In both PD and AD, gender differences have been observed in the incidence and phenotypic manifestations of the disorder. Although these differences may result from diverse behaviors and lifestyles, previous studies suggest that the underlying causes are more complex and disease-specific, and involve hormonal and genetic influences. In the GenderND project, we investigate whether specific genetic factors contribute to the observed gender differences in neurodegenerative disorders. For AD, we are using animal models and molecular data from human biospecimens to study a candidate gene derived from the statistical analysis of multiple large-scale omics datasets from AD case/control studies. The corresponding sex-linked gene, ubiquitin-specific peptidase 9 (USP9), has a gender-biased activity in the human brain and encodes an enzyme reported to regulate the phosphorylation of MAPT, a protein thought to play a central role in AD pathogenesis. We had previously shown that the knockdown of USP9 results in a decreased MAPT gene expression in zebrafish embryos and in a human cell culture model. More recently, preliminary experiments in a mouse model for AD based on the injection of amyloid-beta oligomers into brain hippocampi revealed significant, gender-specific alteration patterns in USP9 and MAPT. Moreover, in a second AD model, brains of mice expressing different human APOE isoforms (APP/E3 and APP/E4 mice) also displayed gender-dimorphic differences in USP9 and MAPT gene expression.

For PD, an initial analysis of gender-linked genes using an integrated analysis of omics datasets from PD case/control studies revealed multiple candidate disease genes with gender-specific changes in PD. These candidates are currently further investigated using analyses of PD-related changes in the surrounding molecular interaction network. The resulting information on disease-linked gender differences in the brain transcriptome can provide new insights to support the development of patient-tailored diagnostic and therapeutic approaches for the studied neurodegenerative disorders.

Digital biomarkers (DMs) from sensors and devices have the potential to change our understanding of Parkinson’s Disease (PD) fundamentally, because they allow for a quantitative and continuous monitoring of disease symptoms, also outside clinics. This includes the possibility to monitor the response to treatment, which opens the opportunity to adapt medication pathways quickly, if necessary. The aim of this project is to evaluate, in how far DMs extracted from a mobile gait sensor system as well as recordings of voice and face movement could help for accurate disease diagnosis and treatment dependent prognosis for each individual patient. This could help to take better-informed medical decisions for each patient at the right time.
Our project brings together medical and computational experts from multiple countries in the field of PD research. Starting from pre-existing data and advanced AI methods developed by project partners in the past, we are assessing different types of DMs regarding their ability to predict different types of disease trajectories and the treatment dependent change of disease symptoms over time. Moreover, we use statistical and AI methods to study the relation of different types of DMs to each other, to clinical outcome scores and to molecular disease mechanisms, opening the opportunity to a so far unseen level of interpretation of DMs. Finally, we provide important insights into feasible legal pathways for the future use of AI and DMs in clinical routine.
Altogether, this project is contributing to defining the technical, legal, ethical and social basis for the future use of DMs within Personalized Medicine based PD treatment. The study is funded by the EU Horizon 2020 ERA PerMed programme.

Sleep-wake disturbances (SWD) such as sleep fragmentation and excessive daytime sleepiness are early symptomatic manifestations of prodromal Parkinson’s disease (PD) and occur in up to 90% of patients over the course of the disease. They are a major source of PD-related disability, diminished quality of life, and an important disease-modifying factor leading to accelerated motor and cognitive decline, and psychiatric manifestations. Interventions that rebalance SWD may, therefore, have the potential to relevantly lessen the burden of symptoms and even slow the progression of PD. The usefulness of available pharmacological treatment strategies is limited and associated with treatment-related complications. In this context, deep brain stimulation (DBS) as a well-established
symptomatic treatment for motor symptoms in PD might offer a powerful intervention to rebalance the impaired neural circuit switching and imbalance between inhibitory and excitatory neuronal populations that are mediating sleep disturbances in PD.
The recent introduction of novel DBS technology allows for innovative treatment approaches. Specifically, segmented electrode contacts for directional steering of the electrical stimulation field, and multiple independent current circuits for the applications of different stimulation patterns and frequencies at different electrode contacts of the same DBS lead, open up new opportunities. They allow for individualizing the stimulation parameters and adapting them to the neuroanatomical-functional requirements. In this consortium, we wish to investigate prospectively a PD patient cohort with regard to clinical, neuroimaging, EEG sleep recordings, and omics biomarkers, while comparing an intervention group treated with novel DBS paradigms with a matched control group.

Proteins with neurotrophic and neuroprotective functions are thought to play a key role in age-related disorders, such as Alzheimer’s and Parkinson’s disease. Sequence variations in corresponding genes can affect the susceptibility for neurodegenerative disorders, and the drug-based activation of neuroprotective proteins is widely studied as a possible new treatment strategy. However, previously no central database had been available that captures the current information on neurotrophic/protective proteins in the literature and allows researchers to study the available experimental evidence for a protein’s trophic/protective actions. By using literature mining in combination with subsequent manual curation, we have recently created a web-based database of known neuroprotective/-trophic proteins (www.neuroprodb.net). The web-service provides details on the reported in vitro and in vivo evidence for neurotrophic and/or –protective functions in the peer-reviewed literature, and the condition-specificity of these functions. This information is complemented by details on relevant gene expression changes in common neurodegenerative disorders, on protein sub-cellular localizations, tissue-specific gene/ protein expression and protein-protein interactions. Currently, we are using the assembled database to investigate to which extent neuroprotective/trophic functions of a protein can be predicted by machine learning algorithms, and to study disease-associated network perturbations around neuroprotective genes in Parkinson’s and Alzheimer’s disease.

Check out our full list of tools for analysing omics data and general gene and protein lists