Regional expression of genes mediating trans-synaptic alpha-synuclein transfer predicts regional atrophy in Parkinson disease

Selective vulnerability and resilience to Alzheimer's disease tauopathy as a function of genes and the connectome

Brain regions in Alzheimer's (AD) exhibit distinct vulnerability to the disease's hallmark pathology, with the entorhinal cortex and hippocampus succumbing early to tau tangles while others like primary sensory cortices remain resilient. The quest to understand how local/regional genetic factors, pathogenesis, and network-mediated spread of pathology together govern this selective vulnerability (SV) or resilience (SR) is ongoing. Although many risk genes in AD are known from gene association and transgenic studies, it is still not known whether and how their baseline expression signatures confer SV or SR to brain structures. Prior analyses have yielded conflicting results, pointing to a disconnect between the location of genetic risk factors and downstream tau pathology. We hypothesize that a full accounting of genes' role in mediating SV/SR would require the modeling of network-based vulnerability, whereby tau misfolds, aggregates, and propagates along fiber projections. We therefore employed an extended network diffusion model (eNDM) and tested it on tau pathology PET data from 196 AD patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Thus the fitted eNDM model becomes a reference process from which to assess the role of innate genetic factors. Using the residual (observed - model-predicted) tau as a novel target outcome, we obtained its association with 100 top AD risk-genes, whose baseline spatial transcriptional profiles were obtained from the Allen Human Brain Atlas (AHBA). We found that while many risk genes at baseline showed a strong association with regional tau, many more showed a stronger association with residual tau. This suggests that both direct vulnerability, related to the network, as well as network-independent vulnerability, are conferred by risk genes. We then classified risk genes into four classes: network-related SV (SV-NR), network-independent SV (SV-NI), network-related SR (SR-NR), and network-independent SR (SR-NI). Each class has a distinct spatial signature and associated vulnerability to tau. Remarkably, we found from gene-ontology analyses, that genes in these classes were enriched in distinct functional processes and encompassed different functional networks. These findings offer new insights into the factors governing innate vulnerability or resilience in AD pathophysiology and may prove helpful in identifying potential intervention targets.

LAG-3 as the third checkpoint inhibitor

Lymphocyte activation gene 3 (LAG-3) is an inhibitory receptor that is highly expressed by exhausted T cells. LAG-3 is a promising immunotherapeutic target, with more than 20 LAG-3-targeting therapeutics in clinical trials and a fixed-dose combination of anti-LAG-3 and anti-PD-1 now approved to treat unresectable or metastatic melanoma. Although LAG-3 is widely recognized as a potent inhibitory receptor, important questions regarding its biology and mechanism of action remain. In this Perspective, we focus on gaps in the understanding of LAG-3 biology and discuss the five biggest topics of current debate and focus regarding LAG-3, including its ligands, signaling and mechanism of action, its cell-specific functions, its importance in different disease settings, and the development of novel therapeutics.

exFINDER: identify external communication signals using single-cell transcriptomics data

Cells make decisions through their communication with other cells and receiving signals from their environment. Using single-cell transcriptomics, computational tools have been developed to infer cell-cell communication through ligands and receptors. However, the existing methods only deal with signals sent by the measured cells in the data, the received signals from the external system are missing in the inference. Here, we present exFINDER, a method that identifies such external signals received by the cells in the single-cell transcriptomics datasets by utilizing the prior knowledge of signaling pathways. In particular, exFINDER can uncover external signals that activate the given target genes, infer the external signal-target signaling network (exSigNet), and perform quantitative analysis on exSigNets. The applications of exFINDER to scRNA-seq datasets from different species demonstrate the accuracy and robustness of identifying external signals, revealing critical transition-related signaling activities, inferring critical external signals and targets, clustering signal-target paths, and evaluating relevant biological events. Overall, exFINDER can be applied to scRNA-seq data to reveal the external signal-associated activities and maybe novel cells that send such signals.

exFINDER: identify external communication signals using single-cell transcriptomics data

Cells make decisions through their communication with other cells and receiving signals from their environment. Using single-cell transcriptomics, computational tools have been developed to infer cell-cell communication through ligands and receptors. However, the existing methods only deal with signals sent by the measured cells in the data, the received signals from the external system are missing in the inference. Here, we present exFINDER, a method that identifies such external signals received by the cells in the single-cell transcriptomics datasets by utilizing the prior knowledge of signaling pathways. In particular, exFINDER can uncover external signals that activate the given target genes, infer the external signal-target signaling network (exSigNet), and perform quantitative analysis on exSigNets. The applications of exFINDER to scRNA-seq datasets from different species demonstrate the accuracy and robustness of identifying external signals, revealing critical transition-related signaling activities, inferring critical external signals and targets, clustering signal-target paths, and evaluating relevant biological events. Overall, exFINDER can be applied to scRNA-seq data to reveal the external signal-associated activities and maybe novel cells that send such signals.

Machine learning within the Parkinson's progression markers initiative: Review of the current state of affairs

The Parkinson's Progression Markers Initiative (PPMI) has collected more than a decade's worth of longitudinal and multi-modal data from patients, healthy controls, and at-risk individuals, including imaging, clinical, cognitive, and 'omics' biospecimens. Such a rich dataset presents unprecedented opportunities for biomarker discovery, patient subtyping, and prognostic prediction, but it also poses challenges that may require the development of novel methodological approaches to solve. In this review, we provide an overview of the application of machine learning methods to analyzing data from the PPMI cohort. We find that there is significant variability in the types of data, models, and validation procedures used across studies, and that much of what makes the PPMI data set unique (multi-modal and longitudinal observations) remains underutilized in most machine learning studies. We review each of these dimensions in detail and provide recommendations for future machine learning work using data from the PPMI cohort.

The Interplay between α-Synuclein and Microglia in α-Synucleinopathies

Synucleinopathies are a set of devastating neurodegenerative diseases that share a pathologic accumulation of the protein α-synuclein (α-syn). This accumulation causes neuronal death resulting in irreversible dementia, deteriorating motor symptoms, and devastating cognitive decline. While the etiology of these conditions remains largely unknown, microglia, the resident immune cells of the central nervous system (CNS), have been consistently implicated in the pathogenesis of synucleinopathies. Microglia are generally believed to be neuroprotective in the early stages of α-syn accumulation and contribute to further neurodegeneration in chronic disease states. While the molecular mechanisms by which microglia achieve this role are still being investigated, here we highlight the major findings to date. In this review, we describe how structural varieties of inherently disordered α-syn result in varied microglial receptor-mediated interactions. We also summarize which microglial receptors enable cellular recognition and uptake of α-syn. Lastly, we review the downstream effects of α-syn processing within microglia, including spread to other brain regions resulting in neuroinflammation and neurodegeneration in chronic disease states. Understanding the mechanism of microglial interactions with α-syn is vital to conceptualizing molecular targets for novel therapeutic interventions. In addition, given the significant diversity in the pathophysiology of synucleinopathies, such molecular interactions are vital in gauging all potential pathways of neurodegeneration in the disease state.

