Microcephaly with altered cortical layering in GIT1 deficiency revealed by quantitative neuroimaging

Badea, Alexandra; Schmalzigaug, Robert; Bonner, Pamela E; Ahmed, Umer; Johnson, G. Allan; Cofer, Gary P.; Foster, Mark P.; Badea, Cristian T.; Premont, Richard T.

doi:10.1016/j.mri.2020.09.023

Cited by 4 publications

(3 citation statements)

References 81 publications

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“…Observed in Figure 6 are 13 major protein nodes with 5 or more connections (including autoregulation). These include (in order of 11 to 5 connections): NOTCH1 (Notch receptor 1, NOTCH signaling, vasculature permeability [ 43 ]; CYLD (cylindromatosis, lysine 63 deubiquitinase, dementia [ 44 ]; RIF1 (Rap1-Interacting Factor 1, telomere and chromosome integrity [ 45 ]; VWF (von Willebrand Factor, vascular inflammation, dementia [ 42 , 46 ]; GRN (Granulin precursor, secreted growth factor progranulin, Parkinson’s disease, dementia [ 47 , 48 ]; low-density lipoprotein receptor [LDLR]-related protein 1 (LRP1, dementia, blood–brain barrier, AD [ 49 ]; TNFAIP3 (Tumor Necrosis Factor Alpha Induced Protein 3, A20 protein, autoimmunity, NFkB regulatory protein [ 50 ]; CTCF (CCCTC-binding Factor, chromatin structure, epigenetics, intellectual disability [ 51 ]; EGR1 (Early Growth Response gene 1, neurodegeneration, neuroinflammation [ 52 ]; F8 (factor VIII, bleeding disorders, VWF connection [ 53 ]; GIT1 (G Protein-Coupled Receptor Kinase-Interacting Protein-1), regulator of neuronal function, brain development, memory [ 54 ]; ITGB2 (Integrin subunit β 2, immunomodulation, CD18 protein, brain immune homeostasis, aging brain [ 55 ]; PCSK5 (Proprotein Convertase Subtilisin/Kexin type 5, lipoprotein metabolism, cognitive impairment [ 56 ].…”

Section: Resultsmentioning

confidence: 99%

Distinguishing Alzheimer’s Disease Patients and Biochemical Phenotype Analysis Using a Novel Serum Profiling Platform: Potential Involvement of the VWF/ADAMTS13 Axis

et al. 2021

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It is important to develop minimally invasive biomarker platforms to help in the identification and monitoring of patients with Alzheimer’s disease (AD). Assisting in the understanding of biochemical mechanisms as well as identifying potential novel biomarkers and therapeutic targets would be an added benefit of such platforms. This study utilizes a simplified and novel serum profiling platform, using mass spectrometry (MS), to help distinguish AD patient groups (mild and moderate) and controls, as well as to aid in understanding of biochemical phenotypes and possible disease development. A comparison of discriminating sera mass peaks between AD patients and control individuals was performed using leave one [serum sample] out cross validation (LOOCV) combined with a novel peak classification valuation (PCV) procedure. LOOCV/PCV was able to distinguish significant sera mass peak differences between a group of mild AD patients and control individuals with a p value of 10−13. This value became non-significant (p = 0.09) when the same sera samples were randomly allocated between the two groups and reanalyzed by LOOCV/PCV. This is indicative of physiological group differences in the original true-pathology binary group comparison. Similarities and differences between AD patients and traumatic brain injury (TBI) patients were also discernable using this novel LOOCV/PCV platform. MS/MS peptide analysis was performed on serum mass peaks comparing mild AD patients with control individuals. Bioinformatics analysis suggested that cell pathways/biochemical phenotypes affected in AD include those involving neuronal cell death, vasculature, neurogenesis, and AD/dementia/amyloidosis. Inflammation, autoimmunity, autophagy, and blood–brain barrier pathways also appear to be relevant to AD. An impaired VWF/ADAMTS13 vasculature axis with connections to F8 (factor VIII) and LRP1 and NOTCH1 was indicated and is proposed to be important in AD development.

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Section: Resultsmentioning

confidence: 99%

Distinguishing Alzheimer’s Disease Patients and Biochemical Phenotype Analysis Using a Novel Serum Profiling Platform: Potential Involvement of the VWF/ADAMTS13 Axis

et al. 2021

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“…Some of well-defined gene-encoded productss and their cellular localizations were illustrated in Fig. 6 b, e.g., dendrite and synapse related genes such as cell adhesion molecule 1 (CADM1), which is a synaptic cell adhesion molecule that regulates synaptogenesis [ 49 ]; G protein-coupled receptor kinase interacting protein 2 (GIT2), which regulates cytoskeletal structure and presynaptic neurotransmitter release [ 50 , 51 ]; cell adhesion molecule L1 like (CHL1), which is accumulated in presynaptic membranes and regulates synaptic activity and plasticity [ 52 ]; calcium/calmodulin-dependent serine protein kinase (CASK), which is a synapse scaffolding protein that is involved in synapse formation and function [ 53 ]; AGRIN, a heparan sulfate proteoglycan that promotes the formation of excitatory synapses [ 54 ]; G protein-coupled receptor kinase interacting protein 1 (GIT1), which regulates microtubule assembly and promotes synapse formation and maintenance [ 55 ]; Src Substrate Cortactin (CTTN), which is involved in actin polymerization and activity-dependent synaptic plasticity [ 56 ]; PSD95, a postsynaptic scaffolding protein that is required for activity-driven synapse stabilization [ 57 ]; Adhesion G protein-coupled receptor L1 (ADGRL1), which is involved in synapse formation and brain development [ 58 ]. In addition, some of the genes are highly related to cytoskeleton or microtubule formation, e.g., polyphosphoinositide phosphatase synaptojanin 1 (SYNJ1), which participates in actin cytoskeleton polymerization and synaptic vesicle recycling [ 59 ]; erythrocyte membrane protein band 4.1 like 3 (EPB41L3), which is related to actin binding and protein-protein interactions at synapses [ 60 , 61 ]; microtubule-associated protein tau (MAPT) gene, which encodes tau protein that is involved in axonal transport, synaptic plasticity and function [ 62 ].…”

