The UNC5C T835M mutation associated with Alzheimer’s disease leads to neurodegeneration involving oxidative stress and hippocampal atrophy in aged mice

The UNC5C T835M mutation associated with Alzheimer’s disease leads to neurodegeneration involving oxidative stress and hippocampal atrophy in aged mice | 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 Research Article The UNC5C T835M mutation associated with Alzheimer’s disease leads to neurodegeneration involving oxidative stress and hippocampal atrophy in aged mice Devi Krishna Priya Karunakaran, Makenna Ley, Joanna Guo, Ammaarah Khatri, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5462618/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Jun, 2025 Read the published version in Molecular Neurodegeneration → Version 1 posted 5 You are reading this latest preprint version Abstract Alzheimer’s disease (AD) is characterized by amyloid plaques, neurofibrillary tangles, and synaptic and neuronal loss. Recently, a rare autosomal dominant coding mutation, T835M, in the Un-coordinated 5c (UNC5C) netrin receptor gene was segregated with late-onset AD (LOAD). Overexpression of T835M in primary hippocampal neurons increased cell death in response to neurotoxic stimuli including beta-amyloid (Aβ) suggesting a mechanism by which T835M may confer increased risk of LOAD. However, the molecular mechanism of T835M-mediated cell death remained under explored. Toward this end, we generated a mouse T835M knock-in (KI) model and employed biochemical and histological analyses to understand the molecular mechanism of T835M-mediated pathogenesis in late onset Alzheimer's disease. We show that homozygous KI mice have significantly reduced hippocampal volume, increased ventricular volume, dendritic disorganization (CA1 region) and reduced UNC5C protein level by 12–18 months of age. Further, we show that the neuronal cell death is observed in the KI mice by 12 months of age by TUNEL analysis and activated Caspase 3/7 assay. Proteomic analysis of hippocampal samples showed upregulation of oxidative stress and downregulation of chaperone proteins at 18 months corroborating the biochemical and histological results showing increased c-Jun N-terminal Kinase (JNK) phosphorylation NADPH oxidase, and decreased Netrin1 levels. Moreover, Unc5c KI/KI mice also show morphological changes in the astrocytes with increased number of branched processes, reduced GFAP levels, and significantly increased activation of microglia. Overall, these results suggest that T835M mutation causes neurodegeneration by creating an oxidative stress environment leading to synaptic degeneration and weakened astrocytes, thereby leading to neuronal cell death via apoptosis. Furthermore, to assess the effects of amyloid pathology on the mutation, we crossed Unc5c KI/KI mice with App NL−G−F/NL−G−F mice and observed an exacerbation of mutation-associated changes along with increased levels of Aβ 42 , suggesting that the T835M mutation increases the susceptibility of neurons to cell death and elevated Aβ 42 levels, thus promoting AD pathogenesis. Understanding the molecular mechanism of cell death in regions susceptible to neurodegeneration such as the hippocampus could shed light on the players and pathways involved in cell death in AD pathogenesis and therefore could inform therapeutic approaches for AD. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Alzheimer’s disease (AD) is a progressive neurodegenerative disease that is the 6th leading cause of mortality in the US and is the most common cause of dementia in the geriatric population, accounting for 60–80% of all cases of dementia. It primarily affects the brain and causes gradual decline of cognitive abilities. Currently, about 6.7 million Americans are reported to live with AD and it is estimated to reach 13 million by 2050 ( https://alz.org ). The main hallmarks of the disease are amyloid plaques and neurofibrillary tangles, ultimately leading to progressive neuron loss. While drugs targeting AD pathologies, β-amyloid (Aβ) plaques and tau neurofibrillary tangles, are in development and anti-Aβ antibodies have been approved, therapies targeting other mechanisms of AD are desperately needed. Anti-Aβ antibodies slow but do not halt AD, benefit only early AD, and can have serious side effects. Therefore, discovering new safe drugs that benefit all stages of AD is of paramount importance. Understanding the molecular mechanism of cell death in regions susceptible to neurodegeneration such as the hippocampus could shed light on pathways involved in cell death, allowing development of AD therapeutics directed at novel targets. Genome wide association studies (GWAS), Whole genome sequencing (WGS) and linkage studies have enabled the identification of genes that affect AD risk. About 95% of cases of AD are late-onset and sporadic and are associated with mutations in many genes. Unc5c (Uncoordinated C. elegans receptor 5c) is a candidate gene containing single nucleotide polymorphisms (SNPs) associated with late-onset AD( 1 ). The T835M mutation (rs137875858) in the hinge region of UNC5C was shown to increase the susceptibility of hippocampal neurons to cell death and segregated with late-onset AD (LOAD)( 1 ). Further studies by Hashimoto et al. , showed that UNC5C T835M activated the cell death pathway in cell culture, suggesting a molecular mechanism involving JNK/PKD/NADPH oxidase signaling( 2 ). UNC5C acts as a chemorepellent for Netrin1, while its antagonist, deleted in colorectal cancer (Dcc), acts as a chemoattractant during axon guidance( 3 – 5 ). Besides its crucial role in various carcinomas, specifically in Colorectal cancer( 6 ), UNC5C has also been implicated in various neurological disorders such as Autism spectrum disorder( 7 ), Schizophrenia( 8 ), and Parkinson’s disease( 9 ). Multiple SNPs in UNC5C have been shown to be associated with AD ( 1 , 10 – 13 ). UNC5C protein contains two Immunoglobulin domains (Ig), two thrombospondin domains (TS), a transmembrane domain (TM), a zona occludens-5 domain (ZU-5), a UPA domain, and a death domain (DD)( 12 ). It belongs to a class of transmembrane receptors called dependence receptors, which depending on the presence or absence of the ligand, could promote cell survival or induce cell death, respectively ( 14 , 15 ). Therefore, it is imperative to understand the molecular underpinnings of the T835M mutation in UNC5C-related brain atrophy and neuronal cell death to inform the potential of UNC5C as a therapeutic target for AD. To gain deeper insights into the role of UNC5C in AD, here we analyze the CNS phenotype of UNC5C T835M targeted replacement mice ( Unc5c KI/KI ). Unc5c KI/KI mice have age-related neurodegeneration, including reduced hippocampal volume, increased ventricular volume and reduced white matter connectivity beginning at 12–18 months of age. Moreover, the UNC5C T835M mutation results in decreased pre-synaptic but increased post-synaptic protein levels, dendritic disorganization in the hippocampal CA1 region, and increased apoptotic neuronal death. In addition, astrocytes and microglia in CA1 have reduced Glial fibrillary acidic protein (GFAP) levels and increased activation, respectively. Proteomic studies reveal increased oxidative stress proteins in the hippocampus and decreased chaperone proteins, along with increased c-Jun N-terminal Kinase (JNK) phosphorylation, NADPH oxidase, and decreased Netrin1 levels. To understand the effects of the UNC5C T835M mutation on Alzheimer’s-related phenotypes, we generated mice that were homozygous for both UNC5Cc T835M and the APP targeted replacement, App NL−G−F/NL−G−F (NLGF)( 16 ), which develop amyloid plaques and synaptic loss by 6 months but no significant neuron loss. In NLGF; Unc5c KI/KI double knock in (dKI) mice compared to NLGF mice alone, we observed an exacerbation of UNC5C T835M-associated phenotypes, such as increased cell death, reduced hippocampal area, and decreased Netrin1 and GFAP levels at 6 and 12 months of age. Overall, these results suggest that the UNC5C T835M mutation causes neurodegeneration by increasing oxidative stress leading to synaptic degeneration and neuronal apoptosis, which are all worsened in the presence of cytotoxic stressors such as amyloid, therefore increasing AD susceptibility. Materials and Methods Mice: Unless indicated, no significance was noted between the genders, and the data presented were the means of both male and female animals. UNC5C T835M KI (KI) mice were generated by Genentech Inc. (JA and RW) and acquired by Northwestern University. Briefly, the construct for targeting the Unc5c locus in mouse ES cells to generate the T835M KI allele, was made using a combination of recombineering, gene synthesis and standard molecular cloning techniques. The resulting targeting vector enabled insertion of the T835M point mutation in Unc5c exon 15 with an FRT- Pgk1 -em7-Neo-FRT cassette inserted in intron 15 at genomic position mm10 chr3:141,827,785. The vector was confirmed by DNA sequencing, linearized with NotI and used to target C57BL/6 C2 ES cells using standard methods (G418 positive and gancyclovir negative selection). Positive clones were identified using PCR and TaqMan analysis and confirmed by sequencing of the modified locus. Correctly targeted ES cells were infected with Adeno-Flpo ( 17 ) to remove the selection marker with a single FRT site remaining, resulting in the final Unc5c T835M KI allele ( Unc5c KI/KI ). Validated ES cells were injected into blastocysts using standard techniques, and germline transmission was obtained after crossing resulting chimaeras with C57BL/6N females. All animal work was performed in Northwestern University in accordance with Northwestern University Institutional Animal Care and Use Committee approval. The number of biological replicates for each experiment is specified in the figure legends. NLGF mice were obtained from Dr. Takaomi Saido, RIKEN Brain Science Institute, Japan, and NLGF homozygous mice were bred with Unc5c KI homozygous mice to obtain the double KI (dKI) mice. U nc5c KO mice were obtained as heterozygotes from Dr. Susan Ackerman at University of California, San Diego, and bred in-house to obtain the homozygotes. Genotyping is performed by Transnetyx using custom probes for Unc5c (WT and mutant specific probes) and standard probes for NLGF mice. Antibodies The antibodies used were as follows: rabbit anti-actin (#926–42210, LI-COR), mouse anti-BACE1 (3D5) (made in Vassar lab)( 18 ), rabbit anti-BACE1 (#ab108394, Abcam), rat anti-MBP (#ab7349, Abcam), mouse anti-PSD95 (#K28/43, DSHB), rabbit anti-C1q (#ab227072, Abcam), rat anti-CD68 (#14-0681-82, Invitrogen), chicken anti-MAP2 (#ab5392, Abcam), rabbit anti-Cdk5 (#14145S, Cell Signaling), chicken anti-NeuN (#ABN91, Millipore), mouse anti–b-tubulin (Tuj1) (a gift from L. Binder), anti-synaptophysin (WB: mouse #ab8049, Abcam; IF: goat #AF5555, R&D Systems), rabbit anti-NADPH oxidase (#17772-1-AP, Proteintech), rabbit anti-total JNK/SAPK (#9252, Cell Signaling), rabbit anti-phospho-JNK (#9251S, Cell Signaling), rabbit anti-PKD (#PA5-13749, Invitrogen), mouse anti-SMI312 (#837904, BioLegend), anti-Iba1 (WB: rabbit #ab178846, Abcam; IF: goat #NB100-1028, Novus), anti-GFAP (WB: rabbit #G9269, Sigma-Aldrich; IF: chicken #ab4674, Abcam), rabbit anti-GAPDH (#2118, Cell Signaling), rabbit anti-Unc5c (polyclonal antibody which was custom made by Proteintech Inc., against the C-terminal 400 amino acids of Unc5c protein), rabbit anti-Ab 42 (#700254, Invitrogen), rat anti-Lamp1 (#1D4B, DSHB), 3D6 mouse anti-Ab monoclonal antibody( 19 ) (gift of Dr. Lisa Conlogue, Elan Pharmaceuticals) (antigen is 1–5 N-terminal amino acids of Aβ 42 ), Rabbit anti-Netrin-1 (#MBS821997, My Biosource Inc,.) and Human Netrin-1 Protein, His Tag (NEI-H52H3, Acro Biosystems). 1ug of the purified protein was loaded to confirm the Netrin1 band around 85kD. Tissue extraction and immunoblot analysis Tissue extraction and immunoblot analysis: Mice were deeply anesthetized by intraperitoneal injection of xylazine (15 mg/kg) and ketamine (100 mg/kg), perfused with ice-cold phosphate-buffered saline (PBS) with phenylmethylsulfonyl fluoride (20 µg/ml), leupeptin (0.5 µg/ml), sodium orthovanadate (20 µM), and dithiothreitol (0.1 mM), followed by decapitation and brain removal. The hemibrain was dissected on ice into the cortex, hippocampus, and cerebellum, and then snap-frozen in liquid nitrogen and stored at − 80°C. Tissues were homogenized in radioimmunoprecipitation assay buffer ((RIPA; 50 mM tris, 0.15 M NaCl, 1% octylphenoxypolyethoxyethanol (IGEPAL), 1 mM EDTA, 1 mM EGTA, 0.1% SDS, 0.5% sodium deoxylate (pH 8)), followed by sonication and centrifugation. All buffers contained protease inhibitor cocktail III (#535140, Millipore) and Halt phosphatase inhibitor (#78420, Thermo Fisher Scientific). Protein concentration was determined using bicinchoninic acid assay (BCA) assay (#23225, Thermo Fisher Scientific). Equal amount of protein was separated under reduced and denatured conditions, transferred onto a polyvinylidene difluoride or nitrocellulose membrane, and developed using Pierce ECL (enhanced chemiluminesence) (Thermo Fisher Scientific) on a ProteinSimple FCR imager and Biorad imager. Chemiluminescent signals were quantified using AlphaView software (ProteinSimple) and Imagelab (Biorad). Immunofluorescence Hemibrains were fixed in 10% formalin and preserved in 30% sucrose/PBS solution. Brains were sectioned as coronal sections at 30 µm on freezing-sliding microtome and stained using the free-floating method. Sections were serially placed in a 12-well plate in a cryoprotective solution (1xPBS, 30% sucrose, and 30% ethylene glycol) and stored at − 20°C until use. Immunofluorescence staining was performed by first washing sections three times in 1xTBS and then incubating sections in 16 mM glycine in 1xTBS for 1 hour at room temperature. After 3 additional washes in 1XTBS, sections were blocked in 5% donkey serum in 0.25% Triton X-100 in 1xTBS for 2 hours at room temperature. The sections were then incubated overnight in primary antibodies in a solution of 0.25% Triton X-100, 1% bovine serum albumin and 1xTBS at 4°C. Alexa Fluor secondary antibodies (Invitrogen) were used at a concentration of 1:750. Sections were mounted using ProLong Gold (#P36934, Thermo Fisher Scientific) and imaged on a Nikon A1 laser scanning confocal microscope (Northwestern University Center for Advanced Microscopy). Terminal d-UTP nick-end labeling (TUNEL) Cell death detection For cell death detection, sections were permeabilized with Triton X-100 for an hour, followed by a 1-hour incubation of reaction mix from the In Situ Cell Death Detection Kit, TMR Red (#12156792910 Roche, Sigma-Aldrich) at 37°C following the directions on the user manual. Activated Caspase-3/7 Fluorescence assay: We assayed the activity of caspase-3/7 using Caspase-3/7 Fluorescence assay kit (Cayman chemical, cat # 10009135). Briefly, 90µl of diluted hippocampal homogenates were added to a black 96-well plate. 10µl of assay buffer was added to each well to assay for the endogenous activity of cleaved caspase-3/7. 100µl of positive control of active Caspase-3 was added to a couple of wells as a positive control. 100µl of the caspase-3/7 substrate solution was added to each well and incubated for 60–90 minutes. The relative fluorescence intensity was read at 535 nm. ELISA & MSD-ELISA For NLGF and dKI hippocampal samples, we treated the extracts with Guanidine hydrochloride (7.2 µl of 2 mg/ml brain homogenates were added to 12.8 µl of freshly made 8.2 M guanidine hydrochloride (GuHCl); 82 mM Tris HCl (pH 8.0) (5 M GuHCl final) and mixed for three days on a nutator) and the samples were then diluted with the assay buffer and ELISA for Aβ 42 (Invitrogen, cat# KHB3441) was performed according to instructions in user’s manual. MSD-ELISA was performed on the hippocampal samples from NLGF and dKI mice at 6- and 12- months using V-PLEX Aβ Peptide Panel 1 (6E10) assay Kit (Meso Scale Discovery, cat# K15200E) following the directions on the user’s manual. Briefly, the hippocampal samples were treated with Guanidine hydrochloride as described above, to obtain the insoluble Aβ species. Samples were further diluted 128-fold with diluent-35 (from the kit) and assay was performed. MRI Brain region volumetric analysis Acquisition was done on a 7Tesla Bruker Clinscan MRI using a 3D multiple echo GRE sequence with isotropic spatial resolution. After realignment, to avoid errors in volume/morphological estimation associated with different head position, data was extracted and comparison between relevant brain regions (ventricle and hippocampus) across cohorts was done by averaging the regional volumes extracted via segmentation and normalized using each whole brain volume. The original 3D high resolution MR images (110µm) were used to derive these quantities. Each segmentation was implemented using a semi-automated approach which combined the use of the threshold automatic segmentation tool contained in IT-SNAP followed by manual corrections using a graphical pen and a dedicated Wacom tablet to enable accurate delineation of anatomical regions. The manual corrections were implemented to avoid artifacts from automated segmentation and to reliably capture volumetric data from the same exact regions across different subjects. Delineation was performed by two experienced neuroimaging scientists (DP) using a standard mouse brain atlas as reference guide for identification of relevant anatomical regions. MRI cortical thickness analysis: Assessment of cortical changes was done through linear measurements of cortical thickness using the built-in image annotation and linear measurement tool included in ITK SNAP. Measurements were done on the re-aligned high resolution 3D brain images at same exact anatomical location (prefrontal cortical area) for all subjects and at two symmetric locations ~ 2 mm off the brain's midline and ~ 1 mm in front of bregma (as shown in representative MRI image in Fig. 1 M). Choice of using average of two contralateral measurements was done to reduce subjective choice of position and uncertainties in realignment. Linear measurements were selected for cortical assessment instead of volumetric analysis to avoid uncertainties linked to confounding selection of total cortical region to be included in the assay. Fractional Anisotropy (FA) analysis: We identified an MRI derived biomarker (thresholded FA -> 0.25) which can provide a quantitative volumetric value reflective of connectivity patterns (the index comes from dividing each thresholded volume FA ~ 0.25 by the whole brain volume). Imaging quantification and analysis using ImageJ and NIS-elements: For hippocampal and cortical area measurement, we employed immunofluorescence on serial sections for each animal stained with NeuN antibody. 10x images were obtained using Nikon Ti2 widefield microscope in the Northwestern Feinberg School of Medicine imaging core facility. Using ImageJ software, polygon selection was used to draw the outline of hippocampi on all the sections. We selected three different bregma positions (-1.34mm (anterior), -1.70mm (center), -2.06mm (posterior)) to obtain the area of hippocampi and cortices (9 mice (5 females, 4 males) in WT-KI analysis, 10 mice (5 females, 5 males) in NLGF-dKI analysis). The average of three positions are compared between WT and KI mice. For signal intensity measurement, region of interest was outlined using polygon selection in ImageJ, then the mean intensity was obtained. For counting cells as in counting TUNEL + cells in the hippocampus, CD68 + cells, and both CD68 + andC1q + cells in microglia, we used the multi-point tool to count the cells manually in the stitched 40x and 20x hippocampal images, respectively, by someone blind to the genotypes of the animals. The images were obtained using AXR laser scanning confocal microscope (Northwestern University Nikon Imaging Centre) and the maximum projected images were used for quantification. For the image analysis and quantification using Nikon NIS-Elements Software (Northwestern University Nikon Imaging Centre), recipes were created by setting intensity, size and background threshold for each staining to be quantified for the numbers or area covered (for GFAP and NeuN) in the region of interest (ROI). Once ROIs were drawn in cortex and hippocampus, a binary channel was created to run the recipe for each region. 10x images obtained using a Ti2 wide-field microscope were used in these analyses. For plaque analysis and NeuN analysis in dKI and NLGF mice, the average of 3–5 sections from Bregma coordinates of about − 1.30 to − 2.52 mm was obtained. Recipes were created to obtain the different plaque core sizes by binning them into different size thresholds. Section selection, tracing, and volume analysis were performed by someone blind to the genotypes of the animals. IMARIS image reconstruction: Astrocyte and Microglial morphology were analyzed using the IMARIS software (v9.1) by reconstructing the z-stacks of 60x confocal images obtained from A1R laser scanning confocal microscope at the Nikon imaging facility at Northwestern university. For astrocyte 3D reconstruction, confocal z-stacks were imported into IMARIS. Maximum projection confocal images were used to define GFAP (for astrocytes) signal for each image. Two sections per animal (n = 7–8 females, 3–5 males/genotype for Unc5c +/+ and Unc5c KI/KI mice, n = 5 females, 5 males/genotype for NLGF and dKI mice) were used and the average was used for each animal. 18–22 cells/animal were used for the analysis and the average was calculated. Using the surface tool, GFAP channel was chosen. Using the filaments tool, the IMARIS software used slice rendering and calculated mean process length, area, volume, soma volume, number of processes, number of process branch points, and number of process terminal points. For analyzing TUNEL signal (red) within GFAP + astrocytes, we employed “section” tool to obtain the orthogonal view of a single plane to show that GFAP + astrocytes contained TUNEL signal. Synaptic dendritic orientation analysis: Maximum intensity projected 60x images with PSD95 staining were analyzed using the OrientationJ plugin in ImageJ software by selecting the OrientationJ distribution and setting the local window σ to 3 pixels and gradient to “gaussian”( 20 ). Images were obtained with the CA1 neuronal layer and were rotated 90° to right or left. The pixel orientation distribution is displayed as histogram of gaussian window ranging from − 90 to + 90°. Pixels that are parallel to the vector field (CA1 layer) are closer to 0° (parallel to the direction of dendritic processes emerging from the CA1 neuronal layer). As the dendrites digress from the parallel orientation, we checked for the degree of deviation in the KI mice. The resulting graph is presented as a Gaussian curve and the degree of deviation is represented by more distribution of orientation towards the ends of the curve (-89.5° and 89.5°). Tandem-mass tagged based Mass spectrometry (TMT-MS) sample preparation: TMT-MS sample preparation was performed as previously described( 21 ). Briefly, 200 µg homogenized hippocampal brain extracts were extracted using methanol-chloroform precipitation. The extracted protein was then resuspended in 6M guanidine in 100 mM triethylammonium bicarbonate (TEAB) buffer (Thermo Scientific, Cat# 90114). Subsequently, reduction and alkylation at Cysteine residues of proteins were performed by subsequent incubation with 5 mM dithiothreitol (DTT) and alkylated at free SH groups of cysteine residues with 20mM iodoacetamide (IAA). Proteins were first digested for 3 h at room temperature (RT) with 1 µg of LysC (Promega, Cat# PI90307) and then overnight at 37°C with 2 µg of Trypsin. The digest was then acidified with formic acid and desalted using C18 HyperSep columns (ThermoFisher Scientific, Cat# 60108-302). The eluted peptide solution was dried before resuspension in 100 mM TEAB. Micro-BCA assay (Thermo Fisher Scientific, Cat#23235) was subsequently performed to determine the concentration of peptides and 100 µg of peptides from each sample was then used for isobaric labeling. TMT 10-plex labeling was performed on peptide samples according to the manufacturer’s instructions (ThermoFisher Scientific). After incubating for 75 min at room temperature, the reaction was quenched with 0.3% (v/v) hydroxylamine. Isobaric labeled samples were then combined 1:1:1:1:1:1:1:1:1:1 and subsequently desalted with C18 HyperSep columns. The combined isobaric labeled peptide samples were fractionated into eight fractions using high pH reversed-phase columns (Thermo Fisher Scientific, Cat# PI84868). Peptide solutions were dried, and stored at − 80°C. TMT-MS Analysis: TMT-MS analysis was performed as previously described( 21 ). In short, samples were resuspended in 20µL of buffer A (5% acetonitrile, 0.125% formic acid), and micro-BCA was performed. 