Regional gene expression patterns are associated with task-specific brain activation during reward and emotion processing measured with functional MRI

The exploration of the spatial relationship between gene expression profiles and task-evoked response patterns known to be altered in neuropsychiatric disorders, for example depression, can guide the development of more targeted therapies. Here, we estimated the correlation between human transcriptome data and two different brain activation maps measured with functional magnetic resonance imaging (fMRI) in healthy subjects. Whole-brain activation patterns evoked during an emotional face recognition task were associated with topological mRNA expression of genes involved in cellular transport. In contrast, fMRI activation patterns related to the acceptance of monetary rewards were associated with genes implicated in cellular localization processes, metabolism, translation, and synapse regulation. An overlap of these genes with risk genes from major depressive disorder genome-wide association studies revealed the involvement of the master regulators TCF4 and PAX6 in emotion and reward processing. Overall, the identification of stable relationships between spatial gene expression profiles and fMRI data may reshape the prospects for imaging transcriptomics studies.

Bioinformatics-based study reveals that AP2M1 is regulated by the circRNA-miRNA-mRNA interaction network and affects Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurological disease that worsens with time. The hallmark illnesses include extracellular senile plaques caused by β-amyloid protein deposition, neurofibrillary tangles caused by tau protein hyperphosphorylation, and neuronal loss accompanying glial cell hyperplasia. Noncoding RNAs are substantially implicated in related pathophysiology, according to mounting data. However, the function of these ncRNAs is mainly unclear. Circular RNAs (circRNAs) include many miRNA-binding sites (miRNA response elements, MREs), which operate as miRNA sponges or competing endogenous RNAs (ceRNAs). The purpose of this study was to look at the role of circular RNAs (circRNAs) and microRNAs (miRNAs) in Alzheimer's disease (AD) as possible biomarkers. The Gene Expression Omnibus (GEO) database was used to obtain an expression profile of Alzheimer's disease patients (GSE5281, GSE122603, GSE97760, GSE150693, GSE1297, and GSE161435). Through preliminary data deletion, 163 genes with significant differences, 156 miRNAs with significant differences, and 153 circRNAs with significant differences were identified. Then, 10 key genes, led by MAPT and AP2M1, were identified by the mediation center algorithm, 34 miRNAs with obvious prognosis were identified by the cox regression model, and 16 key circRNAs were selected by the database. To develop competitive endogenous RNA (ceRNA) networks, hub circRNAs and mRNAs were used. Finally, GO analysis and clinical data verification of key genes were carried out. We discovered that a down-regulated circRNA (has_circ_002048) caused the increased expression of numerous miRNAs, which further inhibited the expression of a critical mRNA (AP2M1), leading to Alzheimer's disease pathology. The findings of this work contribute to a better understanding of the circRNA-miRNA-mRNA regulating processes in Alzheimer's disease. Furthermore, the ncRNAs found here might become novel biomarkers and potential targets for the development of Alzheimer's drugs.

Association between LAG3/CD4 gene variants and risk of Parkinson's disease

BACKGROUND/OBJECTIVES

Several recent studies suggest a possible role of lymphocyte activation 3 (LAG3) protein. LAG3 can behave as an α-synuclein ligand, and serum and cerebrospinal fluid-soluble LAG3 levels have been proposed as a marker of Parkinson's disease (PD). In this study, we aimed to investigate whether there is an association between 3 common single-nucleotide variations (SNVs) in the LAG3 gene and its closely related CD4 molecule gene and the risk of PD in a Caucasian Spanish population. Two of them have been previously associated with the risk of PD in Chinese females.

METHODS

We analysed genotypes and allele frequencies for CD4 rs1922452, CD4 951818 and LAG3 rs870849 SNVs, by using specifically designed TaqMan assays, in a cohort composed of 629 PD patients and 865 age- and gender-matched healthy controls.

RESULTS

The frequencies of the CD4 rs1922452 A/A genotype, according to the dominant and recessive genetic models, and of the CD4 rs1922452/A allelic variant were significantly lower, and the frequencies of the CD4 rs951818 A/A genotype, according to the dominant genetic model, and of the CD4 rs951818/A allele, were significantly higher in PD patients than in controls. The differences were not significant after stratifying by sex. These two SNVs showed strong linkage. Regression models showed a lack of relation between the 3 SNVs studied and the age at onset of PD.

CONCLUSIONS

These data suggest a possible role of CD4 rs1922452 and CD4 rs951818 polymorphisms in the risk of PD.

NUCKS1 is a highly modified, chromatin-associated protein involved in a diverse set of biological and pathophysiological processes

The Nuclear Casein and Cyclin-dependent Kinase Substrate 1 (NUCKS1) protein is highly conserved in vertebrates, predominantly localized to the nucleus and one of the most heavily modified proteins in the human proteome. NUCKS1 expression is high in stem cells and the brain, developmentally regulated in mice and associated with several diverse malignancies in humans, including cancer, metabolic syndrome and Parkinson's disease. NUCKS1 function has been linked to modulating chromatin architecture and transcription, DNA repair and cell cycle regulation. In this review, we summarize and discuss the published information on NUCKS1 and highlight the questions that remain to be addressed to better understand the complex biology of this multifaceted protein.

Identifying Alzheimer's genes via brain transcriptome mapping

BACKGROUND

Alzheimer's disease (AD) is one of the most common neurodegenerative disorders characterized by progressive decline in cognitive function. Targeted genetic analyses, genome-wide association studies, and imaging genetic analyses have been performed to detect AD risk and protective genes and have successfully identified dozens of AD susceptibility loci. Recently, brain imaging transcriptomics analyses have also been conducted to investigate the relationship between neuroimaging traits and gene expression measures to identify interesting gene-traits associations. These imaging transcriptomic studies typically do not involve the disease outcome in the analysis, and thus the identified brain or transcriptomic markers may not be related or specific to the disease outcome.

