Chronic lithium exposure reshapes PI3K–mTOR-linked proteostatic networks in the hippocampus of an Alzheimer’s disease mouse model

Chronic lithium exposure reshapes PI3K–mTOR-linked proteostatic networks in the hippocampus of an Alzheimer’s disease mouse model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); 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 Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Chronic lithium exposure reshapes PI3K–mTOR-linked proteostatic networks in the hippocampus of an Alzheimer’s disease mouse model Caíque de Oliveira Portugal Couto, Maria Luiza Hass das Eiras, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9077098/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Alzheimer’s disease (AD) involves a progressive loss of neuronal integrity in which chronic neuroinflammation is increasingly recognized as a contributing factor. Sustained activation of innate immune pathways, including Toll-like receptor 4 (TLR4) signaling, has been linked to microglial reactivity and hippocampal dysfunction in experimental models of the disease. Lithium has been reported to exert neuroprotective and anti-inflammatory effects, yet the molecular pathways underlying these actions in AD remain poorly defined. Here, we investigated how long-term lithium exposure influences hippocampal protein networks in a triple transgenic mouse model of AD (3xTg-AD). Hippocampal tissue from wild-type and 3xTg-AD mice treated chronically with subtherapeutic (1 mM) or therapeutic (2 mM) lithium concentrations for eight months was analyzed using liquid chromatography–tandem mass spectrometry. Proteomic profiling revealed widespread lithium-associated changes in proteins related to immune response, cellular stress, and inflammatory regulation. Network-based analyses highlighted components of the TLR4/NF-κB and MAPK signaling pathways as prominent nodes that were reduced in lithium-treated animals relative to controls. Together, these data indicate that chronic lithium treatment is associated with a remodeling of hippocampal inflammatory signaling in a mouse model of AD. Although additional functional studies will be necessary to establish mechanistic links, the present findings support the view that lithium-sensitive immune pathways may be relevant to the modulation of neuroinflammatory processes in Alzheimer’s disease. Phosphatidylinositol-3-kinase Alzheimer's disease Lithium Ontological pathways Neuroplasticity Figures Figure 1 Figure 2 Figure 3 Introduction Alzheimer’s disease (AD) is the most prevalent cause of dementia worldwide and represents a major challenge for aging societies (Xinyi Zhang et al., 2024 ). The disease is neuropathologically defined by extracellular amyloid-βeta (aβ) plaques and intracellular neurofibrillary tangles composed of hyperphosphorylated tau, which together disrupt synaptic integrity and neuronal survival. Beyond these hallmark lesions, AD is increasingly recognized as a disorder of intracellular signaling and proteostatic imbalance (Zhang 2021). Among the signaling cascades implicated in AD, the phosphatidylinositol 3-kinase (PI3K) pathway plays a central role in regulating neuronal metabolism, stress responses, protein synthesis, and survival through downstream effectors such as Akt and the mechanistic target of rapamycin (mTOR). Dysregulation of PI3K–mTOR signaling has been linked to both amyloid- and tau-mediated toxicity, positioning this pathway as a potential therapeutic target (Nishtha Singh 2024 and Matysek 2022). Lithium, a long-established mood stabilizer, has been shown to modulate several intracellular pathways relevant to neurodegeneration, including inhibition of glycogen synthase kinase-3β and modulation of phosphoinositide-dependent signaling. Although experimental evidence suggests neuroprotective effects of lithium in AD models, the molecular networks reshaped by chronic lithium exposure in the hippocampus remain poorly defined. In the present study, we applied an integrative proteomic and network-based approach to investigate how long-term lithium treatment modulates hippocampal PI3K-associated protein networks in a triple transgenic mouse model of AD. By focusing on the intersection of amyloid-β and tau signaling with PI3K pathways, we aimed to identify lithium-sensitive molecular processes relevant to proteostasis and neuronal resilience. Materials and Methods a-Transgenic Animals The present project was conducted using a CEUA-approved sample (protocol: 1293/09). No biological material collection was required. The 48 mice (from The Jackson Laboratory) were male, as the necessary differential analysis of the menstrual cycle of females would not be possible; however, this is a limitation of the study. Currently, the importance of including females is understood, even with the necessary additional control. (bacegato 2024) Of the mice, 24 were B6129SF2/J (wild type; WT) and 24 were B6;129-Psen1tm1Mpm Tg(APPSwe,tauP301L)1Lfa/Mmjax (3xTg-AD; Tg). At the beginning of the experiment, the animals were approximately 12 weeks old and were maintained at the Central Animal Facility of the School of Medicine, USP, throughout the treatment period, under controlled temperature (22 ± 1°C) and relative humidity (50–60%), with a 12 h light/12 h dark cycle, and free access to food and fresh water ad libitum during the entire study. The animals (WT and Tg) were divided into three subgroups: Li0 (standard chow); Li1 (chow containing 1.0 g lithium carbonate/kg); and Li2 (chow containing 2.0 g lithium carbonate/kg). The treatment lasted for eight months. After this period, the animals were euthanized by decapitation with a guillotine. The use of the guillotine is justified because it is a method that keeps proteins stable for up to 4 hours, thus being ideal for proteome data. Brain tissue was removed from the cranial cavity and the hippocampi were dissected (Hunsucker 2008). b-Protein Analysis The protein fraction (100 µg) from hippocampal tissues was precipitated using methanol/chloroform, according to Wessel and Flügge (Wessel & Flügge, 1984 ). The protein pellets were resuspended in 100 mM Tris buffer, pH 8.5, containing 8 M urea, and digested following a protocol adapted from Klammer and MacCoss (Klammer & MacCoss, 2006 ). Briefly, disulfide bonds were reduced with 5 mM tris(2-carboxyethyl) phosphine hydrochloride (TCEP) for 20 min at 37°C, and cysteines were subsequently alkylated with 25 mM iodoacetamide (IAM) for 20 min at room temperature in the dark. Urea was then diluted to 2 M with 100 mM Tris buffer (pH 8.5), and proteins were digested with trypsin (mass) at a 1:100 enzyme-to-protein ratio in the presence of 1 mM CaCl₂, by overnight incubation at 37°C. After overnight trypsin digestion, the reaction was quenched with 2% formic acid, and samples were stored at − 20°C until use. c-Liquid Chromatography/Mass Spectrometry (LC-MS) LC-MS/MS experiments were performed using an HP 1100 Series quaternary HPLC pump (Agilent Technologies) coupled to an LTQ-Velos Orbitrap mass spectrometer (Thermo Scientific). Electrospray ionization was conducted directly from the tip of the analytical column using Solution A (5% acetonitrile and 0.1% formic acid), Solution B (80% acetonitrile and 0.1% formic acid), and Solution C (500 mM ammonium acetate, 5% acetonitrile, and 0.1% formic acid). The flow rate was approximately 300 nL/min. Biphasic MudPIT columns (150 µm ID / 360 µm OD fused silica capillaries) were prepared in-house by sequential packing of 2.5 cm of SCX resin (5 µm Partisphere, Whatman) followed by 2.5 cm of reverse-phase C18 resin (5 µm ODS-AQ C18, Yamamura Chemical Laboratory). Analytical capillary columns (100 µm ID / 360 µm OD) were packed with 20 cm of reverse-phase C18 material behind a 5 µm ID pulled tip. MudPIT columns were loaded with 20 µg of peptide mixture, and