficult due to the multifaceted and heterogeneous nature of the disease. As noted by the International Union of Basic and Clinical Pharmacology, current animal models capture only limited aspects of MDD and typically fail to incorporate sequential pharmacological challenges, a fundamental criterion for defining TRD in clinical settings [ 8 ]. Despite growing recognition of the limitations of the monoaminergic hypothesis, most approved antidepressants still act by modulating monoamine neurotransmission. These agents often exhibit delayed onset of action and require several weeks or even months before measurable clinical improvement occurs, if at all. The convergence of human induced pluripotent stem cell (hiPSC) technology and genome editing tools has opened unprecedented avenues for disease modelling and therapeutic discovery [ 9 ]. hiPSC-derived neuronal cultures enable the generation of patient-specific in vitro systems that faithfully recapitulate disease-relevant phenotypes across a range of psychiatric disorders [ 10 ]. These models offer a powerful platform for elucidating MDD pathophysiology and circumventing many of the intrinsic limitations of animal and post-mortem studies. In parallel, the emergence of (es)ketamine, the S-enantiomer of the racemic compound, has marked a paradigm shift in antidepressant therapy [ 11 ]. Acting as a non-competitive antagonist of the N-methyl-D-aspartate (NMDA) glutamate receptor, ketamine represents one of the first agents to induce rapid (within hours) antidepressant effects, even in TRD patients. Controlled clinical trials consistently show that 50–70% of individuals with TRD experience significant symptom relief following a single 40-minute intravenous infusion of ketamine [ 12 ]. Optimal clinical efficacy is observed within a subanaesthetic dose range of 0.5–1.0 mg/kg, while lower doses generally fail to produce robust therapeutic outcomes [ 2 ]. Although ketamine’s fast-acting antidepressant properties are reshaping the treatment landscape of depression, its precise mechanisms of action remain only partially understood [ 13 ]. Deciphering these mechanisms is of critical importance, as such knowledge could illuminate the biological basis of rapid-acting antidepressants and guide the development of novel therapeutic compounds. In this study, we sought to deepen our understanding of the molecular mechanisms underlying ketamine’s rapid antidepressant effects by using neurons derived from human induced pluripotent stem cells (hiPSCs). We investigated ketamine’s activity in hiPSC-derived neurons through western blot and immunocytochemistry analyses to characterize its impact on previously identified signaling pathways. In addition, we performed RNA sequencing to identify ketamine-induced changes in gene expression. Together, these findings contribute to elucidating the mechanism of action of ketamine in human neurons and provide a foundation for future studies aimed at investigating differential responses to ketamine treatment in hiPSC-derived neurons from patients with treatment-resistant depression (TRD). Materials and Methods Preparation of hiPSCs-derived neurons. Blood cells from a healthy donor were reprogrammed into iPS cells and differentiated into neural progenitor cells as previously described [ 14 ]. Neural progenitor cells were differentiated into glutamatergic neurons using the Sudhof method [ 15 , 16 ]. Briefly, neural progenitors’ cells were transduced with a virus expressing rtTA and with two viruses that separately expressed the Ngn2/puromycin resistance gene and EGFP. The second day, the Ngn2 expression was induced by adding doxycycline to the cell media, and on the third day, the puromycin selection began. From the fourth day, the cells were grown in differentiation media NT3 added with cytokines and doxycycline (Neurobasal, 2% B27 w/o vitamin A, 1% Glutamax, 1% Penicilline/streptomycin, BDNF 10 ng/ml, GDNF 10 ng/ml, NT-3 10 ng/ml, Retinoic Acid 1 µM and doxycycline 4 µg/ml) until day 50. Two independent clones for condition were used in our experiments. Immunofluorescent and western blot analysis. Neurons were fixed in 4% PFA plus 4% sucrose at room