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Utilizing an Animal Model to Identify Brain Neurodegeneration-Related Biomarkers in Aging.

ABSTRACT: Glycine N-methyltransferase (GNMT) regulates S-adenosylmethionine (SAMe), a methyl donor in methylation. Over-expressed SAMe may cause neurogenic capacity reduction and memory impairment. GNMT knockout mice (GNMT-KO) was applied as an experimental model to evaluate its effect on neurons. In this study, proteins from brain tissues were studied using proteomic approaches, Haemotoxylin and Eosin staining, immunohistochemistry, Western blotting, and ingenuity pathway analysis. The expression of Receptor-interacting protein 1(RIPK1) and Caspase 3 were up-regulated and activity-dependent neuroprotective protein (ADNP) was down-regulated in GNMT-KO mice regardless of the age. Besides, proteins related to neuropathology, such as excitatory amino acid transporter 2, calcium/calmodulin-dependent protein kinase type II subunit alpha, and Cu-Zn superoxide dismutase were found only in the group of aged wild-type mice; 4-aminobutyrate amino transferase, limbic system-associated membrane protein, sodium- and chloride-dependent GABA transporter 3 and ProSAAS were found only in the group of young GNMT-KO mice and are related to function of neurons; serum albumin and Rho GDP dissociation inhibitor 1 were found only in the group of aged GNMT-KO mice and are connected to neurodegenerative disorders. With proteomic analyses, a pathway involving Gonadotropin-releasing hormone (GnRH) signal was found to be associated with aging. The GnRH pathway could provide additional information on the mechanism of aging and non-aging related neurodegeneration, and these protein markers may be served in developing future therapeutic treatments to ameliorate aging and prevent diseases.

SUBMITTER: Yang MH

PROVIDER: S-EPMC8004625 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND: Neurodegenerative diseases including Parkinson's and Alzheimer's diseases progress slowly and steadily over years or decades. They show significant between-subject variation in progress and clinical symptoms, which makes it difficult to predict the course of long-term disease progression with or without treatments. Recent technical advances in biomarkers have facilitated earlier, preclinical diagnoses of neurodegeneration by measuring or imaging molecules linked to pathogenesis. However, there is no established "biomarker model" by which one can quantitatively predict the progress of neurodegeneration. Here, we show predictability of a model with risk-based kinetics of neurodegeneration, whereby neurodegeneration proceeds as probabilistic events depending on the risk. RESULTS: We used five experimental glaucomatous animals, known for causality between the increased intraocular pressure (IOP) and neurodegeneration of visual pathways, and repeatedly measured IOP as well as white matter integrity by diffusion tensor imaging (DTI) as a biomarker of axonal degeneration. The IOP in the glaucomatous eye was significantly increased than in normal and was varied across time and animals; thus we tested whether this measurement is useful to predict kinetics of the integrity. Among four kinds of models of neurodegeneration, constant-rate, constant-risk, variable-risk and heterogeneity models, goodness of fit of the model and F-test for model selection showed that the time course of optic nerve integrity was best explained by the variable-risk model, wherein neurodegeneration kinetics is expressed in an exponential function across cumulative risk based on measured IOP. The heterogeneity model with stretched exponential decay function also fit well to the data, but without statistical superiority to the variable-risk model. The variable-risk model also predicted the number of viable axons in the optic nerve, as assessed by immunohistochemistry, which was also confirmed to be correlated with the pre-mortem integrity of the optic nerve. In addition, the variable-risk model identified the disintegrity in the higher-order visual pathways, known to underlie the transsynaptic degeneration in this disease. CONCLUSIONS: These findings indicate that the variable-risk model, using a risk-related biomarker, could predict the spatiotemporal progression of neurodegeneration. This model, virtually equivalent to survival analysis, may allow us to estimate possible effect of neuroprotection in delaying progress of neurodegeneration.

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Utilizing an Animal Model to Identify Brain Neurodegeneration-Related Biomarkers in Aging.

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