Cell Physiology and Applications
Below, you can find the updated list of Bachelor and Master internships available at the moment.
For more information on available internships on Single-cell Physiology and on Host microbiome interactions, please contact directly the teamleaders in this group:
Jurgen Haanstra – j.r.haanstra[at]vu[dot]nl
Herwig Bachmann – h.bachmann@vu.nl
Remco Kort – r.kort@vu.nl
| Project title | Type of research | Supervisor(s) |
|---|---|---|
| Understanding changes in energy and NAD+ metabolism in response to oxidative stress. | Experimental (Master) Duration: Min 6 months | Johan van Heerden j.van.heerden[at]vu[dot]nl Personal page |
| Background: There is growing awareness that oxidative stress plays a key role in the aging process as well as many diseases including Parkinson’s Disease, cancer, diabetes, chronic inflammation and metabolic syndrome. To mitigate oxidative stress, cells rely on various detoxification and repair processes. These processes, in turn, are critically dependent on NAD+, a molecule that functions as a coenzyme in cellular redox reactions, and as a substrate for stress response and repair pathways. Ensuring a sufficient supply of NAD+ is therefore a key determinant of cellular robustness against oxidative stress. Not surprisingly then, diseases associated with oxidative stress are also often characterized by decreased levels of NAD+. In this ongoing research project, we use the model eukaryote, S. cerevisiae (aka brewer's yeast), to gain insight into the metabolic process, and with a focus on NAD+ metabolism, that underlie cellular responses to oxidative stress. We combine physiological experiments, with gene expression measurements and quantification of key metabolites, using a variety of fluorescent reporter systems that allow for in vivo quantification in single cells. What you will learn: During this project you will become familiar with the controlled cultivation of S. cerevisiae, the generation and implementation of fluorescent reporter systems for either protein expression or metabolite changes, and their use in various population and single-cell level measurement techniques. You will learn to use a flow cytometer and an inverted fluorescent microscope to observe changes in vivo and in single cells. In addition, image processing will be used to quantify and analyse microscopy data. Your background: Ideally, you have a strong background in experimental microbiology, cell physiology, or biochemistry and an interest in metabolism. Some basic programming experience will be useful (Python, R, Matlab or similar). Supervisor: Dr. Johan van Heerden |
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| 1. Accessory functions in the pangenome of the human vaginal commensal Lactobacillus crispatus 2. Bacterial strain engraftment in the gorilla gut microbiome after fecal transplantation | Two Internships Bioinformatics (Master) | Dr Douwe Molenaar Prof Remco Kort (r.kort@vu.nl) |
| 1. The accessory genome in bacteria can be considered the cradle for adaptive evolution. For this internship a set of whole genome sequences of L. crispatus will be analyzed and accessory functions in the pangenome will be evaluated with a particular emphasis on functions that are important for sustained colonization in the host. Techniques: differential analysis of a large set of sequenced genomes. Duration: 6 months Further reading: van der Veer C, Hertzberger RY, Bruisten SM, Tytgat HLP, Swanenburg J, de Kat Angelino-Bart A, Schuren F, Molenaar D, Reid G, de Vries H, Kort R. (2019) Comparative genomics of human Lactobacillus crispatus isolates reveals genes for glycosylation and glycogen degradation: implications for in vivo dominance of the vaginal microbiota. Microbiome 7:49. Hertzberger R, May A, Kramer G, van Vondelen I, Molenaar D, Kort R (2022) Genetic elements orchestrating Lactobacillus crispatus glycogen metabolism in the vagina. Int J Mol Sci. 23:5590. 2. A fecal transplantation has been carried out to cure Akili, the ARTIS silverback gorilla, by recovery of the gut microbiota after antibiotic treatment. Longitudinal 16S rRNA profiling and metagenome data have been collected in donor and recipient feces to monitor bacterial population dynamics in the gut before, during and after the fecal transplantation intervention. The data will be analyzed with particular emphasis on bacterial strain engraftment. techniques: comparative metagenome analysis (optional metabolic analysis) duration: 4-6 months Further reading: Houtkamp IM, van Zijll Langhout M, Bessem M, Pirovano W, Kort R. (2023) Multiomics characterisation of the zoo-housed gorilla gut microbiome reveals bacterial community compositions shifts, fungal cellulose-degrading, and archaeal methanogenic activity. Gut Microbiome. 4:e12 |
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| Exploring resource allocation strategies and metabolic architectures | Computational Bachelor (with strong interest in computational methods)/Master | Pranas Grigaitis (p.grigaitis@vu.nl) |
