Pathogenic O-GlcNAc dyshomeostasis associated with cortical malformations and hyperactivity

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The study examined the mechanistic basis of syndromic intellectual disability caused by the pathogenic OGT C921Y missense variant using a rodent model, assessing behavior and multiple aspects of brain architecture. The mice displayed behavioral impairments including hyperactivity, impulsivity, and associative learning deficits, along with structural abnormalities such as reduced skull size, microcephaly, decreased cortical thickness, hypoplastic corpus callosum, and histological dysplasia mainly affecting superficial neocortical (cingulate) layers. Quantitative proteomics linked O-GlcNAc dyshomeostasis to perturbed molecular pathways related to brain development; the authors frame the work as a platform for mechanism dissection and potential treatments rather than a definitive therapeutic evaluation. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Missense variants in the O-GlcNAc transferase ( OGT) gene have recently been shown to segregate with a syndromic form of intellectual disability (OGT-ID), underscoring the importance of protein O-GlcNAcylation in brain function. However, the underlying pathophysiological mechanisms linking ID to potential OGT malfunction—whether developmental, neurophysiological, or both—remain unclear. Here, we present comprehensive analyses encompassing behaviour and brain architecture of a rodent model carrying the pathogenic C921Y OGT-ID variant. These mice show a range of behavioural deficits, including hyperactivity, impulsivity, and associative learning phenotypes. Structural studies, using micro-computed tomography and magnetic resonance imaging, revealed reduced skull size, microcephaly, reduced cortical thickness and hypoplastic corpus callosum. Detailed histological analyses revealed dysplastic changes in the neocortex, predominantly affecting the superficial layers of cingulate cortex. Mechanistically, quantitative proteomic analyses revealed O-GlcNAc dyshomeostasis associated with distinct perturbed molecular pathways involved in brain development. Taken together, these data reveal neurodevelopmental defects associated with O-GlcNAc dyshomeostasis and provide a platform for dissecting mechanism and treatments of OGT-ID.
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Nyengaard , Carsten Scavenius , Benedetta Attianese , Oscar G. Sevillano-Quispe , Simon Fristed Eskildsen , View ORCID Profile Jesper Skovhus Thomsen , Brian Hansen , View ORCID Profile Daan M. F. van Aalten doi: https://doi.org/10.1101/2025.04.27.650822 Florence Authier 1 Section for Neurobiology and DANDRITE, Department of Molecular Biology and Genetics, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Asad Jan 2 Core Center for Molecular Morphology, Department of Clinical Medicine, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Islam Faress 1 Section for Neurobiology and DANDRITE, Department of Molecular Biology and Genetics, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christian Stald Skoven 3 CFIN, Aarhus Hospital , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christian Stald Skoven Iria Esperon-Abril 1 Section for Neurobiology and DANDRITE, Department of Molecular Biology and Genetics, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shagana Tharmakulasingam Balasubramaniam 1 Section for Neurobiology and DANDRITE, Department of Molecular Biology and Genetics, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kévin-Sébastien Coquelin 1 Section for Neurobiology and DANDRITE, Department of Molecular Biology and Genetics, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jens R. Nyengaard 2 Core Center for Molecular Morphology, Department of Clinical Medicine, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Carsten Scavenius 1 Section for Neurobiology and DANDRITE, Department of Molecular Biology and Genetics, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Benedetta Attianese 1 Section for Neurobiology and DANDRITE, Department of Molecular Biology and Genetics, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Oscar G. Sevillano-Quispe 1 Section for Neurobiology and DANDRITE, Department of Molecular Biology and Genetics, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Simon Fristed Eskildsen 3 CFIN, Aarhus Hospital , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jesper Skovhus Thomsen 4 Department of Biomedicine, Aarhus University , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jesper Skovhus Thomsen Brian Hansen 3 CFIN, Aarhus Hospital , Aarhus, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daan M. F. van Aalten 1 Section for Neurobiology and DANDRITE, Department of Molecular Biology and Genetics, Aarhus University , Aarhus, Denmark 5 Division of Cell and Developmental Biology, School of Life Sciences, University of Dundee , Dundee, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Daan M. F. van Aalten For correspondence: daan{at}mbg.au.dk Abstract Full Text Info/History Metrics Preview PDF Abstract Missense variants in the O-GlcNAc transferase ( OGT) gene have recently been shown to segregate with a syndromic form of intellectual disability (OGT-ID), underscoring the importance of protein O-GlcNAcylation in brain function. However, the underlying pathophysiological mechanisms linking ID to potential OGT malfunction—whether developmental, neurophysiological, or both—remain unclear. Here, we present comprehensive analyses encompassing behaviour and brain architecture of a rodent model carrying the pathogenic C921Y OGT-ID variant. These mice show a range of behavioural deficits, including hyperactivity, impulsivity, and associative learning phenotypes. Structural studies, using micro-computed tomography and magnetic resonance imaging, revealed reduced skull size, microcephaly, reduced cortical thickness and hypoplastic corpus callosum. Detailed histological analyses revealed dysplastic changes in the neocortex, predominantly affecting the superficial layers of cingulate cortex. Mechanistically, quantitative proteomic analyses revealed O-GlcNAc dyshomeostasis associated with distinct perturbed molecular pathways involved in brain development. Taken together, these data reveal neurodevelopmental defects associated with O-GlcNAc dyshomeostasis and provide a platform for dissecting mechanism and treatments of OGT-ID. Introduction Intellectual disability (ID) is a prevalent neurodevelopmental condition estimated to affect 1-3% of the global population and is characterized by a significant impairment in cognitive function, along with compromised adaptive and social behaviour 1 . Developmental anomalies in the human cerebral cortex, particularly the neocortex, are common causes of intellectual disability (ID) 2 , 3 . A recently reported syndromic form of ID is associated with missense variants in the OGT gene, which resides on chromosome Xq13.1, and encodes the O-linked N-acetylglucosamine transferase (OGT). This enzyme facilitates the covalent attachment of O-linked b-N-acetylglucosamine ( O -GlcNAc) to the hydroxyl groups of serine and threonine (Ser/Thr) residues on nuclear, cytoplasmic and mitochondrial proteins 4 , 5 . O -GlcNAcylation is a dynamic and highly conserved modification governed solely by two enzymes, OGT for attachment 6 , 7 and O-GlcNAcase (OGA) for removal 8 . This sets O-GlcNAcylation apart from other post-translational modifications, such as phosphorylation and ubiquitination, that rely on hundreds of “writer” and “eraser” enzymes, associated with specific sets of “reader” proteins 9 , 10 . O-GlcNAcylation regulates cellular functions, such as transcriptional activation 11 , gene expression 12 , stress response 13 and proteostasis 11 , in response to physiological changes. Structural studies of OGT have demonstrated that the enzyme is composed of an N-terminal tetratricopeptide repeat (TPR) domain, involved in substrate recognition and binding of OGT interactors 9 , 10 , and a catalytic glycosyltransferase domain. In addition to catalysing O-GlcNAcylation, the catalytic domain is involved in the proteolytic activation of the transcriptional coregulator Host Cell Factor 1 (HCF-1), itself an ID-associated protein 14 , 15 . OGT and OGA are notably abundant in the brain, with particularly enrichment in hippocampal formation and related structures 16 . Thus, O-GlcNAcylation plays a critical role in neuronal survival, development, and synaptic function. For instance, pan-neuronal knockout of OGT results in severe neurodevelopmental defects and early neurodegeneration 17 , 18 . Moreover, deletion of OGT in the dopaminergic neurons of substantia nigra or peripheral neurons in the dorsal root ganglion induces widespread apoptosis 19 , 20 . At the synapse, OGT and OGA display distinct localization patterns: OGA is absent from the post-synaptic density, while OGT is distributed evenly between pre- and post-synaptic compartments in excitatory synapses 21 , 22 . In this context, aynaptosome mass spectrometry (MS) data have revealed that nearly 20% of synaptic proteins, including ankyrin G, CaMKIV, and GluA2, are modified by O-GlcNAc, which dynamically regulates excitability and transmission 23 , 24 . To date, 17 pathogenic missense variants in the OGT gene have been identified as causal for a newly described syndromic form of ID, termed OGT-ID, also known as O-GlcNAc Transferase Congenital Disorder of Glycosylation (OGT-CDG) 25 – 29 . This syndrome is frequently comorbid with neurological and psychiatric disorders such as epilepsy and autism spectrum disorder (ASD), both of which are associated with focal cortical dysplasia (FCD) 30 – 32 . In addition to ID and maladaptive behaviour, OGT-ID patients exhibit a plethora of muscular, facial and neurological abnormalities, such as hypotonia, craniofacial dysmorphia, microcephaly, fifth finger clinodactyly, and developmental delay 25 , 26 , 33 . Missense and exon-skipping variants in OGT have been found across both functional domains (i.e. TPR and catalytic), with affected patients exhibiting similar clinical features. These observations hint towards pathogenic mechanisms whereby the OGT variants affect neurodevelopmental processes and/or brain function leading to intellectual disability. Nevertheless, understanding the potential impact of OGT-ID variants on brain development has remained challenging, primarily due to the lack of viable vertebrate models since germline knockout (KO) of Ogt in mice leads to embryonic lethality 34 . Among OGT-ID variants, the catalytically impaired C921Y variant has been the most extensively studied. It was identified in three affected brothers born to a healthy, non-consanguineous couple in Denmark. The affected individuals exhibit developmental delay, autistic features, dysmorphic traits, osteoporosis and seizures 35 . Functional studies have shown that the OGT C921Y variant has decreased glycosyltransferase activity, both in vitro and in mouse embryonic stem cells (mESCs). In OGT C921Y mESCs, the mutation reduces O-GlcNAcylation and decreases expression of stem cell markers Oct4, Sox2, and alkaline phosphatase (ALP) 32 , suggesting that OGT plays a critical role in embryonic stem cell self-renewal and pluripotency 32 . In Drosophila melanogaster models, the OGT C921Y mutation has been shown to reduce O-GlcNAcylation during development 36 , a defect that can be rescued by genetic or pharmacological inhibition of OGA. This mutation disrupts larval neuromuscular junction development and shortens sleep bout duration, with both effects being partially reversible through OGA inhibition. These findings suggest that certain aspects of OGT-ID pathology are developmental in origin, while others may be reversible. Although these studies indicate that early differentiation and development are affected, a vertebrate model is required to dissect the developmental and brain-wide effects of OGT-ID variants, as the precise mechanisms remain unknown. Here, we use an OGT C921Y mouse model of OGT-ID to reveal a range of behavioural deficits, including hyperactivity, impulsivity, and associative learning phenotypes. Structural studies, using micro-computed tomography and magnetic resonance imaging, revealed reduced skull size, microcephaly, reduced cortical thickness and hypoplastic corpus callosum. Detailed histological analyses uncovered nodular cortical dysplasia, predominantly affecting the superficial layers of cingulate cortex. Mechanistically, quantitative proteomic analyses suggested perturbed regulation of distinct perturbed molecular pathways involved in brain development in the presence of O-GlcNAc dyshomeostasis. Taken together, these data reveal neurodevelopmental defects associated with O-GlcNAc dyshomeostasis and provide a platform for dissecting mechanism and treatments of OGT-ID. Materials & Methods Animal husbandry The OGT C921Y line was previously generated and reported 37 and was maintained under C57BL/6J background (Janvier, France). Animal cohorts were obtained from crossing of male OGT WT with female OGT C921Y/+ . Only male mice were used in all experiments. Animals were housed in digitally ventilated cages (Tecniplast, Italy) with water and food available ad libitum and 12/12 h light/dark cycles in the Skou animal facility of Aarhus University. All animal studies and breeding were performed in accordance with the ARRIVE guidelines and the European Communities Council Directive (2010/EU) and were approved by the Danish Animal Experiments Inspectorate (Dyreforsøgstilsynet), under Breeding license 2022-15-0202-00135 and Project licenses: 2023-15-0201-01426 and 2020-15-0201-00421. Non-invasive monitoring of spontaneous activity in home cages (DVC) Patterns of locomotion and spontaneous activity were continuosly monitored over 24 hours in home cages through a specialized digitally ventilated cages (DVC) platform (Tecniplast, Italy) between the age of 30 days and 105 days (termination). This platform is based on electrical capacitance sensing technology, with incorporates a sensor board equipped with an integrated circuit comprising 12 electrodes directly beneath the floor of the cages 38 . The DVC circuit measures changes in the electrical capacitance signal from each electrode in response to the movement of a water-filled body (animal) close to or away from a given electrode. The measurements, performed approximately 4 times per second, are remotely relayed to the centralized DVC analytics platform (Tecniplast, Italy). In this web-based interface, time-stamped data for each cage can be visualized using in-built tools (e.g. daily rhythms, cumulative activity/locomotion index aggregated per minute/hour/day, bedding status, light or dark period activity, heatmaps etc.). In the default setup, the DVC analytics web-interface plots the animal locomotion index as arbitrary units normalized between 0% and 100%, representing the overall activity performed in the cage by the animals, i.e., the signal is measured for each cage and not each animal. Behaviour assessment Prior to behavioural testing, all animals were handled daily by the experimenter for one week. All handling took place in the experimental room with an ambient light setting of 25-30 Lux. All animals were housed in groups of 2 to 3 individuals. Animals were 10 weeks old at the start of behavioural testing, reaching 14 weeks old at the end. Before any behavioural test, mice were place in the experiment room for at least 30 min prior to testing. The data analyses and video quantification were performed blindly with respect to the genotype. The sample size was determined based on previous experience and validated using Post-hoc Power Calculation using ClinCalc online tool ( https://clincalc.com/stats/Power.aspx ). Locomotor behaviour and sensorimotor coordination Open Field: The activity of mice in an open field maze was recorded using ANY-maze video tracking software (Stoelting Europe, Ireland). Individual animals were placed in a 33.5 x 33.5 x 39 cm opaque box and were allowed to explore for 10 min over three consecutive days. Time spent in the centre and periphery