In line with the ‘European Charter for Researchers’, applicants will have the freedom to choose your research topic within the research focus areas of GenomMed and a host supervisor, which is relevant to their research and career development. The short project descriptions have been written by the supervisors, and applicants can consider taking them as a starting point for their research proposal. Link in the name of the research group opens up the webpage of the research group.
Scientific training and research is divided into two main fields, cardiovascular and metabolic diseases and neurosciences, connected by shared research questions and genomics approach and translational applications.
Cardiovascular Genomics - Minna Kaikkonen-Määttä (Associate professor)
The research group of Associate Professor Minna Kaikkonen-Määttä is looking for a PhD candidate with bioinformatics background and basic understanding of biology to integrate as a part of a team working on various aspects of genomics and genetics of cardiovascular diseases. The research plan of the applicant is hoped to contain description of bioinformatics methods that could be used in these studies together with a short summary of the biological question elaborated from descriptions below.
1. Study of gene regulatory mechanisms driving the expression of CAD-associated genes
Despite intense efforts to determine the role of various genes in atherogenesis, we lack knowledge from which cell type the gene expression originates from and at what level are the genes being regulated. In this study, we aim to decipher the cell-type specific contribution of macrophages, endothelial and smooth muscle cells in the development of coronary artery disease (CAD) by characterizing the gene regulatory processes that take place in transcriptional, post-transcriptional and translational level in response to proartherogenic stimuli. The project takes advantage of four state-of-the-art next-generation sequencing technologies, namely GRO-, RNA-, miRNA- and RIBO-Seq. Advanced bioinformatics analysis is used to integrate data from all levels of gene regulation to construct comprehensive models of gene expression that will advance our knowledge of atherogenesis.
2. Role of CAD-associated genetic variation in the regulation of cell-type specific enhancer activity
Here, we aim to bring the functional characterization of genetic variants associated with CAD to date by identifying and interpreting the role of enhancer variants across five disease relevant cell types (macrophages, endothelial cells, smooth muscle cells, hepatocytes and adipocytes). By combination of massively parallel enhancer activity measurements, collection of novel eQTL data throughout cell types under disease relevant stimuli, identification of the target genes in physical interaction with the candidate enhancers, we hope to identify causal enhancer variants and link them with target genes to obtain a more complete picture of the gene regulatory events driving disease progression and the genetic basis of CAD. Correlating these findings with extensive patient data for cardiovascular risk factors, genotype, gene expression and tissue biomarkers has the potential to improve risk prediction, biomarker identification and treatment selection in clinical practice. This study provides pioneering steps towards a novel integrative genomics framework to prioritize the common non-coding variants associated with CAD risk.
Molecular interactions between genes and diet - Jussi Pihlajamäki (Professor in Clinical Nutrition)
The risk of common metabolic diseases can be partly attributed to the genetic risk. However, the inherited risk can be modified and in many cases even abolished by healthy lifestyle. Our research group aims to find the exact molecular pathways mediating these interactions. We have specifically focused on metabolic and molecular consequences of obesity and non-alcoholic fatty liver disease (NAFLD). The main molecular findings have been related to cholesterol, bile acid and fatty acid metabolism. We investigate regulation of genes at the level transcription and RNA processing, including also the role of epigenetic mechanisms.
Molecular Physiology - Pasi Tavi (Professor in Cardiovascular cell physiology)
- Metabolic signaling in cardiac remodeling
Both physiological and pathological cardiac hypertrophy involve remodeling of gene expression, which eventually leads to drastic alterations in cardiac function parallel with the changes in cardiac metabolism. It is, however, unclear whether metabolic changes predispose heart to functional alterations or whether altered cardiac function automatically results in metabolic changes. This study will seek nexuses between metabolic pathways and pathways controlling cardiac contractile phenotype in order to pinpoint potential targets for interventions aimed at controlling cardiac remodeling. Project focuses on cardiac effects of transcription modulators such as PGC-1α-isoforms, metabolic modulators and factors interfering with mitochondrial function. Used models include mouse models of cardiac diseases as well as TG and KO mice, human-iPSC-cardiomyocytes and other cell models. The mechanism are studied with in vivo (Echocardiography), ex vivo (Langendorf-heart) and in vitro with cell imaging and electrophysiological tools. Successful candidate should have experience in in vivo cardiac methods and/or in vitro cell physiological techniques. Experience in transcriptional and metabolic analysis is an advantage.
