Biology Education

Alzheimer´s disease (AD) is a neurodegenerative disease and most common form of dementia. It is characterized by neuronal loss, extracellular amyloid b (Ab) plaques and intraneuronal deposits of neurofibrillary tangles (NFTs)(Selkoe, 2003). AD pathology also manifests reactive gliosis that reflects the activation of microglia and astrocytes. Besides above, myelin degeneration has been increasingly proposed as a key contributor to AD. Microglia are resident immune cells in the central nervous system (CNS). They play essential roles during development by modulating brain homeostasis, neuronal circuits and synaptic pruning(Schafer et al., 2012). In brain diseases, microglia are responsible for inflammatory responses, including phagocytosis and the secretion of soluble factors, such as cytokines, that contribute to immune responses and tissue repair. In the context of AD, activated microglia can reduce Ab aggregation by increasing its phagocytosis, clearance and degradation(Frautschy et al., 1998). Microglia have a long lifespan(Réu et al., 2017), which gives them the capacity to retain their inflammatory past, potentially leading to long-lasting effects. Although inflammation in the body is intended to be protective, an aggressive inflammatory reaction can lead to or contribute to pathological conditions(Heneka et al., 2001). Increasing evidence and my recent study(Yang et al., 2023) have shown that microglia primed with systemic infections had contradictory responses in AD animal models (Tejera et al., 2019; Wendeln et al., 2018). These studies manifest diverse microglia profiles and their impact on Ab accumulation depends on the stimulation and the duration of exposure. Moreover, other cell types, such as astrocytes and oligodendrocytes, may also play roles in AD progression.

The research project aims to identify distinct phenotypes of neurons and one glial cell type (microglia, astrocytes, and oligodendrocytes) through single-cell RNA sequencing method. The objective is to investigate how early-life inflammation influences their transcriptomic and proteomic alterations, with the ultimate goal of targeting specific pathways that modulate neuron-glia interactions. Additionally, preclinical findings will be validated using data from Alzheimer’s disease (AD) patient cohorts at the project’s conclusion.

Supervisor and contact: Yiyi Yang, yiyi.yang@med.lu.se

Affiliation: Experimental neuroinflammation laboratory, Department of Experimental Medical Sciences (EMV), Faculty of Medicine.

Despite advancements in prevention and treatment, cardiovascular disease remains the leading global cause of mortality and is projected to result in 20.5 million deaths by 2025. Both diabetes and aging independently elevate the risk of vascular inflammation, which contributes to atherosclerotic lesions and vascular dysfunction in organs such as the heart, kidneys, and brain. The growing prevalence of diabetes, coupled with an aging population, underscores an urgent need for innovative therapeutic approaches targeting diabetes-related vascular dysfunction. Our research focuses on the role of vascular smooth muscle cells as key regulators of vascular health.

The Swedish Cardiopulmonary Bioimage Study (SCAPIS) is a population-based research project involving 30,000 participants aged 50–64 from six Swedish hospitals. SCAPIS stands out due to its large scale and comprehensive phenotyping. It aims to advance the prevention, diagnosis, and treatment of cardiovascular and pulmonary diseases by combining advanced imaging techniques (e.g., CT, ultrasound) with extensive data collection on lifestyle, genetics, and biomarkers. By identifying new risk factors, biomarkers, and mechanisms underlying conditions like heart attacks, strokes, and COPD, SCAPIS contributes to precision medicine and early intervention strategies.

This project aims to analyze genotyping data from SCAPIS to improve predictions of diabetic vascular disease and cognitive dysfunction. The study involves using PLINK for genetic data analysis and developing machine learning algorithms to explore associations between specific genetic profiles and disease outcomes.

The research group (Molecular Vascular Physiology) is situated at BMC D12 in Lund. The project is ideal for a master student with an interest in deep learning models and polygenic risk scoring for vascular disease. We are seeking a motivated trainee to join our team in the spring of 2025. While our lab currently has limited bioinformatics expertise, we are in the process of recruiting a data analyst to support the group. Additionally, we collaborate with the research group “Artificial Intelligence and Bioinformatics in Thoracic Surgical Science” on this project.

Contact info: Sebastian.Albinsson@med.lu.se

Traditionally, the choice of codons to encode amino acid sequences of proteins has been assumed to be neutral terms of fitness. We now know that synonymous substitutions that do not change amino acid identities can strongly influence the function of proteins. This is because codons affect mRNA stability, expression levels, protein folding rates, and how the protein folds as it emerges from the ribosome. We study how the choice of codons is correlated with the three-dimensional structure of proteins and the evolution of codon usage. Our work has resulted in a database where codon usage is correlated with 3D protein structure and other features. This database can be mined to identify correlations between structural features in protein, such as secondary structure, and codon usage. We also built large language models of codon sequences to understand what features control codon usage in coding sequences.

Project: We would like to develop better ways to visualize our data by creating a webpage that displays the data in our database. This includes mapping codon sequence biases onto 3D structures, visualizing codon conservation in protein families, and mapping coding usage on species phylogenetic trees.

Desired background: The candidate should have a strong interest in visualization and a solid background in Python programming.

Environment: In addition to research on codon usage, the research group (andrelab.lu.se) also does research in protein structure prediction, computational protein design, and protein evolution. The group uses a range of computational approaches (from deep learning to molecular simulation) and experiments to complement computational projects, providing a multidisciplinary environment for a thesis student to learn.

Contact: If this sounds interesting to you, contact Ingemar André, ingemar.andre@biochemistry.lu.se, for further information.

Our lab works on identifying the genes that regulate immune cell formation and function in humans. For this, we have produced large datasets of paired immunophenotypic data (1500 immune cell features measured by flow cytometry) and genome-wide genotype data in thousands of individuals. The flow cytometry data is analysed by a method called “gating”, where each cell is classified into different categories according to parameters like size, complexity and surface protein expression. To automate the gating process, we have developed pattern recognition tool (AliGater). We are now looking for a student who will use AliGater and extend the tool as needed to gate a new set of samples.

This project is suitable for a for ambitious students who are comfortable with Python, ideally as a 30 credit project. You will get support from the developers of AliGater and work together with a diverse team of researchers with clinical, wet lab and bioinformatics expertise.

For more information, contact: Aitzkoa Lopez de Lapuente Portilla aitzkoa.lopez_de_lapuente_portilla@med.lu.se