RNA editing is a posttranscriptional mechanism that targets changes in RNA transcripts to modulate innate immune responses. We report the role of astrocyte-specific, ADAR1-mediated RNA editing in neuroinflammation in Parkinson's disease (PD). We generated human induced pluripotent stem cell-derived astrocytes, neurons and cocultures and exposed them to small soluble alpha-synuclein aggregates. Oligomeric alpha-synuclein triggered an inflammatory glial state associated with Toll-like receptor activation, viral responses, and cytokine secretion. This reactive state resulted in loss of neurosupportive functions and the induction of neuronal toxicity. Notably, interferon response pathways were activated leading to up-regulation and isoform switching of the RNA deaminase enzyme, ADAR1. ADAR1 mediates A-to-I RNA editing, and increases in RNA editing were observed in inflammatory pathways in cells, as well as in postmortem human PD brain. Aberrant, or dysregulated, ADAR1 responses and RNA editing may lead to sustained inflammatory reactive states in astrocytes triggered by alpha-synuclein aggregation, and this may drive the neuroinflammatory cascade in Parkinson's.

Parkinson's disease (PD) affects millions of people worldwide, and up to 40% of these patients develop dementia, profoundly affecting their quality of life. Whether Parkinson's disease dementia (PDD) simply represents a late stage of PD or constitutes a distinct neurodegenerative process remains unresolved. To clarify this, we generated the largest single nuclear transcriptomic atlas of PD and PDD to date - almost one million nuclei derived from the anterior cingulate cortex and inferior parietal lobule of 64 post-mortem donors. By integrating these data with long-read RNA-seq, we found that the cellular compositions, biological pathways, and molecular profiles diverge substantially between PD and PDD, with minimal overlap in differentially expressed genes and pathways. While PD was characterised by widespread upregulation of gene expression programs and robust regional signatures, PDD showed extensive pathway downregulation, loss of cortical regional identity, and significant shifts in transcript usage, including alterations in APP isoforms that may influence pathological amyloid beta accumulation. These findings reveal that PD and PDD represent fundamentally distinct disease states, offering important insights for understanding their underlying mechanisms and will guide the development of targeted therapies and more effective clinical trials. ### Competing Interest Statement The authors have declared no competing interest.

Neuronal insulin resistance is linked to the pathogenesis of Parkinson’s disease through unclear, but potentially targetable, mechanisms. We delineated neuronal and glial mechanisms of insulin resistance and glucagon-like 1 peptide (GLP-1) receptor agonism in human iPSC models of synucleinopathy, and corroborated our findings in patient samples from a Phase 2 trial of a GLP-1R agonist in Parkinson’s ([NCT01971242][1]). Human iPSC models of synucleinopathy exhibit neuronal insulin resistance and dysfunctional insulin signalling, which is associated with inhibition of the neuroprotective Akt pathways, and increased expression of the MAPK-associated p38 and JNK stress pathways. Ultimately, this imbalance is associated with cellular stress, impaired proteostasis, accumulation of α-synuclein, and neuronal loss. The GLP-1R agonist exenatide led to restoration of insulin signalling, associated with restoration of Akt signalling and suppression of the MAPK pathways in neurons. GLP-1R agonism reverses the neuronal toxicity associated with the synucleinopathy, through reduction of oxidative stress, improved mitochondrial and lysosomal function, reduced aggregation of α-synuclein, and enhanced neuronal viability. GLP-1R agonism further suppresses synuclein induced inflammatory states in glia, leading to neuroprotection through non cell autonomous effects. In the exenatide-PD2 clinical trial, exenatide treatment was associated with clinical improvement in individuals with higher baseline MAPK expression (and thus insulin resistance). Exenatide treatment led to a reduction of α-synuclein aggregates, and a reduction in inflammatory cytokine IL-6. Taken together, our patient platform defines the mechanisms of GLP-1R action in neurons and astrocytes, identifies the population likely to benefit from GLP-1R agonism, and highlights the utility of GLP-1R agonism as a disease modifying strategy in synucleinopathies.### Competing Interest StatementThe authors have declared no competing interest. [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT01971242&atom=%2Fbiorxiv%2Fearly%2F2024%2F02%2F28%2F2024.02.28.582460.atom

