Polygenic risk scores validated in patient-derived cells stratify for mitochondrial subtypes of Parkinson\textquoterights disease 2023.05.12.23289877

E-print/Working paper (2023)

Background Parkinson's disease (PD) is the fastest growing neurodegenerative disorder, with affected individuals expected to double during the next 20 years. This raises the urgent need to better ... [more ▼]

Background Parkinson's disease (PD) is the fastest growing neurodegenerative disorder, with affected individuals expected to double during the next 20 years. This raises the urgent need to better understand the genetic architecture and downstream cellular alterations underlying PD pathogenesis, in order to identify more focused therapeutic targets. While only ~10\% of PD cases can be clearly attributed to monogenic causes, there is mounting evidence that additional genetic factors could play a role in idiopathic PD (iPD). In particular, common variants with low to moderate effect size in multiple genes regulating key neuroprotective activities may act as risk factors for PD. In light of the well-established involvement of mitochondrial dysfunction in PD, we hypothesized that a fraction of iPD cases may harbour a pathogenic combination of common variants in nuclear-encoded mitochondrial genes, ultimately resulting in neurodegeneration.Methods: To capture this mitochondria-related 'missing heritability', we leveraged on existing data from previous genome-wide association studies (GWAS) i.e., the large PD GWAS from Nalls and colleagues. We then used computational approaches based on mitochondria-specific polygenic risk scores (mitoPRSs) for imputing the genotype data obtained from different iPD case-control datasets worldwide, including the Luxembourg Parkinson\textquoterights Study (412 iPD patients and 576 healthy controls) and the COURAGE-PD cohorts (7270 iPD cases and 6819 healthy controls).Results: Applying this approach to gene sets controlling mitochondrial pathways potentially relevant for neurodegeneration in PD, we demonstrated that common variants in genes regulating Oxidative Phosphorylation (OXPHOS-PRS) were significantly associated with a higher PD risk both in the Luxembourg Parkinson\textquoterights Study (odds ratio, OR=1.31[1.14-1.50], p=5.4e-04) and in COURAGE-PD (OR=1.23[1.18-1.27], p=1.5e-29). Functional analyses in primary skin fibroblasts and in the corresponding induced pluripotent stem cells-derived neuronal progenitor cells from Luxembourg Parkinson's Study iPD patients stratified according to the OXPHOS-PRS, revealed significant differences in mitochondrial respiration between high and low risk groups (p < 0.05). Finally, we also demonstrated that iPD patients with high OXPHOS-PRS have a significantly earlier age at disease onset compared to low-risk patients.Conclusions: Our findings suggest that OXPHOS-PRS may represent a promising strategy to stratify iPD patients into pathogenic subgroups in which the underlying neurodegeneration is due to a genetically defined mitochondrial burden potentially eligible for future, more tailored mitochondrially targeted treatments. [less ▲]

Results and Lessons Learned from the sbv IMPROVER Metagenomics Diagnostics for Inflammatory Bowel Disease Challenge

in Scientific Reports (2023), in press

A growing body of evidence links gut microbiota changes with inflammatory bowel disease (IBD), raising the potential benefit of exploiting metagenomics data for non-invasive IBD diagnostics. The sbv ... [more ▼]

A growing body of evidence links gut microbiota changes with inflammatory bowel disease (IBD), raising the potential benefit of exploiting metagenomics data for non-invasive IBD diagnostics. The sbv IMPROVER Metagenomics Diagnosis for Inflammatory Bowel Disease Challenge (MEDIC) investigated computational metagenomics methods for discriminating IBD and nonIBD subjects. Participants in this challenge were given independent training and test metagenomics data from IBD and nonIBD subjects, which could be wither either raw read data (sub-challenge 1, SC1) or processed taxonomy- and function-based profiles (sub-challenge 2, SC2). A total of 81 anonymized submissions were received between September 2019 and March 2020. Most participants’ predictions performed better than random predictions in classifying IBD vs nonIBD, Ulcerative Colitis (UC) vs nonIBD, and Crohn’s Disease (CD) vs nonIBD. However, discrimination between UC and CD remains challenging, with the classification quality similar to the set of random predictions. We analyzed the class prediction accuracy, the metagenomics features by the teams, and computational methods used. These results will be openly shared with the scientific community to help advance IBD research and illustrate the application of a range of computational methodologies for effective metagenomic classification. [less ▲]

