Exome Sequencing of Consanguineous Pashtun Families With Familial Epilepsy Reveals Causative and Candidate Variants in TSEN54, MOCS2, and OPHN1.

Next-generation sequencing is advancing in low- and middle-income countries, but accessibility remains limited. In Pakistan, many members of the Pashtun population practice familial marriage and maintain distinct socio-cultural traditions, isolating them from other ethnic groups. As a result, they may harbor genetic variants that could unveil new gene-disease associations. To investigate the genetic basis of epilepsy in the Pashtun community we recently established a collaboration between Bannu University and the University of Tuebingen. Here we report our first results of exome sequencing of four families with presumed monogenetic epilepsy and Mendelian inheritance pattern. In Family #201, we identified distinct disease-causing variants. One had a homozygous pathogenic missense variant in TSEN54 (c.919G > T, p.(Ala307Ser)), linked to Pontocerebellar Hypoplasia Type 2A. The second individual had a homozygous class IV missense variant in MOCS2 (c.226G > A, p.(Gly76Arg)) which is associated with Molybdenum cofactor deficiency. In family EP02, one affected individual carried a heterozygous class III variant in OPHN1 (c.1490G > A, p.(Arg497Gln)), related to syndromic X-linked intellectual disability with epilepsy. Our small study demonstrates the promise of next-generation sequencing in genetic epilepsies among the Pashtun population. Diagnostic next-generation sequencing should be established in Pakistan as soon as possible, and if not feasible, genetic research projects may pioneer this path.

A framework for translating tauopathy therapeutics: Drug discovery to clinical trials.

The tauopathies are defined by pathological tau protein aggregates within a spectrum of clinically heterogeneous neurodegenerative diseases. The primary tauopathies meet the definition of rare diseases in the United States. There is no approved treatment for primary tauopathies. In this context, designing the most efficient development programs to translate promising targets and treatments from preclinical studies to early-phase clinical trials is vital. In September 2022, the Rainwater Charitable Foundation convened an international expert workshop focused on the translation of tauopathy therapeutics through early-phase trials. Our report on the workshop recommends a framework for principled drug development and a companion lexicon to facilitate communication focusing on reproducibility and achieving common elements. Topics include the selection of targets, drugs, biomarkers, participants, and study designs. The maturation of pharmacodynamic biomarkers to demonstrate target engagement and surrogate disease biomarkers is a crucial unmet need. HIGHLIGHTS: Experts provided a framework to translate therapeutics (discovery to clinical trials). Experts focused on the "5 Rights" (target, drug, biomarker, participants, trial). Current research on frontotemporal degeneration, progressive supranuclear palsy, and corticobasal syndrome therapeutics includes 32 trials (37% on biologics) Tau therapeutics are being tested in Alzheimer's disease; primary tauopathies have a large unmet need.

Genomics 2 Proteins portal: a resource and discovery tool for linking genetic screening outputs to protein sequences and structures.

Recent advances in AI-based methods have revolutionized the field of structural biology. Concomitantly, high-throughput sequencing and functional genomics have generated genetic variants at an unprecedented scale. However, efficient tools and resources are needed to link disparate data types-to 'map' variants onto protein structures, to better understand how the variation causes disease, and thereby design therapeutics. Here we present the Genomics 2 Proteins portal ( https://g2p.broadinstitute.org/ ): a human proteome-wide resource that maps 20,076,998 genetic variants onto 42,413 protein sequences and 77,923 structures, with a comprehensive set of structural and functional features. Additionally, the Genomics 2 Proteins portal allows users to interactively upload protein residue-wise annotations (for example, variants and scores) as well as the protein structure beyond databases to establish the connection between genomics to proteins. The portal serves as an easy-to-use discovery tool for researchers and scientists to hypothesize the structure-function relationship between natural or synthetic variations and their molecular phenotypes.

Dopamine Pathway and Parkinson's Risk Variants Are Associated with Levodopa-Induced Dyskinesia.