Summary The role of the SNCA gene locus in driving Parkinson’s disease (PD) through rare and common genetic variation is well-recognized, but the transcriptional diversity of SNCA in vulnerable cell types remains unclear. We performed SNCA long-read RNA sequencing in human dopaminergic neurons and show that annotated SNCA transcripts account for only 5% of expression. Rather, the majority of expression (75%) at the SNCA locus originates from transcripts with alternative 5’ and 3’ untranslated regions. Importantly, 10% originates from transcripts encoding open reading frames not previously annotated, which are translated and detectable in human postmortem brain. Defining the 3’ untranslated regions enabled the rational design of antisense oligonucleotides targeting the majority of SNCA transcripts, leading to the effective reversal of PD pathology, including protein aggregation, mitochondrial dysfunction, and toxicity. Resolving the complexity of the SNCA transcriptional landscape impacts RNA therapies and highlights differences in protein isoforms and their contribution to disease.

Parkinson’s disease is a common, incurable neurodegenerative disorder that is clinically heterogeneous: it is likely that different cellular mechanisms drive the pathology in different individuals. So far it has not been possible to define the cellular mechanism underlying the neurodegenerative disease in life. We generated a machine learning-based model that can simultaneously predict the presence of disease and its primary mechanistic subtype in human neurons. We used stem cell technology to derive control or patient-derived neurons, and generated different disease subtypes through chemical induction or the presence of mutation. Multidimensional fluorescent labelling of organelles was performed in healthy control neurons and in four different disease subtypes, and both the quantitative single-cell fluorescence features and the images were used to independently train a series of classifiers to build deep neural networks. Quantitative cellular profile-based classifiers achieve an accuracy of 82%, whereas image-based deep neural networks predict control and four distinct disease subtypes with an accuracy of 95%. The machine learning-trained classifiers achieve their accuracy across all subtypes, using the organellar features of the mitochondria with the additional contribution of the lysosomes, confirming the biological importance of these pathways in Parkinson’s. Altogether, we show that machine learning approaches applied to patient-derived cells are highly accurate at predicting disease subtypes, providing proof of concept that this approach may enable mechanistic stratification and precision medicine approaches in the future.

Mutations or multiplications of the SNCA (Synuclein Alpha) gene cause rare autosomal dominant Parkinson’s disease (PD). The SNCA G51D missense mutation is associated with a synucleinopathy that shares PD and multiple system atrophy (MSA) characteristics. We generated induced pluripotent stem cell (iPSC) lines from two individuals with SNCA G51D missense mutations at risk of PD. Dermal fibroblasts were reprogrammed to pluripotency using a non-integrating mRNA-based protocol. The resulting human iPSCs displayed normal morphology, expressed markers associated with pluripotency, and differentiated into the three germ layers. The iPSC lines could facilitate disease-modelling and therapy development studies for synucleinopathies.

ABSTRACT Parkinson’s disease (PD) is a common, devastating, and incurable neurodegenerative disorder. Several molecular mechanisms have been proposed to drive PD, with genetic and pathological evidence pointing towards aberrant protein homeostasis and mitochondrial dysfunction. PD is clinically highly heterogeneous, it is likely that different mechanisms underlie the pathology in different individuals, each requiring a specific targeted treatment. Recent advances in stem cell technology and fluorescent live-cell imaging have enabled the generation of patient-derived neurons with different mechanistic subtypes of PD. Here, we performed multi-dimensional fluorescent labelling of organelles in iPSC-derived neurons, in healthy control cells, and in four different disease subclasses. We generated a machine learning-based model that can simultaneously predict the presence of disease, and its primary mechanistic subtype. We independently trained a series of classifiers using both quantitative single-cell fluorescence variables and images to build deep neural networks. Quantitative cellular profile-based classifiers achieve an accuracy of 82%, whilst image based deep neural networks predict control, and four distinct disease subtypes with an accuracy of 95%. The classifiers achieve their accuracy across all subtypes primarily utilizing the organellar features of the mitochondria, with additional contribution of the lysosomes, confirming their biological importance in PD. Taken together, we show that machine learning approaches applied to patient-derived cells are able to predict disease subtypes, demonstrating that this approach may be used to guide personalized treatment approaches in the future.