RESULTS

We propose an innovative two-stage approach to identify genes whose expression profiles are related to diagnosis phenotype via brain transcriptome mapping. Specifically, we first map the effects of a diagnosis phenotype onto imaging traits across the brain using a linear regression model. Then, the gene-diagnosis association is assessed by spatially correlating the brain transcriptome map with the diagnostic effect map on the brain-wide imaging traits. To demonstrate the promise of our approach, we apply it to the integrative analysis of the brain transcriptome data from the Allen Human Brain Atlas (AHBA) and the amyloid imaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Our method identifies 12 genes whose brain-wide transcriptome patterns are highly correlated with six different diagnostic effect maps on the amyloid imaging traits. These 12 genes include four confirmatory findings (i.e., AD genes reported in DisGeNET) and eight novel genes that have not be associated with AD in DisGeNET.

CONCLUSION

We have proposed a novel disease-related brain transcriptomic mapping method to identify genes whose expression profiles spatially correlated with regional diagnostic effects on a studied brain trait. Our empirical study on the AHBA and ADNI data shows the promise of the approach, and the resulting AD gene discoveries provide valuable information for better understanding biological pathways from transcriptomic signatures to intermediate brain traits and to phenotypic disease outcomes.

Modeling seeding and neuroanatomic spread of pathology in amyotrophic lateral sclerosis

The neurodegenerative disorder amyotrophic lateral sclerosis (ALS) is characterized by the progressive loss of upper and lower motor neurons, with pathological involvement of cerebral motor and extra-motor areas in a clinicopathological spectrum with frontotemporal dementia (FTD). A key unresolved issue is how the non-random distribution of pathology in ALS reflects differential network vulnerability, including molecular factors such as regional gene expression, or preferential spread of pathology via anatomical connections. A system of histopathological staging of ALS based on the regional burden of TDP-43 pathology observed in postmortem brains has been supported to some extent by analysis of distribution of in vivo structural MRI changes. In this paper, computational modeling using a Network Diffusion Model (NDM) was used to investigate whether a process of focal pathological 'seeding' followed by structural network-based spread recapitulated postmortem histopathological staging and, secondly, whether this had any correlation to the pattern of expression of a panel of genes implicated in ALS across the healthy brain. Regionally parcellated T1-weighted MRI data from ALS patients (baseline n=79) was studied in relation to a healthy control structural connectome and a database of associated regional cerebral gene expression. The NDM provided strong support for a structural network-based basis for regional pathological spread in ALS, but no simple relationship to the spatial distribution of ALS-related genes in the healthy brain. Interestingly, OPTN gene was identified as a significant but a weaker non-NDM contributor within the network-gene interaction model (LASSO). Intriguingly, the critical seed regions for spread within the model were not within the primary motor cortex but basal ganglia, thalamus and insula, where NDM recapitulated aspects of the postmortem histopathological staging system. Within the ALS-FTD clinicopathological spectrum, non-primary motor structures may be among the earliest sites of cerebral pathology.

Network structure and transcriptomic vulnerability shape atrophy in frontotemporal dementia

Connections among brain regions allow pathological perturbations to spread from a single source region to multiple regions. Patterns of neurodegeneration in multiple diseases, including behavioural variant of frontotemporal dementia (bvFTD), resemble the large-scale functional systems, but how bvFTD-related atrophy patterns relate to structural network organization remains unknown. Here we investigate whether neurodegeneration patterns in sporadic and genetic bvFTD are conditioned by connectome architecture. Regional atrophy patterns were estimated in both genetic bvFTD (75 patients, 247 controls) and sporadic bvFTD (70 patients, 123 controls). First, we identified distributed atrophy patterns in bvFTD, mainly targeting areas associated with the limbic intrinsic network and insular cytoarchitectonic class. Regional atrophy was significantly correlated with atrophy of structurally- and functionally-connected neighbours, demonstrating that network structure shapes atrophy patterns. The anterior insula was identified as the predominant group epicentre of brain atrophy using data-driven and simulation-based methods, with some secondary regions in frontal ventromedial and antero-medial temporal areas. We found that FTD-related genes, namely C9orf72 and TARDBP, confer local transcriptomic vulnerability to the disease, modulating the propagation of pathology through the connectome. Collectively, our results demonstrate that atrophy patterns in sporadic and genetic bvFTD are jointly shaped by global connectome architecture and local transcriptomic vulnerability, providing an explanation as to how heterogenous pathological entities can lead to the same clinical syndrome.

Neurogenetic traits outline vulnerability to cortical disruption in Parkinson's disease

The genetic traits that underlie vulnerability to neuronal damage across specific brain circuits in Parkinson's disease (PD) remain to be elucidated. In this study, we characterized the brain topological intersection between propagating connectivity networks in controls and PD participants and gene expression patterns across the human cortex - such as the SNCA gene. We observed that brain connectivity originated from PD-related pathology epicenters in the brainstem recapitulated the anatomical distribution of alpha-synuclein histopathology in postmortem data. We also discovered that the gene set most related to cortical propagation patterns of PD-related pathology was primarily involved in microtubule cellular components. Thus, this study sheds light on new avenues for enhancing detection of PD neuronal vulnerability via an evaluation of in vivo connectivity trajectories across the human brain and successful integration of neuroimaging-genetic strategies.

Seven mysteries of LAG-3: a multi-faceted immune receptor of increasing complexity

Despite three decades of research to its name and increasing interest in immunotherapies that target it, LAG-3 remains an elusive co-inhibitory receptor in comparison to the well-established PD-1 and CTLA-4. As such, LAG-3 targeting therapies have yet to achieve the clinical success of therapies targeting other checkpoints. This could, in part, be attributed to the many unanswered questions that remain regarding LAG-3 biology. Of these, we address: (i) the function of the many LAG-3-ligand interactions, (ii) the hurdles that remain to acquire a high-resolution structure of LAG-3, (iii) the under-studied LAG-3 signal transduction mechanism, (iv) the elusive soluble form of LAG-3, (v) the implications of the lack of (significant) phenotype of LAG-3 knockout mice, (vi) the reports of LAG-3 expression on the epithelium, and (vii) the conflicting reports of LAG-3 expression (and potential contributions to pathology) in the brain. These mysteries which surround LAG-3 highlight how the ever-evolving study of its biology continues to reveal ever-increasing complexity in its role as an immune receptor. Importantly, answering the questions which shroud LAG-3 in mystery will allow the maximum therapeutic benefit of LAG-3 targeting immunotherapies in cancer, autoimmunity and beyond.

