Projects Involved
Understanding the molecular responses in respect to aging and longevity
Aging, and in particular, longevity have always been a hallmark of scientific research. With the advance of molecular technologies, it is now possible to study aging on multiple levels of biological hierarchy and, even more excitingly, in an integrated manner to establish the molecular interactome leading to longer life.
One may envision comparisons between older persons with kidney failure as rapidly aging persons (and tissues) such as in cohorts collected at CECAD, that can be compared to individuals aging at an average rate and individuals with an exceptionally healthy aging trajectory (collected at LUMC).
This project will focus on establishing a collaborative exchange with world-leading researchers in the aging field in and around the Cologne campus across the CECAD, University Hospital Cologne, MPI-AGE, CMMC, and LUMC.
LUMC currently holds the Leiden Longevity Study (LLS) on long-lived individuals and their siblings, their middle-aged offspring and the partners thereof as including clinical, molecular, and demographic data. Relevant for this project are the participants of IOP2 and 3 and an intervention study GOTO. In parallel, the Dept. 2 of Internal Medicine (UHC/CECAD) holds several cohorts suffering from chronic kidney disease (CKD) including intervention studies with similar data depth.
CKD is one of the key morbidities leading to premature aging and increased risk of aging-associated diseases in humans. The dietary and physical exercise intervention substudies of the LLS and the CKD cohorts will complement the knowledge on beneficial and adverse molecular profiles in the circulation related to kidney aging with a specific focus on proteomic responses. Dietary interventions are among the most powerful tools to increase lifespan and organismal fitness and are conserved in evolution.
Use of such approaches in elderly individuals and patients suffering from CKD is expected to revert aging-associated changes. Inclusion of data before and after these interventions allows for a dynamic view in a longitudinal fashion. Taken together, the combination of these large datasets is a unique asset and allows for a deep molecular phenotyping of aging combined with clinical characteristics in several conditions of human aging and longevity. One approach to utilise this large cohort of data is to develop a network representation of the interactions between every possible combination of features.
In its simplest form this can be represented by a correlation network. However, correlation does not always perform well when data is zero-inflated for example - a hallmark of count based datasets. For this reason, we will use data property relevant modelling techniques to link features with each other. For zero-inflated data this could employ hurdle or negative binomial, for demographics binomial, and for normally distributed data gaussian models. Such an approach then allows us to develop a better understanding of the relationships between molecular responses and the aging processes.
In addition, more accurate biomarkers predicting age and disease could highlight key biological processes that need to be further studied in the context of longevity and general health or detect confounding factors associated with disease.
Building resilience against mental illness during endocrine-sensitive life stages
Throughout human life, the human brain undergoes significant changes shaped by the interactions between environmental, genetics, and hormonal factors. These changes occur mainly during key hormonal transition stages, including pre and postnatal development, puberty, the peripartum period, and the transition to older age. Based on the individual’s resilience and susceptibility, any disruptions during these pivotal periods can increase vulnerability to mental illness, including depression.
Approximately one in seven individuals worldwide experiences mental illness at some point in their lives, contributing significantly to the global economic burden. As a result, researchers’ efforts are increasingly focused on understanding mental illnesses to mitigate their impact and improve outcomes.
Current diagnostic and treatment approaches for mental illness are predominantly symptoms-based and lack the biological profiling tailored to individual patients. Therefore, the conventional diagnostic strategies are not able to differentiate between the different subgroups of mental disorders, overlooks the individual’s variability, and fail to address sex and age related differences.
The major obstacles to improve prevention and treatment of mental illness include the lack of information regarding susceptibility or resilience factors, the gap between the molecular and neurobiological understanding of mental states, and the insufficient knowledge of how genetic and environmental factors interact to shape mental states
To address these challenges, the RE-MEND project aims to identify environmental and genetic risk and protective factors that influence the individual’s mental health. This will be achieved through analysing existing and newly generated data on the epigenome, transcriptome, proteome, adductome, metabolome, endocrinology, and neural markers to improve the molecular and neurobiological understanding of resilience and susceptibility mechanisms in the progression of mental illness.
These datasets represent hierarchical layers of biology that work in concert to achieve a given biological function, phenotype, or disease manifestation. While a single layer is often sufficient to distinguish the difference between healthy and diseased states, softer endpoints-such as changes in behavior or increased susceptibility-require a more detailed representation of the biological system. Data integration is thus essential, enabling the combination of multiple datasets and hierarchical layers to uncover the interactions shaping mental health outcomes. The complexity and the size of such datasets often necessitate the need for machine learning (ML) and artificial intelligence (AI) approaches to analyze and extract key molecular factors associated with adversity.
Evaluating the molecular basis of chronic kidney diseases
Chronic kidney disease (CKD) is a substantial burden to the human population. Approximately 1:10 adults are affected by chronic kidney disease, particularly with increasing age, and about 1:10000 children are impacted by this disease group. Regardless of the underlying factors and age group, a key clinical parameter in defining chronic kidney disease is the glomerular filtration rate (GFR) which represents the functional ability of the kidney. To reduce impact on individuals, estimated GFR (eGFR) rates can be calculated using simple blood markers, the age and gender of the individual, and in the case of children, the height of the child. While the eGFR can give a good account of current functional activity of the kidney, the decrease in eGFR over time, or the eGFR slope, provides information on severity of disease and can enable different treatment or management options for the patient.
To improve our understanding of the molecular state of CKD within adults and children, our group is working closely with Prof. Roman-Ulrich Müller to establish an improved understanding of the molecular basis of Autosomal dominant polycystic kidney disease (ADPKD) which manifests in around 1:1000 individuals. Together with Prof. Müller we have developed predictive models that associate proteomic responses to eGFR slope, are exploring the impact of metabolomics on ADPKD patients, exploring the impact of diets on various aspects of kidney health in these patients, and aim to establish a real-time database analysis and visualisation system of the underlying cohort.
Furthermore, we are also exploring the how genes are modulated on a single cell level within CKD. Within the KFO 329 we are developing approaches to evaluate functional units within kidneys by utilising spatial transcriptomics technologies linked to state-of-the-art computational methodologies and can show how cellular compositions and gene levels change as a result of damage to podocytes, a key cell type involved in renal function.
On the paediatric level, we work closely together with Prof. Max Liebau across multiple cohorts, including the ESCAPE, 4C, 3H, and Neocyst cohorts, associating molecular responses to eGFR slope, identifying novel markers that may drive the loss of kidney function, and exploring how to improve patient stratification with minimal invasive techniques.