BACKGROUND:The molecular drivers of early sporadic Parkinson's disease (PD) remain unclear, and the presence of widespread end stage pathology in late disease masks the distinction between primary or causal disease-specific events and late secondary consequences in stressed or dying cells. However, early and mid-stage Parkinson's brains (Braak stages 3 and 4) exhibit alpha-synuclein inclusions and neuronal loss along a regional gradient of severity, from unaffected-mild-moderate-severe. Here, we exploited this spatial pathological gradient to investigate the molecular drivers of sporadic PD.METHODS:We combined high precision tissue sampling with unbiased large-scale profiling of protein expression across 9 brain regions in Braak stage 3 and 4 PD brains, and controls, and verified these results using targeted proteomic and functional analyses.RESULTS:We demonstrate that the spatio-temporal pathology gradient in early-mid PD brains is mirrored by a biochemical gradient of a changing proteome. Importantly, we identify two key events that occur early in the disease, prior to the occurrence of alpha-synuclein inclusions and neuronal loss: (i) a metabolic switch in the utilisation of energy substrates and energy production in the brain, and (ii) perturbation of the mitochondrial redox state. These changes may contribute to the regional vulnerability of developing alpha-synuclein pathology. Later in the disease, mitochondrial function is affected more severely, whilst mitochondrial metabolism, fatty acid oxidation, and mitochondrial respiration are affected across all brain regions.CONCLUSIONS:Our study provides an in-depth regional profile of the proteome at different stages of PD, and highlights that mitochondrial dysfunction is detectable prior to neuronal loss, and alpha-synuclein fibril deposition, suggesting that mitochondrial dysfunction is one of the key drivers of early disease.

Mutations in the SNCA gene cause autosomal dominant Parkinson’s disease (PD), with progressive loss of dopaminergic neurons in the substantia nigra, and accumulation of aggregates of α-synuclein. However, the sequence of molecular events that proceed from the SNCA mutation during development, to its end stage pathology is unknown. Utilising human induced pluripotent stem cells (hiPSCs) with SNCA mutations, we resolved the temporal sequence of pathophysiological events that occur during neuronal differentiation in order to discover the early, and likely causative, events in synucleinopathies. We adapted a small molecule-based protocol that generates highly enriched midbrain dopaminergic (mDA) neurons (>80%). We characterised their molecular identity using single-cell RNA sequencing and their functional identity through the synthesis and secretion of dopamine, the ability to generate action potentials, and form functional synapses and networks. RNA velocity analyses confirmed the developmental transcriptomic trajectory of midbrain neural precursors into mDA neurons using our approach, and identified key driver genes in mDA neuronal development. To characterise the synucleinopathy, we adopted super-resolution methods to determine the number, size and structure of aggregates in SNCA -mutant mDA neurons. At one week of differentiation, prior to maturation to mDA neurons of molecular and functional identity, we demonstrate the formation of small aggregates; specifically, β-sheet rich oligomeric aggregates, in SNCA -mutant midbrain immature neurons. The aggregation progresses over time to accumulate phosphorylated aggregates, and later fibrillar aggregates. When the midbrain neurons were functional, we observed evidence of impaired physiological calcium signalling, with raised basal calcium, and impairments in cytosolic and mitochondrial calcium efflux. Once midbrain identity fully developed, SNCA -mutant neurons exhibited bioenergetic impairments, mitochondrial dysfunction and oxidative stress. During the maturation of mDA neurons, upregulation of mitophagy and autophagy occured, and ultimately these multiple cellular stresses lead to an increase in cell death by six weeks post-differentiation. Our differentiation paradigm generates an efficient model for studying disease mechanisms in PD, and highlights that protein misfolding to generate intraneuronal oligomers is one of the earliest critical events driving disease in human neurons, rather than a late-stage hallmark of the disease.