and time moving in the periphery were analysed to investigate parameters of locomotion and anxiety. Rotarod: Coordination skills were assessed using rotarod apparatus. Mice were placed on a Ugo Basile NB 80534 rotarod with an increase in speed from 4 to 40 rpm and an acceleration time of 2 min (40 rpm/min is reach after 2min) for maximum 5 min. Latency to fall from the rotarod and speed were measured. Static rods : Mice were placed at the extremity of the rod with head facing the void. Time spent by the mouse to perform a t-turn and to reach the goal platform were recorded. If the animal fall <5 s (presumably due to a misplacement), the mouse was tested again. After three consecutive fails or an up-side down of a mouse, a 120 s score was reported. Five rod diameters (35, 28, 22, 15 and 10 mm) were used for each animal. Pole test : Mice were placed close to the top of a pole (40 cm) with the head of the mouse facing up. Times to perform a t-turn and reach the ground were recorded. The test was performed three times for each mouse. If the mouse succeeded the three attempts, a score of 3 was reported. For each slide and fail to perform the test within the 120 s cut off, an additional 1 point was incremented in the total score. Cognitive performance Novel Object Recognition : Novelty associated short- and long-term memory were assessed in an open-field maze. During the familiarisation phase mice are free to explore the maze containing two identical familiar objects for 10 min. After 90 min for short-term and 24 h for long-term memory, one of the familiar objects is replaced by a novel object and mice are free to explore the maze for 10 min. Time spent exploring objects and number of explorations was measured until reaching a total exploration time of 20 s for each mouse. Spontaneous alternation: The spontaneous alternation test was performed in a T-maze and was used to assess spatial working memory. Mice were free to choose left or right arms for 7 trials. Each arm entry was recorded to calculate the percentage of spontaneous alternation corresponding to the number of correct Left-Right (L/R) or Right-Left (R/L) sequences. Mice that were able to remember which arms they had entered most recently would choose a different one to explore. Aversive conditioning and recall : The apparatus consisted of an open-top cage (24 × 20 × 30 cm) with metal floor bars, placed inside a soundproof cubicle (55 × 60 × 57 cm) (Ugo Basile, Italy). Three minutes after being placed in the conditioning chamber, the mice were conditioned using four tone-foot shock (CS-US) pairings (n = 9 per group). Each pairing consisted of a 25 s, 7 kHz tone (CS), followed by a 25 s gap (trace period), and then a 1.5 s foot shock (US) at 0.5 mA. After the conditioning session, the animals were isolated for 10–15 min before being returned to their home cage with their littermates. Long-term memory recall was assessed in a novel context 24 h after conditioning. Following a 3 min acclimatisation period in the novel context, the mice were exposed to four CS presentations without foot shocks. The intertrial interval ranged from 120 to 180 s for both the conditioning and testing sessions. The behavioural responses were recorded using a top-mounted camera and freezing and locomotor activity were automatically scored using ANY-maze software (Stoelting Europe, Ireland). The freezing percentage represents the time the mouse spent freezing during the CS presentation or the total duration of the time bin. Anxiety behaviour Elevated-plus maze: Anxiety-like behaviour was assessed using the elevated plus maze paradigm. Mice were placed in the centre of the cross-shaped maze comprised of two open arms (125 Lux) and two closed arms (25 Lux) and allowed to explore the maze for 5 min. Time, distance and number of entries in each of the open and closed arms were analysed as an approximation for anxiety. Dark-light paradigm : Light-like anxiety behaviour was assessed using the dark-light paradigm at 180 Lux. Mice were placed in an arena containing both dark and light compartments. At the start of the test, mice were placed in the dark compartment and were free to explore both compartments for 10 min. Time, distance and number of entries in the light compartment were analysed to investigate the level of anxiety. Compulsive behaviour Marbles : Before the test, 20 glass marbles (1.6 cm in diameter) were placed on the top of the bedding (in five rows of four marbles) in a cage with 5 cm bedding. At the end of the test, the number of buried marbles (covered by at least 75% of bedding) was counted. Digging and self-grooming: Mice were placed in a cage with 5 cm (digging) or 1 cm (self-grooming) bedding for 3 min. Number of digging/self-grooming/rearing events and time spent digging/self-grooming/rearing were recorded. Nesting: Mice were placed in a cage with 1 cm bedding in the presence of a cotton pad as nesting material for 1 h. Cotton pads were weighed before and 24 h after testing to allow drying. Percentage of material removed was quantified. Litter burrowing: Mice were placed in a cage in the presence of a PVC tube filled with 120 g of litter bedding for 30 min. Percentage of litter material removed was quantified. Brain perfusion for structural analyses Brains were perfusion fixed and prepared for in-skull MRI (OGT WT n = 15 and OGT C921Y n = 16). Perfusion fixation was performed after the mice had been anesthetized by an intraperitoneal injection of Euthanimal (250 mg/kg, Alfasan, 088672). Then, the brain was fixed by transcardiac perfusion at 125 mmHg, to be close to physiologic brain perfusion as previously reported 39 using 25 mL heparinized (Heparin, 0.2 mL/100 mL, 5000 IU/mL, Pan Pharma, 482480) Natriumchlorid (9 mg/mL, B Braun, 5/389885/0417) for 3 min 30 s, followed by 25 mL of buffered 10% formalin solution (VWR Qpath Chemicals, 11699404) for 3 min 30 s. After decapitation, the mandible and extracranial tissue were removed from the skull to avoid susceptibility artifacts from air bubbles trapped in fur and cavities during imaging. Hereafter, the in-skull brains were stored in 10% formalin solution for at least one week prior imaging. One WT mouse was excluded due to misperfusion. Magnetic resonance imaging (MRI) and image analysis Before imaging, the fixed brain samples were washed in PBS for at least 24 h to increase MRI signal by removal of excess fixative 40 . For imaging, the samples were subsequently mounted in a 15 mL centrifuge tube filled with a perfluorocarbon based liquid (Fluorinert, 3M, PN: FC-770) as is standard 41 – 46 . MRI data collection: MRI scans were acquired on a 9.4 T preclinical system (BioSpec 94/20, Bruker Biospin, Ettlingen, Germany) using a bore-mounted 25 mm quadrature transmit-receive coil. To avoid sample vibrations, the tube containing the sample was secured in a custom polyethylene foam cylinder inside the coil. In-house 3D-printed sample holders ensured consistent positioning of the samples throughout experiments. High-resolution B0 maps were acquired before each sequence, allowing shimming using Bruker’s MAPSHIM. Both DKI data and structural data were acquired for each sample. Diffusion kurtosis analysis: DKI data was collected using an 8-segmented diffusion-weighted spin-echo EPI sequence with a 150 × 150 μm in-plane resolution and 250 μm slice thickness (60 slices for whole-brain coverage). Five unweighted volumes were acquired for signal normalization followed by 30 isotropically distributed encoding directions at each of three non-zero b-values (0.5, 1.0, 2.0 ms/μm 2 ). Additional scan parameters were time between diffusion gradients (Δ) = 15 ms, diffusion gradients duration (δ) = 6 ms, 20 averages, effective echo time (TE) = 27.7 ms, repetition time (TR) = 3500 ms, bandwidth = 278 kHz, resulting in a DKI scan time of 19h26m40s per animal. In addition, a rapid acquisition with relaxation enhancement (RARE) sequence with a 50 × 50 μm in-plane resolution and 250 μm slice thickness was performed. Here, the 60 slices were positioned identically to the DKI data to allow for precise multi-atlas segmentation (MAS, details below) and ROI-specific extraction of DKI parameters. The scan parameters used were effective TE = 10.5 ms, TR = 3000 ms, 30 averages, and RARE factor = 2, with a scan time of 2h28m30s per animal. For the volumetric analysis, we acquired data with an isotropic resolution of 50 μm using a 3D fast low-angle shot (FLASH) sequence. Scan parameters for this were: TR = 88.5 ms, TE = 13.7 ms, matrix size = 360 × 198 × 300, FOV = 18 × 9.9 × 15 mm, and 4 averages, resulting in a scan time of 6h31m21s per animal. For all scan types, data quality was ensured by visual inspection and samples rescanned if needed to ensure consistently high data quality for subsequent analyses. For diffusion kurtosis (DKI) analysis all DKI data from all samples were pre-processed in MATLAB (MathWorks Inc., v. 2022a) for noise floor correction, denoising and Gibbs ringing removal as described in 46 , 47 . After pre-processing, DKI data analysis was performed using inhouse MATLAB scripts as previously described 47 , 48 yielding metrics of mean water diffusivity (MD), tissue anisotropy (FA) and the mean kurtosis (MK, an index of tissue microstructure) in each voxel 47 . Multi-atlas segmentation : To systematically extract regional information from the mouse brain, a multi-atlas segmentation (MAS) 46 , 49 , 50 was performed using 10 ex vivo NeAt templates from C57/BL6J mice (60,61). This followed procedures as described previsouly 46 . Thus, high-resolution labelled images were obtained and then down sampled to match the in-plane resolution of the DKI data allowing extraction of DKI metrics from anatomically well-defined regions of interest (ROIs). Regional voxel values of DKI metrics were filtered for outliers (defined as values exceeding 3 times the median absolute deviation) and used for statistical analysis. Volumetric analysis : The high-resolution FLASH images were processed using an in-house pipeline 46 applying B1 inhomogeneity correction 53 , denoising 54 , and intensity normalization. Spatial alignment with a high-resolution template of the C57BL/6J mouse 55 was done by manually initializing a linear registration 56 followed by a non-linear registration 57 . Neuroanatomical labels from the C57BL/6J mouse atlas were subsequently transformed and resampled to data native space using the calculated deformation fields and affine transformations for calculation of individual regional volumes. From this, absolute brain volume and regional volumes were calculated. For all brains, relative regional volume (RRV; region size as % of total brain volume) was calculated to account for total brain size variation. Statistics : To investigate group differences, permutation tests were performed for either total brain volume (1M permutations), regional volumes (40 regions, 100k permutations) or extracted voxelwise DKI metrics for each region (20 regions, 100k permutations). Uncorrected significance is reported for p values < 0.05 and indicated by asterisks (*). Pound symbols (#) indicate significance below α = 0.05 divided by total number regions tested for group mean (green) and group median (red), respectively. Section signs (§) indicate significance ( p < 0.05) with p values adjusted for false discovery rate (Benjamini-Hochsberg) 58 . Cortical thickness : Cortical thickness was calculated as previously described 59 . Briefly, Laplace’s equation, with fixed boundary conditions for each of the inner and outer surfaces, was solved. For this, the inner and outer surfaces of the cortex were defined based on the anatomical atlas and transformed to the given mouse. For each point on the cortical surface, the length of a streamline connecting the inside and outside surfaces was used to define the thickness. Cortical thickness was averaged within the bilateral frontal, occipital, and parieto-temporal lobes as well as the entorhinal cortex. Statistical maps of group differences in cortical thickness were generated by fitting a general linear model at each surface vertex (SurfStat, http://www.math.mcgill.ca/keith/surfstat/ ). Given the multiple comparisons performed, statistical maps were family-wise error (FWE) corrected using random field theory [38] with α = 0.001 as cluster defining threshold. All statistical maps were thresholded at p = 0.05 (uncorrected and corrected). Micro Computated Tomography (MicroCT) Following MRI, the in-skulls brain samples were imaged using MicroCT scanning (vivaCT 80, Scanco Medical AG, Brüttisellen Switzerland). Skulls of 20 week old mice were placed in the scanner and imaged using 500 projections over 180°, an isotropic voxel size of 39 µm, X-ray voltage of 55 kVp, X-ray current of 105 µA, and an average time of 200 ms. Images were reconstructed and converted to DICOM files that were exported for subsequent analysis in 3D slicer ( http://www.slicer.org ) and rendered in 3D. Skulls were isolated applying a threshold that separates bone from soft tissue. Segmented skulls and extracted endocasts were exported as 3D models. Furthermore, the coordinates of 45 surface landmarks were registered in a semi-automated fashion: a random skull was chosen as template model and landmarked manually, and these landmarks were automatically applied to the rest of the dataset using the ALPACA module in 3D Slicer. Additionally, the PseudoLMGenerator module was used to obtain a dense network of surface landmarks consisting of 768 points from the skulls and used as input for Principal Components Analysis (PCA). The image processing was performed with the operator blinded for the group distribution. For 3D visualization of differences between WT and OGT C921Y mice by heat maps, models for the average shapes of OGT WT and OGT C921Y skulls were generated from the General Procrustes Analysis (GPA) module, aligned, superimposed, and the model-to-model distance was measured. Skull shape and size differences were assessed by measuring distances between the 45 surface landmarks and performing Euclidean distances matrix analysis. A previously collected cohort of 8 weeks old mice skulls were also analysed with this pipeline 60 . One WT skull was excluded from analysis due to imaging artefact. Histology and immunofluorescence (IF) microscopy Following MicroCT scanning, formalin fixed and paraffin embedded brain sections (10 µm thickness) were obtained from the male WT and OGT C921Y mice (n = 6 per group). To better characterize the radial and tangential cytoarchitecture in the neocortex, as well as to assess the bilaterality of any incidental findings in brain structure, one hemisphere was cut in sagittal orientation while the opposite hemisphere was cut in coronal orientation. Sections were deparaffinised and stained with haematoxylin and eosin, as described 61 . Serial sections were stained with cresyl violet for assessing the cellular arrangement of neurons in cortical layers, and separately with Luxol Fast blue for visualizing white matter distribution, including the arrangement of large tracts 61 .Then, high resolution views were obtained with an Olympus VS120 digital slide scanner equipped for bright field imaging. Slide scans were imported into Qupath (v. 0.5.1) 62 and regions of interest (ROI) were outlined manually guided by the Mouse Brain Atlas (Paxinos and Franklin’s The Mouse Brain in Stereotaxic Coordinates, 4th Edition). ROI were segmented using the Qupath cell detection on the haematoxylin channel. Cellular identity was further verified by immnuofluorescence (IF) microscopy using the following primary antibodies: anti-NeuN (1:500; ABN78; Sigma-Aldrich) and anti-glial fibrillarly acidic protein- GFAP (1:500; ab68428; abcam). For this purpose, sections were deparaffinised and incubated in a blocking buffer comprising 5% normal goat serum in Tris-buffered saline for 1 h at room temperature. Then, the sections were incubated overnight at 4°C with the primary antibodies diluted in phosphate-buffered saline containing 0.3% Triton-X and 0.5% bovine serum albumin- BSA: IF detection was performed by fluorophore conjugated secondary antibodies: Alexa-Fluor488 