a 10-hour MudPIT separation method was employed, consisting of one transfer step followed by seven separation steps (five 60-minute steps, one 120-minute step, and one 180-minute step). The transfer step consisted of a gradient up to 50% B over 20 min, followed by an increase to 100% B in 4 min, 100% B for 2 min, and re-equilibration with 100% A for 4 min. The five 60-minute gradients consisted of 2 min of salt injection (10%, 20%, 30%, 50%, and 70% C), followed by 2 min of 100% A, a linear gradient from 0 to 40% B over 45 min, and re-equilibration with 100% A for 10 min. The 120-minute gradient included 2 min of 100% C followed by 2 min of 100% A, a linear gradient from 0 to 50% B over 85 min, an increase to 100% B over 15 min, 100% B for 3 min, and re-equilibration with 100% A for 12 min. The final 180-minute gradient consisted of 5 min at 90% C / 10% B, followed by 4 min of 100% A, a gradient from 0 to 10% B over 15 min, 10 to 20% B over 60 min, 20 to 50% B over 60 min, an increase to 100% B over 15 min, 100% B for 10 min, and re-equilibration with 100% A for 10 min. The LTQ-Velos Orbitrap was operated in data-dependent acquisition (DDA) mode. Full MS1 scans were collected in the Orbitrap (300–1200 m/z range, 60K resolution, AGC target of 5×10⁵), and the 20 most abundant ions per scan were selected for CID MS2 in the ion trap (minimum intensity of 500, AGC target of 1×10⁴). Maximum injection times were set to 250 ms for MS1 and 100 ms for MS2. Dynamic exclusion was enabled with a repeat count of 1, repeat duration of 150 s, exclusion list size of 500, and exclusion duration of 120 s. d-Protein Characterization Peptide and protein identification was performed using the Integrated Proteomics Pipeline – IP2 (Integrated Proteomics Applications, Inc., www.integratedproteomics.com ). Tandem mass spectra were extracted from RAW files using RawXtract 1.9.9.2 (The gene replacement and quantitative mass spectrometry approaches validate guanosine monophosphate synthase as essential for Mycobacterium tuberculosis growth – ScienceDirect) and searched with ProLuCID v.1.3.1 (Xu T and Park SK, 2015) against the Mus musculus reference proteome database from UniProtKB, including reversed sequences. The search dataset included candidates for fully and semi-tryptic peptides, using cysteine carbamidomethylation as a static modification. Searches were performed with a precursor ion tolerance of 50 ppm and a fragment ion tolerance of 600 ppm. Peptide candidates were filtered using DTASelect 2.0 (Klammer & MacCoss, 2006 ), applying a peptide-spectrum match (PSM) mass deviation threshold of < 10 ppm, a minimum of two peptides per protein, and a false discovery rate (FDR) of 1% based on decoy counts. e-Protein–Protein Interactions (PPI) 7,768 proteins were identified. To evaluate the differences in co-expression between differentially expressed proteins identified in the proteomic analysis, a gene co-expression network analysis was performed. The choice to use co-expression is due to the fact that this analysis seeks to understand how proteins work together and not just individual expression changes (Carr, 2022). The correlation between each gene and its partners was calculated using the Pearson Correlation Coefficient (PCC). Differences in PCC values between groups was compared against 1,000 randomized co-expression difference lists obtained by permuting case and control labels, i.e., 1,000 random datasets (p-value ≤ 0.05). Additionally, the Weighted Gene Coexpression Network Analysis (WGCNA) approach was applied to identify coexpression modules; the software allows for the identification of functional modules and divides them by protein hub (Langfelder & Horvath, 2008 ). The resulting networks was visualized using Cytoscape (Cline et al., 2007 ), and differences in the topological properties of gene networks were examined to prioritize partners associated with inflammatory pathways in Alzheimer’s disease (AD) and to compare findings within hippocampal tissue. To identify broker and bridge genes (Son et al., 2003; Aluise et al., 2010), the Interactome database was used. Data analyses were conducted using R and Cytoscape software (v3.2), comparing lithium-treated groups (Li1, Li2) and controls (Li0) through gene ontology enrichment analysis. f-Gene Ontology and Analytical Methods The inflammatory pathways of the identified proteins were compared using Gene Ontology (GO) analysis through ClueGO 2.5.10, a Cytoscape 3.10.3 plug-in that visualizes non-redundant biological terms for large gene clusters within a functional network. ClueGO was used to perform single-cluster analyses as well as multiple-cluster (protein list) comparisons, applying the Benjamini & Hochberg False Discovery Rate correction (p ≤ 0.05) for statistical testing. The analyses included the Gene Ontology categories Biological Process, Cellular Component, and Molecular Function. After the comparison, we used KEGG to analyze the pi3k pathway and manually added related proteins that were not found in the analysis. Then, using a Venn diagram, we found the intersection of proteins present in the APP and Tau processes, both in wild-type and transgenic control specimens. The proteins found were then used as seeds in the STRING, and subsequently grown to the first 50 neighbors. Results To elucidate the PI3K-centered protein network, supported by beta-amyloid and tau signaling in Alzheimer's disease, the proteins analyzed in the proteome were selected using the Venny diagram. It was used to find the intersection between the interactions of tau protein with the PI3K pathway and amyloid beta protein with the phosphatidylinositol pathway. We found 157 proteins in common at the intersections of tau with PI3K and amyloid beta with PI3K, and then compared these with proteins from the control group (Li0) and transgenic group (Li0). Thus, demonstrating an important convergence between amyloid and tau signaling in PI3K-associated networks. Subsequently, we examined the functional organization of this network associated with PI3K using protein-protein interaction and gene ontology analyses. The results indicated that the PI3K pathway was involved in processes such as hormonal response, cell migration, cellular response to endogenous stimuli, positive regulation of the response to stimuli, and regulation of catalytic activity. We then consulted the Kyoto Encyclopedia of Genes and Genomes (KEGG) to identify which proteins from the phosphatidylinositol pathway should be included in the STRING enrichment analysis, along with MAPT and APP. KEGG, a database developed by the Institute of Biological Sciences at Kyoto University in Japan. KEGG provides analysis and visualization tools that facilitate the interpretation and exploration of biological data, and is therefore used to determine new seeds for analysis, revealing that the protein networks associated with PI3K cluster into functional units related to cell signaling, proteostasis, and translational control. To identify the main molecular units likely related to the lithium response within this network, we consulted KEGG. Eighteen proteins related to the PI3K pathway were found, including tau (MAPT) and beta-amyloid (APP) proteins, with a confidence interval of 0.7. These proteins were then used for enrichment in STRING, and analyzed, described below. Of these, we found 7 shared proteins (FKBP1A → mTOR, HSPs → proteostasis, RPLs → translation), suggesting that they represent binding centers for PI3K signaling in proteostatic regulation. In the protein-protein interaction (PPI) analysis, used to identify ontological interactions, only biological pathways with at least four interactions and statistical significance, with a confidence interval of 0.7, were selected. The following relationship between the seeds was found in figure 1: Subsequently, we grew these seeds with the first 50 neighbors, maintaining the confidence interval and obtained the following result shown in figure 1. We analyzed cellular localization of interactions, molecular functions and pathways of biological processes as described in Tables 1, 2 and 3. Figure 1: (1)Elements present in the phosphatidylinositol pathway and their interactions with each other. (2)Containing the elements present in the phosphatidylinositol pathway with the MAPT and APP proteins. *It was searched in uniprot and NCBI and no results were found for the organism Mus musculus and therefore it was removed from the list of official seeds. (3)Diagrammatic representation of the intersection of proteins present in beta-amyloid and Tau protein compared to WT and transgenic control groups. (4)Relationship between the seeds selected for analysis, with a confidence interval of 0.7. 