BACKGROUND: Levodopa-induced dyskinesia (LID) is a common adverse effect of levodopa, one of the main therapeutics used to treat the motor symptoms of Parkinson's disease (PD). Previous evidence suggests a connection between LID and a disruption of the dopaminergic system as well as genes implicated in PD, including GBA1 and LRRK2. OBJECTIVES: Our goal was to investigate the effects of genetic variants on risk and time to LID. METHODS: We performed a genome-wide association study (GWAS) and analyses focused on GBA1 and LRRK2 variants. We also calculated polygenic risk scores (PRS) including risk variants for PD and variants in genes involved in the dopaminergic transmission pathway. To test the influence of genetics on LID risk we used logistic regression, and to examine its impact on time to LID we performed Cox regression including 1612 PD patients with and 3175 without LID. RESULTS: We found that GBA1 variants were associated with LID risk (odds ratio [OR] = 1.65; 95% confidence interval [CI], 1.21-2.26; P = 0.0017) and LRRK2 variants with reduced time to LID onset (hazard ratio [HR] = 1.42; 95% CI, 1.09-1.84; P = 0.0098). The fourth quartile of the PD PRS was associated with increased LID risk (ORfourth_quartile = 1.27; 95% CI, 1.03-1.56; P = 0.0210). The third and fourth dopamine pathway PRS quartiles were associated with a reduced time to development of LID (HRthird_quartile = 1.38; 95% CI, 1.07-1.79; P = 0.0128; HRfourth_quartile = 1.38; 95% CI = 1.06-1.78; P = 0.0147). CONCLUSIONS: This study suggests that variants implicated in PD and in the dopaminergic transmission pathway play a role in the risk/time to develop LID. Further studies will be necessary to examine how these findings can inform clinical care. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Multi-Source Data ETL (Extract, Transform, Load) for a Genetic Epilepsy Diagnosis and Treatment Dashboard.

The growing number of genes identified in relation to epilepsy represents a major breakthrough in diagnosis and treatment, but experts face the challenge of efficiently accessing and consolidating the vast amount of genetic data available. Therefore, we present the process of transforming data from different sources and formats into an Entity-Attribute-Value (EAV) model database. Combined with the use of standard coding systems, this approach will provide a scalable and adaptable database to present the data in a comprehensive way to experts via a dashboard.

Association of Body Mass Index and Parkinson Disease: A Bidirectional Mendelian Randomization Study.

BACKGROUND AND OBJECTIVES: The role of body mass index (BMI) in Parkinson disease (PD) is unclear. Based on the Comprehensive Unbiased Risk Factor Assessment for Genetics and Environment in PD (Courage-PD) consortium, we used 2-sample Mendelian randomization (MR) to replicate a previously reported inverse association of genetically predicted BMI with PD and investigated whether findings were robust in analyses addressing the potential for survival and incidence-prevalence biases. We also examined whether the BMI-PD relation is bidirectional by performing a reverse MR. METHODS: We used summary statistics from a genome-wide association study (GWAS) to extract the association of 501 single-nucleotide polymorphisms (SNPs) with BMI and from the Courage-PD and international Parkinson Disease Genomics Consortium (iPDGC) to estimate their association with PD. Analyses are based on participants of European ancestry. We used the inverse-weighted method to compute odds ratios (ORIVW per 4.8 kg/m2 [95% CI]) of PD and additional pleiotropy robust methods. We performed analyses stratified by age, disease duration, and sex. For reverse MR, we used SNPs associated with PD from 2 iPDGC GWAS to assess the effect of genetic liability toward PD on BMI. RESULTS: Summary statistics for BMI are based on 806,834 participants (54% women). Summary statistics for PD are based on 8,919 (40% women) cases and 7,600 (55% women) controls from Courage-PD, and 19,438 (38% women) cases and 24,388 (51% women) controls from iPDGC. In Courage-PD, we found an inverse association between genetically predicted BMI and PD (ORIVW 0.82 [0.70-0.97], p = 0.012) without evidence for pleiotropy. This association tended to be stronger in younger participants (≤67 years, ORIVW 0.71 [0.55-0.92]) and cases with shorter disease duration (≤7 years, ORIVW 0.75 [0.62-0.91]). In pooled Courage-PD + iPDGC analyses, the association was stronger in women (ORIVW 0.85 [0.74-0.99], p = 0.032) than men (ORIVW 0.92 [0.80-1.04], p = 0.18), but the interaction was not statistically significant (p-interaction = 0.48). In reverse MR, there was evidence for pleiotropy, but pleiotropy robust methods showed a significant inverse association. DISCUSSION: Using an independent data set (Courage-PD), we replicate an inverse association of genetically predicted BMI with PD, not explained by survival or incidence-prevalence biases. Moreover, reverse MR analyses support an inverse association between genetic liability toward PD and BMI, in favor of a bidirectional relation.