goat anti-rabbit (1:1000; A-11034; Thermo Fisher) and Alexa-Fluor594 goat anti-mouse (1:1000; A-11005; Thermo Fisher). Image acquisition was performed on a Leica DM6000 upright microscope equipped with a fluorescence light source. Multiplex IHC analyses were performed in Discovery Ultra automated staining system (Roche Diagnostics) after deparaffinization and heat-induced antigen retrieval using DISCOVERY CC1 buffer (Roche Diagnostics catalogue # 06414575001) for 32 minutes at 95 °C. Then, sections were sequentially incubated with the following primary antibodies: anti-NeuN (Rabbit polyclonal, Sigma-Aldrich catalogue # ABN78; dilution, 1:500), anti-Olig2 (clone: EP112; Rabbit monoclonal, Roche Diagnostics catalogue # 07667973001; dilution, 1:500), anti-Reelin (Goat polyclonal, Novus Biologicals catalogue # AF3820, dilution: 1:100) and anti- Pou3F2/BRN2 (Rabbit polyclonal, Proteintech catalogue # 14596-1-AP; dilution, 1:250). Chromogenic detection was performed using Discovery Omnimap Horseradish peroxidase (HRP)-conjugated secondary antibodies with following chromogens: DISCOVERY Teal HRP (for NeuN; Roche Diagnostics catalogue # 08254338001), DISCOVERY ChromoMap DAB (for Olig2: Roche Diagnostics catalogue # 05266645001), DISCOVERY Purple (for Reelin; Roche Diagnostics catalogue # 07053983001) and DISCOVERY yellow HRP (for POU3F2; Roche Diagnostics catalogue # 8502641001). In order to avoid cross-reaction between antibodies, denaturation between chromogenic detection of markers was performed using ULTRA CC2 buffer (Roche Diagnostics catalogue # 05424542001) at 100 °C for 24 minutes. High resolution views were obtained with an Olympus VS120 digital slide scanner in bright field imaging mode. Tissue collection and dissociation for biochemical analyses The prefrontal cortex was rapidly isolated from whole 16 week old male mouse brain (n = 3 per group), snap frozen and stored at −80 °C until processing. Tissues were disrupted in phosphate-buffered saline (PBS) two times at 5000 rpm for 30 s with 10 s break using a Precellys ® 24 Touch homogenizer (Bertin Technologies). Homogenates were split in half for further protein and RNA extractions. Mass Spectrometry (MS) and data analysis Protein extracts from prefrontal cortex tissues were prepared for mass spectrometry using S-Trap micro spin columns (Protifi), including three washes with 50% CHCl₃/50% MeOH. Trypsin digestion (Proteomics grade, Sigma-Aldrich) was performed for 16 h at 37 °C. The resulting peptides were lyophilized and dissolved in 0.5% formic acid. LC-MS/MS was conducted using an EASY-nLC 1200 system (Thermo Scientific) connected to an Orbitrap Eclipse Tribrid Mass Spectrometer (Thermo Scientific) with a 2 cm trap column (100 μm i.d.) and a 15 cm analytical column (75 μm i.d.), both packed in-house with ReproSil-Pur C18-AQ 1.9 μm resin (Dr. Maisch GmbH). Peptides were eluted at 250 nl/min using an 80 min gradient from 5% to 44% phase B (0.1% formic acid and 80% acetonitrile), followed by a 30 s gradient to 100% phase B and 5 min at 100% B. Protein identification and quantification were performed using Proteome Discoverer 2.5 (Thermo Scientific). Data were searched against the mouse reference proteome ( uniprot.org ) using the Sequest search engine with the following parameters: MS error tolerance of 10 ppm, MS/MS error tolerance of 0.02 Da, trypsin as the protease with two missed cleavages, and carbamidomethylation as a fixed modification. Variable modifications included HexNAc (ST) and oxidation (M). Label-free quantification was based on precursor ions using unique peptides quantified in at least 2 out of 3 replicates. Peptide intensities were normalized to total peptide intensity and scaled using the average of all samples. Protein ratios were based on summed peptide abundances with imputation using replicate-based resampling. Significantly regulated proteins were identified using ANOVA, with adjustments for multiple testing. Western immunoblotting Brain homogenates were lysed using 10x RIPA buffer (Cell Signaling) as previously described 37 . For MS, 50 mL of lysates were stored at −80 °C until further processing. For western blot, the rest of the lysates were centrifuged at 14,000 rpm for 20 min at 4 °C, and the protein concentration was determined with Pierce TM BCA Protein Assay kit (Thermo Scientific, 23227). Proteins (20 μg) were separated on precast 4-12% NuPAGE Bis–Tris Acrylamide gels (Invitrogen) and transferred to nitrocellulose membrane. Membranes were incubated with primary antibodies in 5% bovine serum albumin in Tris-buffered saline buffer with 0.1% Tween-20 overnight at 4 °C. Anti-OGA (1:1000 dilution; HPA036141; Sigma), anti-O-GlcNAc (RL2) (1:1000 dilution; NB300-524, Novus Biologicals), anti-OGT (F-12) (1:1000 dilution; sc-74546; Santa Cruz), mouse anti-actin (1:5000 dilution; A5441; Merck) antibodies were used. Next, the membranes were incubated with IR680/800-labeled secondary antibodies at room temperature for 1 h. Blots were imaged using a Li-Cor Odyssey infrared imaging system (Li-Cor), and signals were quantified using Emperia software (Li-Cor). Results were normalized to the mean of each corresponding WT replicates set and represented as a fold change relative to WT. RT-qPCR Total RNA was purified from brain homogenates using RNAeasy Kit (Qiagen) as previously described 37 . The threshold-crossing value was normalized to internal control transcripts ( 18S , Actb , and Pgk1 ). Results were normalized to the mean of each corresponding WT replicate set and represented as a fold change relative to WT. Statistics Statistical analyses were performed with Prism 9 (Graph Pad) unless specified otherwise. D’Agostino & Pearson, Shapiro–Wilk, and Kolmogorov-Smirnov normality tests were performed to verify normality. For data that fulfilled normality requirements, unpaired t tests were used for pairwise comparisons of WT and OGT C921Y data or Two-way Anova for multiple comparisons were used. For data sets that did not fulfil normality, Mann-Whitney tests were used for pairwise comparisons. Results OGT C921Y mice display postnatal growth development delay and hyperactivity In patients, OGT-ID variants associate with balance difficulties, ataxia and hypotonia, suggesting possible locomotor defects in addition to ID and behavioural deficits 63 . To investigate whether the OGT C921Y variant phenocopies such deficits in mice, we first assessed a range of behavioural traits, including locomotion, anxiety, compulsivity, learning and memory in a mouse line carrying this variant, the generation of which has recently been reported 37 . As part of our overall behavioural screening, animal body weight was monitored weekly. During the initial period after weaning (3 to 8 weeks old), no difference in body weight was observed between OGT WT and OGT C921Y mice suggesting similar developmental trajectories between both genotypes. However, we observed statistically significant lower gain in body weight in the OGT C921Y mice from 9 to 20 weeks old compared to the WT mice (Time x Genotype p = <0.0001, 2-way ANOVA) ( Fig. 1A ), suggesting that OGT C921Y mice show postnatal growth development delay. Download figure Open in new tab Figure 1: OGT C921Y mice show postnatal growth development delay and increased spontaneous activity. Significance is shown as * p < 0.05, ** p < 0.01, *** p < 0.001 and ****p < 0.0001. (A) Body weight of male OGT WT (n=15) and OGT C921Y (n=16) mice from 3 weeks to 20 weeks old. 2-way ANOVA (Alpha, 0.05) followed by Tukey’s column comparisons. (B) Line chart representing the cumulative locomotion index (x-axis, days) for 75 days. Two-way ANOVA (Alpha, 0.05) followed by Tukey’s column comparisons (OGT WT , n = 10 housed in 5 cages; OGT C921Y , n = 10 housed in 5 cages). (C) Line chart representing the cumulative locomotion index with light (6 am – 6 pm) and dark (6 pm – 6 am) periods in DVC cages over a longitudinal period up to 75 days (x-axis showing time in hours, 24-h format). The arrows indicate increased nocturnal activity by OGT C921Y mice compared to WT littermates. (D) Heat maps representation of the cumulative locomotion index with light and dark periods in DVC cages for 75 days (x-axis showing time in hours over 24-hour period each day, y-axis showing days with Day 31 as the starting point on top). The arrow indicates increased nocturnal activity by OGT C921Y mice compared to WT littermates. (E) Representative tracking plot over three consecutive days of a male OGT WT and male OGT C921Y mice in the open field arena. (F) Distance travelled over three consecutive days of male OGT WT (n = 15) and male OGT C921Y (n = 16) mice in the open field arena. 2-way ANOVA (Alpha, 0.05) followed by Tukey’s column comparisons. (G) Maximal speed displayed by male OGT WT (n = 15) and male OGT C921Y (n = 16) mice in the open field arena. 2-way ANOVA (Alpha, 0.05) followed by Tukey’s column comparisons. Shortly after weaning, we placed the animals in digitally ventilated cages to continuously monitor the patterns of spontaneous activity in a non-invasive manner 38 . For this purpose, we tracked animal activity longitudinally, starting at postnatal day 30 until 105 days of age (data collected over 75 consecutive days; n = 10 per group, housed in five separate cages). These experiments revealed hyperactive patterns of home cage activity by the OGT C921Y mice, compared to the WT littermates, as early as day 35 ( p = <0.0001, 2-way ANOVA) ( Fig. 1B ). Intriguingly, this period (postnatal 9-20 weeks) also marks the stage reflecting the observed postnatal growth development delay in the OGT C921Y mice ( Fig. 1A ). Evaluation of daily rhythms and home cage activity during light/dark periods revealed that the OGT C921Y mice exhibited frequent periods of hyperactivity during the dark phase (night time, active time for rodents), compared to the WT ( Fig. 1C, D ). However, the resting time (light phase, day time) was similar between the genotypes ( Fig. 1C, D ) ruling out potential disturbances in circadian rhythms, which otherwise could affect their normal day/night patterns of home cage activity. We next investigated general locomotor activity in an open field arena. Mice were placed in open arenas and allowed to explore for 10 min on three consecutive days to evaluate both exploration of, and habituation to, an unfamiliar environment. Over the total three day period, OGT C921Y mice showed an increase in activity in the arena as reflected by an increase in total distance travelled (Day 1 p = 0.0011; Day 2 p = <0.0001; Day 3 p = <0.0001, 2-way ANOVA) and maximum speed (Day 1 p = 0.0013; Day 2 p = 0.001; Day 3 p = <0.0001, 2-way ANOVA) compared to WT mice ( Fig. 1E, F, G ). To assess whether this hyperactivity was due to higher anxiety levels in OGT C921Y mice, we evaluated the fraction of time spent in the periphery versus centre of the arena. Over the three days of testing, OGT C921Y mice demonstrated thigmotaxic behaviour and increased distance travelled in both periphery (Day 1 p = 0.0004; Day 2 p = <0.0001; Day 3 p = <0.0001, 2-way ANOVA) and centre areas (Day 1 p = 0.014; Day 2 p = 0.0012; Day 3 p = 0.0193, 2-way ANOVA), but no preference for the periphery of the arena compared to WT, as shown by similar time spent in the periphery (Day 1 p = 0.1771; Day 2 p = 0.2657; Day 3 p = 0.8885, 2-way ANOVA) and centre areas (Day 1 p = 0.1785; Day 2 p = 0.2543; Day 3 p = 0.8849, 2-way ANOVA) between both genotypes ( Fig. S1A-D ). During Elevated Plus Maze (EPM), a test to assess elevated and open space anxiety, OGT C921Y mice covered more distance ( p = <0,0001, t -test) and performed higher number of entries in the centre ( p = <0,0001, t -test) resulting in an increase in open/close time ratio ( p = 0,0063, t -test) ( Fig. S1E-H ). Similarly, OGT C921Y mice travelled more distance (0-5 min p = 0.009; 5-10 min p = <0.0001, 2-way ANOVA) and displayed frequent light-dark transitions compared to WT mice during the dark-light paradigm test (0-5 min p = 0.001; 5-10 min p = 0.3374, 2-way ANOVA), with no difference in time spent in the light compartment between both genotypes (0-5 min p = 0.0926; 5-10 min p = 0.0608, 2-way ANOVA) ( Fig. S1I-L ). These findings potentially rule out anxiety-like behaviour in both the OGT C921Y and the WT mice. We also assessed motor skills, including sensorimotor coordination and balance. No differences were observed in mean speed ( p = 0.9056, t -test) or time spent on the rotarod ( p = 0.814, t -test) between WT and OGT C921Y mice ( Fig. S2A, B ). In the pole test, both OGT C921Y and WT mice were able to perform a t-turn and reach the ground with similar scores ( p = 0.1523, Mann-Whitney test) ( Fig. S2C ). During static rod tests, OGT C921Y mice spent more time than their WT littermates to complete the required t-turn (35 mm p = 0.0018; 28 mm p = 0.0003; 22 mm p = 0.1201; 15 mm p = 0.0005; 10 mm p = <0.0001, 2-way ANOVA) although all mice reached the goal platform ( Fig. 2A, B, C ). Taken together these data rule out gross defects in motor coordination and balance in the OGT C921Y animals, while revealing a degree of hyperactivity and stereotypy. Download figure Open in new tab Figure 2: Motor, digging and burying activity. Significance is shown as * p < 0.05, ** p < 0.01 and *** p < 0.001. Student t test was used for statistics. (A) Representative image of experimental set-up for the static rods test. (B) Time to t-turn of male OGT WT (n = 15) and OGT C921Y (n = 16) during the static rods test. (C) Delta time defined as (Total time – time to turn) of male OGT WT (n = 15) and OGT C921Y (n = 16) during the static rods test. (D) Representative images of the marble test at 0 min (T0) and 30 min (T30). (E) Number of buried marbles by male OGT WT (n = 15) and OGT C921Y (n = 16) during the marble test. (F) Representative image of the litter burrowing test. (G) Percentage of litter removed by male OGT WT (n = 15) and OGT C921Y (n = 16) during the litter burrowing test. (H) Number of digging events by male OGT WT (n = 15) and OGT C921Y (n = 16) during 3 min observation. (I) Time spent digging by male OGT WT (n = 15) and OGT C921Y (n = 16) during 3 min observation. (J) Number of rearing events by male OGT WT (n = 15) and OGT C921Y (n = 16) during 3 min observation. (K) Time spent rearing by male OGT WT (n = 15) and OGT C921Y (n = 16) during 3 min observation. OGT C921Y mice exhibit reduced digging behaviour In order to further characterize the stereotypic phenotypes that could reflect ASD features observed in some OGT-ID patients 63 , we assessed compulsive and repetitive behaviour. The OGT C921Y showed a reduced number of buried marbles in a marble burying test ( p = 0.0002, t -test) ( Fig. 2D, E ) and a reduction in litter displacement in a burrowing test ( p = 0.0107, t -test) ( Fig. 2F, G ) compared to WT mice. To investigate whether the reduction in burying/burrowing activity was due to reduced digging behaviour, WT and OGT C921Y were individually placed in cages containing 5 cm deep litter. The number of digging events and time spent digging during 3 min were measured. The OGT C921Y mice exhibit both reduced number of digging events ( p = 0.0004, t -test) and digging time ( p = 0.0003, t -test) compared to WT mice ( Fig. 2H, I ). These findings were not due to lower activity of the OGT C921Y mice, but rather to an increase in rearing events ( p = 0.1978, t -test) and time spent rearing ( p = 0.0317, t -test) compared to WT mice ( Fig. 2J, K ). The number of self-grooming events ( p = 0.7421, t -test) and time spent self-grooming ( p = 0.3785, t -test) were similar between the two genotypes ( Fig. S2D, E ). In addition, OGT C921Y mice removed the same amount of material as WT mice during the nesting test ( p = 0.9774, t -test) ( Fig. S2F ). OGT C921Y mice manifest features of impulsive behaviour and enhanced long term memory recall We next investigated the effect of the OGT C921Y variant on memory. First, we evaluated spontaneous alternation using a T-maze test as a measure of working spatial memory. The mice were placed in a T-shape maze and allowed to choose freely between right (R) and left (L) arms for 7 successive trials ( Fig. 3A ). Both OGT C921Y and WT mice show similar percentage of spontaneous alternation ( p = 0.2347, t -test) ( Fig. 3B ), suggesting intact short-term spatial working memory in the OGT C921Y mice. Nevertheless, the OGT C921Y mice display a significantly reduced latency of choice ( p = 0.0175, Mann-Whitney test) compared to WT mice ( Fig. 3C ), suggesting possible impulsive behaviour. Download figure Open in new tab Figure 3: OGT C921Y show impulsivity, enhanced long term memory and delayed aversive associated learning. Significance is shown as * p < 0.05, ** p < 0.01 and *** p < 0.001. (A) Schematic of the T-maze paradigm. Mice are free to explore both arms for 7 additional trials. Each choice and latency were recorded. (B) Percentage of correct alternation (L-R/R-L sequences) of OGT WT (n = 15) and OGT C921Y (n = 16) during the T-maze test. Student t test was used for statistics. (C) Latency of arm entry of OGT WT (n = 15) and OGT C921Y (n = 16) during the T-maze test. Student t test was used for statistics. (D) Representative image of the Novel Object Recognition (NOR) test. During the familiarization phase, mice are free to explore an arena with two identical objects. During the test phase, mice were free to explore the same arena where one of the familiar objects has been replaced by a novel object to assess short (90 min) and long (24 h) memory. (E) Discrimination index of OGT WT (n = 15) and OGT C921Y (n = 16) during the short term (90 min) NOR test. Student t test was used for statistics. (F) Discrimination index of OGT WT (n = 15) and OGT C921Y (n = 16) during the long term (24h) NOR test. Student t test was used for statistics. (G) Diagram showing the behavioural paradigm (top), tone, conditioned stimulus (CS), trace period, and unconditioned stimulus, foot shock (US) lengths (bottom). (H) Freezing behaviour of OGT WT and OGT C921Y (n = 9 per group) during the acquisition session of the aversive conditioning. ITI, Intertrial interval. 