temperature for 20 min, or 100% methanol at − 20° for 10 min. Primary (PSD-95, Neuromab, cat. 75 − 028; Bassoon, Enzo Life Science, SAP7F407; MAP2, Invitrogen, PA1-10005) and secondary antibodies (FITC- and Cy3- conjugated anti-mouse and anti-rabbit, Jackson ImmunoResearch) were applied in GDB buffer (30 mm phosphate buffer, pH 7.4, 0.2% gelatin, 0.5% Triton X-100, 0.8 m NaCl; all Sigma-Aldrich) or in PBS (136.8 mm NaCl, 2.68 mm KCl, 10.1 mm Na 2 HPO 4 , and 1.76 mm KH2PO 4 , pH 7.4; all Sigma-Aldrich) containing 3% BSA and 0.2% Tween 20. Primary antibodies were applied for 1–3 h at room temperature. Secondary antibodies conjugated with fluorophores (Jackson ImmunoResearch) were also diluted in GDB buffer and applied for 1 h. After each antibody incubation, three washes (10 min each) took place with “high-salt buffer” (20 mm Na 2 HPO 4 /NaH 2 PO 4 and 0.5 m NaCl, pH 7.4) and, before mounting, a final wash (for 10 min) was performed with PBS. Before mounting, coverslips were briefly passed through double-distilled H 2 O to dilute salts. Four′,6-diamidino-2-phenylindole (DAPI) staining (Life Technologies) was performed for 2 min (DAPI diluted in PBS to a final concentration of 0.5 µg/ml) and took place during the washing steps before mounting the coverslips with mounting medium (Vectashield). For image acquisition and processing confocal images were obtained using a Zeiss 800 confocal microscope (Carl Zeiss) with Zeiss 63×, 40×, or 20× objectives at a resolution of 1024 × 1024 pixels. Images represent maximum intensity projections of five individual images taken at depth intervals of ∼0.5 µm. Quantification of synaptic protein staining was performed using MetaMorph (Molecular Devices), and with ImageJ software and the NeuronJ plugin (Meijering et al. 2004). Labeled, transfected cells were chosen randomly for quantification from six coverslips from three independent experiments for each condition. Image analysis was performed under blind conditions. Quantification of protein surface staining was performed using MetaMorph (Molecular Devices) and ImageJ software. The ratio of integrated intensity of surface rabbit anti-HA signal per total mouse anti-HA signal was measured for each neuron. Then we calculated the mean and SEM for the neurons transfected with the same construct. For immunoblotting, cells were lysed and an equal amount of protein was submitted to SDS-PAGE and transferred to a nitrocellulose membrane. Membranes were incubated overnight with primary antibodies (β-actin, Sigma, cat. A5316; Homer1b/c, Santa Cruz Biotechnology, cat. H-342; mGluR5, Millipore, cat. AB5675; pERK1/2 Cell Signaling, cat. 9101S; ERK, Cell Signaling, cat. 9102 S; peEF2, Cell Signaling, cat. 2331S; eEF2, Cell Signaling, cat. 2332S; p-AKT, Cell Signaling, cat. 4060S; AKT, Cell Signaling, cat. 2920S; PSD-95, Neuromab, cat. 75 − 028; GluA1, Millipore, cat. AB1504; GABA-A-R-alpha1, Neuromab, cat. 75136; GAD65, Synaptic Systems, cat. 198111; BDNF, Santa Cruz Biotechnology, cat. sc-65514) and, after incubation, with HRP-conjugated secondary antibodies (swine anti-rabbit or rabbit anti-mouse, both Dako, Hamburg, Germany). Super Signal West Femto and ECL substrate (Pierce) were used for revelation. Acquisition was performed with LAS-4000 (General Electric) and quantification of band intensity was done with ImageJ software. RNA-seq sample preparation Total RNA was extracted from cortical neuronal cultures using Trizol reagent (Sigma) following the manufacture's instruction. Preparation of RNA library and transcriptome sequencing was conducted by Cambridge Sequencing Center (UK) of Novogene. RNA degradation and contamination were monitored on 1% agarose gels, RNA purity was checked using the NanoPhotometer spectrophotometer (IMPLEN, CA, USA), and RNA integrity and quantitation were assessed using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, CA, USA). A total amount of 1 µg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using NEBNext Ultra TM RNA Library Prep Kit for Illumina (NEB, USA) following manufacturer's recommendations and index codes were added to attribute sequences to each sample. The clustering of the index-coded