3µg of each fraction was loaded for LC–MS analysis via an auto-sampler with a Thermo EASY nLC 100 UPLC pump onto a vented Pepmap100, 75µm × 2 cm, nanoViper trap column coupled to a nanoViper analytical column (Thermo Scientific) with a stainless steel emitter tip assembled on the nanospray flex ion source with a spray voltage of 2000 V. Orbitrap Fusion was used to generate MS data. The chromatographic run was performed with a 4 h gradient beginning with 100% buffer A and 0% B and increased to 7% B over 5 min, then to 25% B over 160 min, 36% B over 40 min, 45% B over 10 min, 95% B over 10 min, and held at 95% B for 15 min before terminating the scan. Buffer A contained 5% acetonitrile (ACN) and 0.125% formic acid in H 2 O, and buffer B contained 99.875 ACN with 0.125% formic acid. Multinotch MS3 method was programmed with the following parameters: ion transfer tube temp = 300°C, easy-IC internal mass calibration, default charge state = 2, and cycle time = 3 s. MS1 detector was set to orbitrap with 60 K resolution, wide quad isolation, mass range = normal, scan range = 300–1800 m / z , max injection time = 50ms, AGC target = 6 × 10 5 , microscans = 1, RF lens = 60%, without source fragmentation, and datatype = positive and centroid( 21 ). Monoisotopic precursor selection was set to include charge states 2–7 and reject unassigned. Dynamic exclusion was allowed; n = 1 exclusion for 60 s with 10 ppm tolerance for high and low. The intensity threshold was set to 5 × 10 3 . Precursor selection decision = most intense, top speed, 3 s. MS2 settings include isolation window = 0.7, scan range = auto normal, collision energy = 35% CID, scan rate = turbo, max injection time = 50 ms, AGC target = 6 × 10 5 , and Q = 0.25. In MS3, the top 10 precursor peptides selected for analysis were then fragmented using 65% higher-energy collisional dissociation before orbitrap detection. A precursor selection range of 400–1200 m / z was chosen with mass range tolerance. An exclusion mass width was set to 18 ppm on the low and 5 ppm on the high. Isobaric tag loss exclusion was set to TMT reagent. Additional MS3 settings include an isolation window = 2, orbitrap resolution = 60 K, scan range = 120–500 m / z , AGC target = 6 × 10 5 , max injection time = 120 ms, microscans = 1, and datatype = profile. TMT-MS Data Analysis and Quantification: TMT-MS data analysis was performed as previously described( 21 ). In short, protein identification, TMT quantification, and analysis were performed with The Integrated Proteomics Pipeline-IP2 (Integrated Proteomics Applications, Inc., http://www.integratedproteomics.com/ ). Proteomic results were analyzed with ProLuCID, DTASelect2, Census, and QuantCompare. MS1, MS2, and MS3 spectrum raw files were extracted using RawExtract 1.9.9 software ( http://fields.scripps.edu/downloads.php)(22) . Pooled spectral files from all eight fractions for each sample were then searched against the Uniprot mouse protein database and matched to sequences using the ProLuCID/SEQUEST algorithm (ProLuCID ver. 3.1) with 50 ppm peptide mass tolerance for precursor ions and 600 ppm for fragment ions( 23 ). Fully and half-tryptic peptide candidates were included in the search space, all that fell within the mass tolerance window with no miscleavage constraint, assembled, and filtered with DTASelect2 (ver. 2.1.3) through the Integrated Proteomics Pipeline (IP2 v.5.0.1, Integrated Proteomics Applications, Inc., CA, USA). Static modifications at 57.02146 C and 229.1629 K were included( 24 ). The target-decoy strategy was used to verify peptide probabilities and false discovery ratios( 25 , 26 ). A minimum peptide length of 6 was set for the process of each protein identification, and each dataset included a 1% FDR rate at the protein level based on the target-decoy strategy. Isobaric labeling analysis was established with Census 2 as previously described( 27 ). TMT channels were normalized by dividing it over the sum of all channels( 26 ). No intensity threshold was applied. The fold change was then calculated as the mean of the experimental group standardized values, and p -values were then calculated by Student’s t -test with Benjamini-Hochberg adjustment. Protein ontologies were determined with protein analysis through The D atabase for A nnotation, V isualization and I ntegrated D iscovery (DAVID). The gene-ontology term (GO term) obtained were sorted based on their Benjamini score. Protein ontologies with Fisher statistical tests with false discovery rate correction less than 0.05 were considered significant. Statistics: Statistics were calculated using Prism10 (GraphPad Software). Unpaired two-way Student’s t-test and ordinary one-way ANOVA using Tukey’s multiple comparison tests with Bartlett’s test correction were used in the data analysis. For the UNC5C blot, we performed both multiple t-test and two-way ANOVA with Sidak’s multiple comparisons test. Numbers of replicates and P values are stated in each figure legend. All data are plotted as means ± SEM. Significance was concluded when the P value was less than 0.05, indicated by *P < 0.05, **P < 0.01, ***P 0.05. Results 1. Hippocampal degeneration is evident in Unc5c KI/KI mice by 18 months of age. The UNC5C T835M mutation is associated with LOAD (1), but the pathogenic mechanism of this variant in CNS neurodegeneration is unclear. To shed light on the pathophysiology of UNC5C T835M in AD, we used standard homologous recombination in ES cells to generate a targeted replacement mouse line that constitutively expresses this variant (Fig. S1A). We then analyzed the CNS phenotypes associated with UNC5C T835M expression in the targeted replacement mice, which were bred to homozygosity and aged up to 24 months. Using NeuN immunofluorescence microscopy, we found a significant reduction in hippocampal area in Unc5c KI/KI mice compared to wildtype control Unc5c +/+ at 12 and 18 months compared to 6 months (Fig. 1A, B), while Unc5c KI/KI and Unc5c +/+ cortical areas were equal at all ages, even up to 24 months of age (Fig. S1B, C). This observation may be related to higher expression levels of Unc5c in hippocampus compared to cortex (1). We confirmed hippocampal volume loss in Unc5c KI/KI mice in vivo over time using longitudinal magnetic resonance imaging (MRI) (Fig. 1C, D). We found that ventricular volume increased significantly between 13- and 18-months in Unc5c KI/KI mice compared to Unc5c +/+ mice, (Fig. 1E, Fig.S1D) while hippocampal volume decreased significantly in Unc5c KI/KI mice, both suggesting neurodegeneration (Fig. 1F, Fig.S1E). As in the immunofluorescence microscopy-based analysis, cortical thickness was equivalent in both the Unc5c KI/KI and Unc5c +/+ mice and did not change over time (Fig. 1G, H). We used diffusion tensor imaging (DTI) to measure the fractional anisotropy (FA) index, which is indicative of white matter connectivity. Between 13 and 18 months of age, Unc5c KI/KI mice had a significantly greater decrease in FA than Unc5c +/+ mice, indicating reduced gray-matter interconnectivity in Unc5c KI/KI mice (Fig. 1I, J). 2. Synaptic protein levels and dendritic organization are significantly altered in Unc5c KI/KI mice. Since the longitudinal MRI study strongly suggested white matter atrophy in the hippocampus, we hypothesized that the Unc5c KI/KI mice had axonal and synaptic degeneration in the hippocampal region. Therefore, we assessed the levels of axonal and synaptic proteins in these mice. Immunoblot analysis of hippocampal homogenates of 18-month-old Unc5c KI/KI mice appeared to have reduced presynaptic/axonal markers synaptophysin (SYP), β-secretase (BACE1), Neurofilament/pan-axonal marker (SMI312), and Myelin basic protein (MBP) (Fig. 2A, B-E, Fig.S2). In contrast, immunoblots for the post-synaptic marker PSD95 surprisingly showed a significant increase in the Unc5c KI/KI mice (Fig. 2A, F, Fig.S2). Post-synaptic changes were corroborated by immunofluorescence microscopy (Fig. 2G, H). Both MAP2 and PSD95 (post-synaptic markers) had increased immunostaining intensity in hippocampal CA1 (Fig. 2G-I). Previous studies have shown that homeostatic synaptic plasticity exists to maintain the balance between pre- and post-synaptic sides of the synapse in the developing nervous system (28–30). We hypothesized that if UNC5C T835M affects normal excitability or synaptic homeostasis, an abnormal decrease of the pre-synapse could cause a compensatory increase of the post-synapse. Another UNC protein, MUNC13, has also recently been implicated in controlling postsynaptic AMPA receptor density and clustering (31–33), suggesting a possible role for the UNC family, including UNC5C, in the post-synaptic spine. Taken together, these data suggest that UNC5C T835M-mediated presynaptic degeneration is coupled with compensatory postsynaptic sprouting. Additionally, we observed that PSD95 + dendritic processes in the hippocampal CA1 region of the Unc5c KI/KI mice showed abnormal organization compared to Unc5c +/+ , which we measured as a loss of linearity in dendrites that run parallel to each other and perpendicular to the CA1 cell layer (Fig. 2J, K). When dendritic processes are parallel to each other, the angle between them is near 0°, as observed in 3-month-old mice and the 18-month-old Unc5c +/+ mice, but in 18-month Unc5c KI/KI mice, the orientation is distributed nearly evenly, with no clear peak, indicating a loss of parallel organization. Even at 3 months in Unc5c KI/KI mice, the peak near 0 o is less pronounced and there are more values in the tails of the distribution, indicating that loss of linear organization may occur early and worsen with age. Taken together, our results show that the hippocampal atrophy exhibited by Unc5c KI/KI mice is associated with reduced hippocampal presynaptic and axonal proteins, compensatory postsynaptic sprouting, and disorganized dendrites, suggestive of a neurodegenerative process. 3. Proteomic analysis reveals upregulation of oxidative stress and down-regulation of chaperone proteins in Unc5c KI/KI mice. We performed bulk proteomics on hippocampal extracts of Unc5c +/+ and Unc5c KI/KI mice at 18 months to identify in an unbiased fashion the proteins that may reveal pathways and networks with important roles in UNC5C-mediated cell death (Fig. 3A, S8). We performed Gene Ontology:Biological processes (GO:BP) enrichment analysis for significantly upregulated proteins using D atabase for A nnotation, V isualization and I ntegrated D iscovery (DAVID)(34). The terms that were most significantly upregulated included oxidative phosphorylation, Alzheimer’s disease, Parkinson’s disease, Huntington’s disease (pathways leading to neurodegeneration), and endocytosis (Fig. 3B, D, S8). To corroborate our proteomic results, we quantified by immunoblot the levels of specific upregulated proteins, including Calmodulin-1 (CALM1), ubiquinol-cytochrome c reductase binding protein (UQCRB), and Capping actin protein of muscle Z-line β subunit (CAPZB) that are involved in oxidative stress pathways leading to neurodegeneration and endocytosis (underlined in red in Fig. 3D, F, S8). Notably, UQCRB and CAPZB were significantly increased in Unc5c KI/KI compared to Unc5c +/+ mice (Fig. 3G, I). Although CALM1 levels were not increased in the original Unc5c KI/KI sample (Fig. 3H), normalization against PonceauS showed that CALM1 was significantly increased in Unc5c KI/KI hippocampus (Fig.S3C). Remarkably, the down-regulated proteins by GO terms in the Unc5c KI/KI mice were chaperone, myelin sheath, and glutamatergic synapse proteins (Fig. 3C, E). We validated the proteomic results with immunoblot analysis for some of the representative proteins from each term: heat shock protein family D (HSP60) member 1 (HSPD1/HSP60) (chaperone/protein folding), Glial fibrillary acidic protein (GFAP) (myelin sheath) and Calcium voltage-gated channel auxiliary β subunit 4 (CACNB4) (glutamatergic synapses) (underlined in red in Fig. 3E, J). Notably, GFAP was significantly decreased in the Unc5c KI/KI compared to the Unc5c +/+ mice, while HSP60 and CACNB4 levels trended towards decrease in the Unc5c KI/KI mice (Fig. 3K-M, Fig.S3), which were significant upon normalization with PonceauS (Fig.S3F, G). Previously, it has been shown that increased endocytic activity along with increased trafficking to endosomes could possibly generate Aβ that could contribute to amyloid pathology and accelerate AD(35). Together, our findings suggest that UNC5C T835M may promote oxidative stress, which in the presence of a cytotoxic stressor, such as pathologic Aβ or tau (AD) or α-synuclein (Parkinson’s disease), may cause disease pathogenesis. 4. Unc5c KI/KI mice exhibit increased apoptosis. Since it was shown previously that UNC5C T835M increases susceptibility to death of hippocampal primary neurons (1), we employed the bioinformatic tool Polyphen-2 software (http://genetics.bwh.harvard.edu/pph2/)(36) to assess the impact of the T835M mutation on the structure and function of UNC5C protein. Polyphen-2 showed that the T835M substitution was “possibly damaging” to UNC5C protein structure with a score of 0.929 out of 1.0 (a mutation with a score of 0.0 is “tolerated” while that with 1.0 is “deleterious”; Fig. S4A), suggesting potential functional consequences that may explain the previously reported findings (1) and our own results of hippocampal atrophy suggesting neurodegeneration, as the mutation is in/near the hinge region and could affect the UNC5C open/closed state that triggers cell death or growth activities. Since cleavage of the intracellular domain of UNC5C and other UNC5 family members affect cell death via the apoptotic pathway (37–39), we generated an antibody against the C-terminal 400 amino acids of UNC5C to assess how the T835M mutation affects protein levels and proteolytic processing in the hippocampus. We hypothesized that since the mutation is in the hinge region, it could cause a change in protein conformation favoring the ‘open’ state, thereby exposing the death domain that is susceptible to cleavage by activated caspase 3, triggering the apoptotic cascade. We observed that full length (FL) UNC5C levels were significantly reduced in Unc5c KI/KI hippocampal homogenates by immunoblot analysis (Fig. 4A, B, Fig.S4B). Additionally, we observed two bands at ~ 57 kD and 52 kD – denoted as “CL1 and CL2”, respectively, corresponding to the expected cleaved products of UNC5C with activated caspase3 (boxed regions, Fig. S4C), in Unc5c +/+ and Unc5c KI/KI mice, but that were absent in Unc5c constitutive knockout mouse hippocampal homogenates ( Unc5c KO/KO ) (Fig. 4A, B, Fig.S4B). Both CL1 and CL2 levels were increased in Unc5c KI/KI mice, although CL1 did not reach statistical significance (Fig. 4A, B). Importantly, the individual and summed ratios of CL1 and CL2 to full-length UNC5C were elevated in Unc5c KI/KI mice, with the increase in CL2 being the main driver of the change (Fig. 4C). This observation, that signal for FL band is decreased while those of CL bands are increased, suggests a ‘precursor - product’ relationship. These results suggests that the T835M mutation might increase the cleavage of the FL UNC5C protein into fragments that once released could in turn trigger the apoptotic cascade downstream of UNC5C when it is in its open conformation. To support this hypothesis, we measured apoptotic cell death in Unc5c +/+ and Unc5c KI/KI brains. Using a standard TUNEL (Terminal deoxynucleotidyl transferase (TdT) dUTP Nick-End Labeling) assay, we observed a significant increase in TUNEL + cells (Fig. 4D, Fig.S4D), specifically, NeuN+/TUNEL + cells (~ 60–80 cells) in the hippocampus of Unc5c KI/KI mice at 18 and 24 months (Fig. 4D, E). The number of NeuN-/TUNEL + cells, representing microglia, astrocytes, endothelial cells, oligodendrocytes, and other cells, were far fewer compared to NeuN+/TUNEL + cells (~ 10–20 cells), and remained unchanged in Unc5c KI/KI mice compared to that in Unc5c +/+ mice, indicating that only neurons are more susceptible to apoptosis in the Unc5c KI/KI mice (Fig. 4F, Fig. S4E, F). Co-staining with GFAP (astrocytes), and Iba1 (microglia) showed there were very few TUNEL + GFAP + and TUNEL + Iba1 + cells in both Unc5c +/+ and Unc5c KI/KI hippocampi (Fig. S4E, F). Additionally, NeuN-covered area was decreased by ~ 31% in the hippocampus of Unc5c KI/KI compared to Unc5c +/+ mice (Fig. 4G). The activity of caspase-3, an effector caspase in the apoptotic process, was increased in hippocampal homogenates from Unc5c KI/KI compared to Unc5c +/+ mice at 12 and 18 months of age (Fig. 4H), confirming that the UNC5C T835M mutation increases the susceptibility of neurons to cell death via apoptosis with age. To further understand the molecular pathway of UNC5C-mediated neuron death, we performed immunoblot analysis for certain kinases known to be involved in apoptosis. Previous studies have shown that Protein Kinase-D (PKD) decreases induction of apoptosis by modulating the c-Jun N-terminal Kinase (JNK) pathway and phosphorylation of c-Jun(40). Additionally, PKD1 has been shown to play an anti-apoptotic role in protecting neuronal cells in early stages of oxidative stress (41) by modulating JNK phosphorylation and preventing apoptosis. Since PKD1 has been shown to play a protective role in oxidative stress (42, 43), decreased PKD1 levels could lead to JNK phosphorylation and induce apoptosis via NAPDH oxidase (NOX1), as previously reported (2). NOX1 has been associated with activated caspases in AD brains(44, 45). Therefore, reduction in PKD levels might activate the JNK via increased phosphorylation, which then leads to elevated NOX1 levels. Consistent with increased apoptosis, we found a significant decrease in PKD in the hippocampus of Unc5c KI/KI mice (Fig. 4I, J, Fig.S4G) as well as increased phosphorylation of JNK/SAPK, which has been shown to act downstream of UNC5C T835M (2) (Fig. 4I, K, Fig.S4G). Additionally, we observed increased Cyclin-Dependent Kinase 5 (CDK5) levels in Unc5c KI/KI hippocampus (Fig. 4I, L, Fig.S4G), which have been implicated in apoptosis (46, 47). Another study has shown that CDK5 induces c-Jun phosphorylation through activation of JNK by promoting oxidative stress (48). Further, NADPH oxidase (NOX1) levels were increased in Unc5c KI/KI hippocampus (Fig. 4I, M, Fig.S4G), providing additional evidence of an oxidative stress environment with age in the Unc5c KI/KI mice. Taken together, our results strongly suggest that UNC5C T835M increases susceptibility to hippocampal neuron loss by creating an oxidative stress environment that leads to death via an apoptotic mechanism in vivo. We also analyzed Netrin 1 levels, which were significantly reduced at 12 months of age in the hippocampal samples from Unc5c KI/KI compared to Unc5c +/+ mice (Fig. 4I, N, Fig.S4H). Since UNC5C is a member of the “dependence” receptor family, reduced Netrin1 (ligand) could initiate the apoptotic pathway (39). This supports the hypothesis that the T835M mutation could cause a change in protein conformation that makes UNC5C more prone to adopting the “open” conformation when Netrin1 levels decrease, thus triggering apoptosis and neurodegeneration. Cytotoxic stressors such as Aβ could exacerbate this mechanism. Furthermore, reduced Netrin1 levels are also associated with increased amyloidogenic processing of APP (49), so the UNC5C mutation could have the dual effect of both inducing apoptotic neuronal death and driving amyloid pathology through Netrin1. 5. Reduced GFAP levels and morphological changes are observed in astrocytes of Unc5c KI/KI mice. UNC5C is expressed in astrocytes as well as neurons (50) (https://brainrnaseq.org/?2327723709=1271088613) (51, 52). Notably, we observed reduced GFAP levels in Unc5c KI/KI hippocampal homogenates by proteomic analysis (Fig. 3J, K). Therefore, we used immunofluorescence microscopy to assess whether the UNC5C T835M mutation affected astrocytic phenotype (Fig. 5A-C). At 12 and 18 months, we observed a significant decrease in GFAP immunofluorescence (Fig. 5D) and GFAP + coverage area (Fig. 5E) in Unc5c KI/KI mice, but no change in cell number (Fig. 5F) in the CA1 region of Unc5c KI/KI mice. This suggested that astrocytes in Unc5c KI/KI hippocampi are altered as a result of the mutation, since UNC5C is also expressed in astrocytes and our proteomics study showed that GFAP levels were down-regulated in the Unc5c KI/KI mice (Fig. 3A, E). We speculate that UNC5C signaling may modulate GFAP expression in astrocytes, and that T835M may cause the observed GFAP reduction in Unc5c KI/KI mice. GFAP comprises intermediate filaments of astrocytes and it has been shown that reduction/knockout of GFAP in astrocytes does not necessarily affect their survival (53). Therefore, reduced GFAP levels could affect the cytoskeletal structure of astrocytes, which, in turn could affect their morphology. So, we next sought to examine the morphology of astrocytes in Unc5c KI/KI mice. We used IMARIS software to perform 3D image reconstructions to measure astrocyte process length, volume, number of branch points, number of branch terminal points, number of dendrite terminal points, and soma area (Fig. S5A-M). Although Unc5c KI/KI astrocytes exhibited more filamentous structure with increased branches, branch points and terminal points, we observed no change in the soma area of astrocytes (Fig. S5E). We speculate that astrocytes in Unc5c KI/KI mice may compensate for neuronal cell death by branching out as a way of neuroprotection. Alternatively, the astrocytic changes could be playing a role in cell death, as decreased GFAP levels could be negatively affecting astrocytes functions such as providing cytoskeletal structure to astrocytes, as well as supporting neurons and endothelial cells in the neurovascular unit (54). 6. Microglia show increased activation in Unc5c KI/KI mice. Although microglia do not express UNC5C under normal conditions, other members of UNC5 family can be upregulated under pathological/stress conditions in cultured microglia, AD mice and AD human brain (1, 55). In addition, neuronal apoptosis and astrocytic changes caused by the Unc5C T835M mutation could affect microglia indirectly. To assess the effects of UNC5C T835M in microglia, we performed immunofluorescence microscopy, which revealed that Unc5c KI/KI hippocampi had increased Iba1 and activated phagocytic microglial marker, CD68 compared to Unc5c +/+ mice at 18 months of age (Fig. 6A-E), demonstrating increased microglial activation. Additionally, the overall number of CD68+/Iba1 + cells were significantly increased in Unc5c KI/KI mice, supporting increased activated phagocytic microglia in the Unc5c KI/KI mice (Fig. 6E). Previous studies have shown that increased C1q expression in microglia correlated with increased synaptic engulfment and plays a role in neurodegeneration in an Alzheimer’s disease mouse model, and increased C1q levels were observed in hippocampi of patients with multiple sclerosis (56–58). Further, excessive pruning of the excitatory synapses via complement-dependent pathway via C1q activation in microglia has been recently reported in AD mice(59). As anticipated, we found increased C1q in Unc5c KI/KI microglia by immunofluorescence microscopy, suggesting an elevation of complement-dependent synaptic engulfment by microglia in Unc5c KI/KI mice (Fig. 6A, B, F, G). The Increase in Iba1, CD68 and C1q indicated activation of microglia in Unc5c KI/KI mice, suggesting that microglia were reacting to degenerating neurons and possibly to astrocytic dysfunction (Fig. 6E, G). Alternatively, synaptic pruning by microglia could result from increased levels of PSD95 observed in Unc5c KI/KI mice at 18 months to balance pre- and post-synaptic protein homeostasis (Fig. 2). 7. UNC5C T835M-mediated neurodegeneration is exacerbated in dKI mice. Since the UNC5C T835M mutation was shown to be associated with increased AD risk(1), we studied the UNC5C T835M phenotype in the context of an amyloid pathology to determine if it became worsened. Previous studies have shown that cytotoxic stressors such Aβ 42 , glutamate, and staurosporine exacerbate UNC5C T835M-mediated cell death in primary hippocampal neurons(1), so we hypothesized that T835M mutation would increase cell death in the App NL−G−F mice (NLGF) mouse model, in which the APP gene is humanized with the Swedish double mutation (KM670,671NL), as well as the Arctic (E693G) and Iberian (I716F) mutations, that are all associated with autosomal dominant AD and promote amyloid pathology (16, 60). We crossed Unc5c KI/KI and NLGF mice to generate doubly homozygous NLGF; Unc5c KI/KI (referred to as double KI, (dKI)) and NLGF; Unc5c +/+ (referred to as NLGF) mice. We observed a significant reduction in the neuronal area (NeuN stain) in dKI mice compared to NLGF mice at 12 months and a trend at 6 months, indicating increased hippocampal cell death in the dKI mice (Fig. 7A, B). To ascertain if there is increased cleaved caspase-3 activity, we measured active caspase-3 in hippocampal extracts from 6- and 12-month-old mice (Fig. 7C). Although there was no significant difference in caspase 3 activity between NLGF and dKI mice at 6 months, we observed a small but significant increase in caspase 3 activity in the dKI mice compared to NLGF mice at 12 months of age (Fig. 7C). Also, we observed a significant increase in caspase 3 activity in both genotypes at 12 months compared to 6 months, suggesting that NLGF mice alone do exhibit apoptotic cell death (61), which is significantly increased further in dKI mice (Fig. 7C). Next, we determined whether amyloid pathology exacerbated UNC5C T835M-mediated effects on plaque-associated neuritic dystrophy, another indicator of neuronal dysfunction. To accomplish this, we measured the thickness of the LAMP1 positive halo, a commonly used marker of dystrophic neurites, around the Aβ 42 -defined plaque area (62) and found it to be increased in dKI mice (Fig. 7D, D’, E). Another measure of neuritic dystrophy, the ratio of LAMP1:Aβ42, was also significantly increased in dKI mice (Fig. 7F), supporting increased neuronal/axonal damage and dysfunction in dKI compared to NLGF mice. We repeated these analyses with plaques binned by size and found that the dystrophic neurite increase observed in dKI was primarily driven by increased LAMP1 around smaller plaques under plaque core diameter of 20 µm (Fig. S6A, B), which are thought to be most actively growing and most toxic to surrounding neuropil (63). We then measured plaque coverage using a pan-Aβ antibody (3D6) and found it was increased in the hippocampal region of dKI mice compared to NLGF mice (Fig. 7D, G). To quantify each Aβ species, we performed MSD analysis to determine Aβ 38 , Aβ 40 , and Aβ 42 levels in hippocampal homogenates (Fig. 7H-K). We observed that Aβ 42 and Aβ 42 /Aβ 40 ratio were increased at 12 months (Fig. 7J, K) while Aβ 38 and Aβ 40 levels remained unchanged (Fig. 7H, I), further supporting the amyloid-associated exacerbation of UNC5C T835M-induced neuronal degeneration in the presence of cytotoxic stressors(1). We also used conventional ELISA kit to detect insoluble Aβ 42 levels, and corroborated increased Aβ 42 levels in dKI hippocampal lysates measured by MSD (Fig. 7J, Fig,S6C). We further wanted to understand how the molecular underpinnings of the UNC5C-mediated cell death pathway were affected in the presence of amyloid at 6- and 12-months. Since we observed an increase in p-JNK, CDK5 and NOX1 levels at 12 months in Unc5c KI/KI mice, we expected that amyloid would exacerbate the UN5C-mediated apoptotic pathway. Indeed, we observed in the hippocampi of dKI mice a significant increase in NOX1 and phosphorylated JNK/SAPK/total JNK at 6 months (Fig. 7L-N, Fig.S6D, E), while CDK5 was significantly increased by 12 months (Fig. 7L, O, Fig.S6D, E), suggesting that the UNC5C T835M-mediated apoptotic pathway was exacerbated by amyloid pathology in dKI mice. Netrin1 (NTN1) has been shown to bind to APP and promote non-amyloidogenic processing of APP, thereby reducing the production of Aβ (64). Conversely. reduced NTN1 correlates with increased Aβ in APP transgenic mice and human AD (9, 49, 65–67). Therefore, NTN1 has been proposed as the therapeutic strategy for AD and may also reduce neuroinflammation (64, 67–72). Furthermore, reduction of Netrin1 has been associated with Parkinson’s disease as well (49, 73–76). We hypothesized that the reduced NTN1 levels in Unc5c KI/KI mice (Fig. 4N) could lead to increased Aβ levels in dKI mice. Indeed, we not only observed increased Aβ 42 levels and amyloid deposition in dKI mice (Fig. 7D, G, J), but also noted significantly decreased NTN1 levels in dKI compared to NLGF mice at 6 months, further supporting the hypothesis that the T835M mutation reduces Netrin1 levels, which in turn leads to increased Aβ in dKI mice (Fig. 7L, P, Fig.S6D, E). 