2-way ANOVA time x genotype interaction p < 0.0001, F(11,176) = 5.572. (I) Locomotor activity of OGT WT and OGT C921Y (n = 9 per group) evoked by the US. The data represent the 3 s bin following the foot shock. Two-way ANOVA time x genotype interaction p = 0.3323, F(3,48) = 1.166. (J) Freezing behaviour of OGT WT and OGT C921Y (n = 9 per group) in the recall session where the CS were presented without US. The trace period refers to the 25 s following the CS presentation. Two-way ANOVA time x genotype interaction p = 0.5337, F(11,176) = 0.9082. We also investigated novelty-associated memory using the Novel Object Recognition (NOR) paradigm. We used two different sets of objects to assess both short (90 min) and long (24 h) term memory. During the familiarisation phase, the mice were placed in an open field arena with two identical objects for 10 min. After 90 min or 24 h, mice were placed in the same arena containing one familiar object and one novel object ( Fig. 3D ). Time spent exploring each object was recorded allowing 20 s total exploration of both objects. Discrimination indexes were approached by calculating (t novel – t familiar ) / t total . After a 90 min interval, OGT C921Y mice showed a similar discrimination index as WT mice ( p = 0.1482, t -test), suggesting normal short term memory function in this assay ( Fig. 3E ). However, while both WT and OGT C921Y show discrimination indices greater than 0.2 after 24 h, OGT C921Y mice showed significantly greater discrimination index compared to WT mice ( p = 0.0434, t -test), potentially indicating enhanced long term memory recall ( Fig. 3F ). OGT C921Y mice exhibit impaired plasticity during associative learning To assess learning and memory function, we subjected OGT C921Y mice and WT littermates to aversive Pavlovian trace conditioning - an established hippocampus- and cortex-dependent learning and memory paradigm where mice learn to associate a neutral tone with an aversive foot shock in a non-contiguous manner. Learning involves the presentation of a tone as the conditioned stimulus (CS), followed by a trace period, after which a foot shock is delivered as the unconditioned stimulus (US) ( Fig. 3G ). We used freezing, defined as the complete cessation of movement (except for breathing), as the conditioned response (CR) and a surrogate measure of short-term adaptation and aversive learning. Freezing behaviour was monitored during the acquisition phase and again 24 h later during a recall session ( Fig. 3G ). We observed that OGT C921Y mice exhibited significant learning deficits, as indicated by a delay in freezing behaviour compared to WT littermates (Time x Genotype p = <0.0001, 2-way ANOVA) ( Fig. 3H ). This learning deficit cannot be attributed to somatosensory anomalies, as both genotypes displayed comparable responses to the US, as indicated by locomotor activity evoked by the foot shock (Time x Genotype p = 0.3323, 2-way ANOVA) ( Fig. 3I ). Furthermore, mutant mice reached the same CR levels as WT mice by the fourth CS-tone presentation ( Fig. 3H ), suggesting unimpaired auditory function. During the 24 h recall, there were no differences in freezing behaviour in OGT C921Y mice compared to WT mice (Time x Genotype p = 0.5337, 2-way ANOVA) ( Fig. 3J ), suggesting an impaired plasticity during the acquisition phase. Taken together, these data suggest that OGT C921Y exhibit impaired plasticity during associative learning. OGT C921Y mice exhibit reduced skull size and shape deformation OGT-ID is often associated with microcephaly or craniofacial deformities, which were also recapitulated in our initial report using a small cohort (n < 5) of 2 month old OGT C921Y mice 37 . Here we aimed for a comprehensive skull shape analyses using micro-computed tomography (μCT) in larger cohorts of older (5 month) mice (WT, n = 14; OGT C921Y , n = 16). Average skull shapes were superimposed for OGT WT and OGT C921Y mice and skull shape differences between the two groups were analysed. The OGT C921Y skulls were smaller in both the anterior and posterior areas, whereas the top of the skull appeared more curved compared to the WT skulls ( Fig. 4A ). We performed similar analyses on the previously reported μCT data collected from 2 month old animals 37 to detect possible temporal aspects to these defects. The 2 month old OGT C921Y mice show similar but more prominent shape differences than the 5 month old OGT C921Y mice, suggesting an age-dependent convergence of skull shapes ( Fig. 4A ). Next, surface landmarks were used to perform Principal Component Analysis (PCA) allowing comparison of skull shape independently of size differences ( Fig. S3B ) . Skulls from 5 month old OGT WT and OGT C921Y mice were separated along the first principal component (PC1) with a higher dispersion in the OGT C921Y group suggesting heterogeneity in skull shape deformation phenotype penetrance. More than 63 % of the OGT C921Y skulls display a positive PC1 value while 93 % of the WT skulls display a negative PC1 value. The skull of the OGT C921Y mice has a shorter and rounder shape than the average WT mice ( Fig. 4B ). We performed Euclidean Distance Matrix Analysis (EDMA) using 45 relevant landmarks from the Richtsmeier laboratory resource ( https://getahead.la.psu.edu ) to identify the key regions contributing to the shape differences ( Fig. S3A ). At 5 months of age, 55% of the distances were at least 2% shorter in the OGT C921Y skulls than in WT skulls, with the largest differences affecting the cranial base length ( Fig. S3C ). Similarly, in skulls from 2 month old animals, distances that are shorter than 5% in the OGT C921Y skulls were predominantly located at the base of the skull ( Fig. S3C ). The main bone contributing to the cranial base length is the sphenoid, and shortening in the sphenoid bone determines the curvature of the cranial vault postnatally 64 . This may indicate that defects in the postnatal development of the skull could cause the observed phenotype in the OGT C921Y mice. Similar skull shape deformities have been reported in mouse models of Fragile X syndrome 65 and skeletal defect syndromes, such as osteogenesis imperfecta 66 . Download figure Open in new tab Figure 4: Micro-computed tomography of OGT-ID mouse skulls indicates a reduced endocast volume and shorter skull length. Significance is shown as * p < 0.05, ** p < 0.01 and *** p < 0.001. (A) Heatmaps obtained of the average OGT C921Y skull (n = 16) to the average OGT WT skull (n = 14). Red and blue regions indicate that the average OGT C921Y skull is smaller or larger respectively than the average OGT WT in those areas. (B) PCA biplot of PC1 and PC2 of the surface landmarks of 20 weeks old male OGT WT and OGT C921Y skulls (top). Percentages in the axis indicate the explained variance. Deformations in PC1 showing shape differences across this axis are shown (bottom). (C) Volume of the skulls of 8 weeks and 20 weeks old male OGT WT and OGT C921Y mice. Student t test was used for statistics. (D) Volume of the extracted endocranial cavity of 8 weeks and 20 weeks old male OGT WT and OGT C921Y mice. Student t test was used for statistics. At both ages (i.e., 2 and 5 months old), the OGT C921Y mice, the reduction in cranial base distances and overall reduction in skull size were obvious (2 months p = 0.0016; 5 months p = 0.0101, 2-way ANOVA) ( Fig. 4C ). From 3D reconstruction of the skulls, we determined the endocranial cavity volume as an indirect measure of the brain volume. At both ages tested, OGT C921Y mice had a smaller internal cranial volume compared to their WT littermates (2 months p = 0.013; 5 months p = <0.0001, 2-way ANOVA) ( Fig. 4D ), suggesting a smaller brain volume. Taken together, these findings indicate that the OGT C921Y mice exhibit antero-posterior skull growth defects leading to reduced skull size and shape deformation, suggesting dysmorphic features and reduced brain size. OGT C921Y display reduced cortical thickness and hypoplastic changes in brain structures Neocortex hypoplasia, changes in cortical thickness and white matter integrity defects have been reported in mouse models of neurodevelopment disorders including ASD 67 , CHARGE 68 and Rett 69 syndromes. To assess whether the reduction in brain size is general or localized to specific brain regions, we employed magnetic resonance imaging (MRI). Specifically, we performed volumetry analysis of anatomically well-defined brain regions based on high-resolution MRIs of 50 µm isotropic resolution, covering both the grey and white matter. The OGT C921Y mice showed a reduced total brain volume compared to WT ( Fig. 5A ). Regional volumetric analyses showed a reduction in absolute volumes in most brain regions ( Fig. S4 ). When expressed as regional relative volume (RRV) variation, the OGT C921Y mice showed significantly reduced (group median) volumes of several brain regions including the frontal lobe of the cerebral cortex, corpus callosum, basal forebrain, globus pallidus, internal capsule and stria terminalis ( Fig. 5C , S5 ). In contrast, RRV was increased in the lateral septum, medulla, hippocampus and hypothalamus regions of the OGT C921Y mice compared to WT mice ( Fig. 5C , S5 ). Cortical thickness analysis revealed significantly thinner cortex bilaterally in posterior regions in the OGT C921Y mice compared to WT mice, with differences on the order of 100 µm ( Fig. 5B ). Download figure Open in new tab Figure 5: OGT C921Y mice show changes in regional brain volume and reduced cortical thickness. Volumetry and cortical thickness from high resolution T1-weighted Magnetic Resonance (MR) images. (A) Total brain volume of 20 weeks old male OGT WT and OGT C921Y mice. Groupwise total brain volume where each dot represents one subject. The horizontal lines correspond to group extrema and median. Asterisks (*) mark significance (p < 0.05) based on permutation tests (1M permutations) of either the mean (green asterisk) or median (red asterisk) of the two groups. (B) Heatmaps of group differences in cortical thickness between male OGT WT and OGT C921Y mice. Maps are effect sizes (top row), p-values of significant differences both uncorrected for multiple comparisons (middle row) and corrected with family-wise error (FWE). N = 15 per genotype. One mutant was not perfused correctly and removed from analysis. (C) Regional brain volumes represented as percentage of whole brain volume of male OGT WT and OGT C921Y mice. Asterisks (*) indicate uncorrected significance (p < 0.05), based on permutation tests (100k permutations for each region). Pound symbols (#) indicate significance below alpha (0.05) divided by total number regions tested (40) for mean (green) and median (red), respectively. Section signs (§) indicate significance (p < 0.05) with p-values adjusted for false discovery rate (Benjamini-Hochsberg, BH). Y-axes are scaled to individual ROIs to highlight group variation and difference. We next assessed potential local brain microstructural defects using diffusion kurtosis imaging (DKI) 70 – 73 , which provides sensitive indices of water mobility (mean diffusivity, MD) and diffusion directionality (fractional anisotropy, FA) in tissue as well as markers of tissue complexity (mean kurtosis, MK). Collectively, the MRI markers employed here are known to be sensitive to subtle tissue alterations in both rodent 41 , 42 , 48 , 74 – 76 and human brain 77 – 80 . We observed no difference in these DKI metrics between OGT C921Y and WT mice in any of the 20 automatically segmented regions including neocortex ( Figs. S6-9 ). Taking together, these MRI data suggest that OGT C921Y show preserved brain microstructure at the resolution (150 µm x 150 µm x 250 µm) of the DKI experiments yet reduced cortical thickness. OGT C921Y mice exhibit dysmorphic features in superficial cortical layers, predominantly affecting the cingulate Previous studies indicate that congenital dysplasia in cortical organization manifests as a range of malformations including disrupted cortical laminar organization, neuronal heterotopia in the subcortical white matter, misplaced neurons in cortical lamina I, clustering of neurons in the grey matter, and the presence of dysmorphic neurons 81 – 83 . To evaluate cortical cytoarchitecture at the microscopic level and for detecting any features suggestive of focal cortial dysplasia (FCD) in OGT C921Y mice, we subjected the brain sections to histological analyses using H&E (for cells), cresyl violet (for neurons), luxol fast (for white matter) staining ( Fig. S10 ). In the H&E analyses, we observed an overall normal 6-layered cortical organization across the regions involved in primary sensorimotor modalities, including the somatomotor cortex M1/M2, primary somatosensory cortex S1/S2, auditory cortex and primary visual cortex V1 ( Fig. S11 ). Intriguingly, part of the cingulate (retrosplenial Area 29/Area 30, according to Paxinos and Franklin) found within the paramedian portion (sagittal section, interaural 0.36) showed FCD in 5 out of 6 animals in the OGT C921Y cohort ( Fig. 6A-C , compared to WT). The most conspicuous microscopic finding suggestive of focal cortical dysplasia was seen in the form of pseudosulcus formation in the cingulate, such that the superficial cortical layers (I-III) appeared to be displaced inwards ( Fig. 6B-C , black arrow), and resembled polymicrogyria similar to those observed in other congenital brain malformations 84 , 85 . In two extreme cases, nodular arrangements of cells within the superficial layers (II/III bordering IV), with a central halo containing loosely arranged eosinophilic tissue, were observed ( Fig. 6B-C , yellow arrow ). Having ruled out gross structural alterations (except in the cingulate cortex), we next assessed cortical cell density, which would reveal hypo or hyperproliferative neurodevelopmental anomalies. Total cell density analyses in the neocortical regions (H&E stained serial sections, reflecting both neuronal and non-neuronal cells), did not indicate drastic differences between the two groups in the major cortical regions examined ( Fig. 6D ).Staining analyses using cresyl violet indicated that the nodular collections in the layers II/III bordering IV