Yuya Hayashi

Yuya Hayashi says: “From an organ to another organ, cells send signals to coordinate the physiology of the entire body. A well-known example is signalling by hormones, but what if cells instead wish to deliver more complex messages than signals? A striking discovery in the past years is the packaged delivery of small RNA in nano-sized vesicles called exosomes to “stream” the RNA language over a long distance. Much remains unknown, however, about the precise context of such messages that are exchanged between cells of a living animal. This project aims to decipher the secret RNA codes delivered by exosomes starring zebrafish embryos as a research model that allows genetic manipulation to capture target exosomes and live imaging of the exosome transport through the bloodstream. Furthermore, the uncovered role of exosomes will be tested using advanced nanotechnology. The deeper understanding of the exosome-powered RNA communication between distant cells will identify novel targets for non-viral gene therapy.”

Niklas Pfister

The CausalBiome project will develop a new unified framework for statistical analysis and causal inference on human microbiome data.

Microorganisms, such as bacteria, fungi and viruses interact in diverse ways with their surroundings. The human body is estimated to be a habitat for more than 10,000 different microbial species and they have been associated with various health outcomes such as cardiovascular disease, metabolic diseases, obesity, mental illness, and autoimmune disorders. Thanks to recent advances in gene sequencing technology, scientists are now able to directly measure these microbes. However, to understand how they interact with their human host, sophisticated statistical tools are needed to analyze the highly complex data. Unfortunately, current techniques do not offer a unified approach that incorporates all available knowledge into the analysis.

The CausalBiome project will fill this gap by developing novel statistical and data science analysis methods, which will lead to a better understanding of how the microbiome interacts with its host. All results will be made publicly available to help other scientists gain new insights into how microbes affect our health.

Shilpa Garg

The project aims to develop new computational methods for analysing and integrating data from both short-read and long-read DNA/RNA sequencing experiments.

The field of DNA and RNA sequencing has for many years been dominated by the so-called next-generation or second-generation sequencing technologies with rely on massive parallel sequencing of short reads, and methods for analyzing such data are well developed. Recently, however, new third-generation technologies have emerged which produce much longer reads enabling scientists to fill gaps and study phenomena such as repetitive sequences and structural variants.

However, computational methods to process and integrate these data types are missing. This project therefore aims to develop efficient, high-quality computational methods and open-source software packages for processing massive datasets for integrative sequencing analysis of complex diseases. Such new methods will significantly improve the understanding of genetic variation in novel megabase-sized repetitive regions and to study cell heterogeneity underlying complex diseases.

The computational tools will be useful to large-scale initiatives such as the Human Pangenome Reference Consortium and the Danish National Genome Center, and may yield new insights into complex diseases, such as cancer and Type 2 diabetes.

Jesper Madsen

This project will shed new light on the regulatory and functional architecture of the human pancreatic Islets of Langerhans with the aim of furthering the understanding of how it contributes to health and disease, most notably to diabetes.

In recent years, hundreds of human islet samples have been analysed with new high-throughput technologies, providing insight into single cell transcriptomes, spatial organisation, chromatin accessibility, etc. This project aims to collect, integrate, and jointly analyse these data sets with new data science methods to build a comprehensive map of the human islet.

The resulting web resource will be made publicly available for other researchers to pave the way for new insight that could lead to prevention, diagnosis and treatment of diabetes and other pancreas-related diseases.

Signe M. Jensen

This project aims to improve the analysis methods for high-throughput plant phenotyping to provide an efficient and accurate method for evaluating ecotoxicological hazard of new and existing pesticides.

Crop production needs to increase dramatically to feed the growing world population, particularly if meat consumption and production is reduced to lower CO2 emissions. Agrochemicals, such as herbicides, insecticides, fungicides, and chemical growth regulators, are widely used to achieve the goal of higher, more stable yields but to avoid harm to the environment, such products must undergo rigorous environmental toxicological risk assessment.

The project seeks to expand and improve upon the so-called benchmark dose methodology – a statistical methodology used for ecotoxicological risk assessment – to use large-scale, high-throughput and high-dimensional dose-response data to assess the efficacy and ecotoxicological hazards of chemicals used in agriculture or in private gardens. The methodology should be equally useful for non-chemical stressors, e.g., evaluating the effect of climate-related stress on plants in the context of climate change.

Guillaume Ramstein

Guillaume Ramstein says: “Quantitative genetics relies on associations between DNA changes and observed differences. These associations are useful to predict plants’ performance and select the most promising varieties. However, they are only correlations and cannot tell us what exact DNA changes are causing the observed differences. In SIEVE, I will develop new ways of detecting associations which avoid the confusion between correlation and causation. Using machine learning, I will learn the impact of mutations on fitness based on sequence conservation across species. Then, I will validate my predictions by evaluating the impact of induced mutations in experimental populations. I will assess whether my predictions can explain observed differences for traits like metabolite production and survival.
My predictions about the effect of DNA changes will allow breeders to target the appropriate edits for improving the fitness of important crops, for example to increase grain yield in wheat or biomass in barley.”

Klaus Herburger

Klaus Herburger says: “Plant cells are surrounded by cell walls, which are complex networks of polysaccharides and provide the structural backbone of plants. As such, cell walls constitute the bulk of green biomass and are a fundamental natural resource for our society, including food, fodder, fibres, and fuel. Cell walls are highly dynamic structures and constantly remodelled by various proteins to make the cell wall more rigid or expand it when plants grow. This project aims to understand how these proteins work together in living plants and then to engineer them to enhance the strength of cell walls, for example by introducing additional crosslinks between polysaccharides. Such plants will be more resistant to tissue failure caused by wind or water lodging. Thus, this project may provide means to strongly reduce major causes of crop failure and stronger cell wall biomass that can be used for a vast number of applications, such as superior construction materials.”

Phillip Newton

Linda Engström Ruud

Linda Engström Ruud says: “My project aims at understanding the brain-mediated mechanisms behind the treatment strategy of long-acting glucagon-like 1-receptor (GLP-1R) agonists. GLP-1R agonists have highly beneficial effects on body weight and management of blood glucose levels and are used in the clinic to treat type 2 diabetes and, in the case of liraglutide, also obesity. Still, it is unclear how the appetite-reduction and improvement of glucose tolerance are mediated by the brain. It is also unclear how they induce nausea, a problematic side-effect. I intend to use a top modern approach that will allow me to in vivo specifically study and manipulate neurons that are directly and indirectly activated by long-acting GLP-1R agonists in mice. I will for the first time identify these neurons and study how they are connected. The results can aid in more specific targeted treatment and avoidance of side-effects. Moreover, they will also impact on our basic knowledge of food intake and glucose metabolism regulation.”

Photo by: Johan Wingborg