8. UNC5C T835M-mediated axonal degeneration/disorganization is exacerbated in dKI mice. Since NLGF mice have synaptic loss by 6 months of age (16), and T835M Unc5c KI/KI mice have a progressive loss of presynaptic markers with age, we hypothesized that the synaptic dysfunction in NLGF mice would be worsened by the UNC5C T835M mutation. Interestingly, by immunoblot analysis, synaptophysin, which is decreased in Unc5c KI/KI compared to Unc5c +/+ was not different between NLGF and dKI hippocampi, although synaptophysin did decrease between 6 and 12 months in both genotypes (Fig. 8A, B, G, Fig.S7A). Likewise, post-synaptic marker PSD95, which was increased in Unc5c KI/KI compared to Unc5c +/+ was unchanged between NLGF and dKI mice (Fig. 8A, C, G, Fig.S7A), suggesting that the synaptic changes of NLGF mask those of the UNC5C T835M mutation. Another presynaptic protein, β-secretase (BACE1), which was decreased in Unc5c KI/KI compared to Unc5c +/+ mice (Fig. 2C) was significantly increased in dKI hippocampi at 12 months compared to NLGF (Fig. 8A, D, G, Fig.S7A). This observation is likely due to increased dystrophic neurites in dKI mice as indicated by increased LAMP1:Aβ 42 ratios (Fig. 7F), since BACE1 is well known to accumulate in dystrophic neurites (18, 77, 78) (Fig. 8A, D, G, Fig.S7A). This is supported by the observation that neurofilament/pan-axonal marker (SMI312), which also accumulates in dystrophic neurite clusters surrounding plaques in AD brains (79) is increased in dKI mice (Fig. 8A, E, G, Fig.S7A). Overall, our results strongly suggest that dKI mice have increased dystrophic neurites with age, as measured by LAMP1, BACE1 and SMI312 immunolabeling and immunoblots (Fig. 7F, 8D, E, G, Fig.S7A). Interestingly, GFAP, which is usually increased in gliosis associated with amyloid, showed significantly decreased levels at 12 months in the dKI mice (Fig. 8A, F, G, Fig.S7A), similar to what we observed in the hippocampi of Unc5c KI/KI mice starting at 12 months. Further analysis of GFAP + astrocytes in NLGF and dKI mice showed that astrocytic morphology was not altered between the two genotypes, suggesting that plaques affect astrocytes similarly in both NLGF and dKI mice. However, only dKI mice showed significantly reduced GFAP levels (Fig. 8A, F, G, Fig. S7A-J). Unlike the synaptic proteins, synaptophysin and PSD95, for GFAP, the effects of UNC5C T835M appear to over-ride the amyloid-associated phenotype typically observed in NLGF mice, although the astrocytic morphology remained unchanged. Finally, we measured the effect of UNC5C T835M on the degree of disorganization in CA1 dendrites. As before, we measured linearity in the dendritic processes that run parallel to each other and perpendicular to the CA1 cell layer. We observed that there was a significant difference near − 10° to + 10° in the dKI mice, compared to NLGF, similar to the difference seen between Unc5c KI/KI and Unc5c +/+ mice, suggesting that there is an exacerbation of the dendritic disorganization in the dKI mice compared to NLGF mice (Fig. 8H). When we compared the degree of disorganization between NLGF and dKI mice at 12 months (Fig. 8H) and Unc5c +/+ and Unc5c KI/KI mice at 18 months (Fig. 2L), we found that the dendrites in the CA1 region of the hippocampus in dKI mice had the highest degree of disorganization compared to the other genotypes (Fig. 8I). Discussion Here, we report the phenotypic characterization of a novel KI mouse model of the UNC5C T835M variant associated with LOAD, which makes the neurons more susceptible to stress-induced cell death, as previously reported by Wetzel Smith-Hunkapillar et al ( 1 ). UNC5C is necessary and essential for survival and maintenance of neurons and astrocytes during development and in aging by the protein dimerization and Netrin1 binding (Fig. 9 , left). We hypothesize that UNC5C T835M could either introduce a kink in the protein conformation or reduce Netrin1 levels or both, which then could lead to biasing the protein to be in the open conformation leading to caspase 3 cleavage and activation of the downstream apoptotic cascade involving reduced Netrin1 and PKD levels, consequent JNK phosphorylation, and increased NADPH oxidase levels. NADPH oxidase activates additional caspases leading to apoptosis of neuronal cells. Although other modes of cell death such as necroptosis, ferroptosis, pyroptosis, and parthanatos in principle could be engaged with aging and neurodegenerative diseases such as in AD ( 80 ), UNC5 family members have been well established to be involved in the apoptotic pathway ( 14 , 37 – 39 , 81 ). Therefore, we focused on whether or not the UNC5C T835M mutation increased apoptosis by investigating Capsase-3 activity and TUNEL signal in Unc5c KI/KI brains. Our results showed that there is indeed a significant increase in caspase-3 activity and TUNEL + neurons, suggesting neuronal cell death via apoptosis (Fig. 4 A-H). Taken together, our results strongly suggest that UNC5C T835M increases the open conformation leading to elevated caspase cleavage and apoptosis, as well as phosphorylation of JNK pathway through decreased and increased PKD and CDK5, respectively. Subsequently, NOX1 levels are increased causing an oxidative stress environment that in turn increases caspase-3 activity in a vicious cycle, resulting in hippocampal neuron death via apoptosis (Fig. 4 J-M). Neuronal degeneration could affect astrocytes or astrocytic disfunction to diminish neuronal health. However age, being the most important contributing factor for AD, in the context of UNC5C T835M may result in a significant reduction in hippocampal volume and increase in ventricular volume of the brain (Fig. 1 E, F), reinforcing the association of UNC5C T835M with LOAD ( 1 ). Further, the mutation may affect microglia indirectly because of chronic insults to neurons and astrocytes. Unc5c has been shown to be expressed at high levels in oligodendrocytes (( 51 , 52 ). Indeed, we observed significantly reduced levels of MBP (Fig. 2 A, B) and our proteomics data showed that the GO term “myelin sheath” was downregulated in Unc5c KI/KI mice (Fig. 3 C, E). These data suggest that the UNC5C T835M mutation could affect oligodendrocytes as well, which is a topic to be investigated in future studies. These individual cellular phenotypes combined appear to contribute to oxidative stress in the Unc5c KI/KI mouse brain, providing an ideal environment for neurodegenerative diseases such as AD, PD, or Huntington’s disease (Fig. 9 , right). Further, reduced Netrin1 levels have been shown to increase the amyloidogenic processing of APP leading to increased Aβ 42 levels ( 70 ), which we show to be the case in dKI mice (Fig. 7 D, G, J, P). The whole mechanism, including neurodegeneration, synaptic degeneration/disorganization and oxidative stress leading to apoptosis, is exacerbated in the presence of amyloid (dKI mice) resulting in neuronal cell death and AD pathogenesis (Fig. 9 , right). In this study, we employed Unc5c KI/KI homozygous mice, although in human patients, the mutation typically presents in a heterozygous condition. Since the phenotypes in Unc5c KI/KI mice exhibited as late as 12–18 months, we did not explore these phenotypes in Unc5c KI/+ mice, which we predict would present at even older ages. In humans, where LOAD occurs over the age of 70 and takes at least 2 decades to develop, a single UNC5C T835M mutant allele may be sufficient to increase susceptibility to AD pathogenesis. Behavioral studies were conducted on 9-month and 12-month-old Unc5c +/+ and Unc5c KI/KI mice but did not yield any conclusive evidence of hippocampal memory impairment in the Unc5c KI/KI mice (data not shown), probably because the neuronal loss just begins around that age causing behavioral deficits to manifest much later. Overall, we report an age-associated loss of hippocampal volume and increased hippocampal neuronal loss with age in UNC5C T835M targeted replacement mice, which could serve as a model to study other neurodegenerative diseases such as Parkinson’s and Huntington’s disease. Future studies inhibiting NOX1 or blocking activation of UNC5C-cleaving caspases, or supplementing with Netrin1, to test their ability to ameliorate AD pathology and neurodegeneration, could potentially lead to new therapeutic agents for AD. Declarations Acknowledgements We thank our collaborators at Genentech in the Genetically Engineered Mouse Models (GEM), Microinjection, and Embryo Technology labs for allele design and creation, and in the Genetic Analysis Lab and Animal Resources for technical assistance. We would like to greatly thank David Kirchenbuechler from the Center for Advanced microscopy and Nikon Imaging Center at Northwestern University for his assistance with imaging and analysis. Authors' contributions J. Atwal (from Genentech) generated the homozygous UNC5C T835M KI mice. D. Karunakaran and R. Vassar conceived the study. D. Karunakaran, M. Ley, J. Guo, A. Khatri, A. Upadhyay, J. Popovic, and K. Sadleir performed the experiments. Specifically, A. Upadhyay (under the guidance of J. Savas) helped with the TMT-MS Proteomics study. D. Procissi performed the MRI studies. D. Karunakaran and R. Vassar wrote the manuscript. K. Sadleir and R. Vassar reviewed the manuscript. All authors read and approved the final manuscript. Funding This work was supported by National Institute on Aging R01 grant AG0577277 (to R.V) and Alzheimer’s Association Research Fellowship AARF-16-443173 (to D.K). Imaging work and analysis was performed at the Northwestern University Center for Advanced Microscopy generously supported by NCI CCSG P30 CA060553 awarded to the Robert H Lurie Comprehensive Cancer Center. Availability of data and material All datasets generated are included in this article and the supplemental information files. Ethics approval and consent to participate This study does not contain any human data. All experimental procedures were approved by the IACUC office of Northwestern University. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Wetzel-Smith MK, Hunkapiller J, Bhangale TR, Srinivasan K, Maloney JA, Atwal JK, et al. A rare mutation in UNC5C predisposes to late-onset Alzheimer's disease and increases neuronal cell death. Nature Medicine. 2014;20(12):1452-7. Hashimoto Y, Toyama Y, Kusakari S, Nawa M, Matsuoka M. An Alzheimer Disease-linked Rare Mutation Potentiates Netrin Receptor Uncoordinated-5C-induced Signaling That Merges with Amyloid β Precursor Protein Signaling. J Biol Chem. 2016;291(23):12282-93. Hong K, Hinck L, Nishiyama M, Poo MM, Tessier-Lavigne M, Stein E. A ligand-gated association between cytoplasmic domains of UNC5 and DCC family receptors converts netrin-induced growth cone attraction to repulsion. Cell. 1999;97(7):927-41. Ackerman SL, Kozak LP, Przyborski SA, Rund LA, Boyer BB, Knowles BB. The mouse rostral cerebellar malformation gene encodes an UNC-5-like protein. Nature. 1997;386(6627):838-42. Finger JH, Bronson RT, Harris B, Johnson K, Przyborski SA, Ackerman SL. The netrin 1 receptors Unc5h3 and Dcc are necessary at multiple choice points for the guidance of corticospinal tract axons. The Journal of neuroscience : the official journal of the Society for Neuroscience. 2002;22(23):10346-56. Coissieux MM, Tomsic J, Castets M, Hampel H, Tuupanen S, Andrieu N, et al. Variants in the netrin-1 receptor UNC5C prevent apoptosis and increase risk of familial colorectal cancer. Gastroenterology. 2011;141(6):2039-46. Tsang KM, Croen LA, Torres AR, Kharrazi M, Delorenze GN, Windham GC, et al. A genome-wide survey of transgenerational genetic effects in autism. PloS one. 2013;8(10):e76978. Whitton L, Apostolova G, Rieder D, Dechant G, Rea S, Donohoe G, et al. Genes regulated by SATB2 during neurodevelopment contribute to schizophrenia and educational attainment. PLoS genetics. 2018;14(7):e1007515. Chen G, Kang SS, Wang Z, Ahn EH, Xia Y, Liu X, et al. Netrin-1 receptor UNC5C cleavage by active δ-secretase enhances neurodegeneration, promoting Alzheimer's disease pathologies. Sci Adv. 2021;7(16). Yang HS, Chhatwal JP, Xu J, White CC, Hanseeuw B, Rabin JS, et al. An UNC5C Allele Predicts Cognitive Decline and Hippocampal Atrophy in Clinically Normal Older Adults. J Alzheimers Dis. 2019;68(3):1161-70. Yang HS, White CC, Chibnik LB, Klein HU, Schneider JA, Bennett DA, et al. UNC5C variants are associated with cerebral amyloid angiopathy. Neurol Genet. 2017;3(4):e176. Sun JH, Wang HF, Zhu XC, Yu WJ, Tan CC, Jiang T, et al. The Impact of UNC5C Genetic Variations on Neuroimaging in Alzheimer's Disease. Mol Neurobiol. 2016;53(10):6759-67. Li Q, Wang BL, Sun FR, Li JQ, Cao XP, Tan L. The role of UNC5C in Alzheimer's disease. Ann Transl Med. 2018;6(10):178. Llambi F, Causeret F, Bloch-Gallego E, Mehlen P. Netrin-1 acts as a survival factor via its receptors UNC5H and DCC. Embo j. 2001;20(11):2715-22. Mehlen P, Rabizadeh S, Snipas SJ, Assa-Munt N, Salvesen GS, Bredesen DE. The DCC gene product induces apoptosis by a mechanism requiring receptor proteolysis. Nature. 1998;395(6704):801-4. Saito T, Matsuba Y, Mihira N, Takano J, Nilsson P, Itohara S, et al. Single App knock-in mouse models of Alzheimer's disease. Nat Neurosci. 2014;17(5):661-3. Caothien R, Yu C, Tam L, Newman R, Nakao B, Alcantar T, et al. Accelerated embryonic stem cell screening with a highly efficient genotyping pipeline. Mol Biol Rep. 2022;49(4):3281-8. Zhao J, Fu Y, Yasvoina M, Shao P, Hitt B, O'Connor T, et al. Beta-site amyloid precursor protein cleaving enzyme 1 levels become elevated in neurons around amyloid plaques: implications for Alzheimer's disease pathogenesis. J Neurosci. 2007;27(14):3639-49. Johnson-Wood K, Lee M, Motter R, Hu K, Gordon G, Barbour R, et al. Amyloid precursor protein processing and Aβ42 deposition in a transgenic mouse model of Alzheimer disease. Proceedings of the National Academy of Sciences. 1997;94(4):1550-5. Rezakhaniha R, Agianniotis A, Schrauwen JT, Griffa A, Sage D, Bouten CV, et al. Experimental investigation of collagen waviness and orientation in the arterial adventitia using confocal laser scanning microscopy. Biomechanics and modeling in mechanobiology. 2012;11(3-4):461-73. Jongkamonwiwat N, Ramirez MA, Edassery S, Wong ACY, Yu J, Abbott T, et al. Noise Exposures Causing Hearing Loss Generate Proteotoxic Stress and Activate the Proteostasis Network. Cell Rep. 2020;33(8):108431. He L, Diedrich J, Chu YY, Yates JR, 3rd. Extracting Accurate Precursor Information for Tandem Mass Spectra by RawConverter. Analytical chemistry. 2015;87(22):11361-7. Xu T, Park SK, Venable JD, Wohlschlegel JA, Diedrich JK, Cociorva D, et al. ProLuCID: An improved SEQUEST-like algorithm with enhanced sensitivity and specificity. Journal of proteomics. 2015;129:16-24. Cociorva D, D LT, Yates JR. Validation of tandem mass spectrometry database search results using DTASelect. Current protocols in bioinformatics. 2007;Chapter 13:Unit 13.4. Elias JE, Gygi SP. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nature methods. 2007;4(3):207-14. McAlister GC, Nusinow DP, Jedrychowski MP, Wühr M, Huttlin EL, Erickson BK, et al. MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Analytical chemistry. 2014;86(14):7150-8. Park SK, Aslanian A, McClatchy DB, Han X, Shah H, Singh M, et al. Census 2: isobaric labeling data analysis. Bioinformatics (Oxford, England). 2014;30(15):2208-9. Turrigiano GG, Nelson SB. Homeostatic plasticity in the developing nervous system. Nature reviews Neuroscience. 2004;5(2):97-107. Turrigiano G. Homeostatic synaptic plasticity: local and global mechanisms for stabilizing neuronal function. Cold Spring Harbor perspectives in biology. 2012;4(1):a005736. Turrigiano GG, Leslie KR, Desai NS, Rutherford LC, Nelson SB. Activity-dependent scaling of quantal amplitude in neocortical neurons. Nature. 1998;391(6670):892-6. Richmond JE, Davis WS, Jorgensen EM. UNC-13 is required for synaptic vesicle fusion in C. elegans. Nature neuroscience. 1999;2(11):959-64. Gioia DA, Alexander N, McCool BA. Ethanol Mediated Inhibition of Synaptic Vesicle Recycling at Amygdala Glutamate Synapses Is Dependent upon Munc13-2. Frontiers in neuroscience. 2017;11:424. Varoqueaux F, Sigler A, Rhee JS, Brose N, Enk C, Reim K, et al. Total arrest of spontaneous and evoked synaptic transmission but normal synaptogenesis in the absence of Munc13-mediated vesicle priming. Proceedings of the National Academy of Sciences of the United States of America. 2002;99(13):9037-42. Sherman BT, Hao M, Qiu J, Jiao X, Baseler MW, Lane HC, et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic acids research. 2022;50(W1):W216-w21. Cataldo AM, Barnett JL, Pieroni C, Nixon RA. Increased neuronal endocytosis and protease delivery to early endosomes in sporadic Alzheimer's disease: neuropathologic evidence for a mechanism of increased beta-amyloidogenesis. The Journal of neuroscience : the official journal of the Society for Neuroscience. 1997;17(16):6142-51. Adzhubei IA SS, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR. A method and server for predicting damaging missense mutations. Nature methods. 2010. Tinel A, Janssens S, Lippens S, Cuenin S, Logette E, Jaccard B, et al. Autoproteolysis of PIDD marks the bifurcation between pro-death caspase-2 and pro-survival NF-kappaB pathway. Embo j. 2007;26(1):197-208. Williams ME, Lu X, McKenna WL, Washington R, Boyette A, Strickland P, et al. UNC5A promotes neuronal apoptosis during spinal cord development independent of netrin-1. Nat Neurosci. 2006;9(8):996-8. Zhu Y, Li Y, Nakagawara A. UNC5 dependence receptor family in human cancer: A controllable double-edged sword. Cancer Lett. 2021;516:28-35. Wagner EF, Nebreda AR. Signal integration by JNK and p38 MAPK pathways in cancer development. Nature reviews Cancer. 2009;9(8):537-49. Asaithambi A, Kanthasamy A, Saminathan H, Anantharam V, Kanthasamy AG. Protein kinase D1 (PKD1) activation mediates a compensatory protective response during early stages of oxidative stress-induced neuronal degeneration. Molecular neurodegeneration. 2011;6:43. Rozengurt E, Rey O, Waldron RT. Protein kinase D signaling. J Biol Chem. 2005;280(14):13205-8. Van Lint J, Rykx A, Maeda Y, Vantus T, Sturany S, Malhotra V, et al. Protein kinase D: an intracellular traffic regulator on the move. Trends in cell biology. 2002;12(4):193-200. Wilkinson BL, Landreth GE. The microglial NADPH oxidase complex as a source of oxidative stress in Alzheimer's disease. Journal of Neuroinflammation. 2006;3(1):30. Abramov AY, Canevari L, Duchen MR. Beta-amyloid peptides induce mitochondrial dysfunction and oxidative stress in astrocytes and death of neurons through activation of NADPH oxidase. The Journal of neuroscience : the official journal of the Society for Neuroscience. 2004;24(2):565-75. Zhang Q, Ahuja HS, Zakeri ZF, Wolgemuth DJ. Cyclin-dependent kinase 5 is associated with apoptotic cell death during development and tissue remodeling. Developmental biology. 1997;183(2):222-33. Ahuja HS, Zhu Y, Zakeri Z. Association of cyclin-dependent kinase 5 and its activator p35 with apoptotic cell death. Developmental genetics. 1997;21(4):258-67. Sun KH, Lee HG, Smith MA, Shah K. Direct and indirect roles of cyclin-dependent kinase 5 as an upstream regulator in the c-Jun NH2-terminal kinase cascade: relevance to neurotoxic insults in Alzheimer's disease. Mol Biol Cell. 2009;20(21):4611-9. Zhu K, Wang H, Ye K, Chen G, Zhang Z. Netrin-1 signaling pathway mechanisms in neurodegenerative diseases. Neural Regeneration Research. 2025;20(4):960-72. Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, et al. Proteomics. Tissue-based map of the human proteome. Science (New York, NY). 2015;347(6220):1260419. Bennett ML, Bennett FC, Liddelow SA, Ajami B, Zamanian JL, Fernhoff NB, et al. New tools for studying microglia in the mouse and human CNS. Proc Natl Acad Sci U S A. 2016;113(12):E1738-46. Clarke LE, Liddelow SA, Chakraborty C, Münch AE, Heiman M, Barres BA. Normal aging induces A1-like astrocyte reactivity. Proceedings of the National Academy of Sciences. 2018;115(8):E1896-E905. Menet V, Giménez YRM, Sandillon F, Privat A. GFAP null astrocytes are a favorable substrate for neuronal survival and neurite growth. Glia. 2000;31(3):267-72. Yang Z, Wang KK. Glial fibrillary acidic protein: from intermediate filament assembly and gliosis to neurobiomarker. Trends Neurosci. 2015;38(6):364-74. Gerrits E, Brouwer N, Kooistra SM, Woodbury ME, Vermeiren Y, Lambourne M, et al. Distinct amyloid-β and tau-associated microglia profiles in Alzheimer's disease. Acta Neuropathol. 2021;141(5):681-96. Michailidou I, Willems JG, Kooi EJ, van Eden C, Gold SM, Geurts JJ, et al. Complement C1q-C3-associated synaptic changes in multiple sclerosis hippocampus. Annals of neurology. 2015;77(6):1007-26. Györffy BA, Tóth V, Török G, Gulyássy P, Kovács R, Vadászi H, et al. Synaptic mitochondrial dysfunction and septin accumulation are linked to complement-mediated synapse loss in an Alzheimer's disease animal model. Cell Mol Life Sci. 2020;77(24):5243-58. Zhong L SX, Wang W, Li Y, Zhuo R, Wang K, Zhang L, Hu DD, Hong Y, Chen L, Rao H, Li T, Chen M, Lin Z, Zhang YW, Wang X, Yan XX, Chen X, Bu G, Chen XF. . TREM2 receptor protects against complement-mediated synaptic loss by binding to complement C1q during neurodegeneration. Immunity. 2023. Gomez-Arboledas A, Acharya MM, Tenner AJ. The Role of Complement in Synaptic Pruning and Neurodegeneration. ImmunoTargets and therapy. 2021;10:373-86. Masuda A, Kobayashi Y, Kogo N, Saito T, Saido TC, Itohara S. Cognitive deficits in single App knock-in mouse models. Neurobiol Learn Mem. 2016;135:73-82. Pang K, Jiang R, Zhang W, Yang Z, Li LL, Shimozawa M, et al. An App knock-in rat model for Alzheimer's disease exhibiting Aβ and tau pathologies, neuronal death and cognitive impairments. Cell Res. 2022;32(2):157-75. Sharoar MG, Palko S, Ge Y, Saido TC, Yan R. Accumulation of saposin in dystrophic neurites is linked to impaired lysosomal functions in Alzheimer's disease brains. Mol Neurodegener. 2021;16(1):45. Condello C, Schain A, Grutzendler J. Multicolor time-stamp reveals the dynamics and toxicity of amyloid deposition. Sci Rep. 2011;1:19. Lourenço FC, Galvan V, Fombonne J, Corset V, Llambi F, Müller U, et al. Netrin-1 interacts with amyloid precursor protein and regulates amyloid-beta production. Cell Death Differ. 2009;16(5):655-63. Ju T, Sun L, Fan Y, Wang T, Liu Y, Liu D, et al. Decreased Netrin-1 in Mild Cognitive Impairment and Alzheimer's Disease Patients. Front Aging Neurosci. 2021;13:762649. Eisele YS, Obermüller U, Heilbronner G, Baumann F, Kaeser SA, Wolburg H, et al. Peripherally applied Abeta-containing inoculates induce cerebral beta-amyloidosis. Science. 2010;330(6006):980-2. Sun L, Ju T, Wang T, Zhang L, Ding F, Zhang Y, et al. Decreased Netrin-1 and Correlated Th17/Tregs Balance Disorder in Aβ(1-42) Induced Alzheimer's Disease Model Rats. Front Aging Neurosci. 2019;11:124. Cai M, Zheng Q, Chen Y, Liu S, Zhu H, Bai B. Insights from the neural guidance factor Netrin-1 into neurodegeneration and other diseases. Front Mol Neurosci. 2024;17:1379726. Shabani M, Haghani M, Tazangi PE, Bayat M, Shid Moosavi SM, Ranjbar H. Netrin-1 improves the amyloid-β-mediated suppression of memory and synaptic plasticity. Brain Res Bull. 2017;131:107-16. Spilman PR, Corset V, Gorostiza O, Poksay KS, Galvan V, Zhang J, et al. Netrin-1 Interrupts Amyloid-β Amplification, Increases sAβPPα in vitro and in vivo, and Improves Cognition in a Mouse Model of Alzheimer's Disease. J Alzheimers Dis. 2016;52(1):223-42. Zamani E, Parviz M, Roghani M, Hosseini M, Mohseni-Moghaddam P, Nikbakhtzadeh M. Netrin-1 protects the SH-SY5Y cells against amyloid beta neurotoxicity through NF-κB/Nrf2 dependent mechanism. Mol Biol Rep. 2020;47(12):9271-7. Borel F, Marzocca F, Delcros JG, Rama N, Mehlen P, Ferrer JL. Molecular characterization of Netrin-1 and APP receptor binding: New leads to block the progression of senile plaques in Alzheimer's disease. Biochem Biophys Res Commun. 2017;488(3):466-70. Chen G, Ahn EH, Kang SS, Xia Y, Liu X, Zhang Z, et al. UNC5C Receptor Proteolytic Cleavage by Active AEP Promotes Dopaminergic Neuronal Degeneration in Parkinson's Disease. Adv Sci (Weinh). 2022;9(7):e2103396. Ahn EH, Kang SS, Liu X, Cao X, Choi SY, Musazzi L, et al. BDNF and Netrin-1 repression by C/EBPβ in the gut triggers Parkinson's disease pathologies, associated with constipation and motor dysfunctions. Prog Neurobiol. 2021;198:101905. Jasmin M, Ahn EH, Voutilainen MH, Fombonne J, Guix C, Viljakainen T, et al. Netrin-1 and its receptor DCC modulate survival and death of dopamine neurons and Parkinson's disease features. Embo j. 2021;40(3):e105537. Kang EJ, Jang SM, Lee YJ, Jeong YJ, Kim YJ, Kang SS, et al. The couple of netrin-1/α-Synuclein regulates the survival of dopaminergic neurons via α-Synuclein disaggregation. BMB Rep. 2023;56(2):126-31. Sadleir KR, Kandalepas PC, Buggia-Prévot V, Nicholson DA, Thinakaran G, Vassar R. Presynaptic dystrophic neurites surrounding amyloid plaques are sites of microtubule disruption, BACE1 elevation, and increased Aβ generation in Alzheimer's disease. Acta Neuropathol. 2016;132(2):235-56. Kandalepas PC, Sadleir KR, Eimer WA, Zhao J, Nicholson DA, Vassar R. The Alzheimer's β-secretase BACE1 localizes to normal presynaptic terminals and to dystrophic presynaptic terminals surrounding amyloid plaques. Acta Neuropathol. 2013;126(3):329-52. Masliah E, Mallory M, Hansen L, Alford M, DeTeresa R, Terry R. An antibody against phosphorylated neurofilaments identifies a subset of damaged association axons in Alzheimer's disease. The American journal of pathology. 