Mitochondrial dysfunction is central to the pathogenesis of Parkinson's disease (PD), integrating both genetic and environmental factors. Therefore, reliable blood-based biomarkers reflecting mitochondrial alterations are needed. Emerging evidence suggests that somatic changes to mitochondrial DNA (mtDNA) may reflect early disease-associated processes relevant to PD conversion and clinical manifestation. In this study, we analysed somatic mtDNA major arc deletions as a measure of mitochondrial genome integrity and evaluated 7S DNA abundance as well as copy number as complementary readouts in whole blood (n=776) from a large cohort, including idiopathic and genetic PD patients, individuals at risk, PD converters, patients with primary mitochondrial disease, and healthy controls. This work was complemented by analyses in CSF samples (n=72). Finally, mtDNA measures were integrated with genetic, protein, and clinical data, including mitochondrial polygenic risk scores, alpha-synuclein seeding assays, and serum neurofilament light chain levels. In blood, the strongest effects occurred in PINK1/PRKN-PD (deletions: P<0.0001; 7S DNA: P<0.0001) and early-onset idiopathic PD (7S DNA: P=0.0009-0.0030). Individuals with prodromal signs conferring a high risk for PD also showed increased mtDNA deletions (P=0.0045) and reduced 7S DNA (P=0.0046). In PD converters, these alterations were detectable prior to clinical diagnosis (deletions: P=0.0024; 7S DNA: P=0.0091). In CSF-derived extracellular vesicles, we observed an age-associated increase in mtDNA copy number in healthy controls (R2=0.121, P=0.035) that was absent in idiopathic PD (R2=0.014, P=0.548). Across all PD patients, those with the highest mtDNA deletion burden and lowest 7S DNA exhibited a higher risk of developing cognitive impairment and depression, while also showing a longer time to postural instability (deletions: P=0.0187; 7S DNA: P=0.0281). Integration of mtDNA readouts, mitochondrial polygenic risk scores, alpha-synuclein seeding, and serum neurofilament light chain levels revealed complementary contributions to biological heterogeneity in PD, with receiver operating characteristic analyses showing moderate group-level discrimination using mtDNA measures alone (AUC=0.66) and substantially improved discrimination when combined with alpha-synuclein and neurodegeneration markers (AUC up to 0.96). Alpha-synuclein seeding activity was associated with later age at onset, whereas mtDNA deletion burden showed an inverse association, indicating that these biomarkers capture distinct biological dimensions of PD. MtDNA damage markers, particularly deletion burden, capture mitochondrial dysfunction arising from both genetic and environmental influences and are detectable across early clinical stages of PD. While not serving as stand-alone diagnostic biomarkers, mtDNA measures provide complementary biological information within a multimodal framework and may support patient stratification based on mitochondrial involvement using a minimally invasive approach.

Epilepsy is a disease that affects millions of people worldwide. To improve treatment for its various forms and causes, personalized or precision medicine is increasingly being pursued. Precision medicine offers new therapeutic approaches that deviate from the guidelines and precision medicine provides new insights into the treatment of genetically caused epilepsy. To support this, requirements have been identified for a digital diagnostic board designed for use in interdisciplinary epileptology case conferences. he development of a system that displays patient data clearly and interactively. The definition of the requirements for such a system is necessary to clearly present patient-specific and genetic information and support diagnostic and therapeutic decisions in cases of genetic epilepsy. Participatory observations, including eye tracking, thinking-aloud protocols, expert interviews, and focus group discussions, were conducted to identify requirements. The resulting information was used to determine the system's necessary requirements through needs and requirements analysis. These requirements were then evaluated through focus group discussions and cognitive walkthroughs. Based on the requirements, an interaction concept was designed. Finally, a high-fidelity prototype was developed based on the requirements and interaction concept. Participatory observations and expert interviews provided insight into the researchers' methods of gathering information and processing cases. These insights were used to identify patient-centered and external data sources, such as OMIM, gnomAD, and ClinVar databases, as well as scores, that will be incorporated into the system. Then, based on these requirements, a high-fidelity prototype was created using Figma. This work introduces a novel system concept that supports case conferences on rare and complex forms of epilepsy by coherently visualizing patient-specific molecular and clinical data. A structured requirements analysis informed the definition of core tasks, the development of an interaction concept, and the creation of a high-fidelity prototype, all of which were guided by principles of visual information seeking. These results lay the groundwork for data-driven hypothesis formation in epilepsy research and underscore the necessity of further evaluating the validity, usability, and user acceptance of the requirements.

