Cynomolgus Monkeys With Spontaneous Type-2-Diabetes-Mellitus-Like Pathology Develop Alpha-Synuclein Alterations Reminiscent of Prodromal Parkinson’s Disease and Related Diseases

Abstract

Available evidence suggests that diabetes mellitus (DM) is a non-genetic risk factor for Parkinson's disease (PD). PD and DM have shared similarities in pathogenetic mechanisms, including age, environmental factors, inflammatory reaction, and protein aggregation, etc. α-Synuclein is the primary protein component in the protein inclusions in PD, while islet amyloid polypeptide (IAPP) aggregates to form amyloid structures in β cells in type 2 diabetes mellitus (T2DM). Pancreatic and cerebral functions, pancreas and brain α-synuclein deposition as well as striatal alterations, were assessed in spontaneously developed T2DM monkeys and age-matched normal monkeys. We demonstrated increased accumulation, aggregation, and phosphorylation of α-synuclein, and IAPP in the pancreatic islets of spontaneously developed T2DM monkeys, compared to the age-matched normal subjects. Double immunofluorescence analyses showed complete overlap between α-synuclein and IAPP in the pancreatic islets. In addition, in T2DM monkeys' brain, we observed concomitantly increased accumulation and phosphorylation of α-synuclein in the cortex, pre-commissural putamen and dopaminergic neurons in the substantia nigra, which interestingly showed high correlation with levels of fasting plasma glucose (FPG), triglyceride (TG), and high density lipoprotein (HDL). Our data indicates the close association between IAPP and α-synuclein and the potential link between T2DM and PD, which implies that T2DM may facilitate PD disease onset and progress by interfering with the pathological protein aggregation both in the pancreatic islets and the brain.

α-Synuclein Regulates Peripheral Insulin Secretion and Glucose Transport

“…Recently, pathological pS129-positive α-syn deposits were found in the pancreatic beta cells of 93% of subjects with PD, in 68% of subjects with T2D following neuropathological examination and in 17% of control subjects (Martinez-Valbuena et al, 2018). Further, increased accumulation, aggregation, and phosphorylation of α-syn was observed in the pancreatic islets of non-human primate models of spontaneous T2D (Sun et al, 2020). Serum α-syn levels were also inversely correlated with insulin resistance indicators such as body mass index, homeostatic model assessment for IR (HOMA-IR) and immunoreactive insulin (Rodriguez-Araujo et al, 2015).…”

Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder

“…In order to validate the effectiveness of our high-order FC network, we compare it with C-FCN and LoD-FCN. Specifically, the comparison method utilizes the C-FCN matrix and extracts statistical features based on central moment (i.e., CM-FCN) ( Zhao et al, 2020 ) and root-mean-square (RMS) ( Chen et al, 2017 ) from LoD-FCN as original features, and then perform feature selection for SVM classification. The best classification performances of comparison methods are summarized in Table 4 .…”

“…FC network has been of great importance for discovering the functional organization of human brain and searching for the biomarkers of the neuropsychiatric disorders, such as Alzheimer’s disease ( Chen et al, 2017 ; Hao et al, 2017 ) and autism spectrum disorder (ASD) ( Zhao et al, 2018 ; Wee, Yap & Shen, 2016 ). Currently, researchers have proposed various FC network modeling methods for ASD assisted diagnosis ( Liu & Huang, 2020 ; Zhao et al, 2020 ; Zhao et al, 2021 ). For example, Liu & Huang (2020) estimated the severity of ASD by multivariate model analysis, and they found that some FCs suffer from abnormal alterations in ASD patients.…”

“…This leads to the sensitivity of LoD-FCN to its subnetwork, which limits its use in comparative studies. In order to eliminate the sensitivity, the central moment method was used to extract features from LoD-FCN ( Zhao et al, 2020 , 2021 ). The central moment feature is a common translation invariant feature, which reflects the shape information of the target and is often used in feature extraction of sequences or waveforms.…”

“…Our motivation is based on the hypothesis that FC of ASD children may change at the moment-level, which may be due to miswiring during abnormal development. CM-FCN reflects the topographical information of the center moment feature of dynamic FC, which provides rich discriminative information for disease recognition and classification ( Zhao et al, 2020 ). However, CM-FCN is essentially a low-order network, since it captures pair-wise FC topographical information.…”