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Parkinson’s disease (PD) is the second most common neurodegenerative disease which is characterized by neuronal loss of dopaminergic neurons (mDA) in the substantia nigra. The underlying complexity of the disease and limited amount of patient material limits current interventions to only symptomatic and no curative treatment despite intensive research. We use patient-derived induced pluripotent stem cells to generate mDAs and investigate disease mechanisms by multiomics characterization including single cell RNA-sequencing and bulk proteomics and metabolomics. For this purpose, we developed an extended Non- Negative TriMatrix Factorization approache that allows to integrate the heterogeneous omics data with knowledge of molecular databases including protein-protein, genetic and metabolic interactions as well as co-expression profiles. Our approach was able to identify already PD-associated but also new druggable candidate genes of PD development.

Continuous surveillance of COVID-19 diffusion remains crucial to control its diffusion and to anticipate infection waves. Detecting viral RNA load in wastewater samples has been suggested as an effective approach for epidemic monitoring and the development of an effective warning system. However, its quantitative link to the epidemic status and the stages of outbreak is still elusive. Modelling is thus crucial to address these challenges. In this study, we present a novel mechanistic model-based approach to reconstruct the complete epidemic dynamics from SARS-CoV-2 viral load in wastewater. Our approach integrates noisy wastewater data and daily case numbers into a dynamical epidemiological model. As demonstrated for various regions and sampling protocols, it quantifies the case numbers, provides epidemic indicators and accurately infers future epidemic trends. Following its quantitative analysis, we also provide recommendations for wastewater data standards and for their use as warning indicators against new infection waves. In situations of reduced testing capacity, our modelling approach can enhance the surveillance of wastewater for early epidemic prediction and robust and cost-effective real-time monitoring of local COVID-19 dynamics.

In the summer of 2021, European governments removed most NPIs after experiencing prolonged second and third waves of the COVID-19 pandemic. Most countries failed to achieve immunization rates high enough to avoid resurgence of the virus. Public health strategies for autumn and winter 2021 have ranged from countries aiming at low incidence by re-introducing NPIs to accepting high incidence levels. However, such high incidence strategies almost certainly lead to the very consequences that they seek to avoid: restrictions that harm people and economies. At high incidence, the important pandemic containment measure test-trace-isolate-support becomes inefficient. At that point, the spread of SARS-CoV-2 and its numerous harmful consequences can likely only be controlled through restrictions. We argue that all European countries need to pursue a low incidence strategy in a coordinated manner. Such an endeavour can only be successful if it is built on open communication and trust. Copyright (C) 2021 The Authors. Published by Elsevier Ltd., Funding Agencies|European UnionEuropean Commission [101016233]; Max Planck SocietyMax Planck SocietyFoundation CELLEX; "Netzwerk Universitatsmedizin" (NUM) project egePan [01KX2021]; Digital Society research program - Ministry of Culture and Science of the German State of North Rhine-Westphalia; University of Malta; Luxembourg National Research Fund (FNR) as part of the COVID-19 Fast-Track research project Cov-Screen [COVID-19/2020- 1/14715687]; European Research Council (ERC) under the European UnionEuropean Research Council (ERC) [863664]; Slovenian Research AgencySlovenian Research Agency - Slovenia [P1-0403, J1-2457]; University of Crete; Maastricht University; European CommissionEuropean CommissionEuropean Commission Joint Research Centre; UK Economic and Social Research Council (ESRC)UK Research & Innovation (UKRI)Economic & Social Research Council (ESRC) [ES/T014164/1]; UK Medical Research Council (MRC)UK Research & Innovation (UKRI)Medical Research Council UK (MRC) [MR/S035818/1]; Leverhulme TrustLeverhulme Trust [RPG-2020-295]; Wellcome TrustWellcome Trust [209519/Z/17/Z, 106612/Z/14/Z]; Polish National Science Centre OPUS grant [2019/33/B/NZ2/00956]; Luxembourg National Research Fund (FNR) as part of the COVID-19 Fast-Track research project CO-INFECTOMICS [COVID-19/2020-1/14729513]; European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programmeEuropean Research Council (ERC) [863664]