5: Seeds with the first 50 neighbors created in STRING , with a confidence interval of 0.7. Table 1. Description of the most relevant cellular localization interactions. Table 2 . Description of the most relevant molecular function interactions. Table 3. Description of the most relevant biological process interactions. Subsequently, we evaluated how chronic lithium treatment modulates the expression of core proteins associated with PI3K in a dose-dependent manner. The seven proteins shared between the PI3K pathways with tau and beta-amyloid are: Fkbp 1a, Rash, Hspa1b, Hspa8, Rpl13, Rpl19, and Rpl24. We used these proteins to visualize differential expression in each group treated with different lithium doses, analyzing, through specific graphs, the variations in expression in response to the administered doses, as described in Figure 2. Thus, highlighting a non-linear and specific protein response to lithium dosage. Figure 2 . Selected proteins present in both samples. a) Expression of the FKB1A protein at different lithium concentrations; b) Expression of the HS71B protein at different lithium concentrations; c) Expression of the HSP7C protein at different lithium concentrations; d) Expression of the RASH protein at different lithium concentrations; e) Expression of the RL13 protein at different lithium concentrations; f) Expression of the RL19 protein at different lithium concentrations; g) Expression of the RL24 protein at different lithium concentrations Integrating these results into account, they indicate that lithium activates PI3K signaling in a non-linear fashion, but selectively modulates a PI3K-centered network that involves mTOR regulation, protein folding, and ribosomal function. Discussion In this study, we investigated how chronic lithium treatment influences hippocampal proteomic networks centered on PI3K signaling in a triple transgenic mouse model of Alzheimer’s disease. Using an integrative network-based approach, we identified a set of proteins linking amyloid-β and tau-associated pathways that were selectively modulated by lithium in a dose-dependent but non-linear manner. Rather than inducing uniform activation of PI3K signaling, lithium was associated with coordinated remodeling of interconnected molecular processes related to proteostasis, translational control, and cellular stress responses (Caccamo et al., 2010 ; Nixon, 2013 ). Seven proteins—FKBP1A, RAS-related proteins, heat shock proteins (HSPA1B and HSPA8), and ribosomal subunits (RPL13, RPL19, and RPL24)—emerged as shared nodes connecting PI3K signaling with amyloid and tau pathways. These proteins converge functionally on regulation of mTOR activity, protein folding, and ribosomal function, suggesting that lithium-sensitive effects occur at the level of proteostatic control rather than isolated signaling events (Saxton and Sabatini, 2017 ; Liu et al., 2022 ). The observation that subtherapeutic and therapeutic lithium doses produced distinct expression patterns further supports the notion that lithium acts through selective network modulation (Malhi et al., 2013 ). FKBP1A plays a key role in linking PI3K signaling to mTOR regulation through its interaction with rapamycin-sensitive complexes. Changes in FKBP1A expression across lithium doses are consistent with a model in which lithium influences mTOR-dependent control of protein synthesis and stress adaptation (Cline et al., 2007 ; Saxton and Sabatini, 2017 ). Similarly, the involvement of heat shock proteins highlights a potential role for lithium in maintaining protein folding capacity and chaperone-mediated quality control, processes that are compromised in Alzheimer’s disease (Hipp et al., 2019 ). Alterations in ribosomal proteins further suggest that lithium impacts translational machinery, which is increasingly recognized as a critical component of synaptic maintenance and neuronal resilience (Hernandez-Ortega et al., 2016 ). Importantly, our findings indicate that lithium-associated modulation of PI3K signaling does not follow a linear dose–response relationship. Instead, the observed proteomic patterns suggest threshold- and context-dependent effects, consistent with previous reports describing differential biological outcomes of lithium exposure at varying concentrations (Malhi et al., 2013 ; Forlenza et al., 2014 ). This non-linearity may be particularly relevant in the context of long-term treatment strategies aimed at minimizing toxicity while preserving neuroprotective potential. Several limitations should be acknowledged. The present study is based on proteomic associations and does not include direct functional validation of individual signaling nodes. In addition, only male mice were analyzed, and behavioral correlates were not assessed. These factors limit direct inference regarding functional outcomes and highlight the need for future studies incorporating cell-type–specific analyses, functional assays, and behavioral endpoints. In conclusion, our results support a model in which chronic lithium treatment is associated with remodeling of a PI3K–mTOR-centered proteostatic network in the hippocampus of an Alzheimer’s disease mouse model. By influencing interconnected pathways involved in protein synthesis, folding, and degradation, lithium may modulate molecular processes relevant to neurodegeneration (Caccamo et al., 2010 ; Nixon, 2013 ). While further validation is required, these findings provide mechanistic insight into how lithium-sensitive signaling networks intersect with core features of Alzheimer’s disease pathology. Conclusion We observed that the changes caused by lithium are dose-dependent, altering different proteins with distinct biological functions, but with a point of convergence: the mTOR pathway, which plays a fundamental role in neuronal health. In addition to the already known alterations of lithium in relation to the pi3k pathway, the effect of its use appeared beneficial; however, it is important to emphasize that there are side effects and more studies with humans are needed (Fig. 3 ). Declarations Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author Contribution VJRP supervised the study, obtained funding, and contributed to data analysis, interpretation of the results, and manuscript preparation.COPC performed the experiments, analyzed the data, and wrote the manuscript.MLHE contributed to manuscript writing, discussion of the results, and data analysis.JLJM and CWLCJ contributed to data analysis and interpretation of the results.OVF contributed to manuscript writing and discussion. All authors reviewed and approved the final version of the manuscript. Data Availability All data supporting the findings of this study are available within the paper. References Becegato M (2024) Silva à direita. Roedores fêmeas na neurociência comportamental: Revisão narrativa sobre as armadilhas metodológicas. Comportamento fisiológico. 1º de outubro de 284:114645. 