Biology subjects

Glaab, E.

Publications and source records attributed to Glaab, E..

13 recordsLinked to original sources

bioRxiv2025

Multi-Omics Integration Reveals Lipid Metabolic Reprogramming as a Driver of Cystogenesis in Autosomal Dominant Polycystic Kidney Disease (ADPKD)

Autosomal dominant polycystic kidney disease (ADPKD) is a most common hereditary kidney disorder and a major contributor to end-stage kidney disease (ESKD), but its molecular progression mechanisms are unclear. We performed metabolomic and transcriptomic profiling on a longitudinal cohort of 254 and 47 ADPKD patients, respectively, to identify molecular alterations and then applied Multi-Omics Factor Analysis (MOFA) to uncover coordinated signatures. Disease progression was associated with a marked shift in dysregulated lipids profile, increased acylcarnitines, and increased glycolytic metabolites, suggesting a shift from fatty acid oxidation (FAO) toward glycolysis. Transcriptomic analysis revealed enrichment of PPAR signaling, cytoskeletal remodeling, and cystogenesis, providing a mechanistic link between metabolic alterations and structural dysregulation. Integrative analysis identified a central axis where inflammation, metabolic reprogramming, and cytoskeletal remodeling converges. Our integrated multi-omics analysis defines a molecular framework where transcriptomic dysregulation drives metabolic reprogramming contribute to cystogenesis, providing potential biomarkers and insights to guide precision therapy in ADPKD progression. Lay SummaryThe clinical heterogeneity of ADPKD limits current prognostic tools for early disease progression, often delaying therapeutic intervention. This study integrates longitudinal clinical data with multi-omics profiling (whole-blood transcriptomics and plasma metabolomics) in a large ADPKD cohort using Multi-Omics Factor Analysis. This approach identified coordinated molecular signatures linking aberrant gene expression and metabolic reprogramming directly to disease progression, hypertension, and mortality status. These findings uncover novel mechanistic drivers of ADPKD and suggest biomarkers to enhance early risk stratification, supporting the development of precision medicine strategies.

bioinformatics↗

bioRxiv2025

Sex-specific microRNA regulators of Parkinson disease: insights from cohort-stratified simulations of compensatory pathway dynamics

Parkinsons disease (PD) exhibits sex differences in prevalence, symptom severity, and progression, suggesting distinct underlying molecular mechanisms. However, the pathophysiological mechanisms remain largely obscure, particularly in the context of the post-transcriptional regulations, where microRNAs (miRNAs) suppress the expression of multiple genes. Bulk transcriptomic data often blur these effects, especially when regulatory patterns vary by sex or cell type. miRNAs have emerged as key regulators of PD-related processes, but their complexity demands computational methods that enable capturing the functional impact at the pathway level. In this study, we investigated how sex may affect miRNA-regulated PD pathways using Boolean modeling across two large PD cohorts, the Parkinsons Progression Markers Initiative (PPMI) and the Luxembourg Parkinsons Study (LuxPark). First, differential expression analysis identified significant variations in miRNA expression between the sexes across both cohorts. These miRNAs were analysed to identify molecular pathways that are over-represented among the predicted targets of the dysregulated microRNAs (i.e. enrichment analysis). The enriched pathways were used to build Boolean models to simulate the effects of sex-specific miRNA dysregulation. These simulations showed consistent male-specific impairment in mitochondrial biogenesis, and respiratory chain activity. Mitophagy and oxidative stress response pathways were also disrupted, alongside dysregulation of autophagy-related protein-folding mechanisms. Our findings suggest that sex-specific miRNA dysregulation contributes to differences in molecular patterns in PD by influencing compensatory related pathways and responses. These results highlight the need for sex-stratified approaches in modeling, translational research, and precision medicine strategies for disease-modifying treatments.

systems biology↗

bioRxiv2025

ZFHX4 is necessary for dopaminergic neuron differentiation and controls cell cycle by regulating LIN28A

The selective degeneration of midbrain dopaminergic neurons (mDANs) is the main pathological hallmark of Parkinsons disease (PD). Although many transcription factors (TFs) guiding mDAN development have been identified, the details of the underlying regulatory networks remain elusive. We have previously generated time-series transcriptomic and epigenomic profiles of human induced pluripotent stem cell (hiPSC)-derived mDANs. Integrative analysis of the data identified ZFHX4 as a prominent super-enhancer-controlled TF induced in mDAN differentiation. ZFHX4 has been associated with neurodevelopmental processes in several species and shows reduced expression in midbrain of PD patients. Using in vitro knockdown (KD) and overexpression experiments, we show that ZFHX4 is necessary but not sufficient for mDAN differentiation. ZFHX4 binds preferentially at active promoter regions and transcriptomic analysis upon ZFHX4 depletion during mDAN differentiation revealed putative primary target genes to be enriched for targets of cell-cycle-related TFs and pathways. Consistently, ZFHX4-depleted cells accumulated in G2-phase of the cell cycle, preventing normal cell cycle progression and exit. The RNA-binding protein LIN28A, involved in stem-cell maintenance and microRNA (miRNA) maturation, emerged as one of the most upregulated genes upon ZFHX4-KD, in parallel with downregulation of neurogenic miRNA miR-9. Moreover, the LIN28A locus was enriched for ZFHX4 binding in CUT&Tag analysis. Taken together, our analysis indicates a pivotal role for ZFHX4 in regulating the cell cycle, specifically in silencing multipotency and proliferative programs, while maintaining mDANs in a post-mitotic state by controlling LIN28A-miR-9 axis.