| In this topic, we blend existing knowledge of biochemistry and physiology together with different types of computational modeling to understand how metabolisms work and interact with each other. More information about Pranas' work and current vacancies are posted on this website. Techniques: genome-scale metabolic modelling (both conventional and proteome-constrained) (PySCes CBMpy, COBRA etc.), kinetic modelling (COPASI), programming with Python and/or R for data analysis and visualization |
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| Metabolism in health and disease | Experimental or computational (combinations are possible) Bachelor/master | Jurgen Haanstra |
| The work in this topic aims to understanding control and regulation of metabolism to reveal selective drug targets in pathogens and other disease-causing cells. In addition, we also want to understand these aspects for healthy cells to make sure that interventions against the disease will not harm them. We work with the parasite Trypanosoma brucei and with liver cancer cells in the wetlab, but also do research on the parasite Schistosoma mansoni, on head- and neck cancer and blood cell precursors in the dry-lab (always in collaboration with experimental labs Techniques: Wetlab: cell culture, metabolite measurements, enzyme assays. Dry lab: kinetic modelling (COPASI, PySCes (python-based), genome-scale modelling |
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| Kinetic modelling to understand the link between NAD+ metabolism and oxidative stress. | Theoretical (Master) | Daniëlle Gramsbergen d.s.gramsbergen[at]vu[dot]nl Personal page |
| What you will be working on and the core skills you will learn. In this ongoing project, we are systematically building a kinetic model to better understand the link between NAD+ metabolism, central energy metabolism and oxidative stress in human liver cells. An existing model describes biosynthesis and consumption pathways of NAD+, but still lack details of central energy metabolism (glycolysis and respiration). We are looking for a motivated student to work on and expand the current model with kinetic descriptions of central energy metabolism. You will learn how to translate biological knowledge to ordinary differential equations, how to code such a model in Mathematica and which questions kinetic models of metabolism allow us to answer. Why this work is relevant. Oxidative stress is defined as an overabundance of reactive oxygen species (ROS) and is associated with a decline in concentration of this abundant and important redox cofactor: NAD+. How this NAD+ decrease manifests on a biochemical level and how the NAD+ metabolome is influenced by oxidative stress is largely unknown. However, because oxidative stress is linked to ageing, neurodegenerative disease, viral infection and many more pathologies, gaining an understanding of the interplay between oxidative stress and the NAD+ metabolome on a biochemical level is the first step in designing novel strategies to combat oxidative stress in humans. Because the total poolsize of NAD+ is mostly dependent on the balance of biosynthesis and consumption of NAD+, these were the first pathways included in the model and already gave us some insight on potential strategies to perturb this NAD+ metabolome to reduce oxidative stress. However, because NAD+ is also an important redox factor in central carbon and energy metabolism, we would like to find out using kinetic modelling how changes in energy metabolism during oxidative stress influences these biosynthesis and consumption pathways. |
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| Exploration of the nutritional status on metabolic robustness in Caenorhabditis elegans | Experimental (Master) Filled until Jan 2025 | Johan van Heerden j.van.heerden[at]vu[dot]nl Personal page and Samantha Hughes (Toxicology) s.hughes[at]vu[dot].nl |
| Background: There is a close interaction between oxidative stress, immune activation, energy metabolism and cell viability. There is also growing awareness that oxidative stress plays a key role in the aging process as well as diseases including Parkinson’s Disease, cancer, diabetes, and chronic inflammation. To mitigate oxidative stress, cells rely on various detoxification and repair processes. These processes, in turn, are critically dependent on NAD+, a molecule that functions as a coenzyme in cellular redox reactions, and as a substrate for stress response and repair pathways. Ensuring a sufficient supply of NAD+ is therefore a key determinant of cellular robustness against oxidative stress. Not surprisingly then, diseases associated with oxidative stress are also often characterized by decreased levels of NAD+. Nutritional status of the cell can influence NAD+ levels and it is possible that this confers some protection against oxidative stress. To be able to test this hypothesis, it is important to have a clearly defined culture medium