10.1016/j.physbeh.2024.114645 Epub 2024 Jul. 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Physiol Rep 5(7):e13246. 10.14814/phy2.13246 PMID: 28408639; PMCID: PMC5392530 Wada A, Yokoo H, Yanagita T, Kobayashi H Lítio: terapêutica potencial contra lesões cerebrais agudas e doenças neurodegenerativas crônicas. J Pharmacol Sci. 2005 Dezembro; 99(4):307 – 21. 10.1254/jphs.crj05009x . Epub 2005 7 de dezembro. PMID: 16340157 Liu ZSJ, Truong TTT, Bortolasci CC, Spolding B, Panizzutti B, Swinton C, Kim JH, Kidnapillai S, Richardson MF, Gray L, Dean OM, McGee SL, Berk M, Walder K Efeitos de drogas psicotrópicas em genes ribossômicos e síntese de proteínas. Int J Mol Sci. 2022 28 de junho; 23(13):7180. 10.3390/ijms23137180 . PMID: 35806181; PMCID: PMC9266764. Molina JR, ADJEI, Alex A (2006) The Ras/Raf/MAPK pathway. Journal of Thoracic Oncology, [S.l.], v. 1, n. 1. 7–9. 10.1016/S1556-0864(15)31506-9 Rockenstein E, Torrance M, Adame A, Mante M, Bar-on P, Rose JB, Crews L, Masliah E (2007) Neuroprotective effects of regulators of the glycogen synthase kinase-3beta signaling pathway in a transgenic model of Alzheimer's disease are associated with reduced amyloid precursor protein phosphorylation. J Neurosci 27(8):1981–1991. 10.1523/JNEUROSCI.4321-06.2007 PMID: 17314294; PMCID: PMC6673566 ADICIONAR, AO MENDELEY Ates N, Caglayan A, Balcikanli Z, Sertel E, Beker MC, Dilsiz P, Caglayan AB, Celik S, Dasdelen MF, Caglayan B, Yigitbasi T, Ozbek H, Doeppner TR, Hermann DM, Kilic E (2022) Maio;351:113996 A fosforilação de PI3K / Akt em Thr308, mas não a fosforilação da MAPK quinase, medeia a neuroproteção induzida por lítio contra isquemia cerebral em camundongos. Exp Neurol. 10.1016/j.expneurol.2022.113996 . Epub 2022 2 de fevereiro. PMID: 35122865 Hino M, Kunii Y, Shishido R, Nagaoka A, Matsumoto J, Akatsu, Hiroyasu, Hashizume, Yoshio, Hayashi, Hideki, Kakita, Akiyoshi, Tomita, Hiroaki, Yabe, Hirooki (2024) Marked alteration of phosphoinositide signaling-associated molecules in postmortem prefrontal cortex with bipolar disorder. Neuropsychopharmacol Rep 44. 10.1002/npr2.12409 Caccamo A, Majumder S, Richardson A, Strong R, Oddo S (2010) Molecular interplay between mammalian target of rapamycin (mTOR), amyloid-β, and tau: effects on cognitive impairments. J Biol Chem 285(15):13107–13120. 10.1074/jbc.M110.100420 PMID: 20178983; PMCID: PMC2852950 Cline EN, Bicca MA, Viola KL, Klein WL (2018) The Amyloid-β Oligomer Hypothesis: Beginning of the Third Decade. J Alzheimers Dis 64(s1):S567–S610. 10.3233/JAD-179941 Forlenza OV, De-Paula VJR, Diniz BS (2014) Neuroprotective effects of lithium: implications for the treatment of Alzheimer’s disease and related neurodegenerative disorders. ACS Chem Neurosci. ;5(6):443–450. 10.1021/cn5000309 . PMID: 24720827 Hernandez-Ortega K, Garcia-Esparcia P, Gil L, Lucas JJ, Ferrer I (2016) Altered machinery of protein synthesis in Alzheimer’s: from the nucleolus to the ribosome. Brain Pathol. ;26(5):593–605. 10.1111/bpa.12335 . PMID: 26503462 Hipp MS, Kasturi P, Hartl FU (2019) The proteostasis network and its decline in ageing. Nat Rev Mol Cell Biol. ;20(7):421–435. 10.1038/s41580-019-0101-y . PMID: 30737470 Liu ZSJ, Truong TTT, Bortolasci CC, Spolding B, Panizzutti B, Swinton C, Kim JH, Kidnapillai S, Richardson MF, Gray L, Dean OM, McGee SL, Berk M, Walder K (2022) Effects of psychotropic drugs on ribosomal genes and protein synthesis. Int J Mol Sci 23(13):7180. 10.3390/ijms23137180 PMID: 35806181; PMCID: PMC9266764 Malhi GS, Tanious M, Das P, Coulston CM, Berk M (2013) Potential mechanisms of action of lithium in bipolar disorder: current understanding. CNS Drugs. ;27(2):135–153. 10.1007/s40263-013-0039-0 . PMID: 23371914 Nixon RA (2013) The role of autophagy in neurodegenerative disease. Nat Med. ;19(8):983–997. 10.1038/nm.3232 . PMID: 23921753 Saxton RA, Sabatini DM (2017) mTOR signaling in growth, metabolism, and disease. Cell. ;168(6):960–976. 10.1016/j.cell.2017.02.004 . PMID: 28283069 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 14 May, 2026 Reviews received at journal 12 May, 2026 Reviews received at journal 12 May, 2026 Reviews received at journal 11 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviewers invited by journal 10 Apr, 2026 Editor assigned by journal 07 Apr, 2026 Submission checks completed at journal 07 Apr, 2026 First submitted to journal 09 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. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9077098","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":625003123,"identity":"7af747ba-2d20-4833-b094-2d58e6318166","order_by":0,"name":"Caíque de Oliveira Portugal Couto","email":"","orcid":"","institution":"Institute of Psychiatry of the Hospital das Clínicas of the Faculty of Medicine of the University of São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Caíque","middleName":"de Oliveira Portugal","lastName":"Couto","suffix":""},{"id":625003124,"identity":"ec32df17-31c5-4564-a12a-d52130705e87","order_by":1,"name":"Maria Luiza Hass das Eiras","email":"","orcid":"","institution":"Institute of Psychiatry of the Hospital das Clínicas of the Faculty of Medicine of the University of São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Luiza Hass das","lastName":"Eiras","suffix":""},{"id":625003127,"identity":"232eba7d-c049-4d29-aeef-1dc331c30089","order_by":2,"name":"João Lucas Juliao de Morais","email":"","orcid":"","institution":"Institute of Psychiatry of the Hospital das Clínicas of the Faculty of Medicine of the University of São Paulo","correspondingAuthor":false,"prefix":"","firstName":"João","middleName":"Lucas Juliao","lastName":"de Morais","suffix":""},{"id":625003129,"identity":"cf32c71e-73c3-418b-b738-9230b0defe68","order_by":3,"name":"Carlos Wagner Leal Cordeiro Júnior","email":"","orcid":"","institution":"Institute of Psychiatry of the Hospital das Clínicas of the Faculty of Medicine of the University of São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"Wagner Leal Cordeiro","lastName":"Júnior","suffix":""},{"id":625003131,"identity":"f414b65c-5e50-4ad5-a955-cafed336bb28","order_by":4,"name":"Orestes Vicente Forlenza","email":"","orcid":"","institution":"Institute of Psychiatry of the Hospital das Clínicas of the Faculty of Medicine of the University of São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Orestes","middleName":"Vicente","lastName":"Forlenza","suffix":""},{"id":625003133,"identity":"b9954e51-7cab-4c5e-8502-41ac78b30f1b","order_by":5,"name":"VANESSA De Jesus Rodrigues de Paula","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYBADZjD5gMEGSDI2HiBeSwJDGkhLA1FaGKBaDoNpvFoMjp89+PDHHzt289nNzyQSas7brW0/DLSlxiYap5YzecnGvG3JzDJ3jplJJBy7nbztTCJQy7G03AYcWiQbcsykGRuYmSUkEoBa2G4nmx0AamFsOIxbS/8b858//tQDtaR/k0j4dy7Z7PxD/Fr4JXLMGHjYDgO15JhJJLYdsDO7QcAWfok3xtK8bceZJWTOFFsk9iUnmN0A2pKAxy9s/DmGH3/8qU6WkG7feOPDNzt7s/PpDx98qLHBqQUGkhkkIIxEsMoEAspBwA6mxZ4IxaNgFIyCUTDCAACca19GJqaeIQAAAABJRU5ErkJggg==","orcid":"","institution":"Institute of Psychiatry of the Hospital das Clínicas of the Faculty of Medicine of the University of São Paulo","correspondingAuthor":true,"prefix":"","firstName":"VANESSA","middleName":"De Jesus Rodrigues","lastName":"de Paula","suffix":""}],"badges":[],"createdAt":"2026-03-09 22:08:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9077098/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9077098/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107367813,"identity":"1bdd45c8-4a03-415c-9c3d-44f964720a1b","added_by":"auto","created_at":"2026-04-20 20:34:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1822254,"visible":true,"origin":"","legend":"\u003cp\u003e(1)Elements present in the phosphatidylinositol pathway and their interactions with each other. (2)Containing the elements present in the phosphatidylinositol pathway with the MAPT and APP proteins. *It was searched in uniprot and NCBI and no results were found for the organism Mus musculus and therefore it was removed from the list of official seeds. (3)Diagrammatic representation of the intersection of proteins present in beta-amyloid and Tau protein compared to WT and transgenic control groups. (4)Relationship between the seeds selected for analysis, with a confidence interval of 0.7. 