were predominantly neuronal cell bodies ( Fig. 6E ). In luxol fast stained sections, these malformations contained collections of ectopic white matter, which populated the center of nodular malformations ( Fig. 6F ) and/or found as patchy deposits in the vicinity of superficial layers (II-IV) ( Fig. 6C ).To further establish the cell identity in the cortical malformations, we performed immunofluorescence analyses using neuronal nuclei marker (NeuN) and astroglial marker glial acidic fibrillary protein (GFAP). These analyse further corroborated the notion that the foci of FDC in the cingulate of OGT C921Y mice were predominantly populated by neuronal cells and were devoid of astrocytes, which otherwise would indicate reactive astrogliosis seen in states of brain trauma ( Fig. 6G-H , compare WT in Fig. 6G to OGT C921Y in Fig. 6H ). Furthermore, normal localization of astroglial cells in the subcortical white matter was evident in the WT and OGT C921Y cohorts ( Fig. 6G-H , merged images in the panels on right). In parallel, multiplex IHC analyses confirmed that the nodular cortical malformations predominantly affected layers II-V, as indicated by the distribution of neuronal markers Reelin and POU class 3 homeobox 2 (POU3F2), and were populated by neurons and oligodendrocytes in the vicinity ( Fig. S12 ). Taken in conjunction with the MRI (reduced cortical thickness), the cortical dysplasia in revealed by the histology analyses suggest a neurodevelopmental component to OGT-ID affecting the cortical superficial layers, which in OGT C921Y mice predominantly affects the cingulate. Download figure Open in new tab Figure 6: Pseudosulcus formation, cortical dysplasia and ectopic white matter in the cingulate cortex of OGT C921Y mice. (A-B) Representative low magnification (10X) sagittal views of WT (in A) and OGT C921Y mice (in B), showing cortical malformations in the cingulate cortex of the latter by H&E staining. The black arrow points to instances of pseudosulcus formation and the yellow arrow points to instances of cortical dysplasia observed in the layers II-IV (scale bar = 500 µm). (C) Representative high magnification (40X) views extracted from the insets in A except one marked with *, which is depicting nodular arrangement of cells in a brain section from a different OGT C921Y animal (scale bar = 100 µm). (D) Regional cell density assessed in 10X views (H&E staining) of two-serial sections from WT and OGT C921Y mice. Error bars indicate Mean ± SEM (n = 6 / group; Mann-Whitney test, none significant). Regions examined include: (coronal sections) primary motor cortex M1/M2, primary somatosensory cortex S1/S2, auditory cortex (A); (sagittal sections) visual cortex V1 and cingulate cortex (C). Also see Fig. S10 depicting coordinates and region demarcations (dashed rectangles) used in the cell density analyses. (E-F) Representative low magnification (10X) sagittal views showing cresyl violet staining for neuronal cell bodies (in E) and myelin staining by luxol fast (in F). Notice the instances of ectopic white matter (red arrows) in the cingulate cortex of OGT C921Y mice (scale bar = 200 µm). in 6F. Quantitative proteomics analyses revealed distinctly perturbed molecular pathways in prefrontal cortex of the OGT C921Y mice In order to decipher the molecular phenotypes that could be associated with the cortical defects in the OGT-ID mutant strain, we performed proteome analysis on the prefrontal cortex (PFC) of male OGT C921Y and WT mice using label-free quantification. A total of 5488 proteins were identified with a 1 % false discovery rate which we then ranked as top 50 most affected (and potentially revealing) factors. This ranking was based on defined cut-off criteria for both the upregulated ( p value ≤ 0.05; log-2 fold change ≥ 1.45) and down-regulated proteins ( p value ≤ 0.05, log-2 fold change ≤ −1.33). Then we performed pathways enrichment and downstream analyses using the built-in tools of the STRING database ( Fig. 7A, B ). To begin with, the expression of OGA was significantly downregulated in the cortex of OGT C921Y ( Fig. 7B ) in the mass spectrometry analyses, as well as in western blotting analyses of the cortex ( p = 0.0223, t -test) ( Fig. S13A, C ) as we have reported previously 60 .Although no significant changes in the expression of OGT were observed in OGT C921Y cortex ( p = 0.788, t -test) ( Fig. S13A, D ), there was a significant increase in OGT/OGA protein ratio in OGT C921Y mice ( p = <0.0001, t -test) ( Fig. S13E ), which is driven by the reduction in OGA levels to compensate for reduced OGT activity. Despite this compensatory mechanism, global O-GlcNAcylation of proteins in the brain was drastically impaired in OGT C921Y brain compared to WT ( p = 0.0018, t -test) ( Fig. S13O, Q ). The perturbed regulation of Ogt/Oga ratio (Oga mRNA p = 0.0087; Ogt mRNA p = 0.0894; Ogt/Oga ratio p = <0.0001, t -test) was further confirmed at the transcriptional level by RT-PCR ( Fig. S14A, B, C ). Of note, similar O-GlcNAc dyshomeostasis at protein/mRNA levels was observed in other brain regions analysed except for OGT protein levels that were found reduced in both hippocampus and cerebellum ( Fig. S14 ), suggesting brain region specific effect of the OGT C921Y mutation. In the prefrontal cortex region, gene ontology analyses in the STRING database pointed to significant upregulation in the pathways regulating cellular processes (GO:0009987), cellular metabolic pathways (GO:0044237), nervous system development (GO:0007399), protein catabolic process (GO:0030163) and lysosomal transport (GO:0007041) ( Fig. 7C ). Among the top up regulated proteins identified in our dataset ( Fig. 7A ), thirty-five proteins belong to neurodevelopmental pathways, including proteins involved in neuronal migration (PLXND1, FAT3, ASTN2, NEUROD1), neurogenesis (NEUROD1, RBBP5, RBBP6) and synaptic function (CLCN3, SORCS3, AP3B1). Download figure Open in new tab Figure 7: Quantitative proteomics analyses revealed distinctly perturbed molecular pathways in prefrontal cortex of the OGT C921Y mice. (A) List of Top 50 proteins up-regulated (cut off criteria p value 0.05, log 2 change 1.45). (B) List of Top 50 proteins down-regulated (cut off criteria p value 0.05, log 2 change −1.33). (C) Up-regulated biological pathways enrichment in the STRING database. (D) Down-regulated biological pathways enrichment in the STRING database. Intriguingly, we also identified significant downregulation in the pathways regulating small molecule metabolic process (GO: 0044281), carbohydrate derivative biosynthetic process (GO: 1901137), cellular respiration (GO: 0045333), ATP metabolic process (GO: 00046034) and mitochondrial acetyl-CoA biosynthetic process from pyruvate (GO:0061732) ( Fig. 7D ). Moreover, the perturbed proteomic signatures in the PFC corresponded to distinct Monarch enrichment profiles 86 in the STRING database including abnormal CNS myelination (HP:0011400), agenesis of corpus callosum (HP: 0001274), upper motor neuron dysfunction (HP: 0002493) and abnormal muscle physiology and function (HP: 0011804; HP: 0003808; HP: 0001252; HP: 0001319; HP 0003394) ( Fig. S13A, B ). Taken together, these data suggest that perturbations in protein O-GlcNAcylation in brain of OGT C921Y are associated with distinct alterations in molecular pathways that potentially impact brain development. Discussion Although several OGT variants linked to ID have been reported, the mechanisms underlying the disorder remain unknown. Potential mechanisms that have been proposed 63 ( Fig. 8A ) include loss of O-GlcNAcylation on OGT substrates important for brain function and development 28 , 37 , 63 , OGT aggregation due to reduced OGT stability 63 , impaired OGT interactome 87 , misprocessing of HCF1 15 , or loss of OGA 27 , 28 , 37 , a common feature observed in models of OGT-ID. Lastly, O-GlcNAc dyshomeostasis has been recently suggested as a common mechanism in OGT-ID variants and has been proposed as a biomarker to identify new pathogenic variants using a stem cell reporter line 88 . Furthermore, these hypotheses are not mutually exclusive and can individually or in combination account for the cognitive dysfunction due to impaired brain development, defects in synaptogenesis/synaptic pruning and/or neural transmission impairment ( Fig. 8 ). Vertebrate models are a key step towards deciphering the mechanisms linked to the disease, and here we have described a detailed characterization of a mouse model of one OGT-ID variant (OGT C921Y ). Download figure Open in new tab Figure 8: Graphical summary of the neurodevelopmental, structural and behaviour defects identified in the OGT C921Y mice. This Figure was created using BioRender.com Sevillano quispe, O. (2026) https://BioRender.com/n21l133 The OGT C921Y variant is found in a Danish family with three affected male siblings that inherited the variant through their affected mother. The male siblings exhibit dysmorphic features, ID with poor language skills and late onset epilepsy. Autistic features including repetitive mannerisms, and hyperactivity have also been reported 32 . Mild hyperactivity has also been reported in a patient carrying another OGT-ID variant 89 . The OGT C921Y mice recapitulate features of impaired cognitive function, impulsive behaviour and increased spontaneous activity (or hyperactivity) both in their home cage and in the open field arena. These phenotypes (hyperactivity and impulsivity) are often observed in mouse models recapitulating human ID and autism disorders. For instance, Fmr1 Ko mice exhibit hyperactivity in open field assays and increased exploratory behaviour as seen by an increased number of light-dark transitions in the dark-light paradigm similarly to our observations in the OGT C921Y mice 90 – 92 . Moreover, hyperactivity and impulsivity behaviours present in a haploinsufficiency mouse model of Kdm6b were rescued with methylphenidate, a neurostimulant used to alleviate attention deficit and hyperactivity in individuals with ADHD 93 . In addition, the stereotypy and increased rearing activity observed in the OGT C921Y mice suggest locomotor compulsive behaviour similar to that reported in the individuals carrying the C921Y variant 32 . We have observed that, unlike the WT mice, the locomotor activity of the OGT C921Y mice remained unaffected during repeated measurements in the open field test, which could suggest an impairment in spatial habituation learning. Interestingly, defects in habituation learning were also observed in Drosophila models carrying OGT-ID variants during a light-off jump habituation test 94 . As habituation corresponds to the simplest form of learning 95 , these data hint towards some learning and memory defects in OGT C921Y mice. This is further indicated by a delay in freezing behaviour during acquisition compared to WT littermates during the aversive Pavlovian paradigm, a well-established hippocampus- and cortex-dependent learning and memory. Reduction of brain size has been reported in OGT-ID patients, including microcephaly and brain atrophy 29 . In the present study, both the shape and morphology of the skulls of the OGT C921Y mice were affected. The morphological alteration of the skulls reduced the endocast volume (as an estimate of brain volume) significantly in both 2 month old and 5 month old OGT C921Y mice compared to their WT littermates. MRI brain structural analyses indicate that redistribution of regional volumes rather than overall reduction underpins the lower total brain volume in the OGT C921Y mice. The frontal lobes of the cerebral cortex were prominently affected and associated with bilateral reduced cortical thickness suggesting overall reduction of cortical grey matter. Reduced brain volume, in particular the prefrontal cortex region, has also been associated with several neurodevelopment disorders in human including ADHD 96 , 97 and associated rodent models 98 . Moreover, reduced brain volume and grey matter density in cerebellum and cortical regions are observed in adolescents with ASD associated with intellectual deficits 96 . While the MRI data suggest an overall reduction in the size of the prefrontal cortex, histological analyses revealed FCD and pseudosulci formation in areas of the cingulate (retrosplenial) cortex in the OGT C921Y mice with patchy distribution of ectopic white matter in the vicinity of these cortical malformations. These features are reminiscent of cortical polymicrogyria seen in some types of CDG disorders such as muscular dystrophy-dystroglycanopathy (MDDG) syndromes 84 , 85 , and in some inborn errors of metabolism characterized by defective neural migration 99 . Given that OGT C921Y mice exhibit considerable reduction in cortical protein O-GlcNAcylation, it is plausible to postulate that these forms of cortical dysplasia (psuedosulci) arise as neurodevelopmental sequelae to the perturbed O-GlcNAcome. FCD has been linked to cognitive impairment in certain X-linked intellectual disabilities (XLIDs), such as Fragile X syndrome 100 . FCD arises from localized malformations in the cortex caused by disruptions in neuronal proliferation, differentiation, and migration during corticogenesis 101 – 103 . However, some studies suggest that similar disruptions in the organization of cortical layers may also arise due to disorders of post-migrational development of neural progenitors and not solely due to defects in proliferation or migration 104 . Therefore, it remains to be determined whether the features of cortical malformation (microcephaly and disturbed cytoarchitecture in cingulate) in the OGT C921Y mice pertain to these categories. Behavioural and neuroimaging studies indicate that distinct regions within the cingulate participate in complex cognitive tasks involving affective response and decision making 105 , 106 . Meta-analyses of neuroimaging studies in human subjects with psychiatric disease (major depressive disorder, bipolar disorder, schizophrenia, anxiety and addiction) purport grey matter loss in the dorsal anterior cingulate as a frequent finding 107 , 108 . Taken together, these suggest a link between the cortical structure defects and the behavioural deficits observed in the OGT C921Y mice. However, further systemic interventions (e.g. optogenetic manipulations) will be needed to investigate whether the behavioural phenotypes of OGT C921Y mice solely arise from cortical dysplasia in the cingulate cortex. There is a significant dearth of information on possible cortical malformations in patients with OGT-ID. This is largely due to the lack of clinical data available as brain imaging has only been reported in a few cases, or that the defects are beyond the limits of detection/resolution in routine clinical MRI scans. Nevertheless, defects in white matter composition have been reported in patients with OGT-ID including thin corpus callosum and periventricular leukomalacia 29 . Both defects have previously been associated with learning difficulties and ID 96 , 109 . Changes in white matter structures have also been found in neuroimaging analysis of neurodevelopment disorders such as Fragile-X 110 , developmental delay 111 , 112 and ADHD 113 . Together, these observations suggest that changes in white matter structures could underline learning and behavioural deficits in neurodevelopment disorders. Interestingly, the OGT C921Y mice also showed reduced volumes of several white matter structures including corpus callosum, internal capsule, cerebral peduncle, corticospinal tracts and stria terminalis. These observations could guide future studies in investigating whether OGT-ID variants cause impaired myelination processes leading to cognitive impairment. Our proteomics analyses revealed a number of perturbed molecular pathways in the prefrontal cortex of the OGT C921Y mice. Interestingly, some of the deregulated proteins are associated with brain malformation including progressive microcephaly (ARSA, CHMP1A, QARS1) 114 – 116 , cortical dysplasia (RALGAPB, PACS2) 90 , 9 , myelination defects (ARSA, CHMP1A, GABRA2, HEXA, RMND1) 114 , 117 – 121 and agenesis of the corpus callosum (EFNB1, TUBA8, CLCN3, PLXND1) 