Network perturbation analysis of omics data for complex diseases using convex optimization

Poster (2023)

Complex diseases like neurodegenerative or cancer disorders are characterized by deregulations in multiple genes and proteins. Previous research has shown that neighboring genes in a molecular network ... [more ▼]

Complex diseases like neurodegenerative or cancer disorders are characterized by deregulations in multiple genes and proteins. Previous research has shown that neighboring genes in a molecular network tend to undergo coordinated expression changes. We describe an approach that allows identifying such jointly differentially expressed genes from input expression data and a graph encoding pairwise functional associations between genes (such as protein interactions). We cast this as a feature selection problem in penalized two-class (cases vs. controls) classification, and we propose a novel pairwise elastic net (PEN) penalty that favors the selection of discriminative genes according to their connectedness in the interaction graph. Experiments on large-scale gene expression data for Parkinson’s disease demonstrate marked improvements in feature grouping over competitive methods. [less ▲]

Machine learning applied to higher order functional representations of omics data reveals biological pathways associated with Parkinson‘s Disease

Poster (2022, September 18)

Background: Despite the increasing prevalence of Parkinson’s Disease (PD) and research efforts to understand its underlying molecular pathogenesis, early diagnosis of PD remains a challenge. Machine ... [more ▼]

Background: Despite the increasing prevalence of Parkinson’s Disease (PD) and research efforts to understand its underlying molecular pathogenesis, early diagnosis of PD remains a challenge. Machine learning analysis of blood-based omics data is a promising non-invasive approach to finding molecular fingerprints associated with PD that may enable an early and accurate diagnosis. Description: We applied several machine learning classification methods to public omics data from PD case/control studies. We used aggregation statistics and Pathifier’s pathway deregulation scores to generate higher order functional representations of the data such as pathway-level features. The models’ performance and most relevant predictive features were compared with individual feature level predictors. The resulting diagnostic models from individual features and Pathifier’s pathway deregulation scores achieve significant Area Under the Curve (AUC, a receiver operating characteristic curve) scores for both cross-validation and external testing. Furthermore, we identify plausible biological pathways associated with PD diagnosis. Conclusions: We have successfully built machine learning models at pathway-level and single-feature level to study blood-based omics data for PD diagnosis. Plausible biological pathway associations were identified. Furthermore, we show that pathway deregulation scores can serve as robust and biologically interpretable predictors for PD. [less ▲]

Penalised regression with multiple sources of prior effects

E-print/Working paper (2022)

In many high-dimensional prediction or classification tasks, complementary data on the features are available, e.g. prior biological knowledge on (epi)genetic markers. Here we consider tasks with ... [more ▼]

In many high-dimensional prediction or classification tasks, complementary data on the features are available, e.g. prior biological knowledge on (epi)genetic markers. Here we consider tasks with numerical prior information that provide an insight into the importance (weight) and the direction (sign) of the feature effects, e.g. regression coefficients from previous studies. We propose an approach for integrating multiple sources of such prior information into penalised regression. If suitable co-data are available, this improves the predictive performance, as shown by simulation and application. The proposed method is implemented in the R package `transreg' (https://github.com/lcsb-bds/transreg). [less ▲]

Ten Quick Tips for Biomarker Discovery and Validation Analyses Using Machine Learning

in PLoS Computational Biology (2022), 18(8), 1010357

High-throughput experimental methods for biosample profiling and growing collections of clinical and health record data provide ample opportunities for biomarker discovery and medical decision support ... [more ▼]