Polygenic Risk Scores Validated in Patient-Derived Cells Stratify for Mitochondrial Subtypes of Parkinson's Disease.

OBJECTIVE: The aim of our study is to better understand the genetic architecture and pathological mechanisms underlying neurodegeneration in idiopathic Parkinson's disease (iPD). We hypothesized that a fraction of iPD patients may harbor a combination of common variants in nuclear-encoded mitochondrial genes ultimately resulting in neurodegeneration. METHODS: We used mitochondria-specific polygenic risk scores (mitoPRSs) and created pathway-specific mitoPRSs using genotype data from different iPD case-control datasets worldwide, including the Luxembourg Parkinson's Study (412 iPD patients and 576 healthy controls) and COURAGE-PD cohorts (7,270 iPD cases and 6,819 healthy controls). Cellular models from individuals stratified according to the most significant mitoPRS were subsequently used to characterize different aspects of mitochondrial function. RESULTS: Common variants in genes regulating Oxidative Phosphorylation (OXPHOS-PRS) were significantly associated with a higher PD risk in independent cohorts (Luxembourg Parkinson's Study odds ratio, OR = 1.31[1.14-1.50], p-value = 5.4e-04; COURAGE-PD OR = 1.23[1.18-1.27], p-value = 1.5e-29). Functional analyses in fibroblasts and induced pluripotent stem cells-derived neuronal progenitors revealed significant differences in mitochondrial respiration between iPD patients with high or low OXPHOS-PRS (p-values < 0.05). Clinically, iPD patients with high OXPHOS-PRS have a significantly earlier age at disease onset compared to low-risk patients (false discovery rate [FDR]-adj p-value = 0.015), similar to prototypic monogenic forms of PD. Finally, iPD patients with high OXPHOS-PRS responded more effectively to treatment with mitochondrially active ursodeoxycholic acid. INTERPRETATION: OXPHOS-PRS may provide a precision medicine tool to stratify iPD patients into a pathogenic subgroup genetically defined by specific mitochondrial impairment, making these individuals eligible for future intelligent clinical trial designs. ANN NEUROL 2024;96:133-149.

Distances from ligands as main predictive features for pathogenicity and functional effect of variants in NMDA receptors.

Genetic variants in genes GRIN1 , GRIN2A , GRIN2B , and GRIN2D , which encode subunits of the N-methyl-D-aspartate receptor (NMDAR), have been associated with severe and heterogeneous neurologic diseases. Missense variants in these genes can result in gain or loss of the NMDAR function, requiring opposite therapeutic treatments. Computational methods that predict pathogenicity and molecular functional effects are therefore crucial for accurate diagnosis and therapeutic applications. We assembled missense variants: 201 from patients, 631 from general population, and 159 characterized by electrophysiological readouts showing whether they can enhance or reduce the receptor function. This includes new functional data from 47 variants reported here, for the first time. We found that pathogenic/benign variants and variants that increase/decrease the channel function were distributed unevenly on the protein structure, with spatial proximity to ligands bound to the agonist and antagonist binding sites being key predictive features. Leveraging distances from ligands, we developed two independent machine learning-based predictors for NMDAR missense variants: a pathogenicity predictor which outperforms currently available predictors (AUC=0.945, MCC=0.726), and the first binary predictor of molecular function (increase or decrease) (AUC=0.809, MCC=0.523). Using these, we reclassified variants of uncertain significance in the ClinVar database and refined a previous genome-informed epidemiological model to estimate the birth incidence of molecular mechanism-defined GRIN disorders. Our findings demonstrate that distance from ligands is an important feature in NMDARs that can enhance variant pathogenicity prediction and enable functional prediction. Further studies with larger numbers of phenotypically and functionally characterized variants will enhance the potential clinical utility of this method.

Gut microbiome is not associated with mild cognitive impairment in Parkinson's disease.

Gut microbiome differences between people with Parkinson's disease (PD) and control subjects without Parkinsonism are widely reported, but potential alterations related to PD with mild cognitive impairment (MCI) have yet to be comprehensively explored. We compared gut microbial features of PD with MCI (n = 58) to cognitively unimpaired PD (n = 60) and control subjects (n = 90) with normal cognition. Our results did not support a specific microbiome signature related to MCI in PD.