122 – 125 , providing potential candidates underlying the brain defects identified in the OGT C921Y mice. In addition, the RHO GTPase cycle pathway (HAS-9012999) was significantly upregulated from the Reactome database. RHO GTPase family are involved in cell migration, division and polarity and play crucial roles in neurodevelopment 126 . While a detailed description of all the top 50 upregulated proteins is beyond the scope of this Discussion, the ubiquitin ligase RB binding protein 6 (RBBP6) promotes ubiquitination of the Y box protein 1 (YBX1) followed by its degradation by the proteosome. YBX1 is a nucleic acid binding protein required for forebrain specification, cell proliferation and neuronal differentiation through the suppression of RNA polymerase II mediated transcription 127 . Interestingly, while RBBP6 was found up regulated, its target YBX1 was identified as significantly down regulated in our dataset, suggesting a role of the RBB6P/YBX1 axis in the brain structural defects observed in the OGT C921Y mice. Future studies at early timepoints during brain development will be needed to explore these candidate conveyors of OGT-ID. Whereas structural brain defects suggest a neurodevelopment origin, it is as yet unknown whether the associated cortical dysfunction originates from structural defects during neurodevelopment or neurophysiological defects due to the continuous presence of the catalytically impaired OGT C921Y variant in neurons, their synapses and glia. Non-invasive monitoring of the OGT C921Y mice activity patterns (in DVC) showed that spontaneous hyperactivity was not detected from weaning but continuously progressed from approximatively 7 weeks of age, coinciding with a delay in postnatal growth. With reasonable caution, this is reminiscent of clinical findings in patients with OGT-ID who generally start developing symptoms in the first two years. From a translational perspective, these features hint towards a progressive postnatal appearance of behaviour and morphological deficits in the OGT C921Y mice, thus potentially offering a therapeutic window for future interventions. It is hoped that cognitive impairment due to prefrontal cortical dysfunction can partly be rescued postnatally even in presence of brain structural defects, with notable examples from research in models of Fragile X and Retts neurodevelopmental syndromes 69 , 97 , 100 . Conclusions In conclusion, we report that O-GlcNAc dyshomeostasis in brains of OGT C921Y mice is associated with distinct behavioural phenotypes reflecting hyperactivity, impulsivity and learning deficits. These phenotypes were accompanied by features consistent with perturbed neurodevelopment including skull deformation, microcephaly and focal cortical dysplasia in the cingulate cortex ( Fig. 8B ). Moreover, the glimpse offered by changes in the neocortical proteome of OGT C921Y mice and the observed O-GlcNAc dyshomeostasis will guide future studies in unravelling the pathophysiology of the disorder, and hold promise for the development of novel therapeutic interventions in OGT-ID. Data availability Data will be available upon request. Funding This work was funded by a Wellcome Trust Investigator Award (110061), a Novo Nordisk Fonden Laureate award (NNF21OC0065969) and a Villum Fonden Investigator (00054496) to D.M.F.v.A. Supported in part by the Danish Research Institute of Translational Neuroscience – DANDRITE of the Nordic-EMBL Partnership for Molecular Medicine and Lundbeckfonden. The Novo Nordisk Foundation is gratefully acknowledged for funding the Scanco µCT equipment as a part of the Aarhus X-ray Imaging Alliance (AXIA). Competing interests The authors declare no competing interests. Author contributions F.A. and D.M.F.v.A conceived the study; F.A., I.F., C.S.S., S.T.B., K.S.C., B.A., J.S.T. performed experiments; C.S. performed mass spectrometry; F.A., I.F., C.S.S., K.S.C., I.E.A, S.F.E., B.H. analysed data; A.J. and J.R.N. performed histology and image analyses; O.G.S.Q. provided illustration used in Figure 8 ; F.A., A.J. and D.M.F.v.A. interpreted the data and wrote the manuscript with input from all authors. Supplementary material Download figure Open in new tab Supplementary Figure S1: OGT C921Y mice show similar anxiety-like behaviour and increased exploration Significance is shown as * p < 0.05, ** p < 0.01 and *** p < 0.001. (A) Distance travelled in periphery by male OGT WT (n = 15) and OGT C921Y (n = 16) mice in the open field arena. Two-way ANOVA (Alpha, 0.05) followed by Tukey’s column comparisons. (B) Time spent in periphery by male OGT WT (n = 15) and OGT C921Y (n = 16) mice in the open field arena. Two-way ANOVA (Alpha, 0.05) followed by Tukey’s column comparisons. (C) Distance travelled in centre by male OGT WT (n = 15) and OGT C921Y (n = 16) mice in the open field arena. Two-way ANOVA (Alpha, 0.05) followed by Tukey’s column comparisons. (D) Time spent in centre by male OGT WT (n = 15) and OGT C921Y (n = 16) mice in the open field arena. Two-way ANOVA (Alpha, 0.05) followed by Tukey’s column comparisons. (E) Representative tracking plot of male OGT WT and OGT C921Y mice in the elevated plus maze (EPM). (F) Total distance travelled of male OGT WT (n = 15) and OGT C921Y (n = 16) mice in the EPM. Student t test was used for statistics. (G) Number of centre entries of male OGT WT (n = 15) and OGT C921Y (n = 16) mice in the EPM. Student t test was used for statistics. (H) Ratio between time spent in open and close arms by male OGT WT (n = 15) and OGT C921Y (n = 16) mice in the EPM. Student t test was used for statistics. (I) Representative tracking plot of male OGT WT and OGT C921Y mice in the light compartment during the dark-light paradigm (D/L). (J) Total distance travelled of male OGT WT (n = 15) and OGT C921Y (n = 16) mice in the light compartment during the dark-light paradigm (D/L). Two-way ANOVA (Alpha, 0.05) followed by Tukey’s column comparisons. (K) Number of centre entries of male OGT WT (n = 15) and OGT C921Y (n = 16) mice in the light compartment during the dark-light paradigm (D/L). Two-way ANOVA (Alpha, 0.05) followed by Tukey’s column comparisons. (L) Time spent of male OGT WT (n = 15) and OGT C921Y (n = 16) mice in the light compartment during the dark-light paradigm (D/L). Two-way ANOVA (Alpha, 0.05) followed by Tukey’s column comparisons. Download figure Open in new tab Supplementary Figure S2: OGT C921Y mice show similar motor skills Significance is shown as * p < 0.05, ** p < 0.01 and *** p < 0.001. (A) Mean score reached by male OGT WT (n = 15) and OGT C921Y (n = 16) mice on the rotarod. (B) Mean time spent by male OGT WT (n = 15) and OGT C921Y (n = 16) mice on the rotarod. (C) Score reached by male OGT WT (n = 15) and OGT C921Y (n = 16) mice during the pole test. (D) Number of self-grooming events male OGT WT (n = 15) and OGT C921Y (n = 16) mice during 3 min observation. (E) Time spent self-grooming by male OGT WT (n = 15) and OGT C921Y (n = 16) mice during 3 min observation. (F) Percentage of material removed from a cotton pad by male OGT WT (n = 15) and OGT C921Y (n = 16) mice during 3 min during the nesting test. Download figure Open in new tab Supplementary Figure S3: OGT C921Y mice show reduced distances in the cranial base. (A) Lateral and superior views of a representative microCT 3D reconstruction of a mouse skull presenting the 45 landmarks used for Euclidean distance matrix analysis (EDMA) (left) and surface markups used for Principal Components Analysis and average shapes generation (right). (B) Lateral and inferior views of representative 3D reconstructions of mouse skulls presenting the linear distances that are at least 5% shorter in 2 and 5 months old OGT C921Y skulls (p < 0.01, two-tailed unpaired t-test used) Download figure Open in new tab Supplementary Figure S4: Volumetry from segmentation of T1-weighted structural images (FLASH) from Magnetic Resonance Imaging (MRI) illustrated as absolute volumes. Asterisks (*) indicate uncorrected significance ( p < 0.05), based on permutation tests (100k permutations for each region). Pound symbols (#) indicate significance below alpha (0.05) divided by total number regions tested (40) for mean (green) and median (red), respectively. Section signs (§) indicate significance ( p < 0.05) with p values adjusted for false discovery rate (Benjamini-Hochsberg, BH). Y-axes are scaled to individual ROIs to highlight group variation and difference. Download figure Open in new tab Supplementary Figure S5: Relative volumetry from T1 segmentation. Volumetry from segmentation of T1-weighted structural images (FLASH) from Magnetic Resonance Imaging (MRI) illustrated as regional relative volumes (divided by individual total brain volume). Asterisks (*) indicate uncorrected significance ( p < 0.05), based on permutation tests (100k permutations for each region). Pound symbols (#) indicate significance below alpha (0.05) divided by total number regions tested (40) for mean (green) and median (red), respectively. Section signs (§) indicate significance ( p < 0.05) with p values adjusted for false discovery rate (Benjamini-Hochsberg, BH). Y-axes are scaled to individual ROIs to highlight group variation and difference. Download figure Open in new tab Supplementary Figure S6: OGT C921Y mice show conserved DKI metrics for Neocortex (A) Middle coronal section of subject:207 illustrating the different calculated DKI metrics: Mean Diffusivity (MD), Fractional Anisotropy (FA) and Mean Kurtosis (MK). (B) Extracted DKI metrics (MD, FA and MK) for Neocortex of all subjects in each group for neocortex of all mice. Download figure Open in new tab Supplementary Figure S7: Subject-wise median of extracted Fractional Anisotropy (FA) values in 20 selected regions of interest (ROI). Download figure Open in new tab Supplementary Figure S8: Subject-wise median of extracted Mean Diffusivity (MD) values in 20 selected regions of interest (ROI). Download figure Open in new tab Supplementary Figure S9: Subject-wise median of extracted Mean Kurtosis (MK) values in 20 selected regions of interest (ROI). Download figure Open in new tab Supplementary Figure S10: Panoramic views of the H&E stained sagittal and coronal brain sections depicting neuroanatomical landmarks used for measurements of cortical thickness and cell density. (A) Representative images showing coronal panoramic views of the brain sections used for the measurements of cortical cell density. The sections were used to sample regions in the cortex at the level of Begma 0.14 and Bregma −2.06. Regional cell counts were obtained from primary motor cortex M1/M2 and primary somatosensory cortex S1/S2, and from the auditory cortex. (B) Representative images showing sagittal panoramic views of the brain sections used for the measurements of cortical cell density. The sections were used to sample medial (interaural, 0.36, in C) and lateral (interaural, 2.28, in D) regions of the cortex. Regional cell counts were obtained from cingulate cortex and visual cortex V. Abbreviations: AuD/AuV, auditory cortex (in B); CPu, caudate putamen; superior colliculus. Neuroanatomical annotations are based on the Paxinos and Franklin’s Mouse Brain in Stereotaxic Coordinates, Elsevier Publishing, 4th Edition. Download figure Open in new tab Supplementary Figure S11: Low magnification (10X) views of the H&E stained sagittal and coronal brain sections used for assessing cortical organization. Representative images showing low magnification (10X) views of select cortical regions from OGT WT (in A) and OGT C921Y (in B) mice. Areas shown include primary motor cortex M1/M2, primary somatosensory cortex S1/S2, auditory cortex and visual cortex V1. Notice a mild degree of cortical dysplasia in layers II-III in the auditory cortex of OGT C921Y mice (yellow arrow). The images were obtained from coronal sections (Bregma, 0.14, for M1/M2 and S1/S2; Bregma, −2.06, auditory cortex) and sagittal sections (Interaural 2.28, visual cortex V1). Scale bar = 200 µm. Also see Fig. S10 . Neuroanatomical annotations are based on the Paxinos and Franklin’s Mouse Brain in Stereotaxic Coordinates, Elsevier Publishing, 4th Edition. Download figure Open in new tab Figure S12. Histological characterization of cortical malformations by multiplex IHC. (A-B) Representative low magnification (20X) sagittal views of WT (A) and OGT C921Y mice (B), showing cellular populations in the vicinity of nodular cortical malformation in the cingulate cortex of the latter using multiplex IHC chromogenic detection (NeuN in teal, Olig2 in brown, Reelin in purple and POU3F2 in yellow)- scale bar = 200 µm. Numerals I-VI in S12a-d indicate neocortical layers. (C-D) Representative high magnification (40X) views extracted from the insets in A (wild type) and B (OGT C921Y ) respectively. Notice the undulating pattern of NeuN in layers II/III (black arrow, in B) and mixed cellular populations in the vicinity of cortical malformations in layers II-V of an OGT C921Y animal-scale bar = 100 µm. Download figure Open in new tab Supplementary Figure S13: OGT C921Y mice show altered OGT/OGA ratio and global reduction in O-GlcNAc levels in brain regions. Significance is shown as * p < 0.05, ** p < 0.01 and *** p < 0.001 (n = 3 per group). Student t test was used for statistics. (A) Western blot of OGA and OGT level in male prefrontal cortex and hippocampus of OGT WT and OGT C921Y mice. Actin antibodies were used as loading control. (B) Western blot of OGA and OGT level in male striatum and cerebellum of OGT WT and OGT C921Y mice. Actin antibodies were used as loading control. Quantification of OGA (C), OGT (D) and OGT/OGA ratio (E) proteins levels in prefrontal cortex from the Western blot in panel A. Quantification of OGA (F), OGT (G) and OGT/OGA ratio (H) proteins levels in striatum from the Western blot in panel B. Quantification of OGA (I), OGT (J) and OGT/OGA ratio (K) proteins levels in hippocampus from the Western blot in panel A. Quantification of OGA (L), OGT (M) and OGT/OGA ratio (N) proteins levels in prefrontal cortex from the Western blot in panel B. (O) Western blot of O-GlcNAc levels in prefrontal cortex and hippocampus of male OGT WT and OGT C921Y mice. Actin antibodies were used as loading control. (P) Western blot of O-GlcNAc levels in striatum and cerebellum of male OGT WT and OGT C921Y mice. Actin antibodies were used as loading control.Quantification of O-GlcNAc levels in prefrontal cortex (Q), hippocampus, striatum (S) and cerebellum (T) from the Western blot in panel O and P.). Download figure Open in new tab Supplementary Figure S14: OGT C921Y mice show altered Ogt / Oga mRNA ratio levels in brain regions. Significance is shown as * p < 0.05, ** p < 0.01 and *** p < 0.001 (n = 3 per group). Student t test was used for statistics. Quantification of Oga (A), Ogt (B) and Ogt/Oga ratio (C) mRNA levels in prefrontal cortex Quantification of Oga (D), Ogt (E) and Ogt/Oga ratio (F) mRNA levels in hippocampus. Quantification of Oga (G), Ogt (H) and Ogt/Oga ratio (I) mRNA levels in striatum. Quantification of Oga (J), Ogt (K) and Ogt/Oga ratio (L) mRNA levels in cerebellum. Download figure Open in new tab Supplementary Figure S15: Perturbed proteomic signatures in the prefrontal cortex corresponded to distinct Monarch enrichment profiles in the OGT C921Y mice (A) Up-regulated human phenotypes enrichment in the Monarch database. (B) Down-regulated human phenotypes enrichment in the Monarch database. Acknowledgements The authors would like to thank Trine Mikkelesen (JRN lab) for the assistance with the histology workflow, Kristian Graff (Department of Molecular Biology and Genetics, Aarhus University), for the assistance in behaviour equipment build-up and Kamilla Zahll Hornbek (Department of Biomedicine, Aarhus University) for breeding and animal care assistance. Footnotes This version contains additional analysis presented in Figures 6, S12 and S13. References 1. ↵ Tassé MJ , Luckasson R , Schalock RL . The Relation Between Intellectual Functioning and Adaptive Behavior in the Diagnosis of Intellectual Disability . Intellect Dev Disabil . 