High-throughput experimental methods for biosample profiling and growing collections of clinical and health record data provide ample opportunities for biomarker discovery and medical decision support. However, many of the new data types, including single-cell omics and high-resolution cellular imaging data, also pose particular challenges for data analysis. A high dimensionality of the data in relation to small numbers of available samples, influences of additive and multiplicative noise, large numbers of uninformative or redundant data features, outliers, confounding factors and imbalanced sample group numbers are all common characteristics of current biomedical data collections. While first successes have been achieved in developing clinical decision support tools using multifactorial omics data, there is still an unmet need and great potential for earlier, more accurate and robust diagnostic and prognostic tools for many complex diseases. Here, we provide a set of broadly applicable tips to address some of the most common pitfalls and limitations for biomarker signature development, including supervised and unsupervised machine learning, feature selection and hypothesis testing approaches. In contrast to previous guidelines discussing detailed aspects of quality control, statistics or study reporting, we give a broader overview of the typical challenges and sort the quick tips to address them chronologically by the study phase (starting with study design, then covering consecutive phases of biomarker signature discovery and validation, see also the overview in Fig. 1). While these tips are not comprehensive, they are chosen to cover what we consider as the most frequent, significant, and practically relevant issues and risks in biomarker development. By pointing the reader to further relevant literature on the covered aspects of biomarker discovery and validation, we hope to provide an initial guideline and entry point into the more detailed technical and application-specific aspects of this field. [less ▲]

Body-First Subtype of Parkinson's Disease with Probable REM-Sleep Behavior Disorder Is Associated with Non-Motor Dominant Phenotype

in Journal of Parkinson's Disease (2022)

Background: The hypothesis of body-first vs. brain-first subtype of PD has been proposed with REM-Sleep behavior disorder (RBD) defining the former. The body-first PD presumes an involvement of the ... [more ▼]

Background: The hypothesis of body-first vs. brain-first subtype of PD has been proposed with REM-Sleep behavior disorder (RBD) defining the former. The body-first PD presumes an involvement of the brainstem in the pathogenic process with higher burden of autonomic dysfunction. Objective: To identify distinctive clinical subtypes of idiopathic Parkinson’s disease (iPD) in line with the formerly proposed concept of body-first vs. brain-first subtypes in PD, we analyzed the presence of probable RBD (pRBD), sex, and the APOE ɛ4 carrier status as potential sub-group stratifiers. Methods: A total of 400 iPD patients were included in the cross-sectional analysis from the baseline dataset with a completed RBD Screening Questionnaire (RBDSQ) for classifying as pRBD by using the cut-off RBDSQ≥6. Multiple regression models were applied to explore (i) the effect of pRBD on clinical outcomes adjusted for disease duration and age, (ii) the effect of sex on pRBD, and (iii) the association of APOE ɛ4 and pRBD. Results: iPD-pRBD was significantly associated with autonomic dysfunction (SCOPA-AUT), level of depressive symptoms (BDI-I), MDS-UPDRS I, hallucinations, and constipation, whereas significantly negatively associated with quality of life (PDQ-39) and sleep (PDSS). No significant association between sex and pRBD or APOE ɛ4 and pRBD in iPD was found nor did we determine a significant effect of APOE ɛ4 on the PD phenotype. Conclusion: We identified an RBD-specific PD endophenotype, characterized by predominant autonomic dysfunction, hallucinations, and depression, corroborating the concept of a distinctive body-first subtype of PD. We did not observe a significant association between APOE ɛ4 and pRBD suggesting both factors having an independent effect on cognitive decline in iPD. [less ▲]

Parkinson's Disease progression, resilience and inflammation markers during the COVID-19 pandemic

in Movement Disorders (2022), in press (doi: 10.1002/mds.29212)(in press),

Single-cell transcriptional profiling and gene regulatory network modeling in Tg2576 mice reveal gender-dependent molecular features preceding Alzheimer-like pathologies

in Molecular Neurobiology (2022), in press (doi:10.1007/s12035-022-02985-2)(in press),

Alzheimer’s disease (AD) onset and progression is influenced by a complex interplay of several environmental and genetic factors, one of them gender. Pronounced gender differences have been observed both ... [more ▼]