Mutations in the SNCA gene cause autosomal dominant Parkinson’s disease (PD), with loss of dopaminergic neurons in the substantia nigra, and aggregation of α-synuclein. The sequence of molecular events that proceed from an SNCA mutation during development, to end-stage pathology is unknown. Utilising human-induced pluripotent stem cells (hiPSCs), we resolved the temporal sequence of SNCA- induced pathophysiological events in order to discover early, and likely causative, events. Our small molecule-based protocol generates highly enriched midbrain dopaminergic (mDA) neurons: molecular identity was confirmed using single-cell RNA sequencing and proteomics, and functional identity was established through dopamine synthesis, and measures of electrophysiological activity. At the earliest stage of differentiation, prior to maturation to mDA neurons, we demonstrate the formation of small β-sheet-rich oligomeric aggregates, in SNCA -mutant cultures. Aggregation persists and progresses, ultimately resulting in the accumulation of phosphorylated α-synuclein aggregates. Impaired intracellular calcium signalling, increased basal calcium, and impairments in mitochondrial calcium handling occurred early at day 34–41 post differentiation. Once midbrain identity fully developed, at day 48–62 post differentiation, SNCA -mutant neurons exhibited mitochondrial dysfunction, oxidative stress, lysosomal swelling and increased autophagy. Ultimately these multiple cellular stresses lead to abnormal excitability, altered neuronal activity, and cell death. Our differentiation paradigm generates an efficient model for studying disease mechanisms in PD and highlights that protein misfolding to generate intraneuronal oligomers is one of the earliest critical events driving disease in human neurons, rather than a late-stage hallmark of the disease.

Aggregation of alpha-synuclein (α-Syn) drives Parkinson’s disease (PD), although the initial stages of self-assembly and structural conversion have not been directly observed inside neurons. In this study, we tracked the intracellular conformational states of α-Syn using a single-molecule Förster resonance energy transfer (smFRET) biosensor, and we show here that α-Syn converts from a monomeric state into two distinct oligomeric states in neurons in a concentration-dependent and sequence-specific manner. Three-dimensional FRET-correlative light and electron microscopy (FRET-CLEM) revealed that intracellular seeding events occur preferentially on membrane surfaces, especially at mitochondrial membranes. The mitochondrial lipid cardiolipin triggers rapid oligomerization of A53T α-Syn, and cardiolipin is sequestered within aggregating lipid–protein complexes. Mitochondrial aggregates impair complex I activity and increase mitochondrial reactive oxygen species (ROS) generation, which accelerates the oligomerization of A53T α-Syn and causes permeabilization of mitochondrial membranes and cell death. These processes were also observed in induced pluripotent stem cell (iPSC)–derived neurons harboring A53T mutations from patients with PD. Our study highlights a mechanism of de novo α-Syn oligomerization at mitochondrial membranes and subsequent neuronal toxicity.

ABSTRACT The human genome contains numerous duplicated regions, such as parent-pseudogene pairs, causing sequencing reads to align equally well to either gene. The extent to which this ambiguity complicates transcriptomic analyses is currently unknown. This is concerning as many parent genes have been linked to disease, including GBA1, causally linked to both Parkinson’s and Gaucher disease. We find that most of the short sequencing reads that map to GBA1 , also map to its pseudogene, GBAP1 . Using long-read RNA-sequencing in human brain, where all reads mapped uniquely, we demonstrate significant differences in expression compared to short-read data. We identify novel transcripts from both GBA1 and GBAP1 , including protein-coding transcripts that are translated in vitro and detected in proteomic data, but that lack GCase activity. By combining long-read with single-nuclear RNA-sequencing to analyse brain-relevant cell types we demonstrate that transcript expression varies by brain region with cell-type-selectivity. Taken together, these results suggest a non-lysosomal function for both GBA1 and GBAP1 in brain. Finally, we demonstrate that inaccuracies in annotation are widespread among parent genes, with implications for many human diseases.