Brain atrophy progression in Parkinson's disease is shaped by connectivity and local vulnerability

Brain atrophy has been reported in the early stages of Parkinson's disease, but there have been few longitudinal studies. How intrinsic properties of the brain, such as anatomical connectivity, local cell-type distribution and gene expression combine to determine the pattern of disease progression also remains unknown. One hypothesis proposes that the disease stems from prion-like propagation of misfolded alpha-synuclein via the connectome that might cause varying degrees of tissue damage based on local properties. Here, we used MRI data from the Parkinson Progression Markers Initiative to map the progression of brain atrophy over 1, 2 and 4 years compared with baseline. We derived atrophy maps for four time points using deformation-based morphometry applied to T1-weighted MRI from 120 de novo Parkinson's disease patients, 74 of whom had imaging at all four time points (50 Men: 24 Women) and 157 healthy control participants (115 Men: 42 Women). In order to determine factors that may influence neurodegeneration, we related atrophy progression to brain structural and functional connectivity, cell-type expression and gene ontology enrichment analyses. After regressing out the expected age and sex effects associated with normal ageing, we found that atrophy significantly progressed over 2 and 4 years in the caudate, nucleus accumbens, hippocampus and posterior cortical regions. This progression was shaped by both structural and functional brain connectivity. Also, the progression of atrophy was more pronounced in regions with a higher expression of genes related to synapses and was inversely related to the prevalence of oligodendrocytes and endothelial cells. In sum, we demonstrate that the progression of atrophy in Parkinson's disease is in line with the prion-like propagation hypothesis of alpha-synuclein and provide evidence that synapses may be especially vulnerable to synucleinopathy. In addition to identifying vulnerable brain regions, this study reveals different factors that may be implicated in the neurotoxic mechanisms leading to progression in Parkinson's disease. All brain maps generated here are available on request.

Network-constrained technique to characterize pathology progression rate in Alzheimer's disease

Current methods for measuring the chronic rates of cognitive decline and degeneration in Alzheimer’s disease rely on the sensitivity of longitudinal neuropsychological batteries and clinical neuroimaging, particularly structural magnetic resonance imaging of brain atrophy, either at a global or regional scale. There is particular interest in approaches predictive of future disease progression and clinical outcomes using a single time point. If successful, such approaches could have great impact on differential diagnosis, therapeutic treatment and clinical trial inclusion. Unfortunately, it has proven quite challenging to accurately predict clinical and degeneration progression rates from baseline data. Specifically, a key limitation of the previously proposed approaches for disease progression based on the brain atrophy measures has been the limited incorporation of the knowledge from disease pathology progression models, which suggest a prion-like spread of disease pathology and hence the neurodegeneration. Here, we present a new metric for disease progression rate in Alzheimer that uses only MRI-derived atrophy data yet is able to infer the underlying rate of pathology transmission. This is enabled by imposing a spread process driven by the brain networks using a Network Diffusion Model. We first fit this model to each patient’s longitudinal brain atrophy data defined on a brain network structure to estimate a patient-specific rate of pathology diffusion, called the pathology progression rate. Using machine learning algorithms, we then build a baseline data model and tested this rate metric on data from longitudinal Alzheimer’s Disease Neuroimaging Initiative study including 810 subjects. Our measure of disease progression differed significantly across diagnostic groups as well as between groups with different genetic risk factors. Remarkably, hierarchical clustering revealed 3 distinct clusters based on CSF profiles with >90% accuracy. These pathological clusters exhibit progressive atrophy and clinical impairments that correspond to the proposed rate measure. We demonstrate that a subject’s degeneration speed can be best predicted from baseline neuroimaging volumetrics and fluid biomarkers for subjects in the middle of their degenerative course, which may be a practical, inexpensive screening tool for future prognostic applications.

Transcriptomic Signatures Associated With Regional Cortical Thickness Changes in Parkinson's Disease

Cortical atrophy is a common manifestation in Parkinson's disease (PD), particularly in advanced stages of the disease. To elucidate the molecular underpinnings of cortical thickness changes in PD, we performed an integrated analysis of brain-wide healthy transcriptomic data from the Allen Human Brain Atlas and patterns of cortical thickness based on T1-weighted anatomical MRI data of 149 PD patients and 369 controls. For this purpose, we used partial least squares regression to identify gene expression patterns correlated with cortical thickness changes. In addition, we identified gene expression patterns underlying the relationship between cortical thickness and clinical domains of PD. Our results show that genes whose expression in the healthy brain is associated with cortical thickness changes in PD are enriched in biological pathways related to sumoylation, regulation of mitotic cell cycle, mitochondrial translation, DNA damage responses, and ER-Golgi traffic. The associated pathways were highly related to each other and all belong to cellular maintenance mechanisms. The expression of genes within most pathways was negatively correlated with cortical thickness changes, showing higher expression in regions associated with decreased cortical thickness (atrophy). On the other hand, sumoylation pathways were positively correlated with cortical thickness changes, showing higher expression in regions with increased cortical thickness (hypertrophy). Our findings suggest that alterations in the balanced interplay of these mechanisms play a role in changes of cortical thickness in PD and possibly influence motor and cognitive functions.

Imaging Transcriptomics of Brain Disorders

Noninvasive neuroimaging is a powerful tool for quantifying diverse aspects of brain structure and function in vivo, and it has been used extensively to map the neural changes associated with various brain disorders. However, most neuroimaging techniques offer only indirect measures of underlying pathological mechanisms. The recent development of anatomically comprehensive gene expression atlases has opened new opportunities for studying the transcriptional correlates of noninvasively measured neural phenotypes, offering a rich framework for evaluating pathophysiological hypotheses and putative mechanisms. Here, we provide an overview of some fundamental methods in imaging transcriptomics and outline their application to understanding brain disorders of neurodevelopment, adulthood, and neurodegeneration. Converging evidence indicates that spatial variations in gene expression are linked to normative changes in brain structure during age-related maturation and neurodegeneration that are in part associated with cell-specific gene expression markers of gene expression. Transcriptional correlates of disorder-related neuroimaging phenotypes are also linked to transcriptionally dysregulated genes identified in ex vivo analyses of patient brains. Modeling studies demonstrate that spatial patterns of gene expression are involved in regional vulnerability to neurodegeneration and the spread of disease across the brain. This growing body of work supports the utility of transcriptional atlases in testing hypotheses about the molecular mechanism driving disease-related changes in macroscopic neuroimaging phenotypes.

Emerging evidence implicating a role for neurexins in neurodegenerative and neuropsychiatric disorders

Synaptopathies are brain disorders characterized by dysfunctional synapses, which are specialized junctions between neurons that are essential for the transmission of information. Synaptic dysfunction can occur due to mutations that alter the structure and function of synaptic components or abnormal expression levels of a synaptic protein. One class of synaptic proteins that are essential to their biology are cell adhesion proteins that connect the pre- and post-synaptic compartments. Neurexins are one type of synaptic cell adhesion molecule that have, recently, gained more pathological interest. Variants in both neurexins and their common binding partners, neuroligins, have been associated with several neuropsychiatric disorders. In this review, we summarize some of the key physiological functions of the neurexin protein family and the protein networks they are involved in. Furthermore, examination of published literature has implicated neurexins in both neuropsychiatric and neurodegenerative disorders. There is a clear link between neurexins and neuropsychiatric disorders, such as autism spectrum disorder and schizophrenia. However, multiple expression studies have also shown changes in neurexin expression in several neurodegenerative disorders, including Alzheimer's disease and Parkinson's disease. Therefore, this review highlights the potential importance of neurexins in brain disorders and the importance of doing more targeted studies on these genes and proteins.