molecular biology↗

bioRxiv2024

Insulin resistance compromises midbrain organoid neural activity and metabolic efficiency predisposing to Parkinsons disease pathology

Growing evidence indicates that Type 2 Diabetes (T2D) is associated with an increased risk of developing Parkinsons disease through shared disease mechanisms. Studies show that insulin resistance, which is the driving pathophysiological mechanism of T2D plays a major role in neurodegeneration by impairing neuronal functionality, metabolism, and survival. To investigate insulin resistance caused pathological changes in the human midbrain, which could predispose a healthy midbrain to PD development, we exposed iPSC-derived human midbrain organoids from healthy individuals to either high insulin concentrations, promoting insulin resistance, or to more physiological insulin concentrations restoring insulin signalling function. We combined experimental methods with metabolic modelling to identify the most insulin resistance-dependent pathogenic processes. We demonstrate that insulin resistance compromises organoid metabolic efficiency, leading to increased levels of oxidative stress. Additionally, insulin-resistant midbrain organoids showed decreased neural activity and reduced amount of dopaminergic neurons, highlighting insulin resistance as a significant target in PD prevention.

neuroscience↗

bioRxiv2023

An improved PDE6D inhibitor combines with Sildenafil to synergistically inhibit KRAS mutant cancer cell growth

The trafficking chaperone PDE6D (or PDE) was proposed as a surrogate target for K-Ras, leading to the development of a series of inhibitors that block its prenyl-binding pocket. These inhibitors suffered from low solubility and intracellular potency, preventing their clinical development. Here we developed a highly soluble PDE6D inhibitor (PDE6Di), Deltaflexin3, which has the currently lowest off-target activity, as we demonstrate in dedicated assays. We further increased the K-Ras focus, by exploiting that PKG2-mediated phosphorylation of Ser181 lowers K-Ras binding to PDE6D. Thus, the combination of Deltaflexin3 with the approved PKG2-activator Sildenafil synergistically inhibits cell- and microtumor growth. However, the overall cancer survival of the high PDE6D/ low PKG2 target population is higher than of the group with the opposite signature. Our results therefore suggest re-examining the interplay between PDE6D and K-Ras in cancer, while recommending the development of PDE6Di that plug, rather than stuff the hydrophobic pocket of PDE6D. SignificanceCombinations of a novel PDE6D inhibitor with Sildenafil synergistically focus the inhibition on K-Ras, however, survival data of the target population suggest an interplay of K-Ras and PDE6D that needs further exploration.

biochemistry↗

bioRxiv2023

SARS-CoV-2 infection induces dopaminergic neuronal loss in midbrain organoids during short and prolonged cultures

COVID-19 is mainly associated with respiratory symptoms, although several reports showed that SARS-CoV-2 affects the nervous system. We evaluated the effects of infection in prolonged culture of midbrain organoids, showing that the virus induces changes in gene expression, and fragmentation and loss of dopaminergic neurons. Our findings highlight the direct viral-induced damage to midbrain organoids indicating the relevance of assessing the neurological long-term evolution of COVID-19 patients.

neuroscience↗

bioRxiv2021

Synaptic decline precedes dopaminergic neuronal loss in human midbrain organoids harboring a triplication of the SNCA gene

1.Increased levels of the protein alpha-synuclein (-syn) are associated with the development of neurodegenerative diseases like Parkinsons disease (PD). In physiological conditions, -syn modulates synaptic plasticity, neurogenesis and neuronal survival. However, its pathogenic accumulation and aggregation results in toxicity and neurodegeneration. Here, we used a PD patient specific midbrain organoid model derived from induced pluripotent stem cells harboring a triplication in the SNCA gene to study PD-associated phenotypes. The model recapitulates the two main hallmarks of PD, which are -syn aggregation and loss of dopaminergic neurons. Additionally, impairments in astrocyte differentiation were detected. Transcriptomics data indicate that synaptic function is impaired in PD specific midbrain organoids. This is further confirmed by alterations in synapse number and electrophysiological activity. We found that synaptic decline precedes neurodegeneration. Finally, this study substantiates that patient specific midbrain organoids allow a personalized phenotyping, which make them an interesting tool for precision medicine and drug discovery.

neuroscience↗

bioRxiv2021

A pharmacophore model for SARS-CoV-2 3CLpro small molecule inhibitors and in vitro experimental validation of computationally screened inhibitors

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 {micro}M), and synthetic compounds previously not characterized (e.g. compound CID 46897844, IC50 = 31 {micro}M). 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.