Functional prediction of proteins from the human gut archaeome.

The human gastrointestinal tract contains diverse microbial communities, including archaea. Among them, Methanobrevibacter smithii represents a highly active and clinically relevant methanogenic archaeon, being involved in gastrointestinal disorders, such as inflammatory bowel disease and obesity. Herein, we present an integrated approach using sequence and structure information to improve the annotation of M. smithii proteins using advanced protein structure prediction and annotation tools, such as AlphaFold2, trRosetta, ProFunc, and DeepFri. Of an initial set of 873 481 archaeal proteins, we found 707 754 proteins exclusively present in the human gut. Having analysed archaeal proteins together with 87 282 994 bacterial proteins, we identified unique archaeal proteins and archaeal-bacterial homologs. We then predicted and characterized functional domains and structures of 73 unique and homologous archaeal protein clusters linked the human gut and M. smithii. We refined annotations based on the predicted structures, extending existing sequence similarity-based annotations. We identified gut-specific archaeal proteins that may be involved in defense mechanisms, virulence, adhesion, and the degradation of toxic substances. Interestingly, we identified potential glycosyltransferases that could be associated with N-linked and O-glycosylation. Additionally, we found preliminary evidence for interdomain horizontal gene transfer between Clostridia species and M. smithii, which includes sporulation Stage V proteins AE and AD. Our study broadens the understanding of archaeal biology, particularly M. smithii, and highlights the importance of considering both sequence and structure for the prediction of protein function.

Genomics 2 Proteins portal: A resource and discovery tool for linking genetic screening outputs to protein sequences and structures.

Recent advances in AI-based methods have revolutionized the field of structural biology. Concomitantly, high-throughput sequencing and functional genomics technologies have enabled the detection and generation of variants at an unprecedented scale. However, efficient tools and resources are needed to link these two disparate data types - to "map" variants onto protein structures, to better understand how the variation causes disease and thereby design therapeutics. Here we present the Genomics 2 Proteins Portal (G2P; g2p.broadinstitute.org/): a human proteome-wide resource that maps 19,996,443 genetic variants onto 42,413 protein sequences and 77,923 structures, with a comprehensive set of structural and functional features. Additionally, the G2P portal generalizes the capability of linking genomics to proteins beyond databases by allowing users to interactively upload protein residue-wise annotations (variants, scores, etc.) as well as the protein structure to establish the connection. The portal serves as an easy-to-use discovery tool for researchers and scientists to hypothesize the structure-function relationship between natural or synthetic variations and their molecular phenotype.

Resting-state EEG measures cognitive impairment in Parkinson's disease.

Cognitive dysfunction is common in Parkinson's disease (PD). We developed and evaluated an EEG-based biomarker to index cognitive functions in PD from a few minutes of resting-state EEG. We hypothesized that synchronous changes in EEG across the power spectrum can measure cognition. We optimized a data-driven algorithm to efficiently capture these changes and index cognitive function in 100 PD and 49 control participants. We compared our EEG-based cognitive index with the Montreal cognitive assessment (MoCA) and cognitive tests across different domains from National Institutes of Health (NIH) Toolbox using cross-validations, regression models, and randomization tests. Finally, we externally validated our approach on 32 PD participants. We observed cognition-related changes in EEG over multiple spectral rhythms. Utilizing only 8 best-performing electrodes, our proposed index strongly correlated with cognition (MoCA: rho = 0.68, p value < 0.001; NIH-Toolbox cognitive tests: rho ≥ 0.56, p value < 0.001) outperforming traditional spectral markers (rho = -0.30-0.37). The index showed a strong fit in regression models (R2 = 0.46) with MoCA, yielded 80% accuracy in detecting cognitive impairment, and was effective in both PD and control participants. Notably, our approach was equally effective (rho = 0.68, p value < 0.001; MoCA) in out-of-sample testing. In summary, we introduced a computationally efficient data-driven approach for cross-domain cognition indexing using fewer than 10 EEG electrodes, potentially compatible with dynamic therapies like closed-loop neurostimulation. These results will inform next-generation neurophysiological biomarkers for monitoring cognition in PD and other neurological diseases.

Dissecting genetics of spectrum of epilepsies with eyelid myoclonia by exome sequencing.