BACKGROUND:Antimicrobial resistance genes (ARGs) and virulence factors (VFs) are central contributors to the global health crisis surrounding drug-resistant infections. FINDINGS:We introduce PathoFact 2.0, an enhanced pipeline for improved ARG, VF, toxin, and biosynthetic gene clusters (BGCs) prediction. Key improvements include an updated machine learning (ML) model for VF identification, expanded hidden Markov model profiles for VFs and toxin-associated proteins, a new ML model for toxin and toxin-associated proteins identification, and the integration of antiSMASH 7.0 for predicting BGCs. CONCLUSIONS:Our upgrades make PathoFact 2.0 a more powerful and user-friendly platform for predicting microbiome-based pathogenicity and resistance, providing a crucial tool for better understanding and addressing the challenges posed by antimicrobial resistance and infectious diseases.PathoFact 2.0 is available at https://gitlab.com/uniluxembourg/lcsb/systems-ecology/pathofact2. It is compatible with Linux operating systems.

To investigate whether antidiabetic drugs have a biological basis to be repurposed in PD prevention, we applied a drug target Mendelian randomization framework to assess associations between genetic variation in antidiabetic drug targets and PD risk or age at onset (AAO). Instrumental variables (IVs) were derived from GWAS summary statistics on fasting glucose (FG), glycated hemoglobin (HbA1c), and gene expression data from GTEx. Apart from SGLT2 inhibitors, all other antidiabetic drugs of interest could be instrumented through our methods. Positive and negative control analyses were carried out to validate 20 IVs in the FG arm and 23 IVs in the HbA1c arm. DPP-4 inhibitors failed the positive control. GWAS summary statistics for PD risk and AAO data were sourced from the IPDGC and COURAGE-PD consortia, resulting in 42 083 cases/457 090 controls for risk and 37 103 PD cases for AAO. MR analyses showed no significant associations across consortia or in meta-analysis. These findings do not support a causal role of genetic variation in antidiabetic drug targets in PD risk or AAO.

IntroductionAn association between severe GBA1 variants and the progression of non-motor symptoms in PD has been reported, but the role of Parkinson’s-risk (PD-risk) GBA1 variants is less clear.MethodsWe assessed symptom progression in individuals with severe and PD-risk variants compared to non-carriers. We analyzed longitudinal data from 726 individuals with typical PD, including 22 carriers of severe GBA1 variants and 47 carriers of PD-risk GBA1 variants.ResultsThe findings were not significant after adjusting for Bonferroni correction; however, linear mixed models analyses showed that at a nominal significance level of 5%, carriers of PD-risk or severe variants were associated with faster cognitive decline compared to non-carriers. Moreover, carriers of PD-risk variants were associated with faster worsening of apathy, quality of sleep, tremor, and non-motor symptoms [Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS I)] compared to non-carriers; however, we did not observe this tendency in individuals with severe variants.DiscussionThe exploratory study suggests associations between PD-risk variants and a more rapid disease progression among carriers compared to non-carriers. Nevertheless, the findings should be interpreted cautiously and require confirmation in an independent cohort before any reevaluation of their pathologic relevance.