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 up

Stress hormones are believed to skew the CD4 T-cell differentiation towards a Th2 response via a T-cell-extrinsic mechanism. Using isolated primary human naïve and memory CD4 T cells, here we show that both adrenergic- and glucocorticoid-mediated stress signalling pathways play a CD4 naïve T-cell-intrinsic role in regulating the Th1/Th2 differentiation balance. Both stress hormones reduced the Th1 programme and cytokine production by inhibiting mTORC1 signalling via two parallel mechanisms. Stress hormone signalling inhibited mTORC1 in naïve CD4 T cells (1) by affecting the PI3K/AKT pathway and (2) by regulating the expression of the circadian rhythm gene, period circadian regulator 1 (PER1). Both stress hormones induced the expression of PER1, which inhibited mTORC1 signalling, thus reducing Th1 differentiation. This previously unrecognized cell-autonomous mechanism connects stress hormone signalling with CD4 T-cell differentiation via mTORC1 and a specific circadian clock gene, namely PER1.

Bistable biological regulatory systems need to cope with stochastic noise to fine tune their function close to bifurcation points. Here, we study stability properties of this regime in generic systems to demonstrate that cooperative interactions buffer system variability, hampering noise-induced regime shifts. Our analysis also shows that, in the considered cooperativity range, impending regime shifts can be generically detected by statistical early warning signals from distributional data. Our generic framework, based on minimal models, can be used to extract robustness and variability properties of more complex models and empirical data close to criticality. Our generic framework, based on minimal models, can be used to extract robustness and variability properties of more complex models and empirical data close to criticality.

Mutations in PINK1 and Parkin are two of the main causes of recessive early-onset Parkinson’s disease (PD). We generated human induced pluripotent stem cells (hiPSCs) from fibroblasts of a 64-year-old male patient with a homozygous ILE368ASN mutation in PINK1, who experienced disease onset at 33 years, and from fibroblasts of a 61-year-old female patient heterozygous for the R275W mutation in Parkin, who experienced disease onset at 44 years. Array comparative genomic hybridization (aCGH) determined genotypic variation in each line. The cell lines were successfully used to generate midbrain dopaminergic neurons, the neuron type primarily affected in PD.

Astrocytes, the most abundant cell in the central nervous system, have a star-shaped morphology and play a central role in brain homeostasis as metabolic mediators between neurons and blood vessels. Recent evidence put astrocytes therefore in the focus of neurodegeneration (ND), since in case of metabolic dysfunctions as e.g. observed in Alzheimer’s disease or Parkinson’s disease, they cannot provide neurons with sufficient amount of nutrients. Furthermore, progression of ND is often accompanied by changes in astrocytic morphology further indicating the essential role of astrocytes in the brain. Despite its importance, the complex astrocytic morphology is often neglected in modelling of metabolic reactions [1]. In this study, we propose a computational model that describes cellular metabolism through a reaction-diffusion system including two fundamental pieces of information: the intracellular spatial arrangement of the reaction sites and the real, complex geometries by using the previously developed method CutFEM([2][3]). Our findings show how intracellular spatial organisation and diffusion limitation as well as the physio- logical cell shape must be taken into account to go towards biological models that are closer to reality. In particular, the spatial distribution of mitochondria notably impacts the cellular ATP : ADP ratio, which is an indicator of the energetic state of the cell. Finally, we solve our system in a 3D human astrocytic morphology and study the different spatial arrangements of the reaction sites simulating physiological and dysfunctional behaviour. In this regard, we believe, that the proposed model is a useful instrument to gain insights into the role astrocytes play in neurodegeneration.

Mathematical models are often used to describe and better understand the mechanisms underlying dynamic processes. To receive a more quantitative perspective of the underlying dynamic process, this thesis combines experimental data, computer simulations and mathematical modeling. More specifically, stochastic and deterministic mathematical models were developed to dissect the mechanisms of gene expression regulation by investigating intrinsic noise and external cues., Mathematische Modelle werden oft genutzt, um dynamische Prozesse zu beschreiben und deren Mechanismen zu verstehen. Um eine quantitative Darstellung des dynamischen Prozesses zu erhalten, wurden in der vorliegenden Arbeit experimentelle Daten, Computersimulationen und mathematische Modellierung kombiniert. Genauer gesagt, wurden stochastische und deterministische Modelle aufgestellt, um genregulatorische Mechanismen unter Genexpressionsrauschen und externen Signalen zu untersuchen.