OBJECTIVE: Epilepsy with eyelid myoclonia (EEM) spectrum is a generalized form of epilepsy characterized by eyelid myoclonia with or without absences, eye closure-induced seizures with electroencephalographic paroxysms, and photosensitivity. Based on the specific clinical features, age at onset, and familial occurrence, a genetic cause has been postulated. Pathogenic variants in CHD2, SYNGAP1, NEXMIF, RORB, and GABRA1 have been reported in individuals with photosensitivity and eyelid myoclonia, but whether other genes are also involved, or a single gene is uniquely linked with EEM, or its subtypes, is not yet known. We aimed to dissect the genetic etiology of EEM. METHODS: We studied a cohort of 105 individuals by using whole exome sequencing. Individuals were divided into two groups: EEM- (isolated EEM) and EEM+ (EEM accompanied by intellectual disability [ID] or any other neurodevelopmental/psychiatric disorder). RESULTS: We identified nine variants classified as pathogenic/likely pathogenic in the entire cohort (8.57%); among these, eight (five in CHD2, one in NEXMIF, one in SYNGAP1, and one in TRIM8) were found in the EEM+ subcohort (28.57%). Only one variant (IFIH1) was found in the EEM- subcohort (1.29%); however, because the phenotype of the proband did not fit with published data, additional evidence is needed before considering IFIH1 variants and EEM- an established association. Burden analysis did not identify any single burdened gene or gene set. SIGNIFICANCE: Our results suggest that for EEM, as for many other epilepsies, the identification of a genetic cause is more likely with comorbid ID and/or other neurodevelopmental disorders. Pathogenic variants were mostly found in CHD2, and the association of CHD2 with EEM+ can now be considered a reasonable gene-disease association. We provide further evidence to strengthen the association of EEM+ with NEXMIF and SYNGAP1. Possible new associations between EEM+ and TRIM8, and EEM- and IFIH1, are also reported. Although we provide robust evidence for gene variants associated with EEM+, the core genetic etiology of EEM- remains to be elucidated.

Forecasting the dynamics of a complex microbial community using integrated meta-omics.

Predicting the behaviour of complex microbial communities is challenging. However, this is essential for complex biotechnological processes such as those in biological wastewater treatment plants (BWWTPs), which require sustainable operation. Here we summarize 14 months of longitudinal meta-omics data from a BWWTP anaerobic tank into 17 temporal signals, explaining 91.1% of the temporal variance, and link those signals to ecological events within the community. We forecast the signals over the subsequent five years and use 21 extra samples collected at defined time intervals for testing and validation. Our forecasts are correct for six signals and hint on phenomena such as predation cycles. Using all the 17 forecasts and the environmental variables, we predict gene abundance and expression, with a coefficient of determination ≥0.87 for the subsequent three years. Our study demonstrates the ability to forecast the dynamics of open microbial ecosystems using interactions between community cycles and environmental parameters.

Trans-ancestry polygenic models for the prediction of LDL blood levels: an analysis of the United Kingdom Biobank and Taiwan Biobank.

Polygenic risk score (PRS) predictions often show bias toward the population of available genome-wide association studies (GWASs), which is typically of European ancestry. This study aimed to assess the performance differences of ancestry-specific PRS and test the implementation of multi-ancestry PRS to enhance the generalizability of low-density lipoprotein (LDL) cholesterol predictions in the East Asian (EAS) population. In this study, we computed ancestry-specific and multi-ancestry PRSs for LDL using data obtained from the Global Lipid Genetics Consortium, while accounting for population-specific linkage disequilibrium patterns using the PRS-CSx method in the United Kingdom Biobank dataset (UKB, n = 423,596) and Taiwan Biobank dataset (TWB, n = 68,978). Population-specific PRSs were able to predict LDL levels better within the target population, whereas multi-ancestry PRSs were more generalizable. In the TWB dataset, covariate-adjusted R 2 values were 9.3% for ancestry-specific PRS, 6.7% for multi-ancestry PRS, and 4.5% for European-specific PRS. Similar trends (8.6%, 7.8%, and 6.2%) were observed in the smaller EAS population of the UKB (n = 1,480). Consistent with R 2 values, PRS stratification in EAS regions (TWB) effectively captured a heterogenous variability in LDL blood cholesterol levels across PRS strata. The mean difference in LDL levels between the lowest and highest EAS-specific PRS (EAS_PRS) deciles was 0.82, compared to 0.59 for European-specific PRS (EUR_PRS) and 0.76 for multi-ancestry PRS. Notably, the mean LDL values in the top decile of multi-ancestry PRS were comparable to those of EAS_PRS (3.543 vs. 3.541, p = 0.86). Our analysis of the PRS prediction model for LDL cholesterol further supports the issue of PRS generalizability across populations. Our targeted analysis of the EAS population revealed that integrating non-European genotyping data with a powerful European-based GWAS can enhance the generalizability of LDL PRS.