2016 ; 54 ( 6 ): 381 – 390 . doi: 10.1352/1934-9556-54.6.381 OpenUrl CrossRef PubMed 2. ↵ Barkovich AJ , Guerrini R , Kuzniecky RI , Jackson GD , Dobyns WB . A developmental and genetic classification for malformations of cortical development: update 2012 . Brain J Neurol . 2012 ; 135 (Pt 5 ): 1348 – 1369 . doi: 10.1093/brain/aws019 OpenUrl CrossRef PubMed Web of Science 3. ↵ Kurabayashi N , Fujii K , Otobe Y , et al. Neocortical neuronal production and maturation defects in the TcMAC21 mouse model of Down syndrome . iScience . 2023 ; 26 ( 12 ): 108379 . doi: 10.1016/j.isci.2023.108379 OpenUrl CrossRef PubMed 4. ↵ Torres CR , Hart GW . Topography and polypeptide distribution of terminal N-acetylglucosamine residues on the surfaces of intact lymphocytes. Evidence for O-linked GlcNAc . J Biol Chem . 1984 ; 259 ( 5 ): 3308 – 3317 . OpenUrl Abstract / FREE Full Text 5. ↵ Wulff-Fuentes E , Berendt RR , Massman L , et al. The human O-GlcNAcome database and meta-analysis . Sci Data . 2021 ; 8 ( 1 ): 25 . doi: 10.1038/s41597-021-00810-4 OpenUrl CrossRef PubMed 6. ↵ Gao Y , Wells L , Comer FI , Parker GJ , Hart GW . Dynamic O-glycosylation of nuclear and cytosolic proteins: cloning and characterization of a neutral, cytosolic beta-N-acetylglucosaminidase from human brain . J Biol Chem . 2001 ; 276 ( 13 ): 9838 – 9845 . doi: 10.1074/jbc.M010420200 OpenUrl Abstract / FREE Full Text 7. ↵ Fujiki R , Hashiba W , Sekine H , et al. GlcNAcylation of histone H2B facilitates its monoubiquitination . Nature . 2011 ; 480 ( 7378 ): 557 – 560 . doi: 10.1038/nature10656 OpenUrl CrossRef PubMed Web of Science 8. ↵ Park SY , Kim HS , Kim NH , et al. Snail1 is stabilized by O-GlcNAc modification in hyperglycaemic condition . EMBO J . 2010 ; 29 ( 22 ): 3787 – 3796 . doi: 10.1038/emboj.2010.254 OpenUrl Abstract / FREE Full Text 9. ↵ Levine ZG , Fan C , Melicher MS , Orman M , Benjamin T , Walker S . O-GlcNAc Transferase Recognizes Protein Substrates Using an Asparagine Ladder in the Tetratricopeptide Repeat (TPR) Superhelix . J Am Chem Soc . 2018 ; 140 ( 10 ): 3510 – 3513 . doi: 10.1021/jacs.7b13546 OpenUrl CrossRef 10. ↵ Meek RW , Blaza JN , Busmann JA , Alteen MG , Vocadlo DJ , Davies GJ . Cryo-EM structure provides insights into the dimer arrangement of the O-linked β-N-acetylglucosamine transferase OGT . Nat Commun . 2021 ; 12 ( 1 ): 6508 . doi: 10.1038/s41467-021-26796-6 OpenUrl CrossRef 11. ↵ Fujiki R , Hashiba W , Sekine H , et al. GlcNAcylation of histone H2B facilitates its monoubiquitination . Nature . 2011 ; 480 ( 7378 ): 557 – 560 . doi: 10.1038/nature10656 OpenUrl CrossRef PubMed Web of Science 12. ↵ Constable S , Lim JM , Vaidyanathan K , Wells L . O-GlcNAc transferase regulates transcriptional activity of human Oct4 . Glycobiology . 2017 ; 27 ( 10 ): 927 – 937 . doi: 10.1093/glycob/cwx055 OpenUrl CrossRef PubMed 13. ↵ Dikic I , Wakatsuki S , Walters KJ . Ubiquitin-binding domains - from structures to functions . Nat Rev Mol Cell Biol . 2009 ; 10 ( 10 ): 659 – 671 . doi: 10.1038/nrm2767 OpenUrl CrossRef PubMed Web of Science 14. ↵ Capotosti F , Guernier S , Lammers F , et al. O-GlcNAc Transferase Catalyzes Site-Specific Proteolysis of HCF-1 . Cell . 2011 ; 144 ( 3 ): 376 – 388 . doi: 10.1016/j.cell.2010.12.030 OpenUrl CrossRef PubMed Web of Science 15. ↵ Lazarus MB , Jiang J , Kapuria V , et al. HCF-1 Is Cleaved in the Active Site of O-GlcNAc Transferase . Science . 2013 ; 342 ( 6163 ): 1235 – 1239 . doi: 10.1126/science.1243990 OpenUrl Abstract / FREE Full Text 16. ↵ O’Donnell N , Zachara NE , Hart GW , Marth JD . Ogt-Dependent X-Chromosome-Linked Protein Glycosylation Is a Requisite Modification in Somatic Cell Function and Embryo Viability . Mol Cell Biol . 2004 ; 24 ( 4 ): 1680 . doi: 10.1128/MCB.24.4.1680-1690.2004 OpenUrl Abstract / FREE Full Text 17. ↵ Lee BE , Kim HY , Kim HJ , et al. O-GlcNAcylation regulates dopamine neuron function, survival and degeneration in Parkinson disease . Brain J Neurol . 2020 ; 143 ( 12 ): 3699 – 3716 . doi: 10.1093/brain/awaa320 OpenUrl CrossRef PubMed 18. ↵ Su C , Schwarz TL . O-GlcNAc Transferase Is Essential for Sensory Neuron Survival and Maintenance . J Neurosci . 2017 ; 37 ( 8 ): 2125 . doi: 10.1523/JNEUROSCI.3384-16.2017 OpenUrl Abstract / FREE Full Text 19. ↵ Cole RN , Hart GW . Cytosolic O-glycosylation is abundant in nerve terminals . J Neurochem . 2001 ; 79 ( 5 ): 1080 – 1089 . OpenUrl CrossRef PubMed Web of Science 20. ↵ Lagerlöf O , Hart GW , Huganir RL . O-GlcNAc transferase regulates excitatory synapse maturity . Proc Natl Acad Sci U S A . 2017 ; 114 ( 7 ): 1684 – 1689 . doi: 10.1073/PNAS.1621367114 OpenUrl Abstract / FREE Full Text 21. ↵ Vosseller K , Trinidad JC , Chalkley RJ , et al. O-linked N-acetylglucosamine proteomics of postsynaptic density preparations using lectin weak affinity chromatography and mass spectrometry . Mol Cell Proteomics MCP . 2006 ; 5 ( 5 ): 923 – 934 . doi: 10.1074/mcp.T500040-MCP200 OpenUrl Abstract / FREE Full Text 22. ↵ Trinidad JC , Barkan DT , Gulledge BF , et al. Global identification and characterization of both O-GlcNAcylation and phosphorylation at the murine synapse . Mol Cell Proteomics MCP . 2012 ; 11 ( 8 ): 215 – 229 . doi: 10.1074/mcp.O112.018366 OpenUrl Abstract / FREE Full Text 23. ↵ Vaidyanathan K , Niranjan T , Selvan N , et al. Identification and characterization of a missense mutation in the O-linked β- N-acetylglucosamine ( O-GlcNAc) transferase gene that segregates with X-linked intellectual disability . J Biol Chem . 2017 ; 292 ( 21 ): 8948 – 8963 . doi: 10.1074/JBC.M116.771030 OpenUrl Abstract / FREE Full Text 24. ↵ Willems AP , Gundogdu M , Kempers MJE , et al. Mutations in N-acetylglucosamine (O-GlcNAc) transferase in patients with X-linked intellectual disability . J Biol Chem . 2017 ; 292 ( 30 ): 12621 – 12631 . doi: 10.1074/jbc.M117.790097 OpenUrl Abstract / FREE Full Text 25. ↵ Vaidyanathan K , Niranjan T , Selvan N , et al. Identification and characterization of a missense mutation in the O-linked β-N-acetylglucosamine (O-GlcNAc) transferase gene that segregates with X-linked intellectual disability . J Biol Chem . 2017 ; 292 ( 21 ): 8948 – 8963 . doi: 10.1074/jbc.M116.771030 OpenUrl Abstract / FREE Full Text 26. ↵ Willems AP , Gundogdu M , Kempers MJE , et al. Mutations in N-acetylglucosamine (O-GlcNAc) transferase in patients with X-linked intellectual disability . J Biol Chem . 2017 ; 292 ( 30 ): 12621 – 12631 . doi: 10.1074/jbc.M117.790097 OpenUrl Abstract / FREE Full Text 27. ↵ Pravata VM , Muha V , Gundogdu M , et al. Catalytic deficiency of O-GlcNAc transferase leads to X-linked intellectual disability . Proc Natl Acad Sci . 2019 ; 116 ( 30 ): 14961 – 14970 . doi: 10.1073/pnas.1900065116 OpenUrl Abstract / FREE Full Text 28. ↵ Pravata VM , Gundogdu M , Bartual SG , et al. A missense mutation in the catalytic domain of O-GlcNAc transferase links perturbations in protein O-GlcNAcylation to X-linked intellectual disability . FEBS Lett . 2020 ; 594 ( 4 ): 717 – 727 . doi: 10.1002/1873-3468.13640 OpenUrl CrossRef PubMed 29. ↵ Pravata VM , Omelková M , Stavridis MP , et al. An intellectual disability syndrome with single-nucleotide variants in O-GlcNAc transferase . Eur J Hum Genet . 2020 ; 28 ( 6 ): 706 – 714 . doi: 10.1038/s41431-020-0589-9 OpenUrl CrossRef PubMed 30. ↵ Taylor DC , Falconer MA , Bruton CJ , Corsellis JAN . Focal dysplasia of the cerebral cortex in epilepsy . J Neurol Neurosurg Psychiatry . 1971 ; 34 ( 4 ): 369 – 387 . doi: 10.1136/jnnp.34.4.369 OpenUrl Abstract / FREE Full Text 31. Casanova MF , El-Baz AS , Kamat SS , et al. Focal cortical dysplasias in autism spectrum disorders . Acta Neuropathol Commun . 2013 ; 1 : 67 . doi: 10.1186/2051-5960-1-67 OpenUrl CrossRef PubMed 32. ↵ Omelková M , Fenger CD , Murray M , et al. An O-GlcNAc transferase pathogenic variant linked to intellectual disability affects pluripotent stem cell self-renewal . Dis Model Mech . 2023 ; 16 ( 6 ): dmm049132 . doi: 10.1242/dmm.049132 OpenUrl CrossRef 33. ↵ Selvan N , George S , Serajee FJ , et al. O-GlcNAc transferase missense mutations linked to X-linked intellectual disability deregulate genes involved in cell fate determination and signaling . J Biol Chem . 2018 ; 293 ( 27 ): 10810 – 10824 . doi: 10.1074/jbc.RA118.002583 OpenUrl Abstract / FREE Full Text 34. ↵ Shafi R , Iyer SPN , Ellies LG , et al. The O-GlcNAc transferase gene resides on the X chromosome and is essential for embryonic stem cell viability and mouse ontogeny . Proc Natl Acad Sci . 2000 ; 97 ( 11 ): 5735 – 5739 . doi: 10.1073/pnas.100471497 OpenUrl Abstract / FREE Full Text 35. ↵ Omelková M , Fenger CD , Murray M , et al. An O -GlcNAc transferase pathogenic variant linked to intellectual disability affects pluripotent stem cell self-renewal . Dis Model Mech . 2023 ; 16 ( 6 ): dmm049132 . doi: 10.1242/dmm.049132 OpenUrl CrossRef 36. ↵ Czajewski I , Swain B , Xu J , McDowall L , Ferenbach AT , Van Aalten DM . Rescuable sleep and synaptogenesis phenotypes in a Drosophila model of O-GlcNAc transferase intellectual disability . eLife . 2024 ; 13 : e90376 . doi: 10.7554/eLife.90376 OpenUrl CrossRef PubMed 37. ↵ Authier F , Ondruskova N , Ferenbach AT , McNeilly AD , van Aalten DMF . Neurodevelopmental defects in a mouse model of O-GlcNAc transferase intellectual disability . Dis Model Mech . 2024 ; 17 ( 4 ): dmm050671 . doi: 10.1242/dmm.050671 OpenUrl CrossRef PubMed 38. ↵ Piilgaard L , Rose L , Justinussen JL , et al. Non-invasive detection of narcolepsy type I phenotypical features and disease progression by continuous home-cage monitoring of activity in two mouse models: the HCRT-KO and DTA model . Sleep . 2023 ; 46 ( 9 ): zsad144 . doi: 10.1093/sleep/zsad144 OpenUrl CrossRef 39. ↵ Schwarzmaier SM , Knarr MRO , Hu S , Ertürk A , Hellal F , Plesnila N . Perfusion pressure determines vascular integrity and histomorphological quality following perfusion fixation of the brain . J Neurosci Methods . 2022 ; 372 : 109493 . doi: 10.1016/j.jneumeth.2022.109493 OpenUrl CrossRef 40. ↵ Shepherd TM , Thelwall PE , Stanisz GJ , Blackband SJ . Aldehyde Fixative Solutions Alter the Water Relaxation and Diffusion Properties of Nervous Tissue . Magn Reson Med Off J Soc Magn Reson Med Soc Magn Reson Med . 2009 ; 62 ( 1 ): 26 – 34 . doi: 10.1002/mrm.21977 OpenUrl CrossRef PubMed Web of Science 41. ↵ Khan AR , Chuhutin A , Wiborg O , et al. Biophysical modeling of high field diffusion MRI demonstrates micro-structural aberration in chronic mild stress rat brain . NeuroImage . 2016 ; 142 : 421 – 430 . doi: 10.1016/j.neuroimage.2016.07.001 OpenUrl CrossRef PubMed 42. ↵ Chuhutin A , Hansen B , Wlodarczyk A , Owens T , Shemesh N , Jespersen SN . Diffusion Kurtosis Imaging maps neural damage in the EAE model of multiple sclerosis . NeuroImage . 2020 ; 208 : 116406 . doi: 10.1016/j.neuroimage.2019.116406 OpenUrl CrossRef 43. Bay V , Kjølby BF , Iversen NK , et al. Stroke infarct volume estimation in fixed tissue: Comparison of diffusion kurtosis imaging to diffusion weighted imaging and histology in a rodent MCAO model . PloS One . 2018 ; 13 ( 4 ): e0196161 . doi: 10.1371/journal.pone.0196161 OpenUrl CrossRef PubMed 44. Jespersen SN , Bjarkam CR , Nyengaard JR , et al. Neurite density from magnetic resonance diffusion measurements at ultrahigh field: comparison with light microscopy and electron microscopy . NeuroImage . 2010 ; 49 ( 1 ): 205 – 216 . doi: 10.1016/j.neuroimage.2009.08.053 OpenUrl CrossRef PubMed Web of Science 45. Vestergaard-Poulsen P , Wegener G , Hansen B , et al. Diffusion-weighted MRI and quantitative biophysical modeling of hippocampal neurite loss in chronic stress . PloS One . 2011 ; 6 ( 7 ): e20653 . doi: 10.1371/journal.pone.0020653 OpenUrl CrossRef PubMed 46. ↵ Knopper RW , Skoven CS , Eskildsen SF , Østergaard L , Hansen B . The effects of locus coeruleus ablation on mouse brain volume and microstructure evaluated by high-field MRI . Front Cell Neurosci . 2024 ; 18 . doi: 10.3389/fncel.2024.1498133 OpenUrl CrossRef 47. ↵ Hansen B , Khan AR , Shemesh N , et al. White matter biomarkers from fast protocols using axially symmetric diffusion kurtosis imaging . NMR Biomed . 2017 ; 30 ( 9 ): 10 . 1002 /nbm.3741. doi: 10.1002/nbm.3741 OpenUrl CrossRef PubMed 48. ↵ Ardalan M , Chumak T , Quist A , et al. Reelin cells and sex-dependent synaptopathology in autism following postnatal immune activation . Br J Pharmacol . 2022 ; 179 ( 17 ): 4400 – 4422 . doi: 10.1111/bph.15859 OpenUrl CrossRef PubMed 49. ↵ Ma D , Cardoso MJ , Modat M , et al. Automatic structural parcellation of mouse brain MRI using multi-atlas label fusion . PloS One . 2014 ; 9 ( 1 ): e86576 . doi: 10.1371/journal.pone.0086576 OpenUrl CrossRef PubMed 50. ↵ Ma D , Holmes HE , Cardoso MJ , et al. Study the Longitudinal in vivo and Cross-Sectional ex vivo Brain Volume Difference for Disease Progression and Treatment Effect on Mouse Model of Tauopathy Using Automated MRI Structural Parcellation . Front Neurosci . 2019 ; 13 : 11 . doi: 10.3389/fnins.2019.00011 OpenUrl CrossRef 51. Ma Y , Hof PR , Grant SC , et al. A three-dimensional digital atlas database of the adult C57BL/6J mouse brain by magnetic resonance microscopy . Neuroscience . 2005 ; 135 ( 4 ): 1203 – 1215 . doi: 10.1016/j.neuroscience.2005.07.014 OpenUrl CrossRef PubMed Web of Science 52. Ma Y , Smith D , Hof PR , et al. In Vivo 3D Digital Atlas Database of the Adult C57BL/6J Mouse Brain by Magnetic Resonance Microscopy . Front Neuroanat . 2008 ; 2 : 1 . doi: 10.3389/neuro.05.001.2008 OpenUrl CrossRef PubMed 53. ↵ Sled JG , Zijdenbos AP , Evans AC . A nonparametric method for automatic correction of intensity nonuniformity in MRI data . IEEE Trans Med Imaging . 1998 ; 17 ( 1 ): 87 – 97 . doi: 10.1109/42.668698 OpenUrl CrossRef PubMed Web of Science 54. ↵ Coupe P , Yger P , Prima S , Hellier P , Kervrann C , Barillot C . An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images . IEEE Trans Med Imaging . 2008 ; 27 ( 4 ): 425 – 441 . doi: 10.1109/TMI.2007.906087 OpenUrl CrossRef PubMed Web of Science 55. ↵ Dorr AE , Lerch JP , Spring S , Kabani N , Henkelman RM . High resolution three-dimensional brain atlas using an average magnetic resonance image of 40 adult C57Bl/6J mice . NeuroImage . 2008 ; 42 ( 1 ): 60 – 69 . doi: 10.1016/j.neuroimage.2008.03.037 OpenUrl CrossRef PubMed Web of Science 56. ↵ Collins DL , Neelin P , Peters TM , Evans AC . Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space . J Comput Assist Tomogr . 1994 ; 18 ( 2 ): 192 – 205 . OpenUrl CrossRef PubMed Web of Science 57. ↵ Avants BB , Tustison NJ , Stauffer M , Song G , Wu B , Gee JC . The Insight ToolKit image registration framework . Front Neuroinformatics . 2014 ; 8 : 44 . doi: 10.3389/fninf.2014.00044 OpenUrl CrossRef PubMed 58. ↵ Benjamini Y , Hochberg Y . Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing . J R Stat Soc Ser B Methodol . 1995 ; 57 ( 1 ): 289 – 300 . doi: 10.1111/j.2517-6161.1995.tb02031.x OpenUrl CrossRef PubMed 59. ↵ Lerch JP , Carroll JB , Dorr A , et al. Cortical thickness measured from MRI in the YAC128 mouse model of Huntington’s disease . NeuroImage . 2008 ; 41 ( 2 ): 243 – 251 . doi: 10.1016/j.neuroimage.2008.02.019 OpenUrl CrossRef PubMed 60. ↵ Authier F , Ondruskova N , Ferenbach AT , McNeilly AD , van Aalten DMF . Neurodevelopmental defects in a mouse model of O-GlcNAc transferase intellectual disability . Dis Model Mech . 2024 ; 17 ( 4 ): dmm050671 . doi: 10.1242/dmm.050671 OpenUrl CrossRef PubMed 61. ↵ Robson K. Neuropathology Techniques . Neuropathol Appl Neurobiol . 2005 ; 31 ( 2 ): 204 – 205 . doi: 10.1111/j.1365-2990.2005.00585.x OpenUrl CrossRef 62. ↵ Bankhead P , Loughrey MB , Fernández JA , et al. QuPath: Open source software for digital pathology image analysis . Sci Rep . 2017 ; 7 ( 1 ): 16878 . doi: 10.1038/s41598-017-17204-5 OpenUrl CrossRef PubMed 63. ↵ Pravata VM , Omelková M , Stavridis MP , et al. An intellectual disability syndrome with single-nucleotide variants in O-GlcNAc transferase . Eur J Hum Genet. Published online February 2020 . doi: 10.1038/s41431-020-0589-9 OpenUrl CrossRef PubMed 64. ↵ Adasooriya D , Kyeong M , Cho SW . Sphenoid Bone Determines the Curvature of the Cranial Vault in Postnatal Skull Development in C57BL/6 Mice . J Bone Metab . 