Alzheimer’s disease (AD) onset and progression is influenced by a complex interplay of several environmental and genetic factors, one of them gender. Pronounced gender differences have been observed both in the relative risk of developing AD and in clinical disease manifestations. A molecular level understanding of these gender disparities is still missing, but could provide important clues on cellular mechanisms modulating the disease and reveal new targets for gender-oriented disease-modifying precision therapies. We therefore present here a comprehensive single-cell analysis of disease-associated molecular gender differences in transcriptomics data from the neocortex, one of the brain regions most susceptible to AD, in one of the most widely used AD mouse models, the Tg2576 model. Cortical areas are also most commonly used in studies of post-mortem AD brains. To identify disease-linked molecular processes that occur before the onset of detectable neuropathology, we focused our analyses on an age with no detectable plaques and microgliosis. Cell-type specific alterations were investigated at the level of individual genes, pathways, and gene regulatory networks. The number of differentially expressed genes (DEGs) was not large enough to build context-specific gene regulatory networks for each individual cell type, and thus, we focused on the study of cell types with dominant changes and included analyses of changes across the combination of cell types. We observed significant disease-associated gender differences in cellular processes related to synapse organization and axonogenesis, and identified a limited set of transcription factors, including Egr1 and Klf6, as key regulators of many of the disease-associated and gender-dependent gene expression changes in the model. Overall, our analyses revealed significant celltype-specific gene expression changes in individual genes, pathways and subnetworks, including gender-specific and gender-dimorphic changes in both upstream transcription factors and their downstream targets, in the Tg2576 AD model before the onset of overt disease. This opens a window into molecular events that could determine gender-susceptibility to AD, and uncovers tractable target candidates for potential gender-specific precision medicine for AD. [less ▲]

A Pharmacophore Model for SARS-CoV-2 3CLpro Small Molecule Inhibitors and in Vitro Experimental Validation of Computationally Screened Inhibitors

in Journal of Chemical Information and Modeling (2021), 61(8), 4082-4096

Among the biomedical efforts in response to the current coronavirus (COVID-19) pandemic, pharmacological strategies to reduce viral load in patients with severe forms of the disease are being studied ... [more ▼]

Among the biomedical efforts in response to the current coronavirus (COVID-19) pandemic, pharmacological strategies to reduce viral load in patients with severe forms of the disease are being studied intensively. One of the main drug target proteins proposed so far is the SARS-CoV-2 viral protease 3CLpro (also called Mpro), an essential component for viral replication. Ongoing ligand- and receptor-based computational screening efforts would be facilitated by an improved understanding of the electrostatic, hydrophobic and steric features that characterize small molecule inhibitors binding stably to 3CLpro, as well as by an extended collection of known binders. Here, we present combined virtual screening, molecular dynamics simulation, machine learning and in vitro experimental validation analyses which have led to the identification of small molecule inhibitors of 3CLpro with micromolar activity, and to a pharmacophore model that describes functional chemical groups associated with the molecular recognition of ligands by the 3CLpro binding pocket. Experimentally validated inhibitors using a ligand activity assay include natural compounds with available prior knowledge on safety and bioavailability properties, such as the natural compound rottlerin (IC50 = 37 µM), and synthetic compounds previously not characterized (e.g. compound CID 46897844, IC50 = 31 µM). In combination with the developed pharmacophore model, these and other confirmed 3CLpro inhibitors may provide a basis for further similarity-based screening in independent compound databases and structural design optimization efforts, to identify 3CLpro ligands with improved potency and selectivity. Overall, this study suggests that the integration of virtual screening, molecular dynamics simulations and machine learning can facilitate 3CLpro-targeted small molecule screening investigations. Different receptor-, ligand- and machine learning-based screening strategies provided complementary information, helping to increase the number and diversity of identified active compounds. Finally, the resulting pharmacophore model and experimentally validated small molecule inhibitors for 3CLpro provide resources to support follow-up computational screening efforts for this drug target. [less ▲]