Section: Resultsmentioning

confidence: 99%

Nuclear speckle specific hnRNP D-like prevents age- and AD-related cognitive decline by modulating RNA splicing

Zhang

Jin

et al. 2021

Mol Neurodegeneration

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Background Aberrant alternative splicing plays critical role in aging and age-related diseases. Heterogeneous nuclear ribonucleoproteins (hnRNPs) reportedly regulate RNA splicing process. Whether and how hnRNPs contribute to age-related neurodegenerative diseases, especially Alzheimer’s disease (AD), remain elusive. Methods Immunoblotting and immunostaining were performed to determine expression patterns and cellular/subcellular localization of the long isoform of hnRNP D-like (L-DL), which is a hnRNP family member, in mouse hippocampus. Downregulation of L-DL in WT mice was achieved by AAV-mediated shRNA delivery, followed by memory-related behavioural tests. L-DL interactome was analysed by affinity-precipitation and mass spectrometry. Alternative RNA splicing was measured by RNA-seq and analyzed by bioinformatics-based approaches. Downregulation and upregulation of L-DL in APP/PS1 mice were performed using AAV-mediated transduction. Results We show that L-DL is specifically localized to nuclear speckles. L-DL levels are decreased in the hippocampus of aged mouse brains and downregulation of L-DL impairs cognition in mice. L-DL serves as a structural component to recruit other speckle proteins, and regulates cytoskeleton- and synapse-related gene expression by altering RNA splicing. Mechanistically, these splicing changes are modulated via L-DL-mediated interaction of SF3B3, a core component of U2 snRNP, and U2AF65, a U2 spliceosome protein that guides U2 snRNP’s binding to RNA. In addition, L-DL levels are decreased in APP/PS1 mouse brains. While downregulation of L-DL deteriorates memory deficits and overexpression of L-DL improves cognitive function in AD mice, by regulating the alternative splicing and expression of synaptic gene CAMKV. Conclusions Our findings define a molecular mechanism by which hnRNP L-DL regulates alternative RNA splicing, and establish a direct role for L-DL in AD-related synaptic dysfunction and memory decline.

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“…Using laminar-specific neuroimaging, we can better understand both the computational role of and connectivity between different cortical layers in cognitive functions such as language processing [16], working memory [17], and predictive processing [18]. Brain imaging studies have also found that the normal formation of a six-layered cortex is disrupted during embryonic and postnatal development in disorders such as autism [19] and microcephaly [20].…”

Section: Layering In Biological Brainsmentioning

confidence: 99%

Meta-brain Models: biologically-inspired cognitive agents

Alicea

Parent

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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Artificial Intelligence (AI) systems based solely on neural networks or symbolic computation present a representational complexity challenge. While minimal representations can produce behavioral outputs like locomotion or simple decision-making, more elaborate internal representations might offer a richer variety of behaviors. We propose that these issues can be addressed with a computational approach we call meta-brain models. Meta-brain models are embodied hybrid models that include layered components featuring varying degrees of representational complexity. We will propose combinations of layers composed using specialized types of models. Rather than using a generic black box approach to unify each component, this relationship mimics systems like the neocortical-thalamic system relationship of the mammalian brain, which utilizes both feedforward and feedback connectivity to facilitate functional communication. Importantly, the relationship between layers can be made anatomically explicit. This allows for structural specificity that can be incorporated into the model's function in interesting ways. We will propose several types of layers that might be functionally integrated into agents that perform unique types of tasks, from agents that simultaneously perform morphogenesis and perception, to agents that undergo morphogenesis and the acquisition of conceptual representations simultaneously. Our approach to meta-brain models involves creating models with different degrees of representational complexity, creating a layered meta-architecture that mimics the structural and functional heterogeneity of biological brains, and an input/output methodology flexible enough to accommodate cognitive functions, social interactions, and adaptive behaviors more generally. We will conclude by proposing next steps in the development of this flexible and open-source approach.

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Microcephaly with altered cortical layering in GIT1 deficiency revealed by quantitative neuroimaging

Cited by 4 publications

References 81 publications

Distinguishing Alzheimer’s Disease Patients and Biochemical Phenotype Analysis Using a Novel Serum Profiling Platform: Potential Involvement of the VWF/ADAMTS13 Axis

Distinguishing Alzheimer’s Disease Patients and Biochemical Phenotype Analysis Using a Novel Serum Profiling Platform: Potential Involvement of the VWF/ADAMTS13 Axis

Nuclear speckle specific hnRNP D-like prevents age- and AD-related cognitive decline by modulating RNA splicing

Meta-brain Models: biologically-inspired cognitive agents

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