1993;142(3):871-82. Della Torre L, Beato A, Capone V, Carannante D, Verrilli G, Favale G, et al. Involvement of regulated cell deaths in aging and age-related pathologies. Ageing Res Rev. 2024;95:102251. Mehlen P, Rabizadeh S, Snipas SJ, Assa-Munt N, Salvesen GS, Bredesen DE. The DCC gene product induces apoptosis by a mechanism requiring receptor proteolysis. Nature. 1998;395(6704):801-4. Supplementary Files Additionalfileslegends.docx Supplementaryfigure1S1.tif Supplementaryfigure2S2.tif Supplementaryfigure3S3.tif Supplementaryfigure4S4.tif Supplementaryfigure5S5.tif Supplementaryfigure6S6.tif Supplementaryfigure7S7.tif SupplementaryfileS8.xlsx Cite Share Download PDF Status: Published Journal Publication published 04 Jun, 2025 Read the published version in Molecular Neurodegeneration → Version 1 posted Reviewers agreed at journal 01 Apr, 2025 Reviewers invited by journal 01 Apr, 2025 Editor assigned by journal 01 Apr, 2025 First submitted to journal 31 Mar, 2025 Editorial decision: Minor revision 04 Dec, 2024 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5462618","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436899691,"identity":"6a1d9445-60db-4191-9d67-a7ba646d6df3","order_by":0,"name":"Devi Krishna Priya Karunakaran","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-9746-0200","institution":"Northwestern University Feinberg School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Devi","middleName":"Krishna Priya","lastName":"Karunakaran","suffix":""},{"id":436899692,"identity":"428a9a8b-6485-4923-a520-7ff5d802e304","order_by":1,"name":"Makenna Ley","email":"","orcid":"","institution":"Northwestern University Feinberg School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Makenna","middleName":"","lastName":"Ley","suffix":""},{"id":436899693,"identity":"f9779f8e-d754-4035-b756-ee92e9cf94e5","order_by":2,"name":"Joanna Guo","email":"","orcid":"","institution":"Northwestern University Feinberg School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Joanna","middleName":"","lastName":"Guo","suffix":""},{"id":436899694,"identity":"93f07b65-a5b7-4f10-8970-d23707debb8f","order_by":3,"name":"Ammaarah Khatri","email":"","orcid":"","institution":"Northwestern University Feinberg School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ammaarah","middleName":"","lastName":"Khatri","suffix":""},{"id":436899695,"identity":"92a7c294-06d4-4ca9-b615-328b7bc16282","order_by":4,"name":"Katherine Sadleir","email":"","orcid":"","institution":"Northwestern University Feinberg School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Katherine","middleName":"","lastName":"Sadleir","suffix":""},{"id":436899696,"identity":"6b594dd8-9b73-4eb4-9823-83d00856fd17","order_by":5,"name":"Jelena Popovic","email":"","orcid":"","institution":"Northwestern University Feinberg School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jelena","middleName":"","lastName":"Popovic","suffix":""},{"id":436899697,"identity":"1d4b40a7-e828-421f-81e3-516c589514f7","order_by":6,"name":"Arun Kumar Upadhyay","email":"","orcid":"","institution":"Northwestern University Feinberg School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Arun","middleName":"Kumar","lastName":"Upadhyay","suffix":""},{"id":436899698,"identity":"463a3142-740c-4c72-bff2-113412c5f249","order_by":7,"name":"Jeffrey N Savas","email":"","orcid":"","institution":"Northwestern University Feinberg School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jeffrey","middleName":"N","lastName":"Savas","suffix":""},{"id":436899699,"identity":"3ec612e9-1f29-44ef-b8d7-34742c0c0bcc","order_by":8,"name":"Daniele Procissi","email":"","orcid":"","institution":"Northwestern University Feinberg School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Daniele","middleName":"","lastName":"Procissi","suffix":""},{"id":436899700,"identity":"d9d5ba6e-b02a-4037-92bc-a66685cda0e5","order_by":9,"name":"Jasvinder Atwal","email":"","orcid":"","institution":"Genentech USA Inc South San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Jasvinder","middleName":"","lastName":"Atwal","suffix":""},{"id":436899701,"identity":"a93fd067-1a11-497c-a91c-dd76e9cb348c","order_by":10,"name":"Robert Vassar","email":"","orcid":"https://orcid.org/0000-0002-1358-504X","institution":"Northwestern University Feinberg School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Vassar","suffix":""}],"badges":[],"createdAt":"2024-11-15 19:27:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5462618/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5462618/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13024-025-00850-z","type":"published","date":"2025-06-04T15:57:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79780743,"identity":"ee8712b3-fdad-48b5-b00b-45ee595e6907","added_by":"auto","created_at":"2025-04-02 14:59:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":6611637,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eUnc5c\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cstrong\u003eKI/KI\u003c/strong\u003e\u003c/em\u003e\u003c/sup\u003e\u003cstrong\u003e mice show significant hippocampal atrophy with age. A. \u003c/strong\u003eCoronal sections of hemibrains immunostained for NeuN (green) in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003eand \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice at 6, 12, and 18 months. Scale bar, 1.16mm. Three different bregma positions (-1.34mm (anterior), -1.70mm (center), -2.06mm (posterior)) were chosen for each animal to do the area analysis. Hippocampus is highlighted by dashed yellow region.\u0026nbsp; \u003cstrong\u003eB.\u003c/strong\u003e Quantification of hippocampal area by ImageJ from 6-18 months. Blue circles - males; pink triangle - females. n=4 females, 2 males (6 months), n=5 females, 4 males (12 months), n=6 females, 2 males (18 months). \u003cstrong\u003eC.\u003c/strong\u003e Schematic representation of the longitudinal MRI study. n=8/genotype (4 males, 4 females) \u003cstrong\u003eD.\u003c/strong\u003e (Left) MRI slices with superimposed segmented regions of interest (Hippocampus (Blue), ventricle (red)) visualizing the changes in ventricle and hippocampus size over the course of ~ 5-6 months (13 to 18 month) for \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003e(top) and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e (bottom) mice.\u0026nbsp; (Right) Representative 3D rendered MR images are shown for both \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003eand \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice at 18 months. \u003cstrong\u003eE-F.\u003c/strong\u003e\u0026nbsp; Quantification of the change over time (13 months to 18 months) in ventricular volume (E) and hippocampal volume (F). \u003cstrong\u003eG.\u003c/strong\u003e Representative MRI image slice showing the cortical thickness measurement. Red lines indicate various positions where the thickness was measured. Measurements (in white) in three different regions are similar in both \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+ \u003c/em\u003e\u003c/sup\u003eand \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003emice at 18 months.\u0026nbsp; \u003cstrong\u003eH. \u003c/strong\u003eQuantification of cortical thickness in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003eand \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003emice at 13 and 18 months. \u003cstrong\u003eI.\u003c/strong\u003e Representative 2D MRI slices depicting the brains of \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003eand \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice with superimposed fractional anisotropy (FA) patterns thresholded at ~ 0.2 (red) across the whole brain. \u003cstrong\u003eJ.\u003c/strong\u003e Quantification of the change over time (∆) in FA/FA\u003csub\u003eWT\u003c/sub\u003e (13 months to 18 months). Statistics calculated using two-tailed unpaired student’s t-tests and ordinary one-way ANOVA using Tukey’s multiple comparison tests with Bartlett’s test correction. Data are presented as mean ± SEM. ns=non-significant. *\u003cem\u003ep\u003c/em\u003e-value ≤ 0.05, **\u0026nbsp;\u003cem\u003ep\u003c/em\u003e-value ≤ 0.01, *** \u003cem\u003ep\u003c/em\u003e-value ≤ 0.001, and ****\u003cem\u003ep\u003c/em\u003e-value of ≤ 0.0001.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5462618/v1/f1022c366c02007a8b6edbb8.png"},{"id":79780746,"identity":"e3eb42b4-6b4c-452a-b049-226bca9a9c6c","added_by":"auto","created_at":"2025-04-02 14:59:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3613402,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eUnc5c\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cstrong\u003eKI/KI\u003c/strong\u003e\u003c/em\u003e\u003c/sup\u003e\u003cstrong\u003e mice have axonal and synaptic degeneration and dendritic disorganization. A.\u003c/strong\u003e Immunoblots of presynaptic/axonal and postsynaptic proteins in the hippocampal samples of 18-month-old \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+ \u003c/em\u003e\u003c/sup\u003eand \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e \u003c/em\u003emice. \u003cstrong\u003eB-F.\u003c/strong\u003e Quantification of the immunoblots in (\u003cstrong\u003eA\u003c/strong\u003e) normalized to GAPDH. Blue circles - males; pink triangle – females. n=4-5 females, 3-4 males/genotype. \u003cstrong\u003eG.\u003c/strong\u003e CA1 region of hippocampus stained for post-synaptic proteins such as PSD95 at 3-months and 18-months and MAP2 at 18 months in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice. \u003cstrong\u003eH, I.\u003c/strong\u003e Quantification of mean fluorescence intensity of PSD95 (H) and MAP2 (I). \u003cstrong\u003eJ, K. \u003c/strong\u003eGraph showing the distribution of orientation of dendrites with respect to the CA1 nuclear layer obtained at 3 months (J) and 18 months (K) in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003e(black) and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e \u003c/em\u003e(red) mice. n=6 (\u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e), n=7 (\u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e). Statistics calculated using two-tailed unpaired student’s t-tests and ordinary one-way ANOVA using Tukey’s multiple comparison tests with Bartlett’s test correction. Data are presented as mean ± SEM. ns=non-significant. \u003cem\u003ep\u003c/em\u003e-value ≤ 0.05, *\u003cem\u003ep\u003c/em\u003e-value ≤ 0.01, **\u003cem\u003ep\u003c/em\u003e-value ≤ 0.001, and ****\u003cem\u003ep\u003c/em\u003e-value of ≤ 0.0001.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5462618/v1/647707ebf5ced545e1cfcd44.png"},{"id":79780762,"identity":"50b5a6c1-1f47-4f81-99f9-610a73f6fd03","added_by":"auto","created_at":"2025-04-02 14:59:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3911436,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProteomics reveal upregulation of oxidative stress and down-regulation of chaperone proteins in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eUnc5c\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cstrong\u003eKI/KI\u003c/strong\u003e\u003c/em\u003e\u003c/sup\u003e\u003csup\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003emice hippocampi.\u003c/strong\u003e \u003cstrong\u003eA.\u003c/strong\u003e Volcano plot of up-and down-regulated proteins obtained by TMT-MS of hippocampal homogenates of \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI \u003c/em\u003e\u003c/sup\u003emice at 18 months (n=5 females/genotype). Number of up-regulated (red) and down-regulated proteins (blue) are shown along with total number of proteins obtained. \u003cstrong\u003eB, C.\u003c/strong\u003e DAVID analysis of up-regulated proteins (B) and down-regulated proteins (C) showing the most significant GO terms. \u003cstrong\u003eD, E.\u003c/strong\u003e List of proteins under each significantly up-regulated (D) and down-regulated (E) biological process/GO term in DAVID analysis. \u003cstrong\u003eF.\u003c/strong\u003e Immunoblot analysis of hippocampal homogenates from 18-month \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice for proteins (underlined in red in D) under each of the GO categories listed in D. \u003cstrong\u003eG-I.\u003c/strong\u003e Quantification of the immunoblots for UQCRB (G), CALM1 (H) and CAPZB (I) normalized to β-tubulin (n=5 females/genotype). \u003cstrong\u003eJ.\u003c/strong\u003e Immunoblot analysis of hippocampal homogenates from 18-month \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice for proteins (underlined in red in E) under each of the GO categories listed in E. \u003cstrong\u003eK-M.\u003c/strong\u003e Quantification of the immunoblots for GFAP (K), HSPD1 (L) and CACNB4 (M) normalized to β-tubulin (n=5 females/genotype). Statistics calculated using two-tailed unpaired student’s t-tests. Data are presented as mean ± SEM. ns=non-significant. \u003cem\u003ep\u003c/em\u003e-value ≤ 0.05, **\u003cem\u003ep\u003c/em\u003e-value ≤ 0.01, ***\u003cem\u003ep\u003c/em\u003e-value ≤ 0.001, and ***\u003cem\u003ep\u003c/em\u003e-value of ≤ 0.0001.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5462618/v1/18a8900e5338d9c54051da6c.png"},{"id":79781437,"identity":"2265a745-399f-4c64-90db-42011f052705","added_by":"auto","created_at":"2025-04-02 15:07:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":7890515,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIncreased neuronal apoptosis in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eUnc5c\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cstrong\u003eKI/KI\u003c/strong\u003e\u003c/em\u003e\u003c/sup\u003e\u003csup\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003emice. A. \u003c/strong\u003eImmunoblot analysis using an Unc5c-specific antibody on 12-month-old hippocampal samples from \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003eand \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice. \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKO/KO\u003c/em\u003e\u003c/sup\u003e is used as a negative control. The Full-length (FL) UNC5C band was observed around 115 kDa. Note two additional lower bands that are specific to the UNC5C antibody labeled Cleaved 1 (CL1) and Cleaved 2 (CL2) above and below the 50k D marker, respectively. N=10 (5 females, 5 males) \u003cstrong\u003eB.\u003c/strong\u003e Quantification of FL, CL1, CL2 and combined (CL1+CL2) bands of UNC5C normalized to GAPDH and presented as arbitrary units (a.u.). \u003cstrong\u003eC. \u003c/strong\u003eQuantification of the ratios of CL1, CL2, and combined CL1+CL2 to FL bands.\u003cstrong\u003e \u003c/strong\u003eBlue circles – males; pink triangles – pink. \u003cstrong\u003eD.\u003c/strong\u003e Confocal images of CA1 region from 18m \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003emice stained for TUNEL-positive neurons (NeuN, magenta; TUNEL\u003csup\u003e+\u003c/sup\u003e, red). Scale bar, 100μm. Sections around -1.70mm Bregma position were chosen for analysis. White boxed region in upper panels is enlarged in lower panels. Yellow arrowheads show TUNEL\u003csup\u003e+\u003c/sup\u003e cells, of which some are NeuN\u003csup\u003e+ \u003c/sup\u003e(white arrowheads). Scale bar, 20μm. \u003cstrong\u003eE, F.\u003c/strong\u003e Quantification of number of TUNEL\u003csup\u003e+\u003c/sup\u003e neurons (TUNEL\u003csup\u003e+\u003c/sup\u003e NeuN\u003csup\u003e+\u003c/sup\u003e) (E) and non-neuronal TUNEL\u003csup\u003e+ \u003c/sup\u003ecells (TUNEL\u003csup\u003e+\u003c/sup\u003e NeuN\u003csup\u003e-\u003c/sup\u003e) (F)\u003csup\u003e \u003c/sup\u003ein hippocampal sections of \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003e(black) and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e (red) mice. Blue circles - males; pink triangle - females. N=5-7 males, n=5-8 females/genotype/age. \u003cstrong\u003eG.\u003c/strong\u003e Quantification of the %NeuN covered area in the hippocampus at 18 months. n=6 mice/genotype (2 sections/animal). \u003cstrong\u003eH.\u003c/strong\u003e Quantification of caspase-3 activity assay expressed as relative fluorescent units. n=9-10 mice/genotype \u003cstrong\u003eI.\u003c/strong\u003e Immunoblot analysis of hippocampal homogenates from \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003emice for proteins involved in UNC5C T835M-mediated apoptosis pathway at 12 months.\u0026nbsp;\u0026nbsp; \u003cstrong\u003eJ-N. \u003c/strong\u003eQuantification of immunoblot signals \u0026nbsp;for Protein kinase-D (PKD) (\u003cstrong\u003eJ\u003c/strong\u003e), phospho-JNK/JNK (\u003cstrong\u003eK\u003c/strong\u003e), cycline-dependent kinase (CDK5) (\u003cstrong\u003eL\u003c/strong\u003e), NADPH oxidase (NOX1) (\u003cstrong\u003eM\u003c/strong\u003e), Netrin1 (NTN1) (\u003cstrong\u003eN\u003c/strong\u003e) normalized to GAPDH. Blue circles - males; pink triangle - females. n=3-5 females, n=2-5 males/genotype. Statistics calculated using two-tailed unpaired student’s t-tests, multiple t-test, two-way ANOVA using Sidak’s multiple comparisons test (for panels B and C) and ordinary one-way ANOVA using Tukey’s multiple comparison tests with Bartlett’s test correction. Data are presented as mean ± SEM. ns=non-significant. *\u003cem\u003ep\u003c/em\u003e-value ≤ 0.05, **\u003cem\u003ep\u003c/em\u003e-value ≤ 0.01, ***\u003cem\u003ep\u003c/em\u003e-value ≤ 0.001, and ****\u003cem\u003ep\u003c/em\u003e-value of ≤ 0.0001.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5462618/v1/f88e4beebbc0fe0ef9b2fcd7.png"},{"id":79782138,"identity":"c504b8d7-c718-4112-9e99-b1339cff0c63","added_by":"auto","created_at":"2025-04-02 15:15:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5023201,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAstrocyte morphology is significantly altered in the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eUnc5c\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cstrong\u003eKI/KI\u003c/strong\u003e\u003c/em\u003e\u003c/sup\u003e\u003cstrong\u003e mice. A-C. \u003c/strong\u003eConfocal images of CA1 region of \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003emice at 3 months (\u003cstrong\u003eA\u003c/strong\u003e), 12 months (\u003cstrong\u003eB\u003c/strong\u003e) and 18 months of age (\u003cstrong\u003eC\u003c/strong\u003e) immunostained for GFAP (astrocytes). Scale bar, 33μm. Sections around -1.70mm Bregma position were chosen for analysis. White-dashed boxed region is enlarged in \u003cstrong\u003eA’\u003c/strong\u003e (3 months), \u003cstrong\u003eB’\u003c/strong\u003e (12 months), and \u003cstrong\u003eC’\u003c/strong\u003e (18 months). Scale bar, 50μm. \u003cstrong\u003eD. \u003c/strong\u003eQuantification of mean fluorescent intensity of GFAP in the CA1 region of hippocampus at 3-6 months, 12 months and 18 months in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003emice. \u003cstrong\u003eE.\u003c/strong\u003e Quantification of the % GFAP covered area in the hippocampus at 12 months and 18 months. Blue circles - males; pink triangle - females. n=5-7 females, 5-7 males/genotype/age (2 sections/animal were used in the analysis). \u003cstrong\u003eF.\u003c/strong\u003e Quantification of number of astrocytes in the CA1 region of hippocampus at 18 months. n=4 females, 3 males. Statistics calculated using two-tailed unpaired student’s t-tests and ordinary one-way ANOVA using Tukey’s multiple comparison tests with Bartlett’s test correction. Data are presented as mean ± SEM. Only comparisons with significant p-value are indicated. * \u003cem\u003ep\u003c/em\u003e-value ≤ 0.05, **\u0026nbsp;\u003cem\u003ep\u003c/em\u003e-value ≤ 0.01, **\u0026nbsp;\u003cem\u003ep\u003c/em\u003e-value ≤ 0.001, and **** \u003cem\u003ep\u003c/em\u003e-value of ≤ 0.0001.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5462618/v1/af8cd100c6116643cbb0280e.png"},{"id":79780747,"identity":"b82a5a95-485c-4f37-8ae7-d16a65ade32c","added_by":"auto","created_at":"2025-04-02 14:59:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":6415038,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicroglia show increased activation in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eUnc5c\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cstrong\u003eKI/KI\u003c/strong\u003e\u003c/em\u003e\u003c/sup\u003e\u003cstrong\u003e mice. A. \u003c/strong\u003eConfocal microscope images of CA1 of \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI \u003c/em\u003e\u003c/sup\u003emice at 18 months of age immunostained for Iba1 (red), CD68 (magenta) and C1q (green).\u003cstrong\u003e \u003c/strong\u003eScale bar, 33μm. \u003cstrong\u003eB. \u003c/strong\u003eHigher magnification of images in A showing increased activation of microglia (C1q\u003csup\u003e+\u003c/sup\u003e, CD68\u003csup\u003e+\u003c/sup\u003e) in the \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003emice. Scale bar, 7μm. \u003cstrong\u003eC, D, F. \u003c/strong\u003eQuantification of mean fluorescence intensity of Iba1 (C), CD68 (D), and C1q (F) in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003emice.\u003cstrong\u003e E, G. \u003c/strong\u003eQuantification of number of microglia (Iba1\u003csup\u003e+\u003c/sup\u003e) that are CD68\u003csup\u003e+ \u003c/sup\u003e(E) and both CD68\u003csup\u003e+\u003c/sup\u003e and C1q\u003csup\u003e+ \u003c/sup\u003e(G). Blue circles - males; pink triangle - females. n=4-6 females, 3-5 males /genotype/age (2 sections/animal were used in the analysis). Statistics calculated using two-tailed unpaired student’s t-tests. Data are presented as mean ± SEM. Only comparisons with significant p-value are indicated. *\u003cem\u003ep\u003c/em\u003e-value ≤ 0.05, **\u0026nbsp;\u003cem\u003ep\u003c/em\u003e-value ≤ 0.01, ***\u0026nbsp;\u003cem\u003ep\u003c/em\u003e-value ≤ 0.001, and **** \u003cem\u003ep\u003c/em\u003e-value of ≤ 0.0001.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5462618/v1/83c4d44a44dba9376f52a03c.png"},{"id":79780745,"identity":"ba09f6e5-98e9-4d5c-842e-fa919eb99fa0","added_by":"auto","created_at":"2025-04-02 14:59:21","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":7429543,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNeurodegeneration and Unc5c-mediated apoptosis are exacerbated in dKI mice. A. \u003c/strong\u003eConfocal microscope images of CA1 of NLGF and dKI\u003csup\u003e\u003cem\u003e \u003c/em\u003e\u003c/sup\u003emice at 6 and 12 months of age immunostained for NeuN (red). \u003cstrong\u003eB.\u003c/strong\u003e Quantification of the %NeuN covered area in the hippocampus at 6 and 12 months. n=5 mice/genotype/age (2 sections/animal). \u003cstrong\u003eC.\u003c/strong\u003e Quantification of cleaved Caspase-3 activity assay expressed as relative fluorescent units in NLGF and dKI mice at 6 and 12 months. n=9-10 mice/genotype/age. \u003cstrong\u003eD.\u003c/strong\u003e Coronal sections of hemibrains immunostained with LAMP1 (green), 3D6 (red), and Aβ\u003csub\u003e42 \u003c/sub\u003e(white) from NLGF\u003csup\u003e \u003c/sup\u003eand dKI mice at 12 months. Scale bar, 500μm. Inset in each panel is a high-magnification image of the CA1 region outlined by a dashed box in the respective low-magnification image. \u003cstrong\u003eD’. \u003c/strong\u003eOutline of a plaque with the ‘halo’ marked by LAMP1 (green) and Aβ\u003csub\u003e42\u003c/sub\u003e\u003cstrong\u003e \u003c/strong\u003e(white) to indicate how the diameter was measured. The diameter of the plaque core (Aβ\u003csub\u003e42\u003c/sub\u003e) is indicated by solid line while the dashed line is the diameter of LAMP1.\u003cstrong\u003e E-G.\u003c/strong\u003e diameter of the dystrophic neurites (LAMP1 – Aβ\u003csub\u003e42\u003c/sub\u003e diameter) (\u003cstrong\u003eE\u003c/strong\u003e), Quantification of LAMP1/Aβ\u003csub\u003e42\u003c/sub\u003e fill area (\u003cstrong\u003eF\u003c/strong\u003e), and 3D6 fill area (\u003cstrong\u003eG\u003c/strong\u003e) of NLGF and dKI mice at 12 months. n=5 mice/genotype/age (3-5 sections/animal). \u003cstrong\u003eH-K. \u003c/strong\u003eMSD ELISA results of Aβ species – Aβ\u003csub\u003e38\u003c/sub\u003e (\u003cstrong\u003eH\u003c/strong\u003e), Aβ\u003csub\u003e40 \u003c/sub\u003e(\u003cstrong\u003eI\u003c/strong\u003e), Aβ\u003csub\u003e42 \u003c/sub\u003e(\u003cstrong\u003eJ\u003c/strong\u003e) and Aβ\u003csub\u003e42\u003c/sub\u003e/Aβ\u003csub\u003e40 \u003c/sub\u003e(\u003cstrong\u003eK\u003c/strong\u003e) of hippocampal homogenates from 6-month and 12-month-old mice. \u003cstrong\u003eL.\u003c/strong\u003e Immunoblot of hippocampal homogenates from NLGF and dKI\u003csup\u003e \u003c/sup\u003emice for proteins involved in the UNC5C T835M pathway at 6 and 12 months. \u003cstrong\u003eM-P. \u003c/strong\u003eFor immunoblots in L, quantification of NADPH oxidase (NOX1) (\u003cstrong\u003eM\u003c/strong\u003e), phospho-JNK/total JNK (\u003cstrong\u003eN\u003c/strong\u003e), cyclin-dependent kinase (CDK5) (\u003cstrong\u003eO\u003c/strong\u003e), Netrin1 (\u003cstrong\u003eP\u003c/strong\u003e) normalized to GAPDH. Blue circles - males; pink triangle - females. n=5 mice/genotype/age. Statistics calculated using two-tailed unpaired student’s t-test and ordinary one-way ANOVA using Tukey’s multiple comparison tests with Bartlett’s test correction. Data are presented as mean ± SEM. Only data with significant p values are indicated. * \u003cem\u003ep\u003c/em\u003e-value ≤ 0.05, **\u0026nbsp;\u003cem\u003ep\u003c/em\u003e-value ≤ 0.01, ***\u0026nbsp;\u003cem\u003ep\u003c/em\u003e-value ≤ 0.001, and ****\u0026nbsp;\u003cem\u003ep\u003c/em\u003e-value of ≤ 0.0001.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5462618/v1/38f23af5abeee279c1dacf6b.png"},{"id":79780749,"identity":"6f297e01-3a2b-4893-ac69-467e5216f192","added_by":"auto","created_at":"2025-04-02 14:59:21","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":6218401,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSynaptic abnormalities and dendritic disorganization are exacerbated in dKI mice. A. \u003c/strong\u003eImmunoblot blot analysis of synaptic/axonal proteins in NLGF and dKI mice – PSD95, Synaptophysin (SYP), BACE1, GFAP and SMI312, at 6 and 12 months. n=5/sex/genotype/age. \u003cstrong\u003eB-F. \u003c/strong\u003eQuantification of immunoblot signals for proteins in A normalized to GAPDH. \u003cstrong\u003eG.