- Induced pluripotent stem cell derived cardiomyocyte models of human cardiac hypertrophy
Limited amount of patient material combined with a lack of research models recapitulating disease progress pose a fundamental challenge for understanding of human heart disease. Pathological growth (i.e. hypertrophy) of heart left ventricle is the ubiquitous symptom and cause of many common cardiac diseases such as hypertension, valvular heart disease, heart failure, myocardial infarction and ischemia associated with coronary artery disease. In this project patient specific human iPS-derived cardiomyocytes (hiPS-CM) carrying hypertrophic cardiomyopathy (HCM) causing mutations are used to study the cellular mechanism of cardiac hypertrophy and failure and to identify endogenous genetic mechanisms affecting development of human myocardial hypertrophy. Human iPSC-cardiomyocytes and 3-D cell cultures are studied with different methods of cell physiology and biophysics and patient data is analyzed together with geneticists and clinical cardiologists. Successful candidate should have experience in stem cell work and cell physiological experiments. Experience in gene editing, bioengineering and sequencing methods is advantageous.
Biomedical MRI - Olli Gröhn (Professor of Biomedical NMR)
A. I. Virtanen Institute houses the National Center for experimental MRI, with three high-field (7T - 9.4T) MRI scanners. We have recently developed an advanced EEG-fMRI platform capable of longitudinal EEG/fMRI studies in awake animals. We are planning to use this setup, in combination with brain stimulation techniques, to study how node activity changes will influence large-scale brain connectivity and how these changes are associated with outcome of the progressive neuro diseases. Specifically we will apply this concept in two different disease models and have following PhD projects available:
- a study of progressive changes in brain activity at a focal and a network level in rodent models of Alzheimer’s disease
- a study of progressive changes in brain activity at a focal and a network level in rodent models of traumatic brain injury
Biomedical image analysis - Jussi Tohka (Associate Professor of Biomedical image and signal analysis)
Rapid advances in non-invasive neuroimaging methods have revolutionized the possibilities to study changes occurring in living brain across a variety of time-scales ranging from seconds to an entire lifespan. A large part of these advances can be attributed to the development of dedicated computational algorithms and applied mathematics, which are essential to extract quantitative information from images. The primary research focus of my group lies in identifying surrogate markers of brain disorders from neuroimaging data using machine learning.
1. Automated segmentation of small animal magnetic resonance images
Applying machine learning techniques to predict disease progression in rodents based on magnetic resonance imaging (MRI) is challenged by the lack of robust automatic image processing tools. This is unlike the case in the human brain MRI, where automatic processing tools are mature. This project will develop a new automated framework to process large image databases of rodent MRI, with a specific goal of enabling the prediction of epileptogenesis after traumatic brain injury.
2. Multi-view learning in brain imaging
Machine learning (ML) algorithms are central in trying to decipher the intricate pattern of brain changes indicative of a disease. They solve the model that generated output (the state of the subjects after the follow-up) based on input data (image data at the baseline) based on a training set of images and follow-up information. The idea is that the inferred model predicts automatically the outputs corresponding to inputs not belonging to the training set. These predictive models would be immensely useful for early diagnosis, optimization of treatment response, and planning of clinical trials. However, using data from multiple imaging modalities and combining imaging, genetics, and behavioral data would be likely to lead to more accurate predictions. This project develops multi-view learning strategies to better enable the combination of different types of data for more accurate predictions.