Neuronal Trans-differentiation by Transcription Factors Ascl1 and Nurr1: Induction of a Dopaminergic Neurotransmitter Phenotype in Cortical GABAergic Neurons

Decoding degeneration: the implementation of machine learning for clinical detection of neurodegenerative disorders

Machine learning represents a growing subfield of artificial intelligence with much promise in the diagnosis, treatment, and tracking of complex conditions, including neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. While no definitive methods of diagnosis or treatment exist for either disease, researchers have implemented machine learning algorithms with neuroimaging and motion-tracking technology to analyze pathologically relevant symptoms and biomarkers. Deep learning algorithms such as neural networks and complex combined architectures have proven capable of tracking disease-linked changes in brain structure and physiology as well as patient motor and cognitive symptoms and responses to treatment. However, such techniques require further development aimed at improving transparency, adaptability, and reproducibility. In this review, we provide an overview of existing neuroimaging technologies and supervised and unsupervised machine learning techniques with their current applications in the context of Alzheimer's and Parkinson's diseases.

Circuit formation in the adult brain

Neurons in the mammalian central nervous system display an enormous capacity for circuit formation during development but not later in life. In principle, new circuits could be also formed in adult brain, but the absence of the developmental milieu and the presence of growth inhibition and hundreds of working circuits are generally viewed as unsupportive for such a process. Here, we bring together evidence from different areas of neuroscience—such as neurological disorders, adult‐brain neurogenesis, innate behaviours, cell grafting, and in vivo cell reprogramming—which demonstrates robust circuit formation in adult brain. In some cases, adult‐brain rewiring is ongoing and required for certain types of behaviour and memory, while other cases show significant promise for brain repair in disease models. Together, these examples highlight that the adult brain has higher capacity for structural plasticity than previously recognized. Understanding the underlying mechanisms behind this retained plasticity has the potential to advance basic knowledge regarding the molecular organization of synaptic circuits and could herald a new era of neural circuit engineering for therapeutic repair.

α-Synuclein Impacts on Intrinsic Neuronal Network Activity Through Reduced Levels of Cyclic AMP and Diminished Numbers of Active Presynaptic Terminals

α-synuclein (α-Syn) is intimately linked to synucleinopathies like Parkinson’s disease and dementia with Lewy bodies. However, the pathophysiological mechanisms that are triggered by this protein are still largely enigmatic. α-Syn overabundance may cause neurodegeneration through protein accumulation and mitochondrial deterioration but may also result in pathomechanisms independent from neuronal cell death. One such proposed pathological mechanism is the influence of α-Syn on non-stimulated, intrinsic brain activity. This activity is responsible for more than 90% of the brain’s energyconsumption, and is thus thought to play an eminent role in basic brain functionality. Here we report that α-Syn substantially disrupts intrinsic neuronal network burst activity in a long-term neuronal cell culture model. Mechanistically, the impairment of network activity originates from reduced levels of cyclic AMP and cyclic AMP-mediated signaling as well as from diminished numbers of active presynaptic terminals. The profound reduction of network activity due to α-Syn was mediated only by intracellularly expressed α-Syn, but not by α-Syn that is naturally released by neurons. Conversely, extracellular pre-formed fibrils of α-Syn mimicked the effect of intracellular α-Syn, suggesting that they trigger an off-target mechanism that is not activated by naturally released α-Syn. A simulation-based model of the network activity in our cultures demonstrated that even subtle effect sizes in reducing outbound connectivity, i.e., loss of active synapses, can cause substantial global reductions in non-stimulated network activity. These results suggest that even low-level loss of synaptic output capabilities caused by α-Syn may result in significant functional impairments in terms of intrinsic neuronal network activity. Provided that our model holds true for the human brain, then α-Syn may cause significant functional lesions independent from neurodegeneration.

Roles of Transcription Factors in the Development and Reprogramming of the Dopaminergic Neurons

The meso-diencephalic dopaminergic (mdDA) neurons regulate various critical processes in the mammalian nervous system, including voluntary movement and a wide range of behaviors such as mood, reward, addiction, and stress. mdDA neuronal loss is linked with one of the most prominent human movement neurological disorders, Parkinson’s disease (PD). How these cells die and regenerate are two of the most hotly debated PD research topics. As for the latter, it has been long known that a series of transcription factors (TFs) involves the development of mdDA neurons, specifying cell types and controlling developmental patterns. In vitro and in vivo, TFs regulate the expression of tyrosine hydroxylase, a dopamine transporter, vesicular monoamine transporter 2, and L-aromatic amino acid decarboxylase, all of which are critical for dopamine synthesis and transport in dopaminergic neurons (DA neurons). In this review, we encapsulate the molecular mechanism of TFs underlying embryonic growth and maturation of mdDA neurons and update achievements on dopaminergic cell therapy dependent on knowledge of TFs in mdDA neuronal development. We believe that a deeper understanding of the extrinsic and intrinsic factors that influence DA neurons’ fate and development in the midbrain could lead to a better strategy for PD cell therapy.

Single-cell transcriptomics of human iPSC differentiation dynamics reveal a core molecular network of Parkinson's disease

Parkinson's disease (PD) is the second-most prevalent neurodegenerative disorder, characterized by the loss of dopaminergic neurons (mDA) in the midbrain. The underlying mechanisms are only partly understood and there is no treatment to reverse PD progression. Here, we investigated the disease mechanism using mDA neurons differentiated from human induced pluripotent stem cells (hiPSCs) carrying the ILE368ASN mutation within the PINK1 gene, which is strongly associated with PD. Single-cell RNA sequencing (RNAseq) and gene expression analysis of a PINK1-ILE368ASN and a control cell line identified genes differentially expressed during mDA neuron differentiation. Network analysis revealed that these genes form a core network, members of which interact with all known 19 protein-coding Parkinson's disease-associated genes. This core network encompasses key PD-associated pathways, including ubiquitination, mitochondrial function, protein processing, RNA metabolism, and vesicular transport. Proteomics analysis showed a consistent alteration in proteins of dopamine metabolism, indicating a defect of dopaminergic metabolism in PINK1-ILE368ASN neurons. Our findings suggest the existence of a network onto which pathways associated with PD pathology converge, and offers an inclusive interpretation of the phenotypic heterogeneity of PD.

α-Synuclein Negatively Regulates Nurr1 Expression Through NF-κB-Related Mechanism

The nuclear receptor-related 1 protein (Nurr1) is critical for the development and survival of midbrain dopamine neurons that are predominantly affected and progressively degenerated in Parkinson’s disease (PD). The expression level of Nurr1 has been proposed to be modulated by α-synuclein (α-SYN), an important pathological hallmark of PD. However, the underlying molecular mechanisms of α-SYN-Nurr1 interaction are still rarely explored. In this study, we investigated the effect and mechanism of α-SYN on the transcription level of Nurr1. Our results showed that overexpression of α-SYN (WT or A53T) reduced Nurr1 and its downstream gene expressions. α-SYN neither affected the mRNA stability nor bound with the promoter of Nurr1, but modulated the transcription activity of Nurr1 promoter region ranging from −605 bp to −418 bp, which contains the binding site of nuclear factor-kappa B (NF-κB). Moreover, overexpression of α-SYN (WT or A53T) down-regulated NF-κB expression level, thereby inhibiting the transcription factor activity of NF-κB and decreasing the binding quantity of NF-κB with Nurr1 promoter. These findings may give us new insights to better understand the molecular mechanisms underlying the α-SYN-regulated Nurr1 function, which may fascinate the investigation of dopamine neuron degeneration in PD pathogenesis.