\u003c/strong\u003e Immunofluorescence microscopy for neuronal/synaptic proteins – BACE1, GFAP, SMI312 (phosphorylated neurofilament), PSD95, synaptophysin, and APP in NLGF and dKI mice at 12 months. Yellow star in panels with SMI312 and GFAP indicate the position of amyloid plaque. \u0026nbsp;\u003cstrong\u003eH.\u003c/strong\u003e Graph showing the distribution of orientation of dendrites emerging from CA1 neuronal layer in NLGF and dKI mice at 12 months. There was a significant difference in the regions closer to 0° from (-8.5° to 3.5°) \u003cstrong\u003eI.\u003c/strong\u003e Overlay of the graph in \u003cstrong\u003eH\u003c/strong\u003e and graph in Fig. 2L showing dendritic orientation in NLGF and dKI mice at 12 months as compared to that of \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e at 18 months as shown in Fig. 2L. Blue circles - males; pink triangle - females. n=5/sex/genotype/age. Statistics calculated using two-tailed unpaired student’s t-test and ordinary one-way ANOVA using Tukey’s multiple comparison tests with Bartlett’s test correction. Data are presented as mean ± SEM. Only data with significant p values are indicated. *\u003cem\u003ep\u003c/em\u003e-value ≤ 0.05, *\u003cem\u003ep\u003c/em\u003e-value ≤ 0.01, ***\u0026nbsp;\u003cem\u003ep\u003c/em\u003e-value ≤ 0.001, and ****\u0026nbsp;\u003cem\u003ep\u003c/em\u003e-value of ≤ 0.0001.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-5462618/v1/52d646f400248696f4f3e8e8.png"},{"id":79781443,"identity":"a7565ef4-1a79-4aaf-8577-6547b14acb0d","added_by":"auto","created_at":"2025-04-02 15:07:22","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1221596,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMechanism of Unc5c T835M-mediated cell death and neurodegeneration. (Left)\u003c/strong\u003e In WT mice (\u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e), Unc5c performs its function as dependence receptor in axon guidance pathway in the presence of its ligand Netrin1. \u003cstrong\u003e(Right)\u003c/strong\u003e UNC5C T835M (\u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e) mutation leads to neurodegeneration via synaptic degeneration, disorganization and apoptotic cell death in neurons, reduced GFAP levels and changed morphology with more processes in astrocytes and activated microglia. The mutation results in decreased and increased PKD and CDK5 levels, respectively, leading to activation of the JNK pathway, with further increase in NADPH oxidase (NOX1) creating an oxidative stress environment. With reduced chaperone proteins, NOX1 could trigger the activation of cleaved caspase-3 leading to apoptosis. The UNC5C T835M mutation also results in decreased Netrin1, which could elevate Aβ\u003csub\u003e42\u003c/sub\u003e production, in addition to increased apoptosis. Overall, this pathway leads to increased neuronal loss, reduced hippocampal volume and increased ventricular volume in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice. Thus, \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice provide an ideal environment for the study of various neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, or Huntington’s disease. UNC5C-mediated neurodegeneration, oxidative stress, and apoptosis are exacerbated in the presence of β-amyloid. Images created with BioRender.com.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-5462618/v1/b1e1b80ac9beec3b81e3e119.png"},{"id":84242533,"identity":"68cb2f52-6a0a-41cb-83d0-c7825f756cb3","added_by":"auto","created_at":"2025-06-09 16:09:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":47067705,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5462618/v1/b160ed86-d393-4d3f-8625-b3b6574c8cda.pdf"},{"id":79780757,"identity":"70b422e3-68b4-4dc1-bfc6-63c15a70474b","added_by":"auto","created_at":"2025-04-02 14:59:21","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":55113,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfileslegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-5462618/v1/8fdc83a83bc320906dffd89c.docx"},{"id":79780766,"identity":"d117974f-c58c-4521-a471-72e3b5a13b29","added_by":"auto","created_at":"2025-04-02 14:59:22","extension":"tif","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":1435600,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure1S1.tif","url":"https://assets-eu.researchsquare.com/files/rs-5462618/v1/7aee51cbb850efca21a0784d.tif"},{"id":79780775,"identity":"060efa06-3be7-4967-8e22-24fe760710b4","added_by":"auto","created_at":"2025-04-02 14:59:22","extension":"tif","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":8963908,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure2S2.tif","url":"https://assets-eu.researchsquare.com/files/rs-5462618/v1/13ee86235171990118895e3d.tif"},{"id":79782137,"identity":"0ffafd46-d26e-4dc4-9c0c-56e48b5aeba7","added_by":"auto","created_at":"2025-04-02 15:15:22","extension":"tif","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":5977952,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure3S3.tif","url":"https://assets-eu.researchsquare.com/files/rs-5462618/v1/63dbf32035159e9cfa31ff3c.tif"},{"id":79781447,"identity":"1f6bf17b-45d1-4178-a5c2-adf4a60ac761","added_by":"auto","created_at":"2025-04-02 15:07:22","extension":"tif","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":15518064,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure4S4.tif","url":"https://assets-eu.researchsquare.com/files/rs-5462618/v1/2d9eaa19b1966f0a8589905f.tif"},{"id":79781441,"identity":"a04efbca-7e1a-49df-bb26-e235c91b5d0b","added_by":"auto","created_at":"2025-04-02 15:07:22","extension":"tif","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":13410996,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure5S5.tif","url":"https://assets-eu.researchsquare.com/files/rs-5462618/v1/599aa434092d0ae080e971e7.tif"},{"id":79781439,"identity":"db7c4e57-ac54-4036-b7b0-08ec092a1e5e","added_by":"auto","created_at":"2025-04-02 15:07:21","extension":"tif","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":8877676,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure6S6.tif","url":"https://assets-eu.researchsquare.com/files/rs-5462618/v1/92470b8fe8b2009eb837724c.tif"},{"id":79781445,"identity":"79d3c41c-1417-47fa-9e51-300df355d47b","added_by":"auto","created_at":"2025-04-02 15:07:22","extension":"tif","order_by":19,"title":"","display":"","copyAsset":false,"role":"supplement","size":15357328,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure7S7.tif","url":"https://assets-eu.researchsquare.com/files/rs-5462618/v1/da2d7e72de244e6aa81a9f1a.tif"},{"id":79780777,"identity":"ef5f2cac-9ba9-41a7-a5bd-c4b7beefb47f","added_by":"auto","created_at":"2025-04-02 14:59:22","extension":"xlsx","order_by":20,"title":"","display":"","copyAsset":false,"role":"supplement","size":3823273,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfileS8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5462618/v1/f2ebbcefeaf9c81576acca35.xlsx"}],"financialInterests":"","formattedTitle":"The UNC5C T835M mutation associated with Alzheimer’s disease leads to neurodegeneration involving oxidative stress and hippocampal atrophy in aged mice","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) is a progressive neurodegenerative disease that is the 6th leading cause of mortality in the US and is the most common cause of dementia in the geriatric population, accounting for 60\u0026ndash;80% of all cases of dementia. It primarily affects the brain and causes gradual decline of cognitive abilities. Currently, about 6.7\u0026nbsp;million Americans are reported to live with AD and it is estimated to reach 13\u0026nbsp;million by 2050 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://alz.org\u003c/span\u003e\u003cspan address=\"https://alz.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The main hallmarks of the disease are amyloid plaques and neurofibrillary tangles, ultimately leading to progressive neuron loss. While drugs targeting AD pathologies, β-amyloid (Aβ) plaques and tau neurofibrillary tangles, are in development and anti-Aβ antibodies have been approved, therapies targeting other mechanisms of AD are desperately needed. Anti-Aβ antibodies slow but do not halt AD, benefit only early AD, and can have serious side effects. Therefore, discovering new safe drugs that benefit all stages of AD is of paramount importance. Understanding the molecular mechanism of cell death in regions susceptible to neurodegeneration such as the hippocampus could shed light on pathways involved in cell death, allowing development of AD therapeutics directed at novel targets.\u003c/p\u003e \u003cp\u003eGenome wide association studies (GWAS), Whole genome sequencing (WGS) and linkage studies have enabled the identification of genes that affect AD risk. About 95% of cases of AD are late-onset and sporadic and are associated with mutations in many genes. \u003cem\u003eUnc5c\u003c/em\u003e (Uncoordinated C.\u003cem\u003eelegans\u003c/em\u003e receptor 5c) is a candidate gene containing single nucleotide polymorphisms (SNPs) associated with late-onset AD(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The T835M mutation (rs137875858) in the hinge region of UNC5C was shown to increase the susceptibility of hippocampal neurons to cell death and segregated with late-onset AD (LOAD)(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Further studies by Hashimoto \u003cem\u003eet al.\u003c/em\u003e, showed that UNC5C T835M activated the cell death pathway in cell culture, suggesting a molecular mechanism involving JNK/PKD/NADPH oxidase signaling(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). UNC5C acts as a chemorepellent for Netrin1, while its antagonist, deleted in colorectal cancer (Dcc), acts as a chemoattractant during axon guidance(\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Besides its crucial role in various carcinomas, specifically in Colorectal cancer(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), UNC5C has also been implicated in various neurological disorders such as Autism spectrum disorder(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), Schizophrenia(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), and Parkinson\u0026rsquo;s disease(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Multiple SNPs in UNC5C have been shown to be associated with AD (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). UNC5C protein contains two Immunoglobulin domains (Ig), two thrombospondin domains (TS), a transmembrane domain (TM), a zona occludens-5 domain (ZU-5), a UPA domain, and a death domain (DD)(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). It belongs to a class of transmembrane receptors called dependence receptors, which depending on the presence or absence of the ligand, could promote cell survival or induce cell death, respectively (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Therefore, it is imperative to understand the molecular underpinnings of the T835M mutation in UNC5C-related brain atrophy and neuronal cell death to inform the potential of UNC5C as a therapeutic target for AD.\u003c/p\u003e \u003cp\u003eTo gain deeper insights into the role of UNC5C in AD, here we analyze the CNS phenotype of UNC5C T835M targeted replacement mice (\u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e). \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice have age-related neurodegeneration, including reduced hippocampal volume, increased ventricular volume and reduced white matter connectivity beginning at 12\u0026ndash;18 months of age. Moreover, the UNC5C T835M mutation results in decreased pre-synaptic but increased post-synaptic protein levels, dendritic disorganization in the hippocampal CA1 region, and increased apoptotic neuronal death. In addition, astrocytes and microglia in CA1 have reduced Glial fibrillary acidic protein (GFAP) levels and increased activation, respectively. Proteomic studies reveal increased oxidative stress proteins in the hippocampus and decreased chaperone proteins, along with increased c-Jun N-terminal Kinase (JNK) phosphorylation, NADPH oxidase, and decreased Netrin1 levels. To understand the effects of the UNC5C T835M mutation on Alzheimer\u0026rsquo;s-related phenotypes, we generated mice that were homozygous for both UNC5Cc T835M and the APP targeted replacement, App\u003csup\u003eNL\u0026minus;G\u0026minus;F/NL\u0026minus;G\u0026minus;F\u003c/sup\u003e (NLGF)(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), which develop amyloid plaques and synaptic loss by 6 months but no significant neuron loss. In NLGF;\u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e double knock in (dKI) mice compared to NLGF mice alone, we observed an exacerbation of UNC5C T835M-associated phenotypes, such as increased cell death, reduced hippocampal area, and decreased Netrin1 and GFAP levels at 6 and 12 months of age. Overall, these results suggest that the UNC5C T835M mutation causes neurodegeneration by increasing oxidative stress leading to synaptic degeneration and neuronal apoptosis, which are all worsened in the presence of cytotoxic stressors such as amyloid, therefore increasing AD susceptibility.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMice:\u003c/h2\u003e \u003cp\u003eUnless indicated, no significance was noted between the genders, and the data presented were the means of both male and female animals. UNC5C T835M KI (KI) mice were generated by Genentech Inc. (JA and RW) and acquired by Northwestern University. Briefly, the construct for targeting the \u003cem\u003eUnc5c\u003c/em\u003e locus in mouse ES cells to generate the T835M KI allele, was made using a combination of recombineering, gene synthesis and standard molecular cloning techniques. The resulting targeting vector enabled insertion of the T835M point mutation in \u003cem\u003eUnc5c\u003c/em\u003e exon 15 with an FRT-\u003cem\u003ePgk1\u003c/em\u003e-em7-Neo-FRT cassette inserted in intron 15 at genomic position mm10 chr3:141,827,785. The vector was confirmed by DNA sequencing, linearized with NotI and used to target C57BL/6 C2 ES cells using standard methods (G418 positive and gancyclovir negative selection). Positive clones were identified using PCR and TaqMan analysis and confirmed by sequencing of the modified locus. Correctly targeted ES cells were infected with Adeno-Flpo (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) to remove the selection marker with a single FRT site remaining, resulting in the final \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003eT835M\u003c/sup\u003e KI allele (\u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e). Validated ES cells were injected into blastocysts using standard techniques, and germline transmission was obtained after crossing resulting chimaeras with C57BL/6N females.\u003c/p\u003e \u003cp\u003e All animal work was performed in Northwestern University in accordance with Northwestern University Institutional Animal Care and Use Committee approval. The number of biological replicates for each experiment is specified in the figure legends. NLGF mice were obtained from Dr. Takaomi Saido, RIKEN Brain Science Institute, Japan, and NLGF homozygous mice were bred with Unc5c KI homozygous mice to obtain the double KI (dKI) mice. U\u003cem\u003enc5c\u003c/em\u003e KO mice were obtained as heterozygotes from Dr. Susan Ackerman at University of California, San Diego, and bred in-house to obtain the homozygotes. Genotyping is performed by Transnetyx using custom probes for Unc5c (WT and mutant specific probes) and standard probes for NLGF mice.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAntibodies\u003c/h3\u003e\n\u003cp\u003eThe antibodies used were as follows: rabbit anti-actin (#926\u0026ndash;42210, LI-COR), mouse anti-BACE1 (3D5) (made in Vassar lab)(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), rabbit anti-BACE1 (#ab108394, Abcam), rat anti-MBP (#ab7349, Abcam), mouse anti-PSD95 (#K28/43, DSHB), rabbit anti-C1q (#ab227072, Abcam), rat anti-CD68 (#14-0681-82, Invitrogen), chicken anti-MAP2 (#ab5392, Abcam), rabbit anti-Cdk5 (#14145S, Cell Signaling), chicken anti-NeuN (#ABN91, Millipore), mouse anti\u0026ndash;b-tubulin (Tuj1) (a gift from L. Binder), anti-synaptophysin (WB: mouse #ab8049, Abcam; IF: goat #AF5555, R\u0026amp;D Systems), rabbit anti-NADPH oxidase (#17772-1-AP, Proteintech), rabbit anti-total JNK/SAPK (#9252, Cell Signaling), rabbit anti-phospho-JNK (#9251S, Cell Signaling), rabbit anti-PKD (#PA5-13749, Invitrogen), mouse anti-SMI312 (#837904, BioLegend), anti-Iba1 (WB: rabbit #ab178846, Abcam; IF: goat #NB100-1028, Novus), anti-GFAP (WB: rabbit #G9269, Sigma-Aldrich; IF: chicken #ab4674, Abcam), rabbit anti-GAPDH (#2118, Cell Signaling), rabbit anti-Unc5c (polyclonal antibody which was custom made by Proteintech Inc., against the C-terminal 400 amino acids of Unc5c protein), rabbit anti-Ab\u003csub\u003e42\u003c/sub\u003e (#700254, Invitrogen), rat anti-Lamp1 (#1D4B, DSHB), 3D6 mouse anti-Ab monoclonal antibody(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) (gift of Dr. Lisa Conlogue, Elan Pharmaceuticals) (antigen is 1\u0026ndash;5 N-terminal amino acids of Aβ\u003csub\u003e42\u003c/sub\u003e), Rabbit anti-Netrin-1 (#MBS821997, My Biosource Inc,.) and Human Netrin-1 Protein, His Tag (NEI-H52H3, Acro Biosystems). 1ug of the purified protein was loaded to confirm the Netrin1 band around 85kD.\u003c/p\u003e\n\u003ch3\u003eTissue extraction and immunoblot analysis\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eTissue extraction and immunoblot analysis:\u003c/div\u003e \u003cp\u003eMice were deeply anesthetized by intraperitoneal injection of xylazine (15 mg/kg) and ketamine (100 mg/kg), perfused with ice-cold phosphate-buffered saline (PBS) with phenylmethylsulfonyl fluoride (20 \u0026micro;g/ml), leupeptin (0.5 \u0026micro;g/ml), sodium orthovanadate (20 \u0026micro;M), and dithiothreitol (0.1 mM), followed by decapitation and brain removal. The hemibrain was dissected on ice into the cortex, hippocampus, and cerebellum, and then snap-frozen in liquid nitrogen and stored at \u0026minus;\u0026thinsp;80\u0026deg;C. Tissues were homogenized in radioimmunoprecipitation assay buffer ((RIPA; 50 mM tris, 0.15 M NaCl, 1% octylphenoxypolyethoxyethanol (IGEPAL), 1 mM EDTA, 1 mM EGTA, 0.1% SDS, 0.5% sodium deoxylate (pH 8)), followed by sonication and centrifugation. All buffers contained protease inhibitor cocktail III (#535140, Millipore) and Halt phosphatase inhibitor (#78420, Thermo Fisher Scientific). Protein concentration was determined using bicinchoninic acid assay (BCA) assay (#23225, Thermo Fisher Scientific). Equal amount of protein was separated under reduced and denatured conditions, transferred onto a polyvinylidene difluoride or nitrocellulose membrane, and developed using Pierce ECL (enhanced chemiluminesence) (Thermo Fisher Scientific) on a ProteinSimple FCR imager and Biorad imager. Chemiluminescent signals were quantified using AlphaView software (ProteinSimple) and Imagelab (Biorad).\u003c/p\u003e\n\u003ch3\u003eImmunofluorescence\u003c/h3\u003e\n\u003cp\u003eHemibrains were fixed in 10% formalin and preserved in 30% sucrose/PBS solution. Brains were sectioned as coronal sections at 30 \u0026micro;m on freezing-sliding microtome and stained using the free-floating method. Sections were serially placed in a 12-well plate in a cryoprotective solution (1xPBS, 30% sucrose, and 30% ethylene glycol) and stored at \u0026minus;\u0026thinsp;20\u0026deg;C until use. Immunofluorescence staining was performed by first washing sections three times in 1xTBS and then incubating sections in 16 mM glycine in 1xTBS for 1 hour at room temperature. After 3 additional washes in 1XTBS, sections were blocked in 5% donkey serum in 0.25% Triton X-100 in 1xTBS for 2 hours at room temperature. The sections were then incubated overnight in primary antibodies in a solution of 0.25% Triton X-100, 1% bovine serum albumin and 1xTBS at 4\u0026deg;C. Alexa Fluor secondary antibodies (Invitrogen) were used at a concentration of 1:750. Sections were mounted using ProLong Gold (#P36934, Thermo Fisher Scientific) and imaged on a Nikon A1 laser scanning confocal microscope (Northwestern University Center for Advanced Microscopy).\u003c/p\u003e\n\u003ch3\u003eTerminal d-UTP nick-end labeling (TUNEL) Cell death detection\u003c/h3\u003e\n\u003cp\u003eFor cell death detection, sections were permeabilized with Triton X-100 for an hour, followed by a 1-hour incubation of reaction mix from the In Situ Cell Death Detection Kit, TMR Red (#12156792910 Roche, Sigma-Aldrich) at 37\u0026deg;C following the directions on the user manual.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eActivated Caspase-3/7 Fluorescence assay:\u003c/h2\u003e \u003cp\u003eWe assayed the activity of caspase-3/7 using Caspase-3/7 Fluorescence assay kit (Cayman chemical, cat # 10009135). Briefly, 90\u0026micro;l of diluted hippocampal homogenates were added to a black 96-well plate. 10\u0026micro;l of assay buffer was added to each well to assay for the endogenous activity of cleaved caspase-3/7. 100\u0026micro;l of positive control of active Caspase-3 was added to a couple of wells as a positive control. 100\u0026micro;l of the caspase-3/7 substrate solution was added to each well and incubated for 60\u0026ndash;90 minutes. The relative fluorescence intensity was read at 535 nm.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eELISA \u0026 MSD-ELISA\u003c/h3\u003e\n\u003cp\u003eFor NLGF and dKI hippocampal samples, we treated the extracts with Guanidine hydrochloride (7.2 \u0026micro;l of 2 mg/ml brain homogenates were added to 12.8 \u0026micro;l of freshly made 8.2 M guanidine hydrochloride (GuHCl); 82 mM Tris HCl (pH 8.0) (5 M GuHCl final) and mixed for three days on a nutator) and the samples were then diluted with the assay buffer and ELISA for Aβ\u003csub\u003e42\u003c/sub\u003e (Invitrogen, cat# KHB3441) was performed according to instructions in user\u0026rsquo;s manual.\u003c/p\u003e \u003cp\u003eMSD-ELISA was performed on the hippocampal samples from NLGF and dKI mice at 6- and 12- months using V-PLEX Aβ Peptide Panel 1 (6E10) assay Kit (Meso Scale Discovery, cat# K15200E) following the directions on the user\u0026rsquo;s manual. Briefly, the hippocampal samples were treated with Guanidine hydrochloride as described above, to obtain the insoluble Aβ species. Samples were further diluted 128-fold with diluent-35 (from the kit) and assay was performed.\u003c/p\u003e\n\u003ch3\u003eMRI Brain region volumetric analysis\u003c/h3\u003e\n\u003cp\u003eAcquisition was done on a 7Tesla Bruker Clinscan MRI using a 3D multiple echo GRE sequence with isotropic spatial resolution. After realignment, to avoid errors in volume/morphological estimation associated with different head position, data was extracted and comparison between relevant brain regions (ventricle and hippocampus) across cohorts was done by averaging the regional volumes extracted via segmentation and normalized using each whole brain volume. The original 3D high resolution MR images (110\u0026micro;m) were used to derive these quantities. Each segmentation was implemented using a semi-automated approach which combined the use of the threshold automatic segmentation tool contained in IT-SNAP followed by manual corrections using a graphical pen and a dedicated Wacom tablet to enable accurate delineation of anatomical regions. The manual corrections were implemented to avoid artifacts from automated segmentation and to reliably capture volumetric data from the same exact regions across different subjects. Delineation was performed by two experienced neuroimaging scientists (DP) using a standard mouse brain atlas as reference guide for identification of relevant anatomical regions.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMRI cortical thickness analysis:\u003c/h2\u003e \u003cp\u003eAssessment of cortical changes was done through linear measurements of cortical thickness using the built-in image annotation and linear measurement tool included in ITK SNAP. Measurements were done on the re-aligned high resolution 3D brain images at same exact anatomical location (prefrontal cortical area) for all subjects and at two symmetric locations\u0026thinsp;~\u0026thinsp;2 mm off the brain's midline and ~\u0026thinsp;1 mm in front of bregma (as shown in representative MRI image in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eM). Choice of using average of two contralateral measurements was done to reduce subjective choice of position and uncertainties in realignment. Linear measurements were selected for cortical assessment instead of volumetric analysis to avoid uncertainties linked to confounding selection of total cortical region to be included in the assay.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFractional Anisotropy (FA) analysis:\u003c/h2\u003e \u003cp\u003eWe identified an MRI derived biomarker (thresholded FA -\u0026gt; 0.25) which can provide a quantitative volumetric value reflective of connectivity patterns (the index comes from dividing each thresholded volume FA\u0026thinsp;~\u0026thinsp;0.25 by the whole brain volume).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eImaging quantification and analysis using ImageJ and NIS-elements:\u003c/h2\u003e \u003cp\u003eFor hippocampal and cortical area measurement, we employed immunofluorescence on serial sections for each animal stained with NeuN antibody. 10x images were obtained using Nikon Ti2 widefield microscope in the Northwestern Feinberg School of Medicine imaging core facility. Using ImageJ software, polygon selection was used to draw the outline of hippocampi on all the sections. We selected three different bregma positions (-1.34mm (anterior), -1.70mm (center), -2.06mm (posterior)) to obtain the area of hippocampi and cortices (9 mice (5 females, 4 males) in WT-KI analysis, 10 mice (5 females, 5 males) in NLGF-dKI analysis). The average of three positions are compared between WT and KI mice. For signal intensity measurement, region of interest was outlined using polygon selection in ImageJ, then the mean intensity was obtained.\u003c/p\u003e \u003cp\u003eFor counting cells as in counting TUNEL\u0026thinsp;+\u0026thinsp;cells in the hippocampus, CD68\u0026thinsp;+\u0026thinsp;cells, and both CD68\u0026thinsp;+\u0026thinsp;andC1q\u0026thinsp;+\u0026thinsp;cells in microglia, we used the multi-point tool to count the cells manually in the stitched 40x and 20x hippocampal images, respectively, by someone blind to the genotypes of the animals. The images were obtained using AXR laser scanning confocal microscope (Northwestern University Nikon Imaging Centre) and the maximum projected images were used for quantification.