Lunch Pitch with Sandra Jämtgård, Andreas Kohler and Magali Frauendorf
April 24 @ 12:00 - 13:00
Lunch Pitches with Sandra Jämtgård, Andreas Kohler and Magali Frauendorf
Pitch 1: Sandra Jämtgård: Do proteins hold the key to nitrogen limitation in boreal forests?
Associate Professor, Department of Forest Ecology and Management, UPSC, SLU![](https://icelab.se/wp-content/uploads/2024/01/Sandra-Jaemtgard-2.jpg")
Nitrogen availability limits plant growth in most terrestrial environments. A paradox in the boreal forest is that the amount of nitrogen in soil is large but is yet a limiting nutrient. The majority of this nitrogen (up to 80%) occurs as organic nitrogen in the form of proteins and peptides. Even though plants have the capacity to take up different forms of organic nitrogen such amino acids, peptides and proteins, they are nitrogen-limited in these soils.
Sandra Jämtgård has expertise in mass-spectroscopy analysis of amino acids and dipeptides and her research revolves around understanding the mechanisms governing plant nitrogen availability at the root-soil interface. A key tool in her research group is the sampling technique microdialysis which they are applying as an approach for seeing nitrogen availability from a roots perspective, investigating key aspects of root physiology and plant-microbial interactions and how that influence plant nitrogen availability at the root surface. Sandra aims to unravel the disconnect between nitrogen availability and uptake capacity in boreal soils. One key in this is to understand the composition and the nature of proteins in soils.
Pitch 2: Andreas Kohler: Deciphering the mystery of the quality control of mitochondrially-encoded proteins
Assistant professor, Department of Medical Biochemistry and Biophysics![](https://icelab.se/wp-content/uploads/2024/01/Andreas-Kohler.webp")
Mitochondria are particularly special organelles, as they harbour their own DNA, encoding for essential core subunits of the respiratory chain. Thus, the correct translation of these mitochondrially-encoded proteins is an essential process for cellular respiration, providing our cells with the majority of energy needed.
Like newly produced proteins encoded by nuclear DNA, nascent mitochondrially-encoded proteins need to pass a quality check before they are assembled into functional multiprotein complexes. While we have a sophisticated understanding of how this quality control works for proteins produced in the cytosol, the systems involved in the quality control of mitochondrially-encoded proteins and their molecular mechanisms remain largely elusive.
Our newly established group focuses on deciphering the spatio-temporal organisation of protein quality control systems dedicated to mitochondrially-encoded proteins. We aim to identify these systems, characterize their functional principles and investigate how disruption or stimulation of these systems alters mitochondrial functionality and cellular fitness during ageing. We aim to find novel strategies to counteract age-dependent decline of cellular functionality, which is an underpinning of many human age-related disorders like Alzheimer’s or Parkinson’s disease.
What we can offer:
Our main model organism is the yeast Saccharomyces cerevisiae and we have expertise with advanced yeast genetics to extensively modify the nuclear, and also the mitochondrial genome (e.g. by introducing fluorescence reporters) and run automated genome-wide screens based on cellular growth, fluorescence microscopy and flow cytometry. We further use cutting-edge biochemical methods to explore the spatiotemporal organisation of protein networks and investigate the dynamics of mitochondrial translation, protein quality control as well as protein complex assembly. With our cell biological methods, we can characterise mitochondrial functionality and cellular viability.
Where we would need help:
We would like to extend our work with structural analyses of identified protein quality control systems, which often are large transmembrane complexes. In addition, our data collections from genome-wide screens would benefit from advanced bioinformatic analysis and the possibility to predict functions and interactions by the usage of machine learning approaches.
Pitch 3: Magali Frauendorf: Using computer vision for quick estimation of ungulate reproduction from camera trap images
Postdoctoral researcher, Department of Wildlife, Fish and Environmental Studies, SLU![](https://icelab.se/wp-content/uploads/2024/01/magali-frauendorf1800-jw.webp")
Camera traps are a useful tool in wildlife research and management, also providing information on life history traits (e.g. calf recruitment). However, methods of processing the vast image data are very time-consuming. The automatic identification of species on camera trap images has already been applied globally, but identifications of other features (e.g. sex) to extract demographic parameters are rare. Especially with changing climate, there is need for quick management adaptation. For example, moose monitoring in Sweden takes place during autumn hunt of the previous year meaning that the reproductive season is not considered. This becomes relevant in years with extreme environmental events as reproduction may be negatively affected. To overcome this challenge I provide deep learning models that classifies automatically sex and age of ungulates with high accuracy leading to quicker image processing, shorter feedback loop between monitoring and management and improved management. Next, I will describe other animal attributes I plan to extract from camera images in the future with the help of deep learning models.
Interested in: We would like to find collaborators with expertise in computer vision and an interest in citizen science and wildlife to develop well performing deep learning models to extract various kind of information (e.g. body size, distance) from camera trap images to improve wildlife monitoring and management.