Convergent structural network and gene signatures for MRgFUS thalamotomy in patients with Parkinson's disease

MRgFUS has just been made available for the 1.7 million Parkinson's disease patients in China. Despite its non-invasive and rapid therapeutic advantages for involuntary tremor, some concerns have emerged about outcomes variability, non-specificity, and side-effects, as little is known about its impact on the long-term plasticity of brain structure. We sought to dissect the characteristics of long-term changes in brain structure caused by MRgFUS lesion and explored potential biological mechanisms. One-year multimodal imaging follow-ups were conducted for nine tremor-dominant Parkinson's disease patients undergoing unilateral MRgFUS thalamotomy. A structural connectivity map was generated for each patient to analyze dynamic changes in brain structure. The human brain transcriptome was extracted and spatially registered for connectivity vulnerability. Genetic functional enrichment analysis was performed and further clarified using in vivo emission computed tomography data. MRgFUS not only abolished tremors but also significantly disrupted the brain network topology. Network-based statistics identified a U-shape MRgFUS-sensitive subnetwork reflective of hand tremor recovery and surgical process, accompanied by relevant cerebral blood flow and gray matter alteration. Using human brain gene expression data, we observed that dopaminergic signatures were responsible for the preferential vulnerability associated with these architectural alterations. Additional PET/SPECT data not only validated these gene signatures, but also suggested that structural alteration was significantly correlated with D1 and D2 receptors, DAT, and F-DOPA measures. There was a long-term dynamic loop between structural alteration and dopaminergic signature for MRgFUS thalamotomy, which may be closely related to the long-term improvements in clinical tremor.

Integrated transcriptomic and neuroimaging brain model decodes biological mechanisms in aging and Alzheimer's disease

Both healthy aging and Alzheimer's disease (AD) are characterized by concurrent alterations in several biological factors. However, generative brain models of aging and AD are limited in incorporating the measures of these biological factors at different spatial resolutions. Here, we propose a personalized bottom-up spatiotemporal brain model that accounts for the direct interplay between hundreds of RNA transcripts and multiple macroscopic neuroimaging modalities (PET, MRI). In normal elderly and AD participants, the model identifies top genes modulating tau and amyloid-β burdens, vascular flow, glucose metabolism, functional activity, and atrophy to drive cognitive decline. The results also revealed that AD and healthy aging share specific biological mechanisms, even though AD is a separate entity with considerably more altered pathways. Overall, this personalized model offers novel insights into the multiscale alterations in the elderly brain, with important implications for identifying effective genetic targets for extending healthy aging and treating AD progression.

Dysfunction of RAB39B-Mediated Vesicular Trafficking in Lewy Body Diseases

Intracellular vesicular trafficking is essential for neuronal development, function, and homeostasis and serves to process, direct, and sort proteins, lipids, and other cargo throughout the cell. This intricate system of membrane trafficking between different compartments is tightly orchestrated by Ras analog in brain (RAB) GTPases and their effectors. Of the 66 members of the RAB family in humans, many have been implicated in neurodegenerative diseases and impairment of their functions contributes to cellular stress, protein aggregation, and death. Critically, RAB39B loss-of-function mutations are known to be associated with X-linked intellectual disability and with rare early-onset Parkinson's disease. Moreover, recent studies have highlighted altered RAB39B expression in idiopathic cases of several Lewy body diseases (LBDs). This review contextualizes the role of RAB proteins in LBDs and highlights the consequences of RAB39B impairment in terms of endosomal trafficking, neurite outgrowth, synaptic maturation, autophagy, as well as alpha-synuclein homeostasis. Additionally, the potential for therapeutic intervention is examined via a discussion of the recent progress towards the development of specific RAB modulators. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Cingulate networks associated with gray matter loss in Parkinson's disease show high expression of cholinergic genes in the healthy brain

Structural covariance networks are able to identify functionally organized brain regions by gray matter volume covariance across a population. We examined the transcriptomic signature of such anatomical networks in the healthy brain using postmortem microarray data from the Allen Human Brain Atlas. A previous study revealed that a posterior cingulate network and anterior cingulate network showed decreased gray matter in brains of Parkinson's disease patients. Therefore, we examined these two anatomical networks to understand the underlying molecular processes that may be involved in Parkinson's disease. Whole brain transcriptomics from the healthy brain revealed upregulation of genes associated with serotonin, GPCR, GABA, glutamate, and RAS-signaling pathways. Our results also suggest involvement of the cholinergic circuit, in which genes NPPA, SOSTDC1, and TYRP1 may play a functional role. Finally, both networks were enriched for genes associated with neuropsychiatric disorders that overlap with Parkinson's disease symptoms. The identified genes and pathways contribute to healthy functions of the posterior and anterior cingulate networks and disruptions to these functions may in turn contribute to the pathological and clinical events observed in Parkinson's disease.

Fibrillar α-synuclein toxicity depends on functional lysosomes

Neurodegeneration in Parkinson's disease (PD) can be recapitulated in animals by administration of α-synuclein preformed fibrils (PFFs) into the brain. However, the mechanism by which these PFFs induce toxicity is unknown. Iron is implicated in PD pathophysiology, so we investigated whether α-synuclein PFFs induce ferroptosis, an iron-dependent cell death pathway. A range of ferroptosis inhibitors were added to a striatal neuron-derived cell line (STHdhQ7/7 cells), a dopaminergic neuron-derived cell line (SN4741 cells), and WT primary cortical neurons, all of which had been intoxicated with α-synuclein PFFs. Viability was not recovered by these inhibitors except for liproxstatin-1, a best-in-class ferroptosis inhibitor, when used at high doses. High-dose liproxstatin-1 visibly enlarged the area of a cell that contained acidic vesicles and elevated the expression of several proteins associated with the autophagy-lysosomal pathway similarly to the known lysosomal inhibitors, chloroquine and bafilomycin A1. Consistent with high-dose liproxstatin-1 protecting via a lysosomal mechanism, we further de-monstrated that loss of viability induced by α-synuclein PFFs was attenuated by chloroquine and bafilomycin A1 as well as the lysosomal cysteine protease inhibitors, leupeptin, E-64D, and Ca-074-Me, but not other autophagy or lysosomal enzyme inhibitors. We confirmed using immunofluorescence microscopy that heparin prevented uptake of α-synuclein PFFs into cells but that chloroquine did not stop α-synuclein uptake into lysosomes despite impairing lysosomal function and inhibiting α-synuclein toxicity. Together, these data suggested that α-synuclein PFFs are toxic in functional lysosomes in vitro. Therapeutic strategies that prevent α-synuclein fibril uptake into lysosomes may be of benefit in PD.