\u003c/p\u003e \u003cp\u003eFor the image analysis and quantification using Nikon NIS-Elements Software (Northwestern University Nikon Imaging Centre), recipes were created by setting intensity, size and background threshold for each staining to be quantified for the numbers or area covered (for GFAP and NeuN) in the region of interest (ROI). Once ROIs were drawn in cortex and hippocampus, a binary channel was created to run the recipe for each region. 10x images obtained using a Ti2 wide-field microscope were used in these analyses. For plaque analysis and NeuN analysis in dKI and NLGF mice, the average of 3\u0026ndash;5 sections from Bregma coordinates of about \u0026minus;\u0026thinsp;1.30 to \u0026minus;\u0026thinsp;2.52 mm was obtained. Recipes were created to obtain the different plaque core sizes by binning them into different size thresholds. Section selection, tracing, and volume analysis were performed by someone blind to the genotypes of the animals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIMARIS image reconstruction:\u003c/h2\u003e \u003cp\u003eAstrocyte and Microglial morphology were analyzed using the IMARIS software (v9.1) by reconstructing the z-stacks of 60x confocal images obtained from A1R laser scanning confocal microscope at the Nikon imaging facility at Northwestern university. For astrocyte 3D reconstruction, confocal z-stacks were imported into IMARIS. Maximum projection confocal images were used to define GFAP (for astrocytes) signal for each image. Two sections per animal (n\u0026thinsp;=\u0026thinsp;7\u0026ndash;8 females, 3\u0026ndash;5 males/genotype for \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice, n\u0026thinsp;=\u0026thinsp;5 females, 5 males/genotype for NLGF and dKI mice) were used and the average was used for each animal. 18\u0026ndash;22 cells/animal were used for the analysis and the average was calculated. Using the surface tool, GFAP channel was chosen. Using the filaments tool, the IMARIS software used slice rendering and calculated mean process length, area, volume, soma volume, number of processes, number of process branch points, and number of process terminal points. For analyzing TUNEL signal (red) within GFAP\u0026thinsp;+\u0026thinsp;astrocytes, we employed \u0026ldquo;section\u0026rdquo; tool to obtain the orthogonal view of a single plane to show that GFAP\u0026thinsp;+\u0026thinsp;astrocytes contained TUNEL signal.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSynaptic dendritic orientation analysis:\u003c/h2\u003e \u003cp\u003eMaximum intensity projected 60x images with PSD95 staining were analyzed using the OrientationJ plugin in ImageJ software by selecting the OrientationJ distribution and setting the local window σ to 3 pixels and gradient to \u0026ldquo;gaussian\u0026rdquo;(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Images were obtained with the CA1 neuronal layer and were rotated 90\u0026deg; to right or left. The pixel orientation distribution is displayed as histogram of gaussian window ranging from \u0026minus;\u0026thinsp;90 to +\u0026thinsp;90\u0026deg;. Pixels that are parallel to the vector field (CA1 layer) are closer to 0\u0026deg; (parallel to the direction of dendritic processes emerging from the CA1 neuronal layer). As the dendrites digress from the parallel orientation, we checked for the degree of deviation in the KI mice. The resulting graph is presented as a Gaussian curve and the degree of deviation is represented by more distribution of orientation towards the ends of the curve (-89.5\u0026deg; and 89.5\u0026deg;).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eTandem-mass tagged based Mass spectrometry (TMT-MS) sample preparation:\u003c/h2\u003e \u003cp\u003eTMT-MS sample preparation was performed as previously described(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Briefly, 200 \u0026micro;g homogenized hippocampal brain extracts were extracted using methanol-chloroform precipitation. The extracted protein was then resuspended in 6M guanidine in 100 mM triethylammonium bicarbonate (TEAB) buffer (Thermo Scientific, Cat# 90114). Subsequently, reduction and alkylation at Cysteine residues of proteins were performed by subsequent incubation with 5 mM dithiothreitol (DTT) and alkylated at free SH groups of cysteine residues with 20mM iodoacetamide (IAA). Proteins were first digested for 3 h at room temperature (RT) with 1 \u0026micro;g of LysC (Promega, Cat# PI90307) and then overnight at 37\u0026deg;C with 2 \u0026micro;g of Trypsin. The digest was then acidified with formic acid and desalted using C18 HyperSep columns (ThermoFisher Scientific, Cat# 60108-302). The eluted peptide solution was dried before resuspension in 100 mM TEAB. Micro-BCA assay (Thermo Fisher Scientific, Cat#23235) was subsequently performed to determine the concentration of peptides and 100 \u0026micro;g of peptides from each sample was then used for isobaric labeling. TMT 10-plex labeling was performed on peptide samples according to the manufacturer\u0026rsquo;s instructions (ThermoFisher Scientific). After incubating for 75 min at room temperature, the reaction was quenched with 0.3% (v/v) hydroxylamine. Isobaric labeled samples were then combined 1:1:1:1:1:1:1:1:1:1 and subsequently desalted with C18 HyperSep columns. The combined isobaric labeled peptide samples were fractionated into eight fractions using high pH reversed-phase columns (Thermo Fisher Scientific, Cat# PI84868). Peptide solutions were dried, and stored at \u0026minus;\u0026thinsp;80\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTMT-MS Analysis:\u003c/h2\u003e \u003cp\u003eTMT-MS analysis was performed as previously described(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In short, samples were resuspended in 20\u0026micro;L of buffer A (5% acetonitrile, 0.125% formic acid), and micro-BCA was performed. 3\u0026micro;g of each fraction was loaded for LC\u0026ndash;MS analysis via an auto-sampler with a Thermo EASY nLC 100 UPLC pump onto a vented Pepmap100, 75\u0026micro;m \u0026times; 2 cm, nanoViper trap column coupled to a nanoViper analytical column (Thermo Scientific) with a stainless steel emitter tip assembled on the nanospray flex ion source with a spray voltage of 2000 V. Orbitrap Fusion was used to generate MS data. The chromatographic run was performed with a 4 h gradient beginning with 100% buffer A and 0% B and increased to 7% B over 5 min, then to 25% B over 160 min, 36% B over 40 min, 45% B over 10 min, 95% B over 10 min, and held at 95% B for 15 min before terminating the scan. Buffer A contained 5% acetonitrile (ACN) and 0.125% formic acid in H\u003csub\u003e2\u003c/sub\u003eO, and buffer B contained 99.875 ACN with 0.125% formic acid. Multinotch MS3 method was programmed with the following parameters: ion transfer tube temp\u0026thinsp;=\u0026thinsp;300\u0026deg;C, easy-IC internal mass calibration, default charge state\u0026thinsp;=\u0026thinsp;2, and cycle time\u0026thinsp;=\u0026thinsp;3 s. MS1 detector was set to orbitrap with 60 K resolution, wide quad isolation, mass range\u0026thinsp;=\u0026thinsp;normal, scan range\u0026thinsp;=\u0026thinsp;300\u0026ndash;1800 \u003cem\u003em\u003c/em\u003e/\u003cem\u003ez\u003c/em\u003e, max injection time\u0026thinsp;=\u0026thinsp;50ms, AGC target\u0026thinsp;=\u0026thinsp;6 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e, microscans\u0026thinsp;=\u0026thinsp;1, RF lens\u0026thinsp;=\u0026thinsp;60%, without source fragmentation, and datatype\u0026thinsp;=\u0026thinsp;positive and centroid(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Monoisotopic precursor selection was set to include charge states 2\u0026ndash;7 and reject unassigned. Dynamic exclusion was allowed; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1 exclusion for 60 s with 10 ppm tolerance for high and low. The intensity threshold was set to 5 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e. Precursor selection decision\u0026thinsp;=\u0026thinsp;most intense, top speed, 3 s. MS2 settings include isolation window\u0026thinsp;=\u0026thinsp;0.7, scan range\u0026thinsp;=\u0026thinsp;auto normal, collision energy\u0026thinsp;=\u0026thinsp;35% CID, scan rate\u0026thinsp;=\u0026thinsp;turbo, max injection time\u0026thinsp;=\u0026thinsp;50 ms, AGC target\u0026thinsp;=\u0026thinsp;6 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e, and \u003cem\u003eQ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.25. In MS3, the top 10 precursor peptides selected for analysis were then fragmented using 65% higher-energy collisional dissociation before orbitrap detection. A precursor selection range of 400\u0026ndash;1200 \u003cem\u003em\u003c/em\u003e/\u003cem\u003ez\u003c/em\u003e was chosen with mass range tolerance. An exclusion mass width was set to 18 ppm on the low and 5 ppm on the high. Isobaric tag loss exclusion was set to TMT reagent. Additional MS3 settings include an isolation window\u0026thinsp;=\u0026thinsp;2, orbitrap resolution\u0026thinsp;=\u0026thinsp;60 K, scan range\u0026thinsp;=\u0026thinsp;120\u0026ndash;500 \u003cem\u003em\u003c/em\u003e/\u003cem\u003ez\u003c/em\u003e, AGC target\u0026thinsp;=\u0026thinsp;6 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e, max injection time\u0026thinsp;=\u0026thinsp;120 ms, microscans\u0026thinsp;=\u0026thinsp;1, and datatype\u0026thinsp;=\u0026thinsp;profile.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eTMT-MS Data Analysis and Quantification:\u003c/h2\u003e \u003cp\u003eTMT-MS data analysis was performed as previously described(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In short, protein identification, TMT quantification, and analysis were performed with The Integrated Proteomics Pipeline-IP2 (Integrated Proteomics Applications, Inc., \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.integratedproteomics.com/\u003c/span\u003e\u003cspan address=\"http://www.integratedproteomics.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Proteomic results were analyzed with ProLuCID, DTASelect2, Census, and QuantCompare. MS1, MS2, and MS3 spectrum raw files were extracted using RawExtract 1.9.9 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://fields.scripps.edu/downloads.php)(22)\u003c/span\u003e\u003cspan address=\"http://fields.scripps.edu/downloads.php)(22)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Pooled spectral files from all eight fractions for each sample were then searched against the Uniprot mouse protein database and matched to sequences using the ProLuCID/SEQUEST algorithm (ProLuCID ver. 3.1) with 50 ppm peptide mass tolerance for precursor ions and 600 ppm for fragment ions(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Fully and half-tryptic peptide candidates were included in the search space, all that fell within the mass tolerance window with no miscleavage constraint, assembled, and filtered with DTASelect2 (ver. 2.1.3) through the Integrated Proteomics Pipeline (IP2 v.5.0.1, Integrated Proteomics Applications, Inc., CA, USA). Static modifications at 57.02146 C and 229.1629 K were included(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The target-decoy strategy was used to verify peptide probabilities and false discovery ratios(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). A minimum peptide length of 6 was set for the process of each protein identification, and each dataset included a 1% FDR rate at the protein level based on the target-decoy strategy. Isobaric labeling analysis was established with Census 2 as previously described(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). TMT channels were normalized by dividing it over the sum of all channels(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). No intensity threshold was applied. The fold change was then calculated as the mean of the experimental group standardized values, and \u003cem\u003ep\u003c/em\u003e-values were then calculated by Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test with Benjamini-Hochberg adjustment. Protein ontologies were determined with protein analysis through The \u003cb\u003eD\u003c/b\u003eatabase for \u003cb\u003eA\u003c/b\u003ennotation, \u003cb\u003eV\u003c/b\u003eisualization and \u003cb\u003eI\u003c/b\u003entegrated \u003cb\u003eD\u003c/b\u003eiscovery (DAVID). The gene-ontology term (GO term) obtained were sorted based on their Benjamini score. Protein ontologies with Fisher statistical tests with false discovery rate correction less than 0.05 were considered significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStatistics:\u003c/h2\u003e \u003cp\u003eStatistics were calculated using Prism10 (GraphPad Software). Unpaired two-way Student\u0026rsquo;s t-test and ordinary one-way ANOVA using Tukey\u0026rsquo;s multiple comparison tests with Bartlett\u0026rsquo;s test correction were used in the data analysis. For the UNC5C blot, we performed both multiple t-test and two-way ANOVA with Sidak\u0026rsquo;s multiple comparisons test. Numbers of replicates and P values are stated in each figure legend. All data are plotted as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM. Significance was concluded when the P value was less than 0.05, indicated by *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001. N.S. (not significant) denotes P\u0026thinsp;\u0026gt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1. Hippocampal degeneration is evident in\u003c/strong\u003e\u003cstrong\u003eUnc5c\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eKI/KI\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003emice by 18 months of age.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003cp\u003eThe UNC5C T835M mutation is associated with LOAD (1), but the pathogenic mechanism of this variant in CNS neurodegeneration is unclear. To shed light on the pathophysiology of UNC5C T835M in AD, we used standard homologous recombination in ES cells to generate a targeted replacement mouse line that constitutively expresses this variant (Fig. S1A). We then analyzed the CNS phenotypes associated with UNC5C T835M expression in the targeted replacement mice, which were bred to homozygosity and aged up to 24 months. Using NeuN immunofluorescence microscopy, we found a significant reduction in hippocampal area in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice compared to wildtype control \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e at 12 and 18 months compared to 6 months (Fig. 1A, B), while \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e cortical areas were equal at all ages, even up to 24 months of age (Fig. S1B, C). This observation may be related to higher expression levels of Unc5c in hippocampus compared to cortex (1). We confirmed hippocampal volume loss in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice \u003cem\u003ein vivo\u003c/em\u003e over time using longitudinal magnetic resonance imaging (MRI) (Fig. 1C, D). We found that ventricular volume increased significantly between 13- and 18-months in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice compared to \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e mice, (Fig. 1E, Fig.S1D) while hippocampal volume decreased significantly in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice, both suggesting neurodegeneration (Fig. 1F, Fig.S1E). As in the immunofluorescence microscopy-based analysis, cortical thickness was equivalent in both the \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e mice and did not change over time (Fig. 1G, H). We used diffusion tensor imaging (DTI) to measure the fractional anisotropy (FA) index, which is indicative of white matter connectivity. Between 13 and 18 months of age, \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice had a significantly greater decrease in FA than \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e mice, indicating reduced gray-matter interconnectivity in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice (Fig. 1I, J).\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e2. Synaptic protein levels and dendritic organization are significantly altered in\u003c/strong\u003e\u003cstrong\u003eUnc5c\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eKI/KI\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003emice.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince the longitudinal MRI study strongly suggested white matter atrophy in the hippocampus, we hypothesized that the \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice had axonal and synaptic degeneration in the hippocampal region. Therefore, we assessed the levels of axonal and synaptic proteins in these mice. Immunoblot analysis of hippocampal homogenates of 18-month-old \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice appeared to have reduced presynaptic/axonal markers synaptophysin (SYP), β-secretase (BACE1), Neurofilament/pan-axonal marker (SMI312), and Myelin basic protein (MBP) (Fig.\u0026nbsp;2A, B-E, Fig.S2). In contrast, immunoblots for the post-synaptic marker PSD95 surprisingly showed a significant increase in the \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice (Fig.\u0026nbsp;2A, F, Fig.S2). Post-synaptic changes were corroborated by immunofluorescence microscopy (Fig.\u0026nbsp;2G, H). Both MAP2 and PSD95 (post-synaptic markers) had increased immunostaining intensity in hippocampal CA1 (Fig.\u0026nbsp;2G-I). Previous studies have shown that homeostatic synaptic plasticity exists to maintain the balance between pre- and post-synaptic sides of the synapse in the developing nervous system (28–30). We hypothesized that if UNC5C T835M affects normal excitability or synaptic homeostasis, an abnormal decrease of the pre-synapse could cause a compensatory increase of the post-synapse. Another UNC protein, MUNC13, has also recently been implicated in controlling postsynaptic AMPA receptor density and clustering (31–33), suggesting a possible role for the UNC family, including UNC5C, in the post-synaptic spine. Taken together, these data suggest that UNC5C T835M-mediated presynaptic degeneration is coupled with compensatory postsynaptic sprouting. Additionally, we observed that PSD95\u003csup\u003e+\u003c/sup\u003e dendritic processes in the hippocampal CA1 region of the \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice showed abnormal organization compared to \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e, which we measured as a loss of linearity in dendrites that run parallel to each other and perpendicular to the CA1 cell layer (Fig.\u0026nbsp;2J, K). When dendritic processes are parallel to each other, the angle between them is near 0°, as observed in 3-month-old mice and the 18-month-old \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e mice, but in 18-month \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice, the orientation is distributed nearly evenly, with no clear peak, indicating a loss of parallel organization. Even at 3 months in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice, the peak near 0\u003csup\u003eo\u003c/sup\u003e is less pronounced and there are more values in the tails of the distribution, indicating that loss of linear organization may occur early and worsen with age. Taken together, our results show that the hippocampal atrophy exhibited by \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice is associated with reduced hippocampal presynaptic and axonal proteins, compensatory postsynaptic sprouting, and disorganized dendrites, suggestive of a neurodegenerative process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Proteomic analysis reveals upregulation of oxidative stress and down-regulation of chaperone proteins in\u003c/strong\u003e\u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e\u003cstrong\u003emice.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed bulk proteomics on hippocampal extracts of \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice at 18 months to identify in an unbiased fashion the proteins that may reveal pathways and networks with important roles in UNC5C-mediated cell death (Fig.\u0026nbsp;3A, S8). We performed Gene Ontology:Biological processes (GO:BP) enrichment analysis for significantly upregulated proteins using \u003cstrong\u003eD\u003c/strong\u003eatabase for \u003cstrong\u003eA\u003c/strong\u003ennotation, \u003cstrong\u003eV\u003c/strong\u003eisualization and \u003cstrong\u003eI\u003c/strong\u003entegrated \u003cstrong\u003eD\u003c/strong\u003eiscovery (DAVID)(34). The terms that were most significantly upregulated included oxidative phosphorylation, Alzheimer’s disease, Parkinson’s disease, Huntington’s disease (pathways leading to neurodegeneration), and endocytosis (Fig.\u0026nbsp;3B, D, S8). To corroborate our proteomic results, we quantified by immunoblot the levels of specific upregulated proteins, including Calmodulin-1 (CALM1), ubiquinol-cytochrome c reductase binding protein (UQCRB), and Capping actin protein of muscle Z-line β subunit (CAPZB) that are involved in oxidative stress pathways leading to neurodegeneration and endocytosis (underlined in red in Fig.\u0026nbsp;3D, F, S8). Notably, UQCRB and CAPZB were significantly increased in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e compared to \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e mice (Fig.\u0026nbsp;3G, I). Although CALM1 levels were not increased in the original \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e sample (Fig.\u0026nbsp;3H), normalization against PonceauS showed that CALM1 was significantly increased in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e hippocampus (Fig.S3C). Remarkably, the down-regulated proteins by GO terms in the \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice were chaperone, myelin sheath, and glutamatergic synapse proteins (Fig.\u0026nbsp;3C, E). We validated the proteomic results with immunoblot analysis for some of the representative proteins from each term: heat shock protein family D (HSP60) member 1 (HSPD1/HSP60) (chaperone/protein folding), Glial fibrillary acidic protein (GFAP) (myelin sheath) and Calcium voltage-gated channel auxiliary β subunit 4 (CACNB4) (glutamatergic synapses) (underlined in red in Fig.\u0026nbsp;3E, J). Notably, GFAP was significantly decreased in the \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e compared to the \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e mice, while HSP60 and CACNB4 levels trended towards decrease in the \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice (Fig.\u0026nbsp;3K-M, Fig.S3), which were significant upon normalization with PonceauS (Fig.S3F, G). Previously, it has been shown that increased endocytic activity along with increased trafficking to endosomes could possibly generate Aβ that could contribute to amyloid pathology and accelerate AD(35). Together, our findings suggest that UNC5C T835M may promote oxidative stress, which in the presence of a cytotoxic stressor, such as pathologic Aβ or tau (AD) or α-synuclein (Parkinson’s disease), may cause disease pathogenesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Unc5c\u003c/strong\u003e\u003csup\u003e\u0026nbsp;\u003cstrong\u003eKI/KI\u003c/strong\u003e\u0026nbsp;\u003c/sup\u003e\u003cstrong\u003emice exhibit increased apoptosis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince it was shown previously that UNC5C T835M increases susceptibility to death of hippocampal primary neurons (1), we employed the bioinformatic tool Polyphen-2 software (http://genetics.bwh.harvard.edu/pph2/)(36) to assess the impact of the T835M mutation on the structure and function of UNC5C protein. Polyphen-2 showed that the T835M substitution was “possibly damaging” to UNC5C protein structure with a score of 0.929 out of 1.0 (a mutation with a score of 0.0 is “tolerated” while that with 1.0 is “deleterious”; Fig. S4A), suggesting potential functional consequences that may explain the previously reported findings (1) and our own results of hippocampal atrophy suggesting neurodegeneration, as the mutation is in/near the hinge region and could affect the UNC5C open/closed state that triggers cell death or growth activities. Since cleavage of the intracellular domain of UNC5C and other UNC5 family members affect cell death via the apoptotic pathway (37–39), we generated an antibody against the C-terminal 400 amino acids of UNC5C to assess how the T835M mutation affects protein levels and proteolytic processing in the hippocampus. We hypothesized that since the mutation is in the hinge region, it could cause a change in protein conformation favoring the ‘open’ state, thereby exposing the death domain that is susceptible to cleavage by activated caspase 3, triggering the apoptotic cascade. We observed that full length (FL) UNC5C levels were significantly reduced in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e hippocampal homogenates by immunoblot analysis (Fig.\u0026nbsp;4A, B, Fig.S4B). Additionally, we observed two bands at ~ 57 kD and 52 kD – denoted as “CL1 and CL2”, respectively, corresponding to the expected cleaved products of UNC5C with activated caspase3 (boxed regions, Fig. S4C), in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice, but that were absent in \u003cem\u003eUnc5c\u003c/em\u003e constitutive knockout mouse hippocampal homogenates (\u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKO/KO\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e (Fig.\u0026nbsp;4A, B, Fig.S4B). Both CL1 and CL2 levels were increased in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice, although CL1 did not reach statistical significance (Fig.\u0026nbsp;4A, B). Importantly, the individual and summed ratios of CL1 and CL2 to full-length UNC5C were elevated in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice, with the increase in CL2 being the main driver of the change (Fig.\u0026nbsp;4C). This observation, that signal for FL band is decreased while those of CL bands are increased, suggests a ‘precursor - product’ relationship. These results suggests that the T835M mutation might increase the cleavage of the FL UNC5C protein into fragments that once released could in turn trigger the apoptotic cascade downstream of UNC5C when it is in its open conformation.