Abstract Background Low-frequency heteroplasmic mitochondrial DNA (mtDNA) variants are associated with aging and neurological diseases, including Parkinson’s disease (PD). Targeted deep mtDNA sequencing using PacBio HiFi long reads has the potential to resolve heteroplasmy across the full mitochondrial genome with high accuracy. Methods To validate Vega PacBio sequencing for detecting mtDNA heteroplasmy, we analyzed four predefined mixtures of two mtDNA haplotypes. We generated a single long-range PCR amplicon covering the entire mitochondrial genome. These amplicons were mixed at predefined ratios (minor mixture haplotype component: 5%, 2%, 1%, and 0.1%). Variant calling was performed using Mutserve2 , and accuracy was assessed by calculating the F 1 score from comparisons between expected and detected variants. Full-length mtDNA PacBio sequencing was applied to investigate heteroplasmy across fibroblast passages derived from five LRRK2 p.Gly2019Ser variant carriers (n=3 affected with PD and n=2 unaffected carriers). Changes in mtDNA heteroplasmy level and variant load were assessed longitudinally using a linear mixed model. Results The single-amplicon approach enabled full-length haplotype resolution without amplification bias associated with overlapping PCR strategies. The F 1 score of the predefined mixtures was 1.0 for heteroplasmy levels between 5% and 1% and remained high (0.91) at 0.1%. We detected n=10/62 variants discordant with the Illumina reference at the 0.1% mixture, but sensitivity remained very high at 1.00 in that mixture. Detected minor variants closely matched expected heteroplasmy levels, with average variant levels of 0.057 (5%), 0.022 (2%), 0.011 (1%), and 0.001 (0.1%). Across twelve fibroblast passages, we observed fewer mtDNA heteroplasmic variants (β=-3.2, p=0.026). Increased heteroplasmic variant load over time was also associated with older age (β=1.50, p=0.001) and PD affection status (β=5.0, p=1.0 × 10 - ⁴) in LRRK2 variant carriers. Notably, we observed distinct patterns of heteroplasmic variants that either increased or decreased in heteroplasmy level across passages. Conclusion PacBio HiFi sequencing, combined with a single-amplicon strategy, enables accurate full-length mtDNA heteroplasmy detection and longitudinal analysis, providing a valuable tool for studying mitochondrial variation and dynamics in disease.

We investigated the role of copy number variations (CNVs) in Parkinson's disease (PD) using genotyping data from 10,815 patients (2731 early-onset PD, EOPD) and 8901 controls from the COURAGE-PD consortium. CNVs were analyzed using a sliding window genome-wide association and burden approach. No genome-wide significant CNVs were detected in the overall cohort, but a robust deletion spanning exons 2-6 of PRKN was identified in EOPD cases, validated by MLPA, and replicated in the GP2 dataset (23,089 cases, 18,824 controls). CNV burden was significantly enriched in PD-related genes, primarily driven by PRKN, with the strongest effect observed in EOPD. PRKN CNV carriers showed earlier age at onset, confirmed by survival analysis. No association was observed for genome-wide or large CNV burden. Our findings reinforce the pivotal role of PRKN deletions in early-onset PD and highlight the need for high-resolution CNV analysis in large cohorts to uncover additional rare contributors to PD risk.

REM Sleep Behaviour Disorder (RBD) is a hallmark of the prodromal phase of α-synucleinopathies. We aimed to describe the prevalence of probable RBD and to assess its associations with demographics, cognition, and location in a large sample of older adults in Luxembourg, as a first step toward identifying individuals with RBD symptoms for future prodromal-marker assessment. In 2021, residents of Luxembourg aged 55–75 were invited to complete an online survey including the RBD Screening Questionnaire (RBDSQ); with a threshold of ≥ 7 defining screen-positive probable RBD (sppRBD). Screen-positive participants underwent a telephone interview, and those confirmed were categorised as telephone-assessed probable RBD (pRBD). Bayesian spatial mapping assessed the geographical distribution of pRBD, and logistic regression identified determinants of sppRBD and pRBD. Among 15,915 participants (54% male; median age 62 [IQR 58–67]), 12.4% had sppRBD. The telephone interview confirmed only 34.8% of these as pRBD, yielding a projected prevalence of 4.3%. Self-reported cognitive impairment, male sex, and Portuguese as questionnaire language were associated with pRBD, which showed heterogeneous geographical distribution. Online questionnaires may yield false positives, potentially reflecting e-health literacy issues; therefore, a confirmation step is essential. This analysis identifies individuals with RBD symptoms warranting further prodromal-marker assessment, a candidate group for, rather than a validated instance of, an at-risk-for-α-synucleinopathy cohort.

Rare Mendelian disorders affect 300-400 million people globally. Although genetic testing has become widely adopted, gene-specific evidence for tailored variant interpretation remains scattered across resources. We present Gene Portals, a framework for gene-centered multimodal knowledge bases that co-localize expert-harmonized clinical data, functional assays, population variation, structural annotations and gene-specific ACMG/AMP specifications within a single resource. A modular interface integrates this unified evidence with VCEP-refined ACMG specifications to enable automated gene-specific variant classification, infer molecular mechanisms, and support cross-gene analyses. We demonstrate the framework's utility across five Gene portals spanning eleven neurodevelopmental disorder-associated genes, integrating data from 4,423 individuals with 2,838 unique variants, 36,149 ClinVar submissions, and 1,044 expert-curated molecular readouts. By organizing evidence that is otherwise dispersed across multiple sources into a unified, queryable framework, the SCN, GRIN, CACNA1A, SATB2 and SLC6A1 Gene Portals became widely used community resources and provide an extensible template for standardized rare-disease variant interpretation and mechanism-aware discovery.