Network model of pathology spread recapitulates neurodegeneration and selective vulnerability in Huntington's Disease

Huntington's Disease (HD), an autosomal dominant genetic disorder caused by a mutation in the Huntingtin gene (HTT), displays a stereotyped topography in the human brain and a stereotyped progression, initially appearing in the striatum. Like other degenerative diseases, spatial topography of HD is divorced from where implicated genes are expressed, a dissociation whose mechanistic underpinning is not currently understood. Cell autonomous molecular factors characterized by gene expression signatures, including proteolytic and post translational modifications, play a role in vulnerability to disease. Non-autonomous mechanisms, likely involving the brain's anatomic or functional connectivity patterns, might also be responsible for selective vulnerability in HD. Leveraging a large dataset of 635 subjects from a multinational study, this paper tests various cell-autonomous and non-autonomous models that can explain HD topography. We test whether the expression patterns of implicated genes is sufficient to explain regional HD atrophy, or whether the network transmission of protein products is required to explain them. We find that network models are capable of predicting, to a high degree, observed atrophy in human subjects. Lastly, we propose a model of anterograde network transmission, and show that it is the most parsimonious yet most likely to explain observed atrophy patterns in HD. Collectively, these data indicate that pathology spread in HD may be mediated by the brain's intrinsic structural network organization. This is the first study to systematically and quantitatively test multiple hypotheses of pathology spread in living human subjects with HD.

Imaging Transcriptomics in Neurodegenerative Diseases

Imaging transcriptomics investigates the relationship between neuroanatomical/neuroimaging features and gene expression. The spatial and temporal distribution of the expressed genes and their pattern of spreading over time can contribute to elucidating cellular and molecular processes involved in neurodegeneration. In this study, we review recent findings regarding the correlation between neuroimaging and expression data in neurodegenerative diseases with a focus on Alzheimer's disease and Parkinson's disease. An association between gene expression data and different neuroimaging neurodegeneration features, such as R2 relaxation time and volumetric cortical atrophy, was established. Several positive and negative expression correlations were identified, and they confirmed the focal nature of neurodegeneration. Positively correlated genes were associated with cell motility, immune system activity, neuroinflammation, and microglia. Data from connectome studies support the hypothesis of selective network vulnerability and a temporal spreading pattern in neurodegenerative pathologies. Genes related to cellular mobility and transport are overexpressed in the neuroimaging-defined delineated areas of degeneration. In addition, expression enrichment of genes involved in immunological processes in vulnerable regions-such as the Toll-like receptor, a receptor involved in innate immunity-plays a major role in neuroinflammation in neurodegenerative diseases. However, substantial limitations must be overcome in future studies: the lack of high-quality resolution expression data, the lack of standardized study protocols, and insufficient sensitive early stage neuroimaging markers of degeneration. Identifying neuroimaging and expression prodromal biomarkers and investigating their causal relation in the preclinical disease stage may enable early targeted therapy before the onset of irreversible brain changes.

Differences in network controllability and regional gene expression underlie hallucinations in Parkinson's disease

Visual hallucinations are common in Parkinson's disease and are associated with poorer prognosis. Imaging studies show white matter loss and functional connectivity changes with Parkinson's visual hallucinations, but the biological factors underlying selective vulnerability of affected parts of the brain network are unknown. Recent models for Parkinson's disease hallucinations suggest they arise due to a shift in the relative effects of different networks. Understanding how structural connectivity affects the interplay between networks will provide important mechanistic insights. To address this, we investigated the structural connectivity changes that accompany visual hallucinations in Parkinson's disease and the organizational and gene expression characteristics of the preferentially affected areas of the network. We performed diffusion-weighted imaging in 100 patients with Parkinson's disease (81 without hallucinations, 19 with visual hallucinations) and 34 healthy age-matched controls. We used network-based statistics to identify changes in structural connectivity in Parkinson's disease patients with hallucinations and performed an analysis of controllability, an emerging technique that allows quantification of the influence a brain region has across the rest of the network. Using these techniques, we identified a subnetwork of reduced connectivity in Parkinson's disease hallucinations. We then used the Allen Institute for Brain Sciences human transcriptome atlas to identify regional gene expression patterns associated with affected areas of the network. Within this network, Parkinson's disease patients with hallucinations showed reduced controllability (less influence over other brain regions), than Parkinson's disease patients without hallucinations and controls. This subnetwork appears to be critical for overall brain integration, as even in controls, nodes with high controllability were more likely to be within the subnetwork. Gene expression analysis of gene modules related to the affected subnetwork revealed that down-weighted genes were most significantly enriched in genes related to mRNA and chromosome metabolic processes (with enrichment in oligodendrocytes) and upweighted genes to protein localization (with enrichment in neuronal cells). Our findings provide insights into how hallucinations are generated, with breakdown of a key structural subnetwork that exerts control across distributed brain regions. Expression of genes related to mRNA metabolism and membrane localization may be implicated, providing potential therapeutic targets.

Dementia risk in Parkinson's disease is associated with interhemispheric connectivity loss and determined by regional gene expression

Parkinson's dementia is a common and devastating part of Parkinson's disease. Whilst timing and severity vary, dementia in Parkinson's is often preceded by visual dysfunction. White matter changes, representing axonal loss, occur early in the disease process. Clarifying which white matter connections are affected in Parkinson's with visual dysfunction and why specific connections are vulnerable will provide important mechanistic insights. Here, we use diffusion tractography in 100 Parkinson's patients (33 low visual performers) and 34 controls to identify patterns of connectivity loss in Parkinson's with visual dysfunction. We examine the relationship between regional transcription and connectivity loss, using the Allen Institute for Brain Science transcriptome atlas. We show that interhemispheric connections are preferentially affected in Parkinson's low visual performers. Interhemispheric connection loss was associated with downweighted genes related to the smoothened signalling pathway (enriched in glutamatergic neurons) and upweighted metabolic genes. Risk genes for Parkinson's but not Alzheimer's or Dementia with Lewy bodies were over-represented in upweighted genes associated with interhemispheric connection loss. Our findings suggest selective vulnerability in Parkinson's patients at highest risk of dementia (those with visual dysfunction), where differences in gene expression and metabolic dysfunction, affecting longer connections with higher metabolic burden, drive connectivity loss.