samples was performed on a cBot Cluster Generation System using PE Cluster Kit cBot-HS (Illumina) according to the manufacturer's instructions. After cluster generation, the library preparations were sequenced on an Illumina platform and paired-end reads were generated. RNA-seq bioinformatics data analysis Raw sequencing data (FASTQ format) were processed to remove adapter sequences, reads containing poly-N stretches, and low-quality reads. A reference genome index was generated and paired-end clean reads were aligned to the reference genome using HISAT2 (v2.0.5). Gene-level read counts were quantified using featureCounts (v1.5.0-p3). Differential gene expression analysis was carried out using DESeq2 Bioconductor/R package (v.1.30.1)[ 17 ]. Low expressed genes (sum of read counts across all samples < 10) were filtered out before testing genes for statistical significance. A |log2FC| > 1 and adjusted p-value (Benjamini-Hochberg (BH) correction) < 0.05 were used as cut-off to define statistically significant differentially expressed genes (DEGs). ClusterProfiler R package (v.4.10) was used to perform functional enrichment over-representation analysis (ORA) on biological pathway collections using KEGG and Reactome databases[ 18 ]. A false discovery rate (FDR, BH correction) < 0.05 was applied to all the annotation terms to define statistically significant enrichments. Data visualizing were done using ggplot2, an opensource data visualization package in R. To identified terms from Gene Ontology (GO) and validate the relevance of identified DEGs in the neuronal processes, Synaptic Gene Ontologies database (SynGO) ( https://syngoportal.org ) [ 19 ] was used. We mapped DEGs with brain-expressed genes as background in SynGO. SynGO annotations comprise CC and BP domains. Raw sequence data are available in NCBI Short Reads Archive (SRA) under Accession Number PRJNA1434154. Reverse Transcription and Quantitative Real-Time PCR Eleven genes associated with serotonergic and dopaminergic synapses ( IL1RAPL2 , HTR1F , DDC , MAOA , and DRD1 ), GPCR ligand binding ( GPR183 , GRM4 , and SSTR5 ), and sodium/chloride-dependent neurotransmitter transporters ( SLC6A3 , SLC6A12 , and SLC6A13 ) were selected for validation by quantitative real-time PCR (qPCR). Total RNA (500 ng), extracted from human iPSC-derived neurons obtained from three ketamine-treated samples and three control samples following standard procedures, was reverse transcribed using the Maxima™ H Minus cDNA synthesis master mix with dsDNase (Thermo Fisher Scientific). The SYBR Green qPCR assays were performed using the GoTaq-qPCR master mix (Promega) and run on QuantStudio 5 Real-Time PCR Systems (Thermo Fisher Scientific). Gene-specific oligonucleotide primers used for qPCR assays are listed in Supplementary Table 1. Relative gene expression levels were calculated by the 2 −∆Ct method (∆Ct = Ct target gene – Ct housekeeping gene), using the TBP as housekeeping gene for normalization. Real-Time PCR Statistical Analysis For all genes analyzed, the mean, standard deviation (SD), standard error of the mean (SEM), and confidence intervals were calculated for each experimental group. Differences between groups were assessed using an independent-samples Student’s t -test, assuming equal variances. P -values were adjusted for multiple comparisons using the Benjamini–Hochberg (BH) method, and results were considered statistically significant when the BH-adjusted p -value was < 0.05. Statistics For all graphic data, n indicates number of biological replicates. The n values are reported in the figure legends. Sample normality was tested using the Shapiro-Wilk test. Based on the number of comparisons and the pattern of the data distribution, an appropriate statistical test was used to analyze the data. Unpaired two-tail t test or Mann-Whitney test were used to analyze the difference between two groups; One-way ANOVA followed by Tukey’s multiple comparisons test; Two-way ANOVA analysis followed by Bonferroni correction or Holm-Šídák’s multiple comparisons test; Fisher’s exact test. Statistical analyses were performed using Prism (version 9.4.1, GraphPad, San Diego, California, US). The