Abstract
BackgroundNeuroinflammation is an underlying pathology of all neurological conditions, the understanding of which is still being comprehended. A specific molecular pathway that has been overlooked in neuroinflammation is glycosylation (i.e., post-translational addition of glycans to the protein structure). N-glycosylation is a specific type of glycosylation with a cardinal role in the central nervous system (CNS), which is highlighted by congenital glycosylation diseases that result in neuropathological symptoms such as epilepsy and mental retardation. Changes in N-glycosylation can ultimately affect glycoproteins’ functions, which will have an impact on cell machinery. Therefore, characterisation of N-glycosylation alterations in a neuroinflammatory scenario can provide a potential target for future therapies.MethodsWith that aim, the unilateral intrastriatal injection of lipopolysaccharide (LPS) in the adult rat brain was used as a model of neuroinflammation. In vivo and post-mortem, quantitative and spatial characterisation of both neuroinflammation and N-glycome was performed at 1-week post-injection of LPS. These aspects were investigated through a multifaceted approach based on positron emission tomography (PET), quantitative histology, reverse transcription-quantitative polymerase chain reaction (RT-qPCR), liquid chromatography and matrix-assisted laser desorption ionisation mass spectrometry imaging (MALDI-MSI).ResultsIn the brain region showing LPS-induced neuroinflammation, a significant decrease in the abundance of sialylated and core fucosylated structures was seen (approximately 7.5% and 8.5%, respectively), whereas oligomannose N-glycans were significantly increased (13.5%). This was confirmed by MALDI-MSI, which provided a high-resolution spatial distribution of N-glycans, allowing precise comparison between normal and diseased brain hemispheres.ConclusionsTogether, our data show for the first time the complete profiling of N-glycomic changes in a well-characterised animal model of neuroinflammation. These data represent a pioneering step to identify critical targets that may modulate neuroinflammation in neurodegenerative diseases.
Highlights
Neuroinflammation is an underlying pathology of all neurological conditions, the understanding of which is still being comprehended
In vivo positron emission tomography (PET) imaging of the 18 kD 18 kD Translocator protein (TSPO) showed a statistically significant increased Binding potential (BPND) in the LPS-injected striatum compared to the non-injected (NI) contralateral side (Fig. 2a, b, n = 9, Paired Student’s t test, p < 0.0011). This was correlated with the TSPO mRNA expression, where a significant increase was detected by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) in the LPS-injected side (Fig. 2c, n = 11, R = 0.87, Paired Student’s t test, p < 0.0004)
In vivo data was correlated with post-mortem data through Spearman correlation analysis, showing positive associations between all different inflammation-related markers (Fig. 2d), between 0.74 (GFAP mRNA expression vs TSPO BPND) and 0.85 (Vim mRNA expression vs TSPO BPND)
Summary
Neuroinflammation is an underlying pathology of all neurological conditions, the understanding of which is still being comprehended. Neuroinflammation is a complex pathology with multiple players that underlies most of the penetrating injuries (such as traumatic brain injury (TBI) and spinal cord injury (SCI)), and neurodegenerative conditions (such as Parkinson’s disease (PD), Alzheimer’s disease (AD), multiple sclerosis (MS) and amyotrophic lateral sclerosis (ALS) amongst others) [1,2,3,4]. Neuroinflammatory cascades are mainly governed by glial cells, microglia, which are the resident macrophages (immune cells) of the CNS. If the blood-brain barrier (BBB) is compromised, peripheral immune and endothelial cells can infiltrate the CNS, exacerbating the inflammatory cascades [10]. Chronic microglial activation can amplify inflammatory cascades, induce neuronal death and feed into a degenerative after-effect [10]
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