Regional transcriptional architecture of Parkinson's disease pathogenesis and network spread

Although a significant genetic contribution to the risk of developing sporadic Parkinson's disease has been well described, the relationship between local genetic factors, pathogenesis, and subsequent spread of pathology throughout the brain has been largely unexplained in humans. To address this question, we use network diffusion modelling to infer probable pathology seed regions and patterns of disease spread from MRI atrophy maps derived from 232 de novo subjects in the Parkinson's Progression Markers Initiative study. Allen Brain Atlas regional transcriptional profiles of 67 Parkinson's disease risk factor genes were mapped to the inferred seed regions to determine the local influence of genetic risk factors. We used hierarchical clustering and L1 regularized regression analysis to show that transcriptional profiles of immune-related and lysosomal risk factor genes predict seed region location and the pattern of disease propagation from the most likely seed region, substantia nigra. By leveraging recent advances in transcriptomics, we show that regional microglial abundance quantified by high fidelity gene expression also predicts seed region location. These findings suggest that early disease sites are genetically susceptible to dysfunctional lysosomal α-synuclein processing and microglia-mediated neuroinflammation, which may initiate the disease process and contribute to spread of pathology along neural connectivity pathways.

Origins of atrophy in Parkinson linked to early onset and local transcription patterns

There is enormous clinical value in inferring the brain regions initially atrophied in Parkinson disease for individual patients and understanding its relationship with clinical and genetic risk factors. The aim of this study is to leverage a new seed-inference algorithm demonstrated for Alzheimer's disease to the Parkinsonian context and to cluster patients in meaningful subgroups based on these incipient atrophy patterns. Instead of testing brain regions separately as the likely initiation site for each patient, we solve an L1-penalized optimization problem that can return a more predictive heterogeneous, multi-locus seed patterns. A cluster analysis of the individual seed patterns reveals two distinct subgroups (S1 versus S2). The S1 subgroup is characterized by the involvement of the brainstem and ventral nuclei, and S2 by cortex and striatum. Post hoc analysis in features not included in the clustering shows significant differences between subgroups regarding age of onset and local transcriptional patterns of Parkinson-related genes. Top genes associated with regional microglial abundance are strongly associated with subgroup S1 but not with S2. Our results suggest two distinct aetiological mechanisms operative in Parkinson disease. The interplay between immune-related genes, lysosomal genes, microglial abundance and atrophy initiation sites may explain why the age of onset for patients in S1 is on average 4.5 years later than for those in S2. We highlight and compare the most prominently affected brain regions for both subgroups. Altogether, our findings may improve current screening strategies for early Parkinson onsetters.

Lymphocyte-Activation Gene 3 (LAG3) Protein as a Possible Therapeutic Target for Parkinson's Disease: Molecular Mechanisms Connecting Neuroinflammation to α-Synuclein Spreading Pathology

Parkinson’s disease (PD) is the most common neurodegenerative movement disorder without any objective biomarker available to date. Increasing evidence highlights the critical role of neuroinflammation, including T cell responses, and spreading of aggregated α-synuclein in PD progression. Lymphocyte-activation gene 3 (LAG3) belongs to the immunoglobulin (Ig) superfamily expressed by peripheral immune cells, microglia and neurons and plays a key role in T cell regulation. The role of LAG3 has been extensively investigated in several human cancers, whereas until recently, the role of LAG3 in the central nervous system (CNS) has been largely unknown. Accumulating evidence highlights the potential role of LAG3 in PD pathogenesis, mainly by binding to α-synuclein fibrils and affecting its endocytosis and intercellular transmission, which sheds more light on the connection between immune dysregulation and α-synuclein spreading pathology. Serum and cerebrospinal fluid (CSF) soluble LAG3 (sLAG3) levels have been demonstrated to be potentially associated with PD development and clinical phenotype, suggesting that sLAG3 could represent an emerging PD biomarker. Specific single nucleotide polymorphisms (SNPs) of the LAG3 gene have been also related to PD occurrence especially in the female population, enlightening the pathophysiological background of gender-related PD clinical differences. Given also the ongoing clinical trials investigating various LAG3-targeting strategies in human diseases, new opportunities are being developed for PD treatment research. In this review, we discuss recent preclinical and clinical evidence on the role of LAG3 in PD pathogenesis and biomarker potential, aiming to elucidate its underlying molecular mechanisms.

Transcriptomic signatures of brain regional vulnerability to Parkinson's disease

The molecular mechanisms underlying caudal-to-rostral progression of Lewy body pathology in Parkinson's disease remain poorly understood. Here, we identified transcriptomic signatures across brain regions involved in Braak Lewy body stages in non-neurological adults from the Allen Human Brain Atlas. Among the genes that are indicative of regional vulnerability, we found known genetic risk factors for Parkinson's disease: SCARB2, ELOVL7, SH3GL2, SNCA, BAP1, and ZNF184. Results were confirmed in two datasets of non-neurological subjects, while in two datasets of Parkinson's disease patients we found altered expression patterns. Co-expression analysis across vulnerable regions identified a module enriched for genes associated with dopamine synthesis and microglia, and another module related to the immune system, blood-oxygen transport, and endothelial cells. Both were highly expressed in regions involved in the preclinical stages of the disease. Finally, alterations in genes underlying these region-specific functions may contribute to the selective regional vulnerability in Parkinson's disease brains.

Association of LAG3 genetic variation with an increased risk of PD in Chinese female population

BACKGROUND

Emerging evidence suggests that α-synuclein (α-syn) aggregation and intercellular transmission contributes to pathogenesis of Parkinson's disease (PD) and the toxic fibrillary α-syn binds lymphocyte-activation gene 3 (LAG3) receptor that mediates α-syn transmission. The deletion of LAG3 in animal models was shown to limit α-syn spreading and alleviate the pathological changes of dopaminergic neurons and animal behavioral deficits induced by α-syn aggregation. However, little is known about the genetic association of LAG3 variation with human PD development.

OBJECTIVE

Here we investigated LAG3 single nucleotide polymorphisms (SNPs) and examined the levels of soluble LAG3 (sLAG3) of CSF and serum from Chinese PD patients.

METHODS

We enrolled 646 PD patients and 536 healthy controls to conduct a case-control study. All the participants were genotyped using Sequenom iPLEX Assay and the partial cerebrospinal fluid (CSF) and serum samples were assessed by Meso Scale Discovery electrochemiluminescence (MSD-ECL) immunoassay to measure sLAG3 concentration.

