Impact of cognition-related single nucleotide polymorphisms on brain imaging phenotype in Parkinson’s disease

Multiple single nucleotide polymorphisms may contribute to cognitive decline in Parkinson’s disease. However, the mechanism by which these single nucleotide polymorphisms modify brain imaging phenotype remains unclear. The aim of this study was to investigate the potential effects of multiple single nucleotide polymorphisms on brain imaging phenotype in Parkinson’s disease. Forty-eight Parkinson’s disease patients and 39 matched healthy controls underwent genotyping and 7 T magnetic resonance imaging. A cognitive-weighted polygenic risk score model was designed, in which the effect sizes were determined individually for 36 single nucleotide polymorphisms. The correlations between polygenic risk score, neuroimaging features, and clinical data were analyzed. Furthermore, individual single nucleotide polymorphism analysis was performed to explore the main effects of genotypes and their interactive effects with Parkinson’s disease diagnosis. We found that, in Parkinson’s disease, the polygenic risk score was correlated with the neural activity of the hippocampus, parahippocampus, and fusiform gyrus, and with hippocampal-prefrontal and fusiform-temporal connectivity, as well as with gray matter alterations in the orbitofrontal cortex. In addition, we found that single nucleotide polymorphisms in α-synuclein(SNCA) were associated with white matter microstructural changes in the superior corona radiata, corpus callosum, and external capsule. A single nucleotide polymorphism in catechol-O-methyltransferase was associated with the neural activities of the lingual, fusiform, and occipital gyri, which are involved in visual cognitive dysfunction. Furthermore, DRD3 was associated with frontal and temporal lobe function and structure. In conclusion, imaging genetics is useful for providing a better understanding of the genetic pathways involved in the pathophysiologic processes underlying Parkinson’s disease. This study provides evidence of an association between genetic factors, cognitive functions, and multi-modality neuroimaging biomarkers in Parkinson’s disease.

A multiple-tissue-specific magnetic resonance imaging model for diagnosing Parkinson’s disease: a brain radiomics study

Brain radiomics can reflect the characteristics of brain pathophysiology.However,the value of T1-weighted images,quantitative susceptibility mapping,and R2*mapping in the diagnosis of Parkinson’s disease(PD)was underestimated in previous studies.In this prospective study to establish a model for PD diagnosis based on brain imaging information,we collected high-resolution T1-weighted images,R2*mapping,and quantitative susceptibility imaging data from 171 patients with PD and 179 healthy controls recruited from August 2014 to August 2019.According to the inclusion time,123 PD patients and 121 healthy controls were assigned to train the diagnostic model,while the remaining 106 subjects were assigned to the external validation dataset.We extracted 1408 radiomics features,and then used data-driven feature selection to identify informative features that were significant for discriminating patients with PD from normal controls on the training dataset.The informative features so identified were then used to construct a diagnostic model for PD.The constructed model contained 36 informative radiomics features,mainly representing abnormal subcortical iron distribution(especially in the substantia nigra),structural disorganization(e.g.,in the inferior temporal,paracentral,precuneus,insula,and precentral gyri),and texture misalignment in the subcortical nuclei(e.g.,caudate,globus pallidus,and thalamus).The predictive accuracy of the established model was 81.1±8.0%in the training dataset.On the external validation dataset,the established model showed predictive accuracy of 78.5±2.1%.In the tests of identifying early and drug-naïve PD patients from healthy controls,the accuracies of the model constructed on the same 36 informative features were 80.3±7.1%and 79.1±6.5%,respectively,while the accuracies were 80.4±6.3%and 82.9±5.8%for diagnosing middle-to-late PD and those receiving drug management,respectively.The accuracies for predicting tremor-dominant and non-tremor-dominant PD were 79.8±6.9%and 79.1±6.5%,respectively.In conclusion,the multiple-tissue-specific brain radiomics model constructed from magnetic resonance imaging has the ability to discriminate PD and exhibits the advantages for improving PD diagnosis.

Plasmid-based generation of neural cells from human fibroblasts using non-integrating episomal vectors

Differentiation of human fibroblasts into functional neurons depends on the introduction of viral-mediated transcription factors, which present risks of viral gene integration and tumorigenicity. In recent years, although some studies have been successful in directly inducing neurons through sustained expression of small molecule compounds, they have only been shown to be effective on mouse-derived cells. Thus, herein we delivered vectors containing Epstein-Barr virus-derived oriP/Epstein-Barr nuclear antigen 1 encoding the neuronal transcription factor, Ascl1, the neuron-specific microRNA, miR124, and a small hairpin directed against p53, into human fibroblasts. Cells were incubated in a neuron-inducing culture medium. Immunofluorescence staining was used to detect Tuj-1, microtubule-associated protein 2, neuron-specific nucleoprotein NeuN and nerve cell adhesion molecules in the induced cells. The proportion of Tuj1-positive cells was up to 36.7% after induction for 11 days. From day 21, these induced neurons showed neuron-specific expression patterns of microtubule-associated protein 2, NeuN and neural cell adhesion molecule. Our approach is a simple, plasmid-based process that enables direct reprogramming of human fibroblasts into neurons, and provides alternative avenues for disease modeling and neurodegenerative medicine.