\u003c/p\u003e\n\u003cp\u003eTo support this hypothesis, we measured apoptotic cell death in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e brains. Using a standard TUNEL (Terminal deoxynucleotidyl transferase (TdT) dUTP Nick-End Labeling) assay, we observed a significant increase in TUNEL + cells (Fig.\u0026nbsp;4D, Fig.S4D), specifically, NeuN+/TUNEL + cells (~ 60–80 cells) in the hippocampus of \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice at 18 and 24 months (Fig.\u0026nbsp;4D, E). The number of NeuN-/TUNEL + cells, representing microglia, astrocytes, endothelial cells, oligodendrocytes, and other cells, were far fewer compared to NeuN+/TUNEL + cells (~ 10–20 cells), and remained unchanged in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice compared to that in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e mice, indicating that only neurons are more susceptible to apoptosis in the \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice (Fig.\u0026nbsp;4F, Fig. S4E, F). Co-staining with GFAP (astrocytes), and Iba1 (microglia) showed there were very few TUNEL + GFAP + and TUNEL + Iba1 + cells in both \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e hippocampi (Fig. S4E, F). Additionally, NeuN-covered area was decreased by ~ 31% in the hippocampus of \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e compared to \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e mice (Fig.\u0026nbsp;4G). The activity of caspase-3, an effector caspase in the apoptotic process, was increased in hippocampal homogenates from \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e compared to \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e mice at 12 and 18 months of age (Fig.\u0026nbsp;4H), confirming that the UNC5C T835M mutation increases the susceptibility of neurons to cell death via apoptosis with age.\u003c/p\u003e\n\u003cp\u003eTo further understand the molecular pathway of UNC5C-mediated neuron death, we performed immunoblot analysis for certain kinases known to be involved in apoptosis. Previous studies have shown that Protein Kinase-D (PKD) decreases induction of apoptosis by modulating the c-Jun N-terminal Kinase (JNK) pathway and phosphorylation of c-Jun(40). Additionally, PKD1 has been shown to play an anti-apoptotic role in protecting neuronal cells in early stages of oxidative stress (41) by modulating JNK phosphorylation and preventing apoptosis. Since PKD1 has been shown to play a protective role in oxidative stress (42, 43), decreased PKD1 levels could lead to JNK phosphorylation and induce apoptosis via NAPDH oxidase (NOX1), as previously reported (2). NOX1 has been associated with activated caspases in AD brains(44, 45). Therefore, reduction in PKD levels might activate the JNK via increased phosphorylation, which then leads to elevated NOX1 levels. Consistent with increased apoptosis, we found a significant decrease in PKD in the hippocampus of \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice (Fig.\u0026nbsp;4I, J, Fig.S4G) as well as increased phosphorylation of JNK/SAPK, which has been shown to act downstream of UNC5C T835M (2) (Fig.\u0026nbsp;4I, K, Fig.S4G). Additionally, we observed increased Cyclin-Dependent Kinase 5 (CDK5) levels in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e hippocampus (Fig.\u0026nbsp;4I, L, Fig.S4G), which have been implicated in apoptosis (46, 47). Another study has shown that CDK5 induces c-Jun phosphorylation through activation of JNK by promoting oxidative stress (48). Further, NADPH oxidase (NOX1) levels were increased in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e hippocampus (Fig.\u0026nbsp;4I, M, Fig.S4G), providing additional evidence of an oxidative stress environment with age in the \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice. Taken together, our results strongly suggest that UNC5C T835M increases susceptibility to hippocampal neuron loss by creating an oxidative stress environment that leads to death via an apoptotic mechanism \u003cem\u003ein vivo.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe also analyzed Netrin 1 levels, which were significantly reduced at 12 months of age in the hippocampal samples from \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e compared to \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e mice (Fig.\u0026nbsp;4I, N, Fig.S4H). Since UNC5C is a member of the “dependence” receptor family, reduced Netrin1 (ligand) could initiate the apoptotic pathway (39). This supports the hypothesis that the T835M mutation could cause a change in protein conformation that makes UNC5C more prone to adopting the “open” conformation when Netrin1 levels decrease, thus triggering apoptosis and neurodegeneration. Cytotoxic stressors such as Aβ could exacerbate this mechanism. Furthermore, reduced Netrin1 levels are also associated with increased amyloidogenic processing of APP (49), so the UNC5C mutation could have the dual effect of both inducing apoptotic neuronal death and driving amyloid pathology through Netrin1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Reduced GFAP levels and morphological changes are observed in astrocytes of\u003c/strong\u003e\u003cstrong\u003eUnc5c\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eKI/KI\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003emice.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUNC5C is expressed in astrocytes as well as neurons (50) (https://brainrnaseq.org/?2327723709=1271088613) (51, 52). Notably, we observed reduced GFAP levels in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e hippocampal homogenates by proteomic analysis (Fig.\u0026nbsp;3J, K). Therefore, we used immunofluorescence microscopy to assess whether the UNC5C T835M mutation affected astrocytic phenotype (Fig.\u0026nbsp;5A-C). At 12 and 18 months, we observed a significant decrease in GFAP immunofluorescence (Fig.\u0026nbsp;5D) and GFAP + coverage area (Fig.\u0026nbsp;5E) in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice, but no change in cell number (Fig.\u0026nbsp;5F) in the CA1 region of \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice. This suggested that astrocytes in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e hippocampi are altered as a result of the mutation, since UNC5C is also expressed in astrocytes and our proteomics study showed that GFAP levels were down-regulated in the \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice (Fig.\u0026nbsp;3A, E). We speculate that UNC5C signaling may modulate GFAP expression in astrocytes, and that T835M may cause the observed GFAP reduction in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice. GFAP comprises intermediate filaments of astrocytes and it has been shown that reduction/knockout of GFAP in astrocytes does not necessarily affect their survival (53). Therefore, reduced GFAP levels could affect the cytoskeletal structure of astrocytes, which, in turn could affect their morphology. So, we next sought to examine the morphology of astrocytes in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice. We used IMARIS software to perform 3D image reconstructions to measure astrocyte process length, volume, number of branch points, number of branch terminal points, number of dendrite terminal points, and soma area (Fig. S5A-M). Although \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e astrocytes exhibited more filamentous structure with increased branches, branch points and terminal points, we observed no change in the soma area of astrocytes (Fig. S5E). We speculate that astrocytes in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice may compensate for neuronal cell death by branching out as a way of neuroprotection. Alternatively, the astrocytic changes could be playing a role in cell death, as decreased GFAP levels could be negatively affecting astrocytes functions such as providing cytoskeletal structure to astrocytes, as well as supporting neurons and endothelial cells in the neurovascular unit (54).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6. Microglia show increased activation in\u003c/strong\u003e\u003cstrong\u003eUnc5c\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eKI/KI\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003emice.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough microglia do not express UNC5C under normal conditions, other members of UNC5 family can be upregulated under pathological/stress conditions in cultured microglia, AD mice and AD human brain (1, 55). In addition, neuronal apoptosis and astrocytic changes caused by the Unc5C T835M mutation could affect microglia indirectly. To assess the effects of UNC5C T835M in microglia, we performed immunofluorescence microscopy, which revealed that \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e hippocampi had increased Iba1 and activated phagocytic microglial marker, CD68 compared to \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e mice at 18 months of age (Fig.\u0026nbsp;6A-E), demonstrating increased microglial activation. Additionally, the overall number of CD68+/Iba1 + cells were significantly increased in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice, supporting increased activated phagocytic microglia in the \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice (Fig.\u0026nbsp;6E).\u003c/p\u003e\n\u003cp\u003ePrevious studies have shown that increased C1q expression in microglia correlated with increased synaptic engulfment and plays a role in neurodegeneration in an Alzheimer’s disease mouse model, and increased C1q levels were observed in hippocampi of patients with multiple sclerosis (56–58). Further, excessive pruning of the excitatory synapses via complement-dependent pathway via C1q activation in microglia has been recently reported in AD mice(59). As anticipated, we found increased C1q in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e microglia by immunofluorescence microscopy, suggesting an elevation of complement-dependent synaptic engulfment by microglia in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice (Fig.\u0026nbsp;6A, B, F, G). The Increase in Iba1, CD68 and C1q indicated activation of microglia in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice, suggesting that microglia were reacting to degenerating neurons and possibly to astrocytic dysfunction (Fig.\u0026nbsp;6E, G). Alternatively, synaptic pruning by microglia could result from increased levels of PSD95 observed in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice at 18 months to balance pre- and post-synaptic protein homeostasis (Fig.\u0026nbsp;2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. UNC5C T835M-mediated neurodegeneration is exacerbated in dKI mice.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince the UNC5C T835M mutation was shown to be associated with increased AD risk(1), we studied the UNC5C T835M phenotype in the context of an amyloid pathology to determine if it became worsened. Previous studies have shown that cytotoxic stressors such Aβ\u003csub\u003e42\u003c/sub\u003e, glutamate, and staurosporine exacerbate UNC5C T835M-mediated cell death in primary hippocampal neurons(1), so we hypothesized that T835M mutation would increase cell death in the \u003cem\u003eApp\u003c/em\u003e\u003csup\u003e\u003cem\u003eNL−G−F\u003c/em\u003e\u003c/sup\u003e mice (NLGF) mouse model, in which the APP gene is humanized with the Swedish double mutation (KM670,671NL), as well as the Arctic (E693G) and Iberian (I716F) mutations, that are all associated with autosomal dominant AD and promote amyloid pathology (16, 60). We crossed \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e and NLGF mice to generate doubly homozygous NLGF;\u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e (referred to as double KI, (dKI)) and NLGF;\u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e (referred to as NLGF) mice. We observed a significant reduction in the neuronal area (NeuN stain) in dKI mice compared to NLGF mice at 12 months and a trend at 6 months, indicating increased hippocampal cell death in the dKI mice (Fig.\u0026nbsp;7A, B). To ascertain if there is increased cleaved caspase-3 activity, we measured active caspase-3 in hippocampal extracts from 6- and 12-month-old mice (Fig.\u0026nbsp;7C). Although there was no significant difference in caspase 3 activity between NLGF and dKI mice at 6 months, we observed a small but significant increase in caspase 3 activity in the dKI mice compared to NLGF mice at 12 months of age (Fig.\u0026nbsp;7C). Also, we observed a significant increase in caspase 3 activity in both genotypes at 12 months compared to 6 months, suggesting that NLGF mice alone do exhibit apoptotic cell death (61), which is significantly increased further in dKI mice (Fig.\u0026nbsp;7C).\u003c/p\u003e\n\u003cp\u003eNext, we determined whether amyloid pathology exacerbated UNC5C T835M-mediated effects on plaque-associated neuritic dystrophy, another indicator of neuronal dysfunction. To accomplish this, we measured the thickness of the LAMP1 positive halo, a commonly used marker of dystrophic neurites, around the Aβ\u003csub\u003e42\u003c/sub\u003e-defined plaque area (62) and found it to be increased in dKI mice (Fig.\u0026nbsp;7D, D’, E). Another measure of neuritic dystrophy, the ratio of LAMP1:Aβ42, was also significantly increased in dKI mice (Fig.\u0026nbsp;7F), supporting increased neuronal/axonal damage and dysfunction in dKI compared to NLGF mice. We repeated these analyses with plaques binned by size and found that the dystrophic neurite increase observed in dKI was primarily driven by increased LAMP1 around smaller plaques under plaque core diameter of 20 µm (Fig. S6A, B), which are thought to be most actively growing and most toxic to surrounding neuropil (63).\u003c/p\u003e\n\u003cp\u003eWe then measured plaque coverage using a pan-Aβ antibody (3D6) and found it was increased in the hippocampal region of dKI mice compared to NLGF mice (Fig.\u0026nbsp;7D, G). To quantify each Aβ species, we performed MSD analysis to determine Aβ\u003csub\u003e38\u003c/sub\u003e, Aβ\u003csub\u003e40\u003c/sub\u003e, and Aβ\u003csub\u003e42\u003c/sub\u003e levels in hippocampal homogenates (Fig.\u0026nbsp;7H-K). We observed that Aβ\u003csub\u003e42\u003c/sub\u003e and Aβ\u003csub\u003e42\u003c/sub\u003e/Aβ\u003csub\u003e40\u003c/sub\u003e ratio were increased at 12 months (Fig.\u0026nbsp;7J, K) while Aβ\u003csub\u003e38\u003c/sub\u003e and Aβ\u003csub\u003e40\u003c/sub\u003e levels remained unchanged (Fig.\u0026nbsp;7H, I), further supporting the amyloid-associated exacerbation of UNC5C T835M-induced neuronal degeneration in the presence of cytotoxic stressors(1). We also used conventional ELISA kit to detect insoluble Aβ\u003csub\u003e42\u003c/sub\u003e levels, and corroborated increased Aβ\u003csub\u003e42\u003c/sub\u003e levels in dKI hippocampal lysates measured by MSD (Fig.\u0026nbsp;7J, Fig,S6C).\u003c/p\u003e\n\u003cp\u003eWe further wanted to understand how the molecular underpinnings of the UNC5C-mediated cell death pathway were affected in the presence of amyloid at 6- and 12-months. Since we observed an increase in p-JNK, CDK5 and NOX1 levels at 12 months in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice, we expected that amyloid would exacerbate the UN5C-mediated apoptotic pathway. Indeed, we observed in the hippocampi of dKI mice a significant increase in NOX1 and phosphorylated JNK/SAPK/total JNK at 6 months (Fig.\u0026nbsp;7L-N, Fig.S6D, E), while CDK5 was significantly increased by 12 months (Fig.\u0026nbsp;7L, O, Fig.S6D, E), suggesting that the UNC5C T835M-mediated apoptotic pathway was exacerbated by amyloid pathology in dKI mice. Netrin1 (NTN1) has been shown to bind to APP and promote non-amyloidogenic processing of APP, thereby reducing the production of Aβ (64). Conversely. reduced NTN1 correlates with increased Aβ in APP transgenic mice and human AD (9, 49, 65–67). Therefore, NTN1 has been proposed as the therapeutic strategy for AD and may also reduce neuroinflammation (64, 67–72). Furthermore, reduction of Netrin1 has been associated with Parkinson’s disease as well (49, 73–76). We hypothesized that the reduced NTN1 levels in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice (Fig.\u0026nbsp;4N) could lead to increased Aβ levels in dKI mice. Indeed, we not only observed increased Aβ\u003csub\u003e42\u003c/sub\u003e levels and amyloid deposition in dKI mice (Fig.\u0026nbsp;7D, G, J), but also noted significantly decreased NTN1 levels in dKI compared to NLGF mice at 6 months, further supporting the hypothesis that the T835M mutation reduces Netrin1 levels, which in turn leads to increased Aβ in dKI mice (Fig.\u0026nbsp;7L, P, Fig.S6D, E).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8. UNC5C T835M-mediated axonal degeneration/disorganization is exacerbated in dKI mice.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince NLGF mice have synaptic loss by 6 months of age (16), and T835M \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice have a progressive loss of presynaptic markers with age, we hypothesized that the synaptic dysfunction in NLGF mice would be worsened by the UNC5C T835M mutation. Interestingly, by immunoblot analysis, synaptophysin, which is decreased in Unc5c\u003csup\u003eKI/KI\u003c/sup\u003e compared to Unc5c\u003csup\u003e+/+\u003c/sup\u003e was not different between NLGF and dKI hippocampi, although synaptophysin did decrease between 6 and 12 months in both genotypes (Fig.\u0026nbsp;8A, B, G, Fig.S7A). Likewise, post-synaptic marker PSD95, which was increased in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e compared to \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e was unchanged between NLGF and dKI mice (Fig.\u0026nbsp;8A, C, G, Fig.S7A), suggesting that the synaptic changes of NLGF mask those of the UNC5C T835M mutation. Another presynaptic protein, β-secretase (BACE1), which was decreased in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e compared to \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e mice (Fig.\u0026nbsp;2C) was significantly increased in dKI hippocampi at 12 months compared to NLGF (Fig.\u0026nbsp;8A, D, G, Fig.S7A). This observation is likely due to increased dystrophic neurites in dKI mice as indicated by increased LAMP1:Aβ\u003csub\u003e42\u003c/sub\u003e ratios (Fig.\u0026nbsp;7F), since BACE1 is well known to accumulate in dystrophic neurites (18, 77, 78) (Fig.\u0026nbsp;8A, D, G, Fig.S7A). This is supported by the observation that neurofilament/pan-axonal marker (SMI312), which also accumulates in dystrophic neurite clusters surrounding plaques in AD brains (79) is increased in dKI mice (Fig.\u0026nbsp;8A, E, G, Fig.S7A). Overall, our results strongly suggest that dKI mice have increased dystrophic neurites with age, as measured by LAMP1, BACE1 and SMI312 immunolabeling and immunoblots (Fig.\u0026nbsp;7F, 8D, E, G, Fig.S7A).\u003c/p\u003e\n\u003cp\u003eInterestingly, GFAP, which is usually increased in gliosis associated with amyloid, showed significantly decreased levels at 12 months in the dKI mice (Fig.\u0026nbsp;8A, F, G, Fig.S7A), similar to what we observed in the hippocampi of \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice starting at 12 months. Further analysis of GFAP + astrocytes in NLGF and dKI mice showed that astrocytic morphology was not altered between the two genotypes, suggesting that plaques affect astrocytes similarly in both NLGF and dKI mice. However, only dKI mice showed significantly reduced GFAP levels (Fig.\u0026nbsp;8A, F, G, Fig. S7A-J). Unlike the synaptic proteins, synaptophysin and PSD95, for GFAP, the effects of UNC5C T835M appear to over-ride the amyloid-associated phenotype typically observed in NLGF mice, although the astrocytic morphology remained unchanged. Finally, we measured the effect of UNC5C T835M on the degree of disorganization in CA1 dendrites. As before, we measured linearity in the dendritic processes that run parallel to each other and perpendicular to the CA1 cell layer. We observed that there was a significant difference near − 10° to + 10° in the dKI mice, compared to NLGF, similar to the difference seen between \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e mice, suggesting that there is an exacerbation of the dendritic disorganization in the dKI mice compared to NLGF mice (Fig.\u0026nbsp;8H). When we compared the degree of disorganization between NLGF and dKI mice at 12 months (Fig.\u0026nbsp;8H) and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice at 18 months (Fig.\u0026nbsp;2L), we found that the dendrites in the CA1 region of the hippocampus in dKI mice had the highest degree of disorganization compared to the other genotypes (Fig.\u0026nbsp;8I).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHere, we report the phenotypic characterization of a novel KI mouse model of the UNC5C T835M variant associated with LOAD, which makes the neurons more susceptible to stress-induced cell death, as previously reported by Wetzel Smith-Hunkapillar \u003cem\u003eet al\u003c/em\u003e (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). UNC5C is necessary and essential for survival and maintenance of neurons and astrocytes during development and in aging by the protein dimerization and Netrin1 binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, left). We hypothesize that UNC5C T835M could either introduce a kink in the protein conformation or reduce Netrin1 levels or both, which then could lead to biasing the protein to be in the open conformation leading to caspase 3 cleavage and activation of the downstream apoptotic cascade involving reduced Netrin1 and PKD levels, consequent JNK phosphorylation, and increased NADPH oxidase levels. NADPH oxidase activates additional caspases leading to apoptosis of neuronal cells. Although other modes of cell death such as necroptosis, ferroptosis, pyroptosis, and parthanatos in principle could be engaged with aging and neurodegenerative diseases such as in AD (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e), UNC5 family members have been well established to be involved in the apoptotic pathway (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e). Therefore, we focused on whether or not the UNC5C T835M mutation increased apoptosis by investigating Capsase-3 activity and TUNEL signal in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e brains. Our results showed that there is indeed a significant increase in caspase-3 activity and TUNEL\u0026thinsp;+\u0026thinsp;neurons, suggesting neuronal cell death via apoptosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-H). Taken together, our results strongly suggest that UNC5C T835M increases the open conformation leading to elevated caspase cleavage and apoptosis, as well as phosphorylation of JNK pathway through decreased and increased PKD and CDK5, respectively. Subsequently, NOX1 levels are increased causing an oxidative stress environment that in turn increases caspase-3 activity in a vicious cycle, resulting in hippocampal neuron death via apoptosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ-M). Neuronal degeneration could affect astrocytes or astrocytic disfunction to diminish neuronal health. However age, being the most important contributing factor for AD, in the context of UNC5C T835M may result in a significant reduction in hippocampal volume and increase in ventricular volume of the brain (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, F), reinforcing the association of UNC5C T835M with LOAD (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Further, the mutation may affect microglia indirectly because of chronic insults to neurons and astrocytes. \u003cem\u003eUnc5c\u003c/em\u003e has been shown to be expressed at high levels in oligodendrocytes ((\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Indeed, we observed significantly reduced levels of MBP (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B) and our proteomics data showed that the GO term \u0026ldquo;myelin sheath\u0026rdquo; was downregulated in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, E). These data suggest that the UNC5C T835M mutation could affect oligodendrocytes as well, which is a topic to be investigated in future studies. These individual cellular phenotypes combined appear to contribute to oxidative stress in the \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mouse brain, providing an ideal environment for neurodegenerative diseases such as AD, PD, or Huntington\u0026rsquo;s disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, right). Further, reduced Netrin1 levels have been shown to increase the amyloidogenic processing of APP leading to increased Aβ\u003csub\u003e42\u003c/sub\u003e levels (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e), which we show to be the case in dKI mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD, G, J, P). The whole mechanism, including neurodegeneration, synaptic degeneration/disorganization and oxidative stress leading to apoptosis, is exacerbated in the presence of amyloid (dKI mice) resulting in neuronal cell death and AD pathogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, right).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn this study, we employed \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e homozygous mice, although in human patients, the mutation typically presents in a heterozygous condition. Since the phenotypes in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice exhibited as late as 12\u0026ndash;18 months, we did not explore these phenotypes in \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/+\u003c/em\u003e\u003c/sup\u003e mice, which we predict would present at even older ages. In humans, where LOAD occurs over the age of 70 and takes at least 2 decades to develop, a single UNC5C T835M mutant allele may be sufficient to increase susceptibility to AD pathogenesis. Behavioral studies were conducted on 9-month and 12-month-old \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice but did not yield any conclusive evidence of hippocampal memory impairment in the \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice (data not shown), probably because the neuronal loss just begins around that age causing behavioral deficits to manifest much later.