2023 ; 30 ( 1 ): 93 – 101 . doi: 10.11005/jbm.2023.30.1.93 OpenUrl CrossRef PubMed 65. ↵ Heulens I , Suttie M , Postnov A , et al. Craniofacial characteristics of fragile X syndrome in mouse and man . Eur J Hum Genet . 2013 ; 21 ( 8 ): 816 – 823 . doi: 10.1038/ejhg.2012.265 OpenUrl CrossRef PubMed 66. ↵ Husain TS , Moore JC , Huston LA , et al. Neurocranial growth in the OIM mouse model of osteogenesis imperfecta . Anat Rec . 2024 ; 307 ( 3 ): 581 – 591 . doi: 10.1002/ar.25307 OpenUrl CrossRef PubMed 67. ↵ Fenlon LR , Liu S , Gobius I , et al. Formation of functional areas in the cerebral cortex is disrupted in a mouse model of autism spectrum disorder . Neural Develop . 2015 ; 10 : 10 . doi: 10.1186/s13064-015-0033-y OpenUrl CrossRef PubMed 68. ↵ Donovan APA , Rosko L , Ellegood J , et al. Pervasive cortical and white matter anomalies in a mouse model for CHARGE syndrome . J Anat . 2023 ; 243 ( 1 ): 51 – 65 . doi: 10.1111/joa.13856 OpenUrl CrossRef PubMed 69. ↵ Allemang-Grand R , Ellegood J , Spencer Noakes L , et al. Neuroanatomy in mouse models of Rett syndrome is related to the severity of Mecp2 mutation and behavioral phenotypes . Mol Autism . 2017 ; 8 ( 1 ): 32 . doi: 10.1186/s13229-017-0138-8 OpenUrl CrossRef PubMed 70. ↵ Jensen JH , Helpern JA , Ramani A , Lu H , Kaczynski K . Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging . Magn Reson Med . 2005 ; 53 ( 6 ): 1432 – 1440 . doi: 10.1002/mrm.20508 OpenUrl CrossRef PubMed Web of Science 71. Hansen B , Lund TE , Sangill R , Jespersen SN . Experimentally and computationally fast method for estimation of a mean kurtosis . Magn Reson Med . 2013 ; 69 ( 6 ): 1754 – 1760 . doi: 10.1002/mrm.24743 OpenUrl CrossRef PubMed 72. Hansen B , Lund TE , Sangill R , Stubbe E , Finsterbusch J , Jespersen SN . Experimental considerations for fast kurtosis imaging . Magn Reson Med . 2016 ; 76 ( 5 ): 1455 – 1468 . doi: 10.1002/mrm.26055 OpenUrl CrossRef PubMed 73. ↵ Hansen B , Jespersen SN . Recent Developments in Fast Kurtosis Imaging . Front Phys . 2017 ; 5 . doi: 10.3389/fphy.2017.00040 OpenUrl CrossRef 74. ↵ Qvist P , Eskildsen SF , Hansen B , et al. Brain volumetric alterations accompanied with loss of striatal medium-sized spiny neurons and cortical parvalbumin expressing interneurons in Brd1+/− mice . Sci Rep . 2018 ; 8 ( 1 ): 16486 . doi: 10.1038/s41598-018-34729-5 OpenUrl CrossRef PubMed 75. Joseph JV , Blaavand MS , Cai H , et al. STING activation counters glioblastoma by vascular alteration and immune surveillance . Cancer Lett . 2023 ; 579 : 216480 . doi: 10.1016/j.canlet.2023.216480 OpenUrl CrossRef 76. ↵ Lindhardt TB , Skoven CS , Bordoni L , Østergaard L , Liang Z , Hansen B . Anesthesia-related brain microstructure modulations detected by diffusion magnetic resonance imaging . NMR Biomed . 2024 ; 37 ( 7 ): e5033 . doi: 10.1002/nbm.5033 OpenUrl CrossRef PubMed 77. ↵ Vukovic N , Hansen B , Lund TE , Jespersen S , Shtyrov Y . Rapid microstructural plasticity in the cortical semantic network following a short language learning session . PLoS Biol . 2021 ; 19 ( 6 ): e3001290 . doi: 10.1371/journal.pbio.3001290 OpenUrl CrossRef PubMed 78. Tietze A , Hansen MB , Østergaard L , et al. Mean Diffusional Kurtosis in Patients with Glioma: Initial Results with a Fast Imaging Method in a Clinical Setting . AJNR Am J Neuroradiol . 2015 ; 36 ( 8 ): 1472 – 1478 . doi: 10.3174/ajnr.A4311 OpenUrl Abstract / FREE Full Text 79. Lohmeier J , Radbruch H , Brenner W , et al. Detection of recurrent high-grade glioma using microstructure characteristics of distinct metabolic compartments in a multimodal and integrative 18F-FET PET/fast-DKI approach . Eur Radiol . 2024 ; 34 ( 4 ): 2487 – 2499 . doi: 10.1007/s00330-023-10141-0 OpenUrl CrossRef PubMed 80. ↵ Thaler C , Kyselyova AA , Faizy TD , et al. Heterogeneity of multiple sclerosis lesions in fast diffusional kurtosis imaging . PloS One . 2021 ; 16 ( 2 ): e0245844 . doi: 10.1371/journal.pone.0245844 OpenUrl CrossRef PubMed 81. ↵ Fauser S , Huppertz HJ , Bast T , et al. Clinical characteristics in focal cortical dysplasia: a retrospective evaluation in a series of 120 patients . Brain J Neurol . 2006 ; 129 (Pt 7 ): 1907 – 1916 . doi: 10.1093/brain/awl133 OpenUrl CrossRef PubMed Web of Science 82. Tassi L , Colombo N , Garbelli R , et al. Focal cortical dysplasia: neuropathological subtypes , EEG, neuroimaging and surgical outcome. Brain J Neurol . 2002 ; 125 (Pt 8 ): 1719 – 1732 . doi: 10.1093/brain/awf175 OpenUrl CrossRef PubMed Web of Science 83. ↵ Blümcke I , Thom M , Aronica E , et al. The clinicopathologic spectrum of focal cortical dysplasias: a consensus classification proposed by an ad hoc Task Force of the ILAE Diagnostic Methods Commission . Epilepsia . 2011 ; 52 ( 1 ): 158 – 174 . doi: 10.1111/j.1528-1167.2010.02777.x OpenUrl CrossRef PubMed Web of Science 84. ↵ Paprocka J , Jezela-Stanek A , Tylki-Szymańska A , Grunewald S . Congenital Disorders of Glycosylation from a Neurological Perspective . Brain Sci . 2021 ; 11 ( 1 ): 88 . doi: 10.3390/brainsci11010088 OpenUrl CrossRef PubMed 85. ↵ Squier W , Jansen A . Polymicrogyria: pathology, fetal origins and mechanisms . Acta Neuropathol Commun . 2014 ; 2 : 80 . doi: 10.1186/s40478-014-0080-3 OpenUrl CrossRef PubMed 86. ↵ Putman TE , Schaper K , Matentzoglu N , et al. The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species . Nucleic Acids Res . 2024 ; 52 ( D1 ): D938 – D949 . doi: 10.1093/nar/gkad1082 OpenUrl CrossRef PubMed 87. ↵ Mayfield JM , Hitefield NL , Czajewski I , et al. O-GlcNAc transferase congenital disorder of glycosylation (OGT-CDG): Potential mechanistic targets revealed by evaluating the OGT interactome . J Biol Chem . 2024 ; 300 ( 9 ): 107599 . doi: 10.1016/j.jbc.2024.107599 OpenUrl CrossRef PubMed 88. ↵ Yuan H , Mitchell CW , Ferenbach AT , et al. Exploiting O-GlcNAc dyshomeostasis to screen O-GlcNAc transferase intellectual disability variants . Stem Cell Rep. Published online December 19 , 2024 : 102380 . doi: 10.1016/j.stemcr.2024.11.010 OpenUrl CrossRef 89. ↵ Pravata VM , Gundogdu M , Bartual SG , et al. A missense mutation in the catalytic domain of O-GlcNAc transferase links perturbations in protein O-GlcNAcylation to X-linked intellectual disability . FEBS Lett. Published online October 2019 . doi: 10.1002/1873-3468.13640 OpenUrl CrossRef PubMed 90. ↵ Ding Q , Sethna F , Wang H . Behavioral analysis of male and female Fmr1 knockout mice on C57BL/6 background . Behav Brain Res . 2014 ; 0 : 72 . doi: 10.1016/J.BBR.2014.05.046 OpenUrl CrossRef 91. Peier AM , McIlwain KL , Kenneson A , Warren ST , Paylor R , Nelson DL . (Over)correction of FMR1 deficiency with YAC transgenics: behavioral and physical features . Hum Mol Genet . 2000 ; 9 ( 8 ): 1145 – 1159 . doi: 10.1093/HMG/9.8.1145 OpenUrl CrossRef PubMed Web of Science 92. ↵ Huynh TN , Shah M , Koo SY , Faraud KS , Santini E , Klann E . eIF4E/Fmr1 Double Mutant Mice Display Cognitive Impairment in Addition to ASD-like Behaviors . Neurobiol Dis . 2015 ; 83 : 67 . doi: 10.1016/J.NBD.2015.08.016 OpenUrl CrossRef 93. ↵ Gao Y , Aljazi MB , He J . Kdm6b Haploinsufficiency Causes ASD/ADHD-Like Behavioral Deficits in Mice . Front Behav Neurosci . 2022 ; 16 . doi: 10.3389/FNBEH.2022.905783 OpenUrl CrossRef 94. ↵ Fenckova M , Muha V , Mariappa D , et al. Intellectual disability-associated disruption of O-GlcNAc cycling impairs habituation learning in Drosophila . PLoS Genet . 2022 ; 18 ( 5 ): e1010159 . doi: 10.1371/JOURNAL.PGEN.1010159 OpenUrl CrossRef PubMed 95. ↵ Rankin CH , Abrams T , Barry RJ , et al. Habituation Revisited: An Updated and Revised Description of the Behavioral Characteristics of Habituation . Neurobiol Learn Mem . 2009 ; 92 ( 2 ): 135 . doi: 10.1016/J.NLM.2008.09.012 OpenUrl CrossRef PubMed Web of Science 96. ↵ Spencer MD , Moorhead TWJ , Lymer GKS , et al. Structural correlates of intellectual impairment and autistic features in adolescents . NeuroImage . 2006 ; 33 ( 4 ): 1136 – 1144 . doi: 10.1016/j.neuroimage.2006.08.011 OpenUrl CrossRef PubMed 97. ↵ Kolk SM , Rakic P . Development of prefrontal cortex . Neuropsychopharmacology . 2022 ; 47 ( 1 ): 41 – 57 . doi: 10.1038/s41386-021-01137-9 OpenUrl CrossRef PubMed 98. ↵ Russell VA , Sagvolden T , Johansen EB . Animal models of attention-deficit hyperactivity disorder . Behav Brain Funct BBF . 2005 ; 1 : 9 . doi: 10.1186/1744-9081-1-9 OpenUrl CrossRef PubMed 99. ↵ Schiller S , Rosewich H , Grünewald S , Gärtner J . Inborn errors of metabolism leading to neuronal migration defects . J Inherit Metab Dis . 2020 ; 43 ( 1 ): 145 – 155 . doi: 10.1002/jimd.12194 OpenUrl CrossRef PubMed 100. ↵ Reynolds CD , Nolan SO , Smith GD , et al. Increased Expression of Fragile X Mental Retardation Protein in Malformative Lesions of Patients with Focal Cortical Dysplasia . Neuroreport . 2020 ; 31 ( 14 ): 1036 – 1041 . doi: 10.1097/WNR.0000000000001517 OpenUrl CrossRef PubMed 101. ↵ Raymond AA , Fish DR , Sisodiya SM , Alsanjari N , Stevens JM , Shorvon SD . Abnormalities of gyration, heterotopias, tuberous sclerosis, focal cortical dysplasia, microdysgenesis, dysembryoplastic neuroepithelial tumour and dysgenesis of the archicortex in epilepsy. Clinical, EEG and neuroimaging features in 100 adult patients . Brain J Neurol. 1995 ; 118 ( Pt 3 ): 629 – 660 . doi: 10.1093/brain/118.3.629 OpenUrl CrossRef PubMed Web of Science 102. Barkovich AJ , Kuzniecky RI , Dobyns WB , Jackson GD , Becker LE , Evrard P . A classification scheme for malformations of cortical development . Neuropediatrics . 1996 ; 27 ( 2 ): 59 – 63 . doi: 10.1055/s-2007-973750 OpenUrl CrossRef PubMed Web of Science 103. ↵ Barkovich AJ , Kuzniecky RI , Jackson GD , Guerrini R , Dobyns WB . A developmental and genetic classification for malformations of cortical development . Neurology . 2005 ; 65 ( 12 ): 1873 – 1887 . doi: 10.1212/01.wnl.0000183747.05269.2d OpenUrl CrossRef PubMed 104. ↵ Judkins AR , Martinez D , Ferreira P , Dobyns WB , Golden JA . Polymicrogyria includes fusion of the molecular layer and decreased neuronal populations but normal cortical laminar organization . J Neuropathol Exp Neurol . 2011 ; 70 ( 6 ): 438 – 443 . doi: 10.1097/NEN.0b013e31821ccf1c OpenUrl CrossRef PubMed 105. ↵ Leech R , Sharp DJ . The role of the posterior cingulate cortex in cognition and disease . Brain . 2014 ; 137 ( 1 ): 12 – 32 . doi: 10.1093/brain/awt162 OpenUrl CrossRef PubMed Web of Science 106. ↵ Kolling N , Wittmann MK , Behrens TEJ , Boorman ED , Mars RB , Rushworth MFS . Value, search, persistence and model updating in anterior cingulate cortex . Nat Neurosci . 2016 ; 19 ( 10 ): 1280 – 1285 . doi: 10.1038/nn.4382 OpenUrl CrossRef PubMed 107. ↵ Goodkind M , Eickhoff SB , Oathes DJ , et al. Identification of a common neurobiological substrate for mental illness . JAMA Psychiatry . 2015 ; 72 ( 4 ): 305 – 315 . doi: 10.1001/jamapsychiatry.2014.2206 OpenUrl CrossRef PubMed 108. ↵ Opel N , Goltermann J , Hermesdorf M , Berger K , Baune BT , Dannlowski U . Cross-Disorder Analysis of Brain Structural Abnormalities in Six Major Psychiatric Disorders: A Secondary Analysis of Mega- and Meta-analytical Findings From the ENIGMA Consortium . Biol Psychiatry . 2020 ; 88 ( 9 ): 678 – 686 . doi: 10.1016/j.biopsych.2020.04.027 OpenUrl CrossRef PubMed 109. ↵ Choi JY , Rha D wook, Park ES. The Effects of the Severity of Periventricular Leukomalacia on the Neuropsychological Outcomes of Preterm Children . J Child Neurol . 2016 ; 31 ( 5 ): 603 – 612 . doi: 10.1177/0883073815604229 OpenUrl CrossRef PubMed 110. ↵ Barnea-Goraly N , Eliez S , Hedeus M , et al. White matter tract alterations in fragile X syndrome: Preliminary evidence from diffusion tensor imaging . Am J Med Genet B Neuropsychiatr Genet . 2003 ;118B( 1 ): 81 – 88 . doi: 10.1002/ajmg.b.10035 OpenUrl CrossRef 111. ↵ Cascio C , Styner M , Smith RG , et al. Reduced relationship to cortical white matter volume revealed by tractography-based segmentation of the corpus callosum in young children with developmental delay . Am J Psychiatry . 2006 ; 163 ( 12 ): 2157 – 2163 . doi: 10.1176/ajp.2006.163.12.2157 OpenUrl CrossRef PubMed Web of Science 112. ↵ Pujol J , López-Sala A , Sebastián-Gallés N , et al. Delayed myelination in children with developmental delay detected by volumetric MRI . NeuroImage . 2004 ; 22 ( 2 ): 897 – 903 . doi: 10.1016/j.neuroimage.2004.01.029 OpenUrl CrossRef PubMed Web of Science 113. ↵ Ishii K , Kubo K ichiro, Endo T, et al. Neuronal Heterotopias Affect the Activities of Distant Brain Areas and Lead to Behavioral Deficits . J Neurosci Off J Soc Neurosci . 2015 ; 35 ( 36 ): 12432 – 12445 . doi: 10.1523/JNEUROSCI.3648-14.2015 OpenUrl Abstract / FREE Full Text 114. ↵ Doherty K , Frazier SB , Clark M , et al. A closer look at ARSA activity in a patient with metachromatic leukodystrophy . Mol Genet Metab Rep . 2019 ; 19 : 100460 . doi: 10.1016/j.ymgmr.2019.100460 OpenUrl CrossRef 115. Coulter ME , Dorobantu CM , Lodewijk GA , et al. The ESCRT-III Protein CHMP1A Mediates Secretion of Sonic Hedgehog on a Distinctive Subtype of Extracellular Vesicles . Cell Rep . 2018 ; 24 ( 4 ): 973 – 986 .e8. doi: 10.1016/j.celrep.2018.06.100 OpenUrl CrossRef PubMed 116. ↵ Chan DL , Rudinger-Thirion J , Frugier M , et al. A case of QARS1 associated epileptic encephalopathy and review of epilepsy in aminoacyl-tRNA synthetase disorders . Brain Dev . 2022 ; 44 ( 2 ): 142 – 147 . doi: 10.1016/j.braindev.2021.10.009 OpenUrl CrossRef PubMed 117. ↵ He T , Sun H , Xu B , et al. Novel bi-allelic variants of CHMP1A contribute to pontocerebellar hypoplasia type 8: additional clinical and genetic evidence . Front Neurol . 2023 ; 14 : 1228218 . doi: 10.3389/fneur.2023.1228218 OpenUrl CrossRef 118. Feng Y , Wei ZH , Liu C , et al. Genetic variations in GABA metabolism and epilepsy . Seizure Eur J Epilepsy . 2022 ; 101 : 22 – 29 . doi: 10.1016/j.seizure.2022.07.007 OpenUrl CrossRef PubMed 119. Lv Y , Ji L , Dai H , et al. Identification of key regulatory genes involved in myelination after spinal cord injury by GSEA analysis . Exp Neurol . 2024 ; 382 : 114966 . doi: 10.1016/j.expneurol.2024.114966 OpenUrl CrossRef 120. Gupta A , Colmenero I , Ragge NK , et al. Compound heterozygous RMND1 gene variants associated with chronic kidney disease, dilated cardiomyopathy and neurological involvement: a case report . BMC Res Notes . 2016 ; 9 : 325 . doi: 10.1186/s13104-016-2131-2 OpenUrl CrossRef PubMed 121. ↵ Cesani M , Lorioli L , Grossi S , et al. Mutation Update of ARSA and PSAP Genes Causing Metachromatic Leukodystrophy . Hum Mutat . 2016 ; 37 ( 1 ): 16 – 27 . doi: 10.1002/humu.22919 OpenUrl CrossRef PubMed 122. ↵ Gürsoy S , Hazan F , Öztürk T , Çolak R , Çalkavur Ş. Evaluation of Sporadic and Familial Cases with Craniofrontonasal Syndrome: A Wide Clinical Spectrum and Identification of a Novel EFNB1 Gene Mutation . Mol Syndromol . 2021 ; 12 ( 5 ): 269 – 278 . doi: 10.1159/000515697 OpenUrl CrossRef PubMed 123. Abdollahi MR , Morrison E , Sirey T , et al. Mutation of the Variant α-Tubulin TUBA8 Results in Polymicrogyria with Optic Nerve Hypoplasia . Am J Hum Genet . 2009 ; 85 ( 5 ): 737 – 744 . doi: 10.1016/j.ajhg.2009.10.007 OpenUrl CrossRef PubMed Web of Science 124. Duncan AR , Polovitskaya MM , Gaitán-Peñas H , et al. Unique variants in CLCN3, encoding an endosomal anion/proton exchanger, underlie a spectrum of neurodevelopmental disorders . Am J Hum Genet . 2021 ; 108 ( 8 ): 1450 – 1465 . doi: 10.1016/j.ajhg.2021.06.003 OpenUrl CrossRef PubMed 125. ↵ Tomas-Roca L , Tsaalbi-Shtylik A , Jansen JG , et al. De novo mutations in PLXND1 and REV3L cause Möbius syndrome . Nat Commun . 2015 ; 6 ( 1 ): 7199 . doi: 10.1038/ncomms8199 OpenUrl CrossRef PubMed 126. ↵ Govek EE , Newey SE , Van Aelst L . The role of the Rho GTPases in neuronal development . Genes Dev . 2005 ; 19 ( 1 ): 1 – 49 . doi: 10.1101/gad.1256405 OpenUrl Abstract / FREE Full Text 127. ↵ Evans MK , Matsui Y , Xu B , et al. Ybx1 fine-tunes PRC2 activities to control embryonic brain development . Nat Commun . 2020 ; 11 ( 1 ): 4060 . doi: 10.1038/s41467-020-17878-y OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted February 17, 2026. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. 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