A toolbox of machine learning software to support microbiome analysis.

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.

Early-to-mid stage idiopathic Parkinson's disease shows enhanced cytotoxicity and differentiation in CD8 T-cells in females.

Neuroinflammation in the brain contributes to the pathogenesis of Parkinson's disease (PD), but the potential dysregulation of peripheral immunity has not been systematically investigated for idiopathic PD (iPD). Here we showed an elevated peripheral cytotoxic immune milieu, with more terminally-differentiated effector memory (TEMRA) CD8 T, CD8+ NKT cells and circulating cytotoxic molecules in fresh blood of patients with early-to-mid iPD, especially females, after analyzing > 700 innate and adaptive immune features. This profile, also reflected by fewer CD8+FOXP3+ T cells, was confirmed in another subcohort. Co-expression between cytotoxic molecules was selectively enhanced in CD8 TEMRA and effector memory (TEM) cells. Single-cell RNA-sequencing analysis demonstrated the accelerated differentiation within CD8 compartments, enhanced cytotoxic pathways in CD8 TEMRA and TEM cells, while CD8 central memory (TCM) and naïve cells were already more-active and transcriptionally-reprogrammed. Our work provides a comprehensive map of dysregulated peripheral immunity in iPD, proposing candidates for early diagnosis and treatments.

Accurate long-read sequencing identified GBA1 as major risk factor in the Luxembourgish Parkinson's study.

Heterozygous variants in the glucocerebrosidase GBA1 gene are an increasingly recognized risk factor for Parkinson's disease (PD). Due to the GBAP1 pseudogene, which shares 96% sequence homology with the GBA1 coding region, accurate variant calling by array-based or short-read sequencing methods remains a major challenge in understanding the genetic landscape of GBA1-associated PD. We analyzed 660 patients with PD, 100 patients with Parkinsonism and 808 healthy controls from the Luxembourg Parkinson's study, sequenced using amplicon-based long-read DNA sequencing technology. We found that 12.1% (77/637) of PD patients carried GBA1 variants, with 10.5% (67/637) of them carrying known pathogenic variants (including severe, mild, risk variants). In comparison, 5% (34/675) of the healthy controls carried GBA1 variants, and among them, 4.3% (29/675) were identified as pathogenic variant carriers. We found four GBA1 variants in patients with atypical parkinsonism. Pathogenic GBA1 variants were 2.6-fold more frequently observed in PD patients compared to controls (OR = 2.6; CI = [1.6,4.1]). Three novel variants of unknown significance (VUS) were identified. Using a structure-based approach, we defined a potential risk prediction method for VUS. This study describes the full landscape of GBA1-related parkinsonism in Luxembourg, showing a high prevalence of GBA1 variants as the major genetic risk for PD. Although the long-read DNA sequencing technique used in our study may be limited in its effectiveness to detect potential structural variants, our approach provides an important advancement for highly accurate GBA1 variant calling, which is essential for providing access to emerging causative therapies for GBA1 carriers.

The therapeutic landscape of tauopathies: challenges and prospects.

Tauopathies are a group of neurodegenerative disorders characterized by the aggregation of the microtubule-associated protein tau. Aggregates of misfolded tau protein are believed to be implicated in neuronal death, which leads to a range of symptoms including cognitive decline, behavioral change, dementia, and motor deficits. Currently, there are no effective treatments for tauopathies. There are four clinical candidates in phase III trials and 16 in phase II trials. While no effective treatments are currently approved, there is increasing evidence to suggest that various therapeutic approaches may slow the progression of tauopathies or improve symptoms. This review outlines the landscape of therapeutic drugs (indexed through February 28, 2023) that target tau pathology and describes drug candidates in clinical development as well as those in the discovery and preclinical phases. The review also contains information on notable therapeutic programs that are inactive or that have been discontinued from development.