5: Seeds with the first 50 neighbors created in STRING , with a confidence interval of 0.7.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9077098/v1/274c44252b8b8c0324b54a18.png"},{"id":107487884,"identity":"e8eabe54-3af2-44f6-afae-ad9822c52a70","added_by":"auto","created_at":"2026-04-22 02:43:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":9430249,"visible":true,"origin":"","legend":"\u003cp\u003eSelected proteins present in both samples. a) Expression of the FKB1A protein at different lithium concentrations; b) Expression of the HS71B protein at different lithium concentrations; c) Expression of the HSP7C protein at different lithium concentrations; d) Expression of the RASH protein at different lithium concentrations; e) Expression of the RL13 protein at different lithium concentrations; f) Expression of the RL19 protein at different lithium concentrations; g) Expression of the RL24 protein at different lithium concentrations\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9077098/v1/a125a39a204af4b36b089170.png"},{"id":107487599,"identity":"d04645bc-e632-444a-879a-2c4981554b95","added_by":"auto","created_at":"2026-04-22 02:42:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":16214485,"visible":true,"origin":"","legend":"\u003cp\u003eSynthesis of biological processes: PI3K, and proteins Fkbp1, Ras, chaperone family and ribosomal family.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9077098/v1/8c3d6fa6dd9d7845de79e263.png"},{"id":107489227,"identity":"3a3a2bcd-98c1-4824-a934-f9419a30fbc7","added_by":"auto","created_at":"2026-04-22 02:46:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":19776872,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9077098/v1/0f20a99f-789d-45c6-89e9-7d67d2f0b96a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Chronic lithium exposure reshapes PI3K–mTOR-linked proteostatic networks in the hippocampus of an Alzheimer’s disease mouse model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) is the most prevalent cause of dementia worldwide and represents a major challenge for aging societies (Xinyi Zhang et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The disease is neuropathologically defined by extracellular amyloid-βeta (aβ) plaques and intracellular neurofibrillary tangles composed of hyperphosphorylated tau, which together disrupt synaptic integrity and neuronal survival. Beyond these hallmark lesions, AD is increasingly recognized as a disorder of intracellular signaling and proteostatic imbalance (Zhang 2021).\u003c/p\u003e \u003cp\u003eAmong the signaling cascades implicated in AD, the phosphatidylinositol 3-kinase (PI3K) pathway plays a central role in regulating neuronal metabolism, stress responses, protein synthesis, and survival through downstream effectors such as Akt and the mechanistic target of rapamycin (mTOR). Dysregulation of PI3K\u0026ndash;mTOR signaling has been linked to both amyloid- and tau-mediated toxicity, positioning this pathway as a potential therapeutic target (Nishtha Singh 2024 and Matysek 2022).\u003c/p\u003e \u003cp\u003eLithium, a long-established mood stabilizer, has been shown to modulate several intracellular pathways relevant to neurodegeneration, including inhibition of glycogen synthase kinase-3β and modulation of phosphoinositide-dependent signaling. Although experimental evidence suggests neuroprotective effects of lithium in AD models, the molecular networks reshaped by chronic lithium exposure in the hippocampus remain poorly defined.\u003c/p\u003e \u003cp\u003eIn the present study, we applied an integrative proteomic and network-based approach to investigate how long-term lithium treatment modulates hippocampal PI3K-associated protein networks in a triple transgenic mouse model of AD. By focusing on the intersection of amyloid-β and tau signaling with PI3K pathways, we aimed to identify lithium-sensitive molecular processes relevant to proteostasis and neuronal resilience.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ea-Transgenic Animals\u003c/h2\u003e \u003cp\u003eThe present project was conducted using a CEUA-approved sample (protocol: 1293/09). No biological material collection was required. The 48 mice (from The Jackson Laboratory) were male, as the necessary differential analysis of the menstrual cycle of females would not be possible; however, this is a limitation of the study. Currently, the importance of including females is understood, even with the necessary additional control. (bacegato 2024) Of the mice, 24 were B6129SF2/J (wild type; WT) and 24 were B6;129-Psen1tm1Mpm Tg(APPSwe,tauP301L)1Lfa/Mmjax (3xTg-AD; Tg). At the beginning of the experiment, the animals were approximately 12 weeks old and were maintained at the Central Animal Facility of the School of Medicine, USP, throughout the treatment period, under controlled temperature (22\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C) and relative humidity (50\u0026ndash;60%), with a 12 h light/12 h dark cycle, and free access to food and fresh water ad libitum during the entire study. The animals (WT and Tg) were divided into three subgroups: Li0 (standard chow); Li1 (chow containing 1.0 g lithium carbonate/kg); and Li2 (chow containing 2.0 g lithium carbonate/kg). The treatment lasted for eight months. After this period, the animals were euthanized by decapitation with a guillotine. The use of the guillotine is justified because it is a method that keeps proteins stable for up to 4 hours, thus being ideal for proteome data. Brain tissue was removed from the cranial cavity and the hippocampi were dissected (Hunsucker 2008).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eb-Protein Analysis\u003c/h3\u003e\n\u003cp\u003eThe protein fraction (100 \u0026micro;g) from hippocampal tissues was precipitated using methanol/chloroform, according to Wessel and Fl\u0026uuml;gge (Wessel \u0026amp; Fl\u0026uuml;gge, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). The protein pellets were resuspended in 100 mM Tris buffer, pH 8.5, containing 8 M urea, and digested following a protocol adapted from Klammer and MacCoss (Klammer \u0026amp; MacCoss, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Briefly, disulfide bonds were reduced with 5 mM tris(2-carboxyethyl) phosphine hydrochloride (TCEP) for 20 min at 37\u0026deg;C, and cysteines were subsequently alkylated with 25 mM iodoacetamide (IAM) for 20 min at room temperature in the dark. Urea was then diluted to 2 M with 100 mM Tris buffer (pH 8.5), and proteins were digested with trypsin (mass) at a 1:100 enzyme-to-protein ratio in the presence of 1 mM CaCl₂, by overnight incubation at 37\u0026deg;C. After overnight trypsin digestion, the reaction was quenched with 2% formic acid, and samples were stored at \u0026minus;\u0026thinsp;20\u0026deg;C until use.\u003c/p\u003e\n\u003ch3\u003ec-Liquid Chromatography/Mass Spectrometry (LC-MS)\u003c/h3\u003e\n\u003cp\u003eLC-MS/MS experiments were performed using an HP 1100 Series quaternary HPLC pump (Agilent Technologies) coupled to an LTQ-Velos Orbitrap mass spectrometer (Thermo Scientific). Electrospray ionization was conducted directly from the tip of the analytical column using Solution A (5% acetonitrile and 0.1% formic acid), Solution B (80% acetonitrile and 0.1% formic acid), and Solution C (500 mM ammonium acetate, 5% acetonitrile, and 0.1% formic acid). The flow rate was approximately 300 nL/min. Biphasic MudPIT columns (150 \u0026micro;m ID / 360 \u0026micro;m OD fused silica capillaries) were prepared in-house by sequential packing of 2.5 cm of SCX resin (5 \u0026micro;m Partisphere, Whatman) followed by 2.5 cm of reverse-phase C18 resin (5 \u0026micro;m ODS-AQ C18, Yamamura Chemical Laboratory). Analytical capillary columns (100 \u0026micro;m ID / 360 \u0026micro;m OD) were packed with 20 cm of reverse-phase C18 material behind a 5 \u0026micro;m ID pulled tip.