Tanvi Taparia

Bioinoculants are teams of helpful microbes that protect plants and keep them healthy, even under stress. Yet, these microbial helpers often fail to work when moved from the predictable environment of a research lab to the complex conditions of a farmer’s field. Project Lab2Field explores the social lives of these helpful microbes—how they interact with plants, soils, and other local microbes, in real-world conditions. By uncovering the secrets to their social behaviour, we can create manuals for making these tiny helpers thrive. This will help create reliable and eco-friendly solutions for farmers to use bioinoculants in their fields with success. This research could reduce the need for chemicals like pesticides, promote organic farming practises, help crops adapt to climate change, and support more sustainable food production. 

Nelly S. Raymond

Bio-based fertilisers (BBF) offer a sustainable alternative to synthetic fertilisers, but their adoption is limited by a lack of understanding of their behaviour in soil. This is particularly crucial for phosphorus (P), a finite resource limiting crop productivity in 67% of soils. BBF adds significant carbon (C) and nitrogen (N) to soil, influencing nutrient cycling by microorganisms, key drivers of the P cycle. Their activity is often limited by C and N availability. Plant roots also supply labile C. PRIME-P aims to understand soil P cycling mediated by microorganisms in relation to C and N from BBF and plant roots. Using experimental and modelling approaches, PRIME-P will evaluate BBF and root exudates’ interaction on microbial P mobilisation. The project addresses critical soil-plant-microorganism interactions, paving the way for scalable, bio-based solutions to sustainable soil fertility and beyond.

Jessica Xin Hjaltelin

This project aims to improve early detection of pancreatic cancer (PC) by exploring its link with diabetes, especially new-onset diabetes (NOD). PC is one of the most difficult cancers to diagnose early, resulting in one of the lowest 5-year relative survival rates at only 12%. Using artificial intelligence (AI) and data from Danish health registries, the project will develop models to predict PC by analysing patient histories, including hospital diagnoses, laboratory measurements, GP and hospital medications, and primary care visits. Early detection of PC is crucial since it significantly increases survival rates. By focusing on patients with NOD, who are at a higher risk of developing PC, the project seeks to identify subtle signs of the disease early on. This will involve creating AI models that can accurately pinpoint individuals at high risk. The project aims to make significant strides in early cancer detection through timely diagnosis and intervention.

Sarah Rennie

Beyond serving as simple intermediary molecules in the transfer of information from DNA to proteins, RNAs must be tightly regulated, and their correct processing is essential for maintaining cellular function. mRNA fate is strongly influenced by a diverse group of covalent modifications and interactions with RNA binding proteins, which regulate all stages of its life cycle. Recent technological advances are generating large quantities of data in this field, yet there is a need for new computational methods to fully utilise these datasets. To this end, we will develop powerful new frameworks to 1) investigate how RNA modifications are dynamically regulated at the single-cell level and 2) identify the molecular determinants underlying modification patterns. In addition, further research is required to elucidate emerging links between RNA modifications and neurological disorders. Our newly developed approaches will be applied in the context of Parkinson’s disease, aiming to pinpoint the regulatory roles and timings of RNA modification in dopaminergic neuron development.

Michael Westbury

Preserving biodiversity is not only an urgent matter for the natural world, but also for long-term human health. As the world undergoes dramatic human-induced change, we need to identify species most at risk of extinction and formulate mitigation tactics. Current risk identification approaches are very time and resource intensive, making them unfeasible for many species. The genome of an organism provides valuable information about its past, present, and future and could be leveraged to rapidly identify whether a species is at risk. Despite this, we currently lack the tools to do this for a wide range of species. Our research program will bring the world of genomics to species without access to high quality samples or funding by developing new approaches to reliably assemble and analyse their genomes. Furthermore, we will take advantage of recent advances in machine learning to provide a rapid and efficient method to predict extinction risk in living species based on their genomes.

Jonas Lybker Juul

The COVID-19 pandemic has left a devastating impact on societies across the globe, with immediate and indirect costs in terms of human lives, life quality, and economics. Any improvement in how society deals with future pandemics promises a considerable impact economically and health-wise. This proposal will strengthen epidemic preparedness in three ways:

By developing new statistical methods to leverage large-scale genomic sequencing to understand how diseases spread through populations.

By studying how disease mitigation strategies can be both effective and efficient.

By improving key algorithms used when producing forecasts of case numbers in epidemics.

By improving society’s ability to forecast case numbers, infer how diseases spread, and mitigate outbreaks, InForM aims to save human lives and economic costs in future pandemics.

Jonas Meisner

Genomics-driven precision medicine has the potential to revolutionize global health by predicting disease risk and uncovering underlying causes of complex diseases. To ensure that predictions are accessible, accurate and effective for people from diverse backgrounds, we need to develop innovative computational methods for genomics. The goal is to create tools that can analyze large amounts of genetic data to identify new patterns and connections to help us predict and prevent diseases. By focusing on the most prevalent diseases today, such as cardiometabolic diseases, this project intends to make a meaningful difference in public health. This work combines state-of-the-art techniques to make sense of complex genetic data using haplotype information. This involves developing methods to accurately identify the ancestral backgrounds of individuals, identify genetic risk factors for diseases, and predict disease outcomes based on combined large-scale genetic and health data.

Patrick Munk

Bacteria have waged chemical warfare for billions of years. Their metaphorical armory of weapons and shields are antibiotics and antibiotic resistance respectively. As humans have appropriated antibiotics, resistance proliferates globally and now kills 1.3 million people annually.

To slow this silent pandemic we need to both find new weapons and understand which bacteria are shielded from what weapon. Growing public microbiome datasets can help us do that, but there a number of issues. They contain a large layer of metagenomic dark matter we don’t understand and our current analyses are done sample-by-sample and don’t exploit how different datasets synergistically can inform each other.

There are a number of obstacles to solve before we can recover both the useful and harmful microbial armory hidden in global microbiomes. With this project, I seek to develop new computational approaches, make computer programs and harvest the hidden armory for humanity to exploit and slow the AMR pandemic.

Picture credits: Lars Svankjær / The Young Academy of Denmark

Max Theo Ben Clabbers

My research aims to understand and address health issues related to tiny crystalline structures in cells. These crystallites cause various diseases and form a major concern for public health. Despite their broad impact, we often do not fully understand how they form or affect cellular tissue. Traditional methods typically fall short because the crystals are too small, and samples are studied outside of their natural environment. Our project focuses on improving our understanding of these crystalline pathologies by studying them directly within cells. To achieve this, my research group develops novel methods for in situ electron crystallography that use cryogenic electron microscopy imaging and diffraction to look at these crystals in their natural environment. Specifically, we apply these methods to study crystalline problems associated with type 2 diabetes. Our research will not only benefit diabetes treatment but also pave the way for studying a much broader range of pathologies.

Freja Herborg

Unraveling the complex interactions among neural circuits in the brain is pivotal to advance our understanding of psychiatric disorders and symptoms like social impairments that are common across a spectrum of mental health conditions. Oxytocin is an evolutionarily conserved hormone and signaling molecule that has emerged as a possible mediator of social deficits and is known to interact intricately with other ancient circuits such as the dopaminergic and serotonergic systems, in ways that remain poorly understood. This project will bring together novel genetic disease models of ADHD and depression with state-of-the-art imaging techniques to delineate how disease-relevant changes in dopaminergic and serotonergic signaling affect oxytocin function and influence social behaviors and drug responses. With these efforts, we seek to uncover new insights into the neural processes of social deficits, with potential implications for developing circuit-based strategies to treat social impairments.