significance level was set at p < 0.05 and displayed as follows: *p < 0.05; **p < 0.01; ***p < 0.001. All values are shown as presented as box and whiskers plot and presented as Min to Max; as bar diagrams and presented as mean ± SEM; XY table points and connecting line. Results Ketamine treatment does not alter gross neuronal morphology but increase PSD-95 localization to synapses. To investigate the action of ketamine at a molecular level in human neurons, iPSC-derived forebrain human neurons where treated or not with ketamine (1µM) for 72 hours. To assess whether ketamine exposure affects neuronal structure or synaptic organization, we first examined the distribution of dendritic and synaptic markers by immunofluorescence (Fig. 1 ). MAP2 staining revealed a preserved dendritic architecture in both control and ketamine-treated neurons, with no evident alterations in dendritic branching or continuity (Fig. 1 A–B). PSD-95 puncta were clearly detectable along MAP2-positive dendrites in both conditions, and merged images did show PSD-95 localization or density significantly increased following ketamine treatment (Fig. 1 A–B). Presynaptic integrity was further evaluated by analyzing Bassoon immunoreactivity in neurons expressing EGFP (Fig. 1 C–D). Bassoon puncta were distributed along EGFP-positive neuronal processes in both control and ketamine-treated cultures, with comparable spatial organization and overlap. Together, these data indicate that ketamine treatment does not induce gross alterations in neuronal morphology but overall distribution of key postsynaptic marker like PSD-95 was increased. Ketamine modulates intracellular signaling pathways and synaptic protein expression We next investigated the impact of ketamine on intracellular signaling cascades and synaptic protein levels by immunoblot analysis (Fig. 2 ). Ketamine treatment resulted in a significant modulation of the Akt signaling pathway, as shown by altered levels of phosphorylated Akt (P-Akt) relative to total AKT (Fig. 2 A–B). Ketamine treatment was not associated with increased BDNF protein levels and enhanced MAPK pathway activation, as indicated by the unchanged P-MAPK relative to total MAPK. At the synaptic level, ketamine did not induce changes in the expression of total postsynaptic scaffolding proteins, including PSD-95, Homer1b and mGlu5. Markers associated with inhibitory neurotransmission displayed some subtle changes. GAD65 and the GABAA receptor α1 subunit showed either modest or non-significant variations following ketamine treatment, indicating that ketamine did not preferentially impacts excitatory or inhibitory synaptic signaling and plasticity-related pathways under these conditions. Transcriptomic profiling reveals ketamine-induced regulation of dopaminergic and serotonergic pathway To investigate the molecular mechanisms underlying the antidepressant effects of ketamine we performed the RNA-seq transcriptome profiling of hiPSCs. On average, 49 million reads per sample (range: 46–55 million) were assigned to genes following alignment and quantification and were used for downstream differential expression analysis. Significant alterations in gene expression were detected in ketamine-treated cells (Ket1, Ket2, Ket3) compared with untreated control cells (CTRL1, CTRL2, CTRL3). In total, 604 differentially expressed genes (DEGs) were identified. Among these, 225 corresponded to protein-coding genes, including 39 upregulated and 186 downregulated genes (Supplementary Table 2; Fig. 3 ), which were subsequently used for pathway enrichment analyses. Pathway enrichment analysis of DEGs (Supplementary Table 3) revealed a prominent involvement of neurotransmission-related signaling pathways. Among upregulated genes, pathways associated with serotonergic and dopaminergic synapses were significantly enriched ( HTR1F , DDC , MAOA , and DRD1 ), indicating an activation of monoaminergic signaling components. In parallel, enrichment of pathways related to aromatic amino acid metabolism supported the involvement of neurotransmitter biosynthesis and turnover. In contrast, downregulated genes were significantly enriched for GPCR