\u003c/p\u003e \u003cp\u003e MudPIT columns were loaded with 20 \u0026micro;g of peptide mixture, and a 10-hour MudPIT separation method was employed, consisting of one transfer step followed by seven separation steps (five 60-minute steps, one 120-minute step, and one 180-minute step). The transfer step consisted of a gradient up to 50% B over 20 min, followed by an increase to 100% B in 4 min, 100% B for 2 min, and re-equilibration with 100% A for 4 min. The five 60-minute gradients consisted of 2 min of salt injection (10%, 20%, 30%, 50%, and 70% C), followed by 2 min of 100% A, a linear gradient from 0 to 40% B over 45 min, and re-equilibration with 100% A for 10 min. The 120-minute gradient included 2 min of 100% C followed by 2 min of 100% A, a linear gradient from 0 to 50% B over 85 min, an increase to 100% B over 15 min, 100% B for 3 min, and re-equilibration with 100% A for 12 min. The final 180-minute gradient consisted of 5 min at 90% C / 10% B, followed by 4 min of 100% A, a gradient from 0 to 10% B over 15 min, 10 to 20% B over 60 min, 20 to 50% B over 60 min, an increase to 100% B over 15 min, 100% B for 10 min, and re-equilibration with 100% A for 10 min. The LTQ-Velos Orbitrap was operated in data-dependent acquisition (DDA) mode. Full MS1 scans were collected in the Orbitrap (300\u0026ndash;1200 m/z range, 60K resolution, AGC target of 5\u0026times;10⁵), and the 20 most abundant ions per scan were selected for CID MS2 in the ion trap (minimum intensity of 500, AGC target of 1\u0026times;10⁴). Maximum injection times were set to 250 ms for MS1 and 100 ms for MS2. Dynamic exclusion was enabled with a repeat count of 1, repeat duration of 150 s, exclusion list size of 500, and exclusion duration of 120 s.\u003c/p\u003e\n\u003ch3\u003ed-Protein Characterization\u003c/h3\u003e\n\u003cp\u003ePeptide and protein identification was performed using the Integrated Proteomics Pipeline \u0026ndash; IP2 (Integrated Proteomics Applications, Inc., \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.integratedproteomics.com\" target=\"_blank\"\u003ewww.integratedproteomics.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.integratedproteomics.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Tandem mass spectra were extracted from RAW files using RawXtract 1.9.9.2 (The gene replacement and quantitative mass spectrometry approaches validate guanosine monophosphate synthase as essential for Mycobacterium tuberculosis growth \u0026ndash; ScienceDirect) and searched with ProLuCID v.1.3.1 (Xu T and Park SK, 2015) against the Mus musculus reference proteome database from UniProtKB, including reversed sequences. The search dataset included candidates for fully and semi-tryptic peptides, using cysteine carbamidomethylation as a static modification. Searches were performed with a precursor ion tolerance of 50 ppm and a fragment ion tolerance of 600 ppm. Peptide candidates were filtered using DTASelect 2.0 (Klammer \u0026amp; MacCoss, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), applying a peptide-spectrum match (PSM) mass deviation threshold of \u0026lt;\u0026thinsp;10 ppm, a minimum of two peptides per protein, and a false discovery rate (FDR) of 1% based on decoy counts.\u003c/p\u003e\n\u003ch3\u003ee-Protein–Protein Interactions (PPI)\u003c/h3\u003e\n\u003cp\u003e7,768 proteins were identified. To evaluate the differences in co-expression between differentially expressed proteins identified in the proteomic analysis, a gene co-expression network analysis was performed. The choice to use co-expression is due to the fact that this analysis seeks to understand how proteins work together and not just individual expression changes (Carr, 2022). The correlation between each gene and its partners was calculated using the Pearson Correlation Coefficient (PCC). Differences in PCC values between groups was compared against 1,000 randomized co-expression difference lists obtained by permuting case and control labels, i.e., 1,000 random datasets (p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05). Additionally, the Weighted Gene Coexpression Network Analysis (WGCNA) approach was applied to identify coexpression modules; the software allows for the identification of functional modules and divides them by protein hub (Langfelder \u0026amp; Horvath, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The resulting networks was visualized using Cytoscape (Cline et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and differences in the topological properties of gene networks were examined to prioritize partners associated with inflammatory pathways in Alzheimer\u0026rsquo;s disease (AD) and to compare findings within hippocampal tissue. To identify broker and bridge genes (Son et al., 2003; Aluise et al., 2010), the Interactome database was used. Data analyses were conducted using R and Cytoscape software (v3.2), comparing lithium-treated groups (Li1, Li2) and controls (Li0) through gene ontology enrichment analysis.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ef-Gene Ontology and Analytical Methods\u003c/h2\u003e \u003cp\u003eThe inflammatory pathways of the identified proteins were compared using Gene Ontology (GO) analysis through ClueGO 2.5.10, a Cytoscape 3.10.3 plug-in that visualizes non-redundant biological terms for large gene clusters within a functional network. ClueGO was used to perform single-cluster analyses as well as multiple-cluster (protein list) comparisons, applying the Benjamini \u0026amp; Hochberg False Discovery Rate correction (p\u0026thinsp;\u0026le;\u0026thinsp;0.05) for statistical testing. The analyses included the Gene Ontology categories Biological Process, Cellular Component, and Molecular Function. After the comparison, we used KEGG to analyze the pi3k pathway and manually added related proteins that were not found in the analysis. Then, using a Venn diagram, we found the intersection of proteins present in the APP and Tau processes, both in wild-type and transgenic control specimens. The proteins found were then used as seeds in the STRING, and subsequently grown to the first 50 neighbors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTo elucidate the PI3K-centered protein network, supported by beta-amyloid and tau signaling in Alzheimer\u0026apos;s disease, the proteins analyzed in the proteome were selected using the Venny diagram. It was used to find the intersection between the interactions of tau protein with the PI3K pathway and amyloid beta protein with the phosphatidylinositol pathway. We found 157 proteins in common at the intersections of tau with PI3K and amyloid beta with PI3K, and then compared these with proteins from the control group (Li0) and transgenic group (Li0). Thus, demonstrating an important convergence between amyloid and tau signaling in PI3K-associated networks.\u003c/p\u003e\n\u003cp\u003eSubsequently, we examined the functional organization of this network associated with PI3K using protein-protein interaction and gene ontology analyses. The results indicated that the PI3K pathway was involved in processes such as hormonal response, cell migration, cellular response to endogenous stimuli, positive regulation of the response to stimuli, and regulation of catalytic activity. We then consulted the Kyoto Encyclopedia of Genes and Genomes (KEGG) to identify which proteins from the phosphatidylinositol pathway should be included in the STRING enrichment analysis, along with MAPT and APP. KEGG, a database developed by the Institute of Biological Sciences at Kyoto University in Japan. \u0026nbsp;KEGG provides analysis and visualization tools that facilitate the interpretation and exploration of biological data, and is therefore used to determine new seeds for analysis, revealing that the protein networks associated with PI3K cluster into functional units related to cell signaling, proteostasis, and translational control.