Expression Levels of an Alpha-Synuclein Transcript in Blood May Distinguish between Early Dementia with Lewy Bodies and Parkinson's Disease

Exploring the Regulatory Landscape of Dementia: Insights from Non-Coding RNAs

Dementia, a multifaceted neurological syndrome characterized by cognitive decline, poses significant challenges to daily functioning. The main causes of dementia, including Alzheimer's disease (AD), frontotemporal dementia (FTD), Lewy body dementia (LBD), and vascular dementia (VD), have different symptoms and etiologies. Genetic regulators, specifically non-coding RNAs (ncRNAs) such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), are known to play important roles in dementia pathogenesis. MiRNAs, small non-coding RNAs, regulate gene expression by binding to the 3' untranslated regions of target messenger RNAs (mRNAs), while lncRNAs and circRNAs act as molecular sponges for miRNAs, thereby regulating gene expression. The emerging concept of competing endogenous RNA (ceRNA) interactions, involving lncRNAs and circRNAs as competitors for miRNA binding, has gained attention as potential biomarkers and therapeutic targets in dementia-related disorders. This review explores the regulatory roles of ncRNAs, particularly miRNAs, and the intricate dynamics of ceRNA interactions, providing insights into dementia pathogenesis and potential therapeutic avenues.

Dementia with Lewy Bodies: Genomics, Transcriptomics, and Its Future with Data Science

Dementia with Lewy bodies (DLB) is a significant public health issue. It is the second most common neurodegenerative dementia and presents with severe neuropsychiatric symptoms. Genomic and transcriptomic analyses have provided some insight into disease pathology. Variants within SNCA, GBA, APOE, SNCB, and MAPT have been shown to be associated with DLB in repeated genomic studies. Transcriptomic analysis, conducted predominantly on candidate genes, has identified signatures of synuclein aggregation, protein degradation, amyloid deposition, neuroinflammation, mitochondrial dysfunction, and the upregulation of heat-shock proteins in DLB. Yet, the understanding of DLB molecular pathology is incomplete. This precipitates the current clinical position whereby there are no available disease-modifying treatments or blood-based diagnostic biomarkers. Data science methods have the potential to improve disease understanding, optimising therapeutic intervention and drug development, to reduce disease burden. Genomic prediction will facilitate the early identification of cases and the timely application of future disease-modifying treatments. Transcript-level analyses across the entire transcriptome and machine learning analysis of multi-omic data will uncover novel signatures that may provide clues to DLB pathology and improve drug development. This review will discuss the current genomic and transcriptomic understanding of DLB, highlight gaps in the literature, and describe data science methods that may advance the field.

Peripheral α-synuclein isoforms are potential biomarkers for diagnosis and prognosis of isolated REM sleep behavior disorder

INTRODUCTION

Isolated REM sleep behavior disorder (IRBD) represents an early manifestation of the synucleinopathies Parkinson's disease (PD) and dementia with Lewy bodies (DLB). Aggregation of abnormal α-synuclein and its increased expression in the brain is crucial in the development of the synucleinopathies. Whereas α-synuclein gene (SNCA) transcripts are overexpressed in brain, a concomitant reduction occurs in blood of DLB patients. We assessed whether this decrease is also detectable in IRBD.

METHODS

108 IRBD patients and 149 controls were included of which 29 IRBD and 32 control cases were available for expression studies. Expression of SNCAtv1, SNCAtv2, SNCAtv3 and SNCA126 isoforms, and GBA were determined by real-time PCR. Genotype distribution of SNCA SNPs, rs356219 and rs2736990, and correlation with SNCA expression was analyzed.

RESULTS

Expression of all SNCA transcripts was reduced in IRBD blood whereas GBA expression did not change. SNCAtv3 expression correlated inversely with IRBD duration, being lower in patients with longer follow-up. Rs356219-AA genotype frequency was increased in IRBD patients who later developed PD and DLB. Rs2736990-CC frequency was increased among IRBD cases who remained disease-free. No correlation was observed between rs356219 and rs2736990 genotypes and SNCA transcript levels.

CONCLUSION

SNCA transcript expression is decreased in blood in IRBD, and levels decrease with IRBD duration. Our findings indicate that changes in SNCA expression occur in the earliest stages of the synucleinopathies before motor and cognitive symptoms become apparent.