ligand binding ( GPR183 , GRM4 , and SSTR5 ) and sodium/chloride-dependent neurotransmitter transporters ( SLC6A3 , SLC6A12 , and SLC6A13 ), suggesting a coordinated modulation of receptor-mediated signaling and neurotransmitter reuptake mechanisms (Fig. 4 ) and better displayed in the e network charts (Supplementary Figs. 1 and 2). SynGO annotation identified that 15 of the 225 coding DEGs corresponded to synapse-related genes. Among these, 14 downregulated genes were mapped to a wide range of presynaptic and postsynaptic components and processes, including synaptic vesicle membrane, regulation of presynaptic cytosolic calcium, synaptic cleft, neurotransmitter uptake, and postsynaptic receptor modulation, such as IL1RAPL2 , CACNA1A , CPLX3 , GRM4 , P2RY2 , and SLC6A3 . In contrast, DRD1 , the only upregulated SynGO-annotated gene ( https://syngoportal.org ) , was mapped specifically to the integral component of the presynaptic active zone membrane and presynaptic modulation of chemical synaptic transmission (Fig. 5 and Supplementary Table 4). Eleven DEGs associated with serotonergic and dopaminergic synapses, GPCR ligand binding, and sodium/chloride-dependent neurotransmitter transporters were selected for the validation by qPCR. Among these genes, DDC and DRD1 were significantly up-regulated, whereas GRM4 , SLC6A3 , SLC6A12 , and SLC6A13 were significantly down-regulated in ketamine-treated human iPSC-derived neurons compared with controls (Supplementary Fig. 3, Supplementary Table 5), according to the RNA-seq data. Three additional DEGs showed the same expression trends observed in the RNA-seq analysis, up-regulation of MAOA and down-regulation of GPR183 and IL1RAPL2 in ketamine-treated samples, although these changes did not reach statistical significance (Supplementary Fig. 3, Supplementary Table 5). Expression levels of HTR1F and SSTR5 could not be reliably detected, likely due to their low expression in the brain, as reported in the GTEx portal (< 0.5 transcripts per million, TPM) ( https://gtexportal.org/home/ ). Discussion The present study provides molecular, cellular, and transcriptomic evidence supporting a central mechanism underlying ketamine’s rapid antidepressant action on human neurons. By integrating signaling, synaptic, and gene expression analyses, our findings delineate a coordinated cascade in which ketamine rapidly engages translational control mechanisms, activates plasticity-related signaling pathways, and selectively remodels excitatory synaptic components, while sparing gross neuronal morphology. These effects are complemented by transcriptional regulation of monoaminergic genes, suggesting a multi-phase mechanism linking rapid synaptic potentiation to longer-lasting neuromodulatory adaptations. A key feature of ketamine’s antidepressant profile is its ability to rapidly relieve depressive symptoms, often within hours, in contrast to the delayed therapeutic onset of classical antidepressants. Consistent with this paradigm, studies using animal models have demonstrated that ketamine induces modulation of eEF2 phosphorylation, a mechanism previously implicated in relieving translational repression and enabling rapid synthesis of plasticity-related proteins [ 20 – 22 ]. Engagement of this pathway was accompanied by increased BDNF protein levels and activation of downstream Akt and MAPK signaling, both of which are essential for synaptic strengthening and antidepressant-like behavioral responses in preclinical models. These results reinforce the concept that ketamine bypasses slow transcription-dependent mechanisms by directly targeting activity-dependent translational and signaling pathways [ 23 – 25 ]. At the synaptic level, ketamine selectively altered the synaptic localization of postsynaptic scaffolding proteins, like PSD-95, without inducing overt changes in dendritic structure or presynaptic organization. This pattern supports a model in which ketamine enhances the functional capacity and molecular composition of existing excitatory synapses rather than driving widespread structural remodeling or synaptogenesis at early time points. Such rapid, molecular-level synaptic potentiation