\u003c/p\u003e\n\u003cp\u003eTo identify the main molecular units likely related to the lithium response within this network, we consulted KEGG. Eighteen proteins related to the PI3K pathway were found, including tau (MAPT) and beta-amyloid (APP) proteins, with a confidence interval of 0.7. These proteins were then used for enrichment in STRING, and analyzed, described below. Of these, we found 7 shared proteins (FKBP1A \u0026rarr; mTOR, HSPs \u0026rarr; proteostasis, RPLs \u0026rarr; translation), suggesting that they represent binding centers for PI3K signaling in proteostatic regulation.\u003c/p\u003e\n\u003cp\u003eIn the protein-protein interaction (PPI) analysis, used to identify ontological interactions, only biological pathways with at least four interactions and statistical significance, with a confidence interval of 0.7, were selected. The following relationship between the seeds was found in figure 1: Subsequently, we grew these seeds with the first 50 neighbors, maintaining the confidence interval and obtained the following result shown in figure 1.\u003c/p\u003e\n\u003cp\u003eWe analyzed cellular localization of interactions, molecular functions and pathways of biological processes as described in Tables 1, 2 and 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1:\u003c/strong\u003e (1)Elements present in the phosphatidylinositol pathway and their interactions with each other. (2)Containing the elements present in the phosphatidylinositol pathway with the MAPT and APP proteins. *It was searched in uniprot and NCBI and no results were found for the organism Mus musculus and therefore it was removed from the list of official seeds. (3)Diagrammatic representation of the intersection of proteins present in beta-amyloid and Tau protein compared to WT and transgenic control groups. (4)Relationship between the seeds selected for analysis, with a confidence interval of 0.7. 5: Seeds with the first 50 neighbors created in STRING , with a confidence interval of 0.7.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e\u0026nbsp; Description of the most relevant cellular localization interactions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img1776717106.png\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Description of the most relevant molecular function interactions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img1776717114.png\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Description of the most relevant biological process interactions.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img1776717130.png\"\u003e\u003c/p\u003e\n\u003cp\u003eSubsequently, we evaluated how chronic lithium treatment modulates the expression of core proteins associated with PI3K in a dose-dependent manner. The seven proteins shared between the PI3K pathways with tau and beta-amyloid are: Fkbp 1a, Rash, Hspa1b, Hspa8, Rpl13, Rpl19, and Rpl24. We used these proteins to visualize differential expression in each group treated with different lithium doses, analyzing, through specific graphs, the variations in expression in response to the administered doses, as described in Figure 2. Thus, highlighting a non-linear and specific protein response to lithium dosage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2\u003c/strong\u003e. Selected proteins present in both samples. a) Expression of the FKB1A protein at different lithium concentrations; b) Expression of the HS71B protein at different lithium concentrations; c) Expression of the HSP7C protein at different lithium concentrations; d) Expression of the RASH protein at different lithium concentrations; e) Expression of the RL13 protein at different lithium concentrations; f) Expression of the RL19 protein at different lithium concentrations; g) Expression of the RL24 protein at different lithium concentrations\u003c/p\u003e\n\u003cp\u003eIntegrating these results into account, they indicate that lithium activates PI3K signaling in a non-linear fashion, but selectively modulates a PI3K-centered network that involves mTOR regulation, protein folding, and ribosomal function.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we investigated how chronic lithium treatment influences hippocampal proteomic networks centered on PI3K signaling in a triple transgenic mouse model of Alzheimer\u0026rsquo;s disease. Using an integrative network-based approach, we identified a set of proteins linking amyloid-β and tau-associated pathways that were selectively modulated by lithium in a dose-dependent but non-linear manner. Rather than inducing uniform activation of PI3K signaling, lithium was associated with coordinated remodeling of interconnected molecular processes related to proteostasis, translational control, and cellular stress responses (Caccamo et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Nixon, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeven proteins\u0026mdash;FKBP1A, RAS-related proteins, heat shock proteins (HSPA1B and HSPA8), and ribosomal subunits (RPL13, RPL19, and RPL24)\u0026mdash;emerged as shared nodes connecting PI3K signaling with amyloid and tau pathways. These proteins converge functionally on regulation of mTOR activity, protein folding, and ribosomal function, suggesting that lithium-sensitive effects occur at the level of proteostatic control rather than isolated signaling events (Saxton and Sabatini, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The observation that subtherapeutic and therapeutic lithium doses produced distinct expression patterns further supports the notion that lithium acts through selective network modulation (Malhi et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFKBP1A plays a key role in linking PI3K signaling to mTOR regulation through its interaction with rapamycin-sensitive complexes. Changes in FKBP1A expression across lithium doses are consistent with a model in which lithium influences mTOR-dependent control of protein synthesis and stress adaptation (Cline et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Saxton and Sabatini, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Similarly, the involvement of heat shock proteins highlights a potential role for lithium in maintaining protein folding capacity and chaperone-mediated quality control, processes that are compromised in Alzheimer\u0026rsquo;s disease (Hipp et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Alterations in ribosomal proteins further suggest that lithium impacts translational machinery, which is increasingly recognized as a critical component of synaptic maintenance and neuronal resilience (Hernandez-Ortega et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImportantly, our findings indicate that lithium-associated modulation of PI3K signaling does not follow a linear dose\u0026ndash;response relationship. Instead, the observed proteomic patterns suggest threshold- and context-dependent effects, consistent with previous reports describing differential biological outcomes of lithium exposure at varying concentrations (Malhi et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Forlenza et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This non-linearity may be particularly relevant in the context of long-term treatment strategies aimed at minimizing toxicity while preserving neuroprotective potential.