Synonymous single nucleotide variants (sSNVs), traditionally seen as neutral, are now recognized for their biological impact. To assess their relevance, we developed SyMetrics, a framework that integrates predictors of splicing, RNA stability, evolutionary conservation, codon usage, synonymous variation effects, sequence properties, and allele frequency. We analyzed all possible sSNVs across the human genome, and our machine-learning model achieved 97% accuracy in distinguishing deleterious from benign variants, with a ROC-AUC of 0.89, outperforming individual predictors. Our estimates indicate that about 1.98 ± 0.17% of sSNVs absent from population databases are damaging (roughly 900 000 sSNVs), with an odds ratio of 3.87 for deleteriousness compared to common sSNVs (P < 0.05). To validate predictions, we performed functional assays on selected sSNVs in the AVPR2 gene and additionally used available large scale mutagenesis screens of RAD51C and BAP1 variants. In a clinical cohort, we identified 15 predicted deleterious sSNVs in genes linked to patient phenotypes; 9 were classified as (likely) pathogenic while 6 were variants of uncertain significance (VUS) per American College of Medical Genetics guidelines. For three VUS, segregation data supported their suspected inheritance patterns (de novo, X-linked). Our findings underscore the functional importance of sSNVs. To support further research and clinical applications, we provide a Python package and web application (https://symetrics.org/) for evaluating these variants comprehensively.

Small open reading frames (smORFs), which encode proteins under 100 amino acids, represent an underexplored dimension of the human gut microbiome, despite growing evidence of their essential biological roles. Due to small size and poor annotation, smORFs are typically excluded from metagenomic/metaproteomic analyses. Here, we present a high-resolution multi-omic workflow that integrates smORF prediction into metaproteome searches and enables ultra-deep detection of smORF-encoded proteins (SEPs), without experimental size-based enrichment, utilizing state-of-the-art mass spectrometry instrumentation. Applied to human gut microbiomes, this approach resulted in the largest number of detected SEPs to date, allowing identification of over 25,000 SEPs in the metaproteome, alongside the measurements of the larger proteins. Our multi-omics integrative strategy is critical for advancing human metaproteome research. It also provides a generalizable strategy for comprehensive SEP discovery across diverse microbial ecosystems greatly expanding the previously hidden proteomic landscape.

The mechanism(s) causing selective vulnerability of dopaminergic neurons in Parkinson’s disease (PD) remain largely elusive. To improve our understanding of mitochondrial involvement and related pathways suggested to play a role in this selective vulnerability, we used tyrosine hydroxylase (TH)-mCherry reporter-induced pluripotent stem cells generated by CRISPR/Cas9. We sorted neurons into pure TH-positive and TH-negative neurons upon differentiation into a dopaminergic neuron-containing cell culture. We characterized mitochondrial function in both dopaminergic and non-dopaminergic neurons from PD patients and controls and identified differentially expressed genes between patients and controls in both cell populations. Dopaminergic neurons had a lower mitochondrial membrane potential than non-dopaminergic neurons. Furthermore, ATP levels were lower in PRKN mutation carriers than controls, and mitochondrial mass was reduced in PRKN mutation carriers only in the TH-positive but not in TH-negative neurons. Importantly, in PRKN mutation carriers, we demonstrated elevated levels of dopamine, which can serve as a significant source of toxic, oxidized dopamine. Using unbiased RNA sequencing, we detected increased levels of CHCHD2 and decreased expression of GPNMB in TH-positive neurons from Parkin mutation carriers compared to healthy controls. This suggests a possible interaction of these three PD genes in response to a dopaminergic neuron-specific increase in oxidative stress, which further leads to the selective vulnerability of dopaminergic neurons.

BACKGROUND:Precision medicine for complex diseases like epilepsy requires integrating heterogeneous clinical and genomic data but interpreting numerous disease-associated genes remains challenging. OBJECTIVES:How can data from disparate biomedical sources be organized efficiently and flexibly to support precision medicine in early project stages in genetic epilepsy? METHODS:We applied a three-step system design approach tailored for academic medical research, considering project requirements, available resources, and technology selection. RESULTS:The EAV-hybrid model accommodated diverse clinical and genetic data while preserving flexibility for future expansion. Integration with cBioPortal enabled intuitive visualization and interpretation. The design supports future migration to standard CDMs such as OMOP or i2b2. CONCLUSION:A flexible, metadata-driven EAV-hybrid model supports rapid prototyping and structured data integration in early-stage precision medicine projects, providing an infrastructure for molecular boards and clinical decision-making for genetic epilepsies.