Gene Expression Profiling as a Novel Diagnostic Tool for Neurodegenerative Disorders

There is a lack of effective diagnostic biomarkers for neurodegenerative disorders (NDDs). Here, we established gene expression profiles for diagnosing Alzheimer’s disease (AD), Parkinson’s disease (PD), and vascular (VaD)/mixed dementia. Patients with AD had decreased APOE, PSEN1, and ABCA7 mRNA expression. Subjects with VaD/mixed dementia had 98% higher PICALM mRNA levels, but 75% lower ABCA7 mRNA expression than healthy individuals. Patients with PD and PD-related disorders showed increased SNCA mRNA levels. There were no differences in mRNA expression for OPRK1, NTRK2, and LRRK2 between healthy subjects and NDD patients. APOE mRNA expression had high diagnostic accuracy for AD, and moderate accuracy for PD and VaD/mixed dementia. PSEN1 mRNA expression showed promising accuracy for AD. PICALM mRNA expression was less accurate as a biomarker for AD. ABCA7 and SNCA mRNA expression showed high-to-excellent diagnostic accuracy for AD and PD, and moderate-to-high accuracy for VaD/mixed dementia. The APOE E4 allele reduced APOE expression in patients with different APOE genotypes. There was no association between PSEN1, PICALM, ABCA7, and SNCA gene polymorphisms and expression. Our study suggests that gene expression analysis has diagnostic value for NDDs and provides a liquid biopsy alternative to current diagnostic methods.

Joint Modeling Study Identifies Blood-Based Transcripts Link to Cognitive Decline in Parkinson's Disease

BACKGROUND

Cognitive decline in Parkinson's disease (PD) is prevalent, insidious, and burdensome during the progression of the disease.

OBJECTIVES

We aimed to find transcriptome-wide biomarkers in blood to predict cognitive decline and identify patients at high risk with cognitive impairment in PD.

METHODS

We carried out joint modeling analysis to characterize transcriptome-wide longitudinal gene expression and its association with the progression of mild cognitive impairment (MCI) in PD patients. The average time-dependent area under the curves (AUCs) were used for evaluating the accuracy of the significant joint models. A cognitive survival score (CogSs) derived from joint model was leveraged to predict the occurrence of MCI. All predicting models were built in a discovery cohort with 272 patients and replicated in an independent cohort with 177 patients.

RESULTS

We identified five longitudinal varied expression of transcripts that were significantly associated with MCI progression in patients with PD. The most significant transcript IGLC1 joint model accurately predicted the progression of MCI in PD patients in the discovery and replication cohorts (average time-dependent AUCs >0.82). The CogSs derived from the optimal IGLC1 joint model had a high accuracy at early study stage in both cohorts (AUC ≥0.91).

CONCLUSIONS

Our transcriptome-wide joint modeling analysis uncovered five blood-based transcripts related to cognitive decline in PD. The joint models will serve as a useful resource for clinicians and researchers to screen PD patients with high risk of development of cognitive impairment and pave the path for Parkinson's personalized medicine. © 2022 International Parkinson and Movement Disorder Society.

The Impact of SNCA Variations and Its Product Alpha-Synuclein on Non-Motor Features of Parkinson's Disease

Parkinson’s disease (PD) is a common and progressive neurodegenerative disease, caused by the loss of dopaminergic neurons in the substantia nigra pars compacta in the midbrain, which is clinically characterized by a constellation of motor and non-motor manifestations. The latter include hyposmia, constipation, depression, pain and, in later stages, cognitive decline and dysautonomia. The main pathological features of PD are neuronal loss and consequent accumulation of Lewy bodies (LB) in the surviving neurons. Alpha-synuclein (α-syn) is the main component of LB, and α-syn aggregation and accumulation perpetuate neuronal degeneration. Mutations in the α-syn gene (SNCA) were the first genetic cause of PD to be identified. Generally, patients carrying SNCA mutations present early-onset parkinsonism with severe and early non-motor symptoms, including cognitive decline. Several SNCA polymorphisms were also identified, and some of them showed association with non-motor manifestations. The functional role of these polymorphisms is only partially understood. In this review we explore the contribution of SNCA and its product, α-syn, in predisposing to the non-motor manifestations of PD.

Genetic Architecture and Molecular, Imaging and Prodromic Markers in Dementia with Lewy Bodies: State of the Art, Opportunities and Challenges

Dementia with Lewy bodies (DLB) is one of the most common causes of dementia and belongs to the group of α-synucleinopathies. Due to its clinical overlap with other neurodegenerative disorders and its high clinical heterogeneity, the clinical differential diagnosis of DLB from other similar disorders is often difficult and it is frequently underdiagnosed. Moreover, its genetic etiology has been studied only recently due to the unavailability of large cohorts with a certain diagnosis and shows genetic heterogeneity with a rare contribution of pathogenic mutations and relatively common risk factors. The rapid increase in the reported cases of DLB highlights the need for an easy, efficient and accurate diagnosis of the disease in its initial stages in order to halt or delay the progression. The currently used diagnostic methods proposed by the International DLB consortium rely on a list of criteria that comprises both clinical observations and the use of biomarkers. Herein, we summarize the up-to-now reported knowledge on the genetic architecture of DLB and discuss the use of prodromal biomarkers as well as recent promising candidates from alternative body fluids and new imaging techniques.