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. The present study is based on proteomic associations and does not include direct functional validation of individual signaling nodes. In addition, only male mice were analyzed, and behavioral correlates were not assessed. These factors limit direct inference regarding functional outcomes and highlight the need for future studies incorporating cell-type\u0026ndash;specific analyses, functional assays, and behavioral endpoints.\u003c/p\u003e \u003cp\u003eIn conclusion, our results support a model in which chronic lithium treatment is associated with remodeling of a PI3K\u0026ndash;mTOR-centered proteostatic network in the hippocampus of an Alzheimer\u0026rsquo;s disease mouse model. By influencing interconnected pathways involved in protein synthesis, folding, and degradation, lithium may modulate molecular processes relevant to neurodegeneration (Caccamo et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Nixon, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). While further validation is required, these findings provide mechanistic insight into how lithium-sensitive signaling networks intersect with core features of Alzheimer\u0026rsquo;s disease pathology.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe observed that the changes caused by lithium are dose-dependent, altering different proteins with distinct biological functions, but with a point of convergence: the mTOR pathway, which plays a fundamental role in neuronal health. In addition to the already known alterations of lithium in relation to the pi3k pathway, the effect of its use appeared beneficial; however, it is important to emphasize that there are side effects and more studies with humans are needed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eVJRP supervised the study, obtained funding, and contributed to data analysis, interpretation of the results, and manuscript preparation.COPC performed the experiments, analyzed the data, and wrote the manuscript.MLHE contributed to manuscript writing, discussion of the results, and data analysis.JLJM and CWLCJ contributed to data analysis and interpretation of the results.OVF contributed to manuscript writing and discussion. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data supporting the findings of this study are available within the paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBecegato M (2024) Silva \u0026agrave; direita. Roedores f\u0026ecirc;meas na neuroci\u0026ecirc;ncia comportamental: Revis\u0026atilde;o narrativa sobre as armadilhas metodol\u0026oacute;gicas. Comportamento fisiol\u0026oacute;gico. 1\u0026ordm; de outubro de 284:114645. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.physbeh.2024.114645\u003c/span\u003e\u003cspan address=\"10.1016/j.physbeh.2024.114645\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eEpub 2024 Jul. 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PMID: 28283069\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-neurobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"moln","sideBox":"Learn more about [Molecular Neurobiology](https://www.springer.com/journal/12035)","snPcode":"12035","submissionUrl":"https://submission.nature.com/new-submission/12035/3","title":"Molecular Neurobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Phosphatidylinositol-3-kinase, Alzheimer's disease, Lithium, Ontological pathways, Neuroplasticity","lastPublishedDoi":"10.21203/rs.3.rs-9077098/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9077098/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) involves a progressive loss of neuronal integrity in which chronic neuroinflammation is increasingly recognized as a contributing factor. Sustained activation of innate immune pathways, including Toll-like receptor 4 (TLR4) signaling, has been linked to microglial reactivity and hippocampal dysfunction in experimental models of the disease. Lithium has been reported to exert neuroprotective and anti-inflammatory effects, yet the molecular pathways underlying these actions in AD remain poorly defined. Here, we investigated how long-term lithium exposure influences hippocampal protein networks in a triple transgenic mouse model of AD (3xTg-AD). Hippocampal tissue from wild-type and 3xTg-AD mice treated chronically with subtherapeutic (1 mM) or therapeutic (2 mM) lithium concentrations for eight months was analyzed using liquid chromatography\u0026ndash;tandem mass spectrometry. Proteomic profiling revealed widespread lithium-associated changes in proteins related to immune response, cellular stress, and inflammatory regulation. Network-based analyses highlighted components of the TLR4/NF-κB and MAPK signaling pathways as prominent nodes that were reduced in lithium-treated animals relative to controls. Together, these data indicate that chronic lithium treatment is associated with a remodeling of hippocampal inflammatory signaling in a mouse model of AD. Although additional functional studies will be necessary to establish mechanistic links, the present findings support the view that lithium-sensitive immune pathways may be relevant to the modulation of neuroinflammatory processes in Alzheimer\u0026rsquo;s disease.\u003c/p\u003e","manuscriptTitle":"Chronic lithium exposure reshapes PI3K–mTOR-linked proteostatic networks in the hippocampus of an Alzheimer’s disease mouse model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 20:34:38","doi":"10.21203/rs.3.rs-9077098/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-14T08:17:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T20:46:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T20:21:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T10:15:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314488106940646378300475270843251473988","date":"2026-05-04T05:41:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"194626289526030138990420701274742692967","date":"2026-05-01T16:54:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104548339876983611024712648209691544896","date":"2026-04-29T15:33:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-10T07:25:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-08T01:13:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-08T01:12:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Neurobiology","date":"2026-03-09T21:59:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-neurobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"moln","sideBox":"Learn more about [Molecular Neurobiology](https://www.springer.com/journal/12035)","snPcode":"12035","submissionUrl":"https://submission.nature.com/new-submission/12035/3","title":"Molecular Neurobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0f53cd8b-5016-4787-8cb9-3ce1bd14b0f7","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-14T08:17:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T20:46:34+00:00","index":63,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T20:21:52+00:00","index":62,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T10:15:19+00:00","index":61,"fulltext":""},{"type":"reviewerAgreed","content":"314488106940646378300475270843251473988","date":"2026-05-04T05:41:43+00:00","index":59,"fulltext":""},{"type":"reviewerAgreed","content":"194626289526030138990420701274742692967","date":"2026-05-01T16:54:07+00:00","index":56,"fulltext":""},{"type":"reviewerAgreed","content":"104548339876983611024712648209691544896","date":"2026-04-29T15:33:48+00:00","index":47,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T08:25:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 20:34:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9077098","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9077098","identity":"rs-9077098","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}