bioinformatics↗

bioRxiv2020

COVID-19 Disease Map, a computational knowledge repository of SARS-CoV-2 virus-host interaction mechanisms

We describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the COVID-19 Disease Map. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.

systems biology↗

bioRxiv2020

Neurodegeneration and neuroinflammation are linked, but independent of alpha-synuclein inclusions, in a seeding/spreading mouse model of Parkinson disease

A key process of neurodegeneration in Parkinson disease (PD) is the transneuronal spreading of alpha-synuclein. Alpha-synuclein is a presynaptic protein that is implicated in the pathogenesis of PD and other synucleinopathies, where it forms, upon intracellular aggregation, pathological inclusions. Other hallmarks of PD include neurodegeneration and microgliosis in susceptible brain regions. Whether it is primarily transneuronal spreading of alpha-synuclein particles, inclusion formation, or other mechanisms, such as inflammation, that cause neurodegeneration in PD is unclear. We used spreading/aggregation of alpha-synuclein induced by intracerebral injection of alpha-synuclein preformed fibrils into the mouse brain to address this question. We performed quantitative histological analysis for alpha-synuclein inclusions, neurodegeneration, and microgliosis in different brain regions, and a gene expression profiling of the ventral midbrain, at two different timepoints after disease induction. We observed significant neurodegeneration and microgliosis in brain regions not only with, but also without alpha-synuclein inclusions. We also observed prominent microgliosis in injured brain regions that did not correlate with neurodegeneration nor with inclusion load. In longitudinal gene expression profiling experiments, we observed early and unique alterations linked to microglial mediated inflammation that preceded neurodegeneration, indicating an active role of microglia in inducing neurodegeneration. Our observations indicate that alpha-synuclein inclusion formation is not the major driver in the early phases of PD-like neurodegeneration, but that diffusible, oligomeric alpha-synuclein species, which induce unusual microglial reactivity, play a key role in this process. Our findings uncover new features of alpha-synuclein induced pathologies, in particular microgliosis, and point to the necessity of a broader view of the process of prion-like spreading of that protein.

neuroscience↗

bioRxiv2020

A new synuclein-transgenic mouse model for early Parkinson's reveals molecular features of preclinical disease

Understanding Parkinsons disease (PD) in particular in its earliest phases is important for diagnosis and treatment. However, human brain samples are collected post-mortem, reflecting mainly end stage disease. Because brain samples of mouse models can be collected at any stage of the disease process, they are useful to investigate PD progression. Here, we compare ventral midbrain transcriptomics profiles from -synuclein transgenic mice with a progressive, early PD-like striatum neurodegeneration across different ages using pathway, gene set and network analysis methods. Our study uncovers statistically significant altered genes across ages and between genotypes with known, suspected or unknown function in PD pathogenesis and key pathways associated with disease progression. Among those are genotype-dependent alterations associated with synaptic plasticity, neurotransmission, as well as mitochondria-related genes and dysregulation of lipid metabolism. Age-dependent changes were among others observed in neuronal and synaptic activity, calcium homeostasis, and membrane receptor signaling pathways, many of which linked to G-protein coupled receptors. Most importantly, most changes occurred before neurodegeneration was detected in this model, which points to a sequence of gene expression events that may be relevant for disease initiation and progression. It is tempting to speculate that molecular changes similar to those changes observed in our model happen in midbrain dopaminergic neurons before they start to degenerate. In other words, we believe we have uncovered molecular changes that accompany the progression from preclinical to early PD.

neuroscience↗

bioRxiv2020

Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge

Mobile health, the collection of data using wearables and sensors, is a rapidly growing field in health research with many applications. Deriving validated measures of disease and severity that can be used clinically or as outcome measures in clinical trials, referred to as digital biomarkers, has proven difficult. In part due to the complicated analytical approaches necessary to develop these metrics. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of Parkinsons Disease (PD) and severity of three PD symptoms: tremor, dyskinesia and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC=0.87), as well as tremor (best AUPR=0.75), dyskinesia (best AUPR=0.48) and bradykinesia (best AUPR=0.95) severity.

bioengineering↗

bioRxiv2019

Predicting cellular position in the Drosophila embryo from Single-Cell Transcriptomics data

Single-cell RNA-seq technologies are rapidly evolving but while very informative, in standard scRNAseq experiments the spatial organization of the cells in the tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to keep the localization of the cells have limited throughput and gene coverage. Mapping scRNAseq to genes with spatial information increases coverage while providing spatial location. However, methods to perform such mapping have not yet been benchmarked. To bridge the gap, we organized the DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction of cells from the Drosophila embryo from scRNAseq data, leveraging as gold standard genes with in situ hybridization data from the Berkeley Drosophila Transcription Network Project reference atlas. The 34 participating teams used diverse algorithms for gene selection and location prediction, while being able to correctly localize rare subpopulations of cells. Selection of predictor genes was essential for this task and such genes showed a relatively high expression entropy, high spatial clustering and the presence of prominent developmental genes such as gap and pair-ruled genes and tissue defining markers.

systems biology↗