\u003c/p\u003e \u003cp\u003eOverall, we report an age-associated loss of hippocampal volume and increased hippocampal neuronal loss with age in UNC5C T835M targeted replacement mice, which could serve as a model to study other neurodegenerative diseases such as Parkinson\u0026rsquo;s and Huntington\u0026rsquo;s disease. Future studies inhibiting NOX1 or blocking activation of UNC5C-cleaving caspases, or supplementing with Netrin1, to test their ability to ameliorate AD pathology and neurodegeneration, could potentially lead to new therapeutic agents for AD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank our collaborators at Genentech in the Genetically Engineered Mouse Models (GEM), Microinjection, and Embryo Technology labs for allele design and creation, and in the Genetic Analysis Lab and Animal Resources for technical assistance. We would like to greatly thank David Kirchenbuechler from the Center for Advanced microscopy and Nikon Imaging Center at Northwestern University for his assistance with imaging and analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ. Atwal (from Genentech) generated the homozygous UNC5C T835M KI mice. D. Karunakaran and R. Vassar conceived the study. D. Karunakaran, M. Ley, J. Guo, A. Khatri, A. Upadhyay, J. Popovic, and K. Sadleir performed the experiments. Specifically, A. Upadhyay (under the guidance of J. Savas) helped with the TMT-MS Proteomics study. D. Procissi performed the MRI studies. D. Karunakaran and R. Vassar wrote the manuscript. K. Sadleir and R. Vassar reviewed the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Institute on Aging R01 grant AG0577277 (to R.V) and Alzheimer\u0026rsquo;s Association Research Fellowship AARF-16-443173 (to D.K). Imaging work and analysis was performed at the Northwestern University Center for Advanced Microscopy generously supported by NCI CCSG P30 CA060553 awarded to the Robert H Lurie Comprehensive Cancer Center.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets generated are included in this article and the supplemental information files.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study does not contain any human data. All experimental procedures were approved by the IACUC office of Northwestern University.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWetzel-Smith MK, Hunkapiller J, Bhangale TR, Srinivasan K, Maloney JA, Atwal JK, et al. A rare mutation in UNC5C predisposes to late-onset Alzheimer\u0026apos;s disease and increases neuronal cell death. Nature Medicine. 2014;20(12):1452-7.\u003c/li\u003e\n\u003cli\u003eHashimoto Y, Toyama Y, Kusakari S, Nawa M, Matsuoka M. An Alzheimer Disease-linked Rare Mutation Potentiates Netrin Receptor Uncoordinated-5C-induced Signaling That Merges with Amyloid \u0026beta; Precursor Protein Signaling. J Biol Chem. 2016;291(23):12282-93.\u003c/li\u003e\n\u003cli\u003eHong K, Hinck L, Nishiyama M, Poo MM, Tessier-Lavigne M, Stein E. A ligand-gated association between cytoplasmic domains of UNC5 and DCC family receptors converts netrin-induced growth cone attraction to repulsion. Cell. 1999;97(7):927-41.\u003c/li\u003e\n\u003cli\u003eAckerman SL, Kozak LP, Przyborski SA, Rund LA, Boyer BB, Knowles BB. The mouse rostral cerebellar malformation gene encodes an UNC-5-like protein. Nature. 1997;386(6627):838-42.\u003c/li\u003e\n\u003cli\u003eFinger JH, Bronson RT, Harris B, Johnson K, Przyborski SA, Ackerman SL. The netrin 1 receptors Unc5h3 and Dcc are necessary at multiple choice points for the guidance of corticospinal tract axons. The Journal of neuroscience : the official journal of the Society for Neuroscience. 2002;22(23):10346-56.\u003c/li\u003e\n\u003cli\u003eCoissieux MM, Tomsic J, Castets M, Hampel H, Tuupanen S, Andrieu N, et al. Variants in the netrin-1 receptor UNC5C prevent apoptosis and increase risk of familial colorectal cancer. Gastroenterology. 2011;141(6):2039-46.\u003c/li\u003e\n\u003cli\u003eTsang KM, Croen LA, Torres AR, Kharrazi M, Delorenze GN, Windham GC, et al. A genome-wide survey of transgenerational genetic effects in autism. PloS one. 2013;8(10):e76978.\u003c/li\u003e\n\u003cli\u003eWhitton L, Apostolova G, Rieder D, Dechant G, Rea S, Donohoe G, et al. Genes regulated by SATB2 during neurodevelopment contribute to schizophrenia and educational attainment. PLoS genetics. 2018;14(7):e1007515.\u003c/li\u003e\n\u003cli\u003eChen G, Kang SS, Wang Z, Ahn EH, Xia Y, Liu X, et al. Netrin-1 receptor UNC5C cleavage by active \u0026delta;-secretase enhances neurodegeneration, promoting Alzheimer\u0026apos;s disease pathologies. Sci Adv. 2021;7(16).\u003c/li\u003e\n\u003cli\u003eYang HS, Chhatwal JP, Xu J, White CC, Hanseeuw B, Rabin JS, et al. An UNC5C Allele Predicts Cognitive Decline and Hippocampal Atrophy in Clinically Normal Older Adults. J Alzheimers Dis. 2019;68(3):1161-70.\u003c/li\u003e\n\u003cli\u003eYang HS, White CC, Chibnik LB, Klein HU, Schneider JA, Bennett DA, et al. UNC5C variants are associated with cerebral amyloid angiopathy. Neurol Genet. 2017;3(4):e176.\u003c/li\u003e\n\u003cli\u003eSun JH, Wang HF, Zhu XC, Yu WJ, Tan CC, Jiang T, et al. The Impact of UNC5C Genetic Variations on Neuroimaging in Alzheimer\u0026apos;s Disease. Mol Neurobiol. 2016;53(10):6759-67.\u003c/li\u003e\n\u003cli\u003eLi Q, Wang BL, Sun FR, Li JQ, Cao XP, Tan L. The role of UNC5C in Alzheimer\u0026apos;s disease. Ann Transl Med. 2018;6(10):178.\u003c/li\u003e\n\u003cli\u003eLlambi F, Causeret F, Bloch-Gallego E, Mehlen P. Netrin-1 acts as a survival factor via its receptors UNC5H and DCC. Embo j. 2001;20(11):2715-22.\u003c/li\u003e\n\u003cli\u003eMehlen P, Rabizadeh S, Snipas SJ, Assa-Munt N, Salvesen GS, Bredesen DE. The DCC gene product induces apoptosis by a mechanism requiring receptor proteolysis. Nature. 1998;395(6704):801-4.\u003c/li\u003e\n\u003cli\u003eSaito T, Matsuba Y, Mihira N, Takano J, Nilsson P, Itohara S, et al. Single App knock-in mouse models of Alzheimer\u0026apos;s disease. Nat Neurosci. 2014;17(5):661-3.\u003c/li\u003e\n\u003cli\u003eCaothien R, Yu C, Tam L, Newman R, Nakao B, Alcantar T, et al. Accelerated embryonic stem cell screening with a highly efficient genotyping pipeline. Mol Biol Rep. 2022;49(4):3281-8.\u003c/li\u003e\n\u003cli\u003eZhao J, Fu Y, Yasvoina M, Shao P, Hitt B, O\u0026apos;Connor T, et al. Beta-site amyloid precursor protein cleaving enzyme 1 levels become elevated in neurons around amyloid plaques: implications for Alzheimer\u0026apos;s disease pathogenesis. J Neurosci. 2007;27(14):3639-49.\u003c/li\u003e\n\u003cli\u003eJohnson-Wood K, Lee M, Motter R, Hu K, Gordon G, Barbour R, et al. Amyloid precursor protein processing and A\u0026amp;#x3b2;\u0026lt;sub\u0026gt;42\u0026lt;/sub\u0026gt; deposition in a transgenic mouse model of\u0026amp;#x2009;Alzheimer\u0026amp;#x2009;disease. Proceedings of the National Academy of Sciences. 1997;94(4):1550-5.\u003c/li\u003e\n\u003cli\u003eRezakhaniha R, Agianniotis A, Schrauwen JT, Griffa A, Sage D, Bouten CV, et al. Experimental investigation of collagen waviness and orientation in the arterial adventitia using confocal laser scanning microscopy. Biomechanics and modeling in mechanobiology. 2012;11(3-4):461-73.\u003c/li\u003e\n\u003cli\u003eJongkamonwiwat N, Ramirez MA, Edassery S, Wong ACY, Yu J, Abbott T, et al. Noise Exposures Causing Hearing Loss Generate Proteotoxic Stress and Activate the Proteostasis Network. Cell Rep. 2020;33(8):108431.\u003c/li\u003e\n\u003cli\u003eHe L, Diedrich J, Chu YY, Yates JR, 3rd. Extracting Accurate Precursor Information for Tandem Mass Spectra by RawConverter. Analytical chemistry. 2015;87(22):11361-7.\u003c/li\u003e\n\u003cli\u003eXu T, Park SK, Venable JD, Wohlschlegel JA, Diedrich JK, Cociorva D, et al. ProLuCID: An improved SEQUEST-like algorithm with enhanced sensitivity and specificity. Journal of proteomics. 2015;129:16-24.\u003c/li\u003e\n\u003cli\u003eCociorva D, D LT, Yates JR. Validation of tandem mass spectrometry database search results using DTASelect. Current protocols in bioinformatics. 2007;Chapter 13:Unit 13.4.\u003c/li\u003e\n\u003cli\u003eElias JE, Gygi SP. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nature methods. 2007;4(3):207-14.\u003c/li\u003e\n\u003cli\u003eMcAlister GC, Nusinow DP, Jedrychowski MP, W\u0026uuml;hr M, Huttlin EL, Erickson BK, et al. MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Analytical chemistry. 2014;86(14):7150-8.\u003c/li\u003e\n\u003cli\u003ePark SK, Aslanian A, McClatchy DB, Han X, Shah H, Singh M, et al. Census 2: isobaric labeling data analysis. Bioinformatics (Oxford, England). 2014;30(15):2208-9.\u003c/li\u003e\n\u003cli\u003eTurrigiano GG, Nelson SB. Homeostatic plasticity in the developing nervous system. Nature reviews Neuroscience. 2004;5(2):97-107.\u003c/li\u003e\n\u003cli\u003eTurrigiano G. Homeostatic synaptic plasticity: local and global mechanisms for stabilizing neuronal function. Cold Spring Harbor perspectives in biology. 2012;4(1):a005736.\u003c/li\u003e\n\u003cli\u003eTurrigiano GG, Leslie KR, Desai NS, Rutherford LC, Nelson SB. Activity-dependent scaling of quantal amplitude in neocortical neurons. Nature. 1998;391(6670):892-6.\u003c/li\u003e\n\u003cli\u003eRichmond JE, Davis WS, Jorgensen EM. UNC-13 is required for synaptic vesicle fusion in C. elegans. Nature neuroscience. 1999;2(11):959-64.\u003c/li\u003e\n\u003cli\u003eGioia DA, Alexander N, McCool BA. Ethanol Mediated Inhibition of Synaptic Vesicle Recycling at Amygdala Glutamate Synapses Is Dependent upon Munc13-2. Frontiers in neuroscience. 2017;11:424.\u003c/li\u003e\n\u003cli\u003eVaroqueaux F, Sigler A, Rhee JS, Brose N, Enk C, Reim K, et al. Total arrest of spontaneous and evoked synaptic transmission but normal synaptogenesis in the absence of Munc13-mediated vesicle priming. Proceedings of the National Academy of Sciences of the United States of America. 2002;99(13):9037-42.\u003c/li\u003e\n\u003cli\u003eSherman BT, Hao M, Qiu J, Jiao X, Baseler MW, Lane HC, et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic acids research. 2022;50(W1):W216-w21.\u003c/li\u003e\n\u003cli\u003eCataldo AM, Barnett JL, Pieroni C, Nixon RA. Increased neuronal endocytosis and protease delivery to early endosomes in sporadic Alzheimer\u0026apos;s disease: neuropathologic evidence for a mechanism of increased beta-amyloidogenesis. The Journal of neuroscience : the official journal of the Society for Neuroscience. 1997;17(16):6142-51.\u003c/li\u003e\n\u003cli\u003eAdzhubei IA SS, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR. A method and server for predicting damaging missense mutations. Nature methods. 2010.\u003c/li\u003e\n\u003cli\u003eTinel A, Janssens S, Lippens S, Cuenin S, Logette E, Jaccard B, et al. Autoproteolysis of PIDD marks the bifurcation between pro-death caspase-2 and pro-survival NF-kappaB pathway. Embo j. 2007;26(1):197-208.\u003c/li\u003e\n\u003cli\u003eWilliams ME, Lu X, McKenna WL, Washington R, Boyette A, Strickland P, et al. UNC5A promotes neuronal apoptosis during spinal cord development independent of netrin-1. Nat Neurosci. 2006;9(8):996-8.\u003c/li\u003e\n\u003cli\u003eZhu Y, Li Y, Nakagawara A. UNC5 dependence receptor family in human cancer: A controllable double-edged sword. Cancer Lett. 2021;516:28-35.\u003c/li\u003e\n\u003cli\u003eWagner EF, Nebreda AR. Signal integration by JNK and p38 MAPK pathways in cancer development. Nature reviews Cancer. 2009;9(8):537-49.\u003c/li\u003e\n\u003cli\u003eAsaithambi A, Kanthasamy A, Saminathan H, Anantharam V, Kanthasamy AG. Protein kinase D1 (PKD1) activation mediates a compensatory protective response during early stages of oxidative stress-induced neuronal degeneration. Molecular neurodegeneration. 2011;6:43.\u003c/li\u003e\n\u003cli\u003eRozengurt E, Rey O, Waldron RT. Protein kinase D signaling. J Biol Chem. 2005;280(14):13205-8.\u003c/li\u003e\n\u003cli\u003eVan Lint J, Rykx A, Maeda Y, Vantus T, Sturany S, Malhotra V, et al. Protein kinase D: an intracellular traffic regulator on the move. Trends in cell biology. 2002;12(4):193-200.\u003c/li\u003e\n\u003cli\u003eWilkinson BL, Landreth GE. The microglial NADPH oxidase complex as a source of oxidative stress in Alzheimer\u0026apos;s disease. Journal of Neuroinflammation. 2006;3(1):30.\u003c/li\u003e\n\u003cli\u003eAbramov AY, Canevari L, Duchen MR. Beta-amyloid peptides induce mitochondrial dysfunction and oxidative stress in astrocytes and death of neurons through activation of NADPH oxidase. The Journal of neuroscience : the official journal of the Society for Neuroscience. 2004;24(2):565-75.\u003c/li\u003e\n\u003cli\u003eZhang Q, Ahuja HS, Zakeri ZF, Wolgemuth DJ. Cyclin-dependent kinase 5 is associated with apoptotic cell death during development and tissue remodeling. Developmental biology. 1997;183(2):222-33.\u003c/li\u003e\n\u003cli\u003eAhuja HS, Zhu Y, Zakeri Z. Association of cyclin-dependent kinase 5 and its activator p35 with apoptotic cell death. Developmental genetics. 1997;21(4):258-67.\u003c/li\u003e\n\u003cli\u003eSun KH, Lee HG, Smith MA, Shah K. Direct and indirect roles of cyclin-dependent kinase 5 as an upstream regulator in the c-Jun NH2-terminal kinase cascade: relevance to neurotoxic insults in Alzheimer\u0026apos;s disease. Mol Biol Cell. 2009;20(21):4611-9.\u003c/li\u003e\n\u003cli\u003eZhu K, Wang H, Ye K, Chen G, Zhang Z. Netrin-1 signaling pathway mechanisms in neurodegenerative diseases. Neural Regeneration Research. 2025;20(4):960-72.\u003c/li\u003e\n\u003cli\u003eUhl\u0026eacute;n M, Fagerberg L, Hallstr\u0026ouml;m BM, Lindskog C, Oksvold P, Mardinoglu A, et al. Proteomics. Tissue-based map of the human proteome. Science (New York, NY). 2015;347(6220):1260419.\u003c/li\u003e\n\u003cli\u003eBennett ML, Bennett FC, Liddelow SA, Ajami B, Zamanian JL, Fernhoff NB, et al. New tools for studying microglia in the mouse and human CNS. Proc Natl Acad Sci U S A. 2016;113(12):E1738-46.\u003c/li\u003e\n\u003cli\u003eClarke LE, Liddelow SA, Chakraborty C, M\u0026uuml;nch AE, Heiman M, Barres BA. Normal aging induces A1-like astrocyte reactivity. Proceedings of the National Academy of Sciences. 2018;115(8):E1896-E905.\u003c/li\u003e\n\u003cli\u003eMenet V, Gim\u0026eacute;nez YRM, Sandillon F, Privat A. GFAP null astrocytes are a favorable substrate for neuronal survival and neurite growth. Glia. 2000;31(3):267-72.\u003c/li\u003e\n\u003cli\u003eYang Z, Wang KK. Glial fibrillary acidic protein: from intermediate filament assembly and gliosis to neurobiomarker. Trends Neurosci. 2015;38(6):364-74.\u003c/li\u003e\n\u003cli\u003eGerrits E, Brouwer N, Kooistra SM, Woodbury ME, Vermeiren Y, Lambourne M, et al. Distinct amyloid-\u0026beta; and tau-associated microglia profiles in Alzheimer\u0026apos;s disease. Acta Neuropathol. 2021;141(5):681-96.\u003c/li\u003e\n\u003cli\u003eMichailidou I, Willems JG, Kooi EJ, van Eden C, Gold SM, Geurts JJ, et al. Complement C1q-C3-associated synaptic changes in multiple sclerosis hippocampus. Annals of neurology. 2015;77(6):1007-26.\u003c/li\u003e\n\u003cli\u003eGy\u0026ouml;rffy BA, T\u0026oacute;th V, T\u0026ouml;r\u0026ouml;k G, Guly\u0026aacute;ssy P, Kov\u0026aacute;cs R, Vad\u0026aacute;szi H, et al. Synaptic mitochondrial dysfunction and septin accumulation are linked to complement-mediated synapse loss in an Alzheimer\u0026apos;s disease animal model. Cell Mol Life Sci. 2020;77(24):5243-58.\u003c/li\u003e\n\u003cli\u003eZhong L SX, Wang W, Li Y, Zhuo R, Wang K, Zhang L, Hu DD, Hong Y, Chen L, Rao H, Li T, Chen M, Lin Z, Zhang YW, Wang X, Yan XX, Chen X, Bu G, Chen XF. . TREM2 receptor protects against complement-mediated synaptic loss by binding to complement C1q during neurodegeneration. Immunity. 2023.\u003c/li\u003e\n\u003cli\u003eGomez-Arboledas A, Acharya MM, Tenner AJ. The Role of Complement in Synaptic Pruning and Neurodegeneration. ImmunoTargets and therapy. 2021;10:373-86.\u003c/li\u003e\n\u003cli\u003eMasuda A, Kobayashi Y, Kogo N, Saito T, Saido TC, Itohara S. Cognitive deficits in single App knock-in mouse models. Neurobiol Learn Mem. 2016;135:73-82.\u003c/li\u003e\n\u003cli\u003ePang K, Jiang R, Zhang W, Yang Z, Li LL, Shimozawa M, et al. An App knock-in rat model for Alzheimer\u0026apos;s disease exhibiting A\u0026beta; and tau pathologies, neuronal death and cognitive impairments. Cell Res. 2022;32(2):157-75.\u003c/li\u003e\n\u003cli\u003eSharoar MG, Palko S, Ge Y, Saido TC, Yan R. Accumulation of saposin in dystrophic neurites is linked to impaired lysosomal functions in Alzheimer\u0026apos;s disease brains. Mol Neurodegener. 2021;16(1):45.\u003c/li\u003e\n\u003cli\u003eCondello C, Schain A, Grutzendler J. Multicolor time-stamp reveals the dynamics and toxicity of amyloid deposition. Sci Rep. 2011;1:19.\u003c/li\u003e\n\u003cli\u003eLouren\u0026ccedil;o FC, Galvan V, Fombonne J, Corset V, Llambi F, M\u0026uuml;ller U, et al. Netrin-1 interacts with amyloid precursor protein and regulates amyloid-beta production. Cell Death Differ. 2009;16(5):655-63.\u003c/li\u003e\n\u003cli\u003eJu T, Sun L, Fan Y, Wang T, Liu Y, Liu D, et al. Decreased Netrin-1 in Mild Cognitive Impairment and Alzheimer\u0026apos;s Disease Patients. Front Aging Neurosci. 2021;13:762649.\u003c/li\u003e\n\u003cli\u003eEisele YS, Oberm\u0026uuml;ller U, Heilbronner G, Baumann F, Kaeser SA, Wolburg H, et al. Peripherally applied Abeta-containing inoculates induce cerebral beta-amyloidosis. Science. 2010;330(6006):980-2.\u003c/li\u003e\n\u003cli\u003eSun L, Ju T, Wang T, Zhang L, Ding F, Zhang Y, et al. Decreased Netrin-1 and Correlated Th17/Tregs Balance Disorder in A\u0026beta;(1-42) Induced Alzheimer\u0026apos;s Disease Model Rats. Front Aging Neurosci. 2019;11:124.\u003c/li\u003e\n\u003cli\u003eCai M, Zheng Q, Chen Y, Liu S, Zhu H, Bai B. Insights from the neural guidance factor Netrin-1 into neurodegeneration and other diseases. Front Mol Neurosci. 2024;17:1379726.\u003c/li\u003e\n\u003cli\u003eShabani M, Haghani M, Tazangi PE, Bayat M, Shid Moosavi SM, Ranjbar H. Netrin-1 improves the amyloid-\u0026beta;-mediated suppression of memory and synaptic plasticity. Brain Res Bull. 2017;131:107-16.\u003c/li\u003e\n\u003cli\u003eSpilman PR, Corset V, Gorostiza O, Poksay KS, Galvan V, Zhang J, et al. Netrin-1 Interrupts Amyloid-\u0026beta; Amplification, Increases sA\u0026beta;PP\u0026alpha; in vitro and in vivo, and Improves Cognition in a Mouse Model of Alzheimer\u0026apos;s Disease. J Alzheimers Dis. 2016;52(1):223-42.\u003c/li\u003e\n\u003cli\u003eZamani E, Parviz M, Roghani M, Hosseini M, Mohseni-Moghaddam P, Nikbakhtzadeh M. Netrin-1 protects the SH-SY5Y cells against amyloid beta neurotoxicity through NF-\u0026kappa;B/Nrf2 dependent mechanism. Mol Biol Rep. 2020;47(12):9271-7.\u003c/li\u003e\n\u003cli\u003eBorel F, Marzocca F, Delcros JG, Rama N, Mehlen P, Ferrer JL. Molecular characterization of Netrin-1 and APP receptor binding: New leads to block the progression of senile plaques in Alzheimer\u0026apos;s disease. Biochem Biophys Res Commun. 2017;488(3):466-70.\u003c/li\u003e\n\u003cli\u003eChen G, Ahn EH, Kang SS, Xia Y, Liu X, Zhang Z, et al. UNC5C Receptor Proteolytic Cleavage by Active AEP Promotes Dopaminergic Neuronal Degeneration in Parkinson\u0026apos;s Disease. Adv Sci (Weinh). 2022;9(7):e2103396.\u003c/li\u003e\n\u003cli\u003eAhn EH, Kang SS, Liu X, Cao X, Choi SY, Musazzi L, et al. BDNF and Netrin-1 repression by C/EBP\u0026beta; in the gut triggers Parkinson\u0026apos;s disease pathologies, associated with constipation and motor dysfunctions. Prog Neurobiol. 2021;198:101905.\u003c/li\u003e\n\u003cli\u003eJasmin M, Ahn EH, Voutilainen MH, Fombonne J, Guix C, Viljakainen T, et al. Netrin-1 and its receptor DCC modulate survival and death of dopamine neurons and Parkinson\u0026apos;s disease features. Embo j. 2021;40(3):e105537.\u003c/li\u003e\n\u003cli\u003eKang EJ, Jang SM, Lee YJ, Jeong YJ, Kim YJ, Kang SS, et al. The couple of netrin-1/\u0026alpha;-Synuclein regulates the survival of dopaminergic neurons via \u0026alpha;-Synuclein disaggregation. BMB Rep. 2023;56(2):126-31.\u003c/li\u003e\n\u003cli\u003eSadleir KR, Kandalepas PC, Buggia-Pr\u0026eacute;vot V, Nicholson DA, Thinakaran G, Vassar R. Presynaptic dystrophic neurites surrounding amyloid plaques are sites of microtubule disruption, BACE1 elevation, and increased A\u0026beta; generation in Alzheimer\u0026apos;s disease. Acta Neuropathol. 2016;132(2):235-56.\u003c/li\u003e\n\u003cli\u003eKandalepas PC, Sadleir KR, Eimer WA, Zhao J, Nicholson DA, Vassar R. The Alzheimer\u0026apos;s \u0026beta;-secretase BACE1 localizes to normal presynaptic terminals and to dystrophic presynaptic terminals surrounding amyloid plaques. Acta Neuropathol. 2013;126(3):329-52.\u003c/li\u003e\n\u003cli\u003eMasliah E, Mallory M, Hansen L, Alford M, DeTeresa R, Terry R. An antibody against phosphorylated neurofilaments identifies a subset of damaged association axons in Alzheimer\u0026apos;s disease. The American journal of pathology. 1993;142(3):871-82.\u003c/li\u003e\n\u003cli\u003eDella Torre L, Beato A, Capone V, Carannante D, Verrilli G, Favale G, et al. Involvement of regulated cell deaths in aging and age-related pathologies. Ageing Res Rev. 2024;95:102251.\u003c/li\u003e\n\u003cli\u003eMehlen P, Rabizadeh S, Snipas SJ, Assa-Munt N, Salvesen GS, Bredesen DE. The DCC gene product induces apoptosis by a mechanism requiring receptor proteolysis. Nature. 1998;395(6704):801-4.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-neurodegeneration","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mond","sideBox":"Learn more about [Molecular Neurodegeneration](http://molecularneurodegeneration.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mond/default.aspx","title":"Molecular Neurodegeneration","twitterHandle":"@MolNeuro","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5462618/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5462618/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) is characterized by amyloid plaques, neurofibrillary tangles, and synaptic and neuronal loss. Recently, a rare autosomal dominant coding mutation, T835M, in the Un-coordinated 5c (UNC5C) netrin receptor gene was segregated with late-onset AD (LOAD). Overexpression of T835M in primary hippocampal neurons increased cell death in response to neurotoxic stimuli including beta-amyloid (Aβ) suggesting a mechanism by which T835M may confer increased risk of LOAD. However, the molecular mechanism of T835M-mediated cell death remained under explored. Toward this end, we generated a mouse T835M knock-in (KI) model and employed biochemical and histological analyses to understand the molecular mechanism of T835M-mediated pathogenesis in late onset Alzheimer's disease. We show that homozygous KI mice have significantly reduced hippocampal volume, increased ventricular volume, dendritic disorganization (CA1 region) and reduced UNC5C protein level by 12\u0026ndash;18 months of age. Further, we show that the neuronal cell death is observed in the KI mice by 12 months of age by TUNEL analysis and activated Caspase 3/7 assay. Proteomic analysis of hippocampal samples showed upregulation of oxidative stress and downregulation of chaperone proteins at 18 months corroborating the biochemical and histological results showing increased c-Jun N-terminal Kinase (JNK) phosphorylation NADPH oxidase, and decreased Netrin1 levels. Moreover, \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice also show morphological changes in the astrocytes with increased number of branched processes, reduced GFAP levels, and significantly increased activation of microglia. Overall, these results suggest that T835M mutation causes neurodegeneration by creating an oxidative stress environment leading to synaptic degeneration and weakened astrocytes, thereby leading to neuronal cell death via apoptosis. Furthermore, to assess the effects of amyloid pathology on the mutation, we crossed \u003cem\u003eUnc5c\u003c/em\u003e\u003csup\u003e\u003cem\u003eKI/KI\u003c/em\u003e\u003c/sup\u003e mice with \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F/NL\u0026minus;G\u0026minus;F\u003c/sup\u003e mice and observed an exacerbation of mutation-associated changes along with increased levels of Aβ\u003csub\u003e42\u003c/sub\u003e, suggesting that the T835M mutation increases the susceptibility of neurons to cell death and elevated Aβ\u003csub\u003e42\u003c/sub\u003e levels, thus promoting AD pathogenesis. Understanding the molecular mechanism of cell death in regions susceptible to neurodegeneration such as the hippocampus could shed light on the players and pathways involved in cell death in AD pathogenesis and therefore could inform therapeutic approaches for AD.\u003c/p\u003e","manuscriptTitle":"The UNC5C T835M mutation associated with Alzheimer’s disease leads to neurodegeneration involving oxidative stress and hippocampal atrophy in aged mice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-02 14:59:16","doi":"10.21203/rs.3.rs-5462618/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-04-01T13:19:10+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-01T12:57:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-01T07:16:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Neurodegeneration","date":"2025-03-31T23:16:55+00:00","index":"","fulltext":""},{"type":"decision","content":"Minor revision","date":"2024-12-04T07:17:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-neurodegeneration","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mond","sideBox":"Learn more about [Molecular Neurodegeneration](http://molecularneurodegeneration.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mond/default.aspx","title":"Molecular Neurodegeneration","twitterHandle":"@MolNeuro","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0522973f-848a-4187-8f0b-033b56404b23","owner":[],"postedDate":"April 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T16:00:06+00:00","versionOfRecord":{"articleIdentity":"rs-5462618","link":"https://doi.org/10.1186/s13024-025-00850-z","journal":{"identity":"molecular-neurodegeneration","isVorOnly":false,"title":"Molecular Neurodegeneration"},"publishedOn":"2025-06-04 15:57:09","publishedOnDateReadable":"June 4th, 2025"},"versionCreatedAt":"2025-04-02 14:59:16","video":"","vorDoi":"10.1186/s13024-025-00850-z","vorDoiUrl":"https://doi.org/10.1186/s13024-025-00850-z","workflowStages":[]},"version":"v1","identity":"rs-5462618","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5462618","identity":"rs-5462618","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}