RESULTS

As a result, distributions of rs1922452-AA (1.975, 95% confidence interval (CI) 1.311-2.888, p = 0.001) and rs951818-CC (OR = 2.03, 95% CI 1.369-3.010, p = 0.001) genotype frequencies were found higher in the female PD patients than controls, respectively, and a strong linkage disequilibrium (LD) was calculated on the variants. The level of sLAG3 in CSF of PD patients was found to significantly differ from that of controls (51.56 ± 15.05 pg/ml vs 88.49 ± 62.96 pg/ml, p < 0.0001). Meanwhile, the concentration of α-synuclein in CSF of patients was significantly lower than that of controls (939.9 ± 2900 pg/ml vs 2476 ± 4403 pg/ml, p < 0.0001) and the level of sLAG3 was detected to be positive correlation with that of α-synuclein in the control group (r = 0.597, p = 0.0042), but not in PD group (r = 0.111, p = 0.408).

CONCLUSION

In summary, our data suggested that LAG3 SNPs increase the PD risk of Chinese female population and the sLAG3 may be a potential biomarker predicted for PD development.

α-synuclein accumulation in SH-SY5Y cell impairs autophagy in microglia by exosomes overloading miR-19a-3p

Aims: To reveal whether miRNAs in exosomes from α-synuclein transgenic SH-SY5Y cells are able to regulate autophagy in recipient microglia. Materials & methods: Microarray analysis and experimental verification were adopted to assess the significance of autophagy-associated miRNAs in exosomes from neuronal model of α-synucleinopathies. Results: We found that miR-19a-3p increased remarkably in the exosomes from α-synuclein gene transgenic SH-SY5Y cells. Further study inferred that α-synuclein gene transgenic SH-SY5Y cell-derived exosomes and miR-19a-3p mimic consistently inhibited the expression of phosphatase and tensin homolog and increased the phosphorylation of AKT and mTOR, both of which ultimately lead to the dysfunction of autophagy in recipient microglia. Conclusion: The data suggested that enhanced expression of miR-19a-3p in exosomes suppress autophagy in recipient microglia by targeting the phosphatase and tensin homolog/AKT/mTOR signaling pathway.

Neural connectivity predicts spreading of alpha-synuclein pathology in fibril-injected mouse models: Involvement of retrograde and anterograde axonal propagation

In Parkinson’s disease, some of the first alpha-synuclein aggregates appear in the olfactory system and the dorsal motor nucleus of the vagus nerve before spreading to connected brain regions. We previously demonstrated that injection of alpha-synuclein fibrils unilaterally into the olfactory bulb of wild type mice leads to widespread synucleinopathy in brain regions directly and indirectly connected to the injection site, consistently, over the course of periods longer than 6 months. Our previously reported observations support the idea that alpha-synuclein inclusions propagates between brain region through neuronal networks. In the present study, we further defined the pattern of propagation of alpha-synuclein inclusions and developed a mathematical model based on known mouse brain connectivity. Using this model, we first predicted the pattern of alpha-synuclein inclusions propagation following an injection of fibrils into the olfactory bulb. We then analyzed the fitting of these predictions to our published histological data. Our results demonstrate that the pattern of propagation we observed in vivo is consistent with axonal transport of alpha-synuclein aggregate seeds, followed by transsynaptic transmission. By contrast, simple diffusion of alpha-synuclein fits very poorly our in vivo data. We also found that the spread of alpha-synuclein inclusions appeared to primarily follow neural connections retrogradely until 9 months after injection into the olfactory bulb. Thereafter, the pattern of spreading was consistent with anterograde propagation mathematical models. Finally, we applied our mathematical model to a different, previously published, dataset involving alpha-synuclein fibril injections into the striatum, instead of the olfactory bulb. We found that the mathematical model accurately predicts the reported progressive increase in alpha-synuclein neuropathology also in that paradigm. In conclusion, our findings support that the progressive spread of alpha-synuclein inclusions after injection of protein fibrils follows neural networks in the mouse connectome.

Connectomics and molecular imaging in neurodegeneration

Our understanding on human neurodegenerative disease was previously limited to clinical data and inferences about the underlying pathology based on histopathological examination. Animal models and in vitro experiments have provided evidence for a cell-autonomous and a non-cell-autonomous mechanism for the accumulation of neuropathology. Combining modern neuroimaging tools to identify distinct neural networks (connectomics) with target-specific positron emission tomography (PET) tracers is an emerging and vibrant field of research with the potential to examine the contributions of cell-autonomous and non-cell-autonomous mechanisms to the spread of pathology. The evidence provided here suggests that both cell-autonomous and non-cell-autonomous processes relate to the observed in vivo characteristics of protein pathology and neurodegeneration across the disease spectrum. We propose a synergistic model of cell-autonomous and non-cell-autonomous accounts that integrates the most critical factors (i.e., protein strain, susceptible cell feature and connectome) contributing to the development of neuronal dysfunction and in turn produces the observed clinical phenotypes. We believe that a timely and longitudinal pursuit of such research programs will greatly advance our understanding of the complex mechanisms driving human neurodegenerative diseases.

Predictive model of spread of Parkinson's pathology using network diffusion

Growing evidence suggests that a "prion-like" mechanism underlies the pathogenesis of many neurodegenerative disorders, including Parkinson's disease (PD). We extend and tailor previously developed quantitative and predictive network diffusion model (NDM) to PD, by specifically modeling the trans-neuronal spread of alpha-synuclein outward from the substantia nigra (SN). The model demonstrated the spatial and temporal patterns of PD from neuropathological and neuroimaging studies and was statistically validated using MRI deformation of 232 Parkinson's patients. After repeated seeding simulations, the SN was found to be the most likely seed region, supporting its unique lynchpin role in Parkinson's pathology spread. Other alternative spread models were also evaluated for comparison, specifically, random spread and distance-based spread; the latter tests for Braak's original caudorostral transmission theory. We showed that the distance-based spread model is not as well supported as the connectivity-based model. Intriguingly, the temporal sequencing of affected regions predicted by the model was in close agreement with Braak stages III-VI, providing what we consider a "computational Braak" staging system. Finally, we investigated whether the regional expression patterns of implicated genes contribute to regional atrophy. Despite robust evidence for genetic factors in PD pathogenesis, NDM outperformed regional genetic expression predictors, suggesting that network processes are far stronger mediators of regional vulnerability than innate or cell-autonomous factors. This is the first finding yet of the ramification of prion-like pathology propagation in Parkinson's, as gleaned from in vivo human imaging data. The NDM is potentially a promising robust and clinically useful tool for diagnosis, prognosis and staging of PD.