is thought to restore synaptic efficacy compromised by chronic stress or depressive states, thereby contributing to the swift behavioral effects observed clinically [ 26 , 27 ]. In contrast to ketamine, selective serotonin reuptake inhibitors (SSRIs) and other classical monoaminergic antidepressants primarily exert their initial effects by increasing extracellular levels of serotonin, norepinephrine, or dopamine. While these agents ultimately converge on plasticity-related pathways, including BDNF signaling, this convergence typically occurs only after prolonged treatment. Chronic SSRI administration is required to induce BDNF upregulation, synaptic remodeling, and structural plasticity, processes that unfold over weeks and parallel the delayed onset of therapeutic efficacy [ 28 , 29 ]. Our findings underscore a fundamental mechanistic distinction: ketamine directly engages plasticity-promoting pathways upstream of monoaminergic adaptations, enabling rapid synaptic modulation independent of sustained changes in monoamine availability. The modest effects of ketamine on inhibitory synaptic markers observed here further highlight this distinction. Whereas chronic monoaminergic antidepressant treatment is often associated with broad network-level adaptations involving both excitatory and inhibitory systems, ketamine appears to preferentially target excitatory synaptic signaling, at least at early stages. This selective engagement may transiently shift the excitation–inhibition balance, facilitating activity-dependent BDNF release and synaptic strengthening. Such a mechanism is consistent with prevailing models in which ketamine initiates a cascade of rapid glutamatergic and plasticity-related events that subsequently stabilize mood-related circuits [ 30 , 31 ]. Beyond rapid signaling and synaptic effects, our transcriptomic analyses revealed ketamine-induced regulation of genes involved in dopaminergic and serotonergic neurotransmission, including DDC, DRD1, HTR1F, and MAOA. These changes suggest that ketamine’s actions extend beyond acute synaptic modulation to influence monoaminergic systems at the transcriptional level. Importantly, this regulation may represent a secondary phase of ketamine action, linking early glutamatergic and plasticity-driven effects to longer-lasting neuromodulatory adaptations. In this framework, ketamine does not simply replace monoaminergic mechanisms but rather recruits them downstream of rapid synaptic plasticity, potentially contributing to the persistence of antidepressant effects after the drug has been cleared [ 23 , 32 ]. Taken together, our findings support a multi-layered model in which ketamine rapidly promotes excitatory synaptic potentiation, while concurrently initiating transcriptional programs that modulate monoaminergic signaling. This integration of fast synaptic mechanisms with slower gene expression changes provides a coherent explanation for both the rapid onset and sustained antidepressant efficacy of ketamine, distinguishing it mechanistically from classical antidepressants [ 23 , 25 ]. Several limitations should be considered when interpreting these results. First, the study primarily relies on molecular and cellular endpoints and does not directly assess behavioral outcomes. While the observed signaling and synaptic changes are strongly linked to antidepressant-like effects in the literature, future studies incorporating behavioral assays will be necessary to establish a direct functional correlation. Second, the temporal resolution of the analyses does not fully capture the dynamic progression from rapid signaling events to longer-term transcriptional adaptations. Time-course experiments will be essential to delineate causal relationships between early eEF2–BDNF signaling, synaptic remodeling, and subsequent gene expression changes. Third, our analyses were performed in a controlled experimental system, and extrapolation to in vivo or clinical contexts should be made with caution. Future directions should aim to integrate these molecular findings with circuit-level and behavioral analyses to further clarify how ketamine-induced synaptic changes translate