PloS one · 2025
New insights into the molecular biology of Alzheimer's-like cerebral amyloidosis achieved through multi-omics approaches.
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Campanelli Lorenzo, Sendoya Juan M, Brody Scott, Galeano Pablo, Do Carmo Sonia, Cuello A Claudio, Castaño Eduardo M, Gonzalés-Jimenez Andrés, Verheul Julia, Medina-Vera Dina, de Fonseca Fernando Rodríguez, Wernersson Rasmus, Morelli Laura
Аннотация
BACKGROUND: One of the neuropathologic hallmarks of Alzheimer's disease (AD) is amyloid plaques composed of fibrillar amyloid beta (Aβ) that accumulate in the hippocampus and cerebral cortex. The identification of molecular changes and interactions associated with Aβ-dependent cerebral amyloidosis is a need in the field. We hypothesize that structured datasets linking proteins to differentially abundant metabolites may provide an indirect but effective means of elucidating the processes and functions in which these metabolites are involved. The goal of this study was to identify core network modules related to AD-like cerebral amyloidosis to provide new insights into the molecular underpinnings of this brain disorder potentially associated with diet and microbiota modulation.
METHODS: We performed fecal bacterial genotyping and untargeted metabolomic analysis of plasma and feces from wild-type and McGill-R-Thy1-APP transgenic (Tg) rats, a model of AD-like cerebral amyloidosis, that were exposed to a high-fat diet protocol. To identify relevant proteins associated with the discriminant metabolites, we used several structured databases. Protein-metabolite associations (both physical and functional) were retrieved, and a collection of AD-associated protein-protein interaction (PPI) networks were built using a near-neighborhood approach.
RESULTS: A total of 44 bacterial genera and 636 plasma and 576 fecal metabolites were analyzed. From the discriminating metabolites of the Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) models, 657 networks were collected and a subset of the top 20 exploratory networks was defined. The first ranked network in terms of seed protein enrichment and number of participating metabolites showed strong biological signals of innate and adaptive immunity processes, with CD36 emerging as a central hub, orchestrating immunity, metabolic pathways, and fatty acid trafficking.
CONCLUSIONS: The network biology approach enabled a precise definition of the metabolic pathways underlying the disease biology highlighting the role of immune system in the complex interaction of the brain-gut axis.
METHODS: We performed fecal bacterial genotyping and untargeted metabolomic analysis of plasma and feces from wild-type and McGill-R-Thy1-APP transgenic (Tg) rats, a model of AD-like cerebral amyloidosis, that were exposed to a high-fat diet protocol. To identify relevant proteins associated with the discriminant metabolites, we used several structured databases. Protein-metabolite associations (both physical and functional) were retrieved, and a collection of AD-associated protein-protein interaction (PPI) networks were built using a near-neighborhood approach.
RESULTS: A total of 44 bacterial genera and 636 plasma and 576 fecal metabolites were analyzed. From the discriminating metabolites of the Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) models, 657 networks were collected and a subset of the top 20 exploratory networks was defined. The first ranked network in terms of seed protein enrichment and number of participating metabolites showed strong biological signals of innate and adaptive immunity processes, with CD36 emerging as a central hub, orchestrating immunity, metabolic pathways, and fatty acid trafficking.
CONCLUSIONS: The network biology approach enabled a precise definition of the metabolic pathways underlying the disease biology highlighting the role of immune system in the complex interaction of the brain-gut axis.