Objective: Genetic generalized epilepsies (GGEs) comprise the most common genetically determined epilepsy syndromes, following a complex mode of inheritance. Although many important common and rare genetic factors causing or contributing to these epilepsies have been identified in the past decades, many features of the genetic architecture are still insufficiently understood. This study integrates genome-wide association study (GWAS) data from the International League Against Epilepsy Consortium on Complex Epilepsies with transcriptome-wide association studies to identify genes whose genetically regulated expression levels are associated with epilepsy. Methods: To achieve this, we used multiple computational approaches, including MAGMA, a tool for gene analysis of GWAS data, and its derivatives E-MAGMA and H-MAGMA, to improve gene mapping accuracy by utilizing tissue-specific expression and chromatin interaction data. Furthermore, we developed ME-MAGMA to incorporate methylation quantitative trait loci data, providing insights into epigenetic factors. Results: We identified a total of 897 false discovery rate-corrected (<.05) candidates. These include voltage-gated calcium channels, voltage-gated potassium channels, and other genes such as NPRL2, CACNB2, and KCNT1 associated with epilepsy pathogenesis that act as key players in neuronal communication and signaling in the brain. Significance: In this study, we propose new candidate genes to expand the dataset of potential epilepsy-causing genes. Further research on these genes may enhance our understanding of the complex regulatory mechanisms underlying GGE and other types of epilepsy, potentially revealing targets for therapeutic intervention.

Heterozygous GBA1 variants increase Parkinson's disease (PD) risk with variable penetrance. We investigated the interaction between genome-wide polygenic risk scores (PRS) and severity of pathogenic GBA1 variants (GBA1PVs) to assess their combined impact on PD risk. GBA1 variants were identified from whole exome sequencing in the UK Biobank and targeted PacBio sequencing in the Luxembourg Parkinson's Study, with PRS calculated using genome-wide significant SNPs. GBA1PVs were present in 8.8% of PD patients in the UK Biobank and 9.9% in LuxPark, with carriers showing consistently higher PD risk across all PRS categories. In the highest PRS category, PD risk increased 2.3-fold in the UK Biobank and 1.6-fold in LuxPark. Severe and mild GBA1 variants conferred nearly double the risk of PD compared to risk variants. Our findings demonstrate the impact of PRS on GBA1PVs penetrance, highlighting implications for genetic counseling and clinical trial design in GBA1-associated PD.

Background:Parkinson's disease (PD) increases mortality is difficult to predict because of its heterogeneity and the availability of very few reliable which prognostic markers. Objectives:We used electroencephalography (EEG) and the Linear Predictive Coding EEG Algorithm for PD (LEAPD) for binary classification of 3-year mortality status and correlation between LEAPD indices and time to death. Methods:2-minutes resting-state EEG from 94 PD patients (59 channels, 22 deceased within 3 years of recording) was used for binary classification of 3-year mortality status. Single-channel classification using a balanced dataset of 44 was performed using leave-one-out cross-validation (LOOCV). Robustness was evaluated by truncating the recordings. LOOCV Spearman's correlation coefficient (ρ) was obtained between LEAPD indices and time to death. Optimum hyperparameters obtained from a balanced training dataset of 30 were tested on the remaining 64 patients by 10,000 randomized comparisons of 7 vs 7, using 5 channel combinations Hyperparameters for the best ρ, using the same training dataset were for the out-of-sample correlation for the remaining 7 deceased. Results:In LOOCV analysis several channels yielded 100% accuracy with robust performance from five. The correlations ranged between ρ = -0.59 to -0.86; were significant after adjusting for age, cognitive and motor impairment. Out-of-sample testing using the best-performing 5-channel combination yielded a mean accuracy of 83%. Out-of-sample Spearman's ρ was -0.82. Conclusion:LEAPD provides a robust approach for binary classification of mortality in PD from resting-state EEG. LEAPD indices correlate with survival duration, independent of clinical predictors, suggesting potential utility as a continuous neurophysiological biomarker.