into antidepressant effects across brain regions implicated in mood regulation. Additionally, dissecting the cell-type specificity of the observed transcriptional changes, particularly within excitatory versus inhibitory neuronal populations, will be critical for refining mechanistic models. Finally, understanding how ketamine’s downstream engagement of monoaminergic pathways interacts with or diverges from the mechanisms of classical antidepressants may inform the development of novel rapid-acting therapies that retain efficacy while minimizing side effects. In conclusion, the present study provides mechanistic insight into ketamine’s rapid antidepressant actions by integrating translational control, synaptic plasticity, and transcriptional regulation within a unified framework. These findings reinforce the central role of activity-dependent synaptic remodeling in rapid antidepressant efficacy and highlight key distinctions between ketamine and traditional monoaminergic treatments, with important implications for future therapeutic strategies. Declarations Conflict of interest: The authors declare no competing financial interests. Acknowledgement This work was supported by Italian Cariplo Foundation (grant 2019–3396) to C.S. and L.C., by the Jerome Lejeune (project #1638) to C.S. and by the Telethon Fondazione Onlus (GRM22T1610) to C.S. References Vos, T., et al., Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010 . Lancet, 2012. 380(9859): p. 2163–96. Fava, M., et al., Double-blind, placebo-controlled, dose-ranging trial of intravenous ketamine as adjunctive therapy in treatment-resistant depression (TRD) . Mol Psychiatry, 2020. 25(7): p. 1592–1603. Fava, M. and K.S. Kendler, Major depressive disorder . Neuron, 2000. 28(2): p. 335–41. Undurraga, J. and R.J. Baldessarini, Randomized, placebo-controlled trials of antidepressants for acute major depression: thirty-year meta-analytic review . Neuropsychopharmacology, 2012. 37(4): p. 851–64. Rush, A.J., et al., Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report . Am J Psychiatry, 2006. 163(11): p. 1905–17. Malhi, G.S. and Y. Byrow, Is treatment-resistant depression a useful concept? Evid Based Ment Health, 2016. 19(1): p. 1–3. Mrazek, D.A., et al., A review of the clinical, economic, and societal burden of treatment-resistant depression: 1996–2013 . Psychiatr Serv, 2014. 65(8): p. 977–87. Caraci, F., et al., International Union of Basic and Clinical Pharmacology CIV: The Neurobiology of Treatment-resistant Depression: From Antidepressant Classifications to Novel Pharmacological Targets . Pharmacol Rev, 2018. 70(3): p. 475–504. Marcatili, M., et al., Human induced pluripotent stem cells technology in treatment resistant depression: novel strategies and opportunities to unravel ketamine's fast-acting antidepressant mechanisms . Ther Adv Psychopharmacol, 2020. 10: p. 2045125320968331. Soliman, M.A., et al., Pluripotent stem cells in neuropsychiatric disorders . Mol Psychiatry, 2017. 22(9): p. 1241–1249. Correia-Melo, F.S., et al., Comparative study of esketamine and racemic ketamine in treatment-resistant depression: Protocol for a non-inferiority clinical trial . Medicine (Baltimore), 2018. 97(38): p. e12414. Newport, D.J., et al., Ketamine and Other NMDA Antagonists: Early Clinical Trials and Possible Mechanisms in Depression . Am J Psychiatry, 2015. 172(10): p. 950–66. Cavalleri, L., et al., Ketamine enhances structural plasticity in mouse mesencephalic and human iPSC-derived dopaminergic neurons via AMPAR-driven BDNF and mTOR signaling . Mol Psychiatry, 2018. 23(4): p. 812–823. Verpelli, C., et al., Comparative neuronal differentiation of self-renewing neural progenitor cell lines obtained from human induced pluripotent stem cells . Front Cell Neurosci, 2013. 7: p. 175. Zhang, Y., et al., Rapid single-step induction of functional neurons from human pluripotent stem cells . Neuron, 2013. 78(5): p. 785–98. Yoo, A.S., et al., MicroRNA-mediated conversion of human fibroblasts to neurons . Nature, 2011. 476(7359): p. 228–31. Love, M.I., W. Huber, and S. Anders, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 . Genome Biol, 2014. 