and methods to quantify oxidative stress. To this end, the nematode Caenorhabditis elegans is an ideal model. The nematodes can be cultured in a chemically defined culture media and are well characterized in terms of life-history traits (e.g., brood size, growth, lifespan). In addition, the use of fluorescent reporters allows for a quick readout of the level and impact of oxidative stress. What you will learn: During this project you will become familiar with the handling of C. elegans and how to measure standard endpoints of viability including brood size, growth and lifespan. You will learn to use an inverted fluorescent microscope to observe the fluorescent reporters as well as working with a variety of image processing tools, such as ImageJ. Planned activities: • Define the axenic growth media (without bacterial food source) and set up an SOP • Generate a dose-response curve for H2O2 (to induce oxidative stress) for viability, growth and fertility • Characterise the effect of the induction of oxidative stress using translational reporters for ROS • Link the level of oxidative stress to changes in NAD+ metabolism using mutants and reporter strains Supervisors: Dr. Samantha Hughes Dr. Johan van Heerden |
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| Using complex cytometry and omics data to explore how lifestyle changes and ethnicity impact on immunometabolism and disease risk | Theoretical (Master) Duration: Min 6 months | Johan van Heerden j.van.heerden[at]vu[dot]nl Personal page and Jan Van den Bossche (Molecular Cell Biology and Immunology) j.vandenbossche[at]amsterdamumc[dot]nl Personal page |
| Background: Macrophages are innate immune cells that are functionally very multifaceted and are involved in almost all aspects of life; from immunity and host defense, to homeostasis, tissue repair and development. To fulfil these distinct actions, macrophages adopt a plethora of activation states. Understanding their regulation and phenotypic heterogeneity is crucial because macrophages are critical in many diseases and have emerged as attractive targets for therapy of cancer, atherosclerosis, asthma and many more “Western” killers. We demonstrated the crucial role of metabolic reprogramming in distinct macrophage activation states. A such, our in vitro assays with well-defined pro-inflammatory (M1) and anti-inflammatory (M2) macrophages clearly show that the way a macrophage metabolizes its nutrients not only provides energy, but directly dictates its function. To advance our findings to human health, our current knowledge now needs to be translated to the complex in vivo environment, where macrophages and their monocyte precursors are exposed to a complex mixture of microenvironmental and environmental stimuli and don’t classify as M1 or M2. In other words, the metabolic roadmaps of non-M1/M2 macrophage and immune cells subsets in general need to be defined in health and disease. The general goal and what you will learn: By analyzing (single-cell) RNA-sequencing transcriptomic data sets, this project will set out to define how the metabolic profile of immune cell subsets are regulated to lifestyle changes and ethnicity, and how this relates to disease risk relate with their phenotype in function. During this project you will apply various analytical techniques to process, analyze, interpret and visualize (single-cell) RNA-sequencing RNA sequence data. In addition, you will immerse yourself generally in immunometabolism and specifically in macrophage biology. Potential research angles and outcomes: 1) Using RNA-seq and high-end cytometry data obtained from male versus female individuals from European and South-Asian origin, we will demonstrate how ethnicity impacts on immune cell phenotypes and their metabolism, and how this is related to their (cardiovascular) disease risk OR 2) Using available scRNA-seq data from cancer patients, metabolic profiles of immune cells that associate with clinical outcome after therapy can be defined. We hypothesize that distinct microenvironments will results in altered metabolic profiles and associated immune cell phenotypes. Information obtained from scRNA-seq analysis will allow experimental validation by immune staining and confocal microscopy. Interestingly, the sequencing data itself might already provide indications about the immune cell’s (pseudo)location. Overall, this project will reveal potential new targets to (re)shape macrophage metabolism, function and disease outcome. Depending on the duration of the project, observations and hypotheses generated based on scRNA-seq analysis could be validated experimentally in the laboratory. Your background: Ideally, you have a background in bioinformatics/systems biology or a similar discipline, with a strong interest in cell biology. For this project programming skills are a prerequisite (R, Python, Matlab or similar). Supervisors: Dr. Jan Van den Bossche Dr. Johan van Heerden |
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