ABSTRACT Background Type 2 diabetes (T2DM) is an established risk factor for developing Parkinson's disease (PD), but its effect on disease progression is not well understood. Objective The aim of this study was to investigate the influence of T2DM on aspects of disease progression in PD. Methods We analyzed data from the Tracking Parkinson's study to examine the effects of comorbid T2DM on PD progression and quality of life by comparing symptom severity scores assessing a range of motor and nonmotor symptoms. Results We identified 167 (8.7%) patients with PD and T2DM (PD + T2DM) and 1763 (91.3%) patients with PD without T2DM (PD). After controlling for confounders, patients with T2DM had more severe motor symptoms, as assessed by Movement Disorder Society Unified Parkinson's Disease Rating Scale, Part III (25.8 [0.9] vs. 22.5 [0.3] P = 0.002), and nonmotor symptoms, as assessed by Non‐Motor Symptoms Scale total (38.4 [2.5] vs. 31.8 [0.7] P < 0.001), and were significantly more likely to report loss of independence (odds ratio, 2.08; 95% confidence interval [CI]: 1.34–3.25; P = 0.001) and depression (odds ratio, 1.62; CI: 1.10–2.39; P = 0.015). Furthermore, over time, patients with T2DM had significantly faster motor symptom progression ( P = 0.012), developed worse mood symptoms ( P = 0.041), and were more likely to develop substantial gait impairment (hazard ratio, 1.55; CI: 1.07–2.23; P = 0.020) and mild cognitive impairment (hazard ratio, 1.7; CI: 1.24–2.51; P = 0.002) compared with the PD group. Conclusions In the largest study to date, T2DM is associated with faster disease progression in Parkinson's, highlighting an interaction between these two diseases. Because it is a potentially modifiable metabolic state, with multiple peripheral and central targets for intervention, it may represent a target for alleviating parkinsonian symptoms and slowing progression to disability and dementia. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Individual differences in personality are associated with variation in healthy aging. Health behaviours are often cited as the likely explanation for this association; however, an underlying biological mechanism may also exist. Accelerated leukocyte telomere shortening is implicated in multiple age-related diseases and is associated with chronic activation of the hypothalamus–pituitary–adrenal (HPA) axis, providing a link between stress-related personality differences and adverse health outcomes. However, the effects of the HPA axis are tissue specific. Thus, leukocyte telomere length may not accurately reflect telomere length in disease-relevant tissues. Here, we examined the correlation between stress reactivity and telomere length in heart and brain tissue in young (6–9 month) and aging (18 month) zebrafish. Stress reactivity was assessed by tank diving and through gene expression. Telomere length was assessed using quantitative PCR. We show that aging zebrafish have shorter telomeres in both heart and brain. Telomere length was inversely related to stress reactivity in heart but not brain of aging individuals. These data support the hypotheses that an anxious predisposition contributes to accelerated telomere shortening in heart tissue, which may have important implications for our understanding of age-related heart disease, and that stress reactivity contributes to age-related telomere shortening in a tissue-specific manner.

The development of human induced pluripotent stem cells (hiPSC) has greatly aided our ability to model neurodegenerative diseases. However, generation of midbrain dopaminergic (mDA) neurons is a major challenge and protocols are variable. Here, we developed a method to differentiate hiPSCs into enriched populations (>80%) of mDA neurons using only small molecules. We confirmed the identity of the mDA neurons using single-cell RNA-sequencing and detection of classical markers. Single-cell live imaging demonstrated neuronal calcium signalling and functional dopamine transport. Electrophysiology measures highlighted the ability to form synapses and networks in culture. Patient-specific hiPSC lines differentiated to produce functional mDA neurons that exhibit the hallmarks of synucleinopathy including: aggregate formation, oxidative stress as well as mitochondrial dysfunction and impaired lysosomal dynamics. In summary, we establish a robust differentiation paradigm to generate enriched mDA neurons from hiPSCs, which can be used to faithfully model key aspects of Parkinson’s disease (PD), providing the potential to further elucidate molecular mechanisms contributing to disease development.

We derive a sufficient condition by which a multicommodity minimal cost network flow problem can be transformed and solved as an equivalent single-commodity problem. The condition is based on the topological structure of the network and unifies several special results that have been established. The transformation is given, and potential applications are discussed.