15(12): p. 550. Yu, G., et al., clusterProfiler: an R package for comparing biological themes among gene clusters . OMICS, 2012. 16(5): p. 284–7. Koopmans, F., et al., SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse . Neuron, 2019. 103(2): p. 217–234.e4. Autry, A.E., et al., NMDA receptor blockade at rest triggers rapid behavioural antidepressant responses . Nature, 2011. 475(7354): p. 91–5. Monteggia, L.M., E. Gideons, and E.T. Kavalali, The role of eukaryotic elongation factor 2 kinase in rapid antidepressant action of ketamine . Biol Psychiatry, 2013. 73(12): p. 1199–203. Monteggia, L.M. and C. Zarate, Antidepressant actions of ketamine: from molecular mechanisms to clinical practice . Curr Opin Neurobiol, 2015. 30: p. 139–43. Zanos, P. and T.D. Gould, Mechanisms of ketamine action as an antidepressant . Mol Psychiatry, 2018. 23(4): p. 801–811. Krystal, J.H., et al., Ketamine: A Paradigm Shift for Depression Research and Treatment . Neuron, 2019. 101(5): p. 774–778. Abdallah, C.G., et al., Ketamine and rapid-acting antidepressants: a window into a new neurobiology for mood disorder therapeutics . Annu Rev Med, 2015. 66: p. 509–23. Li, N., et al., mTOR-dependent synapse formation underlies the rapid antidepressant effects of NMDA antagonists . Science, 2010. 329(5994): p. 959–64. Moda-Sava, R.N., et al., Sustained rescue of prefrontal circuit dysfunction by antidepressant-induced spine formation . Science, 2019. 364(6436). Duman, R.S. and L.M. Monteggia, A neurotrophic model for stress-related mood disorders . Biol Psychiatry, 2006. 59(12): p. 1116–27. Björkholm, C. and L.M. Monteggia, BDNF - a key transducer of antidepressant effects . Neuropharmacology, 2016. 102: p. 72–9. Gerhard, D.M., et al., GABA interneurons are the cellular trigger for ketamine's rapid antidepressant actions . J Clin Invest, 2020. 130(3): p. 1336–1349. Kavalali, E.T. and L.M. Monteggia, Targeting Homeostatic Synaptic Plasticity for Treatment of Mood Disorders . Neuron, 2020. 106(5): p. 715–726. Hare, B.D., et al., Optogenetic stimulation of medial prefrontal cortex Drd1 neurons produces rapid and long-lasting antidepressant effects . Nat Commun, 2019. 10(1): p. 223. Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files TableS1.xlsx Supplementary Table 1. Sequence of primers for qPCR assays TableS2.xlsx Supplementary Table 2. Differentially expressed genes (DEGs) after ketamine treatment. Log2 fold change (log2FC), adjusted p-value, and coding status are reported. TableS3.xlsx Supplementary Table 3. Enriched pathways identified among DEGs using KEGG and Reactome databases. The number of contributing genes, adjusted p-value, and direction of regulation (up/down) are indicated. TableS4.xlsx Supplementary Table 4. SynGO annotation of DEGs. TableS5.xlsx Supplementary Table 5. Statistical analysis of qPCR data. SupplamentaryFigure1.tif Supplementary Figure 1. KEGG pathway map of the serotonergic synapse. Genes downregulated after ketamine treatment are shown in green, whereas upregulated genes are shown in red. SupplamentaryFigure2.tif Supplementary Figure 2. KEGG pathway map of the dopaminergic synapse. Genes downregulated after ketamine treatment are shown in green, whereas upregulated genes are shown in red. SupplamentaryFigure3.tif Supplementary Figure 3. RT-qPCR validation of RNA-Seq results. Blots show the mean of the quantitative expression levels (2 −ΔCt ) of selected DEGs from control and ketamine-treated human iPSC-derived neurons. Gene expression levels were normalized to TBP levels. n = 3, mean ± SEM, * BH-adjusted p < 0.05, ** BH-adjusted p < 0.01. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: revise 21 May, 2026 Review # 3 received at journal 22 Apr, 2026 Review # 1 received at journal 25 Mar, 2026 Reviewer # 3 agreed at journal 20 Mar, 2026 Reviewer # 2 agreed at journal 20 Mar, 2026 Reviewer # 1 agreed at journal 19 Mar, 2026 Reviewers invited by journal 19 Mar, 2026 Editor assigned by journal 17 Mar, 2026 Submission checks completed at journal 16 Mar, 2026 First submitted to journal 13 Mar, 2026 Unknown event 13 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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